Combining sampling gear to optimally inventory species highlights the efficiency of eDNA metabarcoding
Cite this dataset
Andres, Kara (2022). Combining sampling gear to optimally inventory species highlights the efficiency of eDNA metabarcoding [Dataset]. Dryad. https://doi.org/10.5061/dryad.1c59zw3zk
Biodiversity surveys may require the use of multiple types of sampling gear to maximize the efficiency of species detections, yet few studies have investigated how to optimally distribute effort among gear. In this study, we conducted eDNA metabarcoding and capture-based sampling surveys (electrofishing, fyke netting, gillnetting, and seining) to sample fish species richness in a large northern temperate lake. We evaluated the success of the sampling methods individually and in combination to determine the allocation of effort and cost across sampling gear that provides the optimal approach for lake-wide species inventories. We found that eDNA metabarcoding detected more species than any other sampling method, including 11 species that were not detected with any capture-based approach. Optimal gear combination analyses revealed that detected species richness is maximized when most of the effort or budget is allocated to eDNA metabarcoding, with smaller allocations to seining and fyke netting. eDNA metabarcoding and capture sampling gear showed similar patterns of spatial heterogeneity in the fish community across habitat types, with pelagic samples forming a group that was distinct from nearshore samples. Our results indicate that eDNA metabarcoding is a rapid and cost-efficient tool for biodiversity monitoring and that assessing the complementarity of multiple sampling types can inform the development of optimal approaches for measuring fish species richness.
See manuscript for a complete description of methods.
All scripts used for bioinformatics, taxonomic assignments, and statistical analyses are available on GitHub: https://github.com/karaandres/Oneida_metabarcoding
National Science Foundation of Sri Lanka
Cooperative Institute for Great Lakes Research