SEASONAL AND ANNUAL SPACE USE OF WHITE-TAILED DEER IN URBAN AND RURAL SOUTHERN INDIANA
A THESIS
SUBMITTED TO THE GRADUATE SCHOOL
IN PARTIAL FULFULLMENT OF THE REQUIREMENTS
FOR A MASTER OF SCIENCE DEGREE
BY:
JONATHAN K. TRUDEAU
DEPARTMENT OF BIOLOGY
BALL STATE UNIVERSITY
MUNCIE, INDIANA
ADVISOR: DR. TIMOTHY C. CARTER
DECEMBER 2017 ACKNOWLEDGEMENTS This project was an adventure of a lifetime with its many ups and downs. It amazes me how quickly three years went by. The completion of this project would not have been possible without the countless family, friends, and colleagues who helped me along the way. I am eternally thankful for everybody who helped me on this journey.
The biggest thank you goes to my wife, Katie. From dropping everything in New Hampshire to move to Indiana, to planning almost our entire wedding so I did not have to stress during my field season, Katie has sacrificed so much so I could follow my dreams. I will never be able to express how thankful I am for this. Without her love, patience, and continuous support, this project would not have been possible. A special thank you to my father Jon, for exposing me to the great outdoors and wildlife at a very young age that sparked my love for it. I thank my mother Lucy, for always believing in me and pushing me to be a better person and to my brother, Jared for being my best friend and inspiring me to apply as much energy as possible to excelling in academics. Of course, thank you to my grandparents, aunts, and uncles for all their guidance and support throughout this journey.
I cannot truly express my deep appreciation for the dedication, leadership, and hard work of all the technicians and interns who helped capture, collar, and track all the deer. I thank Teddy Fisher, Meredith Hoggatt, Christine Greve, Tim Arlowe, Cheyenne Yates, Zach Wesner, Sarah Clark, and Ashley Issacs for the amazing contribution each of them made to this project. To the entire Bloomington Field Office of the Indiana Department of Natural Resources, especially Dawn Slacks, thank you for helping us establish relationships with private landowners and for lending us a boat to pull clover traps out of the flood plain. I just wish we had asked to use the boat one hydrolocked Ranger engine earlier . . .
A special thank you to the countless residents of Bloomington and the surrounding areas, especially Greg Ostler, George Hegeman, Brian Faulkner, and Mark Gray. Without exceptional residents such as these, we would not have had nearly as successful capture seasons. I thank my
2 colleagues and friends Kristi Confortin, Keifer Titus, and Clay Delancey for listening to my worries and concerns about research and life in general. I also thank Dr. Jason Doll for putting up with my countless questions about statistics and helping me understand when to properly use an ANOVA! Of course, I thank Garrett Clevinger for being my partner in crime throughout this entire process. We overcame so much throughout and even though there were times we butted heads, we came out great friends and with a better understanding of our research.
I especially thank the members of my thesis committee. To my advisor Dr. Tim Carter, I will be forever grateful for the guidance, trust, support, respect, and friendship that you have shown me over the last three years. I am thankful to not only have had an amazing advisor, but an equally amazing friend who opened his home and family to us, becoming our Indiana family. To my friend and mentor Dr. Maarten Vonhof, thank you for all the advice and council while working in the UP and in Illinois. I will always remember our joint suffering in small caves at the mercy of Tim. To Dr. Kevin Turcotte, thank you for your guidance and wisdom while I pestered you about why things in GIS were not working. Thank you all for your support, guidance, and extreme patience during this project.
Funding for this project was provided by the Indiana Department of Natural Resources through Wildlife Restoration Grant W48R1. Additional funding was provided by the Department of Biology at Ball State University, Muncie, IN.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS ...... 2 LIST OF TABLES ...... 5 CHAPTER 1: HISTORY OF WHITE-TAILED DEER AND WHITE-TAILED DEER MANAGEMENT ...... 6 TAXONOMY AND EVOLUTIONARY HISTORY ...... 7 DISTRIBUTION...... 8 ANATOMY AND PHYSIOLOGY ...... 9 SENSORY FUNCTIONS ...... 10 PAST MANAGEMENT OF WHITE-TAILED DEER ...... 12 TRACKING AND MONITORING ...... 14 HOME RANGE ...... 16 URBANIZATION ...... 20 LITERATURE CITED ...... 22 CHAPTER 2: COMPARISON OF SEASONAL AND ANNUAL SPACE USE OF WHITE-TAILED DEER IN URBAN AND RURAL SOUTHERN INDIANA...... 31 ABSTRACT ...... 32 INTRODUCTION ...... 33 STUDY AREA ...... 35 METHODS ...... 36 STATISTICAL ANALYSIS ...... 40 RESULTS ...... 40 DISCUSSION ...... 41 CONCLUSION ...... 45 ACKNOWLEDGMENTS ...... 46 LITERATURE CITED ...... 46
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LIST OF TABLES
CHAPTER 2 TABLE 1: Summary of model selection for the comparison of seasonal 95% and 50% minimum convex polygon (MCP) and fixed kernel density estimation (KDE). Human development class (HDC) and sex were additional fixed factors used in model construction. Best fit models were determined by Δ AIC ≤ 2.00...... 50 TABLE 2: Seasonal home range size comparison of male and female white-tailed deer in southern Indiana using minimum convex polygon (MCP) and fixed kernel density estimation (KDE) methods. Differing letters denote statistically significant differences between average home range size of each season and sex (p<0.05)...... 51 TABLE 3: Comparison of mean seasonal home range and core area size of urban and rural white-tailed deer in southern Indiana by development class and sex across all seasons. Differing letters denote statistically significant differences between mean areas utilized by each sex within each Human Development Class (HDC) by estimation method (p<0.05)...... 52 TABLE 4: Mean annual fixed kernel density home range size of urban and rural white- tailed deer in southern Indiana. Differing letters denote statistically significant differences between average home range and core area size of each sex and HDC (p<0.05)...... 53 TABLE 5: Table 5: Comparison of seasonal home range size (ha) between multiple studies performed in differing development classes across the USA. Adapted from Storm et al. (2007)...... 54
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CHAPTER 1 HISTORY OF WHITE-TAILED DEER AND WHITE-TAILED DEER MANAGEMENT
Trudeau, J.K.
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Taxonomy and Evolutionary History
White-tailed deer (Odocoileus virginianus Zimmerman 1780) are among the most abundant deer species in the world (Heffelfinger 2011). They belong to the order of hooved and even-toed mammals known as Artiodactyla. Over the last 25 years, there has been great discussion over not only fossil, but molecular evidence, that Cetaceans are closely related to the
Artiodactyla (Graur and Higgins 1994, Geisler et al. 2007). Cetacea are so closely related to
Artiodactyla that there is discussion of the two orders being reclassified as a single order;
Cetariodactyla (Naylor and Adams 2001, Thewisse et al. 2001). Within the order Artiodactyla, there are three suborders; Ruminantia, Suiformes, and Tylopoda. The three suborders differ in stomach structure with Suiformes having the least advanced and Ruminantia having the most advanced. White-tailed deer belong to the suborder Ruminantia and further to the Cervidae family. Cerivds are found on every continent except Antarctica (Heffelfinger 2011) and contain
51 extant species world-wide (Wilson and Reeder 2005). Within the Cervidae family, there are two subfamilies; Capreolinae and Cervinae. White-tailed deer are one of 22 species belonging to the Capreolinae subfamily, more commonly known as the New World deer. The main separation between these two subfamilies is the presence or absence of distal and proximal lateral metacarpal bones. Capreolinae maintained distal lateral metacarpals while Cervinae maintained proximal lateral metacarpals (Heffelfinger 2011).
