Identifying invasive species threats, pathways, and impacts to improve biosecurity
Bibliographic record
Abstract
Abstract Managing invasive species with prevention and early‐detection strategies can avert severe ecological and economic impacts. Horizon scanning, an evidence‐based process combining risk screening and consensus building to identify threats, has become a valuable tool for prioritizing invasive species management and prevention. We assembled a working group of experts from academic, government, and nonprofit agencies and organizations, and conducted a multi‐taxa horizon scan for Florida, USA, the first of its kind in North America. Our primary objectives were to identify high‐risk species and their introduction pathways, to detail the magnitude and mechanism of potential impacts, and, more broadly, to demonstrate the utility of horizon scanning. As a means to facilitate future horizon scans, we document the process used to generate the list of taxa for screening. We evaluated 460 taxa for their potential to arrive, establish, and cause negative ecological and socioeconomic impacts, and identified 40 potential invaders, including alewife, zebra mussel, crab‐eating macaque, and red swamp crayfish. Vertebrates and aquatic invertebrates posed the greatest invasion threat, over half of the high‐risk taxa were omnivores, and there was high confidence in the scoring of high‐risk taxa. Common arrival pathways were ballast water, biofouling of vessels, and escape from the pet/aquarium/horticulture trade. Competition, predation, and damage to agriculture/forestry/aquaculture were common impact mechanisms. We recommend full risk analysis for the high‐risk taxa; increased surveillance at Florida's ports, state borders, and high‐risk pathways; and periodic review and revision of the list. Few horizon scans detail the comprehensive methodology (including list‐building), certainty estimates for all scoring categories and the final score, detailed pathways, and the magnitude and mechanism of impact. Providing this information can further inform prevention efforts and can be efficiently replicated in other regions. Moreover, harmonizing methodology can facilitate data sharing and enhance interpretation of results for stakeholders and the general public.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".