eDNA to Ecosystem Decisions: Molecular Biodiversity Monitoring Integrated with Forecast Models

Environmental DNA (eDNA) and metabarcoding are transforming the field of biodiversity monitoring, enabling scientists to infer species presence, community composition, and ecological patterns from trace genetic material in environmental samples. These molecular approaches provide high sensitivity and broad taxonomic coverage, often outperforming conventional survey methods in detecting elusive, rare, invasive, or cryptic species across terrestrial, freshwater, and marine ecosystems. Recent advances in sequencing and bioinformatics have expanded their potential for real-time biodiversity assessment. However, key challenges persist in connecting raw molecular detections to ecological meaning, specifically, in translating eDNA data into measures of species abundance, functional diversity, and ecosystem processes. Current studies increasingly highlight both the power and the limitations of eDNA signals in capturing ecological complexity, revealing an urgent need for integrative frameworks that bridge molecular ecology, ecological modeling, and environmental management.This Research Topic aims to promote interdisciplinary efforts that transform eDNA and metabarcoding data into actionable ecological knowledge. It seeks to support the development of analytical approaches, theoretical frameworks, and applied models that move from detection toward prediction and decision-making. Central questions include: How can eDNA data be scaled from local detections to ecosystem-level insights? What modeling approaches best integrate molecular data with environmental and ecological predictors? And how can these integrative methods enhance biodiversity forecasting and inform proactive conservation strategies under rapid global change? The ultimate goal is to facilitate more robust, predictive, and operational uses of molecular data for ecological understanding and environmental governance.This Research Topic is global in scope but limited to studies that bridge molecular detection with ecological interpretation, modeling, and management applications. It encourages contributions spanning theory, method development, and real-world implementation.To gather further insights into integrative biodiversity monitoring and modeling, we welcome articles addressing, but not limited to, the following themes:- Methodological advancements in eDNA and metabarcoding for biodiversity assessment, including sampling design, marker selection, and bioinformatics improvement.- Integration of molecular biodiversity data with ecological, statistical, and machine learning models to enhance prediction and interpretability.- Development of early warning systems for detecting invasive species, species decline, and ecosystem regime shifts using eDNA time-series data.- Applications of eDNA-informed models in conservation planning, ecosystem restoration, and policy development.- Standardization, validation, and scalability of eDNA-modeling frameworks across ecosystems and regions.- Case studies linking eDNA-based monitoring with decision-making and management outcomes.- Design and deployment of decision-support tools that operationalize molecular data for managers, conservationists, and policymakers. Topic editors: Minakshi Rajput, Dr. Monika Singh, SUDRIPTA DAS, SUDHANSHU MISHRA Source: https://www.frontiersin.org/research-topics/80863/edna-to-ecosystem-decisions-molecular-biodiversity-monitoring-integrated-with-forecast-models