White-tailed deer are one of two species within the genus Odocoileus. They are closely related to, and often confused with, their close cousin O. hemionus, more commonly known as the mule deer. Odocoileus hemionus differs from O. virginianus by having a black tipped tail instead of a white tipped tail, larger ears, and slightly different antler structures. The males of O. hemionus have bifurcated antlers, unlike O. virginianus (Heffelfinger 2011). Though the two
7 species may occupy similar habitats, mule deer tend to reside in the more open landscapes while white-tailed deer occupy the more heavily forested landscapes (Anthony and Smith 1977). The two species have been known to interbreed, but the extent has been found to be minimal (Carr et al. 1986). Both species are well suited to their environment and occupy a large geographic area.
White-tailed deer occupy such a large area, that there is extensive debate over the exact number of white-tailed deer subspecies (Wilson and Brown 1953, DeYoung et al. 2003, Latch et al.
2009). It has been generally accepted that there are approximately 38 subspecies (Heffelfinger
2011). In the U.S. alone, there are roughly 17 subspecies, with O.v. borealis and O.v. virginianus being the most abundant in the Midwestern US region (Heffelfinger 2011). The physical size of the subspecies can vary based on geographic region with males exceeding 180 kg in the northern most region (Sauer 1984), and the smallest subspecies residing in the southern extent of the white-tailed deer’s distribution weighing a mere 19.1 kg (Hardin et al. 1984).
Distribution
While white-tailed deer can be found worldwide through introductions, their native range is in North America (Heffelfinger 2011). Their northern limit is thought to be influenced most heavily by harsh winter (Heffelfinger 2011) where deep snow packs make it difficult for white- tailed deer to traverse the landscape and find the resources needed to survive the winter (Lesage et al. 2000). Within the United States, white-tailed deer can be found in every state except
Alaska and Hawaii. Their range expands southward through Mexico and into Central and South
America. Their southern extent is Columbia, Venezuela, and Northern Peru (Heffelfinger 2011).
Being a highly sought-after game species, it is not surprising that white-tailed deer have been transported around the world for sport hunting and as a source of meat. White-tailed deer can
8 now be found in countries such as Finland, the British Isles, Australia, Czech Republic, Siberia,
Bulgaria, the Caribbean islands, and even as far as New Zealand.
Anatomy and Physiology
White-tailed deer are an ungulate species with a highly efficient and complex digestive system. As with all ungulate species, white-tailed deer are foregut fermenters. Fermentation of vegetation occurs in the first chambers of the stomach, as opposed to perissodactyla species whom are hind-gut fermenters with fermentation occurring in the intestines (Langer 1987).
White-tailed deer have a dental formula of 0/3, 0/1, 3/3, 3/3 for a total of 32 teeth (Ditchkoff
2011). These teeth are the first step in the digestion of vegetation by carefully selecting and tearing tender vegetation from plants and then vigorously chewing to increase the surface area for digestion (Holechek 1984). The next step in digestion occurs in the first of four chambers in the stomach. The rumen, which is an organ only found in ungulate species, initiates fermentation.
A slurry of digestive enzymes, micro-organisms, and bacteria all mix together in the rumen to initiate the fermentation process. Ungulates can regurgitate the partially digested slurry, a process known as “chewing cud”, and further break up the vegetation, increasing the surface area. The “cud” can then be re-ingested and return to the rumen for further digestion (Browman and Hudson 1957). From the rumen, vegetation then travels to the reticulum, omasum, and the fourth chamber of the stomach, known as the abomasum, where absorption of nutrients and the reabsorption of water from bodily fluids is initiated. This four-chambered stomach makes deer very efficient browsers, able to break down fibrous material well and pulling most of the nutrients from their food (Domingue et al. 1991).
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Having such an efficient and effective digestive system allows white-tailed deer to consume primarily structural carbohydrates (Domingue et al. 1991, Ditchkoff 2011). Structural carbohydrates are fibrous and difficult to breakdown, such as cellulose commonly found in plant cells (Overdieck 2016). Though deer consume theses carbohydrates, they cannot break them down into useable energy without the assistance of micro-organisms. Deer have a symbiotic relationship with the micro-organisms by providing a host and nutrients from carbohydrates, while the deer receive nutrients from the byproducts of the micro-organisms and the excess microorganism (Ditchkoff 2011). This symbiotic relationship is one of the key factors that has helped deer to become such efficient browsers.
Sensory Functions
Evolving as a species of prey, white-tailed deer have very entuned senses. One of their best developed senses is their vision. Having eyes located on the side of the head enables deer to have a wide view of 310° (Müller-Schwarze 1994), allowing them to see predators over a large area. An additional adaptation improving their nocturnal eyesight is the possession of tapetum lucidum. The tapetum lucidum is a dense layer of fibers located behind the retina that reflects light back past the rods and cones. This increasing the differences in the observed brightness by doubling potential photon-photoreceptor stimulation (VerCauteren and Pipas 2003). The tapetum lucidum allows deer to be active not only during diurnal hours, but also crepuscular hours.
Complimenting their impressive eye sight, deer can hear frequencies between 0.25-30.0 kHz, but are most sensitive to sounds within the 4-8 kHz range (D’Angelo et al. 2007). These adaptations enable white-tailed deer to be better equipped to detect predators.
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Arguably, the most evolved sense in deer is their olfactory and chemical sensory system.
Being an herbivore requires deer to have the ability to differentiate between suitable and unsuitable foods. This is accomplished by possessing a large amount of mushroom-shaped circumvallate papillae to ‘test’ each plant for defensive compounds. Many of these defensive compounds have a bitter taste, which allows deer to sense the compound and determine if the vegetation is suitable for consumption (Müller-Schwarze 1994). Olfaction plays multiple roles in the life of a deer, including locating preferred forage, predator detection, and even social interactions (Ditchkoff 2011). Deer rely heavily upon their olfactory sense during the breading season. Males are often observed curling their upper lip, appearing as if they are tasting the air.
This behavior is referred to as flehmening, which is the transfer of a chemical compound to the incisive foramina and then the vomeronasal organ. The process of testing the chemical compounds is known as vomolfaction and allows males to determine the reproductive status of a female in estrus (Müller-Schwarze 1994).
Though white-tailed deer have seven body glands that emit chemical compounds, there are only three that are well understood (Gassett et al. 1996). These glands are used to leave scent markers on objects in the environment for communication of gender, breeding status, and individual identification (Ditchkoff 2011). The interdigital gland, located between the two toes, is used to scent mark trails. As the deer walks, scent is left along the trail indicating age and potentially dominance rank (Gassett et al. 1996). Both sexes use the interdigital gland as well as the forehead gland, which primarily leaves an individual identifying scent (Moores and
Marchinton 1974, Atkenson and Marchinton 1982). As with the interdigital and forehead glands, the tarsal glands are crucial during the breeding season for both sexes. The tarsal glands are located on the posterior of the knee, known as the tarsal bone, where a deer can easily urinate on
11 the glands. After being rubbed together, the urine and chemicals emitted from the tarsal gland mix to form a semiochemical profile that allows for individual recognition and communication of reproductive status (Alexy et al. 2003 and Miller et al 1991). The remaining four glands are less studied and the role they play are not well understood. These glands include the nasal, preorbital, preputial, and metatarsal glands (Ditchkoff 2011).
