STREAM FISH RESPONSES TO CHANGING ENVIRONMENTAL CONDITIONS: USING A FUNCTIONAL BIOGEOGRAPHY APPROACH ACROSS BROAD SPATIAL EXTENTS By Kyle James Brumm A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Fisheries and Wildlife – Doctor of Philosophy 2025 ABSTRACT Stream habitats and the fishes they support are threatened by environmental factors operating within catchments, and efforts to contextualize species responses to those factors are essential to design and implement effective management actions. Investigating the morphological, physiological, phenological, or behavioral characteristics of species can improve mechanistic understanding of how stream fishes respond to environmental factors. Insights gained from such traits-based investigations can be used to predict changes in the structure and function of stream fish assemblages across space and through time. Despite the utility of traits-based investigations, understanding how environmental factors influence functional traits of stream fishes across broad spatial extents (e.g., continental) remains incomplete. Few traits-based investigations have been conducted at such broad extents because availability of standardized datasets representing distributions of stream fishes is often limited, as are datasets characterizing environmental factors consistently over large regions. Therefore, the goal of my dissertation is to use a functional biogeography approach to describe, explain, and predict functional responses of stream fishes to changing environmental conditions at a continental extent. In my first chapter, I use RLQ and fourth-corner analyses to describe relationships between 17 environmental variables and 16 traits for 597 stream fish species within the conterminous United States. I evaluate the generalizability of trait-environment relationships across the study region and show that while the strength and multivariate structure of trait-environment relationships vary, some relationships, including positive associations between migratory species and forested land cover, were significant in multiple ecoregions. In my second chapter, I investigate consequences of biodiversity change on the stability of stream fish metacommunities throughout the conterminous United States. To do so, I develop a structural equation model to integrate predictions of alpha, beta, and gamma diversity, functional redundancy, and compositional and functional variability at local to regional spatial scales. I show that multiple forms of biodiversity contribute to the compositional and functional variability of stream fish metacommunities and generate insights into how local and regional management efforts may help to achieve sustainable outcomes for freshwater ecosystems. In my third chapter, I develop a decision-support framework to promote the use of ecological thresholds (which represent the intensity of a human landscape stressor that leads to a severe decline in fishes) in decision-making applications. This framework integrates threshold status indices, summaries of protected area distributions, and knowledge of multiple stressor configurations within stream catchments to inform conservation and restoration efforts for nearly 1.73 million catchments located throughout the conterminous United States and Europe. The findings highlight the pervasive influences of agricultural land use on stream habitat and indicate that widespread degradation may result from increased urban development within catchments that are poorly protected. Collectively, my dissertation contributes to an improved understanding of how the functional characteristics of stream fishes vary across space and through time and demonstrates the value of using traits-based approaches to characterize vulnerability of stream fishes to human landscape stressors at broad spatial extents. The insights generated in my dissertation can be applied to support management decision-making processes to help limit further degradation of stream habitat and contribute to addressing the global freshwater biodiversity crisis. Copyright by KYLE JAMES BRUMM 2025 To Grandpa, Grandma, Mom, Dad, Tyler, Emma, and Lila v ACKNOWLEDGEMENTS I would like to thank the United States Geological Survey Aquatic GAP Project for providing financial support for the research described in this dissertation. In addition, I would like to thank the Department of Fisheries and Wildlife at Michigan State University for the various grants and fellowships they awarded that allowed me to share this work with a global audience. I am also thankful to the Graduate School at Michigan State University for awarding me with the Theodore Roosevelt Conservation and Environmental Leadership Fellowship that supported my participation in the 2023 Great Lakes Leadership Academy – Emerging Leader Program. This work, and the personal and professional growth that resulted from it, would simply not have been possible without the generosity of each of these organizations. To my mentor and major advisor, Dr. Dana Infante, I wouldn’t be who I am today without your guidance and encouragement. I can’t thank you enough for accepting me into your lab, pushing me out of my comfort zone, and trusting in me throughout the process. I look forward to continuing our work together and building additional collaborations long into the future! I would like to thank my dissertation committee members, Dr. Mariah Meek, Dr. Patricia Soranno, and Dr. Elise Zipkin, for sharing their advice and wisdom with me. Additionally, I would like to thank the Janice Lee Fenske Excellence in Fisheries Management Committee and my mentors Gary Whelan and Dr. Russ Mason, for fostering opportunities for me to engage with the Michigan Department of Natural Resources and investigate barriers to climate adaptation among state fisheries and wildlife management agencies. Thank you to all members of the Aquatic Landscape Ecology lab for your support and inspiration. I owe a special thanks to Arthur Cooper, Jared Ross, and Dr. Hao Yu for their technical and intellectual contributions in support of my dissertation research. I wish the best of vi luck to all our current postdoctoral, doctoral, and master’s students, and to our many research technicians. I know you will all continue to do great things! To my family, especially Grandma, Grandpa, Mom, Dad, and Tyler, thank you for your unwavering love and support that has allowed me to chase my dreams. To my Malinois Lila, thanks for always being there to go on adventures with, and for the lazy couch weekends when I needed them the most. Last but not least, to my wife Emma, I cannot imagine sharing these accomplishments with anyone else. To say I am excited to live this life with you by my side would be an understatement, and I am looking forward to seeing what our future has in store! vii TABLE OF CONTENTS INTRODUCTION .......................................................................................................................... 1 REFERENCES ............................................................................................................................ 6 CHAPTER 1: FUNCTIONAL BIOGEOGRAPHY OF FLUVIAL FISHES ACROSS THE CONTERMINOUS U.S.A.: ASSESSING THE GENERALISABILITY OF TRAIT- ENVIRONMENT RELATIONSHIPS OVER LARGE REGIONS ............................................... 8 CHAPTER 2: COMPOSITIONAL AND FUNCTIONAL STABILITY OF STREAM FISH METACOMMUNITIES AT A CONTINENTAL EXTENT ......................................................... 9 REFERENCES .......................................................................................................................... 28 APPENDIX 2.A: TABLES ....................................................................................................... 35 APPENDIX 2.B: FIGURES ...................................................................................................... 37 APPENDIX 2.C: SUPPLEMENTARY FIGURES .................................................................. 41 CHAPTER 3: APPLYING ECOLOGICAL THRESHOLDS TO INFORM CONSERVATION AND RESTORATION EFFORTS FOR STREAM FISHES ....................................................... 42 REFERENCES .......................................................................................................................... 60 APPENDIX 3.A: TABLES ....................................................................................................... 67 APPENDIX 3.B: FIGURES ...................................................................................................... 69 APPENDIX 3.C: SUPPLEMENTARY FIGURES .................................................................. 76 MANAGEMENT IMPLICATIONS ............................................................................................ 77 viii INTRODUCTION Despite covering less than 1% of the Earth’s surface, freshwater ecosystems support roughly 10% of all species including 51% of known fishes (Tickner et al., 2020; Hughes, 2021). Freshwater ecosystems also support important functions (e.g., nutrient cycling, water purification) and services (e.g., cultural, economic, and social benefits) that enhance human well- being on a global scale (Maltby and Acreman, 2011; see Vári et al., 2022). Nevertheless, the intensity and pervasiveness of human-nature interactions has proven to be unsustainable, particularly in the freshwater realm. Freshwater ecosystems are highly threatened, and recent estimates suggest that 25% of all freshwater fauna are at risk of extinction due to a combination of factors including abstraction of water for irrigation and food production; fragmentation of rivers and streams for hydropower production and navigation; introduction of non-native and invasive species for aquaculture and sport fishing; pollution from mining, farming, and wastewater treatment activities; and land use and climate change (Dudgeon, 2019; Reid et al., 2019; Sayer et al., 2025). Such factors not only affect species through degradation of local habitat, but they can also influence important community assembly processes occurring at the regional scale. To successfully address the global freshwater biodiversity crisis, we must continue to monitor ecological statuses and trends across multiple spatial scales, understand specific drivers of biodiversity loss, and implement conservation plans and policies to mitigate effects of existing stressors and limit further degradation of our vital freshwater ecosystems. Understanding the extent to which abiotic environmental factors influence the structure and function of species assemblages is critical to design and implement effective conservation and restoration actions. Large-scale assessments, in particular, are valuable for understanding how responses of fishes to environmental factors vary across broad (i.e., continental) spatial 1 extents. Insights gained from such assessments can be used to support regional monitoring programs or facilitate the exchange of best management practices between focal systems (Counihan et al., 2018; Brumm et al., 2024). Despite their utility, large-scale assessments are relatively uncommon because they require data from multiple sources to be collated into comprehensive and standardized datasets that are consistent with respect to the methodology used to sample the data and their scope (Heffernan et al., 2014; Soranno and Schimel, 2014). Development of standardized datasets based on information collected at fine spatial resolutions, such as interconfluence stream reaches, is key to generating insights that can be scaled up to inform management at multiple spatial scales. Efforts to conduct large-scale assessments must account for several challenges to enhance the generality of their conclusions. One challenge in doing this includes the fact that natural influences such as climate, geology, and physiography contribute to the heterogeneity of large regions and define the ecological potential of ecosystems (Herlihy et al., 2008; Wang et al., 2023). Studies that span large, multi-regional landscapes must account for the complex, interrelated effects of natural influences and must frame ecological expectations within those landscapes accordingly. A second challenge is that understanding of ecological patterns and processes may not always be transferable across large regions, as primary threats to freshwater fishes are likely to vary in type and intensity across heterogenous landscapes (Esselman et al., 2013; Su et al., 2021). The potential effects of unique stressor gradients and multiple stressor interactions must be considered. Lastly, a third challenge in large-scale assessments is that comparative analyses conducted across broad spatial extents may be limited by taxonomic differences among regional species pools (see Brumm et al., 2024). While traits-based approaches can be used to help overcome such taxonomic limitations (Martini et al., 2021), our 2 understanding of how natural influences and anthropogenic stressors affect the functional characteristics of freshwater fish assemblages remains incomplete. Importantly, all three of these challenges may be addressed by working at the intersection of biogeography and macroecology (Brown and Maurer, 1989; see McGill, 2019), aquatic landscape ecology (Wiens, 2002; Allan, 2004), and functional community ecology (McGill et al., 2006) – that is, by using a functional biogeography approach to evaluate the causes and consequences of biodiversity change in stream fish assemblages at broad spatial extents. Functional biogeography is the study of how forms and functional roles of species vary across space and time. It is an interdisciplinary framework capable of fostering novel theoretical and applied solutions to conservation problems (Violle et al., 2014). For example, the functional biogeography framework has been used to explain spatial variation in the functional trait distributions of estuarine and marine fish assemblages, with implications for global conservation efforts in a changing climate (Frainer et al., 2017; Henriques et al., 2017). In addition, Toussaint et al. (2016) assessed global functional diversity patterns of freshwater fishes and found that the vulnerability of functional diversity to species loss was highest in the Nearctic and Palearctic realms, indicating that the functional diversity of fish assemblages in these regions is largely supported by multiple species at risk of extinction. While such global assessments are important for understanding broad patterns in trait distributions, they are typically conducted at relatively coarse spatial resolutions. Because global assessments often lack acuity, complementary efforts are needed to improve understanding of the functional biogeography of stream fishes to better inform management at local to regional spatial scales. To address this need, the goal of my dissertation is to use a functional biogeography approach to describe, explain, and predict functional responses of stream fishes to changing 3 environmental conditions throughout the conterminous United States and Europe. In my first chapter, I assess the generalizability of trait-environment relationships across the conterminous United States by evaluating responses of 16 traits (e.g., feeding habits, reproductive strategies) to 17 environmental variables (Brumm et al., 2023). Analyses were conducted within nine large ecoregions using a dataset that represented 597 stream fish species collected from over 45,000 interconfluence stream reaches sampled between 1990 and 2019. While the strength and multivariate structure of these relationships varied, some trait-environment relationships were significant in multiple ecoregions, including inverse relationships between human land use and migratory species (Brumm et al., 2023). In my second chapter, I investigate diversity-stability scaling relationships in 76 stream fish metacommunities located throughout the conterminous United States. I develop a structural equation model to integrate multiple predictions for how measures of alpha, beta, and gamma diversity; functional redundancy; and compositional and functional variability relate to one another at local to regional spatial scales. Model results indicate that alpha and beta diversity contribute to ecosystem stability in slightly different ways. My findings highlight the importance of managing both local and spatial processes supporting community assembly of stream fishes, and also offer insights to explain geographic patterns in the functional variability of stream fish metacommunities. In my third chapter, I develop a decision-support framework to promote use of ecological thresholds (i.e., representing severe declines in trait-based metrics with a comparatively small increase in the intensity of a human landscape stressor) in decision-making applications. This framework integrates threshold status indices, summaries of protected area coverages, and knowledge of multiple stressor configurations to inform conservation and restoration efforts for nearly 1.73 million catchments located throughout the conterminous United States and Europe. This work highlights the 4 vulnerability of stream fishes to human landscape stressors in two continents and provides a flexible framework with which to support management decision-making processes that can help to achieve global policy targets and address the freshwater biodiversity crisis. Collectively, my dissertation contributes to a more complete understanding of how the functional characteristics of stream fishes vary across space and through time, generating insights that can be used to inform conservation and restoration efforts to safeguard freshwater ecosystems and the many services they provide. 5 REFERENCES Allan, J.D., 2004. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annual Review of Ecology, Evolution, and Systematics. 35, 257-284. Brown, J.H., Maurer, B.A., 1989. Macroecology: the division of food and space among species on continents. Science. 243, 1145-1150. Brumm, K.J., Xiong, F., Chen, Y., Yu, H., Wang, L., Infante, D.M., 2024. Relationships between environmental variables and fish functional groups in impounded reaches of the Upper Mississippi and Yangtze Rivers. Water Biology and Security. 