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

Area of Science:

• Ecology
• Environmental Science
• Conservation Biology

Background:

• Global ecosystems face threats from anthropogenic environmental change.
• Effective conservation requires rapid, system-level biodiversity surveys across space and time.

Purpose of the Study:

• To integrate environmental DNA (eDNA) and Earth observations for regional biodiversity patterns.
• To provide multi-scalar community-level characterization using ecological models and community science.

Main Methods:

• Collected 278 samples from diverse California sites (coastal, shrub, lowland forest).
• Utilized eDNA analysis, traditional observations, and environmental data (BIOCLIM, Sentinel-2, human impact, topography).
• Applied gradient forest models to predict community turnover and diversity metrics (alpha, beta, zeta).

Main Results:

• Recovered 16,118 taxonomic entries; habitat classification predicted community composition.
• Gradient forest models explained 35% of community turnover variance, with elevation, sand percentage, and NDVI32 as key predictors.
• Found a positive link between predicted families and biotic interactions, indicating potential disproportionate effects of environmental change.

Conclusions:

• Coupling eDNA with remote sensing and environmental data enables testing Essential Biodiversity Variables.
• This integrated approach can establish new landscape biodiversity baselines across the tree of life.
• Findings highlight the capacity for rapid, comprehensive biodiversity assessment vital for conservation.