University of Florida Thesis Or Dissertation

DEMOGRAPHY AND POPULATION DYNAMICS OF THE GUNNISON’S PRAIRIE DOG (CYNOMYS GUNNISONI) IN THE SOUTHWEST UNITED STATES

RASHIDAH H. FARID

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2019

To my beloved grandmothers Rose and Effie and sisters Sabaah, Aishah, Samiayah, you have been my protectors, healers and inspiration. And for this, I am forever grateful.

ACKNOWLEDGMENTS

I thank my advisors Vanessa K. Hull and Raymond R. Carthy for their commitment to my success as a student and as a person. This research would not have been possible without the generous support of the McKnight Fellowship, University of Florida -Department of Wildlife

Ecology and Conservation, UF-Graduate Office of Diversity and Inclusion, National Science

Foundation, Petrified Forest Museum Association, Denver Zoological Foundation, and the

University of Maryland. For permission to use his unpublished data from 1989 through 1995 on

Gunnison's prairie dogs, I thank John Hoogland and his many field assistants who have helped over the years. I thank the Madan Oli Lab for technical assistance and continuous support. The drive for education was instilled by my grandmother Rose Freeman. Born in rural Alabama in

1918, she believed a woman should “get something in your head”, because lady parts “ain’t selling, a man will only buy you a fish sandwich and a coke”. I thank Janet Haslerig for her support, love and insistence that I most get a PhD from the University of Florida. Lastly, I thank my parents through whom I transition into this beautiful world. As praises due to the most high, beneficent and merciful.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

ABSTRACT ...... 11

CHAPTER

1 INTRODUCTION ...... 13

Ground Squirrels ...... 13 Management ...... 14 Decline ...... 15 Ground Squirrel Sociality ...... 16 Cynomys ...... 17 Cynomys gunnisoni ...... 18 Reproduction ...... 18 Population Ecology ...... 19 Overall Objectives ...... 20 Data Collection ...... 21

2 AGE- AND SEX-SPECIFIC SURVIVAL OF GUNNISON’S PRAIRIE DOG ...... 24

Literature Review ...... 24 Materials and Methods ...... 26 Survival Analysis ...... 27 Population Growth & Sensitivity Analysis ...... 29 Results...... 29 Factors Influencing Age-Specific Survival ...... 29 Impact of Reproduction on Survival ...... 30 Influence of Vital Rates on Population Growth ...... 30 Discussion ...... 31

3 TEMPORAL VARIATION IN AGE- SPECIFIC REPRODUCTIVE RATES AND FACTORS INFLUENCING THE FERTILITY OF GUNNISON’S PRAIRIE DOGS ...... 42

Literature Review ...... 42 Reproduction in Ground Squirrels ...... 42 Objectives and Hypotheses ...... 43 Methodology ...... 45 Part I: Probability of Reproduction using Logistic Regression Analysis ...... 45 Part 2: Negative Binomial Regression Analysis ...... 46

Results...... 48 Factors Affecting Probability of Reproduction ...... 48 Factors Affecting Litter Size ...... 48 Discussion ...... 49

4 TRADEOFF BETWEEN MORTALITY AND FECUNDITY IN GUNNISON’S PRAIRIE DOG ...... 60

Literature Review ...... 60 Methodology ...... 63 Simulation Software ...... 63 Data ...... 64 Results...... 65 Deterministic Population Growth Rates ...... 65 Influence of Population Density on Reproductive Output ...... 65 Demographic Factors Affecting Population Growth ...... 66 Time of Extinction ...... 67 Discussion ...... 67

5 UNDERSTANDING GUNNISON’S PRAIRIE DOGS IN A CHANGING ENVIRONMENT: LESSONS AND FUTURE MANAGEMENT ...... 84

Implications for Survival in Ground Squirrels ...... 84 Reproduction: Challenging Our Understanding ...... 85 Population Dynamics: Challenges for Population Management ...... 86

LIST OF REFERENCES ...... 88

BIOGRAPHICAL SKETCH ...... 94

LIST OF TABLES

Table page

2-1 Model selection results for a priori model set for Gunnison’s prairie dogs in Petrified Forest National Park in eastern Arizona...... 35

2-2 Model selection results for a priori model set for Gunnison’s prairie dogs in Petrified Forest National Park in eastern Arizona...... 36

3-1 Independent variables used in logistic regression analysis on factors affecting probability of reproduction in Gunnison’s prairie dog...... 51

3-2 AIC Table for probability of reproduction regression models results. Dependent predictor variables (left) are included in models 1-8 (vertical). Models 6 and 7 are best fit model and simplest, respectively...... 52

4-1 Baseline parameters for stochastic population viability simulations on Gunnison’s prairie dogs...... 69

4.2 Baseline Input Parameters for Stochastic PVA...... 69

4-3 Reproductive parameters used in Gunnison’s prairie dog population density simulation models...... 70

4-4 Deterministic rates for scenario: Density Dependent-Reproduction...... 71

4-5 Deterministic rates for scenario: Density Dependent-Litter Size and No Density Dependence...... 71

4-6 Mean mortality rate (SD) inputs under each population density scenario...... 71

4-7 Input reproductive parameters values under varying densities to determine impact of reproductive output on long-term population growth...... 72

LIST OF FIGURES

Figure page

1-1 Age and sex specific survival of ground-dwelling sciurids...... 23

2-1 Life cycle of female Gunnison’s prairie dogs (Cynomys gunnisoni)...... 36

2-2 Annual total count of Gunnison’s prairie dogs (Cynomys gunnisoni) of each age class captured. A total of 15,278 individuals were uniquely identified, sexed, and weighed from 1989 – 1995, within Petrified Forest National Park, Arizona...... 37

2-3 A) and B). Probability estimates under a best fit model for (a) survival (φ) and (b) capture probability (p) of Gunnison’s prairie dogs (Cynomys gunnisoni) from an age- cohort Cormack-Jolly-Seber (CJS) model...... 37

2-4 Apparent mean survival (φ) of Gunnison’s prairie dogs (Cynomys gunnisoni) for each age class: adults (age > 2 year), juveniles (age < 1 year), yearlings (1 > age < 2 years)...... 38

2-5 Transition probabilities (ψ) for Gunnison’s prairie dogs (Cynomys gunnisoni) from and to reproductive states were estimated from multi-state analysis...... 39

2-6 Annual population growth (λ) relative to yearly population size and fecundities for each age class; yearlings, 2-year-old adults, and 3-year-old adult Gunnison’s prairie dogs (Cynomys gunnisoni)...... 40

2-7 Proportional sensitivity (elasticity) of population growth rate, λ, to changes in fertility (q) and survival probabilities (S)...... 41

3-1 Theory of Biological Relationship in Gunnison’s females. Body weight and precipitation during lactation are expected to have a positive linear effect on litter size (mean = 3.38) and probability of reproduction ...... 53

3-2 AIC Table for litter size regression models results. Dependent predictor variables (left) are included in models 1-10 (vertical)...... 54

3-3 Factors affecting probability of reproduction in Gunnison’s females from model 1. Precipitation and weight variables are scaled. Probability of reproduction has a positive linear relationship with weight...... 55

3-4 Model effect plots for litter size. Positive relationship is evident between litter size and mother mass march; model 2. Precipitation and population density variables lack statistical inference and have biological relevance...... 55

3-5 Density Plot of Population Size and Litter Size: Three cluster effects are present. At low population densities, mean litter size is higher and more variant. As population

sizes increases, mean litter sizes shift more towards overall population average (3.38) and deviation declines...... 56

3-6 Effects plots of mother mass and population density effects on litter size, model 3. Mother mass in March variable significantly affected the litter size parameter...... 57

3-7 Post Results: Theory of Biological Relationship in Gunnison’s females. Body weight and precipitation during lactation have a positive linear effect on probability of reproduction but not litter size...... 58

3-8 Ordinal regression analysis of effect of mother mass in March on probability of litter size as a factor; where each litter is a level. Smaller females’ probability of producing litters over 5 pups was greater than larger females...... 59

4-1 Percent of Cynomys gunnisoni gunnisoni and C. g. zuniensis predicted range (USGS 2011) by state and landowner (USFW 2013)...... 73

4-2 Annual sequence of events (parameters) for simulated populations of Gunnison’s prairie dogs...... 74

4-3 Stochastic growth (r)projections of simulated Gunnison’s prairie dog populations for reproduction density dependent scenarios, in population densities of low, medium, and high...... 75

4-4 Stochastic growth (r)projections of simulated Gunnison’s prairie dog populations for the density-dependent litter size scenario...... 76

4-5 Stochastic growth (r) projections for simulated Gunnison’s prairie dog populations under various mortality rates. PVA models used original rates published for the species in the past...... 77

4-6 Stochastic growth (r) projections for simulated Gunnison’s prairie dog populations under various reproductive rates...... 78

4-7 Comparison of stochastic growth (r) performance for Gunnison’s prairie dog populations under reproductive and mortality scenarios; where population density is medium, and mortality has been reduced by 10 or 20 percent...... 79

4-8 Mean probability of extinction for simulated Gunnison’s prairie dog populations under various demographic conditions...... 80

4-9 Mean probability of survival of simulated Gunnison’s prairie dog populations under various demographic conditions. PVA models used original rates published for the species in the past...... 81

4-10 Population size projections for simulated Gunnison’s prairie dog populations under various demographic rates for medium density populations. PVA models used original rates published for the species in the past...... 82

4-11 Population genetic diversity projections of simulated Gunnison’s prairie dog populations under various demographic rates. PVA models used original rates published for the species in the past...... 83

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

DEMOGRAPHY AND POPULATION DYNAMICS OF THE GUNNISON’S PRAIRIE DOG (CYNOMYS GUNNISONI) IN THE SOUTHWEST UNITED STATES

Rashidah H. Farid

May 2019

Chair: Vanessa K. Hull Cochair: Raymond R. Carthy Major: Wildlife Ecology and Conservation

Population growth is highly sensitive to life history traits. Sensitive to both spatial and temporal environmental changes, survival and reproductive fitness parameters vary by time, age and population density. Thus, research into the diverse factors that influence demographic rates is an important and growing body of literature in the field of population biology. To date, little research has been done on age and sex-specific demography of the Gunnison’s prairie dog

(Cynomys gunnisoni), a colonial ground-dwelling squirrel inhabiting the sagebrush ecosystem of the southwestern United States. To discern impacts of time, age, sex, and reproductive status on survival, a capture-mark-recapture study was conducted in a population of Gunnison’s prairie dogs (n=15,278) from 1989- 1995. Two models were implemented: a age- cohort Cormack Jolly-

Seber (CJS) model and a multistate mark-recapture model. Natural history suggests the probability of reproducing and litter size are influenced by body weight and forage abundance. I evaluated the influence of precipitation, an indicator of forage, and weight of Cynomys gunnisoni, across age classes. Using a negative binominal regression model and generalized linear mixed model (GLMM), I determined which factors influenced the probability of reproduction and litter size, respectively. A clear relationship between litter size and population

density was evident and counter intuitive of our expectations for an asocial colonial ground squirrel. The sensitivity analyses indicated that the reproductive output of yearlings was the most significant driver of population growth and thus the most vulnerable vital rate to environmental changes. These findings provide more accurate estimates for age- and sex-specific survival in this species than were previously available and more broadly inform linkages between demography and sociality in prairie dogs.

CHAPTER 1 INTRODUCTION

Persistence of populations is driven by underlying factors and processes that affect changes in demographic rates: birth, death, emigration and immigration. Identifying the underlying age and sex-specific demographic patterns driving population change is necessary for understanding and predicting long-term population trends (Stacey 1992). Long term datasets are required to study such phenomena, but seldom are such data available. Such datasets are powerful for identifying the links between demographic rates and environmental changes (Ozgul et al. 2010). For example, changes in temperature and precipitation can have direct implications on forage abundance and quality, leading to changes in individual body condition. Furthermore, the probability of survival and reproduction for many species has been shown to be significantly affected by changes in the environment (Regehr et al. 2010; Saether et al. 2013; Turbill and Prior

2016). Knowledge about the role of demographic factors in influencing population dynamics over time as environments change can in turn be instrumental in managing populations that are threatened and require intensive conservation measures.

Ground Squirrels

Population demography is an important topic of research for ground squirrels (subfamily tribe: Marmotini), a group of small mammals that are predominately distributed in the Holarctic realm across the world within diverse ecosystems and latitudes. Ground squirrel habitats include temperate grasslands, arid grasslands, and the arctic tundra. Of the six genera of the Marmotini tribe, prairie dogs (Cynomys), marmots (Manta) and other North American ground squirrels

(e.g., Spermophilus) are widely studied; however, historical and current population estimates are unclear. Colonial ground squirrels are ecologically significant burrowing mammals, contributing to the engineering of the grassland ecosystems that they occupy. Mound construction by

Cynomys gunnisoni contributes to a unique soil composition and a more diverse vegetative community (Gallie and Drickamer 2008). Briggs et al. (2009) noted this phenomenon in small rodents, citing the role of the golden-mantled ground squirrel (Callospermophilus lateralis) as a significant seed disperser of the dominant tree species, Jeffery pine, in eastern Serra Nevada. The black-tailed prairie dog (Cynomys ludovicianus) is perhaps the most well-known keystone species of the North American grasslands and is the primary prey of the endangered black-footed ferret (Mustela nigripes). When compared to adjacent lands, colonies of black-tailed prairie dogs host a higher richness of reptilian and mammalian species (Baker et al. 2013; Shipley and

Reading 2006).