Past Management of White-tailed deer
White-tailed deer are among the most influential species in game management (Hall
1978) with McCullough (1987) stating that white-tailed deer were the most important wild ungulate specoes in North America due to their incredible abundance and revenue generated from hunting and wildlife viewing. Not only have white-tailed deer influenced hunting regulations, but have also contributed to the initiation of wildlife restoration, conservation policies, and even influenced public attitudes of environmental issues (Adams and Hamilton
2011). Management of white-tailed deer has a long history, dating back to the Native Americans.
Tribes depended on the white-tailed deer for a source of food and trade (McDonald and Miller
2004, Hall 1978). Being the original conservationist, Native Americans would implement prescribed burns to improve the habitat quality for cervids by promoting young vegetation growth that was tender and nutrient rich (Stewart 1951, Trefethen 1970).
Prior to the early 1800’s there was upwards of 12 million white-tailed deer across the landscape, (McCabe and McCabe 1984, Newsom 1984, McDonald and Miller 2004). In the early and mid-1800’s, exploitation from market hunting, loss of habitat due to a boom in timber harvest and agriculture, as well as a lack of regulations led to the mass decline in white-tailed deer populations (Noble 1966, Halls 1978, Demaris et al. 2000, McDonald and Miller 2004).
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Downing (1987) estimated that between 1890-1900, there was a mere 300,000 white-tailed deer in North America. The importance of this resource, and the potential impacts exploitation may have on the economy and environment were recognized by a few states. Prior to the mass decline across North America, colonies and states executed short term regulations to manage white-tailed deer population. Rhode Island became the first colony to implement harvest regulations of white-tailed deer by prohibiting the harvest of any deer in 1646. Almost 100 years later, Virginia followed suit by banning the harvest of females in 1738. Recognizing the need to preserve and protect the important resource, many other states implemented their own forms of regulation (McDonals and Miller 2004). Prior to the early 1800’s, white-tailed deer showed promise of recovery, but between 1850-1900 exploitation increased dramatically due to a need for venison and skins in the European market (Jenkins 1953).
The decline in deer populations, as well as in other species, due to market hunting did not go un-noticed and on May 25th, 1900, the Lacey Act was signed into law by President McKinley.
The Lacey Act was the first federal law to protect wildlife by prohibiting the interstate transport of illegally killed wildlife (Adams and Hamilton 2011). To enforce this new law, the Wardens service was born in the early 1900’s and wildlife biologists followed in the 1940’s. Following the signing of the Lacey Act, the North American landscape started going through a change.
Formerly deforested areas started to recover and provide high quality forage, and in some regions, white-tailed deer populations started to flourish (Demaris et al. 2000). Populations in many of the Northern States of the U.S. were doing so well that between 1930’s-1950’s, over
46,000 deer were translocated to other states in restocking efforts. Restocking efforts continued through the 1990’s with a total of 17 states receiving deer from outside states (McDonald and
Miller 2004). The transport of white-tailed deer around the nation resulted in a mixing of genes,
13 and the impact on the genetic diversity of subspecies has been widely speculated (Handley 1952,
Karlin et al. 1989, Leberg and Ellsworth 1999, Ellsworth et al. 1994).
Even though restocking efforts continued into the 1990’s, by the 1960’s and 70’s many herds had recovered to abundances that required active management to maintain or decrease densities. When deer densities increased, browsing pressure increased as well. As a result, many researchers were noting impacts on the plant community in areas where deer populations had rebounded (Webb et al. 1956, Stoeckeler et al. 1957, Harlow and Downing 1970, Waller and
Alverson 1997). High browsing pressure has been found to negatively impact the diversity and ecological succession of forests across their range (Harlow and Downing 1970, Anderson and
Loucks 1979, Marquis 1974, Frelich and Lorimer 1985, Horsley et al. 2003). These impacts do not only affect the forest the deer reside in, but the other species that occupy the region. For this reason, white-tailed deer have been considered a key-stone species by some biologists (Waller and Alverson 1997).
Tracking and Monitoring
To properly understand how animals utilize the landscape, it is important to identify how the animal will be monitored. In white-tailed deer research, there are two primary types of tracking collar used to estimate location; very high frequency (VHF) radio-tracking collars and global positioning system (GPS) collars. VHF collars have been in use for more than half a century (LeMunyan et al. 1959) while GPS collars were first introduced in the last couple of decades. Both collar types are widely used, but understanding the limitations of each is crucial.
GPS collars were originally limited to large species due to collar weight (>1,000 g), but technology has improved so that smaller collars (<300 g) are now available (Girard et al. 2002,
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Recio et al. 2011). Multiple studies have sought to estimate the cost associate with each collar type to determine when each collar type is appropriate. Guthrie et al. (2011) compared the cost of collecting locations on wild turkey (M. g. intermedia) with GPS and VHF transmitters. Initial cost of GPS units were higher than VHF transmitters, but as sampling effort increased, the cost per location decreased. After 181 locations, GPS units became the more cost-effective method
(Guthrie et al. 2011). When more intensive studies on space use and movement are desired, GPS technology is the more cost-effective method, however, if research is focused on long term space use and survival, where sampling efforts may be less intensive, VHF technology may be the more cost-effective method (Guthrie et al. 2011). An additional cost to consider is the recovery of a GPS collar to download the onboard storage. In recent years, release mechanisms have been developed to allow a researcher to remotely release a collar to reduce the cost of retrieval, but failure to release is a common problem (Hebblewhite et al. 2007, Kochanny et al. 2011). The associated cost to recapture an animal can be a hindrance to some studies.
Cost is not the only factor to consider when comparing collar types. Accuracy of the points and the associated accuracy of the output home range are equally as important. Locations based on GPS and VHF data was examined on a free ranging population of white-tailed deer in
Minnesota where Kochanny et al. (2009) found that home range size did not vary widely on a single deer based on collar technology. However, VHF data missed crucial movements between utilization areas, potentially overlooking critical habitat (Johnson et al. 1998, Seaman et al. 1999,
Kernohan et al. 2001, Kochanny et al. 2009). When examined by Guthrei et al. (2011), locational accuracy of GPS data was within 15.5 m of a collar’s true location. Contrary to this, Thogmartin
(2001) found that the locational accuracy of VHF data using radio-telemetry was within 485 m.
When looking at any species, understanding the limitations of the equipment used is crucial. The
15 accuracy, cost, and longevity of a collar are all important factors to consider. VHF collars may have longer battery lives and lower initial cost, but GPS collars provide larger sample size and higher accuracy of data.
Home Range
As previously stated, white-tailed deer have been an important species throughout history and it is no surprise that they were among the earliest species to be thoroughly studied. Research articles referring to white-tailed deer date back to the late 1890’s, looking at predator interactions, species composition, and even the complete description of the deer in the Eastern
United States (Bangs 1896, Allen 1903, 1910, Barbour and Allen 1922). Since then, there have been thousands of publications dedicated to the understanding of white-tailed deer. For any species, the foundation of understanding space use is the knowledge of their home range. It is believed that home range is the most fundamental index in wildlife management and is undoubtedly the most studied aspect of wildlife behavior (Hemson et al. 2005 and Kie et al.
2002).
