‘Sowing and harvesting water’: Revisiting forest restoration in the Peruvian Andes through a multi‐stakeholder analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».