Data from: Testing multiple substrates for terrestrial biodiversity monitoring using environmental DNA (eDNA) metabarcoding
Cite this dataset
van der Heyde, Mieke E et al. (2020). Data from: Testing multiple substrates for terrestrial biodiversity monitoring using environmental DNA (eDNA) metabarcoding [Dataset]. Dryad. https://doi.org/10.5061/dryad.38100f6
Biological surveys based on visual identification of the biota are challenging, expensive, and time consuming, yet crucial for effective biomonitoring. DNA metabarcoding is a rapidly developing technology that can also facilitate biological surveys. This method involves the use of next generation sequencing technology to determine the community composition of a sample. However, it is uncertain as to what biological substrate should be the primary focus of metabarcoding surveys. This study aims to test multiple sample substrates (soil, scat, plant material and bulk arthropods) to determine what organisms can be detected from each and where they overlap. Samples (n = 200) were collected in the Pilbara (hot desert climate) and Swan Coastal Plain (hot mediterranean climate) regions of Western Australia. Soil samples yielded little plant or animal DNA, especially in the Pilbara, likely due to conditions not conducive to long-term preservation. In contrast, scat samples contained the highest overall diversity with 131 plant, vertebrate, and invertebrate Families detected. Invertebrate and plant sequences were detected in the plant (86 Families), pitfall (127 Families), and vane trap (126 Families) samples. In total 278 Families were recovered from the survey, 217 in the Swan Coastal Plain and 156 in the Pilbara. Aside from soil, 22-43% of the Families detected were unique to the particular substrate and community composition varied significantly between substrates. These results demonstrate the importance of selecting appropriate metabarcoding substrates when undertaking terrestrial surveys. If the aim is to broadly capture all biota then multiple substrates will be required.
Australian Research Council, Award: ICI150100041