A biogeographical regionalization of coastal Mediterranean fishes
Bibliographic record
Abstract
Abstract Aim To delineate the biogeographical regions of the continental shelf of the Mediterranean Sea based on the spatial distributions of coastal marine fishes and their evolutionary relationships, with a view to furthering our capacity to answer basic and applied biogeographical, ecological and evolutionary questions. Location Mediterranean Sea. Methods We used a dataset summarizing the occurrences of 203 coastal Mediterranean fishes (0.1° resolution grid system) and a molecular phylogenetic tree to quantify both compositional and phylogenetic dissimilarity (or beta diversity) between cells. We then applied multivariate analyses to delineate biogeographical regions and to evaluate how they related to broad‐scale environmental gradients. We also assessed the differences between the biogeographical regions identified using phylogenetic beta diversity versus those obtained using compositional beta diversity. Results The bioregionalization schemes based on phylogenetic and compositional beta diversity identified broadly similar regions, each consisting of six distinct pools of coastal fishes. Clear separations between northern and southern regions were observed, as well as a disjunct between inshore and offshore areas. These beta diversity patterns were mainly related to a north–south gradient in sea‐surface temperature and bathymetric constraints. Main conclusions Incorporating phylogenetic information into the measurement of beta diversity did not offer further insights to the bioregionalization scheme based solely on compositional beta diversity. This suggests that evolutionary and historical processes played only a minor role in shaping the contemporary patterns of beta diversity in the Mediterranean coastal fish fauna. However, our results support the view that contemporary environmental conditions play a major role in determining the distribution of these species.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".