What drives variation in habitat use by <i>Anolis</i> lizards: habitat availability or selectivity?
Bibliographic record
Abstract
Geographic variation in habitat availability may drive geographic variation in a species’ habitat use; alternatively, species adapted to particular habitat characteristics may use a habitat regardless of its availability within an environment. In this study, we investigated habitat use of two sympatric species of Anolis lizards that are morphologically specialized to use different microhabitats. We examined variation in microhabitat use and availability among four distinct forest types. In each forest type, we quantified available microhabitats (i.e., perch diameter, angle of inclination, and visibility), as well as microhabitats actually used by each species. We found that species consistently differed in microhabitat use, corresponding to each species’ morphological specializations. However, microhabitat use of both species varied among sites. This variation in Anolis gundlachi Peters, 1876 reflected differences in microhabitat availability, while the variation in Anolis krugi Peters, 1876 resulted from differential microhabitat selectivity. These results indicate that both habitat availability and habitat preferences must be examined in multiple localities for a species to understand the causes of variation in its habitat use.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".