Management

The management of ground squirrels in North America is highly dependent on the species status at federal and state levels. The US Fish and Wildlife service currently tracks the population status of 35 species of Sciuridae. Of these, only six species are federally listed as threatened or endangered: the Utah prairie dog (Cynomys parvidens), Carolina northern flying squirrel (Glaucomys sabrinus coloratus), Mount Graham red squirrel (Tamiasciurus hudsonicus grahamensis), Northern Idaho ground squirrel (Urocitellus brunneus), Mexican prairie dog

(Cynomys mexicanus), and Vancouver Island marmot (Marmota vancouverensis) (USFW-

ECOS 2017). The Vancouver Island marmot, endemic to Canada, is the most critically endangered sciurid in North America. The loss of critical habitat is a key component in all of the sciurids’ federal listings and thus a major component in their management. Under the

Endangered Species Act, all habitat occupied by a species, including that on private land, is protected and managed under a five-year conservation plan. The unlawful take, transport, import or sale of the species or damage to its habitat is punishable by law (USFW- ECOS 2017).

Recovery efforts may also include direct interventions such as captive breeding programs or

habitat restoration (USFW- ECOS 2017). State listed endangered species have protections similar to federally listed species within the boundaries of a given state. Species such as the

Washington ground squirrel within Oregon and the Mojave ground squirrel within California have state conservation plans and regulated protections. Species classified by states as threatened or as a “species of concern” are often actively managed on state and federal lands with some guidelines regarding breeding season and bag limits for private land owners. Many states also have best management practice guidelines for the removal and translocation of sciurids regardless of federal or state listing status.

Decline

Ground squirrels face three direct anthropogenic threats: habitat loss/ fragmentation, eradication, and introduced disease. As stated earlier, habitat loss and fragmentation is the foremost cause of the sciurids’ population declines. For example, prairie dogs currently occupy less than two percent of their original North American range (Knowles 2002). Native grassland prairies were lost to agricultural development beginning in 1900. The conversion of grassland prairies to agricultural lands was encouraged by the development of state and federal ground squirrel eradication programs. Sciurids compete with cattle for forage and habitat and thus are considered agricultural pests. Populations of prairie dogs continue to be devastated by eradication methods including poisoning and shooting. Introduced around 1900, prairie dogs are also facing considerable threat from Bubonic plague caused by Yersinia pestis (Salkeld et al.

2013). For example, in Arizona, plague is the foremost threat to Gunnison’s prairie dog’s long- term persistence (Wagner et al. 2006). The Gunnison’s prairie dog is highly susceptible to bubonic plague and once encountered, the disease generally results in 100% mortality (Busch et al. 2013; Cully 1997; Rayor 1985a).

The continuous decline of North American ground squirrels, particularly prairie dogs, has sparked a debate about whether the current understanding of the population biology of these species is sufficient to inform ongoing management measures. In particular, the role of sociality in ground squirrel survival is understudied. Further, the methodology for estimating survival of keystone species such as Gunnison’s and black-tailed prairie dogs have varied and lacked a clear consistency that would be needed to inform conservation measures.

Ground Squirrel Sociality

The degree of sociality in ground squirrels was described by Armitage (1981), who divided individuals into five grades that were later classified into two groups as asocial (grades 1 to 3) and social (grades 4 and 5) (Schwartz et al. 1998). In the asocial group, individuals are defined as either being mostly solitary (grade 1), aggregating in colonies but living independently of one another (grade 2) or as following a system in which a male may defend a territory of individually living females (grade 3) (Figure 1-1). Social squirrel species are characterized by either a harem of females sharing a burrow (grade 4) or as multi-harem colonies

(grade 5) (Figure 1-1) (Armitage 1981). Sciurid sociality is believed to have evolved as a strategy of reproductive investment by retaining the daughters within the home colony beyond weaning (Boero 1999; Hoogland 1999; Lecuff and Pakdaman 2014). The onset of female maturity, age at first reproduction, and delayed dispersal are all functions of the degree of sociality and body weight (Broussard et al. 2008; Dobson and Oli 2007a, 2007b; Russo et al.

2017). Hence, sociality is an important factor that affects age- and sex-specific survival within a species. Intensive research on prairie dogs has broadened our knowledge on the importance of sociality and provides a framework for future comparisons of ground squirrel age-, stage- and sex- specific survival.

Cynomys

Prairie dogs are highly social and ecologically important mammals. From previous works, the behavioral ecology, mating and reproductive strategies, social behavior and dispersal roles of prairie dogs are well understood (Hoogland 1982, 1997, 2001, 2003, 2006). Mating and reproductive strategies vary between the two main groups of prairie dogs (white-tailed and black-tailed). This variation in strategies is based on social behavior. It has been well documented in prairie dogs that social structure (Dobson 1998) and forage availability affect age of first reproduction and dispersal (Hoogland 1999; Garrett et al. 1982). The more social black- tailed group (black-tailed and Mexican prairie dogs) matures and breeds at age two and 95% of females are philopatric (Hoogland 2006). The less social white-tailed group (Gunnison’s, Utah and white-tailed prairie dogs) can mature and breed earlier as yearlings, and female dispersal is more prevalent (Hoogland 2013a). Hoogland (2013b) also determined that the impacts of polyandry on reproductive parameters vary by species. For example, polyandrous behavior had no effect on the reproductive parameters of black-tailed females. However, in Gunnison’s prairie dogs, polyandrous females experienced increased conception and parturition, larger litter size, higher survivorship of the offspring ≥9 months after weaning, and increased production of yearlings (Hoogland 2013a). Hoogland (2013a) also detailed the importance of close kin cooperation and its role in dispersal, determining that the driving factor influencing female dispersal was the presence or absence of her mother or other close kin relatives.

Less is known about the population ecology of prairie dogs. Research by Garrett et al.

(1982), Rayor (1985b) and (1984), Cully (1997) and Hoogland (1995) provide a solid foundation of basic demography and population dynamics. Hoogland (1995) constructed cumulative cohort– specific life tables that indicated a higher annual survivorship for females than for males, and demonstrated a stable age distribution within a colony of black-tailed prairie dogs. Garrett et al.

(1982), Rayor (1985), and Cully (1997) determined that prairie dog population parameters are density-dependent, and that spatio-temporal variations in abundance and quality of forage resources impact survival, litter size, and body mass. However, a thorough understanding of factors and processes that influence demographic parameters, population dynamics, spatio- temporal variation, and population persistence is currently lacking for most prairie dog species.

Cynomys gunnisoni

Gunnison’s prairie dog (GPD) is a colonial ground-dwelling squirrel inhabiting the sagebrush ecosystem of western USA. Gunnison’s prairie dogs occur primarily in the southwestern United

States, including Arizona, and throughout New Mexico, Colorado and Utah. Populations are largely concentrated in New Mexico and Arizona at higher elevations. A medium sized rodent

(250-1100 grams), Gunnison’s prairie dogs are herbivorous but occasionally forage on insects

(Hoogland 2006). Foraging above-ground begins at dawn within 100 meters of the home territory and continues until dusk (Hoogland 1998a). Gunnison’s prairie dogs molt twice a year and are one of two true hibernating species of prairie dogs. Interestingly, Gunnison’s prairie dogs have only 2N=40 chromosomes while all other species of prairie dogs have 2N=50 chromosomes

(Nadler et al. 1971). Gunnison’s prairie dogs are organized into colonies, with sub-colony territories of clans (Hoogland 1996; Dobson 1998). Although all prairie dogs are highly social,

Gunnison’s prairie dogs are a member of the white-tailed group and are considered less social than the black-tailed and Mexican prairie dogs. The degree of their sociality is responsible for the differences observed in colony organization, reproductive strategies and population demography.

Reproduction

Each clan of Gunnison’s prairie dogs is typically comprised of at least one resident male, two or three breeding females, and the offspring of the mated adult females. The matriarchal structure of the clan produces a closer kinship among adult females of the same territory than

more distantly related females of neighboring territories (Hoogland 2013a). Female yearlings are sexually mature at 10 months and may successfully breed until death at approximately 6 years

(Rayor 1985b; Hoogland 2003). Males delay breeding until age two and infrequently live beyond 4 years (Hoogland 2003). Males’ reproductive success is highly dependent on their ability to compete for females (Hoogland 2003). Thus, body size is a significant contributor to male reproductive campaigns (Hoogland 2003).

The breeding season occurs in early March for several weeks. However, each female is receptive to copulation only one day within a season and for a few hours (Hoogland 1998).

Unlike black-tailed prairie dogs, Gunnison’s prairie dog copulation occurs predominately underground (Hoogland 2006; Hoogland 1998). Copulation is thus determined by observation of distinctive behavioral characteristics: self-licking of genitals, dust-bathing, late submergence by females at the end of the day, mating calls by males, and frequent sniffing and chasing of a female by the breeding male. Following copulation, pregnancy lasts 29-30 days until parturition within the natal burrow and young remain there until near weaning, approximately 38-40 days post parturition (Hoogland 1998).

Population Ecology

Despite their important role as a keystone species, little is known about the population ecology of Gunnison’s prairie dogs, including demographic patterns and the factors influencing population dynamics. Few long-term capture mark-recapture studies of Gunnison’s prairie dogs have been conducted that would facilitate advanced demographic analyses (Cully et al. 1997;

Davidson et al. 2014; Hoogland 2013a; Rayor 1985a and b), as they require intensive field work and rely on the longevity of a colony. Research by Garrett et al. (1982), Rayor (1985b), Cully

(1997) and Hoogland (1995) provide a solid foundation of basic demography and population dynamics. Hoogland (2001) and (2013b) constructed cumulative cohort –specific life tables that

indicated a higher annual survivorship for females than for males, and also reported stable age distribution within a colony of black-tailed prairie dogs. Garrett et al. (1982), Rayor (1985b), and

Cully (1997) determined that prairie dog population parameters are density-dependent, and that spatio-temporal variations in abundance and quality of forage resources impacted survival, litter size, and body mass. However, a thorough understanding of factors and processes that influence demographic parameters, spatio-temporal variation thereof, and population dynamics and persistence is currently lacking for most prairie dogs species.

Overall Objectives

I seek to provide a more detailed understanding of the factors and processes that impact demographic parameters and population dynamics in ground squirrels and how these factors interact to drive population dynamics. I propose to focus on Gunnison’s prairie dogs due to the availability of a long-term (1989-1995) dataset from a relatively predation free population in

Petrified Forest National Park in Arizona. My specific objectives are to:

1. Investigate age- and sex-specific survival rates in Gunnison’s prairie dogs and factors influencing these rates.

2. Investigate age-specific reproductive parameters in Gunnison’s prairie dogs and factors influencing these parameters.

3. Discern which demographic parameters and processes are most important to population persistence in Gunnison’s prairie dogs.

Dissertation organization. This dissertation will include three chapters that correspond to the objectives above:

 Chapter 2: Temporal variation in age- and sex- specific survival rates and factors influencing the survival of Gunnison’s prairie dogs.

 Chapter 3: Temporal variation in age-specific reproductive rates and factors influencing the fertility of Gunnison’s prairie dogs.

 Chapter 4: Population Viability Analysis of Gunnison’s prairie dogs.

Data Collection

Field Methods. The study population of Gunnison’s prairie dogs is found in Petrified

Forest National Park of Arizona (1700 m above sea level). Capture-mark-recapture methods were applied continuously from March through September from 1989 to 1995. It is important to note that the colony was devastated by the plague in 1995 and the study was therefore discontinued. A total of 15,278 individuals were uniquely identified, sexed, and weighed. The population size of adults ranged from 80 to 150 individuals across years, all of which were included in the census. Sampling of adults and yearlings began each year in late February, prior to the mating season and was conducted using double door Tommy Hawk traps. At this time, adults were uniquely ear tagged and temporarily body marked using hair dye for observational identification from a distance. Observations of individuals were conducted from four-meter high towers with binoculars and a 60- powered telescope (Hoogland 1995) from within and along the perimeter of the colony.

Females were identified as breeders if observed copulating or if later observed lactating with long, turgid teats in May or June (Hoogland 2001). Behavioral changes provided suggestions of parturition. However, sex and reproductive condition were determined through examination of the pelvic region and teats of adult females. Lactating females were intensely monitored with the expectation of weaned litters emerging approximately 5-6 weeks following parturition (Hoogland 1996, 1997). Only mothers of weaned litters were considered reproductively successful. Juveniles were trapped from late May through July of each year.

Nursery burrows were surrounded with traps shortly after juveniles’ first appearance (Hoogland

2001). Each pup was sexed, weighed, uniquely ear tagged and given a distinct litter marking

(Hoogland 2001). Once litter mixing occurred, litter markings allowed the field crew to distinguish litter siblings from juveniles of other litters.

A male was identified as a breeder if observed copulating or if his scrotum was black in pigment and testes were fully descended during the breeding season (Hoogland 2001). Non- breeding males were identified by having no descended testes and a gray or whitish scrotum

(Hoogland 2001). However, male reproductive success was undistinguishable conclusively.

Thus, this research cannot determine if reproductive status in males would affect their survival.

In 1989, the first year of the study, all captured adults were estimated to be at least two years old.

Therefore, individuals captured as adults in 1989 could not be included in the capture-mark- recapture analyses for which known age is required. Multiple variables limited our ability to discern specific ages for adults in 1989, including variation in body mass as influenced by reproductive status, sexual dimorphism and local abundance of forages (Hoogland 1997, 2003).

All protocols for capture, marking, and the handling of prairie dogs followed the

American Society of Mammologist’s guidelines (Sikes et al. 2016) and were approved by the

International Animal Care and Use Committee at University of Maryland and University of

Florida. Detailed field methodology can be found in Hoogland (1995) and Hoogland (1996).

