WILDFIRE IMPACTS ON SOIL CARBON POOLS AND MICROBIAL COMMUNITIES IN MIXED - CONIFER FORESTS OF CALIFORNIA By Jaron Adkins A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Crop and Soil Science s Doctor of Philosophy Ecology, Evolution ary Biology , and Behavior Dual Major 202 1 ABSTRACT WILDFIRE IMPACTS ON SOIL CARBON POOLS AND MICROBIAL COMMUNITIES IN MIXED - C ONIFER FORESTS OF CALIFORNIA By Jaron Adkins Forest ecosystems are important reservoir s for long term carbon (C) storage. Forests of the western United States account for 20 - 40% of total U.S. carbon C sequestration, and nearly half of the total C in these forests is stored in soil. However, many forests in the western U.S are experiencing wi ldfire conditions that diverge from historical fire regimes. Prior to Euro - American settlement, - conifer forests typically experienced frequent surface fires of low to moderate burn severity, but, due to the combined effects of altered forest structure and climate change, now experience fires that are larger and more severe than historical conditions. Fires have nume rous direct and indirect effects on the soil biological, chemical, and physical characteristics that influence the soil C cycle. Understanding how altered soil characteristics influence the cycling and persistence of soil C, and how they vary with severity , is important for managing forests for C storage and for predicting fire - climate feedbacks. My dissertation work incorporates observational and manipulative experiments to understand the direct and indirect effects of burn severity on soil C cycling and m icrobial communities over the short to intermediate term, with a particular focus on the distribution of soil C between active and slow cycling pools. Soil C can be conceptualized as discrete pools of variable persistence in soil. The active C pool is qu ickly decomposed, contributing to the return of CO 2 to the atmosphere , whereas t he non - active C pool is more stable and contributes to long term C storage. I leveraged a burn severity gradient resulting from a wildfire in a California mixed - conifer forest to determine the structure and kinetics of these C pools at an intermediate time point in post - fire recovery ( i.e. three years). I found that the size of the non - active C pool was smaller in burned areas than unburned areas, and the kinetic rate of the non - active C pool was negatively related to burn severity . I also characterized the soil microbial communities across this severity gradient and identified the environmental characteristics responsible for differences. I found that fungal - to - bacterial rati o and oligotroph - to - copiotroph bacteria ratio decreased with burn severity , and these effects were driven by differences in live and dead tree basal area, soil nutrients, and pH . Leveraging another burn severity gradient, I then determine d whether differen ces in microbial communities and soil C pools were related one - year post - fire in a mixed - conifer forest. I again found lower non - active C pool kinetic rates, and higher abundances of copiotrophic bacteria in burned compared to unburned areas. Differences i n soil C pool kinetics were related to tree basal area, soil nutrients, and bacterial communities . I determined the short - term impacts of fire on soil C pools and cycling using lab experiments in which I manipulated soil heating intensity and pyrogenic or ganic matter (PyOM) additions . I found that high intensity soil heating can decrease microbial biomass C (MBC) accumulation , whereas PyOM ha d minimal effects on MBC in the short - term. Finally, I found that the size of the active C pool increase d with soil heating intensity, while the kinetic rate of the non - active C pool decrease d ; PyOM primarily increased the size of the non - active C pool. Taken as a whole, my research suggests that fire induces short - term soil C losses by increasing the size of the active C pool, but, over the intermediate - term, residual soil C is more persistent. Fire severity is predicted to increase globally throughout the 21 st century, and my research contributes to understanding how forest C storage will be affected by disr upted wildfire regimes. iv This dissertation is dedicated to Hayden, who taught me the meaning of resilience And to Shane, whose example and support gave me the courage to take this journey. v ACKNOWLEDGMENTS I would like to thank my committee members for their support throughout my studies. Thank you to my dissertation advisor, Dr. Jessica Miesel, for giving me the opportunity to pursue my research interests, helping me do my best work, and treating me with co mpassion when I needed it. Thank you to Dr. Kathryn Docherty for your patient training and positive responses to all my attempts . Thank you to Dr. Phillip Robertson for reminding me to focus on the big picture and providing encouragement. Thank you to Dr. Lisa Tiemann for always being willing to give help, feedback, and advice. This work would not have been possible without financial support from Michigan State University, the Department of Plant, Soil and Microbial Sciences, the Department of Forestry, an d the National Science Foundation Graduate Research Fellowship Program. I would like to thank the staff of the USDA Forest Service at the Plumas National Forest and the Lassen National Forest for their logistical support. I thank the staff of the Duke Univ ersity Research Forest for providing access to their properties and facilitating sample collection. I would like to thank all the staff and faculty in the Department of Plant, Soil, and Microbial Sciences and the Department of Forestry who formally or inf ormally supported me during my time at Michigan State University. Finally, I would like to extend my most appreciative thanks to all my lab colleagues and fellow graduate students with whom I have been fortunate to share this journey. vi TABLE OF CONTENTS LIST OF TABLES ................................ ................................ ................................ ......................... ix LIST OF FIGURES ................................ ................................ ................................ ....................... xi KEY TO ABBREVIATIO NS ................................ ................................ ................................ ...... xiv CHAPTER 1: SOIL CARBON POOLS AND FLUXES VARY ACROSS A BURN SEVERITY GRADIENT THREE YEARS AFTER WILDFIRE ................................ ................................ ...... 1 1.1 ABSTRACT ................................ ................................ ................................ ..................... 1 1.2 INTRODUCTION ................................ ................................ ................................ ............ 2 1.3 MATERIALS AND METHODS ................................ ................................ ..................... 8 1.3.1 Site description ................................ ................................ ................................ ........... 8 1.3.2 Field methods ................................ ................................ ................................ ........... 1 1 1.3.3 Lab methods ................................ ................................ ................................ ............. 12 1.3.4 Statistica l analysis ................................ ................................ ................................ .... 16 1.4 RESULTS ................................ ................................ ................................ ....................... 18 1.4.1 Total carbon and nitrogen content ................................ ................................ ........... 18 1.4.2 Carbon and nitrogen pools ................................ ................................ ....................... 20 1. 4.3 Influence of pyrogenic carbon on mineral soil carbon and nitrogen pools and fluxes ................................ ................................ ................................ 27 1.5 DISCUSSION ................................ ................................ ................................ ................ 28 1.5.1 Soil carbon and nitrogen content ................................ ................................ ............. 28 1.5.2 Pyrogenic carbon pools ................................ ................................ ............................ 29 1.5. 3 Mineral soil carbon pools and CO 2 - C efflux ................................ ........................... 30 1.5.4 Inorganic nitrogen pools ................................ ................................ .......................... 33 1.6 CONCLUSIONS ................................ ................................ ................................ ............ 34 REFERENCES... 3 6 CHAPTER 2: HOW DO SOIL MICROBIAL COMMUNITIES RESPOND TO FIRE IN THE INTERMEDIATE TERM? INVESTIGATING DIRECT AND INDIRECT EFFECTS ............. 45 2.1 ABSTRACT ................................ ................................ ................................ ................... 45 2.2 INTRODUCTION ................................ ................................ ................................ .......... 46 2.3 METHODS ................................ ................................ ................................ ..................... 50 2.3.1 Site description and field methods ................................ ................................ ........... 50 2.3.2 Laboratory methods ................................ ................................ ................................ . 53 2.3.3 Statistical methods ................................ ................................ ................................ ... 56 2.4 RESU LTS ................................ ................................ ................................ ....................... 59 2.4.1 Relationships between vegetation and soil characteristics and fire occurrence and severity ................................ ................................ ..................... 59 2.4.2 Relationships between soil microbial communities and fire occurrence and severity ................................ ................................ ..................... 64 vii 2.4.3 Direct and indirect soil and vegetation drivers of soil microbial community characteristics ................................ ................................ ....................... 68 2.5 DISCUSSION ................................ ................................ ................................ ................ 75 2.5.1 A systems approach revealed direct and indirect drivers of severity on plant, soil, and microbial characteristics ................................ ........... 75 2.5.2 Burn severity has direct and indirect effects on fungal abundance ......................... 76 2.5.3 Burn severity impacts on bacterial communities are d riven by nutrients, pH, and soil texture ................................ ................................ ............. 77 2.6 CONCLUSIONS ................................ ................................ ................................ ............ 80 APPENDIX 82 REFERENCES 88 CHAPTER 3: DETERMINING LINKS BETWEEN BACTERIAL LI FE - STRATEGIES AND SOIL CARBON POOLS ONE - YEAR POST - FIRE ................................ ................................ .... 97 3.1 ABSTRACT ................................ ................................ ................................ ................... 97 3.2 INTRODUCTION ................................ ................................ ................................ .......... 98 3.3 MATERIALS AN D METHODS ................................ ................................ ................. 103 3.3.1 Site description ................................ ................................ ................................ ....... 103 3.3.2 Field methods ................................ ................................ ................................ ......... 104 3.3.3 Lab methods ................................ ................................ ................................ ........... 104 3.3.4 Statisti cal analysis ................................ ................................ ................................ .. 108 3.4 RESULTS ................................ ................................ ................................ ..................... 113 3.4.1 Relationships between fire, tree basal area, and soil properties ............................ 113 3.4.2 Relationship of wildfire and burn severity to soil C pools and fluxes ................... 115 3.4.3 Relationships between wildfire, burn severity, bacterial communities, and imputed metabolic pathways ................................ ................................ ........... 118 3.4.4 Relationships between bacterial taxa, soil C pools, and soil nutrients .................. 127 3.5 DISCUSSION ................................ ................................ ................................ .............. 131 3.5.1 Hypothesis 1: Soil properties can explain differences in carbon pools across a burn severity gradient ................................ ................................ ............... 131 3.5.2 Hypothesis 2: Bacteria previously identified as fire responders are positively associated with burn severity ................................ ................................ 134 3.5.3 Hypothesis 3: Burned areas have a higher abundance of copiotrophic bacteria .... 135 3.5.4 Hypothesis 4: Bacterial taxa are associated with carbon pool kinetic rates .......... 140 3.6 CONCLUSIONS ................................ ................................ ................................ .......... 141 APPENDIX 143 REFERENCES . 147 CHAPTER 4: POST - FIRE EFFECTS OF SOIL HEATING INTENSITY AND PYROGENIC ORGANIC MATTER ON MICROBIAL ANABOLISM: A LABORATORY - BASED APPROACH ................................ ................................ ................................ ............................... 158 4. 1 ABSTRACT ................................ ................................ ................................ ................. 158 4.2 INTRODUCTION ................................ ................................ ................................ ........ 159 4.3 MATERIALS AND METHODS ................................ ................................ ................. 163 4.3.1 Site description and sample collection ................................ ................................ ... 163 4.3.2 Generation of pyrogenic organic matter ................................ ................................ 164 4.3.3 Experimental design ................................ ................................ ............................... 165 viii 4.3.4 Statistical analysis ................................ ................................ ................................ .. 169 4.4 RESULTS ................................ ................................ ................................ ..................... 169 4.4.1 Carbon use eff iciency experiment ................................ ................................ ......... 169 4.4.2 Microbial biomass accumulation experiment ................................ ........................ 173 4.5 DISCUSSION ................................ ................................ ................................ .............. 177 4.5.1 Soil heating intensity underlies estimated carbon use efficiency and microbial biomass accumulation ................................ ................................ ........... 177 4.5.2 Pyrogenic organic matter decreases soil respiration ................................ .............. 180 4.6 CONCLUSIONS ................................ ................................ ................................ .......... 182 APPENDIX 184 REFERENCES 186 CHAPTER 5: SOIL HEATING INTENSITY AND PYROGENIC ORGANIC MATTER HAVE IMMEDIATE IMPACTS ON THE STRUCTURE AND KINETICS OF SOIL CARBON POOLS ................................ ................................ ................................ ................................ ........ 196 5. 1 ABSTRACT ................................ ................................ ................................ ................. 196 5.2 INTRODUCTION ................................ ................................ ................................ ........ 197 5.3 MATERIALS AND METHODS ................................ ................................ ................. 200 5.3.1 Site description and sample collection ................................ ................................ ... 200 5.3.2 Experimental design ................................ ................................ ............................... 201 5.3.3 Statistical Analysis ................................ ................................ ................................ . 204 5.4 RESULTS ................................ ................................ ................................ ..................... 206 5.4.1 Soil and char carbon concentrations ................................ ................................ ...... 206 5.4.2 Extractable organic carbon and microbial biomass carbon ................................ ... 206 5.4.3 Carbon mineralization ................................ ................................ ............................ 211 5.4.4 Carbon pools: single pool model ................................ ................................ ........... 214 5.4.5 Carbon pools: double pool model ................................ ................................ .......... 215 5.5 DISCUSSION ................................ ................................ ................................ .............. 219 5.5.1 High intensity soil heating decreases soil carbon persistence over the short term 219 5.5.2 Char increases the size and persistence of the non - active carbon pool ................. 220 5.6 CONCLUSIONS ................................ ................................ ................................ .......... 222 APPENDIX 224 REFERENCES 228 CHAPTER 6: MANAGEMENT IMPLICATIONS: POST - FIRE FOREST MANAGEMENT MAY IMPROVE RECOVERY OF SOIL CARBON STORAGE ................................ ............ 235 REFERENCES 241 ix LIST OF TABLES Table 1.1 11 Table 1.2 19 Table 1.3 Model parameters for models for which a topographic explanatory variable was statistically si 21 Table 1.4 Size of active (C a ), slow (C s ), and resistant (C r 23 Table 1.5 Table of F and p - 24 Table 2.1. Structural equation models describing drivers of soil microbial 72 Table 3.1 114 Table 3.2 Structural equation models explaining di rect and indirect links to C - 1 18 Table 3. 3 Proportion of genera within selected phyla that are significantly correlated with soil carbon pools 1 30 Table S3.1 Elastic - Net selecte d Generalized Linear Models explaining C - pool parameters based on bacterial phyla abundance.. 1 46 Table 4.1 Respiration rate by day and cumulative CO 2 - 1 7 0 Table 4.2 Carbon and nitrogen concentrations for uncharred and charred carbon 1 7 0 Table 5.1 ANOVA tables for extractable organic carbon (EOC) and microbial biomass carbon 208 Table 5.2 ANOVA tables for carbon mineralization rate and cumulative carbon 212 Table 5.3 ANOVA tables for single and double carbon pool 2 14 x Table S5.1 Parameters estimated from single pool carbon 2 26 Table S5.2 Parameters estimated from double pool carbon .......................................................... ............................. 2 27 xi LIST OF FIGURES Figure 1.1 9 Figure 1.2 Photographs depicting aboveground (a - d) and soil surface (e - 11 Figure 1.3 Mean ( ± SE) carbon .. 20 Figure 1.4 Mean ( ± SE) CO 2 - C efflux 22 Figure 1.5 Concentration of ammonium nitrogen (a), nitrate nitrogen (c), and total inorganic nitrogen . 26 Figure 1.6 Mean ( ± SE) net ammonification (a), net nitrification (b), and net nitrogen 27 Figure 2.1 Relationship of fire occurrence (column 1) and Dnbr (column 2) 60 Figure 2.2 Relationship of fire occurrence (column 1), soil burn severity (column 2), and total burn severity ( column 3) to forest floor mass, mineral soil sand+POM (5 cm) and mineral soil Ph . 61 Figure 2.3 Relationship of fire occurrence (column 1), soil burn severity (column 2), and total burn severity (column 3) to soil properties f . 62 Figure 2.4 General model depicting initially fitted structural equation model of direct and indirect links between fire severity metrics and PLFA - based microbial group absolute abundance and 16S - based bacteria phylum absolut . 64 Figure 2.5 Relative abundance of microbial groups based on PLFA analysis (a and b) 65 Figure 2.6 Relationship of microbial community characteristics including lipid - based fungal - to - bacterial ratio,16S - and 16S - 67 Figure 2.7 Principle coordinates analysis (P c oA) plots of lipid - based communities (a) and 16S - Rdna . 69 xii Figure S2.1 Locations of field plots within a burn severity matrix resulting 83 Figure S2.3 Relationship of lipid - based fungal - to - bacteria l ratio, 16S - - based oligotrophic:copiotrophic 84 Figure S2.2 85 Figure S2.3 Log 2 - fold response to fire for OTUs grouped by family within Bacteroidetes 86 Figure S2.4 Log 2 - fold response to fire for OTUs grouped by family within - Proteobacteria 86 Figure S2.5 Log 2 - fold response to fire for OTUs grouped by family within Acidobacteria 87 Figure S2.6 Log 2 - fold response to fire for OTUs grouped by family within Actinobacteria .. . 87 Figure 3.1 Causal diagram depicting structural equation model of direct and indirect links between burn severity, topography, live and dead tree basal area, and soil properties. Figure 3.2 Mean (± SE) CO 2 - C efflux rate (points) over a 300 - day laboratory incubation of mineral soils (0 - 5 cm) grouped by fire - . 117 F igure 3. 3 Relationship between fire occurrence and severity for selected microbial c ommunity characteristic s .. 11 9 Figure 3. 4 Principle coordinates analysis (P c oA) plots based on a weighted UniFrac distance . 1 20 Figure 3. 5 ..1 21 Figure 3. 6 Heat map of showing z - 12 3 Figure 3. 7 Principle coordinates analysis (P c oA) plots based on a Bray - Curtis distance matrix of imputed MetaCyc p athways 12 5 Figure 3. 8 Relationships between burn severity and imputed C - .. 12 6 Figure 3. 9 Correlations between bacterial phyla and active carbon pool size (C a ) and kinetic rate (k a ), non - active carbon pool kinetic rate (k s ), total inorganic nitrogen (TIN), phosphorus (P), and pH 12 9 xiii Figure S3.1. Locations of field plots within a burn severity matrix resulting from the Beaver 1 44 Figure S3.2 Mean (± SE) cumulative CO 2 - C efflux (points) over a 300 - day laboratory incubation of mineral soils (0 - 5 cm) grouped by fire - 1 45 Figure 4.1 A proxy for carbon use efficiency ( CUE 1 7 2 Figure 4.2 Uptake of extractable organic carbon ( a and c ) and 24 - hour cumulative respired CO 2 - 1 7 3 Figure 4. 3 Change in microbial biomass carbon ( a, c, e, g ) and 14 - day cumulative resp ired CO 2 - C ( b, d, f, h 1 76 Figure S4.1 1 85 Figure 5.1 Extractable organic carbon of pre - 207 Figure 5.2 Extractable organi c carbon .. 209 Figure 5.3 Microbial biomass carbon 210 Figure 5.4 2 13 Figure 5.5 Bar charts illustrating the 2 15 Figure 5.6 2 18 Figure S5.1 Fungal - to - 25 xiv KEY TO ABBREVIATI ONS AIC Aikake Information Criterion ANOVA Analysis of Variance C Carbon C a Active Carbon Pool C r Resistant Carbon Pool C s Slow Carbon Pool ; Non - Active Carbon Pool Csoc Total Soil Organic C CO 2 Carbon Dioxide CUE Carbon Use Efficiency DBH Diameter at Breast Height DME Dry Mass Equivalent dNBR Differenced Normalized Burn Ratio EOC Extractable Organic Carbon FAME Fatty Acid Methyl Ester F:B Fungal - to - Bacterial Ratio GLM Generalized Linear Model IAR Inhi bitor Additivity Ratio k a Kinetic Rate of Active Carbon Pool k r Kinetic Rate of Resistant Carbon Pool k s Kinetic Rate of Slow Carbon Pool ; Kinetic Rate of Non - Active Carbon Pool xv KCl Potassium Chloride MAT Mean Annual Temperature MBC Microbi al Biomass Carbon MRT Mean Residence Time N Nitrogen NH 4 Ammonium NO 3 Nitrate O:C Oligotroph - to - Copiotroph Ratio OTU Operational Taxonomic Unit P Phosphorus PcOA Principal Coordinates Analysis PERMANOVA Permutational Analysis of Va riance PLFA Phospholipid Fatty Acid PLSR Partial Least Squares Regression POM Particulate Organic Matter PyC Pyrogenic Carbon PyOM Pyrogenic Organic Matter RdNBR Relative Differenced Normalized Burn Ratio RMSE Root Mean Square Error SBS Soil Burn Severity SEM Structural Equation Model SOM Soil Organic Matter TIN Total Inorganic Nitrogen xvi WFPS Water Filled Pore Space WHC Water Holding Capacity 1 CHAPTER 1: SOIL CARBON POOLS AND FLUXES VARY ACROSS A BURN SE VERITY GRADIENT THREE YEARS AFTER WILDFIRE 1 1.1 ABSTRACT Carbon (C) storage in soils contributes to the strength and stability of total ecosystem C sinks, but both aboveground and belowground C is vulnerable to loss during fire. The distributio n of soil C and nitrogen (N) among various defined pools e.g., active, slow and resistant C, and ammonium and nitrate as forms of inorganic N determines the C storage capacity of forests and the nutrient availability for plant communities recovering from wildfires. Projections of increased wildfire severity due to a warming climate and frequent droughts raise concerns about parallel increases i C and N pools, with potentially long - lasting effects on the strength of the forest C sink and on the ability of forests to recover from disturbance. Therefore, I sought to determine how the sizes and mineralization rates of soil C and N pools vary across a gradient of fire severity three years after the Chips Fire burned 30,500 ha of Sierra Nevada mixed - conifer forest. I measured total C and N in forest floor and mineral soil (0 - 5 cm), the po ol sizes and mean residence times of the active, slow, and resistant C in mineral soil, and the pool sizes and mineralization rates of inorganic N in mineral soil. Forest floor total C was lower in areas that experienced high severity fire than in unburned reference areas, an effect likely attributable to greater combustion of forest floor material in high severity areas. Mineral soil C content did not vary with fire severity. Over 1 Originally published as: Adkins J., Sanderma n J., Miesel J.R., 2019. Soil carbon pools and fluxes vary across a fire severity gradient three years after a wildfire burned Sierra Nevada mixed - conifer forest. Geoderma 333, 10 - 22. 2 a 300 - day lab incubation, mineral soil CO 2 - C efflux rates were consistently lower in soils from areas that experienced high severity fire relative to unburned reference areas and were associated with longer mean residence times of the slow C pool. Forest floor N content was lower in high severity areas than unburned areas, whereas mineral soil total N did not vary with vary with fire severity. Mineral soil ammonium and total inorganic N concentrations increased significantly with fire severity in field - fresh soils, but this trend was no longer apparent after a 300 - day lab incubatio n, indicating that site - specific factors control N availability among fire severity levels. My results indicate that future increases in wildfire severity in mixed - conifer forest may alter the strength of the forest C sink by impacting the amount C stored in forest floor, the stability of mineral soil C, and the availability of N to recovering plant communities. 1.2 INTRODUCTION Changes to the size and persistence of soil carbon (C) pools in temperate forests have the potential to influence atmospheric CO 2 co ncentrations (Trumbore 2000; Lutzow et al. 2006) because of the major role these ecosystems pla y in global C dynamics. For example, temperate forests accounted for ~30% of the global forest C sink from 1990 - 2007 (Pan et al. 2011) , and store 48% of ecosystem C in their soils (Pan et al. 2011) . The majority of soil C is stored as soil organic matter (SOM), a continuum of materials that remains in soil for days to centuries, depending on the ph ysiochemical properties of the SOM and the surrounding matrix, and the physical accessibility of the organic compounds to decomposers (Schmidt et al. 2011) . The SOM continuum is often modelled as three distinct C pools with variable turnover times: an active C pool (C a ) with a mean residence time (MRT) of days to months, a slow pool (C s ) with a MRT of years to decades, and a resistant pool (C r ), potentially stable for centuries (Trumbore 1997; Paul et al. 2006) . The turnover rates and distribution of C among these three C pools are sensitive to 3 changes in environmental conditions and disturbance regimes (Trumbore 1997; Jackson et al. 2017) and influence the strength of the ecosystem C sink (Luo and Weng 2011) . Wildfires are one of the most common forest disturbances in the conterminous United States, burning more than 17,000 km 2 y - 1 and causing 13.40 Tg C y - 1 of direct C emissions during 1990 - 2012 (Chen et al. 2017) . In addition to c ausing combustion emissions, high - severity wildfires can transform forest stands from C sinks to C sources when C losses via decomposition exceed photosynthetic C gains during post - fire forest recovery (Kashian et al. 2006) . When climate and fire regimes are stable, wildfire emissions are balanced by the C uptake of vegetative regrowth during ecosystem recovery, ecosystems transition from C so urces back to C sinks, and the net ecosystem C flux is zero (Bowm an et al. 2009; Loehman et al. 2014) . However, altered disturbance regimes disrupt this equilibrium by affecting the magnitude of C losses and temporal patterns of ecosystem recovery (Luo and Weng 2011) . Fire regimes have shifted in ecosystems worldwide: for example, the global average area burned increased by more than 20% in the second half of the 20th ce ntury compared to the first half (Flannigan et al. 2013) . In western United Sta tes forests (west of 102 º W longitude), wildfire frequency increased four - fold, total area burned increased six - fold, and the length of the fire season increased by 78 days during 1987 - 2003 compared to 1970 - 1986 (Westerling et al. 2006) . In the Sier ra Nevada mountain range, the proportion of burned area that experienced high severity fires nearly doubled between 1984 and 2006 (Miller et al. 2009b) . Wildfire severity is a measure of the magnitude of effects of wildfire on ecosystem biomass (Keeley 2009) , and is correlated with C stock losses from aboveground vegetation and dead wood in mixed - conifer (Campbell et al. 2007; Meigs et al. 2009) and ponderosa pine ( Pinus ponderosa ) forests (Meigs et al. 2009) . The increasing occurrence of high burn severity in an 4 ecosystem that historically experienced frequent fires of primarily low to moderate severity has the potential to alter forest composition and successional pathways and de stabilize forest C stocks, particularly when coupled with the warming temperatures and increased drought frequency expected in climate projections (Earles et al. 2014; Liang et al. 2017b) . Landsat - derived spectral data available since 1982 have greatly expanded the scale and ease with which burned areas can be mapped (García and Caselles 1991) . The increasing availability of fire severity dat a has expanded both interest in and ability to assess the impacts of fire on aboveground components of the ecosystem, whereas the ability to determine effects on belowground C stocks remains challenging because Landsat imagery is more sensitive to changes in vegetation than soil (Miller and Thode 2007; Miller et al. 2 009a) . The storage of C in pools with long residence times increases the strength and stability of the total ecosystem C sink (Luo and Weng 2011) . Thus, the size, structure, and turnover times of soil C pools have potential to influence the transition of forests from C sources to C sinks during recovery from wildfire and may either moderate or exacerbate th e response to shifting patterns of fire severity. Meta - analyses have indicated that wildfires in general decrease soil C stocks in the forest floor layer (Nave et al. 2011) , b ut the response of mineral soil C varies with climatic zones, forest type, soil depth, fire type (i.e., wildfire vs. prescribed fire), and time since fire (Johnson and Curtis 2001; Nave et al. 2011; Wang et al. 2012) . Wildfire - induced changes in soil N stocks generally mirror those of soil C stocks in temperate regions, in which forest floor N stocks generally decrease (Nave et al. 2011; Wang et al. 2012) , whereas the effects on mineral soil N vary with soil depth and fire type (Wan et al. 2001; Nave et al. 2011) . None of the meta - analyses to date have directly assessed the impacts of fir e severity or fire intensity (i.e., energy flux resulting from a fire) on soil C and N, a shortcoming acknowledged by several researchers (Nave 5 et al. 2011; Wang et al. 2012) . However, studies that separate the effects of prescribed fires and wildfires on soil C and N have found that wildfires cause greater losses to forest floor C and N stocks and miner al soil C concentrations than prescribed fires (Nave et al. 2011; Wang et al. 2012) . Because prescri bed fires are often of lower intensity and result in lower severity relative to wildfires, the differences in impacts between prescribed fires and wildfires reported to date suggest that soil C and N storage may also differ across contrasting levels of fir e severity (Alcañiz et al. 2018) . Because of the large proportion of C stored in soil, the size and turnover times of the C a , C s , and C r pools determines the strength and stability of the ecosystem C sink in recovering forests (Luo and Weng 2011) . Fernández et al. (1999) used a two - pool model to assess the impacts of wildfire on labile and recalcitrant soil C pools and their associated kinetics in P. sylvestris and P. pinaster forests in northwest Spain and found that wildfire increased the size and mineralization rate of the labile C pool in soils to 5 cm depth, an effect that persisted for several months, but was no longer apparent after one year. Two years after the wildfire, the labile C pools in burned soils and their mineralization rates were lower than or equal to those in unburned soils; meanwhile, the mineralization rate of the recalcitrant C pool in burned soils was consistently lower than that of unburned soils over th e two year study (Fernández et al. 1999) . The study sites Fernández et al. (1999) used experienced only high intensity fires, and the effects of fire severity level were not considered. To my knowledge, the relationship between wildfire severity and the C a , C s , and C r pools and their associated kinetics has yet to be assessed: this information is important for understanding long term effects of wildfire on forest C storage (Birdsey et al., 2006) . For example, lower mineralization rates and larger sizes of the C r and C s 6 pools may partially offset total ecosystem C losses by increasing the overall MRT, and thus the sink strength, of for est C (Luo and Weng 2011) . Fire directly influences the soil C pool structure through the formation of p yrogenic carbon (i.e., carbon associated with char; PyC), which is generated via the thermal decomposition of biomass and encompasses a spectrum of materials from slightly charred plant matter to highly condensed soot and micrographine sheets (Bird et al. 2015) . PyC was initially viewed solely as a resistant C pool, but emerging evidence shows that PyC consists of an active, slow, and resistant pool (Kuzyakov et al. 2014) . The relative sizes of these PyC pools depends on combustion temperature (Bird et al. 2015) and source material (Hatton et al., 2016, Michelotti and Miesel 2015) , and the amount of PyC generated during fires has been shown to increase with fire severity (Miesel et al. 2015; Maestrini et al. 2017) and fire intensity (Czimczik et al. 2003; Sawyer et al. 2018) . Pyrogenic C contributes directly to soil total C pools, but also influences so il C pools indirectly via impacts on mineralization kinetics of native soil C. For example, PyC induces short - term positive and long - term negative priming effects (Maestrini et al. 2015) , and soil C mineralization rates decrease with increasing PyC concentrations (Michelotti and Miesel 2015) . Thus, severity - based differences in PyC accumulation may have downstream impacts on C flux rates from the soil to the atmosphere. Low soil inorganic N content often limits plant productivity in coniferous forests (Vitousek and How arth 1991) , whereas enhancing N availability can increase soil C stocks by increasing soil C inputs (Nave et al. 2009) and decreasing C loss via respiration (Janssens et al. 2010) . Therefore, the sizes of inorganic N pools in post - fire soils are likely to affect the recovery of aboveground (Grogan et al. 2000) and belowground C stocks. Increases in soil ammonium (NH 4 + ) and nitrate (NO 3 - ) concentrations are typical after wildfires, across a variety of ecosystem 7 types (Wan et al. 2001) . Maximum increases in soil NH 4 + and NO 3 - concentrations are approximately tenfold greater than pre - fire conditions, generally returning to pre - fire levels after one year for NH 4 + , and within five years for NO 3 - (Wan et al . 2001) . Studies ranging from two days to 26 months after fires have variously attributed the N pulse to pyrolysis of forest floor material (Covington and Sackett 1992) , ash dep osition (Christensen 1973) , d ecreased uptake by vegetation due to plant mortality (Ficken and Wright 2017) , and decreased uptake by microbes (Koyama et al. 2012) . Fire also impacts N cycling in soil, over short - and longer time periods after fire. For example, a meta - analysis of N mineralization response to fires showed that fires stimulate a short term (< 3 months) increase in N mineralization, but decrease N miner alization rates over the long term (at least 3 years), a decrease that is greater for prescribed fires than wildfires (Wang et al. 2012) . In addition to the direct effects of increased N availability on N mineralization rates, changes to N mineralization may result from changes to soil moisture and temperature that in turn influence microbial activity (Turner et al. 2007) , or from increases in PyC concentrations (Michelotti and Miesel 2015) . Together, results from these studies suggest that fire severit y level may have an important influence on N mineralization rates in a recovering forest. Direct assessments of soil C and N pools and dynamics after wildfires are needed to improve estimates of the amount and stability of C stored in forest ecosystems (Birdsey et al. 2006) . Meta - analyses separating the effects of wildfire and prescribed fire o n soil C and N content, as well as research on the influence of fire severity on soil PyC suggest that the distribution of soil C and N among different pools may be influenced by severity (Nave et al. 2011; Wang et al. 2012; Miesel et al. 2015; Maestrini et al. 2017) . However, specific estimates of the sizes and turnover rates of the C a , C s , and C r pools are lacking. Therefore, I conducted a 8 field study to investigate patterns in soil C and N across a fire severity gradient three years after wildfire. My specific objectives were to quantify: 1) total C and N content in forest floor and mineral soil, 2) the sizes of the char pool in forest floor and the PyC pool in mineral soil, 3) the sizes and MRTs of mineral soil C a , C s , and C r pools, 4) the sizes and mineralization rates of inorganic N pools in mineral soil, and 5) the influence of mineral soil PyC on C and N mineralization ra tes, across contrasting fire severity levels, including unburned reference areas. 1.3 MATERIALS AND METHODS 1.3.1 Site d escription I investigated the area affected by the Chips Fire (Lat: 40.095 Long: - 121 .199 ; Fig.1 .1 ), which was ignited by lightning on July 28, 2012 and burned 30,500 ha of the Plumas and Lassen National Forests prior to containment on August 31, 2012 by United States Forest Service wildland firefighting crews. Fire severity estimates based on the Relativ e Differenced Normalized Burn Ratio (RdNBR), which is calculated from Landsat imagery (Eidenshink et al. 2007) , indicate that approximately 6,300 ha bu rned at high severity, 9,600 ha at moderate severity, and 12,500 ha at low severity (MTBS 2017) . RdNBR based severity estimates are sensitive to changes to soil color and moisture, but primarily detect changes to vegetation chlorophyll and water content and is therefore considered an aboveground severity metric (Miller and Thode 2007; Safford et al. 2008; Miller et al. 2009a) . RdNBR value s are calibrated to the amount of basal area mortality, resulting in a three - level severity classification where < 25% basal area mortality is classified as low severity, 25 - 75% basal area mortality is classified as moderate severity, and >75% mortality is classified as high severity (Miller et al. 2009a) . The forest type of my study area is cla ssified as California mixed - conifer (Ruefenacht et al. 2008) , consisting of P. ponderosa, P. lambertiana, P. jeffreyi, Abies concolor, Pseudotsuga menziesii, 9 Calocedrus decurrens, and Quercus kelloggii. Ceanothus spp. and Arctostaphylos spp. shrub species are also common. Figure 1.1 Locations of field plots at different fire severity levels in the Chips Fire. The Chips Fire burned mixed - conifer forest in the Plumas and Lassen National Forests, California, USA in 2012. The border denotes the fire perimeter, the color gradient indicates different severity levels, and the symbols indicate the location of field plo ts. Unburned plots were sited outside of the fire perimeter in mixed - conifer stands of similar composition. Prior to Euro - American settlement in the mid - 19th century, the mean fire return interval - conifer forests ranged from 11 to 34 years (Mallek et al. 2013) , and less than 10% of annual burned area experienced high severity fires (Mallek et al. 2013; Miller and 10 Safford 2017) . However, a policy of fire suppression has been i n place in the United States since the early 20th century (Dombeck et al. 2004) , leading to greater tree and fuel density in mixed - conifer forests, and the percentage of burn ed area that experienced high severity fire increased to 25% by 1984 - 2009 (Mallek et al. 2013) Quincy, CA, the 30 year mean annual precipitation is 1080 mm, more than 75% of which occurs in winte r and spring, and mean annual temperature is 10.6 º C (NCEI - NO AA 2017) . My field plots were established in 2014 as part of a broader forest inventory performed by the USDA Forest Service. Between July 6, 2015 and July 28, 2015 (i.e., three years post - fire), I sampled 17 plots (4 unburned, 5 low severity, 4 moder ate severity, 4 high severity) for forest floor and mineral soil (Fig 1. 2). I selected plots that had relatively similar topographic characteristics across severity levels. Selected plots had an elevation range of 1217 - 1641 m, slopes < 50%, and a variety of slope aspects (Table 1 .1 ). Plots were established 100 - 1000 m from roads and trails in areas without post - fire salvage logging to minimize the influence of direct human activity on my research plots. Unburned reference plots were located within 2000 m of the fire perimeter in mixed - conifer stands of similar composition. The averag e distance between any two plots was 6.4 km, with a minimum distance of 400 m. A portion of the forest burned by the Chips Fire had been burned previously by the Storrie Fire in 2000 and was excluded from this study. Most of the area burned by the Chips Fi re had not burned in the last century. My field sites comprised two soil series: Skalan, a loamy - skeletal, isotic, mesic Vitrandic Haploxeralf, and Kinkel, a loamy - skeletal, mixed, superactive, mesic Ultic Palexeralf. These acidic soils are gravelly loams that form from metamorphic parent material and are free of calcium and carbonates. Typical pedons consist of 2.5 - 5 cm thick O horizons, 7.5 - 17.5 cm thick A horizons, and Bt horizons to bedrock (Soil Survey Staff) . 11 Table 1. 1 Topographic characteristics of research plots for each wildfire severity level. Aspect count shows number of plots per aspect category. Severity Elevation (m) Slope (%) Aspect (count) N E S W Unburned 1359 - 1593 22 - 29 0 2 1 1 Low 1217 - 1399 12 - 36 1 0 1 3 Moderate 1466 - 1591 18 - 43 0 1 2 1 High 1451 - 1641 31 - 49 0 4 0 0 Figure 1.2 Photographs depicting aboveground (a - d) and soil surface (e - h) characteristics of unburned mixed - conifer forest stands, and stands that had experienced low, moderate, and high severity fire, respectively, three years previously. The high severity stand in image d had accumulated patchy leaf litter, whereas the high se verity stand in image h exhibited bare mineral soil. Image h illustrates the high gravel content of the soils. 1.3.2 Field m ethods At each plot, I sampled forest floor and mineral soil 17 m from the plot center at azimuths of 0 º , 120 º , and 240 º , for a total of 51 forest floor samples and 51 mineral soil samples. The forest floor includes the plant litter and duff layers, and is equivalent to the combined Oi, Oe, and Oa horizons in the USDA Soil Taxonomy classification system (Perry et al. 2008) . I collected all forest floor material from within a 15 cm radius ci rcular sampling frame and then collected mineral soil to 5 cm depth using a stainless - steel scoop. I collected one additional volumetric 12 mineral soil sample from each plot to estimate bulk density by collecting mineral soil to 5 cm using a 10 cm diameter s ampling cylinder. Samples were shipped within one week to the laboratory for processing. Forest floor samples were air - dried, and mineral soils were stored and shipped on ice and then refrigerated (4 º C) until processing. 1.3.3 Lab m ethods Soil processing and C and N analysis Small amounts of gravel were present in the lower duff layer in the forest floor samples, likely due to mixing with the mineral soil surface over time as the forest floor layer developed after past fires, as a result of annual freeze/thaw cycles, erosion from wind and spring snowmelt, or bioturbation (Fig. 1. 2h). I removed any gravel present in the forest floor by hand to the maximum extent possible, and then processed each sample at 22,000 RPM for 60 second cycles in a Waring commercial la b blender (Conair Inc., Stamford, CT, USA) until all the material passed a 2 mm mesh screen. A blender was used rather than a plant mill to avoid damaging mill blades with any residual mineral material present in the forest floor samples that was impossibl e to remove by hand. I then pulverized a subsample of the blended forest floor in a SPEX 8000D Mixer/Mill (SPEX Sample Prep LLC, Metuchen, NJ, USA) for further analysis. I oven - dried t he pulverized forest floor material at 60 º C and subsampled for determin ation of total C, total N, and char content. I sieved the field - moist mineral soil samples (2 mm mesh screen), and subsampled to determine total C, total N, PyC, C r , inorganic N (NH 4 + and NO 3 - ), and to establish the laboratory incubation to determine C a , C s , and soil CO 2 efflux. The subsamples for mineral soil total C, total N, and PyC analysis were oven dried at 105 º C to completely remove moisture and pulverized as described above. Subsamples for C r analysis were oven dried at 60 º C. I 13 measured total C an d N in forest floor and mineral soil samples using a Costech dry combustion elemental analyzer (Costech Analytical Technologies Inc., Valencia, CA, USA). Determination of forest floor char Throughout this article , I the forest floor, for which associated C was not determined, whereas I associated with pyrogenic material in the mineral soil, determined through chemical oxidation. Cha r concentrations in forest floor samples were predicted from a Fourier transform infrared spectroscopy - based chemometric model developed from a series of laboratory - based standards . M id - infrared spectra were acquired on dried and finely ground samples on a Bruker Vertex 70 (Bruker Optics, Billerica, MA USA) equipped with a Si - based wide - range beam - splitter and detector with cesium iodide windows. Samples were run neat (i.e., undiluted) using a Pike Autodiff diffuse reflectance accessory (Pike Technologies, Madison, WI). I acquired spectra from 6000 - 180 cm - 1 at a 4 cm - 1 resolution. For each set of samples, a background spectrum was obtained (average of 60 scans) and this was subtracted from the sample reflectance spectra (also an average of 60 scans). A previ ously validated, partial least squares regression (PLSR) model developed using The Unscrambler X software (CAMO Inc.) was used to predict char concentration. This model was developed using known mixtures of pine needle litter and char produced from pine ne edles or pine wood at temperatures of 300 and 550° C, so that char concentrations varied from 0 to 100% in 5% increments (J. Miesel, personal communication). A two - factor PLSR model successfully captured this variance in char with 20 - fold cross - validation indicating an R 2 of 0.97 with a root mean square error (RMSE) of 5.0%. Two sub - samples from separate low severity plots and one sub - sample from a high severity plot were poorly represented 14 (i.e., fell outside of 2 s.d.) by the calibration model as determin ed by Mahalanobis and inlier distance metrics. These three samples were excluded from further analysis. Determination of mineral soil PyC I measured mineral soil PyC concentrations using a weak nitric acid digestion technique (Kurth et al. 2006) . I digested 0.5 g mineral soil in 10 mL 1 M nitric acid + 20 mL 30% hydrogen peroxide at 100 º C in a block digester (SEAL Analytical Inc., United Kingdom). The soil C present after digestion is considered PyC and was measured by elemental analysis as described above. Determination of mineral soil C pools and CO 2 - C efflux I incubated soils to determine potential soil CO 2 - C efflux rates and the size and kinetics of the C a and C s pools. I weighed 30 g subsamples of fresh (field - moist), sieved mineral soil into 120 mL specimen cups and adjusted soil moisture to 40% water filled pore space (WFPS). The cups were placed in 1 L glass jars, 5 mL DI water was added to the bottom of each jar to ma intain humidity, and the soils were incubated in the dark for 300 days at ambient temperature (22 º C). Soil moisture change was determined bi - weekly via change in microcosm mass and was readjusted to 40% WFPS. I measured CO 2 evolution on days 10, 14, 28, 4 2, 58, and 90 and every 30 days thereafter until day 300. This incubation length is similar to the length recommended by Robertson et al. (1999) for determining mineral soil C pool sizes. At each measurement event, I flushed the air in jars to ambient CO 2 concentrations, then tightly sealed them for 24 - 48 hours before sampling a 1 mL gas aliquot through septa fitted to the jar lids. I measured CO 2 concentration of the aliquot using a LI - COR LI - 820 CO 2 gas analyzer (LI - COR Inc., Lincoln, NE, USA), which was continuously flushed with CO 2 - free air. I determined CO 2 concentrations via comparison to a calibration curve constructed using standards of known CO 2 concentration. I 15 calculated CO 2 - C efflux rates as the increase in CO 2 - C concentrations in the jars during the time they were sealed. I standardized CO 2 - C efflux rates to the total amount of soil C present in each microcosm . Determination of resis tant mineral soil C I estimated mineral soil C r concentrations as non - hydrolysable C using a modified version of the acid digestion method described by Paul et al. (1997) . Briefly, I slaked 5 g oven - dried soil in 20 mL DI water with eight 4 mm glass beads overnight to disr upt soil aggregates. Undecomposed plant material is resistant to hydrolysis and can bias estimates of C r (Paul et al. 1997) , so I passed the soil solution through a 53 µ m sieve to remove plant residues. Thereafter, 0.5 g of the sieved soil was refluxed with 10 mL 6 M hydrochloric acid at 116 º C for 2 hours in a Mars 6 microwave digester (CEM Corporation, Matthew, NC, USA). The C present after digestion is considered C r and was measured by elemental analysis as described above. Determination of mineral soil inorganic N pools and mineralization rates I extracted fresh mineral soils for NH 4 + - N and NO 3 - - N by shaking 10 g field - moist soils in 50 mL 2 M potassium chloride (KCl) on a shaker table for 1 hour. I then separated the extract from the soil via filtration with Whatman grade 5 (2.5 µ m) filter paper (GE Healthcare UK Limited, Little Chalfont, Buckinghamshire, UK). I determined the extract NH 4 + - N concentration spectrophotom etrically by reacting the extracts with ammonia salicylate and ammonia cyanurate in a 96 - well plate (Sinsabaugh et al. 2000) , after which I measured absorbance at 595 nm (BioTek Elx800, BioTek Instruments Inc., Winuski, VT, USA). I determined the concentration of NO 3 - - N in the extracts spectrophotometrically by reacting the extracts with vanadium (III), sulfanilamide, and N - (1 - naphthyl) - ethylenediamine dihydrochloride in a 96 - well plate, after which I measured absorbance at 540 nm (Doane and Horwáth 2003) . I converted absorbance 16 values to concentrations via comparison to standard curves produced using (NH 4 ) 2 SO 4 and KNO 3 reference solutions. I calculated total inorganic nitrogen (TIN) as the sum of NH 4 + - N and NO 3 - - N. I repeated this procedure after the 300 - day lab incubation, and calculated net ammonification, net nitrification, and net N mineralization over the course of the incubation. 