From marginal croplands to natural habitats: A methodological framework for assessing the restoration potential to enhance wild-bee pollination in agricultural landscapes
Bibliographic record
Abstract
Context: Intensive agriculture increases crop yields, but harms biodiversity and associated ecosystem services such as pollination. To sustain wild-bee pollination in intensive agricultural landscapes, a minimum of (semi-) natural habitat is needed in the vicinity of crop fields. However, restoration of (semi-) natural habitat is a challenge, especially when most land is allocated to commodity production. Objectives: To evaluate the restoration potential of marginal lands to enhance pollination in intensive agricultural landscapes. Methods: We simulated restoration scenarios in marginal agricultural lands (abandoned and degraded fields, and field edges) in La Vallée-du-Richelieu (Quebec, Canada), aimed at enhancing pollination provision and increasing (semi-) natural habitat coverage by at least 20% within 1 km from crop fields, the estimated minimum amount required to sustain wild-bee populations. We then evaluated the extent to which restoration targets were reached in our scenarios. Results: More than half of the agricultural region studied remained with less than 20% (semi-) natural area coverage, and wild-bee pollination provision could not be ensured across the whole agricultural region after restoration. However, our results show that there is still an important potential for increasing natural habitat coverage by restoring marginal agricultural lands alone. Conclusion: Restoration of marginal lands has a key role to play in the transition towards multifunctionality of production landscapes but might not be sufficient to achieve goals such as those adopted at the COP15 (e.g., restoring 30% of degraded land). Our framework can assist landscape planners in evaluating the restoration potential of agricultural landscapes, as well as its limitations. Supplementary Information: The online version contains supplementary material available at 10.1007/s10980-024-01993-y.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".