Plant and Insect Responses to Experimental Warming In

PLANT AND INSECT RESPONSES TO EXPERIMENTAL WARMING

IN A TEMPERATE GRASSLAND

A Thesis

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Master of Science

Troy Shaun Dunn

May, 2017

PLANT AND INSECT RESPONSES TO EXPERIMENTAL WARMING

IN A TEMPERATE GRASSLAND

Troy Shaun Dunn

Thesis

Approved: Accepted:

______Advisor Department Chair Dr. Randall J. Mitchell Dr. Stephen Weeks

______Committee Member Interim Dean of the College Dr. Greg Smith Dr. John Green

______Committee Member Dean of the Graduate School Dr. Peter Nieverowski Dr. Chand Midha

______Date

ii ACKNOWLEDGEMENTS

I would like to thank the graduate faculty at the University of Akron who helped guide me in my research and who played a significant role in my graduate education by preparing me for the next stages of my academic and professional career. I especially want to thank my advisor, Dr. Randall Mitchell, for his guidance, his expertise, his wit, and his friendship. He has encouraged me to grow in knowledge, experience, confidence, and research skills and has challenged me to be a better inquirer, researcher, and teacher.

I also want to thank my committee members, Dr. Greg Smith and Dr. Peter Nieverowski, for accepting the invitation to be on my thesis committee, for their input on my project, and for challenging me through questions and critique of my research.

I cannot forget to be thankful for enthusiastic undergraduate and graduate students who volunteered to help me with field work, gathering data, and insect identification.

Thanks go to Sean Copley, Rebecca Sue Eagle-Malone, and Aaron Kwoleck for their assistance, and to Mohammad Marhabaie for his help in spider identification, and Heath

Garris for his help and suggestions in the design of my experiment and his guidance and feedback on my data analysis and thesis writing.

I am indebted to the University of Akron Field Station, Bath Township, and the

Bath Nature Preserve, the land on which my research experiment was conducted.

Finally, I am thankful for all the encouragement from family, friends, and classmates who kept encouraging me to pursue my academic and professional goals.

iii TABLE OF CONTENTS

Page

LIST OF FIGURES……………………………………………………………………...vii

ABSTRACT..…………………………………………………………………………...viii

CHAPTER

I. INTRODUCTION…………………………………………………………...... 1

II. METHODS………………………………………………………………...... 8

Study Area………………………………………………………………...8

Experimental Plots and Chamber Design…………………………………9

Temperature Monitorin…………………………………………………..10

Invertebrate Sampling……………………………………………………11

Vascular Plant Monitoring…………………………………………….....12

Statistical Analysis……………………………………………………….13

III. RESULTS…………………………………………………………………….15

Temperature……………………………………………………………...16

Plant Biomass……………………………………………………………17

Insect Abundance and Biomass………………………………………….18

IV. DISCUSSION…….…………………………………………………………..24

REFERENCES…………………………………………………………………………..33

iv APPENDICES…………………………………………………………………………...38

APPENDIX A. EXPERIMENTAL DESIGN………………………………….39

APPENDIX B. OPEN-TOP CHAMBER………………………………………40

APPENDIX C. STRUCTURAL CONTROL…………………………………..41

APPENDIX D. UN-MANIPULATED CONTROL……………………………42

APPENDIX E. OPEN-TOP CHAMBER DESIGN……………………………43

APPENDIX F. PETRI DISH OF DRIED INSECT SPECIMENS……………44

APPENDIX G. PETRI DISH OF INSECT FAMILY SPECIMENS………….45

APPENDIX H. DRY BIOMASS……………………………………………….46

APPENDIX I. LIST OF GRAMINOIDS AND FORBS……………………….47

APPENDIX J. LIST OF INSECT ORDERS AND INSECT FAMILIES……..48

APPENDIX K. ANOVA OF EFFECTS OF TREATMENT ON VEGETATION…………………………………………..50

APPENDIX L. ANOVA OF EFFECTS OF TREATMENT ON LITTER……51

APPENDIX M. ANOVA OF EFFECTS OF TREATMENT ON INSECT ABUNDANCE…………………………………52

APPENDIX N. ANOVA OF EFFECTS OF TREATMENT ON SPIDER ABUNDANCE…………………………………..53

APPENDIX O. ANOVA OF EFFECTS OF TREATMENT ON TOTAL ARTHROPOD BIOMASS………..…………….54

APPENDIX P. INSECT TROPHIC LEVEL GROUPS……………………….55

v APPENDIX Q. ANOVA OF EFFECTS OF TREATMENT ON PHYTOPHAGOUS TROPHIC LEVEL………………….56

APPENDIX R. ANOVA OF EFFECTS OF TREATMENT ON PREDATORY/PARASITIC TROPHIC LEVEL INSECTS….57

APPENDIX S. ANOVA OF EFFECTS OF TREATMENT ON OMNIVOROUS TROPHIC LEVEL INSECTS……………....58

APPENDIX T. ANOVA OF EFFECTS OF TREATMENT ON SAPROPHAGOUS TROPHIC LEVEL INSECTS…………..59

vi LIST OF FIGURES

Figure Page

1. Mean Temperature for Open-Top Chambers...…………………………………..16

2. Mean (SE) Measure of Plant Biomass………...…………………………………17

3. LS Means (SE) Insects Collected………………………………………………..18

4. LS Means (SE) Spiders Collected……………………………………………….19

5. LS Means (SE) Dry Weight (in grams) of Arthropods..………………………...19

6. Means (SE) Measures MANOVA of Insect Orders Abundance..…….…………21

7. LS Means (SE) Measures MANOVA for Four Trophic Level Groups………….22

vii ABSTRACT Community structure is being altered by direct and indirect effects of climate change.

Increasing temperatures can threaten community structure resulting in the disruption of interactions within those communities most sensitive to changes in climate. Among those communities at risk for change is the North American grassland habitat and its resident insect community. Climate change can potentially affect primary production and the abundance and diversity of both plants and animals in different ecosystems. Here we have used open-top chambers to study the impact warming temperatures have on the resident plant and insect community on grassland habitat in order to better understand how grassland areas are affected and may change as a result of global warming, and how climate change will impact the community and ecosystem as a whole. Results show that passively warmed open-top chambers have a measureable increase of 1-4°C in ambient temperature above that of the controls. Results also show no significant treatment effects of temperature on primary production, except for litter, and no significant effect on the abundances of the resident insect community as a whole. Interestingly, results do reveal significant effects of treatment on insect taxonomic orders and families as well as significant effects on the trophic levels within the grassland habitat confirming that insects are responding in different ways to artificial warming, which can ultimately alter trophic dynamics directly and indirectly.

viii

CHAPTER I

INTRODUCTION

We live in a warming world. Mounting evidence suggests climate change will impact ecosystems world-wide well into the future. Temperature is predicted to rise anywhere from 1-6°C during the next century (Shaver et al. 2000; Menendez 2007; Bentz et al. 2010; Robinet and Roques 2010). Over the course of the last three decades in the

Midwest and northern Great Plains of the United States mean winter temperatures have already increased by 4°C (Jamieson et al. 2012). This presents a potential threat to ecological communities, and may alter community structure and disrupt interactions among species (Chapin 1983; Melillo et al. 1993; Henry and Molau 1997; Voigt et al.

2003; Gedan and Bertness 2009; Sheldon et al. 2011; Boggs and Inouye 2012). Plants and insects are especially vulnerable to disruption in interactions among them since higher trophic levels in grasslands are more sensitive to environmental heat sources

(Voigt et al. 2003). Climate warming will impact plant-insect interactions by directly and indirectly affecting how plants and the resident arthropod community respond to increasing temperatures in northern latitudes (Koricheva et al. 2000; Voigt et al. 2003;

Pearson and Dyer 2006; Menendez 2007; Barton et al. 2009; Haddad et al. 2009, Robinet and Roques 2010; Haddad et al. 2011; Kingsolver et al. 2011; Boggs and Inouye 2012;

Jamieson et al. 2012). For decades ecologists have turned their attention toward climate warming and its potential impacts on community assembly and trophic interactions

1 within food webs as climate can alter the nature and strength of species interactions

(Melillo et al. 1993; Henry and Molau 1997; Marion et al. 1997; Norby et al. 1997;

Hollister and Webber 2000; Voigt et al. 2003; Emmerson et al. 2004; Ovadia and

Schmitz 2004; Emmerson et al. 2005; Peñuelas et al. 2007; Barton et al. 2009; Barton and

Schmitz 2009; Boggs and Inouye 2012).

