Tree lizard (<i>Urosaurus ornatus</i>) growth decreases with population density, but increases with habitat quality
Bibliographic record
Abstract
Habitat selection models can explain spatial patterns in the relative abundance of animals in different habitats based on the assumption that fitness declines as density in a habitat increases. Ectotherms, such as lizards, may not follow predictions of density-dependent habitat selection models because temperature, which is unaffected by density, strongly influences their habitat selection. If competition for limited resources decreases fitness, then crowding should cause a decrease in body size and growth rates. We used skeletochronology and body size data from tree lizards (Urosaurus ornatus) at six sites that each spanned two habitats varying in quality to test the hypothesis that habitat selection is density dependent because growth is limited by competition for resources and by habitat quality. First, we tested that the maximum body size of lizards decreased with higher densities in a habitat by comparing growth between sites. Second, we tested whether body size and growth were higher in the habitat with more resources by controlling for density in a habitat and comparing growth between habitats in different sites. We found evidence of density-dependent growth in females, but not in males. Females in more crowded sites reached a smaller maximum size. Females in the higher quality habitat also grew larger than females in the lower quality habitat after controlling for differences in density between the habitats. Therefore, we found partial support for our hypothesis that competition for resources limits growth and causes density-dependent habitat selection.
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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 | 0.001 |
| 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) | 0.002 | 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".