Spatial Correlations Don't Predict Changes in Agricultural Ecosystem Services: A Canada-Wide Case Study
Bibliographic record
Abstract
Improving the management of multiple ecosystem services (e.g., food provision, water and air quality regulation, carbon storage, and erosion control) in agricultural landscapes is a critical challenge to improve food system sustainability. However, we currently lack spatially-resolved national-level assessments of the relationships among services in agricultural landscapes over time. This limits our ability to make decisions and predict how environmental changes or agricultural management actions will impact multiple services. How do multiple ecosystem services vary across both space and time, at regional-to-national scales? To address this question, we quantified eight indicators of four ecosystem services across 290 Canadian agricultural landscapes in 1996, 2001, and 2006. We observed consistent correlations between pairs of services across the 290 ecodistricts in each of the 3 years of our study. In particular, ecodistricts with high livestock production had low provision of most regulating services, while ecodistricts with high air quality (ammonia retention) also had high soil and water quality regulation services. However, these ‘snapshot’ correlations poorly predicted how pairs of services changed through time. Ecosystem service change from 1996–2001 to 2001–2006 (as measured by pairwise correlations) showed markedly different patterns than snapshot correlations. In particular, where livestock production increased between years, so did most regulating services. Ecosystem service bundles also showed similar divergent patterns. The distribution of ecosystem service “snapshot” bundles—sets of ecodistricts with similar levels of provision across multiple ecosystem services in a single year—was generally stable between 1996 and 2006; only 15% of ecodistricts changed bundle types in this time period. However, ecosystem service “change” bundles—sets of ecodistricts with similar changes in ecosystem service provision through time—were much more dynamic. Nearly 60% of ecodistricts exhibited a different set of ecosystem service changes from 2001 to 2006 compared to 1996 to 2001. Our results add to the growing evidence that relationships between services across space do not necessarily predict service change through time. Improved understanding of the spatial patterns and temporal dynamics of ecosystem services, and better understanding of underlying processes, is crucial to improve agricultural landscape management for multifunctionality and sustainability.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".