Reproductive characteristics of female white-tailed deer … · 2017. 3. 21. · Reproductive...

lable at ScienceDirect

Theriogenology 94 (2017) 71e78

Contents lists avai

Theriogenology

journal homepage: www.ther iojournal .com

Reproductive characteristics of female white-tailed deer (Odocoileusvirginianus) in the Midwestern USA

Michelle L. Green a, b, Amy C. Kelly a, 1, Damian Satterthwaite-Phillips b, 2,Mary Beth Manjerovic a, b, 3, Paul Shelton c, Jan Novakofski a, Nohra Mateus-Pinilla b, *

a Department of Animal Sciences, University of Illinois at Urbana-Champaign, 205 Meat Science Laboratory, 1503 S. Maryland Drive, Urbana, IL 61801, USAb Illinois Natural History Survey, University of Illinois Urbana-Champaign, 1816 S. Oak Street, Champaign, IL 61820, USAc Division of Wildlife Resources, Illinois Department of Natural Resources, One Natural Resources Way, Springfield, IL 62702, USA

a r t i c l e i n f o

Article history:Received 1 July 2016Received in revised form13 February 2017Accepted 13 February 2017Available online 16 February 2017

Keywords:Female reproductionPregnancyMaternal ageFetusSex ratioCervidae

* Corresponding author.E-mail addresses: [email protected] (M.L. Gr

(A.C. Kelly), [email protected] ([email protected] (M.B. Manjerovic), [email protected] (J. Novakofski), [email protected]

1 Present address: United States Department of AgriService, 1815 N. University, Peoria, IL 61604, USA.

2 Present address: Phillips Research and Analytics,97405, USA.

3 Present address: Lincoln Park Zoo, 2001 North ClUSA.

http://dx.doi.org/10.1016/j.theriogenology.2017.02.0100093-691X/© 2017 The Authors. Published by Elsevier

a b s t r a c t

Knowledge of reproductive characteristics of wild populations is necessary to inform responsible man-agement decisions that promote herd health. As management, goals, and free-ranging populationschange over time and landscapes, updated knowledge of reproductive characteristics are needed toinform responsible management practices. We estimated reproductive characteristics of female white-tailed deer in Illinois, including pregnancy rate, litter size, fetal growth and fetal sex ratio. We foundmaternal age to have an important influence on several reproductive factors. Approximately 66% oftested females (n ¼ 3884) were pregnant and pregnancy rates increased with increasing maternal age,from 20.5% in fawns to 85.8% in adult deer. Litter size ranged from 1 to 5 fetuses per pregnant female. Theaverage litter size was 1.9 ± 0.54 fetuses per pregnant female and also increased with age, from 1.2 infawns to 2.0 in adults, respectively. Breeding season peaked in November with the mean estimatedconception dates of fetuses varying with maternal age. Fawns conceived fetuses later in the breedingseason (December 2) compared to yearlings and adults (November 11 and 8, respectively). We measuredthe body mass index (BMI) of all fetuses and found that litter size and female age influence fetal size. Wefound no bias in fetal sex ratio (average 1.0:1.0, male:female) but we observed a sex bias in fetal size(mean BMI male ¼ 0.71, female 0.67) across all maternal age classes. A comparison of the current studyand previous reports indicate that variation in maternal age within a population is an important driver ofreproductive metrics, likely because maternal age and body size or condition are related. Furthermore,variation in resource availability will influence reproductive rates, especially among fawn females.© 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND

license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction

North American cervids, including white-tailed deer (Odocoileusvirginianus), mule deer (O. hemionus), elk (Cervus canadensis), andmoose (Alces alces) are recreationally valuable and popular gamespecies. The Odocoileus genus has the highest numbers, highest

een), [email protected]. Satterthwaite-Phillips),[email protected] (P. Shelton),u (N. Mateus-Pinilla).culture, Agricultural Research

641 W 22nd Ave, Eugene, OR

ark Street, Chicago, IL 60614,

Inc. This is an open access article u

economic value [1], and is found across a majority of North America[2]. While white-tailed deer are among the most abundant speciespresently, unregulated hunting threatened to eliminate this speciesfrom much of its range in the early 1900's [3]. Since that time,white-tailed deer have been managed extensively and recentdecades have seen dynamic changes in white-tailed deer pop-ulations. For example, deer populations in the United States andCanada in 1948 were estimated at only about 6 million deer [4],whereas in 2015 that same number was actually harvested byhunters during legal hunting seasons [5].

Population management of wildlife species, especially largegame species requires knowledge of population dynamics relativeto carrying capacity of the landscape. Further, studies of othercervids have indicated that population density can influence femalereproduction [6]. Reproductive parameters such as pregnancy rates,birth rates, neonate and juvenile survival (i.e., fawn recruitment),and litter size are important variables that can be used to model

nder the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

http://creativecommons.org/licenses/by-nc-nd/4.0/mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://crossmark.crossref.org/dialog/?doi=10.1016/j.theriogenology.2017.02.010&domain=pdfwww.sciencedirect.com/science/journal/0093691Xwww.theriojournal.comhttp://dx.doi.org/10.1016/j.theriogenology.2017.02.010http://creativecommons.org/licenses/by-nc-nd/4.0/http://dx.doi.org/10.1016/j.theriogenology.2017.02.010http://dx.doi.org/10.1016/j.theriogenology.2017.02.010

M.L. Green et al. / Theriogenology 94 (2017) 71e7872

population growth or decline and ultimately guide managementdecisions. Given the dynamic changes in deer populations in therecent past, and the importance of having contemporary estimatesof reproductive parameters to monitor variations in deer pop-ulations and guide management decisions, the purpose of thispaper is to examine the reproductive characteristics of femalewhite-tailed deer using a large dataset beginning in 2003.

