RESOURCE SELECTION AND SURVIVAL OF FEMALE WHITE-TAILED DEER IN AN AGRICULTURAL LANDSCAPE. Melissa M. Miller

Transcription

1 RESOURCE SELECTION AND SURVIVAL OF FEMALE WHITE-TAILED DEER IN AN AGRICULTURAL LANDSCAPE by Melissa M. Miller A thesis submitted to the University of Delaware in partial fulfillment of the requirements for the degree Master of Science in Wildlife Ecology Spring 2012 Copyright 2012 Melissa M. Miller All Rights Reserved

2 RESOURCE SELECTION AND SURVIVAL OF FEMALE WHITE-TAILED DEER IN AN AGRICULTURAL LANDSCAPE by Melissa M. Miller Approved: Jacob L. Bowman, Ph.D. Professor in charge of thesis on behalf of the Advisory Committee Approved: Douglas W. Tallamy, Ph.D. Chair of the Department of Entomology and Wildlife Ecology Approved: Robin W. Morgan, Ph.D. Dean of the College of Agriculture and Natural Resources Approved: Charles G. Riordan, Ph.D. Vice Provost for Graduate and Professional Education

3 Those who contemplate the beauty of the earth find reserves of strength that will endure as long as life lasts. There is something infinitely healing in the repeated refrains of nature -- the assurance that dawn comes after night, and spring after winter. Rachel Carson iii

4 ACKNOWLEDGEMENTS I would like to thank my graduate advisory committee members, Dr. Jake Bowman, Joe Rogerson and Dr. Greg Shriver, for their knowledge, direction, and support throughout this research project. Also, I am grateful for the funding sources that made this project and my education possible Delaware Department of Natural Resources Division of Fish and Wildlife, USDA McIntire-Stennis Formula Grant and the University of Delaware Department of Entomology and Wildlife Ecology. Thanks and appreciation to the staff at Redden State Forest for their cooperation throughout the project and the many, many private landowners who allowed me to trap deer on their properties. Without the help of numerous technicians, volunteers and fellow students this research would not have been possible; thank you to J. Ashling, J. Baird, C. Corddry, S. Dougherty, A. Dunbar, K. Duren, N. Hengst, D. Kalb, H. Kline, D. Knauss, E. Ludwig, R. Lyon, D. Peters, C. Rhoads, M. Springer, and E. Tymkiw for the countless hours of trapping and telemetry required to complete this research. Lastly, I would like to express my heartfelt appreciation to my friends and family, especially my parents, grandparents and Dave, whose never-ending love and support was paramount in my success. iv

5 TABLE OF CONTENTS LIST OF TABLES... vi LIST OF FIGURES... vii ABSTRACT... viii Chapter 1 INTRODUCTION TO WHITE-TAILED DEER OVERABUNDANCE AND CROP DAMAGE RESOURCE SELECTION OF FEMALE WHITE-TAILED DEER IN AN AGRICULTURAL LANDSCAPE... 4 Abstract... 4 Introduction... 5 Study Area... 9 Methods Results Discussion Management Implications SURVIVAL OF FEMALE WHITE-TAILED DEER IN AN AGRICULTURAL LANDSCAPE Abstract Introduction Study Area Methods Results Discussion Management Implications OVERALL MANAGEMENT IMPLICATIONS LITERATURE CITED v

6 LIST OF TABLES Table 1 Table 2 The average 95% home range and 50% core area by season and year of adult female white-tailed deer in Sussex County, Delaware in 2010 and Results for model selection investigating effects of season, time of day, land type, and amount of crop available on adult female white-tailed deer habitat selection in Sussex County, Delaware from May-January in 2010 and Models are listed with the effects included in each model, the number of parameters (K), QIC, and weight of the model (w) vi

7 LIST OF FIGURES Figure 1 Figure 2 Figure 3 A map highlighting the study area. The state of Delaware with the discontinuous tracts of Redden State Forest darkened in Sussex County The average amount of crop in random and used buffers by season/time of day combination with 95% confidence intervals. Dark bars represent random buffers, light bars represent used buffers Ossified fibrosarcoma on the head of a female white-tailed deer. a) growth at capture 14 March 2010 and b) growth at mortality 16 September vii

8 ABSTRACT Information regarding resource selection by female white-tailed deer in agricultural areas is necessary to develop management strategies to minimize crop damage. Understanding survival rates of white-tailed deer is also imperative for managers to develop management strategies to achieve desired populations of whitetailed deer. The objectives of this study were to investigate resource selection and estimate survival rates of female white-tailed deer in a fragmented agricultural landscape. I collected 13,409 telemetry locations from 44 radio collared adult female white-tailed deer to document mortalities and to estimate home range size and habitat availability. To investigate resource selection, I compared used locations to random available locations and created resource selection functions (RSFs). The 95% fixed kernel home ranges and 50% core areas differed by year (95%, F 1, 39 = 8.87, P = 0.004; 50%, F 1, 39 = 9.58, P = 0.003) and season (95%, F 1, 39 = 13.77, P < 0.001; 50%, F 1, 39 = 18.84, P < 0.001), but not by time of day (95%, F 1, 39 < 0.01, P = 0.978; 50%, F 1, 39 = 0.05, P = 0.825). Deer selected crop more during the nighttime growing season and less during the daytime hunting season. Although deer were using crop fields less during the hunting season, they remained within the property boundary where they used the most crop fields during the growing season. The annual survival rate was 0.43 (SE=0.11) and 0.72 (SE=0.28) for 2010 and 2011, respectively, and viii

9 differed between years (χ 2 1=5.21, P=0.022). The majority of documented mortalities were attributed to harvest (80%, n=16), whereas deer-vehicle collisions (15%, n=3) and natural mortality (5%, n=1) represent fewer mortalities. An extensive amount of snow fell in the area prior to the beginning of the 2010 hunting season and may have affected harvest numbers and overall survival rates the first year. Managers in the southeastern portion of white-tailed deer ranges need to take abnormal weather conditions into consideration when making predictions about harvest numbers and survival rates. My results suggest that farmers should be able to legally harvest deer that cause crop damage on their property. I recommend that farmers encourage hunters to move deeper into forested habitats to increase the likelihood of encountering deer and thus reducing crop damage. ix

