The physiological ecology of preparing for and recovering from hibernation in temperate insectivorous bats
Bibliographic record
Abstract
Temperate insectivorous bats that hibernate must both prepare for and recover from hibernation during times of the year with low resource abundance and low ambient temperatures, resulting in energetic bottlenecks in both seasons. Assessing the energetic challenges of both seasons is important not only for an integrated comprehension of the scope of the annual cycle of hibernating bats, but also important for understanding the effects of white-nose syndrome (WNS) outside of hibernation. In this thesis, I focus on the seasons directly before and after hibernation to demonstrate the energetic demand of both seasons, and how WNS may exacerbate energetic challenges. In Chapter 2, I review literature concerning the post-emergence season, which presents a large knowledge gap in current understanding of the annual cycle of bats. I discuss the possible energetic challenges that bats may face during this season, and present possible challenges of WNS and climate change. In Chapter 3, I use morphometric and plasma metabolite data to analyze how hibernation preparation has changed in a region that has been affected by WNS for over a decade. Post-WNS, adult bats gained more mass before hibernation, and subadult bats, which previously lost mass, instead slightly increased body mass throughout the season. However, plasma triglyceride concentrations did not indicate any intense foraging throughout the season, despite documentation of elevated plasma triglycerides before the introduction of WNS. The results of this study were consistent with an adaptive response to WNS and may indicate changes in behavior and physiology that may result in reproductive trade-offs. Combined, my thesis emphasizes the significance of the pre- and post-hibernation seasons as energetic bottlenecks in the annual cycle, and the importance of considering the effects of WNS outside of the actual hibernation season, opening a variety of avenues for future research in the costs and challenges of hibernation.
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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 | 0.001 | 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".