Social–ecological management results in sustained recovery of an imperiled salmon population
Bibliographic record
Abstract
When faced with the loss of a population, resource managers often feel compelled to choose restoration strategies perceived to have low risk, such as the management of the ecological components of the system or the application of regulatory measures. It can be counterintuitive to share decision‐making and resource management with those who want to harvest an imperiled population, yet this social–ecological strategy resulted in the recovery of a wild Atlantic salmon population in Newfoundland, Canada. Atlantic salmon supported widespread commercial, subsistence, and recreational fisheries but declines in abundance necessitated closures and other conservation strategies across many areas of Atlantic Canada in the 1990s. Recreational angling for Atlantic salmon was closed in Terra Nova National Park's Northwest River in 1995 when counts were below expectations based on available habitat. The population continued to decline, even though commercial and recreational fishing mortality had been eliminated, and by 2001, extirpation seemed imminent. Despite pressure to pursue conventional strategies such as catch and release fishing and stocking, public consultation and human dimensions research revealed that illegal fishing was likely contributing to declines and that distrust of resource managers created an environment conducive to poaching. Disrupting this dynamic could not be achieved with conventional strategies, so instead an adaptive management approach was implemented that incorporated local collaboration and improved transparency, and was responsive to positive changes in behavior. Adoption of social–ecological management led to a rapid and sustained recovery of the salmon population in Northwest River, out‐performing populations in adjacent rivers managed with conventional management strategies.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".