Projecting long‐term impacts of a mortality event on vertebrates: incorporating stochasticity in population assessment

Abstract Wildlife managers must be able to assess the long‐term, population‐wide impacts of mortality events on long lived vertebrates, taking into account the stochastic nature of population fluctuations. Here, we present a case study of the potential impacts on Western gulls ( Larus occidentalis ) of a single, non‐target mortality event, potentially resulting from exposure to rodenticide directed at eradicating house mice ( Mus musculus ) on the Farallon Islands National Wildlife Refuge. Firstly, we conducted a population viability analysis based on over 25 yr of Farallon Western gull demographic data to model future population trends under varying environmental conditions. Future population trends for Farallon Western gulls, independent of any potential mouse eradication‐related mortality, depend on the frequency of years with near‐failure in reproductive success, as was observed in 2009, 2010, and 2011. We modeled population trends under three environmental scenarios defined by the probability of near‐failure in future breeding: optimistic (probability of near‐failure = 0.0), realistic (probability = 0.115), or pessimistic (probability = 0.25). Secondly, we determined the maximum level of additional mortality, C , that would result in a population outcome distribution that cannot be effectively distinguished from a no additional mortality scenario after 20 yr (defined as 95% overlap in the two frequency distributions). We determined the threshold of detection to be an additional mortality of 3.3% beyond normally observed levels under the realistic scenario, 2.8% under the optimistic scenario, and 4.2% under the pessimistic scenario. Results demonstrate that the greater the background stochasticity, the greater C must be to be able to discriminate a long‐term effect of the mortality event against the backdrop of environmental variability. We demonstrate that incorporation of stochasticity is critical for evaluating one‐time mortality events given the high degree of variability characterizing many ecosystems; deterministic projections alone may provide poor guidance. While the need to account for stochasticity in Population Viability Analysis models is well established, this study is innovative in addressing how to evaluate a future or retrospective one‐time mortality event in the context of stochasticity. Our approach can help resource managers' plan for both best‐case and worst‐case scenarios when evaluating impacts of mortality events.