From Detection to Decision: Plant Biosecurity in a Changing Climate

Plant biosecurity is entering a new era characterized by increasing uncertainty, driven in large part by climate change and global trade, leading to accelerated movement of pests and pathogens. While significant advances have been made in diagnostic technologies, including molecular, genomic, and field-deployable tools, a significant challenge remains: translating these advances into timely, evidence-based biosecurity decisions. Early detection of a pest is only the first step. The real challenge of diagnostics lies in their ability to inform rapid and appropriate responses under conditions of uncertainty.This Research Topic aims to bring together interdisciplinary contributions that explore how diagnostic innovations can be effectively integrated into plant biosecurity and plant health management frameworks in a changing climate.We welcome studies that address the interface between diagnostic science, epidemiological modelling, surveillance, machine learning, artificial intelligence, data science, operational response, and that consider the increasing complexity of risk assessment under dynamic pest and pathogen landscapes.We invite original research, reviews, and perspective articles covering, but not limited to:• Advances in plant diagnostic technologies (e.g. Raman spectroscopy, portable assays, genomics, mass spectrometry, remote sensing) for detection and surveillance of plant pests and pathogens;• Decision-support systems and frameworks, including artificial intelligence and machine learning approaches, for translating diagnostic and surveillance data into biosecurity and plant health responses;• Risk assessment, predictive analytics, uncertainty quantification, and threshold setting under climate-driven uncertainty;• Surveillance strategies for emerging and shifting pest and pathogen distributions and their integration into early warning and response systems;• Case studies of biosecurity responses to incursions, including lessons learned• Integration of predictive modelling, machine learning, artificial intelligence, digital decision-support tools, and early warning systems into plant health decision-making;• Integration and analysis of multi-source datasets (e.g. genomic, phenotypic, environmental, remote sensing, and surveillance data) to support biosecurity risk assessment and decision-making;• Validation, harmonization and integration of diagnostic protocols into international plant health frameworks.By focusing on the critical transition from detection to decision-making, this Research Topic addresses a significant gap in current plant biosecurity and plant health research and practice. The Topic emphasizes how diagnostic information can be translated into surveillance, forecasting, risk assessment, predictive analytics, and response actions that support plant health under increasingly dynamic environmental conditions. We also seek contributions demonstrating how artificial intelligence, machine learning, and data-driven decision-support can strengthen plant biosecurity systems.The collection will be of interest to plant pathologists, entomologists, epidemiologists, diagnosticians, bioinformaticians, data scientists, and crop protection practitioners, as well as researchers developing digital and computational solutions for plant biosecurity worldwide.Topic editor Dr Delphine Vincent is an independent AI, data science, and bioinformatics consultant providing private research and analytical services to academic and industry clients. Topic editors: Maryam Rafiqi, Delphine Vincent Source: https://www.frontiersin.org/research-topics/84427/from-detection-to-decision-plant-biosecurity-in-a-changing-climate