The Value of Woody Hedgerows for Moth Diversity on Organic and Conventional Farms
Bibliographic record
Abstract
Habitat destruction and degradation are important drivers of biodiversity loss within agro-ecosystems. However, little is known about the effect of farming practices and the value of woody hedgerows on Lepidoptera in North America. The purpose of this work was to study moth diversity in woody hedgerows and croplands of organic and conventional farms. In addition, the influence of vegetation composition and abiotic variables on species richness, abundance, and composition was examined. Moths were sampled with light traps during six weeks in the summer of 2001. Vegetation data and abiotic variables were obtained for all sites. In total, 26,020 individuals from 12 families and 408 species were captured. Most species were uncommon. Only 35 species included >100 individuals while for 71% of species <10 individuals were found. The Noctuidae represented 221 species and 85% of all individuals captured. Woody hedgerows harbored more species and in greater number than croplands. There was no significant difference in moth diversity between organic and conventional farms, except that the Notodontidae were significantly more species rich in organic than in conventional sites. Results show that species richness, abundance, and composition were greatly influenced by habitat types (hedgerow versus crop field) and abiotic variables (minimum temperature which was correlated to moon illumination, rainfall, and cloud cover). Moth species composition was significantly correlated to vegetation composition. This study broadens our understanding of the factors driving moth diversity and expands our knowledge of their geographic range. The maintenance of noncrop habitats such as woody hedgerows within agro-ecosystems seems paramount to preserving the biodiversity and abundance of many organisms, including moths.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".