Using mounting, orientation, and design to improve bat box thermodynamics in a northern temperate environment
Bibliographic record
Abstract
Wildlife managers design artificial structures, such as bird houses and bat boxes, to provide alternative nesting and roosting sites that aid wildlife conservation. However, artificial structures for wildlife may not be equally efficient at all sites due to varying climate or habitat characteristics influencing thermal properties. For example, bat boxes are a popular measure employed to provide compensatory or supplementary roost sites for bats and educate the public. Yet, bat boxes are often thermally unstable or too cold to fulfill reproductive females needs in northern temperate environments. To help improve the thermodynamics of bat boxes, we tested the effect of (1) three mountings, (2) four orientations, and (3) twelve bat box designs on the internal temperature of bat boxes. We recorded temperatures in bat boxes across a climate gradient at seven sites in Quebec, Canada. Bat boxes mounted on buildings had warmer microclimates at night than those on poles and those facing east warmed sooner in the morning than those facing west or south. Our best new model based on passive solar architecture (Ncube PH1) increased the time in the optimal temperature range (22-40 °C) of targeted species by up to 13% compared to the most commonly used model (Classic 4-chamber) when mounted on a building with an east orientation (other designs presented in the Supplementary Information). Based on bioenergetic models, we estimated that bats saved up to 8% of their daily energy using the Ncube PH1 compared to the Classic 4-chamber when mounted on a building with an east orientation. We demonstrate that the use of energy-saving concepts from architecture can improve the thermal performance of bat boxes and potentially other wildlife structures as well.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".