The biotic community is an association of a number of interacting species in a defined area (Molles 2010). A community may be the plants in a grassland or the insects associated with the grasses of the grassland. Communities are composed of many different interacting species at each trophic level, and may differ in relative abundance and richness of those species; most species are moderately abundant and few are very abundant or extremely rare (Molles 2010). Trophic dynamics of a community describes the transfer of energy from one part of an ecosystem to another (Lindeman 1942). In the climate change context of this study, I analyze the responses of plants and insects across trophic levels under warming conditions.

Past research has shown that climate warming affects different ecosystems and species in different ways. Recent climate change research has focused on plants in arctic tundra, forests, and marshes and wetlands (Henry and Molau 1997; Marion et al. 1997;

Hollister and Webber 2000; Gedan and Bertness 2009; Bentz et al. 2010; Netherer and

Schopf 2010). Plants in these different ecosystems, including grasses, are sensitive to various climate change conditions and respond differently according to temperature stresses they are exposed to (Grime et al. 2000; Otway et al. 2005; Tilman et al. 2006

Haddad et al. 2009, 2011). Fewer research efforts have focused on temperate grasslands in North America, and fewer still have focused on animal community responses. Many

2 of these efforts have focused on plant responses such as leaf temperature, heat stress, and metabolic processes that potentially affect the quality and phenology of plants and their responses under warming conditions (Norby et al. 1997; Cole 2010; Zinn et al. 2010; De

Boeck et al. 2012). Few have dealt with arthropod abundance and diversity. In fact, the majority of climate warming literature, about 85%, is on plants whereas only about 15% is on insects (Tylianakis et al. 2008; Barton et al. 2009; Jamieson et al. 2012). Likewise, there is little information on effects of temperature on primary consumers (herbivores) and secondary consumers (parasitoids and predators) (Emmerson et al. 2005). Polar regions as well as northern-most temperate latitudes are where the effects of climate change are estimated to be the most extreme (Peñuelas et al. 2007). However, temperate grasslands make up approximately twenty-five percent of Earth’s surface, and will also face challenges from climate change (Lauenroth 1979). These grasslands (tall-grass prairie and short-grass prairie) play a major role in providing habitat for arthropods, birds, small mammals, and large ungulates depending on location. Herbivory in temperate grasslands is most often dominated by insects (Hance et al. 2007; Boggs and

Inouye 2012; Jamieson et al. 2012).

Research suggests that when net primary production is diminished, trophic structure may be altered and control can potentially switch from top-down control to bottom-up (Dyer and Stireman 2003; Pearson and Dyer 2006). In temperate grassland, the ecosystem of interest for this study, climate warming is expected to potentially affect the quality and diversity of primary producers thereby impacting the abundance, diversity, trophic structure and interactions of the resident insect community (Koricheva et al. 2000; Voigt et al. 2003; Ovadia and Schmitz 2004; Emmerson et al. 2005; Pearson

3 and Dyer 2006; Tylianakis et al. 2008; Barton and Schmitz 2009; Haddad et al. 2009;

Robinet and Roques 2010; Haddad et al. 2011; Liu et al. 2011). When there is decreased net primary production, as an effect of climate warming, the quality and diversity of the plant community should decrease, resulting in a similar decrease in insect specialists and an increase in insect generalists (Dyer and Stireman 2003; Pearson and Dyer 2006). In contrast, when plant quality and diversity increase, a corresponding increase in arthropod diversity, with more specialists present, is expected (Siemann et al. 1998; Koricheva et al.

2000; Haddad et al. 2011). Yet, some low plant diversity treatments saw an increased abundance of agricultural pest specialist insects (Haddad et al. 2011).

Insects are especially vulnerable to climate warming events (Beck 1983; Logan et al. 2003; Menendez 2007; Robinet and Roques 2010). Arthropod body temperature is approximately the same as the ambient temperature of their environment, and is the single most important environmental abiotic factor directly affecting insect behavior, distribution, development, reproduction, and survival (Yamamura and Kiritani 1998;

Menendez 2007; Robinet and Roques 2010; Jamieson et. al. 2012). We know from previous warming studies in a variety of other ecosystems that temperature has a strong direct influence on insect development, reproduction, and survival (Bale et al. 2002;

Bentz et al. 2010; Netherer and Schopf 2010; Liu et al. 2011). However, since climate change is complex, differences between regions, and responses by insects to climate warming are difficult to predict due to myriad indirect effects, and are likely to be population-specific or species-specific in nature, magnitude, and strength (Bale et al.

2002; Menendez 2007; Helmuth 2009; Robinet and Roques 2010; Bale et al. 2012).

Ecologists typically assume top-down control in grasslands with low species diversity. The top-down view holds that organisms at the top of the food chains are food limited, and that successive lower levels are alternately predator, then food limited

(Power 1992). That is, in top-down control, a population is regulated by top predators, such as spiders in a grassland, which controls the structure or population dynamics of the ecosystem (Molles 2010). Altered abundance of predators in a typical three trophic level system (plant, herbivore, predator), can have indirect effects by changing the abundances of herbivore prey, or altering their behavior (Barton and Schmitz 2009). In grassland habitat higher temperatures are expected near the top of the grass canopy with a gradation to cooler temperatures descending toward the soil surface. In order for insects to remain within the range of their preferred temperature ranges they may descend downward into the canopy in response to warming changes (Barton and Schmitz 2009).

Climate effects on plant and insect communities have not been well understood

(Voigt et al. 2003). Ongoing research in this area is needed to fill in those gaps since research efforts on grasslands and on insect communities in grasslands have been fewer than studies in other ecosystems targeting different plants and animals. This study seeks to better understand climate effects at the intersection of insect community structure and function and the temperate grassland habitat. We know that individual species are sensitive to climate; however, we know less about how community assembly will be disrupted or how localized populations will respond (Voigt et al. 2003). To predict further outcomes about global climate change it must be determined how plant and animal life are impacted in specific ecosystems and locations then to apply those findings to predict larger scale general conclusions (Emmerson et al. 2004, 2005; Tyliankis et al.

2008). Each ecosystem has its own unique environment and specific interactions. Some habitats are more vulnerable to change than others, and temperature is often a significant driver of many processes in biogeochemistry. A paper by Gedan and Bertness (2009) on loss of plant diversity due to warming in New England salt marshes, and the lack of insect data combined with a grassland habitat, inspired me to question the sensitivity of grasslands to warming temperatures and to investigate the potential effects of increased temperatures on primary plant production and the responses of the resident insect community at different trophic levels.

Grasslands in North America as well as those found across other continents are considered at-risk areas (Emmerson et al. 2005; Barton and Schmitz 2009; Haddad et al.

2011). There are not many true grasslands left, especially in the northern latitudes. My thesis research utilizes open-top passive warming chambers to better understand how temperature increase in temperate grassland affects plants, insect abundance, richness, and diversity, and the trophic structure of the resident insect community. I hypothesize that climate change affects the abundance and diversity of the insect community in a temperate grassland. I predict that increased temperatures will reduce primary vegetative production, and that this will cause a corresponding shift in the resident insect community (reduction in herbivores and predators). I further hypothesize that decreased net primary production in elevated temperature conditions will have an effect on the abundance and diversity of insects, which could result in a traditionally top-down control system becoming a resource limited bottom-up controlled system. I aim to answer the following questions. Do the warming chambers show an overall mean elevation in ambient temperature during the day over that of the control treatments (i.e. structured

6 controls, un-manipulated controls)? If so, by how many degrees Celsius? Does plant biomass differ between warmed and control treatments? Does insect abundance and richness change in warmed treatments? Are some insect groups and arachnids more affected by warming than are others? Does the trophic structure differ in warmed and control treatments (i.e. specialist and generalist insects, insects vs spiders)? What do the results suggest about direct and indirect effects of the plant and arthropod community responding to climate warming?

CHAPTER II

MATERIALS & METHODS

Study area

The area of research is a temperate grassland site located in the Grandview Alley section of Bath Nature Preserve in Summit County, Bath Township, Bath, Ohio. This is a managed grassland site that is mowed once a year in the fall. This particular site was dry and hot during the experimental period, receiving water only from occasional precipitation. The dominant vegetation is primarily introduced European grasses

(Poaceae) with some sedges (Cyperaceae), rushes (Juncaceae), and a variety of forbs (See

Appendix A for a list of the most common families of plants and species richness).

Managed fields typically exhibit a heterogenous mix of forbs and grasses, which was noted in the species growing in this location. Managed fields subject to annual mowing have shown an increase in resource abundance as plant diversity increases resulting in a heterogenous habitat where more graminoids and forbs can become established (Pearson and Dyer 2006). During the growing season there were no unexpected disturbances to the experimental area or plots other than the typical wildlife traffic through the area.