The upper Midwest landscape is a mixture of fertile agriculturalcropland interspersed with forests. Illinois has a long history ofdeer management and previous studies have investigated white-tailed deer reproduction [7,8]. However, few data on reproductionhave been reported in recent years. Previous estimates of repro-ductive parameters may not be applicable if deer population size,habitat quality and quantity, or food resources have changed sub-stantially over time [9]. Therefore, additional research is warrantedto provide updated estimates of reproductive parameters. In orderto update this information, we examined baseline reproductivecharacteristics of female white-tailed deer based on a large sampleset obtained over a decade of sampling. Specifically, we investi-gated (1) pregnancy rate and estimated conception peaks, (2) littersize and sex ratio, and (3) fetal growth.

1.1. Species characteristics

White-tailed deer are seasonal breeders. Estrus and matingoccurs during the rutting season, which in central Illinois rangesfrom October to December. With an average gestational length of200 days [10], parturition follows fromMay to July [7]. Although ata later date than older females, fawns may enter estrus in theautumn of their first year [10]. Female white-tailed deer gain morethan half of their adult mass of 60 kg between 0.5 yr and 2e4 yrs[11], when they attain maximum body size [10]. Males do not reachstable body mass of 80 kg until approximately 5 years [10].

1.2. Pregnancy rate and estimated conception date

Research of white-tailed deer and roe deer (Capreolus capreolus)indicates pregnancy rates increase with maternal age [8,12,13].Maternal age and reproduction are linked as a high plane ofnutrition (defined as the quantity and quality of per capita foodintake), results in rapid growth and earlier maturity (i.e., estrus) inmany species [14,15]. Conversely, females in less-than-averagebody condition may exhibit lower pregnancy rates. In the wild,fawn breeding rates vary with habitat quality [16] and deer habitatin Illinois generally is considered high quality [9] leading to anexpectation of relatively high fawn pregnancy rates although yearlyvariation is expected given variation inweather conditions for plantgrowth (resource availability). Older females tend to conceiveearlier than younger females [12] and we therefore expect theestimated breeding dates of older females to occur earlier thanfawns and yearlings.

1.3. Litter size and sex ratio

White-tailed deer typically carry one or two fetuses althoughlitter size varies with maternal age [10]. Fawns in southern Illinois(based on fetal counts) produced a single offspring compared toyearlings and adults that had higher proportions of twin and tripletlitters [8]. Litter size, similar to pregnancy rate, is connected tomaternal age through body condition. Because of the high meta-bolic cost of pregnancy, it is reasonable to expect females withlarger body size (i.e., more fat) to be better suited to tolerate bodyfat loss, and thus multiple fetuses per pregnancy, compared to fe-males of smaller body size. Therefore, we expect litter size to in-crease with maternal age among Illinois deer.

Fetal sex ratios of white-tailed deer appear responsive tomaternal age and environmental conditions but the literature isdivided on the interpretation of available studies [17e20]. A pre-vious study of white-tailed deer at least 1 yr old, indicated that thepercentage of male fawns declinedwith an increase inmaternal ageand litter size [17]. In Ohio, fawn and yearling females carried moremale fetuses, but the sex ratio was balanced among adult females[21]. A study in Missouri found older females have more maleoffspring [22] whereas fetal sex ratios favored females among adultfemales in an Ohio farmland region [23]. Because our study areaincludes agricultural habitat with abundant food resources, weexpect females in our study area to be in relatively good bodycondition and to carry a lower proportion of male offspringcompared to yearlings. We expect the proportion of males todecrease with increasing litter size within an age class.

1.4. Fetal growth

White-tailed deer fetal growth is highly predictable fromapproximately 40 days post-conception until parturition [24].Descriptive growth keys (e.g., Carnegie stages) for white-taileddeer provide accurate age estimates based on morphologicaldescriptions [25,26]. Although descriptive keys may be slightlymore exact, crown-rump length measurements afford a methodthat requires less detailed observation to provide an accurate ageand conception date estimate [25]. Several factors influence birthsize across most mammals including age, size and body reserves ofthe female at conception, and litter size [27]. Maternal sizeinfluences fetal growth because traits that influence growth areheritable andmaternal size is determined by female body condition(i.e., nutrition and percent body fat), which in turn governs fetalsize. Among white-tailed deer, Cothran et al. [28] indicated thatmaternal age, maternal weight or the number of fetuses can impactfetal growth rates after accounting for fetal age. Therefore, weexpect fetal size to increase with maternal age and decrease withincreasing litter size andmale fetuses are expected to be larger thanfemales [29,30].

2. Materials and methods

Live animal work was conducted under the oversight of theIllinois Department of Natural Resources. Laboratory work wasconducted under the oversight of the University of Illinois Institu-tional Biosafety Committee.

2.1. Sampling

White-tailed deer embryos and fetuses were obtained throughthe state of Illinois chronic wasting disease (CWD) control program,an Illinois Department of Natural Resources (IDNR) program.Samples were collected by IDNR personnel from 15 northernIllinois counties each year from 2003 through 2013 during Januarythrough March (Fig. 1). The county where each deer was killed wasrecorded and used as the geographic location for all analyses. Age ofeach female deer was estimated based on sequential developmentof dentition [31]. Deer were classified into age groups usingmanagement terminology as fawn (8e11 months at time of sam-pling), yearling (20e23 months at time of sampling) or adult(>32 months at time of sampling). Deer were brought to one ofseveral IDNR laboratories and processed to allow donation to localfood banks after disease testing. During processing, IDNR biologistsremoved individual fetuses or reproductive tracts of female deer,which were subsequently transported to the University of Illinoisfor examination. Reproduction was assessed only in healthyanimals. For each fetus, we weighed (g), measured crown-rump

Fig. 1. Landscape features in sample collection region (denoted by dark boundary) in northern Illinois. Red boundary denotes the city of Chicago. (For interpretation of the ref-erences to colour in this figure legend, the reader is referred to the web version of this article.)