Annual Survival by Age & Sex Class

Juveniles Juveniles Yearlings Adults Adults (<8 months) (<8 months) (>12 mth) (>24 mth) (>24 mth)

Species Relative Survival Survival Survival Survival Survival YR of data Method References Sociality Male Female (SD) Female (SD) Male U. armatus Uinta Asocial 0.38 0.42 0.50 1964 to 1968 LTRE Oli et al., 2001 U. columbianus Columbian Asocial 0.62 (0.08) 0.5 (0.07) 1981 to 1986 LTRE Dobson & Oli, 2001; Zammuto, 1987 U. brunneus Idaho Asocial 0.07 0.21 0.57 1987 to 1999 CMR Sherman and Runge, 2002 C. ludovicianus Black-tailed Social 0.47 0.54 0.42 0.17 1975 to 1989 Life Table Hoogland, 1995; this paper C. gunnisoni Gunnison's Social 0.17-0.39 0.50 0.50 0.40 1984 to1986 Life Table Cully, 1997; Hoogland, 2001 C. leucurus White-tailed Asocial 0.21 (0.08) 0.36 (0.13) 0.41 (0.10) 0.29 (0.13) 1983 to 1985 Jolly-Seber Menkens & Anderson, 1988; this paper

M. flaviventris Yellow-bellied Social 0.52-0.75 0.3-0.40 0.62-0.80 1976 to 2003 C. Jolly-Seber Ozgul et al., 2006

Figure 1-1. Age and sex specific survival of ground-dwelling sciurids. Life Table (LT) analysis where base on cohort survival to age x, in under experimental (LTRE) conditions of population regulation. Analysis of life-table response experiments (LTRE) for populations of comparing populations under different conditions of population regulation. Capture – mark – recapture (CMR) are individual based survival models for probability of survival in an open population, given presence in previous year. Jolly-Seber and Cormack Jolly-Seber are variations of CMR.

CHAPTER 2 AGE- AND SEX-SPECIFIC SURVIVAL OF GUNNISON’S PRAIRIE DOG

Literature Review

(Dobson and Oli 2001) and for identifying the links between demographic rates and environmental changes over time (Ozgul et al. 2010).

Population demography is an important area of study for the Gunnison’s prairie dog

(Cynomys gunnisoni), a colonial ground-dwelling squirrel inhabiting the sagebrush ecosystem of southwestern USA. Gunnison’s prairie dogs occur primarily in Arizona, and throughout New

Mexico, Colorado and Utah. These medium sized rodents (250-1100 grams) forage during daytime and are largely herbivorous (Hoogland 2006). Gunnison’s prairie dogs are organized into colonies, with sub-colony territories of clans (Hoogland 1996; Dobson 1998). Each clan of

Gunnison’s prairie dogs is typically comprised of at least one resident male, two or three breeding females, and the offspring of the mated adult females.

Across sciurids, the degree of sociality is responsible for the differences observed in colony organization, reproductive strategies and population demography. Sciurid sociality is believed to have evolved as a strategy of reproductive investment by retaining the daughters within the home colony after weaning (Boero 1999; Hoogland 1999; Lecuff and Pakdaman

2014). The onset of female maturity, age at first reproduction, and delayed dispersal are all

functions of the degree of sociality and body weight (Broussard et al. 2008; Dobson and Oli

2007a, 2007b; Russo et al. 2017). Although all prairie dogs are highly social, Gunnison’s prairie dogs are considered less social than other prairie dog species (e.g., black-tailed and Mexican prairie dogs that congregate in harems of females). Their unique position in the sociality continuum in prairie dogs makes them interesting study subjects.

Little is known about the population ecology of Gunnison’s prairie dogs, including demographic patterns and the factors influencing population dynamics. Few long-term capture mark-recapture studies of Gunnison’s prairie dogs have been conducted that would facilitate advanced demographic analyses (Cully et al. 1997; Davidson et al. 2014; Hoogland 2013a;

Rayor 1985a and b), as they require intensive field work and rely on the longevity of a colony.

Research by Garrett et al. (1982), Rayor (1985b), Cully (1997) and Hoogland (1995) provide a solid foundation of basic demography and population dynamics. However, a thorough understanding of age and sex-specific survival and variation through time is lacking for this species.

In this Chapter, I fill these gaps by investigating the factors affecting survival of

Gunnison’s prairie dogs, including temporal variations in sex, age, and reproductive state using a capture-recapture modeling approach on a long-term dataset from a colony in Petrified Forest

National Park, Arizona. I address the following questions: (1) What are the age- and sex- specific survival rates of the population? (2) How does reproductive state impact age- and sex-specific survival? (3) What is the relationship between survival and both population size and population growth rate over time?

Materials and Methods

Field methods. The study population of Gunnison’s prairie dogs occurs in Petrified

Forest National Park of Arizona (elevation: 1700 m above sea level). Capture-mark-recapture methods were applied continuously from late February (prior to the mating season) through

September of 1989 to 1995. A total of 15278 individuals were uniquely identified, sexed, and weighed. The population size of adults ranged from 80 to 150 individuals across years, all of which were included in the census. Sampling of adults and yearlings began each year in late

February, prior to the mating season and was conducted using double door Tomahawk live traps.

Adults were uniquely ear tagged and body marked using temporary hair dye for observational identification from a distance. Lost or missing ear tags were also replaced prior to mating season.

Only mothers of weaned litters were considered reproductively successful. Parturition occurred approximately 28-30 days following copulation (Hoogland 1997). Behavioral changes provided suggestions of parturition. However, sex and reproductive condition were determined through examination of the pelvic region and teats of adult females. Lactating females were intensely monitored with the expectation of weaned litters emerging approximately 5-6 weeks following parturition (Hoogland 1996).

Juveniles were trapped from late May through July of each year. Each pup was sexed, weighed, and uniquely ear tagged. In 1989, the first year of the study, all captured adults were estimated to be at least two years old. Thus, individuals captured as adults in 1989 could not be included in the capture-mark-recapture analyses that require age to be known. Multiple variables limited our ability to discern specific ages for adults in 1989. For example, body mass is influenced by reproductive status, sexual dimorphism and local abundance of forage (Hoogland

1996; Hoogland 2003).

Female Gunnison’s prairie dogs are sexually mature at approximately 11 months

(yearlings) and commonly successfully breed as such (Hoogland 1997). Males are sexually mature by two years of age and reproductive success is highly dependent on their ability to compete for females (Hoogland 2003). Body size is a significant contributor to male reproductive campaigns (Hoogland 2003). However, we were unable to distinguish male reproductive success conclusively. Thus, we could not determine if reproductive status in males affected their survival.

All protocols for capture, marking, and the handling of prairie dogs followed American

Society of Mammologist’s guidelines (Sikes et al. 2016) and were approved by the International

Animal Care and Use Committee at University of Maryland and University of Florida. Detailed field methodology can be found in Hoogland (1995) and (1996).

Survival Analysis

I constructed two types of survival models- single-state and multi-state. For the single- state model, we used an Age-Cohort Cormack Jolly-Seber (CJS) Model mark- recapture modeling framework (Pollok 1981; Cormack 1964; Jolly 1965; Seber 1965) to calculate apparent sex and age-specific survival (φ) and capture probabilities (p). A multi-state mark- recapture modeling framework (Lebreton et al. 2009) was used for the second, multi-state analysis. I calculated apparent survival (S), capture probability (p) and transition probability (ψ) for each age-stage. The multi-state model allowed me to evaluate the effect of reproduction on survival.

Based on Gunnison’s prairie dog ecology, individuals were classified into five states characterized by age and reproductive stage: juveniles, yearling non-breeding, yearling-breeding, adult-non-breeding and adult-breeding (Figure 2-1).

I limited the multi-state analysis to females, due to our inability to determine reproductive success in males. In the analysis, a weaned litter constituted successful breeding for that year.

Thus, a non-breeding female implies a female without a weaned litter and a breeding female is an individual who has successfully weaned a litter. Distinction between individuals who conceived, lactated, and lost their litter and those that never conceived could not be determined and thus, in either situation, these females were regarded as non-breeders. Juveniles survived with the annual survival rate, S0, and all survivors become yearlings. Most yearlings are sexually mature (Hoogland 1997). Thus, transitioning from a juvenile to a yearling depends on survival and reproductive state. In the model, I represented juveniles becoming either breeding (ψab) yearlings or non-breeding (ψac) yearlings. I could not fix the transition between juvenile and breeding yearling to 1, because breeding in our model represents successful weaning. In the consecutive year, yearlings survive at an annual survival rate Sy and become adults at age 2; they either transition from a yearling to a 2-year-old breeding (ψbd) or non-breeding (ψbe) adult.

Finally, adult females, age 3 or more, survive to the next year at an annual rate, Sa, and either remain a successful breeder (ψdd) for the extent of their lives (Hoogland 2001), or become a non- breeding adult (Sa * ψde). Non-biologically feasible transitions, within the annual time step, such as juvenile to adult, yearling to juvenile, adult to juvenile or to yearling were fixed to zero. I explored temporal variation in all parameters including transitions (ψ).

To evaluate goodness of fit of the multi-state models, the over dispersion parameter (ĉ) was calculated as chi-squared divided by the degrees freedom. For model comparison, we used

Akaike’s information criterion (AIC) to identify the most parsimonious model from a candidate model set (Burnham and Anderson 2004). Comparisons between models were evaluated using the ΔAIC. Models within two ΔAIC of one another were considered to be equally supported by the data provided, thus low model selection certainty (Burnham and Anderson 2004).

Population Growth & Sensitivity Analysis

To evaluate the impact of vital rates on the population, I constructed a post-breeding matrix-based model to estimate population growth rate (λ) per year using the survival and reproductive parameters directly from our multistate capture-recapture models (Nichols et al.

2002). Values of λ greater than 1 reflect a positively growing population and values less than 1 indicate that annual recruitment is less than mortality. I conducted a sensitivity analysis to assess the relative contribution of each parameter and state to annual population growth (Caswell 2001).

Proportional sensitivity (elasticity) of population growth rate, λ, to changes in fertility (q) and transition survival probabilities (S), where states were presented as follows: a. juvenile, b. breeding yearling, c. non-breeding yearling, d. breeding adult and e. non-breeding adult. The order the of the state in the subscripts represents “from” one state “to” the next state. a. juvenile, b. breeding yearling, c. non-breeding yearling, d. breeding adult and e. non-breeding adults.

Fertility of yearlings, 2-year-old adults and 3-year-old adults are represented as qaa, qbb, qdd, respectively.

I fit CJS and multistate CMR models with Program Mark using the RMark interface

(Laake et al. 2013; White and Burnham 1999) in R statistical software (R Core Team 2013). All matrix model analyses were done using the R package ‘popbio’ (Stubben and Milligan 2007).

Results

Factors Influencing Age-Specific Survival

Of the total 15,278 prairie dogs identified, sexed, and weighed in this study, 1,756 were followed from the juvenile to the adult phase (Figure 2-2). Among the single-state models, the two best models for predicting survival (accounting for 93% of the total model weights) both included an interactive effect of age and time and either sex alone (wt: 0.63) or sex and age (wt:

0.3) (Table 2-1). Mean age-specific survival for yearlings (0.29± 0.02) was lower than juveniles

(0.46 ± 0.02) and adults (0.482 ± 0.055). Females had higher apparent survival than males. Age- specific survival rates varied over time, as juvenile survival declined but yearling and adult survival increased and then decreased again as the study progressed (Figure 2-3a). Capture probability was higher for females (0.979 ± 0.012) than males (0.841 ± 0.040), but there was no effect of time on this parameter (Figure 2-3b).

Impact of Reproduction on Survival

Regarding the multi-state survival models, our top two models explained over 96% of the data variation (Table 2-2). In both models, survival was affected by the age and time (year) interaction and transitions between states were conditional based on the previous state. Surviving juveniles transitioned to breeding yearlings (ab) at a rate of 0.480 (± SE = 0.027) such that roughly half of female juveniles did not become successful breeders as yearlings (Figure 2-4).

Breeding yearlings were highly likely to become breeding adults at two years of age (0.772 ±

0.066) (bd) and remained breeding adults (dd) the following year (0.765 ± 0.103) (Figure 2-5).

Approximately 25% of non-breeding yearlings became breeding adults (cd) with a mean transition rate from a non-breeding yearling to a non-breeding adult (1-cd) of 0.754 ± 0.068.

Non-breeding adults transitioned into breeding adults (ed) at 3 years of age at a rate of 0.286 ±

0.171.

Influence of Vital Rates on Population Growth

Our population experienced positive growth rate from 1991-1993 (respective λ across years: 1.28, 1.66, 1.18), followed by a negative growth rate in 1994 (lambda = 0.18) (Figure 2-6).

Periods of declining λ did not directly correlate to a decrease in population size. For instance, in

1993, population size of the colony reached its highest at 362 individuals. However, λ declined from 1.66 in 1992 to 1.17 in 1993. These decreases in population size continued until the population collapse due to plague in 1995. The fecundities of 2- and 3-year-old females

consistently followed the same trend through time (Figure 2-7). Yearling fecundity, on the other hand, declined consistently across years (λ from 1991 through 1994 was 1.28, 1.66, 1.18, and

0.18, respectively; Figure 2-7). Population growth rate was equally as sensitive to the survival of juveniles that became breeding yearlings (sab) and the fertility of 2-year-old females (qbb)

(Figure 2-7). However, yearling fertility (qaa) was the most important vital rate in impacting proportional changes in λ (Figure 2-7).

Discussion

I conducted the first long-term capture mark recapture analysis of Gunnison’s prairie dogs. We identified several factors that influence survival including sex, age, and time. I also put the species in context within the broader picture of understanding linkages between social complexity and demography across sciurids.

Mean age-specific survival for yearlings was surprisingly low (29%). This result differs from previous short-term life-table analysis for this species which suggested 39% survival of yearlings (Cully 1997) and studies on black-tailed prairie dogs which reported around 42% survival of yearlings (Hoogland 1995). Arguably, the main significant biological difference between these species is the average age of first reproduction. In other prairie dog species, reproduction is typically delayed until year two; with some variation relative to the abundance of quality forage (Hoogland 2001). However, in Gunnison’s prairie dogs, yearlings mate and many successfully wean litters (Hoogland 1996; Hoogland 2001). Our finding of a lower yearling survival in Gunnison’s prairie dogs relative to other species thus suggests that there may be a reproductive cost to survival (Reznick 1985).

