An Evaluation of Diets on the Rolling Plains Research Ranch, , across La Niña versus El Niño Weather Patterns

by

Cade B. Bowlin, B.S.

A Thesis

In

Wildlife, Aquatic and Wildlands Science and Management

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCES

Approved

Philip S. Gipson, Ph.D. Chair of Committee

Dale Rollins, Ph.D.

John Baccus, Ph.D.

Mark Sheridan, Ph.D. Dean of the Graduate School

December, 2018

©2018, Cade B. Bowlin

Texas Tech University, Cade B. Bowlin, December 2018

ACKNOWLEDGEMENTS

This research could not have come to fruition without the Rolling Plains Quail Research Foundation and the Rolling Plains Quail Research Ranch. I thank my committee members Philip S. Gipson PhD, John Baccus PhD, Dale Rollins PhD, and the Department of Natural Resources Management at Texas Tech University. I would like to additionally thank Mark A. Tyson for his previous work on coyote diets on the Rolling Plains Quail Research Ranch. I also thank Lloyd M. Lacoste for his friendship, encouragement, and assistance in a multitude of capacities during this project. Mr. Lacoste was an integral part of the successful completion of this research project. Additionally I thank the many technicians of the Rolling Plains Quail Research Ranch that contributed to this project. Bradley W. Kubecka compiled and synthesized much of the supporting data from the Rolling Plains Quail Research Ranch. I also recognize Mr. Kubecka for his dedication and contributions, not only to this project but also to the research ranch, the foundation, and its countless associates. I thank the Texas Tech Natural Science Research Laboratory for loan of guard hair samples used for identification. I would also like to thank Hong Seomun and Meghan Mahurin for their assistance in the laboratory. I would be remiss if I didn’t thank J.L.M. Knudsen, the finest naturalist I know, and also Neil Estes, Sean Yancey, and James Morel for their friendships during my time at Texas Tech. Most importantly I would like to thank Rigby, Brutas, and Bailey. It was their courage, resilience, and tireless pursuit that financed my graduate degree. For that I am humbled and forever grateful. As Gene Hill said, “Without them I would have been empty. They have made my life full.”

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TABLE OF CONTENTS

ACKNOWLEDGMENTS ...... ii

ABSTRACT ...... iv

LIST OF TABLES ...... …v

LIST OF FIGURES ...... vi

CHAPTER I. INTRODUCTION ...... …1

Literature Cited ...... 14

CHAPTER II. DIETS OF ON THE ROLLING PLAINS QUAIL RESEARCH RANCH, TEXAS, DURING AN EL NIÑO WEATHER PATTERN ...... 23 Introduction ...... 23 Study Area ...... 25 Methods...... 32 Results ...... 36 Discussion ...... 53 Management Implications ...... 64 Literature Cited ...... 65 CHAPTER III. LONGITUDINAL EVALUATION OF COYOTE DIETS DURING EL NIÑO v LA NIÑA WEATHER PATTERNS ...... 73 Introduction ...... 73 Study Area ...... 77 Methods...... 82 Results ...... 86 Discussion ...... 102 Management Implications ...... 111 Literature Cited ...... 113

iii Texas Tech University, Cade B. Bowlin, December 2018

ABSTRACT

The objective of this study was to evaluate coyote diets on a landscape dedicated exclusively to maximizing production of (Colinus virginianus) and assess whether coyotes (Canis latrans) are important predators of bobwhites and their nests during El Niño versus La Niña weather cycles. Coyote scats were collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas during a La Niña weather pattern (n=356 scats, 2011) and also during an El Niño weather pattern (n=480 scats, 2015-2017). Abundance of bobwhites varied greatly between the 2 study periods (0.13 bobwhites/ha in 2011 vs. 2.3 bobwhites/ha in 2016). No quail remains or remnants of eggshells were identified in coyote scats collected during the La Niña period. Only 3 scats (<1%) collected during the El Niño period contained quail vestiges and 14 scats (2.9%) contained eggshells. Mast (e.g., , ) was especially important during La Niña, but not El Niño diets. Diets of coyotes during the 2016 period were dominated by cotton rats (Sigmodon hispidus). I conclude that coyotes were not important predators of quail or quail nests on a landscape managed exclusively for quail production.

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LIST OF TABLES

Table 2.1 Abundance of coyotes and various species of prey observed during helicopter surveys flown on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2015-2017...... 43 Table 2.2 Frequency of occurrence (freq. occ) and percent occurrence (pct. occ) of all individual food items recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017...... 44 Table 2.3 Frequency of occurrence (freq. occ) and percent occurrence (pct. occ) of major food categories recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017...... 46 Table 2.4 Seasonal percent occurrence of major prey categories recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during 2015-17...... 47 Table 3.1. Percent occurrence of all individual food items recovered from coyote scats (n=356) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, January 2011-December 2011 (Tyson 2012)...... 94 Table 3.2. Percent of coyote scats containing prey categories collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during a La Niña weather pattern (2011, n=356) (Tyson 2012)...... 95 Table 3.3. Percent occurrence of all individual food items recovered from coyote scats (n=360) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, January 2016-December 2016...... 97 Table 3.4. Percent of coyote scats containing prey categories collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during a El Niño weather pattern (2016, n=360)...... 97 Table 3.5. Percent occurrence of rodents recovered from coyote scats collected on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during La Niña (2011, n=356) and El Niño (2016, n=360) weather patterns...... 98 Table 3.6. Percent occurrence of mast recovered from coyote scats collected on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during La Niña (2011, n=356) and El Niño (2016, n=360) weather patterns...... 99

v Texas Tech University, Cade B. Bowlin, December 2018

LIST OF FIGURES

Figure 2.1. Map illustrating the 10 ecoregions in Texas with the Rolling Plains ecoregion shaded. The Rolling Plains Quail Research Ranch (RPQRR), Fisher County, Texas is indicated by the red dot...... 27 Figure 2.2. Map of the Rolling Plains Quail Research Ranch with state of Texas inset, Fisher County highlighted and study site outlined within Fisher County...... 28 Figure 2.3. Map illustrating soil types found on the Rolling Plains Quail Research Ranch, Fisher County, Texas...... 29 Figure 2.4. Annual precipitation (cm) for the Rolling Plains Quail Research Ranch, Fisher County, Texas vs. the 30-year annual mean (dashed line) recorded in Roby, Texas (16o km east of RPQRR) designating La Niña and El Niño weather patterns during this study...... 38 Figure 2.5. Annual average Palmer Modified Drought Index (PMDI) values for the Low Rolling Plains of Texas 2009-2017 with La Niña (2011) and El Niño (2016) weather patterns labeled (NOAA 2017)...... 38 Figure 2.6. Fall minimum known population (MKP) of bobwhites (Colinus virginianus) on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017 (Kubecka 2017). Data derived from annual trapping-banding efforts ...... 39 Figure 2.7. Small mammals captured during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017...... 39 Figure 2.8. Individual Sigmodon hispidus (cotton rat; expressed per 100 trap- nights) captured during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009- 2017...... 40 Figure 2.9. Individual Neotoma micropus (Southern plains woodrat; expressed per 100 trap-nights) captured during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017...... 41 Figure 2.10. Individual hispid pocket mouse (Chaetodipus hispidus; expressed per 100 trap-nights) captured during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017...... 41 Figure 2.11. Mean (푥̅±SE) number of arthropods caught per transect during trapping efforts in July on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2010-2017...... 43 Figure 2.12. Percent of scats (푥̅±SE) containing rodent remains recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017...... 46

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Figure 2.13. Percent of scats (푥̅±SE) containing grass recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017...... 47 Figure 2.14. Percent of scats (푥̅±SE) containing lagomorph remains recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015- February 2017...... 48 Figure 2.15. Percent occurrence of rodent and lagomorph remains recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017...... 48 Figure 2.16. Annual percent occurrence (푥̅±SE) of prey categories recovered from coyote scats (n=360) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during 2016...... 50 Figure 2.17. Annual percent occurrence (푥̅±SE) of individual prey items recovered from coyote scats (n=360) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during 2016...... 51 Figure 2.18. Remains of Northern bobwhite (Colinus virginianus) vs. reported predators of quail nests recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017...... 53 Figure 3.1. Map illustrating the 10 ecoregions in Texas with the Rolling Plains ecoregion shaded. The Rolling Plains Quail Research Ranch (RPQRR), Fisher County, Texas is indicated by the red dot...... 76 Figure 3.2. Map of the Rolling Plains Quail Research Ranch with state of Texas inset, Fisher County highlighted and study site outlined within Fisher County...... 78 Figure 3.3. Map illustrating soil types found on the Rolling Plains Quail Research Ranch, Fisher County, Texas...... 80 Figure 3.4. Annual precipitation (cm) for the Rolling Plains Quail Research Ranch, Fisher County, Texas with 30-year annual mean (dashed line) and La Niña and El Niño weather patterns labeled...... 88 Figure 3.5. Annual average Palmer Modified Drought Index (PMDI) values for the Low Rolling Plains of Texas 2009-2017 with La Niña (2011) and El Niño (2016) weather patterns labeled (NOAA 2017)...... 88 Figure 3.6. Fall minimum known population (MKP) of bobwhites (Colinus virginianus) on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017 (Kubecka 2017). Data derived from annual trapping-banding efforts...... 89 Figure 3.7. Small mammals captured (per 100 trap nights) during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017...... 90

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Figure 3.8. Individual Peromyscus spp. captured per 100 trap nights during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017...... 91 Figure 3.9. Individual Sigmodon hispidus (cotton rat) captured per 100 trap nights during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017...... 91 Figure 3.10. Individual Neotoma micropus (Southern plains woodrat) captured per 100 trap nights during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009- 2017...... 92 Figure 3.11. Mean (푥̅±SE) number of arthropods caught per transect during trapping efforts in July on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2011-2017. White bar indicates samples obtained via pitfall traps and hatched bar represents sweep-net samples...... 92 Figure 3.12. Percent of coyote scats (푥̅±SE) containing prey categories collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during La Niña(2011, n=356) and El Niño (2016, n=360) weather patterns...... 99 Figure 3.13. Remains of confirmed quail vs. reported predators of quail nests recovered from coyote scats (n=356) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during a La Niña weather pattern (January 2011-December 2011) (Tyson 2012)...... 101 Figure 3.14. Remains of confirmed quail vs. reported predators of quail nests recovered from coyote scats (n=360) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during an El Niño weather pattern (January 2016-December 2016)...... 102

viii Texas Tech University, Cade B. Bowlin, December 2018

CHAPTER I INTRODUCTION

The coyote (Canis latrans) is a widespread, indigenous carnivore occurring across North America. Since extirpation of the wolf (C. lupus) across much of its historical range, 19 subspecies of coyotes (Bekoff and Gese 2003) have experienced an increase in population growth and range expansion (Peterson 1996, Gompper 2002). Plasticity in habitats occupied, dramatic landscape alterations, as well as adaptable reproductive, behavioral and dietary requirements, have enabled the coyote to prosper in a multitude of environs (Korschgen 1957, Meinzer et al. 1975, Bekoff and Wells 1986, Litvaitis and Shaw 1980, MacCracken and Hansen 1986). Coyotes range from latitudes of 10°N to 70°S reaching from a southern terminus of Costa Rica to ranges as far north as Alaska (Bekoff and Gese 2003). Coyotes are a generalist predator capable of subsisting on a broad array of food items (Litvaitis and Shaw 1980, Andelt 1985, Gese et al. 1988, Grinder and Krausman 2001.

Coyote Ecology Reproduction.—Coyotes are territorial in nature and occur as individuals (both resident and transient), pairs, and small groups of 2-7 (Knowlton and Gese 1995) with dispersing and transient individuals also a part of the population (Andelt 1985, Gese et al. 1996). Female coyotes are monestrous and give birth to 1 litter in the spring of the year. The alpha pair in a territory are the primary producers of young (Andelt 1985, Knowlton et al. 1999). Gese et al. (1996) proposed subordinate coyotes may reproduce but such is atypical. Yearling coyotes comprised <10% of reproduction annually in a study in (Gier 1968). Coyotes have been known to reproduce at ages up to 10-12 years (Gese 1990). Reproducing pairs bond late into the fall and early winter, with the formed pair remaining socially monogamous (Gier 1968, Knowlton 1972). After pair formation, the pair remains

______Thesis style follows Journal of Wildlife Management.

1 Texas Tech University, Cade B. Bowlin, December 2018

together indefinitely but not necessarily for life (Bekoff and Gese 2003). Females give birth in late spring (e.g., May) to a litter of 3-8 pups after a gestation period of 60-63 days (Gier 1968, Knowlton 1972, Knowlton et al. 1999). Gestation, parturition, and lactation of the female can influence resource requirements with food abundance being the driving factor of litter size (Knowlton and Gese 1995). Coyotes are thought to be compensatory breeders meaning their litter size is influenced by population density and resource availability (Knowlton 1972). Litter size can be related inversely to population density and correlated positively to food abundance during the preceding winter (Knowlton 1972). Additionally, Windberg (1995) found that population growth is related inversely to coyote abundance. Social structure.—Abundance, and subsequent spatial and temporal distributions across the landscape, is influenced by social hierarchies, behavioral characteristics, a territorial land tenure system, and available resources (Gier 1968, Knowlton and Stoddart 1983, Knowlton and Gese 1995). Coyotes are known to be territorial mesocarnivores with an alpha pair being the dominant controllers of an exclusive territory (Bekoff and Wells 1986, Gese et al. 1989, 1996). Use by conspecifics within these areas is dictated in part by availability of prey (Young et al. 2006). Partitioned territories, and home ranges within them, are defended by residents (both directly and indirectly) from encroachment of neighboring or transient coyotes (Andelt 1985, Windberg and Knowlton 1988). Patrolling and defending territory are important behavioral activities of coyotes. Individuals have been known to cover their entire home range area in one month’s time (Cooper et al. 2014). Territorial boundaries and pack spacing are also maintained through howling (Gese and Ruff 1998) indicating presence of alphas (and their pack) to neighbors and potential intruders. Subordinate members and pups can inhabit the alpha pair’s territory until dispersal or mortality (Gese et al. 1996); some remained in their natal territory for life (Carlson and Gese 2008). Dispersal of juveniles occurs in the fall and early winter and is dictated by social cues and resource limitations (Gese et al. 1996). Knowlton and Gese (1995) speculated that young or low-ranking individuals will leave the natal area and

2 Texas Tech University, Cade B. Bowlin, December 2018 more dominant coyotes will remain in an effort to achieve a higher social rank leading to breeding privileges. Territories have been shown to be delineated by physical features on the landscape such as mountain ranges, rivers, lakes and e structures like fence-rows, roads and urban developments (Andelt 1985, Gese et al. 1996). Althoff and Gipson (1981) concluded that these territorial boundaries survive generations. Young et al. (2006) also showed that coyotes maintained similar territories and space use within generational territories in a stable, unexploited population during 2 studies (spaced 25 years apart) in South Texas. Age structure and diets also remained similar in the home areas between the 2 studies (Young et al. 1996). However, Mills and Knowlton (1991) suggested that in a highly-exploited population, territories and home ranges could fluctuate annually and seasonally in response to variations in prey availability. Spatial ecology.—Territories exhibit little overlap and are commonly connected in areas where food and habitat requirements are met (Knowlton et al. 1999). Borders of territories are maintained by direct (physical confrontation) and indirect (vocal and olfactory) methods (Wells and Bekoff 1981, Gese and Ruff 1997,1998; Gese 2001, Bekoff and Gese 2003). Gese et al. (1988) suggested that coyotes in maintained average home ranges of approximately 11.3 km2 for resident coyotes. Transient coyotes that were not member of packs in the same study maintained average home areas of 106.5 km2. Litvaitis and Shaw (1980) recorded an average of 68.7 km2 for adult females and 1.0 km2 for dispersing pups of the year in . Adult males in the same study had a home area average of 31 km2. Adult individuals in Texas have home ranges of 2.7 km2 (males) and 2.6 km2 (females) during the breeding season (Andelt 1985). Cooper et al. (2014) reported from Global Positioning System (GPS) collar data that the mean 95% Kernel Home Range (KHR) of coyotes in Fisher County, Texas, to be 622 ha and 402 ha for males and females, respectively. Population dynamics.—Population density of coyotes varies within the territories they inhabit. Density varies seasonally as well as annually and is influenced by a multitude of factors including: geographic region, prey abundance and availability, weather patterns,

3 Texas Tech University, Cade B. Bowlin, December 2018 reproductive requirements, interspecific/intraspecific competition, territoriality, and social hierarchy (Bekoff and Gese 2003). Densities of coyotes vary across their range in the but are known to typically increase from north to south (Knowlton et al. 1999). In Minnesota, Chesness and Bremicker (1974) recorded a post-whelping density of 0.2-0.4 individuals per square kilometer. A population of coyotes in Montana was known to occur at a density of 0.15 individuals/km2 to a density of 0.39/km2 in summer (Pyrah 1984). Coyotes in Tennessee have been found at pre-whelping densities of 0.35/ km2 (Babb and Kennedy 1989). High densities of coyotes are known to exist in the Great Plains region of the United States (Knowlton and Gese 1995). Camenzind (1978) reported pre-whelping adult densities of 0.53 individuals per km2 in Wyoming. Gier (1968) estimated that a population of coyotes had a post-whelping density of 0.8 individuals/km2 in Kansas. Coyote densities have been recorded as high as 0.26-0.33/km2 in Colorado (Gese et al. 1989). Knowlton (1972) suggested a density of 0.9/km2 coyotes (post-whelping) in South Texas. An additional study in South Texas suggested a population density of 2.0/km2 (Guthery 1977). In an 11-year study in South Texas, Windberg (1995) estimated spring coyote populations of 1.56-2.73/km2. Andelt (1985) estimated a prewhelping population density of 0.8-0.9 rising to 0.9-1.0 coyotes/km2 on the Welder Wildlife Refuge in South Texas in the fall. Henke and Bryant (1999) noted a pre-whelping density of 0.12-0.14/km2 in Andrews and Martin counties of west Texas. Food availability (particularly in winter) is considered to be the driving factor of coyote population regulation (Gier 1968, Windberg 1995). Stoddart (1987) noted a population irruption of jackrabbits (Lepus californicus) in Idaho led to a subsequent 5- fold increase in coyote abundance. As abundance of jackrabbits decreased, coyote abundance trended downward as well. Todd et al. (1981 found similar results in Alberta positively correlating abundance trends of snowshoe hare (Lepus americanus) and coyotes.

Dietary Niche Whereas some carnivores fill a specialized predatory niche, the coyote is an adaptive, opportunistic, and omnivorous mesomammal. Dietary breadth ranges from

4 Texas Tech University, Cade B. Bowlin, December 2018 narrow and homogenous to a euryphagous, i.e., a generalist feeding niche. Food choice and feeding strategies are complex and dependent upon factors such as food item availability, seasonal nutritional requirements, social hierarchies and behavioral tendencies (Stephens and Krebs 1986, Abrams 2000). A generalist predator like the coyote is adaptive to dynamic conditions, including both stochastic and cyclic events including drought, habitat fragmentation, prey population depletions, and fluctuations in abundance of competitors. Resource abundance and distribution dictate diet, dispersal, home range size, and survival of coyotes (Gier 1968, Gese et al. 1996). Mammalian prey, hard and soft mast (the edible vegetative or reproductive part produced by woody species of plants), carrion, insects, , amphibians, reptiles, vegetable crops, and human products/waste are all viable food items for coyotes (Murie 1941, Korschgen 1957, Litvaitis and Shaw 1980, MacCracken and Hansen 1986). Most studies classify the classic feeding behavior of the coyote as an opportunistic generalist that follows fluctuations in food item availability and abundances (Cornell 1976, Bekoff 1977, Litvatis and Mautz 1980, Windberg and Mitchell 1990, Young et al. 2006). However, some research suggests the foraging technique of coyotes is more selective, consuming prey at levels disproportionate to prey availability (Cornell 1976, Johnson and Hansen 1979). MacCracken and Hansen (1987) proposed that the feeding strategy of coyotes follows an optimal foraging theory model where individuals select foods that have a positive net gain of energy (energy consumed exceeds energy expended) regardless of prey item abundance (MacArthur and Pianka 1966, Krebs et al. 1974).). Their dietary study in southeastern Idaho led MacCracken and Hansen (1987) to propose that coyotes followed optimal foraging theory when they observed how the abundance of black-tailed jackrabbits influenced consumption of lower-ranked food items such as Nuttal’s cottontail (Sylvilagus nuttalli) and montane voles (Microtus montanus). Additionally, a long-term (7-year) study in the Chihuahuan Desert (Hernandez et al. 2002) suggested coyotes were not consuming lagomorphs, rodents, and arthropods according to their availabilities. Lagomorphs were selected and consumed at a rate independent of their availability thus supporting the optimal foraging strategy.

