Identifying potentially invasive non‐native marine and brackish water species for the Arabian Gulf and Sea of Oman
Bibliographic record
Abstract
Invasive non-native species (NNS) are internationally recognized as posing a serious threat to global biodiversity, economies and human health. The identification of invasive NNS is already established, those that may arrive in the future, their vectors and pathways of introduction and spread, and hotspots of invasion are important for a targeted approach to managing introductions and impacts at local, regional and global scales. The aim of this study was to identify which marine and brackish NNS are already present in marine systems of the northeastern Arabia area (Arabian Gulf and Sea of Oman) and of these which ones are potentially invasive, and which species have a high likelihood of being introduced in the future and negatively affect biodiversity. Overall, 136 NNS were identified, of which 56 are already present in the region and a further 80 were identified as likely to arrive in the future, including fish, tunicates, invertebrates, plants and protists. The Aquatic Species Invasiveness Screening Kit (AS-ISK) was used to identify the risk of NNS being (or becoming) invasive within the region. Based on the AS-ISK basic risk assessment (BRA) thresholds, 36 extant and 37 horizon species (53.7% of all species) were identified as high risk. When the impact of climate change on the overall assessment was considered, the combined risk score (BRA+CCA) increased for 38.2% of all species, suggesting higher risk under warmer conditions, including the highest-risk horizon NNS the green crab Carcinus maenas, and the extant macro-alga Hypnea musciformis. This is the first horizon-scanning exercise for NNS in the region, thus providing a vital baseline for future management. The outcome of this study is the prioritization of NNS to inform decision-making for the targeted monitoring and management in the region to prevent new bio-invasions and to control existing species, including their potential for spread.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".