Differential Responses of Grassland Ground Beetle (Coleoptera: Carabidae) Communities to Conservation Reserve Program Management Practices

DIFFERENTIAL RESPONSES OF GRASSLAND GROUND BEETLE (COLEOPTERA: CARABIDAE) COMMUNITIES TO CONSERVATION RESERVE PROGRAM MANAGEMENT PRACTICES

A Thesis by

Evan Waite

Bachelor of Science, University of Florida, 2017

Submitted to the Department of Biological Sciences and the faculty of the Graduate School of Wichita State University in partial fulfillment of the requirements for the degree of Master of Science

July 2019

DIFFERENTIAL RESPONSES OF GRASSLAND GROUND BEETLE (COLEOPTERA: CARABIDAE) COMMUNITIES TO CONSERVATION RESERVE PROGRAM MANAGEMENT PRACTICES

The following faculty members have examined the final copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the requirement for the degree of Master of Science with a major in Biological Sciences.

______Mary Jameson, Committee Chair

______Gregory Houseman, Committee Member

______Andrew Swindle, Committee Member

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“We should preserve every scrap of biodiversity as priceless while we learn to use it and come to understand what it means to humanity.”—E.O. Wilson

ACKNOWLEDGEMENTS

This research project would not have been possible without the contributions of numerous individuals who so kindly dedicated extensive amounts of time, expertise, and unwavering support throughout the entire process. I would first like to thank Dr. Mary Liz

Jameson for being the best mentor that anyone could ever ask for; I would not be half the scientist or person I am today without your guidance. Thank you to Alex Morphew and Fraser

Watson for being with me on this journey every step of the way; even while knee deep in CRP.

Having friends willing to help with anything at the drop of a hat is truly something special, and I would not have made it through this without you two. For assistance with the gargantuan task of sorting the carabids from our traps, I thank Morgan Trible and Katie Slavenburg. Your willingness to sort through mountains of beetle biomass was a blessing.

As a new carabidologist, this project would not have gone anywhere without the knowledge and hospitality of Bob Davidson at the Carnegie Museum of Natural History, Dr. Zack Falin at the

Snow Entomological Museum Collection, Dr. Dave Kavanaugh at the California Academy of the

Sciences, and Dr. Kip Will at the University of California at Berkeley. Without access to their specimens and the verification of my identifications, this project would have stayed where these beetles reside; on the ground.

I would like to thank Dr. Andrew Swindle for his guidance on my committee, Dr. Gregory

Houseman for his assistance and willingness to help an entomologist embrace his ecologist side, and Molly Reichenborn for her constant support as a friend and project manager that has the super- human ability to make things happen.

Finally, I would like to thank the people that have been my support system since day one; my family. My mom, Cindy Connors, as well as my grandparents Peggy and Jim Waite have

always supported every weird, buggy adventure I have chosen to pursue. Even over 1,000 miles away I felt your love and belief in me every single day.

The research “Linking CRP grassland management to plant, insect, and bird abundance and diversity”, which is the subject of this thesis, has been financed, in part, with federal funds from the Fish and Wildlife Service, a division of the United States Department of Interior, and administered by the Kansas Department of Wildlife, Parks and Tourism. The contents and opinions, however, do not necessarily reflect the views or policies of the United States Department of Interior or the Kansas Department of Wildlife, Parks, and Tourism.

ABSTRACT

Grasslands of the Great Plains have declined over 70% since European settlement due to conversion of natural habitats to cropland and urban centers. The federally supported

Conservation Reserve Program (CRP) was created to improve water quality, reduce soil erosion, and increase native habitats for wildlife. Within these restored grassland ecosystems, ground beetles (Coleoptera: Carabidae) are a keystone invertebrate group that fill several crucial niches and may serve as bioindicators of successful land management strategies. Using pitfall traps at

108 CRP sites across 650 km and a precipitation gradient of 63.5 cm, we examined the response of carabid beetle richness and abundance to a low diversity (CP2) and relatively high diversity

(CP25) initial seeding and a grazing treatment (grazed or ungrazed) over two field seasons. We captured 4,841 carabid beetles representing 48 species across this wide gradient. Overall, grazing and CP treatments did not have a detectable effect on carabid abundance, biomass, or diversity.

However, we found significant, positive effects of grazing on all three metrics in tallgrass habitats (strongly correlated with precipitation and plant community diversity). There were no effects of CP on the ground beetle community. Additionally, we found that species turnover was correlated with habitat transitions found along the precipitation gradient. Our findings suggest that moderate grazing by cattle over two seasons does not negatively affect carabid communities and that carabid communities respond differently to the effects of land management across a precipitation gradient and grassland habitats.

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TABLE OF CONTENTS

Chapter Page

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

1.1 Introduction………………………………………………………………………..1

2. MATERIALS AND METHODS………………………..………………………..…….....6

2.1 Site Selection…………………….……….……………………………………...... 6 2.2 Insect Sampling……...…………….……………………………………….……...9 2.3 Plant Sampling…………...……………………..………………………………..11 2.4 Laboratory Methods…..…..……………………………………………………...11 2.5 Habitat Designations……………………………………………………………..13 2.6 Data Analyses…………………………………………………………………….13

3. RESULTS…………………………………..…………………………………………....17

3.1 Carabid Beetles………………………..……………………………..………...... 17 3.2 Carabid Abundance………………………………..………………..……………20 3.3 Carabid Biomass…………………………………………………………………21 3.4 Carabid Diversity………………………………………………………………...22 3.5 Response of Carabid Community to Habitat……………………………………..24 3.6 Species Turnover…………………………………………………………………29

4. DISCUSSION………..…………………………………………………………….…….35

4.1 Discussion………………………………………..……………………………....35 4.2 Response of Carabid Communities to Grazing and CP Treatments………..…....35 4.3 Species Turnover in Carabid Communities……………………………………...39 4.4 Carabids as Bioindicators and Comparison with European Ecology……………42 4.5 Significance………………………………………………………………………43

REFERENCES…………………………………………………………………………………..45

APPENDICES……………………………………………………………………….…………..57

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LIST OF TABLES

Table Page

1. Taxon table with abundances and feeding guild….……………….……………………..18

2. Most abundant species in each region……..…………………………………………….19

3. Kruskal-Wallis for overall treatment effects…………………………………………….23

4. PERMANOVA results for region………………………………………………………..26

5. PERMANOVA results for two region effects…………..……………………………….27

6. PERMANOVA for grazing and CP combinations….…………………………………...27

LIST OF FIGURES

Figure Page

1. Plot layout……………………………………..…………………...…………...…………8

2. Overview of pit fall trap design……………………………………………………….....10

3. Rank abundance of carabid species………………………………………...……………20

4. Carabid abundances by treatment and region……………………………………………21

5. Carabid biomass by treatment and region………………………………...………………22

6. Carabid diversity by treatment and region……………………...………………………...23

7. Three-region NMS ordination……………………………….…………...………………25

8. Two-region NMS ordination……………..………………………………………………28

9. Total species turnover..………………………………………...………………………...30

10. Species turnover by Conservation Practice……………………………………………....31

11. Species turnover by grazing treatment…………………………………………………...32

12. Change in the dominant species………………………………………………………….33

13. Habitat designations of each site…………………………………………………………34

LIST OF MAPS

Map Page

1. Kansas map including all study sites……………...………………………………...……..7

LIST OF PLATES

Plate Page

1. Pitfall trap in-situ………………….……………………………………………………..10

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CHAPTER 1

INTRODUCTION

1.1 Introduction

Over the past few years, a worrying trend has emerged in environmental ecology.

Numerous reports demonstrate that insect populations are in severe decline due to anthropogenic stressors (Conrad et al. 2006, Fox et al. 2014, Hallmann et al. 2017, Lister and Garcia 2018,

Benslimane et al. 2019). However, insect community reassembly is positively influenced by habitat restoration (Longcore 2003, Littlewood et al. 2006, Albrecht 2007). While restored habitats will never be the same as the original habitat, restoration efforts can mitigate some of the damage done to ecosystem processes. Restoring habitat is more important now than ever before, and focusing restorations on insect communities is crucial to maintaining healthy ecosystems because insects provide many necessary ecosystem services such as pollination, pest control, carrion and waste removal, and serving as food sources for other organisms (Ratcliffe 1996,

Larochelle and Lariviere 2003, Kremen and Chaplin-Kramer 2007, Nichols et al. 2008).

Ecosystem resiliency, or the ability to recover from disturbance, can be aided by the invertebrate community and the services they provide. One key invertebrate group that provides ecosystem services (Larochelle and Lariviere 2003), is heavily impacted by these human-imposed stressors

(Koivula 2011), and responds positively to habitat restoration (Günther and Assmann 2005) is the ground beetles (Coleoptera: Carabidae). These characteristics have also led carabids to be a focal taxon for NEON’s (National Ecological Observatory Network) terrestrial invertebrate sampling effort (Kao et al. 2012).

Ground beetles fill several crucial niches within the ecosystem as predators of both plant and invertebrate pests, seed dispersers, and prey for other organisms (Larochelle and Lariviere 2003).

The wide diet breadth of this beetle group is considered beneficial for reducing pest insects and weedy plant populations, thus allowing native communities to thrive (Lund and Turpin 1977,

Kromp 1999, Dorner 2002, Holland 2002). In addition, their role as a food source for other organisms cannot be overlooked. They play a large role in the diets of most vertebrate groups with their largest role being in the diet of ground-dwelling birds like pheasant, grouse, and quail

(Wilson et al. 1999, Larochelle and Lariviere 2003). Over 20% of a pheasant’s diet can be composed of carabids, and higher proportions in pheasant chick diets positively influences chick survival rates (Hill 1985, Smith et al. 2015).

Carabid beetles are often confined to microhabitats such as sparsely vegetated areas in grasslands or leaf litter in deciduous forests, thus making them useful as indicators for changes in habitat types (Thiele 1977, Brose 2003b) (e.g., Agonum and Patrobus are leaf-litter associates while Calosoma, Pasimachus, Harpalus, and Scarites are associated with open ground

[Larochelle and Lariviere 2003]). Even genera with less strict associations (like Chlaenius) are found commonly on paths or patches of bare ground in their habitats (Larochelle and Lariviere

2003). The habitats required to support ground beetle communities can be altered by land management regimes, and this also leads to changes in the carabid composition (da Silva et al.