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Food items vary in diversity over years and seasons. Andelt et al. (1987) suggested annual and seasonal variations in coyote diets in south Texas at the Welder Wildlife Refuge were attributable to changes in plant succession and abundance of prey. Other studies have shown similar changes in the seasonal variation of coyote diets as related to the availability of food items (Gipson and Sealander 1976, Litvaitus and Shaw 1980, Harrison and Harrison 1984). Meinzer et al. (1975) suggested coyote diets varied both seasonally and annually in Knox and King counties, Texas. Mammals, particularly rodents and lagomorphs, are staples of coyote diets across their sympatric ranges. Sperry’s (1941) literature review (compiled from examination of >8,000 coyote stomachs from 17 western states) reported diets consisting of 33, 25, 18, and 13.5% of lagomorph, carrion, rodent, and livestock, respectively. Of these stomach contents, 98.2% were remains with only 1.8% vegetative matter. Ferrel et al. (1953) analyzed coyote stomachs (n = 2,222) over 6 years in California and showed similar results with animal remains (96% of which were lagomorphs) being the most important and only 4% plant foods. Coyotes in Idaho had diets consisting of mainly cottontail rabbits (Sylvilagus floridanus), northern pocket gophers (Thomomys talpiodes), and montane voles with diet breadth varying seasonally (MacCracken and Hansen 1982). A population of coyotes in Nebraska exhibited a diet comprised of mostly mammals (73% percent of scats annually) with vegetation occurring in 39% of scats annually (Huebschman et al. 1997). Coyote diets in Kansas (Kamler et al. 2002) consisted of 87% mammals, 33% insects, 15% fruits, 7% human waste, and 2% reptiles by frequency of occurrence. The digestive system of the coyote allows for a diet consisting of up to 70% plant matter (McCloughlin 1982, Simpson and Raubenheimer 2012, Wang et al. 2008). As such, mast can be an important food group for coyotes across their range. Meinzer et al. (1975) noted 9 different native species of fruits (comprising 46% of the annual diet) in coyote diets from Knox County, Texas; rodents and lagomorphs made up 24.5 and 10.5%, respectively. Vela (1985) ranked mast as the primary food consumed by coyotes in the Chihuahan Desert of during his study period. Fruits ranked as the second most important food item for coyotes in an additional study in Mexico (-Mihart et al. 2001). Plant matter was the most frequently consumed food group, occurring in

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72.9% of coyote scats (n=184) in south central Pennsylvania (Bixel 1995). Summer diets of coyotes in Quebec consisted of >60% fruit matter (Crete and Lemieux 1996).

Cotton Rat Ecology Rodents are an important food source for coyotes in Texas and previous research has shown cotton rats (Sigmodon hispidus) are primary prey for coyotes when available. Windberg and Mitchell (1990) recovered cotton rat remains in 32% and 40% of scats during 2 years of their study in south Texas. Hispid cotton rats and southern plains woodrats (Neotoma micropus) accounted for 24.5% of the annual diet of coyotes (n=514) during Meinzer et al.’s (1975) 2-year study in Knox Co., Texas. Kamler et al. (2007) found coyotes in northwestern Texas consumed cotton rats in addition to other prey. Even in a diverse prey community coyotes select for cotton rats in accordance to rat abundance. Windberg and Mitchell (1990) suggested that consumption of cotton rats by coyotes in south Texas was correlated with cotton rat abundance. As primarily grazers, cotton rats favor habitats with tall, dense grasses and forbs for both protection from predation and as a food source (Schmidly and Bradley 2016:523). Guthery et al. (1979) suggested cotton rat density was correlated to standing vegetative biomass due to higher availability of food and protection from predation. They also posited that tall, single-stemmed forbs (eg., ) provide dense canopy cover and freedom of movement at ground level. Cotton rats are known to exhibit irruptive (i.e., boom-bust) cycles and can experience dramatic population increases when environmental conditions are favorable. Irruptions of cotton rats have been documented previously in Texas (Haines 1963, Lehmann 1984:210). Windberg (1998) saw a 46-fold increase in cotton rat populations between 1981-1982 and a 13-fold increase from 1985-1986. Rainfall from the prior growing season was related directly to abundance of cotton rats as a result of the increase in herbaceous cover. Windberg attributed a subsequent population crash to low rainfall for extended period and increase in raptor population. Haines (1963) suggested that drought conditions in 1957 coupled with subsequent rains may have triggered the irruptions. However, in 1959 and 1960 rainfall was average and the population still decreased. Haines (1963) saw a significant die-off between the summers of 1959-1960

7 Texas Tech University, Cade B. Bowlin, December 2018 after an irruption in Texas. Reports of high populations of cotton rats decreased from 58% to 38% and 4% for 1958, 1959, and 1960, respectively. Haines described the die-off in summer of 1959 as “rapid.” Lehmann (1984:210) reported an irruption in numbers of cotton rats in the 1960s in south Texas that left the landscape barren of herbaceous vegetation. The population subsequently crashed the following year, presumably due to lack of resources and disease. Lehmann suggested an abundance of 120 cotton rats/0.4ha in summer of 1966 (10.3kg/0.4ha of rats) (1984:213). The irruption was attributed to favorable rainfall and mild winter temperatures the previous winter. The author also suggested that cotton rats may be the vector from which other wildlife contract disease.

Climatic Influence on Coyote Diets Availability of mammalian and vegetative food items for coyote consumption is influenced in part by weather. Climatic conditions influence differential prey availability, breadth of available food items, plant phenology and ultimately the functional dietary response by coyotes (Andelt et al. 1987). Weather patterns in the southwestern United States are a result of the El Niño Southern Oscillation (ENSO) (Ropelewski and Halpert 1986). The ENSO is caused by the fluctuation of warm and cool midlattitude sea surface temperatures in the equatorial region of the Pacific Ocean. Warmer than normal sea temperatures induce El Niño, below average sea temperatures result in La Niña (NOAA 2017). These ENSO events typically occur on 2-4 year cycles (Kahya and Dracup 1993) influencing global winds, pressure systems, temperatures, and precipitation (NOAA 2017). The ENSO is comprised of 2 contrasting phases: La Niña and El Niño weather patterns. El Niño is characterized in the Southwest by a cool, wet weather phase with above normal precipitation during winter and spring (Ropelewski and Halpert 1986). Conversely, winter and spring in the Southwest are characterized by above average temperature and below normal precipitation during La Niña (Ropelewski and Halpert 1986, Cole et al. 2002). The ENSO cycles can cause ecosystem-wide changes in terrestrial environments (Holmgren et al. 2001) resulting in devastating ecological and economic impacts (Mo et al. 2009). Fluctuations in seasonal, annual, and multiple year weather patterns can

8 Texas Tech University, Cade B. Bowlin, December 2018 influence ecosystem composition, structure, function, and ultimately resource availability (Holmgren et al. 2001). Plant communities affected by precipitation and temperature changes resulting from ENSO cause complex animal ecosystem response (Holmgren et al. 2001). Variations in temperature and precipitation (both annually and seasonally) impact vegetation structure and succession thus altering prey diversity and abundance for coyotes (Andelt et al. 1987, Windberg and Mitchell 1990, Andelt 1995). As such, Hidalgo-Mihart et al. (2001) suggested that climatic changes directly influence the diets of coyotes. Studies in arid regions found that diets of coyotes changed (comprised of more mast) during periods of below normal precipitation periods (Vela 1985, McKinney and Smith 2007). Andelt et al. (1987) noted coyote diets varied annually during a 4-year study (1961-62, 1973-74, 1975-76, 1978-79) on the Welder Wildlife Refuge in south Texas. Variation in diet was attributed to changes in weather conditions, prey abundance, prey vulnerability, and vegetation succession.

Coyotes and the Mesopredator Release Hypothesis The removal of a large predator resulting in the expansion and abundance of smaller predators is known as the mesopredator release hypothesis (MRH) (Soule et al. 1988). Mesopredators are defined by Roemer et al. (2009) as mammalian predators weighing <15kg. Mesopredators are small to mid-sized predators and are believed to be drivers of community structure and functions (Roemer et al. 2009). An absence of top- down predation allows for freedom of movement and increase in available resources by lesser mesomammals. Presence or absence of an apex predator creates or alleviates pressure on lower mesomammals altering their distribution and resource allocation (Levi and Wilmers 2012). The coyote is often the highest-ranking terrestrial predator in its domain (Gompper 2002) but that position is only a recent (last 100 years) occurrence. Traditional apex predators in North America included bears (Ursus spp.), wolves (Canis spp.), and cougars (Puma concolor). Drastic population reductions and even complete extirpation of top predators (Levi and Wilmers 2012) allowed coyotes, in some systems, to assume the role as top predator in the ecological community (Gompper 2002).

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Removal of top-down predation allowed the coyote to be “released” due to decrease in competition for resources and lack of predation. As a result coyotes expanded their range and flourished. Predation and interference competition by coyotes are thought to influence the distribution, movements, diets, and abundances of smaller mesomammals through the MRH (Johnson et al. 1996, Gompper 2002). These mesomammals include striped skunks (Mephitis mephitis), raccoons (Procyon lotor), gray foxes (Urocyon cinereoargenteus), opossums (Didelphis virginiana), and feral house cats (Felis catus) (Rogers and Caro 1998, Kamler and Gipson 2004) all of which are preyed upon by coyotes (Bekoff 1978). Henke and Bryant (1999) showed that the removal of coyotes, decreased rodent richness and diversity while several mesopredators increased in abundance. Although coyotes may be detrimental to some animal populations (Courchamp et al. 1999), their presence could benefit others. Crooks and Soule (1999) suggested that the presence of coyotes might actually lower the extinction rate of birds in fragmented landscapes by controlling feral cat and fox populations. Other studies (Rogers and Caro 1998, Henke and Bryant 1999) have noted similar impacts on predator-prey population dynamics in support of the MRH.

Interactions of Coyotes and Quail Bobwhites have declined across their range for decades (Brennan 1991). The Breeding Survey shows bobwhites in Texas experienced a -1.78% decline annually from 1966-2015 (Sauer 2017). Factors thought to contribute to the population decline are dramatic changes in land use, reduction or elimination of fire, modern farming practices, livestock production, and disease (Brennan 1991). Species in low abundance or that have been experiencing steady population declines could be impacted by shifts in predator- prey dynamics. One such species is the bobwhite (Brennan 1991). Weather, precipitation and temperature in particular, can impact quail productivity and abundance (Bridges et al. 2001, Lusk et al. 2001, Lusk et al. 2002). Guthery et al. (1988) reported a reduced peak breeding season and reduced reproductive effort in arid environments in south Texas. Guthery attributed the reduced breeding

10 Texas Tech University, Cade B. Bowlin, December 2018 season to physiological stressor (heat) and an adaptive response to harsh laying/brooding conditions as a result of high temperatures. Bobwhite production has also been linked with short- and long-term precipitation patterns. Bridges et al. (2001) noted that the Palmer Modified Drought Index (PMDI) correlated highly with bobwhite abundance across 6 ecoregions in Texas. Severe declines in abundance of (bobwhites and scaled quail [ squamata] has also been related to below-average precipitation for extended periods (Campbell et al. 1973, Giuliano and Lutz 1993). Predation has been known to be the primary proximate cause of bobwhite mortalities from nesting to adulthood (Rollins and Carroll 2001). Coyotes, raptors, fire ants, and other predators have also been attributed with the population decline of bobwhites (Brennan 1994, Rollins and Carroll 2001). Coyotes are sympatric with bobwhites in the United States (Gompper 2002) and as such, managers often bemoan the coyote as being a significant predator of bobwhites and their nests. Undoubtedly coyotes consume quail (Lehmann 1946, Beasom 1974,Meinzer et al. 1975, Lehmann 1984:190-196, Guthery 1995) and also depredate nests of quail (Lehmann 1984:190-96, Rollins and Carroll 2001, Henke 2002, Rader et al. 2007). Lehmann (1946) monitored 189 bobwhite nests of which coyotes depredated an estimated 43% as confirmed by evidence at depredated nests (egg shell remains, scat, and tracks) and analysis of coyote stomachs. Lehmann (1984) declared the coyote to be the most common predator of bobwhite in South Texas. An additional South Texas study that incorporated video surveillance of bobwhite nests showed coyotes depredated 32% (n=43) of quail nests (Rader et al. 2007). However, other studies have shown that quail comprise only a small portion of the coyote’s diet. Gipson (1974) found remains of quail in 2% of coyote stomachs (n =168) in Arkansas. Meinzer et al. (1975) found bird and egg shell remains in only 1.1% of coyote scats in north Texas (n =514). Henke (2002) reported that bobwhite or their eggs were found in <1 percent of coyote stomachs (n = 407) in south Texas; he argued that quail and their nests are merely incidental prey for coyotes. Another south Texas study revealed birds as a category made up only 1% of coyote diets (n=6354; Andelt et al. 1987). Bobwhite remains were found in 1.4% of coyote stomachs (n=770) examined by Korschgen (1957). Vestiges of birds occurred in <3% of coyote scats (n=979) in Idaho

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(Johnson and Hansen 1979) and 2% (n=208) of scats in South Dakota (MacCracken and Uresk 1984). Even though coyotes are confirmed quail and quail nest predators, removal programs could be potentially harmful to quail populations via the MRH. Some researchers posit that lesser mesomammals are responsible for more quail nest depredations than are coyotes (Hernandez et al. 1997). Research suggests that mesomammals such as raccoons, striped skunks, ground squirrels, opossums, and gray foxes are the most significant predators of quail nests where they are sympatric (Hernandez et al. 1997, Fies and Puckett 2000, Rollins and Carroll 2001, Rader et al. 2007). Raccoons are a confirmed nest predator in Texas (Hernandez et al. 1997, Rader et al. 2007). As such, Rollins (1999) argued that a significant reduction in smaller mesomammals could lead to a positive influence on quail production through a reduction in nest depredation. Beasom (1974) observed modest gains in quail abundance in south Texas when intensely removing predators from the study site. However, another study by Guthery and Beasom (1977) showed little impact on abundance of bobwhite and scaled quail after intensive predator removal program on a 15-km2 study area in south Texas. Additionally, Rader et al. (2007) suggested broad application of predator removal is ineffective in improving quail production in areas with a diverse nest-predator community. Guthery (1995) proposed that, through the concept of competing risks, removing coyotes from the system does not necessarily result in net gains of quail production. Competing risk proposes removing the coyote from the predator-prey equation only increases opportunity (and potentially predation) for other nest predators (skunks, possums, etc.). When coyotes are removed, nests and adults only become available to other, lesser mesomammals. Guthery (1995) suggested that control of coyotes in effort to increase bobwhite production might in fact be more harmful than productive and that intense predator control has little effect on quail production. In select, fragmented habitats where prey and predators are concentrated, predator control may be desired (Staller et al. 2005). However, broad application of predator control across the landscape is unwarranted and perhaps counterproductive.

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Additionally, movements of mesomammals, e.g., raccoons, are known to be influenced by coyotes on the landscape (Cooper et al. 2014). Without the presence of coyotes, lesser mesomammals could expand their spatial use of the landscape. This could lead to a higher incidence of nest and quail depredations. The magnitude of the impact that coyotes have on bobwhite populations is unknown. Coyotes could possibly be influencing quail populations directly (i.e., via predation) or indirectly (i.e., through changes in predator-prey relationships, MRH). As prey abundances fluctuate spatially and temporally, coyotes could become significant predators of quail and their nests. Significant weather events, both stochastic and cyclical, could influence vegetative phenology/succession, prey abundances, coyote dietary requirements and behaviors. In order for quail and predator managers to make sound management decisions, a deeper understanding of coyote-quail dynamics is required. Tyson (2012) studied coyote diets on the Rolling Plains Quail Research Ranch in 2009-11 concurrent with a La Niña weather pattern. The drought in 2011 was the worst one-year drought in Texas history (Huber 2011). Tyson’s study revealed only 1 quail consumed by coyotes (n=1,080 scats) throughout the study. However, quail numbers were also well below historic average on the site during the study period. The Rolling Plains of Texas entered an El Niño phase during summer of 2015 and as a result, quail numbers were well above 30-year mean and in fact were the highest recorded since the inception of the RPQRR (2007). The purpose of my study was to 1) investigate variation of coyote diets on RPQRR during La Niña versus El Niño weather cycles, 2) assess whether coyotes are significant predators of quail and quail nests, and 3) take a critical look at the role of coyotes in quail management and subsequently provide managers with a strategy for predator management.

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CHAPTER II DIETS OF COYOTES ON THE ROLLING PLAINS QUAIL RESEARCH RANCH, TEXAS, DURING AN EL NIÑO WEATHER PATTERN

Introduction Since the 20th century extirpation of the wolf (Canis lupus) across much of its historical range, coyotes (Canis latrans) have experienced an increase in population and distribution across North America (Peterson 1996, Gompper 2002, Bekoff and Gese 2003). Dramatic changes in land use practices and landscape level-habitat transformations have allowed coyotes to flourish because of their plasticity in habitats occupied, reproductive capabilities, behavioral attributes and adaptable dietary requirements (Meinzer et al. 1975, Bekoff and Wells 1980, Litvaitis and Shaw 1980, MacCracken and Hansen 1986). As the coyote expanded its range, it has influenced community structure and functionality by altering predator-prey dynamics (Gompper 2002). Species in low abundance or species that have been experiencing steady population declines could be impacted by such shifts in predator-prey dynamics. One such species is the northern bobwhite (Colinus virginianus; hereafter bobwhite) (Brennan 1991). While the coyote has thrived and expanded its distribution, bobwhites have declined across their range for decades. The Breeding Bird Survey shows bobwhites in Texas experienced a -1.78% decline annually from 1966-2015 (Sauer 2017). Factors thought to contribute to the population decline are changes in land use practices, habitat fragmentation, reduction or elimination of historic fire regime, modern farming practices, livestock production, and disease (Brennan 1991). Predation is the primary proximate cause of bobwhite mortalities from nesting to adulthood (Rollins and Carroll 2001). Mesomammals, raptors, fire ants, and a host of other predators have been attributed with the population decline of bobwhites (Brennan 1994, Rollins and Carroll 2001). Coyotes and various species of quail occur sympatrically across their respective ranges in the United States. However, it is unclear as to the extent of the coyotes influence on quail populations through predation and community structure influences.

23 Texas Tech University, Cade B. Bowlin, December 2018

Without a doubt coyotes consume quail (Lehmann 1946, Beasom 1974, Meinzer et al. 1975, Lehman 1984:190-196, Guthery 1995) and also depredate nests of quails (Lehmann 1984:190-96, Rollins and Carroll 2001, Henke 2002, Rader et al. 2007). However, the magnitude of the impact coyotes have on bobwhite populations is unknown. Coyotes could possibly be influencing quail populations directly (via predation and nest depredation) or indirectly (through influencing predator-prey relationships and community structure/function). Although coyotes may negatively influence some animal populations (Courchamp et al. 1999), their presence could benefit other species. Predation and interference competition by coyotes are thought to influence the distribution, movements, diets, and abundances of smaller mesomammals through the mesopredator release hypothesis (MRH) (Johnson et al. 1996, Gompper 2002). The MRH suggests the removal of a large predator, and subsequent top-down pressure, allows lesser mesomammal (mammalian predators weighing <15kg) range expansion, freedom of movement, and abundance (Soule et al. 1988, Roemer et al. 2009). Of the multitude of factors contributing to fluctuations in bobwhite productivity and abundance, weather plays a pivotal role, particularly annual precipitation (Bridges et al. 2001, Lusk et al. 2001, Lusk et al. 2002). This is especially important in the Rolling Plains of Texas, one of the last strongholds of northern bobwhites and scaled quail (Callipepla squamata). The El Niño-Southern Oscillation (ENSO) influences precipitation and temperature trends in the Rolling Plains of Texas. Weather phenomena in the southwestern United States are a result of the El Niño Southern Oscillation (ENSO), which influences global winds, pressure systems, temperatures, and precipitation events (Ropelewski and Halpert 1986, NOAA 2017). The ENSO is caused by the fluctuation of warm and cool midlattitude sea surface temperatures in the equatorial region of the Pacific Ocean (NOAA 2017).The ENSO is comprised of 2 contrasting phases, La Niña and El Niña, and occurs on 2-4 year cycles (Kahya and Dracup 1993). El Niño is characterized in the Southwest by a cool, wet weather phase with above normal precipitation during winter and spring (Ropelewski and Halpert 1986). Conversely, La Niña winter and spring in the Southwest are characterized

24 Texas Tech University, Cade B. Bowlin, December 2018 by above average temperature and below normal precipitation (Ropelewski and Halpert 1986, Cole et al. 2002). The ENSO cycles can cause ecosystem-wide changes in terrestrial environments resulting in dynamic ecological impacts (Mo et al. 2009) through ecosystem composition, structure, function, and ultimately resource availability (Holmgren et al. 2001). Variations in temperature and precipitation (both annual and seasonal) impact vegetation structure and succession thus altering prey diversity and abundance (Andelt et al. 1987, Windberg and Mitchell 1990, Andelt 1995). As bobwhites and coyotes are sympatric it is important to understand the role of coyotes in regards to quail management. Prior studies suggest climatic fluctuations directly influence coyote diets (Andelt et al. 1987, Hidalgo-Mihart et al. 2001, McKinney and Smith 2007) and also bobwhite abundance (Guthery et al. 1988, Giuliano and Lutz 1993, Bridges et al. 2001, Lusk et al. 2001, Lusk et al. 2002). As such, it is imperative that wildlife managers have a thorough understanding of how dynamic weather patterns influence complex food webs and predator-prey relationships. The Rolling Plains of Texas experienced an El Niño cycle beginning during the summer of 2015. Favorable environmental conditions resulted in quail abundances well over the 30-year mean across the region. The purpose of this study was to 1) investigate variation of coyote diets on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during an El Niño weather cycle; 2) assess whether coyotes are significant predators of quail and quail nests; and, 3) take a critical look at the role of coyotes in quail management and subsequently provide quail managers with a strategy for predator management.

