Density‐dependent habitat selection predicts fitness and abundance in a small lizard
Bibliographic record
Abstract
Density‐dependent habitat selection has been used to predict and explain patterns of abundance of species between habitats. Thermal quality, a density‐independent component of habitat suitability, is often the most important factor for habitat selection in ectotherms which comprise the vast majority of animal species. Ectotherms may reach high densities such that individual fitness is reduced in a habitat due to increased competition for finite resources. Therefore, density and thermal quality may present conflicting information about which habitat will provide the highest fitness reward and ectotherm habitat selection may be density‐independent. Using ornate tree lizards Urosaurus ornatus at 10 sites each straddling two adjacent habitats (wash and upland), we tested the hypothesis that habitat selection is density‐dependent even when thermal quality differs between habitats. We first tested that fitness proxies decline with density in each habitat, indicating density‐dependent effects on habitat suitability. We also confirmed that the two habitats vary in suitability (quantified by food abundance and thermal quality). Next, we tested the predictions that habitat selection depends on density with isodar analyses and that fitness proxies are equal in the two habitats within a site. We found that monthly survival rates decreased with density, and that the wash habitat had more prey and higher thermal quality than the upland habitat. Lizards preferred the habitat with more food and higher thermal quality, lizard densities in the two habitats were positively correlated, and fitness proxies of lizards did not differ between habitats. These patterns are consistent with density‐dependent habitat selection, despite differences in thermal quality between habitats. We expect that density‐dependent habitat selection is widespread in terrestrial ectotherms when densities are high and temperatures are close to their optimal performance range. In areas where thermal quality is low, however, we expect that depletable resources, such as food, become less limiting because assimilating resources is more difficult.
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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".