10 Chapter 1 INTRODUCTION TO WHITE-TAILED DEER OVERABUNDANCE AND CROP DAMAGE White-tailed deer (Odocoileus virginianus) populations are overabundant and causing problems in some areas of the United States (Warren 2011). Due to their ability to adapt and use resources, white-tailed deer are found in a diversity of habitats and currently inhabit nearly every state in the United States (Halls1984). Negative issues associated with high densities of white-tailed deer include damage to landscaping plants damage to agricultural crops, damage to timber productivity, deervehicle collisions, and functioning as a reservoir of disease (Conover 1997). Conover (1997) estimated that deer have an annual negative monetary value of greater than $2 billion when vehicle damage, crop damage, timber damage, and landscaping plant damage are summed; however, the $2 billion annual estimate does not take into consideration human fatalities, injuries, or illnesses resulting from deer-human interactions (Conover 1997). Although deer have negative impacts, they do have positive value as well, both economically and biologically. White-tailed deer are an important recreational resource both as the premier game mammal in most parts of North and Central America (Halls 1984) and as a source for non-consumptive uses (Conover 1997). In addition to providing a positive monetary value as a recreational resource, deer also hold an intangible ecosystem value as a native ungulate. Whitetailed deer must be managed for sustainability and to reduce human-deer conflicts (Hewitt 2011). 1

11 Within areas of intense agriculture, deer are abundant because of the large quantity of high quality forage (Conover and Decker 1991). Conover (1997) suggested a conservative estimate of $100 million in agricultural damage is caused by deer in the United States each year, while a more recent estimate reported an annual $7.6 million in agricultural damage due to deer in Maryland alone (MDNR 2009). The actual agricultural damage due to white-tailed deer is probably greater than Conover s estimate from Although many species of wildlife also cause damage to crops, white-tailed deer are reported the most (Conover and Decker 1991). Whitetailed deer management in areas of crop damage is difficult because landowners and state biologists may have different goals. Conover and Decker (1991) indicated that wildlife damage had reached a level that was influencing the willingness of landowners to provide wildlife habitat on or near their properties. Comprehensive management approaches need to be developed to maintain deer populations at a level that can reduce their impact on crops while maintaining sustainable populations. Before state biologists can design and implement deer management strategies in agricultural areas, they need to understand how and when deer are using the landscape. Investigating resource selection is one of the best tools available to attempt to understand how deer use different habitat types throughout the year. White-tailed deer resource selection differs for a wide variety of reasons throughout their range (Beier and McCullough 1990, Vercauteren and Hygnstrom 1998, Brinkman et al. 2005, Hiller et al. 2009). Habitat selection can change throughout the year as different food and cover resources are depleted or become available. Changes in resource selection from the growing season to the hunting season may affect management 2

12 because deer cannot be harvested during the growing season in Delaware. Landownership is an important aspect to consider in areas where there are both public and private hunting opportunities which can influence deer behavior. Any changes in resource selection from day to night could affect management because deer cannot be harvested at night in most states. Changes of human activity on the landscape between seasons and time of day may affect deer behavior and therefore resource selection. In addition to resource selection, survival rates of white-tailed deer are important to understanding population dynamics (Dusek et al. 1992, Brinkman et al. 2004). White-tailed deer survival rates can be impacted by development of the area, hunting pressure, predators, environmental pressures, and the age and sex of deer (Grovenburg et al. 2011). Successful deer management is achieved by controlling the number of females so we must have accurate estimates of survival rates of adult females in a population to set harvest goals (Porter et al. 2004). In order to increase our knowledge of white-tailed deer ecology in agricultural landscapes, I investigated white-tailed deer resource selection and survival rates in Sussex County, Delaware. 3

13 Chapter 2 RESOURCE SELECTION OF FEMALE WHITE-TAILED DEER IN AN AGRICULTURAL LANDSCAPE Abstract Harvest, during regular season hunts or with special permits, as a means to reduce crop damage is widely practiced but the effectiveness is generally unknown. Information regarding resource selection by female white-tailed deer in agricultural areas is necessary to develop management strategies to minimize crop damage. The objectives of this study were to investigate seasonal and temporal changes in resource selection of female white-tailed deer in a fragmented agricultural landscape. I collected telemetry locations (n = 13,409) from radio collared adult female whitetailed deer (n = 44) to estimate seasonal and temporal home range sizes and habitat availability. I created resource selection functions (RSFs) by comparing used locations to random available locations. The 95% fixed kernel home ranges and 50% core areas differed by year (95%, F 1, 39 = 8.87, P = 0.004; 50%, F 1, 39 = 9.58, P = 0.003) and season (95%, F 1, 39 = 13.77, P < 0.001; 50%, F 1, 39 = 18.84, P < 0.001), but not by time of day (95%, F 1, 39 < 0.01, P = 0.978; 50%, F 1, 39 = 0.05, P = 0.825). I found season, time of day, and amount of crop available to be important factors for predicting resource selection. Deer used cropland in proportion to its availability during the daytime growing season and nighttime hunting season, but selected crop more during the nighttime growing season and less during the daytime hunting season. Although deer were using crop fields less during the hunting season, they remained 4

14 within the property boundary where they used the most crop fields during the growing season. My results suggest that farmers should be able to legally harvest deer that cause crop damage on their property. I recommend that famers encourage hunters to move deeper into the forest to increase the likelihood of encountering deer. To further increase hunter success, I advise farmers to plant their winter cover crop early to provide a food source for deer during the early hunting season. KEY WORDS: agriculture, Delaware, home range, Odocoileus virginianus, radio telemetry, resource selection, white-tailed deer. Introduction White-tailed deer are the most commonly reported species of wildlife causing crop damage (Conover and Decker 1991). Conover (1997) suggested a conservative estimate of $100 million in agricultural damage is caused by deer in the United States each year, while a more recent estimate reported an annual $7.6 million in agricultural damage due to deer in Maryland alone (MDNR 2009). The actual agricultural damage due to white-tailed deer is probably greater than Conover s estimate from Many farmers who report damage to agricultural crops do not have the knowledge, ability, or authority to deal with the problem and rely on professionals to make biological and economical management decisions (Fagerstone and Clay 1997). Before state and federal biologists can develop management strategies for deer populations in agricultural areas, they need to understand how and when deer are using the landscape. Managers and farmers rely on hunting as the primary means to control deer populations in areas of crop damage, but the effectiveness of hunting for relieving crop 5

