Strong and nonlinear effects of fragmentation on ecosystem service provision at multiple scales
Bibliographic record
Abstract
Human actions, such as converting natural land cover to agricultural or urban land, result in the loss and fragmentation of natural habitat, with important consequences for the provision of ecosystem services. Such habitat loss is especially important for services that are supplied by fragments of natural land cover and that depend on flows of organisms, matter, or people across the landscape to produce benefits, such as pollination, pest regulation, recreation and cultural services. However, our quantitative knowledge about precisely how different patterns of landscape fragmentation might affect the provision of these types of services is limited. We used a simple, spatially explicit model to evaluate the potential impact of natural land cover loss and fragmentation on the provision of hypothetical ecosystem services. Based on current literature, we assumed that fragments of natural land cover provide ecosystem services to the area surrounding them in a distance-dependent manner such that ecosystem service flow depended on proximity to fragments. We modeled seven different patterns of natural land cover loss across landscapes that varied in the overall level of landscape fragmentation. Our model predicts that natural land cover loss will have strong and unimodal effects on ecosystem service provision, with clear thresholds indicating rapid loss of service provision beyond critical levels of natural land cover loss. It also predicts the presence of a tradeoff between maximizing ecosystem service provision and conserving natural land cover, and a mismatch between ecosystem service provision at landscape versus finer spatial scales. Importantly, the pattern of landscape fragmentation mitigated or intensified these tradeoffs and mismatches. Our model suggests that managing patterns of natural land cover loss and fragmentation could help influence the provision of multiple ecosystem services and manage tradeoffs and synergies between services across different human-dominated landscapes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".