SHORT-TERM EFFECTS OF PASSIVE WARMING ON TWO INVADED PLANT
COMMUNITIES USING A NOVEL OPEN-TOP CHAMBER DESIGN
By
Kileigh Browning Welshofer

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Forestry—Master of Science
2017

ABSTRACT
SHORT-TERM EFFECTS OF PASSIVE WARMING ON TWO INVADED PLANT
COMMUNITIES USING A NOVEL OPEN-TOP CHAMBER DESIGN
By
Kileigh Browning Welshofer
Climate change is expected to directly favor exotic plant species over native species because they
tend to have wider climatic tolerances and greater phenological plasticity. Warming is also likely
to indirectly favor exotic species by herbivore behaviors and/or preference and intensifying the
enemy release phenomenon. To examine these direct and indirect effects, I initiated a field
experiment in two heavily invaded plant communities in northern and southern Michigan, USA.
Passive warming methods such as open-top chambers are often used to warm aboveground
systems; however, the limited height of their design restricts their use to low-stature (~0.4 m)
plant communities. I introduced a new chamber design for year-round warming in taller stature
(~1.5 m) plant communities and analyzed its effects on the abiotic environment. During the
second year of warming, I collected data for species cover, phenology (green-up, flowering time,
timing of seed set), and cumulative leaf herbivory. The new chamber design achieved reliable
levels of warming (+1.8 ºC), consistent with all four Representative Concentration Pathways
(RCPs) scenarios for 2046-2065 from the Intergovernmental Panel on Climate Change’s 5th
Assessment Report. The effects of warming were largely consistent with theoretical expectations
at the northern site and favored exotic plant abundance. Warming did not favor exotic species
dominance at the southern site, but rather responses indicated plants at this site experienced
physiological stress, likely because ambient temperatures were already close to plant species’
thermal maxima.

To Nina and Matt--for your kindness and direction throughout this adventure.

iii

ACKNOWLEDGEMENTS

I would like to thank the members of my committee: Phoebe Zarnetske (Chair, MSU), Lars
Brudvig (MSU), Jennifer Lau (MSU, KBS), and Michael Walters (MSU) for their guidance
throughout the scientific learning process. Nina Lany deserves a special mention for her
immense help in all aspects of this work. I thank Matt Chansler, Tori Niewohner, Meredith
Zettlemoyer, Chase Allen, the Kellogg Forest Crew, and all other individuals who assisted in the
field for their enormous help that made this research possible. A big thank you goes to Patrick
Duffy, Bob Vande Kopple, and Jean Tsao whose expertise helped aid this project immensely.
For funding and support, I thank the National Science Foundation Graduate Research Fellowship
Program (GRFP) and Michigan State University Plant Sciences Fellowship. I also greatly thank
Michigan State University’s Forestry Department, Kellogg Biological Station, the University of
Michigan Biological Station, and the National Science Foundation’s Research Experience for
Undergraduates Program for providing resources to complete this research. Finally, I thank my
family and friends for their continued support and advice throughout this educational experience.

iv

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................................ vii
LIST OF FIGURES ..................................................................................................................... viii
KEY TO ABBREVIATIONS ........................................................................................................ ix
INTRODUCTION ...........................................................................................................................1
CHAPTER 1 OPEN-TOP CHAMBERS FOR TEMPERATURE MANIPULATION IN
TALLER-STATURE PLANT COMMUNITIES ............................................................................4
ABSTRACT .....................................................................................................................................5
INTRODUCTION ...........................................................................................................................5
METHODS ......................................................................................................................................7
Study Sites ............................................................................................................................7
Chamber and Experimental Design .....................................................................................8
Abiotic Measurements .........................................................................................................9
Statistical Analyses ............................................................................................................10
RESULTS ......................................................................................................................................11
DISCUSSION ................................................................................................................................15
CHAPTER 2 SHORT-TERM RESPONSES TO WARMING VARY BETWEEN
NATIVE VS. EXOTIC SPECIES AND WITH LATITUTDE IN AN EARLY
SUCCESSIONAL PLANT COMMUNITY ..................................................................................17
ABSTRACT ...................................................................................................................................18
INTRODUCTION .........................................................................................................................18
METHODS ....................................................................................................................................21
Site Description ..................................................................................................................21
Experimental Design ..........................................................................................................24
Data Collection ..................................................................................................................25
Statistical Analysis .............................................................................................................26
RESULTS ......................................................................................................................................27
DISCUSSION ................................................................................................................................34
APPENDICES ...............................................................................................................................37
APPENDIX A: Additional methods used in warming experiment ...............................................38
APPENDIX B: Dates of sensor malfunction due to technology damage or maintenance
at the northern forest clearing and southern old-agriculture field from August 1, 2015July 31, 2016 ..................................................................................................................................41
APPENDIX C: Comparisons of average differences between chambered-ambient plots
in the northern forest clearing at the University of Michigan Biological Station from
August 1, 2015- July 31, 2016 .......................................................................................................42
APPENDIX D: Comparisons of average differences between chambered-ambient plots
in the southern old-agriculture field at Kellogg Biological Station from
v

August 1, 2015- July 31, 2016 .......................................................................................................43
APPENDIX E: Summary of likelihood ratio tests (α=0.05) used to determine the most
parsimonious mixed-effects model. ...............................................................................................44
LITERATURE CITED ..................................................................................................................46

vi

LIST OF TABLES

Table 1: Species list for each site and site-level relative abundance .............................................23
Table 2: Parameter estimates for the most parsimonious general linear mixed-effects
model based on likelihood ratio tests (LRT) for each of five response variables
explained by warming and species’ origin at each site ..................................................................29
Table 3. Dates of sensor malfunction due to technology damage or maintenance at the northern
forest clearing and southern old-agriculture field from August 1, 2015- July 31, 2016................41
Table 4. Comparisons of average differences between chambered-ambient plots in the northern
forest clearing at the University of Michigan Biological Station from August 1, 2015- July 31,
2016................................................................................................................................................42
Table 5. Comparisons of average differences between chambered-ambient plots in the southern
old-agriculture field at Kellogg Biological Station from August 1, 2015- July 31, 2016. ............43
Table 6. Summary of likelihood ratio tests (α=0.05) used to determine the most parsimonious
mixed-effects model.......................................................................................................................44

vii

LIST OF FIGURES

Figure 1: Chamber design and study sites .......................................................................................9
Figure 2: Mean and 95% confidence intervals for the difference between chambered
and ambient plots ...........................................................................................................................11
Figure 3: Time series showing variability in chamber effects .......................................................12
Figure 4: Frequency of hourly daytime temperatures (°C) at 1m height in chambered
and ambient plots.
........................................................................................................................................................13
Figure 5: Hourly soil temperatures (°C) at -5cm beneath the soil surface during the
winter to spring transition for a warmed and a control plot at the (A) Northern and
(B) Southern sites ...........................................................................................................................14
Figure 6: Site location map ............................................................................................................22
Figure 7: Species percent cover (mean± 1SE) measured at the end of summer 2016
at the (A) northern forest clearing and (B) southern old agriculture field. ....................................30
Figure 8: Phenological responses measured throughout the 2016 growing season .......................32
Figure 9: Percent of leaf eaten (Mean ± 1SE) at the end of summer 2016 at the
(A) northern forest clearing and (B) southern old agriculture field ...............................................33

viii

KEY TO ABBREVIATIONS

IPCC Intergovernmental Panel on Climate Change
LTER Long Term Ecological Research
KBS Kellogg Biological Station
OTC Open-top Chamber
RCP Representative Concentration Pathways
UMBS University of Michigan Biological Station

ix

INTRODUCTION

Exotic species that become invasive threaten both the composition and function of native
ecosystems (Root et al. 2003; Walther 2010). Recent studies suggest that climate change could
enhance the success of exotic species through increased survival during transport, greater
establishment success, and decreased effectivity of control strategies (Theoharides and Dukes
2007; Hellmann et al. 2008). For example, warmer temperatures during transport may increase
species survival and in turn, increase propagule pressure in the introduced range (Early et al.
2016). Further, exotic species that were once limited by climate constraints may experience
greater establishment success under a warmer climate (Rixon et al. 2005). However, the extent
to which warming will alter existing exotic species dominance remains unclear because it can
depend heavily upon interactions with the biotic community of its introduced range (Van der
Putten et al. 2010; Seabloom et al. 2015). Several mechanisms have been proposed to identify
how warming may increase exotic dominance; however, there is little empirical evidence
supporting these mechanisms once exotics are established in their introduced range (Vilà et al.
2007).
One mechanism by which exotic species may increase their local dominance compared to
co-occurring native species is through unique phenological responses (Wolkovich and Cleland
2011). The vacant niche theory suggests that exotic plants are able to exploit resources in
temporal niche spaces unfilled by native plants leading to greater success relative to natives over
time (Elton 1958; Davis 2009). For example, longer growing seasons associated with climate
change may open opportunities for exotic species to establish earlier and limit native growth
through priority effects (Sale 1977; Seabloom et al. 2003).

1

An additional mechanism by which existing exotic species may gain abundance in their
introduced systems is through greater plasticity and/or more rapid evolution than native species
(Richards et al. 2006; Levine et al. 2008). With rapidly changing environments, exotic species
that are able to respond to such changes may be less vulnerable to extinction (Willis et al. 2008).
Exotic species success may also be facilitated in their introduced range by herbivores that
prefer native species (Keane and Crawley 2002). Climate change may moderate the magnitude
and direction of herbivore impacts on exotic plant success (Fey and Cottingham 2011;
Eisenhauer et al. 2012). For instance, warmer temperatures may directly increase insect survival,
range, and abundance (Bale et al. 2002; Robinet & Roques 2010) as well as increase insect
metabolisms leading to increased herbivore damage (Brown et al. 2004). Warming may also
indirectly alter the extent of herbivore damage by altering plant nutritional quality and/or
desirability. For example, if warming increases plant growth, reduced tissues C:N ratios may
require herbivores to consume greater quantities in order to meet nutritional demands (Hillebrand
et al. 2009; Sardans et al. 2012). If native species experience this rise in herbivory due to
warming, and exotic species are released from herbivores, then we would expect exotic species
to experience greater success and abundance under climate change conditions.
Studies that suggest climate change will favor exotic plant dominance in their introduced
range are largely limited to predictions based on theory and experiments that incorporate
biological complexity are lacking (Hellmann et al. 2008). In this thesis, I utilized passive opentop chambers to experimentally examine the individual and interactive effects of warming and
species origin on short-term plant responses in highly invaded ecosystems. In Chapter 1, I
introduce an updated open-top chamber design for year-round use in taller-stature plant
communities that did not previously exist. In order to test the use of this new design across mid-

2

latitude systems, I document its abiotic effects in two common ecosystem types: a northern forest
clearing and a southern early successional grassland site in Michigan, USA. In Chapter 2, I
examine whether two years of continuous warming favored exotic plant success by measuring
the following responses: species cover, emergence, flowering time, timing of seed set, and
cumulative herbivore damage. This knowledge will allow us to better understand and improve
forecasts of changes in exotic species dominance after establishment in the introduced range
under a rapidly changing climate.

3

CHAPTER 1
OPEN-TOP CHAMBERS FOR TEMPERATURE MANIPULATION IN TALLER-STATURE
PLANT COMMUNITIES.

4

ABSTRACT
Open-top chambers simulate global warming by passively increasing air temperatures in field
experiments. They are commonly used in low-stature alpine and arctic ecosystems, but rarely in
taller-stature plant communities because of their limited height. We present a modified
International Tundra Experiment (ITEX) chamber design for year-round outdoor use in warming
taller-stature plant communities up to 1.5m tall. We report a full year of results for the chambers’
effects on air and soil temperature, relative humidity, and soil moisture in a northern hardwood
forest clearing and a southern early successional grassland site located in Michigan, USA.
Detailed construction plans are also provided. The chambers elevated daytime air temperatures at
1m height by 1.8ºC above ambient levels, on average over an entire year, at both the northern
and southern site. The chambers did not affect relative humidity at either site. The chambers did
not alter average soil temperature or moisture at the northern site and reduced soil temperatures
and soil moisture at the southern site. The chambers increased variability in soil freeze/thaw
cycles at both sites. The chambers achieved predicted levels of warming for mid-century (20462065) scenarios consistent with the majority of Representative Concentration Pathways (RCPs)
in the International Panel on Climate Change 5th Assessment Report, with minimal experimental
artifact. This design is a valuable tool for examining the effects of in situ warming on
understudied taller-stature plant communities and creates the opportunity to expand future
comparisons across a diversity of systems.
INTRODUCTION
Climate warming can have profound impacts on ecological communities (Parmesan &
Yohe 2003; Root et al. 2003). Warmer temperatures affect individual growth and metabolism,
shift development times, and alter survival and reproductive success (Brown et al. 2004).

