Mate Choice, Reproductive Success, and How Population Demography
Influences Fawning Season of White-tailed Deer
by
Timothy Joseph Neuman
A thesis submitted to the Graduate Faculty of
Auburn University
in partial fulfillment of the
requirements for the Degree of
Master of Science
Auburn, Alabama
August 2, 2014
Keywords: White-tailed deer, parentage analysis, reproductive success, mate choice, vaginal
implant transmitter, breeding season, demography
Approved by
Stephen S. Ditchkoff, Chair, Ireland Distinguished Professor of Forestry and Wildlife Sciences
Todd D. Steury, Professor of Forestry and Wildlife Sciences
Mark D. Smith, Professor of Forestry and Wildlife Sciences
ii
Abstract
Mate choice of white-tailed deer based on age and body size is poorly understood. I
studied a captive population to evaluate mate choice and reproductive success. Age differences
between mated pairs did not differ from random pairings and I found no apparent relationship of
skeletal size between mated pairs. My results highlight the plasticity of mating success and
reveal the mating system of white-tailed deer has evolved to maximize fertility.
Sex ratio and age class may influence timing and duration of the fawning season. I
recorded birth date of fawns born within a 174-ha captive facility. The herd was intensively
monitored which allowed me to document an earlier shift in fawning following a maturation of
age structure. Earlier fawning may be important for neonatal development and survival,
especially in areas of the Southeast where coyotes are reducing recruitment. I hypothesize
managers can increase neonate development and survival by increasing male age structure.
iii
Acknowledgments
I thank Dr. Steve Ditchkoff for his professional mentorship which enhanced my ability to
articulate my results scientifically. Chad Newbolt deserves recognition for dealing with my
never-ending requests for supplies and equipment. I thank Dr. Todd Steury for assisting with
data analysis and for helping me understand the concept of biological significance. I appreciate
Dr. Randy DeYoung for providing critical advice on how to properly analyze microsatellites and
how to assign parentage using genetic software. I also thank Dr. Mark Smith for making me a
better writer and for advice on how to handle difficult interactions with the general public. I
greatly appreciate the sources of funding for this project: the Center for Forest Sustainability at
Auburn University, Code Blue Scents/EBSCO Industries, and Record Rack, as well as a large
group of individuals who provided private support during the study. To all of my volunteer
technicians, especially Clayton Glassey, Hunter Brooks and James Garrett, I am very grateful for
your assistance in capturing deer and monitoring transmitters. Without your sacrifice, I would
have never been able to collect the enormous amount of data that helped set my project apart
from others. Finally, I thank my wife, Jenny Neuman, as well as my family back in Minnesota
for supporting my move across the country to become a better wildlife biologist.
iv
Table of Contents
Abstract ......................................................................................................................................... ii
Acknowledgments ....................................................................................................................... iii
List of Tables ............................................................................................................................... vi
List of Figures ............................................................................................................................. vii
Chapter 1: Mate Choice and Reproductive Success of Female White-tailed Deer .................... 1
Abstract ............................................................................................................................ 1
Introduction ...................................................................................................................... 1
Materials and Methods ..................................................................................................... 4
Study Area ........................................................................................................... 4
Capture and Data Collection ................................................................................ 5
Population Monitoring ......................................................................................... 7
Microsatellite Analysis ........................................................................................ 8
Statistical Analysis ................................................................................................ 9
Results ............................................................................................................................ 11
Demography ....................................................................................................... 11
Genotyping ......................................................................................................... 11
Parentage ............................................................................................................ 12
Discussion ...................................................................................................................... 13
Literature Cited .............................................................................................................. 20
v
Chapter 2: How Population Demography Can Influence Fawning Season of White-tailed
Deer ............................................................................................................................... 36
Abstract .......................................................................................................................... 36
Introduction .................................................................................................................... 36
Study Site ....................................................................................................................... 39
Methods .......................................................................................................................... 40
Results ............................................................................................................................ 41
Discussion ...................................................................................................................... 43
Literature Cited .............................................................................................................. 47
vi
List of Tables
Table 1.1. Known white-tailed deer breeding populations by sex, age class, and cohort
birth year from 2008-2013, Auburn Captive Facility, Camp Hill, AL ......................... 28
Table 1.2. Population genetics information (individual locus allelic richness, gene
diversity, FIS, and Hardy-Weinberg probabilities) for white-tailed deer from 2008-
2013 at Auburn Captive Facility, Camp Hill, AL. ........................................................ 30
Table 1.3. Inputs for yearly cohort simulations of white-tailed deer parentage analysis
using CERVUS 3.0 from 2008-2013 at Auburn Captive Facility, Camp Hill, AL. ....... 31
Table 2.1. Known white-tailed deer breeding populations by sex, age class, and cohort
birth year from 2008-2013, Auburn Captive Facility, Camp Hill, AL. ....................... 54
vii
List of Figures
Figure 1.1. Observed age differences between dams and sires from 2008-2013, and
random age differences assuming the occurrence of random mating, Auburn
Captive Facility, Camp Hill, AL .................................................................................... 32
Figure 1.2. Range-graded dot representation of age relationships between mated pairs of
white-tailed deer from 2008-2013, Auburn Captive Facility, Camp Hill, AL .............. 33
Figure 1.3. Size comparison of 18 mated pairs of white-tailed deer for which
measurements were available for both parents from 2008-2013, Auburn Captive
Facility, Camp Hill, AL ................................................................................................. 34
Figure 1.4. Body percentile comparison between mated pairs of white-tailed deer from
2008-2013, Auburn Captive Facility, Camp Hill, AL ................................................... 35
Figure 2.1. Julian birth dates of white-tailed deer fawns captured from 2010 - 2013 using
vaginal implant transmitters at the Auburn Captive Facility, Camp Hill, AL ............... 56
1
Chapter1: Mate Choice and Reproductive Success of Female White-tailed deer
Abstract
Mate choice based on age and body size is poorly understood among cervids. I used 14
microsatellite DNA loci to reconstruct the pedigree of a captive population of white-tailed deer
(Odocoileus virginianus) in order to evaluate mate choice and reproductive success. I assigned
both dam and sire to 87 known-age litters over 6 years. Age differences between mated pairs did
not differ from random pairings and I found no apparent relationship of skeletal size between
pairs. My results highlight the plasticity of mating success for white-tailed deer and I speculate
their mating system has evolved to maximize fertility. My investigation was the first to explore
mated pairs with such a high proportion of candidate parents sampled and the first to incorporate
vaginal implant transmitters to validate genetic sampling techniques. This knowledge could help
local and regional wildlife managers comprehend the unpredictability of mating success in
white-tailed deer.
Introduction
In polygynous ungulates, most species have evolved a mating system that creates sexual
dimorphism where the adult male is larger than the adult female (Isaac 2005). The most widely
accepted theory for the cause of sexual dimorphism is sexual selection and differential parental
investment (Andersson 1994). In most mammals, female reproductive success is limited by
their ability to raise offspring; whereas males are limited by the number of effective matings they
can acquire (Trivers 1972). Male-male competition leads to variance in reproductive success
between the sexes. In natural populations with balanced sex ratios, female reproductive success
is rather fixed, but male reproductive success is highly variable (Bateman 1948). Whenever
2
reproductive success is apportioned to a greater segment of the male population, sexual selection
cannot act as strongly and sexual dimorphism will be less pronounced (Isaac 2005).
Male mammals are only guaranteed paternity if they monopolize breeding with a female
or group of females. Emlen and Oring (1977) described the relationship of ecological constraints
to the degree of monopolization that occurs among species. Open habitats often allow males to
monopolize multiple females, such as with polygynous red deer (Cervus elaphus), where males
gather and defend harems in open meadows (Clutton-Brock et al. 1982). The mating system of
white-tailed deer (Odocoileus virginianus) is also generally characterized as polygynous, with
recent evidence of female promiscuity coming from observations of multiple paternity
(DeYoung et al. 2002, Sorin 2004; DeYoung et al. 2009). Male white-tailed deer, however, do
not typically monopolize >1 female at a time (Sorin 2004; DeYoung et al. 2009). Rather, males
follow and defend a single female for a period up to 72 hours (Hirth 1977). Although habitats
vary greatly within the range of white-tailed deer, they tend to be found near areas of thick
vegetative cover which may explain why males only monopolize one female at a time (DeYoung
and Miller 2011).
Although white-tailed deer are the most studied and abundant ungulate species in North
America, few studies have examined their mating success using genetic techniques. Of the few
studies that have been conducted, emphasis was placed on the physical attributes of males such
as age and body size (Sorin 2004; DeYoung et al. 2009; Jones et al. 2011). There has, however,
been extensive effort using number of fawns born to quantify the attributes of successful females
by age, nutritional status, and body size (Haugen 1975; Kie and White 1985; Ozoga and Verme
1986a, 1986b; Ozoga 1987; Mech and McRoberts 1990; Nixon and Etter 1995; DelGuidice et al.
2007). The results of each study differ by region and nutritional availability, but the trend is
3
similar across areas; fawns rarely breed, yearlings usually have 1 fawn, and 2.5+ year olds
produce more twins than younger age classes. These reproductive parameters are vital for
models that estimate deer populations which are important for making future harvest
recommendations (Hansen 2011).
While individual male and female physical attributes are certainly important when trying
to understand breeding success of white-tailed deer, combined attributes between mated pairs
have received scant attention in the literature. Ozoga and Verme (1985) documented age
relationships between experimentally manipulated male populations and females. When mature
males were absent from the population, yearling males mated with females of all ages and there
were no short term changes to female productivity. They also observed less ritualized breeding
behavior, such as antler rubbing and ground scraping by yearling males which they attributed to
a lack of social structure. Sorin (2004) reported 1.5 year old males were only able to secure
breeding opportunities with young (?2.5 year old) females while mature males concentrated
efforts on older females. Unfortunately, her results were limited to an examination of age of
mated pairs and were not able to provide information about how female or male body size
influenced pairings.
Although speculation abounds, there is uncertainty concerning whether female mate
choice or male-male competition drives the mating system of white-tailed deer. Females only
mate with males during their estrous cycle, but males have been known to act aggressively
toward females that were not allowing them to breed which brings into question if the female is
choosing or if she is breeding for self-preservation (Haugen 1959). It is generally believed that
in cases where intra-male competition occurs, the male is eager to mate with any receptive
female, without discrimination, whereas the female chooses the male (Trivers 1972; Emlen and
4
Oring 1977). However, Berger (1989) noted that when males can only secure a limited number
of matings and females exhibit reliable cues to their reproductive potential, males were more
selective. Margulis (1993) found evidence for selection bias among males by observing that
male mule deer (O. hemionus) chased females that did not recruit offspring during the current
year more than females with fawns present. Sorin (2004) suggested mature males concentrated
efforts on older females because they produced more twins than yearling females. However, the
role of female physical attributes on male mate selection has yet to be firmly established.
