Interaction of ecological and angler processes: experimental stocking in an open access, spatially structured fishery
Bibliographic record
Abstract
Effective management of socioecological systems requires an understanding of the complex interactions between people and the environment. In recreational fisheries, which are prime examples of socioecological systems, anglers are analogous to mobile predators in natural predator-prey systems, and individual fisheries in lakes across a region are analogous to a spatially structured landscape of prey patches. Hence, effective management of recreational fisheries across large spatial scales requires an understanding of the dynamic interactions among ecological density dependent processes, landscape-level characteristics, and angler behaviors. We focused on the stocked component of the open access rainbow trout (Oncorhynchus mykiss) fishery in British Columbia (BC), and we used an experimental approach wherein we manipulated stocking densities in a subset of 34 lakes in which we monitored angler effort, fish abundance, and fish size for up to seven consecutive years. We used an empirically derived relationship between fish abundance and fish size across rainbow trout populations in BC to provide a measure of catch-based fishing quality that accounts for the size-abundance trade off in this system. We replicated our experimental manipulation in two regions known to have different angler populations and broad-scale access costs. We hypothesized that angler effort would respond to variation in stocking density, resulting in spatial heterogeneity in angler effort but homogeneity in catch-based fishing quality within regions. We found that there is an intermediate stocking density for a given lake or region at which angler effort is maximized (i.e., an optimal stocking density), and that this stocking density depends on latent effort and lake accessibility. Furthermore, we found no clear effect of stocking density on our measure of catch-based fishing quality, suggesting that angler effort homogenizes catch-related attributes leading to an eroded relationship between stocking density and catch-based fishing quality at the timescale of annual surveys. We conclude that declines in fishing quality resulting from understocking (due to declines in catch rate with low fish abundance) and overstocking (due to suppressed growth and limited recruitment at high density) give an optimal stocking rate that depends on accessibility and latent effort.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".