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Abstract Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways towards mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future. 

Environmental DNA (eDNA) metabarcoding is a promising method to monitor species and community diversity that is rapid, affordable and non‐invasive. The longstanding needs of the eDNA community are modular informatics tools, comprehensive and customizable reference databases, flexibility across high‐throughput sequencing platforms, fast multilocus metabarcode processing and accurate taxonomic assignment. Improvements in bioinformatics tools make addressing each of these demands within a single toolkit a reality.

The new modular metabarcode sequence toolkitAnacapa(https://github.com/limey-bean/Anacapa/) addresses the above needs, allowing users to build comprehensive reference databases and assign taxonomy to raw multilocus metabarcode sequence data. A novel aspect ofAnacapais its database building module, “Creating Reference libraries Using eXisting tools” (CRUX), which generates comprehensive reference databases for specific user‐defined metabarcoding loci. TheQuality Control and ASV Parsingmodule sorts and processes multiple metabarcoding loci and processes merged, unmerged and unpaired reads maximizing recovered diversity.DADA2then detects amplicon sequence variants (ASVs) and theAnacapa Classifiermodule aligns these ASVs toCRUX‐generated reference databases usingBowtie2. Lastly, taxonomy is assigned to ASVs with confidence scores using a Bayesian Lowest Common Ancestor (BLCA) method. TheAnacapa Toolkitalso includes anrpackage,ranacapa, for automated results exploration through standard biodiversity statistical analysis.

Benchmarking tests verify that theAnacapa Toolkiteffectively and efficiently generates comprehensive reference databases that capture taxonomic diversity, and can assign taxonomy to both MiSeq and HiSeq‐length sequence data. We demonstrate the value of theAnacapa Toolkitin assigning taxonomy to seawater eDNA samples collected in southern California.

TheAnacapa Toolkitimproves the functionality of eDNA and streamlines biodiversity assessment and management by generating metabarcode specific databases, processing multilocus data, retaining a larger proportion of sequencing reads and expanding non‐traditional eDNA targets. All the components of theAnacapa Toolkitare open and available in a virtual container to ease installation.

 

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.