Assessing the success of marine ecosystem restoration using meta-analysis
Bibliographic record
Abstract
Marine ecosystem restoration success stories are needed to incentivize society and private enterprises to build capacity and stimulate investments. Yet, we still must demonstrate that restoration efforts can effectively contribute to achieving the targets set by the UN Decade on Ecosystem Restoration. Here, we conduct a meta-analysis on 764 active restoration interventions across a wide range of marine habitats worldwide. We show that marine ecosystem restorations have an average success of ~64% and that they are: viable for a large variety of marine habitats, including deep-sea ecosystems; highly successful for saltmarshes, tropical coral reefs and habitat-forming species such as animal forests; successful at all spatial scales, so that restoration over large spatial scales can be done using multiple interventions at small-spatial scales that better represent the natural variability, and scalable through dedicated policies, regulations, and financing instruments. Restoration interventions were surprisingly effective even in areas where human impacts persisted, demonstrating that successful restorations can be initiated before all stressors have been removed. These results demonstrate the immediate feasibility of a global ‘blue restoration’ plan even for deep-sea ecosystems, enabled by increasing availability of new and cost-effective technologies. This study evaluated the success of marine ecosystem restoration efforts through a descriptive statistical comparison, a formal meta-analysis conducted on 764 active restoration interventions, and by using a mixed model based on a spectrum of survival data reported in the reviewed literature.
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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 | 0.033 | 0.071 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.039 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".