Can variation among hypoxic environments explain why different fish species use different hypoxic survival strategies?
Bibliographic record
Abstract
ABSTRACT In aquatic environments, hypoxia is a multi-dimensional stressor that can vary in O2 level (partial pressure of O2 in water, PwO2), rate of induction and duration. Natural hypoxic environments can therefore be very different from one another. For the many fish species that have evolved to cope with these different hypoxic environments, survival requires adjusting energy supply and demand pathways to maintain energy balance. The literature describes innumerable ways that fishes combine aerobic metabolism, anaerobic metabolism and metabolic rate depression (MRD) to accomplish this, but it is unknown whether the evolutionary paths leading to these different strategies are determined primarily by species' phylogenetic histories, genetic constraint or their native hypoxic environments. We explored this idea by devising a four-quadrant matrix that bins different aquatic hypoxic environments according to their duration and PwO2 characteristics. We then systematically mined the literature for well-studied species native to environments within each quadrant, and, for each of 10 case studies, described the species' total hypoxic response (THR), defined as its hypoxia-induced combination of sustained aerobic metabolism, enhanced anaerobic metabolism and MRD, encompassing also the mechanisms underlying these metabolic modes. Our analysis revealed that fishes use a wide range of THRs, but that distantly related species from environments within the same matrix quadrant have converged on similar THRs. For example, environments of moderately hypoxic PwO2 favoured predominantly aerobic THRs, whereas environments of severely hypoxic PwO2 favoured MRD. Capacity for aerial emergence as well as predation pressure (aquatic and aerial) also contributed to these responses, in addition to other biotic and abiotic factors. Generally, it appears that the particular type of hypoxia experienced by a fish plays a major role in shaping its particular THR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".