The grassland serves as an appropriate study system for examining direct and indirect temperature effects on plants and insects under warming conditions since vegetation and insects are predicted to respond differently to increases in temperature, especially in more northern and temperate latitudes.

Experimental plots and chamber design

To test for effects of climate change and its impact on the insect community in a temperate grassland I constructed Open Top Chambers for experimental warming

(Marion et al. 1997). I measured responses of plant biomass and insects in regularly spaced plots on a grid (n=18). In June 2012 I warmed plots within a mesic grassland community using open-top chambers (OTCs) and measured responses of the insect community over a ten week period from mid-June to early September. I compared plant biomass, insect abundance and biomass, and temperature in the open-top chamber plots against two types of controls, structural control treatments (SCs) and un-manipulated control treatments (UCs). The experimental design consisted of a sampling grid 30 meters x 30 meters at the chosen site. The entire grid was on a slope with the top of the slope on the south and the bottom of the slope on the north. I then subdivided the large grid into six rows and six columns resulting in 36 individual plots (Appendix A). Within this grid I measured and marked 18 individual 1.5 meter wide x 1.5 meter long x 0.5 meter high plots using a Latin square design to randomize the plot locations within the grid (Cochran and Cox 1957). These plots allowed me to assess the impacts of anticipated climate change trophic dynamics in a localized grassland area. Of the 18 individual plots, there were 6 open-top chambers (OTC’s), 6 structural controls (SC’s), and 6 un-manipulated controls (UC’s). There were six horizontal blocks each with three treatments per block (n=6 per treatment). Each row and each column included one OTC

(Appendix B), one SC (Appendix C), and one UC (Appendix D). This blocking was intended to account for potential elevation and moisture effects along the vertical gradient

9 of the slope. The plots were constructed and completed June 10-12, 2012 and monitored for 3 months. Arthropod collecting began the week of June 20, 2012. The last collection was gathered the week of August 26, 2012 after which the structures were removed.

Open-top chambers (Marion et al. 1997) and structural controls (Gedan and

Bertness 2009) were constructed by driving four 5cm x 5cm x 100cm wooden stakes into the ground at each corner of the plot (4 stakes/plot). The tops of the stakes were angled inward toward the plot center at an approximate angle of ө=63.4 degrees (Appendix E).

Six of the warmed plots (OTCs) were surrounded with Tufflite IV® clear greenhouse plastic (Berry Plastics Corp. Evansville, IN) and 6 were surrounded with a single layer

(1.5cm x 1.5cm mesh) of Easy Gardner Deer Block Protective Mesh Covering to account for potential artifacts on account of the chamber and to mimic the physical structure of the warmed plots without a strong effect on temperature (Moise and Henry 2010). The corners and the center of the final 6 un-manipulated control plots were marked with survey flags, with no stakes or material surrounding them. In each of the 18 plots we placed a flag marking the center. The centers measured 6 meters from the center of one plot to the center of the adjacent plot.

Temperature monitoring

Ambient temperature was monitored in a random set of 12 of the 18 plots (n=5

OTCs, 4 SCs, and 3 UCs). Each monitored plot held data-loggers (Thermochron iButton model DS1921G-F5, Embedded Data Systems Inc.) placed on the center flag marker approximately 12cm above the soil surface to record ambient temperature at 30 minute intervals. Half way through the season (about week 6) the temperature data was collected from these data-loggers followed by replacement of the data-loggers into another 12 plots

10 at random. Similar warming chambers have indicated measurable climate change with a

1-6°C elevation in ambient temperature at northern latitudes (Hollister and Webber

2000).

Invertebrate sampling

To sample arthropods collected from the plots during the experimental season, we placed yellow insect pan traps (referred to as “bee” bowls) on the ground in each plot and replaced them weekly (Leong and Thorp 1999; Campbell and Hanula 2007). The bright yellow plastic bowls (SOLOTM brand) measured 15cm in diameter across the top of the bowl, 7.5cm in diameter at the bottom of the bowl, and 5cm deep. Bowl traps contained eight ounces water and one-half ounce unscented dawn detergent. Insect traps were set and replaced in each plot every week for 10 weeks beginning the week of June 17 through the week of August 26, the week of August 19 being omitted because of heavy rain. All insects collected from the bowl traps during the experimental period were separated and allowed to dry overnight. The next day insects collected from each plot were counted, placed in a petri dish, weighed by group after drying, and identified to order and family and then later sorted into four trophic groups (Appendices F, G, & P).

Each set of plot specimens were counted to determine total number of specimens per plot per collection date. Arachnids were counted separately, but not weighed separately.

Each set from their respective plots per collection date were then divided into taxonomic orders and counted to determine how many insect orders were represented as well as how many insects there were per order. The insect orders were identified then subdivided into taxonomic families to estimate diversity.

The plots were left to stabilize for about a week after construction before the first arthropod samples were collected. Collection dates were about 1 week apart on the following dates during the summer of 2012: June 22, June 29, July 6, July 13, July 20,

July 27, August 3, August 10, August 17, August 31, resulting in 10 sets of samples from bowl traps.

Vascular plant monitoring

A preliminary visual survey of vegetation growing within plots to estimate grasses to forbs percentages was taken in June at the beginning of the experimental period. Each plot was scored into a subset of four squares each square representing 25% of the plot. Plants present in the plots were identified to at least genus and then species where possible. I visually estimated percent plant cover per plot to approximate graminoid to forbs cover using meter sticks to divide the plot into four quarters. Average graminoid to forb cover percentages were approximately 70% graminoids and 30% forbs in the plots, graminoids being the dominant vegetation. There were only a few woody shrubs present, but are being kept at bay by annual mowing. There were about a dozen dominant forb species (wildflowers) growing among the graminoids in the plots.

Vegetation and litter samples were collected at the end of the experimental season for plant biomass analysis. Samples were collected from the center of each plot from a

30.5cm x 30.5cm square. Within this area the standing biomass was clipped at the ground and placed into a brown paper bag for drying. Then the ground litter was collected by scraping and collecting all loose litter down to the soil from the same area.

The litter was also placed in a brown paper bag for drying. After a period of drying the dried biomass was weighed (Appendix H).

Statistical Analysis

All statistical analyses were completed using the computer software program SAS

9.4 (SAS Institute, Inc., Cary, NC). Analyses used plots as the unit of observation n=18.

In most analyses I used MANOVA (Multivariate Analysis of Variance) to test for effects of treatment and block on suites of related response variables, with Block as a random effect (Newman et al. 1997). I used a log transform when residuals deviated from normality. One MANOVA involved both measures of plant biomass (vegetation and litter). A second MANOVA involved overall insect response (total counts and total dry weights over the 10 week experimental period). A third MANOVA considered the numerical abundance of the eight arthropod groups (6 insect orders, arachnids, and unclassified). For this data set insects were grouped into their respective taxonomic orders. Means of insects counted in each order (collected over the 10-week period) were used for the MANOVA. These groupings included Coleoptera, Diptera, Hemiptera

(including the group formerly termed Homoptera), Hymenoptera, Lepidoptera,

Orthoptera, unclassified, and Arachnids. In this analysis a Log transform was necessary.

A fourth MANOVA looks for any treatment effects on the taxonomic orders of insects and spiders, and their collective mass weights. Insect families were grouped into four main trophic levels. A fifth MANOVA analyzes treatment effects on trophic levels of these families of insects.

Temperatures were recorded every 30 minutes in the selected random plots

(OTCs n=5; SCs n=4; UCs n=3). To determine temperature differences among the three treatments I calculated the temperature means for each of the three treatments for June and July 2012. To calculate mean temperature I used the recorded daily temperatures

13 from the Thermochron dataloggers across the five OTC treatments taken by half hour during the day. I calculated the mean of those temps to obtain the mean temp for the time of day represented. I then calculated the mean difference of the open-top chambers in comparison to the mean temperature of the control and reported the OTC difference in warming increases during a 24-hour day. I used a scatterplot to show the mean OTC temperature difference over the course of the day to demonstrate how OTCs are warming in comparison to the controls.

CHAPTER III

RESULTS

Primary production within the experimental plots included three main families of graminoids (Poaceae, Cyperaceae, and Juncaceae) which were all present in the plots along with a variety of forbs. Major graminoids making up the largest percentage of cover in the plots included Carex, Cyperus, Eleocharis, Scirpus, Luzula, Juncus,

Alopecuris, Dactylis, and Phalaris (for a complete list of graminoids and forbs see

Appendix I).

A total of 3,124 insects were collected with an additional 865 arachnids. Of the

3,124 insects placed in their respective taxonomic orders there were 550 Coleoptera, 536

Diptera, 363 Homoptera and Hemipteran combined, 1,284 Hymenoptera, 63 Lepidoptera, and 328 Orthoptera. Another 366 that were not counted among the total number of insects were put into a group of unclassified, unknown, or non-insect such as isopods (i.e. common pillbug). This group also includes a few rare species that were removed from the sampling to reduce noise in the data. Insect orders represented by singletons (i.e.

