Title: Age-at-harvest models as monitoring and harvest management tools for Wisconsin carnivores,
Abstract: Quantifying and estimating wildlife population sizes is a foundation of
wildlife management. However, many carnivore species are cryptic, leading to
innate difficulties in estimating their populations. We evaluated the potential
for using more rigorous statistical models to estimate the populations of black
bears (Ursus americanus) in Wisconisin, and their applicability to other
furbearers such as bobcats (Lynx rufus). To estimate black bear populations, we
developed an AAH state-space model in a Bayesian framework based on Norton
(2015) that can account for variation in harvest and population demographics
over time. Our state-space model created an accurate estimate of the black bear
population in Wisconsin based on age-at-harvest data and improves on previous
models by using little demographic data, no auxiliary data, and not being
sensitive to initial population size. The increased accuracy of the AAH
state-space models should allow for better management by being able to set
accurate quotas to ensure a sustainable harvest for the black bear population
in each zone. We also evaluated the demography and annual harvest data of
bobcats in Wisconsin to determine trends in harvest, method, and hunter
participation in Wisconsin. Each trait of harvested bobcats that we tested
(mass, male:female sex ratio, and age) changed over time, and because these
were interrelated, and we inferred that harvest selection for larger size
biased harvests in favor of a) larger, b) older, and c) male bobcats by hound
hunters. We also found an increase in the proportion of bobcats that were
harvested by hound hunting compared to trapping, and that hound hunters were
more likely to create taxidermy mounts than trappers. We also found that
decreasing available bobcat tags and increasing success have occurred over
time, and correlate with substantially increasing both hunter populations and
hunter interest.