M.L. Green et al. / Theriogenology 94 (2017) 71e78 73

length (cm), determined sex by direct observation of genitalia, andcollected tissue for other studies.


The ability to detect a fetus depends on fetal development andsize. Because of difficulty in detecting fetuses in early pregnancy,we fit a sigmoid curve to all data and determined when our abilityto detect fetuses was constant (Fig. 2). Subsequent analyses wereconducted using samples collected after February 1 (Fig. 2, verticalreference line) to avoid bias resulting from non-detection.

All statistical analyses were completed using SAS v. 9.4 (Cary,N.C.). We used a factorial logistic regression (PROC LOGISTIC) todetermine whether pregnancy was influenced by maternal age(fawn, yearling, adult; treated as factor throughout analyses), year(treated as factor throughout), geographic location (treated asfactor throughout), or any two-way interactions. We testedmaternal age because body condition likely changes as femalesgrow. We also included year and geographic location as covariatesbecause temporal and spatial variation in resource availability havethe potential to contribute to the body condition of females. We didnot include day of sampling (number of days into the year) inpredictive models for pregnancy, litter size or fetal sex ratiobecause there was no correlation with those variables (Pearson'scorrelation coefficient: pregnancy r3383 ¼ 0.02, P ¼ 0.16; litter sizer3887 ¼ 0.01, P ¼ 0.62; fetal sex ratio r2542 ¼ 0.02, P ¼ 0.45).

Because it is impossible to observe large fractions of a popula-tion in the wild, wildlife studies rely on fetal developmentalmilestones or regression analysis to estimate dates of conception.There are potential errors in each of these approaches and weutilized a widely used regression calculated from captive breeding

data [25]. We calculated fetal age in days (FAD) based on apredictive equation for white-tailed deer (FAD ¼ 36.82 þ (0.32crown-rump length)) which is expected to be accurate within �2.4andþ4.9 days [25]. We then estimated the conception date for eachfetus by subtracting fetal age from the collection date and used themean estimated conception date for all fetuses within a litter whenappropriate. We evaluated whether maternal age influenced esti-mated mean conception date using an ANOVA with means sepa-ration (using PDIFF in PROC GLM). We included year, geographiclocation, litter size and all two-way interactions as covariates tocapture variation that may contribute to female body condition.


All fetuses were included in litter size calculations but fetuseswith undetermined sex (n ¼ 58) were omitted from analyses asappropriate. To determine whether litter size varied with age class,we used a Poisson regression with a log link function (PROC GEN-MOD). The dependent variable was litter size and the independentvariables tested were maternal age, year collected, geographiclocation of collection and all two-way interactions. Each pregnantfemale was considered an observation. We included maternal agebecause it likely represents some variation in maternal body size/condition. We included year and geographic location because foodand nutrient availability leading to maternal condition may varywith each. All non-pregnant females were removed from analysesof litter size. Large litter sizes were relatively rare events in each ageclass (fawn triplets [n ¼ 1], yearling quadruplets [n ¼ 3], and adultquintuplets [n¼ 1; Fig. 2b]). To focus on typical reproductive eventsand avoid skewing statistical evaluation of normal litter sizes, weremoved the rare events from analyses.

Fig. 2. Pregnancy rates of female white-tailed deer (Odocoileus virginianus) in northernIllinois with corresponding litter sizes represented as a proportion of pregnant femalesby age class. Panel A: vertical reference line indicates 31 days; fawn n ¼ 1,494, yearlingn ¼ 736, adult n ¼ 2836. Panel B: fawn n ¼ 233, yearling n ¼ 444, adult n ¼ 1924.


The sex ratio of fetuses (male fetuses/total fetuses) was deter-mined for all pregnant females. To determine whether fetal sexratio varied with female age on a population level, we consideredall fetuses as unique observations. We used a binary logisticregression model to test the probability of a fetus being maleregardless of litter size (PROC LOGISTIC). Because maternal bodycondition may influence fetal sex ratio, we included all variablesthat could influence maternal body condition: maternal age, year,geographic location, and litter size. We also included day of sam-pling to account for variation in gestational age and all two-wayinteractions as predictors of fetal sex ratio. Furthermore, we con-ducted a second analysis in which we included only observationsfrom compound litters (>1 fetus) in order to assess within-litterfetal sex ratios. We used a cumulative logistic regression model totest whether the mean crown-rump length of the litter (used toestimate conception date), maternal age, year, geographic location,litter size or the interaction between maternal age and litter sizeinfluenced within-litter fetal sex ratios.

2.4. Fetal growth

We identified extreme growth outliers by comparing withinlitter growth variation (crown-rump length) to the observedgrowth variation of all fetuses. To do this we determined the overallinterquartile (IQ) range of crown-rump lengths from compoundlitters (>1 fetus) and excluded litters where the within-litter rangeexceeded the observed range of the population or exceeded

previously reported within-litter variation of 5.1 cm [32,33], cor-responding to an estimated difference of 16 days in gestational agebased on the predictive equation of Hamilton et al. [25]. Identifiedoutliers included compound litters with sibling lengths thatappeared biologically implausible relative to each other whichpossibly resulted from recording or transcription errors over thedecade of sample collection. In total we removed 21 litters, < 1% offull dataset.