2008). Heavy disturbance results in communities that are composed of smaller, macropterous species (Blake et al. 1994), whereas increased time between disturbances allows for larger- bodied, flightless species to colonize (Ribera et al. 2001). Thus, carabid beetle species are potential indicators of habitat type and environmental disturbance.

Great Plains grasslands have declined over 70% since European settlement displacing native species, communities, and ecosystems due to conversion of natural habitats to cropland, rangeland, and urban centers (Samson et al. 2004). One federal program that restores these

habitats is the Conservation Reserve Program (CRP). Created in 1985, this program pays landowners to replant their agricultural fields with prescribed seed mixes with the initial goals of preserving water quality, reducing erosion, and increasing native habitats (USDA 2015a). Since the program’s inception, 42 Conservation Practices (CP) have been created to fill various roles in restoring distinct habitats; each with its own unique initial seeding mix and allowable management practices (e.g., disking, haying, burning, interseeding, and spraying with herbicide).

Over 22 million acres of land are enrolled in CRP across the nation (USDA and FSA 2019), however these restored lands are often more homogenous and less diverse than the surrounding native habitats (Baer et al. 2002, McIntyre and Thompson 2003, Jog et al. 2006, Baer et al. 2009,

Bakker and Higgins 2009, Rahmig et al. 2009, Questad et al. 2011). Differences in seed mix and management practices create discrete habitats that favor certain invertebrate community structures including those of carabids.

Differences in CRP seed mix composition result in inconsistent plant diversity and structural variability across these CRP sites. Heterogeneity (variability) in both physical plant structure and community composition are identified as key components that support more diverse beetle communities; however, restored CRP lands lack heterogeneity compared to undisturbed habitats (Brose 2003b, Schaffers 2008, Joern and Laws 2013). It is also observed that higher diversity in the plant community leads to higher abundance and richness in the insect community (Siemann et al. 1998, Knops et al. 1999). The structure of plant communities is heavily influenced by precipitation (Milchunas et al. 1988). Precipitation has positive effects on the distribution and populations of insects, potentially because of an increase in ground cover and vegetation height (Dingle et al. 2000, Frampton et al. 2000). Plant diversity on CRP land likely differs between Conservation Practice type, which may change the insect community

composition even in areas with similar abiotic factors, thus resulting in differences in insect communities across a precipitation gradient (USDA 2015b, USDA 2015c). This continuous gradient of habitats and changing insect communities allows us to examine species turnover as it relates to the precipitation gradient. Precipitation on its own usually is not an adequate predictor of species distribution (Ricklefs 1987, Brown 2014). However, in combination with other factors, it may illuminate why certain species occur where they do.

Another factor that strongly influences plant heterogeneity is the role of herbivores.

Historically the Great Plains was grazed by large herbivores in the form of bison (Bison bison L.)

(Samson et al. 2004). Although large herds of bison are not common today, domestic cattle (Bos taurus L.) fulfill a similar role. Disturbance by large herbivores creates heterogeneity by preferentially consuming dominant grasses and allowing non-dominant forbs to thrive (Cid 1998,

Adler et al. 2001). In addition, the waste products left behind by these large herbivores increases plant production by adding nutrients back into the system, creating microhabitat islands, and providing food sources for insects such as fly larvae that dwell in the dung (Williams and Haynes

1995, Larochelle and Lariviere 2003). Though grazing on CRP is typically disincentivized

(USDA/NRCS 2012, USDA/NRCS 2015), this natural disturbance is crucial to maintaining ecosystem functions such as nutrient cycling and habitat creation in grasslands (Milchunas et al.

1988). Grazing is likely to increase carabid abundance because of the benefits it provides to the ecosystem and to the beetle communities.

In this study, we aimed to evaluate carabid community responses to management regimes on restored grasslands. The effects of grazing and plant communities on carabid beetles has been well studied in temperate regions outside of North America (Gardner et al. 1997, Brose 2003,

Melis et al. 2006), but very few studies have examined the multi-trophic interactions on restored

grasslands of North America. In addition, few extensive surveys and studies of ground beetles on the grassland biome of North America have been conducted, leaving a gap in our knowledge about how these faunae respond to grassland restoration. In the US, previous studies focused on the response of carabids to fire rather than planting or management practices (Cook and Holt

2006, Kral et al. 2017). Understanding these invertebrate communities and how land management may affect community composition across a broad precipitation gradient will lead to better informed stewardship of these restored lands.

We examined 108 CRP sites across 650 km representing four grassland types (short-, mixed-, sand-, and tallgrass), and across a 63.5 cm (45.7–109.2 cm) annual precipitation gradient. For two summer seasons, we recorded carabid beetle abundance, diversity, and biomass to assess carabid community response to experimental treatments as well as environmental variables. The goals of this study are to assess the response of carabid communities to: 1) differences in land management (moderately grazed by cattle versus ungrazed) and 2) differences in Conservation Practice that are likely to influence plant diversity (low diversity initial seed mix [CP2] versus a higher diversity initial seed mix [CP25]). Additionally, we sought to examine carabid community composition and species turnover across a natural environmental gradient

(precipitation).

CHAPTER 2

MATERIAL AND METHODS

2.1 Site Selection

As part of a broader three-year study (2017–2019), a randomized list of Conservation

Reserve Program (CRP) landowners in Kansas was obtained from the USDA. Selection of a site was determined by size (>35 acres/14.2 ha), distance from the next closest site (>1 km), and year it was enrolled in CRP (prior to 2012 and with a contract not expiring before 2019). We examined the effects of two conservation practices, CP2 and CP25 on ground beetle communities. CP2 is a lower diversity initial planting mix focused primarily on grasses; CP25 is a higher diversity mix with the goals of restoring rare and declining habitats for wildlife (USDA

2015b, USDA 2015c). In order to test the effects of precipitation, these sites were distributed across the precipitation gradient of Kansas which spans from 45.7 cm of precipitation in the west to 109.2 cm in the east. We established three study regions across the west to east precipitation gradient in Kansas based on the average annual precipitation from data points across the state, in addition to elevation data, for the 30-year period of 1971-2000 (U.S. Department of Agriculture and Natural Resources Conservation Service - National Geospatial Center of Excellence 2012):

West (45.7 – 55.9 cm precipitation), Central (55.9 – 76.2 cm precipitation), and East (86.4 –

109.2 cm precipitation). This regional approach allowed us to make generalizations about precipitation effects on beetle communities. We attempted to balance the distribution of each planting practice in each of our three study regions with a goal of 18 CP2 and 18 CP25 in each region for a total of 108 sites (Appendix A and D). In addition, we implemented cattle grazing on half of the total sites to examine the effects of grazing with the Conservation Practice (Appendix

D). For the grazed sites, landowners provided and managed their own cattle at stocking rates

determined by the local Natural Resource Conservation Service (NRCS) Range Conservationist.

These rates were tailored to each field with a goal of removing 50% of the available plant biomass. Landowners were instructed to allow the cattle to graze 120 and 180 days between

April and October 2017 and 2018. The type of cattle (cow/calf, stocker, etc.) was chosen by the landowner and any combination was permitted if it complied with the NRCS recommendation.

Map 1. Map of Kansas with the location of 108 CRP sites in this study. Red symbols represent

CP2 sites; yellow symbols represent CP25 sites. Triangles are grazed sites; circles are ungrazed sites. White lines represent the 30-year average precipitation isoclines (U.S. Department of

Agriculture and Natural Resources Conservation Service - National Geospatial Center of

Excellence 2012). Image by Jackie Baum.

Within each of the 108 CRP sites (Map 1), a 200x300 m experimental plot was established as close to the center of the field as possible with the longest axis of the site determining the orientation of the plot (Fig. 1). If the property included more than one CRP tract, the plot was placed entirely within the largest area.

Fig. 1. Experimental plot design in each CRP site. Numbered points (1–9) are locations for data collection. Points marked in red indicate (2, 4, 6, 8) the placement of pitfall traps for carabid beetle sampling. Image by Gregory Houseman.

2.2 Insect Sampling

Each study plot contained 9 points spaced 100 m vertically and 75 m horizontally from the next closest point (Fig. 1). Points 2, 4, 6, and 8 each contained one non-baited pitfall trap

(Fig. 2), an effective method to sample ground-dwelling insects (LeVan et al. 2015). Pitfall traps were made of two 650 ml (22 fl. oz.) plastic cups, including an outer cup (with holes drilled on the bottom and sides) and an inner cup (with holes on the sides). The cups are nested within one another (Fig. 2) with the drilled holes aligned to allow rainwater to drain into the soil rather than overflow the trap. The inner cup was filled with 180 ml (6 fl. oz.) of a 50:50 propylene glycol and water solution. The trap was placed in the soil so that the top of the trap was flush with the surface of the ground. A metal grate (“pitfall guard”) with diamond-shaped openings large enough to allow all insects to passively fall into the trap (67 x 27 mm) was placed over the top of the trap and was secured using four landscape staples positioned on each corner. This pitfall guard was designed to prevent the cattle on grazed plots from stepping in the trap and causing injury (Plate 1). Pitfall guards were used on all plots regardless of grazing treatment.

Fig. 2. Pitfall trap design. Drilled holes are indicated in red (to allow drainage of rain from the trap). Cup on left (inner cup) is inserted into cup on right (outer cup), and holes are aligned.

Placement of holes allowed rainwater to drain into the soil rather than overflowing the trap.

Image by Rachel Stone.

Plate 1. Pitfall trap in-situ. Pitfall trap was placed in the ground at plot points 2, 4, 6, and 8 with the rim flush with the soil. On all sites, a pitfall guard was placed on top of the trap and secured with a landscape staple at each corner. The guard prevented injury to cattle on grazed sites. Photo by Evan Waite.

