Comparison of environmental DNA metabarcoding and bottom trawling for detecting seasonal fish communities and habitat preference in a highly disturbed estuary

Abstract

Environmental DNA (eDNA) metabarcoding has been used to study fish diversity in various aquatic ecosystems. However, studies on fish community structure in estuarine ecosystems have not been well corroborated by eDNA metabarcoding, and comparisons with bottom trawling are lacking. We used eDNA sequencing of mitochondrial 12S rRNA gene to investigate the fish species composition and relative abundance, community temporal and spatial variations, and community-environment relationship of the Pearl River Estuary during spring and autumn. Then, we compared these results with those obtained by bottom trawling. Results showed that eDNA metabarcoding detected more species (214 vs 90), genera (148 vs 69), families (67 vs 38), and a significantly greater number of species at each station. Results of nonmetric multidimensional scaling and permutational multivariate analysis of variance based on the Bray-Curtis dissimilarity index indicated that eDNA metabarcoding detected significant differences in fish communities between spring and autumn, which was similar to the bottom trawling results. eDNA metabarcoding revealed that the fish community differences increased with spatial distance among stations. However, when we compared results of the two methods using principal coordinates analysis, we observed discordance in the fish community differences among sites. eDNA metabarcoding may provide new insights into and a more detailed and comprehensive understanding of estuarine ecosystems. Additionally, eDNA metabarcoding revealed that salinity and temperature were closely linked to fish community composition in spring, and salinity and dissolved oxygen were closely associated with fish community composition in autumn. In conclusion, eDNA metabarcoding may represent an important supplementary method, or even replace current methods, to monitor and assess temporalandspatialvariation of fish communities and infer the community-environment relationship, especially in estuarine ecosystems, which are difficult to sample using traditional methods.