Day of sampling was correlated with fetal body size metrics andretained as a variable in body size models (body mass indexr4785 ¼ 0.72, P < 0.0001; weight r4790 ¼ 0.82, P < 0.0001; crown-rump length r4795 ¼ 0.78, P < 0.0001). To determine factors influ-encing fetal growth, we used body mass index (BMI) of individualfetuses as a metric of growth assessed using an ordinary linearregression model (PROC GLM). BMI (weight/crown-rump length2)is a popular indicator of body condition because it is relatively easyto gather robust data, and it can serve as a good measurement ofpopulation-level body size [34]. We selected BMI because it cap-tures the collinear predictors of weight and length that describeallometric growth patterns. The independent variables were day ofsampling, maternal age, litter size, year, geographic location, andthe interaction of maternal age with litter size. Day of sampling isthe day during gestation when each female was collected ratherthan an estimate of length of gestation (fetal age) when collected.Although day of sampling had some correlation with crown-rumplength, it had a significant independent contribution to the modelr2 value and was therefore included in the model. We includedmaternal age as a predictor because it corresponds to body size [10]among females, and we included litter size because the number offetuses reportedly can influence sex ratio and therefore, fetal size[35]. Year and geographic location were included to account forvariation in food and nutrient availability that may influencematernal body condition.

We examined BMI values to determine whether male andfemale fetuses were the same size within a litter at a given stage ofdevelopment. In order to test whether fetal size (BMI) was influ-enced by sexwe nested fetal sex withinmother in order to comparemultiple fetuses from the same litter. We tested this by maternalage class to determinewhethermaternal age influenced differencesbetween fetal sizes of males and females. We also independentlytested whether fetal sex (nested within mother) had a greaterinfluence on fetal weight or length. For all glm tests, Bonferronicorrections of multiple comparisons were applied for comparisonsof significant differences.

3. Results


Our dataset for analyses included 3884 females and 4781 fetuses(Table 1). On average, 65% of females were pregnant. Maternal age(P < 0.0001), year (P < 0.0001) and the interaction of maternal ageand year (P < 0.0001) influenced pregnancy rates (overall modelNagelkerke's r2 ¼ 0.49, likelihood ratio X232 ¼ 1695.07, P < 0.0001).More yearlings were pregnant than fawns and more adults werepregnant than yearlings (Table 1; X22 ¼ 1452.1, P < 0.0001). Preg-nancy rate significantly varied by year (Table 1; X210 ¼ 132.4,P < 0.0001) and geographic location (X211 ¼ 61.1, P < 0.0001).

The mean conception date of all fetuses was estimated to beNovember 10 (314.7 days into the year; SE 0.28). Estimatedconception date was influenced by maternal age which accountedfor the majority of the variation (P < 0.001, hp2 (partial etasquared)¼ 0.082) followed by year (P < 0.001, hp2 ¼ 0.034) and littersize (P < 0.001, hp2 ¼ 0.029). The mean estimated conception date ofadult and yearling fetuses was November 8 (311.9 days; SE 0.24)

Table 1Total number of female deer sampled and reproductive characteristics of female white-tailed deer in northern Illinois, 2003e2013.

Fiscal year Maternal age class Total1 (n) Pregnant (n) Fetuses (n) Pregnancy rate (%) Fetal sex ratio2

(fraction of males)Mean litter size ofpregnant females

2003 Fawn 36 3 3 8.3 0.33 1.0Yearling 17 4 6 23.5 0.38 1.5Adult 77 29 58 37.7 0.53 2.0











Total3 Fawn 1137 233 283 20.5 ± 8.0A 0.50 [0.41, 0.53]4 1.2 ± 0.42A

Yearling 562 436 780 77.6 ± 20.3B 0.52 [0.47, 0.54] 1.8 ± 0.55B

Adult 2185 1874 3718 85.8 ± 16.0C 0.51 [0.49, 0.52] 2.0 ± 0.49C

Overall Total 3884 2543 4781 65.5 ± 31.4 0.51 [0.49, 0.52] 1.9 ± 0.54

1Total number of females sampled; includes only deer sampled after 01 February to account for potentially undetected fetuses.2Number of male fetuses divided by the total number of fetuses. Fetuses with undetermined sex omitted.3Values within a column with different superscript letters differ significantly at P < 0.001.495% confidence limits of the mean in brackets.

M.L. Green et al. / Theriogenology 94 (2017) 71e78 75

and November 11 (315.4 days; SE 0.62), respectively. Fawn femalesconceived later, at a mean of December 2 (336.3 days; SE 1.37).


The mean litter size among all pregnant females was 1.9 fetuses(Table 1). Rare events of large litters (n ¼ 5) were removed fromanalysis, but summary statistics are presented in Table 2. Overall,yearling and adult female deer most commonly had twin litterswhile fawns had single offspring (Fig. 2b). Maternal age class wasthe only significant predictor of litter size (X2 ¼ 75.35, P < 0.0001).Fawns had the smallest litter sizes (1.2 fetuses/female) followed byyearlings (1.8 fetuses/female) and adults had the largest (2.0fetuses/female).

Table 2Summary statistics of rare, large litters relative to maternal age. Fetuses were recovered frlitter was collected, CRL indicates crown-rump length, SE indicates standard error, BMI i

Litter Size Age Date CRL (cm) ± SE

3 fawn 4-Feb 12.67 ± 0.174 yearling 7-Feb 20.13 ± 0.694 yearling 17-Mar 26.00 ± 0.544 yearling 27-Feb 23.50 ± 0.255 adult 7-Mar 26.86 ± 0.40

On a population level, we did not find any significant predictorsof fetal sex ratio. Neither did we find any significant predictors offetal sex ratio when testing within-litter fetal sex ratios of com-pound litters only. Maternal age was not a significant predictor offetal sex ratio. Over time (Table 1) there was no differencebetween age classes with an average fetal sex ratio of 1.0:1.0(male:female). For different years, the sex ratios of fawn mothersranged from 33 to 68% males, yearling mothers ranged from 38 to87% males, and adult mothers from 45 to 54% males (Table 1).While we did observe a numerical decline in the proportion ofmales with increasing litter size, which is correlated with maternalage, the trend was not significant (Table 3). Neither maternal age(P ¼ 0.08) nor litter size (P ¼ 0.97) were significant explanatoryvariables.

omwhite-tailed deer in Illinois. Age reflects maternal age, Date reflects the day eachndicates body mass index.

