Conservation Physiology and Conservation Pathogens: White-Nose Syndrome and Integrative Biology for Host–Pathogen Systems
Bibliographic record
Abstract
Conservation physiology aims to apply an understanding of physiological mechanisms to management of imperiled species, populations, or ecosystems. One challenge for physiologists hoping to apply their expertise to conservation is connecting the mechanisms we study, often in the laboratory, with the vital rates of populations in the wild. There is growing appreciation that infectious pathogens can threaten populations and species, and represent an important issue for conservation. Conservation physiology has much to offer in terms of addressing the threat posed to some host species by infectious pathogens. At the same time, the well-developed theoretical framework of disease ecology could provide a model to help advance the application of physiology to a range of other conservation issues. Here, I use white-nose syndrome (WNS) in hibernating North American bats as an example of a conservation problem for which integrative physiological research has been a critical part of research and management. The response to WNS highlights the importance of a well-developed theoretical framework for the application of conservation physiology to a particular threat. I review what is known about physiological mechanisms associated with mortality from WNS and emphasize the value of combining a strong theoretical background with integrative physiological studies in order to connect physiological mechanisms with population processes and thereby maximize the potential benefits of conservation physiology.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".