Landscape structure affects the provision of multiple ecosystem services
Bibliographic record
Abstract
Understanding how landscape structure, the composition and configuration of land use/land cover (LULC) types, affects the relative supply of ecosystem services (ES), is critical to improving landscape management.While there is a long history of studies on landscape composition, the importance of landscape configuration has only recently become apparent.To understand the role of landscape structure in the provision of multiple ES, we must understand how ES respond to different measures of both composition and configuration of LULC.We used a multivariate framework to quantify the role of landscape configuration and composition in the provision of ten ES in 130 municipalities in an agricultural region in Southern Québec.We identified the relative influence of composition and configuration in the provision of these ES using multiple regression, and on bundles of ES using canonical redundancy analysis.We found that both configuration and composition play a role in explaining variation in the supply of ES, but the relative contribution of composition and configuration varies significantly among ES.We also identified three distinct ES bundles (sets of ES that regularly appear together on the landscape) and found that each bundle was associated with a unique area in the landscape, that mapped to a gradient in the composition and configuration of forest and agricultural LULC.These results show that the distribution of ES on the landscape depends upon both the overall composition of LULC types and their configuration on the landscape.As ES become more widely used to steer land use decision-making, quantifying the roles of configuration and composition in the provision of ES bundles can improve landscape management by helping us understand when and where the spatial pattern of land cover is important for multiple services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".