5

Individual responses can lead to community level changes through shifts in population dynamics
and species distributions (Walther 2010), and the combination of these responses can be difficult
to predict. Therefore, it is critical to collect empirical observations to develop a deeper
understanding of ecological community responses to warming essential to inform the sustainable
management of natural systems.
Several experimental tools exist that allow researchers to manipulate air and/or soil
temperature of in situ plant communities (Aronson & McNulty 2009). Active warming methods
(e.g. overhead infrared lamps, heat resistance cables) are used to achieve a pre-determined level
of warming (Peterjohn et al. 1994; Harte et al. 1995; Norby et al. 1997; Pelini et al. 2011). These
methods have been useful for examining above and belowground ecological responses to
warming; however, they often require extensive energy demands, start-up costs, and maintenance
that are often not feasible for remote and budget limited research projects (Aronson & McNulty
2009).
Passive warming methods such as open-top chambers (OTCs) are often used to warm
aboveground systems, especially in arctic and alpine ecosystems (Marion 1996; Walker et al.
2006). Their simple, cost effective design makes them ideal for field experiments that require
replicate plots across environmental gradients or in remote areas. Passive OTCs also allow
natural levels of precipitation, light, and gas exchange (Marion et al. 1997). Further, they enable
winter warming, a phenomenon that is likely to impact plant community dynamics and demands
further research (Kreyling 2010; Bokhorst et al. 2012). Passive OTCs provide a low-maintenance
and cost effective method for researchers to examine the effects of year-round warming on plant
communities (Arft et al. 1999).

6

Despite their utility, the limited height of passive OTCs (~0.4m) has restricted their use to
low-stature plant communities, early life stages, and low productivity plant systems (Elmendorf
et al. 2012). While some taller-stature OTCs exist, they are typically only used during the
growing season and not in harsh winter conditions (Chiba & Terao 2014). Thus there is a gap in
understanding in situ warming effects on communities such as prairies, savannahs, grasslands,
and scrublands (Settele et al. 2014).
Here we provide a durable, passive, OTC design that warms taller-stature (≤1.5m height)
plant communities. We monitored the effects of 24 chambers on the abiotic environment over
one year in two different temperate ecosystem types (forest clearing and old-agriculture field)
spanning approximately 3 degrees of latitude. We report the chambers’ effects on air and soil
temperature, relative humidity, and soil moisture during different timeframes: year-round, in the
growing season vs. the dormant season, and during daytime vs. nighttime hours. We aim to
facilitate the use of passive OTCs across understudied taller-stature systems and expand
knowledge of climate warming effects in a wider array of natural ecosystems.
METHODS
Study Sites
We tested 24 chambers at two sites separated by 380 km and approximately 3 degrees
latitude in Michigan, USA from August 1, 2015 to July 31, 2016. The southern site, located
within Kellogg Biological Station’s Long Term Ecological Research site (KBS) (42º 24'
40.11"N, 85º 22' 24.46"W, 289 m a.s.l.), is a former agriculture field last cultivated roughly 40
years prior and mowed annually until 2014. The average annual air temperature (1981-2010) is
10.2ºC (Menne et al. 2012). The site’s vegetation is dominated by Solidago spp., Poa pratensis,
and Phleum arvense and reaches approximately 1.5 m height (unpublished data). The northern

7

site, located at the University of Michigan Biological Station (UMBS) (45º 33' 40.38"N, 84º 40'
46.54"W, 239 m a.s.l.), was located within a 1.5 km2 northern deciduous forest clearing created
roughly 25 years prior and mowed annually until 2014. The average annual air temperature
(1981-2010) is 5.5ºC (Menne et al. 2012). Vegetation is dominated by Centaurea stoebe, Poa
compressa, and Pteridium spp. and reaches approximately 0.5 m (unpublished data).
Chamber and Experimental Design
We elevated the hexagonal ITEX open-top chamber design described in Marion et al.
(1997) onto 0.91 m vertical polycarbonate walls with pressure-treated wood framing (Figure 1;
Welshofer et al. 2017). The resulting chamber dimensions were 1.57 m tall x 2.5 m diameter
with a top opening large enough to contain a 1m2 plot. We used clear, 0.32 cm Lexan
polycarbonate sheets without UV protective coating to allow high solar transmittance and natural
ultraviolet conditions (ePlastics, San Diego, CA, Item ID: PCCLR0.125AM48X96). We elevated
the polycarbonate 10 cm above the ground surface to allow migration of ground dwelling
organisms. We used cold and UV resistant cable ties to attach the frame to the polycarbonate
panels through drilled holes. We screwed pressure treated plywood supports to the top 120º
corners of the hexagon and attached 25.4 cm metal spikes to anchor each leg in the soil for extra
support during high-wind and snowfall conditions.

8

Figure 1. Chamber design and study sites. (A) Photograph of OTC in a taller-stature old
agriculture field in Hickory Corners, MI, USA. (B) Photograph of OTC in an early successional
northern hardwood forest clearing in Pellston, MI, USA. (C) Perspective view. (D) Aerial view.
Warmed and ambient treatments were randomly distributed across 24, 1 m2 plots
arranged within a 25 x 36 m deer exclosure at each site. Each OTC was centered on a warmed
plot and ambient control plots were left untreated, with a 1 m buffer area on each side of each
plot. As a part of a longer term fully-factorial experiment, plots additionally received insect and
small mammal reduction treatments (Appendix A).
Abiotic Measurements
At the plot-level, we recorded hourly abiotic conditions from August 1, 2015 to July 31,
2016 with HOBO products (Onset Computer Corporation, Bourne, MA). Three chambered and
three ambient plots at each site were instrumented with 4-channel external U12-008 data loggers
that recorded air temperature at 10 cm above the soil surface and 5 cm below the soil surface,
and with Microstation H21-002 data loggers that recorded hourly air temperature and relative
humidity at 1m above the soil surface along with soil moisture at 5 cm below the soil surface.
9

We installed data loggers (Pendant UA-002-64) to record air temperature at 1m height in the
remaining nine chambers at each site. We installed solar shields above each air temperature
sensor (Appendix A). When a sensor malfunctioned for < 30 days, those dates 44 were removed
from all sensors in the analysis. When a sensor malfunctioned for ≥ 30 days, we removed this
sensor’s results from the analysis to avoid excluding dates from a substantial portion of the
season (Appendix B).
Statistical Analyses
We analyzed the data on air temperature (1 m and 10 cm heights), relative humidity (1m),
soil temperature (-5 cm) and soil moisture (-5 cm) for each site separately. All analyses were
completed using R version 3.1.2 (R Development Core Team 2008). We used two sided,
unpaired t-tests to compare the means of the hourly data (nchambered = 3; nambient=3) during the
following intervals: year-round (24 hour), daytime, nighttime, growing season, and non-growing
season. We assessed variation in the warming treatment by calculating the standard deviation of
hourly air temperature (1 m) across the remaining nine chambered plots. We compared the
variability in winter and spring soil temperature of chambered vs. ambient plots with an F-test
such that Ha:σ2chambered > σ2ambient.
We obtained daily snow depth, snowfall, and maximum five-second wind speed records
at both sites during the study period of August 2015-July 2016 and from the National Centers for
Environmental Information to examine the chambers’ ability to withstand wind speed and heavy
snow conditions (Menne et al. 2012). Data were averaged across weather stations throughout
Emmet County, Michigan for the northern site and across Kalamazoo County, Michigan for the
southern site.

10

RESULTS
Throughout the year, average air temperatures at 1m height at both sites were warmer in
the chambers than in control plots (northern site chambers: increased 0.84±0.25 ºC SE; southern
site chambers: increased 0.70±0.18 ºC; Figure 2A). Warming at 1 m varied according to
irradiance; thus, the greatest magnitude of warming was exhibited during daytime hours
(+1.84±0.79 ºC and +1.73±0.19 ºC at the northern and southern sites, respectively), and
especially during sunny days (Figure 3), as well as during the growing season (Figure 2A, 4A
and 4B). The results of all t-tests are given in Appendix C (northern site) and Appendix D
(southern site).

Figure 2. Mean and 95% confidence intervals for the difference between chambered and
ambient plots. (A) air temperature at 1m above ground, (B) air temperature at 10cm above
ground, (C) relative humidity at 1m above ground, (D) soil temperature at 5cm below the soil
surface, and (E) soil moisture at 5 cm below the soil surface from August 1, 2015- July 31, 2016.

11

Figure 2 (cont’d). Significant differences from unpaired t-tests (n=6) denoted with (*). Some
confidence intervals not visible due to small size.

Figure 3. Time series showing variability in chamber effects. (A) air temperature at 1m
height, (B) relative humidity at 1m height, and (C) soil moisture at 5cm below the soil surface
due to daily weather patterns during a typical week during the growing season (May 1-7, 2016)
at the southern site in Hickory Corners, MI, USA. The chambers warm air temperatures most
during the day during clear sky conditions. The values shown are the average hourly values of
the warmed plots (solid red) and the ambient plot (dashed black).
Air temperatures (1 m) measured in the nine remaining chambers showed that the OTC
warmed consistently across plots, with slightly more variation between treatments in the
southern old-agriculture field (northern site: median hourly standard deviation of 0.02 ºC;

12

southern site: 0.23ºC). The chambers did not significantly warm the air at 10 cm above the soil at
either site, although nighttime air temperature at 10 cm was slightly warmer in the chambers at
the southern site (Figure 2B). The chambers did not significantly alter relative humidity at 1 m at
either site (Figure 2C).

Figure 4. Frequency of hourly daytime temperatures (°C) at 1m height in chambered and
ambient plots. Measurements taken during the growing season at the (A) Northern Site and (B)
Southern Site. Chambered plots were warmer than ambient plots, especially on the hottest days
of the year.
The chambers’ effects on soil temperature and moisture varied between the two sites. The
soil was cooler (-0.21±0.10 ºC, Figure 2D) and drier (-3.66±0.57 %, Figure 2E) year-round in
chambers vs. on ambient plots at the southern site. However, we found no significant difference
in soil temperature and moisture between chambered and control plots at the northern site
13

(Figure 2D and 2E). The chambered plots exhibited greater variability in soil temperature during
the non-growing season at the southern site (F = 0.02, df = 2, p=0.05), with 2 additional spring
freeze/thaw cycles than in the ambient plots (Figure 5).

Figure 5. Hourly soil temperatures (°C) at -5cm beneath the soil surface during the winter
to spring transition for a warmed and a control plot at the (A) Northern and (B) Southern
sites. In winter, chambered plots exhibited greater temperature variability, cooler soil
temperature, and increased freeze thaw cycles than ambient plots likely due to decreased
snowpack insulation. In general, soils were colder, experienced greater variability in
temperature, and experienced more freeze thaw cycles at the southern site than at the northern
site during the winter.
The chambers withstood harsh weather conditions with minimal damage to the
infrastructure. During the study, the northern site experienced a maximum five-second wind
speed of 26.4 m/s and annual snowfall of 244 cm with a maximum snow depth of 63.5 cm. The
14

southern site experienced a maximum five-second wind speed of 24.6 m/s and annual snowfall
of 127 cm with maximum snow depth of 22.9 cm.
DISCUSSION
Our chamber design simulated mid-century global warming scenarios in taller-stature
plant communities. We observed mean daytime warming of 1.8 °C at both sites, consistent with
all Representative Concentration Pathway (RCP) scenarios for 2046-2065 and three RCP
scenarios for 2081-2100 predictions from the International Panel on Climate Change 5th
Assessment (Stocker et al. 2014). This amount of warming is consistent with past warming
experiments using passive, low-stature ITEX chambers, and is known to change the phenology,
growth, survival and reproduction of low-stature plants (Arft et al. 1999).
The increased variability in soil temperature caused by the chambers led to an increase in
freeze/thaw cycles also predicted to occur with climate change, likely due to reduced or
intermittent snowpack providing decreased insulation (Brown & DeGaetano 2011; Henry 2008).
This phenomenon was more pronounced at the southern site, where there is less snowpack that is
more likely to completely melt between snowfall events than at the northern site where the
greater snowpack was likely decreased but not absent. Soil freeze/thaw cycles lead to the lysis of
soil microbes, resulting in changes in abundance and community structure of soil bacteria
(Kumar et al. 2013). The death of these microbes may also release nitrogen and phosphorus in
the soil leading to plant uptake and response (Edwards & Jefferies 2010).
The chambers led to overall cooler and drier soil in the warmed plots vs. ambient plots in
the southern site, but not in the northern site. We suggest greater productivity in the southern
site’s chambered plots led to increased soil shading and transpiration, resulting in cooler and
drier soil on the chambered plots as observed in Hollister et al. (2006). At the northern site,

15

moisture likely drained quickly from the sandy soil on all plots regardless of warming treatment
(x̄Northern Site=11.0±1.9%; x̄Southern Site=20.5±0.5%). This regional variation highlights the
importance of an affordable warming method to assess variability in warming responses across
many sites.
The relatively simple and low-maintenance design of these taller-stature chambers
encourages their use in conjunction with globally coordinated ecological experiments such as
Nutrient Network and Drought-Net (Fraser et al. 2013). By combining experiments, researchers
will gain the opportunity to investigate the interactive effects of numerous global change drivers
in situ across taller-stature systems worldwide.