Chrysopidae: Neuroptera) or too few individuals with no measureable biomass weight

(i.e. Thysanoptera) were omitted as outliers skewing tests for normality.

Major insect families were distributed mostly among four trophic levels: phytophagous, predator and parasitic, omnivorous and saprophagous. Phytophagous families dominated the Hemiptera/Homoptera, and a wide distribution of predatory,

15 saprophagous, and omnivorous families were spread across Coleoptera, Diptera, and

Hymenoptera (For a complete list see Appendix J).

Temperature A total of 4,511 Thermochron temperature samples were taken between June and

August 2012 from 12 random plots (n=12). Some of the open-top chambers exhibited warming more than others. Temperature was taken every 30 minutes for 24 hour periods during the height of the season. As predicted, passively warmed chambers increased ambient temperature above that of controls. On a typical day, open-top chambers increased in mean temperature above that of controls by +1-4°C between 8AM and 5PM

(Fig. 1). There was a slight decline in mean temperature during the early morning hours where dew and moisture in the chamber acted as a cooling mechanism.

-1

-2 OTC OTC Differencefrom Controls (degrees C) 0:00 4:48 9:36 14:24 19:12 0:00 Time of Day

Figure 1. Mean temperature for Open top chambers during a 24-hour period for June- July 2012 in temperate grassland in northeast Ohio. OTCs consistently show a minimum 1-4°C increase in ambient temperature during peak hours (n=47 per point)

Plant Biomass

Plant biomass (live vegetation and litter) did not differ significantly among treatments (Figure 2; MANOVA; Pillai’s Trace = 0.46, F4, 20 =1.50, P>0.24) although there was a non-significant trend for lower biomass in the warmed chambers (OTCs).

MANOVA shows no overall block effect (F10, 20 =2.03, P>0.08). Although the

MANOVA revealed no overall effect of treatment on biomass, because the choice to analyze these together in a MANOVA rather than separately might be disputed, I also inspected the constituent ANOVAs. The effect of treatment on vegetation was not significant whereas litter revealed significant differences in biomass among treatments:

ANOVA for effects of treatment on vegetation is not significant (Appendix K, p=0.40);

ANOVA for effects of treatment on litter is significant (Appendix L, p=0.02).

120

100

60 Veg

40 Litter Driedweight (gms) 20

0 OTC SC UC Treatments

Figure 2. Mean (SE) measures of plant biomass at end of the growing season (n=18), for three treatments, open-top chambers (OTC), structural controls (SC), and un-manipulated controls (UC).

Insect Abundance and Biomass

Counts of insects, counts of spiders, and total biomass of the two combined did not differ significantly among treatments (Figures 3, 4, and 5; MANOVA; Pillai’s Trace

= 0.40, F6, 18 =0.74, P>0.62). MANOVA shows no overall block effect (F15, 30 =0.66,

P>0.80). Although the MANOVA revealed no overall effect of treatment on insect and spider counts or mass weights, I also inspected the constituent ANOVAs for total insect counts, spider counts, and total dry weight biomass. ANOVA for effects of treatment on insect abundance is not significant (Appendix M, p=0.65); ANOVA for effects of treatment on spider abundance is not significant (Appendix N, p=0.79). ANOVA for effects of treatment on total arthropod biomass is not significant (Appendix O, p=0.62).

300

250

200

150

100

Least Least SquareMeans Insect Abundance 0 OTC SC UC Treatments

Figure 3. LS Means (SE) insects collected from pan traps across treatments during the 10 week experimental period.

0 Least Least SquareMeans SpiderAbundance OTC SC UC Treatments

Figure 4. LS Means (SE) spiders collected from pan traps across treatments during the 10 week experimental period.

0.7

0.6

0.5

0.4

0.3

0.2

Biomass Dry Weights (grms) 0.1 Least Least SquareMeans InsectSpider + 0 OTC SC UC Treatments

Figure 5. LS Means (SE) dry weight (in grams) of arthropods (insects and arachnids) collected across treatments during the 10 week experimental season.

Although the ANOVA for arthropod dry biomass is not significant for treatment effects the graph indicates reduced arthropod biomass in the warming chambers resulting in lower weight measurements (Figure 6).

Seven common arthropod orders (6 insects + spiders) occurred in samples from this study. For analysis I lumped the less common orders and the few unidentifiable specimens into a group designated as “other”. Some of these included Collembola,

Thysanoptera, and Neuroptera, for example. The cutoff for being considered rare was three specimens or less or if they had no real measurable biomass weight value (i.e.

Thysanoptera).

The insect community differed significantly among treatments. MANOVA with

Log+1 transform for the abundance of each taxonomic order revealed a significant difference among treatments for all insect orders and arachnids collectively (Figure 6;

Pillai’s Trace = 1.76, F16, 8 =3.62, P>0.04). MANOVA revealed no overall block effect

(F40, 35 =1.23, P>0.27). Although OTCs had reduced abundance of Coleoptera, Diptera, and Lepidoptera, ANOVA shows that only Diptera differed significantly among treatments (p=0.01). Hymenopterans were more prevalent in the OTCs. ANOVAs show that least square means for five insect orders were significant across treatments

(p=.0001), and Lepidotera was also significant for each treatment (p<0.04).

4.5 4 3.5 3 otc 2.5 2 sc 1.5 uc 1 Log of Log Number ofIndividuals 0.5 0 coleop dipter hemhom hymen lepido orthop arach other Taxonomic Insect Orders and Non-insect Groups

Figure 6. Means (SE) measures MANOVA of insect orders abundance with log+1 transform by taxonomic order identified in collection from pan traps over 10 weeks for three treatments, open-top chambers (OTC), structural controls (SC), and un-manipulated controls (UC).

The trophic diversity of the resident insect community across treatments consisted of 6 insect orders and 34 families distributed across those orders. For analysis I grouped the insect families into their trophic levels (Appendix P). Families that fall into more than one category were placed into the category that best represents their usual behavior.

The most diverse trophic level consisted of 15 phytophagous or nectar-feeding insect families, representing 44.1% of total families of insects collected. The second most diverse trophic level represented 10 predatory and parasitoid insect families plus spiders making up 29.4% of total families. The rest of the families identified fell into two other categories including 5 generalist omnivore families at 14.7%; and 4 saprophagous insect

21 families at 11.8%. This analysis omits as an outlier a singleton observation of one family

(Chrysopidae; Neuroptera). Numerically, the predatory and parasitic group as a whole was the most abundant.

Six insect orders are represented among the phytophagous and nectar sucking insects. Four insect orders are represented among the predators and parasitoids, and five predatory spider families identified: Acarina, Linyphiidae, Lycosidae, Onyopidae, and

Phalangida. Four insect orders are represented among the generalist omnivores. Two insect orders are represented among the saprophagous insects.

MANOVA for insect trophic levels by abundance revealed a significant difference among treatments (Figure 7; Pillai’s Trace = 1.76, F8, 16 =14.8, P<0.0001).

MANOVA revealed no overall block effect (F20, 40 =1.66, P<0.08).

140

120

100

80 OTC 60 SC

40 UC

Least Least SquareMeans TrophicAbundance Level 0 phyto predatory omnivore sapro Trophic Levels

Figure 7. LS Means (SE) measures MANOVA for four trophic level groups (n=34 families) across three treatments: open-top chambers (OTC), structural controls (SC), and un-manipulated controls (UC).

Constituent ANOVAs from the above MANOVA show significant effects of treatment on trophic structure across treatments (ANOVA for phytophagous insects see Appendix

Q; ANOVA for predatory insects see Appendix R; ANOVA for omnivorous insects see

Appendix S; ANOVA for saprophagous insects see Appendix T). The graph in figure 7 shows that there are effects on all trophic levels across treatments. A pattern emerges where the warmed OTCs have an effect across all trophic levels to varying degrees.

Phytophagous insects and predatory/parasitic insects and spiders appear to be more abundant across treatments. Most notably, the omnivorous and saprophagous insects are limited across treatments, especially in the warmed OTCs. Saprophytic insects are the least abundant in the OTCs where it was found that litter biomass was also limited.

