We conducted a 13-year survival (i.e., time survived since birth) and cause-specific mortality study, divided into 2 phases (Phase I = years 1–6; Phase II = years 7–13), of 302 female white-tailed deer (Odocoileus virginianus) ≥0.6 years old at capture. The study spanned a period of extreme variability in winter severity (maximum winter severity indexes [WSI] of 45–195) and hunting pressure. Most studies of survival and cause-specific mortality of northern deer have assumed constant survival rates for adults of each sex (≥1.0 yr old pooled) and examined fawns (0.6 ≤ x ≤ 1.0 yr old) separately. We observed U-shaped hazard (i.e., instantaneous risk of death) curves for both phases of the study, indicating that risk of death is highest for younger and older individuals. The estimated hazard for Phase II was generally lower and relatively constant for adults 2–10 years old compared to Phase I, where the instantaneous risk of death began to increase at age 6 years. This difference likely reflected differences in winter severities, associated changes in magnitude of wolf (Canis lupus) predation, and changes in hunting pressure between the 2 phases. The age distribution of our study cohort was relatively stable over the study period. Subsequently, when we included 76 neonates (i.e., ≤0.6 yr old) in the study cohort, the descending arm of the all-causes hazard began its descent at a hazard rate of 2.3 (vs. 1.0 without neonates), clearly demonstrating that the greatest risk of mortality occurs in the first year of life. We compared cumulative survival estimates for these data using the generalized Kaplan–Meier (GKM) and the iterative Nelson estimator (INE), and we illustrate the potential for bias when applying the GKM to left-truncated data. Median age of survival for females was 0.83 years old (90% CI = 0.79–1.45 yr old) using the INE and 0.43 years old (90% CI = 0.17–0.78 yr old) using the GKM. Lastly, we used a simulation approach to examine the potential for bias resulting from pooling adults. These simulations suggest that models using the constructed discrete time variable give nearly unbiased survival estimates and provide support for researchers and managers applying age-specific hazards derived during study periods to determine the reliability of adult age-pooled survival estimates. As indicated by our data, it is important to consider environmental variation and its interactions with natural mortality forces (e.g., predation) and age distribution of the population when setting harvest goals.
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Vol. 70 • No. 6