Perspectives on socio-ecological studies in the Northern and Southern Hemispheres
Notice bibliographique
Résumé
Abstract Socio-ecology studies the relationships between human activities and natural systems and their importance in management and public policy. Our objective was to analyse how published papers in countries with a high Human Development Index (HDI) perform socio-ecological studies and compare them between the Northern and Southern Hemispheres. To do this, we used the Scopus platform as a source for searching and obtaining scientific papers about socio-ecological studies conducted in countries from the Northern and Southern Hemispheres. We calculated the number ( n ) of papers published per year and classified them using the main subject areas of the SCImago Journal & Country Rank database. Then, we analysed whether papers included specific recommendations for natural system management, nature conservation, policies or governance structures, or science in general. Besides, we studied whether the papers addressed socio-ecological studies related to flora and fauna and from what specific group of organisms or systems. Data were compared using the chi-square ( χ 2 ) test (Pearson p < 0.005). A total of 467 papers were analysed, where 34% were from the Southern Hemisphere (mainly Argentina, Australia, Chile, and South Africa) and 66% from the Northern Hemisphere (mainly the USA, Canada, and Spain). The Northern Hemisphere (mainly North America and Europe) played a major role in the socio-ecological knowledge exchange than the Southern Hemisphere (South America and Africa). The results showed socio-ecological studies focused mainly on generating management recommendations in social and environmental science fields. The number of studies coming from the Northern Hemisphere was significantly higher than those from the Southern Hemisphere. Most of them were conducted at a local level (e.g., watersheds or human settlements) in three different systems (i) terrestrial (e.g., forests or grasslands), (ii) freshwater (e.g., rivers or streams) and (iii) marine (e.g., coastlines or seas). Most of the studies (70%) were conducted in production systems, where the majority included livestock (mainly bovine) and aquatic fisheries (e.g., salmon, artisanal coastal fishing, or trout). Most vegetation papers (65%) were on native forests. Papers on wildlife made up 30% of all animal-related studies, with mammals, birds, and marine invertebrates (such as collars) being the most extensively researched species. This work highlighted the socio-ecological approach that was used in the analysed countries with greater HDI to develop management options for natural systems.
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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 | 0,003 | 0,006 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».