Effects of farmland heterogeneity on biodiversity are similar to—or even larger than—the effects of farming practices
Bibliographic record
Abstract
Pressure to increase food production to meet the demands of a growing human population can make conservation-motivated recommendations to limit agricultural expansion impractical. Therefore, we need to identify conservation actions that can support biodiversity without taking land out of production. Previous studies suggest this can be accomplished by increasing “farmland heterogeneity”—i.e. heterogeneity of the cropped portions of agricultural landscapes—by, for example, decreasing field sizes. However, it is not yet clear whether policies/guidelines that promote farmland heterogeneity will be as effective as those targeting farming practices. Here, we estimated the relative effects of six practices—annual/perennial crop, fertilizer use, herbicide use, insecticide use, tile drainage, and tillage—versus two aspects of farmland heterogeneity—field size and crop diversity—on the diversity of herbaceous plants, woody plants, butterflies, syrphid flies, bees, carabid beetles, spiders, and birds in rural eastern Ontario, Canada. The strength of effect of farming practices and farmland heterogeneity varied among taxonomic groups. Nevertheless, we found important effects of both farming practices and farmland heterogeneity on the combined (multi) diversity across these groups. In particular, we found greater multidiversity in untilled, perennial crop fields than tilled, annual crop fields, and greater multidiversity in agricultural landscapes with smaller crop fields and less diverse crops. The directions of effect of these variables were generally consistent across individual taxonomic groups. For example, richness was lower in landscapes with larger fields and more diverse crops than in landscapes with smaller fields and less diverse crops for all taxa except spiders. The negative effect of crop diversity on multidiversity and the richness of most of the studied taxa indicates that this aspect of farmland heterogeneity does not necessarily benefit wildlife species. Nevertheless, a compelling implication of this study is that it suggests that policies/guidelines aimed at reducing crop field sizes would be at least as effective for conservation of biodiversity within working agricultural landscapes as those designed to promote a wildlife-friendly farming practice.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".