ABSTRACT

DOES EXCLUDING A TOP MESOPREDATOR INDUCE A TROPHIC CASCADE? EFFECTS AND DIET OF AN OMNIVOROUS PREDATOR IN A RESTORED PRAIRIE

Kirstie Savage, M.S. Department of Biological Sciences Northern Illinois University, 2018 Dr. Holly P Jones, Thesis Director

Predators can shape prey communities through consumptive and non-consumptive interactions. By inducing fear of predation, predators can cause changes to prey foraging behavior that can negatively impact prey reproduction and body-condition. Furthermore, both consumptive (density-mediated) and non-consumptive (trait-mediated) predator-prey interactions can cascade down the food web to have indirect impacts of vegetation (trophic cascade). As we continue to lose apex predators and undergo land change, understanding how the new top dogs shape prey communities and restorations is increasingly important. Through utilizing predator- exclosure fences, mark-recapture analysis, plant biomass and stable isotopes we evaluated the role of coyotes as top mesopredator in a restored grassland. Results indicate that coyotes do not have strong impacts on small mammal population dynamics or cause non-consumptive impacts to small mammal reproduction. In addition, they do not induce a trophic cascade in a restored prairie. However, coyotes appear to be having a positive impact on Peromyscus spp. body condition. I also found that coyotes eat C4 grasses in the highest proportion, although I suspect this to be driven by corn and corn product consumption, and that they have both individualistic

and seasonal diets. The lack of an impact on prairie communities and a diet comprised largely of

C4 grasses indicate that coyotes may not be filling the role of apex predator and exerting strong top-down control in restored prairie communities. However, given the short time frame of my study and small sample sizes I obtained, more research may be necessary to conclusively determine if coyotes function as an apex mesopredator in restored prairie systems.

NORTHERN ILLINOIS UNIVERSITY DEKALB, ILLINOIS

MAY 2018

DOES EXCLUDING A TOP MESOPREDATOR INDUCE A TROPHIC CASCADE:

EFFECTS AND DIET OF AN OMNIVOROUS PREDATOR

IN A RESTORED PRAIRIE

BY

KIRSTIE SAVAGE

©2018 Kirstie Savage

A THESIS SUBMITTED TO THE GRADUATE SCHOOL

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE

MASTER OF SCIENCE

DEPARTMENT OF BIOLOGICAL SCIENCES

Thesis Director: Holly P. Jones

ACKNOWLEDGEMENTS

I would like to acknowledge The Nature Conservancy and Nachusa Grasslands for allowing me to conduct research and have fences built in their renowned tallgrass prairie. I would also like to acknowledge the organizations that funded my graduate research, Friends of

Nachusa Grasslands, Prairie Biotic Research Inc., and the Northern Illinois University Graduate

School. Thank you to Bill Kleiman, Cody Considine, the seasonal crew, stewards and volunteers at Nachusa for all their help with my project and for making Nachusa the remarkable place that it is. I am so grateful to the ReFuGE team, Jones lab, field assistants, and volunteers for all the time they spent helping and supporting me both in the field and lab. Their friendships have been invaluable through this process. I would like to thank my committee members, Nick Barber and

Rich King, for all their assistance, guidance, and support with fieldwork, statistics, classes,

IACUC procedures and my overall success as a graduate student. I am overwhelmingly grateful to my family for their patience, sacrifice and assistance throughout this whole process. Nothing was better than having you two in the field with me. Finally, a very special thank you to my advisor, Dr. Holly Jones, whose understanding of who I am, what I needed, and brilliance as an advisor and woman in science made this all possible.

DEDICATION

This thesis and all the sacrifices and hard work that were necessary to accomplish it are

dedicated to my husband, Danny, daughter, Maple, and son to be. You’re my world.

TABLE OF CONTENTS

Page

LIST OF TABLES ...... v

LIST OF FIGURES ...... vi

LIST OF APPENDICES ...... viii

Chapter

1. INTRODUCTION ………………………...... 1

2. PRAIRIE COMMUNTITIES ………………………...... 5

Methods ………………………...... 9

Results ………………………...... 21

Discussion ………………………...... 39

3. COYOTE DIET …………….………………………...... 45

Methods ………………………...... 50

Results ………………………...... 68

Discussion ………………………...... 73

4. DISCUSSION ...…………….………………………...... 80

LITERATURE CITED …….………………………...... 83

LIST OF TABLES

Table Page

1: Small mammal sampling sessions by year ...... 13

2: Target species and identifying information ...... 14

3: Non-target species and identifying information ...... 15

4: Unique captured and recaptured small mammals by trapping session; species ordered by frequency of capture. I define unique as each individual caught per trapping session; this could include some overlaps from different trapping sessions (i.e., across months) ……………………………………………………………………………...... 24

5: Total number of unique species captured per site (X after site name indicates exclosure grids) for 2017 ...... 25

6: Total number of unique species captured per site (X after site name indicates exclosure grids) for 2016...... 26

7: Summary statistics for linear mixed effects models……………………………….. 26

8: Description of coyote samples ...... 51

9: Coyote prey sample methods of collection, preparation, and groupings

for analysis ...... 54

10: Models run for coyote diet analysis ……………………...... 65

11: Individual coyote diet broken down by sources. Values represent the median

proportion ……………………...... 66

12: Mean and standard deviation δ15N and δ13C values for grouped sources used for

seasonal coyote diet analysis ...... 66

LIST OF FIGURES

Figures Page

1: Map of trapping grid locations at Nachusa Grasslands ...... 11

2: Trapping grid set up, traps are located on black dots spaced 15m apart ...... 12

3: Bivariate plot of all individuals used in body condition measurement ...... 20

4: Small mammal abundance by treatment/Species ……………………...... 27

5: Small mammal reproduction by treatment ……..……………………...... 28

6: Peromyscus abundance by treatment/Microtus abundance by treatment …………. 29

7: Peromyscus reproduction by treatment /Microtus reproduction by treatment ……. 30

8: Small mammal diversity by treatment ……………………………………………. 31

9: Small mammal community composition measured through NMDS ………..……. 32

10: Body condition measured using the scaled mass index …………………………. 33

11: Reproductive body condition measured using the scaled mass index …………... 34

12: Time series comparing BCI between treatments through trapping sessions in 2017 35

13: Time series comparing difference in new captures for Microtus ochragaster, Peromscus maniculatus, and Peromyscus leucopus for control (left) and exclosure (right) treatments …………………………………………………………………………..... 36

14: Forb/Grass aboveground biomass (g) by treatment …..…………………………. 37

15: a) Plotted sources to visualize for grouping b) Dietary items grouped for seasonal analysis, black dots represent consumers 1-3 segmented values c) Dietary items groups for individual dietary analysis ……………………………………………………….. 63

16: Proportion of dietary items for individual coyotes 1-4 showing differences in diet among individuals …………………………………………………………………… 69 vii

17: Proportion of dietary items through time for coyote 1 showing individual seasonal diet ………………………………………………………………………………….... 70

18: Boxplots comparing δ15N and δ13C values by gender for visual inspection of potential differences …………………………………………………………………………… 71

CHAPTER 1

INTRODUCTION

Top-down control is an important limitation in ecological systems. Apex predators, being primarily carnivores, are known to exert top-down control, and their impacts can cascade down a food chain (Shurin et al. 2002). Through both consumption and instilling the fear of consumption in prey, predators play an important role in shaping community composition by limiting prey densities and both directly (i.e., changes to prey foraging) and indirectly (i.e., stress induced changes to prey foraging) affecting prey behavior (Schmitz et al. 1997; Hawlena and Schmitz

2010). In a, the enemy of my enemy is my friend sort of manner, predators exert positive indirect effects on vegetation or producers. This is commonly referred to as a trophic cascade (Shurin et al. 2002). A classic example of which can be seen in the Aleutian Archipelago and southeast

Alaska, where declines in sea otter populations led to increases in sea urchin herbivores and consequently a dramatic decline in kelp forests (Estes and Duggins 1995).

Trophic cascades can extend beyond the classic model (predator-herbivore-vegetation;

Hairston et al. 1960; Schmitz et al. 2000; Ripple et al. 2014) to include a second predator. In these models, the first predator indirectly influences the prey of the second predator (Oksanen et al. 1981; Flagel et al. 2016). This occurs through intraguild predation or exclusion (density- mediated interactions; Polis and Holt 1992; Holt and Polis 1997) and defensive group formation, increased vigilance, or avoidance behaviors driven by the first predator (trait-mediated interactions; Palomares and Caro 1999). For example, in a wolf-coyote-hare system it was found that coyotes visited high wolf areas only half as often as low wolf areas, and consequently there 2 was three times as much browsing damage by hares in the high wolf areas, due to the reduced top-down control of coyotes (Flagel et al. 2016). Thus, apex predators are capable of limiting both their prey and smaller secondary predators, commonly referred to as mesopredators

(Bestion et al. 2015).

Apex predators are currently declining across the globe due to human persecution and activities. As a result, many apex predators have suffered greatly. This has manifested variably, from considerable range loss to total eradication (Gittleman et al. 2001). In the absence of apex predators, mesopredators have been released from predation and competition, and their populations allowed to flourish (Estes 2011; Ripple et al. 2014). This phenomenon has been observed across ecosystems (both terrestrial and aquatic), spatial scales, and continents, with the exception of Antarctica, and is commonly referred to as “mesopredator release” (Prugh et al.

2009). The release of mesopredators from apex predator control has many implications for biodiversity and ecosystems. For example, mesopredators are more strongly omnivorous and, depending on the mesopredator, can consume a considerable amount of plant tissue in addition to animal prey. One study of predator type and diversity on the strength of trophic cascades found that although pinfish, an omnivorous predator, effectively limited herbivores, plant biomass did not differ between the pinfish and no-predator treatment (Bruno and Connor 2005). This lack of effect was due to the direct consumption of the vegetation by the pinfish (Bruno and Connor

2005), breaking down the classic “enemy of my enemy is my friend” trophic cascade signature.

Furthermore, mesopredators are better adapted to living in the world today. They are better able to survive in fragmented or urban ecosystems and smaller habitat patches, and they opportunistically gain food through trash and crops (Crooks and Soulé 1999, Fuller et al. 2010).

3 In an increasingly fragmented world, with decreasing top-down control, mesopredators are likely to continue to spread and replace apex predators as top predators (Prugh et al. 2009).

Increasing mesopredator populations have commonly caused decreases in, or extirpation of, prey populations and ecosystem collapse (Prugh et al. 2009). For example, in sub-Saharan

Africa, where lion and leopard populations have been declining, the olive baboon, a primate mesopredator, has increased in abundance, contributing to serious reductions in ungulate populations and smaller primate species (Brashares et al. 2010). With the continual decline in apex predators, many mesopredators are increasing in numbers, and where apex predators are extirpated, mesopredators can fill the niche of apex mesopredator and even adapt their behaviors.

For example, when coyotes act as the apex mesopredator in a system, they are able to adapt to form larger packs and hunt larger prey (Gese and Groth 1995). However, despite this behavioral change, coyotes (and other mesopredators) may not fill the apex predator niche, and if they do they cannot truly fill it as they have different relationships with both their surroundings and people (Prugh et al. 2009). For example, unlike apex predators, mesopredators persist despite persecution, allow closer proximity to humans, and feed opportunistically (Prugh et al. 2009). It is these “classic mesopredator traits” that have led to such detrimental effects of mesopredator release in the absence of true apex predators (Prugh et al. 2009).

Historically, the range of coyotes was much more limited across North America; however, today they are found in most regions across the continent and have expanded into virtually all systems, including cities and agricultural lands (Fuller et al. 2010; Kamler et al.

2014). In addition, coyotes prey upon many state- and federally-listed vertebrate species (Ripple et al. 2013). Thus, understanding the extent of top-down control they exert on prey species and also their trophic niche width – that is, the breadth of their food choices – is important to better

4 understand their role in community function, structure, and conservation. In this research, I investigated coyote dietary niche and the impacts of coyotes, ascended to the role of top predator, on community structure in a restored grassland. My specific aims were to 1) quantify the diet of an omnivorous predator (coyotes) filling the apex predator niche, 2) determine how coyotes impact prey community structure (abundance, diveristy, composition), and 3) discover if this top mesopredator causes a trophic cascade with indirect positive impacts on prairie communities.

In Chapter 2, I begin by evaluating the impacts of coyote removal on small mammal communities. Specifically, I look at coyote removal on small mammal diversity, community abundance and reproduction, individual species abundance and reproduction, and individual species body condition. I also investigate if exclusion causes a trophic cascade with positive indirect effects to grass and forb biomass. In Chapter 3, I quantify coyote diet using stable isotope analysis. I look at collective coyote diet, individual coyote diet, and both collective and individual seasonal diet. In Chapter 4, I conclude by discussing my findings and implications for ecology in general and management of restored sites.

CHAPTER 2

PRAIRIE COMMUNITIES

Predators are key drivers in ecological systems. They exert top-down control on their prey that can shape community structure through limiting prey numbers and/or affecting prey behavior (e.g., Sih et al. 1985; Schmitz 1998; Abrams 2008; Laundré et al. 2010). By limiting prey densities, predators can facilitate the coexistence of competitive prey species through increasing the abundance of the inferior competitor (keystone predation effect) (Paine 1966; Holt et al. 1994; Leibold 1996; Shurin and Allen 2001). There is also evidence that predators can impact interspecific competition among prey species not only through changes to prey densities but also behaviors (Bouskila 1995).

In addition to influencing prey, predators can indirectly and positively influence vegetation via, a reduction in or change to, feeding behavior by herbivores, a phenomenon known as trophic cascade (Shurin et al. 2002). Trophic cascades have been documented in terrestrial ecosystems (Schmitz et al. 2001) and can be density-mediated (e.g., Estes and

Palmisano 1974; Paine 1980; Power 1990; Terborgh et al. 2001; Croll et al. 2005; Schmitz 2008) and behaviorally-mediated (Beckerman et al. 1997; Grabowski and Kimbro 2005; Stief and

Holker 2006; Schmitz 2008). Few studies have looked at mammalian herbivore trophic cascades

(Schmitz 2000), yet mammals can significantly impact plants, particularly plant biomass

(Huntley 1991; Olff and Ritchie 1998), which may provide a more accurate and long-term measure of trophic cascades than other measures, such as plant damage (Schmitz 2000).

Furthermore, predation is an important limitation on mammalian herbivore densities, indicating 6 the potential for positive indirect effects of predators on vegetation mediated through mammalian herbivores (Oksanen and Oksanen 1992; Messier 1995).

In two-predator systems, top predators not only exert top-down control on prey but also on smaller mesopredators (Bestion et al. 2015). For example, after the release of wolves back into Yellowstone National Park, small mammal populations significantly increased in plots near wolf dens compared to more distant plots where coyotes, in the absence of top-down control from wolves, exerted top-down control on small mammals (Miller et al. 2012). However, apex predators have, historically and currently, experienced range decreases or eradication in many locations (Gittleman et al. 2001). In their absence, mesopredators have been released from predation and competition, and their populations allowed to flourish (Crooks and Soule 1999;

Estes 2011; Ripple et al. 2014).

Increasing mesopredator populations can have drastic effects on prey populations and their ecosystems (Prugh et al. 2009; Brashares et al. 2010; Loehle and Eschenbach, 2012), and in systems where apex predators have been extirpated; mesopredators can fill the apex predator niche. For example, Midwestern prairies historically supported as many as four apex predators

(bears, mountain lions, wolves, and wolverines), but today's Midwestern prairies have none

(Prugh et al. 2009). Concurrent with apex predator decline in North America, the abundance and geographic range of coyotes (Canis latrans) have increased by as much as 40% in the absence of wolves (C. lupus; Prugh et al. 2009; Newsome and Ripple 2015). However, despite the many occurrences of mesopredators taking over the role of apex predator, coyotes (and other mesopredators) are not equivalent to apex predators and may not actually fill the apex predator niche; this is largely due to the different ways in which mesopredators interact with their environment and people (Prugh et al. 2009). For example, unlike apex predators, mesopredators

7 require smaller habitat patches and are better adapted to fragmented ecosystems, are able to better coexist with humans, and are able to feed opportunistically, switch prey, or utilize alternative food resources (e.g., crops, trash, processed food.). These classic mesopredator traits aid in the persistence and success of mesopredators despite human persecution (Crooks and

Soulé 1999; Prugh et al. 2009).