15 damage is unknown (Vercauteren and Hygnstrom 1998). Resource selection may change from the growing season to the hunting season and farmers may not be able to target the deer that cause damage. Although female white-tailed deer in Nebraska remained within vicinity of potential crop damage (Vercauteren and Hygnstrom 1998), this study did not include privately owned farms where crop damage occurred. Details of resource selection are also important to consider, especially changes from day to night, because deer can only be legally harvested during daytime hours in most states. In order for farmers to confidently relieve crop damage on their property, we need to understand how resource selection changes during the growing season, hunting season, and during different times of the day. White-tailed deer adjust their habitat selection and behavior in response to agricultural activities, changes in predator abundance, and environmental stress (Vercauteren and Hygnstrom 1998, Brinkman et al. 2005, Massé and Côté 2009). Resource selection can happen on the landscape scale (second-order, selecting a home range), within the home range (third-order, habitat components; Johnson 1980, Massé and Côté 2009), and on a seasonal and temporal basis (Godvik et al. 2009). Differences in seasonal and temporal resource selection can effect management because crop damage and legal hunting occur at during different seasons. Crop damage occurs during the major crop growing season of corn and soybeans (May- August; Vecellio et al. 1994, Rogerson 2005), whereas winter cover crops are usually planted in October and are not negatively affected by deer browse (Springer 2010). Hunting season typically starts in the fall and continues into winter months (September January in Delaware). Understanding how resource selection changes between 6

16 seasons is imperative to helping farmers reduce crop damages because hunting and damage may not occur during the same time. Research demonstrates white-tailed deer use of agricultural crops during the growing season (Conover and Decker 1991, Vercauteren and Hygnstrom 1998) but several studies reported agriculture having minimal impacts on white-tailed deer movements and behavior (Brinkman et al. 2005, Hiller et al. 2009). On a National Wildlife Refuge in Nebraska, Vercauteren and Hygnstrom (1998) found deer used corn during the growing season but shifted home ranges deeper into cover after crop harvest; however, hunting during their study was limited to a 3-day muzzleloader hunt and may not be comparable to areas with longer hunting seasons. Changes in deer behavior between day and night are often investigated as activity on the landscape (Kammermeyer and Marchinton 1977, Beier and McCullough 1990) or habitat use and home ranges (Beier and McCullough 1990, Vercauteren and Hygnstrom 1998, Hiller et al. 2009). Definitions of day and night have not previously been based on legal hunting hours. Because legal hunting hours are the only time a farmer could harvest deer and reduce crop damage by deer on their property Delaware, we need to know how deer are using the landscape during this time frame to assist farmers in dealing with issues of crop damage. Factors effecting temporal movement patterns of white-tailed deer have been extensively researched (Kilpatrick and Lima 1999, Porter et al. 2004, Brinkman et al. 2005, Grovenburg et al. 2009), but have not been associated with habitat types or availability for harvest in relation to crop damage. Research about deer resource selection in agricultural landscapes has been 7

17 focused in the Midwest (Vercauteren and Hygnstrom 1998, Brinkman et al. 2005, Storm et al. 2007, Hiller et al. 2009), but information from agricultural landscapes in the East is lacking. Vercauteren and Hygnstrom (2011) suggest that many areas of the Midwest support low deer populations when agriculture exceeds 75% of the landscape and deer distributions are primarily influenced by forest cover and agricultural food. In contrast to the Midwest, white-tailed deer in the East face rapid land-use changes, increased urban sprawl, and fragmentation by commercial, industrial, and residential growth (Diefenbach and Shea 2011). In Minnesota, the study area of Brinkman et al. (2005) was 86% agriculture and only 3% forest. Other Midwest study areas reported more forests in their study areas but have focused on refuges (Vercauteren and Hygnstrom 1998) or include a large grassland component (Storm et al. 2007). In addition to different landscape compositions, the Midwest also differs from the East in weather patterns, specifically winter elements that influence deer habitat use (Beier and McCullough 1990, Brinkmean et al. 2005). A comprehensive assessment of resource selection in an agricultural, fragmented landscape in the East will assist managers in dealing with the issue of white-tailed deer crop damage in eastern habitats. Fall hunting seasons are the primary method used to reduced deer numbers and crop damage caused by deer, so we need to understand if resource selection differs between the agricultural growing season and legal hunting season. In addition to season, harvest of deer is usually restricted by time of day so we need to incorporate a temporal component to understand how timing of resource selection changes and potentially affects availability for harvest. The objective of this study was to 8

18 determine if deer that cause crop damage are available for legal harvest by the affected farmer by estimating home range sizes, and investigating temporal and seasonal resource selection of adult female white-tailed deer in an agricultural landscape in Delaware. Study Area I conducted my research within a mosaic of privately and publicly owned lands in central Sussex County, Delaware (Figure 1). Sussex County is located on the coastal plain bordered on the east by the Atlantic Ocean, on the north by Kent County, Delaware, and on the south and west by Maryland. Sussex County was 41% agricultural, 15% developed, and 44% natural areas (22% upland, 22% wetland). The most common agriculture crops in Sussex County were corn, soybeans, and wheat (USDA 2007). The deer density in Sussex County was 19.4 deer/km 2 in 2009 (DDFW 2009a). The hunting season in Delaware was open from 1 September until 31 January each year with a mixture of primitive and modern weapons. Delaware offers a Severe Deer Damage Assistance Program that allows qualifying landowners to harvest antlerless deer from 15 August to 15 May. I focused trapping efforts on Redden State Forest (hereafter, Redden SF; N, W) and the surrounding private lands. Redden SF was approximately 75% managed loblolly pine (Pinus taeda) plantations with interspersed stands of mixed hardwood. Privately owned forests were 85% mixed hardwood stands with balance being pine stands. Canopy species in the mixed hardwood stands were red maple (Acer rubrum), sweet gum (Liquidambar styraciflua), tulip poplar (Liriodendron tulipifera), loblolly pine, Virginia pine (Pinus virginiana), white oak 9