1.3.4 Statistical a nalysis I used linear mixed models to assess the responses of soil C and N pools to fire severity. All models included fire severity, elevation, slope, and aspect as explanatory variables. I conservatively assumed the azimuth subsamples within each plot were non - independent by including a plot - identifier as a random effect . Models assessing inorganic N concentrations included total N as an additional covariate. Models assessing instantaneous CO 2 - C efflux rates during the lab incubation included fixed effects of incubation day, severity, day by severity interactions, and a r andom day effect. Models assessing cumulative CO 2 - C efflux during the lab incubation included a fixed effect of log incubation day, day by severity interactions, and a random day effect. Models assessing the influence of PyC on mineral soil CO 2 - C efflux an d N transformations included fire severity as a random effect rather than as a fixed effect. For all models, the statistical significance of each explanatory variable was assessed using Type 3 Sums of Squares, and variables that were non - significant at = 0.05 were sequentially removed from models until all remaining explanatory variables were significant or no variables remained. If severity was not significant at = 0.05, but exhibited p - values 0.10, I pooled low, moderate, and high severity treatment s into a single burned treatment to determine whether fire had a significant effect on the response variable even if differences in severity did not. For each model, I tested my assumption of non - independence among subplots by comparing Aikake Information Criterion (AIC) values and residual standard errors between models that did or did not include 17 plot - identifier random effects ( Pinheiro and Bates 2000) . If the model without the random effect had both a lower AIC value ( AIC 2) and improved residual standard errors, I considered subplots independent. When assumptions of normality were not met for a model, I box - cox transformed the response variable using the car package (Fox and Weisberg 2011) in the statistical programming software R (R Core Team 2019) to select an appropriate value for . If the 95% confidence interval for Linear mixed - mode ls were performed in R using the nlme package (Pinheiro et al. 2019) . When Type 3 Sums of Squares indicated that fire severity or aspect were significant model parameters, I performed lsmeans package (Lenth 2016) . I used non - linear regression to fit a three - pool constrained model to CO 2 evolution data resulting from the lab i ncubation according to the method of Paul et al. (2001) to determine the size and kinetics of mineral soil C a and C s pools. Briefly, CO 2 evolution was fit to the following first - order kinetics model: dC/dt = C a × k a e ( - ka × day) + C s × k s e ( - ks × day) + C r × k r ( - kr × day) ( eq 1. 1) where C a is the size of the active C pool, and k a is its mineralization rate coefficient, C s is the slow C pool and k s is its mineralization rate coefficient, and C r is the size of the resistant C pool and k r is its mineraliz ation rate coefficient. In this model, C a , k a , and k s are determined via non - linear regression, the C r pool is determined by acid hydrolysis prior to model fitting, and k r is based on an assumed MRT of 1000 years. C s is constrained to be (C soc C r C a ), where C soc is total soil organic C content. Mineralization rate constants are also presented as MRT (1/k) for ease of interpretation and are scaled to field values using a Q 10 value of 2.0 and a MAT of 10.6 º C. The respiration rates for soils from one of t he unburned plots did not decrease over the 300 - 18 day incubation and were not fit to the model nor included in any subsequent statistical analyses. Non - linear regression was performed with SAS software using PROC NLIN (SAS software, version 9.3, SAS Institut e Inc., Cary, NC, USA). Bonferroni adjusted comparisons of resulting parameters (e.g. C a , k a ) among severity levels were performed using PROC GLIMMIX in SAS. 1.4 RESULTS 1.4.1 Total c arbon and n itrogen c ontent I found significant differences among fire severity levels in forest floor total C content ( p = 0.014). High severity areas had 82% less forest floor mass and 71% lower forest floor total C content than unburned areas (Table 1. 2, Fig. 1. 3 a). Aspect was the only significant predictor of forest floor C concentrations: plots located on eastern aspects had a mean forest floor C concentration of 47.3% compared to a grand mean of 43.0% for all plots (Table 1.3 ). Forest floor N contents were 85% smaller in high sev erity areas than in unburned areas ( p = 0.014; Fig. 1.3 b), whereas there were no differences in forest floor N concentrations. Forest floor C:N ratios varied with fire severity ( p = 0.003): high severity areas had higher C:N ratios than unburned and modera te severity areas (Table 1. 2). For mineral soil samples, there were no differences in total C or N content (Fig. 1.3), total C or total N concentrations, or C:N ratios among severity levels (Table 1.2). Mineral soil C content increased with elevation at a rate of 0.12 ± 0.05 g C m - 2 per 100 m increase in elevation on the log response scale ( p = 0.038; Table 1.3), and mineral soil N content increased with elevation at a rate of 0.11 ± 0.03 g N m - 2 per 100 m increase in elevation on the log response scale ( p = 0.003). 19 Table 1.2 Forest floor mass, carbon concentrations, nitrogen concentrations , C:N ratios, char mass fraction, and total char mass; and mineral soil (0 - 5 cm) carbon concentrations, nitrogen concentrations, C:N ratios, pyrogenic carbon mass fraction, and total pyrogenic carbon mass among fire severity levels. Values are means ± SE. Lowercase letters within rows indicate significant differences among fire severity levels at = 0.05. Unburned (N = 4) Low (N = 5) Moderate (N = 4) High (N = 4) Forest Floor Mass (kg m - 2 ) 4.57 ± 1.46 a 1.63 ± 0.33 ab 1.71 ± 0.58 ab 0.80 ± 0.41 b C Concentration (%) 42.39 ± 1.28 a 39.98 ± 2.25 a 41.71 ± 3.87 a 48.85 ± 1.40 a N Concentration (%) 0.80 ± 0.09 a 0.73 ± 0.03 a 0.86 ± 0.08 a 0.63 ± 0.07 a C:N 60.77 ± 4.88 b 64.27 ± 1.54 ab 54.70 ± 5.50 b 90.52 ± 8.52 a Char Mass Fraction (mg g - 1 ) 180.72 ± 26.64 ab 294.44 ± 53.15 a 273.13 ± 75.42 a 160.75 ± 6.56 b Total Char Mass (g m - 2 ) 739.51 ± 246.82 a 580.24 ± 200.91 a 621.02 ± 356.27 a 120.55 ± 63.42 a Mineral Soil (0 - 5 cm) C Concentration (%) 4.98 ± 0.48 a 4.69 ± 0.22 a 5.02 ± 0.29 a 6.33 ± 0.46 a N Concentration (%) 0.20 ± 0.02 a 0.20 ± 0.12 a 0.20 ± 0.03 a 0.26 ± 0.02 a C:N 28.92 ± 4.00 a 27.73 ± 2.10 a 28.45 ± 1.62 a 28.63 ± 1.53 a PyC Mass Fraction (mg g - 1 ) 10.56 ± 1.88 a 8.42 ± 2.56 a 10.50 ± 2.04 a 11.70 ± 2.68 a Total PyC Mass (g m - 2 ) 310.79 ± 56.54 a 201.83 ± 45.44 a 358.83 ± 80.15 a 299.23 ± 57.8 a 20 Figure 1.3 Mean ( ± SE) carbon (a) and nitrogen (b) stocks in forest floor and 0 - 5 cm mineral soil for each severity level three years after the 2012 Chips wildfire burned mixed - conifer forest in northern California. Stocks are presented as mass per unit area. Lowercase letters indicate significant differences amo ng fire severity levels at = 0.05. 1.4.2 Carbon and n itrogen p ools Pyrogenic carbon in forest floor and mineral soil Low and moderate severity areas had higher mass fraction of char in the forest floor than high severity areas ( p = 0.008; Table 1. 2), but the re were no differences in total content of forest floor char among severity levels (Table 1. 2). Forest floor char mass fraction decreased by 5.1 ± 2.2 mg g - 1 for every percent increase in slope ( p = 0.038; Table 1.3 ). There were no differences in mineral s oil PyC mass fraction or total PyC content among severity levels (Table 1. 2), but there was a significant effect of aspect on PyC mass fraction ( p = 0.005; Table 1.3 ). Eastern aspects had higher PyC mass fractions (1.19 ± 0.15 mg g - 1 ) than western aspects (0.64 ± 0.09 mg g - 1 ). Mineral soil total PyC content increased with elevation at a rate of 0.16 ± 0.08 g m - 2 per 100 m increase in elevation on the log response scale. 21 Table 1.3 Model parameters for models for which a topographic explanatory variable was statistically significant. Prior to log transformation, elevation parameter estimates are change in response variable ± SE per 100 m increase in elevation. Aspect parameter estimates are untransformed means. Lowercase letters for aspect parame ter estimates represent significant differences at = 0.05. Mineral soil C pools and CO 2 - C efflux Non - linear regression indicated that there were differences in the sizes and kinetics of soil C pools among fire severity levels (Fig. 1.4 a; Table 1.4 ). High severity areas had significantly larger C a pools than low and moderate severity areas when estimated on a soil mass fraction basis ( Table 1.4 ), and when estimated as a proportion of C soc , C a pools were smaller in burned areas than unburned areas ( Table 1.4 ). My statistical models indicated that th e size of the C s pools varied with severity when estimated on a soil mass fraction basis ( p = 0.011), but mean comparisons indicated that differences between severity levels were not significant, despite high severity areas exhibiting C s pools that were 64 % larger than those in unburned areas ( p = 0.078; Parameter Estimate F p Forest Floor C Concentration (%) Aspect: North 42.80 ± 4.64 ab - - Aspect: East 47.26 ± 0.99 a - - Aspect: South 41.08 ± 2.12 ab - - Aspect: West 38.74 ± 1.80 b - - Char Mass Fraction (mg g - 1 ) Severity See main text Slope (%) - 5.12 ± 2.20 5.43 0.038 Mineral Soil (0 - 5 cm) Total C Content (g m - 2 ) * Intercept 6.92 ± 0.13 2833.23 < 0.001 Elevation (100 m) 0.12 ± 0.05 5.30 0.038 Total N Content (g m - 2 ) * Intercept 3.77 ± 0.08 2347.67 < 0.001 Elevation (100 m) 0.11 ± 0.03 12.87 0.003 PyC Mass Fraction (mg g - 1 ) Aspect: North Aspect: East Aspect: South Aspect: West 0.71 ± 0.15 ab 1.19 ± 0.23 a 1.26 ± 0.32 ab 0.64 ± 0.09 b - - - - - - - - Total PyC Content (g m - 2 ) * Intercept Elevation (100 m) 5.09 ± 0.19 0.16 ± 0.08 3693.09 4.40 < 0.001 0.042 NO 3 - Concentration ( µ g g - 1 ) Intercept Elevation (100 m) - 1.10 ± 0.77 0.87 ± 0.30 2.04 8.50 0.162 0.011 *Response log transformed; parameter estimates on log scale Response box - cox transformed; parameter estimates on original scale 22 Table 1.4 ). There were no differences in C r pools among severity levels. The mineralization rate coefficient (k a ) associated with the C a pool did not vary significantly with severity, but high severity area s had the smallest k s (corresponding to a longer MRT s ), and low severity areas had a smaller k s than unburned areas ( Table 1.4 ). Figure 1.4 Mean ( ± SE) CO 2 - C efflux rate (points) over a 300 - day laboratory incubation of mineral soils (0 - 5 cm) fit with 3 - pool carbon models (lines) using non - linear regression (a) and mean ( ± SE) cumulative CO 2 - C efflux over the incubation period(b). Soils were collected from contrasting levels of fire severity three years after the Chips wildfire, which burned Califo rnia mixed - conifer forest in 2012. CO 2 - C efflux rate is presented as carbon respired per unit dry soil mass per day, and cumulative CO 2 - C efflux is carbon respired per unit soil carbon. 23 Table 1.4 Size of active (C a ), slow (C s ), and resistant (C r ) soil c arbon pools , their sizes proportional to total soil organic carbon (C a :C soc , C s :C soc , C r :C soc ), and corresponding decomposition rate constants (k x ) and mean residence times (MRT x ; 1/k x ) for 0 - 5 cm mineral soil for each wildfire severity. C a , C s , and C r are expressed on a dry soil mass basis, k a and MRT a are expressed as days, and k s , k r , MRT s are expressed as years. C a , k a , and k s were determined via non - linear regression of CO 2 evolution data from a 300 day lab incubation, C r was determined via acid hy drolysis, and C s is assumed to be the difference between total soil organic carbon and C a + C r . Lab - based values of MRT were scaled to field conditions using a Q 10 correction of 2.0. Values are means ± SE. Lowercase letters within rows represent significan t differences among severity levels at = 0.05. Unburned (N = 3) Low (N = 5) Moderate (N = 4) High (N = 4) C a (g kg - 1 ) 0.99 ± 0.15 ab 0.67 ± 0.08 b 0.64 ± 0.08 b 1.08 ± 0.11 a C s (g kg - 1 ) 28.89 ± 3.49 b* 35.81 ± 5.22 ab 36.40 ± 6.18 ab 46.96 ± 5.14 a* C r (g kg - 1 ) 10.79 ± 0.68 a 11.59 ± 1.23 a 15.05 ± 1.65 a 15.30 ± 1.21 a C a :C soc (%) 2.68 ± 0.33 a 1.58 ± 0.16 b 1.39 ± 0.25 b 1.68 ± 0.19 b C s :C soc (%) 69.90 ± 2.01 a 73.91 ± 0.97 a 68.95 ± 1.83 a 73.80 ± 1.52 a C r :C soc (%) 27.42 ± 1.98 a 24.51 ± 0.90 a 29.66 ± 1.80 a 24.52 ± 1.50 a k a (d - 1 ) 0.030 ± 0.006 a 0.044 ± 0.009 a 0.040 ± 0.010 a 0.035 ± 0.005 a k s (y - 1 ) 0.102 ± 0.011 a 0.055 ± 0.006 b 0.074 ± 0.007 ab 0.031 ± 0.005 c k r (y - 1 ) 0.0025 a 0.0025 a 0.0025 a 0.0025 a Lab MRT MRT a (d) 33.40 ± 6.90 a 22.81 ± 4.56 a 24.94 ± 6.18 a 28.43 ± 4.16 a MRT s (y) 9.82 ± 1.06 c 18.26 ± 1.95 b 13.43 ± 1.25 bc 31.86 ± 5.19 a Field MRT MRT a (d) 84.55 ± 17.46 a 57.73 ± 11.54 a 63.15 ± 15.65 a 71.96 ± 10.54 a MRT s (y) 24.86 ± 2.67 c 46.24 ± 4.93 b 34.00 ± 3.17 bc 80.65 ± 13.13 a a Based on an assumed MRT of 1000 years and constrained in non - linear regression model. * p = 0.071 I fit a non - linear regression to all the plots within each severity level, obtaining a single estimate for each C pool and associated kinetics at each severity level (Fig. 1.4 a, Table 1.4 ). I were thus unable to estimate the influence of topographic variabl es on these parameters, except for the C r pool, which was measured separately using acid hydrolysis. However, I calculated instantaneous CO 2 - C efflux rates (Fig. 1.4 a) and cumulative CO 2 - C efflux (Fig. 1.4 b) over the duration of the incubation and determined the influence of severity and topography on these parameters. For instantaneous efflux rates, there were significant effects of incubation day ( p < 0.001), severity ( p < 0.001), severity by day interaction ( p = 0.001), and hillslope ( p = 0.004). For cumulative CO 2 - C flux, there were significant effects of incubation day (p < 0.001), severity ( p = 0.032), severity by day interaction ( p < 0.001), and aspect ( p = 0.008). The significant 24 severity by day interaction indicates the need to examine sever ity - based differences on each measurement day. When analyzing CO 2 - C efflux over time, analyses based on instantaneous efflux rates are more statistically valid than those based on cumulative C flux (Hess and Schmidt 1995) , so I limit my reporting of differences in cumulative C flux to those present only on the final incubation day (i. e., day 300), in contrast to a more detailed reporting of differences in instantaneous CO 2 - C efflux rates (Table 1.5 ). Table 1.5 Table of F and p - values for determining statistically significant differences in instantaneous CO 2 - C efflux rates during a lab incubation of mineral soils (0 - 5 cm) collected three years after the 2012 Chips Fire in Sierra Nevada mixed - conifer forest. Severity Incubation day F p Unburned (N = 3) Low (N = 5) Moderate (N = 4) High (N = 4) 10 2.07 0.118 -- -- -- -- 14 1.18 0.328 -- -- -- -- 28 7.22 <0.001 a b b b 42 4.26 0.010 a b b b 58 2.27 0.095 -- -- -- -- 90 2.68 0.060 -- -- -- -- 120 3.62 0.021 a ab ab b 150 5.64 0.003 a b b b 180 4.53 0.008 a b ab b 210 3.18 0.034 a ab ab b 240 2.83 0.050 a ab ab b 270 3.86 0.016 a* ab b* b 300 4.22 0.011 a* ab b* b * p < 0.10. On incubation days 28, 42, and 150, soils from unburned areas exhibited higher CO 2 - C efflux rates than burned soils from all severity levels (Table 1.5 , Fig. 1.4 a). Carbon efflux rates for unburned soils were significantly greater than low and high severity soils on day 180 and were greater than soils from high severity areas on day 120 , and from day 210 until the end of the incubation. At the conclusion of the incubation, cumulative CO 2 - C efflux for unburned soils was significantly greater than for soils from low and moderate severity areas (Fig. 1.4 b). Despite exhibiting the lowest ove rall mean cumulative CO 2 C efflux, soils from high severity areas were not significantly different from any other areas. 25 Mineral soil inorganic N pools There were significant differences among severity levels in mineral soil TIN and NH 4 + - N concentration s in fresh, pre - incubated (i.e., incubation day 0) mineral soils ( p = 0.003 and p = 0.009, respectively; Fig. 1.5 ). There was a general trend of greater TIN and NH 4 + - N concentrations at higher severity, although there were no differences between low severity areas and unburned or moderate severity areas, and high and moderate severity soils were not significantly different from one another (Fig. 1.5 a and 1.5 c). Minera l soil total N concentration was a significant covariate explaining TIN; TIN increased by 4.3 ± 1.8 µ g g - 1 for every percent increase in total N concentration ( p = 0.020). There were no significant differences in NO 3 - - N concentrations among severity levels for day 0 soils (Fig. 1.5 c), but NO 3 - - N concentrations increased with elevation at a rate of 0.87 µ g g - 1 100 m - 1 . After the 300 - day lab incubation, there were no longer differences in NH 4 + - N, NO 3 - - N, or TIN concentrations (Fig. 1.5b and 1.5c ). There were no differences in net ammonification, net nitrification, or net N mineralization rates among severity levels over the course of the incubation (Fig. 1.6 ). 26 Figure 1.5 Concentration of ammonium nitrogen (a), nitrate nitrogen (c), and total inorganic nitro gen (e) in fresh mineral soils (0 - 5 cm), and concentration of ammonium nitrogen (b), nitrate nitrogen (d), and total inorganic nitrogen (f) after a 300 day lab incubation. Soils were collected from contrasting levels of fire severity three years after the Chips wildfire, which burned northern California mixed - conifer forest in 2012. Means ( ± SE) for each severity level are displayed. Concentrations are presented on a dry soil mass basis. Lowercase letters indicate significant differences among severity levels at = 0.05. NS indicates there were no significant differences among severity level s. 27 Figure 1. 6 Mean ( ± SE) net ammonification (a), net nitrification (b), and net nitrogen mineralization (c) for mineral soil (0 - 5 cm) for each severity level over the course of a 300 day lab incubation. Soil were collected three years after the 2012 C hips wildfire burned mixed - conifer forest in northern California mixed - conifer forest. NS indicates there were no significant differences among severity levels. 1.4.3 Influence of p yrogenic c arbon on m ineral s oil c arbon and n itrogen p ools and f luxes There was a significant positive correlation between PyC mass fraction and C r mass r pools on average. There was no relationship between either PyC mass fraction or total PyC mass an d 28 inorganic N concentrations, C efflux, net ammonification, net nitrification, or net N mineralization rates over the course of the lab incubation (data not shown). 1.5 DISCUSSION 1.5.1 Soil c arbon and n itrogen c ontent The smaller forest floor total C and N contents in high severity areas can be attributed to differences in forest floor mass, because C and N concentrations did not significantly vary among severity levels. The loss of forest floor may have long - term impac ts on soil C and N cycling because the litter layer is a source of C and N inputs to mineral soil (Heckman et al. 20 13) , and less forest floor insulation may result in higher temperatures and lower moisture content in mineral soils (Kasischke and Johnstone 2005) , thereby influencing soil microbial activity. Higher soil temperatures will have a tendency to increase microbial activity, whereas moisture limitation will decrease microbial activity (Chapin et al. 2011) . This may result in bursts of microbial activity following precipitation events that are more intense but shorter in duration in areas that experienced high severity fire compared to lower severity and unburned - conifer forests may be especially sens itive to the effects of temperature and moisture, because they are seasonally dry and prone to frequent drought. Changes to soil temperature and moisture may affect the amount of C retained in microbial biomass versus lost to respiration (i.e., carbon use efficiency). Carbon use efficiency declines with increasing temperature due to greater respiratory costs and heat stress (Manzoni et al. 2012) and declines with increasing soil moisture variability due to physiological and osmotic stress (Tiemann and Billings 2011) . Loss of the litter layer may also alter the soil microbial community structure. Forest litter is preferenti ally colonized by fungi (Chapin et al. 2011) , and loss of the litter layer 29 may shift the microbial community towards bacterial dominance, thereby altering decomposition pathways. 1.5.2 Pyrogenic c arbon p ools The lower char mass f raction in high severity areas compared to low and moderate severity areas may have resulted from greater forest floor combustion efficiency, or from increased inputs of killed but uncharred biomass in the years following the fire. Despite the lower char m ass fraction in high severity areas, there were no differences in total forest floor char content. This is likely due to decreased forest floor mass in high severity areas offsetting the greater char mass fraction. I did not directly measure PyC concentrat ion of forest floor char, but C concentrations are generally greater for charred material than for uncharred biomass (Czimczik et al. 2002; Maestrini and Miesel 2017) . The magnitude of C en richment in char depends on source material and pyrolysis temperature; for example, C concentrations range from approximately 60% for charred pine needles pyrolyzed at 300 º C to more than 90% for charred pine wood pyrolyzed at 550 º C (Maestrini and Miesel 2017) . The digestion - resistant C fraction (i.e. PyC) ranges from approximately 10% for pine needles charr ed at 300 º C to approximately 90% for pine wood charred at 550 º C. Using these values as constraints, this would translate to forest floor char - C contents ranging from 73 to 109 g char - C m - 2 (12 to 109 g PyC m - 2 ) for high severity areas and contents ranging from 444 to 666 g char - C m - 2 (44 to 599 g PyC m - 2 ) for the unburned areas. The greater mass fraction of char in forest floor from low and moderate severity areas may eventually lead to changes in C pool structure and dynamics as the char b ecomes incorporated into the mineral soil. An experiment in a beech - dominated temperate forest indicated that the MRT of charred biomass in soil is one to two orders of magnitude longer than unburnt biomass; the same experiment indicated that PyC can promo te the formation of SOM - 30 stabilizing aggregates (Singh et al. 2014) . Furthermore, a meta - analysis by Maestrini et al., (2015) indicated that upon incorporation into mineral soil, PyC induces a positive priming effect to the native soil C over short time periods (20 - 200 days), but results in an overall negative priming effect over the long term (> 200 days). Longer MRTs, promotion of aggregate formation, and a negative priming effect should all serve to i ncrease the size and MRTs of the C s and C r pools as charred biomass becomes incorporated into the mineral soil. 1.5.3 Mineral s oil c arbon p ools and CO 2 - C e fflux Wildfires release stored C immediately through combustion, but can also cause delayed C losses as f ire - killed plant biomass decomposes, thereby transforming forests from C sinks to C sources until losses are offset by C accumulation during forest regeneration (Kashian et al. 2006) . Using a Differenced Normalized Burn Ratio (dNBR) severity classification, Meigs et al., (2009) determined that the amount of time required for forest stands to transition from C source to sink is shorter for stan ds that experienced low or moderate severity fire than high severity fire, where net primary production is lower due to greater plant mortality. The structure and dynamics of mineral soil C pools govern the amount of C lost via respiration versus the amoun t retained in the mineral soil (Post and Kwon 2000; Six and Jastrow 2002) . Soil C pools thus determine the magnitude o f overall C losses following wildfires and the time required to transition forests back to C sinks. The low soil CO 2 - C efflux rates I observed in soils from high severity areas indicate that these stands are experiencing similar C losses to low and moderat e severity stands, three years after fire. I also found that the MRT for the C s pool increased following wildfire, a trend that was most pronounced among high severity areas, suggesting that soil C stability is greater in soils from high severity areas than other burned and unburned areas. The C s pool accounts for more than 70% of so il C in high severity areas, indicating that CO 2 - C efflux is likely to remain 31 low in these soils until more labile C is incorporated into the C s pool. The greater C stability and corresponding lower CO 2 - C efflux in high severity soils suggests that the str ength of the C source is not enhanced by soil respiration in these stands, and supports Meigs et al., (2009) finding that the sink - source relationship in recovering forests is driven by plant production rather than soil respiration. There was no relationsh ip between PyC and CO 2 - C efflux, but the absence of differences in mineral soil PyC pools among burned and unburned areas suggests that PyC had not been incorporated into the mineral soil three years after the wildfire. Previous research in sites impacted by the Chips fire has indicated that high severity areas contain a greater proportion of PyC in aboveground C pools than low - to - moderate severity areas (Maestrini et al. 2017) , but lateral transport of PyC due to erosion may exceed the rates of vertical mixing following wildfires in the region (Abney et al. 2017) , perhaps accounting for the lack of differences in soil PyC pools among severity levels. Of th e three C pools addressed in this study, C a is most accessible for microbial processing (Bremer et al. 1994 ; Six and Jastrow 2002) , and thus strongly influences soil biological activity and site fertility (Mandal et al. 2008) . The C a pool is sensitive to environmental change and is an early indicator of the impact of changing environmental conditions on soil C dynamics (Bremer et al. 1994) . Large C a pools are indicative of labile (i.e., easily decomposable) C inputs from plant residues and root exudates (Paul e t al. 1999; Collins et al. 2000; Hoosbeek et al. 2006) . According to the severity classification system I used, high severity wildfire results in 75 - 100% basal area mortality (Miller et al. 2009a) , yet I found that high severity areas had proportionally similarly sized C a pools compared to soils from low and moderate severity areas, and large r C a pools than low and moderate severity areas when estimated on a soil mass fraction basis. The larger C a pools in high severity areas relative to low 32 and moderate areas could result from greater incorporation of root necromass into mineral soils in thes e areas. Tree root survival is low following high severity fire in mixed - conifer forests (Meigs et al. 2009) , stimulating root d ecomposition (Campbell et al. 2016) , and perhaps providing a sustained source of belowground C a inputs. Root decomposition could also account for the tendency for C s pool size to increase with severity and for the longer MRT s in the burned plots. Root - derived C has been shown to account for a greater proportion of soil C and have longer MRT values t han C derived from aboveground biomass (Rasse et al. 2005) . In fact, my field - scaled MRT s values are similar to the estimated MRT for Pse uodotsuga menziesii coarse root necromass of approximately 50 years (Janisch et al. 2005) . Another possible explanation for the larger C a pools in high severity plots is the potential for fast - growing, early successional plant species to inc orporate labile C and N into the soil, a possibility that is supported by the greater TIN and NH 4 + - N concentrations in the high severity plots. Nitrogen - fixing shrubs are often early colonizers following wildfires in mixed - conifer forests (C onard et al. 1985) , and high severity patches create canopy openings that favor shrub establishment (Meigs et al. 2009; Knapp et al. 2012; Collins and Roller 2013) . In fact, I observed a greater proportion of Ceanothus cordulatus, an early successional N - fixin g shrub, in high severity areas than other areas (personal observation; Knapp et al. 2012) . My results are novel because they are the first to describe the structure and dynamics of mineral soil C pools properties that determine the status of soils as C sources or sinks among contrasting levels of fire severity. Fernández et al. (1999) described two mineral soil C pools following wildfire in Galician pine forest, but they did not account for fire severity nor differentiate between the slow and resistant C pools. Nevertheless, my finding of greater MRT for the C s pool agrees with the observation of Fe rnández et al. (1999) of decreased mineralization 33 rates in a recalcitrant C pool. My findings of a larger C a pool in high severity areas than in low and moderate severity areas three years after wildfire contrasts with Fernández et al. (1999) finding decreases in the size of the labile C pool during a two - year post - fire monitoring period . I also found no differe nces in the MRT of the C a pool among severity levels, whereas Fernández et al. (1999) found decreases in the mineralization of the labile C pool that persisted for at least two years. These contrasting results highlight the need for additional studies asse ssing the mineralization kinetics and distribution of soil C among different pools following wildifres across contrasting ecoystem types and on short and long timescales. 1.5.4 Inorganic n itrogen p ools Fires have been known to create a pulse of inorganic N to the soil (DeBano et al. 1979) , because NH 4 + is a product of forest floor pyrolysis (Covi ngton and Sackett 1992) . However, the resulting spike in NH 4 + - N generally dissipates after about a year as NH 4 + is transformed to NO 3 - (Wan et al. 2001) . My observations of a persistent six - fold increase in both NH 4 + - N and TIN in high severity soils relative to unburned soils three years after fire suggests either that high severity fires directly induce a more persistent increase in NH 4 + - N than low and moderate severity fires, or that indirect, post - fire effects drive soil inorga nic N pools. For example, elevated TIN could have resulted from increased N inputs from colonization by N - fixing plants (e.g., from the C. cordulatus plants I observed; Oakley et al. 2003 ), decreased N uptake by plants due to greater plant mortality in areas of higher severity (Grogan et al. 2000; Ficken and Wright 2017) , continuous root decomposition, or differences in the activity rates of soil microbial communities (Smithwick et al. 2005) . If the loss of forest floor resulted in drier soils in high severity areas, the elevated NH 4 + - N could also be the result of decreased nitrification rates (Turner et a l. 2007) . A sustained increase in TIN may support forest regeneration after severe 34 wildfires because N is a limiting nutrient in coniferous forests (Vitousek and Howarth 1991) . Furthermore, fertilization experiments suggest that increased TIN may strengthen the soil C sink by increasing SOM formation (Bradford et al. 2008) and by decreasing soil C respiration, effects postulated to result from microbial groups switching energy sources from N - containing recalcitrant C sources to N - poor but more labile C s ubstrates that can be metabolized more efficiently (Janssens et al. 2010) . However, elevated TI N may also have undesirable effects on the ecosystem if lower N - uptake by plants in high severity areas leads to increased leaching of NO 3 - from soil, resulting in contamination of streams and groundwater (Vitousek et al. 1979) . After my 300 - day lab incubation, there were no longer any differences in NH 4 + - N or TIN concentrations, nor were there differences in ammonification, nitrification, or N mineralization rates, suggesting that site characteristics are the dominant drivers of differences in inorganic N pools. However, my lab incubation did not include living plant biomass, therefore I could not account for the effects of plant N uptake on soil N mineralization, or competition bet ween plants and microbes for inorganic N. Because I measured initial and day 300 inorganic N concentrations, my calculations of net mineralization that occurred represent a relatively coarse time scale, and therefore did not capture any patterns of short t erm mineralization that may have differed among severity levels 1.6 CONCLUSIONS My study found that wildfire severity influences soil C and N pool structure and dynamics. I found that less soil C is stored in areas of high fire severity, but that C may be more stable as evidenced by longer MRT s . The large C a pools and TIN concentrations indicate soils in high severity areas maintain the nutrient cycling processes necessary t o support forest regeneration, and low CO 2 - C efflux rates and long MRT of the C s pool indicate future soil C 35 losses will be low. This suggests that the recovery of forest C will likely be constrained by vegetation regeneration rather than soil C cycling pr ocesses. If fires in mixed - conifer forests continue to occur with increasingly high - severity effects, these forests may not completely reaccumulate C stored in aboveground and belowground pools. Further research is needed to determine whether greater soil sink strength in high severity areas will offset lower aboveground production under future severity scenarios. Determining the mechanisms responsible for greater soil C stability in high severity areas (e.g. PyC inputs, shrub colonization, root decompositi on, etc.) will aid in managing forests for C sequestration. Whether high severity fire consistently leads to larger and/or more persistent increases in TIN than low and moderate severity fire and the role of TIN in driving forest recovery also warrants fur ther investigation. 36 REFERENCES 37 REFERENCES Abney RB, Sanderman J, Johnson D, et al (2017) Post - wildfire erosion in mountainous terrain leads to rapid and major redistribution of soil organic carbon. Front Earth Sci 5:1 16. doi: 10.3389/feart.2017.00099 Alcañiz M, Outeiro L, Francos M, Úbeda X (2018) Effects of prescribed fires on soil properties: A review. Sci Total Environ 613 614:944 957. doi: 10.1016/j.scitotenv.2017.09.144 Bird MI, Wynn JG, Saiz G, et al (2015) The pyrogenic carbon cycle. Annu Rev Earth Planet Sci 43:273 298. doi: 10.1146/annurev - earth - 060614 - 105038 Birdsey R, Pregitzer K, Lucier A (2006) Forest carbon management in the United States. J Environ Qual 35:1461. doi: 10.2134/jeq2005.0162 Bowman DMJS, Bal ch JK, Artaxo P, et al (2009) Fire in the earth system. Science 324:481 484. doi: 10.1126/science.1163886 Bradford MA, Fierer N, Reynolds JF (2008) Soil carbon stocks in experimental mesocosms are dependent on the rate of labile carbon, nitrogen and phosph orus inputs to soils. Funct Ecol 22:964 974. doi: 10.1111/j.1365 - 2435.2008.01404.x Bremer E, Janzen HH, Johnston AM (1994) Sensitivity of total, light fraction and mineralizable organic matter to management practices in a Lethbridge soil. Can J Soil Sci 74 :131 138. doi: 10.4141/cjss94 - 020 Campbell J, Donato D, Azuma D, Law B (2007) Pyrogenic carbon emission from a large wildfire in Oregon, United States. J Geophys Res Biogeosciences. doi: 10.1029/2007JG000451 Campbell JL, Fontaine JB, Donato DC (2016) Carbo n emissions from decomposition of fire - killed trees following a large wildfire in Oregon, United States. J Geophys Res Biogeosciences 121:718 730. doi: 10.1002/2015JG003165 Chapin FS, Matson PA, Vitousek PM (2011) Principles of Terrestrial Ecosystem Ecolog y. Springer New York, New York, NY Chen G, Hayes DJ, David McGuire A (2017) Contributions of wildland fire to terrestrial ecosystem carbon dynamics in North America from 1990 to 2012. Global Biogeochem Cycles 31:878 900. doi: 10.1002/2016GB005548 38 Christens en NL (1973) Fire and the nitrogen cycle in california chaparral. Science 181:66 68. doi: 10.1126/science.181.4094.66 Collins BM, Roller GB (2013) Early forest dynamics in stand - replacing fire patches in the northern Sierra Nevada, California, USA. Landsc Ecol 28:1801 1813. doi: 10.1007/s10980 - 013 - 9923 - 8 Collins HP, Elliott ET, Paustian K, et al (2000) Soil carbon pools and fluxes in long - term Corn Belt agroecosystems. Soil Biol Biochem 32:157 168. doi: 10.1016/S0038 - 0717(99)00136 - 4 Conard SG, Jaramillo AE, Cromack K, Rose S (1985) The role of the genus Ceanothus in western forest ecosystems. Portland, OR Covington W, Sackett S (1992) Soil mineral nitrogen changes following prescribed burning in ponderosa pine. For Ecol Manage 54:175 191. doi: 10.1016/0378 - 1 127(92)90011 - W Czimczik CI, Preston CM, Schmidt MWI, et al (2002) Effects of charring on mass, organic carbon, and stable carbon isotope composition of wood. Org Geochem 33:1207 1223. doi: 10.1016/S0146 - 6380(02)00137 - 7 Czimczik CI, Preston CM, Schmidt MWI, Schulze E - D (2003) How surface fire in Siberian scots pine forests affects soil organic carbon in the forest floor: Stocks, molecular structure, and conversion to black carbon (charcoal). Global Biogeochem Cycles. doi: 10.1029/2002GB001956 DeBano LF, Eber lein GE, Dunn PH (1979) Effects of burning on chaparral soils: I. soil nitrogen. Soil Sci Soc Am J 43:504 509. doi: 10.2136/sssaj1979.03615995004300030015x Doane TA, Horwáth WR (2003) Spectrophotometric determination of nitrate with a single reagent. Anal Lett 36:2713 2722. doi: 10.1081/AL - 120024647 Dombeck MP, Williams JE, Wood CA (2004) Wildfire policy and public lands: Integrating scientific understanding with social concerns across landscapes. Conserv Biol 18:883 889. doi: 10.1111/j.1523 - 1739.2004.00491 .x Earles JM, North MP, Hurteau MD (2014) Wildfire and drought dynamics destabilize carbon stores of fire - suppressed forests. Ecol Appl 24:732 740. doi: 10.1890/13 - 1860.1 Eidenshink JC, Schwind B, Brewer K, et al (2007) A project for monitoring trends in b urn severity. Fire Ecol 3:3 21. Fernández I, Cabaneiro A, Carballas T (1999) Carbon mineralization dynamics in soils after wildfires in two Galician forests. Soil Biol Biochem 31:1853 1865. doi: 10.1016/S0038 - 39 0717(99)00116 - 9 Ficken CD, Wright JP (2017) Contributions of microbial activity and ash deposition to post - fire nitrogen availability in a pine savanna. Biogeosciences 14:241 255. doi: 10.5194/bg - 14 - 241 - 2017 Flannigan MD, Cantin AS, de Groot WJ, et al (2013) Global wildland fire season severi ty in the 21st century. For Ecol Manage 294:54 61. doi: 10.1016/j.foreco.2012.10.022 Fox J, Weisberg S (2011) An R companion to applied regression., 2nd edn. Sage, Thousand Oaks, CA García MJL, Caselles V (1991) Mapping burns and natural reforestation usin g thematic mapper data. Geocarto Int 6:31 37. doi: 10.1080/10106049109354290 Grogan P, Burns TD, Chapin III FS (2000) Fire effects on ecosystem nitrogen cycling in a Californian bishop pine forest. Oecologia 122:537 544. doi: 10.1007/s004420050977 Hatton P J, Chatterjee S, Filley TR, et al (2016) Tree taxa and pyrolysis temperature interact to control the efficacy of pyrogenic organic matter formation. Biogeochemistry 130:103 116. doi: 10.1007/s10533 - 016 - 0245 - 1 Heckman K, Campbell J, Powers H, et al (2013) T he influence of fire on the radiocarbon signature and character of soil organic matter in the Siskiyou National Forest, Oregon, USA. Fire Ecol 9:40 56. doi: 10.4996/fireecology.0902040 Hess TF, Schmidt SK (1995) Improved procedure for obtaining statistical ly valid parameter estimates from soil respiration data. Soil Biol Biochem 27:1 7. doi: 10.1016/0038 - 0717(94)00166 - X Hoosbeek MR, Li Y, Scarascia - Mugnozza GE (2006) Free atmospheric CO 2 enrichment (FACE) increased labile and total carbon in the mineral soi l of a short rotation Poplar plantation. Plant Soil 281:247 254. doi: 10.1007/s11104 - 005 - 4293 - x Jackson RB, Lajtha K, Crow SE, et al (2017) The ecology of soil carbon: Pools, Vulnerabilities, and biotic and abiotic controls. Annu Rev Ecol Evol Syst 48:annu rev - ecolsys - 112414 - 054234. doi: 10.1146/annurev - ecolsys - 112414 - 054234 Janisch JE, Harmon ME, Chen H, et al (2005) Decomposition of coarse woody debris originating by clearcutting of an old - growth conifer forest. Ecoscience 12:151 160. Janssens IA, Dieleman W, Luyssaert S, et al (2010) Reduction of forest soil respiration in response to nitrogen deposition. Nat Geosci 3:315 322. doi: 10.1038/ngeo844 40 Johnson DW, Curtis PS (2001) Effects of forest management on soil C and N storage: Meta analysis. For Ecol Man age 140:227 238. doi: 10.1016/S0378 - 1127(00)00282 - 6 Kashian DM, Romme WH, Tinker DB, et al (2006) Carbon storage on landscapes with stand - replacing fires. Bioscience 56:598 606. doi: 10.1641/0006 - 3568(2006)56[598:CSOLWS]2.0.CO;2 Kasischke ES, Johnstone JF (2005) Variation in postfire organic layer thickness in a black spruce forest complex in interior Alaska and its effects on soil temperature and moisture. Can J For Res 35:2164 2177. doi: 10.1139/x05 - 159 Keeley JE (2009) Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int J Wildl Fire 18:116 126. doi: 10.1071/WF07049 Knapp EE, Phillip Weatherspoon C, Skinner CN (2012) Shrub seed banks in mixed conifer forests of northern California and the role of fire in r egulating abundance. Fire Ecol 8:32 48. doi: 10.4996/fireecology.0801032 Koyama A, Stephan K, Kavanagh KL (2012) Fire effects on gross inorganic N transformation in riparian soils in coniferous forests of central Idaho, USA: Wildfires v. prescribed fires. Int J Wildl Fire 21:69 78. Kurth VJ, MacKenzie MD, DeLuca TH (2006) Estimating charcoal content in forest mineral soils. Geoderma 137:135 139. doi: 10.1016/j.geoderma.2006.08.003 Kuzyakov Y, Bogomolova I, Glaser B (2014) Biochar stability in soil: Decompos ition during eight years and transformation as assessed by compound - specific 14 C analysis. Soil Biol Biochem 70:229 236. doi: 10.1016/j.soilbio.2013.12.021 Lenth R V. (2016) Least - squares means: The R package lsmeans. J Stat Softw 69:1 33. doi: 10.18637/js s.v069.i01 Liang S, Hurteau MD, Westerling AL (2017) Response of Sierra Nevada forests to projected climate wildfire interactions. Glob Chang Biol 23:2016 2030. doi: 10.1111/gcb.13544 Loehman RA, Reinhardt E, Riley KL (2014) Wildland fire emissions, carbon , and climate: Seeing the forest and the trees A cross - scale assessment of wildfire and carbon dynamics in fire - prone, forested ecosystems. For Ecol Manage 317:9 19. doi: 10.1016/j.foreco.2013.04.014 Luo Y, Weng E (2011) Dynamic disequilibrium of the ter restrial carbon cycle under global change. Trends Ecol Evol 26:96 104. doi: 10.1016/j.tree.2010.11.003 41 Lutzow M v., Kogel - Knabner I, Ekschmitt K, et al (2006) Stabilization of organic matter in temperate soils: mechanisms and their relevance under differen t soil conditions - a review. Eur J Soil Sci 57:426 445. doi: 10.1111/j.1365 - 2389.2006.00809.x Maestrini B, Alvey EC, Hurteau MD, et al (2017) Fire severity alters the distribution of pyrogenic carbon stocks across ecosystem pools in a Californian mixed - conifer forest. J Geophys Res Biogeosciences 122:2338 2355. doi: 10.1002/2017JG003832 Maestrini B, Miesel JR (2017) Modification of the weak nitric acid digestion method for the quantification of black carbon in organic matrices. Org Geoc hem 103:136 139. doi: 10.1016/j.orggeochem.2016.10.010 Maestrini B, Nannipieri P, Abiven S (2015) A meta - analysis on pyrogenic organic matter induced priming effect. GCB Bioenergy 7:577 590. doi: 10.1111/gcbb.12194 Mallek CM, Safford H, Viers J, Miller JD (2013) Modern departures in fire severity and area vary by forest type, Sierra Nevada and southern Cascades, California, USA. Ecosphere 4:1 28. doi: 10.1890/ES13 - 00217 Mandal B, Majumder B, Adhya TK, et al (2008) Potential of double - cropped rice ecology to conserve organic carbon under subtropical climate. Glob Chang Biol 14:2139 2151. doi: 10.1111/j.1365 - 2486.2008.01627.x Manzoni S, Taylor P, Richter A, et al (2012) Environmental and stoichiometric controls on microbial carbon - use efficiency in soils. New Phytol 196:79 91. doi: 10.1111/j.1469 - 8137.2012.04225.x Meigs GW, Donato DC, Campbell JL, et al (2009) Forest fire impacts on carbon uptake, storage, and emission: The role of burn severity in the eastern Cascades, Oregon. Ecosystems 12:1246 1267. doi: 10. 1007/s10021 - 009 - 9285 - x Michelotti L, Miesel J (2015) Source Material and Concentration of Wildfire - Produced Pyrogenic Carbon Influence Post - Fire Soil Nutrient Dynamics. Forests 6:1325 1342. doi: 10.3390/f6041325 Miesel JR, Hockaday WC, Kolka RK, Townsend P A (2015) Soil organic matter composition and quality across fire severity gradients in coniferous and deciduous forests of the southern boreal region. J Geophys Res Biogeosciences 120:1124 1141. doi: 10.1002/2015JG002959 Miller JD, Knapp EE, Key CH, et al (2009a) Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens Environ 113:645 656. doi: 10.1016/j.rse.2008.11.009 42 Mil ler JD, Safford HD (2017) Corroborating evidence of a pre - Euro - American low - to moderate - severity fire regime in yellow pine mixed conifer forests of the Sierra Nevada, California, USA. Fire Ecol 13:58 90. doi: 10.4996/fireecology.1301058 Miller JD, Saffor d HD, Crimmins M, Thode AE (2009b) Quantitative evidence for increasing forest fire severity in the Sierra Nevada and southern Cascade Mountains, California and Nevada, USA. Ecosystems 12:16 32. doi: 10.1007/s10021 - 008 - 9201 - 9 Miller JD, Thode AE (2007) Qua ntifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sens Environ 109:66 80. doi: 10.1016/j.rse.2006.12.006 MTBS (2017) Monitoring trends in burn severity. https://www.mtbs.gov. Nave LE, Vance ED, Swanston CW, Curtis PS (2009) Impacts of elevated N inputs on north temperate forest soil C storage, C/N, and net N - mineralization. Geoderma 153:231 240. doi: 10.1016/j.geoderma.2009.08.012 Nave LE, Vance ED, Swanston CW, Curtis PS (2011) Fi re effects on temperate forest soil C and N storage. Ecol Appl 21:1189 1201. NCEI - NOAA (2017) National centers for environmental information. https://www.ncei.noaa.gov. Oakley BB, North MP, Franklin JF (2003) The effects of fire on soil nitrogen associate d with patches of the actinorhizal shrub Ceanothus cordulatus . Plant Soil 254:35 46. doi: 10.1023/A:1024994914639 Science 333:988 993. doi: 10.1126/science.1 201609 Paul EA, Follett RF, Leavitt SW, et al (1997) Radiocarbon dating for determination of soil organic matter pool sizes and dynamics. Soil Sci Soc Am J 61:1058 1067. doi: 10.2136/sssaj1997.03615995006100040011x Paul EA, Harris D, Collins HP, et al (199 9) Evolution of CO2 and soil carbon dynamics in biologically managed, row - crop agroecosystems. Appl Soil Ecol 11:53 65. doi: 10.1016/S0929 - 1393(98)00130 - 9 Paul EA, Morris SJ, Conant RT, Plante AF (2006) Does the acid hydrolysis - incubation method measure me aningful soil organic carbon pools? Soil Sci Soc Am J 70:1023 1035. doi: 10.2136/sssaj2005.0103 43 Perry DA, Oren R, Hart SC (2008) Forest Ecosystems. Johns Hopkins University Press, Baltimore Pinheiro J, Bates D, Debroy S, Sarkar D (2019) nlme: Linear and no nlinear mixed effects models. Pinheiro JC, Bates DM (2000) Mixed - effects models in S and S - Plus. Springer - Verlag, New York Post WM, Kwon KC (2000) Soil carbon sequestration and land - use change: Processes and potential. Glob Chang Biol 6:317 327. doi: 10.1 046/j.1365 - 2486.2000.00308.x R Core Team (2019) R: A language and environment for statistical computing. Rasse DP, Rumpel C, Dignac MF (2005) Is soil carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant Soil 269:341 356. doi: 10.1007/s11104 - 004 - 0907 - y Robertson GP, Wedin D, Groffman PM, et al (1999) Soil carbon and nitrogen availability. In: Robertson GP, Coleman DC, Bledsoe CS, Sollins P (eds) Standard soil methods for long - term ecological research. Oxford University Press, Ne w York, pp 258 271 Ruefenacht B, Finco MV, Nelson MD, et al (2008) Conterminous U.S. and Alaska Forest Type Mapping Using Forest Inventory and Analysis Data. Photogramm Eng Remote Sens 74:1379 1388. doi: 10.14358/PERS.74.11.1379 Safford HD, Miller JD, Schmidt D, et al (2008) BAER soil burn severity maps do not measure fire effects to vegetation: A comment on Odion and Hanson (2006). Ecosystems 11:1 11. doi: 10.1007/s10021 - 007 - 9094 - z Sawyer R, Bradstock R, Bedward M, Morrison RJ (2018) Fire intensity dri ves post - fire temporal pattern of soil carbon accumulation in Australian fire - prone forests. Sci Total Environ 610 611:1113 1124. doi: 10.1016/j.scitotenv.2017.08.165 Schmidt MWI, Torn MS, Abiven S, et al (2011) Persistence of soil organic matter as an eco system property. Nature 478:49 56. doi: 10.1038/nature10386 Singh N, Abiven S, Maestrini B, et al (2014) Transformation and stabilization of pyrogenic organic matter in a temperate forest field experiment. Glob Chang Biol 20:1629 1642. doi: 10.1111/gcb.124 59 Sinsabaugh RL, Reynolds H, Long TM (2000) Rapid assay for amidohydrolase (urease) activity in environmental samples. Soil Biol Biochem 32:2095 2097. doi: 10.1016/S0038 - 0717(00)00102 - 4 44 Six J, Jastrow JD (2002) Organic matter turnover. Encycl Soil Sci 936 942. doi: 10.1081/E - ESS - 120001812 Smithwick EAH, Turner MG, Mack MC, Chapin FS (2005) Postfire soil N cycling in northern conifer rorests affected by severe, stand - replacing wildfires. Ecosystems 8:163 181. doi: 10.1007/s10021 - 004 - 0097 - 8 Soil Survey Staff Official soil series descriptions. In: Nat. Resour. Conserv. Serv. United States Dep. Agric. www.nrcs.usda.gov. Tiemann LK, Billings SA (2011) Changes in variability of soil moisture alter microbial community C and N resource use. Soil Biol Biochem 43:18 37 1847. doi: 10.1016/j.soilbio.2011.04.020 Trumbore S (2000) Age of soil organic matter and soil respiration: radiocarbon constraints of belowground C dynamics. Ecol Appl 10:399 411. Trumbore SE (1997) Potential responses of soil organic carbon to global environmental change. Proc Natl Acad Sci 94:8284 8291. doi: 10.1073/pnas.94.16.8284 Turner MG, Smithwick EA, Metzger KL, et al (2007) Inorganic nitrogen availability after severe stand - replacing fire in the Greater Yellowstone ecosystem. Proc Natl Acad Sci U S A 104:4782 4789. doi: 10.1073/pnas.0700180104 Vitousek PM, Gosz JR, Grier CC, et al (1979) Nitrate losses from disturbed ecosystems. Science 204:469 474. Bio geochemistry 13:87 115. Wan S, Hui D, Luo Y (2001) Fire effects on nitrogen pools and dynamics in terrestrial ecosystems: A meta - analysis. Ecol Appl 11:1349 1365. Wang Q, Zhong M, Wang S (2012) A meta - analysis on the response of microbial biomass, dissolve d organic matter, respiration, and N mineralization in mineral soil to fire in forest ecosystems. For Ecol Manage 271:91 97. doi: 10.1016/j.foreco.2012.02.006 Westerling AL, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increas e western U.S. forest wildfire activity. Science 313:940 943. doi: 10.1126/science.112883 45 CHAPTER 2: HOW DO SOIL MICROBIAL COMMUNITIES RESPOND TO FIRE IN THE INTERMEDIATE TERM? INVESTIGATING DIRECT AND INDIRECT EFFECTS 2 2.1 ABSTRACT Fires transform soil microbial communities directly via heat - induced mortality and indirectly by altering plant and soil characteristics. Emerging evidence suggests the magnitude of changes to some plant and soil properties increases with burn severity, but the persistence of changes varies among plant and soil characteristics, ranging from months to years post - fire. Thus, which environmental attributes shape microbial communities at intermediate time points during ecosystem recovery, and how these characteristics vary with sev erity, remains poorly understood. I identified the network of properties that influence microbial communities three years after fire, along a burn severity gradient in Sierra Nevada mixed - conifer forest. I used phospholipid fatty acid (PLFA) analysis and b acterial 16S - rDNA amplicon sequencing to characterize the microbial community in mineral soil. Using structural equation modelling, I applied a systems approach to identifying the interconnected relationships among severity, vegetation, soil, and microbial communities. Dead tree basal area, soil pH, and extractable phosphorus increased with severity, whereas live tree basal area, forest floor mass, and the proportion of the 53 µ m soil fraction decreased. Forest floor loss was associated with decreased soil moisture across the severity gradient, decreased live tree basal area was associated with 2 Originally published as: Adkins J., Docherty K., Gutknecht J., Miese l J.R., 2020. How do soil microbial communities respond to fire in the intermediate term? Investigating direct and indirect effects associated with fire occurrence burn severity. Science of The Total Environment 745, 140957. . 