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Abstract Wildlife diseases, such as the sea star wasting (SSW) epizootic that outbroke in the mid-2010s, appear to be associated with acute and/or chronic abiotic environmental change; dissociating the effects of different drivers can be difficult. The sunflower sea star, Pycnopodia helianthoides, was the species most severely impacted during the SSW outbreak, which overlapped with periods of anomalous atmospheric and oceanographic conditions, and there is not yet a consensus on the cause(s). Genomic data may reveal underlying molecular signatures that implicate a subset of factors and, thus, clarify past events while also setting the scene for effective restoration efforts. To advance this goal, we used Pacific Biosciences HiFi long sequencing reads and Dovetail Omni-C proximity reads to generate a highly contiguous genome assembly that was then annotated using RNA-seq-informed gene prediction. The genome assembly is 484 Mb long, with contig N50 of 1.9 Mb, scaffold N50 of 21.8 Mb, BUSCO completeness score of 96.1%, and 22 major scaffolds consistent with prior evidence that sea star genomes comprise 22 autosomes. These statistics generally fall between those of other recently assembled chromosome-scale assemblies for two species in the distantly related asteroid genus Pisaster. These novel genomic resources for P. helianthoides will underwrite population genomic, comparative genomic, and phylogenomic analyses—as well as their integration across scales—of SSW and environmental stressors. 

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.