Social-ecological interactions, management panaceas, and the future of wild fish populations
Bibliographic record
Abstract
We explored the social and ecological outcomes associated with emergence of a management panacea designed to govern a stochastic renewable natural resource. To that end, we constructed a model of a coupled social-ecological system of recreational fisheries in which a manager supports naturally fluctuating stocks by stocking fish in response to harvest-driven satisfaction of resource users. The realistic assumption of users remembering past harvest experiences when exploiting a stochastically fluctuating fish population facilitates the emergence of a stocking-based management panacea over time. The social benefits of panacea formation involve dampening natural population fluctuations and generating stability of user satisfaction. It also maintains the resource but promotes the eventual replacement of wild fish by hatchery-descended fish. Our analyses show this outcome is particularly likely when hatchery-descended fish are reasonably fit (e.g., characterized by similar survival relative to wild fish) and/or when natural recruitment of the wild population is low (e.g., attributable to habitat deterioration), which leaves the wild population with little buffer against competition by stocked fish. The potential for release-based panacea formation is particularly likely under user-based management regimes and should be common in a range of social-ecological systems (e.g., fisheries, forestry), whenever user groups are entitled to engage in release or replanting strategies. The net result will be the preservation of a renewable resource through user-based incentives, but the once natural populations are likely to be altered and to host nonnative genotypes. This risks other ecosystem services and the future of wild populations.
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 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".