In addition to the differences associated with these classic traits, mesopredators are often omnivorous, and thus able to impact vegetation both directly and indirectly. For example, coyote scat analyses have shown that coyotes eat a wide array of vegetation including different fruits, seeds, herbaceous material and corn, and that depending on the season vegetation can be a large component of coyote diet (Randa 1996; Kamler et al. 2002; Azevedo et al. 2006). Although predators can induce trophic cascades and have positive indirect effects on plants, mesopredators may or may not have this effect depending on the amount and type of vegetation they consume.

Ultimately, mesopredator range expansion and population increases may have important implications for both community dynamics and vegetative composition, as has been the case with reintroduction of wolves into Yellowstone and the consequent ecosystem changes (Ripple and Beschta 2012).

Coyotes are a unique mesopredator because they are considered to be apex predators in many mesopredator release studies and have been shown to exert top down control on other mesopredators (Ritchie and Johnson 2009). However, while today they have ascended to the role of top predator in many systems, they were historically a mesopredator and display classic mesopredator traits. Hence, treating coyotes as apex/top predators runs the risk of viewing degraded ecosystems as relatively intact or desirable (Prugh 2009), reinforcing the problem of shifting baselines (Baum and Meyers 2004). Even in cases where coyotes are recognized as

8 overpopulated due to a lack of apex predators, coyote control efforts can be costly (Berger

2006). Thus, understanding the role of coyotes as both top predator/mesopredator, and if the fill the apex predator niche, as well as the extent of their top down control on prey communities can have important implications for conservation and restoration. Few studies outside of Yellowstone have looked at the roles of coyotes as a mesopredator and the impacts to communities once top- down control of coyotes is re-established. Such impacts are likely to be site-specific because coyotes are opportunistic hunters, and thus it is important to understand coyote impacts in a broad range of ecosystems where they have ascended to the top predator.

In this research, I examined if coyotes, act as an apex mesopredator and exert top-down control in a grassland ecosystem that carries down the food web to induce a trophic cascade. My main objectives were to quantify in paired coyote-exclosure and control sites: 1) prey species community composition, diversity, abundance, and body condition; and 2) plant biomass.

Coyotes in grasslands are reported to largely consume mammals, especially small mammals

(Randa 1994; Huebschman et al. 1997; Gerads et al. 2001; Kamler et al. 2002; Azevedo et al.

2006), so I focused on small mammals as coyote prey sources. Given the impacts predators have on prey species, I predicted that 1) exclusion of coyote mesopredators will cause a decline in prey species diversity, 2) this decrease in diversity will be driven by an increase in prairie vole

(Microtus orchagaster) abundance, as they are the competitively dominant species (Abramsky et al. 1979), and 3) there will be a decrease in plant biomass between experimental treatments due to the changing plant community composition.

9 Methods

Study area and set up

This study was conducted at Nachusa Grasslands, a tallgrass prairie preserve located in northern Illinois. Nachusa is owned by The Nature Conservancy (TNC) and consists of approximately 1,600 hectares of restored and remnant (never ploughed) tallgrass prairie, interspersed with wetlands and oak savanna that is surrounded by a matrix of agriculture and rural housing development. I chose four restoration sites for this study; each site had different times since restoration ranging from 8-17 years. The study sites were chosen to be of similar size and were all rated as high-quality restorations by project managers at the preserve (Figure 1)

(Barber et al. 2017). Three of the four restorations were not burned during the year of 2017; the fourth site was burned in early spring 2017 (Figure 1). Although bison have been reintroduced to part of the preserve, none of the selected sites were part of the bison unit in order to avoid bison impacts in my study design. All sites selected are part of a long-term small mammal study that began in 2013.

Four 70m x 70m predator exclosure fences were built beginning in fall 2016 and finished in early spring 2017. They were placed approximately 20 meters from previously established unfenced sampling grids (described below), which served as controls. A fifth fence of the same size was built approximately 80m from one of the same established sites (see Figure 1), due to limitations regarding site selection. The exclosures were built to exclude ground but not avian predators (i.e., fences were open at the top). A 15 cm mesh was used as well as barbed and solar- powered electric wiring to prevent coyotes from jumping over the fence, and the fence was built flush with the ground to prevent them from going under to enter the study area. These fences

10 were built specifically to exclude coyotes. Medium sized mesopredators, like raccoons and opossums, could potentially climb over the fences, and small mesopredators, such as weasels, could get through the mesh. Although medium sized mesopredators could have potentially gained access to the sites, I saw no visible signs of damage to the fences or of predators digging under the fences/attempting to climb over it. In addition, I confirmed the presence of a weasel in one of the exclosures. There were no signs of birds of prey using the fence posts as perches, which could act to increase avian predation within the exclosure. Inside each fence and in the adjacent controls, a 60m x 60m small mammal sampling grid was established. Traps were placed every 15 m within the grid in a 5x5 arrangement, for a total of 25 traps (Figure 2).

Mesopredators

Nachusa Grasslands supports a wide variety of fauna, including numerous mammalian predators. I used Browning Trail Cameras (command ops series) to survey for predators near my sampling locations. Traps were placed both at the corners of the sampling grids and along two- track roads adjacent to field sites. Vegetation was cleared around the camera traps and they were faced north or south to increase visualization and minimize the number of false triggers caused by the sun. Cameras were mounted to a wooden pole placed in the ground and baited, using a small scent stick, with meat or Cavern’s Quality Animal Lures (Canine Force or Minnesota Red) located approximately 3 m away. Camera trapping confirmed the presence of coyotes, raccoons, opossums and skunks near my trapping locations.

11

Figure 1: Map of trapping grid locations at Nachusa Grasslands.

12

Figure 2: Trapping grid set up, traps are located on black dots spaced 15m apart.

Sampling

To investigate the predator-prey relationship between small mammals and coyotes, I conducted five small mammal sampling events from fall 2016-fall 2017. Sampling occurred both within the predator exclosures and in paired control-sampling plots (Table 1). At the beginning of each sampling event, twenty-five Sherman live traps were deployed in each sampling plot. I surveyed small mammals for four consecutive nights in each grid. Traps, baited with peanut butter and oats, were opened each night within 2.5 hours prior to sunset, and checked the following morning within four hours of sunrise. If the nighttime temperature was predicted to drop below 5° C, I used cotton bedding in each trap to reduce the chance of injury from exposure to the cold and stress (Sikes et al. 2016). For all target species captured (Table 2) I recorded

13 species, mass, sex, age, reproductive status, right hind foot length (RHF), tail length (TL), body length (BL), and location of capture. Weights and measurements were obtained using a

Pesola scale and calipers. I determined reproductive status and sex by palpating male testes and female ovaries (Krebs et al. 1969).

Non-target species (Table 3) were either processed following the same procedure or were released upon capture. In order to identify previously captured individuals, I placed an 8 mm

Biomark MiniChip Passive Integrated Transponder (PIT) tag (Model HPT8, Biomark, Boise,

Idaho, USA) in the dorsal scruff of all target species using a sterile hypodermic needle (Sikes et al. 2016). Each PIT tag contained a unique 15-digit hexadecimal numeric code that was used to identify individuals using a handheld reader (Model 601, Biomark, Boise, Idaho, USA).

Processed non-target species were marked with a four-digit fingerling ear tag (Size 1005-1

Model [5/16" long, 1/8" wide).

All small mammals captured and handled were done so in compliance with the Northern

Illinois IACUC and Office of Research Compliance permit LA#13-0007. Furthermore, permits to trap on the preserve were obtained through the Illinois Nature Preserve Commission, Illinois

Department of Natural Resources, and The Nature Conservancy.

Table 1: Small mammal sampling sessions by year.

2016 2017 Sampling Sessions 1 4 Sampling Sites 9 9 Trapping Nights 4 4 Total Number of Trapping 400 400 Nights per Site Sampling Months October* April, June, August, September/October *Exclosure fences were not built/closed until after the October 2017 sampling event

14

Table 2: Target species and identifying information.

Species Common Measurements Identifying Tag Name information Peromyscus Deer mouse RHF, TL, BL, sex, RHF, TL PIT tag maniculatus location, reproductive status, age, mass Peromyscus White footed RHF, TL, BL, sex, RHF, TL PIT tag leucopus mouse location, reproductive status, age, mass Microtus Prairie vole RHF, TL, BL, sex, Number of PIT tag ochrogaster location, dots on RHF reproductive status, age, mass Microtus Meadow vole RHF, TL, BL, sex, Number of PIT tag pennsylvanicus location, dots on RHF reproductive status, age, mass Reithrodontomys Western RHF, TL, BL, sex, BL, TL PIT tag megalotis harvest mouse location, reproductive status, age, mass Zapus hudsonius Meadow RHF, TL, BL, sex, Coloration, PIT tag jumping mouse location, BL, TL reproductive status, age, mass

15

Table 3: Non-target species and identifying information.

Species Common Name Measurements Identifying Tag information Tamias striatus Eastern NA Appearance NA chipmunk (five dark stripes) Mus musculus House mouse NA Ears and tail NA appearance, mass Sorex cinereus Masked shrew NA Size, TL, mass NA Blarina Northern short- NA Appearance NA brevicauda tailed shrew Ictidomys 13 lined ground RHF, TL, BL, Appearance Fingerling ear tridecemlineatus squirrel sex, location, (Striped with tag reproductive dots) status, age, mass

Trophic Cascade

To investigate the potential of a community-level trophic cascade, aboveground plant biomass was measured in both experimental treatments. Collection occurred at the end of the growing season from August 1-3, 2017. Ten of the 25 sampling points (Figure 2) were randomly selected, and quadrats (0.01 m2) were placed 1m away; above ground biomass was cut as close as possible to the soil. Biomass was sorted to grasses and forbs, dried to a constant mass, and weighed.

Statistical analyses

Because the fences were not completed until Spring 2017, only data collected during

2017 were used to measure small mammal abundance, reproduction, body condition, and

16 diversity. In addition, due to the proximity of my paired exclosure and control sites and the fact that I am looking at community-level processes, I tested for spatial autocorrelation in the sites (Legendre 1993; Tobler 1970). All statistical analyses were done in RStudio (RStudio Team

2015, version 3.4.1).

Testing for spatial autocorrelation is an important step in ecological studies as its presence violates two important assumptions of linear models: independence and equal distribution of residuals (Anselin 2002). I tested for both positive and negative spatial autocorrelation using Moran’s I correlogram (Legendre and Legendre 1998). I used square root transformed abundance data for the two most commonly captured genera (Microtus and

Peromyscus) and the GPS data for the central point in each sampling grid (Figure 1). Abundance was selected because of the minimal information conveyed in binary data (i.e., presence/absence data; Breslow and Clayton 1993).

Abundance measures were calculated using the number of unique individuals captured per the number of trap nights (in this case 100 trap nights or 25 traps set for four nights). Abundances were calculated in each predator treatment (exclosure, control) for each sampling event. Although alternative and more commonly used methods for calculating population size exist (i.e., minimum number alive and the number of unique individuals captured; Krebs 1966; White et al. 1982), I chose this method to reduce biased values caused by aforementioned methods (Pocock et al. 2004). In addition, I attempted modeling abundance using Program Mark (Laake 2013, version 2.2.4) in order to account for imperfect detection probabilities; however, my capture-information was too low and the variance too high (Grimm et al. 2014). Finally, in order to obtain the best estimate of abundance I accounted for accidentally triggered or improperly set traps (no bait, set on its side, etc.) by taking 0.5 trapping nights for

17 the number of unoccupiable traps from the total number of available trap nights (Beauvais and

Buskirk 1999). This normalizes the data and accounts for the fact that these traps reduce sampling effort and can vary between sampling events and sites (Beauvais and Buskirk 1999).

An animal’s body condition is related to its overall health or “quality”. In order to avoid destructive sampling methods, many researchers use condition indexes (CI) to measure body condition (Stevenson and Woods 2006). I measured body condition of reproductive females

(pregnant, lactating or both) and the non-reproductive individuals for the two most commonly captured species using mass and a morphometric measurement (body length), with the scaled mass index method (Peig and Green 2009; Peig and Green 2010). Mass and body length are connected (i.e., they both increase with time and indicate size; Thompson 1961). Thus, using the

Thorpe-Lleonart (TL) model accounts for this scaling relationship (Thorpe 1975; Lleonart et al.

Lo b 2000; Peig and Green 2009) by using the following equation: m̂ i= m i sma. I performed the [Li ] standardized major axis regression method on natural log transformed data for both right hind foot and body length to determine which would be a better measure to use for calculating the scaled mass index (Peig and Green 2009; Figure 3). Although body length can be a subjective measurement, it was found to be the most correlated with body mass in a study looking at numerous small mammal species including Peromyscus and Microtus (Peig and Green 2009), and within my own measurements body length data was more uniform and linear in comparison to right hind foot (Figure 3). Differences in body condition between reproductive females and non-reproductive individuals were evaluated using a linear model with body condition as the response variable, reproductive status as the explanatory variable and planting and month as random factors. In addition, I tested for outliers using the interquartile range method and found

18 there to be no extreme outliers (Sokal and Rohlf 1981). I evaluated potential changes to reproduction by measuring the ratio of unique pregnant and/or lactating females to non- reproductive females.

Diversity was measured using the inverse Simpson’s index (Simpson 1949). Because small mammal studies are known to capture some species less commonly than others, with some species only encountered a few times at best, I did not want my measure to be weighted towards rare species (Peet 1974). The inverse Simpson’s index is weighted towards the more abundant species (Hill 1973). In addition, I looked at community composition using Non-metric

Multidimensional Scaling (NMDS) (Kruskal 1964). NMDS plots were created using the metaMDS function in the R package Vegan (Oksanen et al. 2008). Distances were measured using the Bray Curtis dissimilarity method given its ability to better handle ecological data (Faith et al. 1987), and I used abundance measures to provide more information than presence/absence data. Using NMDS I created an ordination plot and ran a PERMANOVA that compared the similarity/dissimilarity index of small mammal communities between experimental treatments

(VanNimwegen et al. 2008).

All of the above measures were calculated per treatment, per sampling event. Square root transformations were applied when data did not meet assumptions of normality (Peromyscus abundance, Microtus abundance, Microtus reproduction, Peromyscus body condition, and diversity). One measure, overall abundance across all species, was unable to meet the assumption of normality despite transformations and the data was run using the original untransformed data.

I created linear mixed effects models via the lme4 package (Bates et al. 2015) that were analyzed using F-tests in order to describe the relationship between my response (abundance, diversity, body condition, and reproduction) and explanatory variable (predator treatment). I included as

19 random factors the month of sampling, to account for seasonal effects and repeated sampling, and site, to account for differences in vegetation/small mammals among different sites.

Additionally, I ran the models both with all sites and without the burned site to investigate if there was an impact of burning on small mammal community dynamics. Finally, I used pre

(October 2016) and post (2017) data to visually compare new captures of Peromyscus and

Microtus through time in order to see if the overall trends are changing between treatments.

Biomass data were separated by functional group (forb, grass) and the relationship between treatments was evaluated using linear mixed effects models (Bates et al. 2015). My response variable was biomass of either forb or grass and my explanatory variable was predator treatment. For the reasons highlighted above, site was included in the model as a random variable. Data were transformed using a cube root transformation to meet assumptions of normality. For all data, given my small sample size and the short duration of my study, results were considered statistically significant at p=0.075.

20

Figure 3: Bivariate plot of all individuals used in body condition measurement (both reproductive females and non-reproductive females and males) for visualization comparing a) Microtus right hind foot measurements (p<0.01, r2=0.110) to body length measurements (p<0.01, r2=0.699) and b) Peromyscus right hind foot measurements (p<0.01, r2=0.187) to body length measurements (p<0.01, r2=0.493). Lines represent the standardized major axis.

Results

Results from the Moran’s I test for spatial autocorrelation indicated that there was no effect of positive or negative spatial autocorrelation between my sites. This was tested for the two most commonly captured genera Peromyscus (positive p-value=0.96, two-sided p=0.09) and

Microtus (positive p-value=0.47, two-sided p=0.94)

During 2017, I conducted four sampling events that accounted for 3518.5 trap nights in sampling effort. Throughout this time, I caught 262 unique individual small mammals (Table 4).

Of those, 149 were from the exclosures and 113 were from the control sites. In addition, I recaptured 231 previously-tagged small mammals and of those, 98 were unique individual

21 recaptures (i.e., some individuals were re-caught multiple times) (37% unique recapture rate).

