Key questions for understanding drivers of biodiversity-ecosystem service relationships across spatial scales
Bibliographic record
Abstract
Abstract Context Biodiversity loss is predicted to have significant impacts on ecosystem services based on previous ecological work at small spatial and temporal scales. However, scaling up understanding of biodiversity-ecosystem service (BES) relationships to broader scales is difficult since ecosystem services emerge from complex interactions between ecosystems, people, and technology. Objectives In order to inform and direct future BES research, identify and categorise the ecological and social-ecological drivers operating at different spatial scales that could strengthen or weaken BES relationships. Methods We developed a conceptual framework to understand the potential drivers across spatial scales that could affect BES relationships and then categorized these drivers to synthesize the current state of knowledge. Results Our conceptual framework identifies ecological/supply-side and social-ecological/demand-side drivers, and cross-scale interactions that influence BES relationships at different scales. Different combinations of these drivers in different contexts will lead to a variety of strengths, shape, and directionality in BES relationships across spatial scales. Conclusions We put forward four predictions about the spatial scales that the effects of biodiversity, ecosystem service management, ecosystem co-production, and abiotic linkages or effects will be most evident on BES relationships and use these to propose future directions to best advance BES research across scales.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".