19 (Quercus alba), pin oak (Quercus palustris), and red oak (Quercus rubra). The 30-year average ( ) for daily temperatures in Sussex County was C in January and C in July (Georgetown station; NOAA 2010). Annual precipitation in Sussex County ranged cm ( , Georgetown station; Delaware State Climatologist 2010). The average daily temperatures in January were 0.7 C and -0.6 C and in July were 27.3 C and 27.7 C for 2010 and 2011, respectively (NOAA 2011a). Precipitation during the study totaled 115cm and 120cm in 2010 and 2011, respectively (NOAA 2011b). The average daily temperatures and precipitation during my study were within the range of the long-term averages. During February 2010 the Mid-Atlantic States experienced uncharacteristic snowfall. The long term average snowfall for the month of February was 16.3 cm, but cm of snow fell in Delaware during February 2010 (USGS 2010). Snow remained on the ground for approximately 6 weeks (31 January 10 March; National Weather Service 2012). Methods I captured deer from December 2009 May 2010 and December -April 2011 using drop-nets, Clover traps, and dartguns. I used an intramuscular injection of xylazine (0.5 mg/kg; Conner et al. 1987, Rosenberry et al. 1999, Eyler 2001) to sedate deer captured under drop-nets or in Clover traps. For deer captured via dartgun, I used radio-transmitter darts (Pneu-Dart Inc., Williamsport, PA) filled with Telazol (tiletamine and zolazepam; 3.7 mg/kg) and xylazine (2.2 mg/kg: Bowman 1996, Eyler 2001). After capture, I placed a blindfold over the eyes of each deer to minimize 10

20 stress. I attached to each captured deer 2 self-piercing numbered metal ear tags (Model # , National Band and Tag Company, Newport, KY) and 2 large, black plastic tags (7.6 x 5.7cm) with white numbers (Allflex USA Incorporated, Dallas, TX). I collected 4 standard body measurements (shoulder height, hind limb length, total length, and chest girth; Bowman 1996) and estimated the age of each deer according to tooth replacement and wear (Severinghaus 1949). I attached a VHF radio-collar (650g; Advanced Telemetry Systems, Isanti, MN) with an 8-hour mortality sensor to each adult female deer ( 1.5 years). Before being released, I gave all captured deer an intramuscular injection of yohimbine ( mg/kg), an antagonist for xylazine (Mech et al. 1985). I used an injection of vitamin E (0.1 mg/kg selenium and 2.8 mg/kg vitamin E; Rhoads 2006) to counteract signs of capture myopathy when necessary. I monitored all deer until they left the capture site under their own power. The University of Delaware Institutional Animal Care and Use Committee approved all trapping and handling procedures (#1196). I collected radio telemetry locations on each animal from the time of capture until death or the conclusion of the project. I monitored each animal at least once every 3-5 days using a handheld R410 receiver (Advanced Telemetry Systems, Isanti, MN) and an H-antenna from fixed telemetry stations on the ground. I collected 2-5 bearings for each location and used the best 2 closest to 90 while minimizing time between bearings and distance to the animal. Telemetry bearings were no more than 15 minutes apart and had interior angles of <120 and >60. I considered locations that were 4 hours apart to be independent (Swihart and Slade 1985, Kilpatrick and Spohr 2000, Hellickson et al. 2008). I estimated locations from bearings collected 11

21 during telemetry using the computer program Location of a Signal (LOAS, Ecological Software Solutions, Sacramento, CA). To estimate the accuracy of telemetry, I placed radio collars on soda bottles and suspended them from trees approximately 1 m off the ground throughout the study area. The person taking the test did not know the location of the test collar. I used LOAS to determine error polygons for each test collar for each person. The weighted average error polygon was 2.18 ha (SE = 0.37). I used the Home Range Tools extension (Rodgers et al. 2007) for ArcGIS (Environmental Systems Research Institute Inc. ESRI; Redlands, CA) to estimate home ranges for all deer with a minimum of 30 locations per season. I used the fixed kernel method with the least-square cross validation (LSCV) as a smoothing parameter for estimating 95% home ranges and 50% core areas (Kjaer et al. 2007, Hellickson et al. 2008, Hiller et al. 2008). I designated 1 May 31 August as my growing season because major crops in Sussex County are planted in May or June and most deer browse occurs during the summer months (June-August; Sperow 1985, Rogerson 2005, Colligan 2007). Most deer harvest (>80%) occurs between October and January (DDFW 2009b), so I designated 1 October 31 January as my hunting season. To investigate and compare home ranges temporally I collected 40 diurnal (½ hour before sunrise until ½ hour after sunset) and 40 nocturnal locations per season per deer. I used an analysis of variance (ANOVA; Sokal and Rohlf 1995) to compare home range sizes seasonally, temporally (daytime versus nighttime), and between years. Both habitat type and general landownership type (public/private) could affect deer behavior due to changes in availability of food or cover and different risk of harvest between private and public property. I estimated resource selection for habitat 12

22 type and general landownership type. I defined habitat types as forest (shrub land, clear cuts, idle fields, mixed forests, deciduous forests, evergreen forests, etc.), agriculture (all cropland, pastures, etc.), and other (residential areas, roads, water, etc.) using Delaware s 2007 land use land cover data (DGS 2010). I defined general landownership types as private or public land using Sussex County tax parcel data (DGS 2010). To determine which factors affect adult female resource selection, I created resource selection functions (RSFs) by comparing used locations collected from radio telemetry to random available locations (Manly et al. 2002, Godvik et al. 2009). I characterized each estimated telemetry location by season (growing or hunting) and time (day or night) and then randomly generated an equal number of random locations within the available habitat of each deer. I defined the habitat availability of each deer as the area within a 95% kernel density distribution from recorded locations 1 May through 31 January for each year (Proffitt et al. 2010). I applied 82.5 m radius buffers (2.1 ha) to all locations, random and actual, to account for the estimated telemetry error (Erickson et al. 1998, Millspaugh and Marzluff 2001). Within each buffer, I calculated amount of habitat (forest, agriculture, or other) and general landownership type (private or public). I calculated the probability of crop use based on season, time of day, and amount of crop available (Millspaugh and Marzluff 2001) using a casecontrol logistic regression in SAS (version 9.2, Cary, NC; Allison 1999, Stoke et al. 2000, Manly et al. 2002, Thomas and Taylor 2006). I developed a set of 5 a priori models which represented the potential effects of season, time of day, amount of crop available, and general landownership type on use 13

