Negative effects of early spring mowing on a bee community: a case study in the Niagara Region
Bibliographic record
Abstract
Mowing of bee habitats can change the availability of nesting habitat both above and below ground. Disturbances such as mowing with heavy equipment (“bush-hogging”) that remove all woody vegetation, may affect bees by killing them outright or by altering the availability of nesting and foraging resources. In this case study, we studied the effects of three years (2017 to 2019) of early spring mowing with heavy equipment that removed both herbaceous and woody vegetation from a previously undisturbed, 3 ha meadow (Brock South) in St. Catharines, Ontario, Canada. We predicted that mowing would decrease the abundance of bees that require woody vegetation as their nesting substrate and increase the abundance of below-ground nesters that build nests in exposed soil. We used two approaches to address these predictions. First, using paired, biweekly pan trap collections in 2018 and 2019, we compared bee abundance and diversity from the mowed site (Brock South) to that in an adjacent site of similar size that was unmowed but otherwise very similar (Brock Southwest). Bee abundance and diversity were lower in absolute terms in mowed Brock South than in unmowed Brock Southwest. In mowed Brock South, the carpenter and cavity-nesting bee guilds were lower in relative abundance, while below-ground nesters were higher in relative abundance. Second, we hypothesized that in the absence of mowing, temporal patterns in bee abundance in mowed Brock South should have been like those at undisturbed control sites nearby. Based on pan trap samples collected at three control sites within 1 km of the mowed Brock South site, bee abundance in the absence of disturbance was higher in 2018 and 2019 than in previous years (2008–2017). In contrast, bee abundance in mowed Brock South was not higher in 2018 and 2019 than previous years. Thus, both approaches suggest that even relatively mild disturbances, like mowing once per year, may result in alterations to local bee communities, detectable at small spatial scales of tens to hundreds of metres.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".