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

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

iii

“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

iv

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.

v

TABLE OF CONTENTS

LIST OF TABLES… .................................................................................................... vi

LIST OF FIGURES ...................................................................................................... vii

ABSTRACT ................................................................................................................ viii

Chapter

1 INTRODUCTION TO WHITE-TAILED DEER OVERABUNDANCE AND

CROP DAMAGE .............................................................................................. 1

2 RESOURCE SELECTION OF FEMALE WHITE-TAILED DEER IN AN

AGRICULTURAL LANDSCAPE ................................................................... 4

Abstract .................................................................................................. 4

Introduction ............................................................................................ 5

Study Area ............................................................................................. 9

Methods ................................................................................................ 10

Results .................................................................................................. 15

Discussion ............................................................................................ 16

Management Implications ................................................................... 18

3 SURVIVAL OF FEMALE WHITE-TAILED DEER IN AN

AGRICULTURAL LANDSCAPE .................................................................. 23

Abstract ................................................................................................ 23

Introduction .......................................................................................... 24

Study Area ........................................................................................... 26

Methods ................................................................................................ 27

Results .................................................................................................. 30

Discussion ............................................................................................ 30

Management Implications ................................................................... 33

4 OVERALL MANAGEMENT IMPLICATIONS ............................................ 35

LITERATURE CITED ................................................................................................. 37

vi

LIST OF TABLES

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 2011 .................................................................................. 20

Table 2 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 2011. Models are listed with the

effects included in each model, the number of parameters (K),

∆QIC, and weight of the model (w) ...................................................... 22

vii

LIST OF FIGURES

Figure 1 A map highlighting the study area. The state of Delaware with the

discontinuous tracts of Redden State Forest darkened in Sussex

County .................................................................................................. 19

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

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 2010. ................................................................................... 34

viii

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 white-

tailed 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%, F1, 39 = 8.87, P =

0.004; 50%, F1, 39 = 9.58, P = 0.003) and season (95%, F1, 39 = 13.77, P < 0.001; 50%,

F1, 39 = 18.84, P < 0.001), but not by time of day (95%, F1, 39 < 0.01, P = 0.978; 50%,

F1, 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

ix

differed between years (χ2

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.

1

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, deer-

vehicle 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. White-

tailed deer must be managed for sustainability and to reduce human-deer conflicts

(Hewitt 2011).

2

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 1997. Although many species of wildlife also cause damage

to crops, white-tailed deer are reported the most (Conover and Decker 1991). White-

tailed 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

3

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.

4

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 white-

tailed 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%, F1, 39 = 8.87, P = 0.004; 50%, F1, 39 = 9.58, P =

0.003) and season (95%, F1, 39 = 13.77, P < 0.001; 50%, F1, 39 = 18.84, P < 0.001), but

not by time of day (95%, F1, 39 < 0.01, P = 0.978; 50%, F1, 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

5

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 1997.

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

6

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

7

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

8

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

9

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/km2 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; 38°

44′ 12″ N, 75° 23′ 56″ 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

10

(Quercus alba), pin oak (Quercus palustris), and red oak (Quercus rubra).

The 30-year average (1971-2000) for daily temperatures in Sussex County was

-3.9 ─ 6.4°C in January and 18.2 ─ 30.5°C in July (Georgetown station; NOAA 2010).

Annual precipitation in Sussex County ranged 93 ─162 cm (1971-2000, 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

126.2 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

11

stress. I attached to each captured deer 2 self-piercing numbered metal ear tags

(Model #1005-49, 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 (0.2-0.7 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

12

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 9.3.1

(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

13

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 case-

control 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

14

of crop fields. I used the quasi-likelihood information criterion (QIC; Pan 2001) and

model weights (wi) 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).

15

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%, F1, 39 =

8.87, P = 0.004; 50%, F1, 39 = 9.58, P = 0.003) and season (95%, F1, 39 = 13.77, P

<0.001; 50%, F1, 39 = 18.84, P < 0.001; Table 1), but not by time of day (95%, F1, 39 <

0.01 P = 0.978; 50%, F1, 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 β0 = 0.0457 – 0.0999(Crop) – 0.0091 (Season) – 0.0095 (Time of day).

The specific landowner property in the 95% home range differed by amount of

land ( = 7.6, SE = 10.7, t32 = 4.07, P < 0.001) and by proportion of land ( = 3.9, SE

= 8.4, t32 = 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, t32 = 5.29, P < 0.001)

and proportion of land ( = 16.3, SE = 22.0, t32 = 4.25, P < 0.001) in a deer core use

area.

16

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

17

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

18

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.

19

Figure 1 Map of the study area. The state of Delaware with the discontinuous

tracts of Redden State Forest darkened in Sussex County.

20

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 2011.

2010 2011

N (ha.) SE N (ha.) SE

95% home range

Growing 42 137.8 13.0 54 109.3 9.1

Hunting 22 127.0 13.9 44 82.2 7.1

50% core area

Growing 42 33.1 3.4 54 25.0 2.3

Hunting 22 27.9 3.3 44 18.0 1.7

21

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.

22

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 2011. 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 3 0 0.284

SEASON, CROP 3 0.01 0.283

CROP 2 0.09 0.272

TIME OF DAY, SEASON, 5 2.43 0.084

LANDOWNERSHIP TYPE, CROP

LANDOWNERSHIP TYPE, CROP 3 2.61 0.077

23

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 (χ2

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

24

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

0.66 – 0.84 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).

25

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.

26

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/km2 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; 38°

44′ 12″ N, 75° 23′ 56″ 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

27

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 (1971-2000) for daily temperatures in Sussex County was

-3.9 ─ 6.4°C in January and 18.2 ─ 30.5°C in July (Georgetown station; NOAA

2010). Annual precipitation in Susse County ranged 93 ─162 cm (1971-2000,

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

126.2 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

28

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 #1005-49, 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 (0.2-0.7 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

29

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,

30

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 2010 – 9 May 2011 and the second year (2011) as 10 May

2011 – 9 May 2012.

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 (χ2

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

31

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 hard-

mast 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 2010. In addition to larger home ranges in 2010, harvests occurred earlier in

32

the 2010 hunting season than in 2011. Harvest in Sussex County, Delaware during the

2010 hunting season was greater than the 5-year average (2005-2009) 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

33

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.

34

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 2010.

35

Chapter 4

OVERALL MANAGEMENT IMPLICATIONS

Issues associated with high densities of white-tailed deer include damage to

landscapes, damage to agricultural crops, deer-vehicle collisions, and functioning as a

reservoir of disease. As a native ungulate, white-tailed deer must be managed for

sustainability and reduce conflicts. In order for farmers to relieve crop damage on

their property, we need to understand how resource selection changes during the

growing season, hunting season, and different times of the day. In addition to resource

selection, understanding survival rates and the underlying causes of mortality of

white-tailed deer is imperative for managers to develop management strategies to

achieve desired populations.

Although deer are not as visible during the hunting season on properties where

crop damage by deer may occur, farmers should be able to legally harvest deer that

cause crop damage on their property in Delaware. Hunters on private lands should be

educated about deer resource selection and encouraged to hunt in forested habitats

surrounding crop fields to increase the chance of encountering deer. Furthermore,

farmers should plant winter cover crops early so cover crops sprout before heavy frost.

Providing deer with a high quality forage choice during the hunting season will also

increase the opportunity to harvest deer and therefore reduce crop damage.

36

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. I believe sustained annual harvest should be continued as

the primary management tool for regulating deer populations and reducing deer

vehicle collisions.

37

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