23 of crop fields. I used the quasi-likelihood information criterion (QIC; Pan 2001) and model weights (w i ) to address model uncertainty (Arnold 2010). I averaged the models within 2 ΔQIC of the best model to determine a predictive model based on informative parameters (Arnold 2010). I also investigated specific landownership to determine if deer that used crops during the growing season were available to the same landowner for harvest during the hunting season. For each deer I identified all landowners who had a crop field in its 95% home range and calculated the amount of the crop field that overlapped the home range. Most deer had only 1 landowner in their home range (42.4%, n = 14), 12 deer had 2 landowners in their home range (36.4%), 6 deer had 3 landowners in their home range (18.2%) and 1 deer had no crop in its home range (3.0%). Then, I ranked the size of the crop fields per home range and chose the landowner who owned the largest amount of crop field. The largest portion of a crop field in a home range averaged 12.4 ha. (SE = 1.2) and represented 11.3% (SE = 1.2%) of the home range. For deer with more than 1 landowner in its home range, the smaller crop fields averaged 5.7 ha. (SE = 0.6) and represented 3.9% (SE = 0.5%) of the home range. Once I identified one landowner per deer, I calculated the amount of property owned by that landowner (forest included) in each home range during the growing season and the following hunting season. I compared the amount of land and the proportion of the home range between seasons using a paired t-test (Sokal and Rohlf 1995). 14

24 Results From December 2009 to May 2011 I captured 112 total deer and radio-collared 44 adult females. I collected 13,409 telemetry locations, 6,242 at night and 6,813 during the day. The 95% home ranges and 50% core areas differed by year (95%, F 1, 39 = 8.87, P = 0.004; 50%, F 1, 39 = 9.58, P = 0.003) and season (95%, F 1, 39 = 13.77, P <0.001; 50%, F 1, 39 = 18.84, P < 0.001; Table 1), but not by time of day (95%, F 1, 39 < 0.01 P = 0.978; 50%, F 1, 39 = 0.05, P = 0.825). Selection of crop fields changed with season and time combination (Figure 2). Growing season daytime and hunting season nighttime deer used crop in proportion to availability. Growing season nighttime deer used crop more than it was available. Hunting daytime deer used crop less than it was available to them. The models for resource selection that included crop, season, and time of day had similar ΔQIC and weights (Table 2). Because the ΔQIC for models with land type were >2 ΔQIC, I considered general landownership type to be an uninformative parameter and removed models with that parameter from the averaged model. The average model that included crop, season, and time of day provide a resource selection function of β 0 = (Crop) (Season) (Time of day). The specific landowner property in the 95% home range differed by amount of land ( = 7.6, SE = 10.7, t 32 = 4.07, P < 0.001) and by proportion of land ( = 3.9, SE = 8.4, t 32 = 2.66, P = 0.012) in a deer home range. The specific landowner property in the 50% core area differed by amount of land ( = 5.2, SE = 5.6, t 32 = 5.29, P < 0.001) and proportion of land ( = 16.3, SE = 22.0, t 32 = 4.25, P < 0.001) in a deer core use area. 15

25 Discussion Season and time of day were important factors in determining white-tailed deer habitat selection. In both seasons, deer used crop fields more during the nighttime than daytime. My results support the idea that deer used more closed vegetation types (i.e. forests) during the day (Beier and McCullough 1990, Hiller et al. 2009). Deer selected crop less during the hunting daytime and therefore were less visible in fields during the daytime hours of legal hunting season. Although deer may be less available in crop fields during legal hunting hours, they typically remained within forested habitats on private lands. My data did not show a difference in resource selection based on public or private lands which means deer are not moving to public lands during the hunting season to avoid harvest on private lands. Kernohan et al. (1995) suggested that 24 hour habitat use during the summer could be estimated from diurnal locations alone but my results suggest resource selection differs between day and night. I suggest researchers collect enough data to make temporal comparisons of resource selection to ensure they are taking any differences into consideration. Female deer typically exhibit high site fidelity (Beier and McCullough 1990, Vercauteren and Hygnstrom 1998, Walter et al. 2009), but habitat use can change in response to human activities or availability of food and cover (Massé and Côté 2009). Grovenburg et al. (2009) documented dispersal due to weather factors and limited forest cover. Strong site fidelity suggests that localized management of deer in a suburban area is possible (Porter et al. 2004). Although my study area was more rural, my results support the possibility for localized management by farmers. While deer 16

26 may be using crop fields less during the legal hunting hours and therefore less visible to farmers, they are not completely leaving the property of landowners where they may be causing damage during the growing season. Harvest can be used as a tool to remove deer that are using crops during the growing season and therefore give farmers an opportunity to reduce crop damage. To increase the likelihood of deer remaining near crop fields where they cause damage, farmers should plant their winter cover crop soon after harvest of their summer crop. After harvest of corn or soybeans little to no food persists in the field to encourage deer to use these areas. If cover crops are planted early to reduce the amount of time the ground is bare and to produce quality forage before heavy frost, deer are likely to stay nearby to browse without risk of extensive damage (Springer 2010). In addition to possibly increasing chance of harvest, cover crops also protect soils from water and wind erosion, improve soil tilth, and may improve subsequent crop yield. Seasonal and temporal changes in resource selection within a deer s home range that use crops are important factors when considering how to alter management practices to assist farmers. The home range sizes I estimated were similar to other reported home ranges in areas of agriculture (Vercauteren and Hygnstrom 1998, Rhoads et al. 2010). However, I documented larger home range sizes during the summer growing season in comparison to the hunting season. In contrast, Vercauteren and Hygnstrom (1998) documented larger home ranges in response to a 3 day muzzleloader hunt. Rhoads (2010) also documented increased movement and larger home ranges in response to a 2-day controlled firearms hunt. Deer on my study area 17