Mass (g) ± SE BMI ± SE Sex ratio

48.77 ± 0.22 0.3 ± 0.01 2 M:1 F223.25 ± 30.65 0.54 ± 0.04 3 M:1 F596 ± 16.08 0.88 ± 0.03 3 M:1 F412.23 ± 13.25 0.75 ± 0.01 1 M:3 F477.95 ± 44.14 0.66 ± 0.05 2 M:3 F

Table 3Proportion of male white-tailed deer fetuses by maternal age and litter size.

Maternal age

Litter size Adult Yearling FawnSingle 0.524 0.456 0.539Twin 0.510 0.527 0.435Triplet 0.489 0.567Quadruplet 0.417


3.3. Fetal growth

Fig. 3a illustrates developmental stages throughout gestation. Innearly all conceptuses (98.4%), embryonic development, duringwhich all major systems and structures develop, was complete byFebruary 1. A small proportion of conceptuses from each maternalage class was considered embryonic in the data set (1.5% adult, 1.2%yearling, 4.7% of fawn conceptuses). At the February 1 detectioncutoff date, crown-rump length of fetuses averaged 14.6 cm, whichcorrelated with fetal development (characterized by growth inlength and mass) rather than embryonic development. Oncefetuses reached 35e40 cm in crown-rump length, maturation oftissues was complete andminormorphological characteristics suchas pigmentation, spots, eyelashes and apparent hair follicles werepresent (Fig. 3b). When we removed outlier compound litters withsibling lengths that appeared biologically implausible relative toeach other, we excluded 9 twin and 12 triplet litters (

Fig. 4. Effect of maternal age and litter size (fetus number) on fetal size of white-taileddeer (Odocoileus virginianus) from northern Illinois. Graph represents the group meansof fetal BMI by maternal age class. Error bars depict the standard error of the means.Table below figure panel presents the significance levels for group comparisons(*** < 0.001, ** < 0.01, *


than adults [45]. Because they are still growing, fawns direct moreenergy to growth rather than energy storage [45] during breedingseason and even a small reduction in caloric intake can inhibit fatdeposition [46], potentially suppressing follicle maturation andlimiting fawn reproduction [22,37]. This idea is supported by thefact that pregnant fawns and even pregnant yearlings tend toweighmore than their non-pregnant counterparts [37,47].

5. Conclusion

Our study supported several general trends of female repro-duction including the increase of pregnancy rates with female ageand fawns breeding late in the season compared to older females.We found that fawn reproduction continues to be highly variableand is likely dependent on the interaction between availablehabitat, deer density, and resource availability which influencefawn growth and fat accretion that, in turn, influence estrusonset. We found balanced fetal sex ratios and evidence that malefetuses are larger than female fetuses. Because our study coveredseveral years, multiple habitat types and deer densities leading tobroad scale metrics of female reproduction, future work shouldfocus on studies to better understand the interaction of habitatresources and deer density because they are variable across thelandscape.

Acknowledgments

The Illinois Department of Natural Resources collected all thesamples and field data. Numerous wildlife biologists generouslydonated both time and effort to this study. William Brown createdthe map used in Fig. 1. Funding: This work was supported by theU.S. Fish and Wildlife Service Federal Aid in Wildlife RestorationProject [W-146-R], the Illinois Natural History Survey, and theIllinois Office of the Vice-Chancellor for Research.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.theriogenology.2017.02.010.

References

[1] McCullough DR. The theory and management of Odocoileus populations. In:Wemmer CM, editor. Biol. Manag. Cervidae. Washington D.C: SmithsonianInstitution Press; 1987. p. 535e48.

[2] Heffelfinger JR. Taxonomy, evolutionary history, and distribution. In:Hewitt DG, editor. Biol. Manag. White-tailed deer. Boca Raton: CRC Press;2011. p. 3e39.

[3] Adams KP, Hamilton RJ. Management history. In: Hewitt DG, editor. Biol.Manag. White-tailed deer. Boca Raton: CRC Press; 2011. p. 355e77.

[4] Bartlett IH. White-tailed deer resources: United States and Canada. TransNorth Am Wildl Conf 1949;14. 543e542.

[5] QDMA. Whitetail report. 2017. p. 2017.[6] Stewart KM, Bowyer RT, Dick BL, Johnson BK, Kie JG. Density-dependent

effects on physical condition and reproduction in North American elk: anexperimental test. Oecologia 2005;143:85e93. http://dx.doi.org/10.1007/s00442-004-1785-y.

[7] Nixon CM, Hansen LP, Brewer PA, Chelsvig JE. Ecology of white-tailed deer inan intensively farmed region of Illinois. Wildl Monogr 1991;118:3e77.

[8] Roseberry JL, Klimstra WD. Productivity of white-tailed deer on crab orchardnational wildlife refuge. J Wildl Manag 1970;34:23e8.

[9] Roseberry JL, Woolf A. Habitat-population density relationships for white-tailed deer in Illinois. Wildl Soc Bull 1998;26:252e8.

[10] Ditchkoff SS. Anatomy and physiology. In: Hewitt DG, editor. Biol. Manag.White-tailed deer. Boca Raton: CRC Press; 2011. p. 43e73.

[11] Ditchkoff SS, Welch Jr ER, Starry WR, Dinkines WC, Masters RE, Lochmiller RL.Quality deer management at the McAlester army ammunition plant: a uniqueapproach. Proc Annu Conf Southeast Assoc Fish Wildl Agencies 1997;51:389e99.

[12] Haugen AO. Reproductive performance of white-tailed deer in Iowa.J Mammal 1975;56:151e9.