In this study, I monitored a captive population of white-tailed deer exhibiting natural
breeding behavior and evaluated mate choice using offspring parentage assignments. My goal
was to examine relationships between mated pairs with regards to age and body size, and
specifically investigate the role of female selectivity during the mating process. I predicted age
and body size would be positively correlated between mated pairs as individuals concurrently
seek to maximize fitness (Sorin 2004, Berger 1989). Another goal was to observe how age and
body size influence recruitment. I predicted that older, larger females would recruit more
offspring than younger, smaller females (Ozoga and Verme 1986b; Nixon and Etter 1995). I
predicted that younger males that successfully bred were larger than similar-aged bucks that did
not breed in terms of their age adjusted body size (Jones et al. 2011).
Materials and Methods
Study Area
The white-tailed deer in this investigation resided in the 174-hectare Auburn Captive
Facility (ACF) located in Camp Hill, Alabama, USA. The population consisted of deer that were
in the area at the time the fence was constructed in 2007, and their descendants. The perimeter of
the ACF was bordered by a 2.6-meter deer-proof fence which allowed the study of individuals
throughout their lifetime. Except for dispersal, deer were allowed to move freely and behave
5
naturally. Deer were fed 18% protein pellets (?Deer Feed,? SouthFresh Feeds, Demopolis, AL)
ad libitum year round using 3 free choice feeders. Their diet was supplemented by 4 timed corn
feeders providing approximately 2 kg/day of corn during fall and winter which helped attract
deer for capture.
The two main cover types inside the ACF were open hayfields (40%) maintained for hay
production and mixed forest (60%) managed for wildlife habitat using prescribed fire. The
predominant grass species found inside the ACF was bermuda grass (Cynodon sp.). Other
grasses present included fescue (Festuca sp.), big bluestem (Andropogon sp.), Johnson grass
(Sorghum sp.), dallisgrass (Paspalum sp.), and bahia grass (Paspalum sp.). The mixed forest
consisted of 70 % hardwoods which included various oak (Quercus spp.), hickory (Carya spp.),
and maple (Acer spp.) species and 20 % conifer which consisted of loblolly pine (Pinus taeda).
The remaining 10 % of mixed forest was made up of naturally regenerated thickets of Rubus
spp., sweetgum (Liquidambar styraciflua), eastern red cedar (Juniperus virginiana) and Chinese
privet (Ligustrun sinense).
The general habitat among the wooded areas included a thick closed canopy with little
understory growth. Locations where sunlight could penetrate the canopy along forest edges and
creek bottoms contained dense understory growth. A stable water source was available to deer
from 2 creeks that flowed through the property. Elevation ranged from 190 to 225 meters above
sea level. The climate in this region of East-Central Alabama was moderately warm with mean
high temperatures of 32.5 ?C in July and mean low temperatures of -0.5?C in January. Average
annual precipitation in the area was approximately 131 cm.
Capture and Data Collection
Adult (? 6 months old) deer were captured using either a 0.8 ha capture facility or
cartridge fired dart guns equipped with night vision scopes. Chemical immobilization occurred
6
with an intramuscular injection of Telazol? (Fort Dodge Animal Health, Fort Dodge, Iowa;
125mg/ml given at a rate of 4.5 mg/kg) and xylazine (Lloyd Laboratories, Shenandoah, Iowa;
100mg/ml given at a rate of 2.2 mg/kg) followed by reversal with Tolazine? (Lloyd
Laboratories, Shenandoah, Iowa; 100mg/ml given at a rate of 6.6 mg/kg; Miller et al. 2004). The
capture facility allowed for the capture of multiple individuals with one trapping effort. It
consisted of a modified box trap at the end of a 0.8 ha deer proof fence. Deer entered the trap
through an open gate and once the group was calmly feeding, the gate was closed behind them.
The layout of the fence funneled deer into the box trap, which was closed using a remote gate.
Sorting boxes were positioned at one end of the box trap to facilitate chemical immobilization.
Darting was conducted from tree stands over automated feeders from mid-September to early
June. Dart guns used telemetry darts (2.0 cc, type C, Pneudart Inc, Williamsport, PA) to locate
immobilized deer (Kilpatrick et al. 1996).
Measurements of adult deer included head, body, hind foot, and chest. Chest girth was
measured immediately posterior to the front legs, hind foot length was measured from the tip of
the hoof to the posterior end of the tuber calcis (tarsal), and body length was measured from the
tip of the nose to the base of the tail dorsally along the head and spine (Ditchkoff et al. 2001).
Deer were aged using tooth replacement and wear method (Severinghaus 1949). Although this
method has come under recent scrutiny (Gee et al. 2002), I minimized potential errors by
limiting aging assignments to 3 biologist who were familiar with tooth wear patterns of deer in
the facility. Also, the majority (72%) of age assignments occurred when deer were <20 months
old and had not lost their tricuspid pre-molars. Deer initially captured and aged ?1.5 years old
were considered known-age for my study. Thus, a deer captured at 1.5 years old in 2008, was
considered a known-aged 4.5 year old in 2011. All deer not captured previously were ear tagged
7
and freeze branded with unique numbers in order to identify individuals. Tissue samples were
collected via 1-cm ear notch and stored at -78?C until further analysis.
Fawns were captured using Vaginal Implant Transmitters (M3930, Advanced Telemetry
Systems, Isanti, MN; hereafter VITs) following procedures described by Saalfeld and Ditchkoff
(2007). VITs were placed in females captured from late February to early June. I monitored
VITs every 6 hours during the fawning season to determine if the transmitter was expelled.
Hand held telemetry was used to determine the location of the birthing site using methods of
Carstensen et al. (2003). A thermal imaging camera (Raytheon Palm IR 250D, Waltham, MA)
was used to aid in locating fawns not found at the birth site. All capture and handling procedures
were in accordance with protocols approved by the Auburn University Institutional Animal Care
and Use Committee (PRN numbers: 2008-1417, 2008-1421, 2010-1785, 2011-1971, and 2013-
2372) and were in compliance with guidelines adopted by the American Society of
Mammalogists Animal Care and Use Committee (Sikes et al. 2011).
Population Monitoring
The relatively large area of the ACF combined with rolling terrain did not allow me to
view all animals at one time; therefore I used a combination of methods to estimate population
demographics. I used bi-yearly camera surveys at sites baited with shelled corn and along trails
(McCoy et al. 2011). Ear tags, freeze brands, and unique antler configurations allowed me to
identify individuals and estimate abundance, sex ratio, and age structure of the population. I
applied mark-recapture techniques to estimate the proportion of adults sampled for parentage
assignments (Pollock et al. 1990; Karanth and Nichols 1998). Marked individuals were not fitted
with mortality detectors which created some uncertainty regarding prolonged absence of some
individuals from the camera surveys. I considered marked individuals not seen by camera or
8
field observations for 2 years as possibly deceased and removed them from the pool of candidate
parents. I used camera survey data in conjunction with current capture and mortality records to
reconstruct the total population for each year and generate final estimates of demographics.
The goal was to maintain a population of ?120 adult deer during the study. The
population was not hunted, so annual population regulation occurred via natural mortality,
capture related mortalities, and selective removal of fawns. I captured 10 individuals (5 female,
5 male) <1years of age at random and released them outside the enclosure each trapping season
beginning in September 2010. Deer were removed in this manner to maintain a relatively even
distribution of individuals among cohorts, and prevent negative social effects known to occur in
crowded populations of white-tailed deer (Ozoga and Verme 1982).
Microsatellite Analysis
Microsatellite markers were scored by DNA Solutions (Oklahoma City, Oklahoma) using
the panel first described by Anderson et al. (2002). I estimated allelic richness (El Mousadik
and Petit 1996), gene diversity (Nei 1973), and Fis (Weir and Cockerham 1984) using FSTAT
(Goudet 1995, 2001). The program also tested Hardy-Weinberg equilibrium (1,000 permutations
of alleles among individuals) and linkage disequilibrium among loci (10,000 permutations of
genotypes). A Bonferroni correction was used in order to correct for multiple comparisons (Rice
1989).
I defined reproductive success as the successful birth of offspring, or the siring of a fawn
by males. My sample did not include fetuses or fawns that were born and died prior to me being
able to capture them and collect a tissue sample. The six years of reproductive success data were
divided into yearly offspring cohorts, meaning I compiled lists of candidate parents separately
for each year offspring were born (2008-2013). Parentage assignments were made using the
9
likelihood based approach in CERVUS 3.0 (Kalinowski et al. 2007). For each year I entered
population demographics into CERVUS which included candidate parents, offspring, percentage
of sampled individuals and typing error rates. Simulations provided critical values for the Delta
statistic which CERVUS used when assigning parentage. Typing error rates were calculated in
CERVUS using known mother-offspring pairings from VITs. Accuracy of CERVUS
assignments was calculated by including all candidate mothers and comparing results to known
mother-offspring pairings from VITs. Male and female fawns alive during the breeding season
were included as candidate parents because several studies have documented that fawns are
capable of producing young (Schultz and Johnson 1992, Peles et al. 2000). Parentage
assignments were ordered by delta LOD and assignments were selected based on trio confidence,
which incorporated both parents? genotypes in the likelihood based algorithm (Kalinowsi et al.
2007). To be conservative, only trios with 95% confidence were included in the final analysis of
reproductive success.
Statistical Analysis
I used data from 6 years (2008 to 2013) of reproductive success inside the ACF to
determine physical attributes between mated pairs. For female reproductive success, I used a
generalized mixed effects regression with Poisson distribution in R (R core development team,
version 15.3 accessed 10 December 2013). The number of fawns recruited by females was
compared to age and body size of a random group of females, including a random effect of
individual because some females were measured several times throughout their lifetime. Year
was included as a random effect to account for unknown differences in nutritional availability
between years.
10
Skeletal growth patterns of white-tailed deer differ between the sexes, so our variable
grouping of individuals by age reflects this difference (Ditchkoff et al. 1997, Ditchkoff 2011).
Male skeletal body sizes were grouped into six categories: fawns, 1.5, 2.5, 3.5, 4.5, and 5.5+
years old. Once female white-tailed deer reach 2.5 years of age, most are close to their maximum
body size and can put more resources toward reproduction rather than individual growth. As a
result, females were only grouped into three categories: fawns, 1.5, and 2.5+ years old. Age and
body size relationships between mated pairs were analyzed using linear regression in R (R core
development team, version 15.3 accessed 15 December 2013).
I was unable to capture every adult in the population every year, which left gaps in the
dataset regarding body size of one or both parents in a mated pair. In order to examine size
relationships in years when the dam or sire(s) were not measured, I used percentiles. I did this in
order to include information I had gathered about individuals throughout their lifetime, but may
not have captured them in the year they produced a fawn. I calculated percentiles by pooling all
measurements across all years and grouped them by age. I assigned percentile scores to
individuals with ?2 years of skeletal body measurements. For instance, if a male was initially
captured at 1.5 years old and measured 258 cm (body, hind foot, and chest combined), I
compared his skeletal growth to all other 1.5 year olds measured. Assume this individual ranked
12th out of 36 individuals measured at age 1.5, which would put him in the 68.4 percentile. If
that male were subsequently captured at age 3.5 and 5.5, I calculated the mean percentile score
of his lifetime body size and used that number in my correlation of body size if he sired offspring
at 4.5 years old. Skeletal body sizes were normally distributed around the mean, meaning I felt
confident our percentile assignments did not inflate or deflate individual body rankings when
compared to similar aged males.