Pitfall traps were deployed early in the field season (May 23 to June 23 2017, and May 28 to June 8 2018) and then collected after 3–7 weeks (June 27 to July 25 2017, and June 26 to July

10 2018). To reduce variation in the number of days a trap was deployed (24–47 days or 34 average trap days) in 2017, we narrowed the length of trap deployment to 28–35 days (or 31 average trap days) in 2018. Pitfall traps were retrieved from the field and then stored in the lab until processed.

2.3 Plant Sampling

Vegetation was sampled at each of the 9 sample points twice a year (May 22–June 25, 2017 and June 26–July 28, 2017; May 23–June 22, 2018 and June 25–July 25, 2018). To estimate plant species composition, a 1 m2 quadrat was placed at each of the 9 sample points and all plant species in the quadrat were identified. The percent cover of each plant species, bare ground, and rock was estimated in each quadrat by comparing the aerial cover to reference card of a known size and these values were averaged across all nine points to get a single value for the site. To quantify the physical structure of the plant community, photos of the vertical vegetation were taken against a white 1 x 0.5 m backstop board. These photos were cropped (imageJ 1.51j8, Rasband) and converted into black and white pixels (Adobe Photoshop CS4). The black pixels represented the area covered by the plant community.

2.4 Laboratory Methods

All organisms collected in the pitfall traps were examined using a dissecting microscope

(Leica M80 0.8x–1.0x Achromatic lens, Buffalo Grove, IL, http://www.leica-microsystems.com) and sorted into the following target taxa for both years: Carabidae, Histeridae, Silphidae,

Scarabaeoidea, and other Coleoptera. In 2018, Orthoptera were also sorted. Non-target

specimens such as arachnids, other insect orders, terrestrial isopods, and decapods were sorted into “by-catch” in 2017 and 2018.

Genus-level identifications were conducted using Ball and Bousquet (2001). Species- level identifications were made using keys by Ball (1956: Helluomorphoides, 1959: Dicaelus,

1960: Euryderus, and Anderson 1962: Harpalus, and Negre 1972: Calathus, 2001: Micrixys),

Bell (1960: Chlaenius), Bousquet (2010: Scarites), Erwin (1970: Brachinus), Freitag (1969:

Cyclotrachelus [as Evarthus]), Giadspow (1959: Calosoma, 1973: Scaphinotus), Lindroth (1961:

Amara, Anisodactylus, Geopinus, Harpalus, Panagaeus, Poecilus [as Pterostichus],

Pterostichus, Selenophorus), Purrington (2005: Pasimachus), Reichardt (1967: Galerita), Vaurie

(1955: Amblycheila), Will (1997: Harpalus [Megapangus]). Identification of specimens in the subfamily Cicindelinae (tiger beetles) were conducted using Pearson et al. (2015). Identifications were facilitated with comparative collections (University of Kansas, Carnegie Museum of

Natural History, California Academy of Science, and University of California Berkeley) and with the assistance of Bob Davidson, Dave Kavanaugh, and Kip Will (Carnegie Museum of

Natural History, California Academy of Science, University of California Berkeley, respectively). Higher classification (subfamily, tribe, genus) follows the classification of

Adephaga (Coleoptera) by Bousquet (2012).

Carabid specimens occasionally became disarticulated due to water logging. If large parts of a beetle (i.e. thorax+abdomen, head+thorax) were present and identifiable as Carabidae but were not able to be identified to species, then these were placed into a category of

“Unidentifiable”. This category was included in the abundance and biomass evaluations because these measures are independent of species identity, but it was not included in diversity measures.

Following identification, carabid beetle biomass was dried for 168 hours in a Yamato

DKN 810 (Tokyo, Japan, https://www.yamato-scientific.com/), weighed immediately to prevent absorption of ambient humidity using an Ohaus AR1530 balance (Parsippany, NJ, https://us.ohaus.com/en-us/), and recorded to the nearest 0.001 g.

Voucher specimens are deposited at the following public institutions: Carnegie Museum of Natural History (Pittsburg, PA; CMNH), Wichita State University Invertebrate Collection

(Wichita, KS; WICHI), California Academy of Science (San Francisco, CA; CASC), and the

Snow Entomological Museum Collection at the University of Kansas (Lawrence, KS; SEMC).

2.5 Habitat Designations

In order to understand changes in carabid communities in different habitat types across the precipitation gradient, we examined the representative habitats at each of the 108 sites

(Appendix A). We followed the USDA habitat designation (i.e. short-, mixed-, sand-, or tallgrass grassland) for each county where a study site was located (USDA and NRCS 2012b) (Appendix

A). If there were multiple designations for one county, the EPA list of habitat types was used to determine the habitat based on the location of our study site (Chapman et al. 2001). If designations did not agree, the USDA designation was prioritized.

2.6 Data Analyses.

For each sample point (2, 4, 6, and 8), carabid species richness (number of species), species abundance (number of individuals per species), and total biomass (dried weight) were recorded. Data from all four traps at each site were pooled before analysis. We pooled site data from 2017 and 2018 in order to examine the overall effects of CP and grazing on the ground

beetle community. Over two seasons of data collection, no carabids were recorded at site Central

06; this site was excluded from analyses. Site East 32 had an incredibly high abundance of carabids. This site was excluded from statistical analysis because of the effect it had on the analyses. Our objective was to examine the overall effect of CP and grazing, thus inclusion of this site would lead to false conclusions about the effects of these treatments. Analyses were conducted using R (Version 3.4.3).

Shannon-Weiner species diversity (H) was calculated for each treatment combination within a region (West, Central, East) (“vegan” in RStudio [Oksanen et al. 2016]). This metric, in addition to others like evenness (J), are often used to characterize communities but do not provide easily comparable results between studies. Additionally, Shannon-Wiener diversity is a logarithmic measure meaning that an H value of 2 does not represent a community that is twice as diverse as a community with an H value of 1. This doubling of the Shannon index value also does not correspond to a doubling in the diversity of the community Consequently, we also calculated the effective species number (ESN) for each of our treatments. This metric utilizes the

Shannon-Weiner diversity value but converts it into a linear “true diversity” that provides a value that is directly comparable between sites or treatments (i.e. an ESN of 2 represents a community that is twice as diverse as an ESN of 1) (Jost 2006).

Plant diversity (ESN) was calculated for each site (Fraser Watson, unpublished data). In addition, the coefficient of variation (CV) of the plant structure was calculated for each sample point to show how variable the physical properties were. To quantify the structure of the plants at each of the nine sample points, the proportions of black pixels to the total number of pixels obtained from the backstop photos were used. These values were then used to calculate the CV for plant structure at the site level (Fraser Watson, unpublished data).

For the univariate analysis, carabid data were not normally distributed and standard transformations (log, square root, 4th root) nor the use of alternative distributions (Poisson,

Negative Binomial) adequately meet statistical assumptions. Consequently, non-parametric statistical analyses were conducted on beetle abundance, biomass, and diversity. A Kruskal-

Wallis one-way analysis was used to determine differences in overall abundance, diversity, and biomass of beetles on our treatment combinations as well as effects observed within regions.

Prior to multivariate analysis, species represented by only one individual or found on only one study site were removed. Primer 6 & PERMANOVA+ (Anderson et al. 2009) was used to compare the Bray-Curtis dissimilarity between experimental treatments and detect any significant interactions between treatment levels with a PERMANOVA analysis. For all analyses, a p-value of 0.05 was considered significant. To visualize interactions between beetle species and abundance, covariates (precipitation, percent bare ground, plant diversity, CV of plant structure), and treatments (grazing and Conservation Practice) in multivariate space, Non- metric multidimensional scaling ordinations (NMS) were created in PC-Ord6 (McCune and

Mefford 2011). Vectors (biplots) were plotted on NMS ordination to illustrate the strength of the relationship between the carabid community and the factors examined within the community.

For NMS, stress values above 0.35 are considered to represent completely random community composition while values below 0.10 are considered adequate as to not draw false inferences from the ordination (McCune and Grace 2002).

To assess changes in the dominant species in the carabid community, the average abundance of the most abundant species for each region was plotted across the precipitation gradient. In addition, the total species turnover was calculated for the precipitation gradient using a modified temporal diversity index to calculate species shifts between precipitation isoclines.

The proportion of community change was calculated by adding the species gained and species lost and dividing by the total species pool in neighboring precipitation isoclines. The metric was applied to isoclines by substituting average annual precipitation for the time variable (“codyn”

Hallett et al. 2019). This calculation also allowed us to determine if there were differences in species turnover between CP2 and CP25 or between grazed and ungrazed sites.

CHAPTER 3

RESULTS

3.1 Carabid beetles

A total of 5,098 carabid beetles were collected at 108 study sites over two field seasons

(Table 1, Appendix A). 4,841 individuals represented 48 species (Table 1) (257 were pieces and not identifiable to species). There were no adventive or non-native species. The invasive and rapidly expanding species, Pterostichus melanarius Illiger, was not found. Seven species accounted for 68% of all individuals (Table 2). The three most abundant species in each region were pooled, leaving seven species that also accounted for 68% of the total number of carabids collected (Table 2). These species were Cyclotrachelus sodalis LeConte (1,099), Pasimachus punctulatus Haldeman (518), Tetracha virginica L. (427), Pasimachus elongatus LeConte (424),

Harpalus caliginosus (Fabricius) (366), Cyclotrachelus torvus (LeConte) (355), and Pasimachus californicus (Chaudoir) (299) (Table 2, Fig. 3). Nine species were represented by single individuals: Amara obesa (Say), Amblycheila cylindriformis (Say), Calosoma externum (Say),

Dicaelus furvus Dejean, Galerita bicolor (Drury), Geopinus incrassatus (Dejean), Micrixys distincta (Haldeman), Pterostichus femoralis (Kirby), and Selenophorus granarius (Dejean). An overwhelming majority of the beetles were generalist predators or omnivores (91.9% or 5,066 individuals) while only 8.1% (414 individuals) were granivorous. Because all study sites were restored grasslands and dominated by seed-producing plants, this was unexpected because the proportion of granivorous carabids in a community tends to increase as graminoid vegetation increases (Venn et al. 2013).

Table 1. Ground beetle species and their abundances. All individuals were collected in non-

baited pitfall traps. Feeding guild information obtained from Larochelle and Lariviere (2003) and

Ball and Bousquet (2001)1. An asterisk (*) denote a designation based on congeners due to lack

of information on the species.