3, 100291. Counihan, T.D., Waite, I.R., Casper, A.F., Ward, D.L., Sauer, J.S., Irwin, E.R., Chapman, C.G., Ickes, B.S., Paukert, C.P., … Bayer, J.M., 2018. Can data from disparate long-term fish monitoring programs be used to increase our understanding of regional and continental trends in large river assemblages? PLOS One. 13, e0191472. Dudgeon, D., 2019. Multiple threats imperil freshwater biodiversity in the Anthropocene. Current Biology. 29, R960-R966. Esselman, P.C., Infante, D.M., Wang, L., Cooper, A.R., Wieferich, D., Tsang, Y.P., Thornbrugh, D.J., Taylor, W.W., 2013. Regional fish community indicators of landscape disturbance to catchments of the conterminous United States. Ecological Indicators. 26, 163-173. Frainer, A., Primicerio, R., Kortsch, S., Aune, M., Dolgov, A.V., Fossheim, M., Aschan, M.M., 2017. Climate-driven changes in functional biogeography of Arctic marine fish communities. PNAS. 114, 12202-12207. Heffernan, J.B., Soranno, P.A., Angilletta Jr., M.J., Buckley, L.B., Gruner, D.S., Keitt, T.H., Kellner, J.R., Kominoski, J.S., Rocha, A.V., … Weathers, K.C., 2014. Macrosystems ecology: understanding ecological patterns and processes at continental scales. Frontiers in Ecology and the Environment. 12, 5-14. Henriques, S., Guilhaumon, F., Villéger, S., Amoroso, S., França, S., Pasquaud, S., Cabral, H.N., Vasconcelos, R.P., 2017. Biogeographical region and environmental conditions drive functional traits of estuarine fish assemblages worldwide. Fish and Fisheries. 18, 752-771. Herlihy, A.T., Paulsen, S.G., Sickle, J.V., Stoddard, J.L., Hawkins, C.P., Yuan, L.L., 2008. Striving for consistency in a national assessment: the challenges of applying a reference- condition approach at a continental scale. Journal of the North American Benthological Society. 27, 860-877. Hughes, K., 2021. The World’s forgotten fishes. WWF International, Gland, Switzerland. Maltby, E., Acreman, M.C., 2011. Ecosystem services of wetlands: pathfinder for a new paradigm. Hydrological Sciences Journal 56: 1341-1359. 6 Martini, S., Larras, F., Boyé, A., Faure, E., Aberle, N., Archambault, P., Bacouillard, L., Beisner, B.E., Bittner, L., … Ayata, S.D., 2021. Functional traits-based approaches as a common framework for aquatic ecologists. Limnology and Oceanography. 66, 965-994. McGill, B.J., Enquist, B.J., Weiher, E., Westoby, M., 2006. Rebuilding community ecology from functional traits. Trends in Ecology and Evolution. 21, 178-185. McGill, B.J., 2019. The what, how and why of doing macroecology. Global Ecology and Biogeography. 28, 6-17. Reid, A.J., Carlson, A.K., Creed, I.F., Eliason, E.J., Gell, P.A., Johnson, P.T.J., Kidd, K.A., MacCormack, T.J., Olden, J.D., … Cooke, S.J., 2019. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews. 94, 849-873. Sayer, C.A., Fernando, E., Jimenez, R.R., Macfarlane, N.B.W., Rapacciuolo, G., Böhm, M., Brooks, T.M., Contreras-MacBeath, T., Cox, N.A., … Darwall, W.R.T., 2025. One-quarter of freshwater fauna threatened with extinction. Nature. 638, 138-145. Soranno, P.A., Schimel, D.S., 2014. Macrosystems ecology: big data, big ecology. Frontiers in Ecology and the Environment. 12, 3-3. Su, G., Logez, M., Xu, J., Tao, S., Villéger, S., Brosse, S., 2021. Human impacts on global freshwater fish biodiversity. Science 371: 835-838. Tickner, D., Opperman, J.J., Abell, R., Acreman, M., Arthington, A.H., Bunn, S.E., Cooke, S.J., Dalton, J., Darwall, W., … Young, L., 2020. Bending the curve of global freshwater biodiversity loss: an emergency recovery plan. BioScience. 70, 330-342. Toussaint, A., Charpin, N., Brosse, S., Villéger, S., 2016. Global functional diversity of freshwater fish is concentrated in the Neotropics while functional vulnerability is widespread. Scientific Reports. 6, 22125. Vári, A., Podschun, S.A., Erős, T., Hein, T., Pataki, B., Iojă, I.C., Adamescu, C.M., Gerhardt, A., Gruber, T., … Báldi, A., 2022. Freshwater systems and ecosystem services: challenges and chances for cross-fertilization of disciplines. Ambio. 51, 135-151. Violle, C., Reich, P.B., Pacala, S.W., Enquist, B.J., Kattge, J., 2014. The emergence and promise of functional biogeography. PNAS 111: 13690-13696. Wang, L., Cao, Y., Infante, D.M., 2023. Disentangling effects of natural factors and human disturbances on aquatic systems – needs and approaches. Water. 15, 1387. Wiens, J.A., 2002. Riverine landscapes: taking landscape ecology into the water. Freshwater Biology. 47, 501-515. 7 CHAPTER 1: FUNCTIONAL BIOGEOGRAPHY OF FLUVIAL FISHES ACROSS THE CONTERMINOUS U.S.A.: ASSESSING THE GENERALISABILITY OF TRAIT- ENVIRONMENT RELATIONSHIPS OVER LARGE REGIONS Summary While previous studies have shown that responses of stream fishes to environmental conditions vary across broad spatial extents, biogeographic patterns of trait-environment relationships in freshwater fish assemblages remain poorly understood. To address this need, I conducted RLQ and fourth-corner analyses to describe relationships between functional traits of stream fishes and environmental variables in nine large ecoregions comprising the conterminous United States. I show that while the strength and multivariate structure of trait-environment relationships varied, some relationships were significant in multiple ecoregions. For example, abundances of carnivorous species tended to increase in association with forested land cover and elevation, whereas algivorous species generally increased in association with air temperature and human landscape stressors including agriculture, pasture, and urban land use. These findings highlight how complex interrelationships between natural influences, anthropogenic stressors, and functional traits can contribute to spatial variation in the structure of stream fish assemblages at a continental extent. For a full text of this published work, go to: Brumm, K.J., Infante, D.M., Cooper, A.R., 2023. Functional biogeography of fluvial fishes across the conterminous U.S.A.: Assessing the generalizability of trait-environment relationships over large regions. Freshwater Biology. 68, 790-805. https://doi.org/10.1111/fwb.14064 8 CHAPTER 2: COMPOSITIONAL AND FUNCTIONAL STABILITY OF STREAM FISH METACOMMUNITIES AT A CONTINENTAL EXTENT Abstract Scale-dependent community assembly processes, including environmental filtering and dispersal limitation, contribute to spatial variation in patterns of biodiversity that occur at local to regional spatial scales. While changes in environmental conditions, including abiotic factors that contribute to degradation of stream habitat, are responsible for causing changes in patterns of biodiversity, they may also have indirect consequences for ecosystem stability. Ecosystems that support multiple taxa occupying diverse ecological niches are expected to be more stable in terms of their structure and function compared to systems that are relatively less diverse. However, understanding of diversity-stability scaling relationships remains limited, particularly in freshwater ecosystems at large spatial extents. In this study, we investigated relationships between measures of biodiversity and ecosystem stability (characterized by variability, the inverse of stability) in stream fish metacommunities throughout the conterminous United States. Using a database containing records of fish communities sampled between 1990 and 2019, we developed a structural equation model to assess hypothesized relationships between measures of alpha, beta, and gamma diversity; functional redundancy; and compositional and functional variability at two spatial resolutions (i.e., interconfluence stream reaches nested within regional subbasins). In support of our expectations, we found that alpha diversity was positively associated with gamma diversity and inversely associated with compositional variability. Contributions of beta diversity to metacommunity stability were more complex, as indicated by significant positive relationships with both gamma diversity and compositional variability. Moreover, we show that metacommunities in the Western United States had a relatively high beta diversity, nonnative species richness, and functional variability compared to 9 metacommunities in other ecoregions, representing a potential destabilization pathway. Collectively, our findings illustrate how multiple forms of biodiversity contribute to the compositional and functional variability of stream fish metacommunities, thereby generating insights into how local conservation efforts may help to achieve sustainable outcomes for regional ecosystems. Introduction Freshwater biodiversity is declining globally, yet complex patterns often emerge when investigating trends in measures of biodiversity at finer spatial resolutions (Gonzalez et al., 2016; Vellend et al., 2017; Primack et al., 2018; Dudgeon, 2019; Reid et al., 2019). For example, compositional change among fish communities in the Muddy Boggy River, Oklahoma, USA varied over a 40-year time period, ranging from a state of persistence to complete turnover (Zbinden, 2020). Such complex patterns occur in part because scale-dependent processes – including effects of environmental filtering, dispersal limitation, and biotic interactions – jointly determine the structure and composition of biotic communities (Weiher et al., 2011). These scale-dependent processes not only have a profound influence on community assembly locally, but they can also complicate efforts to understand the consequences of biodiversity change (Jarzyna and Jetz, 2018). In particular, our understanding of how changes in local patterns of biodiversity scale up to influence the stability of ecological functions across broad spatial extents remains limited (Mori et al., 2018). As threats to freshwater ecosystems continue to intensify, multi-scale approaches that decompose the various drivers and potential consequences of biodiversity change are key to improving understanding of biodiversity-stability scaling relationships and ultimately to enhancing the effectiveness of conservation actions (Millenium Ecosystem Assessment, 2005). 10 Due to their hierarchical structure and dendritic connectivity, fluvial ecosystems present unique opportunities for testing such metacommunity concepts (Leibold et al., 2004; Altermatt, 2013; Tonkin et al., 2018). Stream habitats are arranged hierarchically; pools, riffles, and runs are nested within interconfluence reaches that collectively drain subbasins comprising entire watersheds (Frissell et al., 1986). By accounting for this spatial arrangement, metacommunity analyses in streams have effectively generated mechanistic insights into how ecological processes drive changes in community assembly across multiple spatial scales. For example, hydrologic alteration and habitat fragmentation at the watershed scale have been shown to amplify compositional differences among local stream reaches by limiting the dispersal of individual fishes between local communities (Griffiths, 2017; Fox and Magoulick, 2024). Similarly, flow intermittency and concomitant changes in connectivity have been shown to alter metacommunity structure in dryland riverscapes (Rogosch and Olden, 2019). Collectively, these investigations have contributed to a more complete understanding of how spatial processes (i.e., dispersal) influence patterns of biodiversity (e.g., beta diversity) at local to regional scales. However, metacommunity analyses in streams have rarely been used to draw inferences about the consequences of biodiversity change on ecosystem functioning. Since the 1950s, ecologists have debated the potential consequences of biodiversity change, including the strength and direction of diversity-stability relationships (DSRs; McCann, 2000). In recent decades, DSRs have been studied extensively in terrestrial ecosystems using a combination of theoretical models, field experiments, and empirical investigations (Tilman and Downing, 1994). While local (alpha) diversity is largely expected to stabilize aggregate ecosystem properties including measures of abundance, biomass, and composition (Wang and Loreau, 2016), effects of spatial (beta) diversity on ecosystem stability remain ambiguous (Mellin et al., 2014; van der Plas et al., 11 2023). For example, a recent analysis of DSRs across multiple organism groups and ecosystem types revealed that the contributions of beta diversity to metacommunity stability may be system-specific (Wisnoski et al. 2023). At a time when diversity-stability hypotheses are increasingly being used to inform ecosystem management and minimize risks associated with food and timber production (Loreau et al., 2021), improved understanding of the contributions of spatial beta diversity to the temporal dynamics of freshwater ecosystems is warranted (Socolar et al., 2016; Rolls et al., 2023). As an additional consideration, biodiversity is inherently multidimensional, and changes in patterns of taxonomic richness or composition may not necessarily translate into changes in patterns of functional or phylogenetic diversity (Biggs et al., 2020). Few studies have evaluated relationships between measures of compositional and functional stability, despite their potential to inform multidimensional biodiversity conservation efforts (Hillebrand and Matthiessen, 2009; Zhang et al., 2023). This is a particularly important research gap when it comes to understanding how redundancies among species mediate effects of compositional change on the functioning of fluvial ecosystems (Lamothe et al., 2018). Current understanding of how diversity-stability relationships (DSRs) operate across multiple spatial scales remains limited, particularly in fluvial ecosystems at large spatial extents (Czeglédi et al., 2022). Therefore, our goal is to investigate how biodiversity contributes to the functional stability of stream fish metacommunities across the conterminous United States. First, we develop a structural equation model to integrate multiple predictions for how measures of diversity, redundancy, and variability (the inverse of stability) relate to one another at local to regional spatial scales. Based on existing evidence, we expect alpha diversity to promote metacommunity stability and hypothesize that contributions of beta diversity will be more 12 complex. Second, we assess geographic patterns in the functional variability of stream fish metacommunities to generate insights into the spatiotemporal dynamics of regional species pools. Third, we investigate relationships among measures of native and nonnative species richness to explore a potential destabilization mechanism and determine whether species translocations contribute to observed patterns of compositional and functional variability. Our findings are expected to improve understanding of diversity-stability scaling relationships in stream fish metacommunities and provide insights into how local conservation efforts may help to achieve sustainable outcomes for regional ecosystems. Methods Establishing the spatiotemporal metacommunity framework This study was conducted at a national extent, spanning several major ecoregions throughout the conterminous United States. We used an existing database depicting the distribution and abundance of freshwater fish species which was previously assembled to support large-scale assessments (Daniel et al., 2015). Although data were sourced from a collection of state, federal, and academic programs, standardization was achieved by ensuring that all sampling efforts were conducted using single-pass electrofishing techniques that targeted entire fish communities (Daniel et al., 2015). Following standardization, data were further processed by attributing electrofishing locations to the NHDPlusV2 stream network (McKay et al., 2012) and validating species names using the Integrated Taxonomic Information System (ITIS; https://www.itis.gov/). For the purposes of this study, we removed all interconfluence stream reaches having a cumulative upstream drainage area larger than 10,000 km2 to account for differences in eco-hydrological processes characteristic of large and greater rivers, as defined by Wang et al. (2011). The resulting database contained records for 41,912 unique interconfluence 13 stream reaches located throughout the conterminous United States, all of which were sampled between 1990 and 2019. To investigate relationships between measures of biodiversity and ecosystem variability at local to regional scales, we developed a spatiotemporal metacommunity framework according to the following considerations. First, we divided our initial dataset into three discrete time periods: 1990-2001 (T1 = time period 1), 2002-2008 (T2), and 2009-2019 (T3). We based our temporal framework on these specific time periods to achieve a balanced design in which each time period contained an approximately equal number of fish sampling records linked to interconfluence stream reaches (for T1: n = 14,348; T2: n = 14,646; T3: n = 12,918). Second, we delineated stream fish metacommunities by assigning interconfluence stream reaches (e.g., local scale) to regional 8-digit hydrologic units (HUC8s), hereafter referred to as subbasins (McKay et al., 2012). Such hierarchical assignments are typical of metacommunity investigations and are essential for partitioning gamma diversity into its local (alpha) and spatial (beta) components. Determining the number of local sites that provide an accurate representation of ecosystem properties at the regional scale is important for defining metacommunities (Stoczynski et al., 2021). In some cases, the same minimum number of local sites has been used to delineate metacommunities in different ecoregions, irrespective of changes in species-area relationships that are known to occur across large biogeographic extents (Heino et al., 2015; Erős et al., 2020). In temporal investigations, such challenges are further amplified as the distribution of sampling locations often varies through time. Because some subbasins may be densely sampled in one time period but poorly sampled in another, additional steps must be taken to ensure that data quality standards are maintained throughout the entire study duration. 