Linking Angling Catch Rates and Fish Learning under Catch-and-Release Regulations
Bibliographic record
Abstract
Abstract Many recreational fisheries are subject to varying degrees of catch-and-release fishing through regulations and conservation-minded anglers. Clearly, releasing a proportion of the catch improves conservation of the fishery, yet it is not clear how the released catch contributes to angling quality. If fish change their behavior to lower their individual catchability after they have been caught, then angler catch rates may not be proportional to fish density. Therefore, even catch-and-release fisheries could exhibit poor angling quality if there is sufficiently high angler effort. We tested this idea by experimentally fishing five small lakes that contained rainbow trout Oncorhynchus mykiss in the interior of British Columbia. We found that with sustained effort of 8 angler-hours · d−1 · ha−1 and complete release of the catch, catch rates quickly dropped within 7–10 d. Given the individual capture histories of tagged fish, the most parsimonious catchability model incorporated learning and heterogeneity into intrinsic catchability. The best-fit parameter values suggest that the population contained a group of highly catchable fish that were quickly caught and then learned to avoid hooks. There was a seasonal decrease in catchability that was independent of angling; however, it was not sufficient to explain the data. Our results indicate that catch rates may decline because of high angling effort even when the number of fish remains constant. Therefore, management goals that go beyond conservation issues and attempt to maximize angler satisfaction must account for effort density on a recreational fishery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".