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POTENTIAL RESOURCE COMPETITION BETWEEN SWINE (Sus scrofa) AND WHITE-TAILED ( virginianus) ON RANGELANDS

By

CONNOR A. CRANK

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2016

© 2016 Connor A. Crank

ACKNOWLEDGEMENTS

I thank the professors and staff of the Department of Wildlife Ecology and Conservation at the University of Florida for all of their guidance and support. I thank my advisor, Raoul

Boughton, for pushing me and for believing in my potential. I greatly appreciate my laboratory members, Ke Zhang, Wesley Anderson, and Bethany Wight for providing an awesome support system and for their friendship. I would not have been able to do this work without access to the

MacArthur AgroEcology Research Center on Buck Island Ranch and the facilities provided. I especially thank Gene Lollis and Laurent Lollis for helping facilitate my fieldwork while on the ranch. Lastly, I thank my and friends for their support and encouragement.

3 TABLE OF CONTENTS

page

ACKNOWLEDGEMENTS ...... 3

LIST OF TABLES ...... 5

LIST OF FIGURES ...... 6

ABSTRACT ...... 7

CHAPTER

1 INTRODUCTION ...... 9

2 COMPETITION FOR MAST ...... 12

Study Area Description ...... 14 Methods...... 15 Results ...... 20 Discussion ...... 21

3 COMPETITION FOR SUPPLEMENTAL FEED...... 32

Methods...... 33 Results ...... 39 Discussion ...... 40

4 CONCLUSION ...... 47

LIST OF REFERENCES ...... 48

BIOGRAPHICAL SKETCH ...... 53

4 LIST OF TABLES

Table page

2-1 Analysis of variance on time interval between consecutive captures with feral swine and white-tailed deer as the 2-factor levels blocked by camera location in Highlands County, FL, USA, 2015-2016 ...... 31

5 LIST OF FIGURES

Figure page

2-1 Map showing spatial spread of oak camera sampling locations during three discrete sampling periods in Highlands County, FL, USA, 2015-2016 ...... 25

2-2 Daily activity patterns of white-tailed deer and feral swine at shared and non-shared sites with each other based on mean number of photographs taken in Highlands County, FL, USA, 2015-2016 ...... 26

2-3 Mean ± SE of log-transformed time intervals between consecutive captures for 4 combinations of 1st and 2nd capture species in Highlands County, FL, USA 2015-2016 ...... 27

2-4 Spearman’s rank correlation between average proportions of feral swine and white-tailed deer camera-site use per day in Highlands County FL, USA, 2015-2016 ...... 28

2-5 Spearman’s rank correlation between proportion of average daily site use by white-tailed deer relative to standardized daily site-specific mast production in Highlands County FL, USA, 2015-2016 ...... 29

2-6 Spearman’s rank correlation between proportion of average daily site use by feral swine relative to daily site-specific mast production in Highlands County, FL, USA, 2015-2016 ...... 30

3-1 Map showing spatial spread of four types of sampling treatment types in Highlands County, FL, USA, 2015-2016 ...... 44

3-2 Mean + SE of number of feral swine and white-tailed deer video captures within four experimental treatment types in Highlands County, FL, USA, 2015-2016 ...... 45

3-3 Percentage of total visits to supplemental feeding stations by various species in Highlands County, FL, USA, 2015-2016 ...... 46

6 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

POTENTIAL RESOURCE COMPETITION BETWEEN FERAL SWINE (Sus scrofa) AND WHITE-TAILED DEER (Odocoileus virginianus) ON FLORIDA RANGELANDS

By

Connor Crank

August 2016

Chair: Raoul Boughton Major: Wildlife Ecology and Conservation

Feral swine are a highly that present a wide array of management issues.

The current body of feral swine research has focused on highlighting potential disease, loss of crops, soil damage, and watershed impacts as the primary problems associated with the species.

Despite widespread speculation that feral swine negatively impact native such as white-tailed deer, little data exists to support this claim. Given the high population densities and large amount of niche overlap between white-tailed deer and feral swine on Florida rangelands, it is likely that these two species are undergoing resource competition. I examined the relationship between feral swine and white-tailed deer using camera traps on rangeland and pasture .

Camera traps were placed over an area of ~20 km2 in two studies, the first at naturally occurring producing oak and the second using -placed supplemental food.

In the oak tree study I compared the spatial and temporal land use behaviors of white- tailed deer and feral swine at shared and non-shared oak trees in mast. I analyzed the average levels of white-tailed deer activity at oak trees over 24-hours and found that deer increased their diurnal activity and reduced their crepuscular activity to avoid highly nocturnal feral swine.

Temporal avoidance was further supported by a longer time interval between consecutive deer-

7 swine swine-deer visits compared to conspecific deer or swine visits. Average daily white- tailed deer site use was negatively correlated with average daily feral swine use of same site

(p<0.001), suggesting that the two species utilized spatially separate trees. An analysis of the amount of mast production in relation to the site-specific use of each species showed that feral swine dominated the highest producing trees, and that white-tailed deer utilized the lowest producing trees. These relationships suggest that white-tailed deer spatially and temporally avoid feral swine, and that increased feral swine activity at the best oak trees may limit mast availability for white-tailed deer.

In the supplemental feed study I compared the use and behavior of feral swine and white- tailed deer at shared and non-shared baiting stations. Feral swine dominated supplemental feeding stations and decreased bait availability for white-tailed deer, as evidenced by the significantly higher (p<0.001) visitation rates of swine to baited swine-only treatments than deer to baited deer-only treatments. Through two studies using food resources I have provided evidence that feral swine limit both food availability and space for white-tailed deer, leading to a reduction in the realized niche of white-tailed deer on Florida rangelands caused by competition with invasive feral swine. The reduced availability of food and spatial resources for white-tailed deer caused by feral swine competition may have cascading negative effects for deer populations in the long-term if populations of feral swine continue to increase.

8 CHAPTER 1 INTRODUCTION

Invasive species often share characteristics that aid in their success in invading a new region, such as high propagule pressure, lack of predators, high feeding efficiency, and the ability to adjust to multiple habitat types and food sources (Moles et al., 2007). These traits increase the likelihood of exploitative and interference competition between invasive and native species. Competition for resources is most probable when an invasive species has significant niche overlap with a native species, in this study defined in terms of food and spatial habits. The resource theory of competition argues that interspecific competition is also more likely to occur in times of environmental stress; for example, when the availability of a limiting resource decreases (Fraterrigo et al., 2014). Competition often results in greater resource partitioning and a reduction in the absolute niche of one or both species.

Due to extreme competitive advantage, studies have shown that invasive species are a major cause of of native plant and species (Clavero and Garcia-Berthou, 2005).

