The combined effects of multiple stressors in an endangered, long-lived species: Lessons learned and ways forward

Exploring solutions to expanding industrial activities and climate change requires assessments of the combined effects of multiple stressors on wildlife populations. We present a spatially explicit state-space model for the health, survival, reproduction, and somatic growth of individuals in a long-lived, wide-ranging species. The model is applied to critically endangered North Atlantic right whales (Eubalaena glacialis) to investigate the combined effects of three primary stressors affecting the species' viability: entanglements in fishing gear, vessel strikes, and prey availability. We estimate exposure to these stressors in space and time and assess how their effects may combine in the pathway from exposure to vital rates. Results suggest that changes in whale distribution after 2010 led to increased entanglement risk. Poorer prey conditions were associated with an increased effect of carrying fishing gear, but, overall, results on combined effects were not conclusive and depended on model formulation. We also incorporated the estimated effects of stressors into a population viability analysis to explore alternative scenarios of stressor reduction. This integrated analysis highlighted the importance of the declining trend in maximum body length and its effect on reproduction, in addition to the documented impact of entanglements on survival. Model development and application elucidated critical data needs and the influence of underlying mechanistic assumptions. Specifically, models for the combined effects of stressors hinge on the availability of extended longitudinal measurements of individual health and life history outcomes, extensive datasets on the spatiotemporal distribution of stressors, and information on individual space use affecting rates of exposure to stressors. Lessons from this data-rich case study will support the generalization of the modeling approach to other long-lived species where measuring the population-level consequences of multiple stressors directly is unfeasible.