Study Area The Rolling Plains Quail Research Ranch (RPQRR) is a research and demonstration facility where “everything points to quail.” The mission statement of the ranch is “to sustain Texas’ wild quail hunting heritage for this and future generations” (www.quailresearch.org). Since the ranch’s inception in 2007, the property has been managed intensively for bobwhite and scaled quail (Callipepla squamata). The RPQRR provides landowners and managers with current research and management techniques to

25 Texas Tech University, Cade B. Bowlin, December 2018 best maintain their properties for maximum abundance of bobwhites. Habitat on the ranch is managed and manipulated to best suit the needs of bobwhite and scaled quail in the Rolling Plains ecoregion. Brush management (chemical and mechanical), grazing management, prescribed fire regimes, soil disturbance, and supplemental feeding efforts are directed towards maximizing quail abundance on RPQRR (Rollins 2007:128-140). Although predation is the major cause of quail mortalities at all life stages (Rollins and Carroll 2001), no active predator management has occurred on the ranch. The RPQRR lies in the Rolling Plains ecoregion of Texas (Fig. 2.1) and is located 19 km west of Roby, Texas in Fisher County and encompasses 19-km2 (Fig. 2.2). Gently rolling plains with dissected valleys and prominent ridges characterize the Rolling Plains ecoregion (NRCS 2018). Elevation of the RPQRR ranges from 587-925 m above sea level with north to south-oriented ridges on the ranch ranging in elevation from 626-687 m above sea level (NRCS 2018). Climate in the western Rolling Plains ecoregion is typified as a dry, sub-humid climate consisting of hot summers and mild winters (NRCS 2018). Temperatures vary from summer days reaching 38 degrees Celsius to lows of -6 degrees Celsius in winter months. Cold spells are typically short-lived but consecutive summer days over 38 degrees Celsius are not uncommon. A long growing season (April-November) averages 215 frost-free days and 220 freeze-free days annually. Average annual precipitation is 62.9 cm and average annual temperature is 17.6 degrees Celsius (NOAA 2017). Majority of annual precipitation comes in spring and early summer. Very little (25 centimeter annual average) precipitation is received in the form of snowfall (NRCS 2018). The study site features gently rolling hills comprised of mostly sandy and clay loam soils. Dominant soil series consist of Paducah Loam (15%), Miles Fine Sandy Loam (30%), Wichita Clay Loam (15%), and Woodward Loam (20%) (NRCS 2018). Soil types (Fig. 2.3) and climate favor a true mixed-grass prairie, with additional forbs and woody species interspersed. Soils in Paducah Loam sites are deep and well-drained, occurring on 0-5% slopes. Common vegetative communities are comprised of grama grasses (Bouteloua spp.), Texas wintergrass (Nassella leucotricha), sand dropseed (), and

26 Texas Tech University, Cade B. Bowlin, December 2018

Figure 2.1. Map illustrating the 10 ecoregions in Texas with the Rolling Plains ecoregion shaded. The Rolling Plains Quail Research Ranch (RPQRR), Fisher County, Texas is indicated by the red dot.

27 Texas Tech University, Cade B. Bowlin, December 2018

Figure 2.2. Map of the Rolling Plains Quail Research Ranch with state of Texas inset, Fisher County highlighted and study site outlined within Fisher County.

28 Texas Tech University, Cade B. Bowlin, December 2018

Figure 2.3. Map illustrating soil types found on the Rolling Plains Quail Research Ranch, Fisher County, Texas.

29 Texas Tech University, Cade B. Bowlin, December 2018 buffalograss (Buchloe dactyloides). Honey mesquite () is a common invader on most sites (NRCS 2018). Miles Fine Sandy Loams on RPQRR are alluvial soils that are deep, well-drained and moderately permeable. These soils occur on level to moderately sloping areas ranging in slopes of 0-8%. Silver bluestem (Bothriochloa laguroides) and grama grasses are native plant species found on Miles series soils. Woody species known to invade these soils consist of catclaw mimosa (Mimosa aculeaticarpa), catclaw acacia (), lotebush (Ziziphus obtusifolia) , and mesquite (NRCS 2018). Wichita Clay Loam soils on RPQRR are found on level to gently sloping uplands of 0-5% slopes. The deep, well-drained soils in this series support native communities of short and mid-grasses as well as mesquite (NRCS 2018). Key grasses found on RPQRR are silver bluestem (Bothriochloa saccharoides), threeawns (Aristida spp.), and tobosagrass (Pleuraphis mutica). Weathered sandstone bedrock formed the deep, well- drained Woodward Loam series that is found on the steeper summits and escarpment ridgelines (slopes 1-30%). Grama grasses are common on Woodward soils on the study site (NRCS 2018). Decades of fire suppression and repeated overgrazing by cattle have resulted in expansion and proliferation of woody species across the landscape (NRCS 2018). These woody species include redberry juniper (Juniperus pinchotti) and honey mesquite. Additional woody species found on RPQRR are netleaf hackberry ( reticulata), pricklyash (Zanthoxylum hirsutum), gum bumelia (Sideroxylon lanuginosum), skunkbush sumac (), (Yucca spp.), agarita (Mahonia trifoliolata), elbowbush (Forestiera pubescens), wolfberry ( berlandieri), and catclaw mimosa. Drought- tolerant succulents such as prickly pear (Opuntia spp.) and tasajillo ( leptocaulis) are also found on the site. Forbs occurring on RPQRR can be responsible for 8-12% of vegetative composition by weight (NRCS 2018). Significant forbs on RPQRR are sunflowers (Helianthus spp.), western ragweed (Ambrosia psilostachya), field ragweed (A. confertiflora), annual broomweed (Amphiachyris dracunculoides), American basketflower (Centaurea americana), catclaw sensitivebriar (Mimosa nuttallii), rushpea

30 Texas Tech University, Cade B. Bowlin, December 2018

(Hoffmannseggia spp.), golden crownbeard (), crotons ( spp.), and filarees (Erodium spp.). Small mammals constitute an important food source for coyotes and other predators. Two types of lagomorphs occur on the ranch, the eastern cottontail (Sylvilagus floridanus) and the black-tailed jackrabbit (Lepus californicus). A diverse array of rodents are found on the study site including, but not limited to, the following genera: Sigmodon, Reithrodontomys, Peromyscus, Perognathus, Neotoma, Mus, Geomys, Chaetodipus, Dipodomys, and Baiomys. Known inhabitants of RPQRR also include Mexican ground squirrels (Spermophilus mexicanus) and the eastern fox squirrel (Sciurus niger) can be found in the northwestern quadrant of the ranch along Buffalo Creek. Additional mammalian species present on RPQRR include white-tailed deer (Odocoileus virginianus) and feral hogs (Sus scrofa). Data derived from biannual helicopter surveys indicated a low abundance of deer (typically 1 deer per 50 ha) and feral hogs (detected only periodically) on the ranch. American badger (Taxidea taxus), (Lynx rufus), raccoon (Procyon lotor), and striped skunk (Mephitis mephitis) are mesocarnivores also found on RPQRR. In addition to bobwhite and scaled quail, a diverse array of aviformes makes RPQRR their home. Resident and migratory raptors are observed frequently on the study site. These include, but are not limited to, Swainson’s hawk (Buteo swainsoni), red-tailed hawk (Buteo jamaicensis), Cooper’s hawk (Accipiter cooperii), (Circus hudsonius), and (Falco sparverius). Passerines such as meadowlarks (Sturnella spp.) and lark buntings (Calamospiza melanocorys) are common during winter months while dickcissels (Spiza americana) and lark sparrows (Chondestes grammacus) are common summer residents. Other common birds include greater roadrunner (Geococcyx californianus), wild turkey (Meleagris gallopavo), and mourning dove (Zenaida macroura). Snakes, including (Lampropeltis getula, Lampropeltis triangulum, Masticophis flagellum, Pantherophis emoryi, Pituophis catenifer, Thamnophis marcianus, Crotalus spp.) are common across the study site (McEachern 2008).

31 Texas Tech University, Cade B. Bowlin, December 2018

Methods Scat Collection.—Coyote scats were collected from November 2015-February 2017 in order to analyze the diets of coyotes inhabiting the study area. Identification of individual coyotes within the study area was not possible for this study due to resource limitations. As such, observations were made at the population level following a Design I model as defined by Thomas and Taylor (1990). Resource (food) availability was assumed to be equally available among all individuals within the study area. Scats were collected monthly along a 32-km route which serves as the primary route for various counts (e.g., raptors, bobwhites); this, continuous loop is referred to as the Texas Quail Index (TQI) route of the RPQRR. The TQI route is divided into 2, 16- km segments designated as East and West routes. Each route has “mile markers” (steel t-posts with unique signs) posted at roughly 1.6-km intervals. The East route contains 12 markers and the West route contains 13 markers. The markers were used as points of reference for collection start points. In order to reduce sampling bias, randomization was introduced into the collections. A random number generator was used to select the starting point of collection each month on each of the 2 routes. For the East route a number from 0-11 was chosen; for the West route a number from 0-12 was chosen. The number produced from the generator designated the starting waypoint on each respective route during each collection period. An additional randomization was the frequency at which scats were collected along the route. Rather than collecting the first 15 scats per route, a number between 1 and 3 was chosen with the number generator. This number designated how many scats were omitted between collections of individual scats along each route. For example if the number 1 was drawn I collected every other scat. The same number was applied to both routes. Also, a maximum of 2 scats were collected from any latrine. The next scat after the latrine was treated as the first scat encountered. For the purpose of this study a latrine was defined as follows: ≥2 scats within a 20-m segment of the collection route. Scats were identified according to nearby animal tracks, scat morphology, and size (Murie 1954, Danner and Dodd 1982, Elbroch 2003). Collection routes were “swept” 7 days prior to collection to remove all scats. This ensured no scats were collected from the previous month and only fresh scats were collected. The entire 32-km

32 Texas Tech University, Cade B. Bowlin, December 2018 loop was driven and every coyote scat removed. On the seventh day after old scats were removed, scat collections were performed. Disposable latex gloves were worn during collection to prevent cross contamination of scat samples. I placed scats individually in a paper bag, then folded the top twice, and stapled the bag shut (Bowyer et al. 1983). On the outside of the bag I wrote the scat identification number, date of collection, and Universal Transverse Mercator (UTM) coordinates of location (Bowyer et al. 1983). Upon completion of collection, I placed sample bags in a freezer for one month at -20°C to inhibit parasite growth (particularly Echinococcus granulosus) and kill destructive organisms such as dung beetles (Phanaeus spp.) (Colli and Williams 1972). Samples were removed from freezer after 1 month and placed in cabinets to air dry. Once dried, I placed samples in individual nylon bags with a unique identification number on a flat, stamped aluminum disk. I then placed nylon bags in warm water with detergent for at least 24 hours (Johnson and Hansen 1979). After soaking, I placed one month’s worth of scat samples (30 samples) per batch in an automatic washing machine and washed them for 2 cycles or until water ran clear (Johnson and Hansen 1978a, 1978b). Once washing was complete I stored samples at 60° Celsius for 72 hours to dry. Once dried, I removed the contents from the nylon bag and weighed them to the nearest 0.01 g. After weighing I placed each individual scat in a plastic bag for storage. Scat contents were then ready for analysis, food item identification, and quantification. Scat analysis.—I counted the food items per scat using the point-frame analysis method (Chamrad and Box 1964). This technique allows for determination of frequency of occurrence and percent occurrence of food items in scats (defined below) (Chamrad and Box 1964). Point-frame scat analysis technique helps to reduce observer bias by providing a systematic, accurate, and repeatable sampling approach to scat content identification and quantification (Ciucci et al. 2004). A reduction in processing time (Ciucci et al. 2004, Meinzer et al. 1975, Johnson and Hansen 1977) is also experienced when using point-frame as compared to other scat analysis methods while maintaining accuracy and reliability. Accuracy of the point-frame technique for use in carnivore scat analysis has been tested by Johnson and Hansen (1977) and Ciucci et al. (2004) who reported no difference in accuracy of results when compared directly to complete hand separation models, but point analysis results in a dramatic reduction in processing time.

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The point-frame system uses a deep-walled, enameled dissecting tray with 10 evenly-spaced graduations along the top edges of tray (Chamrad and Box 1964). A wooden sampling frame with 5 evenly-spaced pins at 45° inclination is fitted to the top of the sampling tray. The graduations on the tray edges allow the sampling frame to be systematically and accurately moved along the tray. The sampling frame is moved along all 10 graduations and a sample is observed at each pin drop per graduation. Five pin drops for 10 sampling frame positions results in 50 observations per scat. Washed and dried scat contents were separated, mixed well, and randomly- and evenly-spaced across the bottom of the sampling tray. The sampling frame is moved to each of the 10 graduations along the tray. Any food item found under or closest to each pin is identified and recorded. I recorded 50 points per scat to compile percentage data of food items contained per individual scat. I recorded the prey item closest to each pin using macroscopic and microscopic techniques. A printed hair key of Texas mammals (Debelica and Thies 2009) aided in hair identification. Also, a reference slide collection of the hairs of common mammals of RPQRR was compiled using specimens from the Texas Tech Research Science Laboratory. All specimens used for the reference collection were taken from Fisher County, Texas or counties in the surrounding Rolling Plains ecoregion. A photo reference was compiled using the same specimens for skeletal and pelage identification. These reference collections aided in the identification of mammalian hairs using a compound microscope and also macroscopically to identify definitive morphological hair structures. The mammalian hair identification technique outlined by Mayer (1952) was used in this study. I identified bones, skulls, jaws, and teeth from mammals using the Illustrated Key to Skulls of Genera of North American Land Mammals (Jones and Manning 1992). The Mammals of Texas (Schmidly and Bradley 2016) was also used to aid in identification of mammalian remains. Plant material (grasses, seeds, etc.) found in scats were identified using several region-specific reference books (Gould 1978, Shaw 2011, Linex 2014). Any unidentifiable macroparticle food remains were assigned to food item with highest frequency of occurrence in sample (Ciucci et al. 2004). For the purpose of this study, the following quantitative categories were used to assess coyote scat contents:

34 Texas Tech University, Cade B. Bowlin, December 2018

Frequency of occurrence: Number of individual scat specimens out of a sample of scats that a particular food item occurs. Percent occurrence: Percentage of individual scats out of a sample of scats that contain a particular food item (number of scats containing food item/total number of scats x100). Data Analysis.—I assessed coyote diets on a monthly, seasonal, annual, and study period basis. For the purpose for this study seasons were defined as: winter (December, January, February), spring (March, April, May), summer (June, July, August), and fall (September, October, November). Frequency of occurrence and percent occurrence values were determined for individual food items and prey categories identified in coyote scats collected during the study. I used the following prey categories in this study: rodent, grass, lagomorph, mast, large mammal, birds, eggshells, reptiles, insect, mesomammal, and quail. Rodents included any species in the order Rodentia. Cottontail rabbits and black- tailed jackrabbits comprised the lagomorph category. Mast included any fruits produced from woody trees or shrubs. For this purpose of this study, feral hogs and whitetail deer were the only species in the large mammal category. The bird category included any species other than bobwhite or scaled quail. As identification of species from eggshell remains proved tenuous, all eggs found in coyote scats were included in the eggshell category. Determination of reptilian species from remains in scats also proved tenuous and all scaled reptiles were consolidated into the reptile category. Remains from species in the phylum Arthropoda were grouped into the insect category. The mesomammal category included any mammalian predators <15kg that inhabit RPQRR. Northern bobwhite was the only species recovered in scats during this study and comprised the quail category. Abundance Indices.—Several metrics are used to monitor relative population abundances on RPQRR and data from these indices were used to assess prey availability during the study. Although multiple techniques are employed to determine quail abundance on RPQRR (Kubecka 2017), I chose the minimum known population (MKP) derived from trapping efforts as my abundance metric of quail for this study. Quail trapping takes place twice annually, in the spring and fall utilizing walk-in type wire

35 Texas Tech University, Cade B. Bowlin, December 2018 cages across 296 trap sites. The minimum known population value derived from fall trapping efforts was used as my estimate of quail abundance for this study. Small mammal trapping also takes place twice annually (January and June) on RPQRR. Trapping efforts are focused across 8 different vegetation types. Arrays of 25 Sherman traps (5x5-m grids) are placed in 5 locations in each habitat site. Traps are monitored for 4 consecutive nights for a total of 500 trap nights/vegetation type per trapping session. I computed relative abundance of small mammals from these trapping data. Relative abundance and species diversity of arthropods are monitored each July at 8 vegetation types across the ranch. Five pitfall arrays of 6 traps each are placed on each transect in each vegetation type. These traps are checked once daily at 3-day intervals. Sweep-netting is performed by obtaining 25 sweeps and 4 replicates at each pitfall array. Individual arthropods are bagged, dried at air temperature for >72 hours, then identified to Order. Helicopter surveys are conducted by RPQRR staff every spring (March) and fall (November) to monitor quail abundance (Kubecka 2017). The count protocol followed guidelines presented by DeMaso et al. (2010). A total of 83.2 km of transect are flown for each count. During the helicopter surveys, other of interest are monitored as well. These helicopter counts were the only abundance metric in which coyote, white- tailed deer, feral hogs, and lagomorphs are recorded. Although not statistically robust, these indices give an anecdotal suggestion as to relative abundance and population trends of these potential prey species.

Results Weather conditions.—The El Niño system that was in place beginning summer of 2015 resulted in annual rainfall totals at or near the 30-year mean for the duration of the study. Annual precipitation was 84 cm in 2015, 61cm in 2016, and 37cm in 2017 (Fig. 2.4). Timely and abundant spring rains in 2016 coupled with mild temperatures resulted in positive floral and faunal responses. During April, May, and June of 2016, RPQRR received 8.8 cm, 11.4 cm, and 10.6 cm of rainfall, respectively. November was the month with the second highest rainfall during 2016 (11.3 cm). The average Palmer

36 Texas Tech University, Cade B. Bowlin, December 2018

Modified Drought Index (PMDI) values were +2.64, +3.37, and +1.19 for 2015, 2016, and 2017, respectively (Fig. 2.5). For the PMDI, values of -4.0 and below indicate severe drought and range to over +4.0 indicating extremely wet conditions. Prey abundance.—Quail trapping efforts during the study resulted in fall MKPs of 2,920 individuals in 2015, 4,393 individuals in 2016, and 2,126 individuals in 2017 (Fig. 2.6). Winter MKPs were 807, 2810, and 3103 for winter 2015, winter 2016, and winter 2017, respectively. Quail abundance in fall 2016 was the highest ever-recorded on RPQRR (since inception in 2008) at a minimum known population of 4,393 individuals. Record numbers of small mammals were trapped during the time period of my study. In summer 2015, 15.85 individuals per 100 trap-nights were captured during small mammal trapping efforts (Fig. 2.7). Abundance increased to Summer 2016 estimates of 28.8 individuals per 100 trap-nights, then declined sharply in Summer 2017 (1.8 individuals/100 trap-nights). Winter small mammal trapping results were 5.73, 27.83, and 2.65 individuals /100 trap-nights for 2015-2017, respectively. In the summer of 2016, 1,152 unique individuals were captured which is the most ever trapped on RPQRR. The most abundant rodent captured during the study was Sigmodon hispidus. Summer trapping efforts resulted in 532, 1023, and 10 cotton rats captured during 2015, 2016, and 2017, respectively (Fig. 2.8). In winter 2015, 132 cotton rats were trapped on RPQRR, 1027 winter of 2016, while only 26 individuals were captured during Winter 2017. Neotoma sp. (wood rats) were the second most common rodent found during the study with estimates of 53, 83, and 42 individuals captured during summers of 2015, 2016, and 2017, respectively (Fig. 2.9). Winter trapping results showed 9, 20, and 55 woodrat individuals trapped on RPQRR. The third most abundant rodent trapped during the study was the hispid pocket mouse (Chaetodipus hispidus) (Fig. 2.10). During summer trapping efforts in 2015, 28 individuals were caught. Summer 2016 and summer

37 Texas Tech University, Cade B. Bowlin, December 2018

90 83.7 80

70 64.2 57.6 55.6 60.6 60 53.4 48.4 50 42.1 40 37.5

30 21.1

Annual Annual Precipitation (cm) 20

10

0 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 La Nina El Nino

30-yr mean Year

Figure 2.4. Annual precipitation (cm) for the Rolling Plains Quail Research Ranch, Fisher County, Texas with 30-year annual mean (dashed line) and La Niña and El Niño weather patterns labeled.

3.37 4 3 2.64 2.04 2 1.19 1 0 -1

-0.32 value -2 -1.99 -3 -2.53 -4 -3.22 -3.89 -5 -4.61 Palmer Palmer Modified Drought Index (PMDI) -6 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 La Nina El Nino

Year

Figure 2.5. Annual average Palmer Modified Drought Index (PMDI) values for the Low Rolling Plains of Texas 2009-2017 with La Niña (2011) and El Niño (2016) weather patterns labeled (NOAA 2017).

38 Texas Tech University, Cade B. Bowlin, December 2018

5000 4393 4500 4000 3500 2920 3000 2126 2500 2000

1500 Fall MKP Fall bobwhite 1000 715 445 283 248 205 500 77 0 2009 2010 2011 2012 2013 2014 2015 2016 2017 Year

Figure 2.6. Fall minimum known population (MKP) of bobwhites (Colinus virginianus) on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017 (Kubecka 2017). Data derived from annual trapping-banding efforts.

35

30 28 29

25

20 18 16 15

10 5 6

Small Small mammals/100 trap nights 4 3 5 2 2 3 3 3 0 1 1 1 0

Figure 2.7. Small mammals captured (per 100 trap nights) during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009- 2017.

