Inger Maren Rivrud, Aimee, Jonas Kindberg

The Swedish brown bear population is a success story of effective conservation: thanks to targeted measures, the population recovered from ~100 individuals a century ago to over 3000 in 2008. However, the paradigm surrounding large carnivores is shifting from that of recovery to sustainable management. While hunting has been used as a management tool in Sweden for decades, harvest quotas have doubled over the last 4 years with offtake now as high as 25% of the population. This is bound to have dramatic consequences for population demography and has sparked discussions among managers about effective hunting strategies that reduce bear numbers without compromising the population’s viability. A user-friendly harvest model was developed to assist managers with setting hunting quotas, but the parameter estimates in this model are based on data that is over a decade old and unlikely to properly represent the population’s status today. Furthermore, the original studies and modelling frameworks used for estimating the parameters were developed with a focus on one-time scientific publication instead of adaptive management and are consequently lacking the reproducibility that is crucial for updating estimates in a cost-effective manner. Here, we present a new and substantially upgraded modelling workflow that integrates data from multiple monitoring schemes (telemetry, reproductive surveys, DNA detections, dead recoveries). This framework estimates key parameters required for understanding the impacts of hunting on population demography and setting sustainable hunting quotas. We showcase how adopting a “workflow perspective” and investing in transparent reproducible coding can pave the way for a new generation of harvest models. The models can be updated with new data annually and enable managers to make decisions on harvest quotas based on the latest knowledge at minimal costs and allow us to take a deep dive into the effects intensive hunting has on Swedish brown bears.