Spatiotemporal patterns of forest pollinator diversity across the southeastern United States
Bibliographic record
Abstract
Abstract Aim Efforts to understand how pollinating insect diversity is distributed across large geographic areas are rare despite the importance of such work for conserving regional diversity. We sought to relate the diversity of bees (Hymenoptera: Apoidea), hover flies (Diptera: Syrphidae), and butterflies (Lepidoptera) to ecoregion, landscape context, canopy openness, and forest composition across southeastern U.S. forests. Location Nineteen experimental forests across nine states in the southeastern U.S. Methods We established 5–7 plots on each experimental forest. In each, we sampled pollinators monthly (March–September) using coloured pan traps, and collected data on local forest characteristics. We used the National Land Cover Database (NLCD) to quantify surrounding landcover at different spatial scales. Results Bee richness was negatively correlated with both the amount of conifer (pine) forest and the extent of wetlands in the surrounding landscape but was positively correlated with canopy openness. Hover flies and butterflies were less sensitive to landscape context and stand conditions. Pollinator communities differed considerably among ecoregions, with those of the Central Appalachian and Coastal Plain ecoregions being particularly distinct. Bee richness and abundance peaked 2 months earlier in Central Appalachia than in the Coastal Plain and Southeastern Mixed Forest ecoregions. Main Conclusions Our findings reveal ecoregional differences in pollinator communities across the southeastern U.S. and highlight the importance of landscape context and local forest conditions to this diverse fauna. The closed broadleaf forests of Appalachia and the open conifer‐dominated forests of the Coastal Plain support particularly distinct pollinator communities with contrasting seasonality. Our results suggest pine forests may reduce pollinator diversity in regions historically dominated by broadleaf forests. However, efforts to create more open canopies can help improve conditions for pollinators in planted pine forests. Research exploring associations between forest pollinators and different broadleaf tree taxa is needed to better anticipate the impacts of various management activities.
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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 | 0.001 |
| 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".