Responses to Temperature and Hypoxia as Interacting Stressors in Fish: Implications for Adaptation to Environmental Change
Bibliographic record
Abstract
Anthropogenic environmental change is exposing animals to changes in a complex array of interacting stressors and is already having important effects on the distribution and abundance of species. However, despite extensive examination of the effects of stressors in isolation, knowledge of the effects of stressors in combination is limited. This lack of information makes predicting the responses of organisms to anthropogenic environmental change challenging. Here, we focus on the effects of temperature and hypoxia as interacting stressors in fishes. A review of the available evidence suggests that temperature and hypoxia act synergistically such that small shifts in one stressor could result in large effects on organismal performance when a fish is exposed to the 2 stressors in combination. Although these stressors pose substantial challenges for fish, there also is substantial intraspecific variation in tolerance to these stressors that could act as the raw material for the evolution of improved tolerance. However, the potential for adaptive change is, in part, dependent on the nature of the correlations among traits associated with tolerance. For example, negative genetic correlations (or trade-offs) between tolerances to temperature and hypoxia could limit the potential for adaptation to the combined stressors, while positive genetic correlations might be of benefit. The limited data currently available suggest that tolerances to hypoxia and to high-temperature may be positively correlated in some species of fish, suggesting the possibility for adaptive evolution in these traits in response to anthropogenic environmental change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".