ANGLER NUMERICAL RESPONSE ACROSS LANDSCAPES AND THE COLLAPSE OF FRESHWATER FISHERIES
Bibliographic record
Abstract
Recreational angling opportunities in lakes are distributed across landscapes and attract anglers based on the combination of angling quality, travel distance, and availability of facilities. The relationship between angler density and fishing quality, as measured by catch rate, represents a numerical response that is analogous to a predator numerical response to variability in prey abundance. We quantified this numerical response of anglers to rainbow trout, Oncorhynchus mykiss, populations distributed over a large lake district in south-central British Columbia, Canada. We developed a harvest dynamics model by linking this empirical description of the spatial numerical response of anglers to a logistic population growth rate model. The model was parameterized for rainbow trout and simulated spatial patterns of angler density and catch rates over a landscape. At locations distant from urban centers, angler density is low and catch rate high, suggesting near pristine conditions; at intermediate distances angler density is higher while catch rates are lower and approximate maximum sustainable levels; and at short distances angler density is sufficiently high to harvest to local extirpation. We extrapolated the model to other lake districts varying in human population size using an empirically derived angling participation rate relationship. Extrapolation to lake districts with one-tenth the human population maintained viable fisheries close to the urban area, and districts with 10 times the human populations could not maintain viable fisheries across much of their lake district. Landscape-scale spatial patterns differed quantitatively for species varying in rates of intrinsic population growth and carrying capacity, but the qualitative spatial patterns were consistent among species, demonstrating the pervasive impacts of the angler numerical response. To achieve a management goal of sustaining fisheries across landscapes, a change in management perspective is necessary, from that of individual lakes to one of dynamic harvest processes across landscapes. This new approach makes it clear that a one-size-fits-all management approach must be replaced with a mosaic of approaches cognizant of landscape-scale processes.
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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 | 0.003 |
| 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 | 0.001 |
| 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".