SEASONAL VARIATION IN SEX RATIOS AND SURVIVAL RATES OF WHITE- TAILED DEER FAWNS Except where reference is made to the work of others, the work described in this thesis is my own or was done in collaboration with my advisory committee. This thesis does not include proprietary or classified information. __________________________________ Sarah Therese Saalfeld Certificate of Approval: ________________________ _______________________ F. Stephen Dobson Stephen S. Ditchkoff, Chair Professor Associate Professor Biological Sciences Wildlife Sciences ________________________ _______________________ James B. Grand Stephen L. McFarland Associate Professor Acting Dean Wildife Sciences Graduate Schol SEASONAL VARIATION IN SEX RATIOS AND SURVIVAL RATES OF WHITE- TAILED DEER FAWNS Sarah Therese Saalfeld A Thesis Submitted to the Graduate Faculty of Auburn University in Partial Fulfillment of the Degree of Master of Science Auburn, Alabama May 11, 2006 iii SEASONAL VARIATION IN SEX RATIOS AND SURVIVAL RATES OF WHITE- TAILED DEER FAWNS Sarah Therese Saalfeld Permission is granted to Auburn University to make copies of this thesis at its discretion, upon request of individuals or institutions and at their expense. The author reserves all publication rights. _____________________________ Signature of Author _____________________________ Date of Graduation iv VITA Sarah Therese Saalfeld was born in Cincinnati, Ohio on July 13, 1982. She grew up in Northern Kentucky and graduated from Notre Dame Academy High School in Park Hills, Kentucky in 2000. Following this, she attended Thomas More College where she worked for three years at Thomas More College?s Center for Ohio River Research and Education. Sarah graduated from Thomas More College summa cum laude with a Bachelor of Arts in Biology in 2003. She then began her graduate degree as a research assistant in Wildlife Sciences at Auburn University. Sarah finished her degree requirements for a Masters of Science in Wildlife Sciences in May, 2006. v THESIS ABSTRACT SEASONAL VARIATION IN SEX RATIOS AND SURVIVAL RATES OF WHITE- TAILED DEER FAWNS Sarah Therese Saalfeld Master of Science, May 11, 2006 (B.A., Thomas More College, 2003) 80 Typed Pages Directed by Stephen S. Ditchkoff The ability of a female to adjust the sex ratio of her offspring has been well documented in numerous mammalian species. However, few studies have deviated from focusing on maternal condition as the driving factor concerning sex ratio variation. In this study, we investigated how birth date influenced offspring sex ratios in a captive herd of white-tailed deer (Odocoileus virginianus) located in Michigan. Since it has been predicted that females will bias the sex ratio of her offspring in order to increase her lifetime fitness, we hypothesized that more males should be born earlier in the birthing season. Offspring born earlier will have more time for development and therefore have a greater potential for increased dominance and body size later in life, traits related to male reproductive success. In this study, we found that maternal condition did not influence offspring sex ratios, while birth date did. It was found that more males tended to be born during the second and fourth birthing periods, with females dominating the first, third and fifth birthing period. In addition, the mass of male fawns at six months decreased with vi later birthing dates, a trend not as apparent in female fawns. This suggests that by varying offspring sex ratios, a female can improve her lifetime fitness by investing in the sex that will gain the greatest reproductive advantage by being born at certain times. As humans continue to move from the urban epicenter, management of wildlife in these areas is becoming increasingly important. However, since wildlife residing in these areas are exposed to different stresses than their rural counterparts, they may exhibit behavioral or life history modifications, ultimately decreasing the effectiveness of management decisions. One life history characteristic that is important to understand before implementing management decisions is survival. In white-tailed deer, a great deal of natural mortality occurs within the first few months of life, and therefore has the potential to greatly influence population dynamics and management decisions. In this study, we determined the cause, timing, and factors influencing neonatal mortality of a suburban population of white-tailed deer in Alabama. We found a 67% mortality rate, with the leading causes of mortality being predation by coyotes (41.7%) and starvation due to abandonment (25%). We also found that survival rates were different between the two years of the study (23.5% in 2004 and 42.1% in 2005; with fawns born in 2005 being 1.49 more likely to survive than fawns born in 2004), were linearly related to time following birth (survival increasing by 1.36 with each additional week after birth), and were positively associated with mass at birth (survival increasing by 1.69 with each additional kg of birth mass). These mortality rates are greater than most rural populations, suggesting that population growth rates of white-tailed deer found in suburban areas may be limited by increased mortality rates early in life. vii ACKNOWLEDGMENTS I would especially like to thank my advisor, Stephen Ditchkoff, for his guidance and support during this project. I would also like to thank the other members of my committee, Stephen Dobson and Barry Grand, as well as John Ozoga and Mike Mitchell for their direction and assistance. I would like to thank all the faculty, staff, and fellow graduate students at the School of Forestry of Wildlife Sciences for their patience and help along the way and the funding for this project, which was provided by the Center for Forest Sustainability at Auburn University. I would also like to give a special thanks to all those who helped with the field work for this study, especially David Saalfeld, Chad Newbolt, Jordan Smith, Chip Gibson, Vic Jackson, John Lewis, Buck Jolley, Will Underwood, Ashley Hand, Carrie Johnson, Thomas Harms, Dustin Rogers, Will Neighbors, Erin Holms, Chris Nix, Rachel DeFreese, Kyle Marable, and Lauren Bilodeaux. I would like to thank all landowners and Alabama Parks Division for allowing me to conduct this research on their property. I would also like to express my sincere gratitude to my husband, David Saalfeld, for his constant support, patience, and love, without which, my dreams would not be as obtainable. I would also like to thank my family, especially my mom for being there to listen and encourage me through this undertaking and for instilling in me her strength to achieve my goals, my dad for his loving support and encouragement, and my sisters and brothers for their constant understanding, friendship, and support. viii Style manual of journal used: Journal of Mammalogy Computer software used: Microsoft Word 2002 (text and tables), SigmaPlot 8.0 (figures) ix TABLE OF CONTENTS LIST OF TABLES .........................................................................................................xi LIST OF FIGURES.......................................................................................................xii I. SEASONAL VARIATION IN SEX RATIOS PROVIDES DEVELOPMENTAL ADVANTAGES IN WHITE-TAILED DEER Abstract....................................................................................................1 Introduction..............................................................................................2 Materials and Methods .............................................................................6 Results......................................................................................................8 Discussion................................................................................................9 Literature Cited ......................................................................................15 Figures and Tables..................................................................................22 II. SURVIVAL OF WHITE-TAILED DEER FAWNS IN A SUBURBAN POPULATION Abstract..................................................................................................28 Introduction............................................................................................29 Material and Methods.............................................................................31 Results....................................................................................................34 Discussion..............................................................................................36 Literature Cited ......................................................................................42 x Figures and Tables..................................................................................49 APPENDICES Appendix 1. Doe capture data in Auburn, Alabama during 2004 and 2005 ........58 Appendix 2. Fawn capture data in Auburn, Alabama for 2004 and 2005 using vaginal implant transmitters......................................................................62 Appendix 3. Doe capture data in Camp Hill, Alabama during 2005 ...................65 Appendix 4. Fawn capture data in Camp Hill, Alabama for 2005 using vaginal implant transmitters................................................................................66 Appendix 5. Fawn capture data in Auburn and Camp Hill, Alabama for 2004 and 2005 using spotlights and thermal imaging