Thermal quality influences habitat selection at multiple spatial scales in milksnakes
Bibliographic record
Abstract
Factors influencing habitat selection may be scale dependent, leading to different selection patterns at different spatial scales. By limiting habitat-selection studies to a single scale, important selection patterns could be missed. Despite this danger, many studies investigate habitat selection at a single scale, often ignoring macro-habitat selection: the selection of a home range within the study area. We investigated macro- and micro-habitat selection in milksnakes. Because of the importance of thermoregulation to ectotherms, we predicted that snakes would select habitats of high thermal quality at both micro- and macro-habitat scales. In 2003–2004, we located 25 individuals 890 times and characterized the habitat in detail at 279 locations used by milksnakes and at 279 paired random locations. Open habitats (fields, rocky outcrops, marshes) and edges have environmental temperatures that deviate less from the preferred body temperature range of milksnakes and offer characteristics that facilitate thermoregulation compared to forest. At the macro- and micro-habitat scales, milksnakes preferred habitats of high thermal quality: they used fields and rocky outcrops more than forests. Milksnakes also preferred edges at both scales. In addition, milksnakes preferred locations with open canopy and many rocks at the micro-habitat scale. These results support the notion that thermal quality influences habitat use in ectotherms and strengthen the idea that habitat-use studies should be conducted at more than one spatial scale to gain a complete understanding of the factors affecting 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".