39 Texas Tech University, Cade B. Bowlin, December 2018

1200

1027 1023 1000

800

600 532

Individuals Individuals captured 400

200 132 100 49 25 5 3 17 1 2 4 2 3 13 26 10

0

Winter Winter Winter Winter Winter Winter Winter Winter Winter

Summer Summer Summer Summer Summer Summer Summer Summer Summer 2009 2010 2011 2012 2013 2014 2015 2016 2017

Figure 2.8. Individual Sigmodon hispidus (cotton rat) captured during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009- 2017.

40 Texas Tech University, Cade B. Bowlin, December 2018

100

83 80

60 53 55

42

40 Individuals Individuals captured 21 20 20 11 9 9 7 9 9 3 5 5 0 2 1 1

0

Winter Winter Winter Winter Winter Winter Winter Winter Winter

Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Figure 2.9. Individual Neotoma micropus (Southern plains woodrat) captured during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017.

40 39 35 33 32 30 30 27 28 25 23 20 13 14 11 10 8 7

Individuals Individuals captured 5 3 2 3 2

0

Winter Winter Winter Winter Winter Winter Winter Winter Winter

Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Figure 2.10. Individual Hispid pocket mouse (Chaetodipus hispidus) captured during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017.

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2017 results showed 39 and 2 individual hispid pocket mice caught, respectively. Individual pocket mice were caught 32, 14, and 7 times during winter of 2015, 2016, and 2017, respectively. Arthropod abundance obtained from sweepnet efforts resulted in an average of 43, 79, and 26 individual arthropods per transect during 2015, 2016, and 2017, respectively (Fig. 2.11). Pitfall trapping efforts during 2015, 2016, and 2017 indicated averages of 107, 209, and 202 individuals per transect across all 8-habitat types (Fig. 2.11). Sightings of all mammals identified during helicopter surveys for all years of the study can be found in Table 2.1. A total of 42 coyotes were identified during the 6 helicopter surveys that were conducted throughout this study. The highest abundance of coyotes observed occurred during Spring 2016 (17) and Fall 2016 (10) and decreased to 6 individuals in Spring 2017. Jackrabbits observed during helicopter transects were greatest in Spring 2015 (38) and Spring 2017 (30). Individual cottontail rabbits (101) were identified 15 times during Spring 2016 counts, 50 during Fall 2016, and 19 in Spring 2017. A high number of deer (for RPQRR) were counted during Fall 2016 (74) (up from 16 Spring 2016) then decreased to 25 individuals Spring 2017. A sum of 115 deer was surveyed during the study period. Only two feral hogs were identified from helicopter surveys during the study: 1 in Fall 2016 and 1 in Spring 2017. Scat Analysis.—I collected a total of 480 coyote scats from November 2015 to February 2017. The most commonly-consumed individual prey item recovered in scats (n=480) was Sigmodon hispidus (Table 2.2). Remains of cotton rats were found in 350 scats (frequency of occurrence) equaling 72.9% of scats (percent occurrence). During all seasons except Fall 2015, hispid cotton rats were the single most important seasonal food item recovered in scats on RPQRR. In all months of the study except November 2015 and February 2017 cotton rats were the most frequently occurring individual food item in coyote scats. November 2015 wood rats were the most frequently occurring item and cottontail rabbits were most important in February 2017. Grass was the second most identified item; 104 coyote scats (21.7%) contained grass. Sylvilagus and Neotoma were the third and fourth most-consumed prey items during the study.

42 Texas Tech University, Cade B. Bowlin, December 2018

600

500

SE) ±

̅ 400 푥

300

200

Individuals/transect Individuals/transect ( 100

0 2010 2011 2012 2013 2014 2015 2016 2017

Pitfall Sweepnet Figure 2.11. Mean (푥̅±SE) number of arthropods caught per transect during trapping efforts in July on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2010- 2017.

Table 2.1 Abundance of coyotes and various prey species observed during helicopter surveys flown on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2015-2017. Transects total 92 km of effort per survey.

Spring Fall Spring Fall Spring Species 2015 2015 2016 2016 2017 Total Coyote 7 2 17 10 6 42 Jackrabbit 38 0 26 29 30 123 Cottontail 17 0 15 50 19 101 Deer 0 0 16 74 25 115 Feral hog 0 0 0 1 1 2 Turkey 0 0 25 0 34 59

43 Texas Tech University, Cade B. Bowlin, December 2018

Table 2.2 Frequency of occurrence (freq. occ) and percent occurrence (pct. occ) of all individual food items (n=480) recovered from coyote scats collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017.

Food Item Freq. occ Pct. occ Rank Sigmodon hispidus 350 72.9 1 Grass 104 21.7 2 Sylvilagus floridanus 71 14.8 3 Neotoma micropus 69 14.4 4 Lepus californicus 33 6.9 5 Peromyscus spp. 26 5.4 6 Prosopis glandulosa 14 2.9 7 Eggshell 14 2.9 8 Reptile 13 2.7 9 Odocoileus virginianus 12 2.5 10 Unidentified feather 10 2.1 11 Spermophilus spp. 5 1.0 12 Orthoptera 5 1.0 13 Opuntia spp. 5 1.0 14 Sus scrofa 4 0.8 15 Sturnella spp. 4 0.8 16 Sciurus niger 3 0.6 17 Unidentified insect 3 0.6 18 Colinus virginianus 3 0.6 19 Perognathus flavescens 2 0.4 20 Coleoptera 2 0.4 21 Canis lupus f. 2 0.4 22 Taxidea taxus 1 0.2 23 Reithrodonomys 1 0.2 24 Procyon lotor 1 0.2 25 Mus musculus 1 0.2 26 Geomys spp. 1 0.2 27

44 Texas Tech University, Cade B. Bowlin, December 2018

Sylvilagus and Neotoma were recovered from 71 (14.8%) and 69 (14.4%) scats, respectively. All other individual prey items each occurred at ≤7% occurrence throughout the study. As a group, rodents were the most consumed prey category during this study. Remains of rodents were recovered in 414 of scats (86.5% ) (Table 2.3). Consumption of rodents by coyotes on RPQRR was consistently high throughout the duration of the study and showed little seasonal variation until a significant decline in consumption occurred during Winter 2016-17 (Fig. 2.12). December 2016 (n=30), January 2017 (n=30), and February 2017 (n=30) showed rodent vestiges in 76.7% (23), 56.7% (17), and 26.7% (8) of scats, respectively. A noted decline in small mammal population was also recorded during this time period (Fig. 2.7). Rodents were the most important prey category for coyotes during each season of the study (Table 2.4). During Spring, Summer, and Fall of 2016, remains of cotton rats were detected in 83.33 (n=30), 93.3 (n=30), and 86.7% (n=30) of scats, respectively. Lagomorphs were the second-most important prey category on a seasonal basis (Table 2.4) with highest percentages of consumption occurring in December 2016 (30%), January 2017 (40%), and February 2017 (63.33%). Lagomorph and grass tied for the second-most consumed prey categories; each was found in 21.7% of scats (Table 2.3). Peak grass consumption during this study occurred in Summer 2016 (43.3%, n=90) (Table 2.4). Scats examined during Summer 2016 contained 53, 47, and 30% grass in June, July, and August, respectively (Fig. 2.13). Grass occurred in 53.3% (16), 46.7% (14), and 30% (9) of scats collected during June, July, and August 2016, respectively. Lagomorph consumption by coyotes on RPQRR was highest during Fall 2015 (23.3%, 7, n=30), Fall 2016 (24.4%, 22, n=90), and Winter 2016-17 (44.44%, 40, n=90, Fig. 2.14) and coincided with a decrease in rodent consumption (Fig. 2.15). The highest seasonal consumption of cottontail rabbits took place in Winter 2016 (38%). Fall 2015 and Fall 2016 saw the second highest consumption rate of cottontail (20% per season). Scats collected spring 2016 contained the most (17%) jackrabbit remains seasonally during this study.

45 Texas Tech University, Cade B. Bowlin, December 2018

Table 2.3 Frequency of occurrence (freq. occ) and percent occurrence (pct. occ) of major food categories recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017.

Category Freq. occ Pct. occ Rank Rodent 414 86.3 1 Lagomorph 104 21.7 2 Grass 104 21.7 3 Mast 17 3.5 4 Large mammal 16 3.3 5 Birds 14 2.9 6 Eggshells 14 2.9 7 Reptile 13 2.7 8 Insect 10 2.1 9 Quail 3 0.6 10 Mesomammal 2 0.4 11

100.00

80.00

SE) SE)

̅± 푥 60.00

40.00

Percent of scatsPercent ( 20.00

0.00

Jul-16

Jan-16 Jan-17

Jun-16

Oct-16

Feb-16 Sep-16 Feb-17

Apr-16

Dec-15 Dec-16

Mar-16

Nov-16 Nov-15 Aug-16 May-16 Fall Winter 2015- Spring 2016 Summer 2016 Fall 2016 Winter 2016- 2015 2016 2017

Figure 2.12. Percent of scats (푥̅±SE) containing rodent remains recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017.

46 Texas Tech University, Cade B. Bowlin, December 2018

Table 2.4 Seasonal percent occurrence of major prey categories recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during 2015-17.

Fall Winter Spring Summer Fall Winter 2015 2015-16 2016 2016 2016 2016-17 Category (n=30) (n=90) (n=90) (n=90) (n=90) (n=90) Rodent 96.7 95.6 88.9 97.8 92.2 53.3 Lagomorph 23.3 11.1 16.7 11.1 24.4 44.4 Grass 23.3 23.3 8.9 43.3 20.0 12.2 Reptile 3.3 0.0 6.7 2.2 2.2 2.2 Large mammal 0.0 3.3 0.0 1.1 4.4 8.9 Insect 3.3 0.0 0.0 4.4 4.4 1.1 Mast 0.0 0.0 0.0 11.1 7.8 0.0 Mesomammal 0.0 1.1 0.0 0.0 0.0 1.1 Quail 0.0 0.0 0.0 0.0 0.0 3.3 Birds 3.3 0.0 3.3 4.4 2.2 4.4 Eggshells 3.3 0.0 5.6 8.9 0.0 0.0

60.00

50.00

SE) ±

̅ 40.00

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Jan-16 Jan-17

Jun-16

Oct-16

Feb-16 Sep-16 Feb-17

Apr-16

Dec-15 Dec-16

Mar-16

Nov-15 Aug-16 Nov-16 May-16 Fall Winter 2015- Spring 2016 Summer 2016 Fall 2016 Winter 2016- 2015 2016 2017

Figure 2.13. Percent of scats (푥̅±SE) containing grass recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017.

47 Texas Tech University, Cade B. Bowlin, December 2018

80.00

SE) 60.00

̅±

푥 (

40.00

20.00 Percent of scats

0.00

Jul-16

Jan-16 Jan-17

Jun-16

Oct-16

Feb-16 Sep-16 Feb-17

Apr-16

Dec-15 Dec-16

Mar-16

Nov-15 Aug-16 Nov-16 May-16 Fall Winter 2015- Spring 2016 Summer 2016 Fall 2016 Winter 2016- 2015 2016 2017

Figure 2.14. Percent of scats (푥̅±SE) containing lagomorph remains recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017.

100.00

80.00

60.00 Rodent Lagomorph

40.00 Percent of scats

20.00

0.00

Figure 2.15. Monthly trends of rodents and lagomorphs recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017.

48 Texas Tech University, Cade B. Bowlin, December 2018

This study encompassed only one full calendar year (2016) and as such only one year can be evaluated on an annual basis. The most commonly consumed prey category during 2016 was rodents, which occurred in 91.9% of scats (331, n=360) (Fig. 2.16). Grass was found in 22.2% (80, n=360) of scats collected in 2016 and was the second most consumed prey category annually. The third-ranked prey category was lagomorphs at 17.8% (64, n=360). All other prey categories each occurred in ≤4% of scats collected during 2016. Analysis results from 2016 (n=360) indicated 85% of scats contained cotton rat vestiges making it the most important species of prey consumed annually during the study (Fig. 2.17). The second- and third-most consumed individual items used by coyotes annually were grass at 21.7% (104) scats and cottontails (14.8%, 71). Woodrats ranked fourth and were found in 14.4% (69) of scats collected during 2016. Rodents were the most important prey category for coyotes during each season of the study (Table 2.4). During Spring, Summer, and Fall of 2016, remains of cotton rats were detected in 83.33 (n=30), 93.3 (n=30), and 86.7% (n=30) of scats, respectively. Lagomorphs were the second-most important prey category on a seasonal basis (Table 2.4) with highest percentages of consumption occurring in December 2016 (30%), January 2017 (40%), and February 2017 (63.33%). Vestiges of birds (excluding quail) and eggshells were each recovered in 14 scats during the study (n=480) for a total of 2.9% of scats (Table 2.3). Of the 14 scats containing feathers, Sturnella spp. accounted for 4 scats (0.8%) and unknown feathers were recovered from 10 (2.1%) specimens. During 2016, 11 scats (3.1%) (n=360) contained bird remnants (Fig. 2.15). Summer 2016 (n=90) and Winter 2016-17 (n=90) seasons recorded the highest consumption of feathers (4.4% per season) (Table 2.4). Eggshell remains were found most commonly in scats collected in Summer 2016 (8.9%; (n=90) (Table 2.4). Eggshell fragments were recovered in 5 (16.7%), 3 (10.0%), and 4 (13.3%) scats during May (n=30), June (n=30), and July (n=30) of 2016, respectively. The season with the second highest incidence of egg remains was Spring 2016 (n=90). Of the coyote scats collected during this period, 5.6% (5) contained eggshells. Eggshell fragments were found in 3.6% (13) of scats (n=360) during 2016.

49 Texas Tech University, Cade B. Bowlin, December 2018

100.00

SE) 80.00

̅±

푥 (

60.00

40.00 Percent of scats

20.00

0.00

Figure 2.16. Annual percent occurrence (푥̅±SE) of prey categories recovered from coyote scats (n=360) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during 2016.

50 Texas Tech University, Cade B. Bowlin, December 2018

100.0

80.0

SE) SE)

̅±

푥 60.0

40.0 Percent of scats (

20.0

0.0

Figure 2.17. Annual percent occurrence (푥̅±SE) of individual prey items recovered from coyote scats (n=360) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during 2016.

51 Texas Tech University, Cade B. Bowlin, December 2018

Due to the degraded state in which eggshells are recovered in coyote scat, I did not attempt to assign species to eggshell remains. Feathers of Colinus virginianus were confirmed in only 3 scats (0.6%) during the study (n=480), all of which occurred in specimens collected during Winter 2016-2017 (Table 2.4). Quail remains were found in one scat (3.33%) each month of December 2016 (n=30), January 2017 (n=30), and February 2017 (n=30). Annually (n=360) during 2016, quail accounted for 0.3% (1 scat containing quail remains) of coyote diets on RPQRR. Mast was not an important food source for coyotes on RPQRR during this study and occurred in only low frequencies. Only 14 (3.5%) of scats (n=480) contained mast and those were in scats collected during August (33.33%, 10, n=30), September (16.7%, 5, n=30) and October 2016 (6.7%, 2, n=30) (Table 2.4). Honey mesquite (Prosopis glandulosa) and prickly pear (Opuntia spp.) were the only fruits found in coyote scats during this study. Mesquite occurred in scats collected in August (26.7%, 8, n=30), September (13.3%, 4, n=30), and October (6.7%, 2, n=30) of 2016. Coyotes on RPQRR used prickly pear during August (13.3%, 4, n=30) and September (3.3%, 1, n=30) 2016 only. Remains of large mammals (feral hog and whitetail deer) were consumed infrequently (3.3%, 16, n=480) during this study (Table 2.3). Annually during 2016, mammal remains constituted 2.2% of coyote diets. On a seasonal basis, scats collected during winter 2016-17 contained the greatest amount of large mammals (8.9%, 8, n=90). Fall 2016 (n=90) was the season with the second highest occurrence of large mammal remains (4.44%, 4, n=90) (Table 2.4). Insects were not an important food item for coyotes on RPQRR during the study. Remains of insects were found in 2.1% of scats (10, n=480, Table 2.2) the majority of which occurred in Summer 2016 (4.4%, 4, n=90), Fall 2016 (4.4%, 4, n=90), and Fall 2015 (3.3%, 1, n=90) (Table 2.4). Grasshoppers (Orthoptera) and beetles (Coleoptera) were the most common Orders of insects consumed. Scales of reptiles were discovered in 2.7% (13, n=480) of scats examined and were not an important prey item for coyotes (Table 2.3). Interestingly, some snakes, e.g., Crotalus and Masticophis are known predators of quail and quail eggs on RPQRR. Other

52 Texas Tech University, Cade B. Bowlin, December 2018 known nest predators that were found in scats collected during this study (n=480) were American badger (Taxidea taxus) (0.2%, 1), raccoon (Procyon lotor) (0.2%, 1), feral hog (0.8%, 4), and Mexican ground squirrels (Spermophilus mexicanus) (1.0%, 5) (Fig. 2.18).

14

12

10

8

6

Numner Numner scats of 4

2

0 Reptile Ground Feral Hog Badger Raccoon Quail Squirrel

Figure 2.18. Remains of northern bobwhite (Colinus virginianus) and confirmed quail/quail nest predators recovered from coyote scats (n=480) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, November 2015-February 2017.

Discussion Most studies characterize the classic feeding behavior of the coyote as an opportunistic generalist that follows fluctuations in food item availability and abundances (Cornell 1976, Bekoff 1977, Litvaitis and Mautz 1980, Windberg and Mitchell 1990, Young et al. 2006). Food choice and feeding strategies are complex and dependent upon factors such as prey availability, seasonal nutritional requirements, social hierarchies and behavioral tendencies (Stephens and Krebs 1986, Abrams 2000). Diets of coyotes observed during this study reflected mammalian prey abundance, namely Sigmodon. and coyotes adapted their diets to fluctuations in prey availability. As previous researchers have suggested, the coyote’s dietary plasticity allows them to subsist on broad array of prey items that fluctuate in availability and distribution through time (Andelt 1985, Gese et al. 1988, Grinder and Krausman 2001). Climatic conditions influence differential prey detectability, breadth of available food items, plant

53 Texas Tech University, Cade B. Bowlin, December 2018 phenology, and ultimately elicit a functional feeding response by coyotes (Murdoch 1973, Andelt 1987). In South Texas, annual and seasonal variation in coyote diets has been attributed to vegetative succession and prey abundance (Andelt et al. 1987). This was evidenced in coyote diets monitored throughout this study on RPQRR during an El Niño weather period. Rodents.—During the El Niño weather pattern that began in 2015, the ranch received above average rainfall, which led to favorable floral and faunal responses into 2016. Above average rainfall and moderate temperatures led to an irruption in small mammal populations on the study site. Trapping data from Summer 2016 showed the greatest abundance of small mammals ever captured on RPQRR, an increase of 82% from Summer 2015. Cotton rats were the most abundant small mammals captured during trapping efforts, >10 times more prevalent than the second most common rodent (woodrat). Cotton rats are known to follow boom-bust cycles and can experience dramatic population irruptions when environmental conditions are favorable. As primarily grazers, cotton rats favor areas with tall, dense grasses and forbs for both protection from predation and as a food source (Schmidly and Bradley 2016:523). Guthery et al. (1979) suggested cotton rat density was correlated to standing vegetative biomass due to higher availability of food and protection from predation. Above average rainfall beginning in summer of 2015 lead to excellent grass production on RPQRR during 2016. Marestail (Conyza canadensis), a tall annual forb, was prevalent on the study site in 2016 likely due to timely above average precipitation in 2015 (Pettit 1991). Fall and winter precipitation has been shown to induce abundant marestail on ranges in western Oklahoma (Gillen and Berg 1998). An abundance of standing vegetative biomass providing food and protection from predators (via ample vegetative screening cover) was likely the catalyst for an irruption in cotton rat population on the study site in 2016. Cotton rats on RPQRR showed a 44-fold increase from Summer 2014 to Summer 2015 and a 94% increase from Summer 2015 to Summer 2016. Population irruptions of cotton rats of this magnitude are not uncommon in Texas. Windberg and Mitchell (1990) showed a 46-fold increase in abundance of cotton rats in southern Texas from 1981-1982. Lehmann (1984:210) saw an irruption in cotton rat numbers in the 1960s in south Texas

54 Texas Tech University, Cade B. Bowlin, December 2018 that left the landscape barren of herbaceous vegetation. Lehman suggested an abundance of 120 cotton rats/0.4ha in summer of 1966 (10.3kg/0.4ha of rats) (1984:213). Population irruption was accounted to favorable rainfall and mild winter temperatures the previous winter. Haines (1963) suggested a cotton rat population irruption in Texas was attributable to favorable rainfall following several years of drought. Similarly, a severe drought beginning in 2011 followed by average to above average rainfall in 2015 and 2016 on RPQRR led to an irruption in cotton rat populations during my study. Coyotes consumed more cotton rats than any other individual food item during this study due to availability of cotton rats as compared to other prey choices. Other researchers have shown the importance of cotton rats to coyotes in Texas although data from my study showed cotton rats were consumed at a much higher frequency than in other studies. In a two-year study in south Texas, Windberg and Mitchell (1990) recovered cotton rat remains in coyote scats at a frequency of occurrence that was less than half (32% and 40%, n=300) of what I recorded in 2016 (85%, n=360). Hispid cotton rats and southern plains woodrats accounted for 24.5% of the annual diet of coyotes (n=514) during Meinzer et al.’s (1975) 2-year study in Knox Co., Texas. Kamler et al. (2002) found coyotes in northwestern Texas consumed cotton rats in addition to other prey (although author did not provide percent occurrence data). Even in a diverse prey community coyotes use cotton rats in accordance to rat abundance. Windberg and Mitchell (1990) suggested that consumption of cotton rats by coyotes in south Texas was correlated with rat abundance, similar to the results of my study. However, a marked decrease in cotton rat consumption was observed in Winter 2016. This dramatic reduction in frequency of occurrence of cotton rats followed a 97% decrease in cotton rat abundance that was observed from Summer 2016 to Winter 2017. This population bust was reflected in coyote diets, as cotton rat abundance decreased, so did their consumption by coyotes. The environmental driver that caused the population crash of cotton rats on RPQRR during 2016 is not known although there is some speculation that density dependent factors such as disease could play a role in their population dynamics (Schmidley and Bradley 2016:526). Haines (1963) suggested that drought conditions in 1957 coupled with subsequent rains triggered a cotton rat irruption