14 To address the challenges presented above, we implemented a conservative approach to identify and retain subbasins that were adequately sampled across all three time periods. We defined adequacy by first assigning subbasins to one of nine aggregated level III ecoregions (USEPA, 2006) and then selected the top five most heavily sampled subbasins per combination of ecoregion and time period (n = 135 subbasins). For each of these selected subbasins, we developed a species accumulation curve to quantify the minimum number of local stream reaches required to capture 75% of the regional species pool. These values were then averaged within each time period to generate ecoregion-specific cutoff values that were subsequently applied to identify candidate metacommunities that were poorly sampled. This process resulted in an intermediate dataset containing 274 adequately sampled subbasins in T1, 304 subbasins in T2, and 244 subbasins in T3. Lastly, to identify subbasins that were adequately sampled across all three time periods, we performed a spatial intersection of these three subbasin layers, thereby establishing our final dataset which consisted of 76 subbasins representing unique metacommunities (Figure 2.1). Quantifying measures of biodiversity and functional redundancy For each metacommunity, we calculated the inverse of Simpson’s index (hereafter, alpha diversity) of each local community using the hillR package in R (q = 2; Li, 2018; R Core Team, 2024). Similarly, we quantified the Sorensen dissimilarity index (hereafter, beta diversity) among subsets of local communities using the betapart package (Baselga et al., 2023). To account for confounding effects of sampling effort on estimates of beta diversity, we implemented a resampling procedure using the beta.sample function; within each metacommunity, six random local communities – the minimum number of local communities representing a single metacommunity in any of the three time periods – were resampled 1,000 times to calculate a 15 distribution of dissimilarity measures (Baselga and Orme, 2012). Finally, we calculated the pooled spatial and temporal average of the Simpson’s and Sorensen dissimilarity indices to produce a single alpha and beta diversity estimate for each metacommunity (see Catano et al., 2020). In addition to measuring alpha and beta diversity, we quantified gamma diversity and functional redundancy based on the regional species pool of each metacommunity. Gamma diversity was calculated as the average species richness of each subbasin. Functional redundancy was represented as the average difference between measures of species diversity (the Simpson index) and functional diversity (Rao’s quadratic entropy) following de Bello et al. (2007). Quantifying ecosystem variability Compositional variability was defined using a multiple-site Sorensen dissimilarity index, where each row of the corresponding input matrix represented a specific time period. This format allowed us to quantify changes in species composition through time, with larger values indicating a higher compositional variability. To account for potential confounding effects of sampling effort on compositional variability, we quantified sampling variability as the coefficient of variation (CV) of the number of local stream reaches sampled in each metacommunity-time period combination. Because functional diversity is a multifaceted concept comprising richness, evenness, and divergence (Schmera et al., 2023), we defined functional variability as a composite variable following Grace and Bollen (2008). For each metacommunity-time period combination, we calculated the functional richness, functional evenness, and functional divergence of the regional species pool using the dbFD function from the FD package (Villéger et al., 2008; Laliberté and Legendre, 2010; Laliberté et al., 2014). We then calculated three CV terms for each 16 metacommunity to quantify the temporal variability of each functional diversity component. Lastly, we created our composite variable by defining functional variability as a linear combination of those three terms (i.e., CV functional richness + CV functional evenness + CV functional divergence; see Fan et al., 2016). Structural equation modeling To analyze interrelationships among measures of diversity, redundancy, and variability, we performed structural equation modeling (SEM) using the lavaan package (Objective 1; Rosseel, 2012). The pathways included in our a priori model were justified by evidence compiled through a literature review. We removed four influential outliers prior to fitting the model, including data points with a z-score larger than an absolute value of 3 and a generalized Cook’s Distance larger than 1 (Aguinis et al., 2013). While we acknowledge that our sample size is not large, our model is simple in that we are not estimating latent variables, and the ratio of samples to free parameters does satisfy the 5:1 rule described by Bentler and Chou (1971; see Fan et al., 2016). Model parameters were estimated using a robust maximum likelihood estimator which accounted for non-normality using the Satorra-Bentler scaling correction factor (Satorra and Bentler, 1994). To satisfy assumptions of linearity, we natural log (loge) transformed our measure of gamma diversity. Model fit was assessed using multiple criteria, including the chi- square test statistic, the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA; Riseng et al., 2004). Because the chi-square test measures the overall fit between observed and model-implied covariance matrices, larger p-values (> 0.05) are desired. Similarly, TLI values greater than 0.95 (Schumacker and Lomax, 2004) indicate a suitable model fit, and RMSEA values should be less than 0.05 (Riseng et al., 2004). 17 Geographic patterns in the functional variability of stream fish metacommunities and the potential contributions of nonnative species Following development of our structural equation model, we conducted additional analyses to investigate geographic patterns in the functional variability of stream fish metacommunities (Objective 2). We addressed our second objective by conducting a Kruskal- Wallis rank sum test to determine whether functional variability significantly differed among aggregated level III ecoregions. We also conducted post-hoc Wilcoxon rank sum tests to identify significant pairwise differences between ecoregions. Lastly, because compositional variability can be expressed as a function of species gains and losses, we conducted Pearson correlation tests to identify associations between the distribution of nonnative species and observed patterns of functional variability (Objective 3). To determine the nonnative status of fish species, we conducted a spatial query of the US Geological Survey Nonindigenous Aquatic Species database (NAS; USGS, 2024) and summarized records of “established freshwater fishes” within each subbasin. For each subbasin, species were considered to be nonnative if they were obtained via the NAS query; otherwise, all remaining species were assumed to be native. Using these data, we quantified nonnative species richness in each subbasin, in addition to invasion degree, which was represented as the ratio between nonnative and native species richness (Milardi et al., 2019; Gavioli et al., 2022). We then used Pearson correlation tests to assess the significance of relationships between nonnative species richness and measures of alpha and beta diversity, and between invasion degree and measures of compositional and functional variability. 18 Spatial variation in the delineation of metacommunities Results We observed substantial spatial variation in the shapes of species accumulation curves that were used to delineate metacommunities. On average, the number of local stream reaches required to capture 75% of the regional species pool ranged from a low of 9 in the Xeric ecoregion to a high of 26 in the Northern Appalachian ecoregion. The second highest cutoff value was found in the Southern Appalachian ecoregion (23 local stream reaches), followed by the Upper Midwest (19), Western Mountain (18), Temperate Plains (17), Coastal Plains (12), Southern Plains (11), and Northern Plains (10) ecoregions. We considered these values to be conservative because they accounted for spatial variation in species-area relationships that occur across large heterogeneous ecoregions but did not reflect temporal variation in the distribution of sampling effort. After accounting for this additional source of variation, each of the remaining 76 metacommunities consisted of fish sampling records obtained from an average of 49.3 local stream reaches (median = 33). Relationships between measures of diversity, redundancy, and variability Our structural equation model had a satisfactory fit relative to the observed data (p = 0.407; TLI = 0.997; RMSEA = 0.022), and relationships among measures of diversity, redundancy, and variability in stream fish metacommunities were congruent with our hypotheses. The model explained 30.5% of the variation in functional variability, 32.0% of the variation in compositional variability, and 42.1% of the variation in functional redundancy. Results indicated that at the regional scale, functional variability was largest in subbasins having a higher compositional variability (standardized regression coefficient (SRC) = 0.43; p < 0.001) or lower functional redundancy (SRC = -0.26; p = 0.017). In turn, functional redundancy was 19 largest in subbasins having a higher regional species richness (SRC = 0.65; p < 0.001). When investigating relationships across spatial scales, we found that regional species richness had a stronger positive association with alpha diversity (SRC = 1.01; p < 0.001) compared to beta diversity (SRC = 0.30; p < 0.001). Compositional variability, on the other hand, decreased in association with alpha diversity (SRC = -0.33; p < 0.001) but had positive associations both with measures of beta diversity (SRC = 0.29; p = 0.001) and sampling variability (SRC = 0.25; p = 0.005; Figure 2.2). Pearson correlation coefficients among exogenous variables indicated that measures of alpha (r = -0.10; p = 0.379) and beta diversity (r = -0.07; p = 0.622) were not significantly associated with sampling variability. However, we did observe a significant, inverse relationship between measures of alpha and beta diversity (r = -0.34; p = 0.004; Figure 2.2; Table 2.1). Geographic patterns in functional variability and the contributions of nonnative species To investigate geographic patterns in the functional variability of stream fish metacommunities, we mapped and summarized values by ecoregion (Figure 2.3). We found that functional variability was highest in ecoregions of the Western United States, where functional redundancy was relatively low and estimates of compositional variability were moderate. On average, functional variability was highest in the Northern and Southern Plains ecoregions and was lowest in the Temperate Plains and Upper Midwest ecoregions (Figure 2.3). Our Kruskal- Wallis rank sum test indicated that there were significant differences in the functional variability of stream fish metacommunities among ecoregions (χ2 = 22.55; df = 8; p = 0.004). Post-hoc Wilcoxon rank sum tests revealed several significant pairwise differences (Table 2.2). Because compositional variability can be expressed as a function of species gains and losses, we conducted Pearson correlation tests to investigate whether there were significant 20 associations between distributions of nonnative species (i.e., species gains) and patterns of functional variability. Findings suggested that nonnative species richness increased significantly in association with beta diversity (r = 0.34; p = 0.004; Figure 2.4) but had no relationship with alpha diversity (r = -0.06; p = 0.60; Figure C2.1). In turn, the ratio of nonnative to native species richness was found to have positive associations both with measures of compositional variability (r = 0.37; p = 0.001; Figure C2.1) and functional variability (r = 0.38; p = 0.001; Figure 2.4). Discussion Community assembly processes such as environmental filtering and dispersal limitation contribute to shaping local (alpha) and spatial (beta) patterns of biodiversity, and changes in such characteristics can have implications for the temporal stability of ecosystem properties. Understanding how biodiversity contributes to ecosystem stability is important for generating insights into the spatiotemporal dynamics of freshwater ecosystems, which may ultimately be used to inform management strategies. Yet despite the relatively high rates of biodiversity loss observed in freshwater ecosystems (McRae et al., 2017), empirical assessments of diversity- stability relationships are dominated by studies of plant communities in terrestrial ecosystems (see Gianuca et al., 2024). Due to fundamental differences in dispersal strategies exhibited by plants versus animals, additional investigations of diversity-stability relationships in stream fish metacommunities are warranted. In this study, we investigated diversity-stability relationships in stream fish metacommunities throughout the conterminous United States by developing a structural equation model to integrate predictions for measures of diversity, redundancy, and variability at local to regional spatial scales. In addition, we assessed geographic patterns in the functional variability of stream fish metacommunities and evaluated a potential destabilization mechanism by assessing relationships between nonnative species and measures of diversity and 21 ecosystem stability. Our findings provide empirical support for diversity-stability scaling relationships in stream fish metacommunities and provide insights about potential consequences of biodiversity change on the structure and function of freshwater ecosystems at a broad spatial extent. Investigating diversity-stability scaling relationships Factors contributing to compositional variability In this study, we showed that the composition of regional species pools was most stable in stream fish metacommunities that had a relatively high alpha or low beta diversity. Due to the opposing influences of alpha and beta diversity on compositional variability, our findings suggest that local influences and spatial processes both play an important role in regulating the spatiotemporal dynamics of stream fish metacommunities. While we did not conduct an exhaustive test of specific mechanisms to explain why these diversity-stability relationships might have occurred, several plausible explanations exist (Wang and Loreau, 2016). Previous assessments have shown that alpha diversity can promote ecosystem stability through the portfolio effect, which occurs when individual species vary in their sensitivity to environmental change (Tilman et al., 1998; Thibaut and Connolly, 2013). While such local insurance effects are common, their strength is known to vary among organism groups (Wisnoski et al., 2023). In comparison, beta diversity has been shown to stabilize aggregate ecosystem properties by promoting spatial asynchrony in temporal fluctuations among communities or populations (Catano et al., 2020). While our data were not appropriate for assessing population dynamics per se, we did find evidence of an opposite relationship in which beta diversity was positively associated with compositional variability at the regional scale. In the context of rivers and streams, this finding highlights the importance of restoring longitudinal connectivity to regulate 22 dispersal of individuals and contribute to the stability of stream fish metacommunities (Mouquet and Loreau, 2003), for example, by facilitating access to refugia and promoting species recovery in response to environmental degradation (Altermatt et al., 2011; Guelzow et al., 2017). Additional work is required to improve understanding of the various mechanisms driving spatiotemporal dynamics in stream fish metacommunities, particularly as they relate to patterns of beta diversity and compositional variability (Lamy et al., 2021; additional details are provided below in the section on nonnative species). Factors contributing to functional variability Functional variability, which we quantified as a composite variable to reflect temporal changes in the functional richness, evenness, and divergence of stream fish metacommunities, was positively associated with compositional variability at the regional scale. This finding suggests that changes in species composition, including combined effects of species gains and losses, are likely to have consequences for the functional characteristics of stream fish metacommunities. Similar findings were observed in a recent meta-analysis of field and mesocosm experiments, emphasizing the importance of assessing relationships between multiple dimensions of ecosystem stability (Hillebrand et al., 2018; Hillebrand and Kunze, 2020). However, we also showed that the positive relationship between compositional and functional variability could be offset by functional redundancies among species at the regional scale. Functional redundancy has previously been shown to promote community stability in marine and terrestrial ecosystems (Biggs et al., 2020), and here we provide empirical evidence to suggest that functional redundancy has a similar effect on stream fish metacommunities across the conterminous United States. Collectively, these findings suggest that temporal fluctuations in the functional characteristics of metacommunities may be most pronounced in ecosystems where 23 functionally unique species are vulnerable to environmental change (see Buisson et al., 2013), thereby supporting the idea that functional redundancy should be regarded as a valuable conservation target (Mori et al., 2013). Explaining geographic patterns in the functional variability of stream fish metacommunities Our study is unique in that we captured a large environmental gradient by assessing diversity-stability relationships in stream fish metacommunities across the conterminous United States, which