Subfamily Species Number Feeding Guild Brachininae Brachinus alternans Dejean 1825 13 Predator* Carabinae Calosoma affine Chaudoir 1843 4 Predator Calosoma externum (Say 1823) 1 Predator Calosoma marginale Casey 1897 5 Predator Calosoma sayi Dejean 1826 3 Predator Scaphinotus elevatus (Fabricius 1787) 11 Predator Cicindelinae Amblycheila cylindriformis (Say 1823) 1 Predator Cicindela formosa Say 1817 10 Predator Cicindela scutellaris Say 1823 9 Predator Cicindelidia punctulata (Olivier 1790) 211 Predator Dromochorus pruininus Casey 1897 5 Predator Tetracha virginica (Linnaeus 1767) 427 Predator Harpalinae Amara obesa (Say 1823) 1 Omnivore/Granivore* Anisodactylus merula (Casey 1918) 2 Omnivore/Granivore Anisodactylus rusticus (Say 1823) 2 Omnivore/Granivore Anisodactylus ovularis (casey 1914) 11 Omnivore* Calathus opacalus LeConte 1854 113 Omnivore* Chlaenius laticollis Say 1823 157 Omnivore* Chlaenius platyderus Chaudoir 1856 71 Omnivore Chlaenius tomentosus (Say 1823) 38 Predator Cyclotrachelus incisus (LeConte 1846) 29 Omnivore* Cyclotrachelus sodalis (LeConte 1848) 1099 Omnivore Cyclotrachelus substriatus (LeConte 1846) 126 Omnivore Cyclotrachelus torvus (LeConte 1863) 355 Omnivore Dicaelus elongatus Bonelli 1813 8 Predator Dicaelus furvus Dejean 1826 1 Predator* Dicaelus purpuratus Bonelli 1813 12 Predator Euryderus grossus (Say 1830) 15 Granivore* Galerita bicolor (Drury 1773) 1 Predator Geopinus incrassatus (Dejean 1829) 1 Omnivore Harpalus caliginosus (Fabricius 1775) 366 Omnivore/Granivore Harpalus pensylvanicus (DeGeer 1774) 27 Omnivore/Granivore Helluomorphoides ferrugineus (LeConte 1853) 2 Predator

Table 1 (Continued)

Helluomorphoides praeustus (Dejean 1825) 4 Predator* 1 Micrixys distincta (Haldeman 1852) 1 Likely mollucivore Panagaeus fasciatus Say 1823 7 Unknown Poecilus chalcites (Say 1823) 54 Omnivore Poecilus lucublandus (Say 1823) 10 Omnivore Pterostichus femoralis (Kirby 1837) 1 Predator Pterostichus permundus (Say 1830) 180 Omnivore Selenophorus granarius Dejean 1829 1 Omnivore/Granivore* Scaritinae Pasimachus californicus Chaudoir 1850 299 Predator Pasimachus elongatus LeConte 1846 424 Predator Pasimachus punctulatus Haldeman 1843 518 Predator Scarites lissopterus Chaudoir 1881 73 Predator* Scarites quadriceps Chaudoir 1843 2 Predator Scarites subterranaeus Fabricius 1775 36 Predator Scarites vicinus Chaudoir 1843 94 Predator Other Unidentified Carabidae (parts) 257 Total Number 5,098

Table 2. Most abundant ground beetle species in each region and their abundances.

Cyclotrachelus sodalis (*) was a dominant species found in all regions. Other species are unique

to their region.

West Number Central Number East Number

Pasimachus Cyclotrachelus Cyclotrachelus punctulatus 468 sodalis* 308 sodalis* 450

Cyclotrachelus sodalis* 341 Cyclotrachelus torvus 273 Tetracha virginica 377 Pasimachus Harpalus californicus 202 Pasimachus elongatus 164 caliginosus 336

Fig. 3. Rank abundance of all 48 restored grassland carabid beetle species from most to least abundant. Seven species (red box) represent 68% of all carabids collected across 108 sites in

2017 and 2018.

3.2 Carabid Abundance

Kruskal-Wallis tests showed no significant difference in overall carabid abundance between CP25 sites (high diversity initial seeding mix) or CP2 sites (low diversity initial seeding mix) or grazed versus ungrazed treatments (Table 3). However, there was a significant difference in beetle abundance between grazed and ungrazed sites in the East region (467 beetles on ungrazed versus 927 beetles on grazed sites) (χ2 = 4.53; df = 1; p = 0.03) (Fig. 4a). One study site, East 32, had an exceptionally high abundance of carabids with 548 beetles collected over both years (9.3% of the total carabids collected and 3.3 times as many beetles as on the site with the second highest abundance [East 34]). Including this site in analyses resulted in different conclusions than if it were excluded. Including East 32 resulted in a significant effect of

Conservation Practice on abundance (χ2 = 4.59; df = 1; p = 0.032) and a nonsignificant effect of grazing in the East (χ2 = 2.91; df = 1; p = 0.087). Because we are interested in the average effect of CP or grazing, including this site will lead to misinformed interpretation of these effects on an average CRP site. Because this was an extreme outlier, we excluded this site from statistical tests. Excluding this site allows for an understanding of the average effects of CP and grazing on the beetle community and not the effect that is driven by this one sample.

4a 4b

Fig. 4 (a-b). Mean carabid abundance by study Region (West, Central, East) and treatment (± standard error). Results of grazing treatment (Fig. 4a) show significant effects of carabid beetle abundance on grazed versus ungrazed sites in the East region (p = 0.033). Results of

Conservation Practice treatment (Fig. 4b) show no significant effects on carabid abundance between CP25 (high diversity initial seeding mix) versus CP2 (low diversity initial seeding mix).

3.3 Carabid Biomass

Much like the results for carabid beetle abundance, Kruskal-Wallis tests showed there was no overall effect of CP on beetle biomass (Table 3) and beetle biomass was higher on grazed sites in the East region (Grazed= 98.127 g, Ungrazed= 46.401 g) (χ2 = 4.49; df = 1; P = 0.033)

(Fig. 5a). Similar to the effects seen with carabid beetle abundance, inclusion of site East 32 resulted in a nonsignificant effect of grazing on biomass in the East (χ2 = 2.68; df = 1; p = 0.101) and a significant effect of CP on carabid biomass in the East ( χ2 = 4.41; df = 1; p = 0.035).

5a 5b

Fig. 5 (a-b). Mean carabid biomass by study Region (West, Central, East) and treatment (± standard error). Results of grazing treatment (Fig. 5a) show a significant effect on carabid beetle biomass between grazed and ungrazed sites in the East region (p = 0.033). Results of

Conservation Practice treatment (Fig. 5b) show no significant effects on carabid biomass between CP25 (high diversity initial seeding mix) and CP2 (low diversity initial seeding mix).

3.4 Carabid Diversity

Ground beetle diversity was similar across the Conservation Practice treatment and the grazing treatment (Table 3). There was a significant difference in carabid diversity between grazed and ungrazed sites in the East region (ESN= 3.56 and 2.69, respectively; χ2 = 6.47; df = 1; p = 0.01) (Fig. 6a). In this case, the inclusion of East 32 slightly increased the p-value but remained significant (χ2 = 4.31; df = 1; p = 0.03).

6a 6b

Fig. 6 (a-b). Effective species numbers by study Region (West, Central, East) and treatment (± standard error). Results of grazing treatment (Fig. 6a) show significant positive effect of grazing on carabid diversity on grazed versus ungrazed sites in the East region (p = 0.010). Results of

Conservation Practice treatment (Fig. 6b) show no significant effects of CP25 (high diversity initial seeding mix) or CP2 (low diversity initial seeding mix) on carabid diversity.

Table 3. Kruskal-Wallis results for overall carabid beetle community measures, treatments, and

year. There were no significant differences between these treatments (p ≥ 0.05).

Kruskal- Treatments Carabid Beetle Wallis Community Measure Output CP2/CP25 Grazed/Ungrazed Abundance Chi 2.14 0.959 df 1 1 p 0.142 0.327 Biomass Chi 1.319 2.964 df 1 1 p 0.250 0.085 Diversity Chi 2.307 1.557 df 1 1 p 0.128 0.212

3.5 Response of Carabid Community to Habitat

PERMANOVA results indicated that the assumption of homogeneity of dispersion was violated. The dispersion of data in the Central region was significantly different than the dispersion of the West and East regions (Table 4) so it is not possible to statistically test the differences in these communities. However, an NMS ordination can be used to visualize this community regardless of the dispersion of data. The ordination (Fig. 7) shows the relationship of

39 ground beetle species with study region and habitat factors (CV of plant structure, plant diversity, bare ground, precipitation) within community space. Nine species represented by single specimens were eliminated from the analysis (Table 1). The NMS provides visual representation of the beetle communities in the West and East regions and shows they are distinct from one another, but both share carabid species with the Central region. The strength of the correlation between the habitat factors and the beetle community is shown by the length of the biplots (or variable vectors, represented by blue arrows). Biplots (vectors) that are most strongly correlated with the beetle community are precipitation and plant diversity (tau= 0.608 and 0.278, respectively). Variation in physical plant structure and cover of bare ground were not strongly correlated with beetle communities (tau = -0.210 and -0.020, respectively). The stress of this three-region plot is 0.23. While stress levels above 0.20 are usually interpreted with caution

(Clark and Warwick 2001), the size of our sample allows us to accrue more stress but still be confident that the data is not randomly associated (Sturrock and Rocha 2000).

Fig. 7. Carabid community ordination plot showing the results of NMS analysis of the West,

Central, and East regions represented by red, gray, and purple polygons, respectively. Each study site in a region is represented by colored shapes (West- triangles, Central- circles, East- squares).

The position of each species in ordination space is determined by its abundance and the locations where it was found. Species are represented by black dots labeled with the first three letters of the genus and the first three letters of the specific epithet (see Table 1). To observe interactions, biplots (variable vectors) were overlain with community data. All biplots that were examined

(precipitation, bare ground, CV of plant structure, and plant diversity) were significant (r > .001)

The biplots show that precipitation and plant diversity were the strongest predictors of the carabid community. Stress is 0.23 (Appendix B)

Table 4. PERMANOVA results for the dispersion between regions. Combinations that include the Central region are significant (that is, the homogeneity of dispersion assumption is violated).