11
Results
Demography
Population estimating methods indicated that minimum annual herd size ranged from 69
to 122 individuals from 2008 to 2013 (Table 1.1). Adult sex ratio gradually shifted over the
course of the study from a female majority in 2008 to a male majority in 2012. Approximately
90% of adult deer have been captured and marked. The proportion of known-age animals in the
population has increased from 50.7% in 2008 to 81.8% in 2013. Mean adult (>0.5 years old)
male age increased from 1.42 in 2008 to 3.92 in 2013, while mean adult female age increased
from 2.14 in 2008 to 4.17 in 2013. Initial density was 0.4 deer/ha in 2007 and peaked in 2011 at
0.7 deer/ha. Initial sex ratio was 1:2 M:F, which gradually shifted toward parity with an
estimated ratio of 1:0.9 M:F in 2013.
Genotyping
DNA Solutions, Inc. genotyped 224 deer captured from October 2007 to July 2013.
Forty-four of 224 (19.6 %) deer were first captured as neonates, and 180 of 224 (80.4 %) were
captured when ?6 months old. DNA Solutions, Inc. originally genotyped 14 loci, but 3 loci (Q,
D, and P) deviated significantly from Hardy-Weinberg equilibrium and were subsequently
excluded from parentage analysis (Table 1.2). Allelic richness ranged from 4 to 16 alleles per
locus (x = 9.93). Equilibrium tests revealed linkage disequilibrium at 9 of 91 pairwise
combinations of loci (Cervid and BL25, L and O, L and P, BM6506 and D, N and BM6438,
BM6438 and Q, O and S, D and OAR, and P and S). All remaining loci were retained despite
observed genotypic disequilibrium as linkages at this level are not likely to alter parentage
assignments (Sorin 2004). The proportion of candidate parents sampled varied from 50 % in
2008 to 90 % from 2009 to 2012 (Table 1.3). The 90 % sampled rate from 2009 to 2012 was a
12
conservative estimate based on our population monitoring methods. In 2013, sampled
percentage was set to 80 because at least ten individuals born in 2012 that remained uncaptured
when data were analyzed.
Parentage
I assigned both sire and dam to 87 known-age litters at the 95% confidence level.
Twenty-four of 87 (27.6 %) assigned dams were known-age whereas 30 of 87 (34.5 %) assigned
sires were known-age individuals. CERVUS correctly assigned maternity for 35 of 37 (94.6 %
accuracy) offspring collected using known mothers by way of VITs. The comparison of age
differences among breeding pairs to a random distribution of available pairings yielded no
difference (?2 = 20.69, d.f. = 18, P = 0.295; Fig. 1.1). The general relationship between dam and
sire age did not differ from what would be expected if random mating had occurred (t = 1.017,
d.f. = 84, P = 0.312; Fig. 1.2). Collectively, male fawns and yearlings mated with 13 females, of
which, 6 females were ?3.5 years old. One yearling male bred with a 7.5 year old female. Male
reproductive success was highly variable and changed according to available male age structure.
In 2008, 7 of 14 (50 %) mated pairs included 1.5 year old males, whereas only 1 of 6 (17 %)
mated pairs included a 1.5 year old male in 2013 when male age structure was more mature.
Multiple paternity occurred in 10 of 27 (37 %) sets of twins. Nine of 10 cases of multiple
paternity involved dissimilar aged males, and the one case of same-aged males occurred between
dissimilar sized males.
Herd reconstruction using assigned parentage allowed me to compute minimum
recruitment values for females. As expected, reproductive success for females ?2.5 years old did
not vary as much as with males. Physically mature females (?2.5 years old) for which
recruitment data were available recruited 1.22 (? 0.073 SE, n = 54) offspring into the fall
13
population. Adolescent females (1.5 years old) for which recruitment data were available
recruited 0.76 (? 0.077, n = 13). I documented 9 fawns that recruit offspring into the fall
population, and they each recruited one individual. Females ?1.5 years old failed to recruit more
than 1 offspring during the study period. Two different females each recruited one litter of
triplets during the study period. Mean age for females that recruited 2 or more individuals into
the fall population was 4.44 (? 0.359, n = 16) years old.
Generalized mixed model regression analysis indicated that for every 10 cm increase in
skeletal size, females recruited 1.35 (1.07-1.74; 95% C.L., P = 0.036) times as many fawns.
Additionally, I found no relationship (t = 1.48, d.f. = 16, P = 0.158) between skeletal sizes of 18
mating pairs for which we had measurements of both parents (Fig 1.3). Using lifetime body
percentile as a surrogate for body size allowed me to compare size relationships for 82 pairs,
which also resulted in no relationship (t = 0.487, d.f. = 81, P = 0.628; Fig. 1.4).
Discussion
My findings do not support my original hypothesis that white-tailed deer selectively
choose mates of similar age or body characteristics as themselves. Sorin (2004) found that
yearling males only mated with young females (?2.5 years old), but I found yearling males
reproduced with older females (>2.5 years old) as well. Sorin (2004) stated that experienced
females might not tolerate advances by young males, but of the 10, 1.5 year old males known to
have sired offspring during our study, 4 mated with females ?3.5 years old. My results show that
tolerance of advances made by young males occurred, with evidence of an extreme example in
2008 when a 1.5 year old male mated with a 7.5 year old female. Other studies described similar
results regarding young males breeding. Ozoga and Verme (1985) indicated 1.5 year old males
gained mating opportunities with females of all ages, although no older males were competing
14
against them within that experimental population. DeYoung et al. (2009) reported physically
immature males (1.5 and 2.5 years old) collectively fathered 30-33% of offspring in 3 separate
populations, even when mature males were present. They reasoned that the overall spatial
dispersion of females within populations combined with temporal breeding synchrony would
limit the number of estrous females an individual male could locate and breed. This in turn
allowed mating opportunities for males of all age classes.
Most studies of reproductive success in white-tailed deer have been presented with
regards to age, but it is uncertain if deer are capable of perceiving age of potential mates or if
they simply use physical characteristics such as body size. If a situation arises where a male
must choose between 2 females in estrus, Berger (1989) suggested the male should choose the
larger female, thereby increasing his odds of siring more offspring than if he mated with the
smaller female. Although not significant, I noticed a trend where female skeletal size was
associated with a female?s ability to recruit offspring. The inability of our data to document
productivity bias by skeletal size could have been an artifact of our sampling of litters, which
mostly (80.4%) consisted of recruited individuals rather than neonates. I did not document a
body size preference but instead found that mating occurred between a wide range of male and
female body sizes. This finding suggests that males may not be choosy when mating. Rather
they may just pursue females based on chemical signals regarding receptiveness (Murphy et al.
1994) rather than physical attributes.
The wide range of ages and body sizes I documented between mated pairs highlights the
plasticity of mate choice in white-tailed deer. There is an inherent choice a female must make
when she is being pursued by a lesser quality mate: should she breed during her first estrous
cycle or wait until a larger, more dominant buck arrives? This decision is important because late
15
breeding may put a female?s offspring at a reproductive disadvantage later in life due to retarded
development and later age of puberty of offspring (Zwank and Zeno 1986; Gray et al. 2002).
Additionally, females who breed during the peak breeding season may have reduced predation
on their offspring due to the predator swamping effect (Whittaker and Lindzey 1999). Our
results suggest females of all ages and sizes will mate with a younger, smaller male which
supports a female choosing to mate instead of holding out for a better quality male. Similar
results were reported by Haugen (1959), when mature females outside of estrus refused to accept
advances made by a young male inside a small pen by fighting him off with their front feet. On
the day the females entered estrus; however, their demeanor changed and they stood quietly and
calmly until serviced by the male (Haugen 1959). Although speculative, I hypothesize that
choosiness during mate selection changes as a female approaches the end of her period of
receptivity. Females that do not tolerate advances from young males (Sorin 2004) may not be in
estrus or may only be in the beginning stages of estrus. White-tailed deer have evolved a mating
system that allows nearly all reproductive-aged females to be fertilized (Verme and Ullrey 1984;
DelGuidice et al. 2007), which may explain why seemingly poor quality mates of both sexes
successfully breed.
The mate choice decision is confounded by the fact that population demographics play a
central role in the dynamics of choice simply through availability. When there are comparatively
fewer mature males present, females may be more apt to breed with younger males simply
because there are not enough mature males to service each female (Ozoga and Verme 1985;
DeYoung et al. 2009). I observed that as the male age structure matured, the proportion of
breeding by 1.5 year old males decreased. This suggests that young males may change
behavioral breeding strategies based on competition from other males. Male breeding success
16
may not actually be random, but it appeared random in our analysis, possibly as a result of
changing demographics. When the enclosure was first constructed there was a young male age
structure with the oldest males 3.5 years old. In 2013, however, one male had reached 8.5 years
of age, which made any comparisons between years problematic. Experience may also factor
into the pair-bonding process because young males may not adequately service females in their
first attempt to copulate, which may allow enough time for another male to find the pair and
displace the subdominant male. The displacement of individuals in a tending bond mating
system may occur more often when more mature males are cruising the landscape in search of
receptive females, but more research needs to be conducted to confirm this speculation.
I found no evidence that males were able to detect differences in female quality based on
physical attributes, but evidence suggests males may use other cues to assess female quality
(Berger 1989). Margulis (1993) suggested the presence or absence of last year?s fawns may
influence which females a male rocky mountain mule deer (O. hemionus hemionus) will chase.
Reproductive expenditures such as gestation and lactation put a strain on the body of females
that may lead to reduced success in successive years, also known as alternate-year reproductive
success (Mundinger 1981). In years where nutrition is inadequate, females will not allocate
resources to their fawns in lieu of maintaining their body mass (Therrien et al. 2007). This is a
strategy that helps facilitate lifetime reproductive success by increasing the female?s chance of
survival at the cost of losing offspring during the current year. The nutritional demands of
reproduction/lactation may mirror those associated with nutritional restriction due to climate or
food shortages. According to Pekins et al. (1998), the total energetic costs of gestation are
16.4% greater than the requirements for non-pregnant does, so pregnant does cannot afford to put
resources toward body growth. Lactation is even more demanding as it requires 1.7 times more
17
energy than gestation (National Research Council 2007) and reduced fecundity can occur after
successfully weaning offspring (Clutton-Brock et al. 1989; Therrien et al. 2007). This has been
found in other ungulate species such as bighorn sheep (Ovis canadensis) where females reduced
their reproductive effort when population density increased and if they had weaned a lamb the
previous year (Martin and Festa-Bianchet 2010). I did not observe a male bias against females
that had recruited fawns the previous year, but our results may differ from wild populations
because our population had access to supplemental feed.
Our results support previous studies (Verme 1969; Haugen 1975; Folk and Klimstra
1991) that found an influence of skeletal size, age, and nutritional status on reproductive success
in adult, female white-tailed deer. Although quality of offspring is certainly important, most data
available for white-tailed deer are reported by quantity of offspring produced by female age
class, which is correlated with body size. Body size varies by region and nutritional availability
as deer from the fertile soils of the Midwest tend to have larger bodies than deer in the Southeast,
and their fetal rates reflect this difference (Ditchkoff 2011). Verme (1969) compared
reproductive patterns of white-tailed deer related to a nutritional plane and found that deer on a
low quality diet had less fawns per doe than those on a high quality diet. The effects of body
size on reproductive potential tend to be more pronounced in younger age classes. Rhodes et al.
