Building an inclusive conservation vision founded upon ecological values and social opportunities
Notice bibliographique
Résumé
Abstract We developed a spatially explicit model for the eastern United States to help identify where we work to conserve a network of ecologically important lands with the input of communities often excluded from conservation planning. We built multiple individual and composite maps from ecological data selected from four broad themes: ecological integrity, connectivity, biodiversity, and ecosystem services. Datasets selected from these themes identify lands important for a conservation network resilient to global change stressors that provide important functions upon which people depend. We also built multiple individual and composite maps using spatial data from existing efforts to measure social conditions constructed around three broad themes: frontline communities, historically marginalized populations, and people who experience the impacts of climate change most accurately. We view these areas as having high social opportunity through improvement of the environmental conditions experienced by these communities. We also hope to engage a greater representation of values, experience, and knowledge held by communities not typically part of conservation planning. We assert that these aims can only be achieved by amplifying excluded community voice and leadership when developing approaches to conservation. This spatially explicit social-ecological model is comparable to models we have built to facilitate development of various collaboratives and initiatives in our place-based work. This is a type of decision support tool, not a decision maker. We present a broad spatial summary of three conditions: 1) co-occurring high social opportunity and nationally significant ecological value, 2) areas of high social opportunity, and 3) areas of high ecological value. These analyses are presented as a foundation for large scale and collaborative conservation planning that seeks to conserve key ecological areas while addressing the needs of a broader spectrum of people. We envision regional conservation efforts that support collective ecological and social well-being. Our framework and data can also be rescaled to smaller extents to identify projects where social and ecological well-being might intersect in local areas. While our combined spatial data synthesizes myriad information, our collection of the individual criteria can serve as a geospatial library of resources available to regional and local conservation efforts. While we intend for this work to guide conservation strategies including land protection, land management, and land stewardship initiatives in conjunction with social initiatives, this work is a tool and not itself a method or blueprint for the challenging work ahead.
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 | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».