Taking too many? Try SAMSE: A tool to include stochasticity for estimating limits to human-caused mortality of wildlife.

Oliver Manlik, School of Biological, Earth and Environmental Sciences, Univerisity of New South Wales, Sydney, Australia, Robert C. Lacy, Chicago Zoological Society, Brookfield, IL, William B. Sherwin, Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences;, University of New South Wales, Sydney, Australia, Hugh Finn, Curtin Law School, Faculty of Business and Law, Curtin University, Bentley, Australia, Neil R. Loneragan, Environmental and Conservation Sciences, College of Science, Health, Engineering and Education and Centre for Sustainable Aquatic Ecosystems, Harry Butler Institute,, Murdoch University, Murdoch, Australia and Simon J. Allen, School of Biological Sciences, University of Bristol, Bristol, United Kingdom; Department of Anthropology, University of Zurich, Zurich, Switzerland; School of Biological Sciences,, University of Western Australia, Perth, Australia

Background/Question/Methods
Human-caused mortality of wildlife is an ongoing threat to biodiversity. Assessing the population-level impact of mortality from fisheries bycatch and other anthropogenic sources has typically relied upon deterministic methods based on limited data. However, population declines are often accelerated by stochastic factors, including ecological connections, that are not accounted for in such conventional methods.
Building upon the widely applied Potential Biological Removal (PBR) equation, we introduce a novel population modelling approach for estimating a sustainable limit to human-caused mortality. Our approach, termed ‘Maximum Anthropogenic Mortality in Stochastic Environments’ (MAMSE), incorporates stochasticity, including variation in vital rates and the dependency of offspring on their mothers. The MAMSE mortality limit indicates the maximum number of individuals that can be removed without causing negative stochastic population growth. We used MAMSE to assess the impact of dolphin mortality from bycatch for an Australian trawl fishery. This model relied on surrogate vital rates from a stable, well-studied, congeneric population.
Results/Conclusions
Our results suggest that even the lower skipper-reported dolphin bycatch mortality rates are unsustainable in the long-term. We calculated a PBR of 16.2 dolphins per year, using the best abundance estimate available, a recovery factor of 0.5, and a default growth rate. In contrast, the MAMSE model, based on mean reproductive rates, indicated that only 2.3 to 8.0 dolphins could be removed per year without causing a population decline in a stochastic environment. Bycatch mortality in this fishery could be sustained only if dolphin reproductive rates were consistently higher than average.
The difference between the PBR and MAMSE mortality limits confirms previous studies that deterministic approaches like PBR may underestimate the true impact of bycatch mortality. This highlights the importance of integrating stochasticity when evaluating the impact of bycatch or other anthropogenic sources of mortality on impacted populations. Although population viability analysis (PVA) has previously been used to evaluate the impact of human-caused mortality, MAMSE represents a novel application of PVA to set acceptable levels of human-caused mortality. It offers a broadly applicable, stochastic addition to the demographic toolbox to evaluate the impact of human-caused mortality on wildlife. This is particularly salient given the broadening spectre of climate change-induced fluctuations in even historically stable ecosystems.