Phylogeny, niche conservatism and the latitudinal diversity gradient in mammals
Bibliographic record
Abstract
Biologists have long searched for mechanisms responsible for the increase in species richness with decreasing latitude. The strong correlation between species richness and climate is frequently interpreted as reflecting a causal link via processes linked to energy or evolutionary rates. Here, we investigate how the aggregation of clades, as dictated by phylogeny, can give rise to significant climate-richness gradients without gradients in diversification or environmental carrying capacity. The relationship between climate and species richness varies considerably between clades, regions and time periods in a global-scale phylogenetically informed analysis of all terrestrial mammal species. Many young clades show negative richness-temperature slopes (more species at cooler temperatures), with the ages of these clades coinciding with the expansion of temperate climate zones in the late Eocene. In carnivores, we find steeply positive richness-temperature slopes in clades with restricted distributions and tropical origins (e.g. cat clade), whereas widespread, temperate clades exhibit shallow, negative slopes (e.g. dog-bear clade). We show that the slope of the global climate-richness gradient in mammals is driven by aggregating Chiroptera (bats) with their Eutherian sister group. Our findings indicate that the evolutionary history should be accounted for as part of any search for causal links between environment and species richness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".