Data from: 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 the 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.