The term “home range” was first used in 1909 by Ernest Seton in his book Life-histories of northern animals: an account of the mammals of Manitoba. Ernest described a home range as a home-region which is not random and corresponds somewhat to the size of the animal. This definition was virtually synonymous with the term “territory”, which is why the two terms were used interchangeably for over three decades. In 1943, William Burt redefined a home range as,
“the area, usually around a home site, over which an animal normally travels in search of food,” while territory was described as the portion of a home range that is actively defended. Burt’s differentiation, no matter how slight, demonstrates that a home range and territory may be used
16 as synonyms, but they do not always correspond to the same area. Though home range is the most commonly studied aspect of an animal’s space use, the core area is the most intensively used portion of the home range. The core area can be used to identify key features of habitat utilized by a deer (Jacoby et al. 2012).
A home range is based on repeat observations of an individual animal and there are multiple methods to estimate the area of the home range. The most common and simplest form of estimation is minimum area method (MAM), also knowns as minimum convex polygon
(MCP) (Mohr 1947). The MCP method is undoubtable the most widely used method for home range estimation (Hansteen et al. 1997, Powell 2000, Girard et al. 2002). By containing all, or a specified percentage, of the locations collected on an individual, the MCP method is simply the connection of outermost points (Huck et al. 2008). Though the MCP is a simple method, by regularly containing all points collected, it causes the MCP to be sensitive to sample size (White and Garrott 1990, Powell 2000, Kenward et al. 2001). Due to the heavy influence of outlier locations, MCP’s often over estimate home range size and including areas not regularly utilized by an animal (Bekoff and Mech 1984, Worton 1987). Powell (2000) suggests selecting 90 or
95% of points to eliminate the extraneous points, and reducing the possibility of over estimating the home range. Though Powell’s recommendation addresses one flaw in MCP’s, it does not address the largest flaw. The MCP assumes that an animal utilizes its whole home range equally, which is not necessarily true. MCP’s do not give any weight to areas of higher utilization and virtually ignore the value of interior locations. Though there are serious flaws in the method, due to its simplicity and long history of use the MCP method makes it easy to compare results to previous literature and therefore is still a useful tool (Harris et al. 1990, Huck et al. 2008).
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The most common non-parametric method of estimating home range size is the kernel density estimation (KDE), which has been extensively studied (Worton 1989, 1995, Silverman
1986, Seaman and Powell 1996, Marzluff et al. 2004). In simple terms, a KDE estimates the likelihood of an observation occurring at a given location. Each observation will provide weight towards the value of a given kernel, also known as a grid cell, that contains a probability density estimate; the outcome is a home range in the form of a probability distribution, or a utilization distribution (Huck et al. 2008). Observations that occur close in proximity to the kernel will influence the probability density more than observations that are further away (Seaman and
Powell 1996). Because of this, KDE provides a researcher with a more accurate depiction of the shape of an animal’s home range as well as estimate of utilization across the home range
(Silverman 1986).
Though KDE is now widely used to estimate home ranges, like any other method, sample size has been found to have a large influence on the outcome (Seaman and Powell 1996, Seaman et al. 1999, Girard et al. 2002). The KDE method is often criticized for over-estimating home range size when sample sizes are small (Naef-Daenzer 1993, Seaman and Powell 1996). Multiple studies have sought to determine the minimum number of locations needed to estimate home range (Seaman et al. 1999, Girard et al. 2002). On an annual basis, a minimum of 30-100 locations per individual are needed, while a minimum of 30-50 locations per individual are needed to estimate a seasonal home range (Seaman et al. 1999, Girard et al 2002). It is possible to estimate home range with fewer locations, but it is recommended to use caution when comparing results as any home range estimation method becomes increasingly inaccurate as sample size decreases (Seaman et al. 1999).
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Biologically, home range size of white-tailed deer can be influenced by a multitude of factors including but not limited to sex, age (Cederlund and Sand 1994, Nicholson et al. 1997,
Relyea et al. 2000), body size (McNab 1963 and Swihart et al. 1988), geographic location
(Kilpatrick and Spohr 2000, Grund et al. 2002, Storm et al. 2007), season (Nicholson et al. 1997) and competition (Riley and Dood 1984, Loft et al. 1993, Kie et al. 2002). Each of these factors can play a role in the area an individual occupies. Sex and season have been found to have the largest influence on the home range size of white-tailed deer (Nicholson et al. 1997, Grund et al.
2002). It is believed that overall males have higher metabolic demands, therefore requiring larger areas to meet these requirements (Nicholson et al. 1997, Beirer and McCullough 1990, Barboza and Bowyer 2000, 2001, Long et al. 2008).
During the fall breeding season, variation in home range size of both males and females has been found to be the most dramatic. Males are more transient during the fall as they are actively searching for females to breed (Nelson and Mech 1981 and 1984). Nelson and Mech
(1984) believe that by having larger home ranges, a single male can increase their likelihood of mating. Home range may vary depending on season not only due to breeding behavior, but due to variation in required resources (Marchinton and Hirth, 1984). These variations can be a result of environmental factors such as precipitation and temperature, (Nelson and Mech 1981, Mooty et al. 1987) or due to a reduction in food availability due to agricultural harvest (Nixon et al
1991, Brinkman et al. 2005). In Canada, some populations have been found to have summer home ranges exceeding 1100 ha, almost 10 orders of magnitude larger than their winter home ranges (Lesage et al. 2000). Not only are the home ranges typically larger in the norther regions, but seasonal shifts and migration are common (Tierson et al. 1985, Lesage et al. 2000). These
19 shifts often occur as a result of change in food availability (Nixon et al. 1991, Brinkman et al.
2005) and seasonal weather variations (Parker et al. 1984).
Urbanization
Over the course of the past few decades, the urbanization of natural lands has increased at a steady rate (Waller and Averson 1997). Urbanization can mean the death or salvation for a species. The species that do persist, often flourish and white-tailed deer are among the most abundant species found in urban/suburban areas across North America (Haverson et al. 2007,
Zeder 2012). White-tailed deer are generalist and edge species, often thriving among the manicured grass yards, flower beds, and city parks (Waller and Averson 1997). With the growing presence of white-tailed deer in developed landscapes, the need for research has also grown within these areas. One of the most important questions researchers have sought to answer is why are densities so high in some urban/suburban landscapes? Unsurprisingly, urban/suburban deer have been found to have fewer natural predators (Kilpatrick and Spohr 2000, Stewart 2011) and lower rates of hunter harvest, primarily due to access limitations (Harden et al. 2005). These factors, along with greater access to supplemental food sources such as ornamental shrubbery, flower gardens, bird feeders, and grass yards, are all accredited for producing higher abundances and reduced area needs of suburban/urban white-tailed deer (Kilpatrick and Spohr 2000, Lauber and Knuth 2004, Stewart 2011). White-tailed deer have been found to have home ranges as small as 43 ha in suburban landscapes, which is significantly smaller than many studies of rural populations (Kilpatrick and Spohr 2000).
The need to understand how deer utilize urban/suburban areas is great not only from a biological standpoint, but from a public safety standpoint as well. Deer-vehicle collisions
20 account for upwards of 1.0 million accidents annually in the United states (Romin and Bissonette
1996, Marcoux and Riley 2010). Deer are also vectors for zoonotic diseases such as Lyme
Disease, posing a potential health concern for residents of areas with high deer densities (Adams and Hamilton 2011, Conover 2011). All of these are major concerns for growing suburban sprawls as deer densities increase. The management of white-tailed deer in urban/suburban areas is incredibly difficult though. Unlike most exurban/rural areas, urban/suburban areas regularly have weapon discharge ordinances, land access issues, and landowners unfamiliar to hunting, all of which makes management more difficult (Adams and Hamilton 2011). As hunting is the primary tool used for deer management and urbanized areas typically have hunting limitations, these areas are left with often more expensive, and potentially less effective, methods.