Groups T-Value P (perm) (Central, East) 3.12 0.004* (Central, West) 4.31 0.001* (East, West) 0.89 0.4

PERMANOVA results showed that the homogeneity of dispersion assumption was met for West and East regions (Table 4), and PERMANOVA with just these regions demonstrated a highly significant effect of region (Pseudo-F = 17.472; p = <0.001), a slightly significant effect of grazing (Pseudo-F = 1.878; p = 0.040) and a significant effect of the grazing x CP interaction

(Pseudo-F = 1.802; p = 0.041) (Table 5). Six possible grazing and CP combinations were tested, but the only significant difference was between the CP2 grazed and CP2 ungrazed sites

(PERMANOVA; T-value = 1.461; p = 0.048) (Table 6). To visually represent the findings of the two region PERMANOVA, we created an NMS using data from the East and West region (Fig.

8). Four additional species were eliminated because they were only found in the Central region;

Anisodactylus merula A.rusticus, Cicindela formosa, and Euryderus grossus. This NMS showed a distinct separation of the carabid communities which was expected as “Region” was highly significant in the two region PERMANOVA (Pseudo-F = 17.472; p = <0.001). The stress of the two region NMS is 0.20. Based on sample size as, the stress at which we are 99% certain the data comes from a random sample is 0.396 (Sturrock and Rocha 2000). Thus, a stress value of 0.20 means we can be fairly confident that our data can be interpreted in a meaningful way (Sturrock and Rocha 2000).

Table 5. PERMANOVA effects of study region (East or West), grazing, CP, and the

possible interactions with the carabid community. Significant results indicated with an asterisk

(*).

Source Pseudo-F p-value Region 17.472 0.001* Grazing 1.878 0.04* CP 0.525 0.912 Region x Grazing 1.213 0.239 Region x CP 0.878 0.57 Grazing x CP 1.802 0.041*

Table 6. PERMANOVA for the effects of grazing and CP combinations on the ground beetle community. The only significant difference is between CP2 grazed and ungrazed communities.

Significant results indicated with an asterisk (*). UG= ungrazed, G= grazed, 2= CP2, 25= CP25

Groups T-value p-value UG25, UG2 0.920 0.581 UG25, G25 0.837 0.739 UG25, G2 1.039 0.342 UG2, G25 1.005 0.423 UG2, G2 1.461 0.048* G25, G2 1.094 0.280

Fig. 8. NMS showing carabid community abundance in West (red) and East (purple) regions.

Study sites are represented by shapes (West- triangles, East- squares). The position of each species in ordination space represented by black dots labeled with the first three letters of the genus and the first three letters of the specific epithet (see Table 1). Biplots (variable vectors) have been overlain to observe all interactions. The biplots show that precipitation and plant diversity were still the strongest predictor of the carabid community. Stress is 0.20 (Appendix

C).

3.6 Species Turnover

The turnover metric (Fig. 9) shows that the carabid community shifts as much as 60% between precipitation isoclines. The calculation for species turnover includes a proportion of species that appear or disappear between adjacent precipitation isoclines to give the percent change in the community across the precipitation gradient. The East community is the most stable, having the lowest turnover rate (~40 %) between isoclines, while the Central region has the highest turnover at ~60 % (Fig. 9) coinciding with changes in habitat types (Fig. 13). In addition to the overall turnover, we examined the total turnover between our treatments. The turnover rate across the precipitation gradient for CP2 and CP25 sites was similar (χ2 = 0.471; df

= 1; p = 0.492) (Fig. 10). However, there was a significant difference in the species turnover between grazed versus ungrazed sites (χ2 = 8.70; df = 1; p = 0.003) (Fig. 11). Across the precipitation gradient, the turnover on grazed sites stayed relatively constant while ungrazed sites increased dramatically and then peak in the Central Region. There was a 20–30 % increase in the species turnover on ungrazed sites compared to grazed sites.

The abundances of Tetracha virginica and Harpalus caliginosus had a strong, positive relationship with precipitation, with numbers increasing from west to east. Three species of

Pasimachus collected in this study (P. punctulatus, P. californicus, and P. elongatus) were more abundant in lower precipitation sites (West). Pasimachus punctulatus had the strongest relationship with lower precipitation where, in the 45.7 cm precipitation isocline, an average of

15 beetles per site were collected compared to an average of zero found in the 109.2 cm isocline.

The distribution of Cyclotrachelus torvus was bell-shaped with the highest abundance centered around 68.5 cm of precipitation (Central region). Cyclotrachelus sodalis, the most abundant

species overall and one of the three most abundant species in each Region, had the highest average abundance across the entire precipitation gradient (45.7–109.2 cm) (Fig. 12).

Fig. 9. Community species turnover as it changes with precipitation (45.7–109.2 cm) across 650 km in Kansas grassland habitats. The proportion of community change is shown for each pair of isoclines it changes from west to east across the entire precipitation gradient. Species turnover increases rapidly from the West and turnover peaks in the Central region where there is a convergence of the eastern and western carabid communities associated with three different grassland habitats (mixed-, sand-, and tallgrass). The gap between the Central and East region is due to geographic constraints of our study. That area represents the Flint Hills ecoregion of

Kansas where there is little to no CRP land so we were not able to obtain sites in this portion of the precipitation gradient.

Fig. 10. Species turnover grouped by Conservation Practice treatment (CP2 in red or CP25 in blue) across 650 km in Kansas grassland habitats and precipitation ranging from 45.72 cm to

109.22 cm. The 95% confidence intervals (red CP2 or blue CP25 bands) may overlap

(represented in purple). CP2 and CP25 have similar turnover across the entire precipitation gradient (Kruskal-Wallis: (χ2 = 0.471; df = 1; p = 0.492).

Fig. 11. Species turnover grouped by grazing treatment (grazed or ungrazed; light blue and yellow, respectively) across 650 km in Kansas grassland habitats and precipitation ranging from

45 cm to 109 cm. The 95% confidence intervals (blue Grazed or yellow Ungrazed bands) may overlap (represented in green). While similar in the low (West) and high (East) precipitation areas (45.72–55 cm, 95–109.22 cm), turnover is significantly higher on ungrazed sites in areas of moderate precipitation (Central: 60–90 cm) (Kruskal-Wallis: χ2 = 8.70; df = 1; p = 0.003).

Highest species turnover corresponds with convergence of eastern and western beetle communities and is associated with three different grassland habitats (mixed-, sand-, and tallgrass).

Fig. 12 Average abundance per site of the seven dominant carabid species as it changes across

650 km and four Kansas grassland habitats (short-, mixed-, sand-, and tallgrass). This 650 km space can also be presented as a precipitation gradient ranging from 45.72–109.22 cm.

Fig. 13. Grassland habitat designation of each study site (short-, mixed-, sand-, and tallgrass), grouped by region across the precipitation gradient. Habitat designations based on county-level habitat type in

Kansas (USDA and NRCS 2012b, Chapman et al. 2001).

CHAPTER 4

DISCUSSION

4.1 Discussion

We examined carabid community response to moderate cattle grazing and CRP

Conservation Practice across 108 restored grasslands spanning 650 km and a precipitation gradient of 63.5 cm (139% change in precipitation from west to east). Our grazing treatment was aimed at creating a 50% reduction in plant biomass, and we examined differences in two

Conservation Practices – CP2, a low diversity initial seed mix primarily composed of grasses, and CP25, a higher diversity initial seed mix that contains forbs. Understanding the relationship of insects and these treatments is important for understanding how we can best manage restoration efforts

4.2 Response of Carabid Communities to Grazing and CP Treatments

Across all 108 sites, we found no overall effect of grazing or Conservation Practice on carabid beetle abundance, diversity, or biomass. Within our study, stocking rates were adjusted for plant production within each region (each aimed at a 50% reduction in plant biomass). For each site, stocking rates may not have been equivalent, but each targeted the same grazing pressure. However, when examining these community measures on a regional basis (36 sites each in the West, Central, and East) there was a significant, positive effect of grazing on the carabid beetle community measures in the East. In the East region, we observed a 454-beetle difference in abundance (grazed = 924, ungrazed = 470), 51.468 g difference in biomass (grazed

= 97.995 g, ungrazed 46.53 g) and a 42% difference in carabid diversity (ESN grazed = 3.70,

ESN ungrazed = 2.61) on grazed versus ungrazed sites. This significant, positive effect of

grazing was not observed in the West or Central regions. There were no regional effects associated with the Conservation Practice (CP) treatment. Thus, there is a regional, positive effect of grazing, with high precipitation sites (East) showing an increase in carabid beetle abundance, biomass, and diversity. These results, however, only represent the effect of moderate grazing over two growing seasons. Longer observation or longer grazing periods could yield similar results in other regions.

Similar to studies conducted in dairy pastures and birch-, pine- and boreal forests (Byers et al. 2000, Suominen et al. 2003, Melis et al. 2006), we predicted that grazing would increase carabid abundance. As a direct result of the increase in carabid abundance, we expected an increase in biomass. And, in fact, we found that 38% of the variation in carabid biomass was explained by carabid abundance (r2= 0.38). Grazing may increase carabid diversity (Batary et al.

2008), thus we expected positive effects across the precipitation gradient with increasing carabid diversity associated with increased precipitation. A study in Norway, however, showed that across three levels of grazing pressure, carabid abundance increased with grazing pressure, but diversity indices were similar across all sites (Melis et al. 2006). This corroborates our findings; there was no overall effect on carabid diversity between our grazing treatments (Grazing: p =

0.212) (Table 3), but grazing significantly increased carabid abundance in the East.