[13] Hewison AJM. Variation in the fecundity of roe deer in Britain: effects of ageand body weight. Acta Theriol Warsz 1996;41:187e98.

[14] Takahashi H. Female reproductive parameters and fruit availability: factorsdetermining onset of estrus in Japanese macaques. Am J Primatol 2002;51:141e53. http://dx.doi.org/10.1002/ajp.10041.

[15] Anderson DP, Nordheim EV, Boesch C. Environmental factors influencing theseasonality of estrus in chimpanzees. Primates 2006;47:43e50. http://dx.doi.org/10.1007/s10329-005-0143-y.

[16] Morton GH, Cheatum EL. Regional differences in breeding potential of white-tailed deer in New York. J Wildl Manag 1946;10:242e8.

[17] Verme LJ. Sex ratio variation in Odocoileus: a critical review. J Wildl Manag1983;47:573e82.

[18] Verme LJ. Progeny sex ratio relationships in deer: theoretical vs. observed.J Wildl Manag 1985;49:134e6.

[19] Myers JH. Sex ratio adjustment under food stress: maximization of quality ofnumbers of offspring? Am Nat 1978;112:381e8.

[20] McGinley MA. The adaptive value of male-biased sex ratios among stressedanimals. Am Nat 1984;124:597e9.

[21] NixonCM. Productivity ofwhite-tailed deer inOhio. Ohio J Sci 1971;71:217e25.[22] Hansen L, Beringer J, Schulz JH. Reproductive characteristics of Missouri

white-tailed deer. In: Proc annu conf southeast assoc fish wildl agencies, vol.50; 1996. p. 357e66.

[23] Stoll RJ, Parker WP. Reproductive performance and condition of white-taileddeer in Ohio. Ohio J Sci 1986;86:164e8.

[24] Soprovich DW. Fetal growth rate estimation from length and date of death. In:Brown RD, editor. Biol. Deer. New York: Springer-Verlag New York, Inc; 1992.p. 63e8.

[25] Hamilton RJ, Tobin ML, Moore WG. Aging fetal white-tailed deer. Proc AnnuConf Southeast Assoc Fish Wildl Agencies 1985;39:389e95.

[26] Armstrong RA. Fetal development of the Northern white-tailed deer(Odocoileus virginianus borealis Miller). Am Midl Nat 1950;43:650e66.

[27] Lawrence TLJ, Fowler VR, Novakofski JE. Growth of farm animals. third ed.Tarxien, Malta: Gutenberg Press Limited; 2012.

[28] Cothran EG, Chesser RK, Smith MH, Johns PE. Influences of genetic variabilityand maternal factors on fetal growth in white-tailed deer. Evolution (N Y)1983;37:282e91.

[29] Clutton-Brock TH, Guinness FE, Albon SD. Red deer: behavior and Ecology ofTwo Sexes. Chicago, IL: University of Chicago Press; 1982.

[30] San Jos�e C, Braza F, Arag�on S. The effect of age and experience on thereproductive performance and prenatal expenditure of resources in femalefallow deer (Dama dama). Can J Zool 1999;1722:1717e22.

[31] Severinghaus CW. Tooth development and wear as criteria of age in white-tailed deer. J Wildl Manag 1949;13:195e216.

[32] Haugen AO. Breeding records of captive white-tailed deer in Alabama.J Mammal 1959;40:108e13.

[33] Haugen AO, Davenport LA. Breeding records of whitetail deer in the UpperPeninsula of Michigan. J Wildl Manag 1950;14:290e5.

[34] Stevenson RD, Woods WA. Condition indices for conservation: new uses forevolving tools. Integr Comp Biol 2006;46:1169e90. http://dx.doi.org/10.1093/icb/icl052.

[35] Verme LJ. Maternal investment in white-tailed deer. J Mammal 1989;70:438e42.

[36] Verme LJ. Reproductive patterns of white-tailed deer related to nutritionalplane. J Wildl Manag 1969;33:881e7.

[37] Verme LJ, Ozoga JJ. Relationship of photoperiod to puberty in doe fawn white-tailed deer. J Mammal 1987;68:107e10.

[38] Ozoga JJ, Verme LJ. Activity patterns of white-tailed deer during estrus. J WildlManag 1975;39:679e83.

[39] Jarrell VL, Dziuk PJ. Effect of number of corpora lutea and fetuses on con-centrations of progesterone in blood of goats. J Anim Sci 1991;69:770e3.

[40] Longenecker DE, Waite AB, Day BN. Similarity in the number of corpora luteaduring two stages of pregnancy in swine. J Anim Sci 1968;27:466e7.

[41] Langvatn R, Loison A. Consequences of harvesting on age structure, sex ratioand population dynamics of red deer Cervus elaphus in central Norway. WildlBiol 1999;5:213e23.

[42] Fairbanks WS. Birthdate, birthweight, and survival in pronghorn fawns.J Mammal 1993;74:129e35.

[43] Sams MG, Lochmiller RL, Qualls Jr CW, Leslie Jr DMJ, Payton ME. Physiologicalcorrelates of neonatal mortality in an overpopulated herd of white tailed deer.J Mammal 1996;77:179e90.

[44] Cothran EG, Chesser RK, Smith MH, Johns PE. Fat levels in female white-taileddeer during the breeding season and pregnancy. J Mammal 1987;68:111e8.

[45] Johns PE, Smith MH, Chesser RK. Annual cycles of the kidney fat index in aSoutheastern white-tailed deer herd. J Wildl Manag 1984;48:969e73.

[46] Verme LJ, Ozoga JJ. Influence of protein-energy intake on deer fawns inautumn. J Wildl Manag 1980;44:305e14.

