Eco-evolutionary drivers of survey bias and consequences for fisheries stock assessment

Abstract The uncertainty of survey-based observations of managed marine populations are the result of many factors: biological, ecological, genetic, and observational, all of which can exhibit spatio-temporal variation. While much effort has been placed on optimizing the design of multi-species surveys, there is less understanding of how ecosystem change and evolutionary processes may combine to bias survey observations and subsequently estimated quantities from age-structured stock assessment models. We used a multispecies individual-based ecosystem model that accounts for short-term phenotypic plasticity, physiology, and ecology under a plausible emissions scenario to simulate the spatially explicit future of exploited populations across several trophic levels over a period of 70 years. The survey biomass and age composition data observed from these simulations, which included phenotypic and evolutionary life history trait (LHT) changes to climate variables and fishing, were passed to a state-space stock assessment model to evaluate the degree to which these forcings induce bias in estimated biomass via distortions in the survey data. LHT changes had pronounced effects on population trajectories, though the observation error in survey biomass varied more strongly with spatial heterogeneity than climate or evolutionary scenarios. While biased survey data led to less precise estimates of biomass, we found unexpected patterns in how stock assessments responded to uncertain survey data. Estimation error varied more strongly across species’ trophic level than across climate scenarios, and appears to be driven by varied sensitivity of life history traits to environmental variability and evolutionary pressure. These results provide insight into how eco-evolutionary forces introduce bias in scientific advice for fisheries management, and how their impact might vary across species’ trophic levels and exploitation histories. This study encourages survey optimization efforts to consider the life-history and trophic ecology of surveyed species when optimizing spatial survey designs, and provides a framework for evaluating the characteristics of assessment models for which incorporating ecological information will be beneficial to reducing bias.