Landscape analyses using eDNA metabarcoding and Earth observation predict community biodiversity in California

Unique ecosystems globally are under threat from ongoing anthropogenic environmental change. Effective conservation management requires more thorough biodiversity surveys that can reveal system-level patterns and that can be applied rapidly across space and time. Environmental DNA, community science and remote sensing have each been offered as methods to reduce the discrepancy between the magnitude of change and historical approaches to measure it. Here, we integrate these approaches to evaluate California’s biodiversity and provide critical community-level characterization. We collected 278 samples in Spring 2017 across coastal, shrub and lowland forest sites, and analyzed their eDNA patterns together with environmental variables to produce statewide biodiversity-based maps. We recovered 16,118 taxonomic entries and identified environmental variables that predict alpha, beta, and zeta diversity. Local habitat classification was diagnostic of community composition, illuminating a characteristic of biodiversity hotspots. Using gradient forest models, environmental variables predicted 35% of the variance in eDNA patterns at the family level, with elevation, sand percentage, and greenness (NDVI32) as the top predictors. In addition to this indication of substantial environmental filtering, we also found a positive relationship between environmentally predicted families and their numbers of biotic interactions. In aggregate, these analyses showed that strong eDNA community-environment correlation is a general characteristic of temperate ecosystems, and may explain why communities easily destabilize under disturbances. Integrating eDNA into biodiversity mapping has promise to produce large scale, high resolution assessments that promote a more comprehensive and predictive understanding of the factors that influence biodiversity and enhance its maintenance.