55 Texas Tech University, Cade B. Bowlin, December 2018 in Texas. Haines (1963) then saw a significant die-off between the summers of 1959- 1960 after the irruption. Reports of high populations of cotton rats decreased from 58% to 38% and 4% for 1958, 1959, and 1960, respectively. These data are similar , but less pronounced, to the cotton rat population bust I documented in 2016 on RPQRR. Cyclic irruptions of prey have elicited comparable functional feeding responses by coyotes in other studies. Todd et al. (1981) noted that the rate of recovery of snowshoe hare (Lepus americanus) remains in coyote scats in Alberta was directly attributable to hare density. Increased consumption was noted during times of high hare abundance and decreased as hare population decreased. The relative abundance of cotton rats on RPQRR during my study could have led coyotes to select for them due to reduced foraging time and increased prey detectability. Favorable precipitation and temperature can lead to a decrease in search efficiency and prey detectability through phenological responses in vegetation (Bowman and Harris 1980). However, MacCracken and Hansen (1986) posited that time spent searching for prey is related inversely to prey density. With a high abundance of cotton rats on the landscape, they were the most “profitable” quarry for coyotes as they were the prey item with the least amount of energy expended per capture. Categorically, rodents comprised the top food choices for coyotes on RPQRR during the study period, annually, and every season. My data reveal a significantly higher intake of rodents as compared to other studies of coyotes in Texas. Henke (2002) reported rodents occurred in 26.2% (n=407) of coyote stomachs collected in southern Texas. Rodents as a category made up 24.5% (n=514) of coyotes annual diet in north Texas (Meinzer et al. 1975). While these results show rodents comprised a significantly smaller percentage of overall coyote diets, no rodent abundance data are available therefore it is not possible to ascertain availability. Grass.—The results from my study indicate a dramatically greater level of grass consumption by coyotes than other studies in Texas. Grass was recovered in 2.5% (n=514) of coyote scats collected in King and Knox counties of Texas (Meinzer et al. 1975). During Tyson’s (2012) study on RPQRR, 9.8% of scats (n=1080) examined contained grass, which occurred most frequently during Winter and Spring. It is unclear as to why coyotes consume grass. Although some researchers (Gier 1968) have suggested

56 Texas Tech University, Cade B. Bowlin, December 2018 nutritional or analgesic value is derived from grass, others dismiss grass consumption by coyotes as incidental (Meinzer et al. 1975, Litvaitis and Shaw 1980). It is important to note that only 2 individual scats examined during this study contained solely grass. Grass remnants identified in all other scats occurred with additional prey items (typically rodent remains) and comprised a small percentage by volume of most individual scats. Tyson (2012) showed a correlation between rodent and grass consumption during a study on RPQRR. But for this study, grass consumption and rodent consumption were not correlated (r=0.34; p>0.20, df=14). Additionally, grass recovered in scats showed little digestion. The state of the grass appeared to be chewed, swallowed and passed with little deterioration or assimilation. An abundance of grass on RPQRR during the study could have made grass a by- product of rodent capture. Rodents are highly mobile and elusive prey that require specialized search and capture techniques by coyotes. As such, during periods of high standing crop of grass on the landscape, coyotes could consume grass incidentally during capture and subsequent consumption of rodents. On a seasonal basis, grass consumption was highest during Summer 2016 and rodents were also consumed at their highest level during Summer 2016 which supports my hypothesis. Additional research is needed to assess why coyotes consume grass in the quantities noted here. Lagomorphs.—Categorically, lagomorphs were an important prey item for coyotes on RPQRR during 2016. However, lagomorph consumption was low until a dramatic increase was noted Fall 2016 and Winter 2016. This is interesting to note because lagomorph consumption showed little variation until this point in the study. Until Fall 2016, coyotes on RPQRR were eating primarily rodents each season. But in Winter 2016, rodent consumption decreased as consumption of lagomorphs increased. This decrease in rodent consumption corresponded to the observed dramatic decrease in rodent populations on the study site. As rodents became less abundant, a functional feeding response (Murdoch 1973) led coyotes to use more lagomorphs, which appeared to still be relatively abundant on the site. Although no robust metric of lagomorph abundance is available, helicopter data showed lagomorphs at the highest levels observed on RPQRR during the time of this study.

57 Texas Tech University, Cade B. Bowlin, December 2018

These data suggest lagomorphs were most important to coyotes on RPQRR during winter months. Similarly, Windberg and Mitchell (1990) found lagomorphs as important winter food items for coyotes in south Texas; they were the principal prey during the study (40-55% of winter diets). Andelt (1985) also suggested mammalian prey is most important to coyotes in south Texas during winter months. Primary consumption of lagomorphs took place during winter in south Texas and was directly related to their abundance. However, the authors noted a stronger relationship between cotton rat abundance and lagomorph consumption than lagomorph abundance/consumption alone (Windberg and Mitchell 1990). Although lagomorphs were common on RPQRR during 2016, they still occurred at low densities on the landscape when compared to rodents. This could explain why rodent consumption was still higher than lagomorph use by coyotes until the precipitous population decline of rodents in Fall 2016. Lagomorph consumption during this study was correlated inversely to rodent consumption (r=-0.9, P<0.05, df=14). These data suggest that as availability of rodents (namely cotton rats) decreased, coyotes adapted their diets and switched to a more available prey, lagomorphs. Similar prey switching (Murdoch 1969) by coyotes in response to fluctuations in prey abundance has been noted in other studies. In the southwest Yukon, coyotes switched to hunting voles when snowshoe hare populations are at a cyclic low and small mammal populations remain high (O’Donoghue et al. 1998). Bartel and Knowlton reported similar findings in the Curlew Valley of Utah. When black-tailed jackrabbit abundance was low, coyotes switched to more available rodents. These data support my hypothesis that as rodent populations crashed beginning late summer 2016, coyotes began to switch their diets to more available lagomorphs. Other Food Items.—Very few scats contained remains of insects throughout the study and as such, insects were not important dietary items annually or seasonally. Arthropod trapping data from the study period reflected a record abundance of insects on the landscape (Figure 2.11). This population irruption was likely also a result of favorable phenological plant response experienced from El Niño conditions beginning in 2015 (Andelt et al. 1987). Although an abundance of insects occurred during the study, coyotes did not consume them in large amounts.

58 Texas Tech University, Cade B. Bowlin, December 2018

Although prior research in Texas has shown insects to be important food sources for coyotes, my results did not correspond with those studies (Henke 2002, Young et al. 2006, Tyson 2012). Coyotes typically consume insects during summer months when insect populations are at their peak. Similar results have been found in south Texas (Andelt et al. 1987). The low incidence of insect consumption was likely due to high abundance of rodents still available on RPQRR during the summer of 2016 and into fall. Mast was not an important food source for coyotes at any point during the course of this study. Work in Texas has suggested that mast is an important food source for coyotes that varies seasonally and annually according to availability (Andelt et al. 1987, Young et al. 2006, Tyson 2012). Fruits comprised 20% of annual coyote diets during a four-year study in south Texas (Andelt et al. 1987). Mast comprised 46% of mean annual diets in the Rolling Plains of Texas, with honey mesquite the second most important overall food item for coyotes during the study (Meinzer et al. 1975). Tyson’s (2012) research at RPQRR that took place during the extreme drought of 2011 on RPQRR showed high use of vegetation by coyotes (67%, n=1080). Mesquite and prickly pear constituted the bulk of mast recovered in scats collected during my study. Ripening and subsequent availability of these foods was concurrent with recovery in coyote scats. Although no metric of fruit abundance was available for use/availability comparisons, ranch personnel anecdotally observed low mast production on the ranch during 2016 (L. M. Lacoste, personal communication). This is corroborated by results reported from south Texas concerning water stress influence on honey mesquite pod production. Yousseffi (1992) reported that mesquite pod production was inversely related to rainfall and pod production was highest during years of low rainfall. Additionally, Lee and Felker (1992) reported that mesquite trees produced 3.3 times more pods during a drought year compared to a wet year in a two- year study in south Texas. An abundance of rainfall on RPQRR during 2016 could have led to low mesquite pod production on the study site. Large mammals (deer and feral hog) were not important food sources for coyotes during this study. Deer have been shown to be an important food source for coyotes in Texas (Andelt et al. 1987, Andelt 1995). Consumption of large mammals during my study was greatest during Winter 2016. This was a result of a decrease in available

59 Texas Tech University, Cade B. Bowlin, December 2018 rodents and also coincided with Texas’ deer hunting season with coyotes possibly consuming carrion. Coyotes could also be scavenging carrion from roads that comprise the northern and western boundaries of the study site. Abundance of deer on RPQRR is “low” (estimated at 1 deer/20 ha). However, Windberg and Mitchell (1990) suggested that in south Texas, consumption of deer by coyotes in winter was related more with deer fitness than abundance. Considering the excellent range condition on RPQRR during 2016, deer likely maintained high fitness levels reducing predation by coyotes. Coyote and Quail Dynamics.—On RPQRR, all actions (brush management, prescribed fire, grazing, disking/planting) are directed toward maximizing quail populations. As a result of these efforts and favorable climatic conditions, quail abundance in 2016 was the highest recorded on the ranch from 2008-2016. With such a high population density, quail were readily available for consumption by coyotes during this study. However, positive identification of quail remains was made in <1% of coyote scats. Favorable precipitation and temperatures led to favorable phenological responses in vegetation on RPQRR, which could have impacted prey detection and search efficiency by coyotes (Bowman and Harris 1980). Abundant, dense vegetation may have helped conceal quail and quail nests from detection by obscuring olfactory and visual abilities of coyotes. Low capture success rate coupled with required energy expenditure made quail a low profitability item for coyotes. Also, abundance of other more profitable and susceptible prey, e.g., cotton rats, may have helped buffer predation on quail by coyotes. Confirmed occurrence of quail vestiges occurred in one scat per month during December 2016, January 2017, and February 2017. These months correspond with Texas’ quail hunting season and limited quail hunting occurred on RPQRR. It is possible the quail remains recovered in coyote scats collected during this study were a product of wounding loss. Crippled or wounded quail could be significantly easier for coyotes to capture than a healthy bird. Coyotes could scavenge wounded birds that are not recovered by hunters and subsequently die as a result of wounds. Also, dry and dormant vegetation could make search efficiency and subsequent detectability of quail by coyotes more productive than during the growing season.

60 Texas Tech University, Cade B. Bowlin, December 2018

Summer 2016 saw the highest frequency of occurrence of eggshells in collected scats. These months parallel the nesting season of quails on the Rolling Plains of Texas (Rollins 2007) thus recovered eggshell fragments could have been a result of quail (or other bird) nest depredations. However, June also corresponds with simulated (“dummy”) nest transects on RPQRR and may have confounded my results. Coyotes were not significant predators of quail or quail nests during an El Niño weather period that resulted in record abundance of quail on RPQRR. Other studies in Texas have shown very little evidence of coyotes as important predators of quail. Meinzer et al. (1975) recovered bobwhite vestiges in only 0.2% (n=514) of coyote scats examined during their study. Birds made up only 1% (n=6,354) of coyote diets in a South Texas study (Andelt et al. 1987). Henke (2002) reported bobwhites or their eggs were found in ≤1% of coyote stomachs (n=407); he concluded that quail were only incidental prey for coyotes. Tyson’s (2012) study on RPQRR, conducted during a La Niña weather pattern, revealed quail remains in only 1 scat (<1%, n=1080) and eggshells in 2 scats (<1%). However, some researchers pose coyotes are significant quail nest predators on the landscape. Rader et al. (2007) reported coyotes were responsible for 32% (n=43) of nest depredations of bobwhites in south Texas. Lehmann (1946) reported 43% (n=189) of quail nests in south Texas were depredated by coyotes. Lehmann also reported coyotes were responsible for 36% (n=532) of nest depredations in south Texas (Lehmann 1984:92). Although these data suggest coyotes are significant predators of quail and their nests, predator removal programs may not be warranted. Prior research has shown few positive results in increased quail production after extensive predator removal (Guthery and Beasom 1977). In fact, some researchers suggest removal of coyotes through predator control efforts could actually negatively impact bobwhite quail production (Henke 2002). Guthery (1995) concluded that intense control of coyotes in an attempt to increase abundance of bobwhites was not warranted because intense predator control has little impact on quail production. Through renesting and competing risks, removal of coyotes from the system may not result in a net increase in productivity (Guthery 1995). The

61 Texas Tech University, Cade B. Bowlin, December 2018 competing risk hypothesis suggests removal of coyotes would only make quail and quail nests available to other predators. During this study, coyotes consumed known predators of quail and quail nests and as such could in fact be benefitting quail by regulating populations of known quail predators or influencing their movements across the landscape (Cooper et al. 2014). Coyotes and the Mesopredator Release Hypothesis.—Mesomammals are thought to be drivers of community structure and function (Romer et al. 2009). Presence or absence of an apex predator can create or alleviate pressure on lower mesomammals altering their distribution and resource allocation (Levi and Wilmers 2012). As such, predation and interference competition by coyotes are thought to influence the distribution, movements, diets, and abundances of smaller mesomammals (e.g., skunks, racoons, foxes) through the MRH (Johnson et al. 1996, Rogers and Caro 1998, Henke and Bryant 1999, Gompper 2002). During this study on RPQRR, coyotes consumed 5 different known nest predators of quail (raccoon, ground squirrel, reptiles, feral hog, and badger). Remains of reptiles were recovered in scats and although species identification was not possible, it’s important to note that snakes are confirmed predators of adult quail, and presumably nests, on RPQRR. A total of 7 radio-marked quail were killed and consumed by rattlesnakes during 2016. Ground squirrels have also been confirmed depredating nests on the RPQRR and were found in coyote scats collected during my study. Frequency occurrence of feral hogs was low for the duration of the study and as such were not important prey for coyotes. However, reptiles, ground squirrels, and feral hogs all occurred more frequently in diets of coyote than did quail. Although coyotes did not consume high numbers of mesomammals during the study, their influence on community structure and dynamics could have significant impacts on quail production and survival. Through interference and resource competitions with other mesomammals, coyotes could in fact be beneficial to quail production. Coyotes could be regulating mesomammals populations and movements on RPQRR thus reducing depredations by more efficient nest predators. Cooper et al. (2014) discovered that GPS-collared coyotes influenced movements of raccoons on

62 Texas Tech University, Cade B. Bowlin, December 2018

RPQRR and restricted (especially) female raccoons to u se only dense brush or riparian areas, i.e., not prime nesting habitat for bobwhites on RPQRR. Other research suggests coyotes can increase bird production by controlling mesomammal populations and thus lower the extinction rate of birds in fragmented landscapes (Rogers and Caro 1998, Crooks and Soule 1999, Henke and Bryant 1999, Levi and Wilmers 2012). After only one year of coyote removal in western Texas, Henke and Bryant (1999) recorded an increase in mesomammals on the study site. The authors suggested coyotes may spatially influence other mesomammals and subsequently have a top-down influence on prey populations. The data from this study on RPQRR and previous research in Texas suggest coyotes are not significant quail nest predators and their presence on the landscape could alleviate depredation pressure from other mesomammals. Study Bias.—As with most scat analysis studies, inherent biases must be taken into account when making inferences on ecological significance of results. Differences in prey digestibility, prey size, time and distance between consumption and defecation, and also recovery of food item could lead to erroneous assumptions including the overestimation of smaller prey in diets (Kelly and Garton 1997, Klare et al. 2011). However, scat analysis techniques give an overall view of coyote diets and are low-cost and non-consumptive. Scat analysis also provides a look into rare or unique prey items. As previously stated, accuracy of the point-frame technique (used in this study) for use in carnivore scat analysis has been tested by Johnson and Hansen (1977) and Ciucci et al. (2004) and results suggested no difference in accuracy of results when compared directly to complete hand separation models. All scats collected on RPQRR for analysis in this study were assumed to come from coyotes that reside permanently on the study site. No individual coyotes were collared with Global Positioning System (GPS) or radio telemetry devices during this study, therefore no space use models are available for robust use/availability analyses. However, previous research on RPQRR showed resident coyotes had mean 95% Kernel Home Ranges of 622 ha and 402 ha for males and females, respectively (Cooper et al. 2014). The radio-collared coyotes rarely, if ever, left the confines of RPQRR. This is consistent with other home range studies of coyotes in Texas that suggested coyotes have

63 Texas Tech University, Cade B. Bowlin, December 2018 home ranges of 2.7 km2 (males) and 2.6 km2 (females) (Andelt 1985). Young et al. (2006) showed in 2 studies (spaced 25 years apart) that territories and space use within territories survived generations in an unexploited population.

Management Implications The results from my study suggest that coyotes were not significant predators of quail or quail nests during an El Niño weather pattern that resulted in record abundances of quail on a property managed strictly for quail production,. Through proper habitat management techniques, and timely precipitation, quail production can be maximized in the presence of coyotes. Preservation of grassland habitats on the landscape could benefit rodent populations providing a buffer species between quail and coyotes. Additionally, coyotes, while not significant predators of quail or quail nests on RPQRR, could be important regulators of prey species that compete with quail for resources. If a coyote removal program is deemed necessary, special care must be taken as deviations in top- down predatory threats by coyotes could lead to a change in ecological function of some systems (Cherry et al. 2016) causing unwanted ramifications. If coyotes are reduced significantly, an irruption in prey species could reduce available resources for quail (Guthery 1995).

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CHAPTER III

LONGITUDINAL EVALUATION OF COYOTE DIETS DURING EL NIÑO v LA NIÑA WEATHER PATTERNS

Introduction Weather phenomena in the southwestern United States are a result of the El Niño Southern Oscillation (ENSO), which influences global winds, pressure systems, temperatures, and precipitation events (Ropelewski and Halpert 1986, NOAA 2017). The ENSO is caused by the fluctuation of warm and cool mid-lattitude sea surface temperatures in the equatorial region of the Pacific Ocean (NOAA 2017). The ENSO is comprised of 2 contrasting phases, La Niña and El Niño, and occurs on 2-4 year cycles (Kahya and Dracup 1993). El Niño is characterized in the Southwest by a cool, wet weather phase with above normal precipitation during winter and spring (Ropelewski and Halpert 1986). Conversely, during La Niña winter and spring in the Southwest are characterized by above average temperature and below normal precipitation (Ropelewski and Halpert 1986, Cole et al. 2002). The ENSO cycles can cause ecosystem-wide changes in terrestrial environments resulting in dynamic ecological impacts (Mo et al. 2009) through ecosystem composition, structure, function, and ultimately resource availability (Holmgren et al. 2001). Variations in temperature and precipitation (both annual and seasonal) impact vegetation structure and succession thus altering prey diversity and abundance (Andelt 1995, Andelt et al. 1987, Windberg and Mitchell 1990). The coyote (Canis latrans), a ubiquitous predator across North America, is influenced directly by these resources fluctuations. Since the extirpation of the wolf (Canis lupus) across much of its historical range in the past century, coyotes have increased in population and range across North America (Peterson 1996, Gompper 2002, Bekoff and Gese 2003). Dramatic changes in land use practices and landscape-level habitat transformations have allowed coyotes to flourish because of their plasticity in habitats occupied, reproductive capabilities, behavioral attributes and adaptable dietary requirements (Meinzer et al. 1975, Litvaitis and Shaw 1980, Bekoff and Wells 1986,

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MacCracken and Hansen 1986). The expansion of coyotes has influenced community structure and functionality by altering predator-prey dynamics (Gompper 2002). Species in low abundance or species that have been experiencing steady population declines could be impacted by such shifts in predator-prey dynamics. One such species is the northern bobwhite (Colinus virginianus; hereafter bobwhite). Unlike coyotes, bobwhites have declined across their range for decades (Brennan 1991). The Breeding Bird Survey shows bobwhites in Texas experienced a -1.78% decline annually from 1966-2015 (Sauer et al. 2017). Factors thought to contribute to the population decline are changes in land use practices, habitat fragmentation, reduction or elimination of historic fire regime, modern farming practices, livestock production, and disease (Brennan 1991). Predation is the primary proximate cause of bobwhite mortalities from nesting to adulthood (Rollins and Carroll 2001). Mesomammals, raptors, fire ants, and a host of other predators have been attributed with the population decline of bobwhites (Brennan 1994, Rollins and Carroll 2001). Coyotes and various species of quails occur sympatrically across their respective ranges in the United States. However, it is unclear as to the extent of the coyote’s influence on quail populations through predation and community structure influences. Without a doubt coyotes consume quail (Lehmann 1946, Beasom 1974, Gipson 1974, Meinzer et al. 1975, Lehmann 1984:190-196, Guthery 1995) and also depredate nests of quails (Lehmann 1984:190-96, Rollins and Carroll 2001, Henke 2002). Lehmann (1946) declared the coyote to be the most common predator of bobwhite in South Texas. More recently, Rader et al. (2010) used video surveillance of bobwhite nests and identified coyotes as one of primary species of predator responsible for depredation of bobwhite nests in South Texas. However, the magnitude of the impact coyotes have on bobwhite populations is unknown. Coyotes could possibly be influencing quail populations directly (via predation and nest depredation) or indirectly (through influencing predator-prey relationships and community structure/function). Andelt (1987) suggested that climatic changes directly influence the diets of coyotes. Variations in temperature and precipitation (both annually and seasonally) impact vegetation structure and succession thus altering prey diversity and abundance for

74 Texas Tech University, Cade B. Bowlin, December 2018 coyotes (Andelt et al. 1987, Windberg and Mitchell 1990, Andelt 1995). Climatic conditions influence differential prey detectability, breadth of available food items, plant phenology and ultimately functional feeding responses by coyotes (Andelt et al. 1987, Harris 2015). Climatic fluctuations directly influence coyote diets (Andelt et al. 1987, Hidalgo- Mihart et al. 2001, McKinney and Smith 2007) and also bobwhite abundance (Guthery et al. 1988, Bridges et al. 2001, Lusk et al. 2001, Lusk et al. 2002, Kubecka 2017). Longitudinal studies are needed to evaluate how cyclic weather patterns (ENSO) impact prey-predator dynamics. Quail-coyote relationships are specifically of interest in the Rolling Plains of Texas. To that end, I studied diets of coyotes on a study site in the Rolling Plains of Texas managed specifically for bobwhites during El Niño and La Niña weather events to investigate how dynamic weather patterns influence complex food webs and predator-prey relationships. The Rolling Plains of Texas frequently (roughly every 5 years) experiences significant climatic extremes as a result of El Niño and La Niña cycles. Bobwhite and scaled quail (Callipepla squamata) occur sympatrically with coyotes across the Rolling Plains ecoregion of Texas (Fig. 3.1). In order to build upon previous research (Tyson 2012) of diets of coyotes on the same study site during a La Niña weather pattern (2010-2012), I collected and analyzed coyote scats on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during an El Niño weather pattern. The objectives of my study were to assess (a) variation in diets of coyotes, (b) whether coyotes are significant predators of quail and quail nests, and (c) to complement information on diets of coyotes reported by Tyson (2012). Using these data, I seek to understand the role of coyotes in quail management and subsequently provide quail managers with a strategy for predator management.

