Bee community responses to a gradient of oak savanna restoration practices
Bibliographic record
Abstract
North American Midwestern oak (Quercus spp.) savannas are rare fire‐dependent ecosystems that can support high levels of biodiversity and are the focus of considerable restoration effort due to widespread fire suppression. Due to the predominance of understory forbs in oak savannas, many of which require insect pollination, restoration practices should be evaluated for their potential impacts on pollinator communities. We evaluated bee community responses during the first 2 years of experimental restoration of fire‐suppressed oak savanna in southern Michigan. We used unmanaged references and two different restoration methods (burning only and burning with thinning) to examine the effects of restoration intensity on the abundance, diversity, and functional groups of bees. We found that thinning and burning rapidly increased bee abundance, richness, and Shannon's diversity, relative to unmanaged references, whereas burn‐only restoration largely failed to do so. Thinning and burning also resulted in a distinct bee community after two seasons, while bee communities in burn‐only restoration plots were similar to those from unmanaged references. Differences in bee diversity and community structure between treatments may be due to the influence of restoration on nesting resources, which is reflected in the differential captures of various nesting guilds. Overall, oak savanna restoration by thinning and burning had positive effects on bee diversity, while burning alone only increased bee abundance. We thus illustrate how restoration strategies that typically target plants have broader‐reaching biodiversity benefits. Although restoring savannas through burning alone may eventually shift bee communities, coupling thinning with burning will influence pollinator communities over the shorter term.
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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) | 0.000 | 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".