is characterized by regional differences in climatic and hydrologic factors known to influence community assembly (i.e., flow intermittence; Messager et al., 2021). We investigated geographic patterns in the characteristics of stream fish metacommunities and found that functional variability was highest in ecoregions of the Western United States, where spatial turnover among freshwater fish communities is relatively high (beta diversity; Qian et al., 2021) and diversity within local communities is comparatively low (alpha diversity; Jenkins et al., 2015). The inverse relationship between measures of alpha and beta diversity suggests a significant trade-off exists between local and spatial processes contributing to patterns in the functional variability of stream fish metacommunities (Kneitel and Chase, 2004). This finding may be explained in part by differences in environmental factors that occur throughout the conterminous United States. For example, Edge et al. (2017) found that patterns of beta diversity among stream fish communities in Canada were strongly influenced by habitat fragmentation, whereas patterns of alpha diversity were more closely associated with habitat degradation resulting from agricultural and urban development practices. More recently, Gianuca et al. (2024) showed that primary factors influencing the regional stability of macroinvertebrate metacommunities in France varied depending on whether rivers were perennial (i.e., influenced 24 by alpha diversity and its effect on local stability) or intermittent (i.e., influenced by spatial asynchrony). Collectively, these findings highlight the need to explicitly account for specific environmental factors to help contextualize diversity-stability relationships and clarify how management strategies may be adapted to better maintain ecosystem functioning (Cid et al., 2022). Investigating relationships between nonnative species and ecosystem stability Species invasions are known to contribute to the homogenization and differentiation of freshwater metacommunities, often represented by a directional change in spatial beta diversity measured at different points in time (Rolls et al., 2023). However, biodiversity can also influence the successful colonization of nonnative species. For example, the biotic resistance hypothesis suggests that competitive exclusion and consumptive resistance (i.e., via predation) can limit colonization of nonnative species in diverse communities at local scales (Gido and Brown, 1999; Alofs and Jackson, 2014). Yet the influence of beta diversity on the successful establishment of nonnative species within metacommunities is understudied compared to known contributions of alpha diversity (Fridley et al., 2007). At the regional scale, we found that nonnative richness increased in association with beta diversity, suggesting that spatial turnover among local communities may be a useful indicator of metacommunity invasibility (see Su et al., 2023). In theory, the probability of invasion should increase in metacommunities that have a high spatial beta diversity or low native dispersal rate (Davies et al., 2005; Brown and Barney, 2021), because combinations of functional traits and the strength of species interactions may exhibit greater variation among local communities that have distinct species compositions (Bower et al., 2023) or environmental characteristics (see Vanschoenwinkel et al., 2013). In addition, we also showed that invasion degree was positively associated with measures of compositional and 25 functional variability, suggesting that the stability of stream fish metacommunities may be expected to decrease along the invasion gradient. Accounting for species invasions may provide insights into the relationships between multiple dimensions of biodiversity and ecosystem stability, although additional metacommunity and trait-based investigations are warranted (Takács et al., 2021; Jarzyna et al., 2022; see Marcolin et al., 2025). Management implications Because measures of alpha and beta diversity contribute to ecosystem stability in complementary ways, management decision-making must account for both local and spatial processes that support the structure and function of stream fish metacommunities. For example, our findings suggest that sites with low levels of alpha diversity should not indiscriminately be disregarded as having low conservation value, because those sites may support unique combinations of species that contribute to patterns of functional redundancy and ecosystem stability at the regional scale. In addition, our findings highlight the need to further limit habitat degradation and safeguard biodiversity within local communities, while also seeking opportunities to improve connectivity (e.g., by removing dams, replacing culverts, or regulating water withdrawals) and enhance the dispersal of individual fishes. Efforts to incorporate such multiscale perspectives into the management of stream fish metacommunities may help to prioritize connectivity restoration projects, which will become increasingly important as species ranges shift in response to climate change (Socolar et al., 2016) and as concerns about the spread of invasive species continue to intensify (Cooper et al., 2021). Similarly, multiscale approaches may be used to increase the efficiency of conservation planning efforts by simultaneously conserving local habitats and promoting connectivity among local communities (Hermoso et al., 2011; Rivers-Moore et al., 2011; Brumm et al., 2022). 26 Conclusions While understanding of how biodiversity contributes to ecosystem stability is a fundamental task in ecology, such relationships remain poorly understood in the context of stream fish metacommunities at broad spatial extents. Our study addresses this need by investigating how biodiversity contributes to the compositional and functional stability of stream fish metacommunities across the conterminous United States and generates insights into how local conservation efforts may help to achieve sustainable outcomes for regional ecosystems. 27 REFERENCES Aguinis, H., Gottfredson, R.K., Joo, H., 2013. Best-practice recommendations for defining, identifying, and handling outliers. Organizational Research Methods. 16, 270-301. Alofs, K.M., Jackson, D.A., 2014. Meta-analysis suggests biotic resistance in freshwater environments is driven by consumption rather than competition. Ecology. 95, 3259-3270. Altermatt, F., 2013. Diversity in riverine metacommunities: a network perspective. Aquatic Ecology. 47, 365-377. Baselga, A., Orme, C.D.L., 2012. betapart: an R package for the study of beta diversity. Methods in Ecology and Evolution. 3, 808-812. Baselga, A., Orme, D., Villeger, S., De Bortoli, J., Leprieur, F., Logez, M., Martinez-Santalla, S., Martin-Devasa, R., Gomez-Rodriguez, C., Crujeiras, R., 2023. betapart: partitioning beta diversity into turnover and nestedness components. R package version 1.6. de Bello, F., Lepš, J., Lavorel, S., Moretti, M., 2007. Importance of species abundance for assessment of trait composition: an example based on pollinator communities. Community Ecology. 8, 163-170. Bentler, P.M., Chou, C.P., 1987. Practical issues in structural modeling. Sociological Methods and Research. 16, 78-117. Biggs, C.R., Yeager, L.A., Bolser, D.G., Bonsell, C., Dichiera, A.M., Hou, Z., Keyser, S.R., Khursigara, A.J., Lu, K., … Erisman, B.E., 2020. Does functional redundancy affect ecological stability and resilience? A review and meta-analysis. Ecosphere. 11, e03184. Bower, L.M., Stoczynski, L., Peoples, B.K., Patrick, C.J., Brown, B.L., 2023. Multiple dimensions of functional diversity affect stream fish taxonomic β-diversity. Freshwater Biology. 68, 437-451. Brown, B.L., Barney, J.N., 2021. Rethinking biological invasions as a metacommunity problem. Frontiers in Ecology and Evolution. 8, 584701. Brumm, K.J., Hanks, R.D., Baldwin, R.F., Peoples, B.K., 2022. A scale-linked conservation planning framework for freshwater ecosystems. Landscape Ecology. 37, 2589-2605. Buisson, L., Grenouillet, G., Villéger, S., Canal, J., Laffaille, P., 2013. Toward a loss of functional diversity in stream fish assemblages under climate change. Global Change Biology. 19, 387-400. Catano, C.P., Fristoe, T.S., LaManna, J.A., Myers, J.A., 2020. Local species diversity, β-diversity and climate influence the regional stability of bird biomass across North America. Proceedings of the Royal Society B. 287, 20192520. 28 Cid, N., Erős, T., Heino, J., Singer, G., Jähnig, S.C., Cañedo-Argüelles, M., Bonada, N., Sarremejane, R., Mykrä, H., … Datry, T., 2022. From meta-system theory to the sustainable management of rivers in the Anthropocene. Frontiers in Ecology and the Environment. 20, 49-57. Cooper, A.R., Infante, D.M., O’Hanley, J.R., Yu, H., Neeson, T.M., Brumm, K.J., 2021. Prioritizing native migratory fish passage restoration while limiting the spread of invasive species: a case study in the Upper Mississippi River. Science of the Total Environment. 791, 148317. Crawford, S., Whelan, G., Infante, D.M., Blackhart, K., Daniel, W.M., Fuller, P., Birdsong, T.W., Wieferich, D.J., McClees-Funinan, R., … Ruhl, P.M., 2016. Through a fish’s eye: the status of fish habitats in the United States 2015. National Fish Habitat Partnership. Czeglédi, I., Specziár, A., Erős, T., 2022. Temporal dynamics of freshwater fish assemblages, their background and methods of quantifications – A synthesis. Fish and Fisheries. 23, 93- 108. Daniel, W.M., Infante, D.M., Hughes, R.M., Tsang, Y.P., Esselman, P.C., Wieferich, D., Herreman, K., Cooper, A.R., Wang, L., Taylor, W.W., 2015. Characterizing coal and mineral mines as a regional source of stress to stream fish assemblages. Ecological Indicators. 50, 50- 61. Davies, K.F., Chesson, P., Harrison, S., Inouye, B.D., Melbourne, B.A., Rice, K.J., 2005. Spatial heterogeneity explains the scale dependence of the native-exotic diversity relationship. Ecology. 86, 1602-1610. Dudgeon, D., 2019. Multiple threats imperil freshwater biodiversity in the Anthropocene. Current Biology. 29, R942-R995. Edge, C.B., Fortin, M.J., Jackson, D.A., Lawrie, D., Stanfield, L., Shrestha, N., 2017. Habitat alteration and habitat fragmentation differentially affect beta diversity of stream fish communities. Landscape Ecology. 32, 647-662. Erős, T., Comte, L., Filipe, A.F., Ruhi, A., Tedesco, P.A., Brose, U., Fortin, M.J., Giam, X., Irving, K., … Olden, J.D., 2020. Effects of nonnative species on the stability of riverine fish communities. Ecography. 43, 1156-1166. Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S.R., Park, H., Shao, C., 2016. Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecological Processes. 5, 19. Fox, J.T., Magoulick, D.D., 2024. Fish beta diversity associated with hydrologic and anthropogenic disturbance gradients in contrasting stream flow regimes. Science of the Total Environment. 945, 173825. 29 Fridley, J.D., Stachowicz, J.J., Naeem, S., Sax, D.F., Seabloom, E.W., Smith, M.D., Stohlgren, T.J., Tilman, D., Holle, B.V., 2007. The invasion paradox: reconciling pattern and process in species invasions. Ecology. 88, 3-17. Frissell, C.A., Liss, W.J., Warren, C.E., Hurley, M.D., 1986. A hierarchical framework for stream habitat classification: viewing streams in a watershed context. Environmental Management. 10: 199-214. Gavioli, A., Milardi, M., Soininen, J., Soana, E., Lanzoni, M., Castaldelli, G., 2022. How does invasion degree shape alpha and beta diversity of freshwater fish at a regional scale? Ecology and Evolution. 12, e9493. Gianuca, A.T., di Cavalcanti, V.R., Cruz, L., Floury, M., Crabot, J., Valette, L., Piffady, J., Datry, T., 2024. River flow intermittence influence biodiversity-stability relationships across spatial scales: implications for an uncertain future. 30, e17457. Gido, K.B., Brown, J.H., 1999. Invasion of North American drainages by alien fish species. Freshwater Biology. 42, 387-399. Gonzalez, A., Cardinale, B.J., Allington, G.R.H., Byrnes, J., Endsley, K.A., Brown, D.G., Hooper, D.U., Isbell, F., O’Connor, M.I., Loreau, M., 2016. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology. 97, 1949-1960. Grace, J.B., Bollen, K.A., 2008. Representing general theoretical concepts in structural equation models: the role of composite variables. Environmental and Ecological Statistics. 15, 191- 213. Griffiths, D., 2017. Connectivity and vagility determine beta diversity and nestedness in North American and European freshwater fish. Journal of Biogeography. 44, 1723-1733. Guelzow, N., Muijsers, F., Ptacnik, R., Hillebrand, H., 2017. Functional and structural stability are linked in phytoplankton metacommunities of different connectivity. Ecography. 40, 719- 732. Heino, J., Soininen, J., Alahuhta, J., Lappalainen, J., Virtanen, R., 2015. A comparative analysis of metacommunity types in the freshwater realm. Ecology and Evolution. 5, 1525-1537. Hermoso, V., Linke, S., Prenda, J., Possingham, H.P., 2011. Addressing longitudinal connectivity in the systematic conservation planning of fresh waters. Freshwater Biology. 56, 57-70. Hillebrand, H., Matthiessen, B., 2009. Biodiversity in a complex world: consolidation and progress in functional biodiversity research. Ecology Letters. 12, 1405-1419. 30 Hillebrand, H., Langenheder, S., Lebret, K., Lindström, E., Östman, Ö., Striebel, M., 2018. Decomposing multiple dimensions of stability in global change experiments. Ecology Letters. 21, 21-30. Hillebrand, H., Kunze, C., 2020. Meta-analysis on pulse disturbances reveals differences in functional and compositional recovery across ecosystems. Ecology Letters. 23, 575-585. Jarzyna, M.A., Jetz, W., 2018. Taxonomic and functional diversity change is scale dependent. Nature Communications. 9, 2565. Jarzyna, M.A., Norman, K.E.A., Lamontagne, J.M., Helmus, M.R., Li, D., Parker, S.M., Rocha, M.P., Record, S., Sokol, E.R., … Surasinghe, T.D., 2022. Community stability is related to animal diversity change. Ecosphere. 13, e3970. Kneitel, J.M., Chase, J.M., 2004. Trade-offs in community ecology: linking spatial scales and species coexistence. Ecology Letters. 7, 69-80. Laliberté, E., Legendre, P., 2010. A distance-based framework for measuring functional diversity from multiple traits. Ecology. 91, 299-305. Laliberté, E., Legendre, P., Shipley, B., 2014. FD: measuring functional diversity from multiple traits, and other tools for functional ecology. R package version 1.0-12.3. Lamothe, K.A., Alofs, K.M., Jackson, D.A., Somers, K.M., 2018. Functional diversity and redundancy of freshwater fish communities across biogeographic and environmental gradients. Diversity and Distributions. 24, 1612-1626. Lamy, T., Wisnoski, N.I., Andrade, R., Castorani, M.C.N., Compagnoni, A., Lany, N., Marazzi, L., Record, S., Swan, C.M., … Sokol, E.R., 2021. The dual nature of metacommunity variability. Oikos. 130, 2078-2092. Leibold, M.A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase, J.M., Hoopes, M.F., Holt, R.D., Shurin, J.B., Law, R., … Gonzalez, A., 2004. The metacommunity concept: a framework for multi-scale community ecology. Ecology Letters. 7: 601-613. Li, D., 2018. hillR: taxonomic, functional, and phylogenetic diversity and similarity through Hill Numbers. Journal of Open Source Software. 3, 1041. Loreau, M., Barbier, M., Filotas, E., Gravel, D., Isbell, F., Miller, S.J., Montoya, J.M., Wang, S., Aussenac, R., … Dee, L.E., 2021. Biodiversity as insurance: from concept to measurement and application. Biological Reviews. 96, 2333-2354. Marcolin, F., Branco, P., Santo, J.M., Reino, L., Santana, J., Ribeiro, J., Chamberlain, D., Segurado, P., 2025. Species traits and invasion history as predictors of freshwater fish invasion success in Europe. Management of Biological Invasions. 16, 277-294. 31 McCann, K.S., 2000. The diversity-stability debate. Nature. 405, 228-233. McKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., Rea, A., 2012. NHDPlus Version 2: User Guide. McRae, L., Deinet, S., Freeman, R., 2017. The diversity-weighted Living Planet Index: controlling for taxonomic bias in a global biodiversity indicator. PLOS One. 12, e0169156. Messager, M.L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H., Snelder, T., Tockner, K., Trautmann, T., Watt, C., Datry, T., 2021. Global prevalence of non-perennial rivers and streams. Nature. 594, 391-397. Milardi, M., Gavioli, A., Soininen, J., Castaldelli, G., 2019. Exotic species invasions undermine regional functional diversity of freshwater fish. Scientific Reports. 9, 17921. Millenium Ecosystem Assessment, 2005. Ecosystems and human well-being: multiscale assessments: findings of the sub-global assessments working group (volume 4). Island Press, Washington, D.C., USA. Mori, A.S., Furukawa, T., Sasaki, T., 2013. Response diversity determines the resilience of ecosystems to environmental change. Biological Reviews. 88, 349-364. Mori, A.S., Isbell, F., Seidl, R., 2018. β-diversity, community assembly, and ecosystem functioning. Trends in Ecology and Evolution. 33, 549-564. Mouquet, N., Loreau, M., 2003. Community patterns in source-sink metacommunities. The American Naturalist. 162, 544-557. van der Plas, F., Hennecke, J., Chase, J.M., van Rujven, J., Barry, K.E., 2023. Universal beta- diversity-functioning relationships are neither observed nor expected. Trends in Ecology and Evolution. 38, 532-544. Primack, R.B., Miller-Rushing, A.J., Corlett, R.T., Devictor, V., Johns, D.M., Loyola, R., Maas, B., Pakeman, R.J., Pejchar, L., 2018. Biodiversity gains? The debate on changes in local- vs global-scale species richness. Biological Conservation. 219, A1-A3. R Core Team, 2024. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org Reid, A.J., Carlson, A.K., Creed, I.F., Eliason, E.J., Gell, P.A., Johnson, P.T.J., Kidd, K.A., MacCormack, T.J., Olden, J.D., … Cooke, S.J., 2019. Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews. 94, 849-873. Rivers-Moore, N.A., Goodman, P.S., Nel, J.L., 2011. Scale-based freshwater conservation planning: towards protecting freshwater biodiversity in KwaZulu-Natal, South Africa. Freshwater Biology. 56, 125-141. 