A freshwater perspective on the United Nations decade for ecosystem restoration
Bibliographic record
Abstract
Abstract Globally, ecosystems have suffered from anthropogenic stressors as we enter the sixth mass extinction within the Anthropocene. In response, the UN has declared 2020–2030 the Decade for Ecosystem Restoration, aiming to mitigate ecosystem degradation and biodiversity loss. Freshwater ecosystems are disproportionately impacted relative to marine or terrestrial systems and ecological restoration is needed to preserve biodiversity and ecosystem services. Paradoxically, freshwater is among Earth's most vital ecosystem services. Here we identify meaningful considerations from a freshwater perspective that will lead to progression toward the restoration of freshwater ecosystems: work across terrestrial and freshwater boundaries during restoration, emulate nature, think and act on a watershed scale, design for environmental heterogeneity, mitigate threats alongside restoration, identify bright spots, think long term (a decade is not long enough), and embrace social–ecological systems thinking. Further, we reflect upon the three implementation pathways identified by the UN to translate these considerations into practice in hopes of “bending the curve” for freshwater biodiversity and ecosystems. Pathway 1, building a global movement, could create a network to share experiences and knowledge promoting vicarious learning, ultimately leading to more effective restoration. Pathway 2, generating political support, will be necessary to institutionalize ecosystem protection and restoration by demonstrating the value of freshwater ecosystems and biodiversity. Pathway 3, building technical capacity, aims to improve the current and often ineffective restoration toolbox by incorporating evidence syntheses (i.e., appraisal of evidence base) and Indigenous ways of knowing (i.e., two eyed seeing). Given that freshwater ecosystems are in dire need of repair, it is our hope that these considerations and implementation pathways will contribute to an actionable and productive Decade for Ecosystem Restoration.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".