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 FRic 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 FRic index was weakly correlated with both SR and the PD index. Specifically, SR and FRic displayed a triangular relationship, that is, increasing variability in FRic 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 FRic 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".