From Catastrophe to Recovery: Stories of Fishery Management Success
Bibliographic record
Abstract
Abstract.—Since the mid-1800s, human activities have increasingly dominated ecosystems within the Okanagan River basin, which spans the Canada–United States border between British Columbia and Washington State. Over the past 50 years, fisheries for anadromous salmon in the Okanagan River basin virtually disappeared as once abundant stocks, such as Sockeye Salmon Oncorhynchus nerka, declined to fewer than 10,000 adults returning annually (on average) in the 1990s. Threat assessments suggested degradation of freshwater habitat in the Columbia River basin as the general cause for the decline. However, recent record returns (2008–2016 average >200,000 adults) indicated surprising resilience and recovery. Review of recent stock management and restoration efforts focused on Okanagan Sockeye Salmon indicated that management actions and fortuitous events facilitated the restoration of salmon to levels exceeding recorded, historic maxima. Actions and events identified include (1) assessment to determine whether large increases in escapement provided evidence of historic underuse of spawning (Okanagan River) and rearing environment (Osoyoos Lake) capacities; (2) development of a decision support system to facilitate fish-friendly water management, which reduced losses of eggs or fry to density-independent events (Okanagan River and Lake); (3) a small contribution (<10% of total production) of hatchery-origin fish; and (4) a coincidental return to favorable marine conditions for Okanagan Sockeye Salmon. Recovery success also involved development of an ecosystem-based sustainability strategy incorporating a shared vision for dealing with human and natural system impacts on salmon from local (Okanagan River basin) to global (North Pacific Ocean) scales. Key elements that characterized efforts to restore Okanagan Sockeye Salmon were the development of ecosystem-based management (including elevated levels of engagement, cooperation, and collaboration among responsible parties to support a common cause); the creation of new knowledge of complex cause-and-effect ecological, economic, and cultural associations; and the creation of new resource management tools (e.g., models and decision support systems). Science-based collaboration to restore aquatic ecosystems and Okanagan salmon is an example of positive outcomes resulting from implementation of Canada’s 2005 Wild Salmon Policy.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".