In contrast to the low yearling survival, juvenile survival in this study population was higher and comparable to adults (around 46-48%). This result makes sense in the context of sciurid sociality (Knowles 2002). Asocial species mature earlier as yearlings and thus have lower

juvenile survival rates. For example, in asocial Uinta ground squirrels, juvenile survival (0.38) is less than both yearlings (0.42) and female adults (0.50) (Oli et al. 2001). Similar patterns are observed in other asocial Urocitellus species (Murie and Michener 1984). In contrast, black- tailed prairie dogs are one of the most social ground dwelling squirrels (Hoogland 1996).

Juvenile male survival (0.47) in black-tailed prairie dogs was the highest in comparison to

Urocitellus species and yellow-bellied marmot. Black-tailed juvenile males remain in the natal territory until age two, and thus prolonged dispersal decreases the probability of mortality by predators and increases overall survivorship. Because Gunnison’s prairie dogs are the more social of the white-tailed group, we expected them to have similar juvenile survival rates as black-tails. In addition, the typical sources of juvenile mortality in prairie dogs are infanticide and predation (Dobson 2000; Hoogland 1985; Sherman 1982), both of which were highly limited in our study population (Hoogland 1996).

The higher survival of females compared to males is consistent with past research on prairie dogs. Across sciurids, female juvenile survival is higher than male juvenile survival due to their natal philopatry. For example, female juvenile survival in Idaho ground squirrels (0.21) is three times higher than juvenile male survival of only 7% (Sherman and Runge 2002). Adult male survival is consistently lower than that of adult females across prairie dog species (Figure

1-1). Proportionally, adult male survival is approximately 10% of that of the female rate for

Gunnison’s and white-tailed prairie dogs (Cully 1997; Menkens and Anderson 1989).

Nonetheless, adult survival rates are highly variable following density trends from year to year

(Menkens and Anderson 1991); an observation that our study also supports. The generally philopatric behavior of female prairie dogs also explains the consistency of females’ high capture probability observed across years (Hoogland 1999) in our population of Gunnison’s prairie dogs.

In contrast, males often disperse as yearlings and thus our results are consistent with the biology of the species. Although capture probabilities were not calculated, similar sex variation trends were observed by other authors when presenting total captured data (Garrett et al. 1982;

Hoogland 1999).

The multi-state model results also suggest that a much smaller percentage of each cohort is contributing to the lifetime reproductive success of the Gunnison’s prairie dog population than was previously thought and a considerable number of yearling females are delaying reproduction. Approximately half (48.0 +/- 2.7) of female yearlings successfully weaned litters each year. Successful yearling breeders overwhelmingly transitioned to successful adult breeders and remained so throughout their lives. Yet consistently, less than 25% of all non-breeding yearlings transitioned to became successful breeding adults. Life history theory suggests that by delaying reproduction, yearlings would increase their survival. Lower survival would therefore be expected for individuals that successfully weaned litters during the first year of life (Reznick

1985; Stearns 1989). My data strongly supports this theory. Previous work determined mating as yearlings to be highly prevalent (100%) in Gunnison’s prairie dogs, a rate normally only seen in similar species under conditions of high forage quality and abundance. (Garrett et al. 1982;

Hoogland 1998). Our results are also consistent with previous works where an average of 82% of Gunnison’s prairie dogs, totaled across age classes, weaned litters each year (Hoogland 2001).

Our population growth and sensitivity analysis revealed that overwhelmingly, the reproductive output of yearling females (fecundity) was the most significant driver of population growth. Yearling fecundity represents 70% of proportional sensitivity of population growth

(Figure 2-7). Lower survival likely contributed to the decline of the population but did not appear to be a major underlying factor prior to the plague devastation in 1995. In other small

mammal populations, predation is a major contributor to population decline (Korpimäki and

Krebs 1996). However, natural predation at this study site was rare (Hoogland 1996). Annual growth rates varied temporally, suggesting environmental factors influenced lambda. Other studies have found precipitation and temperature to be highly correlated to annual survival, mammal lactation and ultimately population growth (Armitage 2013; Davidson 2014; Wang et al. 2013). Further research should explore the potential impact of environmental factors on yearling fecundity and other demographic parameters.

Table 2-1. Model selection results for a priori model set for Gunnison’s prairie dogs in Petrified Forest National Park in eastern Arizona. Model K AICc ΔAICc Weight Deviance φ (age * time) p (sex) 19 2923.61 0.00 0.63 121.38 φ (age * time) p (age + sex) 20 2925.09 1.48 0.30 120.83 φ (age * time) p (time + sex) 24 2928.22 4.61 0.06 115.81 φ (age * time) p (Time) 19 2935.35 11.74 0.00 133.12 φ (age * time) p (age) 19 2936.24 12.63 0.00 134.01 φ (age * time) p (1) 18 2936.49 12.87 0.00 136.28 φ (age * time) p (time + age) 24 2938.30 14.69 0.00 125.89 φ (age * time) p (time) 23 2940.31 16.70 0.00 129.94 φ (age * sex) p (time + age) 13 2954.71 31.10 0.00 164.64 φ (age + sex) p (time + age) 11 2956.17 32.56 0.00 170.14 φ (age * sex) p (time) 12 2960.65 37.03 0.00 172.60 φ (age * sex) p (time + sex) 13 2961.02 37.41 0.00 170.95 φ (age + sex) p (time) 10 2961.31 37.70 0.00 177.30 φ (age + sex) p (time + sex) 11 2962.32 38.71 0.00 176.29 φ (age) p (time + age) 10 2967.23 43.62 0.00 183.22 These model sets explore age and time variation in survival (φ) and detection probabilities (p) using an Age-cohort Cormack-Jolly-Seber (CJS) model. AIC is Akaike’s information criterion, ΔAICc is the difference between the AICc value of a focal model and the low-AIC model in the set, K is the number of model parameters and weight is the total variation of parameters explained by the model. Differences in Akaike’s information criterion corrected for small sample size (ΔAICc), K, AICc, and weights are given for each model. Model symbols included: age, age effect; time, time effect; sex, sex effect. Additive effect is indicated by a plus sign (+) and an interactive effect is indicated by an (*) multiplicative symbol.

Table 2-2. Model selection results for a priori model set for Gunnison’s prairie dogs in Petrified Forest National Park in eastern Arizona. Model K AICc ΔAICc Weight Deviance S(age * time) p (1) ψ(-1 + 23 2111.49 0.00 0.594433 120.16 stratum:tostratum) S(age * time) p(age) ψ(-1 + 24 2112.44 0.96 0.368404 119.04 stratum:tostratum) S(age * time) p(stratum) ψ(-1 + 27 2117.03 5.55 0.037105 117.39 stratum:tostratum) S(stratum * time) p(1) ψ(-1 + 36 2130.95 19.47 0.000035 112.41 stratum:tostratum) S(stratum * time) p(age) ψ(-1 + 37 2132.01 20.52 0.000021 111.35 stratum:tostratum) S(stratum * time) p(stratum) ψ(-1 + 40 2136.64 25.15 0.000002 109.61 stratum:tostratum) S(time) p(age) ψ(-1 + stratum:tostratum) 13 2154.28 42.79 0.000000 183.53 S(time) p(stratum) ψ(-1 + 16 2155.95 44.47 0.000000 179.06 stratum:tostratum) S(time) p(1) ψ(-1 + stratum:tostratum) 12 2155.97 44.48 0.000000 187.26 S(age) p(1) ψ(-1 + stratum:tostratum) 9 2319.95 208.47 0.000000 357.34 These model sets explore age, time and stage variation in survival, transition and detection probabilities using a multi-state mark – recapture modeling framework. AIC is Akaike’s information criterion, ΔAICc is the difference between the AICc value of a focal model and the low-AIC model in the set, K is the number of model parameters and weight is the total variation of parameters explained by the model. Differences in Akaike’s information criterion corrected for small sample size (ΔAICc), K, AICc, and weights are given for each model.

Figure 2-1. Life cycle of female Gunnison’s prairie dogs (Cynomys gunnisoni). Survival (S) occurs each year, times the probability of transitioning in (psi, Ψ) either breeder or non-breeder state; with fecundity (F) of each age-state. Where, a juvenile survives (S0) to become a breeding yearling as survival times transitions probability (Ψab). Thus, a breeding yearling can survive (Sy) to become a breeding adult with transition probability (Ψbd) or non-breeder (1-Ψbd). Adults survive (Sa) and remain in breeding state (Ψdd).

Figure 2-2. Annual total count of Gunnison’s prairie dogs (Cynomys gunnisoni) of each age class captured. A total of 15,278 individuals were uniquely identified, sexed, and weighed from 1989 – 1995, within Petrified Forest National Park, Arizona.

a Survival Probability, φ b Capture Probability, p

Figure 2-3. A) and B). Probability estimates under a best fit model for (a) survival (φ) and (b) capture probability (p) of Gunnison’s prairie dogs (Cynomys gunnisoni) from an age- cohort Cormack-Jolly-Seber (CJS) model. Survival estimates varied by both age and time. Survival estimates varied by both age and time.

Figure 2-4. Apparent mean survival (φ) of Gunnison’s prairie dogs (Cynomys gunnisoni) for each age class: adults (age > 2 year), juveniles (age < 1 year), yearlings (1 > age < 2 years).

Figure 2-5. Transition probabilities (ψ) for Gunnison’s prairie dogs (Cynomys gunnisoni) from and to reproductive states were estimated from multi-state analysis, where states were presented as follows: a. juvenile, b. breeding yearling, c. non-breeding yearling, d. breeding adult and e. non-breeding adult. The order the of the state in the subscripts represents “from” one state “to” the next state. Note: Transitions not presented are deduced from their complement transition: ac (1-ab), be (1-bd), de (1-dd) and ee (1- ed).

3 375

2.5 300 Population Size, N

2 Lambda (λ) 225 Yearling Fecunity 1.5

150 Population Size 2 yrs, Fecunity 1

3 yrs or >, Fecunity Estimates Estimates (Fecunity, Lambda) 75 0.5

0 0 1991 1992 1993 1994 Year

Figure 2-6. Annual population growth (λ) relative to yearly population size and fecundities for each age class; yearlings, 2-year-old adults, and 3-year-old adult Gunnison’s prairie dogs (Cynomys gunnisoni).

Figure 2-7. Proportional sensitivity (elasticity) of population growth rate, λ, to changes in fertility (q) and survival probabilities (S). Proportional sensitivity (elasticity) of population growth rate, λ, to changes in fertility (q) and survival probabilities (S), where states were presented as follows: a. juvenile, b. breeding yearling, c. non- breeding yearling, d. breeding adult and e. non-breeding adult. The order in the subscripts represents “from” one state “to” the next state. Fertility of yearlings, 2- year-old adults and 3-year-old adults are represented as qaa, qbb, qdd, respectively.

CHAPTER 3 TEMPORAL VARIATION IN AGE- SPECIFIC REPRODUCTIVE RATES AND FACTORS INFLUENCING THE FERTILITY OF GUNNISON’S PRAIRIE DOGS

Literature Review

Population growth of a given animal population is highly sensitive to reproductive life history traits such as litter size, age of mother and age at first reproduction (Ibler and Fischer

2016; Roff 2002; Stearns 1989). Sensitive to both spatial and temporal environmental changes

(Millesi et al. 1999; Ozgul et al. 2007b), reproductive fitness parameters vary by time, age and population density. Because reproductive parameters are important for the longevity of any population, ongoing research into the diverse factors that influence reproduction rates is an important and growing body of literature in the field of population biology (Ozgul et al. 2007;

Oli and Dobson 2003).

Reproduction in Ground Squirrels

The importance of this research topic applies particularly to the case of ground squirrels such as prairie dogs. Utah, black-tailed and Gunnison’s prairie dogs reproduce slowly and have short lifespans (Hoogland 2001). Breeding and reproduction are similar in these three species, but some variation also occurs. Black-tailed prairie dogs lack true hibernation and thus begin breeding in mid-February and continue through early April (Hoogland 1995). Gunnison’s and

Utah prairie dogs on the other hand emerge from hibernation in mid- March, and breed through early April (Hoogland 1999). In most prairie dog species, female breeding is typically delayed until year two. However, Gunnison’s prairie dogs go against this trend because 100% of females will breed in their first year of life (Hoogland 2001). Forty nine percent of Utah yearling males breed. However, Gunnison’s and Utah yearling male prairie dogs are unlikely to breed in the first year (Hoogland 2001). Gunnison’s prairie dog males are sexually mature by two years of age and reproductive success is highly dependent on their ability to compete for females (Hoogland

2003). Body size is a significant contributor to male reproductive campaigns (Hoogland 2003).

Male breeding success is dependent on weight, as heavier sires are more successful than

(younger) light weight males (Hoogland 2001). In both sexes, forage quality and abundance are key factors in breeding and reproductive success.

Reproduction in prairie dogs also varies across time and space due to environmental variation. For instance, the availability and quality of forage varies spatially and temporally within a colony site. Previous studies have found that black-tailed and Utah prairie dogs can breed within the first year of life (as opposed to waiting until year 2) if an abundance of quality forage is present (Hoogland 2001; Garrett et al. 1982). The availability and quality of forage is dependent on climatic factors such as temperature and precipitation. Environmental factors also interact with sociality in affecting reproduction. For instance, in black-tailed prairie dog populations, social territories limit the spatial availability of feeding grounds by confining individuals to their clans within black-tailed populations (Hoogland 1999, 2006). Thus, in the more social species, the social status, age and location, are limitations of total reproductive output of an individual within her lifetime (Hoogland 1999, 2006; Moore et al. 2016). On the other hand, in the less social Gunnison’s prairie dog, social restrictions are narrow and strong spatial variation in reproduction parameters have not been observed. Instead, it is possible that temporal differences in temperature, precipitation and age may be key drivers of variation in reproductive outputs in Gunnison’s prairie dogs, yet research on the topic is scarce to date. In fact, a comprehensive picture of the various factors influencing age-specific reproduction in

Gunnison’s prairie dogs is lacking and in need of further exploration.

