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

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.

Flip it and reverse it: Reasonable changes in designated controls can flip synergisms to antagonisms.

Ecological systems are subjected to multiple stressors that can interact in complex ways resulting in "ecological surprises". We examine the pivotal role of 'control' assignment in the categorization of stressors into five classes: additive, +synergistic, -synergistic, +antagonistic, and -antagonistic. We demonstrate if an alternate treatment can reasonably be considered the experimental control, nonlinear interaction classifications change, both in sign (+/-) and in direction (synergistic/antagonistic). Further, switching of interaction classifications is not predictable as changing control can result in multiple possible alternate nonlinear classifications. To explore the magnitude of this problem, we evaluate publications gathered for a recent meta-analysis to 1) explore rationales for choice of controls and 2) quantify how frequently it is reasonable to reassign the control. We found controls were designated with a variety of implicit and explicit justifications, with two overall rationales: 1) controls based on 'natural' conditions (historic, current, or future); 2) controls based on direction of impact, such that stressors always have negative impacts. We reasoned that control re-assignment was justified if an alternate treatment met one of these rationales. Of the 844 interactions classified in the meta-analysis, we determined >95% could be reassigned. Based on these findings, we recommend a new approach to meta-analyses, where the 'control' is strictly and consistently defined by the authors of the meta-analysis. These controls should be based on their broader question, rather than following the common practice of defaulting to controls assigned by the authors of each study, as we found these rationales vary broadly based on the specific questions of each study. Consistent control designation within the ecological or toxicological framework of each meta-analysis may provide deeper and more consistent insight into the nature of interactive effects between multiple stressors. Gaining this insight is crucial because stressor interactions are certain to increase in the Anthropocene.