The role of genetic observatory networks in the detection and forecasting of marine non-indigenous species

Marine biological invasions threaten ocean health, yet management remains reactive rather than proactive. Predictive tools, such as species distribution models, have the potential to provide indications about which areas are particularly likely to be colonised by non-indigenous species, allowing a more proactive management approach. Here we introduce an integrated framework utilising DNA-based monitoring data from genetic observatory networks to identify non-indigenous species for modelling and to independently validate the species distribution models forecasting invasion risk areas across European seas. We modelled habitat suitability for 69 marine non-indigenous species using global occurrence data and an ensemble modelling approach based on 6,555 individual species distribution models built with five different algorithms. Model validation against independent DNA-based detections from observatory networks showed 90% of observations occurred in predicted suitable habitat, confirming robust predictive capacity. Under current conditions, models identify invasion hotspots in the North Sea, North Atlantic, Mediterranean, and Black Sea. Climate projections to 2100 reveal pronounced vulnerability in Arctic and subarctic regions (up to 300% increase in habitat suitability under SSP5-8.5 scenario), while Mediterranean regions show modest change. We further demonstrate how the models can be applied in preventive action by supporting decisions in ballast water management. By coupling standardised and spatiotemporally consistent molecular monitoring with predictive modelling, we provide a scalable approach for marine biosecurity forecasts in a rapidly changing ocean.