Capture technique and fish personality: angling targets timid bluegill sunfish,<i>Lepomis macrochirus</i>
Bibliographic record
Abstract
Size-selective harvesting associated with commercial and recreational fishing practices has been shown to alter life history traits through a phenomenon known as fishing-induced evolution. This phenomenon may be a result of selection pathways targeting life-history traits directly or indirectly through correlations with behavioral traits. Here, we report on the relationship between individual differences in behavior and capture technique (beach seining versus angling) in wild-caught juvenile bluegill sunfish ( Lepomis macrochirus ). Both fish caught by using a seine net (seined) and fish caught by using a lure (angled) were individually tested under standardized laboratory conditions for their boldness, water-column use, and general activity. Observed inter-individual differences in boldness were strongly correlated with method of capture in the wild. Fish caught by angling were more timid and had fewer ectoparasites than fish caught using a seine net. However, this relationship did not carry over to an experiment in a large outdoor pool with seine-caught, individually tagged wild fish, where bolder individuals were more likely to be angled in open water away from refuges than more timid individuals, based on their previously assessed boldness scores. Our study is both novel and important, as it describes the relationship between capture technique and boldness in a natural population and underscores the potential risk of sampling biases associated with method of animal capture for behavioral, population, and conservation biologists.
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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 | 0.000 |
| 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".