Grazing and precipitation have a combined effect on the plant community (Milchunas et al. 1989) which may mediate the response of the carabid community. The combined effect of precipitation and grazing is important to carabids and other insects because cattle preferentially graze certain plants (Reppert 1960) which modifies the plants available in a habitat. Grazing preferences change with plant availability (Towne et al. 2005), a factor heavily driven by precipitation (Cleland et al. 2013). Changes in the plant community can incite shifts in the insect

community as well. For example, in grasshopper communities in Mongolia, grazing had a significant positive effect on grasshopper diversity when coupled with study location. Study location, here, was used as a proxy for the changing plant community across a 200 mm precipitation gradient (Hao et al. 2015). So, it is plausible that the West and Central region plant communities responded differently than the East to the same grazing pressure due to different levels of precipitation. This differential response prevented strong changes in the carabid community in the West and Central regions.

Another habitat factor that is positively altered by grazing is structural heterogeneity of the plant community (Adler et al. 2001). Differences in structural heterogeneity led us to predict that CP25 (higher diversity initial seeding mix) sites would have higher abundance, diversity, and biomass of carabids than CP2 (lower diversity initial seeding mix) sites. The initial seeding mix for CP2 is designed to increase wildlife habitat with a focus on grassland birds (USDA/FSA

2015c); this mix is almost entirely native, warm season, perennial bunchgrasses (USDA/NRCS

2012a). CP25 is primarily used to restore declining habitats in order to provide resources to wildlife, especially pollinators (USDA/FSA 2015b). This seed mix is more diverse and includes many of the same plants as CP2 but with the addition of four to ten native forb species

(USDA/NRCS 2012b). Plant communities shape the physical structure of most habitats (Tews et al. 2004) so a more diverse plant community should provide more habitat structure. However, over time these high (CP25) and low diversity (CP2) plantings converge on equally diverse plant communities, similar to findings by Bach et al. (2012). This convergence could explain why we did not see a significant effect of CP on any of our carabid community measures; there may have been little to no difference between them.

Variation in the physical plant community structure has a positive effect on insect communities (Joern and Laws 2013). Carabid communities specifically, are more diverse on heterogeneous field margins than within crop fields (Werling and Gratton 2008). This pattern is seen with other invertebrate taxa as well. In Scottish grasslands, general arthropod diversity is higher in tussocks of mixed-height vegetation than in surrounding swards of short grass (Dennis

1997). Despite this pattern, structural heterogeneity was only a weak predictor of the carabid community in our study (Figs. 9–10) (tau= -0.210 and 0.015, for axis 1 and 2 with all three study regions included; tau= -0.227 and -0.012 for axis 1 and axis 2 for East and West regions only).

We found that plant diversity and precipitation were stronger predictors of the carabid community than plant structure.

The weak relationship of plant community structure with the carabid community in our study potentially can be explained by our conservative cattle stocking rates. Typically, grazing is not an allowable management strategy on CRP land despite it being a historically important disturbance in the Great Plains. For this study, we were granted special permission from the

Farm Service Agency (FSA) and Natural Resources Conservation Service (NRCS) to utilize grazing for two years in order to examine the effects of grazing on the ground beetle community.

The implemented grazing regiment complied with NRCS’ 528 Prescribed Grazing plan (NRCS

2009), and stocking rates (the number of grazing animals allowed to graze an area for a specified time) were set by NRCS Range Conservationists to accomplish a moderate level of grazing for

120–180 days. These rates were set on a site by site basis with “take half, leave half”, or 50% removal of plant biomass, as the target. After two years of grazing on half of our sites (54 sites), we found that the prescribed stocking rates did not remove the targeted amount of biomass in any study regions with an average reduction of only 24% (Fraser Watson, unpublished data). Insects,

including carabids, show a preference for more intensively grazed habitats (Kruess and

Tscharntke 2002, Melis et al. 2006). It is possible that our observed 24% reduction in plant biomass was not enough to cause a significant shift in the ground beetle community. Long-term studies address lag effects of different management strategies (Milchunas et al. 1994, Patton et al. 2007). However, the time needed to see a lag effect may be longer than the two seasons of

120–180 days of grazing that we implemented for this study.

4.3 Species Turnover in Carabid Communities

We found that the proportion of species changing (species turnover) between isoclines for the entire carabid beetle community was not constant across the 63.5 cm precipitation gradient and 108 sites (Fig. 11). In the Central region (precipitation isoclines 55.88–76.20 cm), species turnover was 20% higher than the West region (precipitation isoclines 45.72–55.88 cm) or the

East region (precipitation isoclines 86.36–109.22 cm) (Fig. 11). Across the precipitation gradient, species turnover was similar in CP2 (low diversity initial seed mix) and CP25 (higher diversity initial seed mix) sites (Fig. 10). However, grazed and ungrazed sites affected species turnover differentially. We observed a relatively constant species turnover on grazed sites while ungrazed sites had a 20–30% increase in turnover in areas of moderate precipitation (Fig.11;

55.88 – 76.2 cm precipitation). Assessing how species communities differ can be used to explain changes in habitats, available resources, and land use (Krauss et al. 2003). Species turnover calculations utilize the proportion of species shared between two areas and the proportion of species gained and lost to address shifts in communities (Koleff and Gaston 2002). Species that can coexist with one another have filtered down through multiple layers of large-scale exclusionary factors such as climate and a species’ ability to utilize a certain habitat. Biotic

filtering through competition, predation, and food availability is the last step in determining the species that are able to live together in one location (Velland 2016). Observations of communities over a large landscape (such as this study) allows us to examine species turnover and drivers such as competition or habitat changes (Cadotte and Tucker 2017). For carabid beetles, the driver of turnover appears to be habitat change.

Individual carabid species have associations with habitat types (Thiele 1977, Larochelle and Lariviere 2003), meaning that each habitat has its own unique species pool that it can support. Generalist species are able to survive in a broader habitat range than specialist species because they can utilize more than one habitat type (Jonsen and Fahrig 1997, Holt and Hochberg

2001). This can lead to higher species turnover at the junction of multiple habitats because each habitat has specialists as well as generalist species shared between them. We hypothesize this is why we see such high species turnover in the Central region. Our study sites span 650 km and four grassland habitats (short-, mixed-, sand-, and tallgrass) (Fig. 13). In the Central region, three of these habitat types are present (mixed-, sand-, and tallgrass) meaning there are three distinct carabid species pools based on habitats alone. The rapid increase and peak in species turnover observed on ungrazed sites in the Central region can be attributed to the inherent species turnover between changing habitats. In the West and East, the habitat types are relatively constant (short- and mixed grasslands in the West; tall grasslands in the East) so species turnover is lower because the sites within these regions support similar beetle species pools. Observed differences in species turnover between grazed and ungrazed sites may be due to grazing (a form of disturbance) that maintains species pools (Cadotte 2007). Across the precipitation gradient, grazed sites had relatively constant species turnover (Fig. 13). Grazing may act as a filter on species that are unable to adapt to disturbance by cattle, whereas carabid communities on

ungrazed sites (sites lacking disturbance pressures) were able to shift with changes in habitat types.

Species turnover tells us about change in communities across the precipitation gradient but it does not provide a gauge of diversity between sites. Out of 108 sites, those with the highest and lowest diversitieswere CP25 sites in neighboring precipitation isoclines: East 32 with

ESN=6.03 and East 31 with ESN=1.12 (respectively). We expected CP25 sites to be overall more diverse than CP2 sites so the exceptionally low diversity on East 31is not consistent with plant community predictions (Siemann et al. 1998, Knops et al. 1999). In addition East 31 is a grazed site. Grazing is expected to increase diversity, so the low diversity associated with East

31 is not consistent with that prediction (Melis et al. 2006). Both sites occur in the East region where we observe the highest levels of annual precipitation. The precipitation isoclines for these sites (E31: 106.68 cm and E32: 109.22 cm) are the two isoclines with the highest average precipitation in this study. With so many similarities, we would predict that these two East sites that have the highest and lowest carabid diversities would be more similar than observed.

One possible explanation for this discrepency is land history. All 108 of our sites have unique histories comprised of different seeding dates, management strategies, and historical land use. This information is often hard to acquire and may be lost when ownership changes or details are forgotten over time. While we do not have complete histories for our sites, we know that the least diverse and most diverse sites for carabid beetles (East region site 31 and East region site

32) were seeded in 2007 and 1997, respectively. Thus, our most diverse site had a decade longer to accrue carabids, although this is likely not the only reason for this striking contrast.

Differences in management such as burning, disking, or herbicide application could explain the

drastically different diversities on sites that are otherwise similar in terms of treatments and amount of precipitation.

4.4 Carabids as Bioindicators and Comparison with European Ecology

Carabid beetles are widely used as bioindicators of management and disturbance throughout Europe because of a long history of Carabidology that created an excellent understanding of their life histories and diverse ecological roles (Kotze et al. 2011, Dave

Kavanaugh, personal communication June 6 2019). Carabid beetle species and communities found in hemlock forests and grasslands in Scotland (Dennis et al. 1997, Dennis et al. 1998,

Latty et al. 2006), temporary wetlands in Germany (Brose 2003a), Hungarian grasslands (Batary et al. 2008), and Russian taiga (Ermakov 2003) are useful bioindicators of land management

(Latty et al. 2006, Batary 2008), plant community traits (Brose 2003a), and anthropogenic pollution (Ermakov 2003). In North America, carabids are surveyed to compare agricultural systems (Byers et al. 2000), effects of burning (Cook and Holt 2006, Kral et al. 2017), effects of grazing (Byers et al. 2000), and habitats (Willand and Wodika 2011), but the interactions of management practices and the resulting change in the carabid community is less studied. Carabid species and communities are not used as bioindicators of successful management strategies in the

US due to taxonomic constraints and lack of life history information.

The carabid fauna of Europe is virtually 100% known and has been for more than 100 years (Stephens 1829, Personal communication Dave Kavanaugh June 6 2019). More importantly, the ecological requirements of most European species are well-known, thus allowing them to be used as monitors of environmental quality, disturbance, and habitat change.

In contrast, the North American fauna is relatively unknown. New species are described

annually, and even more crucially, the life histories and ecological requirements of North

American species remain largely unknown (Dave Kavanaugh Personal communication June 6

2019). Until this knowledge is developed, use of carabid beetles as bioindicators (the types of studies routinely conducted by Europeans) is less feasible in the US.