Objectives and Hypotheses

In this Chapter, I investigated age-specific reproductive factors (probability of reproduction and litter size) in Gunnison’s prairie dogs and factors influencing these parameters;

including temporal variations in (1) age, (2) age at first reproduction, (3) weight, (4) population size, (5) mother’s pack size, (6) previous litter size, and (7) precipitation. I addressed the following questions using the long-term dataset from Petrified Forest National Park, Arizona:

1. Which age-specific reproductive factors contribute to the probability of reproduction?

2. Which age-specific reproductive factors explain litter size?

3. How does temporal variation (and in turn precipitation) affect both the probability of reproduction and litter size?

I had several hypotheses regarding these key questions. Hoogland (2001) and Rayor

(1991) derived the probability of reproducing as a proportion of litters weaned by females that copulated. This information is highly valuable; however, it leaves unanswered questions regarding the processes that affect an individuals’ probability of reproducing. Life history suggests a reproductive cost to current individual fitness (Reznick 1985, Sterns 1989) and future reproduction (Reznick 1985, Sterns 1989, Roff 2002). Moore et al. (2016) corroborated life history theory in finding that the cost of reproduction can transcend a generation in golden– mantled ground squirrels (Callospermophilus lateralis). However, golden–mantled ground squirrels are asocial like Gunnison’s prairie dogs, yet yearling females frequently delay reproduction until second year of life (Kneip et al. 2011). Thus, I predicted that intra- generational variables (i.e. precipitation, weight) would be highly predictive of reproduction in

Gunnison’s prairie dogs. Inter-generational variables (i.e. maternal pack size, previous litter size) were expected to have a limited effect on the predictability of reproduction, as these variables are confounded in social constraints.

Regarding litter size, previous research on Gunnison’s prairie dogs has demonstrated litter size to be correlated to weight, predation (Hoogland 2001) and population density (Cully

1985; Rayor 1991). Hoogland (2001) described the relationship between litter size and weight as

being correlated: increases in weight produce a parallel increase in litter size. It is commonly accepted that smaller females’ litters will fall victim to predation and infanticide more readily than the litters of large females (Hoogland 2001; Hoogland et al. 2006). Thus, in the absence of predation and infanticide, I expected weight to be a strong predictor of litter size. I predicted weight would not be correlated with age-class, but rather a more relative measure of body condition. Finally, precipitation is often used as an indicator of forage availability in habitats occupied by this species. Therefore, I predicted that precipitation would be positively correlated to litter size and follow a similar trend as weight. The biological model below (Figure 3-1) visualizes my predictions based on current theory.

Methodology

The analysis for this chapter included two main approaches to investigating reproductive parameters in the Gunnison’s prairie dog. I analyzed factors influencing probability of reproduction using mixed effects logistic regression analysis and assessed factors influencing litter size using negative binominal regression analysis.

Part I: Probability of Reproduction using Logistic Regression Analysis

Logistic regression is a statistical method that predicts probability of an event occurring and the relationships between one binary response (dependent) variable and several explanatory

(independent) variables simultaneously (Kleinbaum 1994; Hosmer and Lemeshow 2000). The analysis returns a positive binomial probability in the form (Rupert et al., 2008):

푃 = 푒푥/(1 + 푒푥) where,

P is the probability of event x is 훽0 + 훽1푥1 + 훽2푥2+. . . +훽푖푥푖; 훽푖 are logistic regression coefficients; 푥푖 are values for the independent variables; _푖 is the number of variables.

The coefficient 훽0 represents the reference group, consisting of those individuals designated as the reference for each and every other variable 푥1…푖 (Sperandei 2014).

For this study, I constructed a series of mixed effects logistic regression models to analyze the relationship between probability of reproduction of known age Gunnison’s prairie dog females (age 1 or greater; n= 360) and several independent variables listed in Table 3-1.

Variables were chosen based on past research on prairie dog reproduction and included both fixed and random effects. Individual identity was a random effect because individuals were measured repeatedly across years (1989-1995) creating temporal autocorrelation. Maternal pack size and previous litter size were grouped variables nested in prior generational and potential environmental effects. Mothers and daughters were also not independent. For females with unknown age in 1989, I assigned their age as 1 and added +1 to their age for every time step until death, thus creating the variable age class.

I constructed a total of 8 models with different predictor variables included in them and compared their relative performance using Akaike’s Information Criterion (AIC) (Table 3-2). A p-value was also calculated for each independent variable, indicating the variable’s overall statistical significance in the regression model (Rupert et al. 2008; Menard 2002). P-values of

0.10 or lower were considered statistically significant.

Part 2: Negative Binomial Regression Analysis

For the second main analysis of this chapter, I conducted a negative binomial regression analysis, a type of generalized linear mixed model (GLMM), to determine the factors influencing litter size in Gunnison’s prairie dogs. Negative binomial regression was employed because it can handle non-normal datasets, such as count data, and random effects typically associated with linear mixed models (Bolker et al. 2009). This method was appropriate for this study because the litter dataset consisted of counts from 0-8 that followed a negative binominal distribution.

Thus, transformation of data to linear form using log, natural log, or squared root transformations was not appropriate (Bolker et al. 2009). Instead, I analyzed the data using GLMMs by selecting a negative binomial distribution with a logarithmic link function, specifying the random effects and allowing fixed effects to vary randomly within the population (Bolker et al. 2009).

I modeled the data using only known age females with known mothers (n= 312; unique individuals = 242). The same independent variables that were used in the logistic regression analysis were also used for this negative binomial regression analysis, but I also added the additional fixed effect of lactation weight (Table 3-1). This variable corresponded to body mass of female during the month of June. Females are typically lactating in June.

I constructed ten models with different combinations of predictor variables and compared them again using AIC (Figure 3-2). Fixed and random effect parameter estimation was done using maximum likelihood estimates. However, this could not be accomplished by classical

ANOVA methods for the following reasons: treatments had unequal sample sizes, random effects were nested effects, and the response variable was non-normal (Bolker et al. 2009). I used the Laplace Approximation technique for GLMM parameter estimation (Pinheiro and Chao

2006), computing maximum likelihood estimates and thus likelihood-based inference (Bolker et al. 2009). Statistical significance of fixed effect variables was determined from the individual beta coefficients. I also constructed a density plot to visualize the relationship between litter size and population density and detect any clustering patterns. All analysis in this chapter were performed using the R Statistical Computing Software (R Core Team 2013).

Results

Factors Affecting Probability of Reproduction

The top model predicting probability of reproduction in the study population of female

Gunnison’s prairie dogs included the following predictors: age, mother weight, ID as a random effect, and population density (model 6, Table 3-2). Model 1 includes the variable precipitation in addition to the variables from model 6. Thus, effect plots for reproduction are derived from model 1 to include any biological relevance of precipitation (Figure 3-3). As predicted, probability of reproducing was significantly affected by mother weight (p< 0.01; β= 2.869 ±

0.413). This relationship was positive and linear, as heavier females were more likely to reproduce (wean a litter) than smaller females (Figure 3-3). Probability of reproduction did on average increase with precipitation, although the variation around this relationship was high and biologically insignificant (Figure 3-3). Population density was also statistically significant in affecting probability of reproduction; though the measurable effect was minimal (Figure 3-3). No other variables significantly predicted probability of reproduction when modeled as lone predictor variables.

Factors Affecting Litter Size

The top two models for predicting litter size in Gunnison’s prairie dogs were not significantly different from one another, while one included weight and the second included both weight and precipitation (AIC Table 3-3). In both models, mother weight was the only significant predictor that was positively and linearly related to litter size (β=0.064, ± 0.036; p<

0.1) (Figure 3-2, Figure 3-4). When controlling for age class the heaviest mothers produced an average of 1 pup more than the smallest female. Both population density and precipitation had a negative (though statistically non-significant) relationship with litter size; β = -0.086 and -0.062 respectively (Figure 3-2).

When populations were at low densities, litter size was not only higher but also more variant than at higher population densities (Figure 3-5 density plot). As population sizes increased, mean litter sizes shifted more toward the population average (3.38 pups) and deviation declined (Figure 3-6).

Discussion

The results of this study support many of the initial predications and further our understanding of how reproductive factors are affected by the environment in Gunnison’s prairie dogs. The result showing significance of mother weight in predicting probability of reproduction and litter size was not surprising, given past research. For example, Cully (1997) described body weight as an indicator of forage availability and a predictor of reproduction and litter size in

Gunnison’s prairie dogs, concluding that larger body sizes increased the likelihood of successful reproduction. However, the results on this long-term dataset provide some context for understanding the biological significance of this relationship, which was lower than hypothesized

(Figure 3-7). When controlling for age, larger females weaned only 1 greater pup than the smallest female, a difference that may have lower overall impacts on population trends than hypothesized in the past.

Cully (1997) described further how fluctuations in population densities can impact reproductive factors. This current study also supports this past observation and provides new context for it, as I found low population densities produced higher mean litter sizes with large deviations. As the population grew, mean litter sizes reduced as did deviations around litter size.

This implies that an increase in population density may reduce reproductive output. Lacking significant impact from precipitation, the study population may have experienced maximum carrying capacity in the year before colony collapse from plague. The study thus provides

empirical evidence of density dependence acting on reproductive output in Gunnison’s prairie dogs (Figure 3-8).

One surprising result from this analysis was that precipitation was not a significant predictor of probability of reproduction or litter size. The (positive) direction of the relationship that I originally hypothesized was supported by the data, but the effect was minimized in the presence of the much larger impact of mother’s weight. Future studies should attempt to further tease apart the relationship between precipitation and mother’s weight and how the two interact to affect reproduction.

The future conservation of Gunnison’s prairie dogs will depend on our understanding of factors that affect reproduction. Survival estimates alone only present a partial picture of how populations grow and decline. Past research has focused on the impacts of forage, colony age, and sociality on reproductive parameters (Rayor et al. 1999; Garret et al. 1982; Hoogland 1999).

These studies produced valuable data such as age of first reproduction, mean litter size relative to population density and/or forage availability (Cully 1997; Hoogland 1999) but did not clarify the role of environmental and life history factors in influencing probability of weaning and litter size at different ages. This chapter filled these key gaps and in turn will inform broader understanding and management of Gunnison’s prairie dogs by elucidating keys for maintaining future population sizes. The results in this chapter will also directly feed into the population viability analysis (PVA) for Gunnison’s prairie dogs that will be constructed in Chapter 4.

Table 3-1. Independent variables used in logistic regression analysis on factors affecting probability of reproduction in Gunnison’s prairie dog. Variable Description

Fixed Effects

Age Current age of a reproductive mature female

Precipitation Three month mean of rainfall during periods of lactation of a given year;

May, June and July

Age at first reproduction Age at which a female weans her first litter of pups

March weight Body mass of female at emergence from hibernation

Population Size Total number of reproductively mature females in a given year

Random Effects

Individual ID Unique ear tag combination assigned at birth and tracked through the

individual’s life

Maternal pack size For successful mothers, the litter size a female was born into, including her

siblings

Previous litter size For successful mothers, the size of the litter she weaned in the previous year

Table 3-2. AIC Table for probability of reproduction regression models results. Dependent predictor variables (left) are included in models 1-8 (vertical). Models 6 and 7 are best fit model and simplest, respectively. Probability of Reproduction Regression Model Dependent Variable Model 1 2 3 4 5 6 7 8 *** *** *** age 1.501 3.236 1.330 (0.296) (0.477) (0.279) precip_s 0.261** 0.186 0.204 0.204 (0.129) (0.128) (0.129) (0.129) wt.grams_s 1.502*** 2.382*** 2.359*** 2.359*** 2.329*** 2.869*** 2.292*** 1.574*** (0.27) (0.328) (0.343) (0.343) (0.334) (0.413) (0.291) (0.278) age:wt.grams_s -1.969*** (0.316) pop_density_s 0.852*** 0.644** 0.872*** 0.872*** 0.790*** 1.104*** 0.876*** (0.235) (0.256) (0.257) (0.257) (0.236) (0.227) (0.213) Constant -3.744*** -2.997*** -3.175*** -3.175*** -3.122*** -4.531*** -2.776*** -3.745*** (0.432) (0.424) (0.455) (0.455) (0.438) (0.554) (0.337) (0.444) Observations 939 939 965 965 965 964 965 964 Log Likelihood -259.02 -275.63 -292.65 -292.65 -293.94 -245.1 -303.18 -279.26 Akaike Inf. Crit. 532.033 563.254 595.305 595.305 595.873 502.2 612.356 568.516 Bayesian Inf. Crit. 565.946 592.323 619.666 619.666 615.362 531.427 626.973 592.872 Significance *p<0.1; **p<0.05; ***p<0.01

Figure 3-1. Theory of Biological Relationship in Gunnison’s females. Body weight and precipitation during lactation are expected to have a positive linear effect on litter size (mean = 3.38) and probability of reproduction

Figure 3-2. AIC Table for litter size regression models results. Dependent predictor variables (left) are included in models 1-10 (vertical). Models 3 and 4 are best fit models and also the simplest. Models 8-10 were run on a subset of data for only those individuals with a previous litter (to test the effect of previous litter size).