Examples of invasive species with high resource-use efficiencies leading to the extirpation of their native competitors include the gray squirrel (Sciurus carolinensis) in , which displaced the native red squirrel (Sciurus vulgaris) via both interference and exploitative competition. Gray squirrels more efficiently neutralize acorn phytotoxins than native red squirrels, and are benign carriers of parapoxvirus, a fatal disease for red squirrels. Gray squirrels are also more efficient feeders than red squirrels, leading to decreased food availability of tree and, subsequently, deceased red squirrel fitness and recruitment levels (Gurnell et al., 2004;

Mooney and Cleveland, 2001). Another example involves the mudsnail Ilyanassa obsolete, which invaded and displaced a native snail (Cerithidia californica) due to its high efficiency to translate food resources into offspring compared to the native snail species

9 (Grosholz, 2002; Mooney and Cleveland, 2001). Unfortunately, impacts of invasive mammals on their native counterparts are often complex and difficult to isolate (Garrison and Geder, 2006).

Current Eltonian niche overlap models can in theory be used to test for interspecific resource competition; however, these models require large amounts of data on variables including population size, density, mortality, immigration, and emigration (Soberon, 2007). Given these limitations, I focused on testing for indirect evidence of competition between a native and invasive ungulate species with overlapping niche requirements.

Feral swine (Sus scrofa) are believed to have existed in Florida since their introduction as in the 1500’s by Spanish settlers and exist today as hybrids of released domestic stock and introduced Eurasian (Mayer and Brisbin, 2008). Current feral swine populations are roughly estimated between 500,000 to 1 million individuals in Florida, with much of the southeastern United States, Texas, and California also infested by large populations.

Feral swine present a wide array of management issues, including potential transmission of various such as , tuberculosis, and salmonellosis to , livestock, and other wildlife (Saliki et al., 1998; Timmons et al., 2011). A major E. coli outbreak responsible for 205 illnesses and deaths occurred because of contamination of spinach fields by feral swine in California (Jay et al., 2007). Brucella suis (a bacterial disease transmissible to humans, , dogs, and other species) is common in feral swine, with studies finding up to 18% of feral swine in positive for the disease (Meng et al., 2009; Wood et al., 1976). The high occurrence of B. suis poses a serious threat to nearby domestic swine and wild boar hunters, which can contract the disease via contact with contaminated tissue. Other management problems often stem from feral swine rooting and wallowing behaviors, which can negatively impact soil and vegetation through accelerating erosion, increasing disturbance and exotic plant

10 invasion potential, altering succession, and changing hydrological parameters (Seward et al.,

2004). The current body of feral swine research has focused on highlighting potential disease, loss of crops, soil damage, and watershed impacts as the primary problems associated with the species (Campbell and Long, 2009). Despite widespread speculation that feral swine negatively impact and compete with native mammals, few data exist to support this claim. White-tailed deer

(Odocoileus virginianus) are speculated to have a high likelihood of negative interactions with feral swine, as these species rely on many of the same food and habitat resources (Garrison and

Geder, 2006).

I hypothesized that due to a combination of niche overlap, high feral swine population densities, and often-limited resources, feral swine and white-tailed deer compete for food and space on Florida rangelands. According to the Competitive Exclusion Principle, two species relying on the same resources cannot coexist indefinitely, resulting in the niche differentiation of at least one of the species (Hardin, 1960). Niche differentiation often takes the form of temporal segregation (feeding on the same resource at different times), and/or spatial segregation (feeding on the same resource at different locations). I aim to generate causal inference regarding the presence of behavioral shifts that could indicate competitive exclusion of white-tailed deer by feral swine on Florida rangelands.

11 CHAPTER 2 COMPETITION FOR OAK MAST

White-tailed deer (Odocoileus virginianus) in rely upon oak (Quercus spp.) mast as an important dietary source of protein, , and fat for physiological maintenance and survival (Harlow et al., 1975). Mast availability often has marked effects on deer population dynamics; for example, a positive correlation between fawn and yearling body weights and acorn yield from the previous autumn was found in (Feldhamer et al.,

1989). In central Florida, an increased mast was correlated with increased deer fecundity

(Labisky and Richter, 1985). Mast availability has also been linked to additional human-valued traits such as increased development (Hale et al., 1993). According to deer requirements in

Louisiana and Texas, roughly 27.9 kg/ha of available fallen is required to sustain the needs of an average-sized deer population for 90 days (Boyd et al., 1971).

Acorn requirements of deer may not be met annually, as mast is highly variable from year to year due to a short ten-day fertilization window that is sensitive to temperature and precipitation (Carmen et al., 2008). Many oak species require 2-3 years for their developmental cycles to transition from flower bud initiation to mature acorns, and unfavorable environmental conditions at any stage of acorn development may result in a reduction of useable mast

(Abrahamson and Layne, 2003). Deer populations may suffer during low-mast years; for example, poor mast years in the southern Appalachians resulted in does in the 2.5-year age class weighing an average of 2.3 kg less than in good mast years, and bucks in the >3.5-year age class weighing an average of 3.6 kg less than in good mast years (Hale et al., 1993). However, the relationship between mast and deer demographics is not clear-cut in all cases. For example, mass of young bucks (an indicator of survival) in Virginia showed no significant relationship with mast production when preferred alternatives were highly abundant. However, the digestible

12 energy in those mast-free buck diets fell below the 2.12 kcal/g minimum needed for long-term survival. Estimated digestible energy for mast-inclusive diets was 3.1 kcal/g, suggesting that acorns are likely a necessary component of young buck diets (Harlow et al., 1975).

Feral swine (Sus scrofa) also rely on acorns for physiological maintenance and overwinter survival, which may provide an additional stressor on white-tailed deer populations during fall, winter, and spring foraging (Conley and Henry, 1972; Ostfield and Keesing, 2000).

Stomach contents of feral swine in South Carolina showed that acorns constituted 43.8% of diets in the fall, 79.8% of diets in the winter, and 24.2% of diets in the spring (Roark and Wood,

1980). Feral swine diets in Tennessee consisted primarily of acorns even in years of extremely low mast production, supporting the hypothesis that competition between native and feral swine is high and would likely increase in poor resource years (Conley and Henry, 1972).

Aspects of feral swine life history may allow them a competitive advantage over white-tailed deer during low mast years. The broader omnivorous diets of feral swine provide high-protein alternatives to acorn mast such as , , and animal protein to aid in their overwinter survival (Roark and Wood, 1980). Competition for limited acorns resulting in decreased deer survival and fecundity may be compounded by the fact that deer reproduce at a much lower rate than feral swine even under good environmental conditions. A single female feral hog can produce two litters of 4.2-7.5 piglets per year (Seward et al., 2004). Feral swine fecundity in

Texas was reported to be four times higher than that of native , with possible cascading effects on overall ungulate community structure (Taylor et al., 1998). These reproductive differences are particularly concerning for white-tailed deer in Florida, as Florida deer have lower overall fecundity than deer in northern states due to poor soils with low forage productivity and mineral deficiencies (Labisky and Richter, 1985; Main and Schaefer, 2012).