CHAPTER IV

DISCUSSION

This study sought to quantify plant and insect responses to a warming experiment on grassland habitat. Plants and insects respond differently to warming depending on such factors as type of ecosystem, geographic location, and season. Each plant species is likely to respond differently to climate change (Wahid et al. 2007; Zinn et al. 2010; De

Boeck et al. 2012). Likewise, predictions are that insects will also respond differently to climate change. Therefore, I examined potential changes in community structure in the context of climate change by quantifying warming effects on plants and insects. I hypothesized that climate warming affects primary production, and also the abundance and diversity of the resident insect community in a temperate grassland. I predicted that open-top chambers would exhibit an increase in ambient temperature, potentially reducing net primary vegetative production with corresponding trophic shifts within the insect community affecting the abundance and diversity of arthropods. The trophic structure within the grassland habitat may respond in ways that interrupt interactions among plants, insects, and predators, and potentially changing a top-down controlled system to a resource limited bottom-up controlled system.

The results of this study show that the open-top chambers consistently warmed 1-

4°C above that of the controls throughout the experimental season. Despite these increased temperatures, primary production and overall insect abundance of the plots

24 were unaffected. Other studies using similar methods have, however, found effects of

OTC warming on primary plant production due to heat stress (Gedan and Bertness 2009;

Zinn et al. 2010; De Boeck et al. 2012; Garris 2013). As temperature exceeds the tolerance level of grassland plants it can cause heat stress conditions that negatively affect primary production, the quality of that production, which have an effect on the phytophagous insects feeding on that production (Garris 2013). Although there was no significant treatment effect on vegetation biomass in the current study, there was a non- significant tendency toward lower productivity in the open-top chambers.

My study found that total insect and spider abundances did not vary with treatment. However, there was a significant effect of warming on abundance of particular insect orders. For most insect orders the overall patterns across treatment are similar, but Coleoptera, Diptera, and Lepidoptera showed a noticeable decline in the

OTCs compared to the controls. This suggests that Coleopteran, Dipteran and

Lepidopteran families may be more directly susceptible to warming conditions where the increase of ~+5°C potentially exceeds their heat tolerance since individual trophic levels are likely to respond differently to temperature changes (Voigt et al. 2003). However, in addition to direct effects of warming, there are several indirect effects on plants and insects and spiders that may alter their behavior that are important to consider as these all work together collectively when talking about altered trophic structure.

Interestingly, the multivariate analysis shows significant treatment effects on abundance of insects in the different trophic levels, most notably for the predatory/parasitic, omnivorous and saprophagous groups. This indicates that there are real trophic shifts in this temperate grassland community despite there being no

25 significant shift in vegetative productivity. These results confirm, as shown in previous research, that the trophic structure of the resident grassland arthropod community is directly altered by warming itself, or indirectly by changed behaviors among the trophic levels within the plots. Omnivorous and saprophagous insects appear to be the least abundant in comparison to other trophic levels, most notably in the OTCs. Phytophagous and predatory and parasitic insects tend to be more abundant across treatments in general.

The overall pattern of the warmed chambers (OTCs) suggests that temperature, as an effect of treatment, differentially affects the insects in different trophic levels shown by statistically significant results in data analysis (p<0.0001). For example, this could be direct effects of temperature, or indirect effects of predatory insects on other trophic levels. These results may also suggest, as has been found in previous studies, that generalist insects with a higher heat tolerance are more abundant while specialist insects have shifted their behavior. In this case, a variety of phytophagous insects and predatory and parasitic insects were the dominant groups even in the OTCs.

The community in this experiment consisted of 11 forb species, 10 graminoid species, 6 insect orders, and 34 insect families divided into 4 trophic levels. The fundamental trophic structure of this grassland community was numerically dominated by the combined predatory-parasitoid insect group (which includes spiders) in comparison to the other three groups. However, when evaluating the number of families represented within each order, phytophagous insects were the most diverse (44.1% of families) while predatory insects represented 29.5% of total families, omnivorous 14.7% of total families, and saprophagous 11.8% of total families. Similar results have been found in field experiments where phytophagous insects such as Homopterans show an increase in

26 abundance and diversity regardless of the response of vegetation (Masters et al. 1998), and different trophic levels exhibited various sensitivities to warming conditions (Voigt et al. 2003)..

Climate change has direct and indirect effects on communities and ecosystems

(Masters et al. 1998; Netherer and Schopf 2010; Boggs and Inouye 2012). My experiment continues to investigate how community structure and trophic dynamics are impacted by climate, whether increased temperature conditions differentially affect insects at various trophic levels, and how food webs are altered on both small and large scales (Ovadia and Schmitz 2004; Boggs and Inouye 2012). My experimental site is a temperate grassland, which would typically be a top-down control system. Barton et al.

(2009) claims research in ecology assumes top-down forces of predation and herbivory, for example, where predators reduce herbivores and therefore herbivory in a three-trophic level community. In terrestrial systems top-down dominance was restricted to lower levels regulated by predation potentially resulting in systems of low species diversity whereas in bottom-up control upper levels are regulated by resource limitation (Terborgh et al. 2001; Tylianakis et al. 2008).

Primary producers such as plants have primacy in ecosystems in general and grasslands in particular because they provide the resources for the consumers (herbivores in a grassland), which comprise the trophic structure (Power 1992). A loss of plant diversity generally tends to cause a loss of diversity in the arthropod community, potentially altering trophic structure (Haddad et al. 2009). In my study there was no significant treatment effect on vegetation, although litter was limited (p=0.02). When there is resource limitation (such as plant availability for herbivores or predators limited

27 by prey), as an effect of climate warming (hotter temperatures, less moisture), the quality and diversity of the plant community should change, which can lead to a shift in the fundamental trophic structure (Dyer and Stireman 2003; Pearson and Dyer 2006). In a top-down controlled system with good growing conditions, increased plant herbivory and limited plant growth would be expected. An increase in plant quality and diversity may, in turn, increase arthropod diversity with more herbivores and predators (Siemann et al.

1998; Koricheva et al. 2000; Haddad et al. 2011). With high quality plant tissue insect specialists will visit their preferred plant and eat less, doing less damage because they will gain more nutritional value per unit consumed; however, in a system of poor plant quality phytophagous insects will be required to eat more for the same nutritional value and thus inflicting more tissue damage on the plant (Emmerson et al. 2005). In the context of this study the overall pattern was that there was generally less of everything in the OTCs. With a significant decrease in litter and significant treatment effects on insect orders and trophic levels, particularly the order Diptera, and all of the trophic levels, especially limited omnivorous and saprophagous insects, an argument could be made for a resource limited bottom-up control explanation of the treatment effects.

A decade long study by Haddad et al. (2009) reveals and confirms that diversity of arthropods and diversity of plants are interlinked. Increases in temperature should directly affect food webs, altering the movement and behavior of herbivores and predators as well as other insects making up the community and potentially weakening the functioning of the ecosystem (Voigt et al. 2003; Emmerson et al. 2004; Emmerson et al. 2005; Tillman 2006; Barton et al. 2009; Barton and Schmitz 2009; Gedan and

Bertness 2009; Haddad et al. 2011). Evidence suggests that insects are more susceptible

28 to thermal changes and that thermal safety margins may be smaller than previously indicated (Memmott et al. 2007; Menendez 2007; Kingsolver et al. 2011). For example, if predator mortality occurs due to increased temperatures and prey experience lower mortality, this will weaken top-down indirect effects of predators on plants (Barton and

Schmitz 2009). The findings of this study could suggest that if climate warming in an area such as a grassland can result in resource limitation interrupting the top-down model, a potential switch to a bottom-up model could emerge over time.

The results of my experiment adds another voice to the compendium of research on this topic by looking specifically at plant and insect responses to artificial warming on temperate grasslands, and by confirming that warming increases strongly affect the resident insect community and the trophic structure it comprises, while vegetative biomass and insect abundance were not altered. An interesting aspect of studying warming responses in a grassland is that in this type of habitat ecologists assume a top- down effect on plants and arthropods, which is typical of a more diverse managed field as opposed to a less diverse unmanaged field that could become bottom-up controlled

(Pearson and Dyer 2006). My research suggests that bottom-up control factors may be at play here.

Preservation of native grassland areas across North America is of particular interest to conservationists, and better understanding of how they will respond to global warming is required if we are to continue to debate the three trophic level system, top- down vs. bottom-up controls, and fully understand how cascades work. Amidst the challenges and controversies of mixed results there are still unanswered questions. The debated question remains how climate change alters the plant and insect community in

29 the temperate grassland habitat, and how can we identify the direct and indirect effects on trophic structure.

My research is a piece of the puzzle that adds to the larger picture that is being put together as we engage in further current and future research to gain a more thorough understanding of how the individual parts of an ecosystem work together, their interactions, and how plants and animals are affected by abiotic factors such as temperature within the range of their habitats. Understanding these processes better will enable us to make more accurate and specific predictions. Some generalizations can be made about warming trends and its impact on certain sensitive ecosystems (Henry and

Molau 1997; Hollister and Webber 2000; Gedan and Bertness 2009). However, plant and insect responses under warming conditions are varied and site-specific. Exploring plant and insect responses under warming conditions that is site-specific, such as grassland, will provide further clues as to how sensitive grasslands may be. To accomplish this multiple similar climate warming experiments over seasons and over years need be compiled to accurately see and interpret trends that may be taking shape.