(1985) indicated that fawns in South Carolina had an average of 1.06 fetuses, yearlings had an
average of 1.56 fetuses, and 2.5 year old does had an average of 1.73 fetuses. Beyond 2.5 years
old, the combined litter size was 1.76 for all older age classes. The same trend was found in a
study on the productivity of female deer in Illinois in which fawns had 1.00 fetuses, yearlings
had 1.76, 2.5 year old does had 1.90, and combined older age classes had 1.93 fetuses
(Rosenberry and Klimstra 1970). Verme and Ullrey (1984) found that female fawns must reach
18
a critical weight of 36 kg in order to reach puberty and ovulate, which occurred in at least 9 of 63
(14%) female fawns on our study site. Similarly, our results demonstrated that a younger,
smaller body size correlates with less fawn recruitment than older, larger deer: but only to a
certain age.
Although our observations of mated pairs were derived with small sample sizes from
only one population, similar tendencies would be expected across the white-tailed deer?s range. I
concentrated efforts inside a 174-ha high fence enclosure which minimized losses of
reproductive aged females due to emigration and hunting mortality, and also allowed deer to be
intensively monitored throughout their lifetime. Multi-year reproductive success is difficult to
estimate in the wild because white-tailed deer are an inherently cryptic species and yearlings can
disperse long distances (>50 km) from their natal range (Long et al. 2005). Because of the
closed-nature of the system, I was able to collect detailed data from most individuals in the
population, enabling me to examine factors that are extremely difficult in free-range settings.
Mate choice was difficult for early researchers to evaluate because adequate genetic techniques
were unavailable or mate choice was limited by small enclosure size and number of available
mates. For example, using behavioral observations, Hirth (1977) was only able to record 4
copulations over 3 years. Although our facility was only 174 ha in size, and some of the spatial
attributes of the population were compromised (e.g., the size of the facility was less than the
typical home range of white-tailed deer, closed population), I feel my data are representative of
behaviors and mate choices found in a free-range setting. Caution should be used when
interpreting body percentiles because they were calculated using measurements pooled across all
years. Even though supplemental protein was provided ad libitum throughout the entirety of the
study, availability of natural forage from climactic factors could have affected body growth
19
(Ozoga and Verme 1982). I did not determine if there was an influence of climatic factors on
body size because of small sample sizes by age class across years.
Additional studies focusing on the reproductive success of white-tailed deer might
incorporate individual behavioral variables, or monitor fine-scale movements of both sexes in
order to get a better understanding of how young, physically immature males obtain breeding
opportunities, even in the presence of larger more mature males. More research also needs to be
conducted on how female mate choice changes over the duration of the estrous cycle in order to
maximize fertilization. Employing VITs in a greater proportion of the herd would increase
sample size and further validate typing error rates, because more offspring would have at least
one known parent. Currently, there is no mechanism other than behavioral observations that can
identify males that bred females but were unable to conceive offspring. Future research on
reproductive success of free-ranging deer populations with similar proportions sampled as this
study would further our understanding of mate-choice in this cryptic species as well as help
validate our results.
Acknowledgments
This study was supported by the Alabama Agriculture Experiment Station, Center for
Forest Sustainability, and School of Forestry and Wildlife Sciences, Auburn University. I thank
V. Jackson for maintaining feeders and habitat within our study site. I appreciate the assistance
of many undergraduate volunteer technicians from the School of Forestry and Wildlife Sciences
at Auburn University for helping me monitor transmitters and assist in data collection, most
notably C. Glassey, J. Garrett, and J. Brooks. Additional thanks to M. Smith and T. Steury for
reviewing my manuscript.
20
Literature Cited:
ANDERSON, J. D., ET AL. 2002. Development of microsatellite DNA markers for the automated
genetic characterization of white-tailed deer populations. Journal of Wildlife
Management 66:67-74.
ANDERSSON, M. 1994. Sexual selection. Princeton University Press, Princeton, New Jersey.
BATEMAN, A. J. 1948. Intra-sexual selection in Drosophila. Heredity 2:349-368.
BERGER, J. 1989. Female reproductive potential and its apparent evaluation by male mammals.
Journal of Mammalogy 70:347-358.
BOWMAN, J. L., AND H. A. JACOBSON. 1998. An improved vaginal-implant transmitter for
locating white-tailed deer birth sites and fawns. Wildlife Society Bulletin 26:295-298.
CARSTENSEN, M., G. D. DELGIUDICE, AND B. A. SAMPSON. 2003. Using doe behavior and
vaginal-implant transmitters to capture neonate white-tailed deer in north-central
Minnesota. Wildlife Society Bulletin 31:634-641.
CLUTTON-BROCK, T. H., S. D. ALBON, AND F. E. GUINNESS. 1989. Fitness costs of gestation and
lactation in wild mammals. Nature 337:260-262.
CLUTTON-BROCK, T. H., F. E. GUINNESS, AND S. D. ALBON. 1982. Red deer: behavior and
ecology of two sexes. University of Chicago Press, Chicago, Illinois.
DELGIUDICE, G. D., M. S. LENARZ, AND M. C. POWELL. 2007. Age-specific fertility and
fecundity in northern free-ranging white-tailed deer: Evidence for reproductive
senescence? Journal of Mammalogy 88:427-435.
DEYOUNG, R. W., S. DEMARAIS, K. L. GEE, R. L. HONEYCUTT, M. W. HELLICKSON, AND R. A.
GONZALES. 2009. Molecular evaluation of the white-tailed deer (Odocoileus virginianus)
mating system. Journal of Mammalogy 90:946-953.
21
DEYOUNG, R. W., S. DEMARAIS, R. A. GONZALES, R. L. HONEYCUTT, AND K. L. GEE. 2002.
Multiple paternity in white-tailed deer (Odocoileus virginianus) revealed by DNA
microsatellites. Journal of Mammalogy 83:884-892.
DEYOUNG, R. W., AND K. V. MILLER. 2011. White-tailed deer behavior. Pp. 311-351 in
Biology and management of white-tailed deer. (D. G. Hewitt, ed.). CRC Press, Boca
Raton, Florida.
DITCHKOFF, S. S. 2011. Anatomy and Physiology. Pp. 43-73 in Biology and management of
white-tailed deer. (D. G. Hewitt, ed.). CRC Press, Boca Raton, Florida.
DITCHKOFF, S. S., E. R. WELCH, JR, W. R. STARRY, W. C. DINKINES, R. E. MASTERS, AND R. L.
LOCHMILLER. 1997. Quaility deer management at the McAlester army ammunition plant:
a unique approach. Proceedings of the Annual Conference of the Seatheastern
Association of Fish and Wildlife Agencies 51:389-399.
ELMOUSADIK, A., AND R. J. PETIT. 1996. High level of genetic differentiation for allelic
richness among populations of the argan tree [Argania spinosa (L) Skeels] endemic to
Morocco. Theoretical and Applied Genetics 92:832-839.
EMLEN, S. T., AND L. W. ORING. 1977. Ecology, sexual selection, and evolution of mating
systems. Science 197:215-223.
FOLK, M. J., AND W. D. KLIMSTRA. 1991. Reproductive-performance of female key deer.
Journal of Wildlife Management 55:386-390.
FULLER, T. K., R. M. PACE, J. A. MARKL, AND P. L. COY. 1989. Morphometrics of white-tailed
deer in north-central Minnesota. Journal of Mammalogy 70:184-188.
22
GEE, K. L., J. H. HOLMAN, M. K. CAUSEY, A. N. ROSSI, AND J. B. ARMSTRONG. 2002. Aging
white-tailed deer by tooth replacement and wear: a critical evaluation of a time-honored
technique. Wildlife Society Bulletin 30:387-393.
GOUDET, J. 1995. FSTAT: a computer program to calculate F-statistics. Version 1.2. Journal of
Heredity 86:485-486.
GOUDET, J. 2001. FSTAT, a program to estimate and test gene diversities and fixation indices.
Version 2.9.3. http://www2.unil.ch/popgen/softwares/fstat.htm. Updated from Goudet
(1995). Accessed 4 December 2013.
GRAY, W. N., II, S. S. DITCHKOFF, K. CAUSEY AND C. W. COOK. 2002. The yearling
disadvantage in Alabama deer: effect of birth date on development. Proceedings of the
Annual Conference of the Seatheastern Association of Fish and Wildlife Agencies
56:255-264.
HANSEN, L. 2011. Extensive management. Pp. 409-451 in Biology and management of white-
tailed deer. (D. G. Hewitt, ed.). CRC Press, Boca Raton, Florida.
HAUGEN, A. O. 1959. Breeding records of captive white-tailed deer in Alabama. Journal of
Mammalogy 40:108-113.
HAUGEN, A. O. 1975. Reproductive-performance of white-tailed deer in Iowa. Journal of
Mammalogy 56:151-159.
HIRTH, 1977. Social behavior of white-tailed deer in relation to habitat. Wildlife Monographs
53:1-55.
ISAAC, J. L. 2005. Potential causes and life-history consequences of sexual size dimorphism in
mammals. Mammal Review 35:101-115.
23
JONES, P. D., B. K. STRICKLAND, S. DEMARAIS, AND R. W. DEYOUNG. 2011. Inconsistent
association of male body mass with breeding success in captive white-tailed deer. Journal
of Mammalogy 92:527-533.
KALINOWSKI, S. T., M. L. TAPER, AND T. C. MARSHALL. 2007. Revising how the computer
program CERVUS accommodates genotyping error increases success in paternity
assignment. Molecular Ecology 16:1099-1106.
KARANTH, K. U., AND J. D. NICHOLS. 1998. Estimation of tiger densities in India using
photographic captures and recaptures. Ecology 79:2852-2862.
KIE, J. G., AND M. WHITE. 1985. Population-dynamics of white-tailed deer (Odocoileus
virginianus) on the welder wildlife refuge, Texas. Southwestern Naturalist 30:105-118.
KILPATRICK, H. J., A. J. DENICOLA, AND M. R. ELLINGWOOD. 1996. Comparison of standard
and transmitter-equipped darts for capturing white-tailed deer. Wildlife Society Bulletin
24:306-310.
LONG, E. S., D. R. DIEFENBACH, C. S. ROSENBERRY, B. D. WALLINGFORD, AND M. R. D.
GRUND. 2005. Forest cover influences dispersal distance of white-tailed deer. Journal of
Mammalogy 86:623-629.
MARGULIS, S. W. 1993. Mate choice in rocky-mountain mule deer bucks (Odocoileus hemionus
hemionus) is there a preference for does without fawns? Ethology Ecology & Evolution
5:115-119.
MARTIN, J. G. A., AND M. FESTA-BIANCHET. 2010. Bighorn ewes transfer the costs of
reproduction to their lambs, American Naturalist 176:414-423.
24
MCCOY, J. C., S. S. DITCHKOFF, AND T. D. STEURY. 2011. Bias associated with baited camera
sites for assessing population characteristics of deer. Journal of Wildlife Management
75:472-477.
MCELLIGOTT, A. G., ET AL. 2001. Sexual size dimorphism in fallow deer (Dama dama): do
larger, heavier males gain greater mating success? Behavioral Ecology and Sociobiology
49:266-272.
