title: Determining the causes behind the collapse of a small pelagic fishery using Bayesian population modeling.
abstract: Small pelagic fish species present complex dynamics that challenge population biologists and prevent effective management. Huge fluctuations in abundance have traditionally been associated with external environmental forcing on recruitment, exempting other processes from contributing to fisheries collapse. On the other hand, theory predicts that density dependence and overexploitation can increase the likelihood of population oscillations. Here, we combined nonlinear population modeling with Bayesian analysis to examine the importance of different regulatory mechanisms on the collapse of European anchovy (Engraulis encrasicolus) in the Bay of Biscay. The approach relied on detailed population data and in a careful characterization of changes in the environment experienced by anchovy early stages based mainly on satellite remote sensing. Alternative hypotheses about external forcing on recruitment determined prediction skill and provided alternative interpretations of the causes behind the collapse. Density dependence was weak and unable to generate huge oscillations. Instead, models considering changes in phytoplankton phenology or in larval drift presented the best prediction skill. Nevertheless, an extensive surrogate analysis showed that environmental fluctuations alone barely explain anchovy collapse without considering the impact of fishing. Our results highlight the effectiveness of a Bayesian approach to analyze the dynamics and collapse of managed populations.
authors.forename: Fernando G
authors.initials: FG
authors.lastname: Taboadai
authors.forename: Ricardo
authors.initials: R
authors.lastname: Anadón
mesh_terms: D000818:Animals; D001267:Atlantic Ocean; D001499:Bayes Theorem; D061006:Bays; D018533:Biomass; D057231:Climate Change; D003198:Computer Simulation; D004784:Environmental Monitoring; D005398:Fisheries; D005399:Fishes / Q000502:physiology*; D008954:Models, Biological; D011157:Population Dynamics; D012621:Seasons; D013997:Time Factors