The patterns in this research revealed significant treatment effects of artificial warming on trophic structure in grasslands. However, my study failed to confirm the predictions that there would be a reduction in plant productivity due to increasing temperatures. These results raise some questions to consider. First, were these results a phenomenon of the particular year in which they were gathered? The experimental season was hot and dry with only a few rain events. Throughout the season, vegetation within the OTCs became dryer and sparse in comparison to the controls. Perhaps in a cooler or wetter year, plants and different trophic levels would have responded differently

30 to temperature in warmed OTCs. Continued monitoring of these plots over years would be valuable, but was not possible for this study. Second, was this a large enough sample?

Results could be different if more treatment plots were utilized, or more insect samples gathered. Other collection methods could be considered other than pan traps. Previous studies have used sweep-netting and vacuum type collections methods (Koricheva et al.

2000; Haddad et al. 2009). Regarding statistical power, the number of replicate plots in the experiment was n=18 (6/trt). This was typical of other studies on the topic (Masters et al. 1998; Pearson and Dyer 2006; Liu et al. 2011; Garris 2013). Some accumulated several years of replicate data. An n of 18 seemed reasonable to me for this study. Third, are grasslands sensitive to warming as expected where a particular climate threshold affecting temperate grasslands wasn’t met? Perhaps additional research and understanding is needed on heat tolerances of temperate grasses to determine when plant quality begins to break down thus affecting the trophic level insects that feed on the vegetation. Additional insight on plant heat and lack of moisture adaptation mechanisms would also be helpful. This experimental site was managed grassland, which may present different results than an unmanaged grassland habitat.

This study was able to confirm that there were trophic shifts occurring in response to warming in OTCs. Although the data do not suggest that there were effects of warming on vegetative biomass and insect abundance, there was a hint of response of litter. Community structure and trophic dynamics is complex. Due to the complexity with which we are faced, there needs to be new, innovative and novel approaches to how we approach research in ecology especially in regards to climate change. We need more in depth understanding of how plants and arthropods respond to climate change, in

31 particular warming, as in this study. Grasslands and grassland-insect interactions particularly deserve more attention and study if we are to make accurate predictions about how trophic structure is altered in general and to understand the effects of climate change on plant-insect interactions in particular. By gaining deeper insight into the specifics of community structure and trophic dynamics and their unique interactions, ecologists will be able to more accurately broaden the compendium of understanding of how individual ecosystems function such as that of a temperate grassland. Ecologists continue to fill gaps in our knowledge and understanding of how these interactions change when affected by climate and refine what we know about climate change, how climate change will affect the environment, how it will alter trophic dynamics in ecological communities, and how it will impact the biodiversity of plants and animals

(Barton and Schmitz 2009; Barton et al. 2009; Sheldon et al. 2011). We have learned that climate change can alter trophic structure within particular communities such as a grassland. More work needs to be done to understand how global climate warming may impact individual parts of community structure and how a single factor such as temperature can affect the individual trophic levels within a community. We seek more understanding as to how grassland areas change, both in species diversity as well as species interactions, as a result of warming temperatures.

LITERATURE CITED

Bale, J. S., Masters, G. J., Hodkinson, I. D., Awmack, C., Bezemer, T. M., Brown, V. K., Butterfield, J., Buse, A., Coulson, J. C., Farrar, J., Good, J. E. G., Harrington, R., Hartley, S., Jones, T. H., Lindroth, R. L., Press, M. C., Symrnioudis, I., Watt, A. D. & J. B. Whittaker (2002) Herbivory in global climate change research: direct effect of rising temperature on insect herbivores. Global Change Biology 8: 1-6. Barton B. T., Beckerman A. P. and O. J. Schmitz. (2009) Climate warming strengthens indirect interactions in an old-field food web. Ecology 90(9): 2346-2351. Barton, B. T. & O. J. Schmitz (2009) Experimental warming transforms multiple predator effects in a grassland food web. Ecology Letters 12: 1317-1325. Beck, S. D. (1983) Insect thermoperiodism. Annual Review of Entomology 28: 91-108. Bentz, B. J., Regniere, J., Fettig, C. J., Hansen, E. M., Hayes, J. L., Hicke, J. A., Kelsey, R. G., Negron, J. F., & S. J. Seybold (2010) Climate Change and Bark Beetles of the Western United States and Canada: Direct and Indirect Effects. BioScience 60(8): 602- 613. Boggs, C. & D. Inouye (2012) A single climate driver has direct and indirect effects on insect population dynamics. Ecology Letters 15: 502-508. Campbell, J. W. & J. L. Hanula (2007) Efficiency of malaise traps and colored pan traps for collecting flower visiting insects from three forested ecosystems. Journal of Insect Conservation 11(4): 399-408. Chapin, III, F. S. (1983) Direct and indirect effects of temperature on arctic plants. Polar Biology 2: 47-52. Cochran, W. G. & G. M. Cox, editors. 1957. Experimental Designs. 2nd edition. John Wiley & Sons, Oxford, England. Cole, K. L. (2010) Vegetation response to early Holocene warning as an analog for current and future changes. Conservation Biology 24(1): 29-37. De Boeck, H. J., De Groote, T. & I. Nijs (2012) Leaf temperatures in glasshouses and open-top chambers. New Phytologist 194: 1155-1164. Dyer, L. A. & J. O. Stireman (2003) Community-wide trophic cascades and other indirect interactions in an agricultural community. Basic and Applied Ecology 4: 423-432.

Emmerson, M., Bezemer, M., Hunter, M. & H. Jones (2005) Global change alters the stability of food webs. Global Change Biology 11: 490-501. Emmerson, M., Bezemer, T. M., Hunter, M., Jones, T. H., Masters, G. J. & N. M. Van Dam (2004) How does global change affect the strength of trophic interactions? Basic and Applied Ecology 5: 505-514. Garris, H. (2013) Restructuring of wetland communities in response to a changing climate at multiple spatial and taxonomic scales. Doctoral Dissertation. The University of Akron, Akron, Ohio. Downloaded from OhioLink. Gedan, K. & M. Bertness (2009) Experimental warming causes rapid loss of plant diversity in New England salt marshes. Ecology Letters 12: 842-848. Grime, J. P., Brown, V. K., Thompson, K., Masters, G. J., Hillier, S. H., Clarke, I. P., Askew, A. P., Corker, D., & J. P. Kielty (2000) The response of tow contrasting limestone grasslands to simulated climate change. Science 289(5480): 762-765. Haddad, N. M., Crutsinger, G. M., Gross, K., Haarstad, J. & D. Tilman (2011) Plant diversity and the stability of foodwebs. Ecology Letters 14: 42-46. Haddad, N. M., Crutsinger, G. M., Gross, K., Haarstad, J., Knops, J. M. H. & D. Tilman (2009) Plant species loss decreases arthropod diversity and shifts trophic structure. Ecology Letters 12: 1029-1039. Hance, T., J. van Baaren, Vernon, P. & G. Boivin (2007) Impact of Extreme Temperatures on Parasitoids in a Climate Change Perspective. Annual Review of Entomology 52: 107-126. Harrington, R., Woiwood, I. & T. Sparks (1999) Climate Change and Trophic Interactions. TREE 14: 146-150. Hegland, S. J., Nielsen, A., Lazaro, A., Bjerknes, A. & O. Totland (2009) How does climate warming affect plant-pollinator interactions? Ecology Letters 12: 184-195. Helmuth, B. (2009) From cells to coastlines: How can we use physiology to forecast the impacts of climate change? Journal of Experimental Biology 212: 753-760. Henry, G. H. R. & U. Molau (1997). Tundra plants and climate change: the International Tundra Experiment (ITEX). Global Change Biology 3 (Suppl. 1): 1-9. Hollister, R. D. & P. J. Webber (2000) Biotic validation of small open-top chambers in a tundra ecosystem. Global Change Biology 6: 835-842. Jamieson, M.A., Trowbridge, A. M., Raffa, K. F. & R. L. Lindroth (2012) Consequences of Climate Warming and Altered Precipitation Patterns for Plant-Insect and Multitrophic Interactions. Plant Physiology 160: 1719-1727. Kingsolver, J. G., Woods,H. A., Buckley, L. B., Potter, K. J., MacLean, H. J. & J. K. Higgins (2011) Complex Life Cycles and the Responses of Insects to Climate Change.