46 increased shrub coverage, and increased dead tree basal area was associated with incre ases in total and inorganic soil nitrogen. Soil fungal abundance decreased across the severity gradient, despite a slightly positive response of fungi to lower soil moisture in high severity areas. Bacterial phylogenetic diversity was negatively related to severity and was driven by differences in nutrients and soil texture. The abundance of Bacteroidetes increased and the abundance of Acidobacteria decreased across the severity gradient due to differences in soil pH. Overall, I found that the effects of bu rn severity on vegetation and soil physicochemical characteristics interact to shape microbial communities at an intermediate time point in ecosystem recovery. 2.2 INTRODUCTION Wildfire activity has increased globally over the past several decades (Flannigan et al. 2013) . In the western United States, wildfire frequency increased four - f old and total burned area increased six - fold during 1987 - 2003 compared to 1970 - 1986 (Westerling et al. 2006) ; from 1984 - 2011, total wildfire area in the region increased by >350 km 2 y - 1 , and the number of wildfires > 405 ha increased by seven fires per year (Dennison et al. 2014) . Burn severity, which nic matter (Keeley 2009) , has also increased. In the mixed - da mountain range, t he proportion of high severity fire has more than quadrupled compared to pre - settlement (Miller and Safford 2017) , including approximately doubling between 1984 - 2006 (Miller et al. 2009b) . Fire disturbances can influence ecosystem functions for months to years by impacting plant and microbial communities (Treseder et al. 2004; Holden et al. 2016; Pérez - Valera et al. 2019) . Soil microbial community structure may affect the resilience of the community to future disturbances (Jansson and Hofmockel 2020) , and has been linked to key soil ecosystem p rocesses, including CO 2 flux (Bier et al. 2015) . Responses of microbial 47 communities to fire may thus affect ecosystem stability and govern the transition of an ecosystem from a C source to a C sink during forest recovery (Balser et al. 2006) , but the timescale over which these impacts persist remains poorly characterized. Knowledge of the plant and soil characteristics that shape microbial com munities at intermediate time points during post - fire recovery is currently limited (Dove et al. 2020; McLauchlan et al. 2020) . Most existing publications describing impacts of fire on microbial comm unities focus on changes that occur within one year (e.g. Weber et al., 2014; Whitman et al., 2019; Xiang et al., 2014) or several decades (e.g. Cutler et al., 2017; LeDuc et al., 2013; Treseder et al., 2004) post - fire, and boreal forests are currently over - repr esented (Xiang et al. 2014a; Holden et al. 2016; Whitman et al. 2019) relative to other ecosystem types. Furthermore, despite decades - long calls for studies assessing the role of burn severity in shaping microbial communities (Hart et al. 2005; Pressler et al. 2018) , relatively few publications have accounted for severity (but see Holden et al., 2016; Sáenz de Miera et al., 2020; Weber et al., 2014; Whitman et al., 2019; Xiang et al., 2014) . Therefore, it is relatively unknown how forecasted increases in fire activity will influence the environmental drivers and characteristics of microbial communities at intermedi ate time points during post - fire recovery. This is a critical knowledge gap, as the response of microbial communities to changing fire regimes may either exacerbate or modulate the magnitude of fire feedbacks to climate change. For example, fire can cause decreases in fungal biomass and increases in copiotrophic bacteria, which may lead to increases in post - fire soil C efflux because bacterial biomass has faster turnover times compared to fungal biomass (Rousk and Bååth 2011) , and copiotrophic bacteria are associated with faster decomposition rates compared to oligotrophic taxa (Orwin et al. 2018) . 48 Immediate, direct effects of fire on soil microbial communities are driven by soil heating, which alters community structure via differential survival of heat - sensitive versus heat - re sistant microbes. For example, lower fungal biomass is frequently observed in recently burned ecosystems relative to unburned controls (Dooley and Treseder 2012; Pressler et al. 2018) , as fungi may be more sensitive to soil heating than bacteria (Neary and DeBano 2005) . Relatedly, the spore - forming ability of the bacterial phyla Firmicutes and Actinobacteria may convey heat resistance and explain the increase in their dominance in the immediate aftermath of fires (Prendergast - Miller et al. 2017; Whitman et al. 2019) . Fire also appears to favor microorganisms that exhibit a copiotrophic growth strategy. Predicted 16S - rRNA gene copy number, a trait associated with rapid growth rates, increases in respons e to fire in environments ranging from Mediterranean shrubland (Pérez - Valera et al. 2019) to Canadian boreal forests (Whitman et al. 2019) . As post - fire ecosystem succession progresses, the direct im pacts of fire on microbes may become less important as environmental characteristics become dominant drivers of microbial communities (Hart et al. 2005; Ferrenberg et al. 2013) . Fires may influence microbial communities over intermediate (i.e. 1 - 10 years) and long timescales (>10 years) by changing t he soil environment (i.e. soil pH, nutrient status, organic matter pools, texture, moisture, and temperature) (Certini 2005; Hart et al. 2005; Neary and DeBano 2005) . Fire induced losses to plant biomass can indirectly influence the soil environment and microbial community by leading to less soil nutrient and water uptake by plants, fewer litte r inputs, and decreased influence of plant canopy on soil temperature (Hart et al. 2005; Neary et al. 2005; Ficken and Wright 2017) . The persistence of fire - induced changes varies for different plant and soil properties and can range from months or years (e.g. pH, nutrient status; Certini, 2005; Wan et al., 2001) to decades (e.g. plant canopy coverage, soil organic matter pools; Fornwalt et al., 2018; Neary et 49 al., 2005) . The magnitude of these changes likely depends on burn severity, but only a few studies have investigated the links between severity and soil properties or how plant communities modulate these changes. Studies that have directly assessed relationships between burn severity and soil properties in mixed - conifer forests have found that severity affects the magnitude of change to pH (We ber et al. 2014) , total inorganic nitrogen (TIN) concentrations, forest floor (i.e. organic horizon) mass (Adkins et al. 2019b) , forest floor pyrogenic C (i.e. charcoal associated C; PyC) concentrations (Maestrini et al. 2017) , and soil texture (Ulery and Graham 1993) . Differential impacts of burn s everity on soil properties may lead to differences in microbial communities across severity gradients. For example, soil pH affects microbial diversity and community structure (Lauber et al. 2009; Rousk et al. 2010; Docherty et al. 2015) , loss of forest floor may decrease habitat availability for fungi (Joergensen and Wichern 2008; Baldrian et al. 2012) , and increases in PyC may favor lignolytic microbes adapted to decomposing aromatic substrates (Czimczik and Masiello 2007) . The direct and indirect effects of fire on soils represent a system level change to the microbial environment. A systems approach to assessing changes in microbial communities will improve understanding of the linkages between fire and microbial communities by disentangling the interconnected effects of fire on soil and vegetation. Previous research on the impacts of fire on soil microbial communities has tended to focus solely on soil characteristics, and researchers ha ve called for studies that consider vegetation, soil, and microbes as an interacting network (Pressler et al. 2018) . Here, I examined the impacts of burn severity on soil microbial community structure via plant and soil properties three years after a wildfire burned California mixe d - conifer forest in the Sierra Nevada mountain range. I took a systems approach to understanding the impacts of burn severity by utilizing structural equation models (SEM) that accounted for 50 impacts of severity, plant coverage, and soil properties on micro bial communities. My overarching hypothesis is that soil microbial community structure varies with burn severity, and that such differences can be explained by severity - associated differences in mineral soil nutrient concentrations, soil moisture content, and soil texture. I also hypothesized that plant coverage would influence microbial communities only indirectly via impacts on soil nutrients. Lastly, I hypothesized that accounting for burn severity instead of fire occurrence only would better explain cha nges to the microbial community by accounting for more of the variability in plant and soil characteristics in the post - fire environment. I used two methodological approaches which examine different characteristics of soil microbial communities. I used pho spholipid fatty acid (PLFA) analysis to quantify total microbial biomass and the abundance of ecologically distinct guilds of microbes, including general fungi, Gram - positive bacteria, and Gram - negative bacteria. I used 16S - rDNA amplicon sequencing to exam ine bacterial communities at a finer resolution, characterizing the abundance of bacteria at the phylum level, bacterial phylogenetic diversity, and the ratio of oligotrophic - to - copiotrophic (O:C) bacterial taxa. 2.3 METHODS 2.3.1 Site description and field methods My study was conducted in mixed - conifer forest (Ruefenacht et al. 2008) in the northern Sierra Nevada mountain range, California, USA (Plumas and Lassen National Forests; Fig . S 2. 1). The forest is dom inated by Pinus ponderosa Lawson & C. Lawson , P. lambertiana Douglas , P. jeffreyi Balf. , Abies concolor (Gord. & Glend.) Lindl. ex Hildebr. , Pseudotsuga menziessi (Mirb.) Franco , and Calocedrus decurrens (Torr.) Florin , with lesser cover by Quercus kelloggi Newberry . Soils in my plots were from the Skalan soils series, a loamy - skeletal, isotic, mesic Vitrandic Haploxerlaf, and the Kinkel series, a loamy - skeletal, mixed, superactive, mesic 51 Ultic Palexeralf. During the peri od from approximately 1500 - 1850 C.E., the mean fire rotation in the region was 23 years for dry mixed - conifer forests and 31 years for moist mixed - conifer forests of the Sierra Nevada and Cascade mountain ranges in California; between 6 - 8% of burned forest experienced high severity fires on average (Mallek et al. 2013) . The proportion of high severity fires in mixed - conifer forests increased to 25 - 30% for the period from 1984 - 2009 (Mallek et al. 2013) . The 30 year mean annual precipitation is 10 80 mm and mean annual temperature is 10.6 º C (determined at the nearest weather station, Quincy, CA). My study focuses on the Chips Fire (Lat: 40.095 Long: - 121.199), which was ignited by lightning and burned approximately 32,000 ha between July 28 to Augu st 31, 2012. Extended differenced Normalized Burn Ratios (dNBR) fire severity estimates, determined via Landsat TM imagery at a 30 m resolution, are collected in the growing season after fire occurrence, and are sensitive to post - versus pre - fire changes in vegetation and soil exposure (Parsons et al. 2010) . The dNBR metric is correlated with absolute change in biomass and thus the magnitude of soil heating (i.e. more biomass burned equals greater heat flux) (Safford et al. 2008) . dNBR therefore represent a combination of effects to both vegetation and soil, so I unburned pixels) are continuous, but can be thresholded into severity ca tegories: approximately 20% of the area affected by the Chips Fire was classified as high severity, 30% as moderate severity, 38% as low severity, and 12% as unburned (MTBS 2017) . Immediately after wildfires, the USDA Burned Area Emergency response team often assesses soil burn severity (SBS). During the SBS mapping process, dNBR maps are modified b ased on field assessment of soil conditions, including loss to soil litter and duff layers, ash color and depth, soil structure, soil water repellency, and damage to fine roots (Parsons et al. 2010) . 52 The USDA Forest Service established perma nent plots and evaluated forest composition and structure across categories of total burn severity in summer 2014 (Alvey 20 16; Maestrini et al. 2017) . I randomly selected 17 of these plots (4 unburned, 6 low SBS, 5 moderate SBS, and 2 high SBS; dNBR range 1 - 1004) for soil and tree basal area sampling in summer 2015 (i.e. three years post - fire; Fig. S 2. 1 ). Plot level shrub coverage estimates were provided by USDA Forest Service staff based on measurements performed in 2014 (i.e. two years post - fire) within a 16 m sampling radius following a standard protocol for the Common Stand Exam (USDA 2015) . Although it is possible that small changes in shrub cover may have changed between 2014 and 2015, shrub data from 2014 were the only data available as they were not assessed at the plot level during my soil sampling. Because the shrub species present at this site are perennial species, I assumed that shrub cover in 2014 represented cover in 2015. Plot characteristics and sampling methods have been described in detail previously (Maestrini et al. 2017; Adkins et al. 2019b) . Briefly, plot elevation ranged fr om 1217 - 1641 m asl and were located on a variety of aspects with slopes < 50%. At each plot, I measured tree diameters at breast height ( DBH ) for all live and dead stems >10 cm DBH within an 11.3 m sampling radius, and I used these values to calculate live and dead tree basal areas at the plot level. I collected forest floor and mineral soil at azimuths of 0 º , 120 º , and 240 º at 17 m from the plot center, resulting in a total of 51 forest floor samples and 51 mineral soil samples . The forest floor comprises the plant litter and duff layers, and is equivalent to the combined Oi, Oe, and Oa horizons in the USDA Soil classification system (Perry et al. 2008) . At each sampling azimuth, I measured forest floor depth, collected all forest floor material from within a 15 cm diameter circular sampling frame, and collected mineral soi l to 5 cm using a stainless - steel scoop. I stored mineral soils on ice for 2 - 7 days after collection and shipped them to the lab on ice. Subsamples for PLFA and DNA 53 analysis were stored at - 80 º C, and the remainder of the soils were refrigerated at 4 º C un til processing. 2.3.2 Laboratory methods Soil processing and chemical analyses Detailed methods for bulk sample processing and chemical analysis have been described previously (Adkins et al. 2019b) . Briefly, I determined total forest floor dry mass, and pulverized forest floor subsamples by sequentially processing in a Waring commercial lab b lender (Conair Inc., Stamford Ct, USA) and a ball mill (SPEX Sample Prep LLC, Metuchen, NJ, USA). I oven - dried the pulverized forest floor at 60 analysis (Costech Analytical Technologies Inc., Valencia , CA, USA). I sieved field - moist mineral soils (2 mm ) and subsampled for determination of total C, N, PyC, ammonium (NH 4 ), nitrate (NO 3 ), extractable phosphorus (P), pH, proportion of sand + particulate organic matter (sand+POM ), soil moisture, PLFA, and 16S - rDNA analysis. For C, N and PyC analysis, I oven - dried subsamples at 105 C (C and N) or 60 C (PyC) and pulverized in a ball mill prior to analysis. I used field - moist subsamples for NH 4 , NO 3 , and extractable P analysis, an d air - dried subsamples for pH determination. I oven - dried subsamples at 60 sand+POM proportion. I determined soil moisture gravimetrically as the mass difference between subsamples oven - dried at 105 C and field - moist samples. I det ermined PyC concentrations using weak nitric acid digestion (Kurth et al. 2006; Maestrini et al. 2017) . I measured NH 4 and NO 3 concentrations spectrophotometrically following extraction with 2 M KCl (Sinsabaugh et al. 2000; Doane and Horwáth 2003) . I measured P concentration after extraction with 0.5 M NaHCO 3 (Olsen et al. 1954) . I determined pH using a 1:2 (w:v) soil slurry (Oakton pH 700, Oakton Instruments, Vernon Hills, IL, USA). I quantified sand+POM as the 54 mass proportion of dispersed soil that did not pass through a 53 µ m sieve during wet sieving (Cambardella and Elliott 1993) . PLFA analysis I performed PLFA analysis using a modified vers ion of the Bligh and Dyer method (Bligh and Dyer 1959; Schmidt et al. 2015) . I freeze - dried 6 g mineral soil subsamples and e xtracted fatty acids three times using a 0.9:1:2 mixture of 0.15 M citrate buffer:chloroform:methanol. I separated the phases using a 0.9:1 ratio of citrate buffer:chloroform to isolate lipids in the chloroform phase. I separated lipid classes via silica a cid chromatography, followed by methylation of phospholipids through alkaline methanolysis. I analyzed the isolated FAMEs on a Gas Chromatograph System (Agilent 7890, Agilent Technologies Inc., Santa Clara, CA, USA). I converted peak areas to nmol lipid g soil - 1 using internal standards. I used the sum of all lipids with less than 19 carbons as an index for microbial biomass. I used specific lipids as biomarkers to distinguish microbial groups within the community. I considered the sum of 18:1 - negative bacterial indicators, and the sum of all iso - and anteiso - branched lipids as Gram - positive bacterial indicators. I considered the sum of 14:0, 15:0, 16:0, and 18:0 lipids as non - specific bacterial indicators. I used the ratio of mean abundances of fungal biomarkers to bacterial l subsamples (one from a high severity plot and one from an unburned plot) were lost during PLFA processing and were excluded from further analysis. 55 DNA extraction and sequence analysis I extracted DNA from 0.25 g of fresh soil using a DNA isolation kit ( MoBIO PowerSoil, MoBIO laboratories, Carlsbad, CA), according to the manufacturer's instructions. I used Illumina - MiSeq to amplify the V4 region of the 16S - rRNA gene using 515f/806r universal primers (Caporaso et al. 2010) . DNA sequences were processed using the QIIME (v 1.9) bioinformatics pipeline (Caporaso et al. 2010) . I merged forward and reverse sequence reads using the pandaseq (v 2.6) algorithm (Masella et al. 2012) , and I removed chimeric sequences de novo using the USEARCH (v 6.1) algorith m (Edgar 2010) . I clustered sequences into operational taxonomic units (OTUs) using a 97% similarity thres hold via comparison to the SILVA SSURef database (v 128) (Quast et al. 2013) . I removed contaminant OTUs using the filter_otus_from_otu_table.py command, and I removed OTUs associated with Archaea, mitochondria, and chloroplasts using the filter_taxa_from_otu_table.py command. I calculated OTU relative abundance at the phylum level using the summarize_taxa.py command. I PD_whole_tree option t o the alpha_diversity.py command (Faith 1992) . I calculated O:C as the ratio of the sum of relative abundances of all taxa classified within the phyla Acidobacteria and Verrucomicrobia to the sum of the relative abundances of all taxa classified within Actinobacteria , - proteobacteria , Firmicutes , and Bacteroidetes (Fierer et al. 2007; Fierer et al. 2012a; Ramirez et al. 2012) . After sequence processing, there were 19,527 - 45,893 sequences per sample (mean=30,570; SD=4827). I did not rarefy my sa mples because rarefying reduces statistical power and is not useful when library size varies by less than ten - fold (McMurdie and Holmes 2014; Weiss et al. 2017) . Raw sequence data are accessible from the NCBI Sequence Read Archive (SRA) under project ID PRJNA632607, accession numbers SAMN14195883 - 5931. 56 2.3.3 Stati stical methods Univariate relationships between fire, soil, vegetation, and microbial characteristics I assessed univariate relationships using maximum - likelihood linear mixed models constructed in the nlme package (v 3.1.140) (Pinheiro et al. 2019) in the R statistical computing envir onment (v 3.6.1) (R Core Team 2019) . My linear models included a plot - identifier as a random effect, and either fire occurrence, total burn severity, or SBS as the explanatory variable; I treated fire occurrence and SBS as categorical variabl es and total burn severity as a continuous variable. The four unburned plots were included in my severity models. I examined residual distributions to assess the assumption of normality and determined that lipid - derived absolute abundance and microbial bio mass values should be log - transformed. I determined whether fire occurrence, total burn severity, or SBS best explained each response by comparing Akaike Information Criterion (AIC) values among the models. For models assessing the response of vegetation, I compared only fire occurrence and total burn severity models. I performed differential expression analysis and assessed the log 2 - fold change in individual bacterial OTUs in response to fire occurrence using the edgeR package (v 3.28.1) (Robinson et al. 2010) . I aggregated OTUs within bacterial families and calculated mean log 2 - fold change to identify families that exhibited different abundances in burned and unburned areas. Multivariate relationships between fire, soil, vegetation, and microbial communities I assessed the relationships between fire occurrence, burn severity, and lipid - based and 16S - based communities by performing permutational analysis of variance (PERMANOVA) analysis on Bray - Curtis dissimilatory m atrices. Dissimilatory matrices were based on the relative abundances (%) of lipids and bacterial phyla, and PERMANOVAs were performed using the adonis2 function within the vegan package (v 2.5.6) (Oksanen et al. 2019) . I assessed 57 relationships of fire occurrence and severity, vegetation, soil characteristics, and whole microbial communities using PCoA analysis within the vegan package. I c alculated the correlations of variables with the resulting principle coordinates using the envfit function in vegan. System scale relationships between fire, soil, vegetation, and microbial communities using structural equation modelling I constructed SE Ms using the piecewiseSEM package (v 2.1.0) (Lefcheck 2016) and the nlme package (Pinheiro et al. 2019) . Piecewise SEM is a multivariate statistical technique that incorporates multiple explanatory and response variables into a single causal network, repr esented as a set of regression equations (Lefcheck 2016) . SEMs quantify path coefficients for direct and indirect drivers of an explanatory variable on a response variable, which can be combined into a single compound coefficient to assess the overall effect (Grace 2006) . I used SEMs to determine direct and indirect drivers of the absolute abundance (nmol l ipid g - 1 soil) of lipid - derived microbial guilds and the absolute abundance (count of sequences within phyla) of the four most abundant bacterial phyla determined using 16S - rDNA analysis. I recognize that there are potential issues with the use of absolute rather than relative abundance of 16S - rDNA due to differences in sample sequence depth and rRNA gene copy number among phyla. However, due the similarity in library sizes among my samples, using absolute versus relative abundance is unlikely to affect my results. Moreover, the use of proportional abundance for analyzing microbiome data has been found to increase false positive rates and spurious correlations (Friedman and Alm 2012; McMurdie and Holmes 2014) . I also constructed SEMs for microbial biomass, F:B, bacterial phylogenetic diversity, and O:C. I constructed an initial SEM metamodel composed of multivariate linear mixed models with vegetation and soil characteristics as response variables. The component linear mixed 58 models assessing the response of vegetation included total burn severity as the explanatory variable, and the mixed model assessing shrub cover also initially included live and dead tree basal area as explanatory variables. Component mixed mod els assessing soil characteristics included total burn severity and the three plot - level vegetation characteristics as explanatory variables. The model assessing mineral soil moisture also included the overlaying forest floor mass as an explanatory variabl e. After initial fit, the SEM metamodel was modified by sequentially removing explanatory variables that exhibited p - values 0.15. The resulting SEM metamodel was then used as a starting model to construct SEMs describing drivers of individual microbial c ommunity characteristics. All initially fit SEMs included a direct link between the microbial characteristic of interest and total burn severity, and direct links from each soil and vegetation variable to the microbial characteristic. SEMs describing lip id - derived microbial groups also included a direct link to microbial biomass, and SEMs describing 16S - derived microbial groups included a direct link to F:B. All SEM paths were initially fit using linear mixed models that included a plot identifier as a ra ndom effect. I evaluated the assumption of conditional independence between explanatory variables in my SEMs by examining the significance of correlation coefficients provided in the output of the psem function in the piecewiseSEM package. If the correlati on between explanatory variables was highly significant (i.e. p 0.005) I specified correlated errors between those variables in my SEMs. I then sequentially removed the least significant path from the SEM until all paths exhibited p - values I asse ssed the goodness of fit for each SEM - value. A non - - value (p>0.05) indicates that the modeled relationships are supported by the data (Lefcheck 2016) . I then fitted an alternate version of the SEM in which fire occurrence was substituted for total burn severity, and the relative support for 59 these models was compared using the full - model AIC (Shipley and Douma 2020) . I considered an SEM including severity versus fire occurrence to have more support if the full - model AIC between the models was fficients in all SEMs are presented on the standardized scale implemented in the piecewiseSEM package, which standardizes coefficients by multiplying the raw coefficient by the ratio of the standard deviation of the explanatory variable to the standard dev iation of the response variable. I considered path coefficients statistically significant 2.4 RESULTS 2.4.1 Relationships between vegetation and soil characteristics and fire occurrence and severity Univariate analyses Live and dead tree basal area and s hrub coverage were significantly related to fire occurrence and total burn severity (Fig. 2. 1). AIC values indicated that total burn severity had more support than fire occurrence for explaining dead tree basal area, which increased with total burn severity (Fig 2. 1b). AIC values did not indicate a preference for total burn severity versus fire o ccurrence for explaining live tree basal area or shrub coverage. Live tree basal area was ~3.7 times lower in burned areas compared to unburned areas ( Fig. 2. 1c) , and shrub coverage was ~3.9 times higher in burned areas compared to unburned areas (Fig. 2. 1 e). AIC values indicated that fire occurrence had more explanatory power than total burn severity for differences in forest floor mass. Forest floor mass was ~3.5 times lower in burned areas compared to unburned areas (Fig. 2. 2a). Total burn severity was supported over fire occurrence for explaining differences in mineral soil sand+POM proportion, pH, total C, total N, TIN, and extractable P (Figs. 2. 2 and 2. 3). Sand+POM was negatively associated with total burn 60 severity (Fig. 2. 2d), and pH was positively associated with total burn severity (Fig. 2. 2f). Total soil N, TIN, and extractable P all increased with total burn severity (Figs. 2. 3d, 2. 3f, and 2. 3h). For all univariate models (including for microbial groups), AIC values indicated that SBS only had mo re explanatory power than both fire occurrence and total burn severity for TIN concentrations (Fig. S 2. 2), so I do not discuss SBS extensively in the remainder of this paper (but see supplemental figures). Figure 2.1 Relationship of fire occurrence (col umn 1) and dNBR (column 2) to live tree basal area, dead tree basal area, and shrub coverage in 17 plots within the Chips Fire perimeter. 61 Figure 2.2 Relationship of fire occurrence (column 1), soil burn severity (column 2), and total burn severity (colu mn 3) to forest floor mass, mineral soil sand+POM (5 cm) and mineral soil pH. Marginal r 2 values are provided for soil properties that linear mixed models indicated were significantly affected by the explanatory variable of interest at =0.05. Capital lett ers in soil burn severity figures denote Tukey - adjusted significant differences among severity levels. Slope values are provided in total burn severity figures for properties that were significantly affected at =0.05. 62 Figure 2.3 Relationship of fire occurrence (column 1), soil burn severity (column 2), and total burn severity (column 3) to soil properties for mineral soils collected to 5 cm depth. Marginal r 2 values are provided for soil properties that linear mixed models indicat ed were significantly affected by the explanatory variable of interest at =0.05. Capital letters in soil burn severity figures denote Tukey - adjusted significant differences among severity levels. Slope 63 values are provided in total burn severity figures fo r properties that were significantly affected at =0.05. SEM analyses My SEM metamodel revealed direct and indirect drivers of severity on soil characteristics (Fig. 2. 4), some of which were not captured by univariate analyses. Similar to my univariate a nalyses, SEM revealed direct relationships between total burn severity and live tree basal area, dead tree basal area, extractable P, and pH. Additionally, I found a direct, negative link between total burn severity and sand+POM that was tempered by a nega tive relationship between live tree basal area and sand+POM, leading to a compound path coefficient between total burn severity and sand+POM of - 0.40. For other characteristics, the relationship with severity was entirely indirect. Total burn severity affe cted forest floor mass indirectly via a negative association with live tree basal area (compound coefficient= - 0.42). Forest floor mass was positively associated with soil moisture, leading to an indirect negative relationship between moisture and total bur n severity (compound coefficient= - 0.20). The positive relationship between total burn severity and shrub coverage was driven by the negative relationship between live tree basal area and shrub coverage (compound coefficient=0.31). The positive relationship between total burn severity and total N was driven by a positive relationship with dead tree basal area (compound coefficient=0.30). The positive relationship between total burn severity and TIN was driven by total N and a negative relationship between TI N and live tree basal area (compound path coefficient=0.31). 64 Figure 2.4 General model depicting initially fitted structural equation model of direct and indirect links between fire severity metrics and PLFA - based microbial group absolute abundance and 1 6S - based bacteria phylum absolute abundance. Microbial biomass was also included as an explanatory variable of PLFA - based microbial group abundance, and fungal - to - bacterial ratio (F:B) was included as an explanatory variable for 16S - based models. Paths wer e fit using linear mixed models with a random plot effect. Standardized coefficients are displayed for links between total burn severity (dNBR) and endogenous variables. Forest floor mass was negatively related to soil burn severity; soil burn severity is a categorical variable, so a coefficient is not displayed. Abbreviations: N= nitrogen concentration, PyC = pyrogenic carbon concentration, C = carbon concentration, TIN = total inorganic nitrogen concentration, P = phosphorus concentration, POM + Sand = p roportion of particulate organic matter plus sand soil fraction 2.4.2 Relationships between soil microbial communities and fire occurrence and severity Microbial abundance based on lipid indicators Fire affected lipid communities primarily by impacting fungal lipids. PERMANOVAs of a Bray - Curtis dissimilatory matrix indicated that lipid communities were significantly related to fire occurrence (r 2 =0.11; p=0.002) and total burn severity (r 2 =0.14; p=0.0 01). Based on AIC, 65 total burn severity best explained the relative abundance of general fungal lipids and F:B. Fire occurrence had more explanatory power for the relative abundance of Gram - negative bacteria; neither fire occurrence nor total burn severity had more power for explaining the relative abundance of total bacterial lipids, Gram - positive lipids, or total microbial biomass. Despite differences in AIC values, based on univariate analyses, the only lipid group significantly related to fire occurrence or burn severity was the relative abundance of general fungal lipids, which exhibited a negative relationship with total burn severity (Fig. 2. 5a; slope= - 0.008; marginal r 2 =0.17; p=0.038). Figure 2.5 Relative abundance of microbial groups based on PLFA analysis (a and b) and the nine most abundant bacterial phyla (c and d) across a gradient of soil burn severity and total burn severity in mineral soils (0 - 5 cm). Total burn severity is grouped by approximate dNBR quartiles in my data. Bacterial composi tion and diversity based on 16S - rDNA analysis Fire occurrence and burn severity had numerous effects bacterial communities, analyzed with 16S - rDNA methods. Each soil sample harbored bacterial sequences representing 3,959.49 ± 153.69 bacterial OTUs. PERMAN OVAs of bacterial communities indicated that they were significantly related to fire occurrence (r 2 =0.12; p=0.002) and total burn severity (r 2 =0.24; 66 p=0.001). AIC values indicated that neither fire occurrence nor total burn severity better s phylogenetic diversity, which was negatively related to fire occurrence (Fig. 2. 6c). Total burn severity best explained differences in O:C ratio, which was negatively related to burn severity (Fig. 2. 6f). Among all soil samples, the most abundant bacterial groups were Bacteroidetes (18.65 ± 1.35%) , - Proteobacteria ( 13.89 ± 0.56%), Acidobacteria ( 12.50 ± 1.44%), and Actinobacteria ( 9.65 ± 0.40%) (Fig. 2. 5b). Based on AIC values, total burn severity better e xplained differences in Bacteroidetes and Acidobacteria relative abundance, whereas fire occurrence best explained - Proteobacteria and Actinobacteria relative abundance . Bacteroidetes relative abundance increased with total burn severity (slope=0.012; mar ginal r 2 =0.42; p < 0.001). The mean log 2 - fold change in abundance of Bacteroidetes OTUs in response to fire was 0.58 ± 0.06 (Fig S 2. 4). The five most abundant families in Bacteroidetes ( Chitinophagaceae , Cytophagaceae , Sphingobacteriaceae , Flavobacteriacea e , and unclassified env.OPS 17) all exhibited significantly positive log 2 - fold change in abundance in response to fire occurrence. - Proteobacteria relative abundance was not significantly related to fire occurrence at the class level (p=0.48). The mean log 2 - - Proteobacteria OTUs in response to fire was 0.20 ± 0.05 (Fig. S 2. 5). Acidobacteria relative abundance was negativel y related to burn severity (Fig. 2. 5b; slope= - 0.015; marginal r 2 =0.49; p<0.001) ; the mean log 2 - fold change in abundance of Acidobacteria OTUs in response to fire was - 0.59 ± 0.05 (Fig. S 2. 6). All four identified families within Acidobacteria exhibited nega tive log 2 - fold response to fire occurrence. Actinobacteria relative abundance decreased from 11.06 ± 0.40% in unburned areas to 9.22 ± 0.45% in burned areas (marginal r 2 =0.08; p=0.007); the mean log 2 - fold change in abundance of Actinobacteria OTUs in response to fire was 0.03 ± 0.05 (Fig. S 2. 7) . At the family 67 level, OTUs within some families (e.g., Frankiaceae and Mycobacteriaceae ) exhibited negative log 2 - fold response to fire occurrence, while Micrococcaceae exhibited a positive response. Figu re 2.6 Relationship of microbial community characteristics including lipid - based fungal - to - bacterial ratio,16S - - based oligotrophic:copiotrophic bacterial taxa ratio, with fire occurrence (column 1), soil burn se verity (column 2), and total burn severity (column 3) in mineral soils (0 - 5 cm). Marginal r 2 values are provided for microbial community characteristics that linear mixed models indicated were significantly affected by the explanatory variable of interest at =0.05. Capital letters in soil burn severity figures denote Tukey - adjusted significant differences among severity levels. Slope 68 values are provided in total burn severity figures for characteristics that were significantly affected at =0.05. 2.4.3 Direct an d indirect soil and vegetation drivers of soil microbial community characteristics Principle coordinates analysis For lipid communities, the first and second PCoA axes explained 33.59% and 22.06% of community variation, respectively (Fig. 2. 7a). In addition to fire occurrence (r 2 =0.14; p=0.001) and total burn severity (r 2 =0.47, p=0.001), four environmental variables were significantly correlated with the PCoA ordination: dead tree basal area (r 2 =0.22; p=0.009), pH (r 2 =0.48; p=0.001), sand+POM (r 2 =0.2 8; p=0.003), and extractable P (r 2 =0.19; p=0.011). For 16S - rDNA - based soil microbial communities, the first and second PCoA axes explained 48.87% and 11.13% of community variation, respectively (Fig. 2. 7b). In addition to fire occurrence (r 2 =0.15; p=0.00 4) and total burn severity (r 2 =0.47; p=0.001), eight environmental variables were significantly correlated with the PCoA ordination: dead tree basal area (r 2 =0.19; p=0.005), shrub coverage (r 2 =0.15; p=0.033), pH (r 2 =0.60; p=0.001), soil moisture (r 2 =0.25; p=0.001), sand+POM (r 2 =0.25; p=0.002), TIN (r 2 =0.16; p=0.031), extractable P (r 2 =0.16; p=0.024), and forest floor mass (r 2 =0.15; p = 0.033). 69 Figure 2.7 Principle coordinates analysis (PCoA) plots of lipid - based communities (a) and 16S - rDNA based bacteria l communities at the phylum level (b) in mineral soils (0 - 5 cm). Vectors represent variables that are significantly correlated with one of the PCoA axes, and vector lengths are scaled based on r 2 values. Solid vectors represent environmental or edaphic var iables, and dashed vectors in a) represent microbial groups based on lipid groupings. Abbreviations: F:B = fungi - to - bacteria ratio, FF Mass = forest floor mass, P = extractable phosphorus concentration, TIN = total inorganic nitrogen concentration, DT - BA = dead tree basal area, SC = shrub coverage 70 SEM analysis SEM models revealed relationships between severity and microbial communities that were not captured by univariate analysis, and full - model AIC values indicated that SEMs including total burn severity had more explanatory power than fire - occurrence models for every microbial characteristic measured (Table 2. 1). Microbial biomass was positively related to live tree basal area, shrub coverage, sand+POM, and total N, resulting in a negative relationship w ith total burn severity that was not captured by univariate analysis. Absolute fungal abundance was directly, negatively related to total burn severity. Additionally, fungal abundance responded negatively to soil moisture, leading to an indirect, positive relationship between total burn severity and fungal abundance. There was also an indirect negative relationship between fungal abundance and burn severity via the relationship of total burn severity with total microbial biomass. My SEMs also revealed relationships between severity and F:B, total bacterial, Gram - negative, and Gram - positive bacterial abundances that were not captured by univariate analyses. F:B was directly, negatively influenced by total burn severity, and indirectly influenced via soil moisture and total N. Total bacterial lipid abundance was negatively influenced by total burn severity via the total microbial biomass path. Gram - negative bacteria had a direct positive link to total burn severity, but relationships wit h live tree basal area, P, and microbial biomass resulted in an overall negative relationship between severity and Gram - negative bacteria. Gram - positive bacteria was indirectly linked to total burn severity via relationships with live tree basal area, soil moisture, TIN, pH, and microbial biomass, leading to a negative relationship with severity. SEMs revealed that total burn severity affected the absolute abundances of Bacteroidetes and Acidobacteria directly and indirectly via pH mediated impacts. There was an indirect, positive relationship between total burn severity and Bacteroidetes via the positive response of 71 Bacteroidetes to pH, and an indirect, negative relationship between total burn severity and Acidobacteria due to the negative response of Acid obacteria to pH. The abundance of Proteobacteria was indirectly related to severity via the influence of pH, live tree basal area, and TIN. Actinobacteria absolute abundance was associated with dead tree basal area and sand+POM, effects that offset one a nother and resulted in an essentially neutral relationship between severity and Actinobacteria . There was an indirect, negative relationship between total burn severity and bacterial phylogenetic diversity via associated decreases in sand+POM and increases in TIN and P. Total burn severity negatively influenced O:C via a direct, negative link and an indirect, negative link via pH; these negative links were tempered by an indirect, positive link via shrub coverage. 72 Table 2.1. Structural equation mo dels describing drivers of soil microbial communities three years after fire in mixed - conifer forest. Standardized coefficients are presented for paths that were significant at =0.05, but non - significant paths were retained in the final model at p 0.15. T he compound coefficient between total burn severity (dNBR) and the microbial characteristic of interest represents the combined direct and indirect effects of severity. Abundance of microbial groups determined via PLFA are presen ted on the log scale, and a bundance of microbial groups determined via 16S - rDNA are sequence counts. Full - model AIC values are presented for the final models that included severity and for models in which fire occurrence (burned versus unburned) was substituted for severity. Abbrevi ations: C = total carbon, DBA = dead tree basal area, F:B = fungal - to - bacterial ratio, FFM = forest floor mass, LBA = live tree basal area, MB = microbial biomass, N = total nitrogen concentration, O:C = oligotroph - to - copiotroph ratio, P = extractable phos phorus, PyC = pyrogenic carbon SC = shrub coverage, , SM = soil moisture, SP = sand+POM, TIN = total inorganic nitrogen Response Variable Structural Equation Model dNBR Compound Coefficient - value) Full - Model AIC Microbial Biomass Biomass=0.57LBA + 0.42N + 0.49SC + 0.31SP LBA= - 0.64dNBR N=0.49DBA SC= - 0.48LBA SP= - 0.70dNBR 0.47LBA DBA=0.61dNBR - 0.22 30.54 (0.062) Severity: 216.13 Fire Occurrence: 224.53 General Fungi Abundance = MB= 0.57LBA + 0.42N + 0.49SC + 0.31SP SM=0.47FFM N=0.49DBA 0.47LBA FFM=0.66LBA DBA=0.61dNBR - 0.32 70.16 (0.41) Severity: 216.18 Fire Occurrence: 230.41 Table 2.1 73 Gram Positive Bacteria Abundance= 0.10LBA + MB=0.57LBA + 0.42N + 0.49SC + 0.31SP pH=0.56dNBR SM=0.47FFM N=0.49DBA SP= - 0.70dNBR 0.47LBA FFM=0.66LBA DBA=0.61dNBR - 0.24 78.76 (0.75) Severity: 119.18 Fire Occurrence: 134.09 Gram Negative Bacteria Abundance=0.11dNBR+ 0.16LBA + 0.95MB + 0.08P MB=0.57LBA + 0.42N + 0.49SC + 0.31SP P=0.41dNBR N=0.49DBA SP= 0.70dNBR 0.47LBA DBA=0.61dNBR - 0.17 54.11 (0.32) Severity: 193.52 Fire Occurrence: 213.36 Total Bacteria Abundance = 1.00MB MB=0.57LBA + 0.42N + 0.49SC + 0.31SP N=0.49DBA SP= - 0.70dNBR 0.47LBA DBA=0.61dNBR - 0.22 64.88 (0.20) Severity: 157.97 Fire Occurrence: 169.5 Bacterial Ratio N=0.49DBA SM=0.66FFM DBA=0.61dNBR FFM=0.66LBA - 0.33 66.66 (0.52) Severity: 420.23 Fire Occurrence: 454.55 Table 2.1 74 Bacteroidetes Abundance=0.29dNBR + 0.48pH pH=0.56dNBR 0.56 0.00 (1.00) Severity: 761.37 Fire Occurrence: 768.15 pH=0.56dNBR N=0.49DBA DBA=0.61dNBR 0.10 44.27 (0.38) Severity: 797.49 Fire Occurrence: 812.71 Acidobacteria 0.21SP pH=0.56dNBR SP= 0.70dNBR 0.47LBA - 0.80 19.81 (0.34) Severity: 782.2 Fire Occurrence: 804.83 Actinobacteria Abundance=0.39DBA + 0.54SP DBA=0.61dNBR 0.02 3.78 (0.71) Severity: 829.24 Fire Occurrence: 837.64 Bacterial Phylogenetic Diversity 0.43TIN P=0.41dNBR N=0.49DBA DBA=0.61dNBR - 0.45 86.23 (0.59) Severity: 837.10 Fire Occurrence: 853.86 Copiotroph Ratio pH=0.56dNBR - 0.80 35.47 (0.49) Severity: 18.49 Fire Occurrence: 37.02 75 2.5 DISCUSSION 2.5.1 A systems approach revealed direct and indirect drivers of severity on plant, soil, and microbial characteristics My results demonstrate that burn severity influences microbial communities at the ecosystem scale via simultaneous and interacting effects on plant and soil characteristics. I found evidence to support my overarching hypothesis that severity shapes microbial communities primarily via soil properties, both through direct linkages and indirectly by altering vegetation characteristics. My PCoA analyses iden tified severity, and vegetation and soil properties related to severity, as drivers of microbial community structure. My results agree with a previous study that found that bacterial communities were related to soil NH 4 , pH, and moisture one year after a w ildfire in boreal forest in China, but that study did not find that microbial community structure differed between the two burn severity categories they considered (Xiang et al. 2014a) . The after fire, fire occurrence alone is sufficient for exp laining differences in microbial communities. Severity may become more important later through the trajectory of forest recovery if impacts of severity on plant communities become more dominant drivers of soil properties (e.g. by three years post - fire as i n my study) (Hart et al. 2005) . This illustrates the importance of taking an ecosystem recovery approach by simultaneously assessing plant and soil effects on microbial communities. By leveraging a systems approach to assessing impacts of burn severity on microbial communities, after enough time for differentiation of stands r ecovering to different burn severities, I disentangled direct versus indirect drivers of soil microbial communities. I found some support for my hypothesis that vegetation characteristics functioned as indirect, rather than 76 direct, drivers of soil microbia l communities via an influence on soil properties, however there were several instances where vegetation properties were directly linked to microbial groups (Table 2. 1). Direct links between vegetation and microbial groups could be caused by differences in the amount or type of litter inputs to soil, mutualistic relationships with mycorrhizae and rhizospheric bacteria, or losses in canopy shading affecting soil temperature (Hart et al. 2005) . I found support for my hypothesis that accounting for burn severity instead of fire occurrence only would better explain changes to the m icrobial community. Full - model AICs indicated that SEMs that included severity had more explanatory power than fire occurrence in every instance, despite univariate model AICs indicating that fire occurrence was more supported for explaining differences in some microbial groups. This may be because severity - based linear models capture more of the variability of the responses of soil and vegetation characteristics to fire and tend to exhibit lower AIC values than fire - occurrence linear models (for example, s ee Figs 2. 1 - 2. 3). 2.5.2 Burn severity has direct and indirect effects on fungal abundance My systems approach provided evidence that the direct negative impacts of fire on fungal abundance persist three years post - fire and that these impacts are modulated by indirect effects of burn severity on soil moisture via live tree basal area and forest floor mass. The direct negative link between total burn severity and absolute fungal abundance could be due to soil heating causing increased fungal mortality (Neary and DeBano 2005) or slower fungal recovery compared to bac teria . The greater tolerance of bacteria compared to fungi for high soil pH has been invoked to explain decreases in fungal abundance in recently burned ecosystems (Dooley and Treseder 2012) , but I did not observe 77 relationships between pH and fungal abundance. Instead, I found that soil moisture and total N were the only edaphic characteristics directly related to fungal abundance. The negative relationship between soil moisture and fungi may resul t in stronger decreases in fungal abundance in high severity areas during the wetter winter and spring months compared to the drier summer and fall. This is because fire - induced changes to forest structure can affect soil moisture in two different ways tha t may vary in importance seasonally. Decreased plant biomass can reduce soil water uptake and evapotranspiration, thereby increasing soil moisture (Neary and DeBano 2005) . Conversely, decreases in canopy and forest floor cover can increase soil exposure to solar radiation, increasing evaporation (Holden et al. 2015) . More than 75% of the annual precipitation in my study area occurs during the winter and spring (NCEI - NOAA 2017) ; thus, in the spring months when s nowmelt and precipitation is abundant, decreased plant evapotranspiration may lead to greater soil moisture in high severity stands compared to lower severity stands, whereas in the summer and fall months when precipitation is limited, increased evaporatio n may lead to lower soil moisture. As a result, fungal abundance may be even lower in high severity areas during the wet months than the dry months when I collected my samples. Changes in soil fungal abundance could impact the soil C cycle by altering the types of C substrates utilized and because fungal biomass has slower turnover than bacterial biomass (Rousk and Båå th 2011) . Therefore, decreases in fungal abundance may increase the post - fire CO 2 efflux, acting as a positive feedback between fire and climate change. 