MECH, L. D., AND R. E. MCROBERTS. 1990. Survival of white-tailed deer fawns in relation to
maternal age. Journal of Mammalogy 71:465-467.
MILLER, B. F., L. I. MULLER, T. DOHERTY, D. A. OSBORN, K. V. MILLER, AND R. J. WARREN.
2004. Effectiveness of antagonists for tiletamine-zolazepam/xylazine immobilization in
female white-tailed deer. Journal of Wildlife Diseases 40:533-537.
MUNDINGER, J. G. 1981. White-tailed deer reproductive-biology in the swan valley, Montana.
Journal of Wildlife Management 45:132-139.
MURPHY, B. P., K. V. MILLER, AND R. L. MARCHINTON. 1994. Sources of reproductive
chemosignals in female white-tailed deer. Journal of Mammalogy 75:781-786.
NATIONAL RESEARCH COUNCIL. 2007. Nutrient requirements of small ruminants: sheep, goats,
cervids, and new world camelids. National Academies Press, Washington, DC.
NEI, M. 1973. Analysis of gene diversity in subdivided populations. Proceedings of the National
Academy of Sciences of the United States of America 70:3321-3323.
NIXON, C. M., AND D. ETTER. 1995. Maternal age and fawn rearing success for white-tailed
deer in Illinois. American Midland Naturalist 133:290-297.
OZOGA, J. J. 1987. Maximum fecundity in supplementally-fed northern michigan white-tailed
deer. Journal of Mammalogy 68:878-879.
25
OZOGA, J. J., AND L. J. VERME. 1982. Physical and reproductive characteristics of a
supplementally-fed white-tailed deer herd. Journal of Wildlife Management 46:281-301.
OZOGA, J. J., AND L. J. VERME. 1985. Comparative breeding-behavior and performance of
yearling vs prime-age white-tailed bucks. Journal of Wildlife Management 49:364-372.
OZOGA, J. J., AND L. J. VERME. 1986a. Initial and subsequent maternal success of white-tailed
deer. Journal of Wildlife Management 50:122-124.
OZOGA, J. J., AND L. J. VERME. 1986b. Relation of maternal age to fawn-rearing success in
white-tailed deer. Journal of Wildlife Management 50:480-486.
PEKINS, P. J., K. S. SMITH, AND W. W. MAUTZ. 1998. The energy cost of gestation in white-
tailed deer. Canadian Journal of Zoology 76:1091-1097.
PELES, J. D., O. E. RHODES, AND M. H. SMITH. 2000. Spermatozoan numbers and testicular
characteristics of male white-tailed deer fawns during the mating season. Acta
Theriologica 45:95-102.
POLLOCK, K. H., J. D. NICHOLS, C. BROWNIE, AND J. E. HINES. 1990. Statistical-inference for
capture-recapture experiments. Wildlife Monographs:1-97.
RHODES, O. E., JR, K. T. SCRIBNER, M. H. SMITH, AND P. E. JOHNS. 1985. Factors affecting the
number of fetuses in a white-tailed deer herd. Proceedings of the Annual Conference of
the Seatheastern Association of Fish and Wildlife Agencies 39:380-388.
RICE, W. R. 1989. Analyzing tables of statistical tests. Evolution 43:223-225.
ROSEBERRY, J. L. AND W. D. KLIMSTRA. 1970. Productivity of white-tailed deer on crab
orchard national wildlife refuge. Journal of Wildlife Management 34:23-28.
SAALFELD, S. T., AND S. S. DITCHKOFF. 2007. Survival of neonatal white-tailed deer in an
exurban population. Journal of Wildlife Management 71:940-944.
26
SCHULTZ, S. R., AND M. K. JOHNSON. 1992. Breeding by male white-tailed deer fawns. Journal
of Mammalogy 73:148-150.
SEVERINGHAUS, C. W. 1949. Tooth development and wear as criteria of age in white-tailed deer.
Journal of Wildlife Management 13:195-216.
SIKES, R. S., W. L. GANNON, AND THE ANIMAL CARE AND USE COMMITTEE OF THE AMERICAN
SOCIETY OF MAMMALOGISTS. 2011. Guidelines of the American Society of
Mammalogists for the use of wild mammals in research. Journal of Mammalogy 92:235-
253.
SORIN, A. B. 2004. Paternity assignment for white-tailed deer (Odocoileus virginianus): Mating
across age classes and multiple paternity. Journal of Mammalogy 85:356-362.
THERRIEN, J. F., S. D. COTE, M. FESTA-BIANCHET, AND J. P. OUELLET. 2007. Conservative
maternal care in an iteroparous mammal: a resource allocation experiment. Behavioral
Ecology and Sociobiology 62:193-199.
TOWNSEND, T. W., AND E. D. BAILEY. 1981. Effects of age, sex and weight on social rank in
penned white-tailed deer. American Midland Naturalist 106:92-101.
TRIVERS, R. L. 1972. Parental investment and sexual selection. Pp. 136-207 in Sexual selection
and the descent of man 1871-1971 (B. G. Campbell, ed.). Aldine Publishing Company,
Chicoga, Illinois.
VERME, L. J. 1969. Reproductive patterns of white-tailed deer related to nutritional plane. Journal
of Wildlife Management 33:881-889.
VERME, L. J. AND D. E. ULLREY 1984. Physiology and nutrition. Pp. 91-118 in White-tailed
deer: ecology and management (L. K. Halls, ed.). Stackpole Books, Harrisburg,
Pennsylvania.
27
WEIR, B. S., AND C. C. COCKERHAM. 1984. Estimating f-statistics for the analysis of population-
structure. Evolution 38:1358-1370.
WHITTAKER, D. G., AND F. G. LINDZEY. 1999. Effect of coyote predation on early fawn survival
in sympatric deer species. Wildlife Society Bulletin 27:256-262.
ZWANK, P. J., AND J. A. ZENO. 1986. Weight of white-tailed deer fawns relative to fawning date.
Proceedings of the Annual Conference of the Seatheastern Association of Fish and
Wildlife Agencies 40:424-429.
28
Table 1.1 ? Known white-tailed deer breeding populations by sex, age class, and cohort birth
year from 2008-2013, Auburn Captive Facility, Camp Hill AL.
______________________________________________________________________________
Age 2008 2009 2010 2011 2012 2013
______________________________________________________________________________
Total popln.a 69 (35b) 84 (51b) 98 (63b) 122 (90b) 114 (89b) 110 (90b)
Males 25 (15) 40 (29) 48 (34) 62 (50) 64 (53) 53 (45)
0.5 11 (9) 16 (15) 14 (11) 21 (21) 13 (13) 8 (8)
1.5 8 (6) 10 (8) 13 (12) 11 (8) 12 (12) 9 (9)
2.5 3 (0) 8 (6) 9 (7) 12 (11) 11 (8) 6 (6)
3.5 3 (0) 3 (0) 6 (4) 8 (6) 11 (10) 9 (6)
4.5 0 3 (0) 3 (0) 6 (4) 8 (6) 8 (8)
5.5 0 0 3 (0) 2 (0) 6 (4) 7 (5)
6.5 0 0 0 2 (0) 2 (0) 4 (3)
7.5 0 0 0 0 1 (0) 1 (0)
8.5 0 0 0 0 0 1 (0)
Females 44 (20) 44 (22) 50 (29) 60 (40) 50 (36) 48 (36)
0.5 16 (11) 7 (6) 13 (10) 12 (12) 9 (9) 6 (6)
1.5 7 (6) 14 (9) 5 (4) 13 (10) 10 (10) 9 (9)
2.5 8 (3) 5 (4) 13 (8) 5 (4) 8 (5) 8 (8)
3.5 7 (0) 7 (3) 5 (4) 12 (7) 3 (3) 5 (2)
29
Table 1.1 ? Continued
______________________________________________________________________________
Age 2008 2009 2010 2011 2012 2013
______________________________________________________________________________
4.5 4 (0) 5 (0) 6 (3) 5 (4) 9 (4) 3 (3)
5.5 1 (0) 4 (0) 4 (0) 6 (3) 4 (3) 8 (4)
6.5 0 1 (0) 2 (0) 3 (0) 5 (2) 4 (3)
7.5 1 (0) 0 1 (0) 2 (0) 1 (0) 3 (1)
8.5 0 1 (0) 0 1 (0) 1 (0) 1 (0)
9.5 0 0 1 (0) 0 0 1 (0)
10.5 0 0 0 1 (0) 0 0
Sex Ratio
(M:F)c 1:2.0 1:1.5 1:1.1 1:1.2 1:0.8 1:0.9
______________________________________________________________________________
a Abundances estimated using combination of camera surveys, field observations, capture of live
animals, and recovery of deceased animals. All estimating methods indicated ?90% of animals
in breeding populations were marked during the study yielding largely known population sizes.
b Number of individuals initially captured at ? 2.5 years old.
c For animals >0.5 years old.
30
Table 1.2 ? Population genetics information (individual locus allelic richness, gene diversity, FIS,
and Hardy-Weinberg probabilities) for white-tailed deer from 2008-2013 at Auburn Captive
Facility, Camp Hill, AL.
______________________________________________________________________________
Locus Samples Alleles Gene FIS Pa
diversity
______________________________________________________________________________
Cervid 224 14 0.879 - 0.026 0.891
L 223 9 0.776 0.005 0.469
BM6506 224 12 0.890 - 0.028 0.907
N 224 13 0.874 0.040 0.071
INRA01 224 5 0.303 - 0.090 0.975
BM6438 224 9 0.820 0.026 0.215
O 224 8 0.699 - 0.047 0.912
BL25 224 5 0.516 0.083 0.044
K 224 4 0.150 - 0.012 0.686
Qb 223 14 0.836 0.115 0.001
Db 223 10 0.764 0.184 0.001
OAR 224 12 0.826 - 0.010 0.673
Pb 221 8 0.811 0.191 0.001
S 224 16 0.895 - 0.018 0.806
______________________________________________________________________________
a Indicative adjusted nominal level (5%) was 0.004.
b Loci excluded from parentage analysis due to departures from Hardy-Weinberg equilibrium.
31
Table 1.3 ? Inputs for yearly cohort simulations of white-tailed deer parentage analysis using
CERVUS 3.0 from 2008-2013 at Auburn Captive Facility, Camp Hill, AL.
____________________________________________________________________________
Year 2008 2009 2010 2011 2012 2013
____________________________________________________________________________
Offspring 10,000 10,000 10,000 10,000 10,000 10,000
Candidate Mothers 44 (0.5a) 44 (0.9) 50 (0.9) 60 (0.9) 50 (0.9) 48 (0.8)
Candidate Fathers 25 (0.5a) 40 (0.9) 48 (0.9) 62 (0.9) 64 (0.9) 53 (0.8)
Proportion Loci Typedb 0.9997 0.9997 0.9997 0.9997 0.9997 0.9997
Proportion Loci Mistypedc 0.014 0.014 0.014 0.014 0.014 0.014
Minimum Loci Typed 10 10 10 10 10 10
____________________________________________________________________________
a Proportion sampled. Estimated using camera surveys, field observations, capture of live
animals, and recovery of deceased animals.
bReported in allele frequency analysis in CERVUS 3.0.
cCalculated using known parent-offspring mismatching rates with offspring collected by way of
VITs.
