  Estimates of population abundance are fundamental to wildlife management and
conservation, but are difficult to obtain across large geographic scales and
for cryptic species. We used a state-space model with age-at-harvest data in a
Bayesian framework to model American black bear (Ursus americanus) abundance
and demographic parameters in four management zones in Wisconsin from
2011-2017. We had limited demographic data available from the population, and
relied upon a) the model, b) age-at-harvest data, and c) informative prior
distributions from a literature review. The estimated posterior means and
distributions for abundance and demographic parameters from our models were
reasonable for each management zone, and indicated a decreasing trend in zones
A and B, and a generally stable trend in zones C and D. The age-at-harvest data
updated the posterior distribution and means for initial population size,
harvest season survival, and non-harvest season survival, with a notable
increase in precision for the survival values. A strength of the model for
managers is the formalized process for providing biologically supported
information as prior distributions to transparently accommodate expert opinion
with measures of confidence when estimating wildlife populations, which can
then be updated by the age-at-harvest data and model structure. The integration
of prior information and age-at-harvest state-space models in a Bayesian
framework efficiently leverage all available information for making
zone-specific abundance estimates for the management of harvested species. This
may create more informative data for decision-makers when setting harvest
quotas, and could lead to more effective monitoring, conservation, and
management of cryptic carnivore species.