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Figure 3.1. Map illustrating the 10 ecoregions in Texas with the Rolling Plains ecoregion shaded. The Rolling Plains Quail Research Ranch (RPQRR), Fisher County, Texas is indicated by the red dot.

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Study Area The Rolling Plains Quail Research Ranch (RPQRR) (Fig. 3.2) is a research and demonstration facility where “everything points to quail.” The mission statement of the ranch is “to sustain Texas’ wild quail hunting heritage for this and future generations” (www.quailresearch.org). Since the ranch’s inception in 2007, the property has been managed intensively for bobwhite and scaled quail. The RPQRR provides landowners and managers with current research and management techniques to best maintain their properties for maximum abundance of bobwhites. Habitat on the ranch is managed and manipulated to meet the needs of bobwhite and scaled quail in the Rolling Plains ecoregion. Brush management (chemical and mechanical), grazing management, prescribed fire regimes, soil disturbance, and supplemental feeding efforts are directed towards maximizing abundance of bobwhites on RPQRR (Rollins 2007:128-140). No active predator management has occurred on the ranch since 2008. The RPQRR lies in the Rolling Plains ecoregion of Texas and is located 19 km west of Roby, Texas in Fisher County and encompasses 19-km2. Gently rolling plains with dissected valleys and prominent ridges characterize the Rolling Plains ecoregion (NRCS 2018). Elevation of the RPQRR ranges from 587-925 m above sea level with north to south-oriented ridges on the ranch ranging in elevation from 626-687 m above sea level (NRCS 2018). Climate in the western Rolling Plains ecoregion is typified as a dry, sub-humid climate consisting of hot summers and mild winters (NRCS 2018). Temperatures vary from summer days reaching 38 degrees Celsius to lows of -6 degrees Celsius in winter months. Cold spells are typically short-lived but consecutive summer days over 38 degrees Celsius are not uncommon. A long growing season (April-November) averages 215 frost-free days and 220 freeze-free days annually. Average annual precipitation is 62.9 cm and average annual temperature is 17.6 degrees Celsius (NOAA 2017). Majority of annual precipitation comes in spring and early summer. Very little (25 centimeter annual average) precipitation is received in the form of snowfall (NRCS 2018).

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Figure 3.2. Map of the Rolling Plains Quail Research Ranch with state of Texas inset, Fisher County highlighted and study site outlined within Fisher County.

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The study site features gently rolling hills comprised of mostly sandy and clay loam soils. Soil types (Fig. 3.3) and climate favor a true mixed-grass prairie, with additional forbs and woody species interspersed. Dominant soil series consist of Paducah Loam (15%), Miles Fine Sandy Loam (30%), Wichita Clay Loam (15%), and Woodward Loam (20%) (NRCS 2018) (Fig. 3.3). Soils in Paducah Loam sites are deep and well- drained, occurring on 0-5% slopes. Common vegetative communities are comprised of grama grasses (Bouteloua spp.), Texas wintergrass (Nassella leucotricha), sand dropseed (Sporobolus cryptandrus), and buffalograss (Bouteloua dactyloides). Honey mesquite (Prosopis glandulosa) is a common invader on most sites (NRCS 2018). Miles Fine Sandy Loams on RPQRR are alluvial soils that are deep, well-drained and moderately permeable. These soils occur on level to moderately sloping areas ranging in slopes of 0-8%. Silver bluestem (Bothriochloa laguroides) and grama grasses are native plant species found on Miles series soils. Woody species known to invade these soils consist of catclaw mimosa (Mimosa aculeaticarpa), catclaw acacia (Senegalia greggii), lotebush (Ziziphus obtusifolia) , and mesquite (NRCS 2018). Wichita Clay Loam soils on RPQRR are found on level to gently sloping uplands of 0-5% slopes. The deep, well-drained soils in this series support native communities of short and mid-grasses as well as mesquite (NRCS 2018). Weathered sandstone bedrock formed the deep, well-drained Woodward Loam series that is found on the steeper summits and escarpment ridgelines (slopes 1-30%). Grama grasses are common on Woodward soils on the study site (NRCS 2018). Additional grasses found on RPQRR are silver bluestem (Bothriochloa saccharoides), threeawns (Aristida spp.), and tobosagrass (Pleuraphis mutica). Decades of fire suppression and repeated overgrazing by cattle have resulted in expansion and proliferation of woody species across the landscape (NRCS 2018). These woody species include redberry juniper (Juniperus pinchotti) and honey mesquite. Additional woody species found on RPQRR are netleaf hackberry (Celtis reticulata), pricklyash (Zanthoxylum hirsutum), gum bumelia (Sideroxylon lanuginosum), skunkbush sumac (Rhus trilobata), yucca (Yucca sp.), agarita (Mahonia trifoliolata), elbowbush (Forestiera pubescens), wolfberry (Lycium berlandieri), and catclaw mimosa. Drought- tolerant

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Figure 3.3. Map illustrating soil types found on the Rolling Plains Quail Research Ranch, Fisher County, Texas.

80 Texas Tech University, Cade B. Bowlin, December 2018 succulents such as prickly pear (Opuntia spp.) and tasajillo (Cylindropuntia leptocaulis) are also found on the site. Significant forbs on RPQRR are sunflowers (Helianthus spp.), western ragweed (Ambrosia psilostachya), field ragweed (A. confertiflora) annual broomweed (Amphiachyris dracunculoides), American basketflower (Centaurea americana), catclaw sensitive briar (Mimosa nuttallii), rushpea (Hoffmannseggia spp.), golden crownbeard (Verbesina encelioides), crotons (Croton spp.), and filarees (Erodium spp.). Small mammals constitute an important food source for coyotes and other predators. Two species of lagomorphs occur on the ranch, i.e., the eastern cottontail (Sylvilagus floridanus) and the black-tailed jackrabbit (Lepus californicus). A diverse array of rodents are found on the study site including, but not limited to, the following genera: Sigmodon, Reithrodontomys, Peromyscus, Perognathus, Neotoma, Mus, Geomys, Chaetodipus, Dipodomys, and Baiomys. Mexican ground squirrels (Spermophilus mexicanus) occur overmost of the ranch, and the eastern fox squirrel (Sciurus niger) can be found in the northwestern quadrant of the ranch along Buffalo Creek. Additional mammalian species present on RPQRR include white-tailed deer (Odocoileus virginianus) and feral hogs (Sus scrofa). Data derived from biannual helicopter surveys indicated a low abundance of deer (typically 1 deer per 50 ha) and feral hogs (detected only periodically) on the ranch. American badger (Taxidea taxus), bobcat (Lynx rufus), raccoon (Procyon lotor), and striped skunk (Mephitis mephitis) are mesocarnivores also inhabiting RPQRR. In addition to bobwhite and scaled quail, a diverse array of aviformes makes RPQRR their home. Resident and migratory raptors are observed frequently on the study site. These include, but are not limited to, Swainson’s hawk (Buteo swainsoni), red-tailed hawk (Buteo jamaicensis), Cooper’s hawk (Accipiter cooperii), Northern harrier (Circus hudsonius), and American kestrel (Falco sparverius). Passerines such as meadowlarks (Sturnella spp.) and lark buntings (Calamospiza melanocorys) are common during winter months while dickcissels (Spiza americana) and lark sparrows (Chondestes grammacus) are common summer residents. Other common birds include greater roadrunner (Geococcyx californianus), wild turkey (Meleagris gallopavo), and mourning dove (Zenaida macroura).

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Snakes, including (Lampropeltis getula, Lampropeltis triangulum, Masticophis flagellum, Pantherophis emoryi, Pituophis catenifer, Thamnophis marcianus, Crotalus spp.) are common across the study site (McEachern 2008).

Methods

Scat Collection.—Coyote scats were collected monthly from January-December 2011 (n=356, Tyson 2012) and from January 2016-December 2016 (n=360; Chapter 2, this volume) in order to analyze the diets of coyotes inhabiting the study area. Identification of individual coyotes within the study area was not possible. As such, observations were made at the population level following a Design I model as defined by Thomas and Taylor (1990). Resource (food) availability was assumed to be equal among all individual coyotes within the study area. During each time period scats were collected along a 32-km route which serves as the primary route for various counts at RPQRR (e.g., raptors, bobwhites); this, continuous loop is referred to as the Texas Quail Index (TQI) route. The TQI route is divided into 2, 16-km segments designated as East and West routes. Each route has “mile markers” (steel t-posts with unique signs) posted at roughly 1.6-km intervals. The East route contains 12 markers and the West route contains 13 markers. I used these markers as points of reference for collecting scats. I used a random number generator to select the starting point of collection each month on each of the 2 routes in order to reduce sampling bias. For the East route a number from 0-11 was chosen; for the West route a number from 0-12 was chosen. The number produced from the generator designated the starting waypoint on each respective route during each collection period. An additional randomization was the frequency at which scats were collected along the route. Rather than collecting the first 15 scats per route, a number between 1 and 3 was chosen with the number generator. This number designated how many scats were omitted between collections of individual scats along each route. For example if the number 1 was drawn, I collected every other scat encountered. The same number was applied to both routes. Also, a maximum of 2 scats were collected from any latrine. The next scat after the latrine was treated as the first scat encountered. For the purpose of this study a latrine was defined as ≥2 scats within a 20-m segment of the collection route.

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Scats were identified according to nearby animal tracks, scat morphology, and size (Murie 1954, Danner and Dodd 1982, Elbroch 2003). Collection routes were “swept” (i.e., cleared of all scats) 7 days prior to collection to remove all scats. The entire 32-km loop was driven and every coyote scat removed. This ensured no scats were collected from the previous month and that only fresh scats were collected. I collected scats on the seventh day after old scats were removed,. I wore disposable latex gloves during collection to prevent cross contamination of scat samples. Scats were placed individually in a paper bag; the top of bag folded twice and stapled shut (Bowyer et al. 1983). On the outside of the bag was written the scat identification number, date of collection, and Universal Transverse Mercator (UTM) coordinates of location (Bowyer et al. 1983). Upon completion of collection, all sample bags were placed in a freezer for one month at -20°C to inhibit parasite growth (particularly Echinococcus granulosus) and kill destructive organisms such as dung beetles (Phanaeus sp.) (Colli and Williams 1972). Samples were removed from freezer after 1 month and placed in cabinets to air dry. Once dried, samples were placed in individual nylon bags with a unique identification number on a flat, stamped aluminum disk. Nylon bags were then transferred to a container of warm water with detergent for at least 24 hours (Johnson and Hansen 1979). After soaking, one month’s worth of scat samples (30 samples) per batch were placed in an automatic washing machine and washed for 2 cycles or until water ran clear (Johnson and Hansen 1978a, 1978b). Once washing was completed, samples were stored at 60° Celsius for 72 hours to dry. Once dried, contents were removed from the nylon bag and weighed to the nearest 0.01 g. After weighing, each scat sample was placed in a plastic bag for storage. Scat contents were then ready for analysis, food item identification, and quantification. Scat analysis.—The number of food items per scat were counted using the point- frame analysis method (Chamrad and Box 1964). This technique allows for determination of frequency of occurrence and percent occurrence of food items in scats (defined below) (Chamrad and Box 1964). The point-frame scat analysis technique reduces observer bias by providing a systematic, accurate, and repeatable sampling approach to scat content identification and quantification (Ciucci et al. 2004). A reduction in processing time (Ciucci et al. 2004, Meinzer et al. 1975, Johnson and Hansen

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1977) is also possible when using point-frame as compared to other scat analysis methods while maintaining accuracy and reliability. Accuracy of the point-frame technique for use in carnivore scat analysis has been tested by Johnson and Hansen (1977) and Ciucci et al. (2004) who reported no difference in accuracy of results when compared directly to complete hand separation models, but point analysis results in a dramatic reduction in processing time. The point-frame system uses a deep-walled, enameled dissecting tray with 10 evenly-spaced graduations along the top edges of tray (Chamrad and Box 1964). A wooden sampling frame with 5 evenly-spaced pins at 45° inclination is fitted to the top of the sampling tray. The graduations on the tray edges allow the sampling frame to be systematically and accurately moved along the tray. The sampling frame is moved along all 10 graduations and a sample is observed at each pin drop per graduation. Five pin drops for 10 sampling frame positions results in 50 observations per scat. Washed and dried scat contents were separated, mixed well, and randomly- and evenly-spaced across the bottom of the sampling tray. The sampling frame is moved to each of the 10 graduations along the tray. Any food item found under or closest to each pin is identified and recorded. A total of 50 points per scat were collected to compile percentage data of food items contained per individual scat. The prey item closest to each pin was identified using macroscopic and microscopic techniques. A hair key of Texas mammals (Debelica and Thies 2009) aided in hair identification. Also, a reference slide collection of the hairs of common mammals of RPQRR was compiled using specimens from the Texas Tech Research Science Laboratory. All specimens used for the reference collection were taken from Fisher County, Texas or counties in the surrounding Rolling Plains ecoregion. A photo reference was compiled using the same specimens for skeletal and pelage identification. These reference collections aided in the identification of mammalian hairs using a compound microscope and also macroscopically to identify definitive morphological hair structures. The mammalian hair identification technique outlined by Mayer (1952) was used in this study. Bones, skulls, jaws, and teeth from mammals were identified using the Illustrated Key to Skulls of Genera of North American Land Mammals (Jones and

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Manning 1992) and The Mammals of Texas (Schmidly and Bradley 2016). Plant materials (grasses, seeds, etc.) found in scats were identified using several region-specific reference books (Gould 1978, Shaw et al. 2011, Linex 2014). Any unidentifiable macroparticle food remains were assigned to food item with highest frequency of occurrence in sample (Ciucci et al. 2004). For the purpose of this study, the following quantitative categories were used to assess coyote scat contents: Frequency of occurrence: Number of individual scat specimens out of a sample of scats that contains a particular food item. Percent occurrence: Percentage of individual scats out of a sample of scats that contain a particular food item (number of scats containing food item/total number of scats x100). Data Analysis.—Diets of coyotes were assessed on a seasonal, and study period (annual) basis (i.e., El Niño vs. La Niña). Although Tyson’s study encompassed three years (2009-2011), I only included data from his 2011 scat collections as this was the only year of his study a La Niña was in place across the study site. The years 2009 and 2010 saw near average rainfall on RPQRR and in 2011 annual precipitation was dramatically below 30-year mean. During 2015 and 2016, an El Niño system was in place that brought at or above average rainfall to RPQRR. As such, I chose to compare diets of coyotes on RPQRR during the severe drought of 2011 and more mesic year, 2016. For this study seasons were defined as: Winter (December, January, February), Spring (March, April, May), Summer (June, July, August), and Fall (September, October, November). Frequency of occurrence and percent occurrence values were determined for individual food items and prey categories identified in coyote scats collected during the study. The following prey categories were used in this study: rodent, grass, lagomorph, mast, large mammal, birds, eggshells, reptiles, insect, mesomammal, and bobwhite. Rodents included any species in the order Rodentia. Cottontail rabbits and black-tailed jackrabbits comprised the lagomorph category. Mast included any fruits produced from woody trees or shrubs. Feral hogs and white-tailed deer were the only species in the large mammal category. The bird category included any species other than bobwhite or scaled quail. As identification of species from eggshell remains proved tenuous, all eggs

85 Texas Tech University, Cade B. Bowlin, December 2018 found in coyote scats were included in the eggshell category. Determination of reptilian species from remains in scats also proved tenuous and all scaled reptiles were consolidated into the reptile category. Remains from species in the phylum Arthropoda were grouped into the insect category. The mesomammal category included any mammalian predators <15 kg that inhabit RPQRR. The quail category was comprised solely of northern bobwhite as the only quail species recovered from coyote scats during my study was bobwhite. Abundance Indices of Prey.— Several metrics are used to monitor relative population abundances on RPQRR and data from these indices were used to assess prey availability during the study. Although multiple techniques are employed to determine quail abundance on RPQRR (Kubecka 2017), minimum known population (MKP) derived from trapping efforts was used as the abundance metric of quail for this study. Quail trapping takes place in fall (Nov-Dec) utilizing walk-in funnel traps across 296 trap sites. Minimum known population value derived from fall trapping efforts was used for this study. Small mammal trapping takes place twice annually (January and June) on RPQRR. Trapping efforts are focused across 8 different vegetation types. Arrays of 25 Sherman traps (5x5-m grids) are placed in 5 locations in each habitat site. Traps are monitored for 4 consecutive nights for a total of 500 trap-nights/ecological site per trapping session. Relative abundance of small mammals were computed from these trapping data. Relative abundance and species diversity of arthropods are monitored each July at 8 vegetation types across the ranch. Five pitfall arrays of 6 traps each are placed one each transect in each ecological site. These traps are checked once daily at 3-day intervals. Sweep-netting is performed by obtaining 25 sweeps and 4 replicates at each pitfall array.

Results Weather Conditions.—The collection periods coincided with opposing El Niño /La Niña weather periods and saw dramatic differences in rainfall totals. In 2011, average annual rainfall on RPQRR was 21.1 cm, well below 30-year mean (Fig. 3.4).

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This coincided with the beginning of a La Niña weather pattern that resulted in a historic drought, the worst single-year drought in Texas history (Huber 2011). January 2011 to September 2011, RPQRR received only 6.9 cm of rainfall and Summer 2011 recorded 79 days of temperatures >38oC. An El Niño system characterized Summer 2015 and resulted in annual rainfall total well over the 30-year mean during 2015 (83.7 cm, Fig. 3.4). The El Niño remained in place and near average rainfall (60.6 cm) was recorded during 2016 on RPQRR. The Palmer Modified Drought Index (PMDI) is a metric used to evaluate long- term drought severity conditions (NOAA 2017). Prolonged trends in temperature, precipitation, soil moisture and evapotranspiration are used to assign a drought severity value indicating levels of abnormal wetness or dryness. For the PMDI, values of ≤-4.0 indicate severe drought and range to≥4.0 indicating extremely wet conditions. During the La Niña weather pattern in place during 2011, the PMDI value for the Low Rolling Plains of Texas was-4.6 signifying a severe drought in the region (Fig. 3.5). The El Niño period saw positive PMDI values indicating abnormal moisture. The El Niño system that was in place in 2016 resulted in an annual average value of +3.37 (very moist). Quail Abundance.—Quail trapping-banding efforts indicated low relative abundances of quail on RPQRR during the first collection period. Minimum known population (MKP) derived from trapping-banding efforts during Fall 2011 trapped 248 individuals (Kubecka 2017; Fig. 3.6). The influence of the El Niño system that was in place during the second collection period resulted in dramatic increases in relative abundance of quail on RPQRR. Quail trapping efforts during Fall 2016 resulted in fall MKP of 4,393 individuals in 2016 (Kubecka 2017; Fig. 3.6). Quail abundance in Fall 2016 was the highest ever-recorded on RPQRR (since inception in 2008) and was a 17- fold increase over 2011.

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90 83.7 80

70 64.2 57.6 55.6 60.6 60 53.4 48.4 50 42.1 40 37.5

30 21.1

Annual Annual Precipitation (cm) 20

10

0 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 La Nina El Nino

30-yr mean Year

Figure 3.4. Annual precipitation (cm) for the Rolling Plains Quail Research Ranch, Fisher County, Texas with 30-year annual mean (dashed line) and La Niña and El Niño weather patterns labeled.