27 did not respond to hunting pressure by increasing home ranges most likely because the hunting season began with archery and extended 5 months. Hunting pressure is not as intense throughout the 5 month hunting season whereas a short 2-3 day hunt is constant disturbance. I did not measure impacts of hunting on a fine scale so I was unable to detect fluctuating responses. The extended hunting season in comparison to a short, intense hunt did not cause deer to expand overall home ranges or leave their home ranges as was documented in other studies (Vercauteren and Hygnstrom 1998, Rhoads 2010). Management Implications Crop damage cannot be eliminated completely as long as deer are present but farmers have the potential to reduce damages by harvesting deer that are causing crop damage on their property. If farmers who have concerns about crop damage encourage hunting in forested habitats, they will increase the opportunity to harvest deer and therefore reduce crop damage. Farmers should also consider planting a winter cover crop as early as possible to encourage deer to continue to use fields. My study is the first study relating habitat selection of white-tailed deer to a private landowner s ability to relieve crop damage; I suggest more research be conducted in areas of reported crop damage to investigate trends in habitat selection and availability of harvest. 18

28 Figure 1 Map of the study area. The state of Delaware with the discontinuous tracts of Redden State Forest darkened in Sussex County. 19

29 Table 1 The average 95% home range and 50% core area by season and year of adult female white-tailed deer in Sussex County, Delaware in 2010 and N (ha.) SE N (ha.) SE 95% home range Growing Hunting % core area Growing Hunting

30 Figure 2 The average amount of crop in random and used buffers by season/time of day combination with 95% confidence intervals. Dark bars represent random buffers, light bars represent used buffers. 21

31 Table 2 Results for model selection investigating effects of season, time of day, landownership type, and amount of crop available on adult female white-tailed deer habitat selection in Sussex County, Delaware from May-January in 2010 and Models are listed with the effects included in each model, the number of parameters (K), QIC, and weight of the model (w). Model K QIC w TIME OF DAY, CROP SEASON, CROP CROP TIME OF DAY, SEASON, LANDOWNERSHIP TYPE, CROP LANDOWNERSHIP TYPE, CROP

32 Chapter 3 SURVIVAL OF FEMALE WHITE-TAILED DEER IN AN AGRICULTURAL LANDSCAPE Abstract Understanding survival rates of white-tailed deer is imperative for managers to develop management strategies to achieve desired populations. Research regarding survival rates in areas of agriculture, fragmentation by roads, and exposure to hunting is limited. The objective of my study was to estimate survival rates of adult female white-tailed deer in a fragmented agricultural landscape. I captured 112 deer and radio-collared 44 adult females. The annual survival rate was 0.43 (SE=0.11) and 0.72 (SE=0.28) for 2010 and 2011, respectively, and differed between years (χ 2 1=5.21, P=0.022). The majority of documented mortalities were attributed to harvest (80%, n=16). Deer-vehicle collisions (15%, n=3) and natural mortality (5%, n=1) were not important factors in my study. An extensive amount of snow fell in the area prior to the beginning of the 2010 hunting season and may have affected harvest numbers and overall survival rates the first year. Managers need to take abnormal winter weather conditions into consideration when making predictions about survival rates, mortality causes, and overall population trends. KEY WORDS: Delaware, hunting, Odocoileus virginianus, radio telemetry, survival, white-tailed deer 23

33 Introduction Knowing the survival rates of white-tailed deer is important to understanding the dynamics of the population (Dusek et al. 1992, DelGiudice et al. 2002, Brinkman et al. 2004). Survival rates and mortality data assist managers in setting management goals and give insight into what factors affect survival rates. White-tailed deer survival rates and mortality causes are impacted by the level of development of the area, hunting pressure, natural predators, environmental pressures, and age and sex class (Grovenburg et al. 2011). Survival rates for female white-tailed deer reported in literature range from in agricultural landscapes (Nixon et al. 2001, Brinkman et al. 2004, Ebersole et al. 2007). In deer populations exposed to hunting, harvest is the most common cause of mortality (Brinkman et al. 2004, Bowman et al. 2007, Storm et al. 2007). Hunter harvest accounted for 86% of mortality in exurban Illinois (Storm et al. 2007), 43% of mortality in an intensively farmed region in Minnesota (Brinkman et al. 2004) and 70% of mortalities in agricultural areas of South Dakota and Minnesota (Grovenburg et al. 2011). In populations exposed to limited hunting, vehicle collisions are the most common cause of mortality (Etter et al. 2002, Bowman 2011). The more roads in the home range of an animal, the greater the likelihood of vehicle mortality (Etter et al. 2002). Vehicle collisions accounted for 66% of mortalities in a non-hunted population in Illinois (Etter et al. 2002), 14.3% of mortalities in a hunted population in exurban Maryland (Ebersole et al. 2007), and 15% in a hunted population in South Dakota (Grovenburg et al. 2011). 24

34 Environmental factors, specifically winter severity, are linked to reproductive success, mortality due to starvation, and mortality due to predation (Garroway and Broders 2007, Simard et al. 2010). Most often, winter impacts on survival are documented as increased predation or starvation due to lack of resources (DePerno et al. 2000, DelGiudice et al. 2002). DePerno et al. (2000) documented 71% of mortalities in South Dakota as natural causes, most coincided with spring snowstorms. DelGiudice et al. (2002) found snow depth in Minnesota directly affected rates of predation and starvation. Not only can severe winter weather cause immediate mortalities, but long term impacts are also possible (Garroway and Broders 2007). Garroway and Broders (2007) documented decreased probability of female reproduction the year following a severe winter but effects on long term adult survival have not been documented. Severe winter weather is more likely to influence survival in northern populations of ungulates where snow fall is common and predators are present (DelGiudice et al. 2002, Simard et al. 2010). No studies have investigated how a severe winter affects survival of adult deer in areas that do not typically experience extensive snowfall and do not have natural predators. Survival rates have been documented in forested areas (DePerno et al. 2000), intensely farmed areas (Nixon et al. 2001, Brinkman et al. 2004), and highly fragmented areas (Etter et al. 2002, Storm et al. 2007). Highly fragmented areas are often suburban landscapes where hunting is limited or illegal (Etter et al. 2002). Information regarding survival rates and mortality causes of adult female white-tailed deer in areas that are farmed, fragmented by roads, and exposed to hunting is lacking. 25

