Invasive alien species (IAS) are one of the most severe threats to biodiversity and are the subject of varying degrees of surveillance activity. Predictive early warning systems (EWS), incorporating automated surveillance of relevant dataflows, warning generation and dissemination to decision makers are a key target for developing effective management around IAS, alongside more conventional early detection and horizon scanning technologies. Sophisticated modelling frameworks including the definition of the ‘risky’ species pool, and pathway analysis at the macro and micro-scale are increasingly available to support decision making and to help prioritise risks from different regions and/or taxa. The main challenges in constructing such frameworks, to be applied to border inspections, are (i) the lack of standardisation and integration of the associated complex digital data environments and (ii) effective integration into the decision making process, ensuring that risk information is disseminated in an actionable way to frontline surveillance staff and other decision makers. To truly achieve early warning in biosecurity requires close collaboration between developers and end-users to ensure that generated warnings are duly considered by decision makers, reflect best practice, scientific understanding and the working environment facing frontline actors. Progress towards this goal will rely on openness and mutual understanding of the role of EWS in IAS risk management, as much as on developments in the underlying technologies for surveillance and modelling procedures.
Early warning systems in biosecurity; translating risk into action in predictive systems for invasive alien species
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James Rainford, Andrew Crowe, Glyn Jones, Femke van den Berg; Early warning systems in biosecurity; translating risk into action in predictive systems for invasive alien species. Emerg Top Life Sci ETLS20200056. doi: https://doi.org/10.1042/ETLS20200056
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