Effects of body mass, climate, geography, and census area on population density of terrestrial mammals
Bibliographic record
Abstract
Abstract Aim The aim of this study was to investigate the effects of climate, geography, census area and the distribution of body mass on the mass : density relationship in terrestrial mammal populations. Location The areas covered include most major terrestrial biomes including the tropics, savannas, and temperate forests. Method Data on population density and body mass from 827 populations belonging to 330 different terrestrial mammal species were derived from a review of the literature. Results LOWESS and polynomial regression analysis indicated that the overall mass : density relationship on log‐log scales was not linear and that the slope of this relationship behaves differently across the range of body mass. Body mass explained between 37 and 67% of the variability in population density depending upon the dietary category or the biome group. We also developed two multivariate models that can explain up to 65% of the variability in population density in terrestrial mammals. We also tested for a confounding effect of census area on the mass : density relationship on log‐log scales in terrestrial mammals. Conclusions Our findings support previous studies suggesting that body mass is a major predictor of the variance in population density in terrestrial mammals. We suggest that the non‐linearity of the mass : density relationship may result from the fact that the overall distribution of body mass is a mixture of distributions across dietary groups and biomes. In contrast to body mass, our results indicate that climatic and geographical factors have a minor effect on population density. Although census area was closely correlated with body mass, body mass was generally a better predictor of population density than was census area.
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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 | 0.000 | 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) | 0.000 | 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".