3.37 4 3 2.64 2.04 2 1.19 1 0

-1 -0.32 value -2 -1.99 -3 -2.53 -4 -3.22 -3.89

-5 -4.61 Palmer Palmer Modified Drought Index (PMDI) -6 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 La Nina El Nino

Year

Figure 3.5. Annual average Palmer Modified Drought Index (PMDI) values for the Low Rolling Plains of Texas 2009-2017 with La Niña (2011) and El Niño (2016) weather patterns labeled (NOAA 2017).

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5000 4393

4000

2920 3000 2126

2000 Fall MKP bobwhite MKP Fall 1000 715 445 283 248 205 77 0 2009 2010 2011 2012 2013 2014 2015 2016 2017 Year

Figure 3.6. Fall minimum known population (MKP) of bobwhites (Colinus virginianus) on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017 (Kubecka 2017). Data derived from annual trapping-banding efforts.

Small Mammal Abundance.—We observed low abundances (≤ 5/100 trap-nights) of small mammals during the first collection (La Niña) period (Fig. 3.7). Trapping efforts in 2011 resulted in 5 individuals/100 trap-nights caught in Winter 2011 and 2 individuals/100 trap -nights in Summer 2011. Record numbers of small mammals were trapped during the El Niño collection period. Winter 2016 trapping results yielded 28 individuals/100 trap- nights captured during small mammal trapping efforts (Fig. 3.7). In summer 2016, estimates of 29 individuals/100 trap-nights were recorded (Fig. 3.7) then abundance declined sharply in Summer 2017 (1.8 individuals/100 trap nights). Permoyscus spp. (deer mice) were the most abundant rodents on RPQRR during 2011. Winter 2011 trapping efforts indicated 1.8 individuals/100 trap-nights and Summer 2011 0.3 individuals/100 trap-nights. Peromyscus were less abundant during the El Niño period; trapping data showed 1 individual/100 trap-nights in Summer 2016 and <1 individual/100 trap-nights during Winter 2016 (Fig. 3.8). Sigmodon were the second most prevalent rodent on RPQRR during La Niña as evidenced by trapping data. Cotton rats were trapped at a rate of 1.2 individuals/100 trap-nights during Winter 2011 efforts and <1 individual/100 trap-nights in Summer 2011 (Fig. 3.9). Cotton rats were much more abundant during El Niño and trapped at record

89 Texas Tech University, Cade B. Bowlin, December 2018 rates. Winter 2016 trapping results indicated 25.7 individuals/100 trap-nights and 25.6 individuals/100 trap-nights during Summer 2016 (Fig. 3.9). Abundance of cotton rats subsequently crashed on the study site during late-Summer or early Fall 2016. Winter 2017 trapping efforts congirmed the crash; <1 individual/100 trap-nights were trapped. Relative abundance of woodrats (Neotoma micropus) on the study site was low during the La Niña period. Trapping efforts evidenced <1 individual/100 trap-nights during Winter 2011 and Summer 2011 (Fig. 3.10). Woodrats increased greatly during El Niño conditions and comprised the second most common rodent trapped during the collection period with estimates of 2.1 individuals/100 trap-nights during Summer 2016 efforts (Fig. 3.10). Winter 2016 trapping showed a lower abundance of woodrats at <1 individual/100 trap-nights (Fig. 3.10).

35

30 28 29

25 trap nights 20 18 100 100 16 15

10 5 6 4 3 3 3 5 3

Small Small mammals/ 2 2 0 1 1 1

0

Winter Winter Winter Winter Winter Winter Winter Winter Winter

Summer Summer Summer Summer Summer Summer Summer Summer 2009 2010 2011 2012 2013 2014 2015 2016 2017 Year

Figure 3.7. Small mammals captured (number of individuals per 100 trap-nights) during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017.

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2.5

2.1 1.9 2.0 2.0 1.8 1.8

1.5

1.0 1.0 0.9 0.7 0.7

0.5 0.4 0.3 0.4 0.2 0.3 0.1 0.1 0.1 0.1 0.0

0.0

Winter Winter Winter Winter Winter Winter Winter Winter Winter

Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Figure 3.8. Individual Peromyscus spp. captured per 100 trap-nights during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017.

30 25.7 25.6

25

nights -

20 trap

100 100 15 13.3

10 Individuals/ 5 2.5 3.3 1.2 0.6 0.1 0.1 0.4 0.0 0.1 0.1 0.1 0.1 0.3 0.7 0.3

0

Winter Winter Winter Winter Winter Winter Winter Winter Winter

Summer Summer Summer Summer Summer Summer Summer Summer Summer 2009 2010 2011 2012 2013 2014 2015 2016 2017 Year

Figure 3.9. Individual cotton rats (Sigmodon hispidus) captured per 100 trap-nights during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017.

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2.5 2.1

2.0

n n ights - 1.5 1.4

trap 1.3

100 100 1.1 1.0

0.5 0.5 0.5 Individuals/ 0.2 0.2 0.3 0.2 0.2 0.2 0.1 0.1 0.1 0.0 0.1 0.0 0.0

0.0

Winter Winter Winter Winter Winter Winter Winter Winter Winter

Summer Summer Summer Summer Summer Summer Summer Summer Summer Summer 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Year Figure 3.10. Individual Southern plains woodrat (Neotoma micropus) captured per 100 trap-nights during summer and winter trapping efforts on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2009-2017.

600

500

SE)

̅±

푥 400

300

200

100 Individuals/transect Individuals/transect ( 0 2010 2011 2012 2013 2014 2015 2016 2017

Pitfall Sweepnet Figure 3.11. Mean (푥̅±SE) number of arthropods caught per transect during trapping efforts in July on the Rolling Plains Quail Research Ranch, Fisher County, Texas 2011- 2017.

92 Texas Tech University, Cade B. Bowlin, December 2018

Arthropod abundance.—Arthropod abundance on RPQRR was low during the La Niñacollection period (Fig. 3.11). Sweep-netting efforts in 2011 yielded a mean of <5 individuals/transect and pitfall trapping evidenced a mean of 111 individuals/transect. Arthropod abundance was dramatically higher during the El Niño period. Pitfall trapping data showed an increase to a mean of 209 individuals/transect during 2016 efforts. Sweep-netting data from 2016 indicated a mean of 79 individuals/transect. La Niña Scat Analysis.—Beans of honey mesquite were the most important annual food item for coyotes during the La Niña period. Mesquite mast was recovered in 43% (153, n=356) of scats collected during 2011 (Table 3.1). Hackberry mast was the second most frequently occurring annual food item for coyotes during 2011. Remnants of hackberry fruits were found in 14.6% (52, n=356) of scats collected during the La Niña period. Grass was the third-ranked food item consumed by coyotes during 2011 and occurred in 12.9% (46, n=356) of scats. Remains of Sigmodon were found in 11% (39, n=356) of scats collected during La Niña making cotton rats the fourth most important prey for coyotes. All other individual prey items occurred at frequencies of <10% annually in 2011. Categorically, mast was the most important annual food category for coyotes during the La Niña collection period. Remnants of fruits were found in 63.2% (225, n=356) of scats collected during 2011 (Table 3.2). Rodents were an important annual food source for coyotes during 2011 and were the second most frequently occurring food category during La Niña (39%, 139, n=356). Large mammal and grass categories ranked third and fourth during 2011 and occurred in 13.8% (49) and 12.9% (46) of scats, respectively. All other food categories occurred at frequencies of <10% of scats annually during 2011. Seasonally, mast was the most important food category for coyotes during Summer 2011 (75.6%, n=90), Fall 2011 (97.8%, n=89), and Winter2 2011 (93.1%, n=29) (Table 3.2). Mast was the second most important seasonal food category during Spring 2016 and was recovered in 38.6% (n=88) of scats. Rodents were the most frequently occurring seasonal prey category in Winter 20111 (38.3%, n=60) and Spring 2011 (60.2%, n=88). During all subsequent seasons rodents were the second most important

93 Texas Tech University, Cade B. Bowlin, December 2018

Table 3.1. Percent occurrence of all individual food items recovered from coyote scats (n=356) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, January 2011-December 2011.

Food Item Percent Rank Mesquite beans 43.0 1 Hackberry fruit 14.6 2 Peromyscus 14.0 3 Grass 12.9 4 Sigmodon 11.0 5 Feral Hog 7.0 6 Chittam fruit 7.0 7 Deer 6.7 8 Reithrodontomys 6.5 9 Lotebush 6.2 10 Birds 5.3 11 Neotoma 4.5 12 Perognathus 3.9 13 Baiomys 3.9 14 Skunk 3.9 15 Lagomorphs 2.8 16 Insects 1.7 17 Raccoon 1.7 18 Onchomys 1.7 19 Prickly pear tunas 1.4 20 Juniper berries 1.1 21 Chaetodipus 0.8 22 Geomys 0.8 23 Badger 0.6 24 Spermophilus 0.3 25 Porcupine 0.3 26 Armadillo 0.3 27

94 Texas Tech University, Cade B. Bowlin, December 2018

Table 3.2. Percent of coyote scats containing prey categories collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during a La Niña weather pattern (2011, n=356).

Winter1 Spring Summer Fall Winter2 Annual Category (n=60) (n=88) (n=90) (n=89) (n=29) (n=356) Rank Mast 15.0 38.6 75.6 97.8 93.1 63.2 1 Rodent 38.3 60.2 32.2 33.7 13.8 39.0 2 Lg. mammal 20.0 17.0 10.0 11.2 10.3 13.8 3 Grass 8.3 26.1 14.4 5.6 0.0 12.9 4 Mesomammal 11.7 10.2 3.3 2.2 3.4 6.2 5 Bird 21.7 5.7 1.1 0.0 0.0 5.3 6 Lagomorph 15.0 1.1 0.0 0.0 0.0 2.8 7 Insect 5.0 2.3 1.1 0.0 0.0 1.7 8 Eggshells 0.0 0.0 0.0 0.0 0.0 0.0 9 Quail 0.0 0.0 0.0 0.0 0.0 0.0 10 1Includes January and February 2011 2Includes only December 2011

prey category and occurred in 32.2%, 33.7%, and 13.8% of scats during Summer 2011 (n=90), Fall 2011 (n=89), and Winter 20112 (n=29). Birds were the second most commonly occurring seasonal prey category in Winter 20111 and were recovered in 21.7% (n=60) of scats (Table 3.2). Large mammals ranked third during Winter 20111, Fall 2011, and Winter 20112 and were recovered in 20% (n=60), 11.2% (n=89), and 10.3% (n=29) of scats, respectively. El Niño Scat Analysis.—Sigmodon hispidus were the most commonly consumed prey item recovered from scats (n=360) collected during the El Niño period (Table 3.3). Remains of cotton rats were found in 306 scats (85.0%). Grass was the second most common annual food item found and occurred in 80 scats (22.2%). Lagomorphs were the third most frequently consumed prey and were recovered in 17.8% of scats. Neotoma comprised the fourth most frequently consumed prey item annually for coyotes during 2016. Woodrat remains were recovered from 9.4% of scats from the El Niño period. All other individual prey items each occurred at <5% occurrence annually during the El Niño period. Categorically, rodents were the most consumed annual prey category during the El Niño collection period. Remains of rodents were recovered in 331 (91.9%) of scats

95 Texas Tech University, Cade B. Bowlin, December 2018 collected during 2016 (Table 3.4). Grass was the second most frequently occurring annual food category and was found in 22.2% of scats. Lagomorphs were the third- ranked prey category and were recovered in 17.8% of scats. All other prey categories were recovered in <5% of scats annually during 2016. Seasonally, rodents were the most important prey category during each season of 2016. Rodent remains occurred in 95% (n=60), 88.9% (n=90), 97.8% (n=90), 92.2% (n=90), and 76.7% (n=30) of scats during Winter 20161, Spring 2016, Summer 2016, Fall 2016, and Winter 20162, respectively, (Table 3.4). Lagomorphs were the second most frequently occurring food category in Spring 2016 (16.7%, n=90), Fall 2016 (24.4%, n=90), and Winter 20162 (30%, n=30). Seasonally, grass was the second-ranked category recovered from scats collected during Winter 20161 (18.3%, n=60) and Summer 2016 (43.3%, n=90). Grass was the third-ranked food item in all other seasons during 2016. Lagomorphs were the third- ranked seasonal prey category overall and occurred in 16.7%, 24.4% and 30.0% of scats during Spring 2016 (n=90), Fall 2016 (n=90), and Winter 20162 (n=30), respectively . Mast consumption by coyotes was low during all seasons of 2016 and occurred at the highest frequency in Summer 2016 (11.1%, n=90). All other prey categories occurred at frequencies of <10% during each season of 2016. Rodents.—Rodents were an important prey category for coyotes on RPQRR during both La Niña and El Niño collection periods. However, rodent remains were recovered in notably higher frequencies during 2016 than 2011 (Fig. 3.12). Rodent consumption remained high across all seasons during El Niño and were the top prey category for each season of 2016 (Table 3.4). The most commonly occurring rodent in scats collected during 2016 was Sigmodon which was recovered in 85% of scats (Table 3.5). All other rodents were recovered in frequencies of <10% during 2016. While rodents were the second most frequently occurring prey category in 2011, coyotes consumed rodents less frequently during La Niña than El Niño (Fig. 3.13). Rodent consumption was highest during Spring 2011 (Table 3.2) of the La Niña period. Peromyscus were the most frequently occurring rodents during 2011 and were recovered in 14% of scats (3.5%, Table 3.5). Sigmodon were the second most common rodent

96 Texas Tech University, Cade B. Bowlin, December 2018

Table 3.3. Percent occurrence of all individual food items recovered from coyote scats (n=360) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, January 2016-December 2016.

Food Item Percent Rank Sigmodon 85.0 1 Grass 22.2 2 Lagomorphs 17.8 3 Neotoma 9.4 4 Peromyscus 3.1 5 Mesquite 3.9 6 Eggshell 3.6 7 Birds 3.1 8 Reptiles 2.8 9 Deer 1.9 10 Insects 2.2 11 Prickly pear 1.4 12 Spermophilus 0.8 13 Feral hog 0.3 14 Quail 0.3 15 Sciurus 0.3 16

Table 3.4. Percent of coyote scats containing prey categories collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during a El Niño weather pattern (2016, n=360).

Winter1 Spring Summer Fall Winter2 Annual Category (n=60) (n=90) (n=90) (n=90) (n=30) (n=360) Rank Rodent 95.0 88.9 97.8 92.2 76.7 91.9 1 Grass 18.3 8.9 43.3 20.0 13.3 22.2 2 Lagomorph 13.3 16.7 11.1 24.4 30.0 17.8 3 Mast 0.0 0.0 11.1 7.8 0.0 4.7 4 Eggshells 0.0 5.6 8.9 0.0 0.0 3.6 5 Birds 0.0 3.3 4.4 2.2 6.7 3.1 6 Reptile 0.0 6.7 2.2 2.2 0.0 2.8 7 Lg. mammal 1.7 0.0 1.1 4.4 6.7 2.2 8 Insect 0.0 0.0 4.4 4.4 0.0 2.2 9 Quail 0.0 0.0 0.0 0.0 3.3 0.3 10 Mesomammal 0.0 0.0 0.0 0.0 0.0 0.0 11 1Includes January and February 2011 2Includes only December 2011

97 Texas Tech University, Cade B. Bowlin, December 2018 found in scats collected during La Niña and were identified in 11% of scats (Table 3.5). All other rodents occurred at frequencies of <10% during 2011 (Table 3.5). Mast.—Mast was not important to coyotes during 2016 but were the top food item for coyotes in 2011 (Fig. 3.12). Mast was recovered in only 4.7% of scats collected in 2016 (Table 3.4). Honey mesquite was the most frequently occurring mast recovered in scats (3.8% of scats) collected in 2016 (Table 3.6). The only other mast identified in scats from the El Niño collection period was prickly pear and it occurred in only 1.3% of scats (Table 3.5). Consumption of mast by coyotes in 2016 peaked in Summer (11.1%) and decreased into Fall 2016 (7.8%) (Table 3.4). Mast was not recovered in scats collected in any other seasons in 2016. Coyotes relied heavily on mast during the La Niña period. Mast was recovered more frequently in scats collected in 2011 than in 2016 (Fig. 3.13). Mast was the top food category during the dry La Niña period and remains of various fruits occurred in 63% of scats collected during 2011 (Table 3.2). Summer 2011 (75.6%, n=90), Fall 2011 (n=97.8%, n=89) and Winter2 2011 (93.1%, n=29) saw the highest consumption of mast by coyotes during 2011 (Table 3.2). Beans of Prosopis comprised the bulk of mast consumed by coyotes during 2011; it was found in 43% of scats (Table 3.6). Hackberry was the second most frequently occurring mast during La Niña and was found in 14.6%

Table 3.5. Percent occurrence of rodents recovered from coyote scats collected on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during La Niña (2011, n=356) and El Niño (2016, n=360) weather patterns.

2011 2016 Genus (n=350) (n=360) Sigmodon 10.3 85.0 Peromyscus 13.4 3.1 Neotoma 2.9 9.4 Reithrodontomys 6.0 0.0 Baiomys 4.9 0.0 Perognathus 4.0 0.0 Chaetodipus 1.1 0.0 Spermophilus 0.0 0.8 Sciurus 0.0 0.3 Mus 0.0 0.0 Geomys 0.0 0.0

98 Texas Tech University, Cade B. Bowlin, December 2018

Table 3.6. Frequency of occurrence (%) of mast recovered from coyote scats collected on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during La Niña (2011, n=356) and El Niño (2016, n=360) weather patterns.

Species 2011 (n=356) 2016 (n=360) Prosopis 43.0 3.8 Celtis 14.6 0.0 Sideroxylon 7.0 0.0 Ziziphus 6.2 0.0 Opuntia 1.4 1.3 Juniperus 1.1 0.0

100

80

SE)

̅± 푥

( 60

40 Percent of scats 20

0

La Nina El Nino

Figure 3.12. Percent of coyote scats (푥̅±SE) containing prey categories collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during La Niña (2011, n=356) and El Niño (2016, n=360) weather patterns.

99 Texas Tech University, Cade B. Bowlin, December 2018 of coyote scats (Table 3.6). All other mast occurred at frequencies of <10% during 2011 (Table 3.6). Lagomorph.—Lagomorphs were more important to coyotes during El Niño than during La Niña (Fig. 3.12). Winter 2011 saw the highest seasonal consumption (15%) of lagomorphs during the 2011 collection period (Table 3.2). Lagomorph remains occurred at frequencies ≤1% during all other seasons of 2011. Coyotes consumed lagomorphs most frequently during Fall 2016 (24.4%) and Winter 2016 (30%) during El Niño (Table 3.4). Lagomorph consumption increased as rodent consumption decreased during these seasons. Large Mammals.—Large mammals were consumed by coyotes more frequently during La Niña than during El Niño (Fig. 3.12). Large mammals were the third-ranked prey category for coyotes during La Niña(13.8%, Table 3.2) but were relatively unimportant to coyotes during El Niño (2.2%, Table 3.4, Fig. 3.12). Highest frequency of occurrence of large mammal remains during El Niño was in Fall 2016 (4.4%, n=90) and Winter2 2016 (6.7%, n=30). Seasonal consumption of large mammals during La Niña was highest during Winter1 (20%) and Spring 2011 (26.1%, Table 3.2). Quail and Eggs.—A total of 716 coyote scats were collected across the 2 time periods: January 2011-December 2011 (n=356; La Niña) and January 2016-December 2016 (n=360; El Niño). Quail were not important prey for coyotes during either study period. Quail vestiges or evidence of eggshells were not recovered in any scats collected during 2011. However, remains of birds (other than quail) were recovered in 5.3% of scats (Fig. 3.1). Feathers of bobwhite were confirmed in only 1 scat (<1%) during the El Niño collection period and was collected in December 2016 (Table 3.2). Vestiges of birds other than quail were recovered in 3.1% of scats during the El Niño period. During the El Niño period, 3.6% (13, n=360) of scats contained remnants of eggs and were collected during Spring 2016 and Summer 2016 (Table 3.2). Scats containing eggshells were found in 5 scats collected in May 2016 (n=30), 3 in June 2016 (n=30), 4 in July 2016 (n=30), and 1 in August 2016 (n=30). Due to the degraded state in which eggshells are recovered in coyote scat, I did not attempt to assign species to eggshell remains.

100 Texas Tech University, Cade B. Bowlin, December 2018

Other.—Grass was consumed by coyotes on RPQRR during both La Niña and El Niño periods. Grass was the second most consumed food category during 2016 and occurred in 22.2% of scats (Table 3.4). Grass consumption during El Niño was highest during Summer 2016 (43.3%, n=90) and Fall 2016 (20.0%, n=90). Grass had a lower frequency of occurrence during La Niña and was found in 12.9% of scats (Table 3.2). Highest frequency of grass consumption by coyotes occurred in Spring 2011 (26.1%, n=88) and Summer 2011 (14.4%, n=90, Table 3.2). Evidence of mesomammals was not identified in scats collected during El Niño but 6.2% of scats during La Niña contained remains of mesomammals (Table 3.2). Skunks were the most frequently occurring mesomammal in the La Niña period and occurred at a frequency of 3.9% during 2011 (Table 3.1). Raccoons ranked second and were identified in 1.7% of scats collected in 2011 and badger remains occurred in <1% of scats from La Niña (Table 3.1). Additional predators of quail and quail nest were found in scats from each collection period. During the La Niña weather period, feral hog remains occurred in 7% of scats (Fig. 3.13). Vestiges of Spermophilus were recovered in <1% of scats collected during 2011 and armadillo remains also occurred in <1% of scats (Fig. 3.13). Predators of quail nests were recovered from coyote scats collected during El Niño, and included reptiles (2.8%), ground squirrels (<1%), and feral hog (<1%) (Fig. 3.14).