32 Rogosch, J.S., Olden, J.D., 2019. Dynamic contributions of intermittent and perennial streams to fish beta diversity in dryland rivers. Journal of Biogeography. 46, 2311-2322. Rolls, R.J., Deane, D.C., Johnson, S.E., Heino, J., Anderson, M.J., Ellingsen, K.E., 2023. Biotic homogenisation and differentiation as directional change in beta diversity: synthesising driver-response relationships to develop conceptual models across ecosystems. Biological Reviews. 98, 1388-1423. Rosseel, Y., 2012. lavaan: an R package for structural equation modeling. Journal of Statistical Software. 48, 1-36. Riseng, C.M., Wiley, M.J., Stevenson, R.J., 2004. Hydrologic disturbance and nutrient effects on benthic community structure in midwestern US streams: a covariance structure analysis. Journal of the North American Benthological Society. 23, 309-326. Satorra, A., Bentler, P.M., 1994. Corrections to test statistics and standard errors in covariance structure analysis. In von Eye, A., Clogg, C.C. (Eds.), Latent variable analysis: applications to developmental research, pp. 399-419. Sage, Thousand Oaks, CA, USA. Schmera, D., Ricotta, C., Podani, J., 2023. Components of functional diversity revisited: a new classification and its theoretical and practical implications. Ecology and Evolution. 13, e10614. Schumacker, R.E., Lomax, R.G., 2004. A beginner’s guide to structural equation modeling. Lawrence Erlbaum Associates, New Jersey, USA. Socolar, J.B., Gilroy, J.J., Kunin, W.E., Edwards, D.P., 2016. How should beta-diversity inform biodiversity conservation? Trends in Ecology and Evolution. 31, 67-80. Stoczynski, L., Brown, B.L., Midway, S.R., Peoples, B.K., 2021. Landscape features and study design affect elements of metacommunity structure for stream fishes across the eastern U.S.A. Freshwater Biology. 66, 1736-1750. Su, G., Mertel, A., Brosse, S., Calabrese, J.M., 2023. Species invasiveness and community invasibility of North American freshwater fish fauna revealed via trait-based analysis. Nature Communications. 14, 2332. Takács, P., Abonyi, A., Bánó, B., Erős, T., 2021. Effect of non-native species on taxonomic and functional diversity of fish communities in different river types. Biodiversity and Conservation. 30: 2511-2528. Thibaut, L.M., Connolly, S.R., 2013. Understanding diversity-stability relationships: towards a unified model of portfolio effects. Ecology Letters. 16, 140-150. Tilman, D., Downing, J.A., 1994. Biodiversity and stability in grasslands. Nature. 367, 363-365. 33 Tilman, D., Lehman, C.L., Bristow, C.E., 1998. Diversity-stability relationships: statistical inevitability or ecological consequence? The American Naturalist. 151, 277-282. Tonkin, J.D., Heino, J., Altermatt, F., 2018. Metacommunities in river networks: the importance of network structure and connectivity on patterns and processes. Freshwater Biology. 63, 1-5. USEPA (Environmental Protection Agency), 2006. Wadeable streams assessment: a collaborative survey of the nation’s streams. EPA 841-B-06-002. U.S. Environmental Protection Agency, Office of Water and Office of Research and Development. USGS (Geological Survey), 2024. Nonindigenous Aquatic Species Database, Gainesville, Florida, USA. https://nas.er.usgs.gov, Accessed 11 November 2024. Vanschoenwinkel, B., Buschke, F., Brendonck, L., 2013. Disturbance regime alters the impact of dispersal on alpha and beta diversity in a natural metacommunity. Ecology. 94, 2547-2557. Vellend, M., Dornelas, M., Baeten, L., Beauséjour, R., Brown, C.D., De Frenne, P., Elmendorf, S.C., Gotelli, N.J., Moyes, F., … Sievers, C., 2017. Estimates of local biodiversity change over time stand up to scrutiny. Ecology. 98, 583-590. Villéger, S., Mason, N.W.H., Mouillot, D., 2008. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology. 89, 2290-2301. Wang, L., Infante, D.M., Esselman, P., Cooper, A., Wu, D., Taylor, W.W., Beard, D., Whelan, G., Ostroff, A., 2011. A hierarchical spatial framework and database for the National River Fish Habitat Condition Assessment. Fisheries. 36, 436-449. Wang, S., Loreau, M., 2016. Biodiversity and ecosystem stability across scales in metacommunities. Ecology Letters. 19, 510-518. Weiher, E., Freund, D., Bunton, T., Stefanski, A., Lee, T., Bentivenga, S., 2011. Advances, challenges and a developing synthesis of ecological community assembly theory. Philosophical Transactions of The Royal Society B. 366, 2403-2413. Wisnoski, N.I., Andrade, R., Castorani, M.C.N., Catano, C.P., Compagnoni, A., Lamy, T., Lany, N.K., Marazzi, L., Record, S., … Sokol, E.R., 2023. Diversity-stability relationships across organisms groups and ecosystem types become decoupled across spatial scales. Ecology. 104, e4136. Zbinden, Z.D., 2020. Temporal dynamics of stream fish assemblages and the role of spatial scale in quantifying change. Ecology and Evolution. 10, 952-961. Zhang, R., Tian, D., Wang, J., Niu, S., 2023. Critical role of multidimensional biodiversity in contributing to ecosystem sustainability under global change. Geography and Sustainability. 4, 232-243. 34 Table 2.1. The model-implied correlation matrix derived from our structural equation model. APPENDIX 2.A: TABLES Functional Variability Functional Redundancy Gamma Diversity Compositional Variability Alpha Diversity Beta Diversity Sampling Variability Functional Variability Functional Redundancy Gamma Diversity Compositional Variability Alpha Diversity Beta Diversity Sampling Variability 1.00 -0.36 -0.32 0.49 -0.35 0.17 0.13 1.00 0.65 -0.22 0.59 -0.03 -0.08 1.00 -0.34 0.90 -0.05 -0.12 1.00 -0.45 0.38 0.26 1.00 -0.34 -0.10 1.00 -0.07 1.00 35 Table 2.2. Results of the Wilcoxon rank sum tests (p-values) used to determine the significance of pairwise differences in the functional variability of stream fish metacommunities for ecoregions throughout the conterminous United States. NAP NPL SAP SPL TPL UMW WMT XER CPL NAP NPL 0.10 SAP SPL TPL UMW WMT 0.07 0.58 0.25 0.14 0.76 0.06 0.16 0.07 0.92 0.65 0.77 <0.01 0.57 <0.01 0.02 0.57 0.04 0.13 0.04 0.18 0.14 0.05 0.70 0.23 0.24 0.04 0.19 0.50 0.49 <0.01 0.83 0.01 0.14 0.28 0.22 Note: Significant p-values at an alpha value of 0.10 are shown in bold. 36 APPENDIX 2.B: FIGURES Figure 2.1. Distribution of HUC8 subbasins (grey polygons) that were adequately sampled in all three time periods (T1: 1990 - 2001; T2: 2002 - 2008; T3: 2009 - 2019). We defined adequacy by developing species accumulation curves within aggregated level III ecoregions to identify the average number of local stream reaches required to capture 75% of the regional species pool (see Methods). 37 Figure 2.2. Structural equation model designed to assess hypothesized relationships between measures of biodiversity, redundancy, and variability in stream fish metacommunities at local to regional spatial scales. Solid lines represent standardized regression coefficients and dashed lines between exogenous independent variables represent model-implied Pearson correlation coefficients. Significant positive relationships (at an alpha value of 0.05) are shown in blue, significant negative relationships are shown in red, and non-significant relationships are depicted as black lines. 38 Figure 2.3. Geographic patterns of functional variability in stream fish metacommunities (top), further summarized by ecoregion (bottom). 39 Figure 2.4. Pearson correlation tests revealed a: (top) significant relationship between beta diversity and nonnative species richness; and (bottom) significant relationship between the ratio of nonnative to native species richness and functional variability. 40 APPENDIX 2.C: SUPPLEMENTARY FIGURES Figure C2.1. Pearson correlation tests revealed: (top) a nonsignificant relationship between alpha diversity and nonnative species richness; and (bottom) a significant relationship between the ratio of nonnative to native species richness and compositional variability. 41 CHAPTER 3: APPLYING ECOLOGICAL THRESHOLDS TO INFORM CONSERVATION AND RESTORATION EFFORTS FOR STREAM FISHES Abstract Stream habitats and the fishes they support are adversely affected by landscape stressors including agricultural land use and urban development. Understanding the complex responses of fish assemblages to landscape stressors is critical to design and implement effective management strategies to conserve and restore fish habitat. Landscape stressors often elicit severe responses in stream fish assemblages, causing dramatic reductions in numbers of fishes with a comparatively small increase in the intensity of a given stressor. While threshold responses of stream fish assemblages to landscape stressors have been explored in previous studies, efforts to use thresholds to inform conservation and restoration decision-making processes are lacking, particularly at broad (i.e., intercontinental) spatial extents. In this study, we used known threshold values to characterize the status of nearly 1.73 million stream reaches located throughout the conterminous United States and Europe. We developed a decision-support framework that integrates indices based on thresholds detected from multiple pervasive landscape stressors (e.g., urban and agricultural land) and summaries of protected areas within catchments to help inform conservation and restoration efforts for stream fishes. To illustrate the utility of our framework, we present two case studies: one for the Central and Western Europe ecoregion, and the other for the Middle Missouri ecoregion. Our results showed two consistent opportunities for both ecoregions. First, our framework identified catchments in which proactive conservation strategies could be implemented to help prevent stream reaches from crossing their urban land use threshold. Second, and in contrast, we identified multiple catchments that had passed their agricultural land use threshold despite having a high degree of protection, making them candidates for agricultural restoration. Overall, our findings highlight the vulnerability of 42 stream fish assemblages to human landscape stressors in two continents and provide a flexible framework with which to support management decision-making processes to help achieve global policy targets and address the freshwater biodiversity crisis. Introduction One-quarter of all freshwater fish species are threatened with extinction due to myriad factors including flow regulation, pollution, and land use change, all of which contribute to degradation of stream habitats (Dudgeon, 2019; Sayer et al., 2025). Fish species that depend on streams for survival, growth, and reproduction are particularly vulnerable to human activities, including factors operating at the landscape scale (Allan, 2004). For example, stressors associated with agricultural production and urban development are ubiquitous throughout the world, and they are known to contribute to the freshwater biodiversity crisis by altering natural hydrological regimes, changing physical characteristics of streams (e.g., fragmentation, sedimentation), and reducing water quality (e.g., nutrient enrichment; Booth et al., 2004; Riseng et al., 2011; Feio et al., 2023). To help address the freshwater biodiversity crisis, efforts to inform conservation and restoration actions over large spatial extents must account for complex responses of fishes to multiple landscape stressors (van Rees et al., 2021) and must address broader influences on stream ecosystems, including socioeconomic factors. While it is widely acknowledged that landscape stressors degrade habitats and impact stream fishes, specification of how fish assemblages respond to those stressors is critical to identify opportunities to inform ecosystem management. Landscape stressors are known to elicit abrupt changes in fish assemblages detectable based on dramatic reductions in numbers of fishes with specific levels of disturbance (Allan, 2004; Baker and King, 2010). Such non-linear relationships are characterized by the presence of tipping points, or thresholds, which are defined 43 as the point at which a stream fish assemblage, often characterized by specific metrics (e.g., lithophilic individuals), experiences a sudden decline after a comparatively small increase in the intensity of a human landscape stressor (e.g., agricultural land use in stream catchments). Because thresholds represent specific levels of disturbance leading to changes in stream fish assemblages, thresholds can be used to establish conservation targets and to inform environmental policy and management (Foley et al., 2015; Harper et al., 2024). Management strategies that account for and aim to prevent landscape stressors from surpassing known thresholds have been found to be more effective than those that do not, thus demonstrating the relevance of thresholds for improving management outcomes (Kelly et al., 2015). Additionally, because stream catchments can sometimes support multiple land uses that act as stressors to fishes, identification of catchments that have surpassed multiple thresholds may help to enhance the efficiency of management decisions (Côté et al., 2016; Birk et al., 2020; Carrier-Belleau et al., 2022). While efforts to incorporate thresholds into management of stream fishes have been relatively limited (Hernández Martínez de la Riva et al., 2023), thresholds have great potential to inform decision-making (Hastings et al., 2018; Ross et al., 2023). Effective ecosystem management requires resources to implement conservation and restoration actions, yet such resources are typically limited. Therefore, spatial planning efforts are necessary to develop decision-support frameworks that can inform strategic resource allocation. Spatial planning describes the process of integrating multiple data sources to prioritize management actions and maximize their cost effectiveness (Kukkala and Moilanen, 2013). Data inputs often include details about distributions of priority species or locations of rare habitat types, but they may also reflect vulnerability of sites to human landscape stressors (Moilanen et al., 2022). For example, the irreplaceability – condition – vulnerability (ICV) 44 framework integrates measures of conservation value and stressor exposure for sites within an ecosystem (Lawler et al., 2003; Linke et al., 2007). Within the ICV framework, sites that are more vulnerable to a particular threat are deemed higher priority for management action, thereby incorporating considerations of risk into the decision-making process (Mattson and Angermeier, 2007). Comparable frameworks have also been developed to explicitly incorporate thresholds into spatial planning, although applications of such frameworks have been restricted to relatively small regions. For example, Ettinger et al. (2021) used urban land use thresholds in the Puget Sound basin to develop an index of pre-spawn mortality risk and inform preservation and restoration actions to safeguard critical habitat for coho and Chinook salmon. Similarly, in the Upper Mississippi River basin, Delaney and Larson (2024) used multiple environmental predictors and a model-based classification threshold to develop a submerged aquatic vegetation (SAV) habitat suitability index and to generate insights into the vulnerability and restoration potential of sites. A similar approach can be used to inform management of stream fish assemblages at broad (i.e., intercontinental) spatial extents, where heterogeneity within and among large regions must be taken into consideration to ensure resources are used appropriately. Besides using thresholds to characterize vulnerability of stream fish assemblages to multiple landscape stressors, spatial planning efforts can further be improved by considering currently established protected areas, defined as locations dedicated to long-term conservation of nature through legal or other effective means that limit human degradation of landscapes. Accounting for the distribution of protected areas is essential to ensuring that proposed management actions complement existing management strategies. For example, due to accessibility constraints, it may be more feasible and cost-effective to prioritize implementation of restoration actions in highly protected areas, as opposed to privately owned landscapes in 45 which management interests are more diffuse or lacking altogether (Noss et al., 2009; Palmer and Stewart, 2020). Conversely, strategies that prioritize conservation actions in poorly protected landscapes with a high risk of degradation may be more cost-effective than strategies that prioritize landscapes with the lowest risk (Negret et al., 2024). Spatial planning efforts must also account for whether protected areas are an effective means for achieving conservation targets. Because criteria for a protected area to be considered ‘effective’ are likely to vary according to specific management objectives, such assessments should be included as a component of any spatial planning effort (see Hermoso et al., 2016). To address these needs, the goal of this study is to identify conservation and restoration opportunities for streams based on current levels of human landscape stressors and known threshold values detected for stream fishes. To achieve this, we first calculate a threshold status index to assess the extent to which stream fishes may be affected by agriculture, pasture, and/or urban land use in catchments of nearly 1.73 million stream reaches located throughout the United States (US) and Europe. Second, we investigate relationships between the amount of protected land in catchments and measures of stream fish diversity to generate insights into the effectiveness of protected area networks for supporting stream fishes. Lastly, we develop a framework to prioritize actions for catchments based on their threshold status, protected area coverage, and configuration of multiple landscape stressors. We illustrate the utility of our approach by presenting two case studies: one for the Central and Western Europe ecoregion (Europe), and the other for the Middle Missouri ecoregion (US), both of which span relatively large environmental gradients and are known to have moderate threshold values for stream fishes. More broadly, our approach emphasizes the importance of applying thresholds to inform 46 management decisions and direct allocation of resources in ways that have potential to maximize conservation and restoration outcomes for people and nature. Defining our study region and spatial framework Methods This study was conducted in streams located throughout the conterminous United States (US) and Europe. Stream networks were represented using the National Hydrography Dataset (NHDPlusV2; McKay et al., 2012) and the Catchment Characterization and Modelling Database (CCM2; Vogt et al., 2007), respectively. To account for natural biogeographic differences in climate, geology, and physiography, we conducted our analyses within freshwater ecoregions and further stratified analyses by stream size (Freshwater Ecoregions of the World (FEOW); Figure 3.1; Abell et al., 2008). Stream reaches with a cumulative upstream drainage area larger than 100 km2 were classified as rivers (R), and all other stream reaches were classified as creeks (C; Wang et al., 2011; Solheim et al., 2019). We conducted our analyses within stream size classes (hereafter, strata) for each ecoregion. Identifying threshold responses of fishes to human landscape stressors We used threshold values for landscape stressors identified by Üblacker et al. (2023) for the study area. In their investigation, Üblacker et al. (2023) evaluated a cross-continental database containing records of fish assemblages sampled throughout the US and Europe, and they identified thresholds by testing responses of four different fish metrics (percent of intolerant, migratory, lithophilic, and rheophilic individuals) to three different types of human land use (percent of agriculture, pasture, and urban land use). These fish metrics are commonly used to develop fish-based multimetric indices because they are sensitive and often respond negatively to habitat degradation (de Freitas Terra et al., 2013; de Carvalho et al., 2017). 