An evaluation of vulnerability settings in Ecopath with Ecosim on ecosystem hindcast and forecast skills

Ecological model fitting is a critical step in ensuring that models can reflect historical ecosystem dynamics, allowing for an improved understanding of ecological processes and potentially enhancing the reliability of future projections, despite inherent uncertainties. Vulnerability parameters ( v ), reflecting the predator-prey relationship, play a crucial role in the Ecopath with Ecosim (EwE) model fitting. However, many EwE applications have bypassed tuning the vulnerability parameters due to a lack of historical data, limiting the impacts of vulnerability-unfitted ( v -unfitted) models on evaluating management strategies. In this study, we used model skill metrics, including bias, error, and reliability, to evaluate the hindcast and forecast skills of the v -unfitted models with multiple vulnerability settings. The prediction from vulnerability-fitted ( v -fitted) model was found to have the best fitness and most accurately replicate historical ecosystem dynamics when compared to observed data. In addition, the v -unfitted model with trophic-level-related vulnerability setting ( vTL ) exhibited relatively better hindcast ability among the alternative v settings compared with v -fitted model. In terms of forecast skill under both reduced and increased fishing effort scenarios, only the depletion-related vulnerability setting ( vB ) was found to be robust for v -unfitted models comparing to v -fitted model predictions. We highlight the importance of examining various vulnerability settings, and providing a reference for the application of unfitted models in informing ecosystem-based fisheries management. Our results also reaffirm the critical role of time-series data in applying EwE models. • Vulnerability-fitted models best replicate historical ecosystem dynamics and improve projections. • Trophic-level based vulnerability setting showed better hindcast skills. • Depletion-based vulnerability setting improved forecast reliability.