A Study of Feasibility and Detection Sensitivity of Environmental DNA (eDNA) for Fish Biodiversity Monitoring and Stock Assessment in the Black Sea

Petya Pavlova Ivanova^1*Ivelina Yordanova Zlateva²Nina Dzhembekova²Ivan Popov²Violin Stoyanov Raykov²Dimitar Petkov Dimitrov²Svetlana Mihova²Yordan Raev²Kremena Stefanova²

• ¹Other
• ²Institute Of Oceanology, Bulgarian Academy of Sciences, Varna, Bulgaria

Human activities and their effects on ecosystems are threatening biodiversity and causing a decline in species, population, and genetic diversity. Environmental DNA (eDNA) metabarcoding represents a powerful instrument to assess marine biodiversity, enabling an efficient, cost-effective, and non-invasive approach. eDNA metabarcoding technology has significantly progressed in recent years and has been widely employed to assess marine fish populations. This study assessed its potential, detection sensitivity, and effectiveness for an initial evaluation of fish biodiversity in the Black Sea. The efficacy of eDNA was evaluated by contrasting metabarcoding results with trawling data obtained from two sampling campaigns (summer and autumn 2022) across 16 selected locations along the Bulgarian Black Sea coast. Mitochondrial genes 12S and 16S were utilized as standard markers for metabarcoding given their high specificity and sensitivity in amplifying fish DNA, and the availability of large reference databases. Metabarcoding, employing the 12S mitochondrial gene with MiFish-U primers, exhibited superior sensitivity in identifying a broader array of fish species, highlighting its efficacy in detecting eDNA traces of rare and migratory species. During the autumn survey, eDNA analysis identified 23 fish species, compared to 15 species recorded through trawl sampling. In the summer expedition, 12 species were detected using molecular methods, while trawl surveys recorded 9 species. A multi-model analytical approach utilizing Bayesian regression and Generalized Additive Models (GAMs) was implemented to assess the relationships between eDNA 12S metabarcoding data and abundance indices obtained from catch-per-unit-effort (CPUE). The method addresses significant challenges in predicting abundance from eDNA, including scarce, noisy datasets, excessive zeros, and nonlinear predictor-response relationships. Both frameworks reliably detected biologically meaningful associations between eDNA signal strength, environmental gradients, and trawl-derived abundance. The Bayesian framework provided robust uncertainty quantification and reliable inference with few samples, whereas the GAM framework effectively captured nonlinear environmental and spatial effects. The results indicate that eDNA data can replicate patterns observed in conventional trawl surveys. This supports the assumption that eDNA data may serve as a low-impact, scalable method for monitoring fish populations. Model uncertainty informs the need for more sampling or a redesign of covariates to improve the accuracy of eDNA-based abundance predictions.