2.5.3 Burn severity impacts on bacterial communities are driven by nutrients, pH, and soil texture Bact erial phylogenetic diversity was lower in burned areas than unburned areas, but the impacts of severity on diversity only became clear when accounting for soil nutrients and 78 texture. This provides context to a meta - analysis by Pressler et al. (2018), which found no significant effects of fire on bacterial diversity, although individual studies have found negative relationships between fire and bacterial diversity (Pérez - Valera et al. 2017; Sáenz de Miera et al. 2020) . The apparent lack of response of bacterial diversity to fire in many studies may be due to those studies not accounting for indirect effects of fire on diversity via soil nutrients. Fire - induced in creases in extractable soil P and TIN are ephemeral, often dissipating within two years (Wan et al. 2001; Certini 2005) . Therefore, decreases in bacterial diversity in response to fire may be short - lived and difficult to detect, especially following low - severity fires where the nutrient spike is smaller or dissipates earlier (Adkins et al. 2019b) . Links between microbial communities and soil nutrients may result in seasonally dynamic community characteristics. Increased liberation of P and TIN from plant litter during period s of greater decomposition (e.g. in warm, wet spring months; Dove et al., 2020) could lead to apparently lower phylogenet ic diversity compared to periods when low decomposition limits nutrient availability. Decreases in bacterial biodiversity may affect soil C and nutrient cycles by decreasing functional diversity and redundancy (Wagg et al. 2019) . For example, a met a - analysis of studies that manipulated soil microbial diversity indicated that soil respiration rates increase with diversity; however, the diversity levels in manipulative experiments may not match the high biodiversity present in natural ecosystems, and thus may overestimate the impacts on the C cycle (de Graaff et al. 2015) . In contrast to the effects of post - fire decreases in fungal abundance, decreases in microbial diversity may down - regulate the post - fire CO 2 efflux. Soil pH was a p rimary correlate of changes in abundance of the two most abundant bacterial phyla in my study (i.e. Bacteroidetes , Acidobacteria ). The relationship to pH may explain the commonly observed increases of Bacteroidetes abundance (Weber et al. 2014; Xiang 79 et al. 2014a; Pérez - Valera et al. 2019) and decreases of Acido bacteria abundance (Weber et al. 2014; Rodríguez et al. 2018; Whitman et al. 2019) in response to fire, because increased soil pH is a typical response to fire (Certini 2005) . Bacteroidetes and Acidobacteria phyla are among the most dominant bacterial phyla in biomes across the globe (Janssen 2006; Fierer et al. 2012b; Dochert y et al. 2015) , and due to the different growth strategies of these phyla (Fiere r et al. 2007; Ho et al. 2017) , their relative abundances likely play an important role in soil C cycling (e. g. Fierer et al., 2007) . The changes in Bacteroidetes and Acidobacteria abu ndance drove the decrease in O:C I observed across the severity gradient. This is a contrast to suggestions by other authors that copiotrophic taxa should give way to oligotrophic taxa as soon as 16 weeks after wildfire (Ferre nberg et al. 2013) . However, rather than the pH driven changes I observed, the shift to oligotrophic taxa in other burned systems may be due to losses in soil organic matter (Ferrenberg et al. 2013) . Nutrient availabilit y was not a primary driver of O:C as others have found (Fierer et al. 2007; Fierer et al. 2012a; Ramirez et al. 2012) , which may reflect increased competition by pl ants for soil nutrients in burned ecosystems. Indeed, I found that TIN was negatively related to live tree basal area. Although bacterial taxa are often classified as oligotrophic or copiotrophic at the phylum level, lower taxonomic rankings (e.g. famil y and genera) can exhibit variable growth strategies (Ho et al. 2017) , and divergent responses to fire (Whitman et al. 2019) . For example, although I found that the average log 2 - fold change of Bacteroidetes OTUs in burned versus unburned areas was positive, OTUs within the most abundant Bacteroidetes families exhibited both positive and negative responses to fire occurrence. Chitinophagaceae, Cytophagaceae , Sphingobacteriaceae , a nd Flavobacteriaceae OTUs exhibited higher abundance in burned areas, agreeing with research that has identified OTUs from these families as positive fire responders within one year 80 post - fire in a boreal forest in Canada (Whitman et al. 2019) and ponderosa pine and mixed - conifer forests in New Mexico (Weber et al. 2014) . The response of groups within the Acidobacteria phylum have varied among studies. I and others found that Acidobacteria OTUs from Blastocatellaceae (subgroup 4) and Solibacteraceae (subgroup 3) displayed lower abundance in burned areas (Whitman et al. 2019) , but subgroup 4 genera have also been associated with burned soils (Weber et al. 2014) . Likewise, my finding that Acidobacteriaceae (subgroup 1) OTUs displayed lower abundance in burned areas supports previous observations (Weber et al. 2014) , but others have identified a positive response of OTUs from t his family (Whitman et al. 2019) . Although I found Actinobacteria phylum abundance was lower in burned areas, OTUs from my ndant Actinobacteria family, Micrococcaceae, exhibited greater abundance in burned areas, as others have found (Weber et al. 2014; Sáenz de Miera et al. 2020) . Mycobacteriaceae OTUs e xhibited lower abundance in burned areas, in contrast to a study that found that Mycobacteria were dominant in both burned and unburned soils in a pine - oak forest in New Jersey, USA (Mikita - Barbato et al. 2015) . Some Mycobacteria are effective degraders of aromatic compounds (Bastiaens et al. 2000) , and thus might exhibit higher abundance when fire increases soil PyC. However, Mycobacteria abundanc e is also higher at low pH (Norby et al. 2007) , so fire - driven increases in pH may limit this response. 2.6 CONCLUSIONS I found that that burn severity affects the magnitude of persistent effects on the soil microbial community by influencing vegetation and soil characteristics in mixed - conifer forest. I also found that remotely sensed severity estimates (dNBR) mostly outper formed field - validated soil burn severity estimates for explaining changes in soil properties, for soil samples at an intermediate (three years) time point after fire. This is an important finding that has potential to 81 expand research capabilities at inter mediate post - fire time points because dNBR can be easily calculated in areas where Landsat imagery is available. I linked changes to microbial communities to environmental properties that correlate with severity and are slow to recover from fire (e.g. fore st floor mass, live tree basal area). This suggests that changes to microbial communities may persist for years after fire, which may lead to broad - scale effects on ecosystem functions, especially if forecasted increases in fire activity result in greater extent of high severity burns. My systems approach to addressing the influence of fire on microbial communities demonstrates the importance of considering the interconnectedness of environmental characteristics for understanding drivers of microbial commun ity recovery from fire, and I recommend that researchers apply this approach to future studies. The systems approach could also be extended to investigate how plant, soil, and microbial characteristics interact to influence ecosystem functions over multipl e timescales. For example, previous research has demonstrated that fire alters soil C flux rates for years to decades (Adkins et al. 2019b; Dove et al. 2020) . The fire - induced changes to microbial communities I observed particularly the decreased fung al abundance and O:C could have long - term impacts on the soil C cycle across the severity gradient by replacing slow C cycling microbial groups with fast C cycling groups. A systems approach could elucidate whether changes in C flux are driven by microbial community changes or whether other environmental characteristics are dominant drivers. 82 APPENDI X 83 SUPPLEMENTAL FIGURES Figure S2.1 Locations of field plots within a burn severity matrix resulting from the Chips Fire. dNBR values are grouped into unburned, low, moderate, and high severity thresholds identified by the Monitoring Trends in Burn Severity team. 84 Figure S2.3 Relationship of lipid - based fungal - to - bacterial ratio, 16S - phylogenetic diversity, and 16S - based oligotrophic:copiotrophic bacterial taxa ratio with soil burn severity in mineral soils (0 - 5 cm). Soil burn severity values of 1 are unburned pl ots, 2 are low SBS, 3 are moderate SBS, and 4 are high SBS. Marginal r 2 values are provided for microbial community characteristics that linear mixed models indicated were significantly affected by soil burn severity =0.05. Capital letters in figures den ote Tukey - adjusted significant differences among soil burn severity levels. 85 Figure S2.2 Relationship of soil burn severity with soil properties . Soil burn severity values of 1 are unburned plots, 2 are low SBS, 3 are moderate SBS, and 4 are high SBS. Marginal r 2 values are provided for soil properties that linear mixed models indicated were significantly affected by the soil burn severity at =0. 05. Capital letters denote Tukey - adjusted significant differences among soil burn severity levels. 86 Figure S2. 3 Log 2 - fold response to fire for OTUs grouped by family within Bacteroidetes for mineral soils collected to 5 cm three years after the Chips Fire burned mixed - conifer forest in the Sierra Nevada Mountain Range, California, USA in 2012. Figure S2. 4 Log 2 - fold response to fire for OTUs grouped by family within - Proteobacteria for mineral soils collected to 5 cm . 87 Figure S2. 5 Log 2 - fold respo nse to fire for OTUs grouped by family within Acidobacteria for mineral soils collected to 5 cm. Figure S2. 6 Log 2 - fold response to fire for OTUs grouped by family within Ac tin obacteria for mineral soils collected to 5 cm. 88 REFERENCES 89 REFERENCES Adkins J, Sanderman J, Miesel J (2019) Soil carbon pools and fluxes vary across a burn severity gradient three years after wildfire in Sierra Nevada mixed - conifer forest. Geoderma 333:10 22. doi: 10.1016/j.geoderma.2018.07.009 Alve y EC (2016) Early seral mixed - conifer forest structure and composition following a wildfire reburn in the Sierra Nevada. Humboldt State University forest soil are la rgely different and highly stratified during decomposition. ISME J 6:248 258. doi: 10.1038/ismej.2011.95 Balser TC, McMahon KD, Bart D, et al (2006) Bridging the gap between micro - and macro - scale perspectives on the role of microbial communities in globa l change ecology. Plant Soil 289:59 70. doi: 10.1007/s11104 - 006 - 9104 - 5 Bastiaens L, Springael D, Wattiau P, et al (2000) Isolation of Adherent Polycyclic Aromatic Hydrocarbon ( PAH ) - Degrading Bacteria Using PAH - Sorbing Carriers. 66:1834 1843. Bier RL, B ernhardt ES, Boot CM, et al (2015) Linking microbial community structure and microbial processes: An empirical and conceptual overview. FEMS Microbiol Ecol 91:1 11. doi: 10.1093/femsec/fiv113 Bligh EG, Dyer WJ (1959) A rapid method of total lipid extraction and purification. Can J Biochem Physiol 37:911 917. Cambardella CA, Elliott ET (1993) Methods for physical separation and characterization of soil organic matter fractions. Geoderma 56:449 457. doi: 10.1016/0016 - 7061(93)90126 - 6 Caporaso JG, Kucz ynski J, Stombaugh J, et al (2010) QIIME allows analysis of high - throughput community sequencing data Intensity normalization improves color calling in SOLiD sequencing. Nat Publ Gr 7:335 336. doi: 10.1038/nmeth0510 - 335 Certini G (2005) Effects of fire on properties of forest soils: a review. Oecologia 143:1 10. doi: 10.1007/s00442 - 004 - 1788 - 8 Cutler NA, Arróniz - Crespo M, Street LE, et al (2017) Long - Term Recovery of Microbial Communities in the Boreal Bryosphere Following Fire Disturbance. Microb Ec ol 73:75 90. doi: 10.1007/s00248 - 016 - 0832 - 7 90 Czimczik CI, Masiello C a. (2007) Controls on black carbon storage in soils. Global Biogeochem Cycles 21:1 8. doi: 10.1029/2006GB002798 of the microbial community in Mediterranean maquis soils as affected by fires. Int J Wildl Fire 14:355. doi: 10.1071/WF05032 de Graaff M - A, Adkins J, Kardol P, Throop HL (2015) A meta - analysis of soil biodiversity impacts on the carbon cycle. 1:257 271. do i: 10.5194/soil - 1 - 257 - 2015 Dennison PE, Brewer SC, Arnold JD, Moritz MA (2014) Large wildfire trends in the western United States, 1984 - 2011. Geophys Res Lett 41:2928 2933. doi: 10.1002/2014GL059576 Doane TA, Horwáth WR (2003) Spectrophotometric determinat ion of nitrate with a single reagent. Anal Lett 36:2713 2722. doi: 10.1081/AL - 120024647 Docherty KM, Borton HM, Espinosa N, et al (2015) Key edaphic properties largely explain temporal and geographic variation in soil microbial communities across four biom es. PLoS One 10:1 23. doi: 10.1371/journal.pone.0135352 Dooley SR, Treseder KK (2012) The effect of fire on microbial biomass: A meta - analysis of field studies. Biogeochemistry 109:49 61. doi: 10.1007/s10533 - 011 - 9633 - 8 Dove NC, Safford HD, Bohlman GN, et a 18. doi: 10.1002/eap.2072 Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460 246 1. doi: 10.1093/bioinformatics/btq461 Faith DP (1992) Conservation evaluation and phylogentic diversity. Biol Conserv 61:1 10. doi: 10.1890/0012 - 9658(2006)87[1465:ATTFHF]2.0.CO;2 ses in soil bacterial communities following a wildfire disturbance. ISME J 7:1102 1111. doi: 10.1038/ismej.2013.11 Ficken CD, Wright JP (2017) Contributions of microbial activity and ash deposition to post - fire nitrogen availability in a pine savanna. Biog eosciences 14:241 255. doi: 10.5194/bg - 14 - 241 - 2017 Fierer N, Bradford MA, Jackson RB (2007) Toward an ecological classification of soil bacteria. Ecology 88:1354 1364. doi: 10.1890/05 - 1839 91 Fierer N, Lauber CL, Ramirez KS, et al (2012a) Comparative metageno mic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J 6:1007 1017. doi: 10.1038/ismej.2011.159 Fierer N, Leff JW, Adams BJ, et al (2012b) Cross - biome metagenomic analyses of soil microbial communities and their functional attributes. Proc Natl Acad Sci 109:21390 21395. doi: 10.1073/pnas.1215210110 Flannigan MD, Cantin AS, de Groot WJ, et al (2013) Global wildland fire season severity in the 21st century. For Ecol Manage 294:54 61. doi: 10.1016/j.foreco. 2012.10.022 Fornwalt PJ, Stevens - Rumann CS, Collins BJ (2018) Overstory structure and surface cover dynamics in the decade following the hayman fire, Colorado. Forests 9:1 17. doi: 10.3390/f9030152 Friedman J, Alm EJ (2012) Inferring Correlation Networks f rom Genomic Survey Data. PLoS Comput Biol 8:1 11. doi: 10.1371/journal.pcbi.1002687 Grace JB (2006) Structural Equation Modeling and Natural Systems. Cambridge Univ Press, West Nyack, GB Hart SC, DeLuca TH, Newman GS, et al (2005) Post - fire vegetative dyna mics as drivers of microbial community structure and function in forest soils. For Ecol Manage 220:166 184. doi: 10.1016/j.foreco.2005.08.012 Ho A, Di Lonardo DP, Bodelier PLE (2017) Revisiting life strategy concepts in environmental microbial ecology. FEM S Microbiol Ecol 93:1 14. doi: 10.1093/femsec/fix006 Holden SR, Berhe AA, Treseder KK (2015) Decreases in soil moisture and organic matter quality suppress microbial decomposition following a boreal forest fire. Soil Biol Biochem 87:1 9. doi: 10.1016/j.soi lbio.2015.04.005 Holden SR, Rogers BM, Treseder KK, Randerson JT (2016) Fire severity influences the response of soil microbes to a boreal forest fire. Environ Res Lett 11:035004. doi: 10.1088/1748 - 9326/11/3/035004 Janssen PH (2006) Identifying the dominan t soil bacterial taxa in libraries of 16S rRNA and 16S rRNA Genes. Appl Environ Microbiol 72:1719 1728. doi: 10.1128/AEM.72.3.1719 - 1728.2006 Jansson JK, Hofmockel KS (2020) Soil microbiomes and climate change. Nat Rev Microbiol 18:35 46. doi: 10.1038/s41579 - 019 - 0265 - 7 92 Joergensen RG, Wichern F (2008) Quantitative assessment of the fungal contribution to microbial tissue in soil. Soil Biol Biochem 40:2977 2991. doi: 10.1016/j.soilbio.2008.08.017 Keeley JE (2009) Fire intensity, fire severity and burn severity: A brief review and suggested usage. Int J Wildl Fire 18:116 126. doi: 10.1071/WF07049 Kurth VJ, MacKenzie MD, DeLuca TH (2006) Estimating charcoal content in forest mineral soils. Geoderma 137:135 139. doi: 10.1016/j.geoderma.2006.08.003 Lau ber CL, Hamady M, Knight R, Fierer N (2009) Pyrosequencing - based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol 75:5111 5120. doi: 10.1128/AEM.00335 - 09 LeDuc SD, Lilleskov EA, Hor ton TR, Rothstein DE (2013) Ectomycorrhizal fungal succession coincides with shifts in organic nitrogen availability and canopy closure in post - wildfire jack pine forests. Oecologia 172:257 269. doi: 10.1007/s00442 - 012 - 2471 - 0 Lefcheck JS (2016) piecewiseSE M: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol Evol 7:573 579. doi: 10.1111/2041 - 210X.12512 Mabuhay JA, Nakagoshi N, Isagi Y (2006) Soil microbial biomass, abundance, and diversity in a Japanese red pi ne forest: First year after fire. J For Res 11:165 173. doi: 10.1007/s10310 - 005 - 0201 - 8 Maestrini B, Alvey EC, Hurteau MD, et al (2017) Fire severity alters the distribution of pyrogenic carbon stocks across ecosystem pools in a Californian mixed - conifer fo rest. J Geophys Res Biogeosciences 122:2338 2355. doi: 10.1002/2017JG003832 Mallek CM, Safford H, Viers J, Miller JD (2013) Modern departures in fire severity and area vary by forest type, Sierra Nevada and southern Cascades, California, USA. Ecosphere 4:1 28. doi: 10.1890/ES13 - 00217 Masella AP, Bartram AK, Truszkowski JM, et al (2012) PANDAseq: paired - end assembler for illumina sequences. BMC Bioinformatics 13:31. doi: 10.1186/1471 - 2105 - 13 - 31 McLauchlan KK, Higuera PE, Miesel J, et al (2020) Fire as a fund amental ecological process: research advances and frontiers. J Ecol 1365 - 2745.13403. doi: 10.1111/1365 - 2745.13403 McMurdie PJ, Holmes S (2014) Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Comput Biol. doi: 10.1371/journal.pcbi.1 003531 Mikita - Barbato RA, Kelly JJ, Tate RL (2015) Wildfire effects on the properties and microbial 93 community structure of organic horizon soils in the New Jersey Pinelands. Soil Biol Biochem 86:67 76. doi: 10.1016/j.soilbio.2015.03.021 Miller JD, Safford HD (2017) Corroborating evidence of a pre - Euro - American low - to moderate - severity fire regime in yellow pine mixed conifer forests of the Sierra Nevada, California, USA. Fire Ecol 13:58 90. doi: 10.4996/fireecology.1301058 Miller JD, Safford HD, Crimmins M , Thode AE (2009) Quantitative evidence for increasing forest fire severity in the Sierra Nevada and southern Cascade Mountains, California and Nevada, USA. Ecosystems 12:16 32. doi: 10.1007/s10021 - 008 - 9201 - 9 MTBS (2017) Monitoring trends in burn severity. https://www.mtbs.gov. NCEI - NOAA (2017) National centers for environmental information. https://www.ncei.noaa.gov. Neary D., DeBano L. (2005) Wildland fire in ecosystems effects of fire on soil and water. N eary DG., Ryan KC., DeBano LF (eds) (2005) Wildland Fire in Ecosystems: effects of fire on soil and water. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT Norby B, Fosgate GT, Manning EJB, et al (2007) Environmental my cobacteria in soil and water water physicochemical characteristics §. 124:153 159. doi: 10.1016/j.vetmic.2007.04.015 Oksanen J, Blanchet FG, Friendly M, et al (2019) veg an: Community Ecology Package. Olsen SR, Cole CV, Watanable FS, Dean LA (1954) Estimation of available phosphorus in soils by extraction with sodium bicarbonate. Orwin KH, Dickie IA, Holdaway R, Wood JR (2018) A comparison of the ability of PLFA and 16S rRNA gene metabarcoding to resolve soil community change and predict ecosystem functions. Soil Biol Biochem 117:27 35. doi: 10.1016/j.soilbio.2017.10.036 Parsons A, Robichaud PR, Lewis S a, et al (2010) Field guide for mapping post - fire soil burn severity. Gen. Tech. Rep. RMRS - GTR - 243 Pérez - Valera E, Goberna M, Faust K, et al (2017) Fire modifies the phylogenetic structure of soil bacterial co - occurrence networks. Environ Microbiol 19:317 327. doi: 10.1111/1462 - 2920.13609 Pérez - Valera E, Goberna M, Verdú M (2019) Fire modulates ecosystem functioning through the 94 phylogenetic structure of soil bacterial communities. Soil Biol Biochem 129:80 89. doi: 10.1016/j.soilbio.2018.11.007 Perry DA, Oren R, Hart SC (2008) Forest Ecosyst ems. Johns Hopkins University Press, Baltimore Pinheiro J, Bates D, Debroy S, Sarkar D (2019) nlme: Linear and nonlinear mixed effects models. Prendergast - Miller MT, de Menezes AB, Macdonald LM, et al (2017) Wildfire impact: Natural experiment reveals dif ferential short - term changes in soil microbial communities. Soil Biol Biochem 109:1 13. doi: 10.1016/j.soilbio.2017.01.027 Pressler Y, Moore JC, Cotrufo MF (2018) Belowground community responses to fire: meta - analysis reveals contrasting responses of soil microorganisms and mesofauna. Oikos 1 19. doi: 10.1111/oik.05738 Quast C, Pruesse E, Yilmaz P, et al (2013) The SILVA ribosomal RNA gene database project: Improved data processing and web - based tools. Nucleic Acids Res 41:590 596. doi: 10.1093/nar/gks1219 R Core Team (2019) R: A language and environment for statistical computing. Ramirez KS, Craine JM, Fierer N (2012) Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob Chang Biol 18:1918 1927. doi: 10.1 111/j.1365 - 2486.2012.02639.x Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139 140. Rodríguez J, González - Pérez JA, Turmero A, et al (2018) Ph ysico - chemical and microbial perturbations of Andalusian pine forest soils following a wildfire. Sci Total Environ 634:650 660. doi: 10.1016/j.scitotenv.2018.04.028 Rousk J, Bååth E (2011) Growth of saprotrophic fungi and bacteria in soil. FEMS Microbiol E col 78:17 30. doi: 10.1111/j.1574 - 6941.2011.01106.x Rousk J, Bååth E, Brookes PC, et al (2010) Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J 4:1340 1351. doi: 10.1038/ismej.2010.58 Ruefenacht B, Finco MV, Nelson MD, e t al (2008) Conterminous U.S. and Alaska Forest Type Mapping Using Forest Inventory and Analysis Data. Photogramm Eng Remote Sens 74:1379 1388. doi: 10.14358/PERS.74.11.1379 95 Sáenz de Miera LE, Pinto R, Gutierrez - Gonzalez JJ, et al (2020) Wildfire effects o n diversity and composition in soil bacterial communities. Sci Total Environ. doi: 10.1016/j.scitotenv.2020.138636 Safford HD, Miller JD, Schmidt D, et al (2008) BAER soil burn severity maps do not measure fire effects to vegetation: A comment on Odion and Hanson (2006). Ecosystems 11:1 11. doi: 10.1007/s10021 - 007 - 9094 - z Schmidt J, Schulz E, Michalzik B, et al (2015) Carbon input and crop - related changes in microbial biomarker levels strongly affect the turnover and composition of soil organic carbon. Soil Biol Biochem 85:39 50. doi: 10.1016/j.soilbio.2015.02.024 Shipley B, Douma JC (2020) Generalized AIC and chi - squared statistics for path models consistent with directed acyclic graphs. Ecology 101:e02960. doi: 10.1002/ecy.2960 Sinsabaugh RL, Reynolds H, Lo ng TM (2000) Rapid assay for amidohydrolase (urease) activity in environmental samples. Soil Biol Biochem 32:2095 2097. doi: 10.1016/S0038 - 0717(00)00102 - 4 Treseder KK, Mack MC, Cross A (2004) Relationships among fires, fungi, and soil dynamics in Alaskan b oreal forests. Ecol Appl 14:1826 1838. Ulery AL, Graham RC (1993) Forest Fire Effects on Soil Color and Texture. Soil Sci Soc Am J 57:135 140. doi: 10.2136/sssaj1993.03615995005700010026x USDA (2015) FSVEG Common Stand Exam User Guide. USDA Forest Service, Washington, DC Wagg C, Schlaeppi K, Banerjee S, et al (2019) Fungal - bacterial diversity and microbiome complexity predict ecosystem functioning. Nat Commun 10:1 10. doi: 10.1038/s41467 - 019 - 12798 - y Wan S, Hui D, Luo Y (2001) Fire effects on nitrogen pools and dynamics in terrestrial ecosystems: A meta - analysis. Ecol Appl 11:1349 1365. Weber CF, Lockhart JS, Charaska E, et al (2014) Bacterial composition of soils in ponderosa pine and mixed conifer forests exposed to different wildfire burn severity. Soil Bi ol Biochem 69:242 250. doi: 10.1016/j.soilbio.2013.11.010 Weiss S, Xu ZZ, Peddada S, et al (2017) Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:1 18. doi: 10.1186/s40168 - 017 - 0237 - y 96 Westerling A L, Hidalgo HG, Cayan DR, Swetnam TW (2006) Warming and earlier spring increase western U.S. forest wildfire activity. Science 313:940 943. doi: 10.1126/science.1128834 Whitman T, Whitman E, Woolet J, et al (2019) Soil bacterial and fungal response to wildfires in the Canadian boreal forest across a burn severity gradient. Soil Biol Biochem 138:107571. doi: 10.1016/j.soilbio.2019.107571 Xiang X, Shi Y, Yang J, et al (2014) Rapid recovery of soil bacterial communities after wildfire in a Chinese boreal f orest. Sci Rep 4:1 8. doi: 10.1038/srep03829 97 CHAPTER 3: DETERMINING LINKS BETWEEN BACTERIAL LIFE - STRATEGIES AND SOIL CARBON POOLS ONE - YEAR POST - FIRE 3.1 ABSTRACT Wildfire and burn severity influence soil microbial community structure during post - fire recovery. If post - fire differences in microbial communities affect the dynamics of soil carbon (C) pools, these altered microbial communities could govern the transiti on of forests from C sources to C sinks during post - fire recovery. For example, fire may change the abundance of copiotrophic and oligotrophic bacterial taxa, which have the potential to influence the kinetic rates of soil C pools due to differences in C - a cquisition strategies and nutrient requirements . I assessed differences in soil bacterial community structure and soil C pool kinetics one year after a wildfire in a mixed - conifer forest in northern California, USA. I determined whether differences in bac terial communities and C pool kinetics were related to copiotrophic versus oligotrophic life history strategies. Specifically, I assessed how bacterial taxa were related to the availability of inorganic nitrogen, phosphorus, and the active C pool . I furthe r assessed whether bacterial taxa that have traditionally been classified as oligotrophs or copiotrophs were correlated with C pool kinetic rates. I found that bacteria classified as copiotrophs at the phylum level exhibited greater abundance in burned are as compared to unburned areas, whereas oligotrophic phyla exhibited lower abundance in burned versus unburned areas. I found that soil C persistence increased with burn severity, as evidenced by decreases in the kinetic rate of the non - active C pool. The k inetic rate of the non - active C pool was positively related to Elusimicrobia abundance, a suspected oligotrophic phylum. At the phylum level, copiotrophs were positively correlated with nutrient availability and the active C pool, but genera within the sam e phylum exhibited both positive and negative relationships with nutrient and labile C availability 98 (assessed as active C pool size) . Overall, my results suggest that bacterial life - strategies are related to soil C pool kinetics, but phylum - level life - stra tegy classifications do not capture the breadth of bacterial metabolic diversity . Ph y lum - level classifications may therefore be ineffective at predicting how changes in bacterial communities influence ecosystem functions. 3.2 INTRODUCTION Wildfire frequency and severity have increased in the forests of the western United states over the past several decades (Westerling 2006; Dennison et al. 2014) , a trend that is predicted to continue (Flannigan et al. 2009) . Increased burn severity influences soil carbon (C) cycling for years to decades post - fire by altering soil C pool structure, efflux rates, and decomposition (Holden et al. 2016; Adkins et al. 2019b; Dove et al. 2020) . Understanding drivers of post - fire soil C flux is important because the balance between soil C efflux and photosynthetic C gains governs the transition of ecosystems from C sources to C sinks during ecosyste m recovery (Kashian et al. 2006) . Because soil microbes drive the soil C cycle, it is important to understand how microbial communities are affected by wildfire. Linking microbial community composition to ecosystem function is an important research direction in microbial ecology (Prosser and Martiny 2020) , but is challenging for broad scale ecosystem functions, be cause such functions are the integrative results of reactions performed by a wide swath of microbial groups (Treseder et al. 2012; Fierer 2017) . Additionally, broad scale functions are dependent on other biotic and abiotic mechanisms (Schmidt et al. 2011) . In the case of soil C flux, it has been posited that microbial access to substr ates, rather than microbial community structure, dictates flux rate and magnitude (Schmidt et al. 2011; Schimel and Schaeffer 2012) . However, emerging evidence suggests that microbial structure influences the soil C cycle. A meta - analysis of studies that manipulated microbial diversity 99 indicated that C respiration was positively r elated to bacterial diversity (de Graaff et al. 2015) . Microbial diversity and community complexity have been shown to influenc e decomposition and ecosystem multifunctionality in an experimental grassland (Wagg et al. 2019) and C use efficiency in an experimental forest (Domeignoz - Horta et al. 2020) . The amount of C respired versus retained in soils is dependent on the structure and st ability of soil C pools (Trumbore 2000; Kuzyakov 2011; Torn et al. 2013) . Soil C can be conceptualized as distinct C pools with variable turnover times: an active C pool (C a ) with a mean residence time of days to months and a non - active pool (C s ) with a mean residence time of years to decades (Paul et al. 2006) . The sizes of the active and non - active C pools and their associated kinetic rates (k a and k s , respectively) can be quantified by measuring soil C respiration over long - term lab incubations (Collins et al. 2000; Kuzyakov 2011) . In this approach, C pool s are a function of biological processes rather than intrinsic chemical characteristics (see Schmidt et al., 2011) . Microbial activity therefore reflects soil C pool kinetics, but whether differences in microbial community structure influence the respective cycling rates of the two C pools is unkno wn. Disturbance - induced changes to microbial communities could alter ecosystem processes if the microbial community is not resistant or resilient to disturbance or not functionally redundant with the pre - disturbance community (Allison and Martiny 2008; Treseder et al. 2012) . In the case of fires, microbial communit y composition in forest soil is often not resistant or res ilient over the short - to intermediate - term , as they exhibit decreases in diversity and changes in community composition that persist from months to years post - fire (Weber et al. 2014; Rodríguez et al. 2017; Rodríguez et al. 2018; Whitman et al. 2019; Sáenz de Miera et al. 2020; Adkins et al. 2020) . Altered microbial communi ty composition may not lead to changes in soil 100 function if sufficient functional redundancy exists between pre - fire and post - fire soils. However, p ost - fire soil microbial communities appear to be functionally distinct, becoming predominated by more copiotrophic bacterial taxa compared to oligotrophic taxa, an effect that increases with burn severity (Ferrenberg et al. 2013; Adkins et al. 2020) . Furthermore, due to changes to soil pH and differential heat tolerance, bacteria are more predominant than fungi in post - fire soils for a decade or more post - fire (Dooley and Treseder 2012; Pressler et al. 2018) , so bacter ial communities may play a particularly important role in ecosystem processes in recovering forests over the short - to intermediate - term. The classification of bacterial taxa as copiotrophic or oligotrophic represents a trait - based framework for describing bacterial community structure (Fierer 2017) , and these taxa may differ in their effects on the soil C cycle . Copiotrophs consume labile C, have high nutrient requirements, and exhibit high growth rates. In contrast, oligotrophs exhibit slow growth rates, but have hi gh substrate affinity and may outcompete copiotrophs when nutrient content and/or organic matter quality is low (Fierer et al. 2007; Ramirez et al. 2012) . These ecological strategies are reflected at the gene leve l: metagenomic and metabolic approaches have revealed that soils abundant in copiotrophs harbor more genes associated with carbohydrate utilization and protein degradation, and fewer genes and enzymes associated with recalcitrant C degradation, organic N d ecomposition, and P scavenging (Fierer et al. 2012a; Ramirez et al. 2012; Hartman et al. 2017) . The combined influences of oligotroph vs. copiotroph abundance, nutrient content, and organ ic matter quality may thus alter soil C pool kinetics. For example, if two soils have equally large stocks of labile C ( i.e. easily decomposable C ) , but differ in their abundance of copiotrophic taxa (for example due to differences in soil nutrient content s), the soil with greater copiotroph abundance might exhibit larger k a values due the capacity of copiotrophs for rapid growth . In 101 contrast, soils with large stocks of recalcitrant C (e.g. lignin, pyrogenic C) might exhibit larger k s values when oligotroph abundance is high due to their higher substrate affinity and their greater ability to mine nutrients . Consistent patterns have emerged in post - fire differences in phylum - level bacterial community composition among many ecosystem types, and these differences are often reflected by divergent responses of oligotrophic and copiotrophic taxa to fire and burn severity. The copiotrophic phyla Bacteroidetes , Actinobacteria , and Firmicut es tend to exhibit higher abundance post - fire, whereas the oligotrophic phylum Acidobacteria exhibits lower abundance (Weber et al. 2014; Xiang et al. 2014b; Pérez - Valera et al. 2019; Whitman et al. 2019) . Changes in bacterial phyla abu ndance are likely due to a combination of direct and indirect effects of fire on soils. For example, spore - forming, heat resistant taxa such as Actinobacteria and Firmicutes are more likely to survive soil heating events (Prendergast - Miller et al. 2017) . These and other copiotrophic phyla (e.g . Bacteroidetes , Proteobacteria ) may also positively respond to the fire - induced increases in total inorganic nitrogen (TIN) , active C pools, and dissolved organic C (Fernández et al. 1997; Wan et al. 2001; Wang et al. 2012) . Changes to both microbial communities and nutrient availability hav e been demonstrated to scale with burn severity in mixed - conifer forests (Adkins et al. 2019b; Adkins et al. 2020) ; thus, C - cycling processes that are driven by microbial community structure likely vary with burn severity. Consistent responses to fire among certain bacterial genera h ave also emerged. The genera Masillia , a potential aromatic - C degrader, Arthrobacter , a fast - growing and stress - tolerant taxa, and Flavisolibacter have frequently been found to increase in abundance following fires (Weber et al. 2014; Pérez - Valera et al. 2017; Rodríguez et al. 2017; Whitman et al. 201 9; Sáenz de Miera et al. 2020) . Although a few studies have accounted for the role of burn severity in shaping 102 microbial communities (Weber et al. 2014; Whitman et al. 2019; Sáenz de Miera et al. 2020) , most have not, and identifying the influence of severity on post - fire microbial communities has been identified as a key information need in fire ecology research (Hart et al. 2005; Pressler et al. 2018) . The numerous interacting direct and indirect effects of fire and burn severity on soil and bacterial properties highlights the importance of employing a systems approach to understanding disturbance effects on the soil C cycle. Understanding the relationships between soil characteristics and the C cycle will improve predictions of the impacts of changing fire regimes on the C sink strength of forests, and understanding the influence of bacterial communities on soil functions is necessary for improving ecosystem models (Schimel and Schaeffer 2012; Treseder et al. 201 2; Graham et al. 2016) . Additionally, the variability in soil biological, physical, and chemical properties across burn severity gradients make fire - prone forests valuable ecological systems for testing trait - based frameworks of bacterial communities (see Fierer, 2017b) . Here, my overarching objectives are to 1) determine how C pool structure and kinetics are related to soil properties and bacterial communities one - year post fire, and 2) how the oligotroph - copiotroph framewo rk can explain post - fire differences in soil bacterial communities . In service of these objectives, I addressed four hypotheses. I hypothesized that 1) changes in C pool structure in kinetics across a burn severity gradient can be explained by accounting f or soil properties; 2 ) bacterial OTUs that have previously been identified as positive fire responders will be positively correlated with burn severity ; 3 ) bacterial taxa that are more abundant in burned areas will primarily b e copiotrophs and therefore positively related to nutrient availability and C a size; and 4 ) copiotrophic taxa abundance will be positively associated with k a , whereas oligotrophic taxa abundance will be positively associated with k s . 103 3.3 MATERIALS AND METHODS 3.3.1 Site description My study was conducted in mixed - conifer forest in the Klamath National Forest, located in northern California, USA. The forest type is a California mixed - conifer forest (Ruefenacht et al . 2008) , which consists of Pinus ponderosa Lawson & C. Lawson , P. lambertiana Douglas , P. jeffreyi Balf. , Abies concolor (Gord. & Glend.) Lindl. ex Hildebr. , Pseudotsuga menziessi (Mirb.) Franco , Calocedrus decurrens (Torr.) Florin , Arbutus menziesii Pu rsh. and Quercus kelloggi Newberry . Soils of my study area belong to the Skalan series and its associates (Soil Survey Staff 2015 ) ; Skalan is a loamy - skeletal, isotic, mesic Vitrandic Haploxeralf that forms in weathered gneiss residuum and is slightly acidic (Soil Survey Staff 2018 ) . The mean annual precipitation is 1290 mm and mean annual temperature is 9.0 ° C (NCEI - NOAA 2017) . My study focuses on areas affected by the Beaver Fire (Lat: 41.88993, Long: - 122.87056; Fig. S 3. 1), a wildfire that burned ~13,000 ha between July 3 0, 2014 to August 31, 2014. Burn severity estimates based on the Differenced Normalized Burn Ratio (dNBR) indicate that the Beaver Fire resulted in ~4800 ha of burned area classified as high severity, ~4600 ha classified as moderate severity, ~3700 ha clas sified as low severity, and ~750 ha within the fire perimeter was unburned (MTBS 2017) . dNBR se verity estimates are based on Landsat reflectance images collected in the growing season s immediately before and after fire occurrence, and reflect post - versus pre - fire changes in vegetation and soil exposure (Parsons et al. 2010) , resulting in continuous dNBR values assigned to both burned and unburned plots . dNBR values for unburned plots are typically < 100, whereas the upper limit for burned plots can exceed 1000. 104 3.3.2 Field methods Between August 3, 2015 and August 10, 2015 (i.e. one - year post - fire), I sampled 10 plots (4 unburned, 6 burned ; dNBR 0 - 863 ). At each plot, I determined live and dead tree basal area and sampled for forest floor and mineral soil. I measured tree diameters at breast height ( DBH ) for all live and dead tree s >10 cm DBH within an 11.3 m sampling radius, and I used these values to calculate live and dead tree basal area at the plot level. I sampled forest floor and mineral soil 15 m from the plot center at azimuths of 0 º , 90 º , 180 º and 270 º , for a total of 40 forest floor and 40 mineral soil samples collected. The forest floor includes the plant litter and duff layers, and is equivalent to the combined Oi, Oe, and Oa horizons in the USDA Soil Taxonomy classification system (Perry et al. 2008) . I collected all forest floor material from within a 15 cm radius circular sampling f rame. I inserted a 5 cm radius metal cylinder into the mineral soil to 5 cm and collected mineral soil using a stainless - steel scoop. I collected one additional volumetric mineral soil sample from the center of each plot to estimate bulk density using the same sampling method. Forest floor samples were stored at ambient temperature for ~14 days prior to being transported to the lab. Mineral soil samples were stored under refrigeration for 2 - 7 days and shipped to the lab on ice. Upon arrival at the lab, the forest floor samples were air - dried, and mineral soils were sieved (2 mm) and sub - sampled for DNA analysis. Sub - samples for DNA analysis were stored at - 80 º C, and the remainder of the mineral soils were refrigerated at ~4 º C until analyses. 3.3.3 Lab methods So il processing and chemical analyses I processed the forest floor in a blender to pass a 2 mm mesh screen and then pulverized a subsample of the blended material in a ball mill (SPEX Sample Prep LLC, Metuchen, NJ, USA). 105 I oven - dried t he pulverized forest f loor material at 65 º C and prior to determination of total C and N. I used the sieved mineral soil for determination of total C, N, pyrogenic - C (PyC), NO 3 - N, NH 4 - N, extractable P, and pH. I oven - dried the mineral soil sample to be used for C and N analysis at 105 º C and then pulverized the subsamples as described above. I analyzed one forest floor sample and one mineral soil sample from each subplot for total C and N concentrations on a dry combustion elemental analyzer (Costech Analytical Technologies Inc., Valencia, CA, USA) , using atropine as the quantification standard . I quantified mineral soil PyC concentrations by digesting 0.5 g mineral soil from each subplot in 10 mL 1.0 M HNO 3 + 20 mL 30% H 2 O 2 at 100 C for 16 hours (Kurth et al. 2006) and analyzing post - digested soils for residual C and N. I extracted a 10 g sample of fresh mineral soil from each subplot for NH 4 + - N and NO 3 - - N in 50 mL 2.0 M KCl for 1 hour. I filtered the resulting soil extracts through 2.5 µ m pore - size filter paper (GE Healthcare UK Limited, Little Chalfont, Buckinghamshire, UK). I determined the extract NH 4 + concentration s spectrophotometrically by reacting with ammonia salicylate and ammonia cyanurate (Sinsabaugh et al. 2000) , and measuring absorbance at 595 nm (BioTek Elx800, BioTek Instruments Inc., Winuski, VT, USA). I determined the concentration s of NO 3 - in the extracts spectrophotometrically by reacting the extract s with vanadium (III) chloride, sulfanilamide, and N - (1 - naphthyl) - ethylenediamine dihydrochloride and measuring absorbance at 540 nm (Doane and Horwáth 2003) . I extracted 2.5 g subsamples of fresh mineral soils for phosphorus (P) in 40 mL 0.5 M NaHCO 3 and determined P concentration using t he Olsen method (Olsen et al. 1954) . I measured mineral soil pH of a 1:2 (w:v) soil slurry with a benchtop pH meter (Oakton pH 700, Oakton Instruments, Vernon Hills, IL, USA). 106 Determination of carbon pool structure and kinetics I incubated soils to determine the size and kinetic rates of the C a and C s pools and potential soil C flux rates . I adjusted a 30 g sample of f resh mineral soil from each subplot to 40% water filled pore space (WFPS) in 120 mL specimen cups. The specimen cups were placed in 1 L glass jars, and the soils were incubated in the dark for 300 days at ambient temperature (~23 º C) with biweekly adjustme nts of soil moisture to 40% WFPS. I measured CO 2 evolution on days 10, 14, 28, 42, 58, 90, and every 30 days thereafter until day 300. Prior to each measurement event, I flushed the jars to ambient CO 2 concentrations, then tightly sealed the jars for 24 - 48 hours before sampling a 1 mL gas aliquot through septa fitted to the jar lids. I measured CO 2 concentration of the aliquot using an infrared gas analyzer (LI - COR Inc., Lincoln, NE, USA) , and calculated CO 2 - C efflux as the difference in CO 2 - C concentration s between the soil containing jars and blank jars that contained only a specimen cup and DI water. DNA extraction and bioinformatic analysis DNA was extracted from 0.25 g of soil using the MoBIO PowerSoil DNA isolation kit (MoBIO laboratories, Carlsbad, C A), according to the manufacturer's instructions. Illumina - MiSeq was used to amplify the V4 region of the 16S - rRNA gene using 515f/806r universal primers (Cap oraso et al. 2010) at the Michigan State University Genomics Core . I processed DNA sequences using the QIIME2 bioinformatics pipeline (Bolyen et al. 2019) . I denoised, merged forward and reverse reads, and removed chimeras using the q2 - DADA2 plugin (Callahan et al. 2016) . I inferred phylogenetic trees by applying MAFFT multiple sequence alignment (Katoh and Standley 2013) and FastTree 2 (Price et al. 2010) using the q2 - phylogeny plugin. With the q2 - diversity plugin, I used the phylogenet phylogenetic diversity (Faith 1992) 107 UniFra c distance matrix (Lozupone and Knight 2005) . I used the q2 - feature - classifier plugin (Bokulich et al. 2018) with the classify_sklearn action (Pedregosa et al. 2011) to classify taxonomic composition of my samples employing a Naïve Bayes classifier trained on the SILVA SSURef database version 132 using a 99% similarity th reshold (Quast et al. 2013) . I filtered OTUs associated with Archaea, Eukaryotes, mitochondria, and chloroplasts. I calculated oligotroph - to - copiotroph ratio at the phylum level as the ratio of the sum of relativ e abundances of all taxa classified within the phyla Acidobacteria and Verrucomicrobia to the sum of the relative abundances of all taxa classified within the phyla Actinobacteria , Firmicutes , and Bacteroidetes (Fierer et al. 2007; Fierer et al. 2012a; Ramirez et al. 2012) . I predicted metagenomic functional potential of soil bacterial communities using the PICRUST2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) software (Douglas et al. 2019) . PICRUST2 uses HMMER software (hmmer.org) to perform multiple sequence alignment via hidden Markov models, places reads in a reference tree using EPA - ng (Barbera et al. 2018) , and outputs a tree file with Gappa (Czech et al. 2020) . PICRUST2 then performs hidden - state prediction of gene families (Louca and Doebeli 2017) , and uses MinPath (Ye and Doak 2009) to infer the relative abundance of MetaCyc metabolic pathways (Caspi et al. 2018) . MetaCyc is a metabolic pathway database in which pathways are hierarchically grouped according to metabolic function. I focused my analysis on degradation pathway groups related to C cycling, including carbohydrate degradation, alcohol degradation, amine degradation, amino acid degradation, aromatic compound degrada tion, carboxylate degradation, fatty acid and lipid degradation, nucleoside and nucleotide degradation, and secondary metabolite degradation pathways. 108 3.3.4 Statistical analysis Fire and severity effects on soil properties and tree basal area I performed all s tatistical analysis in the R statistical computing environment (R Core Team 2019) . I used linear mixed models (R Package: nlme ; Pinheiro et al., 2016) to determine the impacts of wildfire occurrence and burn severity (dNBR) on live and dead tree basal area, total C and N, C:N ratio, Py C, NH 4 + - N, NO 3 - - N, TIN, extractable P, pH, gravimetric soil moisture, and forest floor mass. To account for legacy effects of soil properties related to topography, all models initially included elevation, slope, and aspect as covariates. Topographic varia bles that were not significant at < 0.05 were sequentially removed from the models. All models included plot - identifier as a random effect, and the NH 4 + - N, NO 3 - - N, and TIN models included total N as a covariate. I determined that inorganic N variables di d not meet assumptions of normality, so I normalized these variables using B ox - C ox transformations (Box and Cox 1964) . I determined whether wildfire occurrence or dNBR was a better predictor of differences by fitting separate models that included either fire occurrence (burned or unburned) or burn severity as an explanatory variable and comparing model AIC values. I selected fire occurrence or severity as the better predictor if the model containing that va riable was 2.0 AIC points lower than the alternative model. Fire and severity effects on soil C pools and fluxes I assessed the impacts of fire and severity on soil C flux rates and cumulative soil C flux over my soil incubation using the following fix ed effects portion of linear mixed models: C Flux (cumulative or rate) = o + 1 × Incubation Day + 2 3 ×Fire 4 ×ln(Incubation Day)×Fire Variable (1) 109 where i represents the model - fitted intercept or slope coefficient and fire variable is either fire occurrence or burn severity. Each model also initially included all topographic variables and a plot - identifier random effect. Non - significant topographic variable s were sequentially removed, and s election of fire occurrence or severity as a more suitable predictor was performed using AIC values as described above. I assessed the size and mineralization rates of the C a and C s pools using non - linear mixed models. I assessed the sizes of the C a and C s pools and their mineralization rate constants using the following fixed effects portion a two - compartment first - order kinetics model (Kuzyakov 2011) : Rate CO 2 = C a × k a e ( - ka×day) + C s × k s e ( - ks×day) (eq. 2) where Rate CO 2 is the soil CO 2 efflux rate at each measuremen t event, C a is the size of the active C pool, k a is its mineralization coefficient, C s is the size of the non - active C pool and k s is its mineralization rate coefficient. In this model, C s is constrained to be C SOC C a , where C SOC is total soil organic C content. I did not include a passive pool in this model because research shows that methods of isolating passive C pools do not result in biologically meaningful estimates (Greenfield et al. 2013) . I determined whether fire occurrence or severity affect ed soil C pools by first fitting a global non - linear model that included all 40 incubation replicates. I performed model selection procedures for random effects in non - linear models as described by Pinhero and Bates (2000) , which resulted in the inclusion of a plot identifier as a random effect associated with C a and k s , and no random effect associat ed with k a . I assessed how wildfire impacted the size or kinetic rates of the C a and C s pools by adding fire occurrence or severity as a covariate to the non - linear models. Selection of fire occurrence or severity as a more suitable predictor variable was 110 performed using AIC values as previously described. In addition to the global model, I fit separate non - linear models for each incubation replicate to obtain independent estimates of C a , k a , and k s for use in elastic net regularization analysis and structural equation modelling (described below). Five incubation replicates (two replicates fr om separate unburned plots, and one each from plots with dNBR values of 108, 350, and 478) did not converge during non - linear regression due to consistently high respiration rates throughout the incubation and were excluded from further analysis. After exc lusion, each of the ten plots still had C pool parameter estimates from at least three subplots. Fire and severity effects on soil bacterial communities and imputed metagenomes I iversity, ten most abundant bacterial phyla using linear mixed models and AIC model - selection as described above. The ten most abundant bacterial phyla represented all phyla that exhibited >0.5% relative abundance and 94.1% of the total abundance. I used multivariate statistical approaches (R Package: vegan ; Oksanen et al., 2017) to determine the impacts of fire occurrence, burn severity, and soil properties on bacterial community structure. Using a weighted UniFrac distance matrix of the bacterial community, I employed principle coordinates analysis (PCoA), and full and partial Mantel tests to determine the relationships between plant and soil characteristics and bacterial communities. Additionally, I performed PCoA and Mantel tests on a Bray - Curtis distance matrix of imputed MetaCyc metabolic pathways, hierarchically grouped into common C - degradation pathways. I identified positive and negative fire responsive bacterial OTUs using species indicator analysis with f ire occurrence as the explanatory variable, relative abundance of OTUs as 111 response variables, and point biserial correlation as the output statistic (R Package: indicspecies ; De Caceres and Legendre, 2009) . I assessed which positive fire - responder OTUs were also positive ly related to severity by performing linear mix ed - model analysis on the relative abundance of each fire - responsive OTU with dNBR as the explanatory variable and unburned plots excluded from the analysis. Links between ecosystem characteristics and soil carbon pools I determined properties linked to t he size and kinetic rates of soil C pools using structural equation modeling. I constructed an initial SEM meta - model composed of linear mixed models that included live tree basal area, dead tree basal area, forest floor mass, total C, PyC, total N, TIN, e xtractable P, pH, and soil moisture as response variables (R Package: piecewiseSEM ; Lefcheck, 2016) . All component linear mixed models initially included explanatory variables of severity and any topographic variable (i.e. elevation, slope, aspect) that my previous analyses (see above) indi cated was significantly related to the response variable of interest. A ll edaphic response variables initially also included live and dead tree basal area as explanatory variables. The TIN linear model also included total N as an explanatory variable, and the soil moisture model included overlying forest floor mass as an explanatory variable. All linear - mixed models included a plot - identifier as a random effect. I used severity instead of fire occurrence in my SEMs, because AIC values indicated that severit y had more explanatory power for more soil variables (Table 3. 