Aim

To test the capacity of eDNA to characterise the spatial and seasonal patterns found within a range of zooplankton communities, and investigate links with concurrent abiotic data collected as part of Australia’s Integrated Marine Observing System (IMOS) programme.

Location

Samples were sourced seasonally for three years from nine Pan-Australian marine sites (n=90).

Methods

Here we apply a multi-assay metabarcoding approach to environmental DNA extracted from a rare long-term collection of bulk plankton samples. Six assays (targeting both the 16SrRNA and COI genes) were used to amplify and sequence the zooplankton diversity found within each sample. The data generated from each assay was filtered and clustered into OTUs prior to analysis. Abiotic IMOS data collected alongside the plankton collection enabled us to explore the physical and chemical drivers of community composition.

Results

The eDNA metabarcoding generated over 25 million sequences, identified in excess of 500 distinct taxa and detected clear spatial differences in marine metazoan communities. We found that site and sea surface temperature are the most consistent predictors of differences between zooplankton communities. We detected endangered and invasive species such as the bryozoan Membranipora membranace and the mollusc Maoricolpus roseus, and seasonal occurrences of species such as humpback whales (Megaptera novaeangliae). We also estimated the number of samples required to ensure robust marine eDNA metabarcoding biomonitoring programs into the future.

Main Conclusion

Our results demonstrate the ability of eDNA to capture and map zooplankton community changes in response to seasonal and spatial stressors and provide vital evidence to environmental stakeholders. We confirm that eDNA offers a practical opportunity for an ecosystem-wide approach to the long-term biomonitoring and understanding marine biomes where a morphological approach is not feasible.

DNA was extracted from zooplankton samples and sequenced using six different barcoding assays.

The sequences from each assay were sorted into samples, labelled for identification, filtered for quality and uploaded as raw fasta files. A sample key for the sample code used has also been uploaded along with the abiotic data used in this study.