32
Figure 1.1 ? Observed age differences between dams and sires from 2008-2013, and random age
differences assuming the occurrence of random mating, Auburn Captive Facility, Camp Hill, AL.
0
2
4
6
8
10
12
14
16
18
20
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10
Prop
ortion
of
m
ated
pairs
(%)
Difference between dam age and sire age (yrs)
Random Pairs
Observed Pairs
33
Figure 1.2 ? Range-graded dot representation of age relationships between mated pairs of white-
tailed deer from 2008-2013, Auburn Captive Facility, Camp Hill, AL.
-0.5
0.5
1.5
2.5
3.5
4.5
5.5
6.5
-0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5
Male
ag
e (y
ears)
Female age (years)
7
4
1
Count:
34
Figure 1.3 ? Size comparison of 18 mated pairs of white-tailed deer for which measurements
were available for both parents from 2008-2013, Auburn Captive Facility, Camp Hill, AL.
r? = 0.1206
250
260
270
280
290
300
310
320
220 230 240 250 260 270 280
Male
skeletal
size
(cm
)
Female skeletal size (cm)
35
Figure 1.4 ? Body percentile comparison between mated pairs of white-tailed deer from 2008-
2013, Auburn Captive Facility, Camp Hill, AL.
r? = 0.0029
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Male
bo
dy
percentile
Female body percentile
36
Chapter 2: How Population Demography Influences
Fawning Season of White-tailed Deer
Abstract
Although white-tailed deer (Odocoileus virginianus) are one of the most abundant and
studied ungulates in North America, few studies of how population demography affects the
fawning season have appeared to date. Age structure and adult sex ratio of a population may
influence the timing and duration of the fawning season. From 2010 to 2013, I used Vaginal
Implant Transmitters (VITs) to record the birth date of fawns born from native Alabama deer
enclosed within a 174-ha captive facility to elucidate how population demography affects
fawning season. The deer herd was intensively monitored which permitted me to document an
earlier shift in fawning season as male age structure increased from a mean of 2.74 years old in
2010 to 3.92 years old in 2013. Prior to the shift, the mean fawning date was 12 August, and
after a maturation of male age structure, the mean fawning date was 30 July. Earlier fawning
may be important for neonatal survival, especially in areas of the Southeast where coyotes
(Canis latrans) are severely reducing recruitment. The effect of male age structure on the timing
and duration of the fawning season has yet to be firmly established, but I hypothesize managers
can increase neonate development and survival by increasing male age structure.
Introduction
The intensity of management of white-tailed deer (Odocoileus virginianus) has increased
prominently over the last two decades and has shifted from traditional management for
maximum sustained yield to herds managed for quality. Quality deer management was designed
to increase overall herd condition by harvesting an appropriate amount of antlerless deer relative
37
to available habitat and protecting young males from harvest (Miller and Marchinton 1995).
Increases in antler size and hunter satisfaction are well documented byproducts of changes to
population demography that occur under quality deer management, but how demography can
affect the fawning season has received scant attention in the literature. It has been suggested that
increased population age structure and a more balanced sex ratio, products of quality deer
management, can alter the timing and duration of the breeding season (Guynn and Hamilton
1986, Jacobson 1992). In iteroparous mammalian species such as white-tailed deer, the timing
of breeding season has evolved to allow fawns to be born during peak food availability, which
helps ensure that females are able to meet the high nutritional demands of gestation and lactation
(Verme 1965, Bronson 1989). The timing of the breeding season is adjustable as Jacobson
(1992) found that breeding chronology shifted 2-3 weeks earlier with an increase in male age
structure that was accomplished by protecting young males from harvest.
An earlier breeding season may be important for mitigating negative effects of late-born
fawns. Research in the Southeast revealed late born fawns typically have smaller antlers (Gray
et al. 2002) and bodies (Gray et al. 2002, Saalfeld et al. 2007) at age 1.5 than fawns born earlier
in the season. Similarly, research on red deer (Cervus elaphus) revealed yearlings with small
antlers were typically born later than yearlings with larger antlers (Schmidt et al. 2001).
Conversely, fawns born earlier may have a fitness advantage over their late-born counterparts by
having larger bodies and antlers at 1.5 years of age. Late-born fawns may be at a developmental
disadvantage because food availability has passed its peak and nutritional availability relates
directly to the amount of milk a female can provide (Verme 1965), in addition to having less
time for development prior to winter. As for reproduction, female fawns born after the peak
fawning period do not usually reach puberty their first breeding season (Verme and Ullrey 1984),
38
and male fawns may not develop spermatozoa in time to breed during their first rut (Peles et al.
2000). These studies suggest that birth date has impacts on development of fawns through their
first few years.
Whereas timing of the fawning season is certainly important, the duration of the fawning
season may be equally significant. Guynn and Hamilton (1986) found that breeding season
condensed as adult sex ratio shifted from female biased to being balanced between the sexes.
Synchrony of estrus among females during the breeding season may lead to greater survival of
fawns the following summer (White et al. 2001). When fawns are born over a shorter time
range, predators may be overwhelmed by the number of available prey, also known as predator
swamping (Ims 1990, Whittaker and Lindzey 1999). Natural selection has guided fawning dates
to occur during time periods conducive to successfully raising offspring, but the recent
colonization of coyotes (Canis latrans) to southeastern ecosystems has introduced a different
selective pressure to deer in this region. Several studies in the Southeast have recognized
coyotes as being a key predator negatively impacting fawn recruitment (Saalfeld and Ditchkoff
2007, Howze et al. 2009, Kilgo et al. 2012, Jackson and Ditchkoff 2013, McCoy et al. 2013).
Differences in the timing of the breeding season might explain why fawn recruitment is lower in
areas of the Southeast, while other regions within the coyote?s native range do not show similar
impacts (Heugel et al. 1985, Brinkman et al. 2004, Grovenburn et al. 2011). The fawning season
in the Southeast is typically later in the year compared to other areas of the white-tailed deer?s
range (Haugen 1959, Leuth 1967, Gray et al. 2002) which may exacerbate the negative influence
coyotes are having on neonatal survival because coyote pups may be able to hunt by the time
fawning occurs.
39
The effects of population demography on fawning season have yet to be clearly
established, so my objective was to study effects of population age and sex ratio on the timing
and duration of fawning season in central-Alabama during a fluctuating demography. Older
(?3.5 years old) males have been documented performing more ritualized breeding behavior than
young (1.5 year old) males (Ozoga and Verme 1985), thus I predicted that as male structure
became progressively mature, fawning dates would occur earlier (Guynn and Hamilton 1986,
Jacobson 1992). I also predicted that fawning season duration would be more condensed when
more mature males were present in the population (Guynn and Hamilton 1986, Miller et al.
1995).
Study Site
The deer in this study resided in the 174-hectare Auburn Captive Facility (ACF) located
in Camp Hill, Alabama, USA. The population consisted of deer that were in the area at the time
the fence was constructed in 2007, and their descendants. The perimeter of the ACF was
bordered by a 2.6-meter deer-proof fence which allowed the study of individuals throughout their
lifetime. Deer were fed 18% protein pellets (?Deer Feed,? SouthFresh Feeds, Demopolis, AL)
ad libitum year round using 3 free choice feeders and their diet was supplemented by corn during
fall and winter which helped attract deer for capture.
The two main cover types inside the ACF were open hayfields (40%) and mixed forest
(60%). The predominant grass species found inside the ACF was bermuda grass (Cynodon sp.).
Other grasses present included fescue (Festuca sp.), big bluestem (Andropogon sp.), Johnson
grass (Sorghum sp.), dallisgrass (Paspalum sp.), and bahia grass (Paspalum sp.). The mixed
forest species were oak (Quercus spp.), hickory (Carya spp.), maple (Acer spp.), and pine (Pinus
spp.) of varying age. The general habitat within the wooded areas included a thick closed
40
canopy with little understory growth. Forest edges and creek bottoms contained dense
understory growth consisting of Chinese privet (Ligustrun sinense). Water was available to deer
from several creeks that ran throughout the property. Elevation ranged from 190 to 225 meters
above sea level. The climate in this region of East-Central Alabama was moderately warm with
mean high temperatures of 32.5 ?C in July and mean low temperatures of -0.5?C in January.
According to the National Oceanic and Atmospheric Administration weather station in
Alexander City, Alabama (37 km northwest of our study area), average annual precipitation in
the area was approximately 131 cm (National Climate Data Center 2013).
Methods
Vaginal Implant Transmitters (VITs, M3930, Advanced Telemetry Systems, Isanti, MN)
were inserted from 2010 to 2013 in adult female deer beginning mid-February and ending in
June using methods described by Saalfeld and Ditchkoff (2007). Females were captured using
tranquilizer dart guns equipped with night vision scopes or a 0.8 ha modified box trap. Chemical
immobilization occurred with an intramuscular injection of Telazol? (Fort Dodge Animal
Health, Fort Dodge, Iowa; 125mg/ml given at a rate of 4.5 mg/kg) and xylazine (Lloyd
Laboratories, Shenandoah, Iowa; 100mg/ml given at a rate of 2.2 mg/kg) followed by reversal
with Tolazine? (Lloyd Laboratories, Shenandoah, Iowa; 100mg/ml given at a rate of 6.6 mg/kg;
Miller et al. 2004). Deer were aged using the tooth replacement and wear (Severinghaus 1949).
VITs were monitored once a week from insertion until 4 July, after which monitoring increased
to 4 times per 24-hour period until the last transmitter had been expelled.
VITs were temperature sensitive so when expelled, the drop in temperature caused the
pulse rate to double. Event time codes were programmed into the VITs so that expulsion time
could be determined with an accuracy of ? 15 minutes. Upon detecting an expelled transmitter, I
41
used telemetry to home in on the VIT to help narrow the search area (Carstensen et al. 2003). A
thermal imaging camera (Raytheon Palm IR 250D, Waltham, MA) was used to locate fawns not
directly at the birth site. In these instances, the location of the VIT was used as a focal point of a
grid search. Fawns were handled quickly (<10 minutes) to prevent any researcher induced
abandonment (Powell et al. 2005), and data were collected from each fawn as part of another
study (Acker 2013). Retention percentages were calculated by dividing the number of successful
VITs (where a fawn was recovered) by the number of females implanted each year. All capture
and handling procedures were in accordance with protocols approved by the Auburn University
Institutional Animal Care and Use Committee (PRN numbers: 2008-1417, 2008-1421, 2010-
1785, 2011-1971, and 2013-2372) and were in compliance with guidelines adopted by the
American Society of Mammalogists Animal Care and Use Committee (Sikes et al. 2011).
Date of birth was recorded for each successful VIT expulsion and was converted to Julian
date for statistical analysis. VITs that were expelled during the fawning season but not located at
definitive birth sites were not included in analysis, as they were assumed to be premature
expulsions, and birth could not be confirmed. I used ANOVA and t-tests in the program R to
examine differences in fawning season between years (R Core Team 2012). I considered
differences in fawning season significant at ? ? 0.05. I also compared fawning season within the
ACF to estimated fawning dates for adult female (?2.5 years old) deer harvested within 40 km of
our study site. These data were collected during spring reproductive surveys by the Alabama
Department of Conservation and Natural Resources in Tallapoosa county using fetal
measurements (Hamilton et al. 1985).