13 Twinning is also rare in Florida deer, with an average fecundity of 1.28 fetuses/pregnant doe

(Labisky and Richter, 1985). The high reproductive rates of feral swine may allow for faster recovery from low mast yield years than white-tailed deer, resulting in ever-increasing competition for resources.

Using a camera array and a comparative study design, I aimed to provide evidence of behavioral shifts in white-tailed deer that may indicate niche differentiation caused by competition with feral swine for limited mast. I compared white-tailed deer and feral swine behavior in the presence and absence of each other at acorn-producing oak trees where resource competition was highly likely. My first objective was to test whether white-tailed deer altered their daily activity patterns to avoid swine through time and space. I predicted a shift in deer activity patterns away from swine-occupied microhabitats and toward swine-free microhabitats during times of day with high average swine activity. I also predicted that deer would avoid oak tree camera sites for longer periods of time after a recent swine visit than they would in the absence of feral swine. My second objective was to examine whether feral swine presence at acorn-producing oak trees limits mast availability for white-tailed deer. I predicted that spatial avoidance of swine-occupied areas paired with higher swine activity at high-producing oak trees would limit deer ability to utilize high mast-producing oak trees.

Study Area Description

This study was conducted at the MacArthur Agro-Ecology Research Center (MAERC) at

Buck Island Ranch, a 4,170-ha commercial cattle ranch with approximately 3,000 cow– pairs

(Boughton and Boughton, 2014). Buck Island Ranch is located in south-central Florida roughly

25-km southeast of Lake Placid. 200-400 feral swine per year were hunted or trapped at the study site from 2007-2012 (Boughton and Boughton, 2014). Populations of white-tailed deer also

14 inhabit the ranch and are utilized through occasional private deer leases. Topography of the study site consisted of very poorly drained organic soils predominately covered by native grasses and plants, and poorly drained sands predominated by bahiagrass (Paspalum notatum). There are also 627 seasonal wetlands, 267 ha of oak and palm hammocks, and over

500 miles of drainage ditches at the site (Boughton and Boughton, 2014). At the time of study, the site experienced flooding due to unseasonably high winter rainfall.

Methods

Competition for acorns was assessed using a game camera array. A total of 54 game cameras were in operation for three discrete sampling periods [October-November 2015,

November-December 2015, December-January 2016], accumulating to 1,070 total camera-days.

Game camera brands consisted of a mix of Bushnell Trophy Cameras and Cuddeback Attack

Black Flash. All cameras were placed a minimum of 400 m apart, spanning a total network of

~20 km2 (Figure 2-1). Cameras took one photo per every motion detected with an enforced lag time of one minute between photos. Initial sampling occurred in October and November 2015.

Camera trapping protocol during the initial sampling round consisted of placing three game cameras facing in opposite directions away from the base of one oak tree for a total of six trees and 18 game cameras. Cameras were placed at oak trees deemed likely to produce acorns based on visible mast. Given the short production window of acorns and limited number of game cameras at my disposal, I altered the sampling protocol for the remaining two sampling rounds by placing one game camera at the base of 18 Oak trees to maximize sampling area. Cameras operated for 2-4 weeks during each sampling round. Due to the change in sampling protocol between the first and final two sampling rounds, I randomly selected 1/3 of all photos from the

15 first sampling round for inclusion in statistical analyses. Therefore, analyses for each species were conducted from 42 camera locations accumulating to 914 total camera-days.

I recorded mast by randomly tying three large plastic bins, each with a capture area of 1 m2, to branches under each 1/3 of the canopy of the oak tree at every camera location. Mast was weighed from each bin at the end of each sampling round coinciding with camera removal. Metal reflectors were placed at the boundary edge of each tree canopy to allow visual guidance in photos when assessing presence of an animal within the fallen acorn area of that specific tree.

Photos within the boundary were identified to species and time for each trigger event recorded. It is important to note that identifying individual was impossible due to animals being untagged, making population estimates infeasible. The minimum camera distance of 400 m reduced the likelihood of individuals visiting multiple camera locations within a short timeframe.

To generate an index of use (not a measure of population size), I analyzed all photos in terms of presence/absence regardless of number of individuals present.

Assessment of Temporal Avoidance

To assess whether deer altered their daily temporal behaviors to avoid feral swine, I examined how deer and swine activity changed on average during 24 hours between deer-swine shared sites and sites where only deer and only swine occurred. I defined ‘shared’ sites in my analyses as any camera location where at least one deer and one swine visit occurred over the entire sampling period (2-4 weeks). ‘Deer-only’ and ‘swine-only’ sites referred to any camera location where the given species occurred at least once, with zero occurrences of the other species over the entire sampling period. Shared and non-shared sites occurred naturally; no artificial barriers or treatments were used to generate them.

16 Activity levels in shared and non-shared sites were analyzed separately for feral swine and white-tailed deer using Poisson generalized linear mixed models (GLMMS) in package lme4 in R. A Poisson distribution was indicated in the ‘family’ argument for all models due to non- normally distributed count data. Models for activity patterns included frequency of visits, quantified by number of photos, as a function of the fixed effect ‘time of day’, truncated every three hours to consist of eight 3-hour periods starting at midnight (hour 0). I included the random effects of ‘daily mast production’, the amount of mast (g) produced at each sampling location, and ‘sampling period’, the sampling round/month in which the visit occurred. Inclusion of the random effect ‘daily mast production’ was necessary to account for differences in species- specific site use based on acorn availability. Acorn production/day was calculated by dividing acorn mass (g) from my buckets by the total number of days those buckets collected acorns (2-4 weeks). The following model was used to provide a baseline pattern of how the average activity levels of each species, pooled across shared and non-shared plot types, fluctuated as a function of time:

Frequency~Time of day+(1|Daily mast production)+(1|Sampling period)

I interposed this single-species baseline activity model with a more complex model that included the interaction between ‘time’ and the additional fixed effect ‘condition’, a two-level factor indicating whether each visit of either feral swine or white-tailed deer occurred at a shared or non-shared site. This produced the following model:

Frequency~Time of day*Condition+(1|Daily mast production)+(1|Sampling period)

The inclusion of an interaction term between ‘time’ and ‘condition’ allowed for meaningful comparison of the effect of ‘condition’ in relation to each time period. I tested for the

17 significance of the effect of ‘condition’ on the distribution of average single-species activity levels over time by conducting an analysis of variance (ANOVA) between my two models. If

‘condition’ was significant, GLMMS z-values were used to examine where the significant effects of the interaction between ‘condition’ and ‘time’ occurred. Z-values indicated the probability of a fixed effect falling outside of the 95th percentile of the z-distribution; therefore, z>1.96 indicated a significant positive effect, and z<-1.96 indicated a significant negative effect.

