Variation in prescribed fire and bison grazing supports multiple bee nesting groups in tallgrass prairie
Bibliographic record
Abstract
Fire and grazing are historic ecosystem drivers of tallgrass prairie and both are used for restoration management today. The effects of these drivers on animal taxa are still incompletely resolved, especially for wild bees, a growing conservation and restoration priority. Fire and grazing could affect wild bee communities through structural changes to nest site availability via changes to soil conditions, vegetative cover, and availability of plant stems. Here, we sought to determine how different bee nesting groups are affected by the combination of fire and bison grazing management strategies. We grouped bee species by nesting substrate (ground, stem/hole, large cavity) because we expect the availability of these substrates to vary with the application of prescribed fire and grazing. We collected bees in restored and remnant high‐quality tallgrass prairie and analyzed whether the proportion of each nesting group within the total bee community was predicted by fire and/or grazing. Ground‐nesting bees reached their greatest proportion in bee communities immediately after prescribed fire, but declined proportionally over time since the last burn. Stem‐/hole‐nesting bees reached their highest proportion in the bee community with infrequent fire (6‐year interval) and differed in their response to fire depending on the presence/absence of bison. Sampling year affected bee nesting groups and we found that nesting groups did not change in concert (i.e. different nesting groups had different good and bad years from each other). Our results show that spatiotemporal variation of prescribed fire and bison grazing is essential for conservation of multiple bee nesting groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".