eDNA signals improve local predictions of fish abundances in Mediterranean coastal ecosystems

Predicting marine species distribution and abundance is essential for effective conservation and management. Yet, it remains challenging in data‐limited regions where traditional biodiversity surveys are logistically or financially constrained.

Combining underwater visual census and eDNA fish sampling across the northwestern Mediterranean Sea, we tested a novel modelling framework that uses eDNA metabarcoding sequences to complement socio‐environmental covariates in predicting species‐specific local abundances.

The eDNA‐derived community proxies revealed ecological gradients complementary to visual census data, helping to distinguish sites dominated by coastal demersal fishes versus offshore predators, territorial reef fishes versus mobile dispersers and benthic versus pelagic species.

Using joint Species Distribution Models (jSDMs), within the Hierarchical Modelling of Species Communities (HMSC) framework, we compared the predictive performance of models based solely on socio‐environmental covariates with those that additionally incorporated eDNA‐based information.

Including eDNA information significantly improved model fit for 16 out of 26 species, including the endangered dusky grouper ( Epinephelus marginatus ), and contributed to one‐third of explained variance in species local abundances on average.

Synthesis and applications . This study demonstrates that integrating eDNA metabarcoding data into species distribution models can improve fish abundance predictions, especially for site‐attached and reef‐associated species. This approach provides a scalable and cost‐effective tool for monitoring, impact assessment, spatial planning and adaptive management of marine resources.