My second approach to temporal avoidance analyzed potential differences in the average number of minutes between consecutive swine-swine, deer-deer, deer-swine, and swine-deer visits to a camera location (Harmsen et al., 2009). Only consecutive captures where a target species immediately followed the initial capture were included for analysis. Time intervals were analyzed against the cross factors ‘capture 1’, whether the initial capture photo was of a deer or swine, and ‘capture 2’, indicating the subsequently captured species, using a mixed model with camera location as a random blocking factor:

Time interval~Capture 1|Capture2|(1|Location)

Minute-long time interval responses were calculated for each pair of consecutive capture photos.

Time interval responses were log-transformed to induce a normal distribution. An analysis of variance was used to examine potential differences in the distribution of time intervals depending on the interaction of first and second captures species.

Assessment of Spatial Avoidance

Spearman’s rank correlation was used to test the relationship between the proportions of daily feral swine and white-tailed deer visits to a camera location. Non-parametric Spearman’s rank correlation was selected to account for non-normally distributed count data. I calculated the proportion of deer and swine visits for each camera location block (42 camera locations) by

18 dividing the total number of white-tailed deer photos by the total number of single-species photos per location. Proportions of use were further divided by the total number of days of camera operation at each location to standardize the metric for different operational periods of camera sites.

I tested my assumption that both white-tailed deer and feral swine would be attracted to higher producing by correlating the proportions of use/day and average acorn production/day, separately for feral swine and white-tailed deer data, for each camera location

(Spearman’s rank correlation). Proportions of use per day and acorn production per day were calculated for each camera location block in the same manner detailed previously. If swine dominate the best mast trees we would expect a greater positive correlation with acorn production compared to deer.

Assessment of Additional Factors Potentially Influencing Deer Behavior

I examined the effects of additional plausible covariates to support causal inference that feral swine presence impacts deer behavior. To rule out non-target species as a factor influencing deer behavior, I compared deer capture rates between sites shared and not shared with my two most frequently captured non-target species, raccoons (Procyon lotor) and cattle ( spp.).

Other non-target species likely to impact deer behavior, including (Canis latrans) and black (Ursus americanus), were not included for analysis due to their near-zero capture rates. Human hunting pressure was not analyzed as a potential covariate because hunting records indicated that only one hunt took place across all days of sampling. Deer behavior in the presence and absence of cattle and raccoons was analyzed using the same GLMMS methods described previously with feral swine.

19 I attempted to rule out surrounding habitat characteristics that may have influenced the frequency of deer and swine visits to my camera locations. General vegetative characteristics did not differ greatly between my camera locations due to their placement in relatively homogenous oak hammocks. Distance to nearest water source was discounted as a possible influential factor as extreme flooding occurred across all sampling locations and periods. Edge differed between camera locations. Cameras located on an oak tree directly adjacent to an open were classified as ‘edge’. Cameras sites within a hammock surrounded by dense vegetation were classified as ‘not-edge’. GLMMS were analyzed separately for deer and swine data using the same method described previously for non-target species and feral swine. An ANOVA between my baseline model and secondary model indicated whether deer and swine capture rates differed between ‘edge’ and ‘not-edge’ habitat types.

Results

White-tailed deer triggered cameras 121 times over 59 total independent days of sampling. Deer were captured at 35% of the 42 camera locations and 63% of all deer photo captures occurred within deer-swine shared plots. Eighty feral swine trigger events were recorded in 26% of the total camera locations, with 71% of all feral swine captures occurring within deer-swine shared plots.

Average daily white-tailed deer activity patterns indicated that the effect of condition

(whether each visit occurred in a shared or deer-only site) varied across time (ANOVA, p<0.01).

Average deer activity was significantly higher within deer-swine shared plots than deer-only plots for the first five time periods (Figure 2-2). The deer-only plot type showed a greater positive effect on average deer activity than the shared plot type at the time period 1800-2100

20 (Figure 2-2). Daily average mast production was significantly higher in shared plots than in deer- only plots (p<0.001).

The effect of ‘time’ on average levels of feral swine activity did not differ between shared and swine-only plot types (ANOVA, p>0.05). Little to no activity was recorded for feral swine for time periods 0600-0900, 0900-1200, and 1200-1500 (Figure 2-2). Feral swine became active at the time periods 1500-1800 and 1800-2100 (Figure 2-2). Average daily mast production was significantly higher in swine-only plots compared to shared plots (p<0.05).

Responses for time interval between consecutive capture were determined from 35 deer- swine visits, 36 swine-deer visits, 67 swine-swine visits, and 113 deer-deer visits. The interval between consecutive deer captures did not differ from the interval between consecutive swine captures (Figure 2-3, Table 2-1). The intervals between consecutive deer-swine and swine-deer captures were significantly longer than those between consecutive deer-deer and swine-swine captures (Figure 2-3, Table 2-1).

Average daily white-tailed deer site use was negatively correlated with average daily feral swine use of same site (Figure 2-4; Spearman’s rho=-0.87, p<0.001). White-tailed deer daily oak tree site use was positively correlated with daily acorn production (Figure 2-5;

Spearman’s rho=0.52, p<0.01) and feral swine daily oak tree site use exhibited an even stronger positive relationship with daily acorn production (Figure 2-6; Spearman’s rho=0.69, p=0<0.001).

The photographic capture rate of deer did not differ between shared and non-shared sites with raccoons (ANOVA, p>0.1) or cattle (ANOVA, p=0.8). Edge habitat showed no effect on the capture rates of deer (ANOVA, p=0.3) or feral swine (ANOVA, p=0.9).

21 Discussion

This study has provided evidence of behavioral shifts in white-tailed deer on Florida rangelands aimed at avoiding feral swine through time and space. Spatial avoidance of feral swine by white-tailed deer negatively impacted the accessibility of mast for deer at my study site, which may result in a reduction of the realized niche (defined in terms of food and spatial resources) of white-tailed deer in the long-term. I found that although both species showed a preference for the same limited number of high mast-producing oak trees, feral swine disproportionately utilized the highest mast-producing trees. Spatial avoidance of swine- occupied sites by white-tailed deer resulted in increased deer activity at the lowest mast- producing trees. Reduced mast availability for white-tailed deer was further supported by the weaker relationship between daily site use by white-tailed deer and daily mast production than that of feral swine.

Reduced mast availability for deer caused by exploitative competition with feral swine may have negative long-term impacts on deer populations including reduced body mass, survival, and fecundity (Hale et al., 1993; Harlow et al., 1975). Mast availability may be further reduced due to feral swine behaviors that disrupt oak tree recruitment levels; for example, acorn germination rates in California were severely decreased due to hog rooting (Sweitzer and Van

Vuren, 2002). Reduced mast availability caused by consumption by feral swine can also negatively impact small populations, in turn leading to decreased seed caching and subsequent oak recruitment levels (Munoz and Bonal, 2007), inducing a cycle of increased competition for limited mast.