In this study, we utilized 108 sites across a 650 km geographic area that are organized across a 63.5 cm west to east precipitation gradient and including four grassland habitats.

Because of the systematic nature that data were collected, carabid community data are easily comparable between sites across this broad continuum. The scale of our study is also unprecedented, both in the US and in Europe. In comparison, carabid studies in Europe used from three (Melis et al 2006) to 30 (Brose 2003b) study sites; in the United States, carabid studies used from 12 (Barber et al. 2017) to 44 (Byers et al. 2000). The wide range of sample sizes is important when comparing results between studies. Broad generalizations about communities are best informed based on dense and systematic sampling such as employed in our study.

4.5 Significance

Carabid beetles are used as a bioindicators of habitat type, management success (haying, grazing, etc.), and disturbance in European ecosystems (Rainio and Niemela 2003, Melis et al.

2006, Garcia-Tejero et al. 2013), but there is a lack of research that examines carabid community responses in North America. By addressing this gap within US grassland habitats, we provide a baseline comparison for future North American carabid ecology studies. Our study provides foundational data for comparison of un-invaded carabid beetle communities in Great Plains grasslands systems. Pterostichus melanarius, an invasive carabid species that is spreading

rapidly in the US, dominates and restructures invertebrate communities (Carcamo et al. 1995,

Hartley et al. 2007). Originally from Europe, this invasive species currently is distributed across the northern US and Canada and southward into neighboring states such as Iowa and Colorado

(Bousquet 2012); it is predicted to expand its range into Kansas (Busch 2016). It is associated with disturbed habitats (Hartley et al. 2007), thus its likely expansion into restored CRP grasslands will lead to shifts from native carabid communities.

Our results indicate that grazing can positively influences carabid abundance, diversity, and biomass in tallgrass habitats and did not negatively impact carabids in other habitats. In addition, Conservation Practice (CP2 or CP25) did not have any significant effects on the carabid community. These results assist in informing CRP management strategies that build more diverse and abundant insect and carabid beetle communities. One of the primary goals of CRP is to increase habitat for wildlife (USDA 2015a); game animals often rely on the invertebrates for food. When management benefits the invertebrate community, the organisms that use the invertebrates for food also profit. Managing CRP land in ways that benefit carabid beetles will aid game birds like pheasant, grouse, and quail as well as non-game wildlife such as small mammals, herptiles, and bats that use beetles as food (Murdoch 1966, Obrtel 1973, Larochelle

1975, Hill 1985, Arlettaz 1996, Smith et al. 2015) and will assist in ecosystem resiliency.

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APPENDICES

APPENDIX A CARABID AND SITE DATA

APPENDIX B PC-ORD 6 OUTPUT FOR 3 REGION NMS AND CORRELATIONS WITH AXES

********************* Nonmetric Multidimensional Scaling ********************* PC-ORD, 6.0 7 Jun 2019, 14:42:17

All Sites Excluding E32 Ordination of plots in vars space. 106 plots 39 vars The following options were selected: ANALYSIS OPTIONS 1. SORENSEN = Distance measure 2. 2 = Number of axes (max. = 6) 3. 500 = Maximum number of iterations 4. RANDOM = Starting coordinates (random or from file) 5. 1 = Reduction in dimensionality at each cycle 6. NO PENALTY = Tie handling (Strategy 1 does not penalize ties with unequal ordination distance, while strategy 2 does penalize.) 7. 0.20 = Step length (rate of movement toward minimum stress) 8. USE TIME = Random number seeds (use time vs. user-supplied) 9. 999 = Number of runs with real data 10. 999 = Number of runs with randomized data 11. NO = Autopilot 12. 0.000010 = Stability criterion, standard deviations in stress over last 15 iterations. OUTPUT OPTIONS 14. NO = Write distance matrix? 15. NO = Write starting coordinates? 16. YES = List stress, etc. for each iteration? 17. YES = Plot stress vs. iteration? 18. NO = Plot distance vs. dissimilarity? 19. NO = Write final configuration? 20. VARIMAX = Write varimax-rotated, principal axes, or unrotated scores for graph? 21. NO = Write run log? 22. YES = Write weighted-average scores for vars ? ------

1764 = Seed for random number generator.

**************** 2-dimensional solution ****************

List of stress, step length, and magnitude of the gradient vector at each iteration.

Step Stress Instability StepLength Mag(G) 1 42.40211 0.20000 0.000179189 2 39.95336 0.20000 0.000096564 3 38.78129 0.17301 0.000052384 4 38.33630 0.16418 0.000056948 5 38.16449 0.14352 0.000071830 6 37.78824 0.11688 0.000047033 7 37.49646 0.10754 0.000053185 8 37.16190 0.09693 0.000051891 9 36.80370 0.08479 0.000029425 10 36.58228 0.01648711 0.07407 0.000023592 11 36.41840 0.01666895 0.06511 0.000025804 12 36.24727 0.01682227 0.05663 0.000017411 13 36.10454 0.01694217 0.04913 0.000015017 14 35.96639 0.01704213 0.04488 0.000014380 15 35.81671 0.01714139 0.04587 0.000014576 16 35.63141 0.01207040 0.05391 0.000019855 17 35.38517 0.01023733 0.06322 0.000025544 18 35.10721 0.00969394 0.06944 0.000028953 19 34.84259 0.00943778 0.06786 0.000034370 20 34.54716 0.00915604 0.06369 0.000035273 21 34.20264 0.00918646 0.06524 0.000037632 22 33.73043 0.00958401 0.08700 0.000047209 23 33.18020 0.01046511 0.10399 0.000075323 24 32.57586 0.01183129 0.10188 0.000086578 25 31.95884 0.01345161 0.09526 0.000082124 26 31.35482 0.01516356 0.09454 0.000077587 27 30.70629 0.01696181 0.09680 0.000090893 28 30.03033 0.01878370 0.10095 0.000095551 29 29.41114 0.02047953 0.10178 0.000093548 30 28.88876 0.02189302 0.10072 0.000099967 31 28.51299 0.02286988 0.09872 0.000100681 32 28.19684 0.02341861 0.09350 0.000094548 33 27.94483 0.02352367 0.08375 0.000079986 34 27.68566 0.02318911 0.06882 0.000049957 35 27.50377 0.02236771 0.06133 0.000045565 36 27.33142 0.02109485 0.05412 0.000037985 37 27.17855 0.01950531 0.04746 0.000026585 38 27.06211 0.01768995 0.04122 0.000021394 39 26.95187 0.01575777 0.03549 0.000024044 40 26.82590 0.01379160 0.03074 0.000018747 41 26.69049 0.01183621 0.03832 0.000012860 42 26.58150 0.01002489 0.04866 0.000016993 43 26.53939 0.00847828 0.04116 0.000025324 44 26.46959 0.00727115 0.03094 0.000012703 45 26.39139 0.00637895 0.02555 0.000023064

46 26.27586 0.00569223 0.02468 0.000021726 47 26.10397 0.00523720 0.05274 0.000011023 48 26.00239 0.00488954 0.04385 0.000015896 49 25.90588 0.00468589 0.03756 0.000023496 50 25.77211 0.00458278 0.03203 0.000027831 51 25.58349 0.00464117 0.03503 0.000039523 52 25.26352 0.00500496 0.05325 0.000043154 53 24.78361 0.00583410 0.08535 0.000035959 54 24.57723 0.00659579 0.10156 0.000092137 55 24.57483 0.00705536 0.08919 0.000137553 56 24.26213 0.00766485 0.04173 0.000014680 57 24.15507 0.00812243 0.05091 0.000021531 58 24.13142 0.00830811 0.04508 0.000033400 59 24.04916 0.00832772 0.02905 0.000007355 60 23.99895 0.00816113 0.02423 0.000012416 61 23.94165 0.00785556 0.02154 0.000010484 62 23.88478 0.00747999 0.02024 0.000008168 63 23.82073 0.00695915 0.02951 0.000007877 64 23.77367 0.00624760 0.02810 0.000014232 65 23.72206 0.00535913 0.02313 0.000010795 66 23.67362 0.00433248 0.01907 0.000008120 67 23.62574 0.00341644 0.01922 0.000007557 68 23.56767 0.00303659 0.02461 0.000007825 69 23.51863 0.00279630 0.02876 0.000010292 70 23.48869 0.00236223 0.02364 0.000009799 71 23.46068 0.00225296 0.01895 0.000006408 72 23.43462 0.00218509 0.01545 0.000005384 73 23.40936 0.00206039 0.01305 0.000004686 74 23.37892 0.00196026 0.01516 0.000004482 75 23.33939 0.00185776 0.02476 0.000004951 76 23.32565 0.00174019 0.02217 0.000008738 77 23.30901 0.00161557 0.01694 0.000003551 78 23.30037 0.00149358 0.01049 0.000001572 79 23.29551 0.00135483 0.00824 0.000001156 80 23.29207 0.00121012 0.00663 0.000000746 81 23.28962 0.00106033 0.00533 0.000000492 82 23.28786 0.00091021 0.00428 0.000000328 83 23.28654 0.00078504 0.00345 0.000000229 84 23.28549 0.00068183 0.00290 0.000000172 85 23.28459 0.00057850 0.00273 0.000000147 86 23.28375 0.00047521 0.00281 0.000000146 87 23.28292 0.00037088 0.00277 0.000000156 88 23.28212 0.00026427 0.00255 0.000000163 89 23.28121 0.00016881 0.00247 0.000000181 90 23.27985 0.00012398 0.00315 0.000000248 91 23.27740 0.00000000

736 = Number of tie blocks in dissimilarity matrix. 4780 = Number of elements involved in ties. 5565 = Total number of elements in dissimilarity matrix. 85.894 = Percentage of elements involved in ties.

23.27740 = final stress for 2-dimensional solution 0.00000 = final instability 91 = number of iterations

STRESS IN RELATION TO DIMENSIONALITY (Number of Axes) ------Stress in real data Stress in randomized data 999 run(s) Monte Carlo test, 999 runs ------Axes Minimum Mean Maximum Minimum Mean Maximum p ------1 36.735 42.046 47.664 0.000 45.288 54.815 0.0880 2 21.765 22.503 27.699 0.000 28.477 32.064 0.0440 ------p = proportion of randomized runs with stress < or = observed stress i.e., p = (1 + no. permutations <= observed)/(1 + no. permutations)

Conclusion: a 2-dimensional solution is recommended.

