Accurate taxonomic identification is foundational for effective species monitoring and management. When visual identifications are infeasible or inaccurate, genetic approaches provide a reliable alternative. However, these approaches are sometimes less viable (e.g., need for near real-time results, remote locations, funding concerns, molecular inexperience). In these situations, CRISPR-based genetic tools can fill an unoccupied niche between real-time, inexpensive, but error-prone visual identification and more expensive or time-consuming, but accurate genetic identification for taxonomic units that are difficult or impossible to visually identify. Herein, we use genomic data to develop CRISPR-based SHERLOCK assays capable of rapidly (<1 h), accurately (94%-98% concordance between phenotypic and genotypic assignments), and sensitively (detects 1-10 DNA copies/reaction) distinguishing ESA-listed Chinook salmon runs (winter- and spring-run) from each other and from unlisted runs (fall- and late fall-run) in California's Central Valley. The assays can be field deployable with minimally invasive mucus swabbing negating the need for DNA extraction (decreasing costs and labour), minimal and inexpensive equipment needs, and minimal training to conduct following assay development. This study provides a powerful genetic approach for a species of conservation concern that benefits from near real-time management decision-making but also serves as a precedent for transforming how conservation scientists and managers view genetic identification going forward. Once developed, CRISPR-based tools can provide accurate, sensitive, and rapid results, potentially without the prohibitive need for expensive specialty equipment or extensive molecular training. Further adoption of this technology will have widespread value for the monitoring and protection of our natural resources.
Keywords: Oncorhynchus tshawytscha; SHERLOCK; San Francisco Bay Delta watershed; fisheries management; genetic technology; threatened and endangered species.
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