Integrative and Comparative Biology pp. 1-14 Advance Access first published online June 30, 2011 doi: 10.1093/icb/icr015. Koricheva, J., Mulder, C. P. H., Schmid, B., Joshi, J. & K. Huss-Danell (2000) Numerical responses of different trophic groups of invertebrates to manipulations of plant diversity in grasslands. Oecologia 125: 271-282. Lauenroth, W. K. (1979) Grassland primary production: North American grasslands in perspective. In Perspectives in Grassland Ecology (pp. 3-24). Springer New York. Leong, J. & R. W. Thorp (1999) Colour-coded sampling: the pan trap colour preferences of oligolectic and nonoligolectic bees associated with a vernal pool plant. Ecological Entomology 24(3): 329-335. Lindeman, R. L. (1942) The trophic-dynamic aspect of ecology. Ecology 23(4): 399-417. Liu, Y., Reich, P., Li, G. & S. Sun (2011) Shifting phenology and abundance under experimental warming alters trophic relationships and plant reproductive capacity. Ecology 92(6): 1201-1207. Logan, J. A., Regniere, J. & J. A. Powell (2003) Assessing the impacts of global warming on forest pest dynamics. Frontiers in Ecology and the Environment 1(3): 130-137. Marion, G. M., Henry, G. H. R., Freckman, D. W., Johnstone, J., Jones, G. Jones, M. H., Levesque, E., Molau, U., Molgaard, P., Parsons, A. N., Svoboda, J. & R. A. Virginia (1997) Open-top designs for manipulating field temperature in high-latitude ecosystems. Global Change Biology 3 (Suppl. 1): 20-32. Masters, G. J., Brown, V. K., Clarke, I. P., Whittaker, J. B. & J. A. Hollier (1998) Direct and indirect effects of climate change on insect herbivores: Auchenorrhyncha (Homoptera). Ecological Entomology 23: 45-52. Melillo, J. M., McGuire, A. D., Kicklighter, D. W., Moore III, B., Vorosmarty, C. J., & A. L. Schloss (1993) Global climate change and terrestrial net primary production. Nature 363: 234-240. Memmott, J., Craze, P. G., Waser, N. M. & M. V. Price (2007) Global warming and the disruption of plant-pollinator interactions. Ecology Letters 10: 1-8. Menendez, R (2007) How are insects responding to global warming? Tijdschrift voor Entomologie 150: 355-365. Moise, E. R. D. & H. A. L. Henry (2010) Like moths to a street lamp: exaggerated animal densities in plot-level global change field experiments. Oikos 119(5): 791-795. Molles, M.C. (2010) Ecology: Concepts and Applicatons. Fifth Edition. Boston, MA: WCB/McGraw-Hill.

Netherer, S. & A. Schopf (2010) Potential effects of climate change on insect herbivores in European forests – General aspects and the pine processionary moth as specific example. Forest Ecology and Management 259: 831-838. Newman, J. A., Bergelson, J. & A. Grafen (1997) Blocking factors and hypothesis tests in Ecology: is your statistics text wrong? Ecology 78(5): 1312-1320. Norby, R. J., Edwards, N. T., Riggs, J. S., Abner, C. H., Wullschleger, S. D. & C. A. Gunderson (1997) Temperature-controlled open-top chambers for global change research. Global Change Biology 3: 259-267. Otway, S. J., Hector, A. & J. H. Lawton (2005) Resource dilution effects on specialist insect herbivores in a grassland biodiversity experiment. Journal of Animal Ecology 74: 234-240. Ovadia, O. & O. Schmitz (2004) Weather variation and trophic interaction strength: sorting the signal from the noise. Oecologia 140: 398-406. Pearson, C. V. & L. A. Dyer (2006) Trophic diversity in two grassland ecosystems. The Journal of Insect Science 6: 25. Published online 2006 September 28. doi: 10.1673/2006 06 25.1. Penuelas, J., Prieto, P., Beier, C. et al. (2007) Response of plant species richness and primary productivity in shrublands along a north-south gradient in Europe to seven years of experimental warming and drought: reductions in primary productivity in the heat and drought year of 2003. Global Change Biology 13: 2563-2581. Power, M. E. (1992) Top-down and bottom-up forces in food webs: do plants have primacy? Ecology 73(3): 733-746. Robinet, C. & A Roques (2010) Direct impacts of recent climate warming on insect populations. Integrative Zoology 5(2): 132-142. Shaver, G. R., Canadell, J., Chapin III, F. S., Gurevitch, J. Harte, J., Henry, G., Ineson, P., Jonasson, S., Melillo, J., Pitelka, L. & L. Rustad (2000) Global Warming and Terrestrial Ecosystems: A Conceptual Framework for Analysis. BioScience 50(10): 871- 882. Sheldon, K. S., Yang, S. & J. J. Tewksbury (2011) Climate change and community dissaembly: impacts of warming on tropical and temperate montane community structure. Ecology Letters 14: 1191-1200. Siemann, E., Tilman, D., Haarstad, J. & M. Ritchie (1998) Experimental tests of the dependence of arthropod diversity on plant diversity. American Naturalist 152: 738-750 [PubMed] Terborgh, J., Lopez, L., Nunez, P. & M. Rao (2001) Ecological meltdown in predator- free forest fragments. Science 294(5548): 1923-1926.

Tilman, D., Reich, P. B. & J. M. H. Knops (2006) Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441: 629-632. Tylianakis, J. M., Didham, R. K., Bascompte, J. & D. A. Wardle (2008) Global change and species interactions in terrestrial ecosystems. Ecology Letters 11: 1351-1363. Voigt, W., Perner, J., Davis, A. J., Eggers, T., Schumacher, J., Bahrmann, R., Fabian, B., Heinrich, W., Kohler, G., Lichter, D., Marstaller, R. & F. W. Sander (2003) Trophic levels are differentially sensitive to climate. Ecology 84(9): 2444-2453. Wahid, A., Gelani, S., Ashraf, M. & M. R. Foolad (2007) Heat tolerance in plants: An overview. Environmental and Experimental Botany 61: 199-223. Yamamura, K. & K. Kiritani (1998) A simple method to estimate the potential increase in the number of generations under global warming in temperate zones. Applied Entomology and Zoology 33(2): 289-298. Zinn, K. E., Tunc-Ozdemir, M. & J. F. Harper (2010) Temperature stress and plant sexual reproduction: uncovering the weakest links. Journal of Experimental Biology Accessed Online April 30, 2015 doi: 10.1093/jxb/erq053.

APPENDICES

APPENDIX A

EXPERIMENTAL DESIGN

PLOT A B C D E

1 SC UC OTC

2 UC OTC SC

3 OTC SC UC

4 UC SC OTC

5 UC OTC SC

6 OTC SC UC

A latin square design was used to design the experimental area on the grassland site. Six rows and six columns were measured out. Only 5 columns were used as the sixth column contained large amounts of Toxicodenron radicans. The 18 plots representing 6 each of the OTC’s SC’s, and UC’s were then placed at random allowing for a representation of each treatment to be placed in each row and each column. Plot identification is written with the column (letter) first followed by the row (number).

APPENDIX B

OPEN-TOP CHAMBER

A photograph of one of the open-top warming chambers constructed for use in the grassland experiment. The chamber is 1.5 x 1.5 m2. The center is marked with a flag along with a marker pole holding a temperature data logger near the base recording ambient temperature in the chamber every 30 minutes. Notice that this plot consists of graminoids and a small amount of forbs.

APPENDIX C

STRUCTURAL CONTROL

A photograph of one of the structural controls used in the grassland experiment. Notice the single layer of wildlife netting surrounding the plot. The center of the plot is marked with a flag along with a pole marker holding a temperature data logger near the base of the plot recording ambient temperature in the plot every 30 minutes. Notice that this plot consists of mostly graminoids.

APPENDIX D

UN-MANIPULATED CONTROL

A photograph of one of the un-manipulated control plots used in the grassland experiment. The corners of the plot are marked with flags, the center is marked with a flag, and there is also a pole marker in the center of the plot holding a temperature data logger near the base of the plot to record ambient temperature in the plot every 30 minutes during the experimental period. Notice that this plot consists of graminoids and forbs.

APPENDIX E

OPEN-TOP CHAMBER DESIGN

The above figure represents the design of the open-top warming chambers used in the grassland field experiment. Six warming chambers were constructed and placed at random within the gridded area of the experimental area. The corner structures of the plot included wooded stakes driven into the ground at an incline toward the center of the plot then wrapped with a single layer of Tufflite IV clear greenhouse plastic, which was stapled to the stakes at the corners for stability. The center of the plot was marked with a flag.