30 25 25

20

15 14

10 Number Number scats of 6 5 2 1 1 0 Feral hog Skunk Raccoon Badger Ground Armadillo squirrel

Figure 3.13. Remains of confirmed predators of quail and quail nests recovered from coyote scats (n=356) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during a La Niña weather pattern (January 2011-December 2011).

101 Texas Tech University, Cade B. Bowlin, December 2018

12 10 10

8

6

4 3 Number Number scats of

2 1

0 Reptiles Ground squirrel Feral hog

Figure 3.14. Remains of confirmed predators of quail or quail nests recovered from coyote scats (n=360) collected monthly on the Rolling Plains Quail Research Ranch, Fisher County, Texas, during an El Niño weather pattern (January 2016-December 2016).

Discussion Our findings suggest the diets of coyotes on RPQRR varied annually, seasonally, and between study periods in accordance to resource availability. These data are similar to other studies in Texas that showed variation in coyote diets in response to fluctuations in prey abundances, and plant succession (Meinzer et al. 1975, Andelt et al. 1987, Windberg and Mitchell 1990). Rodents.—Rodents were an important food source for coyotes during both La Niña and El Niño weather patterns. We noted a higher intake of rodents as compared to other studies of coyotes in Texas. Henke (2002) reported rodents occurred in 26.2% of coyote stomachs (n=407) collected in southern Texas. Rodents comprised 24.5% (n=514) of annual diet of coyotes in north Texas (Meinzer et al. 1975). Sperry’s (1941) literature review (compiled from examination of >8,000 coyote stomachs from 17 western states) reported diets consisting of 18% rodents. Similar to other studies of coyote diets in Texas, rodent consumption fluctuated according to small mammal availability (Windberg and Mitchell 1990). While relative abundance of rodents was low on the study site during the La Niña period, rodents were still an important food source (>35%) for coyotes. Peromyscus was the most important species of rodent for coyotes during 2011 but were of less importance during El Nino.

102 Texas Tech University, Cade B. Bowlin, December 2018

Peromyscus were the most common rodent on RPQRR during the La Niña collection period. The availability of Peromyscus was notably lower during 2016 and as such, deermice were consumed at lower rates in 2016 than 2011. Sigmodon were the second most frequently consumed rodent during La Niña even though they were present at low relative abundance. Other studies suggest coyotes selectively feed on cotton rats (Windberg and Mitchell 1990). Woodrats were of little importance during La Niña and could have been a factor of a low relative abundance of woodrats on RPQRR. Andelt et al. (1987) noted a similar trend in coyote diets in south Texas. As woodrat abundance decreased, frequency of consumption by coyotes also decreased. Frequency of occurrence of rodent remains in coyote scats more than doubled from La Niña to El Niño, probably as a factor of availability. An irruption in Sigmodon populations occurred beginning Summer 2015 as a result of favorable weather conditions. Rodent populations reached recorded highs, their zenith occurring during the El Niño period and, as such, were the principle food item for coyotes on RPQRR during 2016. Sigmodon were consumed significantly more than any other food item during 2016 in response to the irruption of cotton rats. Neotoma were also important to coyotes during El Niño as their populations also saw an increase from the La Niña trapping efforts. As woodrats became more available on the landscape, coyotes utilized them accordingly. Cotton rats are known to be important food sources for coyotes in Texas as documented from previous research. Windberg and Mitchell (1990) recovered remains of cotton rats in 32% and 40% of scats during 2 years of their south Texas study. Hispid cotton rats and southern plains woodrats collectively accounted for 24.5% of the annual diet of coyotes (n=514) during Meinzer et al.’s (1975) 2-year study in Knox Co., Texas. Kamler et al. (2002) found coyotes in northwestern Texas consumed cotton rats in addition to other prey but did not report frequency of occurrence data. A high consumption of cotton rats during the El Niño period was likely attributable to their abundance. Favorable environmental factors lead to an 8-fold increase in cotton rat populations between Winter 2015 and Winter 2016. Similar irruptions in abundance of cotton rats have been documented in Texas (Haines 1963, Lehmann 1984:210). Windberg (1998) saw a 46-fold increase in cotton rat populations

103 Texas Tech University, Cade B. Bowlin, December 2018 in 1982 and 13-fold increase in 1986. Rainfall from the prior growing season was directly related to abundance of cotton rats as a result of increase in herbaceous cover. As primarily grazers, cotton rats favor areas with tall, dense grasses and forbs for both protection from predation and as a food source (Schmidly and Bradley 2016:523). With the abundance of food and protection from predators via ample vegetative screening cover, the increase in grass production on RPQRR could have been a catalyst for an irruption in cotton rat population. Haines (1963) suggested that drought conditions coupled with subsequent rains prompted irruptions of cotton rats; my findings support Haines’ proposal. Several years of below average rainfall on RPQRR followed by generous rainfall in 2015 and 2016 lead to ideal range conditions to support a cotton rat boom on RPQRR. The high relative abundance of cotton rats on RPQRR during 2016 may have enabled coyotes to select for them due to reduced foraging time and increased availability/detectability. Favorable precipitation and temperature can lead to a decrease in search efficiency and prey detectability through phenological responses in vegetation (Bowman and Harris 1980). However, MacCracken and Hansen (1986) posited that time spent searching for prey is inversely related to prey density. With a high abundance of cotton rats on the landscape, they were the most energetically “profitable” quarry for coyotes as they were probably the prey item with the least amount of energy expended per capture. Similar cyclical irruptions of prey have elicited comparable functional feeding responses by coyotes in other studies. Todd et al. (1981) noted that the occurrence of snowshoe hare (Lepus americanus) remains in coyote scats recovered in Alberta was directly proportional to hare density. Sigmodon populations subsequently crashed between Summer 2016 and Winter 2016-17. The environmental driver(s) that caused the population crash of cotton rats on RPQRR during 2016 is not known although there is some speculation that density dependent factors such as disease can play a role in such crashes (Schmidley and Bradley 2016:526). Of note, several dead cotton rats were discovered during the summer and fall of 2016; their cause of death was undetermined but no indication of trauma (i.e., predation) was apparent. Lehmann (1984:210) described an irruption in cotton rats in the

104 Texas Tech University, Cade B. Bowlin, December 2018

1960s in south Texas that left the landscape barren of herbaceous vegetation. Similar “swathed” landscapes were noted on RPQRR. The population subsequently crashed the following year due to lack of resources and disease. Intense predation by coyotes (and other predators, including raptors) may have contributed to the decline in cotton rat abundance but I do not believe predation was the proximate cause of the decline in cotton rats. Lagomorphs.—Consumption of lagomorphs increased dramatically as populations of cotton rats declined through Fall 2016. As rodents became less abundant, a functional feeding response (Murdoch 1973) led coyotes to use more lagomorphs, which appeared to still be relatively abundant on RPQRR. Windberg and Mitchell (1990) reported that lagomorphs were important winter food items for coyotes in south Texas; they were principal prey during the study (comprising 40-55% of winter diets). Andelt (1985) also concluded mammalian prey were most important to coyotes in south Texas during winter months. Primary consumption of lagomorphs took place during winter in south Texas and was related directly to their abundance. However, the authors noted a stronger relationship between cotton rat abundance and lagomorph consumption than lagomorph abundance/consumption alone (Windberg and Mitchell 1990). Lagomorphs were of negligible importance to coyotes during 2011. Henke (2002) noted lagomorphs were important to coyotes in southern Texas and occurred in 26% of coyote stomachs (n=407). Meinzer et al. (1975) reported lagomorphs as the third ranked prey category for coyotes in the Rolling Plains of Texas (10.5%, n=514). Mast.—Mast is known to be an important food item for coyotes across their range. Vela (1985) ranked mast as the primary food consumed by coyotes in the Chihuahan Desert of Mexico. Fruits ranked as the second most important food item for coyotes in an additional study in Mexico (Hidalgo-Mihart et al. 2001). Plant matter was the most frequently consumed food group, occurring in 72.9% of coyote scats (n=184) in south central Pennsylvania (Bixel 1995). Summer diets of coyotes in Quebec consisted of >60% fruit matter (Crete and Lemieux 1996). Studies from south Texas listed mast an important food source for coyotes that varies seasonally and annually according to availability (Andelt et al. 1987, Young et al. 2006). My data illustrate the importance of mast to coyotes in the Rolling Plains,

105 Texas Tech University, Cade B. Bowlin, December 2018 especially during La Niña periods. While mast was not important for coyotes during 2016 because of availability of more profitable food items, mast was the top food category for coyotes during the La Niña period. With a low availability of other food items (e.g., rodents, lagomorphs), coyotes depended largely on fruits for sustenance. Availability and search efficiency of static items like mast could have led coyotes to using more mast. Search efficiency is likely greater for static food items as opposed to mobile prey thus making mast more energetically profitable for coyotes during the severe drought conditions of 2011. Drought tolerant species, e.g., mesquite, provided the majority of sustenance for coyotes during the drought. Hackberry fruits were the second most consumed mast during 2011. During the extreme drought of 2011, beans of mesquite were the primary food item for coyotes. Mesquite consumption increased throughout each season of 2011 and peaked in Fall 2011 and Winter 2011. This trend was concurrent with a decrease in rodent consumption. Rodent availability also decreased from Winter 2011 to Summer 2011. As availability of other food items decreased, mast from mesquite and hackberry provided nutrition for coyotes during the severe drought of 2011. Static food items such as mast could have proved more profitable than more mobile prey. My results mirror those of Meinzer et al. (1975) who noted that mast comprised 46% of mean annual diets in a study on the Rolling Plains. Beans of mesquite were the second most important overall food item for coyotes during the study (Meinzer et al. 1975). Meinzer et al. (1975) noted more mesquite beans being produced in a drier year than the following, more mesic year, and consumption of beans by coyotes followed availability. It is important to reiterate that when availability of other prey species was reduced during drought conditions, coyotes switched to fruits of Prosopis, and Ziziphus. Thus mast-producing plants on the landscape could buffer predation on other species (Tyson 2012). Maintaining a landscape with a diversity of mast-producing shrubs is thus a desirable goal for land managers. Large Mammals.—Deer have been shown to be an important food source for coyotes in Texas (Andelt et al. 1987, Andelt 1995). Large mammals (deer and feral hog) were not important food sources for coyotes during El Niño but were of greater importance during La Niña. The notably higher frequency of large mammal consumption

106 Texas Tech University, Cade B. Bowlin, December 2018 during the La Niña period could be attributed to environmental factors induced by drought conditions. Windberg and Mitchell (1990) suggested that in south Texas, consumption of deer by coyotes in winter was related more with deer fitness than abundance. Deer and feral hog fitness may have been compromised due to drought conditions making them more susceptible to predation from, or scavenging by, coyotes. Coyote and Quail Interactions.—My results suggest coyotes are not significant predators of quail or quail nests on a landscape managed exclusively for quail, regardless of weather phenomena or quail abundance. La Niña and El Niño weather patterns during our study resulted in record low (2012) and high (2016) quail abundances on RPQRR, respectively. However, quail and their nests were not important food items (never >3%) for coyotes during either period. We speculate that the quail consumed during December 2016 were possibly a result of crippling loss from quail hunts during that time. Weather, precipitation and temperature in particular, is known to impact quail productivity and abundance (Bridges et al. 2001, Lusk et al. 2001, Hernandez et al. 2002, Lusk et al. 2002). As a result of low rainfall and extreme high temperatures in the Rolling Plains, abundance of quail was well below the 30-year mean (TPWD 2017) during the initial scat collection period. The drought of 2011 was the most severe one- year drought in Texas history (Huber 2011) and resulted in the lowest recorded abundance of quail on RPQRR in 2012. Similar declines in abundance of quail have been attributed to below-average precipitation for extended periods (Giuliano and Lutz 1993) and quail abundance in Texas has been shown to be highly correlated to drought indices (Bridges et al. 2001). With a low abundance of quail on the landscape during 2011, it is not surprising quail and their nests were not important food for coyotes. A low incidence of quail consumption could be attributed to low relative abundance of quail during the scat collection period. The El Niño system that was in place on the Rolling Plains in 2015 led to above average rainfall and moderate temperatures. As a result, quail abundance in 2016 was the highest recorded on RPQRR from 2008-2016. With such a high relative abundance, quail were readily available for consumption by coyotes. However, even under such high quail populations, positive identification of quail remains was made in <1% of coyote

107 Texas Tech University, Cade B. Bowlin, December 2018 scats, similar to results from La Niña. The same favorable precipitation and temperatures during El Niño that led to favorable phenological responses in vegetation on RPQRR could have impacted prey detection and search efficiency by coyotes limiting effectiveness as quail and nest predators (Bowman and Harris 1980). Abundant, dense vegetation may have provided spatial heterogeneity of nesting cover reducing search efficiency of coyotes (Bowman and Harris 1980). Also, abundance of other more profitable and susceptible prey may have helped buffer predation on quail by coyotes. Low capture success rate coupled with required energy expenditure may have made quail a low profitability item for coyotes. It is important to note I did confirm a low occurrence of quail vestiges from the El Niño period occurred during December 2016. This correspond with Texas’ quail hunting season and limited quail hunting took place on RPQRR during these months. It is possible the quail remains recovered in coyote scat collected during this study were a product of wounding loss. Alternatively, dry and dormant vegetation during winter could make search efficiency and subsequent detectability of quail by coyotes more productive than during the growing season. My data are similar to other studies in Texas that have shown little evidence of coyotes as important predators of quail. Meinzer et al. (1975) recovered bobwhite vestiges in only 0.2% (n=514) of coyote scats examined during their study on the Rolling Plains of Texas. Birds made up only 1% (n=6,354) of coyote diets in a South Texas study (Andelt et al. 1987). Henke (2002) reported that bobwhite or their eggs were found in <1 percent of coyote stomachs (n = 407) in South Texas leading the author to argue that quail and their nests are merely incidental prey for coyotes. While coyotes may not be significant predators of quail, prior research suggests that coyotes could be significant nest predators. In south Texas, Lehmann (1946) monitored 189 bobwhite nests of which he concluded that coyotes depredated an estimated 43% of depredated nests (conclusions based on eggshell remains, scat, and tracks) and analysis of coyote stomachs. Species-identification based on eggshell remains is arguable (Hernandez et al. 1986). Rader et al. (2007) reported 32% (n=43) of nests in a south Texas study were depredated by coyotes as confirmed by video surveillance of nests. Conversely, coyotes were responsible for only 1% (n=85) of nest

108 Texas Tech University, Cade B. Bowlin, December 2018 depredations in northern Florida (Staller et al. 2005). A study in the northern Rolling Plains showed coyotes were responsible for 6.8% (n=59) of nest depredations (Lusk et al. 2006). Meinzer (1975) recovered egg remains in <1% of scats (291) and quail eggs in <1% of stomachs (94) in the Rolling Plains. Similarly, I conclude that coyotes are not significant predators of quail nests on a landscape managed exclusively for quail production. Low consumption rate of eggs by coyotes during our study could have been due to availability of alternate foods (Schmidt 1999). I recovered evidence of eggshells in scats from the El Niño collection period only. Lack of eggshell remains in coyote scats collected during 2011 could be attributed to lack of nesting effort by quail due to harsh environmental conditions. During the severe drought of 2011, only 13 bobwhite nests were recorded on the study site (RPQRR, unpublished data). Guthery et al. (1988) reported a reduced peak breeding season and reduced reproductive effort in arid environments in South Texas. Guthery attributed the reduced breeding season to physiological stress (heat) and an adaptive response to harsh laying/brooding conditions as a result of high temperatures. The extreme temperatures and limited rainfall of 2011 could have curtailed reproductive efforts of quail reducing availability of nests for coyotes to depredate. During 2016, quail fitted with radio-transmitters on RPQRR attempted 31 nests, a three-fold increased from 2011. Favorable environmental conditions in 2015 induced ideal nesting conditions on RPQRR leading into the quail nesting season of 2016. Summer 2016 saw the highest frequency of occurrence of eggshells in collected scats. These months parallel quail nesting season on the Rolling Plains of Texas thus recovered eggshell fragments could have been a result of quail (or other bird) nest depredations. Due to the nature of scats analysis, it was not possible to make positive identification of egg shell species. As such, some eggshell fragments could have been species other than quail. One possibly confounding factor regarding eggshells is that the RPQRR uses simulated (“dummy”) nests in some experiments to estimate hatch rates (Carter et al. 2002). These dummy nests (3 chicken eggs) were in place each June for a 28-day period and were present during both periods of our study. Coyotes vs Mesocarnivores.—My data suggest that coyotes are not significant predators of quail or their nests even on a landscape managed for maximum quail

109 Texas Tech University, Cade B. Bowlin, December 2018 production,. During periods of low or high quail abundance coyotes were not responsible for significant bobwhite mortalities. There is no doubt that when presented the opportunity, coyotes will consume quail and quail eggs (Beasom 1974, Gipson 1974, Meinzer et al. 1975, Guthery 1995). However, it appears quail are merely incidental prey for coyotes. Lesser mesocarnivores (e.g., raccoons, striped skunks) may be more significant predators of quail nests than are coyotes (Hernandez et al. 1997). Research suggests that mesocarnivores such as raccoons, striped skunks, ground squirrels, opossums, and gray foxes are the most significant predators of quail nests where they are sympatric (Hernandez et al. 1997, Rogers and Caro 1998, Rollins and Carroll 2001, Rader et al. 2007). In Brooks County, Texas, skunks (24%) and badgers (12%) were responsible for quail nest depredations but coyotes were the top nest predator (32%, n=43, Rader et al. 2007). A study in the Rolling Plains credited skunks with 10% of nest depredations raccoons 7%, and coyotes were responsible for only 6.8% of nest depredations (n=59, Lusk et al. 2006). In south Texas, skunks were responsible for 23.1% of nest depredations (n=532, Lehmann 1984:92). In northern Florida raccoons (20%, n=85) were documented as quail nest predators and coyotes depredated only 1% of nests (Staller et al. 2005). Coyotes could in fact be influencing quail populations positively by impacting the movements or populations of these mesocarnivores. Predation and interference competition by coyotes are thought to influence the distribution, movements, diets, and abundances of smaller mesomammals, a model referred to as the mesopredator release hypothesis (MRH) (Johnson et al. 1996, Gompper 2002). The MRH suggests the removal of a large predator, and subsequent relaxation of top-down pressure, allows lesser mesomammals opportunities for range expansion, freedom of movement across the landscape, and increased abundance (Soule et al. 1988, Roemer et al. 2009). Movements of mesomammals at RPQRR, e.g., raccoons, are known to be influenced by coyotes on the landscape (Cooper et al. 2014). Without the presence (and influence) of coyotes, lesser mesomammals could expand their populations and spatial use of the landscape. For example, after only 1 year of coyote removal in western Texas, Henke and Bryant (1999) recorded an increase in mesomammals on the study site. This could lead to a higher incidence of nest depredations and quail mortality.

110 Texas Tech University, Cade B. Bowlin, December 2018

Badgers, raccoons, ground squirrels, skunk, snake, and feral hog remains were recovered in scats collected during our study, all of which are known nest predators on RPQRR. During this study, coyotes consumed mesomammals only during the La Niña weather period. Raccoons, skunks, and badgers were consumed during the La Niña period but did not occur in scats from 2016. Fewer mesomammals were consumed during the El Niño period probably because of other food item availability such as rodents. Although coyotes did not consume high numbers of mesomammals during the study, their influence on community structure and dynamics could have had significant impacts on quail production and survival. Through interference and resource competitions with other mesomammals, coyotes could in fact have been beneficial to quail production. Predation and interference competition by coyotes could have influenced distribution, movements, diets, and abundances of smaller mesomammals through the MRH thus benefitting quail populations by controlling more efficient nest predators (Johnson et al. 1996, Henke and Bryant 1999, Gompper 2002, Cooper et al. 2014). The magnitude of the impact that coyotes have on bobwhite populations is unknown. Coyotes could possibly be influencing quail populations directly (i.e., via predation) or indirectly (i.e., through changes in predator-prey relationships, MRH). As prey abundances fluctuate spatially and temporally, or in less favorable habitats for quail, coyotes could become significant predators of quail and their nests. Significant weather events, both stochastic and cyclical, could influence vegetative phenology/succession, prey abundances, coyote dietary requirements and behaviors. The presence of suitable escape cover and nesting cover across the landscape at RPQRR may accrue benefits to quail. Similarly, the presence of various mast-producing shrubs and cacti likely buffers predation during the summer season.

Management Implications These results show that coyotes were not important predators of quail or their nests irrespective of quail abundance. As such, through proper habitat management techniques and favorable environmental factors, quail production can be maximized in

111 Texas Tech University, Cade B. Bowlin, December 2018 the presence of coyotes. Coyotes could in fact be benefitting quail by regulating populations of known predators of quail, and also by regulating prey species that compete directly with quail for resources. Quail management should focus on proper habitat management primarily and predator management secondarily. Rodents and mast are important food items for coyotes on the Rolling Plains of Texas and may buffer predation on quail and nests by coyotes. Grassland habitats critical for quail production also support populations of buffer prey species such as rodents and lagomorphs. Tolerance of drought-hardy plants such as mesquite and prickly pear can alleviate predation pressures by coyotes during times of drought. These plants not only produce an abundant food source for coyotes (and quail) but also provide important nesting habitat in La Niña periods.

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