Manipulating glucocorticoids in wild animals: basic and applied perspectives
Bibliographic record
Abstract
One of the most comprehensively studied responses to stressors in vertebrates is the endogenous production and regulation of glucocorticoids (GCs). Extensive laboratory research using experimental elevation of GCs in model species is instrumental in learning about stressor-induced physiological and behavioural mechanisms; however, such studies fail to inform our understanding of ecological and evolutionary processes in the wild. We reviewed emerging research that has used GC manipulations in wild vertebrates to assess GC-mediated effects on survival, physiology, behaviour, reproduction and offspring quality. Within and across taxa, exogenous manipulation of GCs increased, decreased or had no effect on traits examined in the reviewed studies. The notable diversity in responses to GC manipulation could be associated with variation in experimental methods, inherent differences among species, morphs, sexes and age classes, and the ecological conditions in which responses were measured. In their current form, results from experimental studies may be applied to animal conservation on a case-by-case basis in contexts such as threshold-based management. We discuss ways to integrate mechanistic explanations for changes in animal abundance in altered environments with functional applications that inform conservation practitioners of which species and traits may be most responsive to environmental change or human disturbance. Experimental GC manipulation holds promise for determining mechanisms underlying fitness impairment and population declines. Future work in this area should examine multiple life-history traits, with consideration of individual variation and, most importantly, validation of GC manipulations within naturally occurring and physiologically relevant ranges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".