Alternatives are available, such as immunocontraception, but most have been found to have limited success (Fagerstone et al. 2002), and variable effectiveness (Walter et al. 2002).
Immunocontraception may be an option to maintain current local population levels, but it is rarely effective enough to reduce populations, and is extremely costly to implement (Warren
2000).
Though there has been extensive research performed on suburban/urban white-tailed deer to show trends of reduced area requirements and alternative management methods, but there has been very little research designated to examine the true effect of urbanization on localized populations. There has been a wide acceptance that urban deer require smaller areas (Kilpatrick and Spohr 2000, Grund et al. 2002, Storm et al. 2007), but without direct comparison with rural populations, these are largely assumptions. Storm et al. (2007) compared suburban, exurban, and rural populations of white-tailed deer. Though their study found difference in home range size and survival, their comparison of development classes were done across different years. Storm’s
21 rural data was collected over 10 years earlier than the suburban/exurban data by Cornicelli et al. in 1996. The results that Storm et al. (2007) reported may have been due to annual variation in weather, temperature, or food availability. Research is needed to compare deer across a development gradient during the same temporal scale in order to truly understanding exactly how these populations interact. This information can aid agencies in manipulating management techniques to best manage the population as a whole.
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CHAPTER 2 COMPARISON OF SEASONAL AND ANNUAL SPACE USE OF WHITE-TAILED DEER IN URBAN AND RURAL SOUTHERN INDIANA
Trudeau, J.K., Clevinger, G.B., and Carter, T. C.
To be submitted to the Journal of Fish and Wildlife Management
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Abstract
White-tailed deer (Odocoileus virginianus) have been extensively studied throughout their distribution and in varying habitats. Though much is known about urban and rural deer populations, little is known about how these two populations differ in response to the effects of urbanization. In particular, understanding the differences between urban and rural white-tailed deer space use in adjacent areas is essential to effectively manage the two populations. Previous research has focused primarily on studying populations of deer residing in urban/suburban or exurban/rural landscapes and comparing these results to other studies separated either temporally or spatially. Though this approach has identified characteristics that differ between the two landscape types, there is a lack of knowledge of exactly how these two populations differ within a relatively localized area under the same period. We were specifically interested in how annual and seasonal home range and core area sizes differ between urban and rural deer populations, as well as any differences in the seasonal space use shifts.
Our study took place in the City of Bloomington, Indiana and the surrounding rural areas of Monroe and Brown counties in southern Indiana. Bloomington was similar to many moderately sized cities, supporting a healthy urban white-tailed deer population among residential housing and local businesses. Using a drop net, dart projectors, suspended net-gun, and clover traps we captured 45 urban and 41 rural adult white-tailed deer between January and
July of 2015/2016. Of the 85 deer collared, 51 were fitted with Global Positioning System (GPS) collars and 34 were fitted with very high frequency (VHF) radio transmitter collars. Locations were collected every 6-8 hours and 2-4 times a week on GPS and VHF collared deer, respectively. We utilized both the fixed kernel density estimation (KDE) and minimum convex polygon (MCP) methods to estimate home range sizes. We expected urban deer would utilized
32 smaller areas than the surrounding rural population. However, we did not find this to be the case for both females and males. Females utilized areas approximately 60% smaller in urbanized landscapes than rural landscapes both seasonally and annually (p<0.05), but males were found to have no difference in space use between the two human development classes (HDC). As expected, we found males to have larger area requirements than females regardless of HDC. We had expected rural deer to have larger variations in seasonal home range size and shift than urban populations, but found no evidence to suggest that urban deer shift their space use in response to seasonality, rather sex had a larger influence on shifting spaces use. Our findings suggest that female deer have adjusted to living in an urbanized environment more than males, but further research is needed to identify factors that may influence habitat characteristics that both sexes are selecting for between HDCs.
Introduction
As a result of federal legislation, habitat management, and reintroduction efforts, white- tailed deer (Odocoileus virginianus) abundances increased rapidly during the early 20th century
(Barick 1951, Allen 1965, Beckwith 1965). Historically, white-tailed deer mainly occupied forested landscapes, but as human development has steady increased (Brown et al. 2005,
Hammer et al. 2009) so have deer densities in these urbanized areas (Swihart et al. 1995,
Kilpatrick and Spohr 2000, Grund et al. 2002, Waller and Averson 1997). Deer have been found to thrive in suburban and urban areas (Haverson et al. 2007, Zeder 2012) but these increased densities of deer in developed areas can have negative impacts on human’s due to increased vehicle-deer collisions, property damage, and the transport of disease carrying ticks (Romin and
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Bissonette 1996, Steeve et al. 2004). For these reasons, there has been increased pressure to manage urbanized populations of deer. In order to accomplish effective management, it is paramount to understand how deer utilize areas of differing densities of human development.
It has been suggested that examining an animal’s home range is one of the most important aspects to understand space use (Hemson et al. 2005). For this reason, white-tailed deer (hereafter referred to as deer) home ranges have been extensively studied for decades
(Marshall and Whittington 1969, Larson et al. 1978, Lesage et al. 2000, Grund et al. 2002). Rural populations haven been the most studied with some populations’ average home ranges exceeding
500 ha (Tierson et al. 1985, Nixon et al. 1991, Lesage et al 2000), while the less studied urban populations have been found to consistently have average home range sizes below 100 ha and even as small as 43 ha annually (Cornicelli et al. 1996, Kilpatrick and Spohr 2000, Grund et al.
2002, Storm et al. 2007). Storm et al. (2007) examined the difference between exurban and suburban adult female deer in Illinois and found that suburban females had smaller home ranges than females occupying areas with fewer homes and greater agriculture cover in exurbia only 5 km away. When examining the literature, it is easy to see the trends of urban deer populations having reduced home range sizes. The reasons for such drastic differences in the home range sizes of urban deer populations have been attributed to a multitude of factors. Not only do urban deer have increased access to continuous anthropogenic food sources such as bird feeders and ornamental shrubbery, but there is a reduction in natural predation (Kilpatrick and Spohr 2000,
Stewart 2011) and hunting pressure due to access limitations (Harden et al. 2007). These factors have allowed some urban and suburban deer populations to increase drastically beyond the cultural carrying capacity, or the abundance of deer the public determines to be acceptable, in many areas (Swihart et al. 1995, Messmer et al. 1997, Lauber and Knuth 2004).
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Even though there has been extensive research on rural deer home range characteristics and an increased interest in urban deer home range characteristics, there is little information on the effect of urbanization on deer space use. One of the most important aspects of an animal’s space use is the core area. The core area is the most intensively used part of a deer’s home range, and can indicate the quality of the habitat a deer is using (Grund et al. 2002). While there is little research on home range comparison between different human development classes (HDC) within a localized area, there is even less on the differences between the core area size of deer between
HDCs (Kilpatric and Spohr 2000, Grund et al. 2002, Storm et al. 2007). This highlighted the need for a targeted study aimed to understand the effect of human development on space use of deer by comparing space use of adjacent urban and rural deer populations simultaneously.