APPENDIX F

PETRI DISH OF DRIED INSECT SPECIMENS

Photograph of petri dish with insect and arachnid specimens captured and collected from one of the plot sites in the grassland experiment. After collection, the specimens were dried, counted, and grouped into taxonomic orders and morphospecies.

APPENDIX G

PETRI DISH OF INSECT FAMILY SPECIMENS

Petri dish of insect sampling captured and collected from one plot on one date and being sorted into family. This set was being sorted after drying and weighing. Order: Diptera; Family: Muscidae

APPENDIX H

DRY BIOMASS

Litte Veg r dryi Dryi VE ng Litt ng Veg Litter Grandvi G peri er peri Mass mass ew ma od ma od (subtrac (subtrac Total Alley TREATM ss cod ss Cod ted bag ted bag Biom Plot ENT (g) e) (g) e weight) weight) ass Notes 153 126 Drying period 1 = 11-6-- A1 SC .7 2 .2 1 106.5 79 185.5 >8-2012 120 158 Drying period 2 = 11-8-- A2 UC .1 1 .2 1 72.9 111 183.9 >12-2012 133 125 dried empty bag = A5 UC .1 1 .5 1 85.9 78.3 164.2 47.2g 108 108 A6 OTC .8 1 .4 2 61.6 61.2 122.8 150 83. B3 OTC .8 1 8 1 103.6 36.6 140.2 154 80. B4 UC .8 2 4 1 107.6 33.2 140.8 101 81. B6 SC .6 1 9 2 54.4 34.7 89.1 97. C1 UC 4 1 104 1 50.2 56.8 107 133 95. C3 SC .2 2 7 2 86 48.5 134.5 154 94. C4 SC .8 2 3 2 107.6 47.1 154.7 131 101 C5 OTC .7 1 .4 2 84.5 54.2 138.7 106 D2 OTC .7 2 85 1 59.5 37.8 97.3 170 153 D5 SC .2 2 .8 2 123 106.6 229.6 181 142 D6 UC .6 2 .9 2 134.4 95.7 230.1 134 111 E1 OTC .3 1 .9 1 87.1 64.7 151.8 131 E2 SC .4 1 115 1 84.2 67.8 152 187 159 E3 UC .5 2 .8 2 140.3 112.6 252.9 E4 OTC 177 1 109 2 129.8 61.6 191.4

APPENDIX I

LIST OF GRAMINOIDS AND FORBS

List of Forbs and Grasses Category Genus and Species Forbs Daucus carota Lotus corniculatus Potentilla simplex Ranunculus acris Ranunculus bulbosus Rudbeckia fulgida Rudbeckia triloba Sisyrinchium montanum Toxicodentron (Rhus) radicans Trifolium pretense Trifolium repens Graminoids Cyperaceae Carex scoparia Carex stipata Cyperus spp. Eleocharis spp. Scirpus spp. Juncaceae Luzula multiflora Juncus effuses Poaceae Alopecuris pratensis Dactylis glomerata Phalaris arundinacea

APPENDIX J

LIST OF INSECT ORDERS AND CORRESPONDING INSECT FAMILIES

INSECTS ORDER FAMILY Coleoptera Cerophytidae/Elateridae Coccinellidae Curculionidae Dytiscidae Lampyridae Silphidae Tenebrionidae Diptera Asilidae Calliphoridae Culicidae Dolichopodidae Muscidae Tabanidae Tachinidae Hemiptera Anthocoridae Miridae Pentatomidae Rhopalidae Scutelleridae Homoptera Cercopidae Cicadellidae Membracidae Hymenoptera Apidae Chrysididae Formicidae Halictidae Ichneumonidae Vespidae Lepidoptera Nymphalidae (Phyciodes spp.) Pieridae (Pieris rapae) Neuroptera Chrysopidae Orthoptera Acrididae Gryllidae Tettigoniidae Other: non-insect Collembola Isopoda/Unknown/Uncategorized Acarina

Other: Araneida Linyphiidae Lycosidae Onyopidae Phalangida

APPENDIX K

ANOVA OF EFFECTS OF TREATMENT ON VEGETATION (R2=0.45).

Source DF Sum of Mean F Value Pr>F Squares Square

Model 7 5835.16 833.59 1.16 0.40

Treatment 2 355.25 177.63 0.25 0.79

Block 5 5479.91 1095.98 1.52 0.27

Error 10 7207.75 720.77

Corrected 17 13042.91 Total

APPENDIX L

ANOVA OF EFFECTS OF TREATMENT ON LITTER (R2=0.75).

Source DF Sum of Mean F Value Pr>F Squares Square

Model 7 8786.24 1255.18 4.31 0.02

Treatment 2 2487.62 1243.81 4.27 0.05

Block 5 6298.61 1259.72 4.33 0.02

Error 10 2910.84 291.08

Corrected 17 11697.08 Total

APPENDIX M

ANOVA OF EFFECTS OF TREATMENT ON INSECT ABUNDANCE (R2=0.34)

Source DF Sum of Squares Mean Square F Value Pr>F

Model 7 40635.17 5805.02 0.74 0.64

Treatment 2 25012.0 12506.0 1.60 0.25

Block 5 15623.17 3124.63 0.40 0.83

Error 10 78305.33 7830.53

Corrected 17 118940.5 Total

APPENDIX N

ANOVA OF EFFECTS OF TREATMENT ON SPIDER ABUNDANCE (R2=0.27)

Source DF Sum of Squares Mean Square F Value Pr>F

Model 7 1113.72 159.1 0.53 0.79

Treatment 2 471.44 235.72 0.79 0.48

Block 5 642.28 128.46 0.43 0.82

Error 10 2985.22 298.52

Corrected 17 4098.94 Total

APPENDIX O

ANOVA OF EFFECTS OF TREATMENT ON TOTAL ARTHROPOD BIOMASS

(R2=0.35)

Source DF Sum of Squares Mean Square F Value Pr>F

Model 7 0.35 0.05 0.77 0.62

Treatment 2 0.18 0.09 1.38 0.29

Block 5 0.17 0.03 0.53 0.75

Error 10 0.64 0.06

Corrected 17 0.99 Total

APPENDIX P

INSECT TROPHIC LEVEL GROUPS

Phytophagous Saprophagous Predatory/ Generalist Parasitoids Omnivore Acrididae Calliphoridae Asilidae Anthocoridae Apidae Cerophytidae/Elateridae Chrysididae Culicidae Cercopidae Muscidae Chrysopidae Gryllidae Cicadellidae Silphidae Coccinellidae Tabanidae Curculionidae Dolichopodidae Tenebrionidae Halictidae Dytiscidae Membracidae Formicidae Miridae Ichneumonidae Nymphalidae Lampyridae Pentatomidae Vespidae Pieridae Rhopalidae Scutelleridae Tachinidae Tettigonidae 44.10% 11.80% 29.40% 14.70%

APPENDIX Q

ANOVA OF EFFECTS OF TREATMENT ON PHYTOPHAGOUS TROPHIC LEVEL

INSECTS (R2=0.69)

Source DF Sum of Squares Mean Square F Value Pr>F

Model 7 1724.39 246.34 3.14 0.05

Treatment 2 1020.11 510.06 6.50 0.02

Block 5 704.28 140.86 1.80 0.20

Error 10 784.56 78.46

Corrected 17 2508.94 Total

APPENDIX R

ANOVA OF EFFECTS OF TREATMENT ON PREDATORY/PARASITIC TROPHIC

LEVEL INSECTS (R2=0.89)

Source DF Sum of Squares Mean Square F Value Pr>F

Model 7 2221.22 317.39 11.13 0.0005

Treatment 2 2100.78 1050.39 36.83 <.0001

Block 5 120.94 24.19 0.85 0.55

Error 10 285.22 28.52

Corrected 17 2506.94 Total

APPENDIX S

ANOVA OF EFFECTS OF TREATMENT ON OMNIVOROUS TROPHIC LEVEL

INSECTS (R2=0.98)

Source DF Sum of Squares Mean Square F Value Pr>F

Model 7 1180.72 168.67 78.25 <.0001

Treatment 2 1133.78 566.89 262.99 <.0001

Block 5 46.94 9.39 4.36 0.02

Error 10 21.56 2.16

Corrected 17 1202.28 Total

APPENDIX T

ANOVA OF EFFECTS OF TREATMENT ON SAPROPHAGOUS TROPHIC LEVEL

INSECTS (R2=0.98)

Source DF Sum of Squares Mean Square F Value Pr>F

Model 7 1402.17 200.31 83.32 <.0001

Treatment 2 1339.0 669.5 275.14 <.0001

Block 5 63.17 12.63 5.19 0.01

Error 10 24.33 2.43

Corrected 17 1426.5 Total