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Larval Dispersal Modeling of Pearl Oyster Pinctada margaritifera following Realistic Environmental and Biological Forcing in Ahe Atoll Lagoon
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Edité par CCSD ; Public Library of Science -
International audience. Studying the larval dispersal of bottom-dwelling species is necessary to understand their population dynamics and optimize their management. The black-lip pearl oyster (Pinctada margaritifera) is cultured extensively to produce black pearls, especially in French Polynesia's atoll lagoons. This aquaculture relies on spat collection, a process that can be optimized by understanding which factors influence larval dispersal. Here, we investigate the sensitivity of P. margaritifera larval dispersal kernel to both physical and biological factors in the lagoon of Ahe atoll. Specifically, using a validated 3D larval dispersal model, the variability of lagoon-scale connectivity is investigated against wind forcing, depth and location of larval release, destination location, vertical swimming behavior and pelagic larval duration (PLD) factors. The potential connectivity was spatially weighted according to both the natural and cultivated broodstock densities to provide a realistic view of connectivity. We found that the mean pattern of potential connectivity was driven by the southwest and northeast main barotropic circulation structures, with high retention levels in both. Destination locations, spawning sites and PLD were the main drivers of potential connectivity, explaining respectively 26%, 59% and 5% of the variance. Differences between potential and realistic connectivity showed the significant contribution of the pearl oyster broodstock location to its own dynamics. Realistic connectivity showed larger larval supply in the western destination locations, which are preferentially used by farmers for spat collection. In addition, larval supply in the same sectors was enhanced during summer wind conditions. These results provide new cues to understanding the dynamics of bottom-dwelling populations in atoll lagoons, and show how to take advantage of numerical models for pearl oyster management.
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