47 Thresholds were identified using piecewise linear regression and significant threshold values were verified using a combination of change-point analysis and visual confirmation (for additional details, see Üblacker et al., 2023). For the purposes of our study, we extracted thresholds showing responses of fish metrics to agriculture, pasture, or urban land use summarized within network catchments, which are defined as the cumulative upstream land area draining to each interconfluence stream reach (Wang et al., 2011). Note that these thresholds were specific to ecoregion and stream size strata (see Üblacker et al., 2023). Because we were broadly interested in determining whether each landscape stressor had surpassed the minimum intensity at which changes in fish assemblages were expected to occur, we based our analyses on the lowest threshold value identified for any fish metric. Calculating threshold status indices To address our first objective (Figure 3.2), we quantified the current intensity of human landscape stressors by summarizing percent agriculture, pasture, and urban land use within network catchments (Wieferich et al., 2021). Land use data in the US were sourced from the 2019 National Land Cover Database (Dewitz and US Geological Survey, 2021), and those in Europe were sourced from the 2018 Coordination of Information on the Environment (CORINE) Land Cover Dataset (European Environment Agency, 2019). Previous work by Üblacker et al. (2023) found that agriculture, pasture, and urban land use categories within these datasets are comparable. Within each ecoregion, we then calculated threshold status indices by dividing network catchment land use summaries by their strata-specific threshold values. Using this approach, a threshold status greater than 1 indicates that a catchment has passed the given land use threshold, whereas a threshold status less than or equal to 1 indicates that the catchment remains below that threshold. To facilitate interpretation, we used a quantile classification to 48 assign threshold status index values to one of four categories, designating whether a catchment is “Far Above,” “Just Above,” “Just Below,” or “Far Below” each of its associated threshold values. To identify catchments that have passed multiple thresholds, we combined our threshold status indices together to determine whether each catchment has passed its known thresholds for agriculture, pasture, and/or urban land use. Doing so allowed us to evaluate threshold patterns across the study region and improve understanding of the frequency of multiple stressor configurations within two large, heterogeneous freshwater ecoregions. Summarizing protected areas within catchments To generate insights into the spatial distribution and effectiveness of protected area networks for supporting stream fishes (Figure 3.2), we calculated total protected area coverage by summarizing the World Database of Protected Areas (WDPA; UNEP-WCMC and IUCN, 2024) within network catchments throughout the US (NHDPlusV2) and Europe (CCM2). Protected areas in the WDPA are assigned to International Union for Conservation of Nature (IUCN) management categories. Spatial overlap among features in the WDPA is common, meaning that individual protected areas can have multiple IUCN designations (e.g., a national park located inside an international biosphere reserve). To ensure that protected areas were not counted more than once when summarizing the total coverage of protected areas, we created a flat layer using a series of ‘dissolve’ and ‘difference’ operations in Esri ArcGIS Pro. Within each network catchment, protected area coverage ranged from 0% to 100%. To ease interpretation and enhance operability of these protected area summaries, we assigned network catchments to one of three equal interval categories. Catchments with a protected area coverage less than or equal to 33.3% were considered to have “Low Protection,” those with greater than 33.3% and less than or equal to 66.6% coverage were considered to have “Medium Protection,” and those with 49 greater than 66.6% coverage were considered to have “High Protection.” We used Kruskal- Wallis and Wilcoxon rank sum tests (with Bonferroni corrections) to assess significance of differences between our three protected area categories with respect to the four fish metrics calculated by Üblacker et al. (2023), including percentages of intolerant, migratory, lithophilic, and rheophilic individuals. This approach enabled us to evaluate the effectiveness of protected area networks for supporting the diversity of stream fish assemblages across the study region. Prioritizing conservation and restoration actions Next, we integrated threshold status indices (Objective 1), protected area categories (Objective 2), and knowledge of multiple stressor configurations to prioritize conservation and restoration actions and promote the use of ecological thresholds in decision-making applications (Figure 3.2). In our decision-support framework, we considered catchments to be a high priority for conservation if they had “Low Protection” and were “Just Below” a given land use threshold. Catchments at the intersection of these two categories were relatively vulnerable to crossing a known threshold but were expected to benefit from reserve expansion or other proactive conservation strategies. Similarly, catchments were considered to be a high priority for restoration if they had “Medium” or “High Protection” and were “Just Above” a given land use threshold. In theory, these catchments should require less effort to restore than catchments that were “Far Above” a given threshold, and they should also be more feasible to implement restoration actions within compared to catchments in the “Low Protection” category. After identifying catchments that were a high priority for management, we mapped our results and summarized the frequency of recommended management actions (e.g., percent of catchments identified as a high priority for urban conservation) by ecoregion and strata. 50 Results Summarizing threshold status indices across the conterminous United States and Europe In total, we calculated 44 threshold status indices to characterize vulnerability of stream fishes to agriculture, pasture, and urban land use within 19 ecoregions, including 14 ecoregions in the US and 5 ecoregions in Europe (Table 3.1). Threshold values for agricultural land use summarized within network catchments ranged from 0.10% for rivers in the Middle Missouri ecoregion to 40.06% for creeks in the Western Iberia ecoregion. Thresholds for pasture land use ranged from 1.52% for creeks in the Upper Mississippi ecoregion to 15.43% for rivers in the Colorado ecoregion. Lastly, thresholds for urban land use ranged from 0.08% for rivers in the Western Iberia ecoregion to 12.30% for creeks in the Laurentian Great Lakes ecoregion (Table 3.1; Üblacker et al., 2023). Evaluating the effectiveness of protected areas for supporting stream fishes We found that all four fish metrics, including percentages of intolerant (Kruskal-Wallis chi-squared (χ2) = 2425.7; p < 0.001), migratory (χ2 = 2005.7; p < 0.001), lithophilic (χ2 = 1176.3; p < 0.001), and rheophilic (χ2 = 1125.9; p < 0.001) individuals, were significantly higher in stream reaches having more protected land within their network catchments (Figure 3.3). In addition, all pairwise Wilcoxon rank sum tests revealed significant differences in fish metrics between protected area categories (e.g., Low, Medium, and High protection), and this pattern was observed for all four fish metrics (significance was assessed using an α value = 0.017; all p values < 0.001). These results, based on thousands of records of stream fishes in two continents, clearly show the influence of protected lands on stream fish assemblages. 51 Case Study 1 - Central and Western Europe ecoregion Our first case study was conducted in the Central and Western Europe ecoregion, which consisted of 146,824 catchments including 111,697 creeks and 35,127 rivers. According to our threshold status indices, 43.5% of creeks and 64.6% of rivers within the Central and Western Europe ecoregion had exceeded the threshold for agriculture, whereas 33.2% of creeks and 71.8% of rivers had passed the threshold for urban land use (Figure 3.4a, b; Table 3.2). We found that most catchments within the Central and Western Europe ecoregion were poorly protected, as 59.9% of creeks and 60.9% of rivers had “Low Protection”. In contrast, 13.5% and 26.4% of all catchments in the Central and Western Europe ecoregion had a “Medium” or “High” degree of protection, respectively. Our investigation of multiple stressor configurations indicated that 29.3% of catchments had passed both an agricultural and urban land use threshold, whereas 38.4% of catchments in the Central and Western Europe ecoregion had not passed either threshold (Figure 3.5a). In comparison, 19.2% of catchments had passed a threshold for agriculture but not urban land use, and 13.1% had passed a threshold for urban but not agricultural land use (Figure 3.5a). Based on the framework illustrated in Figure 3.6, we provided conservation or restoration recommendations for 34.9% of creeks and 37.7% of rivers within the Central and Western Europe ecoregion (Figure C3.1a). For creeks, 32.1% of all recommendations were associated with catchments that were a high priority for urban conservation, followed by 25.4% that were a high priority for agricultural conservation, and 17.0% that were a high priority for agricultural restoration. Similarly for rivers, 25.2% of all recommendations were associated with catchments that were a high priority for agricultural restoration, followed by 23.7% that were a high priority for urban restoration. 52 To further demonstrate how our approach could be used to inform decision-making at multiple spatial scales, we highlighted a subset of conservation and restoration recommendations for catchments that were located within subbasins of the Vistula River watershed, Poland (Figure 3.7). Within this watershed, opportunities to implement agricultural and urban conservation actions were concentrated along the mainstem of the Bug River (Figure 3.7a), whereas the Narew River was identified as a high priority subbasin both for agricultural and urban restoration actions (Figure 3.7b). Case Study 2 - Middle Missouri ecoregion Our second case study was conducted in the Middle Missouri ecoregion, which consisted of 181,176 catchments including 138,993 creeks and 42,183 rivers. We found that 82.8% of creeks and 90.8% of rivers within the Middle Missouri ecoregion had passed the threshold for agriculture, whereas 65.6% of creeks and 65.3% of rivers had passed the threshold for urban land use (Figure 3.4c, d; Table 3.2). As we saw in the Central and Western Europe ecoregion, most catchments were poorly protected, as 97.8% of creeks and 97.6% of rivers within the Middle Missouri ecoregion were classified as having “Low Protection”. Conversely, 1.1% and 1.2% of all catchments in the Middle Missouri ecoregion had a “Medium” or “High” degree of protection, respectively. Our investigation of multiple stressor configurations indicated that 63.0% of catchments had passed both an agriculture and urban land use threshold, whereas 12.8% of catchments in the Middle Missouri ecoregion had not passed either threshold (Figure 3.5b). In comparison, 21.7% of catchments had passed a threshold for agriculture but not urban land use, and 2.6% had passed a threshold for urban but not agricultural land use (Figure 3.5b). Overall, we provided broad recommendations for conservation or restoration actions for 18.0% of creeks and 18.9% of rivers within the Middle Missouri ecoregion (Figure C3.1b). For 53 creeks, 92.4% of all recommendations were associated with catchments that were a high priority for urban conservation, followed by 2.8% that were a high priority for agricultural restoration. Similarly for rivers, 84.7% of all recommendations were associated with catchments that were a high priority for urban conservation, followed by 5.6% that were a high priority for agricultural restoration. Discussion While it is widely acknowledged that landscape stressors including agriculture, pasture, and urban land use degrade habitats and impact stream fishes, efforts to contextualize how fish assemblages respond to those stressors are crucial for identifying opportunities to inform ecosystem management (Brumm et al., 2023). In this study, we developed a decision-support framework to promote the use of ecological thresholds in decision-making applications throughout the conterminous United States (US) and Europe. Although protected areas were lacking for many catchments in the Central and Western Europe and Middle Missouri ecoregions, we showed that protected areas within network catchments are effective for supporting stream fishes. Using our framework, we identified conservation gaps in catchments that are at risk of surpassing known thresholds while also helping to inform restoration efforts for streams within protected landscapes. Our findings highlight the pervasive influences of agricultural land use on stream habitat and indicate that widespread degradation may result from increased urban development within catchments that are poorly protected. Collectively, our results characterize the vulnerability of stream fish assemblages to human landscape stressors and provide a flexible framework with which to support management decision-making processes. 54 Addressing the utility of threshold status indices Understanding spatial variation in the vulnerability of stream fishes to human landscape stressors is important for determining where management strategies might best be applied to proactively conserve or retroactively restore natural characteristics of stream habitats (Sievert et al., 2016). By developing threshold status indices, we showed that a large proportion of catchments in the US and Europe have surpassed known thresholds for agricultural, pasture, and/or urban land use, and we also showed that several opportunities exist to make informed management decisions based on configurations of multiple landscape stressors. Moreover, our threshold status indices are unique in that they can be mapped for all catchments to generate a continuous characterization of vulnerability within several large, heterogeneous ecoregions that span an intercontinental spatial extent. This aspect of our study differs from previous assessments that have used thresholds to inform management decisions, because existing applications have largely been restricted to individual watersheds (Ettinger et al., 2021; Delaney and Larson, 2024). Lastly, because catchments represent our basic units of analysis, our indices can be used in local applications or may be scaled up to draw generalized inferences about the relative vulnerability of larger areas ranging from subbasins to entire watersheds. This characteristic makes our approach relevant to a variety of different research and management applications, including national scale assessments (Soulé and Terborgh, 1999; Lessmann et al., 2014), regional planning exercises (Leonard et al., 2017), and structured participatory frameworks for engaging with local communities (Robinson et al., 2019; Lees et al., 2021). Considering the effectiveness of protected areas Whether protected areas are an effective conservation strategy is a topic that has been debated in both the terrestrial and marine realms (Leverington et al., 2010; Laurance et al., 2012; 55 Watson et al., 2014). More recent assessments conducted in freshwater ecosystems have provided evidence to suggest that protected areas, in some cases, can be effective for supporting freshwater biodiversity (see Acreman et al., 2020). Throughout the conterminous United States and Europe, our results clearly showed that more intolerant, migratory, lithophilic, and rheophilic individuals occurred in fish assemblages found in streams with more protected areas in their catchments. Yet in spite of this, we also found that most catchments in the Central and Western Europe and Middle Missouri ecoregions were poorly protected. In the Iberian peninsula, Hermoso et al. (2015) showed that Natura 2000 sites, which are designated to safeguard threatened species and habitats