Stock-environment-recruitment models for North Atlantic albacore (Thunnus alalunga)

Abstract

Different stock–recruitment models were fitted to North Atlantic albacore (Thunnus alalunga) recruitment and spawning stock biomass data. A classical density dependence hypothesis, a recent environmental‐dependence hypothesis and a combination of both were considered. For the latter case, four stock–environment–recruitment models were used: Ricker, Beverton‐Holt, Deriso's General Model (modified to take into account environmental effects) and conditioned Neural Networks. Cross‐validation analysis showed that the modified Deriso model had the best predictive capability. It detected an inverse effect of the North Atlantic Oscillation (NAO) on recruitment, a Ricker‐type behaviour with density dependent overcompensation when environmental conditions are unfavourable and a Beverton–Holt‐type behaviour towards an asymptotic recruitment carrying capacity with favourable environmental conditions. The Neural Network model also detected that under favourable environmental conditions high spawning stock biomass does not necessarily have a depensatory effect on recruitment. Moreover, they suggest that under extremely favourable environmental conditions, albacore recruitment could increase well above the asymptotic carrying capacity predicted by Beverton–Holt‐type models. However, the general decrease in spawning stock biomass in recent years and increasing NAO trends suggest that there is low probability of exceptionally large recruitment in the future and instead there is a danger of recruitment overfishing.