Physiological and Behavioral Consequences of Long‐Term Artificial Selection for Vulnerability to Recreational Angling in a Teleost Fish
Bibliographic record
Abstract
Few studies have examined the physiological and behavioral consequences of fisheries-induced selection. We evaluated how four generations of artificial truncation selection for vulnerability to recreational angling (i.e., stocks selected for high and low vulnerability [HVF and LVF, respectively]) affected cardiovascular physiology and parental care behavior in the teleost fish largemouth bass Micropterus salmoides. Where possible, we compared artificially selected fish to control fish (CF) collected from the wild. Although, compared to control fish, resting cardiac activity was approximately 18% lower for LVF and approximately 20% higher for HVF, maximal values did not vary among treatments. As a result, the HVF had less cardiac scope than either LVF or CF. Recovery rates after exercise were similar for HVF and CF but slower for LVF. When engaged in parental care activities, nesting male HVF were captured more easily than male LVF. During parental care, HVF also had higher turning rates and pectoral and caudal fin beat rates, increased vigilance against predators, and higher in situ swimming speeds. Energetics simulations indicated that to achieve the same level of growth, the disparity in metabolic rates would require HVF to consume approximately 40% more food than LVF. Selection for angling vulnerability resulted in clear differences in physiological and energetic attributes. Not only is vulnerability to angling a heritable trait, but high vulnerability covaries with factors including higher metabolic rates, reduced metabolic scope, and increased parental care activity. Despite these energetically costly differences, HVF and LVF of the same age were of similar size, suggesting that heightened food consumption in HVF compensated for added costs in experimental ponds. Ultimately, angling vulnerability appears to be a complex interaction of numerous factors leading to selection for very different phenotypes. If HVF are selectively harvested from a population, the remaining fish in that population may be less effective in providing parental care, potentially reducing reproductive output. The strong angling pressure in many freshwater systems, and therefore the potential for this to occur in the wild, necessitate management approaches that recognize the potential evolutionary consequences of angling.
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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".