****************************** Output from Graph ****************************** PC-ORD 6.0 7 Jun 2019, 14:50

All Sites Excluding E32

Pearson and Kendall Correlations with Ordination Axes N= 106

Axis: 1 2 3 r r-sq tau r r-sq tau r r-sq tau

Precip .818 .669 .608 -.108 .012 -.175 Bare Gro -.040 .002 -.020 .100 .010 .081 Structur -.298 .089 -.210 .025 .001 .015 Plant Di .443 .196 .278 -.056 .003 -.053

***************************** Operation completed ***************************** 7 Jun 2019, 14:50

APPENDIX C PC-ORD 6 OUTPUT FOR 2 REGION NMS AND CORRELATIONS WITH AXES

********************* Nonmetric Multidimensional Scaling ********************* PC-ORD, 6.0 7 Jun 2019, 14:35:28 East and West Excluding E32 Ordination of plots in vars space. 71 plots 35 vars The following options were selected: ANALYSIS OPTIONS 1. SORENSEN = Distance measure 2. 2 = Number of axes (max. = 6) 3. 500 = Maximum number of iterations 4. RANDOM = Starting coordinates (random or from file) 5. 1 = Reduction in dimensionality at each cycle 6. NO PENALTY = Tie handling (Strategy 1 does not penalize ties with unequal ordination distance, while strategy 2 does penalize.) 7. 0.20 = Step length (rate of movement toward minimum stress) 8. USE TIME = Random number seeds (use time vs. user-supplied) 9. 999 = Number of runs with real data 10. 999 = Number of runs with randomized data 11. NO = Autopilot 12. 0.000010 = Stability criterion, standard deviations in stress over last 15 iterations. OUTPUT OPTIONS 14. NO = Write distance matrix? 15. NO = Write starting coordinates? 16. YES = List stress, etc. for each iteration? 17. YES = Plot stress vs. iteration? 18. NO = Plot distance vs. dissimilarity? 19. NO = Write final configuration? 20. VARIMAX = Write varimax-rotated, principal axes, or unrotated scores for graph? 21. NO = Write run log? 22. YES = Write weighted-average scores for vars ? ------2895 = Seed for random number generator. **************** 2-dimensional solution **************** List of stress, step length, and magnitude of the gradient vector at each iteration.

Step Stress Instability StepLength Mag(G) 1 42.18191 0.20000 0.000264191 2 39.75753 0.20000 0.000124639 3 38.92274 0.17296 0.000073373 4 38.54408 0.16141 0.000086804

5 38.22638 0.14473 0.000087489 6 37.88004 0.12852 0.000088111 7 37.55536 0.11593 0.000072314 8 37.28010 0.10051 0.000084320 9 36.92250 0.08651 0.000070746 10 36.55076 0.01559080 0.07592 0.000061746 11 36.17566 0.01616206 0.07120 0.000060462 12 35.70270 0.01692288 0.08173 0.000076941 13 35.03581 0.01804700 0.10608 0.000109181 14 34.27844 0.01955286 0.11638 0.000173935 15 33.29135 0.02161230 0.11547 0.000242536 16 32.04603 0.02087604 0.12214 0.000207953 17 30.80364 0.02347521 0.15524 0.000266995 18 30.06021 0.02623462 0.16312 0.000456886 19 29.40904 0.02862323 0.16813 0.000548304 20 28.40837 0.03104366 0.14835 0.000409366 21 27.57605 0.03322791 0.14518 0.000448083 22 26.81415 0.03503964 0.14500 0.000491954 23 26.03366 0.03642154 0.13750 0.000425031 24 25.54225 0.03710021 0.12908 0.000405672 25 25.00809 0.03713689 0.10031 0.000208506 26 24.72738 0.03626185 0.08381 0.000198415 27 24.40674 0.03459951 0.07235 0.000155283 28 24.07846 0.03236026 0.06750 0.000149826 29 23.75171 0.02964297 0.06338 0.000110160 30 23.51537 0.02671635 0.05901 0.000102034 31 23.32485 0.02405000 0.05373 0.000086604 32 23.18018 0.02179897 0.04833 0.000069863 33 23.07360 0.01936064 0.04196 0.000050845 34 23.00067 0.01660011 0.03514 0.000034943 35 22.94916 0.01415964 0.02802 0.000022159 36 22.91429 0.01193207 0.02236 0.000014759 37 22.88793 0.00995340 0.01812 0.000010695 38 22.86489 0.00843224 0.01487 0.000008050 39 22.84499 0.00703270 0.01218 0.000005519 40 22.82817 0.00593704 0.01090 0.000004436 41 22.81039 0.00480378 0.01208 0.000005005 42 22.78947 0.00373490 0.01329 0.000007155 43 22.76447 0.00281370 0.01340 0.000008064 44 22.73432 0.00214771 0.01405 0.000010050 45 22.69552 0.00166864 0.01727 0.000008075 46 22.66364 0.00136195 0.02054 0.000009184 47 22.64244 0.00118623 0.01705 0.000009353 48 22.62433 0.00109972 0.01384 0.000006559 49 22.60823 0.00105961 0.01124 0.000005483 50 22.59273 0.00104150 0.00949 0.000004782

51 22.57317 0.00103263 0.01170 0.000004928 52 22.54214 0.00103654 0.01755 0.000009935 53 22.49528 0.00106665 0.01939 0.000018497 54 22.42190 0.00115078 0.02066 0.000039918 55 22.28820 0.00136862 0.02643 0.000038718 56 22.06388 0.00184658 0.05211 0.000024769 57 22.03861 0.00216498 0.05190 0.000069329 58 21.98515 0.00242702 0.03791 0.000038090 59 21.93949 0.00263880 0.01495 0.000003577 60 21.91813 0.00278947 0.01409 0.000003745 61 21.88244 0.00290066 0.02666 0.000009830 62 21.86226 0.00295289 0.02331 0.000023601 63 21.82315 0.00296243 0.01873 0.000013512 64 21.78480 0.00292205 0.01524 0.000014629 65 21.73309 0.00283414 0.01650 0.000018173 66 21.62519 0.00274198 0.02968 0.000025394 67 21.46084 0.00271392 0.05434 0.000049823 68 21.42741 0.00260110 0.04561 0.000088146 69 21.32606 0.00252449 0.03056 0.000016624 70 21.29309 0.00248947 0.02284 0.000020594 71 21.26260 0.00260336 0.01907 0.000012817 72 21.23995 0.00265384 0.01560 0.000010893 73 21.21705 0.00267474 0.01279 0.000006938 74 21.19974 0.00266013 0.01084 0.000004207 75 21.18551 0.00258717 0.01095 0.000005994 76 21.16893 0.00247198 0.01042 0.000004898 77 21.15364 0.00229163 0.01044 0.000003467 78 21.14330 0.00205717 0.01170 0.000002786 79 21.14107 0.00174985 0.00946 0.000004062 80 21.13636 0.00136854 0.00606 0.000000779 81 21.13400 0.00100880 0.00480 0.000001093 82 21.13120 0.00083114 0.00395 0.000001500 83 21.12675 0.00061995 0.00381 0.000002727 84 21.11443 0.00052993 0.00802 0.000003670 85 21.08789 0.00047313 0.02040 0.000004329 86 21.05910 0.00045915 0.02085 0.000009188 87 21.03495 0.00047374 0.01736 0.000006488 88 21.02585 0.00049421 0.01404 0.000006373 89 21.01922 0.00051179 0.01030 0.000003396 90 21.01487 0.00052265 0.00650 0.000001334 91 21.01227 0.00052982 0.00513 0.000001075 92 21.01014 0.00053400 0.00413 0.000000746 93 21.00837 0.00053247 0.00335 0.000000569 94 21.00664 0.00051900 0.00328 0.000000506 95 21.00452 0.00049585 0.00418 0.000000592 96 21.00236 0.00045930 0.00468 0.000000845

97 21.00040 0.00040641 0.00394 0.000000761 98 20.99859 0.00033301 0.00325 0.000000574 99 20.99681 0.00024352 0.00334 0.000000457 100 20.99490 0.00016564 0.00456 0.000000449 101 20.99389 0.00011605 0.00410 0.000000684 102 20.99291 0.00000000

416 = Number of tie blocks in dissimilarity matrix. 1891 = Number of elements involved in ties. 2485 = Total number of elements in dissimilarity matrix. 76.097 = Percentage of elements involved in ties.

20.99291 = final stress for 2-dimensional solution 0.00000 = final instability 102 = number of iterations ------Stress in real data Stress in randomized data 999 run(s) Monte Carlo test, 999 runs ------Axes Minimum Mean Maximum Minimum Mean Maximum p ------1 32.641 38.882 53.767 0.000 45.964 56.032 0.0530 2 19.393 21.367 24.014 0.000 27.095 31.789 0.0310 ------p = proportion of randomized runs with stress < or = observed stress i.e., p = (1 + no. permutations <= observed)/(1 + no. permutations)

Conclusion: a 2-dimensional solution is recommended. ****************************** Output from Graph ****************************** PC-ORD 6.0 7 Jun 2019, 14:40

East and West Excluding E32

Pearson and Kendall Correlations with Ordination Axes N= 71

Axis: 1 2 3 r r-sq tau r r-sq tau r r-sq tau

Precip .862 .742 .548 -.299 .090 -.306 Bare Gro -.004 .000 .030 .096 .009 .055 Structur -.301 .090 -.227 -.040 .002 -.012 Plant Di .551 .303 .353 -.152 .023 -.107

***************************** Operation completed ***************************** 7 Jun 2019, 14:40

APPENDIX D TABLES SHOWING NUMBER OF SITES IN EACH TREATMENT FOR ALL REGIONS

Region Number of CP2 Sites Number of CP25 Sites

West 18 18

Central 18 18

East 14 22

Region Number of Grazed Sites Number of Ungrazed Sites

West 18 18

Central 17 19

East 18 18