16

CHAPTER 2
SHORT-TERM RESPONSES TO WARMING VARY BETWEEN NATIVE VS. EXOTIC
SPECIES AND WITH LATITUTDE IN AN EARLY SUCCESSIONAL PLANT
COMMUNITY

17

ABSTRACT
Climate change is expected to favor exotic plant species over native species because they tend to
have wider climatic tolerances and greater phenological plasticity. Warming is also likely to
intensifying the enemy release phenomenon, leading to greater exotic advantage in a changing
climate. Here, we examine direct effects of warming on plant abundance and phenology, as well
as indirect effects of warming propagated through herbivores, in two heavily invaded plant
communities in Michigan, USA separated by approximately three degrees latitude. At the
northern site, the effects of warming were largely consistent with theoretical expectations.
Warming (+1.8 °C above ambient) increased exotic plant abundance by 19% but decreased
native plant abundance by 31%, indicating that exotic species may be favored in a warmer world.
Warming also resulted in earlier spring emergence (1.65 ± 0.77 days), earlier flowering
(2.18±0.92 days), and greater damage by herbivores (2-fold increase), affecting exotic and native
species equally. Additionally, contrary to expectations, native and exotic plants experienced
similar amounts of herbivory. Warming did not have strong ecological effects at the southern
site, only resulting in a delay of flowering time by 2.42±0.83 days for both native and exotic
species. Consistent with the enemy release hypothesis, exotic plants experienced less herbivory
than native plants at the southern site, and herbivory was lower under warming for both exotic
and native species. Plants and herbivores may not have benefited from warming at the southern
site because ambient temperatures were already close to the critical threshold for physiological
stress, while ambient temperatures at the northern site were further from the thermal maximum.
INTRODUCTION
Understanding the mechanisms for the success of exotic plants in their introduced range is a
longstanding ecological question (Tilman 1985; Davies 2011; Simberloff 2013). One proposed

18

mechanism for exotic success is the ability to take advantage of vacant spatial or temporal niche
space (Elton 1958), especially following disturbance (Herbold & Moyle 1986). A second
proposed mechanism is enemy release, in which exotic plants experience reduced herbivore,
disease, and pest pressure compared to their native counterparts (Williamson 1996; Keane &
Crawley 2002). However, anthropogenic climate change is likely to interact with each of these
mechanisms through extended growing season length and rapidly warming temperatures
(Wolkovich & Cleland 2011). These changes are also expected to shift plant-herbivore
relationships (Dijkstra et al. 2012). Understanding the mechanisms that influence exotic plants’
success under warmer temperatures in their introduced range is critical for understanding and
forecasting plant community dynamics.
Warmer temperatures driven by climate change are known to influence the abundance of
exotic species (Dukes 2010). For example, warmer temperatures could increase climatic
suitability for exotic species (Dukes & Mooney 1999). Further, native species that are stressed by
a rapidly changing climate may have reduced competitive ability and therefore, allow
opportunities for exotic establishment or growth in the introduced range (Alpert et al. 2000;
Chambers et al. 2016; Ma et al. 2017). Determining the mechanisms for exotic success and how
they may be altered with a changing climate is therefore of critical importance.
Climate change may favor exotic species with phenologies that differ from their co-occurring
native species in their introduced range (Wolkovich & Cleland 2011). Warmer temperatures lead
to extended growing seasons and may create open niche space for exotic species that are able to
establish earlier in the season or extend their life cycle past those of native species. Extended
growing seasons have promoted the success of exotic species in both terrestrial and aquatic
systems (Stachowicz et al. 2002; Fridley 2012). Exotic species may also be favored in their

19

introduced range due to high phenological plasticity (Richards et al. 2006). Accordingly, species
with high phenological flexibility may have an advantage in a rapidly changing environment
(Willis et al. 2008; Matesanz et al. 2010).
The success of exotic species under climate change is also likely to depend on their
interactions with herbivores. Exotic plants benefit from enemy release when herbivores prefer
native species and therefore indirectly enable exotic species to thrive under relatively less
consumptive pressure (Bremm et al. 2016). Climate change may moderate the magnitude and
direction of herbivore impacts on exotic plant success by warmer temperatures directly
increasing insect metabolic rate (Bale et al. 2002; Fey & Herren 2014). The warming induced
reduction of plant food quality may also require herbivores to consume greater quantities to meet
nutritional demands (Hillebrand et al. 2009; Sardans et al. 2012). If native species experience
increased herbivory due to warming, but this increase is lower in magnitude for exotic species
(i.e. enemy release), exotic species would experience greater success under warmer climate
conditions. Alternatively, herbivores may limit the growth of competitive plant species and,
therefore, promote community stability (Van der Putten et al. 2010; Blois et al. 2013; Post 2013;
Borer et al. 2014). Thus, forecasting community consequences of exotic plant success may
depend on the influence of herbivores in a warmer world.
Climate change is expected to favor exotic species in their introduced ranges (Dukes &
Mooney 1999; Hellmann et al. 2008), but further research is needed. These predictions are
largely based on theory and observational studies rather than manipulative experiments
(Wolkovich & Cleland 2011; Sandel & Dangremond 2012; Wolkovich et al. 2013). Experiments
that test the differential effects of warming on exotic and native species often use simplified
biotic communities, which may reduce the ability to make predictions under the full complexity

20

of realistic communities (Lu et al. 2015; Munier et al. 2010). Also, in situ field-based
experimental warming of plant communities has been largely focused on high-latitude and highelevation ecosystems with low-stature plant communities, and we lack sufficient understanding
in mid-latitude and lower-elevation ecosystems (Arft et al. 1999). Finally, in order to best
examine the effects of warming on exotic success we must examine ecosystems where
introductions of exotics are more prevalent (Simberloff & Von Holle 1999; Turbelin et al. 2017).
We addressed these research gaps with an in situ field experiment that manipulated warming
in highly invaded early successional, mid-latitude plant communities. We examined the effects
of warming exotic v. native species for one annual cycle to test the following hypotheses:
1) Warming will increase exotic species cover more than co-occurring native species cover.
2) Warming will advance exotic species phenology more than co-occurring native species
phenology.
3) Warming will indirectly favor exotic species through increased leaf tissue damage by
herbivores to co-occurring native species under warming.

METHODS
Site Description
We examined two invaded, early successional plant communities in Michigan, USA
separated by 354 km and approximately three degrees latitude (Figure 6A) from May 2015
through October 2016. The northern site is a forest clearing at The University of Michigan’s
Biological Station (UMBS) located at 45.56° N,-84.71° W and 239 meters above sea level
(Figure 6B). The 1.53 km2 clearing was clear-cut in 1994 and is dominated by Danthonia
spicata, Centaurea stoebe, and Poa pratensis (Table 1). The site was maintained through
mowing to prevent forest succession until the beginning of the experiment. The mean annual
temperature in Emmet County where the site is located is 5.4 °C and mean annual rainfall and
21

snowfall are 76.7 cm and 244 cm, respectively (1981-2010 normals) (Menne et al. 2012a; Menne
et al. 2012b).

Figure 6. Site location map. A. Map of mid-western United States with study site locations and
30-year temperature normals from 1981-2010 (PRISM Climate Group 2013). B. University of
Michigan Biological Station forest clearing site. C. Kellogg Biological Station successional
agriculture field site. D. Open-top chamber experiment in southern old-agriculture field located
at Kellogg Biological Station. In B and C, the location of the experiment is marked with an X.
The southern site is an early successional grassland (previously agriculture field) at
Kellogg Biological Station’s Long Term Ecological Research Site (KBS-LTER) located at
42.40° N, -85.40° W and 289 meters above sea level (Figure 6C). The site is dominated by
Solidago canadensis, Poa pratensis, and Hieracium pratense (Table 1, Figure 6D) and was
maintained through mowing to prevent succession prior to the beginning of the experiment. The

22

mean annual temperature in Kalamazoo County where the site is located is 10.1 °C and mean
annual rainfall and snowfall are 100.4 cm and 127 cm, respectively (1981-2010 climate normals)
(Menne et al. 2012a; Menne et al. 2012b).
Table 1. Species list for each site and site-level relative abundance. All origin values retrieved
from USDA Plant Database at the county level. Species relative abundance based on
composition estimates collected in August 2016. Relative abundance calculated for each site as
the (sum of each species’ cover/sum of all species)*100.
Northern Forest Clearing
Species
Origin
Relative
Abundance
(%)
Danthonia spicata
Native
38.34
Centaurea stoebe
Exotic
29.65
Poa pratensis
Exotic
11.44
Carex pensylvanica Native
6.21
Pteridium aquilinum Native
4.83
Rumex acetosella
Exotic
2.84
Hypericum
Exotic
1.11
perforatum
Hieracium pilosella Exotic
<1
Poa compressa
Exotic
<1
Quercus rubra
Native
<1
Fragaria vesca
Native
<1
Vaccinium
Native
<1
angustifolium
Solidago gigantea
Native
<1
Pilosella aurantiaca Exotic
<1
Asclepias sp.
Native
<1
Betula sp.
Native
<1
Antennaria sp.
Native
<1
Solidago nemoralis
Native
<1
Tragopogon dubius
Exotic
<1
Acer rubrum
Native
<1

Species

Southern Old-Field
Origin

Solidago canadensis
Poa pratensis
Hieracium pratense
Phleum arvense
Achillea millefolium
Trifolium pratense
Centaurea stoebe

Native
Exotic
Exotic
Exotic
Native
Exotic
Exotic

Relative
Abundance
(%)
34.27
33.74
10.17
4.73
4.56
3.28
2.82

Euthamia graminifolia
Rubus allegheniensis
Potentilla recta
Dactylis glomerata
Daucus carota

Native
Native
Exotic
Exotic
Exotic

1.66
1.05
<1
<1
<1

Trifolium repens
Alliaria petiolata
Celastrus orbiculatus
Arrhenatherum elatius
Hypericum perforatum
Elymus repens
Asclepias sp.
Taraxicum officinale
Barbarea vulgaris
Cardamine hirsuta