[47] DelGiudice GD, Lenarz MS, Powell MC. Age-specific fertility and fecundity inNorthern free-ranging white-tailed deer: evidence for reproductive senes-cence? J Mammal 2007;88:427e35. http://dx.doi.org/10.1644/06-MAMM-A-164R.1.

http://dx.doi.org/10.1016/j.theriogenology.2017.02.010http://dx.doi.org/10.1016/j.theriogenology.2017.02.010http://refhub.elsevier.com/S0093-691X(17)30078-X/sref1http://refhub.elsevier.com/S0093-691X(17)30078-X/sref1http://refhub.elsevier.com/S0093-691X(17)30078-X/sref1http://refhub.elsevier.com/S0093-691X(17)30078-X/sref1http://refhub.elsevier.com/S0093-691X(17)30078-X/sref2http://refhub.elsevier.com/S0093-691X(17)30078-X/sref2http://refhub.elsevier.com/S0093-691X(17)30078-X/sref2http://refhub.elsevier.com/S0093-691X(17)30078-X/sref2http://refhub.elsevier.com/S0093-691X(17)30078-X/sref3http://refhub.elsevier.com/S0093-691X(17)30078-X/sref3http://refhub.elsevier.com/S0093-691X(17)30078-X/sref3http://refhub.elsevier.com/S0093-691X(17)30078-X/sref4http://refhub.elsevier.com/S0093-691X(17)30078-X/sref4http://refhub.elsevier.com/S0093-691X(17)30078-X/sref4http://refhub.elsevier.com/S0093-691X(17)30078-X/sref5http://dx.doi.org/10.1007/s00442-004-1785-yhttp://dx.doi.org/10.1007/s00442-004-1785-yhttp://refhub.elsevier.com/S0093-691X(17)30078-X/sref7http://refhub.elsevier.com/S0093-691X(17)30078-X/sref7http://refhub.elsevier.com/S0093-691X(17)30078-X/sref7http://refhub.elsevier.com/S0093-691X(17)30078-X/sref8http://refhub.elsevier.com/S0093-691X(17)30078-X/sref8http://refhub.elsevier.com/S0093-691X(17)30078-X/sref8http://refhub.elsevier.com/S0093-691X(17)30078-X/sref9http://refhub.elsevier.com/S0093-691X(17)30078-X/sref9http://refhub.elsevier.com/S0093-691X(17)30078-X/sref9http://refhub.elsevier.com/S0093-691X(17)30078-X/sref10http://refhub.elsevier.com/S0093-691X(17)30078-X/sref10http://refhub.elsevier.com/S0093-691X(17)30078-X/sref10http://refhub.elsevier.com/S0093-691X(17)30078-X/sref11http://refhub.elsevier.com/S0093-691X(17)30078-X/sref11http://refhub.elsevier.com/S0093-691X(17)30078-X/sref11http://refhub.elsevier.com/S0093-691X(17)30078-X/sref11http://refhub.elsevier.com/S0093-691X(17)30078-X/sref11http://refhub.elsevier.com/S0093-691X(17)30078-X/sref12http://refhub.elsevier.com/S0093-691X(17)30078-X/sref12http://refhub.elsevier.com/S0093-691X(17)30078-X/sref12http://refhub.elsevier.com/S0093-691X(17)30078-X/sref13http://refhub.elsevier.com/S0093-691X(17)30078-X/sref13http://refhub.elsevier.com/S0093-691X(17)30078-X/sref13http://dx.doi.org/10.1002/ajp.10041http://dx.doi.org/10.1007/s10329-005-0143-yhttp://dx.doi.org/10.1007/s10329-005-0143-yhttp://refhub.elsevier.com/S0093-691X(17)30078-X/sref16http://refhub.elsevier.com/S0093-691X(17)30078-X/sref16http://refhub.elsevier.com/S0093-691X(17)30078-X/sref16http://refhub.elsevier.com/S0093-691X(17)30078-X/sref17http://refhub.elsevier.com/S0093-691X(17)30078-X/sref17http://refhub.elsevier.com/S0093-691X(17)30078-X/sref17http://refhub.elsevier.com/S0093-691X(17)30078-X/sref18http://refhub.elsevier.com/S0093-691X(17)30078-X/sref18http://refhub.elsevier.com/S0093-691X(17)30078-X/sref18http://refhub.elsevier.com/S0093-691X(17)30078-X/sref19http://refhub.elsevier.com/S0093-691X(17)30078-X/sref19http://refhub.elsevier.com/S0093-691X(17)30078-X/sref19http://refhub.elsevier.com/S0093-691X(17)30078-X/sref20http://refhub.elsevier.com/S0093-691X(17)30078-X/sref20http://refhub.elsevier.com/S0093-691X(17)30078-X/sref20http://refhub.elsevier.com/S0093-691X(17)30078-X/sref21http://refhub.elsevier.com/S0093-691X(17)30078-X/sref21http://refhub.elsevier.com/S0093-691X(17)30078-X/sref22http://refhub.elsevier.com/S0093-691X(17)30078-X/sref22http://refhub.elsevier.com/S0093-691X(17)30078-X/sref22http://refhub.elsevier.com/S0093-691X(17)30078-X/sref22http://refhub.elsevier.com/S0093-691X(17)30078-X/sref23http://refhub.elsevier.com/S0093-691X(17)30078-X/sref23http://refhub.elsevier.com/S0093-691X(17)30078-X/sref23http://refhub.elsevier.com/S0093-691X(17)30078-X/sref24http://refhub.elsevier.com/S0093-691X(17)30078-X/sref24http://refhub.elsevier.com/S0093-691X(17)30078-X/sref24http://refhub.elsevier.com/S0093-691X(17)30078-X/sref24http://refhub.elsevier.com/S0093-691X(17)30078-X/sref25http://refhub.elsevier.com/S0093-691X(17)30078-X/sref25http://refhub.elsevier.com/S0093-691X(17)30078-X/sref25http://refhub.elsevier.com/S0093-691X(17)30078-X/sref26http://