The influence of past and present climate on the biogeography of modern mammal diversity
Bibliographic record
Abstract
Within most terrestrial groups of animals, including mammals, species richness varies along two axes of environmental variation, representing energy availability and plant productivity. This relationship has led to a search for mechanistic links between climate and diversity. Explanations have traditionally focused on single mechanisms, such as variation in environmental carrying capacity or evolutionary rates. Consensus, though, has proved difficult to achieve and there is growing appreciation that geographical patterns of species richness are a product of many interacting factors including biogeographic history and biological traits. Here, we review some current hypotheses on the causes of gradients in mammal richness and range sizes since the two quantities are intimately linked. We then present novel analyses using recent datasets to explore the structure of the environment-richness relationship for mammals. Specifically, we consider the impact of glaciation on present day mammalian diversity gradients. We conclude that not only are multiple processes important in structuring diversity gradients, but also that different processes predominate in different places.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".