The temporal activity patterns of white-tailed deer in this study did not follow the crepuscular activity patterns generally observed in deer (Beier and McCullough, 1990; Jackson

22 et al., 1972). My data suggested that white-tailed deer increased their diurnal activity and shifted their crepuscular activity to avoid highly nocturnal feral swine. White-tailed deer at deer-swine shared camera locations reduced their temporal activity at dusk, a time period when deer activity levels generally increase (Jackson et al., 1972). White-tailed deer at swine-absent camera sites were highly active at dusk, suggesting that deer altered their temporal activity to avoid times of increasing feral swine activity. The temporal activity patterns of white-tailed deer at swine- absent sites generally followed the expected crepuscular pattern, showing low levels of midday and late night activity. However, deer at deer-swine shared sites exhibited increased activity levels during the mid-day, coinciding with extremely low levels of average feral swine activity.

Increased daytime activity and associated additional respiratory energy loss may negatively impact deer fitness levels and abundance (Creel and Christianson, 2008; Karasov, 1981; Zapata-

Rios and Branch, 2015). This behavioral may reduce the likelihood of interference competition between the two species, as feral swine exhibit aggressive territorial behaviors at the sounder level (Sparklin et al., 2009). Additionally, there is some evidence that feral swine occasionally predate white-tailed deer fawns (Springer, 1977; Wilcox and Van Vuren, 2009).

Remnants of white-tailed deer fawns were found in 4% of analyzed feral swine stomachs in east

Texas, but may have been attributed to feral swine consumption of (Springer, 1977).

However, an analysis of feral swine stomach contents in California found evidence that feral swine engage in active hunting of live prey, including highly agile mammals, during times of environmental stress such as poor mast years (Wilcox and Van Vuren, 2009).

The temporal activity patterns of feral swine vary widely between locations. Feral swine in and South Carolina exhibited highly diurnal activity patterns (Kurz, 1971; Kurz and

Marchinton, 1972), whereas feral swine in , , and some regions of South Carolina

23 exhibited highly nocturnal activity patterns (Hughes, 1985; Lemel et al., 2013; Pei, 2006).

Habitat disturbance has a large effect on the diel behaviors of feral swine, with swine in undisturbed tending toward diurnal activity patterns (Ohashi et al., 2012). Despite the species’ widely ranging temporal activity patterns, the majority of swine-infested regions have reported increased swine activity at sunset (Lemel et al., 2013; Pei, 2006; Spitz and Janeau,

1990). The results of this study indicated a shift in white-tailed deer activity away from swine- occupied areas at dusk when average swine activity began to increase, pointing toward a potential behavioral adaptation in deer as a result of long-term feral swine invasion in Florida.

The shifts in white-tailed deer behavior observed in this study may have been caused by factors other than feral swine. Some studies have shown that higher cattle densities and increased cattle-associated human activity may lead to increased spatial avoidance by deer. However, evidence of shifts in deer behavior caused by cattle farming is largely circumstantial (Hood and

Inglis, 1974), and was not supported by the results of my study. Large predators were uncommon in my study, but may a role in the long-term adaptive spatial and temporal behaviors of white-tailed deer. For example, white-tailed deer in Connecticut actively avoided the urine of ( rufus) and coyotes (Swihart et al., 1991), both of which were present at my study site. Finally, although population sizes of feral swine and white-tailed deer at my study were assumed to be similar, the lack of known population sizes limits this study’s findings as the lower mast use by white-tailed deer may have resulted from a lower deer population size. The higher capture rate of deer than swine at my oak tree camera sites suggests that population size differences was likely not a causal factor; however, it is possible that a small number of the same white-tailed deer individuals visited my camera locations at a high rate.

24 1st sampling round 2nd sampling round 3rd sampling round

Figure 2-1. Map showing spatial spread of oak tree camera sampling locations during three discrete sampling periods in Highlands County, FL, USA, 2015-2016. Note: sampling occurred in October-November 2015, November-December 2015, and December-January 2016.

25

Figure 2-2. Daily activity patterns of white-tailed deer and feral swine at shared and non-shared sites with each other based on mean number of photographs taken in Highlands County, FL, USA, 2015-2016. Note: Graph A represents mean frequency of white- tailed deer photographic capture over time; graph B represents feral swine.

26

8

7.5

7 Second Capture 6.5 Deer (Minutes) 6 Swine 5.5 transformed Time Interval Time transformed -

Log 5

4.5

4 Deer Swine First Capture

Figure 2-3. Mean ± SE of log-transformed time intervals between consecutive captures for 4 combinations of 1st and 2nd capture species in Highlands County, FL, USA 2015- 2016.

27

Figure 2-4. Spearman’s rank correlation between average proportions of feral swine and white-tailed deer camera-site use per day in Highlands County FL, USA, 2015-2016.

28

Figure 2-5. Spearman’s rank correlation between proportion of average daily site use by white- tailed deer relative to standardized daily site-specific mast production in Highlands County FL, USA, 2015-2016.

29

Figure 2-6. Spearman’s rank correlation between proportion of average daily site use by feral swine relative to standardized daily site-specific mast production in Highlands County FL, USA, 2015-2016.

30 Table 2-1. Analysis of variance on time interval between consecutive captures, capture1 followed by capture2a, blocked by camera location in Highlands County, FL, USA, 2015-2016. d.f. SS MS F P Location 17 278.8 16.40 3.29 <0.01 Capture1 1 0.1 0.13 0.03 0.87 Capture2 1 5.3 5.28 1.06 0.30 Capture1:Capture2 1 50.0 49.97 10.04 <0.01 Location:Capture1 7 113.2 16.17 3.25 <0.01 Location:Capture2 8 96.6 12.08 2.43 <0.05 Location:Capture1:Capture2 5 45.8 9.15 1.84 0.11 Residuals 213 1059.8 4.98 aSee Methods for variable descriptions. Note: SS=sum of squares, MS=mean square, P<0.05 values are shown in bold.

31 CHAPTER 3 COMPETITION FOR SUPPLEMENTAL FEED

One possible means of buffering the effects of low-mast years on white-tailed deer

(Odocoileus virginianus) is the human placement of supplemental deer feed such as corn, apples, or commercial blends. Deer feeding stations are often used within the aim to increase body mass and antler growth for improved hunting trophies, and are legal for use in some states for baiting deer. These resources are expensive for hunters to provide and unfortunately are often disproportionately utilized by non-target species including feral swine (Sus scrofa) (Demarais and Lambert, 2001). Use of deer feeding stations by feral swine may also lead to white-tailed deer avoiding the area, as feral swine often exhibit aggressive territorial behaviors to “defend such areas of food abundance during the fall and winter” (Graves, 1984, p. 484). Unintentionally providing additional high-quality feed for feral swine also may allow sows to produce larger litters, adding to the already-growing swine population (Burns, 2009). Another issue associated with feral swine use of deer feeders is the potential for disease spread via higher congregation in specific areas, increasing the possibility of transfer through direct contact or through increased deposition of bodily fluids (Vicente et al., 2007).