1), and previous research indicates that severity - based SEMs outperform fire occurrence SEMs (Adkins et al. 2020) . The initial SEM model was modified by sequentially removing paths that exhibited p - values > 0. 05 , resulting in an SEM meta - model upon which all further models were based . 112 I assessed relatio nships of soil properties, live and dead tree basal area, and topography with C pools using separate SEMs for each C pool parameter (i.e. C a , k a , k s ). Each SEM included C a , k a , or k s as the response variable, and the initial explanatory variables included all soil and topography variables, and live and dead tree basal area . Variables that were no t significant at =0.05 were sequentially removed from the model. I did not construct SEMs for C s because this pool dominated the total C stock so SEMs would likely identify factors impacting total C stock size rather than C s pool size. My SEMs satisf y the recommendations of 5 observations per variable (Grace et al. 2015) , and local - estimation approaches to SEM like those used here have smaller sample size requirements since there only need to be enough degrees of freedom to fit any component model (Shipley et al. 2001). Relationships between bacteria, C pools, and soil nutrients I used correlation analyses and elastic net regularization to assess relationships between log 2 bacteria l abundance at the levels of phylum and genu s with C pools, soil TIN and P, and pH , soil variables that are commonly associated with bacterial copiotrophic versus oligotrophic life - strategies. I considered C a pools size an indicator of labile C availability. I performed univariate correlations betwe en all bacterial phyla and the selected soil variables. At the genus level, I performed correlations within phyla that my linear mixed - models indicated were related to burn severity and that previous research indicates exhibit either copiotrophic or oligot rophic life strategy (i.e. Acidobacteria , Actinobacteria , Bacteroidetes , Proteobacteria , and Verrucomicrobia ). for non - normality of bacterial abundance data and I considered correlations significant at =0.05. I also performed elastic net regularization (R Package: glmnet ; Friedman et al., 2010) to identify bacteria phyla and metabolic pathways that are predictive of C pool parameters. Elastic 113 net regularization selects the best set of predictors (i.e. phyla or metabolic pathways) for a given response by using LASSO and ridge regression, but does not assign significance values to selected predictors; elastic net regularization is frequently used with sparse datasets when the number of potential predicto rs is larger than the number of observations, as is often the case for genomic data (Friedman et al. 2010; Wagg et al. 2019) . 3.4 RESULTS 3.4.1 Relationships between fire, tree basal area, and soil properties Linear mixed models My AIC - based model selection indicated that fire occurrence had more explanatory power for models assessing total soil C, NH 4 + - N and TIN, whereas severity had more explanatory power for dead tree basal area, forest floor mass, total N, pH, and soil moisture (Table 3. 1). Neither explanatory variable had more power for live tree basal area, C :N, NO 3 - - N, and extractable P. Concentrations of NH 4 + - N, NO 3 - - N, and TIN were higher in burned stands than unburned stands (Table 3. 1). Additionall y, total N was a significant covariate for NH 4 + - N, NO 3 - - N, and TIN , all of which were all positively related to total N (p<0.001, p=0.041, and p<0.001, respectively). Univariate relationships between total N and NH 4 + - N, NO 3 - - N, and TIN were not significant indicating total N was not the primary driver of changes to inorganic N . Dead tree basal area increased with both severity (p=0.005) and elevation (p=0.017). Live tre e basal area was not related to severity and increased with elevation (p=0.034). Forest floor mass was negatively associated with severity (p=0.002) and positively related to elevation (p =0.001). Mineral soil gravimetric moisture was negatively related to severity (p=0.0 04 ) , and positively related to elevation (p<0.001) . Soil pH increased in association with severity (p=0.007). Mineral 114 soil total C and N, C:N, PyC, and extractable P were not significantly affected by fire occurrence or burn severity . Total N was positively related to elevation (p=0.012) and C:N was negatively related to elevation (p=0.045). Hillslope and aspect were not related to any of the considered properties. Table 3.1 Relationship of fire occurrence and burn severity (dNBR) to soil pro perties as determined via linear mixed models. Linear mixed models also included elevation, hillslope, and aspect as topographic covariates. Relationships between soil properties and topographic variables are described in the main text. Lowercase letters r epresent differences in soil properties between unburned and burned forest stands at - determined slope parameter and represents change in soil property per unit increase in dNBR. Bold text indicates tween models that included burn status versus dNBR, indicating that one of these variables should be preferred for predicting change in the relevant soil property. Unburned mean ± SE (n=4) Burned mean ± SE (n=6) dNBR coefficient Fire occurrence model AIC dNBR model AIC Live Tree Basal Area (m 2 ha - 1 ) 77. 7 ± 26. 5 a 33.3 ± 11.6 a - 0.046 100.19 98.79 Dead Tree Basal Area (m 2 ha - 1 ) 5. 4 ± 5. 4 b 21. 6 ± 5.6 a 0.044* 78.60 76.07 Forest floor Mass (kg m - 2) ) 2.60 ± 0.48 a 0.95 ± 0.39 b - 0.0026 * 485.46 463.69 Total C ( g kg - 1 ) 6 6.2 ± 1 3.3 a 6 8.8 ± 1 0.3 a 0.0043 206.30 208.35 Total N ( g kg - 1 ) 2.7 ± 0.5 a 2.8 ± 0. 8 a - 0.0011 - 61.16 - 64.56 C:N 34.0 ± 11.5 a 37.9 ± 8.0 a 0.023 356.07 357.73 PyC Proportion (% Total C) 14.3 ± 3.3 a 12.8 ± 2.0 a - 0.000065 - 94.95 - 96.16 NH 4 + - N ( µ g g - 1 ) 1.28 ± 0.52 a 11.9 ± 4. 6 b 0.0016 * 45.06 49.90 NO 3 - - N ( µ g g - 1 ) 0.29 ± 0.29 a 5.21 ± 3.18 b 0.00057 * 16.28 15.97 TIN ( µ g g - 1 ) 1.57 ± 0.75 a 17.1 ± 7.7 b 0.0019 * 61.20 65.88 Extractable P ( µ g g - 1 ) 3 5 . 0 ± 4.2 a 45.0 ± 4. 6 a 0.0084 334.54 336.47 pH 6.14 ± 0.05 a 6.54 ± 0.11 b 0.00090 * 64.11 58.43 Soil Moisture ( g kg - 1 ) 8 8.9 ± 1 6.0 a 56.6 ± 20 . 7 a - 0.0041 * 216.60 208.42 Bulk Density (kg m - 3 ) 1.17 ± 0.19 a 1.26 ± 0.16 a - 0.000045 13.05 13.18 Response variables for inorganic N models were box - cox transformed prior to statistical analysis and slope value is on box - cox scale. Unburned/burned means are on original scale. Inorganic N models included total N concentration as a covariate. * Coefficient significantly different from zero at = 0.05. Structural equation models My SEM meta - model revealed relationships between severity and soil properties that were not captured by my linear mixed - models (Fig 3. 1). Soil moisture was directly, negatively linked to severity (p=0.03), indirectly, negatively linked via forest floor mass (p=0.001), and indirectly, positively linked via dead tree basal area (p=0.047), leading to a standardized 115 compound path coefficient of - 0.52 between severity and soil moisture (marginal r 2 =0.63). Although my univariate analysis did not reveal a relationship between severity and total N (section 2.1.1), my SEM indicated that N was negatively linked to severity, a relationship that was partially offset by a positive relationship between N and dead tree basal area, leading to compound path coefficient of - 0.40 between N and severity (marginal r 2 =0.69). After accounting for dead tree basal area, elevation was no longer a significant predictor of total N. Figure 3.1 Causal diagram depicting structural equation model of direct and indirect links between burn severity, topography, live and dead tree basal area, and soil properties. Paths were fit using linear mixed models with a random plot effect. Standardiz ed coefficients are displayed for all links. All retained paths exhibited p - values 0.05. 3.4.2 Relationship of wildfire and burn severity to soil C pools and fluxes AIC - based comparisons of mixed - models assessing soil CO 2 efflux rate over my 300 - day incubati on did not suggest a preference for fire occurrence versus severity - based models (Fig. 3. 2 ). However, AIC values indicated fire occurrence had more explanatory power when assessing the same data as cumulative CO 2 - C flux (Fig S3.2). There was a negative mai n effect of fire occurrence on flux rate (p=0.01) and a positive interaction effect of fire occurrence × 116 ln (day) (p=0.007) (Fig. 3.8). Together, these effects indicate that initial flux rates were lower in burned soils, and, over the course of the incubati on, flux rates decreased more slowly in burned soils compared to unburned soils. Flux rates were positively related to elevation, but, when the data was assessed as cumulative CO 2 - C flux, elevation was not a significant predictor. AIC - based model selectio n of my global non - linear mixed models quantifying soil C pool structure and kinetics indicated that a fire occurrence model had more explanatory power than a severity model ( AIC=3.08). k s decreased from 0.00017 in unburned soils to 0.000070 in burned soi ls (p=0.002). This is equivalent to C s MRT increasing from 16.1 years to 39.2 years. The sizes of the C a and C s pools and k a were not significantly different between burned and unburned soils. Modeled C a was 1220 ± 210 m g k g soil - 1 (2.08 ± 0.37% of total C), and k a was 0.028 ± 0.002 (MRT=35.7 days). The SEMs indicated that C a was directly positively related to severity, but t he relationship was offset by dead tree basal area, forest floor mass, and total N such that there was only a minimal change in C a with severity (Table 3.2). SEMs indicated that k a was indirectly linked to severity via a relationship with pH. SEMs indicate d that k s was indirectly linked to severity via the path between dead tree basal area and total N. k s was directly positively related to live tree basal area and PyC. 117 Figure 3. 2 Mean (± SE) CO 2 - C efflux rate (points) over a 300 - day laboratory incubation of mineral soils (0 - 5 cm) grouped by fire - occurrence (a) and severity (b). C olored lines represent change in CO 2 - C efflux rates between sampling days and vertical bars represent standard errors . In panel a, SE is based on n=4 unburned plots and n=6 burned plots. In panel b, SE is based on n=3 or 4 subplots per plot. 118 Table 3.2 Structural equation models explaining direct and indirect links to C - pool par ameters . Path coefficients were standardized and only coefficients exhibiting p - values 0.05 were retained in the models. Abbreviations: DBA=dead tree basal area; FFM= forest floor mass; LBA=live tree basal area; N=total nitrogen; PyC=pyrogenic carbon Res ponse Variable Structural Equation Model dNBR Compound Coefficient (p - value) C a C a = 1.68dNBR 1.45DBA + 0.63FFM + 0.70N +0.85ELEV (m. r 2 =0.54) FFM= 0.37dNBR + 0.46ELEV (m. r 2 =0.84) N = 0.92 dNBR + 0.89DBA (m. r 2 =0.69) DBA= 0.93dNBR + 0.79ELEV (m. r 2 =0.72) 0.03 6.29 (0.39) k a k a = 0.51pH (m. r 2 =0.24) pH= 0.51dNBR (m. r 2 =0.35) 0.26 1.64 (0.44) k s k s = 0.26LBA + 0.40N + 0.56PyC (m. r 2 =0.63) LBA= 0.79ELEV (m. r 2 =0.62) N = 0.92 dNBR + 0.89DBA (m. r 2 =0.69) DBA= 0.93dNBR + 0.79ELEV (m. r 2 =0.72) - 0.04 25.30 (0.12) 3.4.3 Relationships between wildfire , burn severity , bacterial communities , and imputed metabolic pathways Bacterial diversity AIC values indicatin g no preference for fire occurrence or severity - based models (Figs. 3. 3 a and 3. 3 b). AIC - based model selection indicated that the severity model better explained OTU richness, which was negatively correlated with severity (Figs. 3. 3 c and 3. 3 di not shown). Fire occurrence had more explanatory power for changes in phyla - level oligotroph - to - copiotroph ratios, which decreased from 1.08 in unburned soils t o 0.48 in burned soils (Figs 3. 3 e and 3. 3 f). 119 Figure 3. 3 Relationship between fire occurrence and severity for selected microbial community characteristics and d), and oligotrophic - to - copiotrophic taxa ratio (e and f). Bacterial community structure I performed PCoA on a weighted UniFrac distance matrix and found that the first axis expla ined 54.9% of variation, and the second axis explained an additional 8.7% (Fig. 3. 4 ). Fire occurrence (r 2 =0.24, p<0.001) and severity (r 2 =0.57, p<0.001) were both significantly correlated with the PCoA ordination. Several soil properties were also signific antly correlated with the ordination, including NH 4 - N concentration (p=0.002), TIN concentration (p=0.008), P concentration (p=0.009), pH (p<0.001), soil moisture (p=0.010), and forest floor mass (p=0.003). 120 Mantel tests between the weighted UniFrac matrix and a Bray - Curtis matrix of soil properties indicated the two matrices were significantly related (Mantel statistic r=0.246, p=0.008). Figure 3. 4 Principle coordinates analysis (PCoA) plots based on a weighted UniFrac distance matrix of bacterial commun ities in mineral soils (0 - 5 cm). Each point represents the bacterial community from a single subplot. Vectors represent variables that are significantly correlated with one of the PCoA axes, and vector lengths are scaled based on r 2 values. Solid and dashe d hulls depict the ordination space that encompasses all burned and unburned samples, respectively. I assessed the relationship between wildfire occurrence and severity and the relative abundances of the ten most abundant bacterial groups using univariate linear mixed models. AIC values indicated that the fire occurrence models had more explanatory power for assessing differences in Bacteroidetes, Acidobacteria, Verrucomicrobia, and Planctomycetes relative abundance. The relative abundances of Bacteroidetes, Actinobacteria, and Firmicutes were 121 higher in burned areas than unburned areas (Fig. 3. 5 ). The rela tive abundances of Acidobacteria, Verrucomicrobia, and Planctomycetes relative abundance were lower in burned areas than unburned areas. There was no significant effect of fire occurrence or burn severity on - - Proteobacteria, - proteobact eria or Gemmatimonadetes relative abundance. Figure 3. 5 Relative abundance of the ten most abundant bacterial phyla across a gradient of burn severity in mineral soils (0 - 5 cm). The ten most abundant phyla accounted for ~9 4 % of the total bacterial commun ity. Abundances at each dNBR level are the means of four subplots. Bacterial OTUs Indicator species analysis identified 53 OTUs as indicators of burned soils (i.e. positive fire responders) and 74 OTUs as indicators of unburned soils (i.e. negative fire responders) ( data not shown ). The positive fire responders most commonly belonged to the Actinobacteria and Bacteroidetes phyla, as well as to the - proteobacteria class, which respectively accounted for 28.6%, 23.2%, and 26.8% of the positive responder OTUs. The seven OTUs that exhibited the 122 strongest positive response to fire (point biserial correlation >0.60) came from the genera Massilia ( - proteobacteria ), Roseomonas ( - proteobacteria ), Segetibacter ( Bacteroidetes ; 2 OTUs), Blastoccus ( Actinobacteria ), unclassified Micrococcaceae genus ( Actinobacteria ), and unclassified Burkholderiaceae genus ( - proteobacteria ). The negative fire responders most frequently belonged to the Planctomycetes phylum, which accounted for 14.7% of these responders, and to the - proteobacteria and - proteobacteria classes, which accounted for 18.7% and 17.3%, respectively. The five OTUs that exhibited the strongest negative response came from the genera Cytophaga ( Bacteroidetes ), IS - 44 ( - proteobacteria ), Mycobacterium ( Actinobacteria ), uncultured Elsteraceae genus ( - proteobacteria ), and uncultured Gemmataceae genus ( Planctomycetes ). I identified 15 OTUs as positively associated with severity based on linear mixed models (Fig. 3. 6 ). Of the 15 OTUs , six were from the Bacteroidetes phylum, two were from Actinobacteria , and one was from Verrucomicrobia . Four severity responders were from the - proteobacteria class, one was from the - proteobacter ia class, and one was from the - proteobacteria class. The abundances of all of the severity - associated OTUs were positively correlated with either TIN or P (data not shown). 123 Figure 3. 6 Heat map of showing z - transformed relative abundance of ba cterial OTUs identified as severity responders. Severity responders were identified by performing indicator species analysis and linear mixed modelling. Abundances at each dNBR level are the means of four subplots. 124 Carbon degradation metabolic pathways Using PICRUST2, I identified 135 MetaCyc metabolic pathways associated with C - degradation functions. PCoA on a Bray - Curtis matrix of the MetaCyc pathways grouped into common C - degradation functions indicated that the first axis explained 45.1% o f variation and the second axis explained 22.1% (Fig. 3. 7 ). Fire occurrence (r 2 =0.16, p<0.001) and severity (r 2 =0.41, p<0.001) were significantly correlated with the ordination. Additionally, several soil properties were significantly correlated with the o rdination, including total C (p=0.009), C:N ratio (p=0.044), NH 4 - N (p=0.008), TIN (p=0.047), extractable P (r 2 =0.32, p=0.002), pH (p=0.010), and forest floor mass (p=0.007). Partial mantel tests between the distance matrix, a dNBR distance matrix, and a so il properties distance matrix indicated that, after accounting for soil properties, there was a significant correlation between severity and the metabolic pathways (Mantel statistic r=0.18, p=0.008). AIC - based model comparisons for linear mixed models in dicated that severity had more explanatory power for differences in carbohydrate degradation, alcohol degradation, amine and polyamine degradation, carboxylate degradation, and nucleotide and nucleoside degradation. Severity positively impacted the relativ e abundance of imputed pathways associated with carbohydrate degradation, alcohol degradation, amine and polyamine degradation, carboxylate degradation, and secondary metabolite degradation (Fig. 3. 8 ). The relative abundance of amino acid degradation pathw ays was higher in burned areas than unburned areas (p=0.021). The relative abundances of aromatic compound degradation pathways and fatty acid and lipid degradation pathways were not related to fire occurrence or severity. 125 Figure 3. 7 Principle coordinates analysis (PCoA) plots based on a Bray - Curtis distance matrix of imputed MetaCyc pathways grouped into common C - degradation functions. Vectors represent variables that are significantly correlated with one of the PCoA axes, and vector lengths a re scaled based on r 2 values. Solid and dashed hulls depict the ordination space that encompasses all burned and unburned samples, respectively. 126 Figure 3. 8 Relationship s between burn severity and imputed C - degradation pathways estimated using PICRUST2. 127 3.4.4 Relationships between bacterial taxa, soil C pools, and soil nutrients At the phyla level, Proteobacteria , Latescibacteria , and Bacteroidetes were positively cor related with C a , whereas Firmicutes was negatively correlated with C a (Fig 3.9). Firmicutes was also positively correlated with k a , while several relatively rare bacterial phyla were negatively correlated with k a . Omnitrophicaeota and Elusimicrobia were po sitively correlated with k s , and no phyla were negatively correlated with k s . Firmicutes , FBP , Bacteroidetes and Actinobacteria were the only phyla positively correlated with TIN or P. Several bacterial phyla were negatively correlated with these nutrients, including the relatively abundant Verrucomicrobia , Planctomycetes , and Acidobacteria . GLMs constructed using elastic net regu larization selected seven bacterial phyla as associated with C a , two phyla associated with k a , and one phylum associated with k s (Table S3.1). Various relationships between bacteria, C pools, and nutrients emerged at the genera level (Table 3.3) . Proteob acteria , Bacteroidetes , Firmicutes , and Actinobacteria are traditionally considered copiotrophic phyla. Within Proteobacteria and Bacteroidetes , more genera were positively correlated with C a than were negatively correlated. More genera within these two ph yla were positively correlated with TIN than were negatively correlated, but nearly 10% of genera within both phyla were negatively correlated with TIN . Within Proteobacteria , more genera were negatively correlated with P, and within Bacteroidetes , an equal proportion of genera were positively and negatively correlated with P. 46.8% of Proteobacteria genera and 45.8% of Actinobacteria genera were not significantly correlated within any of the considered variables. Within Firmicutes an d Actinobacteria , more genera were negatively correlated with C a than were positively correlated, while more genera were positively correlated with TIN than 128 were negatively correlated. More Firmicutes genera were positively correlated P than were negativel y correlated, whereas more Actinobacteria genera were negatively correlated with P than were positively correlated. For both phyla, more genera were positively correlated pH than were negatively correlated. 45.8% of Firmicutes genera and 42.7% of Actinobac teria genera were not significantly correlated with any of the considered variables. Acidobacteria and Verrucomicrobia are typically considered oligotrophic phyla. Within both of these phyla, more genera were positively correlated with C a than were negat ively correlated. Additionally, more genera within both phyla were negatively correlated with TIN, P, and pH. 38.0% of Acidobacteria genera and 51.9% of Verrucomicrobia genera were not significantly correlated with any of the considered variables. 129 Figure 3.9 Correlations between bacteria l phyla and active carbon pool size ( C a ) and kinetic rate ( k a ) , non - active carbon pool kinetic rate ( k s ) , total inorganic nitrogen ( TIN ) , phosphorus ( P ) , and pH. Asterisks indicate correlations that were significant at =0.05 . 130 Table 3.3 Proportion of genera within selected phyla that are significantly correlated with soil carbon pools , total inorganic nitrogen ( TIN ) , phosphorus ( P ) , or pH . Correlations significant at Phylum Genera Count C a k a k s TIN P pH Pos. (%) Neg. (%) Pos. (%) Neg. (%) Pos. (%) Neg. (%) Pos. (%) Neg. (%) Pos. (%) Neg. (%) Pos. (%) Neg. (%) Proteobacteria 412 8.99 1.21 0.73 8.98 4.61 0.49 13.1 9.5 0 6.07 10.4 8.10 14.3 Bacteroidetes 131 7.63 1.53 0.00 1 3.0 9.16 2.29 16.0 9.92 6.87 6.87 13.0 12.2 Actinobacteria 157 3.18 4.46 3.18 6.37 1.91 3.18 12.7 8.28 3.18 15.9 16. 6 8.92 Firmicutes 33 0.00 9.09 12.1 0.00 0.00 9.09 21.1 0.00 12.1 3.03 24.2 3.03 Verrucomicrobia 52 11.5 0.00 0.00 13. 5 5.77 0.00 5.77 19.2 5.77 17.3 5.77 17.3 Acidobacteria 95 8.42 1.05 0.00 8.42 3.16 0.00 4.21 20.0 0.00 19.0 1.05 25. 3 131 3.5 D ISCUSSION 3.5.1 Hypothesis 1: Soil properties are related to differences in carbon pools across a burn severity gradient Soil properties including forest floor mass, total N, and pH were related to C pools, but live and dead tree basal area also played a n important role in explaining differences in C pools (Table 3.2). Dead tree basal area was related to several soil propert ies one - year post - fire, exhibiting direct links to total N and soil moisture and an indirect link to TIN (Fig. 3. 1). I also observed a positive relationship between dead tree basal area and total N in a previous study (Adkins et al. 2020) , an effect that could be caused by decomposition of dead tree roots leading to increased soil N inputs (Fahey et al. 1988) . Root dynamics could also explain the direct negative link between dead tree basal area and C a (Table 3.2), a relationship potentially resulting from decreased root exudation (Boddy et al. 2007; de Graaff et al. 2010) . Along with forest floor mass and total N, dead tree basal area offset the direct positive link between severity and C a , suggesting vegetation dynamics play an important role in mediating the response of soil C stabi lity to fire. In my previous study performed three years post - fire , I found that forest floor mass was directly positively linked to live tree basal area (Adkins et al. 2020), whereas I found no such link here. This suggests that by one year post - fire, new litter inputs have not yet led to re - accumulation of forest floor, and litter deposition may not substantially increase forest floor mass until later in forest recovery. The positive relationships of total N with C a , and k s are likely due to faster decomposition rates of high N (low C:N) soil organic matter (Aber and Melillo 1980; Melillo et al. 1982) . Indeed, I found positive relationships between total N and C flux rate, and negative relationships between C:N and C flux rate during our soil incubation (data not shown). However, 132 total N did not vary wi th severity (Table 3.1), and thus does not account for k s decreasing with severity. Similarly, although there was a tendency for C:N to increase with severity , this effect was not significant and therefore does not account for the decreased k s . Interesting ly, TIN was not related to C pool dynamics , despite indications that high TIN decrease s organic matter decomposition rates in forests (Fog 1988; Janssens et al. 2010) , and would therefore be expected to be negatively associated with k a and/or k s . There are several potential reasons why the impact of TIN on C cycling may be tempered in burned forests. First, TIN may primarily influence decomposition rates in organic surface horizons rather than in mineral soils (Janssens et al. 2010) . This effect may therefore be minimal when fires lead to decreases in the forest floor layer, as I found her e. Secondly, decreased decomposition in response to TIN may be partially attributable to acidifying effects of TIN on soil (Averill and Waring 2018) . Increases in soil pH that typically occur following fires may therefore buffer against the acidifying effects of TIN and negate the potential impacts on decomposition. Finally, TIN may decrease decomposition by suppressing the abundance and activity of my corrhizal (Phillips and Fahey 2007; Janssens et al. 2010) and lignolytic fungi (Fog 1988; DeForest et al. 2004; Entwistle et al. 2018) . Mycorrhizal and saprotrophic fungal abundance is generally lower in burned versus unburned soils due to vegetation and litter loss, low tolerance for soil heating, and pH effects (Dooley and Treseder 2012; Pressler et al. 2018) . Decomposition performed by these fungal groups is thus likely already lower in burned versus un burned areas, so higher TIN might not have any additional effects on their activities. Although my SEMs displayed adequate goodness of fit statistics and identified several variables that were significantly related to C pool parameters, these SEMs do not appear to well explain the patterns of C pool kinetics across the severity gradient. For example, my non - linear 133 models indicated a strong negative effect of fire occurrence and burn severity on k s , but the SEM indicated only a slightly negative effect. Th is suggests there are other unaccounted - for variables necessary for explaining relationships between fire and C pool kinetics. Despite the shortcomings of these SEMs, I present them here because they were still successful at identifying individual variable s that are related to C pool sizes and kinetics, including ecosystem properties that are affected by fire and severity. For example, live and dead tree basal area, forest floor mass, pH, and pyrogenic C content are all properties that are associated with f ire (Certini 2005; Hart et al. 2005; Miesel et al. 2018) and were directly linked to one or more C pool parameter in my SEMs. Differences in soil organic matter composition across the severity gradient could acco unt for the inability of the SEMs to represent the overall relationship between severity and C pool kinetics. For example, soil carbohydrate content decreased immediately following wildfires in P. pinaster forests of Spain (Martín et al. 2009) , and lignin was found to be a more predominant component of soil organic matter in areas of high burn severity i n coniferous and deciduous forests of northern Minnesota, USA (Miesel et al. 2015) . The slower decomposition of lignin relative to carbohydrates could possibly account for the lower k s in burned areas, especially if fire also decreases lignolytic fungal abundance. Accounting for soil organic matter composition could also capture the effects of colonization of burned areas by early successional herbs and shrubs during post - f ire recovery (Hart et al. 2005; Collins and Roller 2013) , which likely results in greater inputs of high N, low lignin deciduous litter over the short - to intermediate term (Hart et al. 2005) . Differences in soil texture could also influence soil C pools in ways that were not accounted for in my SEMs. For example, fire can lead to altered soil textures by disrupting aggregates, degr ading clay at high soil temperatures (>400 ° C), and due to convective forces 134 generated by fire transporting fine soil particles (Certini 2005; Neary and DeBano 2005; Alcañiz et al. 2018) . Soil C is stabilized via occlusion in aggregates and associations with clay (Jastrow et al. 2007) , so changes to these soil physical properties could alter C pool structure and kinetics. In the field, soil microclimate differences between burned and unburned areas could also influence soil C pool kinetics in ways that are difficult to account for durin g lab - based soil incubations. For example, less canopy shading and decreased insulation from forest floor in high burn severity areas could result in higher temperatures and lower soil moisture in mineral soils (Hart et al. 2005; K asischke and Johnstone 2005) , thereby influencing microbial activity and C pool kinetics . 3.5.2 Hypothesis 2 : Bacteria previously identified as fire responders are positively associated with burn severity In support of my hypothesis, s ome of the genera harboring the severity - responsive OTUs (Fig. 3.6) have previously been identified as fire - responsive taxa (e.g. Adhaeribacter , Roseomonas , and Flavisolibacter ) (Weber et al. 2014; Whitman et al. 2019) . All of the genera harboring the severity - responsive OTUs were positively correlated with either TIN or P, suggesting copiotrophic life - strategies. To the best of my knowledge, other OTUs I identified as sev erity - responders have not previously been characterized as fire - responders. For example, Segetibacter accounted for two severity - responsive OTUs (and three positive fire - responsive OTUs) but has not been identified as fire - responsive in previous studies. H owever, Segetibacter was identified as responding positively to PyC additions in a lab incubation study, suggesting post - fire affinity (Woolet and Whitman 2020) . I identified several genera as positive fire - responders (but not severity - responders) that other studies have also identified, including Aeromicrobia , Blastococcus , Massilia , Phenylobacterium , and Devosia (Weber et al. 2014; 135 Whitman et al. 2016; Huffman and Madritch 2018) . Conversely, other studies have consistently identified Arthrobacter as a positive fire - responder (Weber et al. 2014; Huffman and Madritch 2018; Whitman et al. 2019) , but I did not. My identification of unique severity responsive OTUs suggests that high burn severity may cause soil function to become dissimilar from pre - fire conditions. This is further evidenced by the positive associations of several imputed C - metabolism pa thways with severity. The negative responses of bacterial phylogenetic diversity, OTU richness, and phyla - level oligotrophic - to - copiotrophic ratio to fire and burn severity agrees with my previous research where I found similar patterns three years post - fi re in another mixed - conifer forest (Fig. 3.3; Adkins et al. 2020) . This suggests that short - to intermediate - term decreases in bacterial diver sity and oligotrophic - to - copiotrophic ratio are common responses to fire in mixed - conifer forests. 3.5.3 Hypothesis 3 : Burned areas have a higher abundance of copiotrophic bacteria The abundances of several dominant bacterial phyla were associated with fire , and in support of my hypothesis, phyla traditionally classified as copiotrophic tended to be more abundant and oligotrophic phyla less abundant in burned compared to unburned areas . The relative abundance of Bacteroidetes was higher and Acidobacteria was lowe r in burned areas compared to unburned areas, a dynamic that has often been observed following fires (Weber et al. 2014; Xiang et al. 2014b; Rodríguez et al. 2018; Pérez - Valera et al. 2019; Whitman et al. 2019) . Actinobacteria and Firmicutes had higher relative abundance in burned areas one - year post - fire, which contrasts with my previous research where I found no differences in Actinobacteria and Firmicutes abundance three years after fire (Adkins et al. 2020) . Other studies have found increased Actinobacteria and Firmicutes abundance in burned areas in several forest types (Ferrenberg et al. 2013; Weber et al. 2014; Fultz et al. 2016; Prendergast - Miller et al. 136 2017; Huffman and Madritch 2018; Pérez - Valera et al. 2019) , and, like the present study, those studies all occurred within one year post - fire (range: one day to one year post - fire). The lack of studies encompassing longer timeframes makes it difficult to determine whether a parabolic - shaped response of Actinobacteria and Firmicutes abundance in mixed - conifer forests is typical, as suggested by this study and Adkins et al. (2020) together. The increased abundance of Firmicutes and Actinobacteria following fires is likely due to spore - forming ability (Prendergast - Miller et al. 2017) , and thus the abundance of these phyla could decrease later in the post - fire recovery period as environmental characteristics become more important drivers of microbial communities (Ferrenberg et al. 2013; Whitman et al. 2019) . The lower abundance of Verrucomicrobia and Planctomycetes i n burned areas supports a previous study that identified a similar pattern in three months post - fire in ponderosa pine and mixed - conifer forest in New Mexico (Weber et al. 2014) . My hypothesis that greater copiotrophic bacterial abundance is related to post - fire increases in nutrient availability is suppor ted by the positive relationships between TIN and Bacteroidetes and between P and Firmicutes . Support for my hypothesis that copiotrophic bacteria would be associated with higher C a is mixed: Bacteroidetes and Proteobacteria abundance was positively associated with C a while Firmicutes was negatively associated. The positive association of Bacteroidetes with C a is particularly interesting because Bacteroidetes abundance increased with severity, but C a was unchanged. This sugge sts that Bacteroidetes do not necessarily respond to labile C and nutrient availability simultaneously and that higher TIN concentrations maintain the greater Bacteroidetes populations in burned areas. Given that Bacteroidetes is the most abundant phylum i n these soils, the response of Bacteroidetes to greater TIN could influence C cycling in ways that diverge with time post - fire . In the short term, 137 increased TIN could lead to more efficient C use by Bacteroidetes by allowing them to preferentially decompos e labile C substrates that have high C:N ratios. However, if aboveground vegetation losses result in decreased labile C input s in the long - term, Bacteroidetes may switch to recalcitrant C substrates , requiring greater investment in exoenzymes, and conseque ntly, greater C mineralization (Malik et al. 2020) . Shifting abundances of C degradation pathways also supported the hypothesis that bacterial communities are more copiotrophic in burned areas. Higher ratios of genes associated with carbohydrate deg radation versus aromatic compound degradation is a metagenomic indicator of community level copiotrophy (Hartman et al. 2017) , and I found that carbohydrate degradation pathway abundance increased with severity while aromatic degradation pathway abundance was unchanged (Fig. 3.7). This approach can be extended to other compound types. Amino acids are labile C substrates, and ami no acid degradation pathway abundance increased with severity. In contrast, fatty - acids and lipids are slow to decompose , and the abundance of pathways associated with their degradation was unrelated to severity , patterns that also suggest community - level copiotrophy increases with severity . Inputs of recalcitrant C compounds to soil may be high after fire due to the formation of pyrogenic organic matter and wood deposition resulting from tree mortality. If the input of this recalcitrant C is not accompanie d by increases in the metabolic pathways associated with its degradation, recalcitrant C could accumulate in soil, leading to larger C s pool s and soil C stocks over the long - term. Validity of copiotroph - oligotroph classifications depends on bacterial taxon omic level Although I found support for my hypothesis that phyla - level copiotroph versus oligotroph bacterial abundance differed between burned and unburned areas, I also found that these life - history classification depended on the resource (i.e. nutrient s versus C) and taxonomic level 138 considered. Copiotrophic abundance is frequently associated both with labile C and nutrient availability , and, w hen considered at the phylum level, traditional ecological classifications appeared to agree with this framework . For example, in addition to Proteobacteria and Bacteroidetes increasing in abundance with C a , the copiotrophic phyla Actinobacteria increased in abundance with TIN. Furthermore, the oligotrophic phyla Acidobacteria and Verrucomicrobia were negatively associated with nutrient concentrations, suggesting they are outcompeted by copiotrophs at high nutrient availability. A notable excepti on to the phyla - level oligotroph - copiotroph framework occurred for Firmicutes , which is typically classified as a copiotroph. Firmicutes increased in abundance with nutrient concentrations, suggesting copiotrophy, but was negatively correlated with C a , sug gesting oligotrophy . The oligotrophic traits of Firmicutes could be due to evolutionary tradeoffs related to endo spore - forming ability (Malik et al. 2020) . Maintaining sporulation ability is energetically costly, and, in environments that do not selec t for this trait, Firmicutes often lose the capacity for spore - formation and in turn achieve faster growth rates (Filippidou et al. 2016) . Greater post - fire abundance of Firmicutes is likely due to the heat - tolerance of endospores (Ferrenberg et al. 2013; Prendergast - Miller et al. 2017) , so po st - fire soils may be dominated by slow - growing Firmicutes genera that are less able to rapidly respond to labile C availability. This indicates, that even at the phylum level, ecological classification of bacteria depend s on specific environmental characte ristics, as well as the resource or trait considered , and may oversimplify the metabolic diversity of bacteria within phyla (Hartmann et al. 2017; Ho et al. 2017) . The limitations of the oligotroph - copiotroph framework became even clearer when considered at the genus level. For example, despite their copiotrophic classification, a substantial proportion of Proteobacteria and Bacteroidetes genera were negatively related to TIN (Table 139 3.3) . Furthermore, more than half of the genera harbored within Proteobacteria and Bacteroidetes were not significantly correlated with C a or nutrient concentrations . This suggests that phyla - level ecological classification may be based on a minori ty of genera and fail to encompass taxa that are oligotrophic or neutral in their response to resources, especially if responses to C and nutrients are not considered in conjunction. Similar divergence of ecological traits were apparent among genera harbo red within the oligotrophically classified Acidobacteria and Verrucomicrobia phyla. Although a greater proportion of genera exhibited oliogotrophic tendencies in their relationships with nutrient availability, more genera were copiotrophic in their relationship s with labile C availability. Only one of the nine Acidobacteria genera that was positively related to C a was correlated with nutrient concentrations, and 50% of the Verrucomicrobia genera that were positively related to C a were negatively corr elated with nutrient concentrations. This suggests tradeoffs in traits related to C versus nutrient acquisition at the genus level (Malik et al. 2020) , and that genera within the same phylum occupy different ecological niches. Niche differen tiation am ong Aci dobacteria taxa could explain the divergent response of subgroups within this phyla to fire (Weber et al. 2014; Adkins et al. 2020) . The divergence of ecological strategies at the genera level contributes to a growing body of evidence suggestin g that ecological classification of bacteria should occur at a finer taxonomic resolution than the phylum - scale (Hartmann et al. 2017; Ho et al. 2017; Sauvadet et al. 2019) . Furthermore, my results show that the same taxon can exhibit both copiotrophic and oligotrophic traits depending on whether C or nutrients are the resources of interest. The oligotroph - copiotroph dichotomy m ay therefore fail to capture the metabolic breadth of bacteria, and a three - dimensional competitor - stress - ruderal framework (Ho et al. 2013; Malik et al. 2020) may be a more suitable alternative, especially in disturbed 140 ecosystems. Nevertheless, despite the apparent shortcoming of taxonomy - based life - history classifications, the fact that all of the OTUs that were positively associated with burn severity were also positively associated with nutrient concentrations supports the hypothesis that bacterial communities are more copiotrophic in burned areas than unburned areas. 3.5.4 Hypothesis 4 : Bacterial taxa are associated with carbon pool kinetic rates My hypothesis that k a would be positively associated with copiotrophic bacterial taxa is not well supported. Firmicutes was the only phylum that was positively associated with k a , and, althou gh Firmicutes is often considered copiotrophic, I found that this phyla exhibited both copiotrophic and oligotrophic characteristics. In contrast, I found that k s was positively associated with Elusimicrobia abundance. Although Elusimicrobia has not been classified as either copiotrophic or oligotrophic, my results and others suggest that Elusimicrobia is oligotrophic. Elusimicrobia is a recently defined bacterial phylum that appears to be metabolically diverse, with various lineages capable of N - fixation and nitrate reduction (Meheust et al. 2019) . Elusimicrobia may preferentially utilize recalcitrant forms of C as subst rate (Chávez - Romero et al. 2016) , which may explain its association with k s . In fact, Elusimicrobia have been identified as degraders of lignin (Wilhelm 2016) , a plant compound that degrades slowly when other bioavailable C compounds are not available to provide energy for its decomposition (Klotzbücher et al. 2011) . Considered with my observed negative relationship of Elusimicrobia with TIN and P , this suggests Elusimicrobia is an oli gotrophic phylum. Elusimicrobia was a relatively rare phyla in my soils, exhibiting a relative abundance of only 0.21%, so likely is an indicator rather than a driver of higher k s . 141 3.6 CONCLUSIONS My results suggest that soil C is more persistent in burned than unburned areas one year after fire (as indicated by lower k s ) , and that this effect is at least partially influenced by top - down controls exerted by vegetation on soil properties and bacterial communities . Dead tree basal area specifically was directly or indirectly linked to soil moisture, total N, and TIN, and, through these linkages, influenced soil bacterial communities. In concert with the positive association between live tree basal area a nd k s , these results suggest that vegetation structure has a downstream effect on soil C persistence during post - fire recovery. The increase in C persistence may partially offset ecosystem C losses from biomass combustion while vegetation recovers, but it is possible that the positive relationship between fire and fast C - cycling copiotrophic bacterial taxa may negate some of these effects . Differences in bacterial community structure between burned and unburned areas could be explained by the copi otroph - oligotroph life - history framework when considered at the phylum level but was less effective in explaining differences at the genus level. This suggests that coarse life - history classifications fail to capture the metabolic breadth of bacterial taxa and may therefore limit the ability to predict the influence of microbial communities on ecosystem function during post - fire recovery. Certain bacterial taxa were associated with C pool and kinetics. Although my study cannot disentangle correlation versus causation of bacterial communities on soil C pools, these results suggest that post - fire changes in microbial composition are linked to soil C cycling . Future research could incorporate isotopic tracing techniques to further elucidate which bacterial taxa drive differences in C cycling and whether life - history strategy explains these differences . Such information could be incorporated into 142 global ecosystem models to help anticipate the effects of fire regime change on the global C cycle. 143 APPENDIX 144 SUPPLEMENTAL FIGURES Figure S3.1. Locations of field plots within a burn severity matrix resulting from the Beaver Fire. dNBR values are grouped into unburned, low, moderate, and high severity thresholds identified by the Monitoring Trends in Burn Severity team. 145 Figure S3.2 Mean (± SE) cumulative CO 2 - C efflux (points) over a 300 - day laboratory incubation of mineral soils (0 - 5 cm) grouped by fire - occurrence (a) and severity (b). In panel a, colored lines represent the fitted mo del , ribbons are the standard error of prediction , and vertical lines are standard errors of means (n=4 for unburned, n=6 for burned) . In panel b, colored lines represent change in cumulative CO 2 - C efflux between sampling days , and verticals bars are stand ard errors of means (n=3 or 4 per plot). 146 SUPPLEMENTAL TABLES Table S3.1 Elastic - Net selected Generalized Linear Models explaining C - pool parameters based on bacterial phyla abundance. Response Variable Elastic - Net Selected Generalized Linear Model C a 888 10. 6 Armatimonadetes + 118. Bacteroidetes + 13 4 FCPU426 208Firmicutes + 209Kiritimatiellaeota + 203. Proteobacteria + 17. 