Bayesian Analysis of Wildlife Age‐at‐Harvest Data

Abstract

State and federal natural resource management agencies often collect age-structured harvest data. These data represent finite realizations of stochastic demographic and sampling processes and have long been used by biologists to infer population trends. However, different sources of data have been combined in ad hoc ways and these methods usually failed to incorporate sampling error. In this article, we propose a "hidden process" (or state-space) model for estimating abundance, survival, recovery rate, and recruitment from age-at-harvest data that incorporate both demographic and sampling stochasticity. To this end, a likelihood for age-at-harvest data is developed by embedding a population dynamics model within a model for the sampling process. Under this framework, the identification of abundance parameters can be achieved by conducting a joint analysis with an auxiliary data set. We illustrate this approach by conducting a Bayesian analysis of age-at-harvest and mark-recovery data from black bears (Ursus americanus) in Pennsylvania. Using a set of reasonable prior distributions, we demonstrate a substantial increase in precision when posterior summaries of abundance are compared to a bias-corrected Lincoln-Petersen estimator. Because demographic processes link consecutive abundance estimates, we also obtain a more realistic biological picture of annual changes in abundance. Because age-at-harvest data are often readily obtained, we argue that this type of analysis provides a valuable strategy for wildlife population monitoring.

Integrated Population Modeling of Black Bears in Minnesota: Implications for Monitoring and Management

“…We expect to see increased interest in applying age-at-harvest models to wildlife data in coming years, particularly given recent applications in the literature [1], [2], [4]–[6]. There are many ways to fit age-at-harvest models and to judge model reliability.…”

“…Auxiliary datasets previously considered have included those generated by radio telemetry studies to inform survival probabilities [1], [2] and mark-recovery studies to inform survival and harvest rates [6]. Several authors have used hunter effort data to estimate harvest rates [4], [5], [14], and independent indices of abundance have been used to provide additional structure on changes in population size [5].…”

United Nations Population Division

“…Subsequently, a large body of literature developed; Brooks, Catchpole, and Morgan (2000a), Brooks et al (2002) provided reviews while Barry et al (2003), Conn et al (2008) and Corkrey et al (2008) are just a few examples of more recent studies.…”

Application of Cohort Analysis to Large Terrestrial Mammal Harvest Data

“…In contrast, Gove et al (2002) and Conn et al (2008) introduced a method for statistical population estimation of large terrestrial mammals when harvest-at-age is measured with error. Their method will be useful when the reported rate is low or unknown because of an insufficient reporting system or poaching.…”

“…Although radio-telemetry surveys provide reliable estimates of population sizes via capture-mark-recapture methods (Lebreton et al 1992), their efficacy depends on the number of individuals animals included in the surveys, making the method time-and labor-intensive in many cases where accuracy of estimation is of importance. On the other hand, large harvest data sets for game species, often collected by age and sex as part of wildlife agency management programs (Skalski and Millspaugh 2002;Conn et al 2008), are more readily available; under many circumstances, these data can be useful for population estimation.…”