Within this context, our objectives were to quantify the differences between sex and HDC for 1) annual home range and core area size, 2) seasonal home range and core area size, and 3) shifts in the center of utilization between seasonal home ranges. Understanding the impacts urbanization has on the space use of deer populations can illuminate other important population parameters such as density. In turn, this can assist managers in promoting proper management practices in urban areas. Being able to base these decisions on scientific evidence is especially important in today’s world where public perception and support is of equal importance and weight in deer management (Keller 1995).
Study Area
This study occurred in and around the city of Bloomington IN, USA (60.5 km2; population ~85,000). Bloomington is characterized by small residential neighborhoods with vegetated yards, city parks, and small woods lots. The deer population in the surrounding public and private forest, farmland, and floodplains was used as the exurban/rural study area. All work
35 was located in Monroe and Brown counties of southern IN, USA located in the Southern Hills and Lowlands physiographic regions (elevation ~235m). The regions are characterized by dense, even-aged, oak-hickory-beech forest with a mosaic of farm land, creek floodplains, suburban and urban development, and city parks. Yellowwood and Morgan/Monroe State Forests were used heavily throughout the study.
Methods
Capture and Handling
Deer were captured from 5 January to 28 July in 2015 and 2016 using four capture methods; 1) a 7.62 m x 7.62 m remote activated drop net (Wildlife Capture LLC., Flagstaff, AZ),
2) modified clover traps (Clover 1956), 3) dart projectors in conjunction with VHF radio transmitter darts (Pneu-dart Inc., Williamsport, PA), and 4) a suspended, remote activated, net launcher equipped with a 6.10 m x 6.10 m net (Wildlife Capture LLC., Flagstaff, AZ). All capture methods were baited with cracked corn, apples, and peanut butter. Clover traps and the suspended net launcher were not used within the City of Bloomington due public safety concerns and city firearms ordinance. Suburban/urban trapping efforts occurred exclusively on private property while exurban/rural trapping efforts occurred on public and private property.
All captured deer were physically restrained and blindfolded, then immediately injected intramuscularly with BAM (Butorphanol Tartrate 27.3 mg/mL - Azaperone Tartrate 9.1 mg/mL-
Medetomidine HCl 10.9 mg/mL; Zoopharm INC., Laramie, Wyoming, USA; Miller et al. 2009).
Hand injected dosages followed manufacturer dosing guidelines based on animal age and sex.
For darted deer, all darts were pre-filled with a standard 2 ml of BAM to ensure adequate dosages for all deer remotely darted regardless of age or gender.
36
Once a deer reached full anesthesia, the heart rate, respiratory rate, and temperature were monitored and recorded every five minutes. Males and females were equipped with either a 190 g VHF radio collar (M2230B, Advanced Telemetry Systems, Inc., Isanti, MN) or an 825 g
Global Positioning System (GPS) collar (G2110E Iridium; Advanced Telemetry Systems, Inc.,
Isanti, MN). Collars were sex specific with male collars having an expandable neoprene band to compensate for seasonal variation in neck diameter. The two collar types were distributed evenly between urban and rural areas. All capture and handling procedures were approved by the
Institutional Animal Care and Use Committee at Ball State University (IACUC Protocol #:
640852-2).
Monitoring Movements
GPS collars collected locations three to four times per day. VHF collars were monitored two to four times per week using ground radio-telemetry. VHF locations were collected at least
48 hours apart between the hours of 0800 and 2000. Nocturnal locations were not collected due to public perception concerns within Bloomington. When possible, deer locations were collected by “honing-in” on their location using a vehicle mounted 5-element Yagi antenna (Wildlife
Materials Inc.) and receiver (R2000; Advance Telemetry Systems) to obtain a visual or determine the property parcel the deer occupied. When a visual location was established, the location was recorded using a handheld GPS unit (GPSMAP 60Cx; Garmin Ltd., Olathe,
Kansas). If a visual confirmation was not possible, two or three azimuth triangulation was performed to estimate location. Location estimates were generated in real time using the software program LOAS (Ecological Software Solutions LLC. Hegymagas, Hungary) with the maximum- likelihood estimator algorithm used to estimate telemetry error. Only intercepting azimuths of >
30 or < 150 were accepted.
37
Home Range and Core Area Analysis
We used the kernel density estimator and isopleth tools within the Geospatial Modelling
Environment software (GME; Version 0.7.4.0; Beyer, H.L. 2015) to produce fixed kernel density estimates (KDE) for deer fitted with GPS collars. Seasonal space use was calculated with 95% and 50% utilization contours representing home range and core area. We choose to use the least- squares cross-validation (LSCV) smoothing parameter (Storm et al. 2007) and a 30m grid cell.
ArcGIS 10.3 was used to calculate the area of each home range generated. To reduce the influence of excursion events on overall area, locations occurring ≥ 0.50 km from the 95% utilization contour were removed and a final home range was estimated. Three biologically relevant seasons were established; fawning (15 May to 30 September), breeding (1 October to 15
December), and post-breeding (16 December to 14 May; Anderson et al. 2011). Seasonal home ranges were estimated only if a deer was tracked the entirety of a season. Radio-collared deer had reduced location sampling compared to GPS collars and fell below the recommended 30 locations per season minimum to accurately calculate a home range using the KDE method
(Seaman et al. 1999; Girard et al. 2002). For this reason, radio-collared seasonal home ranges were estimated using the minimum convex polygon (MCP) method (Seaman and Powell 1996,
Seaman et al. 1999). MCPs were calculated in ArcGIS 10.3 using the MCP analysis tool in the
Home Range Tools Extension (Rodger et al. 2015), selecting for 95% and 50% of locational home ranges. In order to compare data from VHF and GPS collars together for seasonal home range analysis, subsamples of GPS collar locations were generated using the sample function in program R (R Core Team, 2015). The subsamples obtained were used to calculate 95% and 50%
MCP seasonal home ranges. Subsample size of GPS collar locations were the equivalent of the maximum number of locations collected on VHF collars each season. For consistency with VHF
38 data, subsampled GPS locations were a minimum of 48 hours apart and were collected during diurnal hours.
Annual home ranges and core areas were estimated on deer tracked for a minimum of 11 months. Annual home range estimates did not exceed 12 months of locations. If a deer was tracked over consecutive years, the mean of the two years was used in analysis. If a deer occupied multiple distinct areas during a single season or year, separate fixed KDE home ranges were estimated for each area and the sum was considered the seasonal or annual home range
(Lesage et al. 2000).
Human development class (HDC) was determined for each season and year by calculating the density of homes within 1 km2 around the centroid of each home range. Parcel data was obtained at the county level from state and county databases. Manual spot checks were performed using aerial photography to ensure the quality of parcel data. Suburban/urban areas, hereafter referred to as urban, were classified as containing ≥ 26 dwellings/ km2 while exurban/rural areas, hereafter referred to as rural, contained ≤ 25 dwellings/ km2 (Brown et al.
2005; Hansen et al. 2005).
Seasonal shift Analysis
Following the methods laid out by Grund et al. (2002), we calculated the distance between geometric centered of consecutive seasonal home ranges using ArcGIS. If an individual deer was collared for greater than three consecutive seasons, the average shift between the same seasons over consecutive years was averaged. Home range centroid shift was only measured if deer were located the entirety of two consecutive seasons.
39
Statistical Analysis
All statistical analysis was performed using the R programming language (R Core Team,
2015). Data was log transformed if values were not normally distributed. We used a linear regression model to test for differences between mean log-transformed home range, core area, and seasonal home range shift of urban and rural deer. We performed linear regression models with all two and three-way interaction containing HDC, sex, and season when applicable.
