Climate effect on the seasonal gonad maturity of three commercial penaeid shrimp species in the Gulf of California

Abstract

Penaeid shrimp represents one of the most economically important fisheries worldwide. In Mexico, they comprise a sequential multispecies fishery with highly variable seasonal and interannual production, presenting its maximum national yield in the Gulf of California (GC) by exploiting the species Litopenaues vannamei, L. stylirostris, and Farfantepenaues californiensis. The yield variability can be related to their reproductive climatic adaptability, measured by the Mexican Fisheries Bureau as the intra and inter-seasonal percentage of mature females (%MF). Currently, shrimp fishery management in Mexico does not explicitly consider the environmental effect within the exploitation strategies for each species, although climatic factors significantly affect this fishery. For this reason, the present study aims to determine a function to estimate the monthly %MF for each commercial penaeid species considering climatic fluctuations. For this objective, the monthly relationship of %MF for each species over 15 years (2001–2015) with the seasonal cycle of the sea surface temperature anomalies (1950–2020) were analyzed and classified by intensity scenarios (normal, warm & cold) in the southern GC, fitting a logistic function based on monthly SST differentials and SST time lags per species. As a result, the model was able to explain 40–60% of the %MF variation per species (p < 0.001), defining the natural seasonal peaks in the summer and declining towards the winter. Under the climate scenarios, the weak-cold anomalies resulted in the maximum %MF (up to +142% in F. californiensis). Conversely, a minimum %MF (−50%) for the three species resulted from moderate to strong warming anomalies. The %MF of the species with the most tropical affinity (L. vannamei and F. californiensis) were the least vulnerable under weak-warm anomalies, while the more temperate species was the most sensible (L. stylirostris). The model significantly predicted the seasonality of %MF per species and suggests an inverse relationship with SST anomalies, which may have important implications for resource management under climate change and may support the improvement of stock assessment.