camera..............................67 xi LIST OF TABLES 1. Causes of mortality of white-tailed deer fawns (2004-2005) during the initial 8 weeks of life in Auburn, Alabama............................................................................49 2. Model results from known fate analysis of survival rates during the initial 8 weeks of life for white-tailed deer fawns in Auburn, Alabama from 2004-2005 .......50 3. Parameter estimates and likelihoods from known fate models of survival rates during the initial 8 weeks of life for white-tailed deer fawns in Auburn, Alabama from 2004-2005 .......................................................................................................52 xii LIST OF FIGURES 1. Frequency of white-tailed deer litters in relationship to birth date from 1973-1984 (excluding 1974) at the Cusino deer research facility, Michigan, USA .....................22 2. Litter sex ratio of white-tailed deer offspring in relationship to birthdate from 1973-1984 (excluding 1974) at the Cusino deer research facility, Michigan, USA. Means with the same letters are not different (P > 0.05). Numbers correspond to sample sizes of litters in each time period ................................................................24 3. Mass of 9-month old male and female white-tailed deer fawns born in relationship to birthdate from 1973-1984 (excluding 1974) at the Cusino deer research facility, Michigan, USA. Means with the same letters within an age class are not different (P > 0.05)................................................................................................................26 4. Survival of white-tailed deer fawns in Auburn, Alabama during the initial 8 weeks of life during 2004 and 2005 ....................................................................................53 5. Survival rates of white-tailed deer fawns in Auburn, Alabama during the initial 8 weeks of life as predicted by birth mass and age during 2004 (A) and 2005 (B) .......55 1 I. SEASONAL VARIATION IN SEX RATIOS PROVIDES DEVELOPMENTAL ADVANTAGES IN WHITE-TAILED DEER ABSTRACT Since Trivers and Willard (1973) first proposed their hypothesis concerning the adaptive investment of a mother in the sex of her offspring, it has been at the forefront of scientific research concerning sex ratios with most subsequent studies focusing on maternal condition as a key contributor to variations in sex ratios. Another factor that could greatly influence sex ratios, although has been only infrequently examined in mammalian species, is birth date. We investigated how birth date influenced offspring sex ratios in white-tailed deer (Odocoileus virginianus). Since date of birth can greatly influence an individual?s fitness and reproductive success we suggest that birth date may be an alternative strategy in determining offspring sex ratios. Since it has been predicted that females bias the sex ratio of their offspring in order to produce the sex that will most likely increase their lifetime reproductive fitness, we hypothesized that females will differentially produce male offspring earlier in the season. Offspring born earlier will have a head start in development and therefore have greater potential for increased body size and dominance later in life, traits which greatly influence male reproductive success. In this study, we found that maternal condition did not affect offspring sex ratio in a captive population of white-tailed deer in Michigan; however, birth date did. We found 2 that more males tended to be born during the second and fourth birthing periods, while more females were born during the first, third and fifth periods. In addition, we found that males born earlier in the season had greater mass the following spring than those born later, a trend that was not as dramatic in females. These results lend moderate support to our hypothesis that in mammals with polygynous mating systems females will tend to vary sex of offspring according to timing of birth and relative reproductive advantages gained by a particular sex being born at that time. INTRODUCTION The ability of a female to determine the sex of her offspring has been well documented in a number of mammalian species, including white-tailed deer (Odocoileus virginianus) (Verme and Ozoga 1981; Kojola and Eloranta 1989; Kucera 1991; Landete-Castillejos et al. 2001; Landete-Castillejos et al. 2004; Sheldon and West 2004). This allows females to enhance their lifetime fitness by producing the sex with the greatest potential reproductive value. Some factors shown to influence this selection include maternal age, condition, social status, and habitat quality. Trivers and Willard (1973) proposed that females of sexually dimorphic, polygynous species in good condition would benefit more from producing sons, because mother?s condition would more strongly influence future reproductive success of sons than daughters. Conversely, females in poor condition would benefit most by producing daughters, since female offspring require less maternal investment and will likely reproduce regardless of maternal condition. Poor quality male offspring likely will not develop the physical characteristics necessary to compete successfully for females later in life. This 3 hypothesis is based on three assumptions: condition of offspring is correlated with condition of the mother after the parental investment period, condition of the young following the parental investment period will carry over to adulthood, and conditional advantages will differentially benefit male offspring in reproductive success. Since most ungulate species are polygynous, sexually dimorphic species which commonly exhibit the three assumptions (Hewison and Gaillard 1999), numerous studies have been conducted to test the Trivers-Willard hypothesis. In several of these studies, support for the Trivers-Willard hypothesis has been documented, where older or more dominant individuals gave birth to more males, and younger or subordinate females gave birth to more females (Landete-Castillejos et al. 2001; Landete-Castillejos et al. 2004; Sheldon and West 2004). Despite the many findings that support Trivers and Willard (Landete-Castillejos et al. 2001; Landete-Castillejos et al. 2004; Sheldon and West 2004), numerous studies have reported data that seems to contradict their hypothesis. In a review of published papers on the genus Odocoileus, Verme (1983) found that more males were produced by younger females, females in poor condition, and females in areas of poor habitat quality. Similarly, Mendl et al. (1995) discovered that more female offspring were produced by dominant females in domestic pigs (Sus scrofa). These contradicting trends have been explained by alternative hypotheses for biased sex ratios including: the local resource competition hypothesis (Clark 1978), the advantaged daughter hypothesis (Hiraiwa-Hasegawa 1993), and the advantaged matriline hypothesis (Leimar 1996). According to the advantaged daughter and the advantaged matriline hypotheses, mothers are better able to influence the reproductive success of daughters rather than sons (eg. by 4 the transfer of rank and/or access to high quality resources). Therefore, these hypotheses propose that high quality females are more likely to produce females than males. Conversely, the local resource competition hypothesis states that local resources rather than maternal condition drive biases in sex ratios. In matriarchal species, Clark (1978) proposed that females in good condition will produce more female offspring, whereas females in poor condition will invest more in male offspring. This is because female offspring normally establish home ranges that overlap their mother?s home range, while males tend to disperse creating less competition for resources in the future. While they differ, all of these hypotheses focus on maternal condition as the driving factor in offspring sex ratios of mammals. Another factor that should greatly influence reproductive potential of offspring and offspring sex ratios is birth date. In many sexually dimorphic species, it is believed that time at birth differentially affects the ability of a particular sex to reproduce during its first and subsequent years (Suttie 1983; Clutton-Brock et al. 1984; Kruuk et al. 1999; Festa-Bianchet et al. 2000). Therefore, biasing sex ratios of offspring according to birth date could increase a female?s lifetime fitness. A number of avian species have been shown to vary sex ratios seasonally, including the northern goshawk (Accipiter gentiles: Byholm et al. 2002), European kestrel (Falco tinnunculus: Pen et al. 1999), common sandpiper (Actitis hypoleucos: Andersson et al. 2003), American kestrel (F. sparverius: Smallwood and Smallwood 1998; Griggio et al. 2002), spotted starling (Sturnus unicolor: Cordero et al. 2001), and little grassbirds (Megalurus gramineus: McIntosh et al. 2003). In their work with a nonmigratory population of American kestrels, Smallwood and Smallwood (1998) found more male-biased sex ratios earlier in the season as compared to later and proposed that 5 males that fledged earlier could find nest sites and defend territories better than males that fledged later. Griggio et al. (2002) found the same results in a migratory population of American kestrels, with more males born earlier in the season. Similarly, it was determined that the sex ratio variation could be due to acquisition of nest sites that were limited for this population. In contrast, competition