In total, I caught six different small mammal species in 2017. I conducted one sampling event, totaling 877 trap nights, prior to the fences being built/closed in October of 2016. During this time I caught 25 unique individuals and recaptured 13 individuals, of those 11 were unique (44% unique recapture rate), from four different small mammal species (11 from exclosures and 14 from the controls) (Table 4). For the entirety of this study, from fall 2016 to fall 2017, I caught a total of 287 and recaptured 109 unique small mammal individuals (38% total unique recapture rate), and conducted 4,395.5 trap nights (Table 4).

Microtus ochrogaster, Peromyscus maniculatus, and Peromyscus leucopus were the most commonly captured species in the exclosures in decreasing order of frequency during 2017.

These species accounted for 97% of the total captures in the exclosures (Table 5). In contrast,

Microtus ochrogaster, Peromyscus maniculatus, and Peromyscus leucopus, and

Reithrodontomys megalotus were the most frequently captured species in the control sites, with

Microtus and Peromyscus being equal in numbers (Table 5). These four species accounted for

93% of the total captures in the control sites. Peromyscus maniculatus was the most commonly captured species in both the exclosures and control sites in 2016 and accounted for 84% of the total captures (Table 6). For the entire study, Microtus ochrogaster was the most commonly caught species accounting for 38% of the total captures closely followed by Peromyscus maniculatus, which accounted for 36% of the total captures.

There was no significant difference in small mammal abundance between the mesopredator exclusion treatment and the control treatment (p=0.66)(Table 7; Figure 4). In addition, mesopredator removal did not impact reproduction in small mammals between treatments (p=0.99)(Table 7; Figure 5). Abundance, reproduction, and body condition measures

22 were broken down for the three most commonly captured species, Peromyscus maniculatus,

Peromyscus leucopus, and Microtus ochrogaster (referred to as Microtus). Although during sampling I identified Peromyscus to the species level, for analyses I combined both species and ran statistics at the genus level due to the difficulty of properly identifying the two species

(Bruseo et al. 1999; Kamler et al. 1998; Sternberg and Feldhammer 1997). Predator removal had no influence on abundance (p=0.88; p=0.30) or reproduction (p=0.61; p=0.97) between experimental treatments for both Peromyscus and Microtus (Table 7; Figure 6, 7). There were significant differences in body condition between reproductive females and non-reproductive individuals among experimental treatments for both Peromyscus (p<0.01) and Microtus

(p<0.01). Furthermore, Peromyscus showed increased body condition in the exclosures (p=0.55), and there was not an impact of predator exclosure on Microtus body condition (p=0.41) or reproductive condition in both species (p=0.76; p=0.37)(Table 7; Figure 10, 11). To investigate the biological significance in Peromyscus body condition between treatments I graphed the average BCI per trapping session for both treatments to visually inspect changes to BCI through time (Figure 12).

Small mammal diversity was not different between treatments (p=0.33) (Table 7; Figure

8). This was further supported through community composition NMDS analysis (stress=0.94; p=0.27)(Figure 9). Visual analysis of pre/post data initially showed similar trends of Microtus dominating both the exclosures and the controls. However, through time, Microtus continued to dominate in the exclosures whereas Peromyscus numbers dramatically increased in the controls

(Figure 13).

Aboveground biomass data ranged from a minimum biomass of 0.00 to a maximum of

58.05 grams (g) and a mean biomass of 5.61 g. In the control sites forb biomass ranged from

23 0.00-28.39 g with an average biomass of 6.47 g, and in the exclosures it ranged was from

0.00-58.05 g with an average biomass of 8.18 g. The mean value of forb biomass among treatments was 7.40 g. Grass biomass ranged from 0.00-17.45 g in control sites and 0.00-48.43 g in exclosure sites with a average biomass of 3.52 g and 4.10 g, respectively. The mean value of grass biomass among treatments was 3.83 g. Aboveground biomass for both forbs and grasses were not impacted by predator removal (p=0.64; p=0.91)(Table 7; Figure 14). Finally, there was no change when the burned sites were removed from the model for any of the above measures.

24 Table 4: Unique captured and recaptured small mammals by trapping session; species ordered by frequency of capture. I define unique as each individual caught per trapping session; this could include some overlaps from different trapping sessions (i.e., across months).

Species Total unique 2016 2017 individuals October April June August October Prairie vole 137 2 10(1) 42(7) 38(11) 45(16) (Microtus ochrogaster) Deer mouse 125 21(6) 38(7) 24(9) 24(9) 18(11) (Peromyscus maniculatus) White footed mouse 52 15 8(1) 11(1) 18(3) (Peromyscus leucopus) Western harvest 19 1 2 1 7(1) 8 mouse (Reithrodontomys mega lotus) 13-lined ground 7 2 2 3 squirrel (Ictidomys tridecemlineatus) Meadow jumping 1 1 mouse (Zapus hudsonius) Meadow vole 1 1 (Microtus pennsylvanicus) Total 342 25 67 75 82 93

25 Table 5: Total number of unique species captured per site (X after site name indicates exclosure grids) for 2017.

Species CCE CCEX CCW CCWX SB SBXW SBXE TC TCX Total Prairie vole 5 10 7 10 20 18 21 7 10 108 (Microtus ochrogaster) Deer mouse 11 13 9 2 15 7 15 4 5 81 (Peromyscus maniculatus) White footed 1 1 4 4 6 7 5 20 48 mouse (Peromyscus leucopus) Western 1 6 1 3 6 17 harvest mouse (Reithrodont omys mega lotus) 13-lined 2 4 1 1 8 ground squirrel (Ictidomys tridecemline atus) Meadow 1 1 jumping mouse (Zapus hudsonius) Total 20 24 25 13 46 32 46 22 35 263

26 Table 6: Total number of unique species captured per site (X after site name indicates exclosure grids) for 2016.

Species CCE CCEX CCW CCWX SB SBXW SBXE TC TCX Total Deer mouse 1 1 2 3 4 1 3 3 18 (Peromyscus maniculatus) Prairie vole 1 1 2 (Microtus ochrogaster) Western 1 1 harvest mouse (Reithrodonto mys mega lotus) Meadow vole 1 1 (Microtus pennsylvanicu s) Total 2 1 3 0 5 4 1 3 3 22

Table 7: Summary statistics for linear mixed effects models

Response Variable P-value F-value Degrees of Freedom Small mammal abundance 0.663 0.207 1 Small mammal reproduction 0.985 0.000 1, 6.678 Peromyscus abundance 0.882 0.024 1, 7 Microtus abundance 0.301 1.250 1, 7 Peromyscus reproduction 0.610 0.270 1, 18.734 Microtus reproduction 0.968 0.002 1, 6.633 Peromyscus reproductive BCI 0.760 0.108 1, 3.832 Microtus reproductive BCI 0.362 1.014 1, 4.838 Peromyscus BCI 0.055 5.644 1, 5.990 Microtus BCI 0.408 0.758 1, 8.457 Diversity 0.328 1.108 1, 7 Forb biomass 0.641 0.219 1, 88 Grass biomass 0.908 0.014 1, 7.023

27

Figure 4: a) Small mammal abundance by treatment (p>0.075). b) Small mammal abundance by treatment (p>0.075). In both graphs the error bars represent +/- the standard error. Sites: CCE= Clear Creek East, CCW= Clear Creek West, SB= Stone Barn, TC= Thelma Carpenter.

28

Figure 5: Small mammal reproduction by treatment (p>0.075). Error bars represent +/- the standard error. Refer to Figure 4 for site names.

29

Figure 6: a) Peromyscus abundance by treatment (p>0.075). Error bars represent +/- the standard error. b) Microtus abundance by treatment (p>0.075).. Error bars represent +/- the standard error. Refer to Figure 4 for site names.

30

Figure 7: a) Peromyscus reproduction by treatment (p>0.075). Error bars represent +/- the standard error. b) Microtus reproduction by treatment (p>0.075). Error bars represent +/- the standard error. Refer to Figure 4 for site names.

31

Figure 8: Small mammal diversity by treatment (p>0.075). Error bars represent +/- the standard error. Refer to Figure 4 for site names.

32

Figure 9: Small mammal community composition measured through NMDS (p>0.075). Green symbols represent individual exclosure sites and red symbols indicate individual control sites. Ellipses represent 95% confidence ellipses.

33

Figure 10: Body condition measured using the scaled mass index (mass, body length). a) Peromyscus body condition by treatment (p=0.055). All values in the exclosures are greater than those in the control. Error bars represent +/- the standard error. b) Microtus body condition by treatment (p>0.075). Error bars represent +/- the standard error. Refer to Figure 4 for site names.

34

Figure 11: Reproductive body condition measured using the scaled mass index (mass, body length). a) Reproductive Peromyscus female body condition by treatment (p>0.075). Error bars represent +/- the standard error. b) Reproductive Microtus female body condition by treatment (p>0.075). Error bars represent +/- the standard error. Refer to Figure 4 for site names.

35

Figure 12: Time series comparing BCI between treatments through trapping sessions in 2017.

36

Figure 13: Time series comparing difference in new captures for Microtus ochragaster, Peromscus maniculatus, and Peromyscus leucopus for control (left) and exclosure (right) treatments.

37

Figure 14: a) Forb aboveground biomass (g) by treatment (p>0.075). Error bars represent +/- the standard error. b) Grass aboveground biomass (g) by treatment (p>0.075). Error bars represent +/- the standard error. Refer to Figure 4 for site names.

38 Discussion

Small mammal community dynamics

The aim of this study was to quantify the extent of top-down control that coyotes, ascended to top predator, exert in a grassland ecosystem. I found that over the course of nine months since experimental exclosure, there was no signal of top-down control on small mammal communities. Additionally, I found no differences in plant communities indicating the lack of support for a trophic cascade in the first growing season following predator exclusion.

Although I saw no effect of predator removal on small mammal communities, other studies have shown coyotes can act as an apex predator and exert significant top-down control that impacts small mammal communities (Henke 1992; Miller et al. 2012). Predators, including coyotes, have also been shown to increase prey species diversity (Henke 1992; Chase et al. 2002), and can do this through promoting the coexistence of interspecific competitors.

Microtus and Peromyscus are the two most commonly captured small mammal species at

Nachusa and are interspecific competitors (Abramsky et al. 1979). Wirtz and Pearson (1960) found that when testing interspecies aggression, Microtus pennsylvanicus started majority of the fights (charging/chasing, squeaking and nipping) and took resources from Peromyscus leucopus.

In contrast, Peromyscus often responded to aggressive behavior by fleeing, clinging to the cage bars, or crouching against the wall. When aggressive behavior was exhibited by Peromyscus, it was not acknowledged by Microtus (Wirtz and Pearson 1960). Competitive dominance has also been demonstrated in Microtus ochrogaster over Peromyscus maniculatus (Abramsky et al.

1979) and among other species of Microtus and Peromyscus (Grant 1971; Petticrew and Sadleir

1974; Redfield et al. 1977; Yunger 2004).

39 One mechanism that facilitates the coexistence of interspecific competition between prey species is higher consumption of the competitive dominant, which serves to limit their densities and interspecific competition (Paine 1966; Paine 1971; Holt et al. 1994; Leibold 1996;

Shurin and Allen 2001). Previous studies have shown Microtus makes up one of the most important dietary items for coyotes in tallgrass prairie (Randa 1996). Furthermore, coyotes have been demonstrated to hunt more frequently in areas with high Microtus densities or quality

Microtus habitat (Reichel 1991; Miller et al. 2012). This could in part be due to the larger body size of Microtus, resulting in them not being as good at escaping predators as other target small mammal species (Hoffmeister 1989; Yunger 2004). This preferential consumption of Microtus, a competitive dominant species in the small mammal guild, by coyotes likely results in a stronger predation pressure on voles, which would act to limit their densities and promote the coexistence of Microtus and Peromyscus at Nachusa. Thus, I would expect to see competitive exclusion by

Microtus influencing small mammal abundance and diversity through increases in Microtus and decreases in Peromyscus abundance; however, I did not find this over the course of my study.

In addition to changes in community dynamics, interspecific competitors often compete for space, resources, or both. Furthermore, competition can cause numerical increases in one competitor at the expense of the other (Grant 1972). For example, it was found that in two interspecific competitors, the competitive dominant, Peromyscus polionotus, drove the competitive inferior, Mus musculus, to extinction due to competition for food (Caldwell 1964).

There are many costs to reproduction and lactation in small mammals, including, but not limited to, increased energy demands and calcium requirements (Speakman 2008). Interspecific competition (Watts and Holekamp 2008) and predation (Hawlena and Schmitz 2010; Sheriff et al. 2011) can both have impacts on this demanding function. Therefore, the potential increase in

40 resources and/or space could act to increase the competitive dominants reproduction. Such a response has been demonstrated in Hyenas (Crocuta crocuta), a competitive inferior, which had lower reproductive output when subjected to strong interspecific competition (Watts and

Holekamp 2008). Thus, I expected, but did not find coyote removal to cause an increase in interspecific competition among Microtus and Peromyscus that would act to increase Microtus abundance and reproduction and decrease diversity in the exclusion sites. The short time frame over which my study was conducted could have resulted in my unexpected findings. Other studies that saw significant impacts of coyote top-down control occurred over a two or greater year period (Henke 1992; Miller et al. 2012). Furthermore, one study didn’t see significant changes to small mammal richness or diversity until the second year after coyote control began

(Henke 1992). Thus, changes in small mammal communities due to coyote removal may occur over a longer timescale than the course of this study. This could especially be the case given that

Microtus makes up a large portion of the diet of other vertebrate predators in tallgrass prairie ecosystems (i.e., great horned owls and red tailed hawks; Cooper 1997).

Predators not only impact prey reproduction through changes in interspecific competition but also through behaviorally-mediated effects (Hawlena and Schmitz 2012; Sheriff et al. 2011).

However, given recent findings about predator hunting modes and how they shape responses in prey, I may not expect reproduction to be impacted by coyote removal (Preisser et al. 2007).

Coyotes have a roaming hunting mode, which typically corresponds to density-mediated effects rather than behaviorally-mediated risk effects (Schmitz and Suttle 2001). Roaming predators often do not present a persistent enough cue for risk of predation to be a chronic stressor and thereby impact measures of fitness in prey (Preisser et al. 2007; Schmitz 2008; Hawlena and

Schmitz 2010). For example, a study conducted on wolves (a roaming predator) in Yellowstone

41 found that wolves do not impact elk reproduction and that even in high predator areas, their presence is only detected near prey approximately every nine days, likely not consistent enough to cause behaviorally mediated impacts (White et al. 2011; Middleton et al. 2013). However, this is contested (Creel et al. 2011; Winnie and Creel 2017), and more research is necessary to tease out potential mechanisms, especially given that hunting mode and its relation to risk versus density effects has largely been tested in invertebrates, on small spatial scales, or in roaming prey species. Furthermore, a study conducted in an aquatic system found the presence of a behaviorally-mediated trophic cascade from a roaming predator (Burkholder et al. 2013). This indicates that there may be potential for a roaming predator to cause risk effects in prey, which can be a chronic stressor and impact prey behavior and fitness (Preisser et al. 2007; Hawlena and

Schmitz 2010). Thus, removal of roaming mesopredators may actually be impacting prey reproduction and this should be looked at in more detail.

I saw better Peromyscus body condition in predator exclosures versus the controls, a counterintuitive finding if indeed Microtus are expected to exert stronger competitive effects in the absence of coyotes. It is possible that Peromyscus are the only small mammal species that coyotes exert top-down control on and that the absence of coyotes is driving the increase seen in

Peromyscus body condition. However, we hypothesize that with coyote removal Microtus are likely to increase in abundance and outcompete smaller-bodied and less competitive Peromyscus individuals. This would result in Peromyscus individuals caught in exclosures to exhibit superior body condition relative to Peromyscus outside the exclosures, which may be experiencing less competition. Microtus ochragaster has been shown to competitively exclude Peromyscus maniculatus, supporting this idea (Abramsky et al. 1979). Visual inspection of changes to

Peromyscus body condition through time further supports this idea (Figure 12). Peromyscus

42 body condition decreases in control sites and increases in exclosure sites during the October sampling, a time when I caught Microtus in the highest abundance. This could indicate Microtus pushing out the smaller bodied Peromyscus at this time. More research is necessary to further clarify the mechanisms of competition between these two species and their resulting impacts on body condition.

It is possible that different prescribed fire regimes may have driven some of the patterns I saw. Non-burned sites have increased thatch accumulation, a preferred habitat for Microtus

(Grant et al. 1982; Bueno et al. 2012). In contrast, burning increases open ground a preferential habitat for Peromyscus maniculatus (Grant et al. 1982; Bueno et al. 2012), thus there is the possibility for burning to be driving aspects of small mammal community dynamics. However, I saw no impact of removing or including sites that had been burned on my results, indicating that burning is not having an effect on small mammal communities.

