Managing ecosystem services and biodiversity conservation in agricultural landscapes: are the solutions the same?
Bibliographic record
Abstract
Summary 1. Biodiversity conservation and agricultural production have traditionally been viewed as substantially in conflict and recent declines in biodiversity have been linked to intensive agricultural production. An increased use of ecosystem services to benefit agricultural production has been proposed as one strategy to enhance conservation of biodiversity in agricultural landscapes and attenuate this conflict. 2. We use examples from the literature to examine the relationship between management of agricultural landscapes for the provision of ecosystem services and management for biodiversity conservation. 3. We argue that although there is a relationship between biodiversity conservation and management for ecosystem services, it does not follow that focusing solely on one or the other will provide reciprocal benefits of the kind we should be seeking in land‐use decision‐making. 4. We identify a number of asymmetries in the relationship between management for maximizing ecosystem services and biodiversity conservation. Actions that increase or protect biodiversity in an agricultural landscape will often indirectly help preserve ecosystem services, but actions that focus on enhancing ecosystem services will not necessarily provide good outcomes for biodiversity. 5. Synthesis and applications. Synergies between agricultural productivity and biodiversity conservation can only be achieved if an understanding of ecosystem services leads to a change in management practice that supports greater biodiversity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".