Environmental variation is a major predictor of global trait turnover in mammals
Bibliographic record
Abstract
Abstract Aim To evaluate how environment and evolutionary history interact to influence global patterns of mammal trait diversity (a combination of 14 morphological and life‐history traits). Location The global terrestrial environment. Taxon Terrestrial mammals. Methods We calculated patterns of spatial turnover for mammalian traits and phylogenetic lineages using the mean nearest taxon distance. We then used a variance partitioning approach to establish the relative contribution of trait conservatism, ecological adaptation and clade specific ecological preferences on global trait turnover. Results We provide a global scale analysis of trait turnover across mammalian terrestrial assemblages, which demonstrates that phylogenetic turnover by itself does not predict trait turnover better than random expectations. Conversely, trait turnover is consistently more strongly associated with environmental variation than predicted by our null models. The influence of clade‐specific ecological preferences, reflected by the shared component of phylogenetic turnover and environmental variation, was considerably higher than expectations. Although global patterns of trait turnover are dependent on the trait under consideration, there is a consistent association between trait turnover and environmental predictive variables, regardless of the trait considered. Main conclusions Our results suggest that changes in phylogenetic composition are not always coupled with changes in trait composition on a global scale and that environmental conditions are strongly associated with patterns of trait composition across species assemblages, both within and across phylogenetic clades.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".