Results
42
Population reconstruction indicated minimum annual herd size ranged from 69 (2008) to
122 (2011) individuals (Table 2.1). Adult sex ratio gradually shifted over the course of the study
from 1:2.0 M:F in 2008 to 1:0.8 M:F in 2012. Approximately 90% of adult deer were captured
and marked as part of ongoing research at the study site. The proportion of known-age animals
in the population increased from 50.7% in 2008 to 81.8% in 2013. Mean adult (>0.5 years old)
male age increased from 1.42 in 2008 to 3.92 in 2013, while mean adult female age increased
from 2.14 in 2008 to 4.17 in 2013.
From 2010 to 2013 a total of 55 females were fitted with VITs. I successfully recovered
37 neonates from 24 females. Successful VIT retention was 58.8, 30.0, 60.0, and 71.4% in 2010,
2011, 2012, and 2013, respectively. I censored 8 VITs from analysis in 2013 because batteries
died before parturition occurred. A total of 6 VITs were retained nearly to parturition, but were
not included in statistical analysis because I was unable to locate a birth site and thus confirm
birth. Seventeen VITs were prematurely expelled before fawning season. Mean ages of
implanted females were 3.7, 1.8, 3.2, and 5.7 years of age in 2010, 2011, 2012, and 2013,
respectively. Females implanted in 2011 (n = 3) were 3.87 (?3.66) years younger than females
implanted in 2013 (t = -3.21, P = 0.036). Two females were implanted with VITs in multiple
years, one of which was implanted for three consecutive years, with no adverse effects.
Mean fawning date occurred 12.4 (? 12.72, 95% C.I.) days earlier in 2013 than in 2010
(Figure 2.1), however the difference was not significant (t = 1.877, P = 0.109). The length of
fawning season ranged from 31 (Julian date 210 to 241, 29 July to 29 August) days in 2010 to 25
(208 to 233, 27 July to 21 August) days in 2011, but this difference was not significant (t =
0.192, P = 0.895). Mean fawning dates were 12 August ? 3.11 days (x ? SE), 10 August ?7.45
days, 2 August ?4.02 days, and 30 July ?5.83 days for 2010, 2011, 2012, and 2013, respectively.
43
Mean fawning date did not differ from that of wild Alabama deer harvested in 2013 within 40
km of the ACF (t = -0.441, P = 0.663).
Discussion
I documented the trend toward an earlier fawning season as population demography
changed. Although not significant, our low sample size and near significant results suggest that
population demographics influenced the timing of parturition. Jacobson (1992) documented a
similar shift toward earlier breeding when quality deer management harvest strategies increased
male age structure. When there were more mature males present, the rut occurred 2-3 weeks
earlier and was likely a result of more males available to breed females during their initial
estrous cycle (Jacobson 1992). Our population mimicked the changes one would expect under
quality deer management as male age structure increased over time. Older males have been
documented to perform more ritualized breeding behavior than younger males (Ozoga and
Verme 1985), which may explain the earlier fawning season I observed. The importance of
population demography on breeding season has yet to be clearly established, but our results
suggest that age structure of males can influence timing of the fawning season.
Although I was unable to test for significant differences in sex ratio over the course of
our study, Guynn and Hamilton (1986) found that breeding season condensed as adult sex ratio
shifted from female biased to being balanced between the sexes. I, however, did not document a
significant shortening of the fawning season when more mature males were present in the
population. Having more mature males relative to females in a population increases sexual
competition and leaves fewer females unbred after their first estrous cycle (Miller et al. 1995),
but sex ratios close to parity or slightly skewed toward females seem to function similarly. From
2010 to 2013, our population never had an estimated sex ratio less than 1 male for every 1.2
44
females, so I may not have been able to detect a condensed fawning season because sex ratios
were already balanced when I began using VITs to document the fawning season. Similarly,
Kilgo et al. (2012) found that fawning season at the Savannah River Site in South Carolina still
occurred over a 2.5 month period, even when adult sex ratio approached parity for over 40 years.
Although speculative, I believe the fawning season may have condensed since 2007 when the
research facility was established, but I have no data on fawning season before VITs were utilized
beginning in 2010.
Whether it is age structure or adult sex ratio affecting the fawning season, there is little
doubt that late-born fawns are at a disadvantage relative to more earlier born from their cohort
(Knox et al. 1991, Gray et al. 2002). In terms of physical development, late-born fawns typically
have smaller antlers (Gray et al. 2002) and bodies (Gray et al, 2002, Saalfeld and Ditchkoff
2007) at age 1.5 than fawns born earlier in the season. Earlier breeding and fawning may have
population level effects as earlier born female fawns may reach puberty and have offspring
(Verme and Ullrey 1984). Conversely, late born female fawns usually do not breed, or if they
do, it occurs late in the breeding season which perpetuates the late-born fawn cycle. Lifetime
breeding success may also be influenced by birth date. Evidence suggests negative effects of late
born fawns, such as small body size and reduced fecundity, may even persist into adulthood
(Clutton-Brock et al. 1982, Mech et al. 1991, Monteith et al. 2009). Another disadvantage late
born fawns incur is increased risk of winter mortality, especially in Northern regions where
winter climates are severe (Delgiudice et al. 2006, Carstensen et al. 2009).
The timing of the fawning season is important as the relatively recent colonization of
coyotes to southern regions is a concern to mangers having to deal with reduced fawn survival.
Although information is lacking for coyotes in the Southeast, they have been well studied within
45
their native range (Hilton 1978). Coyote pups are born in early spring and usually do not leave
the vicinity of their den sites until 6-8 weeks old (Harrison and Gilbert 1985). These pups may
be large enough to capture fawns by the time fawning season occurs in Alabama and some other
parts of the Southeast (Saalfeld and Ditchkoff 2007). Having fawns born earlier in the growing
season, may increase fawn survival because coyote pups may lack experience catching prey
earlier in the year, or because pups have differential hunting tactics compared to older, more
dominant coyotes (Gese et al. 1996). Selective pressures to have fawns born during a specific
time period may become more pronounced as coyotes continue to persist on the Southeastern
landscape. Despite the occurrence of coyotes at our study site, I failed to document any
reduction in fawn recruitment. In areas where coyotes are negatively impacting fawn
recruitment, harvest recommendations that influence deer population structure could mitigate
some of these impacts (Kilgo et al. 2012).
Although having fawns born earlier and over a shorter time period can increase survival
and development of fawns, there may be potential negative consequences found in populations
that have increased proportions of mature males. The intense competition found in these
populations can lead to elevated levels of injury and stress. In a population with a large
proportion (>50%) of mature males (?3.5 years old), Ditchkoff et al. (2001) reported mortality of
mature males due to rut-related stress and physical exertion was greater than human-induced
hunting mortality, and speculated that this was due to the high proportion of mature males in the
population and associated levels of competition for breeding. Ozoga and Verme (1985) observed
evidence of differential participation in breeding activity between old (?3.5 year olds) and young
(1.5 year olds) males by manipulating male age structure within a population. When only 1.5
year old males were present, they observed fewer rubs and scrapes than when only older males
46
were present, but no differences in adult male survival were reported. Another potential
consequence of managing populations for increased numbers of older males is increased risk of
intracranial abscessation, which is thought to be caused by breeding activities such as antler
sparring, rubbing, or antler casting (Davidson et al. 1990). It has been reported that intracranial
abscessation can be a significant cause of mortality in older males (Davidson et al. 1990, Karns
et al. 2009). Antler breakage patterns are also of management importance as they may be
indicative of a population with a high proportion of mature males (Karns and Ditchkoff 2012).
Although I did not collect data on antler breakage, anecdotal evidence suggests more breakage
occurred in 2013 than in 2008, which also suggests that competition for females in the ACF was
more intense in 2013 compared to 2008.
Although male demographics are certainly important, female nutrition and age also play
an important role as to when an individual will enter estrus (Abler et al. 1976) and give birth
(Verme 1969). Gestation may be extended for females in poor condition while females in good
condition may not gestate as long (Verme 1965). The nutritional plane (due to supplemental
feed provided ad libitum) and age of females fitted with VITs was consistent throughout the
study to isolate the effect of population demographics on fawning date. I did not collect data on
native forage availability during our study, and low sample size prevented any comparisons of
female age to fawning date. Guynn and Hamilton (1986) found that female age was not an
important determinant of when breeding occurred. They documented an earlier shift in the
breeding season even as the age of pregnant females at their study site slightly declined. If age
were an important factor, they should have found later conception dates when younger females
were present.
47
I documented an earlier fawning season with increasing male age structure, which
supports the notion that having more mature males present in a population has a positive
influence on fawning date. Earlier-born fawns get a head start on development which may
persist into adulthood. Managers seeking to improve fawn survival as well as allow more time to
develop prior to winter may consider increasing male age structure, which increases breeding
competition and results in earlier fawning. The positive effects of earlier fawning may not be
apparent the first season quality deer management is implemented, but benefits should continue
to accrue into successive years of the management program as early born fawns reach maturity.
In addition to the positive impacts on fawn development, an earlier fawning season may also be
good for mangers seeking to counteract reduced fawn survival caused by coyote predation.
Acknowledgments
This study was supported by the Alabama Agriculture Experiment Station, Center for
Forest Sustainability, and School of Forestry and Wildlife Sciences, Auburn University. I
appreciate the assistance of many volunteer technicians that helped monitor transmitters and
aided in data collection.
Literature cited
Abler, W. A., D. E. Buckland, R. L. Kirkpatrick, and P. F. Scanlon. 1976. Plasma progestins and
puberty in fawns as influenced by energy and protein. Journal of Wildlife Management
40:442-446.
Acker, P. 2013. Factors influencing reproductive success and camera survey efficiency of white-
tailed deer. Thesis, Auburn University, Alabama, USA.
Brinkman, T. J., J. A. Jenks, C. S. DePerno, B. S. Haroldson, and R. G. Osborn. 2004. Survival
of white-tailed deer in an intensively farmed region of Minnesota. Wildlife Society
Bulletin 32:726-731.
48
Bronson, F. H. 1989. Mammalian reproductive biology. University of Chicago Press, Chicago.
Carstensen, M., G. D. DelGiudice, and B. A. Sampson. 2003. Using doe behavior and vaginal-
implant transmitters to capture neonate white-tailed deer in north-central Minnesota.
Wildlife Society Bulletin 31:634-641.
______, ______, ______, and D. W. Kuehn. 2009. Survival, birth characteristics, and cause-
specific mortality of white-tailed deer neonates. Journal of Wildlife Management 73:175-
183.
Clutton-Brock, T. H., F. E. Guiness, and S. D. Albon. 1982. Red deer: behavior and ecology of
two sexes. G. B. Schaller, editor. University of Chicago Press, Chicago.
Davidson, W. R., V. F. Nettles, L. E. Hayes, E. W. Howerth, and C. E. Couvillion. 1990.
Epidemiologic features of an intracranial abscessation/suppurative meningoencephalitis
complex in white-tailed deer. Journal of Wildlife Diseases 26:460-467.