Using a game camera array and a quasi-experimental design framework, I examined the average use and behavior of feral swine and white-tailed deer at supplemental feeding stations. I examined the spatial activity patterns of white-tailed deer and feral swine in the presence and absence of each other using fenced and unfenced treatments along with baited and non-baited camera stations. Similar to my acorn study in Chapter 2, I hypothesized that white-tailed deer alter their activity patterns to avoid swine through space, resulting in a reduction in available food resources for white-tailed deer. My first objective was to test for shifts in the spatial land use behaviors of white-tailed deer that would suggest avoidance of supplemental feeding stations

32 used by feral swine. I predicted that white-tailed deer would avoid unfenced supplemental feeding stations due to high feral swine use, and that deer would disproportionately utilize baited stations fenced off from feral swine. My second objective was to assess the feeding rates of supplemental feed by feral swine and other non-target species. I predicted that the high activity and feeding rates of feral swine at shared, unfenced supplemental feeding stations would result in the reduced availability of supplemental feed for white-tailed deer.

This study was conducted at MAERC’s Buck Island Ranch, located 25 km southeast of

Lake Placid, Highlands County in south-central Florida. A full description of the study site is provided in Chapter 2.

Methods

Competition for supplemental feed was assessed using baiting stations accompanied by game cameras. Baiting stations were assigned to one of four treatment types (A-D) designed to compare the spatiotemporal activity patterns and feeding rates of deer and swine in the presence and absence of the other species. Data collection took place during four discrete sampling periods in September–October 2015 and January–February 2016.

Treatment type A allowed feral swine access to supplemental corn but excluded entry by deer using 1.2-m x 2.5-m hog traps with 1-m x 1-m open doors. Open hog traps were selected as a cost-effective alternative to constructing enclosure fencing and were effective at excluding deer as deer generally avoid entering tight enclosed spaces but swine enter them willingly. Treatment type B allowed deer access to supplemental corn but excluded entry by feral swine by constructing 100-m2 enclosures with 1.2-m-high fencing. This fencing technique is commonly used for deer feeding stations in areas of high swine density as deer can jump over the fences but swine generally do not. Treatment type C allowed both species access to the corn bait without

33 any fencing or hindrances. Treatment type D was designed in the same manner as treatment type

C but was not baited. Treatment type D served as a negative control to provide an index of random encounter rates of deer and swine in the absence of supplemental corn. Three replicates of each of the four treatment types were erected for a total of nine baited stations (treatment types A-C) and three unbaited stations (treatment type D) per each of the four sampling periods.

I hypothesized that white-tailed deer avoid feral swine through space, and therefore predicted that higher levels of deer activity would occur within the baited, ‘deer-only’ treatments

(treatment type B) than baited, deer-swine ‘shared’ treatments (treatment type C). I also predicted that feral swine do not avoid deer and therefore expected equal swine activity levels between baited ‘swine-only’ treatments (treatment type A) and baited, ‘shared’ treatments

(treatment type C). I expected no difference in the total number of captures between feral swine and white-tailed deer in the unbaited control group (treatment D), as I assumed equal population sizes of deer and feral swine.

All baited treatments (A-C) underwent three days of pretreatment before each sampling period. Pretreatment consisted of erecting the enclosures and baiting them in the same manner as for true sampling periods. This pretreatment served to minimize any potential treatment effects via increasing awareness of the bait and allowing deer and swine more time to become comfortable entering the enclosure. Baiting protocol for pretreatment and true sampling periods consisted of placing one shallow 12-L plastic tub within each replicate of treatment types A-C and adding 11.33 kg of dry corn to the tub every day for three days. Initial bait placement occurred between the hours of 1300 and 1600 on the first day of sampling. Game camera operation began at the same time as initial bait placement. Remaining corn was weighed and recorded and tubs were refilled to 11.33 kg every 24-hours for three days. If precipitation

34 occurred during any 24-hour period, excess water was drained from the tub and a conversion factor of 14% of remaining corn weight was subtracted to account for absorbed water weight.

This conversion factor was determined via soaking a known amount of dry corn in water for 24 hours and then calculating the additional weight caused by absorption of water.

Each treatment replicate was equipped with one Bushnell game camera either facing the corn bait (for treatment types A-C), or in a random direction for the negative control group

(treatment type D). Game cameras operated for the three days of available bait in each sampling period. Game cameras captured 15-second videos of any trigger movement with an enforced delay of three minutes between videos. Videos were selected for use over photos to assess whether an individual fully entered the enclosure, and to account for the total number of individuals entering the enclosure after the initial trigger event. Species, date, time, number of individuals, and duration of stay were recorded for all videos. All individuals within the enclosure were counted regardless of whether they were shown consuming corn or not. It is important to note that distinguishing between individuals at different stations was impossible due to animals being untagged. To minimize the occurrence of the same individual frequenting multiple stations within a short timeframe, all baited and unbaited stations were located a minimum distance of 400 m apart per sampling period and the three replicates of each treatment type were placed within a different block of the study area; north, east, south, or west (Figure 3-

1). One sampling unit was defined as one 24-hour period at one camera station, with 48 cameras accumulating a total of 192 camera-days.

Assessment of Spatial Avoidance

I constructed GLMMS using package lme4 in R to test for the effects of treatment type on deer and swine video capture rates. My first model was a baseline activity model, which

35 included frequency of capture (number of videos) as a function of the random effects ‘location’, the specific treatment replicate within which each visit occurred, and ‘sampling period’, the sampling month during which the visit occurred. This produced the following model:

Frequency~(1|Location)+(1|Sampling Period)

I interposed this model with a second model that included the fixed effect ‘treatment type’, indicating which of the three possible treatment types (treatment types B, C, and D for deer; treatment types A, C, and D for swine) each visit occurred in:

Frequency~Treatment Type+(1|Location)+(1|Sampling period)

A Poisson distribution was evident in the count data and indicated in the ‘family’ argument for all models. Analyses of the effect of ‘treatment type’ were conducted separately for feral swine and white-tailed deer data. I tested for the significance of the effect of ‘treatment type’ on the distribution of average single-species activity levels over time using an ANOVA between the baseline model and treatment type model. If ‘treatment type’ had a significant effect on average levels of species activity, GLMMS z-values were used to determine the direction and magnitude of individual treatment type-effects. Z-values indicated the probability of a fixed effect falling outside of the 95th percentile of the z-distribution; therefore, z>1.96 indicated a significant positive effect, and z<-1.96 indicated a significant negative effect.

I aimed to generate sound causal inference regarding whether the perceived shifts in white-tailed deer spatial use of my experimental treatments were caused by feral swine activity.