Exotic
Exotic
Exotic
Exotic
Exotic
Exotic
Native
Exotic
Exotic
Exotic

<1
<1
<1
<1
<1
<1
<1
<1
<1
<1

23

Experimental Design
At each site, we established 24 1m2 plots within a 25 meter by 36 meter area surrounded
by a 3 meter tall fence to prevent deer browsing. Plots were separated by at least 4 m of buffer
area to prevent edge impacts from chambers (e.g. shading) in neighboring plots and to serve as
walkways for sampling. As part of a larger, long-term experiment, the plots were treated with a
randomized, fully factorial design that included insect reduction and small mammal exclosure in
addition to the warming treatment (Appendix A). We tested for differences between plots with
the additional herbivore reduction treatments and those without herbivore treatments prior to
examining each of our response variables. When the effects of herbivore reduction treatments
were not significant (p > 0.05), we tested our hypotheses across all plots. There were no initial
differences between warmed and ambient plots in average percent cover across species at either
site at the beginning of the experiment (Northern Site: Warmed (t=-0.76, df=126.1, p=0.45) and
Origin (t=-0.01, df=12.89, p=0.99); Southern Site: Warmed (t=-0.84, df=180.21, p=0.40) and
Origin (t=1.86, df=13.01, p=0.09).
We warmed the plots year-round using a passive, open-top chamber designed to warm
taller stature (≤1.5 m) plant communities, minimize edge effects, and allow for migration of
flying and ground dwelling organisms in and out of the chambers (Molau & Mølgaard 1996;
Welshofer et al. in press). We used clear, UV-transmitting, 1/8” Lexan Polycarbonate sheets to
elevate temperatures using sunlight (ePlastics, San Diego, CA; Figure 6D). We installed the
chambers during spring 2015 at both sites. The chambers warmed plots, on average, by
(1.84±0.79 ºC and 1.73±0.19 ºC) in the forest clearing and old agriculture field, respectively,
with no evidence for differences in relative humidity. Warming varied with irradiance and
therefore exhibited the hottest temperatures (up to approximately 45 °C) during the daytime in

24

the growing season. The chambers reduced winter soil temperature, presumably due to reduced
snowpack insulation, and resulted in greater variability in soil freeze/thaw cycles (Welshofer et
al. in press). Here we report ecological data collected during the second growing season (2016),
after nearly a full year of the warming treatment.
Data Collection
Because this study was part of a longer-term experiment, we used non-destructive
sampling methods. We estimated percent cover for each species in each 1m2 plot taken at the
beginning of the experiment (Spring 2015) and at the end of the second growing season (August
2016). During the time of species emergence (March-May), we also visually estimated species
cover in each plot every 3-4 days. KW performed all estimates to prevent bias between plots or
sampling occasions. The origin of each species was classified as either 'native' or 'exotic'
according to the PLANTS Database (USDA 2017).
We monitored three major plant phenological events (green-up, flowering, and seed set)
at each plot every 3-4 days from March through October 2016. We defined ‘emergence’ as the
first appearance of annual species and ≥ 50% greening for perennial species for each species in
each plot. We defined 'flowering' as the period between flower bud break and flower
senescence. ‘Seed set’ was determined when an individual exhibited withering of the stigma
along with swelling of the ovaries. For both flowering and seed set, we recorded the date when at
least one individual for each species in each plot exhibited the phenological stage.
We visually estimated the percent of leaf tissue missing (presumably due to herbivory) at
the end of the second growing season to capture cumulative damage throughout the season
(Schultheis et al. 2015). We haphazardly selected four leaves vertically dispersed along the stem
on three individuals of each species within each plot (n=12 leaves per species per plot). When

25

three individuals of that species were not present within a plot, we sampled the maximum
number of individuals present. We sampled all species except for Achillea millefolium, because
visual estimates of tissue damage on its feather-like leaves were difficult to assess. For
graminoid species, many ends of the blade were removed making it difficult to determine the
amount of tissue eaten. To mediate this concern, we used the average length of undamaged
blades for that species as a reference for each leaf herbivory estimate. We only recorded the
percent of missing plant tissue, as browning of the tissue could have been a response to abiotic
stress or fungal infections (Green et al. 1990). To prevent bias, KW completed all estimates with
visual aids where exact herbivory percentages were calculated using ImageJ software (Schneider
et al. 2012).
Statistical Analysis
We took several steps to prepare the data for analysis. We natural log transformed each
percent cover value to conform to the assumptions of general linear regression. As an index of
spring green-up phenology, we computed the date at which each species reached its median
cover value per plot. For time of flowering and seed set, we computed the median date that each
species was observed in each phenological stage in each plot. For the leaf herbivory models, we
did not consider plots with the reduced insect treatment (n=12 per site).
To evaluate the separate and interactive effects of warming and origin (native vs. exotic)
on plant percent cover, emergence, flowering time, seed set, and leaf herbivory, we fit mixedeffect ANOVA models. For each response variable at each site, we used a 2-step process to
compare complex and reduced models using likelihood ratio tests (α=0.05) to select the most
parsimonious model. To account for the additional treatments applied as part of the long-term
experiment, even though they are not a focus of this study, we first compared the full models

26

including terms for insect reduction and small mammal exclusion treatments to models without
those terms. Next, we compared the model including an interaction between warming and origin
to a model without the interaction to test for interactive effects of warming and plant species
origin on each response variable. A significant interaction term would indicate that the change in
the response variable caused by warming depends on plant species origin. Plant species identity
was included as a random intercept in each model to account for additional species-specific
variation within all response variables. For leaf herbivory models, we used a generalized linear
regression model with Poisson error, and the identity of individual plants was also included as a
random intercept because multiple observations were made on each individual. 95% confidence
intervals were calculated for each parameter estimate using bootstrap methods (number of
simulations=999). We calculated the denominator degrees of freedom using the Satterthwaite
approximation method (Bolker et al. 2009). To additionally test whether warming increased the
aggregate abundance of all exotic plants at each site, we summed the total cover for all exotic
species in each plot (n=24/site) and performed a non-hierarchical ANOVA with temperature
treatment (warmed/ambient) as a fixed effect. We used one-way ANOVA (α=0.05) procedures to
evaluate whether initial differences in species cover between warmed and ambient plots existed
prior to the start of the experiment, and completed all analyses using R version 3.1.2 (R
Development Core Team 2010) and the R package lme4 (Pinheiro and Bates 2000).
RESULTS
At the northern site, the response of plant cover to warming depended on origin (Table 2,
Figure 7A). Warming increased exotic plant cover by 19% and decreased native plant cover by
31% relative to the average cover in ambient plots (((x̄ warmed- x̄ambient)/ x̄ambient)*100). The
greatest increases in total cover were found in Rumex acetosella, Poa pratensis, and Hypericum

27

perforatum (all exotic). However, we did not find evidence that warming influenced plant cover
for either native or exotic species in the southern old agriculture field (Table 2, Figure 7B). We
did not find evidence for an interactive effect between warming and origin on average species
cover this site (Appendix E). However, we observed the greatest increases in total cover due to
warming of Trifolium sp. (exotic) and Euthamia graminofolia (native), with the greatest
decreases in Poa pratensis and Centaurea stoebe (both exotic).

28

Table 2. Parameter estimates for the most parsimonious general linear mixed-effects model
based on likelihood ratio tests (LRT) for each of five response variables explained by
warming and species’ origin at each site. All models included species as a random intercept.
The leaf herbivory model additionally included a random intercept for individual plant identity
and used Poisson error term (i.e. a generalized linear mixed-effect model). 95% confidence
intervals calculated using bootstrap techniques (number of simulations=999). All parameters
significantly different from zero are bolded. Positive origin effects indicate greater values for
native species relative to exotic species.
Response

Species Cover
Warmed
Origin
Warmed*Origin
Residuals
Emergence
Warmed
Origin
Residuals
Flowering Time
Warmed
Origin
Insects
Residuals
Seed Set Time
Warmed
Origin
Residuals
Leaf Herbivory
Warmed
Origin
Residuals

Northern Forest Clearing
Denom.
Estimate
df
95% CI
0.62
0.36
-0.69

Southern Old Agriculture Field
Denom.
Estimate
df
95% CI
-0.17
0.82

213.81
20.49

(-0.37, 0.02)
(-0.22, 1.92)

122.14
21.47
122.14
126

(0.16, 1.07)
(-0.68, 1.42)
(-1.38, -0.07)

-1.65
6.99

106.34
18.4
115

(-3.15, -0.09)
(0.03, 13.73)

-0.51
6.38

249.98
15.29
262

(-3.26, 2.23)
(-1.08, 13.38)

-2.18
-15.17
2.13

84.19
12.88
84.07
91

(-4.02, -0.35)
(-42.94, 9.79)
(0.41, 3.96)

2.42
37.14

211.24
18.94

(0.76, 3.88)
(4.00, 70.88)

0.82

81.09

0.89

183.53

(-2.26, 3.79)

-8.62

11.09
87

(-1.98, 3.70)
(-31.26,
14.66)

33.77

16.75
195

(-1.14, 65.48)

0.68
1.02

102.82
8.54
466

(0.01, 1.29)
(-0.72, 2.74)

-0.70
1.89

187.29
8.15
763

(-1.30, -0.14)
(0.67, 3.22)

225

225

29

Figure 7. Species percent cover (mean± 1SE) measured at the end of summer 2016 at the
(A) northern forest clearing and (B) southern old agriculture field. Data were measured for
each species in each plot at each site (n=24). An ANOVA ran on the most parsimonious model
shows warming increased overall species cover at the northern site (p<0.05), with no other
differences at the southern site or between species origin.
At the northern forest clearing, plant species emerged an average of 1.65±0.77 (SE) days
earlier in warmed plots than ambient plots and exotic species emerged 6.99±3.44 days earlier
than native plant species (Table 2, Figure 8A). Native and exotic plants shifted emergence
phenology by the same amount of time, on average (i.e. the interaction of warming and origin
was not present in the most parsimonious model, Appendix B). Plants flowered -2.18±0.92 days
earlier in the warmed plots than plants in the ambient plots at the northern site (Table 2, Figure
8C), and the magnitude of this shift was also indistinguishable between native and exotic species
(Appendix B). We found that plots at the northern site without the insecticide treatment flowered
30

2.13±0.86 days later than plots with the insecticide treatment (Table 2). The timing of seed set
did not vary according to warming treatment or origin at the northern site (Table 2, Figure 8E).
At the southern old agriculture field, warming did not affect spring emergence phenology for
native or exotic plants (Table 2, Figure 8B). Warming delayed plant flowering by 2.42±0.83 days
for both native and exotic species (Table 2, Figure 8D). At this site, exotic plants flowered
37.14±16.57 days earlier than native plants, regardless of warming (Figure 8D). The timing of
seed set also did not vary according to warming treatment or origin at the southern site (Table 2,
Figure 8F). The interaction between warming and origin was not present in the most
parsimonious model for any of the phenology responses at the northern or southern study site
(Appendix E).

31

Figure 8. Phenological responses measured throughout the 2016 growing season. Data were
measured for each species in each plot at each site. Boxplots display the median (central line),
25th percentile (lower bound), 75th percentile (upper bound), and smallest and largest value (ends
of whiskers) no larger than 1.5x the interquartile range from the upper and lower quartiles.
An ANOVA (α=0.05) was ran on each response’s most parsimonious model to determine
significant differences between groups. A and B show the date where each species reached 50%
of its maximum cover. B. and C. show the median date of flowering. D. and F. show the median
date of seed set.

32

Warming increased leaf herbivory approximately two-fold at the northern site (Table 2,
Figure 9A). Native and exotic species experienced similar levels of herbivory (Table 2, Figure
9A), but the way herbivory varied according to warming treatment was statistically
indistinguishable for native vs. exotic species (Appendix B). Warming decreased leaf herbivory
in the southern old agriculture field by approximately half, and native species experienced about
6-fold greater herbivory than exotic species (Table 2). The most parsimonious model for the
southern site also did not contain a term for the interaction of warming and herbivory (Appendix
B).

Figure 9. Percent of leaf eaten (Mean ± 1SE) at the end of summer 2016 at the (A) northern
forest clearing and (B) southern old agriculture field. Data were measured for four leaves on
three individuals of each species in each plot (n=12) for each site. An ANOVA ran on the most
parsimonious model shows warming increased herbivory at the northern site and decreased
33

Figure 9 (cont’d). herbivory at the southern site. Native species also experienced more
herbivory than exotic species at the southern site.

