Ecosystem service coproduction across the zones of biosphere reserves in Europe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biosphere reserves (BR) balance biodiversity protection and sustainable use through different management restrictions in three zones: core areas, buffer zones, and transition areas. Information about the links between zoning and ecosystem services (ES) is lacking, particularly in terms of the relative roles of natural contributions (ecosystem properties and functions) and anthropogenic contributions (human inputs such as technology and infrastructure) in coproducing ES. This study aimed to: (1) analyse how coproduction of four ES (crop production, grazing, timber production, recreation) differs across the three zones of BRs; and (2) understand which predictors (zoning, natural and anthropogenic contributions, other environmental characteristics) best explain ES provision within BRs. To do this, we collected spatial data on 137 terrestrial BRs in the European Union and on 16 indicators of ES coproduction. We used non-parametric pairwise Wilcoxon rank sum tests to calculate differences in indicators between zones. We used model selection and multiple linear regression to identify predictors of ES provision patterns. Anthropogenic contributions showed most differences between zones, with contributions generally increasing from buffer zones to transition areas. Natural contributions did not, on average, differ between zones, however, for recreation and crop production they decreased from buffer zones to transition areas. ES provision differed between zones only for crop production and grazing, which increased from buffer zones to transition areas. Regression analysis showed that natural contributions are the best predictors of ES provision for all four services. Our results indicate that zoning of BRs has implications for ES coproduction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it