Balancing costs and benefits of managing hibernacula of cavernicolous bats
Bibliographic record
Abstract
ABSTRACT Manipulation of microclimates in caves and mines has gained renewed interest as a conservation and management strategy for populations of hibernating bats devastated by white‐nose syndrome (WNS). WNS creates an energy imbalance for hibernating bats and ultimately leads to starvation, so some researchers and management agencies suggest modifying hibernacula to meet conditions historically thought to minimise energy expenditure during hibernation. Modifying hibernacula has great potential as a management strategy, but an oversimplified view of hibernation physiology and behaviour leads to an incomplete balancing of costs and benefits. Hibernaculum manipulations, as currently being implemented in the USA, carry high risk because cave systems used by bats have all the hallmarks of systems prone to falling into ecological traps. We present an individual‐based model of bat energetics during hibernation, demonstrating the risk of relying on oversimplified descriptions of physiology and environmental conditions to design and implement hibernaculum manipulations. When realistic levels of variation in ambient conditions are included, proposed ‘target’ microclimates are very risky for hibernating bats. Realistic natural conditions in many or most hibernacula mean that modifications to the microclimate may produce modest energy savings for hibernating bats while potentially exposing them to substantial long‐term fitness declines. Due to the risks of creating ecological traps and negative energetic consequences, we generally urge caution when modifying subterranean sites for bat use, and specifically suggest that if hibernacula are modified, the primary goal should be to maximise spatial gradients and minimise temporal variability in ambient conditions (temperature and humidity), as opposed to aiming to achieve a specific midwinter temperature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".