Reality as the leading cause of stress: rethinking the impact of chronic stress in nature
Bibliographic record
Abstract
Summary Chronic activation of the stress axis caused by long‐term uncontrollable and unpredictable factors in the environment has been regarded as causing maladaptive and/or pathological effects, both by those studying animals in the laboratory and in nature. While pathology may apply to the former, I argue that it does not apply to the latter. Our thinking on the role of chronic stress in animals in nature has been heavily influenced by biomedical research, but much less so by the ecological and evolutionary context within which animals actually function. I argue that when such stressors occur (e.g. periods of high predation risk, food limitation, prolonged severe weather, social conflict, etc.), although the animal may be chronically stressed, its responses are adaptive and continue to promote fitness. Chronic stressors in nature can be subdivided into whether they are reactive (direct physiological challenges threatening homeostasis and not requiring cognitive processing – for example, food limitation) or anticipatory (perceived to be threatening and requiring cognitive processing – for example, high predation risk). For anticipatory stressors, their impact on the animal should not be based on their absolute duration (they may be acute), but rather by the duration of their physiological consequences. The anticipatory stressor of persistent high predation risk does not elicit chronic stress in all prey classes. Cyclic snowshoe hare and arctic ground squirrels exhibit evidence of chronic stress when predator numbers are high, but cyclic vole and noncyclic elk populations do not. I suggest that chronic stress has evolved to benefit the fitness of the former and not the later, with the key factors being lifespan and life history. I propose that chronic stress evolves in a species only if it is adaptive.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| 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".