Comparing the activity of exploited and unexploited populations of a reef-dwelling seabream, <i>Chrysoblephus laticeps</i> , during an extreme upwelling and cold-spell event
Bibliographic record
Abstract
Along the south coast of South Africa, extreme upwelling events lead to rapid temperature declines and prolonged marine cold-spells, which have, on occasion, led to large-scale mortalities of coastal fishes. In a somewhat antagonistic process, fishing exploitation has been shown to target specific phenotypes, potentially selecting for higher physiological diversity within exploited populations, reducing resilience to adverse environmental conditions. This study investigated the effects of thermal stress and exploitation in two populations (exploited and unexploited) of the commercially and recreationally targeted red roman Chysoblephus laticeps (family Sparidae) during an intense upwelling and a marine cold-spell event, using acoustic telemetry data. The results show that the acceleration of tagged fish (a proxy for fish activity) during the upwelling event differed significantly between the two populations (p < 0.05), with fish in the unexploited population maintaining their activity, and fish in the exploited population exhibiting reduced activity. The activity of the fish was also significantly different between the populations during the marine cold-spell, with those in the exploited population exhibiting reduced activity over time compared with the unexploited population. These findings highlight the impact of thermal extremes on fish activity and suggest that exploitation may selectively remove individuals that are tolerant to these events, which might consequently reduce resilience of the fish population. Based on these findings, it is concluded that networks of marine protected areas can promote fish populations that are resilient to future climatic conditions.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".