for mates seemed the most likely explanation for male-biased sex ratios early in season in the common sandpiper, because males born earlier have an advantage in obtaining mates (Andersson et al. 2003). Although the explanations for these trends differ, they suggest that by varying offspring sex ratio by birth date, a mother can enhance her own future fitness. Smallwood and Smallwood (1998) coined this trend the ?Early Bird Hypothesis? and predicted that the sex which gains the greatest advantage by hatching earlier is selected for in early conception dates in avian species. The Early Bird Hypothesis (Smallwood and Smallwood 1998), however, has not been evaluated in mammals. While it has been demonstrated that older individuals tend to give birth earlier (Johns et al. 1977; Festa-Bianchet 1988; Christley et al. 2002) and offspring born earlier are more likely to survive due to increased food resources (Festa-Bianchet 1988; Singh et al. 1990; Festa-Bianchet et al. 1997; C?t? and Festa-Bianchet 2001), the link to differential sex ratios based on birth date has not been well documented in mammalian species. We hypothesize that birth date is a driving factor in offspring sex ratios with females producing more male offspring earlier in the breeding season with increasing production of females as the season progresses. Because offspring born earlier should have a longer period of time to grow before their first 6 winter, more males should be produced at this time period because of the relative importance of size on future reproductive success in males and females. MATERIALS AND METHODS We analyzed data from the Cusino enclosure, a 252-ha enclosure located near Shingleton in Michigan?s Upper Peninsula from 1973 through 1984 (excluding 1974). The enclosure consisted mainly of northern hardwoods, mixed hardwood-conifer stands, and pine plantation. Due to initial overbrowsing and subsequent condition decline, deer were supplementally fed ad libitum year-round with a pelleted ration beginning in 1972. Following this, deer were documented to be in good physical condition, even during winter months (Ozoga and Verme 1982). Initially (1972-1976), no deer were intentionally removed from the enclosure, however, to ensure a stable population, the number of individuals within the enclosure was maintained at approximately 40 individuals with the removal of most fawns and older individuals each year starting in 1977 (Ozoga and Verme 1982). A complete population census was obtained annually in March by live-trapping deer and placing them in a holding pen. At this time, all deer were marked with numbered collars and weighed. In addition, adult does were X-rayed to determine stage of pregnancy and number of fetuses (Ozoga and Verme 1982). Age of fetuses was determined by comparing fetal radiograms with known-age fetal specimens (105 specimens ranging from 76-137 days old) determined by observed breeding dates (Ozoga and Verme 1985). Following this, birth dates were ascertained by using a 199-day gestation. Pregnancy in fawns was also monitored at this time. Since fetuses within 7 fawns would be too small to be detected on radiograms due to the late conception date, the presence of pregnancy in fawns was monitored through progesterone assay in peripheral plasma (Plotka et al. 1977). Following the above procedures, deer were released back into a holding pen where they could be observed to confirm mother-fawn lineage from the previous year in some instances, as well as to document the dominance hierarchy among the deer. The dominance number assigned to each individual corresponded to the percentage of the herd that the individual dominated. All deer were released from the holding pen in late March or early April, and thereafter, observations were used to further elucidate behavioral patterns, social habits, birthing periods, rearing success, and sex of offspring. Fawns were captured and tagged whenever the opportunity arose and therefore most were already identified prior to the following March census. Those that were not tagged by the March census were identified and tagged for visual identification at this time (Ozoga and Verme 1982). All research was performed in a humane manner, followed ASM guidelines, and was approved by an institutional animal care and use committee. In order to determine any possible role of birthdate on offspring sex ratio we analyzed data similar to Smallwood and Smallwood (1998). We subdivided fetuses into 10-day intervals for the range of birth dates observed in this study (May 13-July 25). Since not all groups had adequate sample sizes (Fig. 1) we pooled data of groups with samples sizes less than 10 litters. This resulted in the pooling of dates after June 22. We then tested whether sex ratios differed among these groups using repeated measures analysis of variance repeated among years with a compound symmetric covariance structure and birthing group nested within year as our error term (PROC MIXED: SAS 8 Institute 1999). In addition, to determine differences in physical condition by birth date between sexes, we tested if mass of fawns measured the following spring (March) differed among birthing groups (eg. birth date) within each sex using the same repeated measures analysis of variance. To test the validity of maternal-condition based hypotheses within this population, we investigated if any difference existed among maternal condition or age for females that produced sons or daughters using repeated measures analysis of variance repeated among years with a compound symmetric covariance structure. Although we were unable to account for repeatedly sampling females, we tested whether sex ratios among the different birthing periods differed from equality using a Pearson chi-square. RESULTS A total of 428 fawns from 251 litters were examined during the study: mean birth date was 4 June (n = 230; SE = 0.63 days), and mean number of fawns per doe was 1.85 (n = 230; SE = 0.04). The offspring sex ratio (50.4%) for the entire study did not differ from equality (X 2 = 0.02; P = 0.880). We detected a difference (F 4,58 = 2.86; P = 0.031; Fig. 2) in the proportion of males born among time periods with a greater tendency of males being born in the second and fourth periods. We detected a greater proportion of males being born between June 12 - June 21 (70.4%) than between May 13 - May 22 (38.9%; t 58 = -2.30; P = 0.025), June 2 - June 11 (46.5%; t 58 = -2.78; P = 0.007), and June 22 - July 25 (38.9%; t 58 = 2.11; P = 0.040). We also found a tendency for more males being born between May 23 - June 1 (58.0%) than between June 2 - June 11, however only approaching significance (t 58 = 1.78; P = 0.081). We found no differences among 9 other time periods (P > 0.050). Additionally, we found that only the second birthing period had a sex ratio different than equality (X 2 = 3.93; P = 0.047). Body mass of fawns when approximately 9 months of age decreased as birth date became later for males (F 4,31 = 5.11; P = 0.003), but not for females (F 4,30 = 1.94; P = 0.130; Fig. 3). We also did not detect any differences between body mass (t 57 = 1.60; P = 0.211), dominance (t 56 = 0.00; P = 0.973), or age (t 57 = 0.62; P = 0.434) among mothers who produced either sons or daughters. DISCUSSION Our results support our hypothesis that birth date influences offspring sex ratios in white-tailed deer, suggesting benefits for females that adjust the sex ratio of their offspring. However, we found only moderate support for our hypothesis that more male offspring would be produced earlier in the birthing season as compared to females. Although we were unable to detect a significant difference in the sex ratios of the earliest time periods, we did find that more males tended to be born in the second time period resulting in a sex ratio different than parity. This same trend has been demonstrated in other studies on white-tailed deer where it was found that more male offspring were likely to be conceived earlier in the birthing season (S. S. Ditchkoff unpublished data; W. V. Underwood unpublished data). In ungulates, individuals that are born earlier have more time to grow and better food availability than late-born conspecifics (Festa-Bianchet 1988; Singh et al. 1990; Festa-Bianchet et al. 1997; C?t? and Festa-Bianchet 2001). While an earlier birth could be beneficial to both sexes, fitness gain would potentially be greater for males than females in a sexually dimorphic, 10 polygynous species. Increases in body size and quality have been shown to strongly influence a male?s initial reproductive success and lifetime fitness (Suttie 1983; Clutton-Brock et al. 1984; Kruuk et al. 1999; Festa-Bianchet et al. 2000). In addition, early births have also been shown to increase antler quality (Ozoga and Verme 1982; Schmidt et al. 2001; Gray et al. 2002), a trait which strongly affects dominance and ultimately reproductive success in ungulates (Clutton-Brock and Albon 1980; Bowyer 1986). Also, postweaning male fawns have a greater dietary protein requirement than females (Ullrey et al. 1967). Therefore, being born earlier would be most beneficial for males, since earlier born individuals have better food availability than late born individuals. Female fitness, however, is not as strongly influenced by this initial disadvantage in body size. Jorgenson et al. (1993) found that condition in female bighorn sheep (Ovis canadensis) did not have a major effect on age at first reproduction. However, they did detect