Wild turkey roost selection is more consistently associated with tree traits than microclimate
Bibliographic record
Abstract
Animals must cope with a range of climatic conditions across seasons, and they can accomplish this by selecting habitats that are favourable for thermoregulation. Sheltering from environmental conditions can be particularly important for reducing energetic costs when animals are inactive, but the influence of microclimate on fine-scale selection of sleeping sites is often unclear. We compared microclimate at eastern wild turkey ( Meleagris gallopavo silvestris ) roost trees and nearby non-roost trees during summer and winter in southern Ontario, Canada, near the northern part of the turkeys’ range. During both winter and summer, overnight air temperature and wind speeds at turkey roost trees were similar to those at nearby non-roost trees. In summer, however, there was slightly less accumulated precipitation at roost trees compared to non-roost trees. Fine-scale selection of roost trees was better predicted by tree characteristics, with a preference for larger trees in both seasons, and for deciduous trees in summer. Our findings suggest that although roost trees may occasionally provide thermoregulatory benefits related to slight differences in microclimate, turkeys’ choice of specific roost trees within a woodland is more likely influenced by proximate signals related to tree characteristics. Our study highlights the importance of forests with large trees as roosting habitat for wild turkeys, particularly in agricultural landscapes within the northern part of their range.
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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 | 0.000 |
| 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) | 0.002 | 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".