On‐farm habitat restoration counters biotic homogenization in intensively managed agriculture
Bibliographic record
Abstract
To slow the rate of global species loss, it is imperative to understand how to restore and maintain native biodiversity in agricultural landscapes. Currently, agriculture is associated with lower spatial heterogeneity and turnover in community composition (β-diversity). While some techniques are known to enhance α-diversity, it is unclear whether habitat restoration can re-establish β-diversity. Using a long-term pollinator dataset, comprising ∼9,800 specimens collected from the intensively managed agricultural landscape of the Central Valley of California, we show that on-farm habitat restoration in the form of native plant 'hedgerows', when replicated across a landscape, can boost β-diversity by approximately 14% relative to unrestored field margins, to levels similar to some natural communities. Hedgerows restore β-diversity by promoting the assembly of phenotypically diverse communities. Intensively managed agriculture imposes a strong ecological filter that negatively affects several important dimensions of community trait diversity, distribution, and uniqueness. However, by helping to restore phenotypically diverse pollinator communities, small-scale restorations such as hedgerows provide a valuable tool for conserving biodiversity and promoting ecosystem services.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".