DISCUSSION
We found evidence that warming favored exotic species over native species at the northern
forest clearing site, where the effects of warming were largely consistent with our predictions.
However, the same levels of warming did not have strong ecological effects at the southern old
agriculture field site. We suggest that plants and herbivores may not have benefited from
warming at the southern site because ambient temperatures were already close to the critical
threshold for physiological stress, while ambient temperatures at the northern site were cooler
than the thermal maximum.
The moderate ecological effects observed at the northern forest clearing site were consistent
with our expectations. Greater plant cover in warmed plots compared to ambient plots is
consistent with widespread experimental studies that found warming increases plant productivity
(Rustad et al. 2001) and/or increases the abundance of more productive species (Fridley et al.
2016). The observed shifts in emergence and flowering time (either earlier or later) at the
northern site are consistent with the literature (Dunnell & Travers 2011; Calinger et al. 2013).
Warmed plants also experienced greater herbivory than those in ambient conditions. This could
be due to lower food quality and therefore increased herbivore demand (Hillebrand et al. 2009;
Sardans et al. 2012). Additionally, warmer temperatures could also increase the metabolism of
insects within the warmed plots, in turn increasing the amount of leaf tissue required by the
herbivore (Gillooly 2001). Exotic species emerged an entire week earlier than native species,
indicating their broad climate tolerance early in the growing season, but the reproductive
phenology of native and exotic species responded similarly to warming.

34

Responses at the southern old agriculture field site indicate that plants may have experienced
physiological stress (delayed flowering and reduced herbivory) when exposed to warming,
highlighting the dangers of living close to the thermal maximum (Tewksbury et al. 2008). The
thermal optima for individual plant species tend to cluster in a narrow range around 30° C and
are surprisingly insensitive to ambient temperature (Dell et al. 2011). As a result, lower-latitude
communities are more likely to be composed of organisms living at or above their critical
thermal maximum (Laurance et al. 2011; Stuart-Smith et al. 2015). Given the same magnitude of
warming, we would expect more local extinctions and greater declines in performance in lowerlatitude communities if warming exceeds the stress threshold for a greater proportion of species
(Perez et al. 2016).
Overall, we found limited evidence that exotic plant species will respond to climate change
differently than native species, with only one response (cover at the northern site) including an
interaction term in the most parsimonious model. The lack of differential effects of warming
between native or exotic species may be due to similar ruderal traits shared by species of both
origins. Because these systems are both heavily disturbed (clear-cut and agriculture), weedy
species dominate their communities. Weedy species that readily establish in disturbed
environments are likely to have broad climatic tolerances and be relatively insensitive to
moderate warming, regardless of their continent of origin (Theoharides & Dukes 2007). For
example, plant species with affinity for warmer soils and that produce wind-dispersed seeds
tended to be the best colonizers of newly available habitat patches as temperatures increase in an
alpine system (Matteodo et al. 2013).
This experiment suggests climate warming is not likely to differentially benefit exotic
plant species as theory suggests, but rather that the magnitude and direction of community

35

responses may depend on the difference between environmental temperature and the thermal
optima and maxima of individual species. While short-term experiments such as this increase
our understanding of annual ecological variation, they also provide insight into potential
mechanisms that lead to long-term ecological dynamics (Chesson & Huntly 1989; Magnuson
1990; Shriver 2016). Future research will provide insight to whether these short-term direct and
indirect responses to warming translate into greater success of exotic plant species over time.

36

APPENDICES

37

APPENDIX A
Additional methods used in warming experiment.
Insect Reduction Methods- As part of the long-term experiment, we reduced insect herbivory
through the application of Merit-75 WP insecticide at a concentration of 0.031 g/L with 1.2 L
applied for all insect exclusion plots at each site every 13-15 days while measurements were
taken during the growing season (Bayer Environmental Science, Research Triangle Park, NC).
An equal amount of water (1.2 L) was applied evenly across plots where insects were not
reduced within the same treatment session in order to reduce any unequal effects of added water
in the insecticide to the plants. All insecticide applications were performed to dry vegetation
during early morning low-wind conditions and no precipitation in the following 24 hours. Care
was taken to spray all plants across sub-canopies within each plot, such that ground dwelling
plants also received treatment.
Small Mammal Reduction Methods- We also reduced small mammal herbivory as part of the
long-term experiment by installing 91 cm tall, 1.3 cm gauge mesh hardware cloth around 12
plots at each site at the beginning of the experiment. The cloth was buried 20-30 cm beneath the
ground (Brown & Davidson 1977). The fencing was reinforced in Spring 2016 by attaching a
second layer of hardware cloth along the ground to reduce plot access to burrowing mammals
(Nutrient Network: A Global Research Cooperative 2008). We tested the integrity of the fences
during three separate 72 hour events at each site and in each year. During each sampling event
we set Sherman live traps in the fenced and unfenced plots that were baited with peanut butter
and oats (H. B. Sherman Traps, Tallahassee, FL: 6.5 2.0 × 2.5 cm). The traps were checked for
captures and reset every 8 hours.

38

Herbivore treatments do not affect air temperature- To examine whether the herbivore
treatments affected the chamber’s ability to warm the plots, we performed an ANOVA test using
the pendants’ mean hourly temperatures in plots with either type of herbivore reduction (small
mammals and insects) and plots without herbivore reduction. We found no statistical differences
between herbivore treatments in mean temperatures at either site (Northern Site: F = 1.97, df = 2,
p = 0.14; Southern Site: F=0.07, df =2, p = 0.93).
Solar Radiation Shields- To avoid direct radiation on the sensors that would generate inaccurate
readings, radiation shields were constructed using methods described by Fedler (2013).
Time-Period Methods and Statistical Analyses- We analyzed the data on air temperature (1m
and 10 cm heights), relative humidity (1m), soil temperature (-5 cm) and soil moisture (-5 cm)
for each site separately. We used two sided, unpaired t-tests to compare the means of the hourly
data (nchambered = 3; nambient=3) during the following intervals: 24-hour (entire day), daytime,
nighttime, growing season, and non-growing season. We classified ‘daytime’ hours as those
between sunrise and sunset based on nearby cities: Richland, MI and Pellston, MI (United States
Naval Observatory 2016). Similarly, ‘nighttime’ hours were classified as the hours between
sunset and sunrise. We defined ‘growing season’ as beginning on the spring equinox and ending
the day before fall equinox and ‘non-growing season’ as the date beginning fall equinox and
ending the day before spring equinox.
Data Quality Control Methods-Data were graphically and systematically screened for faulty data
due to equipment issues. The sensors used in this experiment record extreme values (i.e. ±
999°C) or N/A values when physically damaged (chewed wires, loose wire connections). These
values were removed from the analyses. Days that included <24 hours were also removed to
standardize analyses between different timeframes. When a sensor malfunctioned for < 30 days,

39

those dates were removed from all sensors in the analysis. When a sensor malfunctioned for ≥ 30
days, we removed this sensor’s results from the analysis to avoid excluding dates from a
substantial portion of the season (Table S1). After screening, 94% and 83% of the total data
across all sensor types were used for analysis at the northern and southern site, respectively.

40

APPENDIX B.

Dates of sensor malfunction at both sites from August 1, 2015- July 31, 2016.
Table 3. Dates of sensor malfunction due to technology damage or maintenance at the
northern forest clearing and southern old-agriculture field from August 1, 2015- July 31,
2016. Each pair includes data from one chamber plot and one ambient plot using one HOBO
Microstation H21-002 4-channel data logger (Air Temperature at 1m, Relative Humidity at 1m,
Soil Moisture at -5cm) and one HOBO 4-channel external U12-008 data logger (Air
Temperature at 10cm and Soil Temperature at -5cm). Each 4-channel data logger collected the
same type of measurement from the chambered (two of the channels) and ambient (remaining
two channels) plot in each pair. When a sensor malfunctioned for < 30 days, these dates were
removed from all sensors in the analysis. When a sensor malfunctioned for ≥ 30 days, we
removed this sensor’s results from the analysis to avoid excluding dates from a substantial
portion of the season.
Measurement Pair Chamber

Ambient Dates
Result
Malfunctioned
Northern Forest Clearing (University of Michigan Biological Station)
Air Temp 1m,
1
x
x
04/01/2016 12:00–
Dates removed from all
RH 1m, Soil
04/02/2016 9:00
sensors for this measurement
Moisture
Air Temp 1m,
2
x
x
05/16/2016 15:00–
Dates removed from all
RH 1m, Soil
06/10/2016 10:00;
sensors for this measurement
Moisture
09/26/2015 10:00
Air Temp
2
x
08/01/2015 00:00–
10cm; Soil
11/24/2015 10:00
moisture
Southern Old-Agriculture Field (Kellogg Biological Station)
Air Temp 1m
1
x
07/01/2016 4:00 –
07/31/2016 23:00
Air Temp
2
x
08/01/2015 0:00 –
10cm; Soil
10/30/2015 12:00
moisture
Air Temp 1m;
3
x
08/01/2015 0:00 –
RH 1m
10/30/2015 12:00
Air Temp
3
x
Variable All Year
10cm
(≥ 30 days)

41

Sensor excluded from analysis

Dates removed from all
sensors for this measurement
Sensor excluded from analysis

Sensor excluded from analysis
Sensor excluded from analysis

APPENDIX C.

Unpaired t-tests for warmed and ambient treatments at the northern forest clearing.
Table 4. Comparisons of average differences between chambered-ambient plots in the
northern forest clearing at the University of Michigan Biological Station from August 1,
2015- July 31, 2016. T-tests were performed on the mean of the hourly values for each of the
intervals described below. Significant differences (p<0.05) are in bold text.

Measurement
Air Temperature, 1m (ºC)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season
Air Temperature, 10cm (ºC)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season
Relative Humidity, 1m (%)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season
Soil Temperature, -5cm (ºC)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season
Soil Moisture, -5cm (%)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season

Difference
(Chamber - Ambient)
Mean
95% CI

t

df

n warmed,
n ambient

p-value

6.93
3.87
-0.52
5.48
5.49

2.18
2.91
3.90
2.31
2.11

3,3
3,3
3,3
3,3
3,3

0.02
0.032
0.633
0.023
0.028

0.84
1.84
-0.15
1.36
0.37

(0.36, 1.33)
(0.29, 3.39)
(-0.97, 0.67)
(0.42, 2.30)
(0.09, 0.64)

2.21
5.00
0.68
9.99
0.20

1.05
1.08
1.08
1.00
1.01

2,2
2,2
2,2
2,2
2,2

0.26
0.11
0.61
0.06
0.87

0.61
1.03
0.37
1.28
0.10

(-2.49, 3.71)
(-1.18, 3.23)
(-5.49, 6.24)
(-0.34, 2.89)
(-6.15, 6.35)

-0.91
-1.16
1.10
-1.77
1.67

2.40
2.91
3.57
2.36
3.73

3,3
3,3
3,3
3,3
3,3

0.45
0.33
0.34
0.20
0.18

-0.48
-2.02
1.05
-1.57
0.52

(-2.41, 1.46)
(-7.63, 3.60)
(-1.75, 3.85)
(-4.90, 1.74)
(-0.37, 1.41)

4.73
2.27
5.06
4.73
1.49

1.07
1.48
1.16
1.07
1.46

2,2
2,2
2,2
2,2
2,2

0.12
0.19
0.10
0.12
0.32

0.67
0.78
0.56
0.67
0.36

(-0.87, 2.20)
(-1.30, 2.83)
(-0.46, 1.58)
(-0.87, 2.20)
(-1.17, 1.90)

0.04
0.00
0.09
-0.21
0.20

3.42
3.45
3.41
3.74
3.28

3,3
3,3
3,3
3,3
3,3

0.97
0.99
0.94
0.84
0.86

0.06
0.00
0.12
-0.21
0.30

(-3.73, 3.84)
(-3.58, 3.57)
(-3.88, 4.11)
(-3.15, 2.72)
(-4.33, 4.93)

42

APPENDIX D.

Unpaired t-tests for warmed and ambient treatments at the southern old-agriculture field.
Table 5. Comparisons of average differences between chambered-ambient plots in the
southern old-agriculture field at Kellogg Biological Station from August 1, 2015- July 31,
2016. T-tests were performed on the mean of the hourly values for each of the intervals
described below. Significant differences (p<0.05) are in bold text.