Individuals were treated as the repeat factor. Interactions were dropped if they were not significant. We used Akaike information criterion (AIC) to select the best fit model. Post hoc
Tukey HSD tests were performed to analyze all significant seasonal and annual results (Grund et al. 2002).
Results
Seasonal Home range, Core area, and Shift
Of the 85 deer collared, 55 deer (11 urban males, 17 urban female, 15 rural female, 12 rural males) were analyzed seasonally. Only deer that had occupied the entirety of a season were analyzed. We delineated 77 seasonal home ranges from 29 GPS collared deer using 45,155 locations and 65 seasonal home ranges from 26 VHF collared deer using 2,508 locations. The average error ellipse for all triangulated locations was 5.49 ha. Best fit models varied between home range estimation method (Table 1). Average MCP and KDE seasonal home range and core area size of males were larger than those of females during and between all seasons (Table 2).
Seasonality did not affect female home range or core area size, however, males exhibited larger home ranges during the breeding season than during the fawning season (Table 2). Urban females had smaller average MCP home ranges and KDE core areas than rural females (p<0.05),
40 but was not dependent on seasonality. Male home range and core area size did not differ between
HDCs (Table 3).
Shift in seasonal home range centroids were not significantly different between season or
HDC. Males had larger average shift in home range centroids than females; 865m and 197m, respectively for MCP home ranges, and 734m and 190m, respectively for KDE home ranges.
Shifts in KDE seasonal home range indicate rural deer, male and females combined, have larger seasonal shifts in home range centroids than urban deer; 393m and 297m, respectively.
Annual home range and core area
Of the 86 deer captured, 24 VHF and 23 GPS annual KDE home ranges and core areas were derived from 2,526 VHF and 30,750 GPS locations respectively. Urban females had smaller average annual home range and core areas (95% CI, 68.44 ±19.54 ha and 13.46 ±3.64 ha, respectively) than rural females (95% CI, 167.84 ±60.16 ha and 33.98 ±11.40 ha, respectively)
(Table 4). Though rural male and female annual home range size differed (p<0.05), core area sizes did not significantly differ (p=0.06).
Discussion
As expected from previous literature of deer in both urban/suburban (Gaughan and
DeStefano 2005, Walter et al. 2011) and exurban/rural (Nelson and Mech 1981, Cornicelli et al.
1996, Storm et al. 2007) landscapes, we found male deer to have larger home ranges than females. This is likely due to higher average nutritional demands and increased movement due to breeding behavior (Nixon et al. 1991, Nicholson et al. 1997, Long et al. 2008). As with other studies, we documented males having larger home ranges during the fall breeding season than during the fawning. Nixon et al. (1991) suggested that in intensively cultivated areas of the
41
Midwest, fawning home range reduction was the result of increased food availability. In our study area agriculture is not as intensive as other parts of the Midwest where small wooded areas are speckled amongst vast expanses of crops; our study site was a mosaic of human developments, croplands, hardwood forests, and flood plains. For this reason, we believe that food availability had less of an influence on seasonal variation in male home range size than breeding behavior. If food availability was the leading factor, we would have expected females to demonstrate similar variations in seasonal home range size and shifts in center of seasonal home ranges.
Across the United States, deer have been found to have varying home ranges sizes in different HDCs and geographic regions. We expected both sexes to exhibited similar trends in space use as seen in other portions of the country (Kilpatrick and Spohr 2000, Grund et al. 2002,
Storm et al. 2007, Haverson et al.2007). Though the two methods we utilized to measure seasonal home range size exhibited slightly differing results, the underlying trends remained the same. We were able to document similar size seasonal home ranges in both HDCs as published elsewhere (Table 5). Any variations between the two home range methods may be due to the differences in location sampling regimes or the smaller sample size of deer used for KDE home range estimates. With higher sampling both in terms of points per deer and deer per treatment, we believe the two methods would have yielded more similar trends. One clear trend observed regardless of estimation method, is urban females utilized smaller areas than rural females
(Tables 3, 4, 5). Though we are not the first to observe this difference (Storm et al. 2007, Porter et al. 2004, Grund et al. Haverson et al. 2007), we are the first study in the Midwest to compare these two populations simultaneously and note this difference. We believe the heterogeneous environment of urban landscapes provide deer with high quality food sources such as manicured
42 lawns, fertilized gardens, and supplemental food from residents. Unlike rural food sources, these urban sources are more consistent throughout and between years. In suburban Connecticut,
Kilpatrick and Sphor (2000) attributed the reduction in space use to this increased food availability.
We estimated urban female home ranges were approximately 60% smaller than those of rural females not only because of increased resource availability, but also due to different behavioral responses to human activity. Haverson et al. (2007) believed that deer reduced their home range size in urbanized areas as they became more domesticated and tolerant of human activity. We regularly observed deer in both urban and rural environments during our capture season and the subsequent tracking of collared deer. Though we did not quantify this, we frequently observed deer in Bloomington slowly and calmly walk away when approached by a human as opposed to rural deer that would run away, snort, and raise tails when encountering humans. We also found urban deer to be more curious, routinely approaching residents attempting to hand feed the deer with various fruits and vegetables. This type of behavior indicates that urban deer are becoming accustomed to human presence, not only through human habitat manipulation, but also through direct interactions. Being less impacted by human presence, urban deer, especially females, show signs of adapting to not only the change in habitat within developed areas, but the change in increased interactions with humans.
We expected males to show similar trends in space use, but found HDC to have little influence on home range size. Our findings contradict those of other studies (Haverson et al.
2007, Gaughan and DeStefano 2005). Gaughan and DeStefano (2005) document rural males having annual home ranges 10 orders of magnitude larger than suburban males in Massachusetts.
This reduction in home range size was attributed to differing winter cover requirements in the
43 suburban and rural landscapes. Rural deer tended to select for heavier canopy non-deciduous forest, while suburban males were selecting for higher densities of homes that likely provided a unique microclimate and protection from harsh winter winds. Our study site experiences mild winters with annual snowfall <38 cm and average monthly winter temperatures rarely falling below 0°C (US Climate Data 2017). In addition, we did not find that males or females exhibited significant differences in seasonal shifts in the center of seasonal home ranges. If decreased temperatures or snowfall played a larger role in the shift of urban or rural seasonal home ranges, we would have expected both sexes to exhibit similar distances in shifts between non-wintering and wintering ranges, but this was not seen. We did regularly observe urban deer bedding against homes during the winter, but we observed the same individuals bedding next to homes throughout the year, leading us to believe that deer were not selecting to bed near homes primarily for thermoregulation. The need for different winter cover is unlikely to have an influence on deer movement and space use in southern Indiana but maybe more crucial to northern populations where snowfall and temperature are more severe (Lesage et al. 2000).
Males appear to be less influenced by human development than females do, and may base home range size more heavily on other factors that we did not examine.
We feel it is important to note that deer were regularly observed utilizing areas across the developmental gradient. Males were observed utilizing both urban and rural areas more frequently, but we also noted females moving across the gradient between the two areas, and specifically documented a single female migrating between separate fawning and breeding/spring home ranges. This female relocated from a highly rural area during the winter to a suburban area during late spring and early fawning of 2016 and 2017. We believe this female utilized a different fawning home range for rearing fawns during the most susceptible time of a
44 neonate’s life. Williamson et al. (2015) found that fawns born within suburban and urban areas had much lower predation rates than those born in rural areas. Though we only documented this occurring in a single individual, it shows that deer may select for varying amounts of human development for reduced predation pressure during the most susceptible time of a deer’s life.
