Managing exploitation of freshwater species and aggregates to protect and restore freshwater biodiversity
Bibliographic record
Abstract
For millennia humans have extracted biological and physical resources from the planet to sustain societies and enable the development of technology and infrastructure. Growth in the human population and changing consumption patterns have increased the human footprint on ecosystems and their biodiversity, including in fresh waters. Freshwater ecosystems and biodiversity face many threats and it is now widely accepted that we are in a biodiversity crisis. One means of protecting and restoring freshwater biodiversity is to better manage the exploitation of freshwater biota and aggregate resources (e.g., sand, gravel, and boulders). Here we outline the threats arising from such exploitation and identify response options to ensure that methods and levels of extraction are sustainable and allow recovery of over-exploited freshwater biodiversity and ecosystems. The guidance we provide will enable practitioners, policy-makers, and resource stewards to embrace effective, sustainable, and evidence-based approaches to resource extraction. Response options for managing species exploitation include strengthening assessment and reporting, using science-based approaches to reduce overexploitation and support recovery, embracing community engagement, and building or tightening legislation. Response options for managing exploitation of freshwater aggregate resources include reducing demand for harvest, strengthening governance, reporting, and monitoring of environmental impacts, and promoting the restoration of degraded ecosystems or compensating for losses. Diverse case studies highlight examples of where various management actions have been implemented in an effort to consider how they can be scaled up and adapted to other contexts. Managing exploitation will be a key aspect of broader initiatives needed to protect and restore freshwater biodiversity around the globe.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".