1 WS2 6 9.0 Unclassified k a 0.02 0 + 0.0016Firmicutes 0.00015Omnitrophicaetoa k s 0.00014 + 2.48 × 10 - 6 Elusimicrobia 3.00×10 - 6 Unclassified 147 REFERENCES 148 REFERENCES Aber JD, Melillo JM (1980) Litter decomposition: measuring relative contributions of organic matter and nitrogen to forest soils. Can J Bot 58:416 421. doi: 10.1139/b80 - 046 Adkins J, Docherty KM, Gutknecht JLM, Miesel JR (2020) How do soil microbial communities respond to fire in the intermediate term? Investigating direct and indirect effects associated with fire occurrence and burn severity. Sci Total Environ 745:140957. doi: 10.1016/j.scitotenv.2020.140957 Adkins J, Sanderman J, Miesel J (2019) Soil carbon pools and fluxes vary across a burn severity gradient three years after wildfire in Sierra Nevada mixed - conifer forest. Geoderma 333:10 22. doi: 10.1016/j.geoderma.2018.07.009 Alcañiz M, Outeiro L, Francos M, Úbeda X (2018) Effects of pr escribed fires on soil properties: A review. Sci Total Environ 613 614:944 957. doi: 10.1016/j.scitotenv.2017.09.144 Allison SD, Martiny JBH (2008) Colloquium paper: resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci U S A 105 Suppl:11512 9. doi: 10.1073/pnas.0801925105 Averill C, Waring B (2018) Nitrogen limitation of decomposition and decay: How can it occur? Glob Chang Biol 24:1417 1427. doi: 10.1111/gcb.13980 Barbera P, Kozlov AM, Czech L, et al (2018) EPA - ng: Massively Parallel Evolutionary Placement of Genetic Sequences. Syst Biol 68:365 369. doi: 10.1093/sysbio/syy054 Boddy E, Hill PW, Farrar J, Jones DL (2007) Fast turnover of low molecular weight components of the dissolved organic carbon pool of temperate grassland field soils. Soil Biol Biochem 39:827 835. doi: 10.1016/j.soilbio.2006.09.030 Bokulich NA, Kaehler BD, Rideout JR, et al (2018) Optimizing taxonomic classification of marker - - feature - classifier plugin. Microbiome 6: 90. doi: 10.1186/s40168 - 018 - 0470 - z Bolyen E, Rideout JR, Dillon MR, et al (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852 857. doi: 10.1038/s41587 - 019 - 0209 - 9 Box GEP, Cox DR (1964) An A nalysis of Transformations. J R Stat Soc Ser B 26:211 243. doi: 10.1111/j.2517 - 6161.1964.tb00553.x 149 Callahan BJ, McMurdie PJ, Rosen MJ, et al (2016) DADA2: High - resolution sample inference from Illumina amplicon data. Nat Methods 13:581 583. doi: 10.1038/nm eth.3869 Caporaso JG, Kuczynski J, Stombaugh J, et al (2010) QIIME allows analysis of high - throughput community sequencing data Intensity normalization improves color calling in SOLiD sequencing. Nat Publ Gr 7:335 336. doi: 10.1038/nmeth0510 - 335 Caspi R, Billington R, Fulcher CA, et al (2018) The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res 46:D633 D639. doi: 10.1093/nar/gkx935 Certini G (2005) Effects of fire on properties of forest soils: a review. Oecologia 143:1 10. doi: 10.100 7/s00442 - 004 - 1788 - 8 Chávez - Romero Y, Navarro - Noya YE, Reynoso - Martínez SC, et al (2016) 16S metagenomics reveals changes in the soil bacterial community driven by soil organic C, N - fertilizer and tillage - crop residue management. Soil Tillage Res 159:1 8. d oi: 10.1016/j.still.2016.01.007 Collins BM, Roller GB (2013) Early forest dynamics in stand - replacing fire patches in the northern Sierra Nevada, California, USA. Landsc Ecol 28:1801 1813. doi: 10.1007/s10980 - 013 - 9923 - 8 Collins HP, Elliott ET, Paustian K, et al (2000) Soil carbon pools and fluxes in long - term Corn Belt agroecosystems. Soil Biol Biochem 32:157 168. doi: 10.1016/S0038 - 0717(99)00136 - 4 Czech L, Barbera P, Stamatakis A (2020) Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data. Bioinformatics. doi: 10.1093/bioinformatics/btaa070 De Caceres M, Legendre P (2009) Associations between species and groups of sites: indices and statistical inference. Ecology de Graaff M - A, Adkins J, Kardol P, Throop HL (2015) A meta - a nalysis of soil biodiversity impacts on the carbon cycle. Soil 1:257 271. doi: 10.5194/soil - 1 - 257 - 2015 de Graaff M - A, Classen AT, Castro HF, Schadt CW (2010) Labile soil carbon inputs mediate the soil microbial community composition and plant residue decom position rates. New Phytol 188:1055 1064. doi: 10.1111/j.1469 - 8137.2010.03427.x DeForest JL, Zak DR, Pregitzer KS, Burton AJ (2004) Atmospheric nitrate deposition and the microbial degradation of cellobiose and vanillin in a northern hardwood forest. Soil Biol Biochem 36:965 971. doi: 10.1016/j.soilbio.2004.02.011 Dennison PE, Brewer SC, Arnold JD, Moritz MA (2014) Large wildfire trends in the western 150 United States, 1984 - 2011. Geophys Res Lett 41:2928 2933. doi: 10.1002/2014GL059576 Doane TA, Horwáth WR (20 03) Spectrophotometric determination of nitrate with a single reagent. Anal Lett 36:2713 2722. doi: 10.1081/AL - 120024647 Domeignoz - Horta LA, Pold G, Liu X - JA, et al (2020) Microbial diversity drives carbon use efficiency in a model soil. Nat Commun 11:3684 . doi: 10.1038/s41467 - 020 - 17502 - z Dooley SR, Treseder KK (2012) The effect of fire on microbial biomass: A meta - analysis of field studies. Biogeochemistry 109:49 61. doi: 10.1007/s10533 - 011 - 9633 - 8 Douglas GM, Maffei VJ, Zaneveld J, et al (2019) PICRUSt2: An improved and extensible approach for metagenome inference. bioRxiv. doi: 10.1101/672295 Appl 0:1 18. doi: 10.1002/eap.2072 Entwistle EM, Zak DR, Argiroff WA (2018) Anthropogenic N deposition increases soil C storage by reducing the relative abundance of lignolytic fungi. Ecol Monogr 88:225 244. doi: 10.1002/ecm.1288 Fahey TJ, Hughes JW, Pu M, Arthur MA (1988) Root decomposition and nutrient flux following whole - tree harvest of northern hardwood forest. For Sci 34:744 768. doi: 10.1093/forestscience/34.3.744 Faith DP (1992) Conservation evaluation and phylogentic diversity. Biol Conserv 61:1 10 . doi: 10.1890/0012 - 9658(2006)87[1465:ATTFHF]2.0.CO;2 Fernández I, Cabaneiro A, Carballas T (1997) Organic matter changes immediately after a wildfire in an atlantic forest soil and comparison with laboratory soil heating. Soil Biol Biochem 29:1 11. doi: 1 0.1016/S0038 - 0717(96)00289 - 1 bacterial communities following a wildfire disturbance. ISME J 7:1102 1111. doi: 10.1038/ismej.2013.11 Fierer N (2017) Embracing the unkno wn: Disentangling the complexities of the soil microbiome. Nat Rev Microbiol 15:579 590. doi: 10.1038/nrmicro.2017.87 Fierer N, Bradford MA, Jackson RB (2007) Toward an ecological classification of soil bacteria. Ecology 88:1354 1364. doi: 10.1890/05 - 1839 151 Fierer N, Lauber CL, Ramirez KS, et al (2012) Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J 6:1007 1017. doi: 10.1038/ismej.2011.159 Filippidou S, Wunderlin T, Junier T, et al (2016) A combination of extreme environmental conditions favor the prevalence of endospore - forming firmicutes. Front Microbiol 7:1 11. doi: 10.3389/fmicb.2016.01707 Flannigan MD, Krawchuk MA, de Groot WJ, et al (2009) Implications of changing climate fo r global wildland fire. Int J Wildl Fire 483 507. doi: 10.1071/WF08187 Fog K (1988) The effect of added nitrogen on the rate of decompositon of organic matter. Biol Rev 63:433 462. doi: 10.1111/j.1469 - 185X.1988.tb00725.x Friedman J, Hastie T, Tibshirani R (2010) Regularizaton paths for generalized linear models via coordinate descent. J Stat Softw 33:1 22. Fultz LM, Moore - kucera J, Davinic M, et al (2016) Forest wildfire and grassland prescribed fire effects on soil biogeochemical processes and microbial co mmunities: Two case studies in the semi - arid Southwest. Appl Soil Ecol 99:118 128. Grace JB, Scheiner SM, Schoolmaster DR (2015) Structural equation modeling: building and evaluating causal models. In: Fox GA, Negrete - Yankelevich S, Sosa VJ (eds) Ecologica l Statistics: Contemporary Theory and Application. Oxford University Press, pp 168 199 Graham EB, Knelman JE, Schindlbacher A, et al (2016) Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes? Front Microbiol 7:1 10. doi: 10.3389/fmicb.2016.00214 Greenfield LG, Gregorich EG, van Kessel C, et al (2013) Acid hydrolysis to define a biologically - resistant pool is compromised by carbon loss and transformation. Soil Biol Biochem 64:122 126. doi: 10.1 016/j.soilbio.2013.04.009 Hart SC, DeLuca TH, Newman GS, et al (2005) Post - fire vegetative dynamics as drivers of microbial community structure and function in forest soils. For Ecol Manage 220:166 184. doi: 10.1016/j.foreco.2005.08.012 Hartman WH, Ye R, Horwath WR, Tringe SG (2017) A genomic perspective on stoichiometric regulation of soil carbon cycling. ISME J 11:2652 2665. doi: 10.1038/ismej.2017.115 Hartmann M, Brunner I, Hagedorn F, et al (2017) A decade of irrigation transforms the soil microbiome o f a semi - arid pine forest. Mol Ecol 26:1190 1206. doi: 10.1111/mec.13995 152 Ho A, Di Lonardo DP, Bodelier PLE (2017) Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol 93:1 14. doi: 10.1093/femsec/fix006 Ho A, Kerckhof F M, Luke C, et al (2013) Conceptualizing functional traits and ecological characteristics of methane - oxidizing bacteria as life strategies. Environ Microbiol Rep 5:335 345. doi: 10.1111/j.1758 - 2229.2012.00370.x Holden SR, Rogers BM, Treseder KK, Randerson J T (2016) Fire severity influences the response of soil microbes to a boreal forest fire. Environ Res Lett 11:035004. doi: 10.1088/1748 - 9326/11/3/035004 Huffman MS, Madritch MD (2018) Soil microbial response following wildfires in thermic oak - pine forests. Biol Fertil Soils 54:985 997. doi: 10.1007/s00374 - 018 - 1322 - 5 Janssens IA, Dieleman W, Luyssaert S, et al (2010) Reduction of forest soil respiration in response to nitrogen deposition. Nat Geosci 3:315 322. doi: 10.1038/ngeo844 Jastrow JD, Amonette JE, Bailey VL (2007) Mechanisms controlling soil carbon turnover and their potential application for enhancing carbon sequestration. Clim Change 80:5 23. doi: 10.1007/s10584 - 006 - 9178 - 3 Kashian DM, Romme WH, Tinker DB, et al (2006) Carbon storage on landscapes with stand - replacing fires. Bioscience 56:598 606. doi: 10.1641/0006 - 3568(2006)56[598:CSOLWS]2.0.CO;2 Kasischke ES, Johnstone JF (2005) Variation in postfire organic layer thickness in a black spruce forest complex in interior Alaska and its effects on soi l temperature and moisture. Can J For Res 35:2164 2177. doi: 10.1139/x05 - 159 Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol 30:772 780. doi: 10.1093/molbev/mst010 K lotzbücher T, Kaiser K, Guggenberger G, Kalbitz K (2011) A new model for the fate of lignin in decomposing. Ecology 95:1052 1062. doi: 10.2307/41151233 Kurth VJ, MacKenzie MD, DeLuca TH (2006) Estimating charcoal content in forest mineral soils. Geoderma 1 37:135 139. doi: 10.1016/j.geoderma.2006.08.003 Kuzyakov Y (2011) How to link soil C pools with CO2 fluxes? Biogeosciences 8:1523 1537. doi: 10.5194/bg - 8 - 1523 - 2011 Lefcheck JS (2016) piecewiseSEM: Piecewise structural equation modelling in r for ecology, 153 e volution, and systematics. Methods Ecol Evol 7:573 579. doi: 10.1111/2041 - 210X.12512 Louca S, Doebeli M (2017) Efficient comparative phylogenetics on large trees. Bioinformatics 34:1053 1055. doi: 10.1093/bioinformatics/btx701 Lozupone C, Knight R (2005) U Communities. Appl Environ Microbiol 71:8228 8235. doi: 10.1128/AEM.71.12.8228 Malik AA, Martiny JBH, Brodie EL, et al (2020) Defining trait - based microbial strategies with consequences for soil carbon cycling under climate change. ISME J 14:1 9. doi: 10.1038/s41396 - 019 - 0510 - 0 Martín A, Díaz - Raviña M, Carballas T (2009) Evolution of composition and content of soil carbohydrates f ollowing forest wildfires. Biol Fertil Soils 45:511 520. doi: 10.1007/s00374 - 009 - 0363 - 1 Meheust R, Castelle CJ, Carnevali PBM, et al (2019) Aquatic Elusimicrobia are metabolically diverse compared to gut microbiome Elusimicrobia and some have novel nitroge nase - like gene clusters. bioRxiv. doi: 10.1101/765248 Melillo JM, Aber JD, Muratore JF (1982) Nitrogen and Lignin Control of Hardwood Leaf Litter Decomposition Dynamics. Ecology 63:621 626. Miesel J, Reiner A, Ewell C, et al (2018) Quantifying Changes in T otal and Pyrogenic Carbon Stocks Across Fire Severity Gradients Using Active Wildfire Incidents. Front Earth Sci 6:1 21. doi: 10.3389/feart.2018.00041 Miesel JR, Hockaday WC, Kolka RK, Townsend PA (2015) Soil organic matter composition and quality across f ire severity gradients in coniferous and deciduous forests of the southern boreal region. J Geophys Res Biogeosciences 120:1124 1141. doi: 10.1002/2015JG002959 MTBS (2017) Monitoring trends in burn severity. https://www.mtbs.gov. NCEI - NOAA (2017) National centers for environmental information. https://www.ncei.noaa.gov. Neary D., DeBano L. (2005) Wildland fire in ecosystems effects of fire on soil and water. Oksanen J, Blanchet FG, Friendly M, et al (2019) vegan: Community Ecology Package. Olsen SR, Col e CV, Watanable FS, Dean LA (1954) Estimation of available phosphorus in soils by extraction with sodium bicarbonate. 154 Parsons A, Robichaud PR, Lewis S a, et al (2010) Field guide for mapping post - fire soil burn severity. Gen. Tech. Rep. RMRS - GTR - 243 Paul EA, Morris SJ, Conant RT, Plante AF (2006) Does the acid hydrolysis - incubation method measure meaningful soil organic carbon pools? Soil Sci Soc Am J 70:1023 1035. doi: 10.2136/sssaj2005.0103 Pedregosa F, Varoquaux G, Gramfort A, et al (2011) Scikit - learn : Machine learning in Python. J Mach Learn Res 12:2825 2830. Pérez - Valera E, Goberna M, Faust K, et al (2017) Fire modifies the phylogenetic structure of soil bacterial co - occurrence networks. Environ Microbiol 19:317 327. doi: 10.1111/1462 - 2920.13609 Pérez - Valera E, Goberna M, Verdú M (2019) Fire modulates ecosystem functioning through the phylogenetic structure of soil bacterial communities. Soil Biol Biochem 129:80 89. doi: 10.1016/j.soilbio.2018.11.007 Perry DA, Oren R, Hart SC (2008) Forest Ecosyst ems. Johns Hopkins University Press, Baltimore Phillips RP, Fahey TJ (2007) Fertilization effects on fineroot biomass, rhizosphere microbes and respiratory fluxes in hardwood forest soils. New Phytol 176:655 664. doi: 10.1111/j.1469 - 8137.2007.02204.x Pinhe iro J, Bates D, Debroy S, Sarkar D (2019) nlme: Linear and nonlinear mixed effects models. Pinheiro JC, Bates DM (2000) Mixed - effects models in S and S - Plus. Springer - Verlag, New York Prendergast - Miller MT, de Menezes AB, Macdonald LM, et al (2017) Wildfi re impact: Natural experiment reveals differential short - term changes in soil microbial communities. Soil Biol Biochem 109:1 13. doi: 10.1016/j.soilbio.2017.01.027 Pressler Y, Moore JC, Cotrufo MF (2018) Belowground community responses to fire: meta - analys is reveals contrasting responses of soil microorganisms and mesofauna. Oikos 1 19. doi: 10.1111/oik.05738 Price MN, Dehal PS, Arkin AP (2010) FastTree 2 - Approximately maximum - likelihood trees for large alignments. PLoS One. doi: 10.1371/journal.pone.0009 490 Prosser JI, Martiny JBH (2020) Conceptual challenges in microbial community ecology. Philos 155 Trans R Soc B Biol Sci 375:2 4. doi: 10.1098/rstb.2019.0241 Quast C, Pruesse E, Yilmaz P, et al (2013) The SILVA ribosomal RNA gene database project: Improved d ata processing and web - based tools. Nucleic Acids Res 41:590 596. doi: 10.1093/nar/gks1219 R Core Team (2019) R: A language and environment for statistical computing. Ramirez KS, Craine JM, Fierer N (2012) Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob Chang Biol 18:1918 1927. doi: 10.1111/j.1365 - 2486.2012.02639.x Rodríguez J, González - Pérez JA, Turmero A, et al (2018) Physico - chemical and microbial perturbations of Andalusian pine forest soils fol lowing a wildfire. Sci Total Environ 634:650 660. doi: 10.1016/j.scitotenv.2018.04.028 Rodríguez J, González - Pérez JA, Turmero A, et al (2017) Wildfire effects on the microbial activity and diversity in a Mediterranean forest soil. Catena 158:82 88. doi: 10.1016/j.catena.2017.06.018 Ruefenacht B, Finco MV, Nelson MD, et al (2008) Conterminous U.S. and Alaska Forest Type Mapping Using Forest Inventory and Analysis Data. Photogramm Eng Remote Sens 74:1379 1388. doi: 10.14358/PERS.74.11.1379 S áenz de Miera LE, Pinto R, Gutierrez - Gonzalez JJ, et al (2020) Wildfire effects on diversity and composition in soil bacterial communities. Sci Total Environ. doi: 10.1016/j.scitotenv.2020.138636 Sauvadet M, Fanin N, Chauvat M, Bertrand I (2019) Can the co mparison of above - and below - ground litter decomposition improve our understanding of bacterial and fungal successions? Soil Biol Biochem 132:24 27. doi: 10.1016/j.soilbio.2019.01.022 Schimel JP, Schaeffer SM (2012) Microbial control over carbon cycling in soil. Front Microbiol 3:1 11. doi: 10.3389/fmicb.2012.00348 Schmidt MWI, Torn MS, Abiven S, et al (2011) Persistence of soil organic matter as an ecosystem property. Nature 478:49 56. doi: 10.1038/nature10386 Sinsabaugh RL, Reynolds H, Long TM (2000) Rapi d assay for amidohydrolase (urease) activity in environmental samples. Soil Biol Biochem 32:2095 2097. doi: 10.1016/S0038 - 0717(00)00102 - 4 Soil Survey Staff Official soil series descriptions. In: Nat. Resour. Conserv. Serv. United States 156 Dep. Agric. www.nrc s.usda.gov. Soil Survey Staff Web Soil Survey. In: Nat. Resour. Conserv. Serv. United States Dep. Agric. http://websoilsurvey.sc.egov.usda.gov/. Torn MS, Kleber M, Zavaleta ES, et al (2013) A dual isotope approach to isolate soil carbon pools of differen t turnover times. Biogeosciences 10:8067 8081. doi: 10.5194/bg - 10 - 8067 - 2013 Treseder KK, Balser TC, Bradford MA, et al (2012) Integrating microbial ecology into ecosystem models: challenges and priorities. Biogeochemistry 109:7 18. doi: 10.1007/s10533 - 011 - 9636 - 5 Trumbore S (2000) Age of soil organic matter and soil respiration: radiocarbon constraints of belowground C dynamics. Ecol Appl 10:399 411. Wagg C, Schlaeppi K, Banerjee S, et al (2019) Fungal - bacterial diversity and microbiome complexity predict ec osystem functioning. Nat Commun 10:1 10. doi: 10.1038/s41467 - 019 - 12798 - y Wan S, Hui D, Luo Y (2001) Fire effects on nitrogen pools and dynamics in terrestrial ecosystems: A meta - analysis. Ecol Appl 11:1349 1365. Wang Q, Zhong M, Wang S (2012) A meta - analysis on the response of microbial biomass, dissolved organic matter, respiration, and N mineralization in mineral soil to fire in forest ecosystems. For Ecol Manage 271:91 97. doi: 10.1016/j.foreco.2012.02.006 Weber CF, Lockhart JS, Charaska E, et al (2014) Bacterial composition of soils in ponderosa pine and mixed conifer forests exposed to different wildfire burn severity. Soil Biol Biochem 69:242 250. doi: 10.1016/j.soilbio.2013.11.010 Westerling AL (2006) Warming and Earlier Spring Increase Wes tern U.S. Forest Wildfire Activity. Science 313:940 943. doi: 10.1126/science.1128834 Whitman T, Pepe - Ranney C, Enders A, et al (2016) Dynamics of microbial community composition and soil organic carbon mineralization in soil following addition of pyrogeni c and fresh organic matter. ISME J 10:2918 2930. doi: 10.1038/ismej.2016.68 Whitman T, Whitman E, Woolet J, et al (2019) Soil bacterial and fungal response to wildfires in the Canadian boreal forest across a burn severity gradient. Soil Biol Biochem 138:10 7571. doi: 10.1016/j.soilbio.2019.107571 Wilhelm RC (2016) Deciphering decomposition and the effects of disturbance in forest soil 157 microbial communities with metagenomics and stable isotope probing. University of British Columbia Woolet J, Whitman T (2020) Pyrogenic organic matter effects on soil bacterial community composition. Soil Biol Biochem 141:107678. doi: 10.1016/j.soilbio.2019.107678 Xiang X, Shi Y, Yang J, et al (2014) Rapid recovery of soil bacterial communities after wildfire in a Chinese boreal forest. Sci Rep 4:1 8. doi: 10.1038/srep03829 Ye Y, Doak TG (2009) A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes. PLOS Comput Biol 5:e1000465. 158 CHAPTER 4: POST - FIRE EFFECTS OF SOIL HEATING INTENS ITY AND PYROGENIC ORGANIC MATTER ON MICROBIAL ANABOLISM : A LABORATORY - BASED APPROACH 4.1 ABSTRACT Wildfires result in direct and indirect CO 2 emissions due to combustion and post - fire decomposition. Approximately half of temperate forest ecosystem carbon (C) is stored in soil, so post - fire soil C cycling likely impacts the strength of forest C sinks. Soil C sink strength is in part determined b y soil microbial anabolism versus catabolism, which dictates the amount of C stored in microbial biomass versus respired as CO 2 . Fires affect soil C availability and composition, changes that could alter carbon use efficiency ( CUE ) and microbial biomass pr oduction , and thus potentially influence recovery of the forest C sink. Wildfire intensity is forecast to increase in forests of the western United States and understanding the impacts of fire intensity on the amount of C retained in soil versus respired t o the atmosphere is necessary for predicting fire - climate feedbacks. My objective was to determine the influence of soil heating intensity and pyrogenic organic matter (PyOM) on microbial anabolism. I determined the short - term impacts of these factors on m icrobial anabolism by measuring the accumulation of microbial biomass carbon (MBC) , C mineralization, and a proxy for CUE . I simulated the effects of fire intensity by heating soils to 100 or 200 ° C for 30 minutes in a muffle furnace, and I amended the soi ls with charred or uncharred organic matter. Higher intensity soil heating (200 ° C) consistently led to lower MBC accumulation, greater metabolic C respiration , and lower CUE proxies compared to unheated soils . In contrast, lower intensity soil heating ( 1 0 0 ° C) resulted in MBC accumulation and estimated CUE that was similar to unheated soils . Soils amended with PyOM exhibited similar MBC accumulation compared to the uncharred organic matter , but CO 2 emissions were lower in soils amended with PyOM. These res ults indicate that 159 high intensity soil heating decreases soil C - sink strength over the short - term by decreasing the amount of microbial anabolism relative to catabolism . These findings suggest that increased wildfire intensity will have detrimental impacts of on soil C storage over the short - term . 4.2 INTRODUCTION Wildfire disturbances are common in mixed - conifer forests of the western United States, resulting in direct CO 2 emissions during biomass combustion (Flannigan et al. 2009; Chen et al. 2017) . Over the long - term, these emissions have a neutral impact on atmospheric carbon (C) concentrations as CO 2 - C is assimilated by plant regrowth during forest recovery, but, over the short - term, represent a decrease in the size of forest C sink (Bowman et al. 2009; Loehman et al. 2014) . Temperate forests store ~50% of ecosystem C in soils (Pan et al. 2011) , so losses to soil C due to combustion (Campbell et al. 2007) , and post - fire necromass decomposition could make substantial contributions to fire - induced C emissions (Meigs et al. 2009; Zhao et al. 2012; Campbell et al. 2016) . Understanding the amount of soil C retained versus respired in the aftermath of fire is thus necessary for accurate representation of the influence of fire on the strength of the forest C sink. The relative amount of microbial anabolism (biomass prod uction ) versus catabolism (C mineralization) determines the fate of soil C over both short and long timescales (Schimel and Schaeffer 2012; Liang et al. 2017a) . Microbial carbon use efficiency (CUE) represents the balance between anabolic and catabolic processes over short timeframes (Manzoni et al. 2012; Sinsabaugh et al. 2013; Spohn et al. 2016a) . An index of the amount of total C uptake that is assimilated into microbial bio mass during decomposition (Geyer et al. 2016 ) , CUE is the proximal driver determining whether C is lost to atmosphere versus retained in soil (Cotrufo et al. 2013) . High CUE indicates more C accumulated in microbial biomass, whereas low CUE 160 indicates more C respired to the atmosphere. Microbial anabolism also influence C storage over longer time scales because microbial necromass is more efficiently stabilized in soils compared to other types of organic matter and may account for >50% of total soil C (Liang et al. 2019; Ni et al. 2020) . The physiological response s of soil microbes to fire may therefore dictate the magnitude of post - fire soil emissions in the short term, and affect the size of soil C stocks and soil - climate feedbacks over the long - term (Allison et al. 2010; Frey et al. 2013; Wieder et al . 2013; Liang et al. 2019) . Identifying the mechanistic processes that impact C sequestration following disturbance has been identified as a key component of managing forests for C storage in the future (Birdsey et al. 2006) . Determining post - fire patterns in microbial anabolism and catabolism will thus contribute to understanding of C source/sink strength of burned ecosystems. CUE a nd microbial biomass production are influenced by substrate quality and complexity, microbial community structure, nutrient availability, and environmental factors such as soil temperature and moisture (Frey et al. 2013; Geyer et al. 2016; Spohn et al. 2016b; Liang et al. 2019; Domeignoz - Horta et al. 2020) . For example, CUE is lower for structurally complex/stable molecules (e.g. lignin, aromatic molecules) that requi re a larger enzyme investment compared to simpler, labile molecules (e.g. sugars) (Manzoni et al. 2012; Frey et al. 2013; Sinsabaugh et al. 2013) . Relatedly, CUE decreases when nutrient availability is low due to microbial direction of metabolism toward catabolic processes that support nutrient acquisition, for example mining of nutrients from complex organic molecules (Geyer et al. 2016; Spohn et al. 2016b; Chen et al. 2020; Soong et al. 2020) . In some cases, CUE increases with microb ial diversity (Dom eignoz - Horta et al. 2020) , and may differ for fungi versus bacteria as consequence of differences in microbial biomass stoichiometry (Six et al. 20 06; Keiblinger et al. 2010; Manzoni et al. 2012; Sinsabaugh et al. 2013) . 161 Fire alters soil C chemistry, nutrient availability, and microbial community structure, with potentially contrasting impacts on microbial biomass production . Fire induces tempor ary pulses in soil dissolved organic carbon (Wang et al. 2012) and inorganic nitrogen (N) (Wan et al. 2001) , potentially increasing microbial biomass production and CUE via positive effects on labile C and N availability. However, fire also generates pyrogenic organic matter (PyOM), which is more chemically stable and aromatic than its precursor material (Preston and Schmidt 2006; Bird et al. 2015) , and thus may be used less efficiently. The conversion of organic matter to PyOM may therefore lead to less bioavailable C and lower microbial biomass production. There is a dearth of information on the influence of PyOM on microbial anabolism in burned systems, but biochar amendment studies can provide some insight. Biochar is a form of PyOM produced under controlled pyroly sis conditions and applied as a soil amendment in agricultural systems. Biochar additions to soil have frequently been found to increase microbial biomass, potentially via beneficial effects on soil nutrient retention, pH, soil moisture, and by providing m icrohabitat (Lehmann et al. 2011) . A study in temperate pastures found that biochar - CUE was lower than values typically reported for non - biochar feedstock (Fang et al. 2018) , but other studies have found that biochar increases overall CUE via beneficial effects on soil bio - physiochemical properties (Jiang et al. 2016; Guo et al. 2020) . In addition to influencing C and N chemistry, fire increases the pre dominance of bacteria relative to fun gi (Dooley and Treseder 2012; Pressler et al. 2018) , in part due lower sensitivity of bacteria to soil heating (Neary and DeBano 2005) . Bacteria may use simple C substrates more efficiently than fungi when N is abundant , exhibit rapid growth rates , and act as the primary decomposers of the labile fraction of PyOM; in contrast, fungi may more efficiently decompose the recalcitrant fraction of PyOM and 162 PyOM derived from wood Guzmán et al. 2020) . Fire intensity is predicted to increase in ecosystems across the globe (Flannigan et al. 2009) , and there are r easons to suspect that effects of fire on microbial anaboli c and catabolic processes vary with fire intensity and/or severity. For example, the amount of PyOM generated has been shown to increase with fire intensity (Czimczik et al. 2003; Sawyer et al. 2018) and severity (Miesel et al. 2015) , which could have negative impacts on microbial anabolism by decreasing C bio - availability, or positive impacts via beneficial effects on soil properties. Changes to labile C and nutrient avai lability could also influence microbial anabolism, but there is a dearth of information regarding the immediate impacts of fire intensity on salt - water extractable organic carbon ( EOC ) and inorganic - N pools. Used as an alternative to field - based research, lab - based soil heating studies have indicated that EOC increase s with soil heating temperature up to at least ~300 ° C (Bárcenas - Moreno and Bååth 2009) . Various experiments have found that i norganic - N concentrations exhibit no changes to moderate increases in soils heated from 150 - 210 ° C (Serrasolsas and Khanna 1995; Choromanska and DeLuc a 2002; Prieto - Fernández et al. 2004; Guerrero et al. 2005) , with larger increases beginning to occur at ~400 ° C (Raison 1979; Choromanska an d DeLuca 2002) . Laboratory - based heating studies have also assessed the effects of heating intensity on microbial biomass carbon (MBC), and microbial community structure (Serrasolsas and Khanna 1995; Díaz - Raviña et al. 1996; Fernández et al. 1997; Prieto - Fern ández et al. 1998; Guerrero et al. 2005; Bárcenas - Moreno and Bååth 2009) , but whether these characteristics lead to post - heating changes in the balance between microbial anabolism and catabolism is not well studied. 163 Small changes in the balance between microbial anabolism and catabolism can have substantial impacts on soil C emissions and stocks (Alli son et al. 2010; Schimel and Schaeffer 2012; Wieder et al. 2013; Liang et al. 2017a) , potentially exacerbating or modulating the effects of fire intensity on ecosystem C loss. Here, my objectives are to determine 1) how a proxy for CUE and the balance o f MBC accumulation versus C mineralization var y with soil heating intensity and 2) whether PyOM influences these processes differently in soil subjected to contrasting soil heating intensities. I hypothesized that 1) estimated CUE and MBC accumulation will increase with soil heating intensity due to greater EOC availability, and 2) estimated CUE and MBC accumulation will be higher in soils amended with uncharred organic matter compared to charred organic matter regardless of soil heating intensity. I used f actorial incubation experiments in which I subjected a forest soil to two levels of heating intensity and applied two C substrates in charred and uncharred form and multiple types of charred and uncharred plant litter to determine a proxy for CUE and the level of MBC accumulation versus C mineralization. 4.3 MATERIALS AND METHODS 4.3.1 Site description and sample collection I collected soil and litter samples from the Plumas National Forest in the Sierra Nevada Mountain Range, California, USA. The ecosystem is a dry mixed - conifer forest dominated by Pinus ponderosa, P. lambertiana, P. jeffreyi, Abies concolor, Pseudotsuga menziesii, and Calocedrus decurrens, with lesser cover by Quercu s kelloggii . Dry mixed - conifer forests of the region are fire - adapted, having exhibited a mean fire rotation of 23 years prior to Euro - American settlement (1500 - 1850 C.E.) (Mallek et al. 2013) . Soils in my plots are from the Skalan series, a loamy - sk eletal, isotic, mesic Vitrandic Haploxerlaf (Soil Survey Staff 2018 ) . The 30 year mean annual precipitation is 1080 mm and mean annual temperature is 10.6 º C. 164 From four sites within the forest that did not exhibit evidence of recent fire activity (i.e. no charred biomass) , I collected mineral soil at two sampling points located 40 m apart. At each sampling location, I removed the litter (Oi) and duff (Oe + Oa) layers overlying the mineral soil, and then collected mineral soil to 10 cm depth using a 10 cm diameter soil auger. I then collected three types of litter from the forest, including Q. kelloggii (black oak), P. ponderosa (ponderosa pine), and mixed litter. I collected black oak and ponderosa pine litter samples from the litter surface directly under a tree of each species, and I collected only leaf material that was recognizable as belonging to the target tree species. The mixed litter sample included litter recognizable as deriving from P. ponderosa and litter that was derived from either A. concolor , P. menziesii or both. Mineral soils and litter samples were stored on ice until being transported to the lab, after which m ineral soils were stored at - 20 ° C, and litter samples were air - dried and stored at ambient temperature until the commencement of the lab experiments. 4.3.2 Generation of pyrogenic organic matter I generated PyOM from two simple C substrates (glucose, ascorbic acid) and from the three litter types collected in the field. Glucose is commonly used in CUE and substrate induced respiration experiments, and ascorbic acid has been demonstrated to effectively induce differential respiration responses among soil types (Degens and Harris 1997) . I charred glucose (210 ° C) and ascorbic acid (200 ° C) at temperatures at the lower end of their thermal degradation ranges (Örsi 1973; Jingyan et al. 2013) . I weighed ~5 g of each substrate into a small aluminum dish, covered the dish with aluminum foil, and heated in a muffle furnace by ramping to the target temperature over 30 minutes and then holding at temperature for another 30 minutes. I sterilized the litter via autoclave (121 ° C) for 30 minutes and then oven - dried overnight at 65 ° C. I charred the litter by placing aluminum - foil wrapped sterile litter in a muffle furnace 165 that had been pre - heated to 200 ° C and held at temperature for one hour. I the n ramped the temperature to 300 ° C and held at temperature for another hour. The litter was then homogenized using a mortar and pestle to pass a 500 - micron sieve. I measured charred substrate and litter C and N concentrations of single replicates using a d ry - combustion elemental analyzer (Costech Analytical Technologies Inc., Valencia, CA, USA) , with acetanilide as the quantification standard. 4.3.3 Experimental design I conducted two experiments to assess the amount microbial anabolism versus catabolism after a soil heating disturbance. I n one experiment, I measured a prox y for community - scale CUE which quantifies gross production efficiency of the microbial community over short timescales (up to 48 hours) before biomass turnov er occurs (Geyer et al. 2016) . In the second experiment, I measured net MBC accumulation and net C mineralization over 14 days . To ac count for potential heterogeneity of soil across my sampling sites, I composited equal masses of sieved (2 mm) mineral soil from the plots before evaluating the short - term impacts of soil heating and PyOM on CUE and MBC accumulation . Carbon use efficiency experiment I determined impacts of soil heating and substrate pyrolysis on a CUE proxy using a full y - factorial experiment in which I applied two levels of soil heating (plus unheated controls) and two labile substrate types (glucose, ascorbic acid) in c harred and uncharred form. Each treatment was replicated 18 times, equally divided between two incubation blocks that were performed consecutively. For each incubation block, I pre - incubated nine soil replicates for seven days. For each replicate, I weighe d 130 g (dry mass equivalent ( DME )) of soil into 0.473 L 166 mason jars, added water to bring soil moisture to 40% water - holding capacity (WHC), and incubated in the dark at ambient temperature (~23 ° C). After pre - incubation, I applied heat treatments of 100 ° C or 200 ° C by covering the mason jars with aluminum foil and heating the jars in a muffle furnace for 45 minutes. Following heating, I weighed 5 g ( DME ) subsamples into 50 mL centrifuge tubes, adjusted soil moisture to 50% WHC, capped the tubes with septa - fitted li ds, and incubated in the dark at ambient temperature. Before adding substrates, I measured CO 2 - C respiration daily until respiration rates differed by <10% among soil heating treatments, which occurred after five days. I waited until respiration rates were similar so that CUE estimates would not be influenced by differences in basal respiration. I measured soil respiration rates by flushing the incubation tubes with ambient air, tightly capping, and measuring CO 2 - C concentrations of 1 mL gas aliquots after ~30 minutes and again after ~6 hours using an infrared gas analyzer (LI - COR Inc., Lincoln, NE, USA). On the sixth day after heating, I added charred and uncharred substrates to the incubation tubes at 1 mg substrate - C g - 1 soil in 0.5 mL DI water. I monito red respiration rates over 24 hours by measuring CO 2 - C concentrations after ~1.5, 4, 8, 12, and 24 hours. I extracted three replicates per treatment in each block for determination of EOC immediately after substrate addition, and I extracted the remaining six replicates after 24 hours. I performed EOC extractions by adding 25 mL K 2 SO 4 directly to the incubation tubes, agitating on a reciprocating shaker for 1 hour, and filtering through 11 µ m pore - size filters (Whatman Grade 1). I determined EOC concentrati ons in the extracts spectrophotometrically after potassium - dichromate oxidation (Cai et al. 2011) . I calculated CUE over the 24 hours after substrate addition as: (1) 167 where EOC is the change in EOC over the 24 hours after substrate addition and is assumed to represent total microbial C uptake; CO 2 - C is cumulative C re spired over the 24 hours (Tiemann and Billings 2011; Geyer et al. 2016) . This calculation is a proxy for CUE because I did not apply isotopically labeled substrates and so cannot directly quantify the amount of substrate - C uptake and respiration. Microbial biomass carbon accumulation e xperiment I determined impacts of soil heating and substrate pyrolysis on MBC accumulation and cumulative C mineralization using a full y - factorial experiment in which I applied two levels of soil heating (plus unheated controls) and three litter types (ponderosa pine , black oak, and mixed litter) in charred and uncharred form. Each treatment was replicated 18 times, equally divided between two incubation blocks that were performed consecutively, except for mixed litter treatments which were replicated nine times and i ncubated in a single block. Soil pre - incubation was performed as described above, except that replicates were 150 g ( DME ) and pre - incubated for ten days. After pre - incubation, I applied heat treatments of 100 ° C or 200 ° C as described above. I then weighed 10 g ( DME ) subsamples into 50 mL centrifuge tubes, added 80 ± 5 mg charred or uncharred litter to each tube, mixed by vortexing for 30 seconds, and added water to bring soil moisture to 50% WHC . I incubated the soils fo r 14 days, measuring respiration on days 1, 2, 3, 4, 5, 7, 9, and 13. Twenty - four hours after heat treatments, I extracted a subset of unamended samples (n=6 per heat treatment) for EOC and microbial biomass C (MBC) using direct chloroform fumigation (Witt et al. 2000) . 14 days after applying treatments , I extracted the remaining soils (n=12 per treatment combination) . I measured MBC as the diffe rence in EOC between fumigated and 168 unfumigated samples, divided by a correction factor of 0.33 (Cai et a l. 2011) . I calculated net accumulation of MBC as the difference in MBC between days 1 and 14 of the incubation. For one incubation block, I determined fungal and bacterial activity in a subset of soils < 24 hours after heating (n=6 per heat treatment) using selective respiratory inhibition (Anderson and Domsch 1973) . Additionally, I incubated an extra set of heated and unheated soils amended with uncharred or charred pine material (n=6) to determine post - incubation differences in fungal and bacterial activity. For selective respiratory inhibition, I weighed four equal soil masses (2.5 g DME ) from each incubation tube into septa - capped 20 mL scintillation vials , applied glucose at 8 mg g - 1 in 0.2 mL DI water, and agitated on a reciprocating shaker for 1 hour. I then applied one of four biocide treatments to each of the vials: no biocide addition, the bactericide bronopol at 100 µ g g - 1 , the fungicide cycloheximide at 8 mg g - 1 , or the addition of both biocides at these concentrations. Vials were capped and placed on a reciprocating shaker for 6 hours, after which accumulated CO 2 - C was measured with an infrared gas analyzer. Fungal and bacterial activity was determined as: (3) Where A is respi ration in the absence of inhibitors, B is respiration in the presence of the fungicide, C is respiration in the presence of bactericide, and D is respiration in the presence of both biocides. The inhibitor additivity ratio (IAR), a measure of non - target or antagonistic effects of the antibiotics was determined as: (4) The concentrations of glucose and antibiotics for determination of bacterial and fungal activity were selected based on the optimization procedures described by Bailey et al. (2003) using the 169 same soil as was used for the CUE experiments. These preliminary procedures yielded an IAR of 1.01 ± 0.07 (SD). An IAR of 1.0 indicates no non - target or antagonistic effects. 4.3.4 Statistical analysis I performed all statistical analysis in the R statistical computing environment (v 3.6.1) (R Core Team 2019) . I applied linear mixed - models using the nlme package (v 3.1.140) (Pinheiro et al. 2019) to assess the response of C respiration, EOC, MBC, and CUE to my experimental treatments. All models initially included soil heating level, litter or substrate type, an d char status as main effects, all possible two - and three - way interaction effects, and incubation block as a random effect. Interaction effects that exhibited p - values 0.15 were sequentially removed from the models. For main effects that were significan t at =0.05, I compared marginal means using Tukey - adjusted p - values using the emmeans package (v.1.4.4) (Lenth 2020) . For the MBC accumulation experiment, I also applied general linear models to determine impact of soil heating and uncharred and charred pine litter on fungal and bacterial activity. 4.4 RESULTS 4.4.1 Carbon us e efficiency experiment Soil heating caused an immediate increase in soil respiration rate that was positively associated with heating intensity (p<0.001; Table 4. 1). Compared to unheated soils, heating soils to 100 °C caused a pulse in respiration that l asted two days, and heating soils to 200 °C caused a pulse that lasted four days. By five days after heating, the heated soils did not differ in respiration rates compared to unheated soils. Over five days post - heating, soils heated to 100 °C respired 8.6% more CO 2 - C, and soils heated to 200 °C respired 58.9% more CO 2 - C compared to unheated soils. 170 Table 4.1 Respiration rate by day and cumulative CO 2 - C respired over five days after soil heating and prior to adding substrates for the carbon use efficiency experiment (mean ± SE) . Lower - case letters indicate Tukey - adjusted significant differences in respiration among soil heating treatments ( =0.05). Respiration Rate ( m g CO 2 - C k g - 1 d - 1 ) Unheated (n=84) 100 ° C (n=84) 200 ° C (n=84) Day 1 22.7 ± 0. 4 a 31.4 ± 0.5 b 43. 7 ± 3. 1 c Day 2 25.5 ± 0. 5 a 30. 2 ± 1. 2 b 56.4 ± 1.2 c Day 3 22.5 ± 0.4 a 23. 5 ± 0. 6 a 40.4 ± 0. 4 b Day 4 27.3 ± 0. 6 a 27. 7 ± 0. 6 a 36. 6 ± 0. 6 b Day 5 31. 6 ± 1. 9 a 28. 2 ± 1. 9 a 28. 7 ± 1.8 a Cumulative Respired ( m g CO 2 - C kg - 1 ) 130 ± 2. 6 a 141 ± 2. 7 b 206 ± 3. 4 c Table 4.2 Carbon and nitrogen concentrations for uncharred and charred carbon substrates and litters used in the carbon use efficiency and microbial biomass carbon accumulation experiment. C (%) N (%) Uncharred Charred Uncharred Charred Glucose 40.0 42.6 Ascorbic Acid 40.9 41. 1 P. Ponderosa Litter 50.6 63. 6 0.25 0.43 Q. Kellogii Litter 47.2 65. 8 2.54 3.31 Mixed Litter 35.0 52.9 0.59 0.75 171 Charring of glucose and ascorbic acid resulted in visual changes in appearance (Fig. S 4. 1) and slight increases in C concentration (Table 4. 2). There were significant effects of soil heating (p<0.001) and a heating × substrate identity interaction (p<0.001) on the CUE proxy , but no main effects of substrate identity or charring on the estimate (Fig . 4 . 1). Soils heated to 200 °C exhibited ~68% lower estimated CUE in response to ascorbic acid application than the other soil heating treatments. The CUE proxy is driven by the level of EOC uptake and CO 2 - C respired, so I separately assessed the response of these metabolic functions to the treatment s . There were significant effects of soil heating (p<0.001) and a charring × substrate interaction (p<0.001) on EOC uptake. Additionally, there were marginally significant differences of substrate identity (p=0.070) and a heating × substrate interaction (p= 0.067). For soils receiving ascorbic acid, differences in EOC uptake were driven by heating effects: soils heated to 200 °C exhibited ~42% less EOC uptake than unheated soils (Fig. 4. 2). In contrast, EOC uptake in soils that received glucose was driven by both charring and soil heating: within heating treatments, EOC uptake was ~32 - 56% lower for charred glucose than uncharred glucose. Within charring treatments, EOC uptake was ~27 - 30% lower for soils heated to 100 °C compared to unheated soils. For CO 2 - C re spired, there were significant effects of soil heating (p<0.001) and a charring × substrate interaction (p<0.001). Similar to EOC uptake, drivers of differences in CO 2 - C respiration varied among substrate type. For soils receiving ascorbic acid, difference s in respiration were driven by heating, with soils heated to 100 °C respiring ~28% less CO 2 - C than the other treatments. Soils that received glucose exhibited differences in respiration due to both substrate charring and soil heating. Within heating treat ments, soils respired ~28 - 60% less CO 2 - C in response to charred glucose application, and, within charring treatments, soils heated to 100 °C respired ~6 - 40% less CO 2 - C. 172 Figure 4.1 A proxy for carbon use efficiency (CUE) for unheated soils and soils heate d to 100 ° C and 200 ° C that received ascorbic acid (a) or glucose (b) in uncharred or charred form. Shading of boxplots indicates whether soils received uncharred or charred substrates. Each boxplot represents the distribution of 12 replicates. Upper - case letters indicate Tukey - adjusted significant differences among the hea ting × charring treatment combinations within substrate types ( =0.05). 173 Figure 4.2 Uptake of extractable organic carbon ( a and c ) and 24 - hour cumulative respired CO 2 - C ( b and d ) for unheated soils and soils heated to 100 ° C and 200 ° C that received asc orbic acid or glucose in uncharred or charred form. Shading of boxplots indicates whether soils received uncharred or charred substrates. Each boxplot represents the distribution of 12 replicates. Upper - case letters indicate Tukey - adjusted significant dif ferences among the heating × charring treatment combinations within substrate types ( =0.05). 4.4.2 Microbial biomass accumulation experiment Soil heating led to increases in EOC in soils extracted 24 hours after heating: unheated soils contained 40.4 ± 3.1 m g kg - 1 EOC compared to 60.6 ± 2. 2 and 12 1 ± 2.0 m g kg - 1 for soils heated to 100 °C and 200 °C, respectively. Microbial biomass was decrease d only in soils heated to 100 °C , where MBC was 85. 1 ± 12.2 m g kg - 1 compared to 138 ± 13. 5 and 134 ± 8.0 m g kg - 1 174 for unheated soils and soils heated to 200 °C , respectively. Litter charring led to increases in C and N concentrations compared to uncharred l itter (Table 4. 2). MBC decreased over the incubation period in soils heated to 200 °C for all litter types (Fig. 4. 3 ), while CO 2 - C respiration was elevated by ~6 - 105% compared to unheated soils (p=0.001). CO 2 - C respiration was also elevated in soils heat ed to 100 °C for soils that received no litter, uncharred oak, and uncharred pine by ~32 - 59%, but t hese soils also accumulated 28 - 390% more MBC over the incubation. Within soils that received oak or pine litter, the charred version induced 33 - 61% less CO 2 - C respiration compared to the uncharred counterpart and exhibited similar MBC accumulation. Within soils that received mixed litter, CO 2 - C respiration and MBC accumulation were similar between charred and uncharred forms. After the 14 - day incubation, soils heated to 200 °C had 28 - 76% less MBC than unheated soils, and soils heated to 100 °C had similar MBC to unheated soils. MBC was similar between charred and uncharred litter among all soil heat treatments. EOC uptake over the 14 - day incubation increased with soil heating intensity for soils that did not receive litter inputs (p<0.001). EOC uptake was 3.9 ± 1. 2 m g kg - 1 for unheated soils, 21. 4 ± 1. 5 m g kg - 1 for soils heated to 100 °C, and 40.5 ± 1. 3 m g kg - 1 for soils heated to 200 °C. When consid ered on a relative basis, EOC uptake was similar for soils heated to 100 °C (34. 7 ± 2. 4 % of initial EOC) and 200 °C (33.5 ± 1.0%), both of which exhibited greater EOC uptake than unheated soils (9. 7 ± 2. 9 %). Cumulative 14 - day respiration was positively cor related with EOC uptake only for soils heated to 100 °C (r=0.61, p=0.037), and MBC accumulation was positively correlated with EOC uptake only for soils heated to 200 °C (r=0.71, p=0.014). Despite my IAR values being very near the target value of 1.0 dur ing preliminary optimization procedures (see methods), the IAR values I observed during the CUE E experiment 175 were substantially higher. Immediately post heating, IAR was 1.42 ± 0.03, and after the 14 - day incubation IAR was 1.27 ± 0.04. These values indicate non - target effects of one of the antibiotics (Bailey et al. 2003; Rousk et al. 2009) . The bactericide was very likely responsible for the non - target effects because the i nhibition resulting from bactericide application alone was similar to the inhibition that resulted from application of both biocides in conjunction. Thus, I limit the dissemination of my results to the impacts of treatments on fungal activity only. Immedia tely after heating, fungal activity did not differ among the soil heating treatments. After the 14 - day incubation, there were significant main effects of soil heating (p=0.017) and litter charring (p=0.047) on fungal activity, but no interaction effects (p =0.12). Fungi contributed to 31.4 ± 3. 5 % of respiration in soils heated to 200 ° C, which was significantly less than 47. 7 ± 5. 5 % in unheated soils. At 40.4 ± 5. 2 %, fungal respiration in soils heated to 100 ° C did not differ from the other treatments. Altho ugh the results of my general linear model indicated that soils amended with charred versus uncharred pine litter exhibited higher fungal activity, pairwise means comparisons did not uncover differences in fungal activity between these treatments. Fungal activity did not signi ficantly affect cumulative C respire d or change in MBC. 176 Figure 4. 3 Change in microbial biomass carbon ( a, c, e, g ) and 14 - day cumulative respired CO 2 - C ( b, d, f, h ) for unheated soils and soils heated to 100 ° C and 200 ° C that no litter, Q. kelloggii ( Oak), P. ponderosa (Pine), or mixed leaf litter in uncharred or charred form. Shading of boxplots indicates whether soils received uncharred or charred litter. Each boxplot represents the distribution of 12 replicates, except for mixed litter treatments wh ich had 6 replicates . Upper - case letters indicate Tukey - adjusted significant differences among the heating × charring treatment combinations within litter treatments ( =0.05). 