Canopy thinning, not agricultural history, determines early responses of wild bees to longleaf pine savanna restoration
Bibliographic record
Abstract
Longleaf pine savannas are highly threatened, fire‐maintained ecosystems unique to the southeastern United States. Fire suppression and conversion to agriculture have strongly affected this ecosystem, altering overstory canopies, understory plant communities, and animal populations. Tree thinning to reinstate open canopies can benefit understory plant diversity, but effects on animal communities are less well understood. Moreover, agricultural land‐use legacies can have long‐lasting impacts on plant communities, but their effects on animal communities either alone or through interactions with restoration are unclear. Resolving these impacts is important due to the conservation potential of fire‐suppressed and post‐agricultural longleaf savannas. We evaluated how historical agricultural land use and canopy thinning affect the diversity and abundance of wild bees in longleaf pine savannas. We employed a replicated, large‐scale factorial block experiment in South Carolina, where canopy thinning was applied to longleaf pine savannas that were either post‐agricultural or remnant (no agricultural history). Bees were sampled using elevated bee bowls. In the second growing season after restoration, thinned plots supported a greater bee abundance and bee community richness. Additionally, restored plots had altered wild bee community composition when compared to unthinned plots, indicating that reduction of canopy cover by the thinning treatment best predicted wild bee diversity and composition. Conversely, we found little evidence for differences between sites with or without historical agricultural land use. Some abundant Lasioglossum species were the most sensitive to habitat changes. Our results highlight how restoration practices that reduce canopy cover in fire‐suppressed savannas can have rapid benefits for wild bee communities.
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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 | 0.000 | 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".