Delgiudice, G. D., J. Fieberg, M. R. Riggs, M. C. Powell, and W. Pan. 2006. A long-term age-
specific survival analysis of female white-tailed deer. Journal of Wildlife Management
70:1556-1568.
Ditchkoff, S. S., E. R. Welch, R. L. Lochmiller, R. E. Masters, and W. R. Starry. 2001. Age-
specific causes of mortality among male white-tailed deer support mate-competition
theory. Journal of Wildlife Management 65:552-559.
Gese, E. M., R. L. Ruff, and R. L. Crabtree. 1996. Foraging ecology of coyotes (Canis latrans):
the influence of extrinsic factors and a dominance hierarchy. Canadian Journal of
Zoology 74:769-783.
49
Gray, W. N., II, S. S. Ditchkoff, K. Causey, and C. W. Cook. 2002. The yearling disadvantage
in Alabama deer: effect of birth day on development. Proceedings of the Annual
Conference of the Seatheastern Association of Fish and Wildlife Agencies 56:255-264.
Grovenburg et al. 2011. Survival of white-tailed deer neonates in Minnesota and South Dakota.
Journal of Wildlife Management 75:213-220.
Guynn, D. C. and R. J. Hamilton. 1986. The effects of adult sex ratio on reproduction in white-
tailed deer. Proceedings of the International Ranchers Roundup 1986:233-240.
Hamilton, R. J., M. L. Tobin, and W. G. Moore. 1985. Aging fetal white-tailed deer. Proceedings
of the Annual Conference of the Seatheastern Association of Fish and Wildlife Agencies
39:389-395.
Harrison, D. J., and J. R. Gilbert. 1985. Denning ecology and movements of coyotes in Maine
during pup rearing. Journal of Mammalogy 66:712-719.
Haugen, A. O. 1959. Breeding recoreds of captive white-tailed deer in Alabama. Journal of
Mammalogy 40:108-113.
Hilton, H. 1978. Systematics and ecology of the eastern coyote. Pages 209-228 in M. Bekoff,
editor. Coyotes: biology, behavior, and management. The Blackburn Press, Caldwell,
New Jersey.
Howze, M. B., L. M. Connoer, R. J. Warren, and K. V. Miller. 2009. Predator removal and
white-tailed deer recruitment in Southwestern Georgia. Proceedings of the Annual
Conference of the Seatheastern Association of Fish and Wildlife Agencies 63:17-20.
Huegel, C. N., R. B. Dahlgren, and H. L. Gladfelter. 1985. Mortality of white-tailed deer fawns
in south-central Iowa. Journal of Wildlife Management 49:377-380.
50
Ims, R. A. 1990. On the adaptive value of reproductive synchrony as a predator-swamping
strategy. American Naturalist 136:485-498.
Jackson, A. M., and S. S. Ditchkoff. 2013. Survival estimates of white-tailed deer fawns at Fort
Rucker, Alabama. American Midland Naturalist 170:184-190.
Jacobson, H. A. 1992. Deer condition response to changing harvest strategy, davis island,
Mississippi. Pages 48-55 in R. D. Brown, editor. The biology of deer. Springer-Verlag,
New York, New York.
Karns, G. R., and S. S. Ditchkoff. 2012. Antler breakage patterns in white-tailed deer.
Proceedings of the Annual Conference of the Southeastern Association of Fish and
Wildlife Agencies 66:114-119.
______, R. A. Lancia, C. S. DePerno, M. C. Conner, and M. K. Stoskopf. 2009. Intracranial
abscessation as a natural mortality factor for adult male white-tailed deer (Odocoileus
virginianus) in Kent county, Maryland, USA. Journal of Wildlife Diseases 45:196-200.
Kilgo, J. C., H. S. Ray, M. Vukovich, M. J. Goode, and C. Ruth. 2012. Predation by coyotes on
white-tailed deer neonates in South Carolina. Journal of Wildlife Management 76:1420-
1430.
Knox, W. M., M. O. Bara, and K. V. Miller. 1991. Effect of fawning date on physical
development of yearling white-tailed deer. Proceedings of the Annual Conference of the
Seatheastern Association of Fish and Wildlife Agencies 45:30-36.
Leuth, F. X. 1967. Reproductive studies of some Alabama deer herds. Proceedings of the Annual
Conference of the Seatheastern Association of Fish and Wildlife Agencies 21:62-68.
51
McCoy, J. C., S. S. Ditchkoff, J. B. Raglin, B. A. Collier, and C. Ruth. 2013. Factors influencing
survival of white-tailed deer fawns in coastal South Carolina. Journal of Fish and
Wildlife Management 4:280-289.
Mech, L. D., M. E. Nelson, and R. E. McRoberts. 1991. Effects of maternal and grandmaternal
nutrition on deer mass and vulnerability to wolf predation. Journal of Mammalogy
72:146-151.
Miller, K.V., and R. L. Marchinton. 1995. Quality whitetails - the why and how of quality deer
management. Stackpole books, Mechanicsburg, Pennsylvania.
______, ______, and J. J. Ozoga. 1995. Deer sociobiology. Pages 118-128 in K.V. Miller and R.
L. Marchinton, editors. Quality whitetails - the why and how of quality deer
management. Stackpole books, Mechanicsburg, Pennsylvania.
Miller, B. F., L. I. Muller, T. Doherty, D. A. Osborn, K. V. Miller, and R. J. Warren. 2004.
Effectiveness of antagonists for tiletamine-zolazepam/xylazine immobilization in female
white-tailed deer. Journal of Wildlife Diseases 40:533-537.
Monteith, K. L., L. E. Schmitz, J. A. Jenks, J. A. Delger, and R. T. Bowyer. 2009. Growth of
male white-tailed deer: consequences of maternal effects. Journal of Mammalogy 90:651-
660.
National Climate Data Center. 2013. National Oceanic and Atmospheric Administration.
. Accessed 12 December 2013.
Ozoga, J. J., and L. J. Verme. 1985. Comparative breeding-behavior and performance of yearling
vs prime-age white-tailed bucks. Journal of Wildlife Management 49:364-372.
52
Peles, J. D., O. E. Rhodes, and M. H. Smith. 2000. Spermatozoan numbers and testicular
characteristics of male white-tailed deer fawns during the mating season. Acta
Theriologica 45:95-102.
Powell, M. C., G. D. DelGiudice, and B. A. Sampson. 2005. Low risk of marking-induced
abandonment in free-ranging white-tailed deer neonates. Wildlife Society Bulletin
33:643-655.
R Core Team. 2012. R: A language and environment for statistical computing. R Foundation for
Statistical Computing, version 2.15.2. . Accessed 4
November 2013.
Saalfeld, S. T., and S. S. Ditchkoff. 2007. Survival of neonatal white-tailed deer in an exurban
population. Journal of Wildlife Management 71:940-944.
Schmidt, K. T., A. Stien, S. D. Albon, and F. E. Guinness. 2001. Antler length of yearling red
deer is determined by population density, weather and early life-history. Oecologia
127:191-197.
Severinghaus, C. W. 1949. Tooth development and wear as criteria of age in white-tailed deer.
Journal of Wildlife Management 13:195-216.
Sikes, R. S., W. L. Gannon, and American Society of Mammalogists. 2011. Guidelines of the
American Society of Mammalogists for the use of wild mammals in research. Journal of
Mammalogy 92:235-253.
Verme, L. J. 1965. Reproduction studies on penned white-tailed deer. Journal of Wildlife
Management 29:74-79.
______. 1969. Reproductive patterns of white-tailed deer related to nutritional plane. Journal of
Wildlife Management 33:881-887.
53
______, and D. E. Ullrey. 1984. Physiology and nutrition. Pages 91-118 in White-tailed deer:
ecology and management (L. K. Halls, editor). Stackpole Books, Harrisburg,
Pennsylvania.
White, G. C., D. J. Freddy, R. B. Gill, and J. H. Ellenberger. 2001. Effect of adult sex ratio on
mule deer and elk productivity in Colorado. Journal of Wildlife Management 65:543-551.
Whittaker, D. G., and F. G. Lindzey. 1999. Effect of coyote predation on early fawn survival in
sympatric deer species. Wildlife Society Bulletin 27:256-262.
54
Table 2.1 ? Known white-tailed deer breeding populations by sex, age class, and cohort birth
year from 2008-2013, Auburn Captive Facility, Camp Hill AL.
______________________________________________________________________________
Age 2008 2009 2010 2011 2012 2013
______________________________________________________________________________
Total popln.a 69 (35b) 84 (51b) 98 (63b) 122 (90b) 114 (89b) 110 (90b)
Males 25 (15) 40 (29) 48 (34) 62 (50) 64 (53) 53 (45)
0.5 11 (9) 16 (15) 14 (11) 21 (21) 13 (13) 8 (8)
1.5 8 (6) 10 (8) 13 (12) 11 (8) 12 (12) 9 (9)
2.5 3 (0) 8 (6) 9 (7) 12 (11) 11 (8) 6 (6)
3.5 3 (0) 3 (0) 6 (4) 8 (6) 11 (10) 9 (6)
4.5 0 3 (0) 3 (0) 6 (4) 8 (6) 8 (8)
5.5 0 0 3 (0) 2 (0) 6 (4) 7 (5)
6.5 0 0 0 2 (0) 2 (0) 4 (3)
7.5 0 0 0 0 1 (0) 1 (0)
8.5 0 0 0 0 0 1 (0)
Females 44 (20) 44 (22) 50 (29) 60 (40) 50 (36) 48 (36)
0.5 16 (11) 7 (6) 13 (10) 12 (12) 9 (9) 6 (6)
1.5 7 (6) 14 (9) 5 (4) 13 (10) 10 (10) 9 (9)
2.5 8 (3) 5 (4) 13 (8) 5 (4) 8 (5) 8 (8)
3.5 7 (0) 7 (3) 5 (4) 12 (7) 3 (3) 5 (2)
55
Table 2.1 ? Continued
______________________________________________________________________________
Age 2008 2009 2010 2011 2012 2013
______________________________________________________________________________
4.5 4 (0) 5 (0) 6 (3) 5 (4) 9 (4) 3 (3)
5.5 1 (0) 4 (0) 4 (0) 6 (3) 4 (3) 8 (4)
6.5 0 1 (0) 2 (0) 3 (0) 5 (2) 4 (3)
7.5 1 (0) 0 1 (0) 2 (0) 1 (0) 3 (1)
8.5 0 1 (0) 0 1 (0) 1 (0) 1 (0)
9.5 0 0 1 (0) 0 0 1 (0)
10.5 0 0 0 1 (0) 0 0
Sex Ratio
(M:F)c 1:2.0 1:1.5 1:1.1 1:1.2 1:0.8 1:0.9
______________________________________________________________________________
a Abundances estimated using combination of camera surveys, field observations, capture of live
animals, and recovery of deceased animals. All estimating methods indicated ?90% of animals
in population were marked during the study yielding largely known population sizes.
b Number of individuals initially captured at ? 2.5 years old.
c For animals >0.5 years old.
56
Figure 2.1 ? Julian birth dates of white-tailed deer fawns captured from 2010 - 2013 using
vaginal implant transmitters at the Auburn Captive Facility, Camp Hill, AL.
195
205
215
225
235
245
2009 2010 2011 2012 2013
Julian
date
Year