Moths versus Bees: Contrasts in Habitat Preferences Across Barrens of the Northeastern <scp>USA</scp>
Bibliographic record
Abstract
Bees and moths are globally important pollinators. Xeric barrens in the largely mesic northeastern USA support high levels of pollinator diversity, including rare bees and moths. We investigated the response of bee vs. moth communities to abiotic and vegetation drivers in barrens across the region. We sampled local environmental conditions, vegetation, bees, and moths for 2-4 years in 19 preserves. Employing random forest analysis, we tested the role of 29 abiotic and vegetation predictors of bee vs. moth abundance, species richness, Shannon-Wiener Index, evenness, and species composition. Variables related to climate, canopy cover, and soils were the most important predictors of abundance, diversity, and species composition for both bees and moths. Vegetation variables, such as species richness of shrubs and hostplants, were also important for bees. The direction of these relationships contrasted sharply between bees and moths: bees were more abundant and species rich in more open, sandy sites and moths the opposite. Habitat preferences for a subset of moth xeric specialists were much more similar to bees than to other moths, with a preference for open, sandy conditions. Contrasts between bees and moths in habitat preferences likely stemmed from differences in their life histories: bees rely on flowers for feeding and porous substrates for nesting, whereas most moth adults feed on flowers, but many moth caterpillars use woody plants as hosts. In sharp contrast to the results for abundance and richness, bees and moths responded similarly for the Shannon-Wiener Index, which raises important general questions about the conservation value of these two metrics. Our results suggest that, because of differences in habitat preferences among pollinators, barrens management for both open and more closed habitats is likely to promote the highest abundance and diversity of local bee and moth pollinator communities jointly.
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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 | 0.000 |
| 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".