“So, where do you come from?” The impact of assumed spatial population structure on estimates of recruitment

Abstract

Understanding population dynamics is essential for achieving sustainable and productive fisheries. However, estimating recruitment in a stock assessment model involves the challenging task of identifying a self-sustaining population, which often includes representing complex geographic structure. A review of several case studies demonstrated that alternative stock assessment models can influence estimates of recruitment. Incorporating spatial population structure and connectivity into stock assessment models changed the perception of recruitment events for a wide diversity of fisheries, but the degree to which estimates were impacted depended on movement rates and relative stock sizes. For multiple population components, estimates of strong recruitment events and the productivity of smaller population units were often more sensitive to connectivity assumptions. Simulation testing, conditioned on these case studies, suggested that accurately accounting for population structure, either in management unit definitions or stock assessment model structure, improved recruitment estimates. An understanding of movement dynamics improved estimation of connected sub-populations. The challenge of representing geographic structure in stock assessment emphasizes the importance of defining self-sustaining management units to justify a unit-stock assumption.