Trophic Cascade

Over the course of this study, I saw no impact of coyote removal on measures of forb and grass biomass. From beginning of predator removal to the time of biomass collection, approximately five months had passed in the growing season. Although, Schmitz et al. (2000) found that a majority (~80%) of trophic cascades in terrestrial systems occurred within a 6- month time frame, the studies being evaluated all consisted of arthropod herbivores and non- mammalian vertebrate and invertebrate predators (Schmitz et al. 2000). Terrestrial trophic cascades between mammalian carnivores and herbivores are understudied and thus the length of time for these processes to occur may be much longer than of those previously evaluated; more research will be necessary to understand these processes through time.

43 In addition to the short time frame of my study, previous studies have found that both higher predator and herbivore diversity can result in top-down effects being attenuated (Strong

1992; Schmitz et al 2000; Finke and Denno 2004). My study site has several small mammal herbivores, larger herbivores (deer, rabbits) as well as insect herbivores. In addition, there are several omnivore and raptor predators present. This could lead to attenuation of top-down control by coyotes in tallgrass prairies. However, previous studies have focused on non-mammalian herbivores and predators, illustrating the need for more time and research to truly determine if there is an effect. Small mammal herbivores have a significant impact on seed recruitment, standing crop, and community composition in prairie restorations (Howe et al. 2006; Pellish et al. 2017). Furthermore, Peromyscus and Microtus have different diets (DeJaco and Batzli 2013), so if with time effects of predator removal on small mammal community dynamics are seen, changes to vegetation may result. Additionally, invertebrates can make up a large proportion of some small mammal diets, so the dynamics of predator removal not only on small mammals communities but also invertebrate communities and consequently vegetation should be investigated.

Finally, investigating community level trophic cascades could potentially mask species- level trophic cascades (Schmitz et al 2000). This could be the result of preferential feeding on edible plants over defended ones or preferential feeding on one functional group over the over

(i.e., preferential consumption of forbs over grasses; Chase 1998; Schmitz et al 2000). Although

I looked at grasses and forbs separately, species-level cascades have been found to be more common and likely to occur in terrestrial ecosystems (Schmitz et al 2000; Polis et al 2000).

Given that both Microtus and Peromyscus have selective diets (DeJaco and Batzli 2013), the effects of top-down control should be further investigated in species-level cascades.

CHAPTER 3

COYOTE DIET

As Earth’s ecosystems continue to undergo change as a result of human use, restoration of native systems will be increasingly necessary in order to maintain biodiversity and ecosystem services (Benayas et al. 2009). Restoration success has typically been measured through recovery of vegetation, although more recently the focus has shifted to diversity, abundance, and ecosystem services (Wortley et al. 2013). In order to restore resilient ecosystems, an understanding of how other food web members beyond primary producers respond to and influence restoration and management is needed (National Research Council 1992; Bowler

2000). Predators can play an important role in maintaining ecosystem stability through top-down control of lower trophic levels. For example, predators can increase the diversity of an ecosystem

(Estes et al. 2011). In addition, predators can help prevent invasive species colonization, especially invasive predators, which can have very damaging impacts on lower trophic levels

(Salo et al. 2007; Wallach et al. 2010). Ultimately, the role of predators in an ecosystem serves to stabilize the system and helps to protect it against climate change, disease transmission, and other contemporary disturbances (Wilmers et al. 2006; Pongsiri et al. 2009; Ritchie et al. 2012).

These services are likely instrumental in the ecosystem recovery process and will aid in establishing resilient restored ecosystems. However, there seems to be little research evaluating the impacts of predators on restoration success.

The Earth is currently in a state of trophic downgrading, defined as the disproportionate loss of large predators (Estes et al. 2011), which has led to numerous incidences of 45 “mesopredator release” where smaller predators are released from top-down control by large predators (Prugh et al. 2009; Brashares et al. 2010). As a result of mesopredator release from wolves, coyotes have both increased in abundance and geographic range across the continent and now serve the role of apex mesopredator in many systems (Gompper 2002; Newsome and Ripple

2015). This mesopredator release of coyotes emphasizes the need to understand their role as the new top dog, especially in restorations, due to the different ways in which they fill the apex predator role (e.g., can reach higher densities due to small home ranges and selection of different prey items, Prugh et al. 2009).

Coyotes are opportunistic and omnivorous and therefore have the potential to impact vegetation both directly and indirectly through consumption and changes to prey foraging behavior or numbers (Henke and Bryant 1999; Bruno and Connor 2005). In addition to being omnivores, coyotes have seasonal and moderately individualistic diets (Prugh et al. 2008).

Individualistic diets can minimize intraspecific competition (Smith 1990; Svanback and Persson

2004). This may have important implications for restoration. For example, in coyotes, intraspecific competition may be reduced, potentially allowing coyotes to reach higher densities and have a larger impact on communities, such as small mammals (those weighting less than

500g such as mice, voles, and ground squirrels), which have been shown to often make up the majority of coyote diet in prairie grasslands (Randa 1994; Huebschman et al. 1997; Gerads et al.

2001; Kamler et al. 2002; Azevedo et al. 2006). Small mammalian granivores impact seedling emergence and shape community composition in tallgrass prairies through seed predation and soil disturbances (Harnett and Keeler 1995; Pellish et al 2017). Furthermore, coyote culling for livestock protection led to decreased small mammal diversity in a shortgrass prairie ecosystem

(Henke and Bryant 1999). Decreased small mammal diversity could have negative ecosystem

46 consequences given that biodiversity can help to increase ecosystem stability (Thebault and

Loreau 2005). Top-down control by coyotes on small mammal communities serves an important role in increasing diversity, which can then have important implications for both vegetation and insect community composition (both of which are important dietary items for small mammals) and potentially the trajectory of a restoration.

In addition to influencing small mammal communities, coyotes can help curb nest predation of avian species (Sovada et al.1995; Rogers and Caro 1997). For example, in a prairie ecosystem, areas where coyotes were the primary predator saw significantly increased nest success for ducks than in areas where foxes (Vulpes vulpes) were the primary predator. This effect was likely due to the top-down control of coyotes on foxes (Sovada et al. 1995).

Furthermore, coyotes can increase scrub bird diversity and increase song sparrow (Melospiza melodia) nest survival, again by suppressing smaller mesopredator densities (Rogers and Caro

1997; Crooks and Soule 1999). Passerines are one of the most threatened bird species today and provide a crucial functional role in the prairie (Donald et al. 2001; Sauer et al. 2003; Thogmartin et al. 2006); therefore, this role of coyotes as top mesopredator can be important to restoration success. The wide dietary breadth, ability to impact prey/smaller predator communities, and individualistic diets of coyotes indicate their potential ability to impact restoration success of both flora and fauna and ultimately a stable system. Prairies have declined by over 90% in North

America and over 99% in Illinois, making them one of the world’s most threatened ecosystems

(White 1978; Samson and Knopf 1996). Thus, understanding the role of coyotes as the new top mesopredator in prairies could have important conservation significance for these imperiled ecosystems and the species they support.

47 Many studies have characterized coyote diet using traditional methods and in various habitats (i.e., scat analysis or analyzing stomach contents; Meinzer et al. 1975; Bowyer et al.

1983; Harrison and Harrison1984; MacCracken and Uresk 1984; Toweill and Anthony 1988;

Brillhart and Kaufman 1994; Azevedo et al. 2006). However, coyote diet is variable across habitats and it is difficult to quantify the relative abundance of different prey species using traditional methods (Bearhop et al. 2004). For example, a dietary scat analysis conducted on

Australian sea lions (Neophoca cinerea), led to an underestimation of ingested otholiths (fish ear bones), with smaller otholiths being found less frequently than larger ones, likely due to complete digestion (Gales and Cheal 1994). Other issues associated with traditional methods include the difficulty of incorporating temporal components into dietary analysis and an inability to incorporate differing prey assimilation rates (Bearhop et al. 2004).

Stable isotope analysis (SIA) provides a way to quantify diet that incorporates temporal scales and evaluates both richness and evenness of prey items (Bearhop et al. 2004). When prey items have distinct isotopic signatures, one can take the tissue of a predator and quantify prey items and their relative abundances based on the isotopic signature of the predator. When using

SIA to measure diet, one calculates the ratio of 13C to 12C and of 15N to 14N within an organism’s tissues. The ratio of 15N to 14N, represented as δ15N (measured as per mil or ‰), quantifies the trophic level at which predators feed (e.g., Bearhop et al. 2002; DeNiro and Epstein 1981;

Hobson et al. 1994). Carbon isotopes (δ13C) are used to indicate reliance on C4 versus C3 plant diets and together both isotopes can indicate what predators eat and in what proportions

(Peterson and Fry 1987).

Given the wide array of dietary items consumed by coyotes and the high potential that their diet items are not isotopically distinct, it can be difficult to quantify their diet with stable

48 isotope mixing models. However, there are ways to cope with several isotopically overlapping dietary source items through a priori or a posteriori groupings of sources and the use of different models (Philips et al. 2005; Parnell et al. 2010). Furthermore, given the elusive behavior of predators, collecting tissue samples for isotope analysis can be difficult. Few studies have evaluated the individual diets of coyotes (Fedriani 2001; Prugh et al. 2008) and none have done so using SIA, to my knowledge. Despite the challenges associated with dietary analyses of omnivores, understanding the role of top mesopredators, especially their resources use, could provide valuable information as to how they will shape prey communities, and vegetation, in the face of continual apex predator loss. This information is critical, especially in a restoration context, given that so much money and other resources are spent in an attempt to repair damaged ecosystems. Thus, it is important to be able to predict the potential impacts of predators in restored ecosystems.

For this study, I aimed to quantify the assimilated diets of individual coyotes using SIA in a restored tallgrass prairie. I also evaluated seasonal trends in coyote diet using segmented coyote hair. I used two isotopes, carbon and nitrogen, to determine coyote diet with respect to plant and animal matter including C3 and C4 plants, mammals, reptiles, invertebrates and birds.

Considering the heavy reliance on mammals and seasonal shifts seen in other studies evaluating coyote diet in grasslands (Randa 1994; Huebschman et al. 1997; Gerads et al. 2001; Kamler et al.

2002; Azevedo et al. 2006) I expected to find that: 1. coyotes rely heavily on mammalian prey, with small mammals making up the majority of their diet; 2. coyote diet shifts with the seasons, reflecting available food resource; and 3. coyotes in the prairie also have moderately individualistic diets.

49 Methods

Study Site

This research was carried out on a tallgrass prairie preserve called Nachusa Grasslands that is ~1,600-ha. This site is owned by the The Nature Conservancy (TNC) and is located in

Franklin Grove, Illinois. Nachusa consists of restored and remnant (never ploughed) tallgrass prairie, wetlands, oak savanna, and a deciduous forest. The surrounding area is mainly rural development intermixed with agricultural fields containing soybeans or corn. Nachusa is a managed site with management applications including an introduced herd of bison (Bison bison) in 2014, with access to approximately half of the preserve, and prescribed fire. In addition, there are roads connecting different parts of the site and surrounding areas, hiking trails, game trails and ATV dirt roads, all of which have been shown to be utilized by coyotes (Sequin et al. 2003;

Gese et al. 2013).

Coyote samples

Different tissues have different turnover rates, meaning they reflect dietary records over varying temporal scales (i.e. when the tissues were formed; Bearhop et al. 2002). Metabolically inert tissues, such as hair, maintain the isotopic signatures for a long time, are stable, and represent the animal’s diet when the deposition occurred (Hobson 1999). Furthermore, hair grows at a relatively constant rate and can be segmented to determine seasonal trends in diet based on the rate of growth, thus I chose to use coyote hair for my dietary analysis.

All coyote hair was collected opportunistically from local hunters that had killed coyotes at or within a two-mile radius of Nachusa Grasslands. Collected hair included both undercoat

50 and guard hair and was cut at the base of the skin. Although this can be considered

“incomplete sampling” because some short pieces of hair and the follicle will not be collected

(Schwertl et al. 2003), I was not in a position to pluck the hairs from the coyotes. In addition, without the follicle to identify the telogen hair phase (no longer actively growing hair) recent dietary information can be lost (Hopps 1977; Schwertl et al. 2003). However, I used several hair samples (~10-15) and so assuming similar patterns to other mammals, the likelihood of getting multiple telogen hairs in my sample is likely less than 0.3% (Harkey 1993; Schwertl et al. 2003).

Three coyote samples were collected in late spring to early summer (May-June) and consisted of two females and one male. All were adults and hair was collected from the body of the coyotes. The fourth sample was collected in late November and was an unknown gender.

Hair samples were laid straight from base to end, secured and then cut into 10 mm segments.

Sample length ranged from 60-110 mm (Table 8). Each 10 mm segment was then washed in a

95% ethanol solution to remove any external contaminants, followed by a DI water rinse. Hair samples were dried at 55°C for at least 48 hours. Guard hair was separated from undercoat for analysis and cut into small sections (Darimont & Reimchen 2002; Kristensen et al. 2011).

Coyote guard hair typically ranges from 50-110 mm and is shed once yearly in late springtime.

Assuming constant growth, my samples collected in the spring should reflect diet over an approximately 12 month time frame indicating late summer, fall, winter and early spring diet and the November collected sample reflecting summer and fall diet (Darimont & Reimchen 2002;

Beckoff & Gese 2003; Murray et al. 2015).

51 Table 8: Description of coyote samples.

Species Sex Hair length Date of collection Season of diet

Canis latrans Female 110 mm 5/3/2017 Summer, Fall,

Consumer 1 Winter, Spring

Canis latrans Female 70 mm 5/24/17 Summer, Fall,

Consumer 2 Winter, Spring

Canis latrans Male 60 mm 6/25/17 Summer, Fall,

Consumer 3 Winter, Spring

Canis latrans Unknown 90 mm 11/20/17 Summer, Fall

Consumer 4

Dietary sources

To select dietary sources to include in my models I evaluated the literature on coyote diet, based on scat or stomach content analysis, conducted in grassland or prairie habitats (Randa

1996; Huebeschman et al. 1997; Gerads et al. 2001; Kamler et al. 2002; Azevedo et al. 2006).

Because these studies were conducted on scat/stomach content analysis, many dietary sources were identified to broad groups only and included, birds/eggshells, reptiles, invertebrates, fruit, vegetation, mammals, small mammals, amphibians, and garbage. Two studies went into more detail on the mammalian species, fruits, invertebrates, and amphibians (Kamler et al. 2002;

Azevedo et al. 2006). Thus, I combined literature findings with known prey items available at my site. Knowledge of available dietary sources at Nachusa came from items seen in coyote scat,

52 discussion with managers, and research conducted at the site including camera trapping, small mammal sampling, nest surveys, snake boards, and invertebrate pitfall trapping. However, given the diversity at Nachusa Grasslands I was not able to obtain all possible dietary items to include in the model.

For birds at Nachusa, I only included samples that I was able to obtain from abandoned nests due to the low occurrence of road kill and a need for more than one sample from each species. However, I was able to obtain several species that included different avian guilds (i.e., insectivores, omnivores and granivores). I was able to get samples from majority, but not all, of the herbivorous mammal and small mammals species at Nachusa, and I intentionally omitted a few from the models due to the rarity of the species at my site (indicating if they are consumed it is likely at very low rates) and because they were found in the overlapping clump of omnivore species (Figure 15a). Difficulty in obtaining a sample or rarity of the species prevented me from getting samples from all mammalian species at Nachusa; however, I was able to get samples from all trophic levels and hopefully covered the range of isotopic signatures of mammals. The previous literature found grasshoppers, beetles, and other invertebrates in coyote diet. I chose to include a grasshopper, beetle and cricket species in my model. To save both time and money I only included one species of each but chose a predatory beetle, herbivorous grasshopper and detritivorous cricket species to cover all insect guilds and hopefully the range of isotopic signatures within. For reptile species I included a state-listed ornate box turtle species that is of particular interest to managers and a snake species commonly found at my field site. For fruit I selected mulberries due to its frequent occurrence in coyote scat during the summer and because it is one of the only fruits available at my study site. However, if residents in the area were growing fruit in their yards I would have missed a potential fruit source. Finally two of the

53 previous studies (Huebeschman et al. 1997; Gerads et al. 2001) found “vegetation” to be an important dietary item. Due to the non-specific nature of this description and that other studies have found grass (Poaceae) (Santana and Armstrong 2017) to be in coyote diet, I included a range of common C4 and C3 grasses as well as corn in my model. I did not include any amphibian species for my analysis due to the difficulty in obtaining samples and low occurrence at my site.

