Social–ecological feedbacks drive spatial exploitation in a northern freshwater fishery: A halo of depletion
Bibliographic record
Abstract
Abstract Freshwater fisheries are complex social–ecological systems spatially structured by coupled feedbacks between people and nature. Spatial exploitation dynamics depend on angler preferences for multiple attributes that influence their site choices. Anglers then reciprocally impact local fish populations through size‐selective catch and harvest. Thus, feedbacks among angler site choices, their capture efficiency (i.e. catchability) and fish population dynamics permeate through whole landscapes. We studied the coupled feedbacks and effects of spatial exploitation in an iconic northern freshwater fishery of conservation concern. Specifically, we evaluated several coupled feedbacks in the spatially structured Yukon lake trout fishery using a Bayesian multinomial choice model fitted to onsite interviews and fishery‐independent population assessments to identify whether: (a) trip context (day vs. multi‐day trips) shaped angler preferences and site choices, (b) catch‐based quality was influenced by a size–numbers trade‐off and density‐dependent catchability and (c) fish population structure was associated with the gravity of resource usage resulting from spatial exploitation. Overall, we found that angler site choices were shaped by preferences for multiple characteristics including travel time and catch‐based quality. Angler preferences also varied with trip contexts—for example, anglers on day trips were less willing to travel than anglers on multi‐day trips. We detected strong density‐dependent catchability, which led to hyperstable catches and relatively few anglers dominated most of the catch. There was a strong demographic trade‐off between lake trout body size and abundance that appeared to dynamically interact with anglers' size‐selective preferences for larger lake trout. Coupled feedbacks among angler site choices, size‐selective and hyperstable catches, and density‐dependent growth and survival appeared to structure spatial exploitation patterns leading to a halo of depletion in fish body sizes and fishing quality near urban centres. Synthesis and applications. Feedbacks between fish and anglers affected spatial exploitation patterns leading to a halo of depletion in Yukon lake trout. We recommend fisheries' managers consider the size, distance and behaviours of nearby angler communities using measures of gravity to craft policies aligning expected resource usage with spatial conservation risks. Such approaches may help managers balance stakeholder needs with conservation targets across whole ecosystems.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".