In an effort to rule out non-target species as a potential factor influencing deer behavior, I compared deer capture rates between baited sites shared and not shared with raccoons (Procyon lotor), the most frequently captured non-target species. To rule out surrounding habitat type as a

36 potential factor influencing deer behavior, I classified each treatment replicate as ‘open’, ‘forest’, or ‘edge’ habitat. ‘Edge’ referred to any treatment replicate adjacent to both a forest and open area; ‘open’ referred to any treatment replicate located within a completely open area such as pasture; ‘forest’ referred to any treatment replicate located within an oak hammock or other forest type. Statistical analyses for the effect of raccoons and habitat type on white-tailed deer behavior at baited treatment types (B and C) were analyzed using GLMMS and best model selection in the same manner as described in chapter 2 for my analyses of non-target species and edge habitat.

Assessment of Feral Swine Feeding Rates

To calculate feeding rates of feral swine, it was necessary to first calculate the feeding rates of all non-target species that consumed corn between or during feral swine visits to baited treatment areas. Within each applicable treatment replicate, all species, number of individuals of each species, and duration of visit were recorded for every video recorded for each 24-hour period between bait placements. I calculated the average feeding rate of raccoons (Procyon lotor), the most commonly recorded non-target species, using videos from all 24-hour sampling units where only raccoons occurred. For each raccoon-only sampling day, I divided the amount of corn loss (calculated from the amount of corn weight remaining after each 24-hour period) by the total number of individual raccoons recorded. This provided a daily average consumption rate of corn per raccoon per 15-second video for each raccoon-only sampling day. These consumption rates were averaged across all raccoon-only sampling days to provide a single average feeding rate per raccoon per 15-second video, estimated at 13.6 g.

The consumption rate of the second most common non-target species, crows (Corvus brachyrhynchos), was analyzed using 100 crow-only videos. Pecking rate, or the number of

37 times a crow picked up a kernel of corn within each video, was calculated for each individual crow within each of the 100 crow-only videos. Mean pecking rate per crow was approximately 1 corn kernel per 15-second video. One corn kernel weighed ~0.91 g, therefore mean corn consumption per individual crow per 15-second video was estimated at 0.91 g. Additional non- target species were wild turkeys (Meleagris gallopavo), gray squirrels (Sciurus carolinensis), and Virginia opossums (Didelphis virginiana). Approximate corn loss per each individual of these species was low; individuals of these species were not recorded as feeding continuously and generally consumed a negligible amount of corn. Therefore, I estimated average corn loss for these species as similar to that of crows at 0.91 g per individual per 15-second video.

Using my estimated non-target species feeding rates per video I calculated the total amount of corn consumed by non-target species for each 24-hour sampling period by multiplying each species feeding rate by the total number of individuals and summing all species totals. I did not include any treatment replicates and/or days of sampling with fewer than five recorded swine videos for my feeding rate analyses due to insufficient data, therefore swine feeding rates were calculated from 15 individual days of swine video capture within 10 baited treatment replicates.

Total corn loss due to consumption by non-target species was subtracted from the total corn loss recorded for each day to calculate the total amount of corn consumed by feral swine per treatment replicate per day. Given that unequal numbers of swine consumed corn within the various treatment replicates, I divided the total corn loss from feral swine by the mean number of individual swine to generate an average individual swine consumption rate per day.

I could not generate meaningful feeding rates for deer at my baiting stations due to a low deer capture rate. Instead I used my estimated corn consumption by feral swine and non-target species/day to calculate the amount of corn that would remain available for deer. Within the

38 shared/baited treatment type C, only two deer videos were captured within the same treatment replicates as feral swine. The deer in these videos were not shown consuming corn and therefore were not included in swine-specific feeding rate analyses. Feeding rates were reported in mean ±

SE.

Results

White-tailed deer occurred at 25% of the total possible 36 treatment replicates (treatment types B, C, and D). Swine occurred at 36% of the total possible treatment replicates (treatment types A, C, and D) and within 44% of the total baited treatment replicates (treatment types A and

C). Maximum swine sampling in one day was 137 videos compared to a maximum of eight for deer. Feral swine represented 93.5% of total deer and swine visits to baited treatment types A-C.

We captured a total of seven deer videos and two swine videos within treatment type D, the

‘control-group’ treatment type representing the random encounter rates of each species in the absence of any bait or deterrents.

Treatment type had a significant effect on the capture rate of deer (ANOVA, p<0.001).

Deer were captured at a significantly greater rate within the baited, deer-only treatment type B than treatment types C and D (Figure 3-2). Average deer capture rate did not differ significantly between the baited, shared treatment type C and the unbaited control treatment type D (Figure 3-

2). The average number of feral swine captures did not differ significantly between baited treatment types A and C (Figure 3-2; ANOVA, p>0.05) but were both significantly higher than treatment D (Figure 3-2; ANOVA, p<0.05).

Analyzed factors that potentially influenced deer behavior and use of my supplemental feeding stations indicate that raccoon activity had no effect on the capture rate of deer (ANOVA,

39 p>0.05). Habitat type (open, forest, or edge) showed no effect on the capture rates of white-tailed deer (ANOVA, p=0.46) or feral swine (ANOVA, p>0.05).

Feral swine (without respect to group size) consumed an average of 5.88 ± 0.90 kg of corn/day, or ~52% of total available corn per day within the 10 applicable treatment replicates

(treatment types A and C). Maximum total corn consumption by feral swine per 24-hour sampling period was 10.97 kg. The average number of individual swine per video was 1.48 ±

0.19 and average corn consumption per swine was 3.96 ± 0.29 kg/swine/day.

Raccoons consumed 0.44 ± 0.18 kg of corn/day. Corn loss from other non-target species was negligible, with an average daily corn loss of 0.01 ± 0.004 kg/day from crows and only 16 total recorded visits (approximately 1% of total visits) from other non-target species (Figure 3-3).

Average corn loss from swine and non-target species combined was roughly 6.35 kg/day, or

56.0% of total available corn (11.33 kg/day), of which 92.8% of the total corn consumed/day was attributed to feral swine.

Discussion

This study examined the spatial use behaviors and food resource use of feral swine and white-tailed deer at shared and non-shared supplemental feeding stations. Feral swine were disproportionately represented in relation to deer at baited experimental treatment areas, with swine representing 93.5% of total deer and swine visits to baited camera sites. White-tailed deer spatially avoided baited unfenced treatment areas and exhibited a strong preference for supplemental feeding stations fenced off from feral swine entry, indicating that white-tailed deer shift their behavioral use of feeding sites with an aim to avoid potential interactions with feral swine. Feral swine showed no such preference and used any baited site, shared or not, in a similar fashion, providing evidence that feral swine are a dominant competitor with white-tailed

40 deer at bait stations. The increased activity levels at baited feeding sites and comparatively high use of supplemental feed of feral swine at baiting stations lead to a significant reduction in supplemental feed availability for white-tailed deer.

