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Beginning in 2013, sea stars throughout the Eastern North Pacific were decimated by wasting disease, also known as “asteroid idiopathic wasting syndrome” (AIWS) due to its elusive aetiology. The geographic extent and taxonomic scale of AIWS meant events leading up to the outbreak were heterogeneous, multifaceted, and oftentimes unobserved; progression from morbidity to death was rapid, leaving few tell‐tale symptoms. Here, we take a forensic genomic approach to discover candidate genes that may help explain sea star wasting syndrome. We report the first genome and annotation forPisaster ochraceus, along with differential gene expression (DGE) analyses in four size classes, three tissue types, and in symptomatic and asymptomatic individuals. We integrate nucleotide polymorphisms associated with survivors of the wasting disease outbreak, DGE associated with temperature treatments inP. ochraceus, and DGE associated with wasting in another asteroidPycnopodia helianthoides. InP. ochraceus, we found DGE across all tissues, among size classes, and between asymptomatic and symptomatic individuals; the strongest wasting‐associated DGE signal was in pyloric caecum. We also found previously identified outlier loci co‐occur with differentially expressed genes. In cross‐species comparisons of symptomatic and asymptomatic individuals, consistent responses distinguish genes associated with invertebrate innate immunity and chemical defence, consistent with context‐dependent stress responses, defensive apoptosis, and tissue degradation. Our analyses thus highlight genomic constituents that may link suspected environmental drivers (elevated temperature) with intrinsic differences among individuals (age/size, alleles associated with susceptibility) that elicit organismal responses (e.g., coelomocyte proliferation) and manifest as sea star wasting mass mortality.

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.