in Europe, failed to provide adequate coverage for several freshwater taxa, and similar findings have been observed for stream fishes in the Brazilian Amazon (Frederico et al., 2018) and conterminous United States (Cooper et al., 2019). Collectively, these findings indicate that protected areas can be effective for supporting stream fishes and underscore the need to more explicitly consider freshwater ecosystems in the design of future protected areas to help close gaps in protection of stream fishes and other freshwater conservation targets. Using our framework to inform conservation and restoration decisions Spatial planning, which includes efforts to identify priority areas for biodiversity conservation and ecological restoration, represents a single step in the broader systematic conservation planning process (Margules and Pressey, 2000). Insights gained from our decision- support framework represent management priorities based primarily on the proximity of catchments to known thresholds for landscape stressors. However, the management decision- making process is complex and must be informed by additional data inputs that account for measures of cultural, economic, and social relevance. A recent review of systematic conservation 56 planning efforts conducted in Europe found that stakeholder inputs were rarely considered (Jung et al., 2024), even though stakeholder participation is one of the biggest determinants of success for conservation programs (Farwig et al., 2025). Although our decision-support framework used ecological thresholds to identify catchments that may benefit from conservation and restoration actions across a broad spatial extent, local socioeconomic considerations including sense of place and land ownership must also be incorporated into the design and implementation of on-the- ground management actions (Fischer et al., 2021). Since the 1960s, the focus of nature conservation has drifted from a ‘nature for itself’ to a ‘people and nature’ mindset (Mace, 2014; Hermoso et al., 2016). This change reflects a paradigm shift towards recognition of the need to address multiple competing objectives and navigate trade-offs between biodiversity conservation and ecosystem services (Giakoumi et al., 2025). In support of these efforts, we identified catchments in which biodiversity conservation and human land use practices (i.e., agricultural production, urban development) may be compatible with one another at current levels of low to moderate intensity. For example, we identified priority areas for urban conservation in which stream fish assemblages are likely to benefit from the establishment of protected areas, conservation easements, or implementation of other best management practices including green infrastructure to minimize changes in hydrological regimes and to contain nutrient runoff in urban settings (Liu et al., 2016; Wang et al., 2024). Such proactive conservation strategies are important to limit further degradation of stream habitat and are most likely to be successful in spaces where decisions are made in collaboration with local stakeholders (Doyle-Capitman and Decker, 2018; Mori and Isbell, 2024). Large-scale biodiversity initiatives have also emphasized the need to restore degraded habitat conditions to promote recovery of species and ecological processes within freshwater 57 ecosystems (Palmer et al., 2005; Wohl et al., 2015, Palmer et al., 2020). In support of these efforts, we identified priority areas for agricultural restoration, including catchments that had passed a known agricultural land use threshold despite having a relatively high degree of protection. Prior to implementing restoration actions within these catchments, it is important to conduct local habitat condition assessments to characterize specific mechanisms of impairment and establish clear restoration goals (see USDA NRCS, 2019). Although restoration and monitoring guidelines for rivers and streams are essential for measuring the success of restoration efforts, their availability and rigor are known to vary among restoration programs (Feio et al., 2021). Standardized reporting of restoration projects is necessary to improve restoration outcomes through adaptive management and to foster more effective communication with stakeholders and policymakers (Gann et al., 2019; Löfqvist et al., 2023). Limitations One limitation of our analyses is that we investigated effects of human landscape stressors on fish assemblages but did not account for other stressors known to influence the structure or function of stream fish assemblages. Additional stressors that may be accounted for at smaller spatial extents include in-channel factors associated with channelization, fragmentation, and nutrient enrichment, among others. While assessing responses of stream fish assemblages to in-channel factors can improve mechanistic understanding, such data are not comprehensively available for all stream reaches within the broad extent of our study area but could be incorporated with our results if applied within a single basin. A second limitation of our approach is that we identified priority restoration areas based on the assumption that habitat degradation (e.g., represented by dramatic reductions in numbers of fishes with specific levels of disturbance) is inherently reversible. However, the literature 58 suggests that most restoration projects fall short of attaining pre-disturbance conditions (see Palmer et al., 2020). In part, the effectiveness of restoration projects is limited by uncertainties in the conditions under which a degraded system can fully be restored to a reference state (i.e., hysteresis; Harper et al., 2024). An alternative management strategy, which may be more effective under high levels of uncertainty, is to intentionally direct the transformation of an ecosystem into a new desirable state that better promotes ecosystem services that enhance human well-being (Lynch et al., 2021). Returning a site to its pre-disturbance state may not always be the goal, and decisions about what constitutes a new desirable ecosystem state should be made in collaboration with land use planners, natural resource managers, and other local interest groups. Conclusions Despite recognition of their limitations, initiatives such as the Kunming-Montreal Global Biodiversity Framework and the European Union’s Nature Restoration Law represent formal commitments to addressing the freshwater biodiversity crisis (Cooke et al., 2023; Hughes and Grumbine, 2023; Stoffers et al., 2024). To help achieve the ambitious conservation and restoration targets set forth by such initiatives, spatial planning efforts conducted at broad spatial extents must account for complex responses of fish assemblages to multiple landscape stressors (van Rees et al., 2021). By characterizing vulnerability of stream fishes to multiple landscape stressors, our decision-support framework promotes the use of ecological thresholds in decision- making applications throughout the conterminous United States and Europe. More broadly, our decision-support framework serves as a template to support threshold-based management in other ecosystems throughout the world. 59 REFERENCES Abell, R., Thieme, M.L., Revenga, C., Bryer, M., Kottelat, M., Bogutskaya, N., Coad, B., Mandrak, N., Balderas, S.C., … Petry, P., 2008. Freshwater Ecoregions of the World: a new map of biogeographic units for freshwater biodiversity conservation. BioScience. 58, 403- 414. Acreman, M., Hughes, K.A., Arthington, A.H., Tickner, D., Dueñas, M.A., 2020. Protected areas and freshwater biodiversity: a novel systematic review distils eight lessons for effective conservation. Conservation Letters. 13, e12684. Allan, J.D., 2004. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annual Review of Ecology, Evolution, and Systematics. 35, 257-284. Baker, M.E., King, R.S., 2010. A new method for detecting and interpreting biodiversity and ecological community thresholds. Methods in Ecology and Evolution. 1, 25-37. Birk, S., Chapman, D., Carvalho, L., Spears, B.M., Andersen, H.E., Argillier, C., Auer, S., Baattrup-Pedersen, A., Banin, L., … Hering, D., 2020. Impacts of multiple stressors on freshwater biota across spatial scales and ecosystems. Nature Ecology and Evolution. 4, 1060-1068. Booth, D.B., Karr, J.R., Schauman, S., Konrad, C.P., Morley, S.A., Larson, M.G., Burges, S.J., 2004. Reviving urban streams: land use, hydrology, biology, and human behavior. Journal of the American Water Resources Association. 40, 1129-1388. Brumm, K.J., Infante, D.M., Cooper, A.R., 2023. Functional biogeography of fluvial fishes across the conterminous U.S.A.: assessing the generalizability of trait-environment relationships over large regions. Freshwater Biology. 68, 790-805. Carrier-Belleau, C., Pascal, L., Nozais, C., Archambault, P., 2022. Tipping points and multiple drivers in changing aquatic ecosystems: a review of experimental studies. Limnology and Oceanography. 67, S312-S330. de Carvalho, D.R., Leal, C.G., Junqueira, N.T., de Castro, M.A., Fagundes, D.C., Alves, C.B.M., Hughes, R.M., Pompeu, P.S., 2017. A fish-based multimetric index for Brazilian savanna streams. Ecological Indicators. 77, 386-396. Cooke, S.J., Harrison, I., Thieme, M.L., Landsman, S.J., Birnie-Gauvin, K., Raghavan, R., Creed, I.F., Pritchard, G., Ricciardi, A., Hanna, D.E.L., 2023. Is it a new day for freshwater biodiversity? Reflections on outcomes of the Kunming-Montreal Global Biodiversity Framework. PLOS Sustainability and Transformation. 2, e0000065. 60 Cooper, A.R., Tsang, Y.P., Infante, D.M., Daniel, W.M., McKerrow, A.J., Wieferich, D., 2019. Protected areas lacking for many common fluvial fishes of the conterminous USA. Diversity and Distributions. 25, 1289-1303. Côté, I.M., Darling, E.S., Brown, C.J., 2016. Interactions among ecosystem stressors and their importance in conservation. Proceedings of the Royal Society B. 283: 20152592. Delaney, J.T., Larson, D.M., 2024. Using explainable machine learning methods to evaluate vulnerability and restoration potential of ecosystem state transitions. Conservation Biology. 38, e14203. Dewitz, J., US Geological Survey., 2021. National Land Cover Database (NLCD) 2019 products (ver. 3.0, February 2024). US Geological Survey data release. https://doi.org/10.5066/P9KZCM54. Doyle-Capitman, C.E., Decker, D.J., 2018. Facilitating local stakeholder participation in collaborative landscape conservation planning: a practitioners’ guide. Human Dimensions Research Unit Publication Series 17-12. Department of Natural Resources, College of Agriculture and Life Sciences, Cornell University, Ithaca, New York, United States. Dudgeon, D., 2019. Multiple threats imperil freshwater biodiversity in the Anthropocene. Current Biology. 29, R960-R967. European Environment Agency (EEA)., 2019. CORINE Land Cover 2018. https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac. Ettinger, A.K., Buhle, E.R., Feist, B.E., Howe, E., Spromberg, J.A., Scholz, N.L., Levin, P.S., 2021. Prioritizing conservation actions in urbanizing landscapes. Scientific Reports. 11, 818. Farwig, N., Sprenger, P.P., Baur, B., Böhning-Gaese, K., Brandt, A., Eisenhauer, N., Ellwanger, G., Hochkirch, A., Karamanlidis, A.A., … Mosbrugger, V., 2025. Identifying major factors for success and failure of conservation programs in Europe. Environmental Management. 75, 425-443. Feio, M.J., Hughes, R.M., Callisto, M., Nichols, S.J., Odume, O.N., Quintella, B.R., Kuemmerlen, M., Aguiar, F.C., Almeida, S.F.P., … Yates, A.G., 2021. The biological assessment and rehabilitation of the world’s rivers: an overview. Water. 13, 371. Feio, M.J., Hughes, R.M., Serra, S.R.Q., Nichols, S.J., Kefford, B.J., Lintermans, M., Robinson, W., Odume, O.N., Callisto, M., … Sharma, S., 2023. Fish and macroinvertebrate assemblages reveal extensive degradation of the world’s rivers. Global Change Biology. 29, 355-374. 61 Fischer, J., Riechers, M., Loos, J., Martin-Lopez, B., Temperton, V.M., 2021. Making the UN Decade on Ecosystem Restoration a social-ecological endeavour. Trends in Ecology and Evolution. 36, 20-28. Foley, M.M., Martone, R.G., Fox, M.D., Kappel, C.V., Mease, L.A., Erickson, A.L., Halpern, B.S., Selkoe, K.A., Taylor, P., Scarborough, C., 2015. Using ecological thresholds to inform resource management: current options and future possibilities. Frontiers in Marine Science. 2, 95. Frederico, R.G., Zuanon, J., de Marco Jr., P., 2018. Amazon protected areas and its ability to protect stream-dwelling fish fauna. Biological Conservation. 219, 12-19. de Freitas Terra, B., Hughes, R.M., Francelino, M.R., Araújo, F.G., 2013. Assessment of biotic condition of Atlantic Rain Forest streams: a fish-based multimetric approach. Ecological Indicators. 34, 136-148. Gann, G.D., McDonald, T., Walder, B., Aronson, J., Nelson, C.R., Jonson, J., Hallett, J.G., Eisenberg, C., Guariguata, M.R., … Dixon, K.W., 2019. International principles and standards for the practice of ecological restoration: second edition. Restoration Ecology. 27, S1-S46. Giakoumi, S., Richardson, A.J., Doxa, A., Moro, S., Andrello, M., Hanson, J.O., Hermoso, V., Mazor, T., McGowan, J., … Katsanevakis, S., 2025. Advances in systematic conservation planning to meet global biodiversity goals. Trends in Ecology and Evolution. 40, 395-410. Harper, M., Rytwinski, T., Creed, I.F., Helmuth, B., Smol, J.P., Bennett, J.R., Hanna, D., Saravia, L.A., Rocha, J., … Cooke, S.J., 2024. A multi-realm perspective on applying potential tipping points to environmental decision-making. Environmental Reviews. 32, 131-144. Hastings, A., Abbott, K.C., Cuddington, K., Francis, T., Gellner, G., Lai, Y.C., Morozov, A., Petrovskii, S., Scranton, K., Zeeman, M.L., 2018. Transient phenomena in ecology. Science. 361, 6406. Hermoso, V., Filipe, A.F., Segurado, P., Beja, P., 2015. Effectiveness of a large reserve network in protecting freshwater biodiversity: a test for the Iberian Peninsula. Freshwater Biology. 60, 698-710. Hermoso, V., Abell, R., Linke, S., Boon, P., 2016. The role of protected areas for freshwater biodiversity conservation: challenges and opportunities in a rapidly changing world. Aquatic Conservation: Marine and Freshwater Ecosystems. 26, 3-11. Hernández Martínez de la Riva, A., Harper, M., Rytwinski, T., Sahdra, A., Taylor, J.J., Bard, B., Bennett, J.R., Burton, D., Creed, I.F., … Cooke, S.J., 2023. Tipping points in freshwater ecosystems: an evidence map. Frontiers in Freshwater Science. 1, 1264427. 62 Hughes, A.C., Grumbine, R.E., 2023. The Kunming-Montreal Global Biodiversity Framework: what it does and does not do, and how to improve it. Frontiers in Environmental Science. 11, 1281536. Jung, M., Alagador, D., Chapman, M., Hermoso, V., Kujala, H., O’Connor, L., Schinegger, R., Verburg, P.H., Visconti, P., 2024. An assessment of the state of conservation planning in Europe. Philosophical Transactions of the Royal Society B. 379, 20230015. Kelly, R.P., Erickson, A.L., Mease, L.A., Battista, W., Kittinger, J.N., Fujita, R., 2015. Embracing thresholds for better environmental management. Philosophical Transactions of the Royal Society B. 370, 20130276. Kukkala, A.S., Moilanen, A., 2013. Core concepts of spatial prioritization in systematic conservation planning. Biological Reviews. 88, 443-464. Laurance, W.F., Useche, D.C., Rendeiro, J., Kalka, M., Bradshaw, C.J.A., Sloan, S.P., Laurance, S.G., Campbell, M., Abernethy, K., … Zamzani, F., 2012. Averting biodiversity collapse in tropical forest protected areas. Nature. 489, 290-294. Lawler, J.J., White, D., Master, L.L., 2003. Integrating representation and vulnerability: two approaches for prioritizing areas for conservation. Ecological Applications. 13, 1762-1772. Lees, C.M., Rutschmann, A., Santure, A.W., Beggs, J.R., 2021. Science-based, stakeholder- inclusive and participatory conservation planning helps reverse the decline of threatened species. Biological Conservation. 260, 109194. Leonard, P.B., Baldwin, R.F., Hanks, R.D., 2017. Landscape-scale conservation design across biotic realms: sequential integration of aquatic and terrestrial landscapes. Scientific Reports. 7, 14556. Lessmann, J., Muñoz, J., Bonaccorso, E., 2014. Maximizing species conservation in continental Ecuador: a case of systematic conservation planning for biodiverse regions. Ecology and Evolution. 4, 2410-2422. Leverington, F., Costa, K.L., Pavese, H., Lisle, A., Hockings, M., 2010. A global analysis of protected area management effectiveness. Environmental Management. 46, 685-698. Linke, S., Pressey, R.L., Bailey, R.C., Norris, R.H., 2007. Management options for river conservation planning: condition and conservation re-visited. Freshwater Biology. 52, 918- 938. Liu, Y., Theller, L.O., Pijanowski, B.C., Engel, B.A., 2016. Optimal selection and placement of green infrastructure to reduce impacts of land use change and climate change on hydrology and water quality: an application to the Trail Creek Watershed, Indiana. Science of the Total Environment. 553, 149-163. 63 Löfqvist, S., Kleinschroth, F., Bey, A., de Bremond, A., DeFries, R., Dong, J., Fleischman, F., Lele, S., Martin, D.A., … Garrett, R.D., 2023. How social considerations improve the equity and effectiveness of ecosystem restoration. 73, 134-148. Lynch, A.J., Thompson, L.M., Beever, E.A., Cole, D.N., Engman, A.C., Hoffman, C.H., Jackson, S.T., Krabbenhoft, T.J., Lawrence, D.J., … Wilkening, J.L., 2021. Managing for RADical ecosystem change: applying the Resist-Accept-Direct (RAD) framework. Frontiers in Ecology and the Environment. 19, 461-469. Mace, G.M., 2014. Whose conservation? Science. 345, 1558-1560. Margules, C.R., Pressey, R.L., 2000. Systematic conservation planning. Nature. 405, 243-253. Mattson, K.M., Angermeier, P.L., 2007. Integrating human impacts and ecological integrity into a risk-based protocol for conservation planning. Environmental Assessment. 39, 125-138. McKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., & Rea, A., 2012. NHDPlus version 2: User Guide. Moilanen, A., Lehtinen, P., Kohonen, I., Jalkanen, J., Virtanen, E.A., Kujala, H., 2022. Novel methods for spatial prioritization with applications in conservation, land use planning and ecological impact avoidance. Methods in Ecology and Evolution. 