All dietary hair, tissue, and vegetation samples were collected opportunistically and came from Nachusa Grasslands or the literature (Table 9). Each collected sample was prepped for processing by doing two or more of the following: washing (95% ethanol), rinsing (DI water), drying (at 55°C) and crushing/cutting (mortar and pestle or scissors), refer to table 9 for item specific sample preparation.

Isotope analysis and mixing models

Coyote and prey samples were weighed to either 1 or 5 mg into tin capsules, depending on the percent protein of the sample (Table 8, 9). Samples were then processed in the Northern

Illinois University Isotope lab using an isotope ratio mass spectrometer (Thermo DELTAplus

Advantage) with an elemental analyzer. Stable isotope ratios (R=δ15N / δ14N and R=δ13C/ δ12C) were obtained using atmospheric air for nitrogen and VPDB for carbon as the standards and

3 calculated by taking δ(‰) = [(Rsample/Rstandard)-1] * 10 . In addition, percent carbon and nitrogen were recorded.

54 Table 9: Coyote prey sample methods of collection, preparation, and groupings (individual or seasonal) for analysis.

Species name Common Tissue type Sample prep Sample collection method Individual Diet Groupings Seasonal Diet Groupings

name

Microtus Prairie Vole Hair Washed, rinsed and Hand collected for this Low N Omnivores

ochragaster dried study

Peromyscus Deer mouse Hair Washed, rinsed and Hand collected for this Mid N Omnivores

maniculatus dried study

Peromyscus White footed Hair Washed, rinsed and Hand collected for this Mid N Omnivores

leucopus mouse dried study

Ictidomys 13-lined Hair Washed, rinsed and Hand collected for this Low N Omnivores tridecemlineat us ground dried study

squirrel

Didelphimorph Opossum Hair Washed, rinsed, Road kill High N Omnivores ia virginana dried and cut

Procyon lotor Racoon Hair Washed, rinsed, Road kill High N Omnivores

dried and cut

(Continued on next page)

5

4

55 Table 9 (continued) Memphitis Skunk Hair Washed, rinsed, Road kill High N Omnivores memphitis dried and cut

Spiza Dickcissel Egg shell Dried and crushed Collected from Mid N Omnivores americana empty/abandoned nests

Odocoileus White tailed Hair Washed, rinsed, Collected off fences at study Herbivore Herbivore virginianus deer dried and cut site

Sylvilagus Rabbit Hair Washed, rinsed, Road kill Herbivore Herbivore floridanus dried and cut

Caelifera Grasshopper Whole Rinsed, dried and Collected from unbaited Herbivore Herbivore passive pitfall traps insect crushed

Coleoptera Beetle Whole Rinsed, dried and Collected from unbaited Beetle Omitted for this analysis passive pitfall traps insect crushed

Gryllidae Cricket Whole Rinsed, dried and Collected from unbaited Low N Omnivores passive pitfall traps insect crushed

Sciurus niger Fox squirrel Hair Washed, rinsed, Road kill Mid N Omnivores

dried and cut

(Continued on next page)

5

5

56 Table 9 (continued) Phasianus Ring-necked Egg shell Dried and crushed Collected from Ring-necked pheasant Ring-necked pheasant colchicus pheasant empty/abandoned nests

Spizella Field sparrow Egg shell Dried and crushed Collected from Low N Omnivores pulsilla empty/abandoned nests

Geothlypis Common Egg shell Dried and crushed Collected from High N Omnivores trichas yellowthroat empty/abandoned nests

Spinus tristis American Egg shell Dried and crushed Collected from Mid N Omnivores

goldfinch empty/abandoned nests

Terrapene Ornate Box Tail clip, Dried and Road kill, Illinois Natural Mid N Omnivores ornata ornata Turtle skin ground/cut into History Survey

small pieces

Pantherophis Fox snake Blood Ethanol was King and Bowden 2013 Mid N Omnivores vulpinus evaporated off,

blood was dried

and ground into a

powder

(Continued on next page)

5

6

57 Table 9 (continued) Mustela Longtail Hair Washed, rinsed, Hand collected for this Mid N Omnivores frenata weasel dried and cut study

Morus Mulberry Full berry Dried and crushed Bushes at Nachusa Mulberry Mulberry

Grasslands

Zea mays Corn Cob N/A Latshaw and Miller 1924; C4 C4

Boone et al. 2008

Sporobolus Dropseed Leaves Dried and crushed Hand collected for this C4 C4 heterolepis study

Andropogon Big blue stem Leaves Dried and crushed Hand collected for this C4 C4 gerardii study

Sorghastrum Indian grass Leaves Dried and crushed Hand collected for this C4 C4 nutans study

Setaria viridis Fox tail Leaves Dried and crushed Hand collected for this C4 C4

study

Phleum Timothy grass Leaves Dried and crushed Hand collected for this C3 C3 pratense study

(Continued on next page)

5

7

58 Table 9 (continued)

Juncus Interior Inland rush Leaves Dried and crushed Hand collected for this C3 C3

study

Bromus Smooth brome Leaves Dried and crushed Hand collected for this C3 C3 inermus study

Carex bicknelli Bicknell’s Leaves Dried and crushed Hand collected for this C3 C3

sedge study

Elymus Canada wild Leaves Dried and crushed Hand collected for this C3 C3 canadensis rye study

Dichanthelium Scribner’s Leaves Dried and crushed Hand collected for this C3 C3 oligosanthes panic grass study scribnerianum

Dichanthelium June grass Leaves Dried and crushed Hand collected for this C3 C3 oligosanthes study scribnerianum

Koeleria White-haired Leaves Dried and crushed Hand collected for this C3 C3 macrantha panic grass study

(Continued on next page)

5

8

59 Table 9 (continued)

Poa pratensis Kentucky blue Leaves Dried and crushed Hand collected for this C3 C3

grass study

Phalaris Reed Leaves Dried and crushed Hand collected for this C3 C3 arundinacea canarygrass study

Agrostis Redtop Leaves Dried and crushed Hand collected for this C3 C3 gigantea study

59

60 Potential diet items were plotted by their δ13C and δ15N values in order to determine if sources were isotopically distinct (Figure 15a). Where sources overlapped, they were grouped by isotopic signatures and biological similarities (Figure 15b, c). To evaluate potential differences in diet by sex, consumer δ13C and δ15N values were each plotted by sex for visualization. Sample size was insufficient to analyze diet differences due to sex. All statistics were conducted using

RStudio (RStudio Team 2015, version 3.4.1). To evaluate the seasonal and individualistic diets of coyotes at Nachusa Grasslands, I used the R package MixSiar (Stock and Semmens 2016a) to run Bayesian mixing models. Bayesian mixing models are a powerful method for measuring the proportion of each diet item in a consumer's diet (Stock and Semmens 2016a). MixSiar is a modeling framework under which users can create their own models specific to their data (Stock and Semmens 2016a). I constructed six separate models to look at individual coyote diet, group seasonal diet and individual seasonal coyote diet (Table 10). Group seasonal diet was only analyzed with coyote samples that were collected in the spring and represent approximately one year of dietary information, in order to ensure that samples corresponded to equivalent time periods. For the seasonal diet model, I used coyote hair segments as a measure of time that was run as a continuous variable. For the individual diet model, I used the average of the δ13C and

δ15N values for the segments to yield an overall prediction of diet across a year. All models incorporated concentration dependence. Differences in the proportional contribution of elemental concentrations between sources can be problematic for model output given that this is an assumption of mixing models and violation can lead to an overestimation of dietary sources.

Differences in elemental contributions have been found to exist in omnivores as they eat a wide variety of dietary items including both vegetation and animals (Phillips and Koch 2002). In addition, both residual and processing error was used for models evaluating seasonal diet and

61 only processing error for individual diet (Stock and Semmens 2016b). One important benefit of mixing models is their ability to incorporate error structures, which account for differences in

δ13C and δ15N data between consumers. For seasonal diet I used both error structures because differences in coyote isotope data is likely due to both unexplained variation (such as difference in digestion) and the predation process by each individual coyote, in such that each coyote will not be sampling the source items equally or infinitely (Stock and Semmens 2016b). However, for individual diet I only used processing error because there was not enough variation in my data to incorporate residual error. This was likely due to my small sample size and because I was taking the average δ13C and δ15N value for consumer diet.

I used adult fox trophic discrimination factors (TDF) based on hair samples that were determined in the lab using a fixed diet (Roth and Hobson 2011). Laboratory studies have identified multiple factors that influence TDFs and that their incorrect use can be a source of error (Caut et al. 2008; Bastos et al. 2017). Ideally, samples would be tissue-, species-, diet- and age-specific (Caut et al. 2008). Unfortunately, I could not find species-specific TDF values to use in the literature, indicating a knowledge gap that should be filled. The TDF values I used were 3.2 ± 0.22 for nitrogen and -2.6 ± 0.27 for carbon (Roth and Hobson 2011). Models were tested for convergence using both the Gelman-Rubin and Geweke diagnostic tests, both of which are provided through the MixSiar package (Geweke 1992; Gelman et al. 2014; Stock and

Semmens 2016a). Some models met convergence diagnostics for one test but not the other. In these situations I visually evaluated trace plots based on the model diagnostics output as confirmation of convergence. Model convergence is important in mixing models to ensure that the true posterior distribution has been reached for each variable (Stock and Semmens 2016a).

Finally, in order to get the most information out of the model while still managing to get the

62 model to converge, different groupings were used for seasonal vs. individual diet of coyotes.

For seasonal diets, six major source groups were established and for individual diet nine groups were used (Figure 15b, Table 11 Figure 15c, Table 12).

63

64

Figure 15: a) Plotted sources to visualize for grouping b) Dietary items grouped for seasonal analysis, black dots represent consumers 1-3 segmented values c) Dietary items groups for individual dietary analysis.

65

Table 10: Models run for coyote diet analysis.

Model Parameters Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Type Individual Diet Group (1-3)- Coyote 1- Coyote 2- Coyote 3- Coyote 4- Seasonal Seasonal Seasonal Seasonal Seasonal Hair Source Average Segments Segments Segments Segments Segments Concentration Yes Yes Yes Yes Yes Yes Dependence Error Structures Processing Processing/ Processing/ Processing/ Processing/ Processing/ Residual Residual Residual Residual Residual Continuous NA Time Time Time Time Time Variable Convergence Yes No Yes No No No Number of Nine Six Six Six Six Six sources

6

5

66 Table 11: Mean and standard deviation δ15N and δ13C values for grouped sources used for seasonal coyote diet analysis.

Value C3 Grasses C4 Grasses Herbivore Omnivores Mulberry Ring-Necked Pheasant Mean -1.31 3.78 3.22 6.59 -0.40 5.07 δ15N SD 1.99 1.68 1.00 1.90 0.42 0.49 δ15N Mean -27.23 -12.56 -26.47 -22.42 -28.30 -11.66 δ13C SD 2.98 1.18 0.83 3.91 0.56 2.26 δ13C

Table 12: Mean and standard deviation δ15N and δ13C values for grouped sources used for individual coyote diet analysis.

Value Beetle C3 Grasses C4 Grasses Herbivore High Low N Mid N Mulberry Ring-Necked Pheasant N Mean 4.36 -1.31 3.78 3.22 8.35 4.22 6.91 -0.40 5.07 δ15N SD 1.03 1.99 1.68 1 0.92 1.19 1.24 0.42 0.49 δ15N Mean -18.7 -27.23 -12.56 -26.47 -19.5 -24.65 -22.78 -28.30 -11.66 δ13C SD 1.04 2.98 1.18 0.83 3.33 2.9 3.83 0.56 2.26 δ13C

6

6

67 Results

Dietary source signatures

Source δ15N values ranged from -0.88 to 10.03 with a mean value of 5.58 and δ13C values ranged from -29.28 to -9.40 with a mean of -21.99. There was a clear difference in vegetation

15 13 that followed the C4 vs. C3 pathway for δ N and δ C values, with mean values of -0.4 and 4.55 and -28.3 and -11.9, respective. Provided the overlapping signatures among sources, dietary items were grouped into isotopically and biologically relevant groups.

Coyote diet

Two of the six models converged on the posterior distribution, whereas the other four did not (Table 10). Converged models included individual coyote diet and coyote 1 seasonal diet

(Table 10). Results discussed here reference converged models due my inability to confidently draw conclusions from non-converged models.

I found that dietary composition of coyotes, based on individual coyote diet, in a restored prairie consisted largely of C4 plants (median range=33.2-19.9%, 95% credible interval range=68.2-48.3% and 5.3-1.6%). The remaining dietary items in order of significance were beetles (median range=17.4-12.7%, 95% credible interval range=76.0-45.9% and 0.8-0.3%), ring-necked pheasants (median range=14.3-10.4%, 95% credible interval range=66.9-49.6% and

0.7-0.2%), high N animals (opossums, skunks, raccoons, common yellowthroats; median range=5.0-2.2%, 95% credible interval range=21.0-14.2% and 0.2-0.1%), low N animals (voles, ground squirrels, crickets, field sparrows; median range=4.9-3.0%, 95% credible interval range=38.4-21.3% and 0.2-0.1%), mid N animals (deer mice, white-footed mice, fox squirrels,

68 weasels, ornate box turtles, fox snakes, dickcissels, American goldfinches; median range=4.5-

2.3%, 95% credible interval range=25.0-14.8% and 0.2-0.1%), herbivores (white-tailed deer, rabbits, grasshoppers; median range=4.0-2.6%, 95% credible interval range=29.1-17.7% and 0.2-

0.1%), C3 grasses (median range=3.7-2.2%, 95% credible interval range=26.9-16.0% and 0.2-

0.1%) and mulberries (median range=3.3-1.9%, 95% credible interval range=24.9-13.5% and

0.1-0.0%; Table 12).

In addition, I found differences among diets of individual coyotes (Figure 16). For example, C4 plants were the dietary item consumed in the highest proportion for all four coyotes; however, the median percent in consumers ranged from 39.5% to 19.9%. Similar patterns were seen in all dietary items with differences in median percent (Table 12). When looking at individual coyote diet, I found evidence for seasonal changes (Figure 17). Visual inspection of coyote diet by sex shows the potential for some difference to exist between male and female coyotes (Figure 18).

Table 12: Individual coyote diet by sources. Values represent median proportion.

Source Consumer 1 Consumer 2 Consumer 3 Consumer 4 C4 Grasses 0.332 0.395 0.251 0.199 Beetles 0.148 0.127 0.174 0.173 Ring-Necked Pheasant 0.143 0.104 0.106 0.111 High N 0.050 0.022 0.026 0.031 Low N 0.049 0.030 0.039 0.048 Mid N 0.045 0.023 0.027 0.033 Herbivore 0.040 0.026 0.035 0.040 C3 Grasses 0.037 0.022 0.029 0.033 Mulberry 0.033 0.019 0.025 0.028

69

Figure 16: Proportion of dietary items for individual coyotes 1-4 showing differences in diet among individuals.

70

Figure 17: Proportion of dietary items through time for coyote 1 showing individual seasonal diet. Individual coyote diet changes through time among the three most important dietary items with ring-necked pheasants decreasing from summer through early spring and C4 plants doing the inverse. Omnivores stay relatively the same through seasons, increasing slightly through time. Time is shown sequentially starting with oldest diet (summer-early fall 2017) to newest diet (late winter-early spring).

71

Gender

Figure 18: Boxplots comparing δ15N and δ13C values by segmented gender (female n=2, male n=1) hair samples for visual inspection of potential differences.

Discussion

I quantified coyote diet using stable isotope analysis in a restored prairie. Given the potential ability of coyotes to shape community structure across taxa, understanding their dietary niche is important to understanding their role in restoration. In contrast to other studies conducted on coyote diet in the prairie, I found that coyotes at Nachusa Grasslands eat C4 grasses in the highest proportion and that small/medium-sized mammals were a relatively minor food sources (such mammals are subsumed in Mid N and Low N groups (Figure 15) and omnivore

72 group (Figure 17)). In addition, I found that coyote diet is moderately individualistic as well as seasonal, with shifts in the proportion of dietary items changing with the seasons. However, the signal of seasonality was not as strong as anticipated from previous studies (Meinzer et al.

1975; Bowyer et al. 1983; Kamler et al. 2002). My findings indicate that coyotes may be playing an important role as top mesopredators in prairie restoration through consuming omnivorous prey species, invertebrates and ring-necked pheasants (though see below paragraph for potential caveats). In addition, coyotes may have a strong influence on restoration of C4 grasses through consumption; although, I hypothesize this is actually driven by corn consumption and thus may have implications for farmers.

