A functional connectivity approach for exploring interactions of multiple ecosystem services in the context of agricultural landscapes in the Canadian prairies
Notice bibliographique
Résumé
• Pioneers integrative ES mapping and network analysis for connectivity in agricultural landscapes. • Novel method highlights key areas for ES interactions hotspots in the landscape. • Results show ∼29 % of the study area, functions as critical connectivity zones for various ES, including pollination, carbon storage, and soil erosion control. • Natural habitats including wetlands, shrublands and grasslands within the connectivity zones are key mediators of ES provisioning and ES interactions. • The study offers actionable insights for land managers on sustainable ecosystem service enhancement. Land-use and land-cover patterns, including their spatial heterogeneity and configuration, are fundamental in shaping landscape-level ecological processes, functions, and services. Despite growing recognition of the importance of these patterns, gaps remain in our understanding of how they influence the functional connectivity of ecosystem services (ES)—a crucial aspect for ecosystem resilience and sustainability. This research aims to bridge this gap by investigating the functional connectivity among multiple ES, such as pollination, carbon storage, soil erosion control, wetland-based ES such as habitat provisioning and water storage capacity from marshes, swamps, and open water wetlands, and agricultural food production within a complex landscape. We define functional connectivity as the extent to which the landscape facilitates or impedes the interactions and interdependencies of ecological processes that combine to create distinct ecosystem services. This definition encompasses the dynamics within a spatially interconnected mosaic of land use and land cover, exemplified by connections such as those from pollination provisioning areas to croplands. The primary goal of this research is to develop an empirical framework that encapsulates ‘network topological’ interactions— essentially, the complex interplay among various components of the ecosystem — specific to agricultural landscapes and then to apply this framework to the Canadian prairies. Our methodology uses the spatial tools including InVEST, ARIES, and GIS to map diverse ES. An ecological network is then constructed for these ES at the landscape scale, designating network nodes based on high-value ES provisioning areas and defining links between pairs of ES according to their functional connections (overlapping and proximal in physical space). These functional connections effectively delineate areas of the landscape where the majority of ES flows occur. Mapping ES connectivity and network building revealed that around 29% of the studied landscape lies within functional connectivity zones for the selected ES, representing hotspots of significant ES interactions. Our findings reveal that although soil erosion-control spans just 1.36% of the total area, a substantial 72.59% of its spatial extent was identified as functionally connected. Land cover analysis in functional connectivity zones revealed that natural habitats such as shrublands, broadleaf forests, wetlands, and grasslands are vital mediators of ES. The variability in ES interconnectivity in the landscape was evident both in the intensity of interactions and observed connections. Our findings, informed by Ecological Network Analysis (ENA), emphasize the need for integrating connectivity and systems thinking in conservation sciences to achieve sustainability and ecosystem resilience. The insights offer a foundation to explore optimal ES provisioning scenarios at the landscape scale.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».