Measurement
Air Temperature, 1m (ºC)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season
Air Temperature, 10cm (ºC)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season
Relative Humidity, 1m (%)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season
Soil Temperature, -5cm (ºC)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season
Soil Moisture, -5cm (%)
Year-round, 24 h
Daytime Only
Nighttime Only
Growing Season
Non-growing Season

Difference
(Chamber - Ambient)
Mean
95% CI

t

df

n warmed,
n ambient

p-value

13.61
17.64
-22.50
21.06
5.35

2.67
2.41
2.23
2.05
1.29

2,3
2,3
2,3
2,3
2,3

0.002
0.001
0.001
0.002
0.08

0.70
1.76
-0.40
0.99
0.38

(0.52, 0.87)
(1.39, 2.12)
(-0.47, -0.33)
(0.79, 1.19)
(-0.16, 0.92)

2.82
-0.35
10.37
1.21
6.19

1.01
1.02
1.08
1.15
2.86

2,2
2,2
2,2
2,2
3,2

0.21
0.78
0.05
0.46
0.01

0.26
-0.09
0.62
0.15
0.41

(-0.89, 1.42)
(-3.02, 2.85)
(-0.02, 1.26)
(-1.21, 1.52)
(0.19, 0.63)

0.84
-1.15
3.00
0.46
1.41

2.15
2.04
2.33
2.64
2.17

2,3
2,3
2,3
2,3
2,3

0.49
0.37
0.08
0.68
0.28

0.75
-1.08
2.65
0.55
0.98

(-2.88, 4.39)
(-5.04, 2.88)
(-0.68, 5.98)
(-3.54, 4.64)
(-1.81, 3.78)

-3.75
-1.93
-1.30
-3.07
-0.47

3.02
3.96
3.85
3.57
2.74

3,3
3,3
3,3
3,3
3,3

0.03
0.13
0.27
0.04
0.67

-0.21
-0.26
-0.18
-0.38
-0.05

(-0.40, -0.03)
(-0.62, 0.11)
(-0.55, 0.20)
(-0.74, -0.02)
(-0.39, 0.29)

-10.49
-12.89
-8.48
-5.56
-4.47

3.00
2.77
2.88
2.71
1.98

2,3
2,3
2,3
2,3
2,3

0.001
0.001
0.004
0.02
0.05

-3.66
-3.53
-3.79
-2.94
-4.41

(-4.77, -2.55)
(-4.44, -2.61)
(-5.24, -2.33)
(-4.74, -1.15)
(-8.69, -0.13)

43

APPENDIX E.

Summary of likelihood ratio tests for mixed-effects model comparisons.
Table 6. Summary of likelihood ratio tests (α=0.05) used to determine the most
parsimonious mixed-effects model. Percent Cover, Emergence, Flowering Time, and Timing of
Seed Set included species identification as a random effect and leaf herbivory included species
identification AND individual plant identification as random effects. Significant results listed in
bold.
Fixed Model Comparisons

ln(Percent Cover) (2015)
[M1]Cover~Warmed+Origin+Warmed*Origin+Insects+
Mammals vs.
[M2]Cover~ Warmed+Origin+Warmed*Origin+Insects
[M1]Cover~Warmed+Origin+Warmed*Origin+Insects vs.
[M2]Cover~Warmed+Origin+Warmed*Origin
[M1]Cover~Warmed+Origin+Warmed*Origin vs.
[M2]Cover~Warmed+Origin
ln(Percent Cover) (2016)
[M1]Cover~Warmed+Origin+Warmed*Origin+Insects+
Mammals vs.
[M2]Cover~ Warmed+Origin+Warmed*Origin+Insects
[M1]Cover~Warmed+Origin+Warmed*Origin+Insects vs.
[M2]Cover~Warmed+Origin+Warmed*Origin
[M1]Cover~Warmed+Origin+Warmed*Origin vs.
[M2]Cover~Warmed+Origin
Emergence
[M1]Emergence
~Warmed+Origin+Warmed*Origin+Insects+Mammals
vs.
[M2]Emergence~
Warmed+Origin+Warmed*Origin+Insects
[M1]Emergence~Warmed+Origin+Warmed*Origin+Inse
cts vs. [M2]Cover~Warmed+Origin+Warmed*Origin
[M1]Emergence~Warmed+Origin+Warmed*Origin vs.
[M2]Emergence~Warmed+Origin
Flowering Time
[M1]Flowering Time~
Warmed+Origin+Warmed*Origin+Insects+Mammals vs.
[M2] Flowering Time~
Warmed+Origin+Warmed*Origin+Insects

44

Northern Site
Summary
Results

Southern Site
Summary
Results

χ2=0.35; df=1;
p=0.55

χ2=0.90; df=1;
p=0.34

χ2=0.30; df=1;
p=0.58
χ2=0.60; df=1;
p=0.44

χ2=0.84; df=1;
p=0.36
χ2=2.46; df=1;
p=0.12

χ2=0.10; df=1;
p=0.75

χ2=2.05; df=1;
p=0.15

χ2=1.11; df=1;
p=0.29
χ2=4.23; df=1;
p=0.04

χ2=0.04; df=1;
p=0.85
χ2=0.76; df=1;
p=0.38

χ2=0.54; df=1;
p=0.46

χ2=1.15; df=1;
p=0.28

χ2=0.10; df=1;
p=0.76
χ2=0.03; df=1;
p=0.86

χ2=1.92; df=1;
p=0.17
χ2=1.51; df=1;
p=0.22

χ2=0.32; df=1;
p=0.57

χ2=0.33; df=1;
p=0.57

Table 6 (cont’d).
[M1] Flowering Time~
Warmed+Origin+Warmed*Origin+Insects vs.
[M2] Flowering Time~Warmed+Origin+Warmed*Origin
[M1] Flowering Time~Warmed+Origin+Warmed*Origin
vs. [M2] Flowering Time~Warmed+Origin
Timing of Seed Set
[M1]Seed Set~
Warmed+Origin+Warmed*Origin+Insects+Mammals vs.
[M2] Seed Set~
Warmed+Origin+Warmed*Origin+Insects
[M1] Seed Set~Warmed+Origin+Warmed*Origin+Insects
vs. [M2] Seed Set~Warmed+Origin+Warmed*Origin
[M1] Seed Set~Warmed+Origin+Warmed*Origin vs.
[M2] Seed Set ~Warmed+Origin
Leaf Herbivory
[M1]Leaf Herbivory~Warmed+Origin+Warmed*Origin+
+Mammals vs.
[M2]Leaf Herbivory~Warmed+Origin+Warmed*Origin
[M1]Leaf Herbivory~Warmed+Origin+Warmed*Origin
vs. [M2]Leaf Herbivory~Warmed+Origin

45

χ2=5.40; df=1;
p=0.02

χ2=1.87; df=1;
p=0.17

χ2=0.65; df=1;
p=0.42

χ2=0.37; df=1;
p=0.54

χ2=0.08; df=1;
p=0.78

χ2=0.05; df=1;
p=0.82

χ2=0.39; df=1;
p=0.53
χ2=0.00; df=1;
p=0.95

χ2=0.32; df=1;
p=0.57
χ2=0.02; df=1;
p=0.89

χ2=2.43; df=1;
p=0.12

χ2=1.41; df=1;
p=0.24

χ2=0.25; df=1;
p=0.62

χ2=0.76; df=1;
p=0.38

LITERATURE CITED

46

LITERATURE CITED
Alpert, P., E. Bone, and C. Holzapfel. 2000. “Invasiveness, Invasibility and the Role of
Environmental Stress in the Spread of Non-Native Plants.” Perspectives in Plant
Ecology, Evolution and Systematics 3 (1): 52–66. doi:10.1078/1433-8319-00004.
Arft, A. M., M. D. Walker, J. Gurevitch, J. M. Alatalo, M. S. Bret-Harte, M. Dale, M. Diemer, et
al. 1999. “Responses of Tundra Plants to Experimental Warming: Meta-Analysis of the
International Tundra Experiment.” Ecological Monographs 69 (4): 491–511.
doi:10.1890/0012-9615(1999)069[0491:ROTPTE]2.0.CO;2.
Aronson, E.L. and S.G. McNulty. 2009. “Appropriate Experimental Ecosystem Warming
Methods by Ecosystem, Objective, and Practicality.” Agricultural and Forest
Meteorology 149 (11): 1791–99. doi:10.1016/j.agrformet.2009.06.007.
Bale, J.S., S. Gregory, J. Masters, I.D. Hodkinson, C.A. T. M. Bezemer, V.K. Brown, J.
Butterfield, et al. 2002. “Herbivory in Global Climate Change Research: Direct Effects of
Rising Temperature on Insect Herbivores.” Global Change Biology 8 (1): 1–16.
doi:10.1046/j.1365-2486.2002.00451.x.
Bokhorst, S., J.W. Bjerke, M.P. Davey, K.Taulavuori, E. Taulavuori, K. Laine, T.V. Callaghan,
and G.K. Phoenix. 2010. “Impacts of Extreme Winter Warming Events on Plant
Physiology in a Sub-Arctic Heath Community.” Physiologia Plantarum 140 (2): 128–40.
doi:10.1111/j.1399-3054.2010.01386.x.
Bolker, B.M., M.E. Brooks, C.J. Clark, S.W. Geange, J.R. Poulsen, M. Henry H. Stevens, and
J.S.S. White. 2009. “Generalized Linear Mixed Models: A Practical Guide for Ecology
and Evolution.” Trends in Ecology & Evolution 24 (3): 127–35.
doi:10.1016/j.tree.2008.10.008.
Borer, E.T., E.W. Seabloom, D.S. Gruner, W.S. Harpole, H. Hillebrand, E.M. Lind, P.B. Adler,
et al. 2014. “Herbivores and Nutrients Control Grassland Plant Diversity via Light
Limitation.” Nature 508 (7497): 517–20. doi:10.1038/nature13144.
Bremm, C., P.C. F. Carvalho, L. Fonseca, G.A. Amaral, J.C. Mezzalira, N.B. Perez, C. Nabinger,
and E.A. Laca. 2016. “Diet Switching by Mammalian Herbivores in Response to Exotic
Grass Invasion.” Edited by Erik Sotka. PLOS ONE 11 (2): e0150167.
doi:10.1371/journal.pone.0150167.
Brown, J.H., J.F. Gillooly, A.P. Allen, V.M. Savage, and G.B. West. 2004. “Toward a Metabolic
Theory of Ecology.” Ecology 85 (7): 1771–89. doi:10.1890/03-9000.
Brown, P.J., and A.T. DeGaetano. 2011. “A Paradox of Cooling Winter Soil Surface
Temperatures in a Warming Northeastern United States.” Agricultural and Forest
Meteorology 151 (7): 947–56. doi:10.1016/j.agrformet.2011.02.014.
47

Calinger, K.M., S. Queenborough, and P.S. Curtis. 2013. “Herbarium Specimens Reveal the
Footprint of Climate Change on Flowering Trends across North-Central North America.”
Edited by Yvonne Buckley. Ecology Letters 16 (8): 1037–44. doi:10.1111/ele.12135.
Chambers, J.C., M.J. Germino, J. Belnap, C.S. Brown, E.W. Schupp, and S.B. St. Clair. 2016.
“Plant Community Resistance to Invasion by Bromus Species: The Roles of Community
Attributes, Bromus Interactions with Plant Communities, and Bromus Traits.” In Exotic
Brome-Grasses in Arid and Semiarid Ecosystems of the Western US, edited by Matthew
J. Germino, Jeanne C. Chambers, and Cynthia S. Brown, 275–304. Cham: Springer
International Publishing. doi:10.1007/978-3-319-24930-8_10.
Charles, H. and J. Dukes. 2007. In Biological Invasions. Vol. 193. Ecological Studies.
Heidelberg: Springer.
Chesson, P. and Nancy H. 1989. “Short-Term Instabilities and Long-Term Community
Dynamics.” Trends in Ecology & Evolution 4 (10): 293–98. doi:10.1016/01695347(89)90024-4.
Chiba, M and Tornio T. 2014. “Open-Top Chambers with Solar-Heated Air Introduction Tunnels
for the High-Temperature Treatment of Paddy Fields.” PLANT PRODUCTION
SCIENCE 17 (2): 152–65.
Cook, D.C., M.B. Thomas, S.A. Cunningham, D.L. Anderson, and P.J. De Barro. 2007.
“Predicting the Economic Impact of an Invasive Species on an Ecosystem Service.”
Ecological Applications 17 (6): 1832–40.
Cranston, B.H., A.Monks, P.A. Whigham, and K.J. M. Dickinson. 2015. “Variation and
Response to Experimental Warming in a New Zealand Cushion Plant Species.” Austral
Ecology 40 (6): 642–50. doi:10.1111/aec.12231.
Tilman, D. 1985. “The Resource-Ratio Hypothesis of Plant Succession.” The American
Naturalist 125 (6): 827–52.
Davies, K.W. 2011. “Plant Community Diversity and Native Plant Abundance Decline with
Increasing Abundance of an Exotic Annual Grass.” Oecologia 167 (2): 481–91.
doi:10.1007/s00442-011-1992-2.
Davis, M.A. 2009. Invasion Biology. Oxford ; New York: Oxford University Press.
Dell, A.I., S. Pawar, and V.M. Savage. 2011. “Systematic Variation in the Temperature
Dependence of Physiological and Ecological Traits.” Proceedings of the National
Academy of Sciences 108 (26): 10591–96. doi:10.1073/pnas.1015178108.
Dickson, T.L., and K.L. Gross. 2013. “Plant Community Responses to Long-Term Fertilization:
Changes in Functional Group Abundance Drive Changes in Species Richness.”
Oecologia 173 (4): 1513–20. doi:10.1007/s00442-013-2722-8.
48

