Multifaceted biodiversity hotspots of marine mammals for conservation priorities
Bibliographic record
Abstract
Abstract Aim Identifying the multifaceted biodiversity hotspots for marine mammals and their spatial overlap with human threats at the global scale. Location World‐wide. Methods We compiled a functional trait database for 121 species of marine mammals characterized by 14 functional traits grouped into five categories. We estimated marine mammal species richness ( SR ) as well as functional ( FD ) and phylogenetic diversity ( PD ) per grid cell (1° × 1°) using the FR ic index (a measure of trait diversity as the volume of functional space occupied by the species present in an assemblage) and the PD index (the amount of evolutionary history represented by a set of species), respectively. Finally, we assessed the spatial congruence of these three facets of biodiversity hotspots (defined as 2.5% and 5% of the highest values of SR , FD and PD ) with human threats at the global scale. Results We showed that the FR ic index was weakly correlated with both SR and the PD index. Specifically, SR and FR ic displayed a triangular relationship, that is, increasing variability in FR ic along the species richness gradient. We also observed a striking lack of spatial congruence (<0.1%) between current human threats and the distribution of the multiple facets of biodiversity hotspots. Main Conclusions We highlighted that functional diversity calculated using the FR ic index is weakly associated with the species richness of marine mammals world‐wide. This is one of the most endangered vertebrate groups playing a key ecological role in marine ecosystems. This finding calls for caution when using only species richness as a benchmark for defining marine mammal biodiversity hotspots. The very low level of spatial congruence between hotspots of current threats and those of the multiple facets of marine mammal biodiversity suggests that current biodiversity patterns for this group have already been greatly affected by their history of exploitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| 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 teacher head, 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".