Pattern, function, planning : modeling ecosystem service dynamics for sustainable multifunctional landscapes in the Canadian prairies
Bibliographic record
Abstract
Multifunctional landscapes are essential for sustaining ecosystem services (ES) including agricultural productivity, yet land-use and management practices often create complex trade-offs and synergies. In the Canadian prairies, an intensively modified agricultural landscapes, the spatial dynamics of ES interactions remain inadequately understood, limiting their long-term resilience and sustainability. This dissertation addresses this gap by developing a novel "Pattern-Function-Planning" framework to systematically explore how landscape patterns govern ES flows and their implications for sustainable land management. The research first uses mechanistic modeling to understand pollination dynamics, then employs network analysis to map ES functional connectivity. It subsequently explores how landscape patterns and ES flows mediate agricultural crop yield and finally identifies spatial patterns of trade-offs and synergies among conservation, production, and climate resilience objectives to inform targeted planning. This is achieved by integrating spatial ES modeling (e.g., ARIES, InVEST), ecological network analysis, non-linear statistical modeling (GAMs), and multi-objective optimization. Key findings reveal that: over 45% of pollination-dependent croplands in the study area lack sufficient wild pollination; approximately 29% of the selected landscape functions as critical ES interaction hotspots, with natural habitats like wetlands and grasslands serving as vital mediators of ES connectivity; landscape configuration (e.g., connectivity) often exerts greater influence on crop yield than the mere amount of natural habitat; and, while an area covering 27.33% of the landscape faces significant production-conservation trade-offs, only 9.11% currently supports synergistic, resilient production systems. This research demonstrates that landscape pattern is a fundamental driver of the ecological and regional functions that dictate ES flows and bundling. The findings underscore that agricultural output is not just a function of field-level inputs but is deeply embedded within, and responsive to, the broader landscape matrix and the ecological processes it supports. A crucial insight is that strategic management of landscape configuration—such as enhancing connectivity and crop diversity—can offer greater returns for both agricultural performance and ES multifunctionality than focusing merely on the quantity of natural habitat. The "Pattern-Function-Planning" framework offers a novel, applied approach to explore ES spatial dynamics and reconcile conflicting objectives. This research thus provides pragmatic decision pathways and a spatially explicit basis for policy and land-use planning aimed at fostering sustainable, multifunctional agricultural landscapes where single-priority management is no longer a viable solution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".