Dijkstra, F.A., E. Pendall, J.A. Morgan, D.M. Blumenthal, Y. Carrillo, D.R. LeCain, R.F. Follett,
and D.G. Williams. 2012. “Climate Change Alters Stoichiometry of Phosphorus and
Nitrogen in a Semiarid Grassland.” New Phytologist 196 (3): 807–15.
doi:10.1111/j.1469-8137.2012.04349.x.
Dukes, J.S. 2010. “Responses of Invasive Species to a Changing Climate and Atmosphere.” In
Fifty Years of Invasion Ecology, edited by David M. Richardson, 345–57. Oxford, UK:
Wiley-Blackwell. doi:10.1002/9781444329988.ch26.
Dukes, J.S. and H.A. Mooney. 1999. “Does Global Change Increase the Success of Biological
Invaders?” Trends in Ecology & Evolution 14 (4): 135–39. doi:10.1016/S01695347(98)01554-7.
Dunnell, K.L. and S.E. Travers. 2011. “Shifts in the Flowering Phenology of the Northern Great
Plains: Patterns over 100 Years.” American Journal of Botany 98 (6): 935–45.
doi:10.3732/ajb.1000363.
Early, R., B.A. Bradley, J.S. Dukes, J.J. Lawler, J.D. Olden, D.M. Blumenthal, P. Gonzalez, et
al. 2016. “Global Threats from Invasive Alien Species in the Twenty-First Century and
National Response Capacities.” Nature Communications 7 (August): 12485.
doi:10.1038/ncomms12485.
Edwards, K.A. and R.L. Jefferies. 2010. “Nitrogen Uptake by Carex Aquatilis during the WinterSpring Transition in a Low Arctic Wet Meadow: Early Spring Nitrogen Uptake by Carex
Aquatilis.” Journal of Ecology 98 (4): 737–44. doi:10.1111/j.1365-2745.2010.01675.x.
Eisenhauer, N., N.A. Fisichelli, L.E. Frelich, and P.B. Reich. 2012. “Interactive Effects of Global
Warming and ‘global Worming’ on the Initial Establishment of Native and Exotic
Herbaceous Plant Species.” Oikos 121 (7): 1121–33. doi:10.1111/j.16000706.2011.19807.x.
Elmendorf, S.C., Gregory H.R. Henry, R.D. Hollister, R.G. Björk, A.D. Bjorkman, T.V.
Callaghan, L. Siegwart Collier, et al. 2012. “Global Assessment of Experimental Climate
Warming on Tundra Vegetation: Heterogeneity over Space and Time: Warming Effects
on Tundra Vegetation.” Ecology Letters 15 (2): 164–75. doi:10.1111/j.14610248.2011.01716.x.
Elton, C.S. 1958. The Ecology of Invasions by Animals and Plants. University of Chicago Press
ed. Chicago: University of Chicago Press.
Erschbamer, B. 2001. Responses of Some Austrian Glacier Foreland Plants to Experimentally
Changed Microclimatic Conditions. Edited by G. R. Walther, C. A. Burga, and P. J.
Edwards. New York: Kluwer Academic/Plenum Publ.
Fedler, M. 2013. Weather Station Solar Rediation Shield. Youtube.com.
https://www.youtube.com/watch?v=KOBt7sxtx0Y.
49

Fey, S.B., and Kathryn L. Cottingham. 2011. “Linking Biotic Interactions and Climate Change to
the Success of Exotic Daphnia Lumholtzi: Temperature Alters Daphnia Competitive
Interactions.” Freshwater Biology 56 (11): 2196–2209. doi:10.1111/j.13652427.2011.02646.x.
Fey, S.B., and C.M. Herren. 2014. “Temperature-Mediated Biotic Interactions Influence Enemy
Release of Nonnative Species in Warming Environments.” Ecology 95 (8): 2246–56.
doi:10.1890/13-1799.1.
Fraser, L.H, H.A.L Henry, C.N. Carlyle, S.R. White, C. Beierkuhnlein, J.F Cahill, B.B. Casper,
et al. 2013. “Coordinated Distributed Experiments: An Emerging Tool for Testing Global
Hypotheses in Ecology and Environmental Science.” Frontiers in Ecology and the
Environment 11 (3): 147–55. doi:10.1890/110279.
Frei, E.R., J. Ghazoul, P. Matter, M. Heggli, and A.R. Pluess. 2014. “Plant Population
Differentiation and Climate Change: Responses of Grassland Species along an
Elevational Gradient.” Global Change Biology 20 (2): 441–55. doi:10.1111/gcb.12403.
Fridley, J.D. 2012. “Extended Leaf Phenology and the Autumn Niche in Deciduous Forest
Invasions.” Nature 485 (7398): 359–62. doi:10.1038/nature11056.
Fridley, J.D., J.S. Lynn, JP. Grime, and A.P. Askew. 2016. “Longer Growing Seasons Shift
Grassland Vegetation towards More-Productive Species.” Nature Climate Change 6 (9):
865–68. doi:10.1038/nclimate3032.
Gillooly, J.F. 2001. “Effects of Size and Temperature on Metabolic Rate.” Science 293 (5538):
2248–51. doi:10.1126/science.1061967.
G.M. Marion, G.H.R. Henry, D.W. Freckman, J. Johnstone, G. Jones, M.H. Jones, E. Levesque,
et al. 1997. “Open-Top Designs for Manipulating Field Temperature in High-Latitude
Ecosystems.” Global Change Biology 3 (1): 20–32.
Green, J.L., J. Capizzi, and O. Maloy. 1990. “A Systematic Approach to Diagnosing Plant
Damage.” Vol. 13 Issue 6. Ornamentals Northwest Archive. Oregon State University
Extension.
Harte, J., M.S. Torn, F.R. Chang, B. Feifarek, A.P. Kinzig, R. Shaw, and K. Shen. 1995. “Global
Warming and Soil Microclimate: Results from a Meadow-Warming Experiment.”
Ecological Applications 5 (1): 132–50. doi:10.2307/1942058.
Hellmann, J.J., J.E. Byers, B.G. Bierwagen, and J.S. Dukes. 2008. “Five Potential Consequences
of Climate Change for Invasive Species.” Conservation Biology 22 (3): 534–43.
doi:10.1111/j.1523-1739.2008.00951.x.
Henry, H.A.L. 2008. “Climate Change and Soil Freezing Dynamics: Historical Trends and
Projected Changes.” Climatic Change 87 (3–4): 421–34. doi:10.1007/s10584-007-93228.
50

Herbold, B. and Moyle, P.B. 1986. “Introduced Species and Vacant Niches.” The American
Naturalist 128 (5): 751–60.
Hillebrand, H., E.T. Borer, M.E.S. Bracken, B.J. Cardinale, J. Cebrian, E.E. Cleland, J.J. Elser,
et al. 2009. “Herbivore Metabolism and Stoichiometry Each Constrain Herbivory at
Different Organizational Scales across Ecosystems.” Ecology Letters 12 (6): 516–27.
doi:10.1111/j.1461-0248.2009.01304.x.
Hollister, R.D., P.J. Webber, F.E. Nelson, and C.E. Tweedie. 2006. “Soil Thaw and Temperature
Response to Air Warming Varies by Plant Community: Results from an Open-Top
Chamber Experiment in Northern Alaska.” ARCTIC ANTARCTIC AND ALPINE
RESEARCH 38 (2): 206–15. doi:10.1657/1523-0430(2006)38[206:STATRT]2.0.CO;2.
Ineson, P., G. K. Self, D.S. Bogie, D. Benham, A.H. Fitter, T.K. Brown, and J.D. Graves. 1999.
“Root Production and Turnover in an Upland Grassland Subjected to Artificial Soil
Warming Respond to Radiation Flux and Nutrients, Not Temperature.” Oecologia 120
(4): 575–81. doi:10.1007/s004420050892.
Brown, J.H. and D.W. Davidson. 1977. “Competition between Seed-Eating Ants and Rodents in
Desert Ecosystems.” Science 196 (4292): 880–82.
Keane, R. and M.J. Crawley. 2002. “Exotic Plant Invasions and the Enemy Release Hypothesis.”
Trends in Ecology & Evolution 17 (4): 164–70. doi:10.1016/S0169-5347(02)02499-0.
Kreyling, J. 2010. “Winter Climate Change: A Critical Factor for Temperate Vegetation
Performance.” Ecology 91 (7): 1939–48. doi:10.1890/09-1160.1.
Kumar, N., P. Grogan, H. Chu, C. Christiansen, and V. Walker. 2013. “The Effect of FreezeThaw Conditions on Arctic Soil Bacterial Communities.” Biology 2 (1): 356–77.
doi:10.3390/biology2010356.
Laurance, W.F., D.C. Useche, L.P. Shoo, S.K. Herzog, M. Kessler, F. Escobar, G. Brehm, J.C.
Axmacher, I.C. Chen, and L.A. Gámez. 2011. “Global Warming, Elevational Ranges and
the Vulnerability of Tropical Biota.” Biological Conservation 144 (1): 548–57.
doi:10.1016/j.biocon.2010.10.010.
Levine, J.M., A. K. McEachern, and C. Cowan. 2008. “Rainfall Effects on Rare Annual Plants.”
Journal of Ecology 96 (4): 795–806. doi:10.1111/j.1365-2745.2008.01375.x.
Lu, X., E. Siemann, H.W., X. Shao, and J. Ding. 2015. “Effects of Warming and Nitrogen on
above- and below-Ground Herbivory of an Exotic Invasive Plant and Its Native
Congener.” Biological Invasions 17 (10): 2881–92. doi:10.1007/s10530-015-0918-z.
Ma, Z., H. Liu, Z. Mi, Z. Zhang, Y. Wang, W. Xu, L. Jiang, and J.S. He. 2017. “Climate
Warming Reduces the Temporal Stability of Plant Community Biomass Production.”
Nature Communications 8 (May): 15378. doi:10.1038/ncomms15378.
51

Magnuson, J.J. 1990. “Long-Term Ecological Research and the Invisible Present.” BioScience 40
(7): 495–501. doi:10.2307/1311317.
Marion, G.M. 1996. “Temperature Enhancement Experiments.” In ITEX Manual, 17–22. Danish
Polar Center, Copenhagen.
Marion, G.M., G.H.R. Henry, D.W. Freckman, J. Johnstone, G. Jones, M.H. Jones, E. LéVesque,
et al. 1997. “Open-Top Designs for Manipulating Field Temperature in High-Latitude
Ecosystems.” Global Change Biology 3 (S1): 20–32. doi:10.1111/j.13652486.1997.gcb136.x.
Matesanz, S., E. Gianoli, and F. Valladares. 2010. “Global Change and the Evolution of
Phenotypic Plasticity in Plants: Global Change and Plasticity.” Annals of the New York
Academy of Sciences 1206 (1): 35–55. doi:10.1111/j.1749-6632.2010.05704.x.
Matteodo, M., S. Wipf, V. Stöckli, C. Rixen, and P. Vittoz. 2013. “Elevation Gradient of
Successful Plant Traits for Colonizing Alpine Summits under Climate Change.”
Environmental Research Letters 8 (2): 024043. doi:10.1088/1748-9326/8/2/024043.
Menne, M.J., I. Durre, B. Korzeniewski, S. McNeill, K. Thomas, X. Yin, S. Anthony, et al. 2012.
“Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.” NOAA
National Climatic Data Center. https://doi.org/10.7289/V5D21VHZ.
Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston. 2012. “An Overview of the
Global Historical Climatology Network-Daily Database.” Journal of Atmospheric and
Oceanic Technology 29 (7): 897–910. doi:10.1175/JTECH-D-11-00103.1.
Munier, A., L. Hermanutz, J.D. Jacobs, and K. Lewis. 2010. “The Interacting Effects of
Temperature, Ground Disturbance, and Herbivory on Seedling Establishment:
Implications for Treeline Advance with Climate Warming.” Plant Ecology 210 (1): 19–
30. doi:10.1007/s11258-010-9724-y.
Norby, R., N. Edwards, J. Riggs, C. Abner, S. Wullschleger, and C. Gunderson. 1997.
“Temperature-Controlled Open-Top Chambers for Global Change Research.” Global
Change Biology 3 (3): 259–67. doi:10.1046/j.1365-2486.1997.00072.x.
Nutrient Network: A Global Research Cooperative. 2008. “Building a Fence.” University of
Minnesota Department of Ecology, Evolution, and Behavior, March.
http://www.nutnet.umn.edu/files/nutnet/NutNet_FenceBuilding.pdf.
Parmesan, C., and G. Yohe. 2003. “A Globally Coherent Fingerprint of Climate Change Impacts
across Natural Systems.” Nature 421 (6918): 37–42. doi:10.1038/nature01286.
Pelini, S.L., F.P. Bowles, A.M. Ellison, N.J. Gotelli, N.J. Sanders, and R.R. Dunn. 2011.
“Heating up the Forest: Open-Top Chamber Warming Manipulation of Arthropod
52