Figure 3-3. Factors affecting probability of reproduction in Gunnison’s females from model 1. Precipitation and weight variables are scaled. Probability of reproduction has a positive linear relationship with weight.

Figure 3-4. Model effect plots for litter size. Positive relationship is evident between litter size and mother mass march; model 2. Precipitation and population density variables lack statistical inference and have biological relevance.

Figure 3-5. Density Plot of Population Size and Litter Size: Three cluster effects are present. At low population densities, mean litter size is higher and more variant. As population sizes increases, mean litter sizes shift more towards overall population average (3.38) and deviation declines.

Figure 3-6. Effects plots of mother mass and population density effects on litter size, model 3. Mother mass in March variable significantly affected the litter size parameter.

Figure 3-7. Post Results: Theory of Biological Relationship in Gunnison’s females. Body weight and precipitation during lactation have a positive linear effect on probability of reproduction but not litter size.

Figure 3-8. Ordinal regression analysis of effect of mother mass in March on probability of litter size as a factor; where each litter is a level. Smaller females’ probability of producing litters over 5 pups was greater than larger females.

CHAPTER 4 TRADEOFF BETWEEN MORTALITY AND FECUNDITY IN GUNNISON’S PRAIRIE DOG

Literature Review

Life history theory suggests that population dynamics is largely driven by demographic factors and their interactions with the environment (Cole 1954 and Lewontin 1965). The development of sensitivity analyses has allowed for direct interpretation of the relative importance of life history variables to population growth across a variety of animal species

(Caswell 2001). With climate change, phenotypic changes in mammals are increasingly observed. However, researchers are unsure if these changes are plastic responses or evolutionary adaptations (Boutin and Lane 2014). Without question, the conservation and management of species relies on our clear understanding of such demographic factors and processes.

The importance of obtaining sound data on population demography for informing on-the- ground management of species is exemplified by the case of Gunnison’s prairie dog. The overall pattern of decline of Gunnison’s prairie dogs since early 1900s caused the US Fish and Wildlife

Service (USFWS) to be petitioned to protect this species under the US Endangered Species Act

(USFW 2013). However, the petition was rejected, a ruling that was partially based on the reported stability of populations located on federal and state lands, coupled with the fact that

Gunnison’s prairie dog is a fast reproducing species, a life history trait which is believed to aid in population recovery and persistence (USFW 2013). USFWS also noted that existing state and federal land management plans were sufficient to manage the continued recovery of the species

(USFW 2013). However, the majority of known Gunnison’s prairie dog populations are located and managed on private (27%) and tribal lands (36%) (USFW 2013), where there are fewer regulations or protective measures in place for the species. In addition, each state varies with respect to its hunting regulations for the species, which puts populations at different levels of risk

for population decline. For instance, Arizona does not allow hunting of Gunnison’s prairie dogs during the breeding season on any type of land, but Utah and New Mexico ban hunting during this time of year for only public lands and also have no bag limits (Figure 4-1, USFW 2013).

The variability in regulations on hunting and managing Gunnison’s prairie dogs reflects a lack of data on sensitivity of the species to anthropogenic impacts and overall population demographic trends. In an attempt to fill this gap, in 2010, the Colorado Department of Wildlife contracted a Population Viability Analysis (PVA) in an effort to understand the anthropogenic and environment threats to Gunnison’s prairie dog population persistence within the state.

Stochastic population models, such as PVAs, are useful management tools that allow ecologists to model population response to various demographic, anthropogenic or environmental scenarios. The model population is a simplified representation of an actual ecosystem. The baseline parameters are based on the most current data available. For the Colorado PVA, Phillip

Miller created input datasets for parameters from published data (Cully 1997; Hoogland 2001,

2007), internal data from the Colorado Department of Wildlife, and information gathered from a

2- day workshop with leading experts in February 2007 (Seglund and Schnurr 2010). Together with Miller’s PVA, several PVAs have been historically successful in contributing to the population management and thus persistence of small mammals (Allen et al. 1992; Ball et al.

2003) including, black-tailed (COSEWIC 2011), white-tailed (Seglund and Schnurr 2010;

Anderson and Williams 1997) and Mexican prairie dogs (Scott-Morales et al. 2005).

While the 2010 PVA model has been instrumental for understanding population demographics in Gunnison’s prairie dogs, there have been several new developments in the research and management of this species since then that make it worthwhile to revisit and improve the model. For instance, the 2010 PVA model estimated the effects of plague based on

data from an older research study (Cully 1997) but we now know more on how plague affects

Gunnison’s prairie dogs and specifically how those impacts are magnified due to both reduced genetic heterogeneity within the species and climate variations (Eads and Hoogland 2017;

Sackett et al. 2012). Climate variations have become far more prevalent and extreme around the world and specifically in Gunnison’s prairie dog habitat (Reeves et al. 2014; Wagner et al. 2006).

Further, anthropogenic threats have also increased since 2010, including sport shooting. In addition, the analyses in Chapters 1 and 2 provide updated estimates of reproductive and survival parameters which will involve accuracy of population viability modelling. Given the growing uncertainty of Gunnison’s prairie dog population persistence in the face of disturbances such as the bubonic plague, integration of this information is critical to creating a more comprehensive picture of the factors that contribute to Gunnison’s prairie dog population persistence.

Objectives. In this Chapter, I will apply the aforementioned new sources of information on population demographics of Gunnison’s prairie dogs to construct an updated PVA model for the species. In doing so, I will discern which demographic parameters and processes are most important to population persistence in Gunnison’s prairie dogs. This process in turn will provide new information to assist in addressing pressing management and conservation concerns regarding Gunnison’s prairie dogs. Specific questions I will address include:

1. What are the primary demographic factors that drive the growth of Gunnison’s prairie dog populations?

2. Are Gunnison’s prairie dogs vulnerable to the risk of extinction? If so, what factors contribute to extinction?

3. Are Gunnison’s prairie dogs fast or slow breeders and what are the implications of either?

4. What level of mortality is tolerable without compromising population persistence?

Methodology

Population viability analysis, or PVA, is the use of stochastic population models to assess a population’s vulnerability to extinction and determine the sensitivity of demographic parameters to environmental changes and/or anthropogenic threats (Allen et al. 1992). PVA models attempt to identify threats to population persistence and allude to underlying population dynamics. Reed et al. (2002) noted that sensitivity analyses are often conducted as a part of

PVA, but require discipline in the interpretation. Identifying vital rates that affect large proportional changes in population growth must be interpreted within the context of the species’ ecology and management practicality (Reed et al. 2002; Gaillard et al. 1998; Mills et al. 1999).

Using a stochastic age- structured matrix population model limits the errors associated with misinterpretation of sensitivity of growth rate to proportional changes in vital rates by utilizing age class and sex specific baseline parameter data (Caswell 2001). However, choosing a reliable and repeatable model is equally as important as accuracy, especially when the long-term monitoring and management of a species is carried out by diverse computationally skilled shareholders (Lacy 2000).

Simulation Software

VORTEX is a no-cost, individual population simulation software copyrighted by the

Chicago Zoological Society (Lacy et al. 2003). This program is used globally for research, teaching and adaptive management. The software allows age- structured matrix modeling, but utilizes a user-friendly platform for windows operating systems. As an individual-based model, the fate of each animal is followed throughout its lifetime in a theoretical population (Figure 4-

2). This theoretical population is modeled to encounter the deterministic forces similarly to a wild population, as well as demographic and stochastic environmental and genetic events

(Seglund and Schnurr 2010). VORTEX uses the Monte Carlo method to create a distribution of

risks, from which the probability of an outcome to a given factor can be determined. For details on the programming of the software, see Lacy (2000) and Miller and Lacy (2003).

Data

For the input data for VORTEX stochastic population viability simulations, I use: estimates of age-specific survival and reproductive rates and covariate data from Chapters 2 and

3; and published data from Cully 1997, Hoogland (2001, 2007), Seglund and Schnurr (2010) and

(Sackett et al. 2012, 2014). Baseline data were used for the following input parameters: dispersal, reproduction system, reproductive rates, mortality rates, mate monopolization, initial population size, carrying capacity, catastrophic events, and harvest rates (Table 4-1 and Table 4-

2). Population scenarios follow an annual sequence for events created based on the biology of the species (Figure 4-1). To understand the potential affect of density dependency, reproductive parameters were characterized for three scenarios: DD-Reproduction, where both probability of reproduction and litter size differed for population sizes of high, medium, and low; DD- Litter size, where population size only affected the standard deviation of a mean litter size and probability of reproduction remains at baseline; No DD-Baseline, where females’ probability of reproduction and litter size was held constant regardless of population size. Reproductive parameters are fully described for each scenario in Table 4-3. Deterministic rates are shown in

Tables 4-4 and 4-5. For scenario DD- Reproduction, weaning percentages and mean litter size for each population size were based on the original PVA; however, standard deviations remained constant across scenarios. For scenarios DD-Litter Size and Baseline, reproductive parameters were based on mean litter size (3.38) and (82%) weaning rate calculations from previous dissertation chapters. To determine the relative importance of demographic parameters to population growth, various mortality and reproductive scenarios were run where either mortality or reproductive parameters were held constant. This allowed for an investigation into projected

population growth with 10% and 20% mortality reduction (Table 4-6) and the comparison of current results to that of the original PVA (Table 4-7).

Results

Deterministic Population Growth Rates

The initial deterministic rates indicated a greater population growth of the Gunnison’s prairie dog population under the Density Dependent (DD)- Litter Size scenario than other scenarios. Both female generation time and net reproductive output, R0, were more productive; thus serving as major contributors to long-term population persistence. Females reproduced and weaned more litters in a shorter amount of time in this scenario (Table 4-4 and Table 4-5).

Population growth declined with population density for both the DD-Reproduction scenario and

DD-Litter size scenario. However, the population decline rate was lower under the DD-Litter size scenario (0.68) compared to the lowest density of the DD-Reproduction scenario (0.81,

Table 4-4 and Table 4-5). Regardless of the scenario, reproductive output peaked under low population densities resulting in longer population persistence, with a mean of 15 years (Table 4-

4 and Table 4-5).

Influence of Population Density on Reproductive Output

High density populations experienced little stochastic population growth regardless of the percent of females reproducing, or else litter size rates were confounded by density dependence

(Figure 4-3). Given equal reproductive rates across populations differing by population density, medium and low initial populations had cyclic growth patterns as population size approached carrying capacity (2N) (Figure 4-4). All populations experienced negative population growth constantly after year 4, with limited possibility of recovery for low density populations beyond year 15 (Figure 4-4). Positive population growth was observable in low density populations, where rates of percent females reproducing and litter size were both highest.

Demographic Factors Affecting Population Growth

To assess the effect of demographic parameters on stochastic population growth, mortality and reproductive rates from the original PVA (PVA low, medium, high) that was published by the Colorado Department of Wildlife were compared to the performance of updated

(updated low, medium, high) rates calculated in this dissertation study. Reproductive rates were consistent under all scenarios of mortality but not for reproduction scenarios. Updated demographic scenarios were able to produce population growth projections similar to the mortality scenarios of the original PVA (Figure 4-5 and Figure 4-6). Only low mortality rates and not higher mortality rates projected positive population growth in both of the original and updated PVA models. However, the updated low mortality rates projected positive population growth farther into the future than original PVA models, beyond 28 years. Updated mortality rate scenarios also projected less severe declines in population growth than original PVA mortality scenarios (Figure 4-5).

Low and medium PVA reproductive rates projected positive population growth well into year 50 (Figure 4-6). However, of the updated scenarios, only low reproductive rates produced consistent positive population growth. Low reproductive rate scenarios, original PVA and updated, paralleled each other with regard to population growth projections (Figure 4-6). Figure

4-7 provides a comparison of stochastic growth performance under reproductive and morality scenarios when all population sizes where medium or low. The reductions of mortality by 20% in medium and 10% in low density populations did not produce positive population growth projections (Figure 4-7). Under no scenario did updated reproductive rates produce consistent population growth in 50 years. Only the original PVA reproduction rates projected population growth, which was seen in a medium density population.

Time of Extinction

Mean time of extinction was between 15-30 years for all scenarios with updated demographic rates (Figure 4-8). Populations survived for up to 50 years for three scenarios:

Mortality at 20% Reduction in the Low Density population, PVA Reproduction in the Low

Density population, and PVA Reproduction in the Medium Density population (Figure 4-9). The relationship between reproductive rates and population size/growth is clear when examining demographic scenarios across medium and low-density populations. Population size increased every five only under the original PVA reproductive rates (Figure 4-10). Even when mortality is reduced by 20%, populations were unable to maintain growth without the original PVA reproduction rates (Figure 4-10). Genetic diversity declined with time, analogous to population size (Figure 4-11); thus, perhaps serving as a driving factor of observed rates of extinction.

Discussion

Results indicated a species in peril. Scenarios based on updated demographic rates projected the loss of our study population with 15-20 years, even in the absence of modelling events such as shooting, catastrophes and plague. In reality, the study population died from bubonic plague after the six years of research documented in this study. Thus, it is important to consider two possibilities to explain this result: A) bubonic plague was present in the population, prior to observable epidemic, affecting demographic rates or B) The demographic rates used for the previous PVA do not accurately represent our study population.

Hoogland (2001) noted that 82% of females produced emergent young in his 7-year study. This population is the same dataset used for this study. However, this previous analysis was limited to known-aged individuals and the dataset subdivided reproduction yearly and by age class. Yet, our rates are consistent with this previous study for a medium sized population. In the original PVA, authors noted a 100% weaning rate (percent of females who mate) for low

density populations. It is reasonable to assume that such a rate could account for the differences observed. Even when mortality was reduced by 20%, populations were unable to maintain growth without the original PVA reproduction rates.

