‘Sowing and harvesting water’: Revisiting forest restoration in the Peruvian Andes through a multi‐stakeholder analysis
Bibliographic record
Abstract
Abstract Efforts to restore Peru's megadiverse Andean Forests are rapidly growing. While ecological determinants for restoration success are well known, knowledge on the socio‐economic and governance conditions that allow for the success of ecological restoration using native species is scarce. (Appendix ) Using a multi‐stakeholder approach, this paper analyses the motivations, preferences, success factors and governance models for effective ecological restoration of Andean Forests, through 75 semi‐structured interviews with local community members, NGOs and government actors in 11 restoration sites in Peru. We find that across sites and stakeholder groups, the primary motivations for Andean Forest restoration were tied to restoring and improving hydrological resources. Stakeholders valued Andean Forests mostly for their provisioning ecosystem services—with water provision valued by all stakeholders and firewood provision predominantly by communities—followed by regulating services (water retention and climate regulation). Restoration success—the degree of perceived achievement of project objectives—was high at all sites and scored between 2.4 and 3 out of 3. Enabling factors for the restoration success were mostly social and institutional. There was no ‘silver bullet’ to successful restoration; rather, enabling factors included high resource dependence of communities, support from NGOs, participatory management and governance, and the creation of communal conservation agreements. Communities emphasized primarily social and institutional limiting factors, while government stakeholders emphasized technical challenges. We further identified three typologies of how projects engage and compensate communities: a ‘payment model’, a ‘capacity model’ and a ‘mixed model’ which differ in their rentability, longevity and socio‐economic benefits provided. All stakeholder groups favoured active forest restoration and community members identified desirable native plant species with local use and hydrological value. Interviewees also highlighted that restoration needs to go beyond forests, and combine native tree planting, agroforestry, restoration of mountain grasslands and peatlands to holistically improve water resources and long‐term economic benefits at a landscape scale. Synthesis and applications . Andean Forest restoration projects need to consider hydrological ecosystem services in all key restoration stages. Communities need to be involved through participatory processes and receive long‐lasting benefits—both ecosystem services and livelihood incentives—to guarantee long‐term project success. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".