35 Sussex County, Delaware is an agricultural area where deer are exposed to both hunting pressure and risk of vehicle collision because the area is fragmented by roads. My objectives were to estimate survival rates and mortality causes of adult female white-tailed deer in Sussex County, Delaware. I hypothesized that harvest would account for most documented mortalities. Study Area I conducted my research within a mosaic of privately and publicly owned lands in central Sussex County, Delaware (Figure 1). Sussex County is located on the coastal plain bordered on the east by the Atlantic Ocean, on the north by Kent County, Delaware, and on the south and west by Maryland. Sussex County was 41% agricultural, 15% developed, and 44% natural areas (22% upland, 22% wetland). The most common agriculture crops in Sussex County were corn, soybeans, and wheat (USDA 2007). The deer density in Sussex County was 19.4 deer/km 2 in 2009 (DDFW 2009a). The hunting season in Delaware was open from 1 September until 31 January each year with a mixture of primitive and modern weapons. Delaware offers a Severe Deer Damage Program that allows qualifying landowners to harvest antlerless deer from 15 August to 15 May. I focused trapping efforts on Redden State Forest (hereafter, Redden SF; N, W) and the surrounding private lands. Redden SF was approximately 75% managed loblolly pine (Pinus taeda) plantations with interspersed stands of mixed hardwood. Privately owned forests were 85% mixed hardwood stands 26

36 with balance being pine stands. Canopy species in the mixed hardwood stands were red maple (Acer rubrum), sweet gum (Liquidambar styraciflua), tulip poplar (Liriodendron tulipifera), loblolly pine, Virginia pine (Pinus virginiana), white oak (Quercus alba), pin oak (Quercus palustris), and red oak (Quercus rubra). The 30-year average ( ) for daily temperatures in Sussex County was C in January and C in July (Georgetown station; NOAA 2010). Annual precipitation in Susse County ranged cm ( , Georgetown station; Delaware State Climatologist 2010). The average daily temperatures in January were 0.7 C and -0.6 C and in July were 27.3 C and 27.7 C for 2010 and 2011, respectively (NOAA 2011a). Precipitation during the study totaled 115cm and 120cm in 2010 and 2011, respectively (NOAA 2011b). The average daily temperatures and precipitation during my study were within the range of the long-term averages. During February 2010 the Mid-Atlantic States experienced uncharacteristic snowfall. The long term average snowfall for the month of February was 16.3 cm, but cm of snow fell in Delaware during February 2010 (USGS 2010). Snow remained on the ground for approximately 6 weeks (31 January 10 March; NWS 2012). Methods I captured deer from December 2009 May 2010 and December -April 2011 using drop-nets, Clover traps, and dartguns. I used an intramuscular injection of xylazine (0.5 mg/kg; Conner et al. 1987, Rosenberry et al. 1999, Eyler 2001) to sedate 27

37 deer captured under drop-nets or in Clover traps. For deer captured via dartgun, I used radio-transmitter darts (Pneu-Dart Inc., Williamsport, PA) filled with Telazol (tiletamine and zolazepam; 3.7 mg/kg) and xylazine (2.2 mg/kg: Bowman 1996, Eyler 2001). After capture, I placed a blindfold over the eyes of each deer to minimize stress. I attached to each captured deer 2 self-piercing numbered metal ear tags (Model # , National Band and Tag Company, Newport, KY) and 2 large, black plastic tags (7.6 x 5.7cm) with white numbers (Allflex USA Incorporated, Dallas, TX). I collected 4 standard body measurements (shoulder height, hind limb length, total length, and chest girth; Bowman 1996) and estimated the age of each deer according to tooth replacement and wear (Severinghaus 1949). I attached a VHF radio-collar (650g; Advanced Telemetry Systems, Isanti, MN) with an 8-hour mortality sensor to each adult female deer ( 1.5 years). Before being released, I gave all captured deer an intramuscular injection of yohimbine ( mg/kg), an antagonist for xylazine (Mech et al. 1985). I used an injection of vitamin E (0.1 mg/kg selenium and 2.8 mg/kg vitamin E; Rhoads 2006) to counteract signs of capture myopathy when necessary. I monitored all deer until they left the capture site under their own power. The University of Delaware Institutional Animal Care and Use Committee approved all trapping and handling procedures (#1196). I collected radio telemetry locations on each animal from the time of capture until death or the conclusion of the project. I monitored each animal at least once every 3-5 days using a handheld R410 receiver (Advanced Telemetry Systems, Isanti, MN) and an H-antenna from fixed telemetry stations on the ground. I collected

38 bearings for each location and used the best 2 closest to 90 while minimizing time between bearings and distance to the animal. Telemetry bearings were no more than 15 minutes apart and had interior angles of <120 and >60. I considered locations that were 4 hours apart to be independent (Swihart and Slade 1985, Kilpatrick and Spohr 2000, Hellickson et al. 2008). I estimated locations from bearings collected during telemetry using the computer program Location of a Signal (LOAS, Ecological Software Solutions, Sacramento, CA). To estimate the accuracy of telemetry, I placed radio collars on soda bottles and suspended them from trees approximately 1 m off the ground throughout the study area. The person taking the test did not know the location of the test collar. I used LOAS to determine error polygons for each test collar for each person. The weighted average error polygon was 2.18 ha (SE = 0.37). When I detected a mortality signal, I located the collar and documented the timing and cause of mortality. Due to the lack of natural predators in the area, I expected mortality causes to be harvest, natural death, or vehicle collision. If the carcass showed bruising along the side of the body, broken bones, and was found near a road, I considered the cause of death to be a vehicle collision. I considered deer reported by hunters or found with a weapon wound to be harvest mortalities. I considered a carcass with no signs of human inflicted trauma to be a natural mortality. If a deer died within 2 weeks of capture, I removed it from the analysis. If the date of mortality was unknown, I used the midpoint between the date of the last telemetry location and the date found dead (Lindsey and Ryan 1998, Murray 2006). I used the Kaplan-Meier procedure in SAS (version 9.1, Cary, NC; Heisey and Fuller 1985, 29

