The effects of regional angling effort, angler behavior, and harvesting efficiency on landscape patterns of overfishing
Bibliographic record
Abstract
We used a coupled social-ecological model to study the landscape-scale patterns emerging from a mobile population of anglers exploiting a spatially structured walleye (Sander vitreus) fishery. We systematically examined how variations in angler behaviors (i.e., relative importance of walleye catch rate in guiding fishing site choices), harvesting efficiency (as implied by varying degrees of inverse density-dependent catchability of walleye), and angler population size affected the depletion of walleye stocks across 157 lakes located near Thunder Bay (Ontario, Canada). Walleye production biology was calibrated using lake-specific morphometric and edaphic features, and angler fishing site choices were modeled using an empirically grounded multi-attribute utility function. We found support for the hypothesis of sequential collapses of walleye stocks across the landscape in inverse proportionality of travel cost from the urban residence of anglers. This pattern was less pronounced when the regional angler population was low, density-dependent catchability was absent or low, and angler choices of lakes in the landscape were strongly determined by catch rather than non-catch-related attributes. Thus, our study revealed a systematic pattern of high catch importance reducing overfishing potential at low and aggravating overfishing potential at high angler population sizes. The analyses also suggested that density-dependent catchability might have more serious consequences for regional overfishing states than variations in angler behavior. We found little support for the hypotheses of systematic overexploitation of the most productive walleye stocks and homogenized catch-related qualities among lakes sharing similar access costs to anglers. Therefore, one should not expect anglers to systematically exploit the most productive fisheries or to equalize catch rates among lakes through their mobility and other behaviors. This study underscores that understanding landscape overfishing dynamics involves a careful appreciation of angler population size and how it interacts with the attributes that drive angler behaviors and depensatory mechanisms such as inverse density-dependent catchability. Only when all of these ingredients are considered and understood can one derive reasonably predictable patterns of overfishing in the landscape. These patterns range from self-regulating systems with low levels of regional fishing pressure to sequential collapse of walleye fisheries from the origin of angling effort.
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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 | 0.002 |
| 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 | 0.001 | 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".