Deciphering Landscape-Scale Plant Cover and Biodiversity From Soil eDNA

Abstract

Biodiversity surveys are critical for detecting environmental change; however, undertaking them at scale and capturing all available diversity through observation is challenging and costly. This study evaluated the potential of soil-extracted eDNA to describe plant communities and compared these findings to traditional, observation-based field surveys. We analyzed 789 soil samples using high-throughput amplicon sequencing and compared DNA-based diversity metrics, indicator taxa, predicted vegetation class, and plant cover in a comparison with co-located field survey data. The results indicated that taxonomically aggregated (genus) eDNA-derived data, while showing slightly reduced Shannon's diversity scores, yielded remarkably similar overall richness and composition estimates. However, the DNA indicator taxa and predictive power for vegetation community classification were also lower overall than those recorded by the field survey. In many cases, plant cover could be inferred from amplicon abundance data with some accuracy despite widely differing scales of sampling—0.25 g crumb of soil versus a 1 m² quadrat. Overall, results from eDNA demonstrated lower sensitivity but were broadly in accordance with traditional surveys, with our findings revealing comparable taxonomic resolution at the genus level. We demonstrate the potential and limitations of a simple molecular method to inform landscape-scale plant biodiversity surveys, a vital tool in the monitoring of land use and environmental change.