Reduced supplemental feed availability for deer due to use by invasive feral swine presents a significant economic loss for supplemental feed providers, both in terms of cost of corn consumed by feral swine and the loss of potential benefits for deer populations (and deer hunters). Examples of the potential benefits of supplemental feed include evidence of accelerated body growth and a 50% increase in 2.5-year age class doe fecundity in a Michigan deer herd provided with unlimited supplemental feed (Ozoga and Verme, 1982). Bucks provided with supplemental feed in Texas exhibited a 14% increase in average antler size, and a 12-23% increase in male body mass at time of harvest (Bartoskewitz, et al., 2003). Although intended to aid deer populations, unintentionally providing supplemental feed to feral swine may also lead to increased swine reproduction and population sizes (Cellina, 2008), and, subsequently, increased competition for resources between feral swine and native ungulates.

Feral swine in my study consumed an average of 52% of total available corn per day.

This estimate falls at the low end of the spectrum compared to similar studies, with estimates of supplemental feed consumption by feral swine ranging from 55-88% of total available corn per day in and the southwest United States (Ballari and Barrios-Garcia, 2014; Cellina, 2008;

Fournier-Chambrillon et al., 1995). The comparatively low rate of use of supplemental feed of feral swine in this study may have been due to swine preference for available oak mast at the time of sampling. During times of limited food resource availability, supplemental food use is likely to increase (Ballari and Barrios-Garcia, 2014). Low sample sizes for white-tailed deer at my baited treatment areas made it impractical to calculate the feeding rates of deer for this study.

41 However, similar studies have provided estimates of deer feeding rates at fenced supplemental feeding stations ranging from 0.23-0.46 kg of corn/deer/day (Linhart et al., 1997), the mid-point of which is more than 11 times lower than the average individual feeding rate of feral swine found in this study (3.96 kg/day). The feeding rates of feral swine at my study site indicate that feral swine would consume feed rapidly and limit supplemental feed availability for deer even in the absence of deer avoidance behaviors toward shared baiting stations. As the results of this study showed, white-tailed deer prefer baited stations fenced off from feral swine and providing barriers and deterrents to feral swine use of baiting stations will save deer hunters considerable losses of bait that would normally be utilized by white-tailed deer populations. In areas such as my study site where non-target species disproportionately utilize supplemental feed intended for deer, the one-time cost of erecting fences at supplemental feeders plus the cost of annual fence maintenance is less than the annual cost of feed lost due to non-target species consumption

(McBryde, 1995).

The results of this study are limited by the lack of known population sizes of white-tailed deer and feral swine at my study site. Although I did attempt to quantify the random encounter rates of each species to provide an index of population sizes, the low sample sizes for deer and swine captured at my unbaited control treatments limits any comparison of population sizes.

With only seven deer and two feral swine captured at random encounter points, it would suggest the population of deer to be much greater than feral swine. If this was a true estimate of populations it would suggest that though deer may have much larger populations than swine on the site, deer are using bait stations less than expected compared to swine populations. Larger deer populations than swine populations but less bait station use by deer supports greater negative interactions with feral swine than first thought. Future studies on feral swine and white-

42 tailed deer interactions would benefit from longer-term data collection to allow for increased data collection on population sizes, and comparisons of seasonal or annual changes in supplemental feed use by deer and swine. Finally, it has been speculated that white-tailed deer may adapt their behavior to avoid corn bait due to human hunting pressure at bait piles; however, this statement is highly anecdotal and not represented in scientific literature. The supplemental bait stations used at my study site (which use corn) are also infrequently used as hunting blinds.

Hunting pressure associated with corn bait may have influenced the behavior of white-tailed deer at my baited experimental treatments, although evidence of use in this study suggests that this is not the case with deer only bait stations being the most visited by deer.

43 Figures

Treatment Type A Treatment Type B Treatment Type C Treatment Type D

Figure 3-1. Map showing spatial spread of four types of sampling treatment types in Highlands County, FL, USA, 2015-2016. Note: Treatment type A refers to baited, swine-only treatments; type B refers to baited, deer-only treatments; type C refers to baited, both species treatments; and type D refers to unbaited control treatments. See page 33 for full treatment descriptions.

44

6

5

4 Feral swine

3 White-tailed deer

2 per camera sitecamera per

1 Average number of video captures captures video of number Average 0 Baited, Single Species Baited, Shared Random Control

Figure 3-2. Mean + SE of number of feral swine and white-tailed deer video captures within four experimental treatment types in Highlands County, FL, USA, 2015-2016.

45

1%

14%

Feral swine

49% Raccoons Crows Other 36%

Figure 3-3. Percentage of total visits to supplemental feeding stations by various species in Highlands County, FL, USA, 2015-2016.

46 CHAPTER 4 CONCLUSION

The presence of feral swine on Florida rangelands causes white-tailed deer to shift their land use behaviors to avoid times and places of high swine activity, leading to increased spatial and temporal segregation of the two species. The results of this study indicate that white-tailed deer increased their diurnal activity to avoid highly nocturnal feral swine, with the potential to increase white-tailed deer respiratory energy loss (Creel and Christianson, 2008; Karasov, 1981;

Zapata-Rios and Branch, 2015cac). Additionally, increased feral swine activity at the highest mast-producing oak trees paired with spatial avoidance of feral swine by white-tailed deer may negatively impact mast accessibility for white-tailed deer. Reduced mast availability may have long-term consequences on white-tailed deer population demographics, and may result in a reduction of the realized niche of deer on Florida rangelands.

Our analysis of supplemental feed use by feral swine and white-tailed deer in the presence and absence of each other found that white-tailed deer spatially avoided shared baiting stations with feral swine. The daily consumption rates of supplemental feed by feral swine in our study were lower than those found in similar studies; however, we found that feral swine disproportionately utilized shared and non-shared baiting stations compared to white-tailed deer.

Deer exhibited a high preference for baited treatments fenced off from swine entry, illustrating the importance of swine fencing at supplemental feeding stations as well as the importance of swine population control efforts as a means of decreasing competition. Additionally, this study highlighted the utility of game camera trapping in species interaction research, which allowed us to generate meaningful comparisons of species behavioral patterns without the added cost and time associated with live trapping or point-count surveys.

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52 BIOGRAPHICAL SKETCH

Born in East Lansing, Michigan, Connor Crank has always enjoyed being outdoors and working with animals. Connor Crank received her bachelor’s degree from Michigan State

University in 2014. During her time in undergraduate, Connor majored in fisheries and wildlife with an additional interest in plant biology. She relocated to Gainesville in 2014 to begin her

Master of Science degree in wildlife ecology and conservation at the University of Florida. Her primary research interest is in charismatic and large carnivore ecology. Upon graduating from the University of Florida, Connor intends to spend time travelling within the country, and plans to seek employment in the field of mammal biology with a governmental or research organization. Outside of her research, Connor enjoys travelling, spending time with her dogs, and hiking.

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