Connecting People to the Landscapes Upon Which They Depend: Modeling Ecosystem Service Flows to Beneficiaries in Canada
Bibliographic record
Abstract
Ecosystem services (ES) are “the benefits people obtain from ecosystems.” ES models estimate the value ecosystems contribute to society’s social and economic activities. Current models present ES values at different locations based on biophysical characteristics or mechanisms but rarely present how these values are distributed to different groups of people in the landscape. Yet decision makers need tools that help identify trade-offs of management activities relevant to different groups, enabling community-based solutions to environmental issues. Focusing on Canada’s landscapes, my thesis advances ES models by quantifying and visualizing ES flows from terrestrial and aquatic ecosystems to specific communities and beneficiary groups, to support decisions that maintain ecosystem areas for the benefits of different groups and future generations. In Chapter 2, I develop a framework for estimating spatial ecosystem “servicesheds” which connects information on both ES supply and beneficiary conditions across spatial areas. I apply the approach to evaluate multiple ES supply-beneficiary relationships in an agricultural and a coastal landscape in Canada. The spatial and quantitative results are usable in real landscape decision-making and the workflow can be incorporated into various science-policy processes. In Chapter 3, I conceptualize ES flows that contribute to agricultural benefits in distinct ways and identify and visualize factors associated with these flows. I develop panel datasets and empirical-statistical models to quantify the flows to pollination, grazing, and water use in agricultural Saskatchewan, Canada. The approach demonstrates ways to use government-collected environmental asset data to help connect environmental accounts with socially relevant outcomes. In Chapter 4, I use climate and land use models and regional agricultural policy targets to develop quantitative assumptions of potential future scenarios. I estimate the losses and gains across multiple ES upon which different communities depend under these scenarios. The results help identify trade-offs and synergies between beneficiary priorities and spatially locate communities vulnerable to the impacts of the scenarios. Overall, my thesis results in geovisual and quantitative measures that show where ecosystem conditions will impact different human beneficiaries and to what extent, as well as the potential consequences of ecosystem changes for the well-being of different communities. I incorporate innovative concepts and uses of existing and growing information on ecosystem conditions and human activities into the development of practical tools for land managers to make more informed decisions. At regional and national levels, the models can support statistical agencies in monitoring and accounting for ecosystem services under established international standards
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Simulation or modeling | low |
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".