Risk of invasiveness of non-native aquatic species in the eastern Mediterranean region under current and projected climate conditions
Bibliographic record
Abstract
Human-induced biological introductions pose a major threat to global biodiversity, and this is especially frequent in the eastern Mediterranean region, which is a globally important biodiversity hotspot area of high conservation value. To predict at which level introduced species in this region might become invasive under current and projected climate conditions, 232 non-native aquatic organisms were screened using the Aquatic Species Invasiveness Screening Kit. Based on receiver operative characteristic curve analysis, thresholds were identified to distinguish between low, medium and high risk species. The “top invasive” (very high risk) species identified were: brown bullhead Ameiurus nebulosus, blue crab Callinectes sapidus, gibel carp Carassius gibelio, Philippine catfish Clarias batrachus, Chinese mitten crab Eriocheir sinensis, bluespotted cornetfish Fistularia commersonii, silver carp Hypophthalmichthys molitrix, silver-cheeked toadfish Lagocephalus sceleratus, half-smooth golden pufferfish Lagocephalus spadiceus, Suez pufferfish Lagocephalus suezensis, signal crayfish Pacifastacus leniusculus, fathead minnow Pimephales promelas, channeled applesnail Pomacea canaliculata, red swamp crayfish Procambarus clarkii, devil firefish Pterois miles and European catfish Silurus glanis. The risk of being invasive of more than half of the screened species increased after taking global warming predictions into account, and several species considered to be globally invasive (cf. high risk) were classified as posing only a medium risk for the eastern Mediterranean region. Region-specific risk screenings, as implemented in this study, are therefore essential for setting priorities in preventative management for the conservation of key biodiversity hotspots and the optimal allocation of resources in view of full risk assessment for the species identified as (very) high risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".