Integrating eDNA and acoustic-trawl data to provide small pelagic biomass estimates for fish stock assessment

Abstract Accurate abundance estimates of fisheries resources are essential for sustainable fisheries management. In response to the growing need for developing more accurate and cost-effective biomass estimation methods, the analysis of environmental DNA (eDNA) has recently emerged as an alternative for fish abundance quantification. However, practical approaches for integrating eDNA data into fisheries assessment remain limited. Here, we introduce a Bayesian joint model that combines acoustic-trawl and eDNA data to estimate fish biomass. Utilizing 209 water eDNA samples and 196 acoustic transects, the model was applied to estimate the distribution and abundance of the European anchovy (Engraulis encrasicolus) in the Bay of Biscay. The joint model produced similar estimates to those derived from acoustic observations alone and consistent with known spatial patterns of anchovy, with eDNA data suggesting a broader distribution and potentially higher abundance. This research demonstrates the value of incorporating eDNA data as a complement to acoustic-trawl for stock assessment and illustrates the versatility of joint Bayesian models and their potential application to various species and datasets. Ultimately, our work opens new avenues for more holistic fisheries assessment, underscoring the growing role of eDNA in that context.