Previous studies that have looked at individual coyote diet found coyotes to have

“moderately” individualistic diets, or that each individual had a diet similar to the average of the population, but with differences in the proportions of each dietary item (Prugh et al. 2008). My results confirm this finding (Figure 15, Table 11). In addition, I found C4 plants to not only make up the majority of all coyote diets but also that there was the most variation in reliance in this dietary item among individual coyotes (39.5-19.9%). However, this difference is smaller when I exclude my fourth coyote sample (39.5-25.1%), indicating coyotes 1-3 consume more C4 plants.

The fourth coyote sample was collected in November, likely missing the peak in C4 consumption in late winter/early spring I found when investigating seasonal diet (Figure 17). Seasonal diet results are based on one coyote's seasonal diet and more research will be necessary to confirm this. In comparison to the wide variation in C4 plants, all other dietary items were eaten in much more consistent proportions with the difference between dietary items among individuals ranging from 1.4-4.7%. My findings of moderately individual diets among coyotes lend support to the likelihood of decreased intraspecific competition among coyotes and the potential for stronger

73 top-down control via increased predator numbers in prairie restorations. However, more research will be necessary to determine if this is actually the case.

Some individual and group seasonal coyote diet models did not converge, indicating that the results from those models with respect to coyote seasonal diet need to be interpreted with caution. However, one coyote’s seasonal diet model did converge. From this individual, I saw a seasonal diet with high reliance on ring-necked pheasants followed by omnivorous species and

C4 plants through the summer and early fall. Late fall and winter diet shifted to an increase in C4 plants and omnivorous species. By late winter and early spring, this coyote relied heavily on C4 plants and to a lesser extent omnivorous species (Figure 17). My findings of omnivorous species to be the second or fourth most consumed set of species, depending on the season, somewhat matches previous studies, which have shown a strong reliance on mammals through winter months (Kamler et al. 2014). However, this group consisted not only of mammalian species but also reptile, invertebrate, and bird species, so I cannot conclusively say that mammals alone make up the second highest proportion of coyote diet through the winter. Given previous studies and the decreased availability of the other three taxa during winter months, mammals are likely driving the contribution of omnivorous species to coyote diet during winter/early spring. These findings support the idea that coyotes are an important predator for mammalian herbivores, potentially limiting their numbers and increasing diversity in grassland restorations.

I found ring-necked pheasants to be the third most important dietary item of coyotes at

Nachusa and that coyotes may strongly rely on them during the summer and early fall. Isotope model accuracy is predicated on the correct selection of sources. Because coyotes are omnivores with large home ranges, ensuring that all potential dietary items were included at a diverse site like Nachusa was a challenge. Therefore, there could be another dietary source consumed by

74 coyotes that has a similar isotope signature to ring-necked pheasants. If this were the case then ring-necked pheasants would likely be overrepresented in my results, indicating the need for more research and method pairing, which are discussed in more detail below. If my findings for ring-necked pheasants are accurate, coyote reliance on pheasants could have important implications for prairie restoration. On the one hand ring-necked pheasants are an important game bird that brings in revenue, which is often put back into maintaining ecosystems. In contrast, ring-necked pheasants are known to have negative impacts on the breeding success of greater prairie chickens (Sharp 1957; Vance and Westemeier 1979; Westemeier et al. 1998), which have been reduced to small flocks and are extirpated across much of their original range

(Svedarsky et al. 2000). Thus, higher consumption of coyotes on ring-necked pheasants may be beneficial or detrimental to restoration depending on the overall goal of the site.

I found C4 plants to be to be the most important dietary item overall for all four coyotes as well as seasonally. This indicates that through consumption coyotes could have strong impacts on the success of C4 grasses in the prairie. However, in contrast to my findings, other studies of seasonal diet have shown a peak in coyote consumption of plant matter during the summer months (Bowyer et al. 1983; Kamler et al. 2002; Kamler et al. 2014). In addition, studies of coyote diet in rural areas or along an urban to rural gradient did not find such a high reliance on

C4 plants and found a similar strong reliance on mammals in both urban and rural habitats

(Morey et al. 2007; Kamler et al. 2014). Most studies have looked at coyote diet using scat analyses, which can lead to complete digestion of vegetation, so scats may not be accurate portrayals of diet. Thus, I hypothesize that this high reliance on C4 plants is being driven by consumption of corn and corn-based products. Kamler et al. (2014), found that coyote diet in fragmented short-grass prairie was 17% crops, although corn did not make up the majority and

75 this study was conducted using scat analyses. This field site had 39% cultivated crops including corn, sorghum, winter wheat, and sunflower (Kamler et al. 2014). In contrast to the study by Kamler et al. (2014), the cultivated fields surrounding Nachusa are largely corn and soybeans and I have directly observed corn to be present in scats in fall. Moreover, the local community has pet owners that set out cat/dog food (many of which contain corn) and coyotes have been seen eating this and repeatedly coming back as it is a constant food source. My model does not include human garbage or processed food items, but these have been found in coyote scat at other sites (Kamler et al. 2002; Gehrt 2007). Although previous studies have found that coyotes consume human food in higher quantities in urban environments (Gehrt 2007), no previous study has found a primary reliance on C4 plants, especially in winter months when most grasses are dead. Finally, hunters in the area commonly use corn pellets as bait, making corn even more readily available to opportune consumers such as coyotes, especially over the fall/winter months during hunting season.

My results indicate that coyotes rely heavily on C4 plants, likely driven by corn, in a fragmented tallgrass prairie. Models evaluating individual coyote diet performed the best and utilized the most dietary sources, underscoring the individualistic diets of the coyotes in this study. Most previous studies looking at coyote diet used scat analysis, which cannot be identified to the individual and based on my findings, may miss an important aspect of coyote diet. In addition, few studies have quantified individual coyote diet using scat analysis paired with fecal genotyping. Of those studies, one tested the effectiveness of this method and found it to be challenging due to the high number of samples needed per individual (at least 10 but ideally 20-

35) and the ability and effort to obtain them (Prugh et al. 2008). I was able to include nine different groups of dietary sources with individual diet, whereas when running seasonal diet

76 models I could only use six (Table 10), and still couldn’t get several models to converge, making their results spurious. When running seasonal diet models, I omitted beetles and combined omnivorous dietary sources further, due to an inability of the model to converge with that many sources (Figure 15b). However, when looking at coyote diet of individuals, beetles were the second most important dietary item accounting for 12.7-17.4% of coyote diet, indicating that my seasonal diet models are likely missing an important dietary item and potentially overestimating the proportion of several source items.

By using SIA mixing models, I was able to characterize some aspects of coyote diet at

Nachusa Grasslands; however, a lot of dietary information was lost due to the need to aggregate sources into isotopically distinct groups so that models would converge. Unfortunately, isotopically distinct sources are necessary for SIA and are a common limitation encountered, especially with omnivorous predators who eat numerous prey items (Phillips 2012), such as coyotes. Future research that could generate a more accurate depiction of coyote diet should pair

SIA with other dietary analysis methods, such as DNA metabarcoding and scat/stomach analyses, in order to provide informative priors to the models and allow for a more precise determination of diet. For example, a study conducted on Arctic Peregrine Falcons (Falco peregrinus tundrius) encountered similar problems to mine (many and overlapping source items) and found that SIA was best able to predict diet when used in conjunction with motion sensitive camera data that allowed for models to be informed with real-time diet information (Robinson et al. 2018). In addition, a larger consumer sample size would allow for other parameters, such as trophic position, to be measured. Finally, some differences between male and female diet have been observed in other studies (Watine & Giuliano 2017) and I see the potential for that to be

77 occurring at my site (Figure 18), though my conclusions are from too few individuals to analyze statistically. Future studies should also investigate potential differences in diet due to sex.

Coyotes are an important top-mesopredator. Understanding their role as top-predator and their dietary habits could have important implications for shaping restorations. I found that, depending on the season, omnivorous prey species can make up to 24% of coyote diet and that individually they make up the fourth, fifth and sixth most important dietary items. Additionally, I found these prey species to be consumed in the highest proportion during the summer/fall season when many plants are going to seed. Given that many of these omnivorous prey species are important herbivores in the prairie and can impact both seedling emergence and community composition (Pellish et al. 2017), the top-down control coyotes are exerting on these species could be providing an important limitation on both their abundances and foraging behavior, especially during a critical time for vegetation. Furthermore, coyotes are consuming smaller mesopredators, which could be decreasing the nest predation rate on threatened avian species.

Ultimately, coyotes could be playing an important role in not only shaping community structure at lower trophic levels but also what vegetation is emerging and persisting in tallgrass prairies.

Thus, future studies should continue to investigate coyote diet and role in tallgrass restorations, among others, and how that impacts restoration trajectory. This is especially pertinent as coyotes are highly persecuted for their impacts on livestock and human interactions (Berger 2006; White and Gehrt 2009). Although I did find that coyotes consume a high proportion of C4 grasses, likely due to corn consumption, future studies will need to investigate the impacts of coyotes on crop yields specifically and the source of high C4 signature in coyote diet (i.e., crops, dog/cat food, human garbage and processed food).

CHAPTER 4

DISCUSSION

Community interactions are complex and understanding relationships among trophic levels is instrumental in restoring ecosystems, executing successful conservation efforts and informing science. Predators in particular can be very influential on community dynamics and some species are considered a keystone species or are referred to as a “keystone predator”. This is because of their ability to impact both the biodiversity and community structure of lower trophic levels (Eisenberg 2013). Not only do predator’s impact prey species but their foraging behavior can also cascade down the food web to have positive indirect effects on vegetation

(Shurin et al. 2002). Predator species are important in maintaining resilient ecosystems that can endure environmental change (Wilmers et al. 2006; Salo et al. 2007; Pongsiri et al. 2009;

Wallach et al. 2010; Ritchie et al. 2012) and have the potential to shape restoration outcomes. In the face of apex predator loss, it is necessary to better understand the role of coyotes as top mesopredators and how they shape communities and restorations. In this research, I investigated coyote dietary niche and impacts on community structure in a restored grassland.

Coyotes have become one of the most abundant predators in North America. They have dominion as top-mesopredator over many ecosystems and their impacts have been documented across the continent. In prairie ecosystems, their diet is reported to consist largely of small mammals species and they can have significant impacts on small mammals communities (Randa

1996; Huebeschman et al. 1997; Gerads et al. 2001; Kamler et al. 2002; Yunger 2004; Azevedo et al. 2006). However, I did not find this. Coyote removal had minor impacts to small mammal 79 community dynamics and, not surprisingly, this did not cascade to grass or forb biomass. In addition, I did not find that coyotes at my study site eat a majority of small mammals. Rather, coyotes appear to be consuming C4 plants in the highest proportions, which we hypothesize is due to corn consumption.

There are many possible explanations for my unique results. Nachusa grasslands is surrounded by an abundance of corn fields. In addition, hunting is allowed in the area and hunters are known to use corn pellets to bait deer. Finally, pet owners often leave out dog and cat food for their pets and there is an abundant source of leftover processed human food/garbage in dumpsters. As opportunistic feeders, coyotes may just be eating a lot more corn products than spending costly energy hunting small mammals and other prey items. Thus top-down control on small mammal communities by coyotes may be lacking at Nachusa.

In contrast, coyotes may in fact be having a larger impact on small mammal dynamics, but with the diverse predator and herbivore guild at Nachusa, more time may be necessary for changes to become significant, especially given my relatively small sample size and short sampling period. In addition, stable isotope mixing models may not be able to accurately predict coyote diet. Coyotes eat a wide array of dietary items that have a lot of overlap among their trophic signatures, which has been shown to be problematic when using mixing models to determine diet (Robinson et al. 2018). Thus, my estimates of coyote diet may be incorrect and coyotes may be eating far greater proportions of small mammals than predicted by my models.

The current literature would suggest the latter explanation for what I’m seeing, but more research will be necessary to get a better handle on the impacts of coyote removal at Nachusa, especially since there is concern at my study site that coyotes, through depredation, are negatively impacting a state-listed ornate box turtle species (Terrapene ornata). Although there

80 have been signs of coyote bite marks on adult ornate box turtles, I found very little evidence that coyotes are consuming these species in high proportions. When looking at individual coyote diet, ornates are among the group of omnivorous species titled Mid N. This group comprises 8 different omnivorous species and at most only makes up 4.5% of coyote diet. This coincides with other literature of coyote diet in the prairie that found reptiles made up 4.2% of coyote diet

(Kamler et al. 2002). Furthermore, many other studies didn’t even find reptile species in coyote diet (Huebeschman et al. 1997; Gerads et al. 2001; Azevedo et al. 2006). Given the literature and my findings, I feel that ornate box turtles are not likely to make up a significant proportion of coyote diet, if at all. Further research utilizing DNA metabarcoding or other techniques will be necessary to conclusively say if coyotes eat ornate box turtles at all. Additionally, I found that coyote exclusion did not have significant impacts on small mammal communities or plant biomass, indicating this could be an effective form of management to deal with these predators should it be necessary. However, as discussed, more time and research is necessary to conclusively say that coyotes do not impact lower trophic levels.

LITERATURE CITED

Abrams PA (2008) Measuring the Impact of Dynamic Antipredator Traits on Predator-Prey- Resource Interactions. Ecology 89:1640–1649 doi: 10.1890/07-0764.1

Abramsky Z, Dyer M, Harrison P (1979) Competition among Small Mammals in Experimentally Perturbed Areas of the Shortgrass Prairie. Ecology 60:530–536 doi: 10.2307/1936073

Anselin L (2002) Under the hood Issues in the specification and interpretation of spatial regression models. Agric Econ 27:247–267 doi: 10.1016/S0169-5150(02)00077-4

Azevedo F, Lester V, Gorsuch W, et al (2006) Dietary breadth and overlap among five sympatric prairie carnivores. J Zool 269:127–135 doi: 10.1111/j.1469-7998.2006.00075.x

Barber NA, Jones HP, Duvall MR, et al (2017) Phylogenetic diversity is maintained despite richness losses over time in restored tallgrass prairie plant communities. J Appl Ecol 54:137–144 doi: 10.1111/1365-2664.12639

Bastos RF, Corrêa F, Winemiller KO, Garcia AM (2017) Are you what you eat ? Effects of trophic discrimination factors on estimates of food assimilation and trophic position with a new estimation method. Ecol Indic 75:234–241. doi: 10.1016/j.ecolind.2016.12.007

Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1-48

Baum JK, Myers RA (2004) Shifting baselines and the decline of pelagic sharks in the Gulf of Mexico. Ecol Lett 7:135–145 doi: 10.1111/j.1461-0248.2003.00564.x

Bearhop S, Adams CE, Waldron S, et al (2004) Determining trophic niche width : a novel approach using stable isotope analysis. J Anim Ecol 73:1007–1012 doi: 10.1111/j.0021- 8790.2004.00861.x

Bearhop S, Waldron S, Votier SC, Furness RW (2002) Factors That Influence Assimilation Rates and Fractionation of Nitrogen and Carbon Stable Isotopes in Avian Blood and Feathers. Physiol Biochem Zool 75:451–458 doi: 10.1086/342800

Beauvais GP, Buskirk SW (1999) Modifying estimates of sampling effort to account for sprung traps. Wildl Soc Bull 39-43

82 Beckerman AP, Uriarte M, Schmitz OJ (1997) Experimental evidence for a behavior- mediated trophic cascade in a terrestrial food chain. Proc Natl Acad Sci 94:10735–10738 doi: 10.1073/pnas.94.20.10735

Bekoff M, Gese EM (2003) Coyote (Canis latrans). In: Feldhamer GA, Thompson BC, Chapman JA (eds) Wild mammals of North America: biology, management, and conservation, 2nd edn. Johns Hopkins University Press, Baltimore, pp 467–481

Benayas JMR, Carnian E, Norian E (2009) Enhancement of Biodiversity and Ecosystem Services by Ecological Restoration: A Meta-Analysis. Science 325:1121–1124 doi: 10.1126/science.1172460

Berger K (2006) Carnivore-Livestock Conflicts: Effects of Subsidized Predator Control and Economic Correlates on the Sheep Industry. Conserv Biol 1–11 doi: 10.1111/j.1523- 1739.2006.00336.x

Bestion E, Cucherousset J, Teyssier A, Cote J (2015) Non-consumptive effects of a top-predator decrease the strength of the trophic cascade in a four-level terrestrial food web. Oikos 124:1597–1602 doi: 10.1111/oik.02196

