On the determinants of census area: implications for mammalian macroecological patterns
Bibliographic record
Abstract
Abstract The aim of this study was to examine the effects of various biological factors such as body mass, trophic level, climate and geography on census area in terrestrial mammals. We also examine the effects of census area on the population density–body mass relationship. The geographic areas covered in this study include most major terrestrial biomes including taïga, desert, savanna, grassland, tropical dry forest, temperate dry forest, tropical rain forest and temperate rain forest. An extensive literature search was conducted and we compiled data on census area and body mass from 377 mammalian populations and 59 communities. Statistical analyses include linear regression, Kruskal–Wallis analysis of variance, LOWESS, and multiple regression. Overall, the regression between log census area (A) and log body mass (M) yielded a slope of 0.710, which did not differ significantly from 0.75, but it was significantly different from 1.0. The analyses also showed that the log A–log M relationship is constrained within a well‐defined space in a similar fashion to the home range–body mass relationship. When mammals were separated into trophic groups, regression lines differed significantly in their intercepts, but not in slopes. At the community level, the census area was particularly affected by the population with the largest body mass within the community. Both the number of species and number of taxa encompassed by the community were found to be correlated positively with log A (r = 0.26, P = 0.0464 and r = 0.27, P = 0.0398, respectively). Sampling of mammalian species is not usually random. Not only is census area significantly associated with the technique used to sample a given species, but it is also influenced by biological factors that have been shown previously to influence population density. Striking similarities were found between the census area–body mass relationship and the home range–body mass relationship, suggesting that investigators may sample mammalian populations over areas that actually reflect the use of space of their focal species.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".