The land-blending strategy: Contribution of metapopulation theory to the land sparing-sharing debate
Bibliographic record
Abstract
Two land management strategies have been proposed to preserve biodiversity while maintaining sufficient agricultural production: land sparing and land sharing. Debate on their efficiency continues, although a third hybrid strategy has emerged. The balance between these strategies is context-dependent, limiting generalizations. We addressed this challenge using a metapopulation-based model to simulate species persistence in agricultural landscapes under different management strategies. Our model captures the influence of contextual factors, such as landscape composition, connectivity, and pest incidence, allowing us to evaluate how landscape management strategies influence biodiversity and ecosystem services such as pest regulation. Our results highlight key factors for designing effective landscape management strategies. First, maintaining intermediate quality habitats (e.g., agroforests) within the landscape is essential to support pest controllers and thus, the provision of ecosystem services. Second, although agroforestry expansion can reduce economic returns compared to conventional agriculture, biodiversity offsets these costs when pest pressure is high and biological control is effective. These findings emphasize the importance of an integrated approach to implement effective landscape management strategies, optimizing both productivity and biodiversity conservation. This study reveals the potential of a hybrid ‘land blending’ strategy, able to outperform traditional land sparing-sharing approaches, while offering greater flexibility for change and uncertainty. To our knowledge, this study represents the first theoretical modelling approach to assess the effectiveness of conservation strategies without considering specific contextual influences. Our findings enhance our understanding of the impact of context on optimal strategies, enriching the debate and suggesting new perspectives beyond its false dichotomy. • Even small amounts of semi-natural systems in the landscape maintain pest control services. • Biodiversity in landscapes provides indirect pest control benefits to intensive agriculture. • Biodiversity offsets semi-natural system costs through provision of pest-control services. • Mixing land sparing and sharing can outperform basic strategies, depending on landscape context. • Agricultural output and biodiversity can be optimized dispelling the debate dichotomy.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".