Biodiversity outcomes of nature-based solutions for climate change adaptation: Characterising the evidence base
Bibliographic record
Abstract
Nature-based solutions (NbS) are increasingly recognised for their potential to address both the climate and biodiversity crises. Both these outcomes rely on the capacity of NbS to support and enhance the health of an ecosystem: its biodiversity, the condition of its abiotic and biotic elements, and its capacity to continue to function despite environmental change. However, while understanding of ecosystem health outcomes of NbS for climate change mitigation has developed in recent years, the outcomes of those implemented for adaptation remain poorly understood. To address this, we systematically reviewed the outcomes of 109 nature-based interventions for climate change adaptation using 33 indicators of ecosystem health across eight broad categories (e.g., diversity, biomass, ecosystem composition). We showed that 88% of interventions with reported positive outcomes for climate change adaptation also reported benefits for ecosystem health. We also showed that interventions were associated with a 67% average increase in species richness. All eight studies that reported benefits for both climate change mitigation and adaptation also supported ecosystem health, leading to a “triple win.” However, there were also trade-offs, mainly for forest management and creation of novel ecosystems such as monoculture plantations of non-native species. Our review highlights two key limitations in our understanding of the outcomes of NbS for ecosystem health. First, a limited selection of metrics are used and these rarely include key aspects such as functional diversity and habitat connectivity. Second, taxonomic coverage is limited: 50% of interventions only had evidence for effects on plants, and 57% of outcomes did not distinguish between native and non-native species. We make suggestions of how to improve assessments of the ecosystem health outcomes of NbS, as well as policy recommendations to enable the upscaling of NbS that support flourishing and resilient ecosystems, and are effective in addressing both climate and biodiversity goals.
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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 | 0.022 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".