Much like a crystal ball, horizon scanning allows us to catch a fleeting glimpse into the future. This is the magic of forward-looking studies, the same which allow businessmen to decide where to invest and meteorologists when to pack an umbrella. For ecologists, this same foresight reveals which biological storms are gathering at the border. In invasion biology, waiting for the rain to fall means you are already soaked. Invasive alien species, alongside climate change and habitat destruction, are primary drivers of global biodiversity loss. Beyond disrupting ecosystems, they impact human health, the economy, and act as subtle vectors for wildlife pathogens. Because managing an invasion after establishment is a massively expensive and often losing battle, science is striving to shift from reactive management to proactive planning. This is precisely where horizon scanning steps in as the answer.
This approach is already successfully applied in sectors like plant and animal health to intercept agricultural pests and livestock diseases before they trigger outbreaks. Notably, this cross-sectoral synergy was a central theme at the 3rd International Scientific Workshop on Horizon Scanning for Plant Health, organised by the European Food Safety Authority (EFSA), where an international panel of experts addressed these shared challenges. The workshop's official summary features a presentation of my own contribution to the field, which serves as the foundational basis for this article. In invasion ecology, this foresight specifically targets the human activities driving species movement. By analyzing trade routes and climate shifts, it flags "door-knocking" species: high-risk disruptors sitting just outside our borders, ready to invade.
The main hurdle in this predictive art is volume. The 2026 exercise faced an initial longlist of nearly 9,000 species, filtered down to some 4,000 by removing clear climate mismatches or species lacking an invasion history. Ultimately, 622 species were debated at the consensus workshop, classifying 57 as "very high risk" and 108 as "high risk." While automated tools like climate matching and occupancy analysis were implemented to streamline the screening, the results highlighted the strict limits of automation. Incomplete baseline data often led to unreliable outputs, and automated models frequently failed to capture complex ecological requirements. This proved a critical lesson: automated tools are useful for flagging risks, but they cannot replace the nuanced, qualitative judgment of expert networks.
Crucially, horizon scanning is a prioritization filter, not a mitigation strategy, just the initial step before a full risk assessment and formal EU listing. The looming invasion of the red imported fire ant (Solenopsis invicta) in Europe exposes the challenge in this pipeline: flagged as a top priority "door-knocker" in 2015 and risk-assessed in 2017, it was officially listed only in 2022. But it was too late. The ant had already established itself in Sicily by 2019, remaining undetected until 2023. This case proves that even the most advanced foresight tools fail without rapid information flow and immediate policy enforcement.
| Fire ant (Solenopsis invicta) Drawing made by Massimiliano Lipperi © European Commission |
The fire ant case demonstrates that without a seamless, rapid handoff from foresight to policy enforcement, the best-scanned horizon remains just a picture of an impending, unmitigated crisis. For a forward-looking study to truly protect biodiversity, the scientific alarm must be met with an immediate, synchronized, and aggressive legislative response. Eventually, the critical challenge is now to integrate, harmonize, and find deep synergies between the methods applied in the field of biological invasions with those utilized in animal and plant health. Alongside this cross-sectoral alignment, we must place a strong emphasis on increasing the efficiency of automated studies. By refining these digital frameworks to minimize baseline data gaps, we can optimize resources, reduce the reliance on purely manual filtering, and perform much quicker screenings. Ultimately, bridging the science-policy gap, embracing automation responsibly, and fostering cross-disciplinary collaboration remain the most critical steps in transforming these prioritized lists from mere warnings on a page into tangible, swift conservation outcomes.