Conclusion
Observing differences in the home range and core area size of urban and rural females indicates that females are more heavily influenced by human development than males. We are hesitant to draw the conclusion that males are not affected by urbanization due to the wide range in home range size we observed in our urban males. During the summer of 2016, we had an urban male utilize a mere 4.31 ha, the smallest seasonal home range recorded, and another urban male utilize 386.96 ha, the second largest seasonal home range recorded, less than 1 km from each other. This single example exemplifies the wide range of home range size that our males exhibited. With larger sample sizes of both urban and rural males, we believe we could draw more sound conclusions on how urbanization affects males.
Overall, we believe urban areas likely present better foraging opportunities, increase suitable cover, and reduce hunting and predation pressure for deer (Swihart et al. 1995,
Kilpatrick and Spohr 2000). With urban females utilizing less area than rural females, reduction of hunter access, and the societal difficulties behind managing urban deer populations (Stewart
2011), urbanized landscapes have the capacity to support the rapid growth in deer densities. As deer densities continue to increase in urbanized areas across the United States, so do the concerns for deer-vehicle collisions, disease spread, and property damage, facilitating the necessity of addressing how deer utilize these areas. The exact features of urban environment that deer select
45 for and similarly, the habitat features that facilitate dispersal in urban areas, are still not well understood. Taking into account abundance estimates in each HDC, higher resolution data, larger sample sizes, and longer-term monitoring, research could identify other life history characteristics that may play a role in space use between HDCs, such as age class and relatedness among individuals. Though there is plethora of knowledge on white-tailed deer, there is still the need for a better understanding of human and deer interactions to improve our ability to manage deer populations in and around urbanized landscapes. It is important to address these distinct differences between localized populations to properly manage this important resource.
Acknowledgments
Funding for this project was provided by the Indiana Department of Natural Resources through Wildlife Restoration Grant W48R1. We are indebted to our field technicians and interns for all their hard work helping to capture, collar, and track the deer analyzed in this study. I thank
Dr. Jason Doll for his assistance with statistical analysis. A special thank you to all the residents of Monroe and Brown counties, Indiana for their support during this project.
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Table 1: Summary of model selection for the comparison of seasonal 95% and 50% minimum convex polygon (MCP) and fixed kernel density estimation (KDE). Human development class (HDC) and sex were additional fixed factors used in model construction. Best fit models were determined by Δ AIC ≤ 2.00.
Model n parameters Δ AIC
HDC+Season+Sex +Sex*Season+HDC*Sex 9 0.00 HDC+Season+Sex+ Sex*Season+HDC*Season+HDC*Sex 11 2.43
HDC+Season+Sex+HDC*Season*Sex+Sex*Season+HDC*Season 13 5.96 MCP95%
HDC+Season+Sex +Sex*Season+HDC*Sex 9 0.00 HDC+Season+Sex+ Sex*Season+HDC*Season+HDC*Sex 11 1.40
HDC+Season+Sex+HDC*Season*Sex+Sex*Season+HDC*Season 13 3.05 MCP50%
HDC+Season+Sex +Sex*Season+HDC*Sex 9 0.00
HDC+Season+Sex + HDC*Sex 7 1.16 HDC+Season+Sex+HDC*Season*Sex+Sex*Season+HDC*Season 13 3.90 KDE 95% KDE HDC+Season+Sex+ Sex*Season+HDC*Season+HDC*Sex 11 3.90
HDC+Season+Sex +Sex*Season+HDC*Sex 9 0.00
HDC+Season+Sex + HDC*Sex 7 0.14 HDC+Season+Sex+ Sex*Season+HDC*Season+HDC*Sex 11 3.95 KDE 50% KDE HDC+Season+Sex+HDC*Season*Sex+Sex*Season+HDC*Season 13 5.40
50
Table 2: Seasonal home range size comparison of male and female white-tailed deer in southern Indiana using minimum convex polygon (MCP) and fixed kernel density estimation (KDE) methods. Differing letters denote statistically significant differences between average home range size of each season and sex (p<0.05).
Fawning Breeding Post-breeding Sex n n n (x̅ ± CI) (x̅ ± CI) (x̅ ± CI)
Female 28 39.80 ± 12.34 A 28 38.75 ± 16.30 A 26 53.33 ± 17.32 A
MCP Male 17 97.27 ± 43.75 B 15 173.94 ± 58.17 C 13 114.49 ± 48.47 BC
Female 14 67.11 ± 26.03 A 15 74.82 ± 13.48 A 13 90.93 ± 36.64 A
KDE Male 7 223.96 ± 70.82 B 9 548.58 ± 152.62 C 5 386.30 ± 189.61 BC
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Table 3: Comparison of mean seasonal home range and core area size of urban and rural white-tailed deer in southern Indiana by development class and sex across all seasons. Differing letters denote statistically significant differences between mean areas utilized by each sex within each Human Development Class (HDC) by estimation method (p<0.05).
MCP KDE
Home range (ha) Core area (ha) Home range (ha) Core area (ha) HDC and Sex n (x̅ ± CI) (x̅ ± CI) n (x̅ ± CI) (x̅ ± CI) Urban Female 43 24.57 ± 4.59 A 6.62 ± 1.60 A 22 58.99 ± 10.35 A 13.36 ± 2.43 A Rural Female 39 64.86 ± 15.57 A 19.77 ± 6.93 B 20 97.31 ± 27.02 B 24.55 ± 7.54 B Urban Male 25 125.11 ± 43.49 B 33.25 ± 12.52 C 11 440.49 ± 129.56 C 97.56 ± 31.77 C Rural Male 20 131.16 ± 41.48 B 30.73 ± 12.15 C 10 359.11 ± 157.30 C 82.51 ± 45.75 C
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Table 4: Mean annual fixed kernel density home range size of urban and rural white-tailed deer in southern Indiana. Differing letters denote statistically significant differences between average home range and core area size of each sex and HDC (p<0.05).
Average Home range (ha) Average Core area (ha) HDC and Sex n (x̅ ± SE) (x̅ ± SE) Urban Female 14 68.44 ± 19.54 A 13.46 ± 3.64 A Rural Female 14 167.84 ± 60.84 B 33.98 ± 11.40 B Urban Male 8 432.69 ± 117.28 C 87.32 ± 31.59 C Rural Male 5 400.21 ± 147.15 C 75.86 ± 30.18 BC
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Table 5: Comparison of seasonal home range size (ha) between multiple studies performed in differing development classes across the USA. Adapted from Storm et al. (2007).
Author State Development class Home-range estimator Fawning Breeding Winter
Nixon et al. (1991) IL Rural Min convex Polygon 55 ---- 177
Cornicelli et al. (1996) IL Suburban Min convex Polygon 17 17 37
Kilpatrick and Spohr CT Suburban Adaptive kernel 33 39 36 (2000)
Grund et al. (2002) MN Suburban Adaptive Kernel 50 93 85
Campbell et al. (2004) WV Rural Fixed kernel 100 99 133
Porter et al. (2004) NY Suburban Min convex polygon 21 ---- 22
Storm et al. (2007) IL Exurban Fixed kernel 53 ---- 91
Min convex polygon 61 54 76 This study (2017) IN Rural Fixed kernel 89 87 118 Min convex polygon 21 24 34 This study (2017) IN Urban Fixed kernel 45 64 67
54