The Power of Physiology in Changing Landscapes: Considerations for the Continued Integration of Conservation and Physiology
Bibliographic record
Abstract
The growing field of conservation physiology applies a diversity of physiological traits (e.g., immunological, metabolic, endocrine, and nutritional traits) to understand and predict organismal, population, and ecosystem responses to environmental change and stressors. Although the discipline of conservation physiology is gaining momentum, there is still a pressing need to better translate knowledge from physiology into real-world tools. The goal of this symposium, ‘‘Physiology in Changing Landscapes: An Integrative Perspective for Conservation Biology’’, was to highlight that many current investigations in ecological, evolutionary, and comparative physiology are necessary for understanding the applicability of physiological measures for conservation goals, particularly in the context of monitoring and predicting the health, condition, persistence, and distribution of populations in the face of environmental change. Here, we outline five major investigations common to environmental and ecological physiology that can contribute directly to the progression of the field of conservation physiology: (1) combining multiple measures of physiology and behavior; (2) employing studies of dose–responses and gradients; (3) combining a within-individual and population-level approach; (4) taking into account the context-dependency of physiological traits; and (5) linking physiological variables with fitness metrics. Overall, integrative physiologists have detailed knowledge of the physiological systems that they study; however, communicating theoretical and empirical knowledge to conservation biologists and practitioners in an approachable and applicable way is paramount to the practical development of physiological tools that will have a tangible impact for conservation.
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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 | 0.048 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.016 | 0.044 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.010 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".