Since 2010, studies on Gunnison’s prairie dog population dynamics have been limited. In this chapter, I aimed to fill knowledge gaps regarding the environmental and anthropogenic factors affecting Gunnison’s prairie dog long-term persistence. Data from this chapter could be instrumental in updating state conservation plans for Gunnison’s prairie dogs and the status of concern for the species. The PVA will allow managers to better predict the impact of potential anthropogenic disturbances on Gunnison’s prairie dogs and weigh management options based on a better understanding of how sensitive various local populations are to decline.

Table 4-1 Baseline parameters for stochastic population viability simulations on Gunnison’s prairie dogs. Input Parameter Description Data Reference

Dispersal Annual percent of emigration Hoogland 1999; 2013a; Sackett et al 2012 Reproductive System Probability of breeding; age Hoogland 2006; Chapter 2 at first birth; polyandrous data mating Reproductive Rate Weaned rate and litter size Hoogland (2001, 2006); Chapter 2 data Mate Monopolization Male breeding probability Hoogland 2013a Catastrophic Events Random events at specific Miller 2010; simulated data probabilities. 1) drought 2) plague epidemics Harvest Rate Percent population culled Simulated data; Colorado annually Small Game Report 2005 Mortality Annual rate of age-specific Chapter 1 data; death Sackett et al 2013

Table 4.2. Baseline Input Parameters for Stochastic PVA. Female Male Average Life Span 5 years 5 years Polygynous 92% Age at First Reproduction 2 year (1-2 years old) 3 year (2- 3 year old) Age of Reproductive Senescence NONE High/ Medium/ Low Population Density 10,000 / 400 / 50 Mean litter size (SD) 4 (1) / 4 (2) / 4 (2.5) Percent of females weaning litters (SD) 82% (10)

Percent of males breeding ALL Carrying capacity (2N) 20,000/ 800/ 100 Inbreeding NONE Morality (SD) Age class (0-1) 54 (3) 59 (2) Age class (1-2) 71 (3) 73 (3) Age class (2-6) 52 (8) 51 (7)

Table 4-3. Reproductive parameters used in Gunnison’s prairie dog population density simulation models. Reproductive Parameter Population Density Scenario Low Medium High Density Adult females 100 (10) 67 (10) 40 (10) Dependence weaning a litter (DD) (%, SD) Mean litter size at 4.6 (2.5) 4 (2) 1.8 (0.5) weaning (SD) DD – Litter Adult females 82 (10) 82 (10) 82 (10) Size weaning a litter (%, SD) Mean litter size at 3.38 (2.5) 3.38 (2) 3.38 (0.5) weaning (SD) Baseline (No Adult females 82 (10) 82 (10) 82 (10) DD) weaning a litter (%, SD) Mean litter size at 3.38 (2) 3.38 (2) 3.38 (2) weaning (SD)

Table 4-4. Deterministic rates for scenario: Density Dependent-Reproduction. High Medium Low r -0.7854 -0.4436 -0.2085 Lambda 0.4559 0.6417 0.8118 R0 0.0875 0.2751 0.5587 Female Generation 3.10 2.91 2.79 Time Male Generation 3.79 3.69 3.62 Time Mean Generation 3.45 3.30 3.20 Time

Table 4-5. Deterministic rates for scenario: Density Dependent-Litter Size and No Density Dependence. High Medium Low r -0.3786 -0.3786 -0.3786 Lambda 0.6848 0.6848 0.6848 R0 0.3366 0.3366 0.3366 Female Generation 2.88 2.88 2.88 Time Male Generation 3.67 3.67 3.67 Time Mean Generation 3.27 3.27 3.27 Time

Table 4-6. Mean mortality rate (SD) inputs under each population density scenario. Scenario Mortality Rates in Populations of Varying Density Pop 1. Pop. 2 Pop. 3 Low Pop. 4 Pop. 5 Low High Medium Density- Medium Density – Density - Density - 20% Morality Density- 10% Standard 10% Morality (Equal) 20% Morality Morality (Equal) Mortality 54 (3) 54 (3) 54 (3) 54 (3) 54 (3) age 0 to 1 Mortality 71 (3) 61 (3) 52 (8) 52 (8) 61 (3) age 1 to 2 Mortality 52 (8) 52 (8) 52 (8) 52 (8) 52 (8) after age 2

Table 4-7. Input reproductive parameters values under varying densities to determine impact of reproductive output on long-term population growth. Reproductive Parameters Population Density Scenario Low Medium High Density Dependence Percent adult females 100 (10) 82 (10) 40 (10) (PVA) weaning a litter (SD)

Mean litter size at weaning 4.6 (2.5) 4 (2) 1.8 (0.5) (SD)

DD – Net Percent adult females 82 (10) 58 (10) 48 (10) Reproductive Output weaning a litter (SD) (Updated) Mean litter size at weaning 5.0 (2.5) 4.2 (2) 3.5 (0.5) (SD)

Note: Density Dependent Scenario values were informed from the original published PVA. Net Reproductive Output values were based calculations from this dissertation.

Figure 4-1. Percent of Cynomys gunnisoni gunnisoni and C. g. zuniensis predicted range (USGS 2011) by state and landowner (USFW 2013).

Figure 4-2. Annual sequence of events (parameters) for simulated populations of Gunnison’s prairie dogs.

Figure 4-3. Stochastic growth (r)projections of simulated Gunnison’s prairie dog populations for reproduction density dependent scenarios, in population densities of low, medium, and high.

Figure 4-4. Stochastic growth (r)projections of simulated Gunnison’s prairie dog populations for the density-dependent litter size scenario.

Figure 4-5. Stochastic growth (r) projections for simulated Gunnison’s prairie dog populations under various mortality rates. PVA models used original rates published for the species in the past. Updated rates reference present calculations amended due to findings in previous chapters of the dissertation. Reproductive rates were consistent under all scenarios

Figure 4-6. Stochastic growth (r) projections for simulated Gunnison’s prairie dog populations under various reproductive rates. PVA models used original rates published for the species in the past. Updated rates reference present calculations amended due to findings in previous chapters of the dissertation. Mortality rates were constant under all scenarios.

Figure 4-7. Comparison of stochastic growth (r) performance for Gunnison’s prairie dog populations under reproductive and mortality scenarios; where population density is medium, and mortality has been reduced by 10 or 20 percent.

Figure 4-8. Mean probability of extinction for simulated Gunnison’s prairie dog populations under various demographic conditions. PVA models used original rates published for the species in the past. Updated rates reference present calculations amended due to findings in previous chapters of the dissertation.

Figure 4-9. Mean probability of survival of simulated Gunnison’s prairie dog populations under various demographic conditions. PVA models used original rates published for the species in the past. Updated rates reference present calculations amended due to findings in previous chapters of the dissertation.

Figure 4-10. Population size projections for simulated Gunnison’s prairie dog populations under various demographic rates for medium density populations. PVA models used original rates published for the species in the past. Updated rates reference present calculations amended due to findings in previous chapters of the dissertation. Morality rates were constant under all scenarios.

Figure 4-11. Population genetic diversity projections of simulated Gunnison’s prairie dog populations under various demographic rates. PVA models used original rates published for the species in the past. Updated rates reference present calculations amended due to findings in previous chapters of the dissertation. Mortality rates were constant under all scenarios.

CHAPTER 5 UNDERSTANDING GUNNISON’S PRAIRIE DOGS IN A CHANGING ENVIRONMENT: LESSONS AND FUTURE MANAGEMENT

Perhaps the best way to understand the totality of this dissertation is to return to the original knowledge gaps. We understand Gunnison’s prairie dogs as social ground squirrels.

However, their asocial nature produces the following predictions; 1) Females breed as yearlings; thus survival of yearlings is assumed to be similar to adults, 2) Probability of reproducing is affected by population density; 3) Female weight and precipitation are significant contributors to explaining litter weaning rate; 4) Population growth rate is higher in young, less dense populations than older populations and the survival of adult females is critical to population growth. These four statements represent years of research that captures our prior understanding of Gunnison’s prairie dog demography and dynamics.

Implications for Survival in Ground Squirrels

My population growth and sensitivity analysis revealed that overwhelmingly, the reproductive output of yearling females (fecundity) was the most significant driver of population growth. Lower survival likely contributed to the decline of the population but did not appear to be a major underlying factor prior to the plague devastation in 1995. In other small mammal populations, predation is a major contributor to population decline (Korpimäki and Krebs 1996).

However, natural predation at this study site was rare (Hoogland 1996). Annual growth rates varied temporally, suggesting environmental factors influenced lambda. Other studies have found precipitation and temperature to be highly correlated to annual survival, mammal lactation and ultimately population growth (Armitage 2013; Davidson 2014; Wang et al. 2013). Further research should explore the potential impact of environmental factors on yearling fecundity and other demographic parameters.

Reproduction: Challenging Our Understanding

Gunnison’s prairie dogs. One surprising result from this analysis was that precipitation was not a significant predictor of probability of reproduction or litter size by itself. The (positive) direction of the relationship that I originally hypothesized was supported by the data, but the effect was minimized in the presence of the much larger impact of mother’s weight. Future studies should attempt to further tease apart the relationship between precipitation and mother’s weight and how the two interact to affect reproduction.

This research supports the idea that Gunnison’s prairie dogs are slow breeders. Hoogland

(2001) clearly defined the reproduction pattern of prairie dogs as slow breeders, despite claims of faster breeders from ranchers. However, uncertainty still persists in understanding if Gunnison’s are r- or k-strategists. Perhaps a species can change strategies based on population density. For example, young females produce larger litters at lower population densities, seemingly, to promote population growth. We do not see this kind of flexibility in adult females. Results from sensitivity analysis and PVA growth projections echo this possibility; reproduction was the most influential parameter in shaping population growth and yearling reproduction was the most sensitive to environmental change. I know of no other mammalian species where evidence of

such r/K dichotomy in reproduction is present. Further research is needed to understand the relationship between population density and reproduction output and how this might affect management.

Population Dynamics: Challenges for Population Management

The differences in juvenile survival among prairie dog species are explained by variation in sociality. Asocial species mature earlier as yearlings and thus have lower juvenile survival rates. For social species, male juvenile survival is consistently higher, relative to asocial male juveniles, due to delayed dispersal at higher body mass. Female juvenile survival is higher due to their philopatric nature to remain in the natal territory. In the absence of reproductive suppression, survival across age, sex, and stage is highly dependent on body size and predation pressure. When resources are abundant for social species, yearlings often reproduce and successfully wean litters. Resource abundance is a determinant of the rate of mass gained, minimum mass at age of dispersal, age at first reproduction, and minimum adult body mass.

Even in highly social ground squirrels, body mass, interpreted as resources available, has been found to have implications for survival. Thus, understanding the (1) social grade of a ground squirrel species as well as the (2) resource availability for a population are predictive characteristics of specific age- and stage- class sensitivity to environmental stochasticity and potential management practices. For ground squirrel populations in crisis, even in the absence of specific survival rates, conservative short-term interventions can be executed by considering the sociality of the species and resource availability for the population, until more concrete demographic analyses can be completed. In the case of Gunnison’s, supplemental feeding prior to hibernation may increase reproduction output of females in the following year. Additionally, managers should consider managing spatial capacity of colonies to reduce population density.

Results indicated that Gunnison’s prairie dogs are a species in peril. Scenarios based on updated demographic rates projected the loss of our study population with 15-20 years in the absence of modeled shooting, catastrophes and plague. Even when mortality is reduced by 20%, populations are unable to maintain growth using reproduction rates estimated in this dissertation..

This finding illustrates the importance of reproduction for population growth and the importance of updated demographic data. The long-term persistence of Gunnison’s prairie dog populations may depend on our ability to protect yearling females. Evolution has equipped Gunnison’s prairie dogs with the ability to adapt to a changing environment, but only if anthropogenic forces can be minimized.

LIST OF REFERENCES

Allen, E. J., J. M. Harris, and L. J. S. Allen. 1992. PERSISTENCE-TIME MODELS FOR USE IN VIABILITY ANALYSES OF VANISHING SPECIES. Journal of Theoretical Biology 155:33-53.

Anderson, S. H., and E. S. Williams. 1997. Plague in a complex of white-tailed prairie dogs and associated small mammals in Wyoming. Journal of Wildlife Diseases 33:720-732.

Armitage, K. B. 1981. SOCIALITY AS A LIFE-HISTORY TACTIC OF GROUND- SQUIRRELS. Oecologia 48:36-49.

Armitage, K. B. 2013. Climate change and the conservation of marmots. Natural Science 5:36- 43.

Baker, B. W., D. J. Augustine, J. A. Sedgwick, and B. C. Lubow. 2013. Ecosystem engineering varies spatially: a test of the vegetation modification paradigm for prairie dogs. Ecography 36:230-239.

Ball, S. J., D. B. Lindenmayer, and H. P. Possingham. 2003. The predictive accuracy of population viability analysis: a test using data from two small mammal species in a fragmented landscape. Biodiversity and Conservation 12:2393-2413.

Bolker, B. M., M. E. Brooks, C. J. Clark, S. W. Geange, J. R. Poulsen, M. H. H. Stevens, and J. S. S. White. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology & Evolution 24:127-135.

Borrego, N., A. Ozgul, K. B. Armitage, D. T. Blumstein, and M. K. Oli. 2008. Spatiotemporal variation in survival of male yellow-bellied marmots. Journal of Mammalogy 89:365- 373.

Boutin, S., and J. E. Lane. 2014. Climate change and mammals: evolutionary versus plastic responses. Evolutionary Applications 7:29-41.

Broussard, D. R., F. S. Dobson, and J. O. Murie. 2008. Previous experience and reproductive investment of female Columbian ground squirrels. Journal of Mammalogy 89:145-152.

Cole, L. C. 1954. THE POPULATION CONSEQUENCES OF LIFE HISTORY PHENOMENA. Quarterly Review of Biology 29:103-137.