Communities at Harvard and Duke Forests.” Methods in Ecology and Evolution 2 (5):
534–40. doi:10.1111/j.2041-210X.2011.00100.x.
Perez, T.M., J.T. Stroud, and K.J. Feeley. 2016. “Thermal Trouble in the Tropics.” Science 351
(6280): 1392–93. doi:10.1126/science.aaf3343.
Peterjohn, W.T., J.M. Melillo, P.A. Steudler, K.M. Newkirk, F.P. Bowles, and J.D. Aber. 1994.
“Responses of Trace Gas Fluxes and N Availability to Experimentally Elevated Soil
Temperatures.” Ecological Applications 4 (3): 617–25. doi:10.2307/1941962.
Post, E.S. 2013. Ecology of Climate Change: The Importance of Biotic Interactions. Princeton:
Princeton University Press. http://site.ebrary.com/id/10718664.
Post, E.S., C. Pedersen, C.C. Wilmers, and M.C. Forchhammer. 2008. “Phenological Sequences
Reveal Aggregate Life History Response to Climatic Warming.” Ecology 89 (2): 363–70.
doi:10.1890/06-2138.1.
R Development Core Team. 2008. R: A language and environment for statistical computing. R
Foundation for Statistical Computing, issued 2008. http://www.R-project.org.
Richards, C.L., O. Bossdorf, N.Z. Muth, J. Gurevitch, and M. Pigliucci. 2006. “Jack of All
Trades, Master of Some? On the Role of Phenotypic Plasticity in Plant Invasions.”
Ecology Letters 9 (8): 981–93. doi:10.1111/j.1461-0248.2006.00950.x.
Rixon, C.A.M., I.C. Duggan, N.M.N. Bergeron, A. Ricciardi, and H.J. Macisaac. 2005.
“Invasion Risks Posed by the Aquarium Trade and Live Fish Markets on the Laurentian
Great Lakes.” Biodiversity and Conservation 14 (6): 1365–81. doi:10.1007/s10531-0049663-9.
Robinet, C., and A. Roques. 2010. “Direct Impacts of Recent Climate Warming on Insect
Populations.” Integrative Zoology 5 (2): 132–42. doi:10.1111/j.1749-4877.2010.00196.x.
Root, T.L., J.T. Price, K.R. Hall, S.H. Schneider, C. Rosenzweig, and J.A. Pounds. 2003.
“Fingerprints of Global Warming on Wild Animals and Plants.” Nature 421 (6918): 57–
60. doi:10.1038/nature01333.
Rustad, L., J. Campbell, G. Marion, R. Norby, M. Mitchell, A. Hartley, J. Cornelissen, and J.
Gurevitch. 2001. “A Meta-Analysis of the Response of Soil Respiration, Net Nitrogen
Mineralization, and Aboveground Plant Growth to Experimental Ecosystem Warming.”
Oecologia 126 (4): 543–62. doi:10.1007/s004420000544.
Sale, P.F. 1977. “Maintenace of High Diversity in Coral Reef Fish Communities.” The American
Naturalist 111 (978): 337–59.
Sandel, B., and E.M. Dangremond. 2012. “Climate Change and the Invasion of California by
Grasses.” Global Change Biology 18 (1): 277–89. doi:10.1111/j.13652486.2011.02480.x.
53

Sardans, J., A. Rivas-Ubach, and J. Peñuelas. 2012. “The C:N:P Stoichiometry of Organisms and
Ecosystems in a Changing World: A Review and Perspectives.” Perspectives in Plant
Ecology, Evolution and Systematics 14 (1): 33–47. doi:10.1016/j.ppees.2011.08.002.
Schneider, C.A, W.S. Rasband, and K.W. Eliceiri. 2012. “NIH Image to ImageJ: 25 Years of
Image Analysis.” Nature Methods 9 (7): 671–75. doi:10.1038/nmeth.2089.
Schultheis, E.H., A.E. Berardi, and J.A. Lau. 2015. “No Release for the Wicked: Enemy Release
Is Dynamic and Not Associated with Invasiveness.” Ecology 96 (9): 2446–57.
doi:10.1890/14-2158.1.
Seabloom, E.W., W.S. Harpole, O.J. Reichman, and D. Tilman. 2003. “Invasion, Competitive
Dominance, and Resource Use by Exotic and Native California Grassland Species.”
Proceedings of the National Academy of Sciences 100 (23): 13384–89.
doi:10.1073/pnas.1835728100.
Seabloom, E.W., E.T. Borer, Y.M. Buckley, E.E. Cleland, K.F. Davies, J. Firn, W. Stanley
Harpole, et al. 2015. “Plant Species’ Origin Predicts Dominance and Response to
Nutrient Enrichment and Herbivores in Global Grasslands.” Nature Communications 6
(July): 7710. doi:10.1038/ncomms8710.
Shaver, G.R., J. Canadell, F.S. Chapin, J. Gurevitch, J. Harte, G. Henry, P. Ineson, et al. 2000.
“Global Warming and Terrestrial Ecosystems: A Conceptual Framework for Analysis.”
BioScience 50 (10): 871. doi:10.1641/0006-3568(2000)050[0871:GWATEA]2.0.CO;2.
Shriver, R.K. 2016. “Quantifying How Short-Term Environmental Variation Leads to LongTerm Demographic Responses to Climate Change.” Edited by Susan Schwinning.
Journal of Ecology 104 (1): 65–78. doi:10.1111/1365-2745.12490.
Simberloff, D.. 2013. Invasive Species: What Everyone Needs to Know. Oxford: Oxford
University Press.
Simberloff, D., and B. Von Holle. 1999. “Positive Interactions of Nonindigenous Species:
Invasional Metldown?” Biological Invasions 1 (1): 21–32.
Stachowicz, J. J., J.R. Terwin, R.B. Whitlatch, and R.W. Osman. 2002. “Nonlinear Partial
Differential Equations and Applications: Linking Climate Change and Biological
Invasions: Ocean Warming Facilitates Nonindigenous Species Invasions.” Proceedings
of the National Academy of Sciences 99 (24): 15497–500. doi:10.1073/pnas.242437499.
Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V.
Bex, and P.M. Midgley. 2014. “Climate Change 2013 - The Physical Science Basis:
Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change.” Cambridge: Cambridge University Press.
doi:10.1017/CBO9781107415324.
54

Stuart-Smith, R.D., G.J. Edgar, N.S. Barrett, S.J. Kininmonth, and A.E. Bates. 2015. “Thermal
Biases and Vulnerability to Warming in the World’s Marine Fauna.” Nature, November.
doi:10.1038/nature16144.
Suzuki, S., and G. Kudo. 1997. “Short-Term Effects of Simulated Environmental Change on
Phenology, Leaf Traits, and Shoot Growth of Alpine Plants on a Temperate Mountain,
Northern Japan.” Global Change Biology 3 (1): 108–15. doi:10.1111/j.13652486.1997.gcb146.x.
Tewksbury, J.J., R.B. Huey, and C.A. Deutsch. 2008. “ECOLOGY: Putting the Heat on Tropical
Animals.” Science 320 (5881): 1296–97. doi:10.1126/science.1159328.
Theoharides, K.A., and J.S. Dukes. 2007. “Plant Invasion across Space and Time: Factors
Affecting Nonindigenous Species Success during Four Stages of Invasion.” New
Phytologist 176 (2): 256–73. doi:10.1111/j.1469-8137.2007.02207.x.
Turbelin, A.J., B.D. Malamud, and R.A. Francis. 2017. “Mapping the Global State of Invasive
Alien Species: Patterns of Invasion and Policy Responses: Mapping the Global State of
Invasive Alien Species.” Global Ecology and Biogeography 26 (1): 78–92.
doi:10.1111/geb.12517.
Ulf M., and P. Mølgaard. 1996. ITEX Manual. 2nd ed. Copenhagen: Danish Polar Center.
United States Navy. 2016. “The United States Naval Observatory Astronomical Applications.”
www.aa.usno.navy.mil/data/docs/RS_OneYear.php.
Van der Putten, W.H., M. Macel, and M.E. Visser. 2010. “Predicting Species Distribution and
Abundance Responses to Climate Change: Why It Is Essential to Include Biotic
Interactions across Trophic Levels.” Philosophical Transactions of the Royal Society B:
Biological Sciences 365 (1549): 2025–34. doi:10.1098/rstb.2010.0037.
Vilà, M., J.D. Corbin, J.S. Dukes, J. Pino, and S.D. Smith. 2007. “Linking Plant Invasions to
Global Environmental Change.” In Terrestrial Ecosystems in a Changing World, edited
by Josep G. Canadell, Diane E. Pataki, and Louis F. Pitelka, 93–102. Berlin, Heidelberg:
Springer Berlin Heidelberg. doi:10.1007/978-3-540-32730-1_8.
Walker, M.D., C.H. Wahren, R.D. Hollister, G.H. R. Henry, L.E. Ahlquist, J.M. Alatalo, M.S.
Bret-Harte, et al. 2006. “From The Cover: Plant Community Responses to Experimental
Warming across the Tundra Biome.” Proceedings of the National Academy of Sciences
103 (5): 1342–46. doi:10.1073/pnas.0503198103.
Walther, G.R. 2010. “Community and Ecosystem Responses to Recent Climate Change.”
Philosophical Transactions of the Royal Society B: Biological Sciences 365 (1549):
2019–24. doi:10.1098/rstb.2010.0021.

55

Welshofer, K.W., Zarnetske, P.L., Lany, N.K., Thompson, L.A.E. in press. Open-top chambers
for temperature manipulation in taller-stature plant communities. Methods in Ecology and
Evolution.
Welshofer, K.W., Zarnetske, P.L., Lany, N.K., Thompson, L.A.E. 2017. Data from: Open-top
chambers for temperature manipulation in taller-stature plant communities. Dryad Digital
Repository. doi:10.5061/dryad.2br8k
Williamson, M.H. 1996. Biological Invasions. 1st ed. Population and Community Biology Series
15. London ; New York: Chapman & Hall.
Willis, C.G., B. Ruhfel, R.B. Primack, J.M.R. Abraham, and C.C Davis. 2008. “Phylogenetic
Patterns of Species Loss in Thoreau’s Woods Are Driven by Climate Change.”
Proceedings of the National Academy of Sciences 105 (44): 17029–33.
Wolkovich, E.M., T.J. Davies, H. Schaefer, E.E. Cleland, B.I. Cook, S.E. Travers, C.G. Willis,
and C.C. Davis. 2013. “Temperature-Dependent Shifts in Phenology Contribute to the
Success of Exotic Species with Climate Change.” American Journal of Botany 100 (7):
1407–21. doi:10.3732/ajb.1200478.
Wolkovich, E.M., and E.E. Cleland. 2011. “The Phenology of Plant Invasions: A Community
Ecology Perspective.” Frontiers in Ecology and the Environment 9 (5): 287–94.
doi:10.1890/100033.

56