A trait‐based approach for assessing and mapping niche overlap between native and exotic species: the<scp>M</scp>editerranean coastal fish fauna as a case study
Bibliographic record
Abstract
Abstract Aim We propose a trait‐based approach for assessing and mapping potential niche overlap between native and exotic species at large spatial scales. Location The Mediterranean Sea. Methods We developed two complementary indices based on ecological, biological and ecomorphological similarities between native and exotic species. The first index (FNNr) allows identifying areas where native species are the most at risk in terms of potential ecological interactions with exotic species. The second index (FGO) is species‐specific and allows identifying native species that display the broadest functional niche overlap and range overlap with exotic species. We illustrated our approach using the Mediterranean coastal fish fauna as a case study. Results We found that endemic and exotic fishes had a similar global functional niche at the Mediterranean scale, that is, they filled the same functional space defined by trait axes. FNNrhotspots were found to be moderately congruent with exotic species richness hotspots. Higher values of FNNrwere observed along the coasts of the Levantine Sea. The computation of the FGO index showed that the geographical range of a given endemic species overlapped in average with 52 exotic species. Species showing the highest FGO values displayed localized and/or fragmented distributions in the eastern Mediterranean basin. Main conclusions Our findings suggest that the number of exotic species alone cannot be used as a broad‐scale indicator of potential impact because this metric does not account for functional relatedness between native and exotic species. The trait‐based indices developed in this study can be used for other taxa in both aquatic and terrestrial ecosystems and should help environmental managers to set up local‐scale studies on areas where the potential impact of exotic species on native biodiversity is the highest.
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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 | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 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".