Genomic and Epigenomic Influences on Resilience across Scales: Lessons from the Responses of Fish to Environmental Stressors
Bibliographic record
Abstract
Understanding the factors that influence the resilience of biological systems to environmental change is a pressing concern in the face of increasing human impacts on ecosystems and the organisms that inhabit them. However, most considerations of biological resilience have focused at the community and ecosystem levels, whereas here we discuss how including consideration of processes occurring at lower levels of biological organization may provide insights into factors that influence resilience at higher levels. Specifically, we explore how processes at the genomic and epigenomic levels may cascade up to influence resilience at higher levels. We ask how the concepts of "resistance," or the capacity of a system to minimize change in response to a disturbance, and "recovery," or the ability of a system to return to its original state following a disturbance and avoid tipping points and resulting regime shifts, map to these lower levels of biological organization. Overall, we suggest that substantial changes at these lower levels may be required to support resilience at higher levels, using selected examples of genomic and epigenomic responses of fish to climate-change-related stressors such as high temperature and hypoxia at the levels of the genome, epigenome, and organism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".