13, 1062-1072. Mori, A.S., Isbell, F., 2024. Untangling the threads of conservation: a closer look at restoration and preservation. Journal of Applied Ecology. 61, 215-222. Negret, P.J., Venegas, R., Sonter, L.J., Possingham, H.P., Maron, M., 2024. Conservation planning for retention, not just protection. Global Change Biology. 30, e17211. Noss, R., Nielsen, S., Vance-Borland, K., 2009. Prioritizing ecosystems, species, and sites for restoration (Chapter 12). A. Moilanen, K.A. Wilson, H.P. Possingham (Eds.), Spatial conservation prioritization: quantitative methods and computation tools, Oxford University Press, Oxford, England, United Kingdom. Palmer, M.A., Bernhardt, E.S., Allan, J.D., Lake, P.S., Alexander, G., Brooks, S., Carr, J., Clayton, S., Dahm, C.N., … Sudduth, E., 2005. Standards for ecologically successful river restoration. Journal of Applied Ecology. 42, 208-217. Palmer, M.A., Stewart, G.A., 2020. Ecosystem restoration is risky… but we can change that. One Earth. 3, 661-664. van Rees, C.B., Waylen, K.A., Schmidt-Kloiber, A., Thackeray, S.J., Kalinkat, G., Martens, K., Domisch, S., Lillebø, A.I., Hermoso, V., … Jähnig, S.C., 2021. Safeguarding freshwater life beyond 2020: recommendations for the new global biodiversity framework from the European experience. Conservation Letters. 14, e12771. 64 Riseng, C.M., Wiley, M.J., Black, R.W., Munn, M.D., 2011. Impacts of agricultural land use on biological integrity: a causal analysis. Ecological Applications. 21, 3128-3146. Robinson, K.F., Fuller, A.K., Stedman, R.C., Siemer, W.F., Decker, D.J., 2019. Integration of social and ecological sciences for natural resource decision making: challenges and opportunities. Environmental Management. 63, 565-573. Ross, J.A., Infante, D.M., Cooper, A.R., Whittier, J.B., Daniel, W.M., 2023. Assessing impacts of human stressors on stream fish habitats across the Mississippi River basin. Water. 15, 2400. Sayer, C.A., Fernando, E., Jimenez, R.R., Macfarlane, N.B.W., Rapacciuolo, G., Böhm, M., Brooks, T.M., Contreras-MacBeath, T., Cox, N.A., … Darwall, W.R.T., 2025. One-quarter of freshwater fauna threatened with extinction. Nature. 638, 138-145. Sievert, N.A., Paukert, C.P., Tsang, Y.P., Infante, D.M., 2016. Development and assessment of indices to determine stream fish vulnerability to climate change and habitat alteration. Ecological Indicators. 67, 403-416. Solheim, A.L., Globevnik, L., Austnes, K., Kristensen, P., Moe, S.J., Persson, J., Phillips, G., Poikane, S., van de Bund, W., Birk, S., 2019. A new broad typology for rivers and lakes in Europe: development and application for large-scale environmental assessments. Science of the Total Environment. 697, 134043. Stoffers, T., Altermatt, F., Baldan, D., Bilous, O., Borgwardt, F., Buijse, A.D., Bondar-Kunze, E., Cid, N., Erős, T., … Hein, T., 2024. Reviving Europe’s rivers: Seven challenges in the implementation of the Nature Restoration Law to restore free-flowing rivers. WIREs Water. 11, e1717. Üblacker, M.M., Infante, D.M., Cooper, A.R., Daniel, W.M., Schmutz, S., Schinegger, R., 2023. Cross-continental evaluation of landscape-scale drivers and their impacts to fluvial fishes: understanding frequency and severity to improve fish conservation in Europe and the United States. Science of the Total Environment. 897, 165101. UNEP-WCMC and IUCN., 2024. Protected Planet: The World Database on Protected Areas (WDPA). Available at: https://www.protectedplanet.net. USDA NRCS., 2019. National Handbook of Conservation Practices. Conservation Practice Standard, Code 395. Stream habitat improvement and management. Vogt, J., Soille, P., de Jager, A., Rimavičiute, E., Mehl, W., Foisneau, S., Bodis, K., Dusant, J., Paracchini, M.L., … Bamps, C., 2007. A pan-European river and catchment database. European Commission. Joint Research Centre, Technical Report EUR 22920 EN. https://doi.org/10.2788/35907. 65 Wang, D., Xu, P.Y., An, B.W., Guo, Q.P., 2024. Urban green infrastructure: bridging biodiversity conservation and sustainable urban development through adaptive management approach. Frontiers in Ecology and Evolution. 12, 1440477. Wang, L., Infante, D., Esselman, P., Cooper, A., Wu, D., Taylor, W., Beard, D., Whelan, G., Ostroff, A., 2011. A hierarchical spatial framework and database for the National River Fish Habitat Condition Assessment. Fisheries. 36, 436-449. Watson, J.E.M., Dudley, N., Segan, D.B., Hockings, M., 2014. The performance and potential of protected areas. Nature. 515, 67-73. Wieferich, D.J., Williams, B., Falgout, J.T., Foks, N.L., 2021. xstrm. U.S. Geological Survey software release. https://doi.org/10.5066/P9P8P7Z0. Wohl, E., Lane, S.N., Wilcox, A.C., 2015. The science and practice of river restoration. Water Resources Research. 51, 5974-5997. 66 APPENDIX 3.A: TABLES Table 3.1. Availability of threshold values for creeks (C) and rivers (R) summarized by ecoregion. Thresholds were sourced from Üblacker et al. (2023) and correspond to the intensity of landscape stressors summarized within network catchments. Ecoregion IDs align with those reported in Figure 3.1. ID Europe Ecoregion % Agriculture % Pasture % Urban US 1 Cantabric Coast – Languedoc 2 Central and Western Europe 3 Dniester – Lower Danube 4 Upper Danube 5 Western Iberia 6 Appalachian Piedmont 7 Central Prairie 8 Chesapeake Bay 9 Colorado 10 Columbia Unglaciated 11 Cumberland 12 Laurentian Great Lakes 13 Middle Missouri 14 Northeast US Atlantic Drainages 15 Ozark Highlands 16 Sacramento – San Joaquin 17 Teays – Old Ohio 18 Upper Mississippi 19 Upper Missouri 1.23 (R) 24.58 (C*) 40.06 (C) 2.49 (C); 0.58 (R) 2.07 (C) 0.19 (R) 0.20 (C); 5.97 (R) 2.83 (R) 0.19 (R) 0.25 (C); 0.10 (R) 1.05 (C); 6.67 (R) 6.19 (R) 7.26 (R) 5.05 (C); 15.43 (R) 5.46 (C) 7.25 (C) 1.52 (C) 1.05 (R) 3.50 (C); 2.25 (R) 4.68 (C); 5.15 (R) 3.90 (R) 1.75 (C); 0.08 (R) 9.31 (R) 7.01 (R) 0.96 (C); 0.61 (R) 10.04 (C) 12.30 (C) 2.54 (C); 2.08 (R) 11.51 (C) 9.58 (R) 3.79 (C); 6.22 (R) 0.88 (R) *The threshold value for rivers was assumed to be the same as that for creeks 67 Table 3.2. Summary of catchments in the Central and Western Europe and Middle Missouri ecoregions that had passed either an agricultural or urban land use threshold. Ecoregion Central and Western Europe Count Agriculture Urban Middle Missouri All Creeks Rivers All Creeks Rivers 146,824 111,697 35,127 181,176 138,993 42,183 71,264 (48.5%) 62,278 (42.4%) 48,572 (43.5%) 37,044 (33.2%) 22,692 (64.6%) 25,234 (71.8%) 153,449 (84.7%) 118,789 (65.6%) 115,144 (82.8%) 91,228 (65.6%) 38,305 (90.8%) 27,561 (65.3%) 68 APPENDIX 3.B: FIGURES Figure 3.1. All ecoregions (n=19) throughout (a) Europe and the (b) United States in which threshold values were identified for landscape stressors summarized within network catchments (Üblacker et al., 2023). The two ecoregions that we present as case studies include the (a; ID #2) Central and Western Europe ecoregion and the (b; ID #13) Middle Missouri ecoregion. Ecoregion IDs align with those reported in Table 3.1. 69 Figure 3.2. A conceptual diagram representing the workflow for incorporating thresholds into our conservation and restoration decision-making framework. 70 Figure 3.3. Relationships between protected area categories (Low, Medium, or High Protection) and stream fish metrics for sites sampled throughout the conterminous United States and Europe. Fish metrics include the percentage of (a) intolerant, (b) migratory, (c) lithophilic, and (d) rheophilic individuals. 71 Figure 3.4. A summary of relative distances from urban (top; a, c) and agriculture (bottom; b, d) land use thresholds for catchments in the Central and Western Europe (left; a, b) and Middle Missouri (right; c, d) ecoregions. Categories for these threshold status indices were derived using quantiles in Esri ArcGIS Pro. 72 Figure 3.5. Multiple stressor configurations for catchments in the (a) Central and Western Europe and (b) Middle Missouri ecoregions. Catchments that have not passed an urban or agriculture threshold are shown in grey, whereas those that have passed both thresholds are shown in purple. 73 Figure 3.6. Relationship between the coverage of protected areas and the urban threshold status index for rivers in the Middle Missouri ecoregion. The red horizontal line that intercepts the y- axis at a value of 1 (left) represents the urban land use threshold. Additional red horizontal lines (right) reflect the threshold status categories shown in Figure 3.4. Vertical red lines (right) are used to classify catchments into low, medium, or high protection categories, consistent with those depicted in Figure 3.3. Rivers that are just below the threshold and in the low protection category are considered a high priority for urban conservation. Similarly, rivers that are just above the threshold and in the medium or high protection categories are considered a high priority for urban restoration. 74 Figure 3.7. Recommendations for subbasins of the Vistula River watershed, Poland, depicted as subsets of Figure C3.1a. The Bug River (a) has a relatively poor coverage of protected areas and has not yet passed its thresholds for agricultural or urban land use. This combination of factors renders the Bug River a high priority for agricultural and urban conservation initiatives. Contiguous sections of the Narew River (b) are moderately to highly protected but have slightly passed both the agriculture and urban land use thresholds, making the Narew River a high priority for agricultural (b) and urban (b, d) restoration efforts. Throughout its course, the Vistula River (c) was identified as a high priority for agricultural restoration, whereas the Wkra River (d) was identified as a high priority for urban restoration. 75 APPENDIX 3.C: SUPPLEMENTARY FIGURES Figure C3.1. Recommendations for the allocation of conservation (“C”) and restoration (“R”) actions based on thresholds for agriculture (“A”) and urban (“U”) land use in the (a) Central and Western Europe and (b) Middle Missouri ecoregions. Additional consideration was given based on the protected area coverage within network catchments, as illustrated in Figure 3.6. High priority (“HP”) catchments are colored, whereas nonpriority catchments are shown in grey. 76 MANAGEMENT IMPLICATIONS Continental-scale assessments are valuable for detecting patterns that may not be identified by analyses conducted over smaller spatial extents (i.e., stream reach, single river basin) because the heterogeneity in conditions exhibited across large regions provides important contrast necessary for assessing variation in responses of stream fishes to environmental factors. Analyses conducted over such broad extents can improve understanding of similarities between regions, and those similarities can be leveraged to support inter-regional partnerships or facilitate exchange of best management practices, decision-support tools, and other resources to improve efficiencies among systems that are facing similar challenges. Conversely, such analyses can also be used to identify substantial differences between regions and highlight characteristics that pose unique management challenges for specific systems. Collectively, the insights generated by my dissertation research can be applied to strengthen management outcomes over smaller extents, as I describe in more detail below. Chapter 1 I assigned 16 functional traits to 597 stream fish species collected from over 45,000 interconfluence stream reaches and investigated trait-based responses of stream fishes to 17 environmental factors known to influence stream habitat. I then assessed the generalizability of significant trait-environment relationships across nine large ecoregions comprising the conterminous United States. My results show that while trait-environment relationships were not consistent in terms of their strength or multivariate structure, some relationships were significant in multiple ecoregions, including an inverse relationship between intensity of human land use and abundance of migratory species, among others. By contributing to a more complete understanding of how variation in natural influences, anthropogenic stressors, and functional 77 traits affect distributions of stream fishes, insights from this study can be used to inform biological monitoring efforts at broad spatial extents. Designing monitoring programs to capture environmental gradients that are most influential in determining the structure and composition of stream fish assemblages is critical for detecting ecological impairment and identifying opportunities to mitigate or prevent further degradation of stream habitat. For example, my findings can be used to determine whether stream fish assemblages in specific regions are sensitive to changes in productivity that may result from human land use and climate change (e.g., as suggested by increases in the abundance of algivorous species, or species that have a high thermal tolerance), or whether species responses are more predictable in association with changes in hydrology (e.g., rheophilic species), streambed sedimentation (e.g., lithophilic species), or fragmentation (e.g., migratory species). Details such as these are important for facilitating regional adjustments to biological monitoring programs to better account for biogeographic variation in assessing the responses of functional traits to changing environmental conditions. Chapter 2 Although we often investigate effects of habitat degradation on biodiversity, additional factors including dispersal limitation and species interactions can have a strong influence on the diversity of stream fishes. By assessing the implications of biodiversity change over time on the stability of ecosystem properties, we gain insights into how multiple factors contribute to variation in the structure and function of stream fish communities. Using an existing dataset containing records of stream fishes collected throughout the conterminous United States between 1990 and 2019, I developed a structural equation model to integrate predictions for how various measures of biodiversity contribute to the compositional and functional variability of stream fish 78 metacommunities at a continental spatial extent. While I show that alpha diversity was positively associated with measures of ecosystem stability, contributions of beta diversity were more complex. Metacommunities with a higher beta diversity supported a significantly higher number of nonnative species and had lower compositional and functional stabilities compared to metacommunities with a lower beta diversity (or higher alpha diversity). Yet patterns of beta diversity also had a significant and positive effect on functional redundancy, which I found to be important for stabilizing trait-based metacommunity dynamics. More broadly, I show that the structure and function of stream fish metacommunities across the conterminous United States changed between 1990 and 2019. For example, the composition of species in some regions differed by more than 40% among the three time periods, whereas functional diversity varied by as much as 15%. These findings illustrate the importance of incorporating a multiscale perspective to improve management decision-making processes in freshwater ecosystems. By providing insights into how biodiversity within and among local communities contributes to spatiotemporal variation in metacommunity dynamics, managers may be better equipped to account for scale- dependent community assembly processes that are known to drive changes in the structure and function of stream fish communities. For example, because factors including hydrologic alteration, fragmentation, and local habitat degradation influence the diversity of stream fish communities in complementary ways, the effectiveness of particular management strategies is likely to vary from one regional context to another. Therefore, multiscale approaches can be used to understand and manage interplay between local and spatial processes and improve our ability to support the maintenance of important ecological functions. 79 Chapter 3 While efforts to incorporate thresholds into management of stream fishes have been relatively limited, thresholds do have great potential to inform decision-making. I used known threshold values to characterize vulnerability of stream fishes to multiple landscape stressors and summarized distributions of established protected areas to develop a decision-support framework that can be implemented to support conservation and restoration decisions in nearly 1.73 million catchments across the United States and Europe. I show that while most catchments in the Central and Western Europe and Middle Missouri ecoregions have low levels of protection, continued establishment of protected areas can contribute to improved outcomes for intolerant, migratory, lithophilic, and rheophilic fishes. My findings also highlight the pervasive influences of agricultural land use on stream habitat and indicate that widespread degradation may result from increased urban development within catchments that are poorly protected. This decision-support framework has important policy implications and can help to achieve the ambitious conservation and restoration targets set forth by recent initiatives such as the Kunming-Montreal Global Biodiversity Framework and the European Union’s Nature Restoration Law. For example, this framework can be used to identify conservation gaps in catchments that are at risk of surpassing known thresholds for agricultural, pasture, or urban land use, while also helping to inform restoration efforts for streams within protected landscapes. This approach is compatible with the discourse around relationships between people and nature because it emphasizes implementation of management actions in areas that are most at risk of surpassing known thresholds. Instead of prioritizing management actions in landscapes with a relatively low vulnerability, this framework identifies opportunities to engage with local communities to better address trade-offs between biodiversity conservation and other cultural, 80 social, and economic considerations. 81