refhub.elsevier.com/S0093-691X(17)30078-X/sref26http://refhub.elsevier.com/S0093-691X(17)30078-X/sref26http://refhub.elsevier.com/S0093-691X(17)30078-X/sref27http://refhub.elsevier.com/S0093-691X(17)30078-X/sref27http://refhub.elsevier.com/S0093-691X(17)30078-X/sref28http://refhub.elsevier.com/S0093-691X(17)30078-X/sref28http://refhub.elsevier.com/S0093-691X(17)30078-X/sref28http://refhub.elsevier.com/S0093-691X(17)30078-X/sref28http://refhub.elsevier.com/S0093-691X(17)30078-X/sref29http://refhub.elsevier.com/S0093-691X(17)30078-X/sref29http://refhub.elsevier.com/S0093-691X(17)30078-X/sref30http://refhub.elsevier.com/S0093-691X(17)30078-X/sref30http://refhub.elsevier.com/S0093-691X(17)30078-X/sref30http://refhub.elsevier.com/S0093-691X(17)30078-X/sref30http://refhub.elsevier.com/S0093-691X(17)30078-X/sref30http://refhub.elsevier.com/S0093-691X(17)30078-X/sref30http://refhub.elsevier.com/S0093-691X(17)30078-X/sref31http://refhub.elsevier.com/S0093-691X(17)30078-X/sref31http://refhub.elsevier.com/S0093-691X(17)30078-X/sref31http://refhub.elsevier.com/S0093-691X(17)30078-X/sref32http://refhub.elsevier.com/S0093-691X(17)30078-X/sref32http://refhub.elsevier.com/S0093-691X(17)30078-X/sref32http://refhub.elsevier.com/S0093-691X(17)30078-X/sref33http://refhub.elsevier.com/S0093-691X(17)30078-X/sref33http://refhub.elsevier.com/S0093-691X(17)30078-X/sref33http://dx.doi.org/10.1093/icb/icl052http://dx.doi.org/10.1093/icb/icl052http://refhub.elsevier.com/S0093-691X(17)30078-X/sref35http://refhub.elsevier.com/S0093-691X(17)30078-X/sref35http://refhub.elsevier.com/S0093-691X(17)30078-X/sref35http://refhub.elsevier.com/S0093-691X(17)30078-X/sref36http://refhub.elsevier.com/S0093-691X(17)30078-X/sref36http://refhub.elsevier.com/S0093-691X(17)30078-X/sref36http://refhub.elsevier.com/S0093-691X(17)30078-X/sref37http://refhub.elsevier.com/S0093-691X(17)30078-X/sref37http://refhub.elsevier.com/S0093-691X(17)30078-X/sref37http://refhub.elsevier.com/S0093-691X(17)30078-X/sref38http://refhub.elsevier.com/S0093-691X(17)30078-X/sref38http://refhub.elsevier.com/S0093-691X(17)30078-X/sref38http://refhub.elsevier.com/S0093-691X(17)30078-X/sref39http://refhub.elsevier.com/S0093-691X(17)30078-X/sref39http://refhub.elsevier.com/S0093-691X(17)30078-X/sref39http://refhub.elsevier.com/S0093-691X(17)30078-X/sref40http://refhub.elsevier.com/S0093-691X(17)30078-X/sref40http://refhub.elsevier.com/S0093-691X(17)30078-X/sref40http://refhub.elsevier.com/S0093-691X(17)30078-X/sref41http://refhub.elsevier.com/S0093-691X(17)30078-X/sref41http://refhub.elsevier.com/S0093-691X(17)30078-X/sref41http://refhub.elsevier.com/S0093-691X(17)30078-X/sref41http://refhub.elsevier.com/S0093-691X(17)30078-X/sref42http://refhub.elsevier.com/S0093-691X(17)30078-X/sref42http://refhub.elsevier.com/S0093-691X(17)30078-X/sref42http://refhub.elsevier.com/S0093-691X(17)30078-X/sref43http://refhub.elsevier.com/S0093-691X(17)30078-X/sref43http://refhub.elsevier.com/S0093-691X(17)30078-X/sref43http://refhub.elsevier.com/S0093-691X(17)30078-X/sref43http://refhub.elsevier.com/S0093-691X(17)30078-X/sref44http://refhub.elsevier.com/S0093-691X(17)30078-X/sref44http://refhub.elsevier.com/S0093-691X(17)30078-X/sref44http://refhub.elsevier.com/S0093-691X(17)30078-X/sref45http://refhub.elsevier.com/S0093-691X(17)30078-X/sref45http://refhub.elsevier.com/S0093-691X(17)30078-X/sref45http://refhub.elsevier.com/S0093-691X(17)30078-X/sref46http://refhub.elsevier.com/S0093-691X(17)30078-X/sref46http://refhub.elsevier.com/S0093-691X(17)30078-X/sref46http://dx.doi.org/10.1644/06-MAMM-A-164R.1http://dx.doi.org/10.1644/06-MAMM-A-164R.1

Reproductive characteristics of female white-tailed deer (Odocoileus virginianus) in the Midwestern USA1. Introduction1.1. Species characteristics1.2. Pregnancy rate and estimated conception date1.3. Litter size and sex ratio1.4. Fetal growth

2. Materials and methods2.1. Sampling2.2. Pregnancy rate and estimated conception date2.3. Litter size and sex ratio2.4. Fetal growth

3. Results3.1. Pregnancy rate and estimated conception date3.2. Litter size and sex ratio3.3. Fetal growth

4. Discussion5. ConclusionAcknowledgmentsAppendix A. Supplementary dataReferences

Date post:	17-Feb-2021
Category:	Documents
Upload:	others
View:	0 times
Download:	0 times

Reproductive characteristics of female white-tailed deer … · 2017. 3. 21. · Reproductive...

Documents