Survival of 265 female and 224 male [Formula: see text]1-year-old white-tailed deer (Odocoileus virginianus) marked on 3 study areas in central and northern Illinois was examined. Females lived, on average, 5.5 years and males 2.5 years from birth. Twenty-four of the 265 females lived for at least 10 years from birth, but only 14 males for at least 5 years. The oldest female was 18 years of age and the oldest male 9 years old when killed. For both sexes, deaths were concentrated in the fall, with males more likely to die than females. Males were more likely to die from hunting and females from other causes. Known wounding deaths were 1 for every 3 retrieved deer for archers and 1 for every 8 for firearms hunters. Dispersing male and female yearlings and 2-year-olds suffered greater mortality than did sedentary deer. Annual survival rates of yearling and older females ranged from 0.56 (dispersing 2-year-olds) to 0.92 (8-year-olds). Survival was significantly reduced for 5-year-old females compared with those both older and younger. Annual survival of rates males ranged from 0.35 (dispersing 2-year-olds) to 0.76 (sedentary yearlings).

Survival of 265 female and 224 male ≥1-year-old white-tailed deer (Odocoileus virginianus) marked on 3 study areas in central and northern Illinois was examined. Females lived, on average, 5.5 years and males 2.5 years from birth. Twenty-four of the 265 females lived for at least 10 years from birth, but only 14 males for at least 5 years. The oldest female was 18 years of age and the oldest male 9 years old when killed. For both sexes, deaths were concentrated in the fall, with males more likely to die than females. Males were more likely to die from hunting and females from other causes. Known wounding deaths were 1 for every 3 retrieved deer for archers and 1 for every 8 for firearms hunters. Dispersing male and female yearlings and 2-year-olds suffered greater mortality than did sedentary deer. Annual survival rates of yearling and older females ranged from 0.56 (dispersing 2-year-olds) to 0.92 (8-year-olds). Survival was significantly reduced for 5-year-old females compared with those both older and younger. Annual survival of rates males ranged from 0.35 (dispersing 2-year-olds) to 0.76 (sedentary yearlings).

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.Abstract: Management of high-density suburban deer populations requires knowledge of survival and movement to predict population trends. However, natural and human-induced influences on survival and movement of suburban deer are poorly understood. Therefore, we marked 208 (60 bucks, 148 does) white-tailed deer (Odocoileus virginianus) from forest preserves in Chicago, Illinois, USA (1994-1998). Seasonal and annual survivorship was >0.80 for 114 does and 13 bucks. Deer-auto collisions produced the highest mortality rates, 0.10 (95% CI 0.06 to 0.14) and 0.17 (95% CI 0.0 to 0.37). Spring dispersal for does was 7% (3 of 41) for fawns and 6% (5 of 83) for yearlings and adults; and for bucks it was 50% (8 of 16) for fawns and 7% (2 of 30) for yearlings and adults. All dispersals were <9 km, except for 1 parous doe that moved 33.9 km. Doe home ranges averaged 51 (95% CI 40.5 to 61.5), 26 (95% CI 22.0 to 30.0), and 32 (95% CI 19.6 to 44.4) ha for winter-spring, summer, and fall, respectively. A priori, we developed a set of 10 logistic regression models for suburban doe survival relative to home range size and traffic exposure indices. Using Akaike's Information Criterion (AIC), the best models included covariates reflecting home range size and traffic exposure. Inference across a 290% confidence set of survival models indicated substantial spatial heterogeneity in mortality risk for suburban does. High survival and philopatry by suburban deer apparently contribute to their overabundance in metropolitan areas. JOURNAL OF WILDLIFE MANAGEMENT 66(2):500-510

Understanding the factors that affect dispersal is a fundamental question in ecology and conservation biology, particularly as populations are faced with increasing anthropogenic impacts. Here we collected georeferenced genetic samples (n = 2,540) from three generations of black bears (Ursus americanus) harvested in a large (47,739 km2), geographically isolated population and used parentage analysis to identify mother-offspring dyads (n = 337). We quantified the effects of sex, age, habitat type and suitability, and local harvest density at the natal and settlement sites on the probability of natal dispersal, and on dispersal distances. Dispersal was male-biased (76% of males dispersed) but a small proportion (21%) of females also dispersed, and female dispersal distances (mean ± SE  =  48.9±7.7 km) were comparable to male dispersal distances (59.0±3.2 km). Dispersal probabilities and dispersal distances were greatest for bears in areas with high habitat suitability and low harvest density. The inverse relationship between dispersal and harvest density in black bears suggests that 1) intensive harvest promotes restricted dispersal, or 2) high black bear population density decreases the propensity to disperse. Multigenerational genetic data collected over large landscape scales can be a powerful means of characterizing dispersal patterns and causal associations with demographic and landscape features in wild populations of elusive and wide-ranging species.

Estimating black bear (Ursus americanus) population size is a difficult but important requirement when justifying harvest quotas and managing populations. Advancements in genetic techniques provide a means to identify individual bears using DNA contained in tissue and hair samples, thereby permitting estimates of population abundance based on established mark‐capture‐recapture methodology. We expand on previous noninvasive population‐estimation work by geographically extending sampling areas (36,848 km2) to include the entire Northern Lower Peninsula (NLP) of Michigan, USA. We selected sampling locations randomly within biologically relevant bear habitat and used barbed wire hair snares to collect hair samples. Unlike previous noninvasive studies, we used tissue samples from harvested bears as an additional sampling occasion to increase recapture probabilities. We developed subsampling protocols to account for both spatial and temporal variance in sample distribution and variation in sample quality using recently published quality control protocols using 5 microsatellite loci. We quantified genotyping errors using samples from harvested bears and estimated abundance using statistical models that accounted for genotyping error. We estimated the population of yearling and adult black bears in the NLP to be 1,882 bears (95% CI = 1,389‐2,551 bears). The derived population estimate with a 15% coefficient of variation was used by wildlife managers to examine the sustainability of harvest over a large geographic area.

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Managers of overabundant deer have failed to incorporate relevant predatorprey theory into management research. In particular, understanding the functional response of deer hunters (deer encountered/time) to declining deer density is important because functional responses determine relative effort (time/deer encountered) required to harvest a deer and may, in turn, influence hunter perceptions of deer density and costs associated with deer removal. We used information-theoretic techniques and nonlinear regression to reanalyze data from controlled hunts in Illinois, Michigan, Wisconsin, and Ontario. Alternate models include killing rates or sighting rates as Type 1 (linear), Type 2 (hyperbolic), or Type 3 (sigmoidal) functions of deer density. Akaike's information criteria suggested that optimal models for most data sets were Type 1, although this may have been a artifact of small sample sizes. Nonetheless, effort curves derived from fitted functional responses indicated that relative effort accelerates as deer density declines. Accelerating effort requirements on the part of deer hunters likely hinders agency efforts to reduce overabundant deer populations and may be a source of hunter perceptions of unrealistically reduced deer herds. If general, this relationship (1) may determine what levels of harvest/removal are realistic, (2) is a potential source of bias in population estimation, and (3) may contribute to hunter distrust of agency efforts to reduce population size.

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