39 Pollock et al. 1989, Ebersole et al. 2007) to estimate annual survival rates. I compared survival rates between years using a log-rank test (Allison 2010). I defined the first year (2010) as 10 May May 2011 and the second year (2011) as 10 May May Results I captured 112 deer and radio-collared 44 adult females. The annual survival rate was 0.43 (SE = 0.11) and 0.72 (SE = 0.28) for 2010 and 2011, respectively, and differed between years (χ 2 1 = 4.12, P = 0.043). I documented 23 mortalities (capture = 3, natural = 1, vehicle collision = 3, harvest = 16). Most mortalities were harvest related (2010 = 83%, n = 10; 2011 = 75%, n = 6). In 2010, 80% of harvests occurred during the early hunting season (3 in September, 5 in October) whereas only 33% of harvests occurred during the same time period in 2011 (0 in September, 2 in October). The second most common mortality cause was deer-vehicle collisions (2010 = 8%, n = 1; 2011 = 25%, n = 2). I observed a single natural mortality event (2010 = 8%, n = 1; 2011 = 0%, n = 0). This natural mortality was caused by an ossified fibrosarcoma on the head of the deer (Figure 3). I captured the deer on 14 March 2010 with a small growth above her left eye but otherwise in good condition. I found her dead on 16 September 2010 and the growth had increased about 10 times in size. Discussion My estimated survival rate for 2011 was similar to other studies however, the survival rate I documented during 2010 was less than all other survival rates for adult 30

40 white-tailed deer females reported in the literature (Nixon et al. 2001, Brinkman et al. 2004, Etter et al. 2002, Ebersole et al. 2007, Storm et al. 2007). During February 2010, almost 8 times more snow fell in Delaware than normal. Garroway and Broders (2007) related a severe winter and lactation throughout the summer with a difficulty for females to acquire the appropriate amount of energy reserves to successfully reproduce the following year. Because of the physiological stress of parturition and lactation, female deer in my study may have been unable to improve their condition until the fall. Poor condition throughout the summer likely led to increased foraging in the early fall to gain necessary energy to reproduce in the winter (Garroway and Broders 2007). Extreme winter conditions are usually directly linked to starvation or predation documented during the winter or spring (Deperno et al. 2000, DelGiudice et al. 2002, Brinkman et al. 2004). I believe the effects of winter stress in my study were not persistent enough to cause starvation, but caused a change in foraging behavior in the fall and therefore an increase in harvest. Ryan et al. (2004) found during years of good hard-mast crop, harvest of white-tailed deer decreased. In 2010, Delaware experienced an above-average hardmast crop (E. Burkentine, personal communication), which I would have expected to cause decreased harvests and smaller home ranges due to an abundance of acorns. Instead, I documented larger documented home ranges (95% and 50%) during the 2010 hunting season in comparison to 2011 likely due to increased foraging by females to compensate for the decreased body condition caused by the severity of the winter in In addition to larger home ranges in 2010, harvests occurred earlier in 31

41 the 2010 hunting season than in Harvest in Sussex County, Delaware during the 2010 hunting season was greater than the 5-year average ( ) and was the second greatest year on record (Delaware Division of Fish and Wildlife unpublished data). Annual survival in my study was primarily influenced by legal harvest. The high mortality due to harvest is similar to what was reported in other studies (43-86%; Dusek et al. 1992, Van Deelen et al. 1997, Brinkman et al. 2004, Grovenburg et al. 2011). Sustained annual harvest contributes to fewer non-harvest mortalities (i.e. deer-vehicle collisions; Dusek et al. 1997). High harvest numbers can also contribute to efforts to reduce overabundance by reducing survival rates (Etter et al. 2002). In populations where harvest makes up the majority of mortality, Jacques et al. (2011) suggested that the presence of a radio collar may bias telemetry based survival estimates. Although Jacques et al. (2011) would suggest my survival estimates are biased high; the survival rate I documented the first year was the lowest reported for adult female white-tailed deer. In addition to the low survival rate I documented, most hunters that harvested a radio collared deer in this study expressed remorse and claimed that they did not see the collar before harvesting the animal; therefore, I believe the presence of a radio collar did not influence my estimates of survival. The proportion of non-harvest mortality during this study was similar to reports from other hunted populations of white-tailed deer (Van Deelen et al. 1997, Brinkman et al. 2004, Ebersole et al. 2007, Grovenburg et al. 2011). Only 15% of the mortalities I documented were due to deer-vehicle collisions which is similar to other 32

42 studies in hunted populations (14-15%; Ebersole et al. 2007, Grovenburg et al. 2011). The percentage of natural mortality (5%) that I documented was comparable to other studies, (0-19%; Van Deelen et al. 1997, Brinkman et al. 2004) but the source of mortality was unique. The only natural mortality in this study was due to an ossified fibrosarcoma (Figure 3). Ossified fibrosarcomas are rare (Sundberg and Nielsen 1981) and the one I documented was larger than other reported cases of occurrence in a wild white-tailed deer (Roscoe et al. 1975). Management Implications My results suggest severe weather factors have delayed effects on harvest risk and survival rates of white-tailed deer in the southern portion of the United States, specifically the East. Managers need to take abnormal winter weather conditions into consideration when making predictions about survival rates, mortality causes, and overall population trends. My study suggests that a large proportion of harvest not only contributes to keeping deer populations in check, but also can decrease the frequency of deer vehicle collisions. With 80% of mortality due to harvest, I believe sustained annual harvest should be continued as the primary management tool for regulating deer populations and reducing deer vehicle collisions. 33

43 a. b. Figure 3 Ossified fibrosarcoma on the head of a female white-tailed deer. a) growth at capture 14 March 2010 and b) growth at mortality 16 September