Boone LV, Vasilas BL, Welch LF. (1984) The nitrogen content of corn grain as affected by hybrid, population, and location. Commun Soil Sci Plant Anal 15(6): 639-650 doi: 10.1080/00103628409367504

Bouskila A (1995) Interactions Between Predation Risk and Competition : A Field Study of Kangaroo Rats and Snakes. Ecology 76:165–178 doi: 10.2307/1940639

Bowler PA (2000) Ecological Restoration of Coastal Sage Scrub and Its Potential Role in Habitat Conservation Plans. Environ Manage 26:85–96 doi: 10.1007/s002670010064

Bowyer TR, McKenna SA, Shea ME (1983) Seasonal changes in coyote food habits as determined by fecal analysis. Am Mid Nat 109:266-273 doi: 10.2307/2425406

Brashares JS, Prugh LR, Stoner CJ, Epps CW (2010) Ecological and conservation implications of mesopredator release. Trophic cascades: predators, prey, and the changing dynamics of nature. Island Press, Washington, DC, pp 221–240

Breslow N, Clayton D (1993) Approximate Inference in Generalized Linear Mixed Models. J Am Stat Assoc 88:9–25 doi: 10.1080/01621459.1993.10594284

Brillhart DE, Kaufman DW (1994) Temporal variation in coyote prey in tallgrass prairie of eastern Kansas. Prairie Nat 26:93-105

Bruno JF, O’Connor MI (2005) Cascading effects of predator diversity and omnivory in a marine food web. Ecol Lett 8:1048–1056 doi: 10.1111/j.1461-0248.2005.00808.x

83 Bruseo J, Vessey S, Graham J (1999) Discriminatino between Peromyscus leucopus noveboracensis and Peromyscus maniculatus nubiterrae in the field. Acta Theriol (Warsz) 44:151–160

Bueno C, Ruckstuhl KE, Arrigo N, et al (2012) Impacts of cattle grazing on small-rodent communities : an experimental case study. Can J Zool 90:22–30 doi: 10.1139/Z11-108

Burkholder DA, Heithaus MR, Fourqurean JW, et al (2013) Patterns of top-down control in a seagrass ecosystem : could a roving apex predator induce a behaviour-mediated trophic cascade ? J Anim Ecol 82:1192–1202 doi: 10.1111/1365-2656.12097

Caldwell LD (1964) An investigation of competition in natural populations of mice. J Mammal 45(1):12-30 doi: 10.2307/1377290

Caut S, Angulo E, Courchamp F (2008) Caution on isotopic model use for analyses of consumer diet. Can J Zool 438–445 doi: 10.1139/Z08-012

Chase JM, Abrams A, Grover JP, et al (2002) The interaction between predation and competition : a review and synthesis. Ecol Lett 5:302–315 doi: 10.1046/j.1461- 0248.2002.00315.x

Cooper D (1997) Foraging responses of the Great-horned Owl (Bubo virginianus) to changes in prey abundance. MS thesis, Northern Illinois University, Ill.

Creel S, Christianson DA, Winnie JA (2011) A survey of the effects of wolf predation risk on pregnancy rates and calf recruitment in elk. Ecol Appl 21:2847–2853 doi: 10.1890/11- 0768.1

Croll D, Maron J, Estes JA, et al (2005) Introduced Predators Transform Subarctic Islands from Grassland to Tundra. Science 307:1959–1961 doi: 10.1126/science.1108485

Crooks KR, Soule ME (1999) Mesopredator release and avifaunal extinctions in a fragmented system. Lett to Nat 400:563–566 doi: 10.1038/23028

Darimont CT, Reimchen TE (2002) Intra-hair stable isotope analysis implies seasonal shift to salmon in gray wolf diet. Cambridge Univ Press 80:1638–1642 doi: 10.1139/Z02-149

DeJaco C, Batzli G (2013) Palatability of plants to small mammals in nonnative grasslands of east-central Illinois. J Mammal 94:427–435 doi: 10.1644/12-MAMM-A-157.1

DeNiro MJ, Epstein S (1981) Influence of diet on the distribution of nitrogen isotopes in animals. Geochim Cosmochim Acta 45:341-351 doi: 10.1016/0016-7037(81)90244-1

Donald PF, Green RE, Heath MF (2001) Agricultural intensification and the collapse of Europe ’ s farmland bird populations. Proc R Soc B 268:25–29 doi: 10.1098/rspb.2000.1325

84 Eisenberg C (2013) The wolf's tooth: keystone predators, trophic cascades, and biodiversity. Island Press

Estes JA, Duggins DO (1995) Sea Otters and Kelp Forests in Alaska: Generality and Variation in a Community Ecological Paradigm. Ecol Monogr 65:75–100 doi: 10.2307/2937159

Estes JA, Terborgh J, Brashares JS, et al (2011) Trophic downgrading of planet Earth. Science 333:301–306 doi: 10.1126/science.1205106

Estes JA, Palmisano J (1974) Sea Otters: Their role in structuring nearshore communities. Science 185:1058–1060 doi: 10.1126/science.185.4156.1058

Faith D, Minchin P, Belbin L (1987) Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69:57–68 doi: 10.1007/978-94-009-4061-1

Fedriani JM, Kohn MH (2001) Genotyping faeces links individuals to their diet. Ecol Lett 4:477–483 doi: 10.1046/j.1461-0248.2001.00250.x

Finke DL, Denno RF (2004) Predator diversity dampens trophic cascades. Lett to Nat 429:407 doi: 10.1038/nature02526.1.

Flagel DG, Belovsky GE, Cramer MJ, et al (2016) Fear and loathing in a Great Lakes forest: cascading effects of competition between wolves and coyotes. J Mammal 98:77-84 doi: 10.1093/jmammal/gyw162

Fuller, TK, Destefano S, Warren P (2010) Carnivore Behavior and Ecology, and Relationship with Urbanization. In: Gehrt, Stanley D., Riley, SPD, Cypher B (ed) Urban Carnivores Ecology, Conflict, and Conservation. The Johns Hopkins University Press, Baltimore, pp 13–19

Gales NJ, Cheal AJ (1992) Estimating the diet composition of the Australian sea lion (Neophoca cinerea) from scat analysis: An unreliable technique. Wildl Res 19:447-455 doi: 10.1071/WR9920447

Gehrt SD (2007) Ecology of coyotes in urban landscapes. 12th Wildl Damage Manag Conf 303– 311

Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2014) Bayesian data analysis (Vol. 2). Boca Raton, FL: CRC press

Gerads JR, Jenks JA, Watters BK (2001) Food habits of coyotes inhabiting the Black Hills and surrounding prairies in western South Dakota. In: Proceedings of the South Dakota Academy of Science 80(200):95–108

85 Gese E, Grothe S (2015) Analysis of Coyote Predation on Deer and Elk during Winter in Yellowstone National Park, Wyoming. Am Midl Nat 133:36–43 doi: 10.2307/2426345

Gese EM, Dowd JLB, Aubry LM (2013) The Influence of Snowmobile Trails on Coyote Movements during Winter in High-Elevation Landscapes. PLoS One 8:1–10 doi: 10.1371/journal.pone.0082862

Geweke J (1992) Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments. In J. M. Bernardo, A. F. M. Smith, A. P. Dawid and J. O. Berger (eds.), Bayesian Statistics 4, New York: Oxford University Press.

Gittleman JL, Funk SM, Macdonald DW, Wayne RK (2001) Carnivore Conservation, 5th edn. Cambridge University Press

Gompper M (2010) Top in the Carnivores Suburbs? Ecological and Conservation Issues Raised by Colonization of North-Eastern North America by Coyotes. Biosci 52(2):185–190 doi: 10.1641/0006-3568(2002)052[0185:TCITSE]2.0.CO;2

Grabowski JH, Kimbro DL (2005) Predator-Avoidance Behavior Extends Trophic Cascades to Refuge Habitats. Ecology 86:1312–1319 doi: 10.1890/04-1216

Grant PR (1971) Experimental studies of competitive interaction in a two-species system. III. Microtus and Peromyscus species in enclosures. J Anim Ecol 40:323-350 doi: 10.2307/3249

Grant P (1972) Interspecific Competition Among Rodents. Annu Rev Ecol Syst 3:79–106 doi: 10.1146/annurev.es.03.110172.000455

Grant WE, Birney EC, French NR, Swift DM (1982) Structure and productivity of grassland small mammal communities related to grazing-induced changes in vegetative cover. J Mammal 63:248–260 doi: 10.2307/1380634

Grimm A, Gruber B, Henle K (2014) Reliability of Different Mark-Recapture Methods for Population Size Estimation Tested against Reference Population Sizes Constructed from Field Data. PLoS One 9(6): e98840 doi: 10.1371/journal.pone.0098840

Hairston NG, Smith FE, Slobodkin LB (1960) Community Structure, Population Control, and Competition. Am Nat 94:421–425 doi: 10.1086/282146

Harkey MR (1993) Anatomy and physiology of hair. Forensic Sci Int 63:9-18 doi: 10.1016/0379-0738(93)90255-9

Harrison DJ, Harrison JA (1984) Foods of adult Maine coyotes and their known-aged pups. J Wildl Manage 48:922-926 doi: 10.2307/3801439

86 Hartnett DC, Keeler KH (1995) Population processes. In: Joern A, Keeler KH (eds) The Changing Prairie: North American Grasslands. Oxford, Oxford University Press, pp 82–99

Hawlena D, Schmitz OJ (2010) Physiological Stress as a Fundamental Mechanism Linking Predation to Ecosystem Functioning. Am Nat 176:537–556 doi: 10.1086/656495

Henke SE, Bryant FC (1999) Effects of coyote removal on the faunal community in western Texas. J Wildl Manage 63:1066-1081 doi: 10.2307/3802826

Hill M (1973) Diversity and Evenness: A Unifying Notation and Its Consequences. Ecology 54:427–432 doi: 10.2307/1934352

Hobson K (1999) Tracing Origins and Migration of Wildlife Using Stable Isotopes: A Review. Oecologia 120:314–326 doi: 10.1007/s004420050865

Hobson KA, Piatt JF, Pitocchelli J (1994) Using stable isotopes to determine seabird trophic relationships. J Anim Ecol 63:786-798 doi: 10.2307/5256

Hoffmeister DE (1989) Mammals of Illinois. University of Illinois Press, Champagne, Ill. Holt RD, Polis G (1997) A theoretical framework for intraguild predation. Am Nat 149:745–764 doi: 10.1086/286018

Holt RD, Grover J, Tilman D (1994) Simple rules for interspecific dominance in systems with exploitative and apparent competition. Am Nat 144:741–771 doi: 10.1086/285705

Hopps HC (1977) The biologic bases for using hair and nail for analyses of trace elements. Sci Total Environ 7:71-89 doi: 10.1016/0048-9697(77)90018-3

Howe HF, Zorn-arnold B, Sullivan A, Brown JS (2006) Massive and Distinctive Effects of Meadow Voles on Grassland Vegetation. Ecology 87:3007–3013 doi: 10.1890/0012- 9658(2006)87[3007:MADEOM]2.0.CO;2

Huebschman JJ, Hygnstrom SE, Gubanyi JA (1997) Coyote Food Habits at DeSoto National Wildlife Refuge, Nebraska. Prairie Nat 29:99–109

Huntly N (1991) Herbivores and the Dynamics of Communities and Ecosystem. Annu Rev Ecol Syst 22:477–503 doi: 10.1146/annurev.es.22.110191.002401

Kamler J, Gipson PS, Perchellet CC (2002) Seasonal Food Habits of Coyotes. Prairie Nat 34:75– 84

Kamler J, Klare U, Ballard W, et al (2014) Comparison of coyote (canis latrans) diets in continuous and fragmented short-grass prairie in the Texas Panhandle. Texas J Sci 66:25– 41

87 Kamler J, Pennock D, Welch C, Pierotti R (1998) Variation in Morphological Characteristics of the White-footed Mouse (Peromyscus leucopus) and the Deer Mouse (P. maniculatus) under Allotopic and Syntopic Conditions. Am Midl Nat 140:170–179 doi: 10.1674/0003- 0031(1998)140[0170:VIMCOT]2.0.CO;2

King RB, Bowden RM (2013) Seasonal, Condition-Dependent, and Individual Variation in Testosterone in a Natricine Snake. J Herpetol 47:179–183. doi: 10.1670/12-001

Krebs CJ (1966) Demographic Changes in Fluctuating Populations of Microtus californicus. Ecol Monogr 36:239–273 doi: 10.2307/1942418

Krebs CJ, Keller B, Tamarin R (1969) Microtus Population Biology: Demographic Changes in Fluctuating Populations of M. Ochrogaster and M. Pennsylvanicus in Southern Indiana. Ecology 50:587–607 doi: 10.2307/1936248

Kristensen D, Kristensen E, Forchhammer M, et al (2011) Arctic herbivore diet can be inferred from stable carbon and nitrogen isotopes in C3 plants, faeces, and wool. Can J Zool 89:892–899 doi: 10.1139/z11-073

Kruskal J (1964) Nonmetric Multidimensional Scaling: A Numerical Method. Psychometrika 29:115–129 doi: 10.1007/BF02289694

Laake JL (2013) RMark: An R Interface for Analysis of Capture-Recapture Data with MARK. AFSC Processed Rep 2013-01, 25p. Alaska Fish. Sci. Cent., NOAA, Natl. Mar. Fish. Serv., 7600 Sand Point Way NE, Seattle WA 98115.

Latshaw WL, Miller EC (1924) Elemental composition of the corn plant. J Agric Res 27:845– 861

Laundré JW, Hernández L, Ripple WJ (2010) The Landscape of Fear: Ecological Implications of Being Afraid. Open Ecol J 3:1–7 doi: 1874-2130/10

Legendre P (1993) Spatial Autocorrelation: Trouble or New Paradigm? Ecology 74:1659–1673 doi: 10.2307/1939924

Legendre P, Legendre L (1998) Numerical Ecology. Second English edition. Oxford: Elsevier

Leibold MA (1996) A graphical model of keystone predators in food webs: Trophic regulations of abundance, incidence and diversity patterns in communities. Am Nat 147:784–812 doi: 10.1086/285879

Lleonart J, Salat J, Torres G (2000) Removing Allometric E 4 ects of Body Size in Morphological Analysis. J Theor Biol 205:85–93 doi: 10.1006/jtbi.2000.2043

88 Loehle C, Eschenbach W (2011) Historical bird and terrestrial mammal extinction rates and causes. Biodivers Res 1–8 doi: 10.1111/j.1472-4642.2011.00856.x

MacCracken JG, Uresk DW (1984) Coyote foods in the Black Hills, South Dakota. J Wildl Manage 48:1420-1423 doi: 10.2307/3801809

Meinzer WP, Ueckert DN, Flinders JT (1975) Food niche of coyotes in the rolling plains of Texas. J Range Manage 28:22-27

Messier F (1995) Trophic interactions in two northern wolf-ungulate systems. Wildl Res 22:131– 146 doi: 10.1071/WR9950131

Middleton AD, Kauffman M, McWhirter D, et al (2013) Linking anti-predator behaviour to prey demography reveals limited risk effects of an actively hunting large carnivore. Ecol Lett 1– 8 doi: 10.1111/ele.12133

Miller BJ, Harlow HJ, Harlow TS, et al (2012) Trophic cascades linking wolves ( Canis lupus ), coyotes ( Canis latrans), and small mammals. Can J Zool 90:70–78 doi: 10.1139/Z11-115

Morey PS, Gese EM, Gehrt SD (2007) Spatial and Temporal Variation in the Diet of Coyotes in the Chicago Metropolitan Area. Am Midl Nat 158:147–161 doi: 10.1674/0003- 0031(2007)158[147:SATVIT]2.0.CO;2

Murray M, Cembrowski A, Latham ADM, et al (2015) Greater consumption of protein-poor anthropogenic food by urban relative to rural coyotes increases diet ... Ecography (Cop) 38:1–8 doi: 10.1111/ecog.01128

Narional Research Council (NRC) (1992) Restoration of aquatic ecosystems: science, technology and public policy. National Academy Press, Washington, DC, USA

Newsome TM, Ripple WJ (2015) A continental scale trophic cascade from wolves through coyotes to foxes. J Anim Ecol 84:49–59 doi: 10.1111/1365-2656.12258

Oksanen L, Fretwell SD, Arruda J, Niemela P (1981) Exploitation Ecosystems in Gradients of Primary Productivity. Am Nat 118:240–261 doi: 10.1086/283817

Oksanen J, Kindt R, Legendre P, et al (2009) vegan: Community Ecology Package. R package version 1.15-4.