177 4.5 DISCUSSION 4.5.1 Soil heating intensity underlies estimated carbon use efficiency and microbial biomass accumulation I did not find support for my hypothesis that the CUE proxy and MBC accumulation would be positively correlated with soil heating intensity. Rather, heating soils to 200 °C decreased the CUE proxy and led to n et negative MBC accumulation . Interestingly, in the MBC accumulation experiment , MBC content measured 24 hours after heating was lower in soils heated to 100 °C than those heated to 200 °C. This suggests rapid microbial growth in the 24 hours after heating in soils heated to 200 °C. EOC content increased with heating intensity , and the assimilation of this flushed EOC by the surviving microbial community could have fueled the rapid growth in the soils heated to 200 °C. Indeed, a laboratory heating study per formed on soils collected from Pinus halepensis forest in Spain found that bacterial growth was several times higher in soils heated between 80 and 400 °C than unheated controls within 2 - 4 days of soil heating (Bárcenas - Moreno and Bååth 2009) . Furthermore, Bárcenas - Moreno and Bååth ( 2009 ) found that peak growth rate was greater and occurred earlier in soils heated to higher temperatures, an effect that correlated with greater initial increases in EOC. Differences in the balance between anabolism versus catabolism in heated soils are likely due to the combined effects of heating on labile C availability, nutrient availability, and microbial biomass stoichiometry. For example, t he greater MBC accumulation in soils heated to 100 ° C versus 200 ° C could be related to the combined effects of micr obial cell lysis and threshold effects of heating on organic molecules impacting the quality of available C . Microbial lysis during heating results in the release of labile organic molecules, for example carbohydrates, proteins, and amino acids (González - Pérez et al. 2004) . Such molecules are likely to be 178 efficiently utilized by the surviving microbial populations in the absence of nutrient limitation (Cotrufo et al. 2013) , potentially explaining why MBC accumulation sometime s increased in soils heated to 100 ° C compared to unheated soils. Additionally, protein and amino acids represent labile form s of organic N (Jones et al. 2004; Schimel and Bennett 2004; Kielland et al. 2007) , and 100 °C is below the threshold of N volatilization (Bodí et al. 2014) . Thus, low intensity soil heating could potentially increase labile organic N availability and allow fast - growing microorganisms with high nutrient demands to rapidly increase in biomass. Concurrently, increased N availability could suppress less efficient decomposition of complex and recalcitrant organic matter (Janssens et al. 2010) and decrease the need for N - mining (Moorhead and Sinsabaugh 2006; Craine et al. 2007) . In contrast, heating soils to 200 ° C likely caused greater mortality to the microbial community (Pingree and Kobziar 2019) and thus greater input of organic molecules, but 200 ° C is above or near the threshold at which these labile organic molecules are destructively distilled, volatized, or pyro lyzed (González - Pérez et al. 2004; Massman et al. 2010) . Thus, although there was an overall increase in EOC after 20 0 ° C heating, this C may be in a less bioavailable form . Studies assessing chemical changes of plant biomass during pyrolysis indicate that loss of thermally labile compounds and aromatization reactions occur at 200 ° C (Hatton et al. 2016) , and carbohydrate loss and transformation is one of the first processes t o occur (Chatterjee et al. 2012) . Moreover, soil carbohydrate c ontent derived from both plants and microbes has been found to decrease immediately following wildfire, and remain depressed for at least 15 months (Martín et al. 2009) . Greater carbohydrate loss and increased dominance of lignin and pyrogenic co mpounds appears to occur at higher fire severity (Miesel et al. 2015) , and water - extractable organic matter is more aromatic after both prescribed fires and wildfires (Vergnoux et al. 2011; 179 Hobley et al. 2019) . Thus, at higher soil heating intensity, the low MBC accumulation could be due to low availability of labile C that can be efficiently transformed into MBC . In addition to the potential effects on C quality , 200 ° C represents the lower threshold for N - volatilization and loss of soil proteins (Russell et al. 1974; Bodí et al. 2014; Lozano et al. 2016) , potentially leadi ng to decreased N availability and increasing the need for N - mining and investment in extracellular enzymes . For example, Prieto - Fernández et al. ( 2004 ) found that chemically labile (acid hydrolysable) organic N was not impacted when soils were heated to 1 50 ° C, but decreased by >50% when heated to 210 ° C and by a similar amount in soils collected immediately after a wildfire. Some of this organic N will be converted to inorganic N and retained in soil as available N , but substantial increases in inorganic N might not occur until soil is heated at much higher temperatures ( ~ 400 ° C) than I employed (Raison 1979; Choromanska and DeLuca 2002; Guerrero et al. 2005) . Even if N availability increased slightly in soils heated to 200 ° C , changes in microbial community composition in response to soil heating could affect CUE and MBC accumulation by leading to a disconnect between microbial biomass stoichiometry and nutrient availability. I f soil heating induces a shift in the microbial community from fungi and oligotrophic bacteria with high C:N biomass ratios and thus lower nutrient requirements towards copiotrophic bacteria with lower C:N ratios a nd higher nutrient requirements (Fierer et al. 2007; Sinsabaugh et al. 2013) , decomposition of labile C could be coupled to ex vivo catabolic processes in order to obtain limiting nutrients from complex organic matter (Moorhead and Sinsabaugh 2006; Craine et al. 2007; Manzoni et al. 2017) . M icrobial communities that have lower fungal - to - bacterial ratios and/or a greater abu ndance of copiotrophic bacteria are less efficient at scavenging for limiting nutrients from complex organic matter , leading to greater C mineralization per unit biomass and therefore lower CUE and MBC 180 accumulation . In fact, I found that fungal activity wa s significantly decreased in soils heated to 200 ° C, and research has shown that the relative abundance of bacterial phyla typically classified as copiotrophic increase within one day to one week post - fire (Pérez - Valera et al. 2017; Prendergast - Miller et al. 2017) , sug gesting shifts to microbial communities with higher nutrient requirements . Indeed, the high respiration rates and lack of associated MBC accumulation (Fig. 4. 3 ) supports the explanation that catabolic, rather than anabolic, processes are dominant in soils heated to 200 ° C. Similarly, the lower CUE proxy in 200 ° C soils that received ascorbic acid could be due to the decomposition of this labile C substrate being coupled to nutrient acquisition. Importantly, higher MBC accumulation in soils heated to 100 ° C does not necessarily reflect increased C sequestration compared to unheated soils. In fact, greater C respiration in the absence of new C inputs reflects a net loss of C, regardless of an associated accumulation of MBC. Thus, the increased C respiration in soils heated to 100 ° C compared to unheated soils indicates a net loss of soil C during the immediate post - heating period (Table 4. 1 and Fig. 4.3 ). However, the high MBC accumulation does represent efficient C recycling , limiting the contribution of mic robial mortality to post - fire emissions and suggests microbial anabolism is high following low - intensity soil heating . In contrast, soils heated to 200 ° C exhibited high post - heating respiration rates, continuing decreases in microbial biomass, and lower e stimated CUE even in soils amended with a relatively labile C source (Figs. 4. 1 and 4. 3 ). This suggests that the effects of high - intensity soil heating results in high microbial catabolism which exacerbates C emissions after heating, at least in the short term. 4.5.2 Pyrogenic organic matter decreases soil respiration I did not find support for my hypothesis that estimated CUE and MBC accumulation would be higher in soils amended with uncharred organic matter compared to charred organic 181 matter . In most cases, re gardless of soil heating intensity, application of PyOM led to decreases in C respiration without negatively affecting EOC uptake or MBC accumulation compared to the uncharred counterpart (Figs 4.2 and 4.3) . These findings indicate that PyOM addition decre ases microbial catabolism in soils without negatively impacting anabolism. The decrease in catabolism was likely not due to pyrolysis releas ing labile C that could be used more efficiently , because EOC extractions of charred and uncharred litter indicated that there was ~90% less extractable C in charred litter. One possible explanation for these patterns is that PyOM additions positiv ely affected soil properties in ways that increased the efficiency of decomposition. For example, PyOM has been shown to inc rease CUE and microbial abundance by increasing nutrient availability, soil oxygen concentrations, pH, and by providing sorption sites for bacteria (Lehmann et al. 2011; Jiang et al. 2016; Fang et al. 2018) . Alternatively, low - intensity soil heating could select for microbial groups that are adapted to efficient use of PyOM (Whitman et al. 2019) . The lower amounts of catabolism in response to PyOM addition could also be due to low bio - availability of the charred biomass. However, low microbial use of PyOM may depend on the availability of an alternative labile C pool , and EOC can remain depressed for decades following wildfire (Prieto - Fernán dez et al. 1998) . In the case of high intensity fires, aboveground plant mortality and biomass combustion may be high, removing a source of future labile soil C inputs from litter deposition and root inputs (González - Pérez et al. 2004; Grady and Hart 2006; Kavanagh et al. 2010) . In these cases, the microbial community may switch to use of PyOM af ter the residual labile C pool is depleted, leading to less efficient C use over the intermediate or long term. An analogous substrate - limitation mechanism has been found to explain lignin degradation when soil carbohydrate pools becomes depleted (Hall et al. 2020) . 182 There was a clear negative impact of soil heating on fungal activity, supporting other research finding that fungal biomass is more negatively affected by fire than b acterial biomass (Dooley and Treseder 2012) . Although I did not observe a relationship between fungal respiration and MBC accumulation , decreased fungal biomass could negativ ely impact MBC accumulation in soils with high PyOM concentrations because fungi may be better able to utilize organic matter characterized by high aromaticity and C:N (Jastrow et al. 2007; Keiblinger et al. 2010; Dungait et al. 2012) . 4.6 CONCLUSIONS High intensity soil heating decreases the strength of soil C sink over the short term by decreasing the micr obial anabolism and increasing catabolism. Higher soil heating intensity likely increases the amount of C lost to combustion during fire events. My results suggest that this effect may be exacerbated by low CUE , low accumulation of MBC, and high soil respi ration rates over the short - term in soils that experience high heating intensity. In addition to exacerbating short - term C losses, th e decreased anabolism could lead to long - term reductions in soil C storage via less microbial necromass, which is a persistent stock of C. Fire intensity is predicted to increase in many fire - prone ecosystems, and a negative relationship between intensity a nd microbial anabolism could lead to positive fire - climate feedbacks. Disrupted fire regimes are already being observed in temperate coniferous forests of the western United States, and if these altered fire regimes lead to lower anabolism and decreased CU E , the C sequestration ability of these forests could be permanently reduced. I found some beneficial impacts of PyOM in ameliorating C losses via decreased respiration , but over longer timeframes, PyOM could either negatively influence CUE due to its chem ical recalcitrance, or positively influence CUE via beneficial effects on soil physicochemical properties. My study used lab - based soil heating to 183 estimate direct effects of fire on a CUE proxy and MBC accumulation , but field - based research is needed to de termine whether the influence s of soil heating and PyOM on CUE and MBC accumulation persist over the long - term. 184 APPENDIX 185 SUPPLEMENTAL FIGURES Figure S4.1 Glucose (a) and ascorbic acid (b) after charring in a muffle furnace at 210 ° C and 200 ° C, respectively. 186 REFERENCES 187 REFERENCES Anderson JPE, Domsch KH (1973) Quantification of bacterial and fungal contributions to soil respiration. Arch Mikrobiol 93:113 127. doi: 10.1007/BF00424942 Bailey VL, Smith JL, Bolton H (2003) Novel antibiotics as inhibitors for the selective respiratory inhibition method of measuring fungal:bacterial ratios in soil. Biol Fertil Soils 38:154 160. doi: 10.1007/s00374 - 003 - 0620 - 7 Bárcenas - Moreno G, Bååth E (2009) Bacterial and fungal growth in soil heated at different temperatures to simulate a range of fire intensities. Soil Biol Biochem 41:2517 2526. doi: 10.1016/j.soilbio.2009.09.010 Bird MI, Wynn JG, Saiz G, et al (2015) The pyrogenic carbon cycle. Annu Rev Earth Planet Sci 43:273 298. doi: 10.1146/annurev - earth - 060614 - 105038 Birdsey R, Pregitzer K, Lucier A (2006) Forest carbon management in the United States. J Environ Qual 35:1461. doi: 10.2134 /jeq2005.0162 Bodí MB, Martin D a., Balfour VN, et al (2014) Wildland fire ash: Production, composition and eco - hydro - geomorphic effects. Earth - Science Rev 130:103 127. doi: 10.1016/j.earscirev.2013.12.007 Bowman DMJS, Balch JK, Artaxo P, et al (2009) Fire in the earth system. Science 324:481 484. doi: 10.1126/science.1163886 Cai Y, Peng C, Qiu S, et al (2011) Dichromate digestion - spectrophotometric procedure for determination of soil microbial biomass carbon in association with fumigation - extraction. Commu n Soil Sci Plant Anal 42:2824 2834. doi: 10.1080/00103624.2011.623027 Campbell J, Donato D, Azuma D, Law B (2007) Pyrogenic carbon emission from a large wildfire in Oregon, United States. J Geophys Res Biogeosciences. doi: 10.1029/2007JG000451 Campbell JL, Fontaine JB, Donato DC (2016) Carbon emissions from decomposition of fire - killed trees following a large wildfire in Oregon, United States. J Geophys Res Biogeosciences 121:718 730. doi: 10.1002/2015JG003165 Chatterjee S, Santos F, Abiven S, et al (2012) Elucidating the chemical structure of pyrogenic organic matter by combining magnetic resonance, mid - infrared spectroscopy and mass spectrometry. Org Geochem 51:35 44. doi: 10.1016/j.orggeochem.2012.07.006 188 Chen G, Hayes DJ, David McGuire A (2017) Contributi ons of wildland fire to terrestrial ecosystem carbon dynamics in North America from 1990 to 2012. Global Biogeochem Cycles 31:878 900. doi: 10.1002/2016GB005548 Chen X, Xia Y, Rui Y, et al (2020) Microbial carbon use efficiency, biomass turnover, and necro mass accumulation in paddy soil depending on fertilization. Agric Ecosyst Environ 292:106816. doi: 10.1016/j.agee.2020.106816 Choromanska U, DeLuca TH (2002) Microbial activity and nitrogen mineralization in forest mineral soils following heating: Evaluati on of post - fire effects. Soil Biol Biochem 34:263 271. doi: 10.1016/S0038 - 0717(01)00180 - 8 Cotrufo MF, Wallenstein MD, Boot CM, et al (2013) The Microbial Efficiency - Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organ ic matter stabilization: do labile plant inputs form stable soil organic matter? Glob Chang Biol 19:988 995. doi: 10.1111/gcb.12113 Craine JM, Morrow C, Fierer N (2007) Microbial nitrogen limitation increases decomposition. Ecology 88:2105 2113. doi: 10.18 90/06 - 1847.1 Czimczik CI, Preston CM, Schmidt MWI, Schulze E - D (2003) How surface fire in Siberian scots pine forests affects soil organic carbon in the forest floor: Stocks, molecular structure, and conversion to black carbon (charcoal). Global Biogeochem Cycles. doi: 10.1029/2002GB001956 Degens BP, Harris J a. (1997) Development of a physiological approach to measuring the catabolic diversity of soil microbial communities. Soil Biol Biochem 29:1309 1320. doi: 10.1016/S0038 - 0717(97)00076 - X Díaz - Raviña M, Prieto A, Bååth E (1996) Bacterial activity in a forest soil after soil heating and organic amendments measured by the thymidine and leucine incorporation techniques. Soil Biol Biochem 28:419 426. doi: 10.1016/0 038 - 0717(95)00156 - 5 Domeignoz - Horta LA, Pold G, Liu X - JA, et al (2020) Microbial diversity drives carbon use efficiency in a model soil. Nat Commun 11:3684. doi: 10.1038/s41467 - 020 - 17502 - z Dooley SR, Treseder KK (2012) The effect of fire on microbial bioma ss: A meta - analysis of field studies. Biogeochemistry 109:49 61. doi: 10.1007/s10533 - 011 - 9633 - 8 Dungait J a J, Hopkins DW, Gregory AS, Whitmore AP (2012) Soil organic matter turnover is governed by accessibility not recalcitrance. Glob Chang Biol 18:1781 1 796. doi: 10.1111/j.1365 - 2486.2012.02665.x 189 Fang Y, Singh BP, Luo Y, et al (2018) Biochar carbon dynamics in physically separated fractions and microbial use efficiency in contrasting soils under temperate pastures. Soil Biol Biochem 116:399 409. doi: 10.10 16/j.soilbio.2017.10.042 Fernández I, Cabaneiro A, Carballas T (1997) Organic matter changes immediately after a wildfire in an atlantic forest soil and comparison with laboratory soil heating. Soil Biol Biochem 29:1 11. doi: 10.1016/S0038 - 0717(96)00289 - 1 Fierer N, Bradford MA, Jackson RB (2007) Toward an ecological classification of soil bacteria. Ecology 88:1354 1364. doi: 10.1890/05 - 1839 Flannigan MD, Krawchuk MA, de Groot WJ, et al (2009) Implications of changing climate for global wildland fire. Int J Wildl Fire 483 507. doi: 10.1071/WF08187 Frey SD, Lee J, Melillo JM, Six J (2013) The temperature response of soil microbial efficiency and its feedback to climate. Nat Clim Chang 3:395 398. doi: 10.1038/nclimate1796 Geyer KM, Kyker - Snowman E, Grandy AS, Frey SD (2016) Microbial carbon use efficiency: accounting for population, community, and ecosystem - scale controls over the fate of metabolized organic matter. Biogeochemistry 127:173 188. doi: 10.1007/s10533 - 016 - 0191 - y González - Pérez JA, González - Vila FJ, Almendros G, Knicker H (2004) The effect of fire on soil organic matter -- a review. Environ Int 30:855 870. doi: 10.1016/j.envint.2004.02.003 Grady KC, Hart SC (2006) Influences of thinning, prescribed burning, and wildfire on soil processes and proper ties in southwestern ponderosa pine forests: A retrospective study. For Ecol Manage 234:123 135. doi: 10.1016/j.foreco.2006.06.031 Guerrero C, Mataix - Solera J, Gómez I, et al (2005) Microbial recolonization and chemical changes in a soil heated at differen t temperatures. Int J Wildl Fire 14:385 400. doi: 10.1071/WF05039 Guo K, Zhao Y, Liu Y, et al (2020) Pyrolysis temperature of biochar affects ecoenzymatic stoichiometry and microbial nutrient - use efficiency in a bamboo forest soil. Geoderma 363:114162. doi : 10.1016/j.geoderma.2019.114162 Hall SJ, Huang W, Timokhin VI, Hammel, KE (2020) Lignin lags, leads, or limits the decomposition of litter and soil organic carbon. Ecology 101:1 7. doi: 10.1002/ecy.3113 Hatton PJ, Chatterjee S, Filley TR, et al (2016) Tre e taxa and pyrolysis temperature interact to control the efficacy of pyrogenic organic matter formation. Biogeochemistry 130:103 116. doi: 10.1007/s10533 - 016 - 0245 - 1 190 Hobley EU, Zoor LC, Shrestha HR, et al (2019) Prescribed fire affects the concentration and aromaticity of soluble soil organic matter in forest soils. Geoderma 341:138 147. doi: 10.1016/j.geoderma.2019.01.035 Janssens IA, Dieleman W, Luyssaert S, et al (2010) Reduction of forest soil respiration in response to nitrogen deposition. Nat Geosci 3: 315 322. doi: 10.1038/ngeo844 Jastrow JD, Amonette JE, Bailey VL (2007) Mechanisms controlling soil carbon turnover and their potential application for enhancing carbon sequestration. Clim Change 80:5 23. doi: 10.1007/s10584 - 006 - 9178 - 3 Jiang X, Denef K, St ewart CE, Cotrufo MF (2016) Controls and dynamics of biochar decomposition and soil microbial abundance, composition, and carbon use efficiency during long - term biochar - amended soil incubations. Biol Fertil Soils 52:1 14. doi: 10.1007/s00374 - 015 - 1047 - 7 Jin gyan S, Yuwen L, Zhiyong W, Cunxin W (2013) Investigation of thermal decomposition of ascorbic acid by TG - FTIR and thermal kinetics analysis. J Pharm Biomed Anal 77:116 119. doi: 10.1016/j.jpba.2013.01.018 Jones DL, Shannon D, Murphy D V., Farrar J (2004) Role of dissolved organic nitrogen (DON) in soil N cycling in grassland soils. Soil Biol Biochem 36:749 756. doi: 10.1016/j.soilbio.2004.01.003 Kavanagh KL, Dickinson MB, Bova AS (2010) A way forward for fire - caused tree mortality prediction: Modeling a ph ysiological consequence of fire. Fire Ecol 6:80 94. doi: 10.4996/fireecology.0601080 Keiblinger KM, Hall EK, Wanek W, et al (2010) The effect of resource quantity and resource stoichiometry on microbial carbon - use - efficiency. FEMS Microbiol Ecol 73:no - no. doi: 10.1111/j.1574 - 6941.2010.00912.x Kielland K, McFarland JW, Ruess RW, Olson K (2007) Rapid cycling of organic nitrogen in taiga forest ecosystems. Ecosystems 10:360 368. doi: 10.1007/s10021 - 007 - 9037 - 8 Lehmann J, Rillig MC, Thies J, et al (2011) Biochar effects on soil biota A review. Soil Biol Biochem 43:1812 1836. doi: 10.1016/j.soilbio.2011.04.022 Lenth R (2020) emmeans: Estimated Marginal Means, aka Least - Squares Means. R package version 1.4.4. Liang C, Amelung W, Lehmann J, Kästner M (2019) Quant itative assessment of microbial necromass contribution to soil organic matter. Glob Chang Biol 25:3578 3590. doi: 191 10.1111/gcb.14781 Liang C, Schimel JP, Jastrow JD (2017) The importance of anabolism in microbial control over soil carbon storage. Nat Microb iol. doi: 10.1038/nmicrobiol.2017.105 Loehman RA, Reinhardt E, Riley KL (2014) Wildland fire emissions, carbon, and climate: Seeing the forest and the trees A cross - scale assessment of wildfire and carbon dynamics in fire - prone, forested ecosystems. For Ecol Manage 317:9 19. doi: 10.1016/j.foreco.2013.04.014 Lozano E, Chrenková K, Arcenegui V, et al (2016) Glomalin - related Soil Protein Response to Heating Temperature: A Laboratory Approach. L Degrad Dev 27:1432 1439. doi: 10.1002/ldr.2415 Mallek CM, Saffo rd H, Viers J, Miller JD (2013) Modern departures in fire severity and area vary by forest type, Sierra Nevada and southern Cascades, California, USA. Ecosphere 4:1 28. doi: 10.1890/ES13 - 00217 c regulation along resource stoichiometry gradients. Ecol Lett 20:1182 1191. doi: 10.1111/ele.12815 Manzoni S, Taylor P, Richter A, et al (2012) Environmental and stoichiometric controls on microbial carbon - use efficiency in soils. New Phytol 196:79 91. do i: 10.1111/j.1469 - 8137.2012.04225.x Martín A, Díaz - Raviña M, Carballas T (2009) Evolution of composition and content of soil carbohydrates following forest wildfires. Biol Fertil Soils 45:511 520. doi: 10.1007/s00374 - 009 - 0363 - 1 Massman WJ, Frank JM, Mooney SJ (2010) Advancing investigation and physical modeling of first - order fire effects on soils. Fire Ecol 6:36 54. doi: 10.4996/fireecology.0601036 Meigs GW, Donato DC, Campbell JL, et al (2009) Forest fire impacts on carbon uptake, storage, and emiss ion: The role of burn severity in the eastern Cascades, Oregon. Ecosystems 12:1246 1267. doi: 10.1007/s10021 - 009 - 9285 - x Miesel JR, Hockaday WC, Kolka RK, Townsend PA (2015) Soil organic matter composition and quality across fire severity gradients in conif erous and deciduous forests of the southern boreal region. J Geophys Res Biogeosciences 120:1124 1141. doi: 10.1002/2015JG002959 Moorhead DL, Sinsabaugh RL (2006) A theoretical model of litter decay and microbial interaction. Ecol Monogr 76:151 174. 192 Neary D., DeBano L. (2005) Wildland fire in ecosystems effects of fire on soil and water. Ni X, Liao S, Tan S, et al (2020) The vertical distribution and control of microbial necromass carbon in forest soils. Glob Ecol Biogeogr 29:1829 1839. doi: 10.1111/geb.13 159 Örsi F (1973) Kinetic studies on the thermal decomposition of glucose and fructose. J Therm Anal 5:329 335. doi: 10.1007/BF01950381 Science 333:988 993. doi: 10.1126/science.1201609 Pérez - Valera E, Goberna M, Faust K, et al (2017) Fire modifies the phylogenetic structure of soil bacterial co - occurrence networks. Environ Microbiol 19:317 327. doi: 10.1111/1462 - 2920.13609 (2020) Corn and hardwood biochars affected soil microbial community and enzyme activities. Agrosystems, Geosci Environ 3:1 12. doi: 10.1002/agg2.20082 Pingree MRA, Kobziar LN (2019) The myth of the biological threshold: A review of biological responses to soil heating associated with wildland fire. For Ecol Manage 432:1022 1029. doi: 10.1016/j.foreco.2018.10.032 Pinheiro J, Bates D, Debroy S, Sarkar D (2019) nlme: Linear and nonlinear mixed effects models. Prendergast - Miller MT, de Menezes AB, Macdonald LM , et al (2017) Wildfire impact: Natural experiment reveals differential short - term changes in soil microbial communities. Soil Biol Biochem 109:1 13. doi: 10.1016/j.soilbio.2017.01.027 Pressler Y, Moore JC, Cotrufo MF (2018) Belowground community responses to fire: meta - analysis reveals contrasting responses of soil microorganisms and mesofauna. Oikos 1 19. doi: 10.1111/oik.05738 Preston CM, Schmidt MWI (2006) Black (pyrogenic) carbon: a synthesis of current knowledge and uncertainties with special consider ation of boreal regions. Biogeosciences 3:397 420. doi: 10.5194/bg - 3 - 397 - 2006 Prieto - Fernández A, Acea MJ, Carballas T (1998) Soil microbial and extractable C and N after wildfire. Biol Fertil Soils 27:132 142. doi: 10.1007/s003740050411 Prieto - Fernández Á , Carballas M, Carballas T (2004) Inorganic and organic N pools in soils burned or heated: Immediate alterations and evolution after forest wildfires. Geoderma 193 121:291 306. doi: 10.1016/j.geoderma.2003.11.016 R Core Team (2019) R: A language and environmen t for statistical computing. Raison RJ (1979) Modification of the soil environment by vegetation fires, with particular reference to nitrogen transformations: A review. Plant Soil 51:73 108. doi: 10.1007/BF02205929 Rousk J, Demoling LA, Bååth E (2009) Con trasting short - Term antibiotic effects on respiration and bacterial growth compromises the validity of the selective respiratory inhibition technique to distinguish fungi and bacteria. Microb Ecol 58:75 85. doi: 10.1007/s00248 - 008 - 9444 - 1 Russell JD, Fraser AR, Watson JR, Parsons JW (1974) Thermal decomposition of protein in soil organic matter. Geoderma 11:63 66. doi: 10.1016/0016 - 7061(74)90007 - X Sawyer R, Bradstock R, Bedward M, Morrison RJ (2018) Fire intensity drives post - fire temporal pattern of soil ca rbon accumulation in Australian fire - prone forests. Sci Total Environ 610 611:1113 1124. doi: 10.1016/j.scitotenv.2017.08.165 Schimel JP, Bennett J (2004) Nitrogen mineralization: challenges of a changing paradigm. Ecology 85:591 602. Schimel JP, Schaeffer SM (2012) Microbial control over carbon cycling in soil. Front Microbiol 3:1 11. doi: 10.3389/fmicb.2012.00348 Serrasolsas I, Khanna PK (1995) Changes in heated and autoclaved forest soils of S.E. Australia. I. Carbon and nitrogen. Biogeochemistry 29:3 24 . doi: 10.1007/BF00002591 Sinsabaugh RL, Manzoni S, Moorhead DL, Richter A (2013) Carbon use efficiency of microbial communities: Stoichiometry, methodology and modelling. Ecol Lett 16:930 939. doi: 10.1111/ele.12113 Six J, Frey SD, Thiet RK, Batten KM (2006) Bacterial and Fungal Contributions to Carbon Sequestration in Agroecosystems. Soil Sci Soc Am J 70:555. doi: 10.2136/sssaj2004.0347 Soil Survey Staff Official soil series descriptions. In: Nat. Resour. Conserv. Serv. United States Dep. Agric. www.nr cs.usda.gov. Soong JL, Fuchslueger L, Marañon - Jimenez S, et al (2020) Microbial carbon limitation: The need for integrating microorganisms into our understanding of ecosystem carbon cycling. Glob Chang Biol 26:1953 1961. doi: 10.1111/gcb.14962 194 Spohn M, Kl aus K, Wanek W, Richter A (2016a) Microbial carbon use efficiency and biomass turnover times depending on soil depth - Implications for carbon cycling. Soil Biol Biochem 96:74 81. doi: 10.1016/j.soilbio.2016.01.016 Spohn M, Pötsch EM, Eichorst SA, et al (2 016b) Soil microbial carbon use efficiency and biomass turnover in a long - term fertilization experiment in a temperate grassland. Soil Biol Biochem 97:168 175. doi: 10.1016/j.soilbio.2016.03.008 Steinbeiss S, Gleixner G, Antonietti M (2009) Effect of bioch ar amendment on soil carbon balance and soil microbial activity. Soil Biol Biochem 41:1301 1310. doi: 10.1016/j.soilbio.2009.03.016 Tiemann LK, Billings SA (2011) Changes in variability of soil moisture alter microbial community C and N resource use. Soil Biol Biochem 43:1837 1847. doi: 10.1016/j.soilbio.2011.04.020 Vergnoux A, Di Rocco R, Domeizel M, et al (2011) Effects of forest fires on water extractable organic matter and humic substances from Mediterranean soils: UV - vis and fluorescence spectroscopy a pproaches. Geoderma 160:434 443. doi: 10.1016/j.geoderma.2010.10.014 Wan S, Hui D, Luo Y (2001) Fire effects on nitrogen pools and dynamics in terrestrial ecosystems: A meta - analysis. Ecol Appl 11:1349 1365. Wang Q, Zhong M, Wang S (2012) A meta - analysis o n the response of microbial biomass, dissolved organic matter, respiration, and N mineralization in mineral soil to fire in forest ecosystems. For Ecol Manage 271:91 97. doi: 10.1016/j.foreco.2012.02.006 Whitman T, Whitman E, Woolet J, et al (2019) Soil ba cterial and fungal response to wildfires in the Canadian boreal forest across a burn severity gradient. Soil Biol Biochem 138:107571. doi: 10.1016/j.soilbio.2019.107571 Wieder WR, Bonan GB, Allison SD (2013) Global soil carbon projections are improved by m odelling microbial processes. Nat Clim Chang 3:909 912. doi: 10.1038/nclimate1951 Witt C, Gaunt JL, Galicia CC, et al (2000) A rapid chloroform - fumigation extraction method for measuring soil microbial biomass carbon and nitrogen in flooded rice soils. Bio l Fertil Soils 30:510 519. doi: 10.1007/s003740050030 Yu Z, Chen L, Pan S, et al (2018) Feedstock determines biochar - induced soil priming effects by stimulating the activity of specific microorganisms. Eur J Soil Sci 69:521 534. doi: 10.1111/ejss.12542 Zha o H, Tong DQ, Lin Q, et al (2012) Effect of fires on soil organic carbon pool and 195 mineralization in a Northeastern China wetland. Geoderma 189 190:532 539. doi: 10.1016/j.geoderma.2012.05.013 196 CHAPTER 5: SOIL HEATING INTENSITY AND PYROGENIC ORGANIC MATTER HAVE IMMEDIATE IMPACTS ON THE STRUCTURE AND KINETICS OF SOIL CARBON POOLS 5.1 ABSTRACT Increases in tree density and fuel loads in the mixed - conifer forests of California (USA) have led to increases in wildfire intensity, potentially leading to positive feedbacks between carbon (C) emissions and climate. In addition to emissions resulting fr om combustion, high intensity fires could alter soil C mineralization rates immediately post - fire due to direct impacts of soil heating on soil properties; however, high intensity fires could also produce more pyrogenic organic matter (PyOM), a source of p ersistent C. The influence of soil heating and PyOM on the relative sizes and stability of the active and slow cycling soil C pools could have long - lasting effects on forest C storage and determine the strength of fire - climate feedbacks. Here, I disentangle the impacts of soil heating intensity and PyOM on the size and kinetic rates of the active (C a ) and non - active (C s ) C pools in forest soils. I hypothesized that C a size and mineralization rate will increase with soil heating intensity, C s size will increase with PyOM additions, and that C s mineralization rates will be inversely related to temperature of PyOM formation. I conducted a laboratory experiment in which I manipulated soil heating temperature (unheated, 200 °C, 300 °C, 400 °C) and char formation temperature (uncharred, 300 °C, 550 °C), and incubated soils for 390 days to determine the sizes and kinetic rates of the C a and C s pools. The C a pool size and mineralization rate increased at the two highest soil heating intensities, and char ad ditions increased C s pool size. Char formation temperature did not influence the size or mineralization rates of either the C a or C s pools. My results suggest that soil C pools are resistant to low intensity soil heating, whereas high intensity soil heatin g leads to immediate decreases in 197 soil C persistence. Over the long term, the impacts of high intensity soil heating could be offset by the persistence of PyOM. This research advances understanding of C persistence in fire - prone forests, and suggests that forest C accounting should consider the impacts of fire intensity on soil C. 5.2 INTRODUCTION Carbon (C) stored in temperate forest soils accounts for ~50% of the ecosystem C stock (Pan et al. 2011) , so disturbance - induced changes to the persistence of soil C could have substantial impacts on the global C balance (Luo and Weng 2011) - conifer forests, selective logging and fire suppressio n have resulted in forests that are more densely populated with small, fire - sensitive trees and have higher surface fuel loads compared to historical reference conditions (Skinner and Taylor 2006; Earles et al. 2014; Taylor et al. 2014) . These structur al changes have led to increased wildfire intensity (Taylor et al. 2014) , which, coupled with greater surface fuel loads, can result in greater heat flux to soil during wildfires (Neary and DeBano 2005; Massman et al. 2010; Busse et al. 2013) . Soil heating can directly impact the amount and composition of soil organic matter (González - Pérez et al. 2004; Knicker 2007) , potentially influencing both the size and persistence of the soil C sink. Understanding the effects of soil heating intensi ty on soil C dynamics is important for predicting the feedbacks between fire and climate under disrupted fire regimes, as well as for determining whether fuel removal treatments designed to decrease fire severity and intensity are also effective at promoti ng resilience of soil functions. The magnitude, depth, and duration of soil heating during a fire can vary substantially, largely depending on pre - fire fuel load and moisture content. Temperatures of 100 - 300 °C are typical at the soil surface, but instanta neous temperatures can exceed 700 - 1000 °C under heavy 198 fuel loads (Certini 2005; Neary and DeBano 2005) . Mineral soil is a poor heat conductor, so temperatures at 5 cm below the mineral soil surface are generally less than 150 °C (Certini 2005) , but can exceed temperatures of 200 °C for several hours under wood piles or smoldering duff (Neary and DeBano 2005; Busse et al. 2013) . Soil heating intensity likely influences soil C cycling due to progressive changes in soil chemistry and biology with temperature (González - Pérez et al. 2004) . For example, soluble organic C content has been shown to increase with soil heating temperature up to ~300 °C, and then exhibit rapid declines (Bárcenas - Moreno and Bååth 2009) . Protein denaturation begins at temperatures <100 °C (Knicker 2007) , cellulose and hemicellulose are lost or transformed at temperatures ~200 °C, and lignin appears to be lost at temperatures >300 °C (Fernández et al. 1997; González - Pérez et al. 2004) . From a biological standpoint, microbial biomass losses increase with soil heating temperature (Choromanska and DeLuca 2002; Bárcenas - Moreno and Bååth 2009) , and soil heating leads to lower fungal - to - bacterial ratios due to greater heat - sensitivity of fungi (Dunn et al. 1985; Guerrero et al. 2005) . Soil C can be conceptually modeled as discrete pools that vary in persistence: an active C pool (C a ) with a mean residence time (MRT) of days to months, and a non - active C pool (C s ) with an MRT of years to decades. The C s pool can be an order of magnitude larger than the C a pool, so fire - induced changes to its size or persistence may be especially i mportant for determining the strength of the soil C sink. inaccurate estimates (Greenfield et al. 2013) . Pyrogenic organic matter (PyOM) en compasses a range of fire - affected organic matter, from slightly charred biomass to highly condensed aromatic compounds (Masiello 2004; Bird et al. 2015) , and tends to exhibit longer MRT in soils co mpared to uncharred biomass (Kuzyakov et al. 2009; Santos et al. 2012; Kuzyakov et al. 199 2014) . PyOM may therefore primarily contribute to the C s pool, represen ting a persistent C sink that offsets C losses resulting from biomass combustion (Jones et al. 2019) . However, a portion of PyOM decomposes relatively quickly in soil, and the size of this fast - cycling PyOM pool is inversely related to pyrolysis temperature (Nguyen et al. 2010; Bird et al. 2015) , indicating that fire intensity may influence the persistence of PyOM. Furthermore, PyOM decomposition can be primed by labile C (Kuzyakov et al. 2009) , and fungi may decompose PyOM more efficiently than bacteria (Zimmermann et al. 2012) , suggesting that the impacts of heating intensity on soil chemical and biological properties can indirectly influence PyOM decomposition dynamics. Previous research has indicated that wildfire can lead to immediate increases in the size of the C a pool and the kinetic rates of both the C a and C s pools (Fernández et al. 1997; Fernández et al. 1999) . However, by months to years post - fire in Pinus sylvestris (Fernández et al. 1997; Fernández et al. 1999) and Sierra - Nevada mixed - conifer fore sts (Adkins et al. 2019b) , the opposite trend is apparent: C a pool size and mineraliz ation rates of the C a and C s pools are lower in burned areas versus unburned areas. This suggests that fire has immediate and distinct impacts on soil C pool dynamics that are not captured by assessments later during ecosystem recovery. The immediate impac ts of fire intensity on soil C pool dynamics has not been investigated, nor has it been determined whether changes are related to soil chemical or biological properties. A better understanding of the immediate impacts of fire on soil C persistence could im prove forest C accounting and inform the management of fire - prone forests for C storage under novel fire regimes. Here, I performed a laboratory study to disentangle the direct and indirect impacts of fire on the structure and persistence of C a and C s pool s by manipulating soil heating intensity, PyOM additions, and microbial communities independently of one another. I hypothesized that 1) C a pool size and kinetic rates would progressively increase with soil heating intensity due to 200 increases in extractable C; 2) C s size would increase with PyOM additions; and 3) C s kinetic rates would be inversely related to the temperature of PyOM formation. 5.3 MATERIALS AND METHODS 5.3.1 Site description and sample collection I collected soil samples from the Lassen National For est in the Sierra Nevada Mountain Range, California, USA. The forest is a dry mixed - conifer forest dominated by Pinus ponderosa, P. lambertiana, P. jeffreyi, Abies concolor, Pseudotsuga menziesii, Calocedrus decurrens , and Quercus kelloggii . Soils at my sites are from the Skalan soils series, a loamy - skeletal, isotic, mesic Vitrandic Haploxerlaf (Soil Survey Staff) . From five sites within the forest, I collected soil along a transect with sampling locations spaced ~15 cm apart until I collected ~10 L of mineral soil. At each sampling location, I removed the organic horizon, inserted a 15.7 cm diameter cylinder to 5 cm, and collected mineral soil using a stainless - stee l scoop. I sieved (4 mm) and air dried the soil at time of sampling. After transport to the lab, I composited equal masses of soils from each of the five sampling sites and sieved to 2 mm. I collected P. taeda (loblolly pine) wood samples from the Duke Fo rest in Durham, North Carolin a , USA. The forest is dominated by P. taeda , with an understory of Liquidambar styracifua , Acer rubrum , Cercis canadensis , and Cornus florida (Hobbie et al. 2014) . Within each plot, I collected five pieces of wood (2.54 - 7.62 cm diameter) from the surface of the forest floor. Duke Forest is a former Free Air Carbon Enrichment (FACE) site, and wood produced under CO 2 fumigation is depleted in 13 C compared to wood produced under CO 2 natur al abundance. I collected wood samples from Duke Forest instead of Lassen National Forest to trace accumulation of C from wood and char into microbial biomass, but I do not report those results here. 201 5.3.2 Experimental design I performed a full - factorial exper iment in which I manipulated soil heating intensity (unheated soil, and soil heated to 200 ° C, 300 ° C, or 400 ° C), wood/char additions (no addition, uncharred wood addition, and addition of char generated at 300 ° C or 550 ° C), and soil inoculum type to man ipulate microbial communities (250 - 2000 µ m soil fraction and a 250 µ m soil fraction). Each treatment combination was replicated 18 times (N=576) , and replicates were divided between four incubation blocks that were destructively sampled after incubating f or 30, 100, 200, and 390 days. Treatment applications and soil incubation I created char by individually wrapping ~15 cm lengths of loblolly pine wood in aluminum foil and heating in a muffle furnace for four hours at the target temperature. I then pulver ized charred and uncharred wood to pass a 250 µ m sieve. For soil heating treatments, I weighed ~1600 g of soil into an aluminum baking dish, added DI water to bring soils to 25% water - filled pore - space (WFPS), and mixed the soil to achieve even moisture th roughout. I then covered the dish with aluminum foil and heated in a muffle furnace for 90 minutes at the target temperature. I measured C and N concentrations of soil, wood, and char using a dry - combustion elemental analyzer (Costech Analytical Technologies Inc., Valencia, CA, USA) , using acetanilide as the quantification standard . I weighed 31.0 ± 1.0 g replicates of the post - he ated soils into 90 mL specimen cups. For microcosms receiving wood or char additions, I added 0.310 ± 0.010 g of material to the soils and stirred to mix. I readjusted the soils to 25% WFPS, capped the specimen cups, and sterilized the samples by autoclavi ng at 121 ° C for 30 minutes. Following sterilization, I sought to manipulate the soil microbial community by reinoculating the sterilized soils with either 0.75 ± 202 0.10 g of unsterilized soil that had passed a 250 µ m sieve, or 1.50 ± 0.10 g of soil that did not pass the sieve (i.e. 250 - 2000 µ m fraction). The different masses of the two inoculum types were selected to apply approximately equal microbial biomass, determined via substrate - induced respiration in preliminary experiments. Previous research indicat es that soil fractions differ in microbial community composition (Wagg et al. 2014; Wagg et al. 2019) , and preliminary experiments based on selectively - inhibited substrate - induced respiration suggested that the 0 - 250 µ m fraction had a higher fungal - to - bacterial ratio than the 250 - 2000 µ m fraction (1.45 ± 0.34 vs 0.68 ± 0.06; p=0.024; Fig. S5.1). After soil inoculation, I adjusted soil moisture of each microcosm to 50% WFPS, placed each specimen cup in a loosely capped 0.473 L glass jar, and incubated in the dark at ambient temp erature (23 ° C). Determination of extractable organic carbon and microbial biomass carbon I destructively sampled incubation blocks at 30, 100, 200, and 390 days for determination of extractable organic carbon (EOC) and microbial biomass carbon (MBC). Fro m each microcosm, I weighed four ~5 g subsamples into 50 mL centrifuge tubes. Two subsamples were immediately extracted for EOC, and the other two were directly exposed to chloroform for 24 hours to lyse microbial cells prior to extraction (Witt et al. 2000) . I also determined EOC from each soil heating treatment at day 0 by extracting three autoclaved and unamended soil replicates. I extracted EO C by adding 25 mL of 0.5 M K 2 SO 4 to the centrifuge tubes, agitating on a reciprocating shaker for 1 hour, and filtering through 11 µ m pore - size filters (Whatman Grade 1). I determined EOC concentrations spectrophotometrically after potassium - dichromate oxidation (Cai et al. 2 011) . Wet - oxidation methods have been shown to underestimate EOC concentrations compared to combustion methods (Bolan et al. 1996) , but conversion factors between methods are not available in the published literature. MBC was determined as the 203 difference in EOC between fumigated and unfumigated subsamples, divided by a correction factor of 0.33 (Cai et al. 2011) . My original intent was to measure fungal - to - bacterial ratios using selectively - inhibited sub strate - induced respiration on each sampling date to determine whether the two soil inoculum treatments affected microbial community structure (Anderson and Domsch 1973; Bailey et al. 2003) . Prior to initiating the experiment, I optimized the selective - inhibition procedure on unheated and unsterilized soils collected from the same site and achieved acceptable results. Howev er, when I applied the procedure on day 30, the inhibition additivity ratio was too high (>1.3) to accept the results . Thus, I did not continue using this technique on the remaining sampling dates. Selectively - inhibited substrate - induced respiration requir es extensive optimization, and different soils require different inhibitor types and concentrations to produce acceptable results (Bailey et al. 2003; Rousk et al. 2009) . This suggests that differences in soil biological characteristics affect the performance of this procedure, and I suspect that changes in the soil biological community over the course of the incubation impacted its efficacy. Determination of c arbon m inera lization r ates For one incubation block, I measured C mineralization rates on incubation days 9, 11, 21, 31, 40, 50, 60, 74, 90, 102, 119, 143, 180, 221, 265, 305, 347, and 389. On measurement days 9 and 11, I determined CO 2 - C production by tightly capping each jar with a septum - fitted l id and mea suring the CO 2 concentrations of 13 mL gas aliquots using a gas chromatograph (Thermo Fisher Scientific Inc., Waltham, MA, USA). Measurements were taken ~30 minutes and 6 hours after capping. Due to equipment malfunction, not all replicates were measured o n day 21, so this data were not analyzed. For the remaining sampling days, I measured CO 2 concentrations of 1 mL gas aliquots using an infrared gas analyzer (LI - COR Inc., Lincoln, NE, USA). For these 204 sampling days, CO 2 was allowed to accumulate in the capp ed jars for 24 - 96 hours prior to measurement, with the longer accumulation times occurring later in the incubation when C mineralization rates were low. Carbon Pool Models I applied single - and double - pool decay models to my C mineralization data to dete rmine the size and rate constants of soil C pools (Kuzyakov 2011) . The single - pool model was fit as: (1) where C t is cumulative CO 2 - C respired at day t, C m is the initial size of the carbon pool available for mineralization, and k m is its kinetic rate constant. The double - pool model was fit as: (2) where C a is the size of the active (or fast cycling) pool, C s is the size of the non - active pool, and k a and k s are the respective kinetic rate constants. In this model, C s is constrained to be the difference between total C in the microcosms (soil C + char C) and C a . The soil and char C values used for this constraint were treatment means. I used both single - and double - pool decay models because visual inspection of cumulative C mineralization curves suggested that two treatment combinations (unheated soil + uncha rred wood, 200 °C soil + uncharred wood) would be better estimated by single - pool models. I did not include a passive C pool in my models, because methods for isolating such a pool do not provide biologically meaningful estimates (Greenfield et al. 2013) . 5.3.3 Statistical Analysis I performed all analyses in the R statistical computing environments (v 3.6.1) (R Core Team 2019) using the nlme package (v 3.1.140) (Pinheiro et al. 2019) . I used genera l linear models to assess the responses of EOC and MBC and linear mixed models to assess the 205 responses of C mineralization rate and cumulative C mineralization. Models initially included main effects of time, soil heating, char type, and inoculum type, and all possible two - and three - way interactions. The mixed models also included a microcosm identifier as a random effect. Non - significant three - way interactions were removed from the models. Additionally, inoculum treatment main and interactive effects were never significant and were removed from all models, but all other effects were retained. Model residuals for the two C mineralization models were not normally distributed. For the C mineralization rate model, this was rectified by specifying time as a con tinuous covariate and adding a log (time) term. For the cumulative C mineralization model, residual non - normality was rectified by specifying time as a factor and an autoregressive correlation structure to account for non - independence of within - group (microcosm) observations (Pinheiro and Bates 2000) . I fit the single - and double - pool decay models by first performing separate non - linear regressions for each in cubation replicate, with cumulative C mineralization in units of mg kg - 1 soil. To fit a single model encompassing all replicates, I then used data visualization approaches as described by Pinheiro and Bates (2000) to determine which parameters were associated with random and interactive effects. These procedures resulted in models that included random intercepts and two - way interactions associated with C a and k s , whereas k a was associated only with main effects. I then fit additional models with cumulative C mineralization in units of percent total microcosm C to obtain estimates of pool sizes on both a soil mass fraction basis and relative to total C. Because C s is constrained in the double - pool models, it is not estimated independently of C a . I therefore calculated 95% confidence intervals for C s by subtracting the confidence limit s of C a from total microcosm C, and I considered differences in C s to be significant if the confidence intervals did not overlap. For all other parameters, if main effects 206 were significant at I performed pairwise comparisons of marginal means usi adjustment in the emmeans package (v 1.4.4) (Lenth 2020 ) . 5.4 RESULTS 5.4.1 Soil and c har c arbon c oncentrations There were no statistically significant effects of soil heating on C concentrations (p=0.79; n=3), although soils heated to 400 °C had slightly lower C concentrations than the pooled average (3. 7 ± 0. 4 % and 3.9 ± 0. 2 %, respectively). C concentrations were 48.7% for uncharred wood, 66.1% for 300 ° C char, and 71.7% for 550 ° C char. 5.4.2 Extractable o rganic c arbon and m icrobial b iomass c arbon Soil heating had immediate impacts on EOC concentrations (p<0.001; Fi g. 5. 1). EOC increased with heating temperature up to 300 °C, then decreased in soils heated to 400 °C. Compared to unheated soils, EOC concentrations were 45.0% higher in soils heated to 300 °C and 12.5% lower in soils heated to 400 °C. There were signifi cant main and interactive effects on both EOC and MBC measured over the incubation (Table 5. 1). EOC was lowest in soils heated to 400 ° C on all four sampling dates regardless of char treatment (Fig . 5. 2). Averaged across all char × time combinations, soils heated to 400 ° C exhibited 50.2% - 55.0% less EOC than the other soil heating treatments. On day 30, unheated soils generally exhibited higher EOC than soils heated to 400 ° C , and soils heated to 200 ° C and 300 ° C exhibited intermediate values . On day 100, soils heated to 200 ° C exhibited the greatest EOC for soils that with uncharred wood or no addition, while unheated soils exhibited greatest EOC for soils with 550 ° C char. Although there were temperature - based differences in EOC on days 200 a nd 390, these differences did not exhibit a clear pattern. 207 Soils with uncharred wood tended to have greater EOC than the soils with char or no addition throughout the incubation . Averaged across all soil heating × time combinations, soil that received un charred wood exhibited 8.6% - 18.2% more EOC than the other addition treatments. In soils that were heated to 200 ° C, EOC was lowest in soils that received 550 ° C char at all sampling dates, averaging 23.9% - 31.3% less EOC than the other char treatments. On day 30, unheated soil generally exhibited lower MBC than heated soils (Fig. 5. 3). Averaged across all char treatments, MBC was 39.1% - 67.6% lower in unheated soils at this time point compared to the other soil heat treatments. Soils heated to 300 ° C and 400 °C exhibited MBC that was higher than the other heat treatments by 1.7 - 2.1 fold on day 30. Soil heating impacted patterns in MBC over time. MBC decreased by 55.4% between days 30 and 390 in soils heated to 300 ° C and by 74.9% in soils heated to 400 ° C. In contrast, MBC in soils heated to 200 ° C only decreased by 16.9% and increased by 48.4% in unheated soils. The impacts of charcoal on MBC did not exhibit a clear pattern. Figure 5.1 Extractable organic carbon of pre - incubated soils exposed to fo ur different heating intensities and then autoclaved. The heights of the bars are treatment means (n=3) and error bars are 95% confidence intervals. Letters above bars represent pairwise significant differences among treatments. 208 Table 5.1 ANOVA tables for extractable organic carbon (EOC) and microbial biomass carbon (MBC) for soils destructively sampled four times over the 390 day incubation. Explanatory Variable F - value P - value EOC Intercept 527.45 <0.001 Time 0.16 0.686 Soil Heat Treatment 52.03 <0.001 Char Treatment 5.16 0.002 Time × Soil Heat 1.81 0.144 Time × Char 6.37 <0.001 Soil Heat × Char 4.08 <0.001 Time × Soil Heat × Char 4.59 <0.001 MBC Intercept 33.01 <0.001 Time 2.83 0.038 Soil Heat Treatment 30.02 <0.001 Char Treatment 0.07 0.978 Time × Soil Heat 7.72 <0.001 Time × Char 2.24 0.019 Soil Heat × Char 23.48 <0.001 Time × Soil Heat × Char 6.51 <0.001 209 Figure 5.2 Extractable organic carbon on four destructive sampling days of soils exposed to different heating intensities and receiving different char additions. The top row of figures (a - d) displays char treatments grouped into levels of soil heating, and the bottom row of figures (e - h ) disp lays soil heating treatments grouped into levels of char treatments. Points are treatment means (n=9) and error bars are 95% confidence intervals. 