Influence of temperature on maternity roost selection by noctule bats (<i>Nyctalus noctula</i>) and Leisler’s bats (<i>N. leisleri</i>) in Białowieża Primeval Forest, Poland
Bibliographic record
Abstract
This study tests whether the temperature of tree cavities determines their selection by bats in Białowieża Primeval Forest (BPF), eastern Poland. Using a data logger, I simultaneously measured the temperature in cavities selected by bats as roosts and the temperature in available but unselected cavities. The maternity roosts chosen by noctule bats, Nyctalus noctula (Schreber, 1774), and Leisler’s bats, N. leisleri (Kuhl, 1817), during late pregnancy and lactation were warmer than unoccupied cavities, but temperature ranges in the two types of cavity did not differ. A logistic regression model showed that the mean cavity temperature during the night (2200–0400) and the minimum cavity temperature over a 24 h period were crucial for roost selection. This suggests that female noctule and Leisler’s bats selected roosts that promoted juvenile growth and used tree cavities that could save them energy while they were active because the cavity temperatures were close to the lower critical temperature of their thermoneutral zone. I also suggest that selection of warmer cavities minimized the bats' energy expenditure prior to emergence from the roost and that passive rewarming inside the cavity was an important factor in minimizing energetic costs of roosting by bats in BPF. Mean and maximum temperatures recorded near tree trunks increased with the height at which the temperature was measured (ground level, 10, 20, and 30 m), suggesting that bats can gain thermal benefits from insolation of the trunk by selecting highly placed cavities, as was observed in BPF.
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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) | 0.001 | 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".