Performance of length-based assessment in predicting small-scale multispecies fishery sustainability.

Autor: Medeiros-Leal, Wendell, Santos, Régis, Peixoto, Ualerson I., Casal-Ribeiro, Morgan, Novoa-Pabon, Ana, Sigler, Michael F., Pinho, Mário
Předmět:
Zdroj: Reviews in Fish Biology & Fisheries; Sep2023, Vol. 33 Issue 3, p819-852, 34p
Abstrakt: Small-scale fisheries play a critical role in food security and contribute to nearly half of reported global fish catches. However, the status of most small-scale fisheries stocks is still poor. In data-limited situations, length-based methods have been widely applied to estimate reference points and to understand stock status. This study applied three different length-based assessment methods (length-based indicators—LBI, length-based spawning potential ratio—LBSPR, and the length-based Bayesian biomass approach—LBB) to predict fisheries stock sustainability in the Azores. Overall, the three methods showed robustness for 15 out of 18 stocks assessed and agreed on their exploitation status. The results showed that 45% of the Azorean stocks were classified as sustainable stocks, 33% possible rebuilding/overfished and 22% overfishing/overfished stock status. Sensitivity analysis showed that biases on the source of initial life-history parameters, especially the asymptotic length (L∞) and the ratio of natural mortality and growth coefficient (M/k), have a stronger influence on the reference points of conservation of mature individuals (LBI), spawning potential ratio and fishing mortality (LBSPR) and the biomass relative to the maximum sustainable yield (LBB). Furthermore, sensitivity analysis indicated that, among the three methods, LBI is more robust. Our findings provide some management recommendations such as (1) catches and effort should be reduced; (2) minimum landing size should be increased; (3) minimum hook size should be increased, to be applied mainly for those stocks classified as possible rebuilding/overfished and overfishing/overfished stock status. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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