Associations between soil characteristics and ground‐nesting bees on farms
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Much of the world's agricultural production depends on pollination services provided by wild bees. At the same time, agriculture changes landscapes in ways that can alter bee habitat. However, little is known about the nesting habitat requirements of the many bee species that nest underground. Here, we asked which soil factors influence the abundance, diversity, and community composition of ground‐nesting bees in agroecosystems around Ottawa, Canada. We measured soil characteristics (texture, hardness, slope, and ground cover) and sampled bee communities at 131 plots on 35 farms over a 2‐year period. We identified the ground‐nesting bees to species. We collected 8661 ground‐nesting bees representing 100 species. Ground‐nesting bee abundance and species richness were higher with increased percentages of bare ground and sand, while Simpson's diversity was negatively associated with slope. The abundance of non‐ground‐nesting (cavity‐nesting) bees was not related to any measured soil properties, suggesting that the associations between soil variables and ground‐nesting bees reflect direct effects of soils on these bees, rather than indirect effects mediated by unmeasured variables. Only a small proportion of the variance in ground‐nesting bee community composition was explained by soil factors; however, sand percentage, slope, soil compaction, and bare ground were all significant predictors, reflecting the fact that relationships between soil predictors and ground‐nesting bee taxa were species‐specific. Compared to floral resources, soils have been neglected as components of bee habitat quality, but understanding the soil characteristics preferred by ground‐nesting bees can assist in efforts to protect this important group of pollinators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it