a minimum mass for young ewes to begin reproduction. Variation in mass above this minimum, however, did not effect age at first reproduction. Therefore, adaptive investment of a mother in the sex of her offspring would potentially allow females to differentially produce the sex that would increase their lifetime fitness the most. In white-tailed deer, most births normally occur within a short time period (e.g., several weeks). For example, 61% of litters in our population were born during a 15 day period (May 28-June 11). We expect that in species that have a much longer birthing period or multiple litters within a year, seasonal sex ratio biases would be more prominent. This is true in Virginia opossums (Didelphis virginiana: Wright et al. 1995), meadow voles (Microtus pennsylvanicus: Dobson and Myers 1989), and gray-tailed voles 11 (Microtus canicaudus: Bond et al. 2003), and this pattern has been termed the first-cohort advantage hypothesis (Wright et al. 1995). In Virginia opossums, females had more male-biased first litters than second litters possibly due to the increased reproductive fitness of males born earlier (Wright et al. 1995). Additionally, in meadow voles, more male-biased litters were born earlier in the season than later corresponding to a similar pattern in litter size, which also declined as the season progressed (Dobson and Myers 1989). On the other hand, Bond et al. (2003), when examining gray-tailed voles, found female-biased litters rather than male-biased litters early in the season. They hypothesized that this trend was seen since females almost always reproduce their first year, while most late-born males delay first breeding until the following spring. Additionally, they speculated that winter survival could also influence gray-tailed vole sex ratios, where males born later had greater survival through winter than females born at the same time. However, this does not discount the importance of seasonal sex-ratio variation in a species like white-tailed deer where births are highly synchronized. Our data demonstrate the relative importance of birth dates between sexes in white-tailed deer. Males born in the earliest time period had 18.1% greater mass the following spring at 9 months of age than males born in the last time period. Early-born females also tended to have greater mass the following spring than late-born individuals, but the difference between the groups was less (9.7%). Since our study was conducted on a supplementally-fed population, we suspect these differences to be less than in a natural population where food would be limited during the winter months. Although these data do not measure future fitness, they provide strong support for the hypothesis that birth 12 date differentially influences future fitness in males and females, as has been demonstrated with a number of avian species (Smallwood and Smallwood 1998; Pen et al. 1999; Cordero et al. 2001; Byholm et al. 2002; Griggio et al. 2002; Andersson et al. 2003; McIntosh et al. 2003). Despite this apparent trend, we did not detect a larger proportion of males born during the earliest time period. Although we are unsure of the specific reasoning for this, we suspect that early in the season resource availability may be unpredictable. While, giving birth early has advantages, it could also have disadvantages if food is unavailable or unpredictable at this time. Therefore, females may not invest in the more costly sex (i.e. males) if they may not be able to provide adequate nutrition for them. Additionally, low sample size during this time period (n = 12) could also prevent us from detecting this trend. Another interesting trend seen in our data was that more males were additionally born later in the season. This trend was also seen in American kestrels, where more male-biased broods were born later in the season, although low sample size precluded statistical analysis (Smallwood and Smallwood 1998). Giving birth late in the season increases the likelihood of producing smaller, disadvantaged offspring of either sex. In elk (Cervus elaphus nelsoni: Smith and Anderson 1998), red deer (C. elaphus: Clutton-Brock et al. 1987), and moose (Alces alces: Keech et al. 2000) offspring of either sex that were late-born had decreased survival and reproduction. We propose that the best strategy for a mother that conceives late may be to produce the dispersing sex. According to the local resource competition hypothesis (Clark 1978), a mother improves her fitness, as well as that of previous and future offspring that reside within her home range by producing the dispersing sex, which reduces competition for resources. Our 13 data support this hypothesis since both males and females born late in the birthing season had low mass the following spring compared to offspring born early. Despite this trend, we were unable to detect a greater proportion of males produced in last time period, possibly due to the low sample size (9 litters). Although our data do not provide an explanation for the mechanism controlling sex ratio variations, there have been numerous hypotheses developed in order to explain this phenomenon (see Clutton-Brock and Iason 1986; see Krackow 1995). These hypotheses focus on three main mechanisms that could potentially control sex ratio variation: fetal reabsorption, differential utilization of X and Y carrying sperm, and variation in hormonal or chemical concentrations at or near implantation. Although it has been found that fetal reabsorption may be a very important way some mammalian species such as rodents control sex ratios, this mechanism is not believed to be the main mechanism in all mammalian species (James 1996). In springbok (Antidorcas marsupialis), Kr?ger et al. (2004) found that fetal mortality did not explain sex ratio variation since very few females were found with reabsorbed fetuses. In addition, since the mean number of fawns per doe in our study was 1.85, fetal reabsorption is not a likely explanation for the observed sex ratio biases. Therefore, we believe that differential utilization of X and Y sperm or differences in hormonal or chemical concentrations around the time of implantation appear to be the most likely mechanisms controlling seasonal sex ratio variation in this species. Variation in glucose (Cameron 2004) and hormonal levels (James 1996), which influence development of male and female offspring, vary throughout the reproductive cycle, potentially influencing offspring sex ratios in relation to conception dates. In addition, adult males might also control sex ratio 14 variation by producing Y-chromosome biased ejaculates. However, DeYoung et al. (2004) did not find this in white-tailed deer. More research is needed in order to fully understand the mechanisms controlling variations in sex ratios. Altering sex ratios according to birth date is an overlooked but perhaps very important tactic for increasing lifetime fitness of white-tailed deer and other polygynous mammals. Although this is not the first documented evidence of male-biased sex ratios early in the birthing season in ungulates (Clutton-Brock et al. 1982; Kohlmann 1999) or large sexually dimorphic mammals (Coulson and Hickling 1961; Stirling 1971), we believe our data provides a better explanation for this phenomenon than has previously been proposed. Although there are numerous factors that influence sex ratios and timing of birth, it appears that timing of birth was the main cause of variation in sex ratios in the population we studied. Although our data were collected from a captive herd of white-tailed deer, we assume that they are representative of a natural population and expect that some of the trends we have seen to be more dramatic in a natural population. 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Verme, L. J. 1983. Sex ratio variation in Odocoileus: a critical review. Journal of Wildlife Management 47:573-582. Verme, L. J., and J. J. Ozoga. 1981. Sex ratio of white-tailed deer and the estrus cycle. Journal of Wildlife Management 45:710-715. Wright, D. D., J. T. Ryser, and R. A. Kiltie. 1995. First-cohort advantage hypothesis: a new twist on facultative sex ratio adjustment. American Naturalist 145:133-145. 22 Figure 1. Frequency of white-tailed deer litters in relationship to birth date from 1973-1984 (excluding 1974) at the Cusino deer research facility, Michigan, USA. 23 M a y 1 3 - M a y 2 2 M a y 2 3 - J u n e 1 J u n e 2 - J u n e 1 1 J u n e 1 2 - J u n e 2 1 J u n e 2 2 - J u l y 1 J u l y 2 - J u l y 1 1 J u l y 1 2 - J u l y 2 1 J u l y 2 2 - J u l y 2 5 F r eq uen cy 0 20 40 60 80 100 24 Figure 2. Litter sex ratio of white-tailed deer offspring in relationship to birthdate from 1973-1984 (excluding 1974) at the Cusino deer research facility, Michigan, USA. Means with the same letters are not different (P > 0.05). Numbers correspond to sample sizes of litters in each time period. 25 M a y 1 3 - M a y 2 2 M a y 2 3 - J u n e 1 J u n e 2 - J u n e 1 1 J u n e 1 2 - J u n e 2 1 J u n e 2 2 - J u l y 2 5 Pe r c en t mal e s 20 30 40 50 60 70 80 (12) (83) (82) (27) (9) A AB A B A 26 Figure 3. Mass of 9-month old male and female white-tailed deer fawns born in relationship to birthdate from 1973-1984 (excluding 1974) at the Cusino deer research facility, Michigan, USA. Means with the same letters within an age class are not different (P > 0.05). 