Principles for ensuring healthy and productive freshwater ecosystems that support sustainable fisheries
Bibliographic record
Abstract
Freshwater ecosystems and the fisheries they support are increasingly threatened by human activities. To aid in their management and protection, we outline nine key principles for supporting healthy and productive ecosystems based on the best available science, including laws of physics and chemistry apply to ecology; population dynamics are regulated by reproduction, mortality, and growth; habitat quantity and quality are prerequisites of fish productivity; connectivity among habitats is essential for movements of fishes and their resources; freshwater species and their habitats are tightly linked to surrounding watersheds; biodiversity can enhance ecosystem resiliency and productivity; global processes affect local populations; anthropogenic stressors have cumulative effects; and evolutionary processes can be important. Based on these principles, we provide general recommendations for managing and protecting freshwater ecosystems and the fisheries they support, with examples of successful implementation for each strategy. Key management strategies include engage and consult with stakeholders; ensure that agencies have sufficient capacity, legislation, and authority to implement policies and management plans; define metrics by which fisheries resources and management success or failure will be measured; identify and account for threats to ecosystem productivity; adopt the precautionary approach to management; embrace adaptive management; implement ecosystem-based management; account for all ecosystem services provided by aquatic ecosystems; protect and restore habitat as the foundation for fisheries; and protect biodiversity. Ecosystems are complex with many intertwined components and ignoring linkages and processes significantly reduces the probability of management success. These principles must be considered when identifying management options and developing policies aiming to protect productive freshwater ecosystems and sustainable fisheries.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".