A functional connectivity approach for exploring interactions of multiple ecosystem services in the context of agricultural landscapes in the Canadian prairies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".