27 M a y 1 3 - M a y 2 2 M a y 2 3 - J u n e 1 J u n e 2 - J u n e 1 1 J u n e 1 2 - J u n e 2 1 J u n e 2 2 - J u l y 2 5 Ma s s ( k g ) 32 34 36 38 40 42 Females Males AB A BC BC C a a a a a 28 II. SURVIVAL OF WHITE-TAILED DEER FAWNS IN A SUBURBAN POPULATION ABSTRACT As humans continue to move further from the urban epicenter, management of wildlife populations in urban/suburban areas is becoming increasingly important. Because wildlife residing in these areas are exposed to different stresses, they may not exhibit the same behavior and life history characteristics as their rural counterparts. Therefore, in order to properly manage these species, it may be necessary to relearn important life history characteristics, such as survival, that influence management decisions. In white-tailed deer (Odocoileus virginianus), a great deal of natural mortality occurs within the first few months of life and has the potential to greatly influence population growth. Therefore, understanding neonatal mortality in this species is important when implementing management decisions. Although numerous studies have determined the causes and timing of death of neonatal white-tailed deer fawns, none have focused on urban/suburban populations. In this study, we determined causes and timing of deaths of neonatal white-tailed deer in a suburban area of Alabama in 2004 and 2005, estimated survival rates, and determined factors that influenced survival for the initial 8 weeks of life. We found a 67% mortality rate during the two year study, with the leading causes of mortality being predation by coyotes (41.7%) and starvation due to 29 abandonment (25%). Survival rates were different between the two years of the study (23.5% in 2004 and 42.1% in 2005) with fawns born in 2005 being 1.49 times more likely to survive than fawns born in 2004. Additionally, we found that survival rates were linearly related to time following birth and increased by a factor of 1.36 for each week following birth. Survival rates were also associated positively with mass at birth and increased by a factor of 1.77 for each additional kilogram of birth mass. Most studies with rural populations have reported fawn survival rates greater than those reported here, suggesting that population growth rates of high density white-tailed deer populations, such as those found in suburban areas, may be limited by increased mortality rates early in life. INTRODUCTION As humans continue to move further from the urban epicenter, wildlife-human conflicts have been increasing. Once a suitable habitat for only a few species, suburban areas are now designed in such a way that allows wildlife to live along side human populations. By creating larger lots and maintaining native vegetation between houses, suburban areas are now able to support wildlife populations that were never able to reside in these areas in the past. Because of this, management of wildlife populations residing in these areas is becoming increasingly important. White-tailed deer is a particular species causing much concern due to overabundant populations and increased wildlife-human conflicts (e.g. vehicular accidents and foraging of landscapes). These management concerns, however, may be hard to address for populations living in these areas. It has been suggested that because wildlife residing in urban areas are exposed to 30 different stresses than their rural counterparts, behavior and life history characteristics may be altered in order to alleviate the stress encountered in these new environments (Ditchkoff et al. 2006). It has been shown that wildlife residing in urban areas may be forced to modify or experience changes in movements, diet, reproduction, health, and survival rates (see Ditchkoff et al. 2006). Since most wildlife management decisions in urban areas are concerned with wildlife-human conflicts and decreasing populations, understanding behavior and life history characteristics related to conflicts (eg. movements and diet) and population growth (eg. survival and reproduction) are essential for effective control. However, currently most management recommendations for wildlife are based on knowledge obtained from rural populations. If wildlife populations living in urban/suburban areas are displaying differences in behavior or life history characteristics essential to management decisions, management recommendations may be inaccurate and ineffective. In order to effectively manage species in these areas, detailed knowledge must be obtained concerning these behaviors and life history characteristics in urban/suburban populations. One very important factor that influences population growth and ultimately management decisions is survival. In white-tailed deer, a great amount of natural mortality occurs during the first few months of life. Although numerous studies on white-tailed deer fawn survival have been conducted (Cook et al. 1971; Garner et al. 1976; Carroll and Brown 1977; Bartush and Lewis 1981; Epstein et al. 1983; Epstein et al. 1985; Huegel et al. 1985; Nelson and Woolf 1987; Kunkel and Mech 1994; Sams et al. 1996; Long et al. 1998; Ballard et al. 1999; Ricca et al. 2002; Vreeland et al. 2004), none have focused on urban/suburban populations. However, it has been shown that 31 wildlife residing in urban/suburban areas are exposed to different predation and mortality risks such as vehicular traffic (Forman and Alexander 1998; Koenig et al. 2002) and predation by domestic animals (Koenig et al. 2002; Gillies and Clout 2003; Lepczyk et al. 2003). These additional mortality risks have the potential to alter survival rates and population growth, factors essential for management decisions. In a study on white-tailed deer in Missouri, it was found that adult mortality rates were similar between urban/suburban areas and forested/agricultural areas, however, the causes of mortality were different with vehicular accidents replacing hunting mortality in urban areas (Hansen and Beringer 2003). In addition, it was found that deer-vehicular collision was the greatest cause of adult white-tailed deer mortality in Chicago, Illinois (Etter et al. 2002). Despite the large impacts such differences in mortality risks can have on management, decisions are still being based on knowledge obtained from rural areas. In this study, we examined the cause and timing of death of neonatal white-tailed deer fawns in a suburban area in Alabama, estimated survival rates, and determined the potential factors that influence survival of fawns in this area. MATERIALS AND METHODS Our study site was located in a suburban area of Auburn, Alabama in Lee County. It consisted of a cluster of suburban developments with 0.4-2.0 ha lots, which maintained much of the native vegetation and wooded areas between houses, suitable for wildlife corridors. In addition, we also examined deer from Chewacla State Park, in Auburn, Alabama, a 281.3 ha tract of land, surrounded by these suburban developments. This study area was intersected with low density two-lane suburban/city streets and bordered 32 by a major interstate (I-85) with high-speed traffic. Deer on this site regularly crossed roads and lived in close proximity to human habitation. Between March and August in 2004 and 2005, 46 female white-tailed deer were captured and fitted with vaginal implant transmitters (Models M3950 and M3930 Advanced Telemetry Systems, Insanti, Michigan). Captured deer were anesthetized using an intramuscular injection of telazol (2.25 ml/45.36 kg) and xylazine (2 ml/45.36 kg), administered with dart guns over areas baited with whole corn. While anesthetized, a vaginal implant transmitter was inserted approximately 15-20 cm into the vaginal canal, with the silicone wings pressed firmly against the cervix (Carstensen et al. 2003). These transmitters were specifically designed to be expelled during the birth process and emit a signal when the temperature of the transmitter changed from 34 ? C to 30 ? C (Bowman and Jacobson 1998). Age of the doe was also determined from annuli in the cementum of the first incisor (Matson's Laboratory Milltown, MT, USA: Low and Cowan 1963; Gilbert 1966). Does were monitored approximately every 8 hours beginning in July through August. During the peak fawning period (throughout August) does were monitored more extensively in order to locate fawns soon after birth. Once a transmitter was expelled, we waited at least 4 hours after the pulse rate switched to locate the birth site through hand-held telemetry. If the fawn had moved from the birth site, we expanded the search area to a 200-300m radius around the birth site as described by Carstensen et al. (2003). In addition, we also used a thermal imaging camera (Thermal-Eye 250D, L-3 Communications Infrared Products, Dallas Texas, USA) to help locate fawns moved from the birth site. Once located, each newborn fawn was captured by hand. Each fawn 33 was weighed in a cloth bag that was rubbed in dirt prior to each new capture to minimize scent transfer. In addition, an expandable radio collar (Model M4210 Advanced Telemetry Systems, Insanti, Minnesota, USA: Diefenbach et al. 2003) was affixed to each fawn allowing us to monitor survival for approximately 6-12 months. These collars were designed to give a signal if activity was undetected for 6 hours and included a coding system allowing us to determine the exact time motion ceased. Following release, each fawn was located at least once per day. In cases of mortality, cause of death was ascertained. In cases of predation, predators were distinguished by comparison of location and description of remains, hair found at site