Cormack, R. M. 1964. ESTIMATES OF SURVIVAL FROM SIGHTING OF MARKED ANIMALS. Biometrika 51:429-&.

Cully, J. F. 1997. Growth and life-history changes in Gunnison's prairie dogs after a plague epizootic. Journal of Mammalogy 78:146-157.

Cully, J. F., A. M. Barnes, T. J. Quan, and G. Maupin. 1997. Dynamics of plague in a Gunnison's prairie dog colony complex from New Mexico. Journal of Wildlife Diseases 33:706-719.

Davidson, A. D., M. T. Friggens, K. T. Shoemaker, C. L. Hayes, J. Erz, and R. Duran. 2014. Population Dynamics of Reintroduced Gunnison's Prairie Dogs in the Southern Portion of their Range. Journal of Wildlife Management 78:429-439.

Dobson, F. S. 1984. Mating systems, population dynamics, and life history patterns in ground squirrels. Dissertation Abstracts International B Sciences and Engineering 45:449-450.

Dobson, F. S. 1998. Social structure and gene dynamics in mammals. Journal of Mammalogy 79:667-670.

Dobson, F. S., R. K. Chesser, and B. Zinner. 2000. The evolution of infanticide: genetic benefits of extreme nepotism and spite. Ethology Ecology & Evolution 12:131-148.

Dobson, F. S., and P. Jouventin. 2010. The trade-off of reproduction and survival in slow- breeding seabirds. Canadian Journal of Zoology 88:889-899.

Dobson, F. S., and J. D. Kjelgaard. 1985. THE INFLUENCE OF FOOD RESOURCES ON POPULATION-DYNAMICS IN COLUMBIAN GROUND-SQUIRRELS. Canadian Journal of Zoology-Revue Canadienne De Zoologie 63:2095-2104.

Dobson, F. S., and M. K. Oli. 2001. The demographic basis of population regulation in Columbian ground squirrels. American Naturalist 158:236-247.

Dobson, F. S., and M. K. Oli. 2007a. Fast and slow life histories of mammals. Ecoscience 14:292-299.

Dobson, F. S., and M. K. Oli. 2007b. Fast and slow life histories of rodents.

Eads, D. A., and J. L. Hoogland. 2017. PRECIPITATION, CLIMATE CHANGE, AND PARASITISM OF PRAIRIE DOGS BY FLEAS THAT TRANSMIT PLAGUE. Journal of Parasitology 103:309-319.

Fay, R., H. Weimerskirch, K. Delord, and C. Barbraud. 2015. Population density and climate shape early-life survival and recruitment in a long-lived pelagic seabird. Journal of Animal Ecology 84:1423-1433.

Gaillard, J. M., M. Festa-Bianchet, and N. G. Yoccoz. 1998. Population dynamics of large herbivores: variable recruitment with constant adult survival. Trends in Ecology & Evolution 13:58-63.

Gallie, J. A., and L. C. Drickamer. 2008. Ecological interactions between two ecosystem engineers: Gunnison's prairie dog and Botta's pocket gopher. Southwestern Naturalist 53:51-60.

Garrett, M. G., J. L. Hoogland, and W. L. Franklin. 1982. DEMOGRAPHIC DIFFERENCES BETWEEN AN OLD AND A NEW COLONY OF BLACK-TAILED PRAIRIE DOGS (CYNOMYS-LUDOVICIANUS). American Midland Naturalist 108:51-59.

Garrison, E. P., J. W. McCown, and M. K. Oli. 2007. Reproductive ecology and cub survival of Florida black bears. Journal of Wildlife Management 71:720-727.

Hoogland, J. L. 1982. PRAIRIE DOGS AVOID EXTREME INBREEDING. Science 215:1639- 1641.

Hoogland, J. L. 1985. INFANTICIDE IN PRAIRIE DOGS - LACTATING FEMALES KILL OFFSPRING OF CLOSE KIN. Science 230:1037-1040.

Hoogland, J. L. 1996. Why do Gunnison's prairie dogs give anti-predator calls? Animal Behaviour 51:871-880.

Hoogland, J. L. 1997. Duration of gestation and lactation for Gunnison's prairie dogs. Journal of Mammalogy 78:173-180.

Hoogland, J. L. 1998. Estrus and copulation of Gunnison's prairie dogs. Journal of Mammalogy 79:887-897.

Hoogland, J. L. 1999. Philopatry, dispersal, and social organization of Gunnison's prairie dogs. Journal of Mammalogy 80:243-251.

Hoogland, J. L. 2001. Black-tailed, Gunnison's, and Utah prairie dogs reproduce slowly. Journal of Mammalogy 82:917-927.

Hoogland, J. L. 2003. Sexual dimorphism of prairie dogs. Journal of Mammalogy 84:1254-1266.

Hoogland, J. L. 2006. Demography and population dynamics of prairie dogs. Conservation of the black-tailed prairie dog: saving North America's western grasslands.:27-52.

Hoogland, J. L. 2013a. Prairie Dogs Disperse When All Close Kin Have Disappeared. Science 339:1205-1207.

Hoogland, J. L. 2013b. Why do female prairie dogs copulate with more than one male?-Insights from long-term research. Journal of Mammalogy 94:731-744.

Hoogland, J. L., K. E. Cannon, L. M. DeBarbieri, and T. G. Manno. 2006. Selective predation on Utah prairie dogs. American Naturalist 168:546-552.

Ibler, B., and K. Fischer. 2016. Costs of reproduction-A demographical approach to examine life-history trade-offs in two old-world deer species. Mammalian Biology 81:455-463.

Kneip, E., D. H. Van Vuren, J. A. Hostetler, and M. K. Oli. 2011. Influence of population density and climate on the demography of subalpine golden-mantled ground squirrels. Journal of Mammalogy 92:367-377.

Menkens, G. E., and S. H. Anderson. 1991. POPULATION-DYNAMICS OF WHITE-TAILED PRAIRIE DOGS DURING AN EPIZOOTIC OF SYLVATIC PLAGUE. Journal of Mammalogy 72:328-331.

Millesi, E., S. Huber, L. G. Everts, and J. P. Dittami. 1999. Reproductive decisions in female European ground squirrels: Factors affecting reproductive output and maternal investment. Ethology 105:163-175.

Mills, L. S., D. F. Doak, and M. J. Wisdom. 1999. Reliability of conservation actions based on elasticity analysis of matrix models. Conservation Biology 13:815-829.

Moore, J. F., C. P. Wells, D. H. Van Vuren, and M. K. Oli. 2016. Who pays? Intra- versus inter- generational costs of reproduction. Ecosphere 7.

Nadler, C. F., R. S. Hoffmann, and Pizzimen.Jj. 1971. CHROMOSOMES AND SERUM PROTEINS OF PRAIRIE DOGS AND A MODEL OF CYNOMYS EVOLUTION. Journal of Mammalogy 52:545-&.

Nichols, J. D., and J. E. Hines. 2002. Approaches for the direct estimation of lambda, and demographic contributions to lambda, using capture-recapture data. Journal of Applied Statistics 29:539-568.

Oli, M. K. 2003. Partial life-cycle analysis: a simplified model for post-breeding census data. Ecological Modelling 168:101-108.

Oli, M. K., and F. S. Dobson. 2003. The relative importance of life-history variables to population growth rate in mammals: Cole's prediction revisited. American Naturalist 161:422-440.

Oli, M. K., N. A. Slade, and F. S. Dobson. 2001. Effect of density reduction on Uinta ground squirrels: Analysis of life table response experiments. Ecology 82:1921-1929.

Ozgul, A., D. Z. Childs, M. K. Oli, K. B. Armitage, D. T. Blumstein, L. E. Olson, S. Tuljapurkar, and T. Coulson. 2010. Coupled dynamics of body mass and population growth in response to environmental change. Nature 466:482-U485.

Ozgul, A., M. K. Oli, L. E. Olson, D. T. Blumstein, and K. B. Armitage. 2007. Spatiotemporal variation in reproductive parameters of yellow-bellied marmots. Oecologia 154:95-106.

Pinheiro, J. C., and E. C. Chao. 2006. Efficient Laplacian and adaptive Gaussian quadrature algorithms for multilevel generalized linear mixed models. Journal of Computational and Graphical Statistics 15:58-81.

Pollock, K. H. 1981. CAPTURE-RECAPTURE MODELS ALLOWING FOR AGE- DEPENDENT SURVIVAL AND CAPTURE RATES. Biometrics 37:521-529.

Pollock, K. H., J. D. Nichols, C. Brownie, and J. E. Hines. 1990. STATISTICAL-INFERENCE FOR CAPTURE-RECAPTURE EXPERIMENTS. Wildlife Monographs:1-97.

Rayor, L. S. 1984. YEARLING BEHAVIOR, REPRODUCTION, AND DISPERSAL IN GUNNISON PRAIRIE DOGS. American Zoologist 24:A2-A2.

Rayor, L. S. 1985a. DYNAMICS OF A PLAGUE OUTBREAK IN GUNNISON PRAIRIE DOG. Journal of Mammalogy 66:194-196.

Rayor, L. S. 1985b. EFFECTS OF HABITAT QUALITY ON GROWTH, AGE OF FIRST REPRODUCTION, AND DISPERSAL IN GUNNISON PRAIRIE DOGS (CYNOMYS- GUNNISONI). Canadian Journal of Zoology-Revue Canadienne De Zoologie 63:2835- 2840.

Rayor, L. S., and K. B. Armitage. 1991. SOCIAL-BEHAVIOR AND SPACE-USE OF YOUNG OF GROUND-DWELLING SQUIRREL SPECIES WITH DIFFERENT LEVELS OF SOCIALITY. Ethology Ecology & Evolution 3:185-205.

Reed, J. M., L. S. Mills, J. B. Dunning, E. S. Menges, K. S. McKelvey, R. Frye, S. R. Beissinger, M. C. Anstett, and P. Miller. 2002. Emerging issues in population viability analysis. Conservation Biology 16:7-19.

Reeves, M. C., A. L. Moreno, K. E. Bagne, and S. W. Running. 2014. Estimating climate change effects on net primary production of rangelands in the United States. Climatic Change 126:429-442.

Regehr, E. V., C. M. Hunter, H. Caswell, S. C. Amstrup, and I. Stirling. 2010. Survival and breeding of polar bears in the southern Beaufort Sea in relation to sea ice. Journal of Animal Ecology 79:117-127.

Reznick, D. 1985. COSTS OF REPRODUCTION - AN EVALUATION OF THE EMPIRICAL- EVIDENCE. Oikos 44:257-267.

Sackett, L. C., S. K. Collinge, and A. P. Martin. 2013. Do pathogens reduce genetic diversity of their hosts? Variable effects of sylvatic plague in black-tailed prairie dogs. Molecular Ecology 22:2441-2455.

Sackett, L. C., T. B. Cross, R. T. Jones, W. C. Johnson, K. Ballare, C. Ray, S. K. Collinge, and A. P. Martin. 2012. Connectivity of prairie dog colonies in an altered landscape: inferences from analysis of microsatellite DNA variation. Conservation Genetics 13:407- 418.

Sackett, L. C., A. Seglund, R. P. Guralnick, M. N. Mazzella, D. M. Wagner, J. D. Busch, and A. P. Martin. 2014. Evidence for two subspecies of Gunnison's prairie dogs (Cynomys gunnisoni), and the general importance of the subspecies concept. Biological Conservation 174:1-11.

Schwartz, O. A., K. B. Armitage, and D. Van Vuren. 1998. A 32-year demography of yellow- bellied marmots (Marmota flaviventris). Journal of Zoology 246:337-346.

Scott-Morales, L. M., E. Gottschalk, and M. Muhlenberg. 2005. Decline in the endemic Mexican prairie dog Cynomys mexicanus: what do we know about extinction risk? Oryx 39:389- 397.

Sherman, P. W. 1982. INFANTICIDE IN GROUND-SQUIRRELS. Animal Behaviour 30:938- 939.

Sherman, P. W., and M. C. Runge. 2002. Demography of a population collapse: The Northern Idaho ground squirrel (Spermophilus brunneus brunneus). Ecology 83:2816-2831.

Sikes, R. S., and M. Anim Care Use Comm Amer Soc. 2016. 2016 Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education. Journal of Mammalogy 97:663-688.

Sperandei, S. 2014. Understanding logistic regression analysis. Biochemia Medica 24:12-18.

Stacey, P. B., and M. Taper. 1992. ENVIRONMENTAL VARIATION AND THE PERSISTENCE OF SMALL POPULATIONS. Ecological Applications 2:18-29.

Stearns, S. C. 1989. TRADE-OFFS IN LIFE-HISTORY EVOLUTION. Functional Ecology 3:259-268.

Stubben, C., and B. Milligan. 2007. Estimating and analyzing demographic models using the popbio package in R. Journal of Statistical Software 22:1-23.

Wagner, D. M., L. C. Drickamer, D. M. Krpata, C. J. Allender, W. E. Van Pelt, and P. Keim. 2006. Persistence of Gunnison's prairie dog colonies in Arizona, USA. Biological Conservation 130:331-339.

White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46:120-139.

BIOGRAPHICAL SKETCH

Dr. Rashidah H. Farid is a native of Abbeville, Alabama and the youngest of seven siblings. She is daughter of Dr. Q. Ashanty Balkom Farid, OMD and Wali Abu Freeman Farid.

She completed her Bachelor of Science at Tuskegee University and Master of Science at

Alabama A&M University, in Animal Science and Plant and Soil Science, respectively. Her master’s thesis focused on genetic connectivity of amphibian populations in a fragmented landscape. In her spare time, Rashidah is president of Natural Resources Diversity Initiative

(NRDI) in Gainesville, Florida. NRDI focuses on STEM education of children in socio- economically disenfranchised areas of Gainesville and Micanopy Florida.