Oksanen L, Oksanen T (1992) Long-term microtine dynamics in North Fennoscandian Tundra – the vole cycle and the lemming chaos. Ecography (Cop) 15:226–236 doi: 10.1111/j.1600- 0587.1992.tb00029.x

Olff H, Ritchie ME (1998) Effects of herbivores on grassland plant diversity. Trends Ecol Evol 13:261–265 doi: 10.1016/S0169-5347(98)01364-0

89

Paine RT (1971) A Short-Term Experimental Investigation of Resource Partitioning in a New Zealand Rocky Intertidal Habitat. Ecology 52:1096–1106 doi: 10.2307/1933819

Paine RT (1980) Food Webs: Linkage, Interaction Strength and Community Infrastructure. J Anim Ecol 49:667–685 doi: 10.2307/4220

Paine RT (1966) Food Web Complexity and Species Diversity. Am Nat 100:65–75 doi: 10.1086/282400

Palomares F, Caro TM (1999) Interspecific Killing among Mammalian Carnivores. 153:492–508 doi: 10.1086/303189

Parnell AC, Inger R, Bearhop S, Jackson AL (2010) Source Partitioning Using Stable Isotopes : Coping with Too Much Variation. PLoS One 5:1–5 doi: 10.1371/journal.pone.0009672

Peet R (1974) The measurement of species diversity. Annu Rev Ecol Syst 5:285–307 doi: 10.1146/annurev.es.05.110174.001441

Peig J, Green A (2009) New perspectives for estimating body condition from mass / length data : the scaled mass index as an alternative method. Oikos 118:1883–1891 doi: 10.1111/j.1600- 0706.2009.17643.x

Peig J, Green A (2010) The paradigm of body condition : a critical reappraisal of current methods based on mass and length. Funct Ecol 24:1323–1332 doi: 10.1111/j.1365- 2435.2010.01751.x

Pellish CA, Sherrard ME, Leytem PA, Jackson LL (2017) Small vertebrate granivores reduce seedling emergence in native tallgrass prairie restoration. Restor Ecol 1–8 doi: 10.1111/rec.12557

Pellish CA, Sherrard ME, Leytem PA, Jackson LL (2017) Small vertebrate granivores reduce seedling emergence in native tallgrass prairie restoration. Restor Ecol 1–8 doi: 10.1111/rec.12557

Peterson BJ, Fry B (1987) Stable Isotopes in Ecosystem Studies. Annu Rev Ecol Syst 18:293– 320 doi: 10.1146/annurev.es.18.110187.001453

Petticrew BG, Sadleir RMFS (1974) The ecology of the deer mouse Peromyscus maniculatus in a coastal coniferous forest I. Population dynamics. Can J Zool 52:107-118 doi: 10.1139/z74-015

Phillips DL, Gregg JW (2003) Source partitioning using stable isotopes : coping with too many sources. Oecologia 136:261–269 doi: 10.1007/s00442-003-1218-3

90 Phillips DL, Newsome SD, Gregg JW (2005) Combining sources in stable isotope mixing models : alternative methods. Oecologia 144:520–527 doi: 10.1007/s00442-004-1816-8

Phillips DL (2012) Converting isotope values to diet composition: the use of mixing models. J Mammal 93:342–352 doi: 10.1644/11-MAMM-S-158.1

Phillips DL, Koch PL (2002) Incorporating concentration dependence in stable isotope mixing models. Oecologia 130:114–125 doi: 10.1007/s004420100786

Pocock M, Frantz A, Cowan D, et al (2004) Tapering bias inherent in minimum number alive (MNA) population indices. J Mammal 85:959-962 doi: 10.1644/BPR-023

Polis, GA, Holt R (1992) Intraguild predation: the dynamics of complex trophic interactions. Trends Ecol Evol 7:151–153 doi: 10.1016/0169-5347(92)90208-S

Pongsiri MJ, Roman JOE, Ezenwa VO, et al (2009) Global Disease Ecology. Bioscience 59:945–954 doi: 10.1525/bio.2009.59.11.6

Power M (1990) Effects of Fish in River Food Webs. Science 250:811–814 doi: 10.1126/science.250.4982.811

Preisser EL, Orrock JL, Schmitz OJ (2007) Predator Hunting Mode and Habitat Domain Alter Nonconsumptive Effects in Predator-Prey Interactions. Ecology 88:2744–2751 doi: 10.1890/07-0260.1

Prugh LR, Arthur SM, Ritland CE (2008) Use of faecal genotyping to determine individual diet. Wildlife Biol 14:318–330 doi: 10.2981/0909-6396(2008)14[318:UOFGTD]2.0.CO;2

Prugh LR, Stoner CJ, Epps CW, et al (2009) The Rise of the Mesopredator. Bioscience 59:779– 791 doi: 10.1525/bio.2009.59.9.9

R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

Randa NLA (1996) Prey Selectivity and Foraging Activity of Canis latrans and Vulpes vulpes in Response to Prey Fluctuations and Habitat in a Heterogeneous Landscape. Northern Illinois University

Redfield JA, Krebs CJ, Taitt MJ (1977) Competition between Peromyscus maniculatus and Microtus townsendii in grasslands of coastal British Columbia. J Anim Ecol 46:607-616 doi: 10.2307/3833

Reichel JD (1991) Relationships Among Coyote Food Habits, Prey Populations, and Habitat Use. Northwest Sci 65:133–137

91 Ripple WJ, Beschta RL (2012) Trophic cascades in Yellowstone : The first 15 years after wolf reintroduction. Biol Conserv 145:205–213 doi: 10.1016/j.biocon.2011.11.005

Ripple WJ, Estes JA, Beschta RL, et al (2014) Status and ecological effects of the world’s largest carnivores. Science (80- ) 343:1241484 doi: 10.1126/science.1241484

Ripple WJ, Wirsing AJ, Wilmers CC, Letnic M (2013) Widespread mesopredator effects after wolf extirpation. Biol Conserv 160:70–79 doi: 10.1016/j.biocon.2012.12.033

Ritchie EG, Elmhagen B, Glen AS, et al (2012) Ecosystem restoration with teeth : what role for predators ? Trends Ecol Evol 27:265–271 doi: 10.1016/j.tree.2012.01.001

Ritchie EG, Johnson C (2009) Predator interactions , mesopredator release and biodiversity conservation. Ecol Lett 12:1–18 doi: 10.1111/j.1461-0248.2009.01347.x

Robinson BG, Franke A, Derocher AE (2018) Stable isotope mixing models fail to estimate the diet of an avian predator. Auk Ornithol Adv 135:60–70 doi: 10.1642/AUK-17-143.1

Rogers M, Caro J (1998) Song sparrows, top carnivores and nest predation: a test of the mesopredator release hypothesis. Oecologia 116:227–233 doi: 10.1007/s004420050583

Roth JD, Hobson KA (2011) Stable Carbon and Nitrogen Isotopic Fractionation Between Diet and Tissue of Captive Red Fox: Implications for Dietary Reconstruction. Can J Zool 78:848–852 doi: 10.1139/z00-008

RStudio Team. (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA. URL http://www.rstudio.com/

Salo P, Korpima E, Banks PB, et al (2007) Alien predators are more dangerous than native predators to prey populations. Proc R Soc B 274:1237–1243 doi: 10.1098/rspb.2006.0444

Sampson F, Knopf F (1994) Prairie conservation in North America. Bioscience 44:418–421 doi: 10.2307/1312365

Santana E, Armstrong J (2017) Food habits and anthropogenic supplementation in coyote diets along an urban-rural gradient. Human-Wildlife Interact 11:156–166

Sauer J, Fallon J, Johnson R (1997) Use of North American breeding bird survey data to estimate population change for bird conservation regions. J Wildl Manage 67:372–389 doi: 10.2307/3802778

Schmitz OJ (1998) Direct and Indirect Effects of Predation and Predation Risk in Old-Field Interaction Webs. Am Nat 151:327–342 doi: 10.1086/286122

92 Schmitz OJ (2008) Effects of predator hunting mode on grassland ecosystem function. Science (80- ) 319:952–954 doi: 10.1126/science.1152355

Schmitz OJ, Hamback PA, Beckerman AP (2000) Trophic Cascades in Terrestrial Systems : A Review of the Effects of Carnivore Removals on Plants. Am Nat 155:141–153 doi: 10.1086/303311

Schmitz OJ, Suttle K (2001) Effects of top predator species on direct and indirect interactions in a food web. Ecology 82:2072–2081 doi: 10.1890/0012- 9658(2001)082[2072:EOTPSO]2.0.CO;2

Schwertl M, Auerswald K, Schnyder H (2003) Reconstruction of the isotopic history of animal diets by hair segmental analysis. Rapid Commun Mass Spectrom 17:1312–1318 doi: 10.1002/rcm.1042

Sequin E, Jaeger MM, Brussard PF, Barrett RH (2003) Wariness of coyotes to camera traps relative to social status and territory boundaries. Can J Zool 81:2015–2025 doi: 10.1139/z03-204

Sharp W (2008) Social and Range Dominance in Gallinaceous Birds: Pheasants and Prairie Grouse. J Wildl Manage 21:242–244 doi: 10.2307/3797597

Sheriff MJ, Krebs CJ, Boonstra R (2011) From process to pattern : how fluctuating predation risk impacts the stress axis of snowshoe hares during the 10-year cycle. Oecologia 166:593–605 doi: 10.1007/s00442-011-1907-2

Shurin J, Borer E, Seabloom E, et al (2002) A cross ecosystem comparison of the strength of trophic cascades. Ecology 785–791 doi: 10.1046/j.1461-0248.2002.00381.x

Shurin JB, Allen EG (2001) Effects of Competition, Predation, and Dispersal on Species Richness at Local and Regional Scales. Am Nat 158:624–637 doi: 10.1086/323589

Sih A, Crowley P, Mcpeek M, et al (1985) Predation, Competition, and Prey Communities: A Review of Field Experiments. Lin. Annu Rev Ecol Syst 16:269–311 doi: 10.1146/annurev.es.16.110185.001413

Sikes R, Mammalogists TAC and UC of the AS of (2016) 2016 Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. J Mammal 97:663–688 doi: 10.1093/jmammal/gyw078

Simpson EH (1949) Measurement of diversity. Nature 163:688 doi: 10.1038/163688a0

Smith T (1990) Resource Use by Bill Morphs of An African Finch : Evidence for Intraspecific Competition. Ecology 71:1246–1257 doi: 10.2307/1938261

93 Sokal RR, Rohlf FJ (1981) Biometry. 2nd edn. Freeman, New York.

Sovada MA, Sargeant AB, Grier JW (1995) Differential effects of coyotes and red foxes on duck nest success. J Wildl Manage 59:1-9 doi: 10.2307/3809108

Speakman JR (2008) The physiological costs of reproduction in small mammals. Philos Trans R Soc B 363:375–398 doi: 10.1098/rstb.2007.2145

Sternburg J, Feldhamer GA (1997) Mensural discrimination between sympatric Peromyscus in southern Illinois. Acta Theriol (Warsz) 42:1–13

Stevenson RD, Woods WA (2006) Condition indices for conservation : new uses for evolving tools. Integr Comp Biol 46:1169–1190 doi: 10.1093/icb/icl052

Stief P, Holker F (2006) Trait-Mediated Indirect Effects of Predatory Fish on Microbial Mineralization in Aquatic Sediments. Ecology 87:3152–3159 doi: 10.1890/0012- 9658(2006)87[3152:TIEOPF]2.0.CO;2

Stock B, Semmens B (2016a) MixSIAR User Manual version 3.1. [Available at https://github.com/brianstock/MixSIAR/.]

Stock B, Semmens B (2016b) Unifying error structures in commonly used biotracer mixing models. Ecology 97:2562–2569 doi: 10.1002/ecy.1517

Strong D (1992) Are Trophic Cascades All Wet? Differentiation and Donor-Control in Speciose Ecosystems. Ecology 73:747–754 doi: 10.2307/1940154

Svanbäck R, Persson L (2004) Individual diet specialization, niche width and population dynamics: implications for trophic polymorphisms. J Anim Ecol 73:973–982 doi: 10.1111/j.0021-8790.2004.00868.x

Svedarsky WD, Westemeier RL, Robel RJ, et al (2000) Status and management of the greater prairie-chicken Tympanuchus cupido pinnatus in North America. Wildlife Biol 6:277–284

Terborgh JW, Lopez L, Nunez V. P, et al (2001) Ecological Meltdown in Predator-Free Forest Fragments. Science 294:192–194 doi: 10.1126/science.1064397

Thebault E, Loreau M (2005) Trophic Interactions and the Relationship between Species Diversity and Ecosystem Stability. Am Nat 166:95–114 doi: 10.1086/444403

Thogmartin WE, Knutson MG, Sauer JR (2006) Predicting regional abundance of rare grassland birds with a hierarchical spatial count model. Condor 108:25–46 doi: 10.1650/0010- 5422(2006)108[0025:PRAORG]2.0.CO;2

94 Thompson DW (1961) On growth and form, Bonner edn. Cambridge University Press

Thorpe R (1975) Quantitative handling of characters useful snake systematics with particular reference to intraspecific variation in the Ringed Snake Nutrix natrix ( L .). Biol J Linn Soc 7:27–43 doi: 10.1111/j.1095-8312.1975.tb00732.x

Tobler WR (1970) A Computer Movie Simulating Urban Growth in the Detroit Region. Econ Geogr 46:234–240 doi: 10.2307/143141

Toweill DE, Anthony RG (1988) Coyote foods in a coniferous forest in Oregon. J Wildl Manage 52:507-512. doi: 10.2307/3801600

Vance DR, Westemeier RL (1979) Interaction of pheasants and prairie chicken in Illinois. Wildl Soc Bull 7:221–225

VanNimwegen R, Kretzer J, Cully Jr. J (2008) Ecosystem engineering by a colonial mammal: How prairie dogs structure rodent communites. Ecology 89:3298–3305 doi: 10.1890/07- 1511.1

Wallach AD, Johnson C, Ritchie EG, O’Neill A (2010) Predator control promotes invasive dominated ecological states. Ecol Lett 13:1008–1018 doi: 10.1111/j.1461- 0248.2010.01492.x

Watine LN, Giuliano WM (2017) Factors Determining Coyote (Canis latrans) Diets. Open J Ecol 7:650–666 doi: 10.4236/oje.2017.713045

Watts H, Holekamp K (2008) Interspecific competition influences reproduction in spotted hyenas. J Zool 276:402–410 doi: 10.1111/j.1469-7998.2008.00506.x

Westemeier RL, Buhnerkempe JE, Edwards WR, et al (1998) Parasitism of Greater Prairie- Chicken Nests by Ring-Necked Pheasants. J Wildl Manage 62:854–863 doi: 10.2307/3802536

White GC (1982) Capture-recapture and removal methods for sampling closed populations. Los Alamos National Laboratory

White J (1978) Illinois Natural Areas Inventory Technical Report, vol. I. Survey Methods and Results. Illinois Department of Conservation, Department of Landscape Architecture, Springfield, Ill.

White LA, Gehrt SD (2009) Coyote Attacks on Humans in the United States and Canada. Hum Dimens Wildl 14:419–432 doi: 10.1080/10871200903055326

95 White P, Garrott R, Hamlin K, et al (2011) Body condition and pregnancy in northern Yellowstone elk: Evidence for predation risk effects? Ecol Appl 21:3–8 doi: 10.1890/09- 2123.1

Wilmers CC, Post E, Peterson R, Vucetich J (2006) Predator disease out-break modulates top- down , bottom-up and climatic effects on herbivore population dynamics. Ecol Lett 9:383– 389 doi: 10.1111/j.1461-0248.2006.00890.x

Winnie J, Creel S (2017) The many effects of carnivores on their prey and their implications for trophic cascades , and ecosystem structure and function A. Food Webs 12:88–94 doi: 10.1016/j.fooweb.2016.09.002

Wirtz WO, Pearson PG (1960) A preliminary analysis of habitat orientation in Microtus and Peromyscus. Am Midl Nat 63:131–142 doi 10.2307/2422933

Wortley L, Hero J, Howes M (2013) Evaluating Ecological Restoration Success : A Review of the Literature. Restor Ecol 21:537–543 doi: 10.1111/rec.12028

Yunger JA (2004) Movement and spatial organization of small mammals following vertebrate predator exclusion. Behav Ecol 139:647–654 doi: 10.1007/s00442-004-1534-2