and bite marks (Cook et al. 1971; White 1973; Garner et al. 1976). When we could not determine conclusively that predation was the cause of death, but evidence suggested predation (eg. found only collar with bite marks or near predator scat or time exceeded 24 hours following death and scavenging could not be eliminated as possibility), it was classified as possible predation. If the cause of death could not be determined in the field, necropsies were performed. Emaciation was identified similar to Sams et al. (1996) by severe atrophy of adipose tissue, absence of gastrointestinal contents, and presence of meconium in lower intestines. If cause of death was unable to be determined by these procedures it was classified as unknown. Survival analysis was performed with a known fate model in program MARK version 4.2 (White and Burnham 1999). Weekly survival rates starting from the date of birth were used to determine the best model explaining survival rates in this population. We modeled survival rates for the initial 8 weeks of life (56 days), the approximate time weaning will begin. Eighteen models were developed to be tested including time 34 following birth (i.e. survival rates are different among the 8 weeks following birth), a linear time trend (i.e. survival rates for each week following week are related in a linear trend over time), and individual covariates: year (coded: 2004 = 1; 2005 = 0), mass of fawn at birth, sex of fawn (coded: male = 1; female = 0), birthdate, and age of mother (divided into 2 groups; coded: ?2.5 = 0 and >2.5 = 1). We used Akaike?s Information Criterion corrected for small size (AICc) to select the best models and determined parameter estimates, likelihoods, and standard errors from the estimates given by MARK. In order to test the goodness of fit of the most general model, we used Hosmer and Lemeshow goodness of fit statistic (PROC LOGISTIC, SAS Institute 1999). In addition, we used a t-test to test for differences in birth date and birth mass between years. We used a chi-square to test for differences in survival rates between years. We also tested for differences in mother?s condition (i.e. age and chest girth) among fawns that lived, were abandoned, and died of all other causes with an analysis of variance. RESULTS We implanted 46 does in 2004 and 2005 with vaginal implant transmitters, of which 28 resulted in the successful captures of fawns. Unsuccessful captures from transmitters resulted from premature expulsion of transmitter (n = 9), transmitter failure (n = 2; one of which was replaced), transmitter malfunction (n = 1), failure to locate fawns after birth (n = 1), and implantation of infertile or post parturition does (n = 6). We captured a total of 36 fawns, 17 in 2004 and 19 in 2005. Mean birth date in 2004 was later (t 34 = -2.64; P = 0.012; 15 August) than in 2005 (4 August), however mean birth mass did not differ (t 34 = 0.14; P = 0.893) between years (2.50 kg in 2004 and 35 2.53 kg in 2005). The overall survival rate for the first 8 weeks of life was 33.3%. Although the survival rate in 2005 (42.1%) tended to be greater than 2004 (23.5%), it was not significant (X 2 = 1.39; P = 0.238). During both years, greater mortality rates occurred immediately following birth with fewer mortalities occurring as the fawns aged (Figure 1). The most common cause of mortality during both years (41.7%) was predation by coyotes, followed by starvation due to abandonment (25.0%), possible predation (20.8%), and accidents and unknown causes (<13.0%; Table 1). When comparing maternal condition among fawns with different fates, we did not detect any differences (P > 0.050) in age or chest girth of mothers for fawns abandoned (age = 3.83, chest girth = 832.50), fawns that died of all other causes (age = 3.33, chest girth = 809.72), and fawns that survived (age = 3.58, chest girth = 845.83). From the known fate analysis the best model (?AICc = 0.00, AICw = 0.43) from the suite of models considered was the additive model of the linear time trend, mass, and year (Table 2), suggesting that the change in weekly survival was linearly related and varied between years and between masses at an equal rate (Figure 2). The second best model (?AICc = 0.61, AICw = 0.31), the additive model of the linear time trend and mass, also has considerable strength of evidence as a good model. Therefore, parameter likelihoods and estimates were determined. The parameter likelihoods illustrated that the linear time trend (0.92), mass (0.81), and year (0.58) were the most important parameters to be included in the best model (Table 3). From the top model it was determined that survival each week following birth increased by a factor of 1.36 (95% CI = 0.08-2.63). Additionally, survival increased by 1.69 with each additional kg of birth weight (95% CI = 0.07-3.31) and fawns born in 2005 were1.49 times more likely to survive than fawns 36 born in 2004 (95% CI = -0.11-3.09). Models containing the variables age of the mother, birth date, sex, and time following birth did not explain a significant proportion of variation in survival rates (Table 2). Similarly, parameter likelihoods also indicated that age of the mother (0.00), birthdate (0.00), sex of fawn (0.00) and time following birth (0.01) were not likely to be included in the best model. The Hosmer-Lemeshow goodness of fit statistic (? = 0.714) indicated that the most general model fit the data well. DISCUSSION Numerous studies have determined cause and timing of mortalities of white-tailed deer fawns across the United States (Cook et al. 1971; Garner et al. 1976; Carroll and Brown 1977; Bartush and Lewis 1981; Epstein et al. 1983; Epstein et al. 1985; Huegel et al. 1985; Nelson and Woolf 1987; Kunkel and Mech 1994; Sams et al. 1996; Long et al. 1998; Ballard et al. 1999; Ricca et al. 2002; Vreeland et al. 2004). However, no studies have examined survival of white-tailed deer fawns in urban/suburban areas. The mortality rate of suburban white-tailed deer fawns detected in our study was 66.7%. This rate is greater than the mean rate of neonatal mortality among temperate ungulates (45%) as calculated by Linnell et al. (1995). In addition, among studies of white-tailed deer, neonatal mortality rates ranged from 23.6%-90.0%, with a mean mortality rate of 54.8%. Additionally, 69% of these studies resulted in mortality rates lower than the rate observed in this study. The high neonatal mortality rate observed in this study could be attributed to sampling biases of other studies. Since most previous studies on neonatal survival of white-tailed deer captured fawns with foot searches or doe behavior, most fawns were a few days to weeks old at time of capture. Therefore, mortality occurring within the first 37 few days of life went undetected and could have resulted in lower rates of mortality. In our study, 50.0% of mortalities occurred in the first week of life. Therefore, in order to accurately measure survival rates, it is critical that fawns are captured as early as possible or analyses account for staggered entry of individuals (Pollock et al. 1989). We also attribute the high mortality rate we detected to a high predation rate on this population. We detected a predation rate of 41.7%-62.5%, of which, 100% was attributed to coyotes. Other studies have found similar coyote predation rates (>50.0% predation rate, of which >50% attributed to coyotes) of neonatal white-tailed deer in various geographic regions, including Oklahoma (Garner et al. 1976), Iowa (Huegel et al. 1985), Illinois (Nelson and Woolf 1987), and Texas (Cook et al. 1971; Carroll and Brown 1977). We suspect that the high rate of predation was due to efficient detection of bedded fawns in the typical open landscape of the suburban area. Coyotes are visual hunters and therefore, it has been suggested that increased predation on neonatal white-tailed deer fawns by coyotes is associated with sparse vegetative cover (Garner et al. 1976; Carroll and Brown 1977; Huegel et al. 1985; Nelson and Woolf 1987; Long et al. 1998). This effect would be most evident within the first 30 days of life since fawns are bedded at this time and therefore rely on camouflage to avoid predation (Huegel et al. 1985). The manicured lawns and landscaped yards in suburban areas would complement the search strategies employed by coyotes. The timing of the birthing season in this population could also contribute to increased predation. In Alabama, the birthing season is much later than most other populations of white-tailed deer, occurring from late July to early September (Gray et al. 2002). This birthing season coincides with the greatest hunting population of coyotes 38 since predispersal coyote pups are hunting independently at this time (Harrison and Harrison 1984; Harrison et al. 1991). In one of the few studies done on food habits of coyotes in Alabama, Hoerath (1990) found that in fall (September-November) white-tailed deer fawns were the most abundant (23.9%) and frequent (33.1%) food source found in coyote scat. In addition, fawns were also an important food source in summer (June-August), third in abundance (15.0%) and frequency (27.5%) only to lagomorphs (Sylvilagus floridanus and S. aquaticus) and cotton rats (Sigmodon hispidus). This suggests that white-tailed deer fawns in Alabama are an important food source to coyotes during the months where fawns are the most vulnerable. The second major cause of mortality in this population (25.0%) was starvation due to abandonment. This cause, while