ABSTRACT
BACKGROUND: The processes that shape microbial biogeography are not well understood, and concepts that apply to macroorganisms, like dispersal barriers, may not affect microorganisms in the same predictable ways. To better understand how known macro-scale biogeographic processes can be applied at micro-scales, we examined seagrass associated microbiota on either side of Wallace's line to determine the influence of this cryptic dispersal boundary on the community structure of microorganisms. Communities were examined from twelve locations throughout Indonesia on either side of this theoretical line. RESULTS: We found significant differences in microbial community structure on either side of this boundary (R2 = 0.09; P = 0.001), and identified seven microbial genera as differentially abundant on either side of the line, six of these were more abundant in the West, with the other more strongly associated with the East. Genera found to be differentially abundant had significantly smaller minimum cell dimensions (GLM: t923 = 59.50, P < 0.001) than the overall community. CONCLUSION: Despite the assumed excellent dispersal ability of microbes, we were able to detect significant differences in community structure on either side of this cryptic biogeographic boundary. Samples from the two closest islands on opposite sides of the line, Bali and Komodo, were more different from each other than either was to its most distant island on the same side. We suggest that limited dispersal across this barrier coupled with habitat differences are primarily responsible for the patterns observed. The cryptic processes that drive macroorganism community divergence across this region may also play a role in the bigeographic patterns of microbiota.
ABSTRACT
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. Using modern ecological models and community science, we integrate environmental DNA and Earth observations to produce a time snapshot of regional biodiversity patterns and provide multi-scalar community-level characterization. We collected 278 samples in spring 2017 from coastal, shrub, and lowland forest sites in California, a complex ecosystem and biodiversity hotspot. We recovered 16,118 taxonomic entries from eDNA analyses and compiled associated traditional observations and environmental data to assess how well they predicted alpha, beta, and zeta diversity. We found that local habitat classification was diagnostic of community composition and distinct communities and organisms in different kingdoms are predicted by different environmental variables. Nonetheless, gradient forest models of 915 families recovered by eDNA analysis and using BIOCLIM variables, Sentinel-2 satellite data, human impact, and topographical features as predictors, explained 35% of the variance in community turnover. Elevation, sand percentage, and photosynthetic activities (NDVI32) were the top predictors. In addition to this signal of environmental filtering, we found a positive relationship between environmentally predicted families and their numbers of biotic interactions, suggesting environmental change could have a disproportionate effect on community networks. Together, these analyses show that coupling eDNA with environmental predictors including remote sensing data has capacity to test proposed Essential Biodiversity Variables and create new landscape biodiversity baselines that span the tree of life.
