Using statistical population reconstruction to estimate effects of changing harvest regulations on fisher in Minnesota

Monitoring the demographic trends of harvested furbearer populations is essential for effective management and to evaluate the consequences of changing harvest regulations such as season lengths, bag limits, or closures. We used age-at-harvest, harvest effort, and telemetry data to parameterize a second-stage statistical population reconstruction model with both random and linear effects to estimate abundance, recruitment, harvest vulnerability, and non-harvest survival rates of fisher (Pekania pennanti) in Minnesota, USA, from 2010 to 2023. We then used this model to investigate the effect of changing the length of the harvest season during this time on predicted population abundance and harvest vulnerability. Our results suggest that the decision to decrease the harvest season from 9 days to 6 days from 2012 to 2018 may have counterintuitively increased the likelihood of fisher being harvested per additional unit of harvest effort (i.e., trap-nights), which may have been attributable to trappers adjusting their strategy to maximize harvest during the shorter period. This analysis illustrates the utility of using statistical population reconstruction with random and linear effects to help management agencies make more informed decisions about changing harvest season lengths and other regulations, and to ensure that unexpected effects can be quickly detected and adjusted for in an adaptive management framework.