Exploitation may influence the climate resilience of fish populations through removing high performance metabolic phenotypes
Bibliographic record
Abstract
Physiological rates and processes underpin the relationships between ectothermic organisms, such as fish, and their environment. The response and persistence of fish populations in an increasingly variable ocean is dependent on the distribution and diversity of physiological phenotypes. Growing evidence suggests that fisheries exploitation can selectively target certain physiological and behavioural phenotypes, which may shift exploited populations to altered physiological states. Here we test if commercial fisheries have the potential to do this in a "natural laboratory" along the South African coast. We compare metabolic traits of exploited and protected populations of the fish species, Chrysoblephus laticeps, which is a major component of the South African hook and line fishery. We find that high-performance aerobic scope phenotypes are reduced in the fished population. The most likely mechanism for this finding is a positive relationship between aerobic scope and capture vulnerability in passive-gear fisheries. Our results further highlight the selective nature of capture-fisheries and suggest that exploitation has the capacity to alter climate responses of fish populations on a physiological level. Our finding also implicates how Marine Protected Areas, through harbouring individuals with a greater diversity of physiological traits, may provide greater fish response diversity to environmental variability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".