210 Figure 5.3 Microbial biomass carbon on four destructive sampling days of soils exposed to di fferent heating intensities and receiving different char additions. The top row of figures (a - d) displays char treatments grouped into levels of soil heating, and the bottom row of figures (e - h ) displays soil heating treatments grouped into levels of char treatments. Points are treatment means (n=9) and error bars are 95% confidence intervals. 211 5.4.3 Carbon m ineralization Cumulative C mineralization (mg CO 2 - C kg - 1 soil C) was not influenced by main effects of soil heating or char treatments, but all interactions involving time were significant (Table 5. 2). By the end of the incubation, soil heating impacted cumulative C mineralized only for the uncharred wood or 30 0 ° C char treatments (Fig. 5. 4). For soils with uncharred wood, the unheated and 200 ° C treatments resulted in 34.8% - 44.6% more C mineralized than the other soil heating treatments. For soils with 300 ° C char, the 400 ° C treatment led to 27.6% more C miner alized than the unheated treatments. Char treatments impacted cumulative C mineralization at all soil heating intensities. In unheated and soils heated to 200 ° C, soils with uncharred wood mineralized 1.5 - 2.1 times more C than the other char treatments. In soils heated to 300 ° C, uncharred wood resulted in ~ 33% more total C mineralization than either char type; and in soils heated to 400 ° C, uncharred wood led to 25.8% more C mineralization than 550 ° C char. For all soil heating temperatures, soils with ch ar exhibited total C mineralization that was less than or equal to soils that received no addition. All main and interactive effects impacted C mineralization rate (mg CO 2 - C kg - 1 soil C d - 1 ) (Table 5. 2). The effect of soil heating on C mineralization chang ed over time. Early in the incubation, mineralization rate was positively associated with soil heating intensity, with the 300 ° C and 400 ° C treatments consistently exhibiting the highest mineralization rates, regardless of char treatment. The opposite pat tern was present late in the incubation, with the 300 ° C and 400 ° C heating treatments typically exhibiting lower mineralization rates compared to the unheated and 200 ° C treatments. The effect of char treatments on mineralization rates depended on levels of soil heating and varied with time. Early in the incubation, soils with no additions exhibited the highest mineralization rates for unheated and 200 ° C treatments. Within the 400 ° C heat 212 treatment, soils with no additions or uncharred wood exhibited high er mineralization rates than soils with char. Late in the incubation, uncharred wood treatments exhibited the highest mineralization rates in soils that were unheated or heated to 200 ° C or 300 ° C. Soils that received char did not differ in mineralization rates from those that received no additions. Table 5.2 ANOVA tables for carbon mineralization rate and cumulative carbon mineralization over the 390 day incubation. Explanatory Variable F - value P - value C Mineralization Rate (mg CO 2 - C kg - 1 soil C d - 1 ) Intercept 2307.01 <0.001 Time 63.55 <0.001 log (Time) 1774.87 <0.001 Soil Heat Treatment 8.99 <0.001 Char Treatment 12.61 <0.001 Time × Soil Heat 11.80 <0.001 Time × Char 17.33 <0.001 Soil Heat × Char 6.66 <0.001 Time × Soil Heat × Char 20.43 <0.001 Cumulative C Mineralization (mg CO 2 - C kg - 1 soil C) Intercept 20.04 <0.001 Time 69.66 <0.001 Soil Heat Treatment 0.36 0.78 Char Treatment 0.73 0.54 Time × Soil Heat 11.70 <0.001 Time × Char 10.32 <0.001 Soil Heat × Char 0.36 0.95 Time × Soil Heat × Char 5.96 <0.001 213 Figure 5.4 Carbon mineralization over 390 days of soils exposed to different heating intensities and receiving different char additions. The top row of figures (a - d) displays char treatments grouped into levels of soil heating, and the bottom row of figures (e - h ) displays soil heating treatments groupe d into levels of char treatments. Points are treatment means of cumulative C mineralization (n=9) and error bars are 95% confidence intervals. The slope of the lines between points represent mineralization rates. Lett ers represent pairwise significant diff erences in cumulative carbon mineralization at the end of the incubation. 214 5.4.4 Carbon pools : single pool model C m was impacted by char treatments and by a soil heat × char interaction, and k m was impacted by all main and interactive effects (Table 5. 3). Uncharred wood increased C m by ~4.5 - 5.5 times in unheated soils and by ~4.1 - 4.3 times in soils heated to 200 ° C (Fig. 5. 5; Table S 5. 1). This pattern was consistent whether C m pool size was cons idered on a soil mass fraction basis or relative to total C. C m was otherwise unaffected by soil heat and char treatments. Similarly, k m decreased in response to uncharred wood additions in unheated soils and soils heated to 200 ° C but was otherwise unchan ged. Table 5.3 ANOVA tables for single and double carbon pool models. Values are from models fit to cumulative respiration per g soil data. ANOVAs for models fits to cumulative respiration per g soil C are not presented but are similar to the results prov ided here. Explanatory Variable F - value P - value Single Pool Model C m Intercept 59.40 <0.001 Soil Heat Treatment 0.25 0.859 Char Treatment 66.69 <0.001 Soil Heat × Char 30.60 <0.001 k m Intercept 85.02 <0.001 Soil Heat Treatment 10.17 <0.001 Char Treatment 12.93 <0.001 Soil Heat × Char 5.51 <0.001 Double Pool Model C a Intercept 146.01 <0.001 Soil Heat Treatment 6.85 <0.001 Char Treatment 23.26 <0.001 Soil Heat × Char 13.03 <0.001 k a Intercept 456.37 <0.001 Soil Heat Treatment 19.78 <0.001 Char Treatment 32.78 <0.001 k s Intercept 74.38 <0.001 Soil Heat Treatment 2.72 0.043 Char Treatment 90.73 <0.001 Soil Heat × Char 21.18 <0.001 215 Figure 5.5 Bar charts illustrating the parameters of the single carbon pool model. The size of the potential mineralizable C pool (C m ) is presented in both absolute size (a) and relative to total soil C (b). The kinetic rate constant (k m ) is presented as d - 1 (c) . The heights of the bars represe nt the marginal means of the parameter estimates, and the error bars represent the 95% confidence intervals of the estimates. Letters above bars indicate pairwise significant differences between char treatments within soil heating levels. Pairwise comparis ons for soil heating levels within char treatments are provided in table S1. 5.4.5 Carbon pools: d ouble p ool m odel Pool s izes on s oil m ass f raction b asis C a was affected by both soil heating and char treatments (Table 5. 3). Within soils that received no addit ions, C a was 22.6% - 38.9% larger in soils heated to 400 ° C than the other heat treatments (Fig. 5. 6; Table S 5. 2). For soils with uncharred wood, C a was 8.9 - 13.1 times larger in the 300 ° C and 400 ° C treatments than the other heating treatments. For char soils with char, C a was 1.5 - 2.1 times larger in the 300 ° C and 400 ° C treatments than the other heating treatments. C a generally did not differ between soils that received char additions from those that received no 216 additions. An exception occurred in soils heated to 200 ° C, where C a was 29.2% smaller for the 300 ° C char treatment compared to the no addition treatment. Among all char treatments, C s was smallest in soils heated to 400 °C and largest for soils heated to 300 °C. C s showed a consistent pattern o f increasing with char treatments as follows: no addition < uncharred < 300 °C < 550 °C. Pool s izes r elative to t otal c arbon Soils heated to 400 °C had C a pools that were 1.5 - 6.2 times larger than the other heat treatments (Fig. 5. 6; Table S 5. 2). For so ils with wood or char additions, soils heated to 300 °C had 1.5 - 4.9 fold larger C a pools than the unheated and 200 °C treatments. For soils with uncharred wood, unheated soils and soils heated to 200 °C had the largest C s pools. For soils that received cha r, the 400 °C heat treatment resulted in the smallest C s pools, and the 300 °C treatment had smaller C s pools than the unheated and 200 °C treatments. Within unheated and 200 °C heated soils, uncharred wood led to the largest C s pools. Char additions incre ased C s pool size compared to no additions in soils heated to 200 °C. Kinetic r ate c onstants Soils heated to 400 ° C exhibited larger k a values than the other soil heating treatments. Compared to unheated soils, the larger k a values in these soils represen t a decrease in C a mean residence time (MRT) of 9.8 - 18.6 days. Soils with uncharred wood exhibited the largest k a values at all soil heating treatments, representing a decrease of 16.0 - 24.9 days in C a MRT. In addition to significant main effects, k s was affected by a soil heat × char interaction. For soils with uncharred wood, k s was largest in the unheated soils and 200 ° C treatments, representing a decrease of 25.8 - 34.2 years in C s MRT. For soils with 300 ° C char, k s was higher in soils heated to 2 00 ° C than in soils heated to 400 ° C, representing a 27.1 year difference in MRT. Within 217 soils that received 550 ° C char, k s was higher for the unheated treatments than for the 200 ° C and 400 ° C treatments, representing a difference in C s MRT of 17.9 - 36.6 years. Soils with uncharred wood had the largest k s values in soils that were unheated or heated to 200 ° C, representing a 22.9 - 37.7 year decrease in C s MRT compared to the two higher soil heating treatments. 218 Figure 5.6 Bar charts illustrating the par ameters of the double carbon pool model. The sizes of the active (C a ) and non - active (C s ) pools are presented in both absolute size (a and c) and relative to total soil C (b and d) . The active C pool kinetic rate constant (k a ) is presented as d - 1 (e) , and slow C pool kinetic rate constant (k s ) is presented as y - 1 (f) . The heights of the bars represent the marginal means of the parameter estimates, and the error bars represent the 95% confidence intervals of the estimates. Letters above bars indicate pai rwise significant differences between char treatments within soil heating levels. Pairwise comparisons for soil heating levels within char treatments are provided in table S2. 219 5.5 DISCUSSION 5.5.1 High intensity soil heating decreases soil carbon persistence over t he short term I found support for my hypothesis that C a pool size and kinetic rates would increase with soil heating intensity. The two highest intensity soil heating treatments increased k m , k a and C a pool size, and the highest intensity decreased C s poo l size, all of which indicate lower soil C persistence over the short term. Thus, in addition to causing greater C emissions during the combustion event itself, high intensity fires could increase soil C emissions via impacts on the structure and stability of soil C pools. My results support previous, field - based research that found increased C a pools and k a values in burned vs unburned mineral soils collected immediately (1 day) after wildfires in P. sylvestris dominated forests in northwestern Spain (Fernández et al. 1997; Fernández et al. 1999) . My previous research found that C a size is lower in burned areas than unburned areas three years post - fire (Adkins et al. 2019) , suggesting the direct effects of soil heating on C a are transient. My ex periment suggests that the immediate changes to C a size and persistence are likely due to the direct effects of heat flux on soil characteristics. For example, soil heating can directly affect soluble C content (Certini 2005; Knicker 2007) , which could in turn influence C a pool size and kinetics, bec ause soluble C is a fast - cycling C source that is often positively correlated with soil respiration rates (Neff and Asner 2001; Wang et al. 2003) . However, contrary to my hypothesis, increases in C a and k a in heated soils do not appear to be related to EOC: soil s heated to 400 °C had the largest C a and k a values, but the lowest EOC. This suggests that nutrient availability may be a stronger driver of active C cycling in heated soils. For example, N volatilization increases with temperature from 200 to 500 °C, at which point >50% of N is lost (Knicker 2007; Bodí et al. 2014) . Lower N availability could increase the need for N - mining from organic matter (Moorhead and 220 Sinsabaugh 2006) , a C inefficient process that could lead t o higher C mineralization rates. In addition to changes to C and nutrient availability, soil heating could influence C pools by destabilizing soil aggregates and degrading clay (Certini 2 005; Mataix - Solera et al. 2011) . Soil aggregation and clay associations are important mechanisms of C stabilization (Jastrow et al. 2 007) , so disruption of these mechanisms could explain the higher C a and k a . Additionally, the higher mineralization rates and k a values in soils heated to 300 °C and 400 °C could be due to indirect effects of soil heating on MBC via changes to soil abi otic properties. Microbes drive heterotrophic respiration and MBC may account for a portion of C a (Wang et al. 2003; Lawrence et al. 2009) , and, early in the incubat ion, MBC was greatest in soils subjected to high heating intensity. Soil heating can directly impact MBC by inducing microbial mortality (Choromanska and DeLuca 2002; Certini 2005; Bárcenas - Moren o and Bååth 2009) ; however, I sterilized and reinoculated the microcosms to ensure similar initial microbial biomass among soil heating treatments. Thus, differences in MBC were likely indirectly affected by soil heating. Furthermore, although soil heat ing can directly impact microbial community structure via differential survival of fungi vs bacteria and by selecting for heat - resistant bacterial taxa (Dooley and Treseder 2012; Prendergast - Miller et al. 2017) , the lack of difference in C pool structure or kinetics between my two inoculation treatments indicates that initial differences in microbial community structure did not drive C cycling. These results furt her support the interpretation that top - down impacts of heating on soil abiotic characteristics determine C pool structure and kinetics. 5.5.2 Char increases the size and persistence of the non - active carbon pool I found support for my hypothesis that C s would increase with PyOM additions, but not for my hypothesis that k s would be inversely related to the temperature of PyOM formation. My 221 findings that char increased the size of the C s pool on a soil mass fraction basis suggest that char generated during fires can ameliorate the negative influence of soil heating on C persistence. This is further supported by my finding that high intensity soil heating decreased k s only in soils with charred or uncharred wood. Previous research has shown that a high inten sity wildfire had minimal immediate impacts on k s (Fernández et al. 1997) . However, research performed months to years following wildfires in mixed - coni fer (Adkins et al. 2019b) and P. sylvestris forests (Fernández et al. 1999) indicate that the absolute size of C s was higher and k s lower at these intermediate time points in post - fire recovery in burned vs unburned areas. Considered with my present results, this suggests that increased C s size and persistence is an indirect effect of fire that may emerge later during ecosystem recove ry as char formed from aboveground biomass becomes incorporated into soil (Abney et al. 2017) . Charcoal production increases with fire intensity and severity (Czimczik et al. 2003; Miesel et al. 2015; Sawyer et al. 2018) , and I found that C s size increased with charring temperature. Theref ore, high intensity fire could result in a large and persistent C pool that offsets the negative direct effects of high intensity soil heating on C persistence. Previous research has shown that char can contribute to the C a pool (Abney et al. 2019) , because a portion of char - associated C is easily decomposed (Kuzyakov et al. 2014; Bird et al. 2015) , and char can induce positive priming of native soil C (Maestrini et al. 2015) . However, I observed no statistically significant effects of char on C a or k a , again suggesting that char primarily led to higher soil C persistence. This effect may be partially attributable to the relativel y high charring temperatures I employed, as previous research has indicated that forest soils amended with char generated at 200 °C exhibited larger C a pools than soils amended with higher temperature chars (Abney et al. 2019) . Additionally, the chemical properties of char, and 222 the influence of char on soil respiration varies with source material (Michelotti and Miesel 2015; Hatton et al. 2016) . In contrast, adding uncharred wood often increased both k a and k s , indicating l ess persistent C a and C s pools. Although wildfires cause immediate declines in woody debris at the soil surface (Miesel et al. 2018) , uncharred wood could reaccumulate over time via fallen branches and stems of fire - killed trees. For example, by 4 - 5 years after a wildfire in P. ponderosa dominated forest in O regon, USA, woody debris accounted for a larger proportion of aboveground C stocks in burned areas compared to unburned areas (Meig s et al. 2009) . 5.6 CONCLUSIONS High intensity fires likely result in greater ecosystem C losses due to combustion. My results suggest that this effect may be exacerbated over the short term by increases in the size and kinetic rate of the active C pool. H owever, over the long term, these losses could be offset by char incorporation into soil, which increases the size of the non - active C pool. The non - active C pool is an order of magnitude larger than the active C pool and exhibits MRT of decades, so char i ncorporation could substantially increase the strength of the soil C sink during forest recovery. These results suggest that accurate estimates of the impacts of disrupted fire regimes on forest C stocks and persistence should account for the influence of fire intensity on the soil C cycle. Low intensity soil heating had minimal impacts on soil C pools and MBC, suggesting that soils are resistant and/or resilient to low intensity fires. Therefore, fuel reduction treatments that decrease soil heating intensi ty could effectively promote resilience of soil functions. Additionally, low intensity prescribed fires could be an effective management tool for reducing forest fuel loads and restoring forest structure without adversely affecting soil C storage. Previous research has shown that the reductions in fire severity resulting from fuel removal treatments enhance permanence of aboveground forest C stocks (North and Hurteau 2011) ; my results suggest that 223 fuel removal that decreases soil he ating intensity could also contribute to maintaining the persistence of soil C. 224 APPENDIX 225 SUPPLEMENTAL FIGURES Figure S5.1 Fungal - to - bacterial activity ratio for the two soil fractions used to reinoculate sterilized soil microcosms. Fungal - to - bacterial activity ratio was determined using selectively inhibited substrate - induced respiration. 226 SUPPLEMENTAL TABLES Table S5.1 Pa rameters estimated from single pool carbon models. Pool size parameters expressed in mg kg - 1 are derived from models fit to cumulative respiration per g soil, and pool size parameters expressed in percent total C are from models fit to respiration per g so il C. Ranges are 95% confidence intervals. Lower - case letters indicate significant differences between soil heating treatments within levels of char treatments. Data is the tabular form of the data shown in figure 5. 5. Single Pool Models C m (mg kg - 1 ) C m (%) k m (d - 1 ) No Addition Unheated Soil 1630.37 - 2733.38 a 4.31 - 6.85 a 0.0046 - 0.0071 b 200 °C Soil 1782.96 - 2952.22 a 4.69 - 7.38 a 0.0046 - 0.0073 b 300 °C Soil 1525.99 - 2626.69 a 3.74 - 6.28 a 0.0065 - 0.0091 b 400 °C Soil 1488.64 - 2587.81 a 4.30 - 6.83 a 0.0090 - 0.0115 a Uncharred Wood Unheated Soil 9727.08 - 12046.08 a 21.82 - 27.17 a - 0.0001 - 0.0023 b 200 °C Soil 8966.37 - 10928.08 a 18.07 - 22.15 a 0.0001 - 0.0025 b 300 °C Soil 2036.83 - 3136.63 b 4.32 - 6.86 b 0.0075 - 0.0100 a 400 °C Soil 1848.34 - 2947.23 b 4.49 - 7.03 b 0.0098 - 0.0123 a 300 ° C Char Unheated Soil 1408.86 - 2513.14 a 3.03 - 5.58 a 0.0046 - 0.0071 b 200 °C Soil 1768.12 - 2878.11 a 3.79 - 6.35 a 0.0036 - 0.0061 b 300 °C Soil 1524.94 - 2624.76 a 3.05 - 5.59 a 0.0070 - 0.0095 a 400 °C Soil 1563.46 - 2662.53 a 3.61 - 6.15 a 0.0092 - 0.0117 a 550 ° C Char Unheated Soil 1843.00 - 2955.72 a 3.89 - 6.46 a 0.0029 - 0.0053 c 200 °C Soil 1867.29 - 2983.54 a 3.92 - 6.50 a 0.0040 - 0.0065 c 300 °C Soil 1617.30 - 2717.82 a 3.18 - 5.72 a 0.0064 - 0.0089 b 400 °C Soil 1506.79 - 2605.96 a 3.40 - 5.94 a 0.0098 - 0.0123 a 227 Table S5.2 Parameters estimated from double pool carbon models. Pool size parameters expressed in mg kg - 1 are derived from models fit to cumulative respiration per g soil, and pool size parameters expressed in percent total C are from models fit to respiration per g soil C. Ranges are 95% confidence intervals. Lower - case letters indicate significant differenc es between soil heating treatments within levels of char treatments. Data is the tabular form of the data shown in figure 5. 6. Double Pool Models C a (mg kg - 1 ) C a (% Total C) C s (mg kg - 1 ) C s (% Total C) k a (d - 1 ) k s (y - 1 ) No Addition Unheated Soil 807.19 - 1116.94 b 2.06 - 2.78 b 37983.06 - 38292.81 b 97.22 - 97.94 a 0.011 - 0.014 b 0.020 - 0.032 a 200 °C Soil 875.44 - 1201.66 b 2.25 - 3.02 b 38028.34 - 38354.56 b 96.98 - 97.75 a 0.012 - 0.014 b 0.023 - 0.036 a 300 °C Soil 968.37 - 1266.18 b 2.34 - 3.04 b 40203.82 - 40501.63 a 96.96 - 97.75 a 0.012 - 0.014 b 0.015 - 0.027 a 400 °C Soil 1261.72 - 1552.01 a 3.54 - 4.22 a 35077.99 - 35368.28 c 95.78 - 98.46 a 0.015 - 0.016 a 0.012 - 0.024 a Uncharred Wood Unheated Soil 19.20 - 293.05 b 0.05 - 0.68 c 43740.95 - 44014.80 b 99.32 - 99.95 a 0.015 - 0.018 b 0.074 - 0.085 a 200 °C Soil - 14.72 - 258.74 b - 0.02 - 0.61 c 43845.23 - 44118.69 b 99.39 - 100.02 a 0.015 - 0.018 b 0.072 - 0.084 a 300 °C Soil 1246.56 - 1533.40 a 2.67 - 3.33 b 44767.48 - 45054.32 a 96.67 - 97.33 b 0.016 - 0.018 b 0.020 - 0.032 b 400 °C Soil 1458.23 - 1741.11 a 3.56 - 4.22 a 39865.78 - 40148.66 c 95.78 - 96.44 c 0.019 - 0.020 a 0.016 - 0.027 b 300 ° C Char Unheated Soil 700.43 - 1003.07 b 1.50 - 2.19 c 44561.97 - 44864.61 c 97.81 - 98.50 a 0.011 - 0.014 b 0.014 - 0.026 ab 200 °C Soil 588.18 - 883.22 b 1.26 - 1.94 c 44893.58 - 45188.62 b 98.06 - 98.74 a 0.012 - 0.014 b 0.020 - 0.032 a 300 °C Soil 1103.95 - 1404.03 a 2.26 - 2.94 b 46610.47 - 46910.55 a 97.06 - 97.74 b 0.013 - 0.014 b 0.011 - 0.023 ab 400 °C Soil 1349.35 - 1639.80 a 3.11 - 3.79 a 41677.56 - 41968.01 d 96.21 - 96.89 c 0.015 - 0.017 a 0.010 - 0.021 b 550 ° C Char Unheated Soil 563.34 - 865.99 b 1.18 - 1.86 c 45476.68 - 45779.33 b 98.14 - 98.82 a 0.010 - 0.013 b 0.020 - 0.032 a 200 °C Soil 617.36 - 920.20 b 1.30 - 1.99 c 45459.69 - 45762.53 b 98.01 - 98.70 a 0.011 - 0.013 b 0.019 - 0.030 a 300 °C Soil 1103.51 - 1408.03 a 2.22 - 2.92 b 47268.30 - 47572.82 a 97.08 - 97.78 b 0.012 - 0.013 b 0.011 - 0.023 ab 400 °C Soil 1364.11 - 1665.19 a 3.08 - 3.78 a 42351.51 - 42652.59 c 96.22 - 96.91 c 0.014 - 0.015 a 0.007 - 0.019 b 228 REFERENCES 229 REFERENCES Abney RB, Jin L, Berhe AA (2019) Soil properties and combustion temperature: Controls on the decomposition rate of pyrogenic organic matter. CATENA 182:104127. doi: 10.1016/j.catena.2019.104127 Abney RB, Sanderman J, Johnson D, et al (2017) Post - wildfire erosion in mountainous terrain leads to rapid and major redistribution of soil organic carbon. Front Earth Sci 5:1 16. doi: 10.3389/fe art.2017.00099 Adkins J, Sanderman J, Miesel J (2019a) Soil carbon pools and fluxes vary across a burn severity gradient three years after wildfire in Sierra Nevada mixed - conifer forest. Geoderma. doi: 10.1016/j.geoderma.2018.07.009 Adkins J, Sanderman J, Miesel J (2019b) Soil carbon pools and fluxes vary across a burn severity gradient three years after wildfire in Sierra Nevada mixed - conifer forest. Geoderma 333:10 22. doi: 10.1016/j.geoderma.2018.07.009 Anderson JPE, Domsch KH (1973) Quantification of ba cterial and fungal contributions to soil respiration. Arch Mikrobiol 93:113 127. doi: 10.1007/BF00424942 Bailey VL, Smith JL, Bolton H (2003) Novel antibiotics as inhibitors for the selective respiratory inhibition method of measuring fungal:bacterial rati os in soil. Biol Fertil Soils 38:154 160. doi: 10.1007/s00374 - 003 - 0620 - 7 Bárcenas - Moreno G, Bååth E (2009) Bacterial and fungal growth in soil heated at different temperatures to simulate a range of fire intensities. Soil Biol Biochem 41:2517 2526. doi: 10 .1016/j.soilbio.2009.09.010 Bird MI, Wynn JG, Saiz G, et al (2015) The pyrogenic carbon cycle. Annu Rev Earth Planet Sci 43:273 298. doi: 10.1146/annurev - earth - 060614 - 105038 Bodí MB, Martin D a., Balfour VN, et al (2014) Wildland fire ash: Production, comp osition and eco - hydro - geomorphic effects. Earth - Science Rev 130:103 127. doi: 10.1016/j.earscirev.2013.12.007 Bolan NS, Baskaran S, Thiagarajan S (1996) An evaluation of the methods of measurement of dissolved organic carbon in soils, manures, sludges, and stream water. Commun Soil Sci Plant Anal 27:2723 2737. doi: 10.1080/00103629609369735 Busse MD, Shestak CJ, Hubbert KR (2013) Soil heating during burning of forest slash piles and 230 wood piles. Int J Wildl Fire 22:786 796. doi: 10.1071/WF12179 Cai Y, Peng C , Qiu S, et al (2011) Dichromate digestion - spectrophotometric procedure for determination of soil microbial biomass carbon in association with fumigation - extraction. Commun Soil Sci Plant Anal 42:2824 2834. doi: 10.1080/00103624.2011.623027 Certini G (2005 ) Effects of fire on properties of forest soils: a review. Oecologia 143:1 10. doi: 10.1007/s00442 - 004 - 1788 - 8 Choromanska U, DeLuca TH (2002) Microbial activity and nitrogen mineralization in forest mineral soils following heating: Evaluation of post - fire effects. Soil Biol Biochem 34:263 271. doi: 10.1016/S0038 - 0717(01)00180 - 8 Czimczik CI, Preston CM, Schmidt MWI, Schulze E - D (2003) How surface fire in Siberian scots pine forests affects soil organic carbon in the forest floor: Stocks, molecular structure, and conversion to black carbon (charcoal). Global Biogeochem Cycles. doi: 10.1029/2002GB001956 Dooley SR, Treseder KK (2012) The effect of fire on microbial biomass: A meta - analysis of field studies. Biogeochemistry 109:49 61. doi: 10.1007/s10533 - 011 - 9633 - 8 Dunn PH, Barro SC, Poth M (1985) Soil moisture affects survival of microorganisms in heated chaparral soil. Soil Biol Biochem 17:143 148. Earles JM, North MP, Hurteau MD (2014) Wildfire and drought dynamics destabilize carbon stores of fire - suppressed f orests. Ecol Appl 24:732 740. doi: 10.1890/13 - 1860.1 Fernández I, Cabaneiro A, Carballas T (1997) Organic matter changes immediately after a wildfire in an atlantic forest soil and comparison with laboratory soil heating. Soil Biol Biochem 29:1 11. doi: 10 .1016/S0038 - 0717(96)00289 - 1 Fernández I, Cabaneiro A, Carballas T (1999) Carbon mineralization dynamics in soils after wildfires in two Galician forests. Soil Biol Biochem 31:1853 1865. doi: 10.1016/S0038 - 0717(99)00116 - 9 González - Pérez JA, González - Vila FJ , Almendros G, Knicker H (2004) The effect of fire on soil organic matter -- a review. Environ Int 30:855 870. doi: 10.1016/j.envint.2004.02.003 Greenfield LG, Gregorich EG, van Kessel C, et al (2013) Acid hydrolysis to define a biologically - resistant pool i s compromised by carbon loss and transformation. Soil Biol Biochem 64:122 126. doi: 10.1016/j.soilbio.2013.04.009 Guerrero C, Mataix - Solera J, Gómez I, et al (2005) Microbial recolonization and chemical 231 changes in a soil heated at different temperatures. I nt J Wildl Fire 14:385 400. doi: 10.1071/WF05039 Hatton PJ, Chatterjee S, Filley TR, et al (2016) Tree taxa and pyrolysis temperature interact to control the efficacy of pyrogenic organic matter formation. Biogeochemistry 130:103 116. doi: 10.1007/s10533 - 0 16 - 0245 - 1 Hobbie EA, Hofmockel KS, van Diepen LTA, et al (2014) Fungal carbon sources in a pine forest: evidence from a 13C - labeled global change experiment. Fungal Ecol 10:91 100. doi: 10.1016/j.funeco.2013.11.001 Jastrow JD, Amonette JE, Bailey VL (2007) Mechanisms controlling soil carbon turnover and their potential application for enhancing carbon sequestration. Clim Change 80:5 23. doi: 10.1007/s10584 - 006 - 9178 - 3 Jones MW, Santín C, van der Werf GR, Doerr SH (2019) Global fire emissions buffered b y the production of pyrogenic carbon. Nat Geosci 12:742 747. doi: 10.1038/s41561 - 019 - 0403 - x Knicker H (2007) How does fire affect the nature and stability of soil organic nitrogen and carbon? A review. Biogeochemistry 85:91 118. doi: 10.1007/s10533 - 007 - 910 4 - 4 Kuzyakov Y (2011) How to link soil C pools with CO2 fluxes? Biogeosciences 8:1523 1537. doi: 10.5194/bg - 8 - 1523 - 2011 Kuzyakov Y, Bogomolova I, Glaser B (2014) Biochar stability in soil: Decomposition during eight years and transformation as assessed by compound - specific 14 C analysis. Soil Biol Biochem 70:229 236. doi: 10.1016/j.soilbio.2013.12.021 Kuzyakov Y, Subbotina I, Chen H, et al (2009) Black carbon decomposition and incorporation into soil microbial biomass estimated by 14C labeling. Soil Biol Biochem 41:210 219. doi: 10.1016/j.soilbio.2008.10.016 Lawrence CR, Neff JC, Schimel JP (2009) Does adding microbial mechanisms of decomposition improve soil organic matter models? A comparison of four models using data from a pulsed rewetting experiment. Soil Biol Biochem 41:1923 1934. doi: 10.1016/j.soilbio.2009.06.016 Lenth R (2020) emmeans: Estimated Marginal Means, aka Least - Squares Means. R package version 1.4.4. Luo Y, Weng E (2011) Dynamic disequilibrium of the terrestrial carbon cycle under global change. Trends Ecol Evol 26:96 104. doi: 10.1016/j.tree.2010.11.003 Maestrini B, Nannipieri P, Abiven S (2015) A meta - analysis on pyrogenic organic matter 232 induced priming effect. GCB Bioenergy 7:577 590. doi: 10.1111/gcbb.12194 Masiello CA (2004) New dire ctions in black carbon organic geochemistry. Mar Chem 92:201 213. doi: 10.1016/j.marchem.2004.06.043 Massman WJ, Frank JM, Mooney SJ (2010) Advancing investigation and physical modeling of first - order fire effects on soils. Fire Ecol 6:36 54. doi: 10.4996/ fireecology.0601036 Mataix - Solera J, Cerdà A, Arcenegui V, et al (2011) Fire effects on soil aggregation: A review. Earth - Science Rev 109:44 60. doi: 10.1016/j.earscirev.2011.08.002 Meigs GW, Donato DC, Campbell JL, et al (2009) Forest fire impacts on carb on uptake, storage, and emission: The role of burn severity in the eastern Cascades, Oregon. Ecosystems 12:1246 1267. doi: 10.1007/s10021 - 009 - 9285 - x Michelotti L, Miesel J (2015) Source Material and Concentration of Wildfire - Produced Pyrogenic Carbon Influ ence Post - Fire Soil Nutrient Dynamics. Forests 6:1325 1342. doi: 10.3390/f6041325 Miesel J, Reiner A, Ewell C, et al (2018) Quantifying Changes in Total and Pyrogenic Carbon Stocks Across Fire Severity Gradients Using Active Wildfire Incidents. Front Earth Sci 6:1 21. doi: 10.3389/feart.2018.00041 Miesel JR, Hockaday WC, Kolka RK, Townsend PA (2015) Soil organic matter composition and quality across fire severity gradients in coniferous and deciduous forests of the southern boreal region. J Geophys Res Biog eosciences 120:1124 1141. doi: 10.1002/2015JG002959 Moorhead DL, Sinsabaugh RL (2006) A theoretical model of litter decay and microbial interaction. Ecol Monogr 76:151 174. Neary D., DeBano L. (2005) Wildland fire in ecosystems effects of fire on soil and water. Neff JC, Asner GP (2001) Dissolved organic carbon in terrestrial ecosystems: Synthesis and a model. Ecosystems 4:29 48. doi: 10.1007/s100210000058 Nguyen BT, Lehmann J, Hockaday WC, et al (2010) Temperature sensitivity of black carbon decomposition and oxidation. Environ Sci Technol 44:3324 3331. doi: 10.1021/es903016y North MP, Hurteau MD (2011) High - severity wildfire effects on carbon stocks and emissions in fuels treated and untreated forest. For Ecol Manage 261:1115 1120. doi: 10.1016/j.foreco.2 010.12.039 233 Science 333:988 993. doi: 10.1126/science.1201609 Pinheiro J, Bates D, Debroy S, Sarkar D (2019) nlme: Linear and nonlinear mixed effects models. Pinheiro JC, Bates DM (2000) Mixed - effects models in S and S - Plus. Springer - Verlag, New York Prendergast - Miller MT, de Menezes AB, Macdonald LM, et al (2017) Wildfire impact: Natural experiment reveals differential short - term changes in soil microbial communities. Soil Biol Biochem 109:1 13. doi: 10.1016/j.soilbio.2017.01.027 R Core Team (2019) R: A language and environment for statistical computing. Rousk J, Demoling LA, Bååth E (2009) Contrasting short - Term antibiotic effects on respiration and bacterial growth compromises the validity of the selective respiratory inhibition technique to distinguish fungi and bacteria. Microb Ecol 58:75 85. doi: 10.1007/s00248 - 008 - 9444 - 1 Santos F, Torn MS, Bird JA (2012) Biological degradation of pyrogenic o rganic matter in temperate forest soils. Soil Biol Biochem 51:115 124. doi: 10.1016/j.soilbio.2012.04.005 Sawyer R, Bradstock R, Bedward M, Morrison RJ (2018) Fire intensity drives post - fire temporal pattern of soil carbon accumulation in Australian fire - p rone forests. Sci Total Environ 610 611:1113 1124. doi: 10.1016/j.scitotenv.2017.08.165 Skinner CN, Taylor AH (2006) Southern Cascades Bioregion. 195 224. Soil Survey Staff Official soil series descriptions. In: Nat. Resour. Conserv. Serv. United States De p. Agric. www.nrcs.usda.gov. Taylor AH, Vandervlugt AM, Maxwell RS, et al (2014) Changes in forest structure, fuels and potential fire behaviour since 1873 in the Lake Tahoe Basin, USA. Appl Veg Sci 17:17 31. doi: 10.1111/avsc.12049 Wagg C, Bender SF, Wid mer F, van der Heijden MGA (2014) Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc Natl Acad Sci 111:5266 5270. doi: 10.1073/pnas.1320054111 Wagg C, Schlaeppi K, Banerjee S, et al (2019) Fungal - bacterial diversi ty and microbiome complexity predict ecosystem functioning. Nat Commun 10:1 10. doi: 10.1038/s41467 - 019 - 12798 - y 234 Wang WJ, Dalal RC, Moody PW, Smith CJ (2003) Relationships of soil respiration to microbial biomass, substrate availability and clay content. So il Biol Biochem 35:273 284. doi: 10.1016/S0038 - 0717(02)00274 - 2 Witt C, Gaunt JL, Galicia CC, et al (2000) A rapid chloroform - fumigation extraction method for measuring soil microbial biomass carbon and nitrogen in flooded rice soils. Biol Fertil Soils 30:5 10 519. doi: 10.1007/s003740050030 Zimmermann M, Bird MI, Wurster C, et al (2012) Rapid degradation of pyrogenic carbon. Glob Chang Biol 18:3306 3316. doi: 10.1111/j.1365 - 2486.2012.02796.x 235 CHAPTER 6 : MANAGEMENT IMPLICATIONS: POST - FIRE FOREST MANAGEMENT MAY IMPROVE RECOVERY OF SOIL CARBON STORAGE Altered forest structure in forests of the western United States has led to increased forest ecosystem carbon (C) stocks due to greater tree density and dead fuel accumulation (North et al. 2009; Earles et al. 2014; Hurteau et al. 2014) . Forest densification has allowed these forests to act as strong C sinks and offset a substantial portion CO 2 - C emissions (Goodale et al. 2002; Pan et al . 2011) . However, compared to forests that are structurally similar to historical (i.e. pre fire - suppression) forests, these C stocks are inherently less stable because dense forests are more susceptible to C losses due to drought and insect - based distu rbances (Earles et al. 2014; Hurteau et al. 2014; Stephens et al. 2020) and could transition to C sources under future climate scenarios (Loudermilk et al. 2013; Liang et al. 2017c) . Furthermore, these dense forests are more susceptible to stand - replacing fires that lead to immediate losses of forest C stocks and are slow to recover the lost stocks due to low post - fire net ecosystem productivity (Kashian et al. 2006; Meigs et al. 2009) . High - - s in which centuries of accumulated C are rapidly lost, and the large magnitude of change to the ecosystem prevents the forest from fully recovering the lost C stocks (Adams 2013) . Approximately half of ecosystem C in temperate forests is stored in soils (Pan et al. 2011) , so understanding the impacts of fire and fire - management on soil C storage is important for managing forests for C sequestration and climate change mitigation (Birdsey et al. 2006) . Here, I discuss the implications of for fo rest and fire management. My research shows soil C storage is lower in burned compared to unburned forest stands, likely due to forest floor mass loss, and this effect increases in magnitude with burn severity 236 (Fig. 1.3). High intensity soil heating also induces short - term increases in soil C mineralization and decreases C stored in microbial biomass (Table 4.1 and Fig. 4.3). Together, these results indicate that increased burn severity has negative impacts on soil C storage. However, despite the short - ter m effects of soil heating on mineral soil C, total mineral soil C storage does not vary among severity levels by three years post - fire (Fig 1.3), and, in fact, may be more stable (Table 1.4). This indicates that recovery of forest soil C storage is primari ly dependent on re - accumulation of forest floor. The importance of forest floor for dictating post - fire soil C storage suggests that managing forests for vegetation recovery will have associated benefits on soil. I found that live tree coverage is substan tially lower in high severity areas (Fig. 2.1d), and previous research has indicated that a negative relationship between severity and tree coverage is common in California mixed - conifer forests (Miller et al. 2016) . Considered with my finding that forest floor mass is positively associated with live tree basal area (Fig. 2.4), this further supports the idea that vegetation man agement is necessary for achieving recovery of soil C storage in high burn severity areas . This has implications for both pre - and post - fire forest management. From a pre - fire perspective, it suggests forest management practices designed to limit the incid ence of high - severity fires will have a positive impact on soil C storage. Severity - reduction treatments (e.g. prescribed fire, stand thinning, fuel removal) have been shown to have numerous positive outcomes, including promoting biodiversity, increasing w ater availability, and stabilizing aboveground C stocks (Stephens et al. 2020) . Severity - reduction treatments stabilize aboveground C stocks by limiting tree mortality wh en wildfires burn treated areas, decreasing combustion emissions and allowing for post - fire photosynthetic C gains (Liang et al. 2018) . My research suggests that the stabilizi ng effects of severity - reduction treatments may also apply to 237 soil C: lower fire severity leads to more C retained in forest floor and faster re - accumulation due to greater tree survival. Additionally, treatments that decrease soil heating (e.g. coarse woo dy debris removal) could have marginal benefits on soil C storage by limiting losses to soil C via microbial biomass loss and post - fire increases in C mineralization. Fire exclusion has led to forest floor accumulation in some forest stands, and prescribed fires in these areas could result in high intensity soil heating due to forest floor combustion. In these cases, prescribed fires may have a temporary negative impact on soil C storage, but, over the long term, could have a positive impact if prescribed f ires lead to lower wildfire severity. From a post - fire perspective, my results suggest that management decisions designed to increase tree regeneration in areas of high burn severity could increase the rate of soil C recovery. Management strategies that promote C storage and are compatible with other goals such as forest restoration or timber production have been identified as integral for forest C management in the 21 st century (Birdsey et al. 2006) . Post - fire seedling planting may represent a management application with multi - faceted benefits by improving forest regeneration following wildfires (Ouzts et al. 2015) , with corollary bene fits on the recovery of soil C storage. Post - fire tree planting is controversial because successful establishment from these practices can be low, and dense plantations can promote high severity fires (Thompson et al. 2007; Ouzts et al. 2015) . However, natural tree regeneration can be low or absent in areas of high burn severity due to poor site conditions and greater distance to seed sources (Crotteau et al. 2013; Feddema et al. 2013; Lopez Ortiz et al. 2019) - vegetation type (Barton 2002; Savage and Mast 2005; Roccaforte et al. 2012; Tepley et al. 2017) . Due to negative impacts of high severity fir e on natural forest recovery, the need for post - fire restoration is becoming increasingly recognized, especially during a critical 3 - 5 year post - fire 238 window when regeneration is most likely to be successful (Tepley et al. 2017; Stewart et al. 2020) . My research indicates that certain site conditions in areas of high burn severity may have a positive influence on artificial regeneration efforts. Shrub coverage was positively correlated with severity three years post - fire (Fig. 2.1f), and inorganic nitrogen concentrations were greater in areas of high burn severity at 1 - 3 y ears post fire (Fig. 1.5 and Table 3.1) Shrubs can positively (Keyes et al. 2009) , and greater nitrogen availability ameliorates nutri ent limitation (Tabo ada et al. 2017) . However, previous research has shown that elevated inorganic nitrogen in burned stands generally does not persist longer than approximately five years post - fire (Wan et al. 2001) . This suggests that artificial regeneration efforts are more likely to be successful in this timeframe, supporting argumen ts that the 3 - 5 year post - fire window is key for successful regeneration (Tepley et al. 2017; Stewart et al. 2020) . By focusing restoration efforts in high severity areas with elevated nutrient concentrations and shrub coverage, forest managers could maximize the success of artificial regeneration efforts and increase th e rate of soil C recovery. Changes to soil microbial communities could have consequences for post - fire forest management. Using phospholipid fatty acid (PLFA) analysis, I found that fungal biomass was negatively related to severity three years post - fire ( Fig. 2.5a). The change in fungal biomass could reflect losses to ectomycorrhizal fungi, which are known to be sensitive to fire (Dahlbe rg et al. 2001; Holden et al. 2013) . Loss of ectomycorrhizal fungi could negatively impact both natural regeneration and artificial restoration efforts because these fungi form symbiotic associations with conifer roots, improving nitrogen and phosphorus acquisition in exchange for carbohydrates (Heijden et al. 2015) . This suggests that artificial regeneration efforts that use seedling transplants, which already have developed mycorrhizal networks, may be more 239 effective than direct seeding, which depend on an existing soil bank of ectomycorrhizal spores. Indeed, in situ mycorrhizal colonization decreases following fires (Dove and Hart 2017) . M oreover, t ransplant of seed l ings that have been inoculated with ectomycorrhiza has been shown to improve seedling performance during reforestation following a variety of disturbance types, including for post - fire restoration in Pinus pinaster stands (Policelli et al. 2020) . Differences in soil bacterial communities in areas of high burn severity also highlight the importance of a ctively managing areas of high burn severity for forest regeneration and C recovery. The abundance of copiotrophic bact eria, which have higher nutrient requirements than oligotrophic bacteria (Fierer et al. 2007; Ho et al. 2017) , w as positively correlated with severity 1 - 3 years post - fire (Figs. 2.6f and 3.3f). Plants and microbes compete for nutrients (Kaye and Hart 1997; Kuzyakov and Xu 2013) , and the higher nutrient requirements of copiotrophic bacteria could result in increased competition between plants and microbes. Increased competition could decrease plant performance following fire, hindering natural forest regeneration a nd the recovery of both aboveground and soil C stocks. Additionally, copiotrophs exhibit faster decomposition rates than oligotrophs (Orwin et al. 2018) , potentially leading to continued soil C losses in the years post - fire. This further highlights the importance of inoculating seedling transplants with ectomycorrhizal fungi, which improve the competitive ability of plants for nutrients. Overall, my results in dicate that high burn severity has detrimental impacts on soil C storage and that microbial communities are altered in ways that could hinder natural forest recovery. Fortunately, these negative effects can be overcome by vegetation management practices th at are already common in forest ecosystems , and the development of new soil - specific management tools does not appear necessary. However, achieving recovery of soil C 240 storage in areas of high burn severity likely requires active forest management rather th an relying on natural regeneration. 241 REFERENCES 242 REFERENCES Adams M (2013) Mega - fires, tipping points and ecosystem services: Managing forests and woodlands in an uncertain future. For Ecol Manage 294:250 - 261. doi: 10.1016/j.foreco.2012.11.039 Barton AM (2002) Intense wildfire in southeastern Arizona: Transformation of a Madrean oak - pine forest to oak woodland. For Ecol Manage 165:205 212. doi: 10.1016/S0378 - 1127(01)00618 - 1 Birdsey R, Pregitzer K, Lucier A (2006) Forest carbon management in the United States. J Environ Qual 35:1461. doi: 10.2134/jeq2005.0162 Crotteau JS, Morgan Varner J, Ritchie MW (2013) Post - fire regeneration across a fire severity gradient in the southern Cascades. For Ecol Manage 287:103 112. doi: 10.1016/j.foreco.2012.09.022 Dahlberg A, Schimmel J, Taylor AFS, Johannesson H (2001) Post - fire legacy of ectomycorrhizal fungal communities in the Swedish boreal forest in relation to fire severity and logging intensity. Biol Conserv 100:151 16 1. doi: 10.1016/S0006 - 3207(00)00230 - 5 Dove NC, Hart SC (2017) Fire reduces fungal species richness and in situ mycorrhizal colonization: A meta - analysis. Fire Ecol 13:37 65. doi: 10.4996/fireecology.130237746 Earles JM, North MP, Hurteau MD (2014) Wildfire and drought dynamics destabilize carbon stores of fire - suppressed forests. Ecol Appl 24:732 740. doi: 10.1890/13 - 1860.1 Feddema JJ, Mast JN, Savage M (2013) Modeling high - severity fire, drought and climate change impacts on ponderosa pine regeneration. Ec ol Modell 253:56 69. doi: 10.1016/j.ecolmodel.2012.12.029 Fierer N, Bradford MA, Jackson RB (2007) Toward an ecological classification of soil bacteria. Ecology 88:1354 1364. doi: 10.1890/05 - 1839 Goodale CL, Apps MJ, Birdsey R a, et al (2002) Forest carbon sinks in the Northern Hemisphere. Ecol Appl 12:891 899. doi: Doi 10.2307/3060997 Heijden MG a Van Der, Martin FM, Selosse M - AA, et al (2015) Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol 205:1406 1423. doi: 10.1111/n ph.13288 243 Ho A, Di Lonardo DP, Bodelier PLE (2017) Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol 93:1 14. doi: 10.1093/femsec/fix006 Holden SR, Gutierrez A, Treseder KK (2013) Changes in Soil Fungal Communities, E xtracellular Enzyme Activities, and Litter Decomposition Across a Fire Chronosequence in Alaskan Boreal Forests. Ecosystems 16:34 46. doi: 10.1007/s10021 - 012 - 9594 - 3 Hurteau MD, Bradford JB, Fulé PZ, et al (2014) Climate change, fire management, and ecologi cal services in the southwestern US. For Ecol Manage 327:280 289. doi: 10.1016/j.foreco.2013.08.007 Kashian DM, Romme WH, Tinker DB, et al (2006) Carbon storage on landscapes with stand - replacing fires. Bioscience 56:598 606. doi: 10.1641/0006 - 3568(2006)56 [598:CSOLWS]2.0.CO;2 Kaye JP, Hart SC (1997) Competition for nitrogen between plants and soil microorganisms. Trends Ecol Evol 12:139 142. Keyes CR, Maguire DA, Tappeiner JC (2009) Recruitment of ponderosa pine seedlings in the Cascade Range. For Ecol Mana ge 257:495 501. doi: 10.1016/j.foreco.2008.09.024 Kuzyakov Y, Xu X (2013) Competition between roots and microorganisms for nitrogen: Mechanisms and ecological relevance. New Phytol 198:656 669. doi: 10.1111/nph.12235 Liang S, Hurteau MD, Westerling AL (201 7) Potential decline in carbon carrying capacity under projected climate - wildfire interactions in the Sierra Nevada. Sci Rep 7:1 7. doi: 10.1038/s41598 - 017 - 02686 - 0 Liang S, Hurteau MD, Westerling AL (2018) Large - scale restoration increases carbon stability under projected climate and wildfire regimes. Front Ecol Environ. doi: 10.1002/fee.1791 Lopez Ortiz MJ, Marcey T, Lucash MS, et al (2019) Post - fire management affects species composition but not Douglas - fir regeneration in the Klamath Mountains. For Ecol Manage 432:1030 1040. doi: 10.1016/j.foreco.2018.10.030 Loudermilk EL, Scheller RM, Weisberg PJ, et al (2013) Carbon dynamics in the future forest: The importance of long - term successional legacy and climate - fire interactions. Glob Chang Biol 19:3502 3515. doi: 10.1111/gcb.12310 Meigs GW, Donato DC, Campbell JL, et al (2009) Forest fire impacts on carbon uptake, storage, and emission: The role of burn severity in the eastern Cascades, Oregon. Ecosystems 12:1246 1267. doi: 10.1007/s10021 - 009 - 9285 - x 244 Miller JD , Safford HD, Welch KR (2016) Using one year post - fire fire severity assessments to estimate longer - term effects of fire in conifer forests of northern and eastern California, USA. For Ecol Manage 382:168 183. doi: 10.1016/j.foreco.2016.10.017 North M, Hur teau M, Innes J (2009) Fire supression and fuels treatment effects on mixed - conifer carbon stocks and emissions. Ecol Appl 19:1385 1396. doi: 10.1890/08 - 1173.1 Orwin KH, Dickie IA, Holdaway R, Wood JR (2018) A comparison of the ability of PLFA and 16S rRNA gene metabarcoding to resolve soil community change and predict ecosystem functions. Soil Biol Biochem 117:27 35. doi: 10.1016/j.soilbio.2017.10.036 Ouzts J, Kolb T, Huffman D, Sánchez Meador A (2015) Post - fire ponderosa pine regeneration with and wit hout planting in Arizona and New Mexico. For Ecol Manage 354:281 290. doi: 10.1016/j.foreco.2015.06.001 Science 333:988 993. doi: 10.1126/science.1201609 Policelli N, Horton TR, Hudon AT, et al (2020) Back to Roots: The Role of Ectomycorrhizal Fungi in Boreal and Temperate Forest Restoration. Front For Glob Chang. doi: 10.3389/ffgc.2020.00097 Roccaforte JP, Fulé PZ, Chancellor WW, La ughlin DC (2012) Woody debris and tree regeneration dynamics following severe wildfires in Arizona ponderosa pine forests. Can J For Res 42:593 604. doi: 10.1139/x2012 - 010 Savage M, Mast JN (2005) How resilient are southwestern ponderosa pine forests after crown fires? Can J For Res 35:967 977. doi: 10.1139/x05 - 028 Stephens SL, Westerling ALR, Hurteau MD, et al (2020) Fire and climate change: conserving seasonally dry forests is still possible. Front Ecol Environ 18:354 360. doi: 10.1002/fee.2218 Stewart JA E, van Mantgem PJ, Young DJN, et al (2020) Effects of postfire climate and seed availability on postfire conifer regeneration. Ecol Appl. doi: 10.1002/eap.2280 Taboada A, Tárrega R, Marcos E, et al (2017) Fire recurrence and emergency post - fire management influence seedling recruitment and growth by altering plant interactions in fire - prone ecosystems. For Ecol Manage 402:63 75. doi: 10.1016/j.foreco.2017.07.029 Tepley AJ, Thompson JR, Epstein HE, Anderson - Teixeira KJ (2017) Vulnerability to forest loss thr ough altered postfire recovery dynamics in a warming climate in the Klamath Mountains. Glob Chang Biol 23:4117 4132. doi: 10.1111/gcb.13704 245 Thompson JR, Spies TA, Ganio LM (2007) Reburn severity in managed and unmanaged vegetation in a large wildfire. Proc Natl Acad Sci U S A 104:10743 10748. doi: 10.1073/pnas.0700229104 Wan S, Hui D, Luo Y (2001) Fire effects on nitrogen pools and dynamics in terrestrial ecosystems: A meta - analysis. Ecol Appl 11:1349 1365.