SWIO 30*30: A social-ecological approach to identify marine priority areas for conservation under the Kunming-Montreal Global Biodiversity Framework in the South West Indian Ocean region.
Notice bibliographique
Résumé
The SWIO 30*30 project is a social-ecological initiative focused on identifying priority marine conservation areas across the South West Indian Ocean (SWIO) region to meet the Kunming-Montreal Global Biodiversity Framework goal of protecting 30% of terrestrial and marine areas by 2030. SWIO marine ecosystems, while hosting exceptional biodiversity, face high ecological pressure from overfishing, pollution, and habitat loss. Despite these challenges, the coverage of Marine Protected Areas (MPAs) across this region remains low, with most countries far from the 30% protection target. This situation highlights the urgent need for strategic conservation planning that is tailored to SWIO’s unique ecological and socio-economic contexts. This project is built on our previous successful Peru 30*30 project, which provided promising results for expanding Peruvian protected areas through a combination of scientific methods and local stakeholder engagement. The current objective is to provide a framework for conservation planning in SWIO marine areas (for Madagascar, Mozambique, Mayotte, Reunion, Comoros) that not only promotes biodiversity but also enhances ecosystem services essential for local communities, such as carbon storage, food provision, and cultural values. To answer the project’s core research questions—namely, identifying priority areas for marine conservation and understanding how to balance various conservation factors—the SWIO 30*30 project adopts a transdisciplinary approach that involves three main methodological components. First, it integrates diverse data, combining information on biodiversity, ecosystem services, and socio-economic factors. These data can be sourced from both space/in situ earth observation agencies (e.g. ESA, CNES) and environmental marine monitoring programs (e.g. CMEMS Copernicus), ensuring that the conservation planning process is informed by high-quality, contextually relevant data. Second, it applies advanced artificial intelligence techniques, including mathematical optimization methods, to address the multi-objective complexity of marine conservation planning. These AI tools allow for more sophisticated prioritization of conservation areas by handling the high combinatorial demands of multi-factor decisions. Third, the project follows a stakeholder co-production model, involving local communities and decision-makers throughout the process. This collaboration increases the transparency, acceptance, and practical utility of conservation recommendations, improving the chances that the proposed MPAs will be adopted and managed effectively at the national level. To adapt conservation strategies to the specific regional needs, the project evaluates four different scenarios: (1) a Biodiversity scenario, focused solely on preserving biodiversity; (2) a Socio-ecological scenario that adds ecosystem services such as carbon storage, food provision, and cultural values; (3) a Pragmatic scenario that incorporates human impacts (e.g., fishing, sea transport) alongside ecological considerations; and (4) an Integrated scenario that combines all previous factors with an emphasis on ecological connectivity. These scenarios will allow decision-makers to weigh conservation trade-offs and synergies and identify the best path forward for MPAs. By offering a context-sensitive and data-driven framework, SWIO 30*30 aims to contribute not only to SWIO biodiversity and ecosystem services conservation but also pave the way to broader global efforts, potentially serving as a model for other biodiverse yet under-protected marine regions worldwide.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».