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Estimates of population abundance are fundamental to wildlife management andconservation, but are difficult to obtain across large geographic scales andfor cryptic species. We used a state-space model with age-at-harvest data in aBayesian framework to model American black bear (Ursus americanus) abundanceand demographic parameters in four management zones in Wisconsin from2011-2017. We had limited demographic data available from the population, andrelied upon a) the model, b) age-at-harvest data, and c) informative priordistributions from a literature review. The estimated posterior means anddistributions for abundance and demographic parameters from our models werereasonable for each management zone, and indicated a decreasing trend in zonesA and B, and a generally stable trend in zones C and D. The age-at-harvest dataupdated the posterior distribution and means for initial population size,harvest season survival, and non-harvest season survival, with a notableincrease in precision for the survival values. A strength of the model formanagers is the formalized process for providing biologically supportedinformation as prior distributions to transparently accommodate expert opinionwith measures of confidence when estimating wildlife populations, which canthen be updated by the age-at-harvest data and model structure. The integrationof prior information and age-at-harvest state-space models in a Bayesianframework efficiently leverage all available information for makingzone-specific abundance estimates for the management of harvested species. Thismay create more informative data for decision-makers when setting harvestquotas, and could lead to more effective monitoring, conservation, andmanagement of cryptic carnivore species.

Estimation of black bear abundance by management zone in Wisconsin