Dealing with data-limited from recreational fisheries to estimate abundances of alabacore, Thunnus alalunga

Abstract

[eng] Fish stocks assessment can be highly benefited by the addition of parameters estimated from recreational fishery-data. However, fisheries regulations can be a potential bias in order to estimate key parameters such abundances. For example, the maximum catch quote can underestimate the actual potential harvest of vessels, because it is not always possible to ensure that vessels exceed this limit, replace their captures by other larger individuals or simply stop fishing. A particular case that includes this problem is the tuna fishing tournaments held annually in the Balearic Islands. Precisely, this works explores the potential usefulness of these tournaments data-limited to estimate 19-years of abundances of albacore (Thunnus alalunga) dealing with the catch limit as the main handicap of the regulations. On one hand, a Bayesian prediction model was developed to estimate new numbers of albacores caught for all the boats which reached the catch limit. On the other hand, albacores caught (new estimations included) were integrated in a Bayesian State-Space Model to estimate relative abundances of albacore and population growth-rates. The results suggested that albacore abundance remained stable along the 19-years period studied. The across-years average growth-rate was 1.08 (95% of CI: from 0.77 to 1.48), with small variations of the annual growth-rate around 1. Neither environmental factor used in this model seemed to be related to growth-rate (NAO and anomaly of SST), but interestingly, wave height played a key role on catchability giving an optimal threshold for fishing albacore. The present work provides a novel and replicable tool to estimate abundances from limited fishery data, with potential repercussion for commercial and recreational fisheries management.