relatively high (46.2%) in the first year, was absent in the second. Although abandonment could be due to disturbance of the birth site or scent transfer while handling fawns, all precautions were taken in order to minimize these effects, including waiting a minimum of four hours after birth to collar fawns, wearing non-scented latex gloves, minimizing handling time to less than 10 minutes, minimizing the number of handlers to one or two, and minimizing disturbance to the birth site. The methods and researchers handling fawns did not change between years. Therefore, we believe that abandonment was not due to disturbance during the handling process. In addition, in a study done on the effects of handling techniques on the rate of abandonment, Carstensen Powell et al. (2005) found that increased scent transfer, increased handling time, time of capture and increased handling stress did not affect the rate of abandonment in a free-ranging herd of white-tailed deer in Minnesota. Therefore, they suggest that removing data on fawns suspected of handling-induced abandonment is 39 unwarranted. One possible explanation for increased abandonment in 2004 is low food availability as indicated by monthly rainfall. In 2004, the total rainfall for March and April (2.24 cm and 11.23 cm) was much lower than in 2005 (26.29 cm and 17.68 cm). In addition, in 2004 the cumulative rainfall from March through September remained below normal with the mean departure being 17 cm below normal, as compared to 5 cm greater than normal in 2005. It has been shown that in years of poor nutritional availability that white-tailed deer dams were more likely to abandon fawns or fawns were more likely to die of malnutrition (Verme 1962; Murphy and Coates 1966; Langenau and Lerg 1976). From the known fate model, year was an important factor in predicting survival for this population. It was estimated that fawns born in 2005 were 1.49 times more likely to survive than fawns born in 2004. Other causes of mortality in this population were due to accidents and unknown reasons. These, however, did not account for the majority of the deaths. Vehicular accidents have been found to be a major cause of mortality of adult white-tailed deer living in urban/suburban areas (66% of mortality: Etter et al. 2002; 73% of mortality: Hansen and Beringer 2003). We had anticipated higher rates of mortality due to vehicular traffic, but it is possible that the sedentary nature of fawns early in life may have resulted in fawns only infrequently crossing roads. During the first three weeks of life fawns are active only 8%-15% of the time, traveling less than 100m in the first week, and less than 200m by the third (Jackson et al. 1972). By the fourth-eighth week, however, fawns are active 15%-20% of the time, and by 4 months they are as active as adults (Jackson et al. 1972). These data suggest that mortality of fawns due to vehicular traffic should increase with age, however, since we monitored survival for the initial 8 40 weeks of life, we may not have observed the time period at which fawns are most vulnerable to this mortality risk. Neonatal fawns exhibit greater vulnerability to mortality early in life. In numerous studies on white-tailed deer it has been shown that mortality rates of younger fawns are greater than that of older fawns (Cook et al. 1971; Garner et al. 1976; Nelson and Mech 1986; Long et al. 1998; Ricca et al. 2002; Vreeland et al. 2004). In this study, we found that neonatal survival was linearly associated with time following birth and increased by a factor of 1.36 with each additional week after birth. Other studies have found similar results with greatest mortality rates occurring within the first week of life (Ricca et al. 2002; Vreeland et al. 2004). Since the leading causes of mortality for this population were abandonment and predation, these results suggest that fawns are more susceptible to these causes of mortality earlier in life. It has been shown that mass at birth can also have substantial impacts on survival, with lighter neonatal white-tailed deer having higher mortality than heavier individuals (Verme 1962, 1977; Nelson and Woolf 1987; Kunkel and Mech 1994; Vreeland et al. 2004). Mass at birth was an important factor in our population predicting survival rates. We found that survival rates increased 1.69 times with each additional kg of birth mass (as predicted by known fate model). In a study done in Pennsylvania on white-tailed deer fawns, Vreeland et al. (2004) found similar results in that fawns were 2.14 times more likely to survive with each additional kg of weight at capture. Other factors that could potentially influence fawn survival include age of the mother, birthdate, and sex of the fawn. It has been suggested that fawn mortality rates are negatively related to the mother?s age since birth weight tends to be associated 41 positively with maternal age and older individuals tend to defend and protect their young better than younger individuals (Smith 1987; Mech and McRoberts 1990). Additionally, fawns survival may be negatively related to date of birth. Since older mothers or mothers in better condition tend to give birth earlier (Johns et al. 1977; Festa-Bianchet 1988; Berger 1992; Christley et al. 2002), and survival of fawns increases as maternal condition and age increases (Verme 1962; Murphy and Coates 1966; Langenau and Lerg 1976; McGinnes and Downing 1977; Rognmo et al. 1983; Smith 1987; Mech and McRoberts 1990), survival may be predicted by date of birth. Activity patterns of male and female fawns also differ, with males being more active, potentially leading to increased predation rates of male fawns (Jackson et al. 1972). Despite the potential influence on fawn survival, maternal age, birthdate, and sex of the fawn were not found to be important factors explaining fawn mortality rates in this population. As humans continue to move away from the urban epicenter into suburban areas, management of suburban wildlife will become increasingly important. However, species living in these areas are exposed to different stresses and can potentially alter behavior and life history characteristics to mitigate this stress. Therefore, basing management decisions of urban wildlife on rural populations may be inaccurate and could potentially lead to ineffective results (Ditchkoff et al. 2006). One important life history characteristic to understand when managing a species is survival. Changes in survival rates and causes of survival have profound impacts on management and control decisions of urban wildlife such as white-tailed deer. 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Causes of mortality of white-tailed deer fawns (2004-2005) during the initial 8 weeks of life in Auburn, Alabama. ____________________________________________________ 2004 2005 ___________ ___________ Cause of Mortality % N % N ____________________________________________________ Predation 38.5 5 45.5 5 Possible Predation 7.7 1 36.4 4 Abandonment 46.2 6 ----- -- Accident a ----- -- 9.1 1 Unknown 7.7 1 9.1 1 ____________________________________________________ a Fawn fell in hole soon after birth and could not escape. 50 Table 2. Model results from known fate analysis of survival rates during the initial 8 weeks of life for white-tailed deer fawns in Auburn, Alabama from 2004-2005. _______________________________________________________________ Model No. Parameters ?AICc a AICw b _______________________________________________________________ S (linear time trend + year + mass) c 4 0.00 0.43 S (linear time trend + mass) 3 0.61 0.31 S (linear time trend + year) 3 2.93 0.10 S (linear time trend) 2 3.25 0.08 S (year + mass) 3 5.06 0.03 S (mass) 2 6.77 0.01 S (year*mass) d 4 6.85 0.01 S (time + year + mass) 10 9.42 0.00 S (time + mass) 9 9.59 0.00 S (year) 2 9.60 0.00 S (.) e 1 11.12 0.00 S (time) 8 11.80 0.00 S (time + year) 9 11.85 0.00 S (sex of fawn) 2 12.50 0.00 S (birthdate) 2 12.93 0.00 51 Table 2. Continued. _______________________________________________________________ Model No. Parameters ?AICc a AICw b _______________________________________________________________ S (age of mother) 2 13.14 0.00 S (time + age of mother) 9 14.02 0.00 S (time*year) f 16 19.83 0.00 _______________________________________________________________ a Difference between model?s Akaike?s Information Criteria, corrected for small sample size and the lowest AICc value b AICc relative weight attributed to model c Model of additive effects of linear time trend, year, and mass d Model of additive effects of year and mass and the interaction e Model of no effects on survival f Numerical convergence not reached 52 Table 3. Parameter estimates and likelihoods from known fate models of survival rates during the initial 8 weeks of life for white-tailed deer fawns in Auburn, Alabama from 2004-2005. ________________________________________________________________________ Explanatory Parameter Parameter Parameter Estimate SE From Variable Likelihood a Estimate b SE c From Top Model Top Model ________________________________________________________________________ Intercept 1.00 0.73 0.22 0.67 0.41 Linear time trend 0.92 0.29 0.01 0.30 0.12 Mass 0.81 0.43 0.22 0.53 0.25 Year 0.58 -0.23 0.17 -0.40 0.24 Age of mother 0.00 0.00 0.00 ----- ----- Birthdate 0.00 0.00 0.00 ----- ----- Sex of fawn 0.00 0.00 0.00 ----- ----- Time 0.01 ----- ----- ----- ----- ________________________________________________________________________ a AICc weights summed across all candidate models that include a given variable b Weighted averages (based on model AICc weight) from all candidate models c Based on unconditional variances over the suite of models considered 53 Figure 1. Survival of white-tailed deer fawns in Auburn, Alabama during the initial 8 weeks of life during 2004 and 2005. 54 Days Following Birth 0 5 10 15 20 25 30 35 40 45 50 55 60 Su rv i v a l 20 30 40 50 60 70 80 90 100 2004 2005 55 Figure 2. Survival rates of white-tailed deer fawns in Auburn, Alabama during the initial 8 weeks of life as predicted by birth mass and age during 2004 (A) and 2005 (B). 56 75 80 85 90 95 100 1.5 2.0 2.5 3.0 3.5 4.0 1 2 3 4 5 6 7 S u r v i v a l B i r t h m a s s ( k g ) W e e k F o ll o w in g B ir th A 75 80 85 90 95 100 1.5 2.0 2.5 3.0 3.5 4.0 1 2 3 4 5 6 7 S u r v i v a l B i r t h m a s s ( k g ) W e e k F o ll o w in g B ir th B 57 APPENDICES 58 Appendix 1. Doe capture data in Auburn, Alabama during 2004 and 2005. ________________________________________________________________________ Date Capture Fawns Doe ID Captured Year Location Transmitter Fate Caught ________________________________________________________________________ 1 March 24 2004 Gentry premature expulsion 0 2 March 29 2004 Gentry premature expulsion 0 3 April 2 2004 Gentry premature expulsion 0 4 April 19 2004 Hammond premature expulsion 0 5 April 28 2004 Boecklen not pregnant 0 6 May 3 2004 Gentry successful 2 7 May 5 2004 Boecklen successful 1 8 May 12 2004 Hammond successful 2 9 May 17 2004 Hammond not pregnant 0 10 May 23 2004 Hammond successful 1 11 May 26 2004 Gentry successful 1 12 June 4 2004 Gentry successful 1 13 June 11 2004 Chewacla successful 2 14 June 18 2004 Chewacla successful 1 15 June 19 2004 Chewacla successful 1 59 Appendix 1. Continued. ________________________________________________________________________ Date Capture Fawns Doe ID Captured Year Location Transmitter Fate Caught ________________________________________________________________________ 16 July 11 2004 Chewacla not pregnant 0 17 July 13 2004 Robin Rd successful 1 18 July 23 2004 Angarano not pregnant 0 19 August 1 2004 Whippoorwill successful 1 20 August 6 2004 Whippoorwill successful 2 21 August 8 2004 Chewacla not pregnant 0 22 August 13 2004 Angarano failure to locate fawns 0 23 August 23 2004 Ogletree Rd successful 1 24 March 1 2005 Gentry successful 1 25 March 8 2005 Chewacla successful 2 26 March 13 2005 Chewacla premature expulsion 0 27 March 18 2005 Boecklen successful 1 28 April 4 2005 Angarano successful 2 29 April 7 2005 Whippoorwill successful 1 30 April 18 2005 Chewacla successful 1 60 Appendix 1. Continued ________________________________________________________________________ Date Capture Fawns Doe ID Captured Year Location Transmitter Fate Caught ________________________________________________________________________ 31 April 20 2005 Chewacla successful 1 32 April 22 2005 Angarano transmitter failure 0 33 May 3 2005 Ogletree Rd premature expulsion 0 34 May 6 2005 Chewacla not pregnant 0 35 May 22 2005 Chewacla successful 1 36 June 4 2005 Whippoorwill transmitter malfunction 0 37 June 4 2005 Gentry successful 1 38 June 12 2005 Chewacla premature expulsion 0 39 June 17 2005 Chewacla premature expulsion 0 40 June 28 2005 Angarano premature expulsion 0 41 July 5 2005 Angarano successful 1 42 July 9 2005 Gentry successful 2 43 July 11 2005 Glover successful 1 44 July 16 2005 Glover successful 2 45 July 19 2005 Angarano successful 1 61 Appendix 1. Continued ________________________________________________________________________ Date Capture Fawns Doe ID Captured Year Location Transmitter Fate Caught ________________________________________________________________________ 46 July 29 2005 Ogletree Rd successful 1 ________________________________________________________________________ 62 Appendix 2. Fawn capture data in Auburn, Alabama for 2004 and 2005 using vaginal implant transmitters. ________________________________________________________________________ Fawn Mother Birth Birth Date of ID ID Date Location Sex Death Cause of Death ________________________________________________________________________ F3 12 Jul-30-04 Whippoorwill F Aug-30-04 Predation F4 17 Aug-05-04 Whippoorwill F Aug-09-04 Unknown F5 10 Aug-05-04 Whippoorwill F Aug-28-04 Predation F6 13 Aug-08-04 Chewacla M Aug-11-04 Abandoned F7 13 Aug-08-04 Chewacla F ------------- Survived F8 20 Aug-09-04 Chewacla F Aug-11-04 Abandoned F9 20 Aug-09-04 Chewacla F Aug-11-04 Abandoned F10 14 Aug-10-04 Chewacla M Aug-16-04 Possible Predation F11 19 Aug-13-04 Whippoorwill M Aug-22-04 Predation F12 7 Aug-18-04 Whippoorwill F ------------- Survived F13 8 Aug-18-04 Whippoorwill M Aug-31-04 Predation F14 8 Aug-18-04 Whippoorwill M ------------- Survived F15 11 Aug-19-04 Whippoorwill M Aug-20-04 Abandoned F16 6 Aug-22-04 Whippoorwill F Aug-24-04 Abandoned 63 Appendix 2. Continued. ________________________________________________________________________ Fawn Mother Birth Birth Date of ID ID Date Location Sex Death Cause of Death ________________________________________________________________________ F17 6 Aug-22-04 Whippoorwill F Aug-23-04 Abandoned F18 23 Aug-28-04 Twin Creeks F Sep-18-04 Predation F19 15 Sep-03-04 Chewacla F ------------- Survived F20 29 Jul-14-05 Whippoorwill F ------------- Survived F21 44 Jul-19-05 Glover F Jul-24-05 Possible Predation F22 44 Jul-19-05 Glover F Aug-23-05 Predation F24 42 Jul-23-05 Whippoorwill F ------------- Survived F25 42 Jul-23-05 Whippoorwill M Aug-11-05 Predation F27 45 Jul-26-05 Angarano M Aug-05-05 Unknown F30 25 Jul-31-05 Outing Club F ------------- Survived F31 25 Jul-31-05 Outing Club F Aug-19-05 Predation F33 28 Jul-31-05 Springwood M Aug-08-05 Possible Predation F34 28 Jul-31-05 Springwood F ------------- Survived F36 27 Aug-06-05 Whippoorwill M ------------- Survived F37 24 Aug-09-05 Whippoorwill F Aug-10-05 Predation 64 Appendix 2. Continued ________________________________________________________________________ Fawn Mother Birth Birth Date of ID ID Date Location Sex Death Cause of Death ________________________________________________________________________ F38 37 Aug-11-05 Whippoorwill M Aug-14-05 Predation F41 31 Aug-14-05 Whippoorwill F ------------- Survived F42 30 Aug-15-05 Chewacla F Aug-17-05 Accident F47 43 Aug-21-05 Grove Hill F Oct-15-05 Possible Predation F49 35 Aug-25-05 Angarano F Oct-10-05 Possible Predation F50 46 Aug-26-05 Ogletree M ------------- Survived F52 41 Sep-01-05 Springwood M ------------- Survived ________________________________________________________________________ 65 Appendix 3. Doe capture data in Camp Hill, Alabama during 2005. _____________________________________________________________ Date Fawns Doe ID Captured Year Transmitter Fate Caught _____________________________________________________________ 200 March 10 2005 transmitter failure 0 201 March 20 2005 failure to locate fawns 0 203 May 10 2005 successful 2 204 May 16 2005 successful 1 205 May 23 2005 successful 2 206 May 28 2005 successful 1 207 June 7 2005 successful 1 208 June 14 2005 not pregnant 0 209 June 14 2005 premature expulsion 0 210 June 16 2005 successful 2 _____________________________________________________________ 66 Appendix 4. Fawn capture data in Camp Hill, Alabama for 2005 using vaginal implant transmitters. __________________________________________________________________ Fawn Mother Birth Date of ID ID Date Sex Death Cause of Death __________________________________________________________________ F100 206 Jul-27-05 M Jul-31-05 Predation F101 205 Jul-29-05 F Aug-18-05 Predation F102 205 Jul-29-05 M ------------- Survived F103 203 Aug-03-05 M Oct-2-05 Predation F104 203 Aug-03-05 M Aug-14-05 Predation F105 210 Aug-08-05 F Aug-13-05 Predation F106 210 Aug-08-05 M Aug-10-05 Predation F107 207 Aug-09-05 M Aug-14-05 Predation F109 204 Aug-22-05 M ------------- Survived __________________________________________________________________ 67 Appendix 5. Fawn capture data in Auburn and Camp Hill, Alabama for 2004 and 2005 using spotlights and thermal imaging camera. ________________________________________________________________________ Fawn Hoof Capture Capture Date of ID Growth Date Location Sex Death Cause of Death ________________________________________________________________________ F1 0.0 Jul-19-04 Chewacla M Aug-22-04 Predation F2 0.0 Jul-19-04 Chewacla M ------------- Survived F23 ---- Jul-22-05 Chewacla M Aug-09-05 Predation F26 4.0 Jul-23-05 Whippoorwill M ------------- Survived F28 1.0 Jul-28-05 Robin Rd F Aug-06-05 Predation F29 5.0 Jul-30-05 Robin Rd M ------------- Survived F32 3.0 Jul-31-05 Whippoorwill F Oct-23-05 Predation F35 6.0 Aug-04-05 Angarano M Oct-11-05 Possible Predation F39 5.0 Aug-13-05 Whippoorwill M Aug-23-05 Predation F40 6.0 Aug-13-05 Whippoorwill M ------------- Survived F43 7.0 Aug-16-05 Whippoorwill M Aug-17-05 Hit by Car F44 8.0 Aug-19-05 Whippoorwill F Aug-24-05 Possible Predation F45 7.0 Aug-19-05 Whippoorwill M Oct-22-05 Domestic Dog F48 5.0 Aug-25-05 Springwood M ------------- Survived 68 Appendix 5. Continued. ________________________________________________________________________ Fawn Hoof Capture Capture Date of ID Growth Date Location Sex Death Cause of Death ________________________________________________________________________ F51 5.0 Aug-28-05 Chewacla F Sep-06-05 Predation F108 5.0 Aug-15-05 Camp Hill F ------------- Survived ________________________________________________________________________