Supporting Pollinators in Canola Fields: The Role of Landscape Composition for Honey Bee Nutrition and Wild bee Diversity
Bibliographic record
Abstract
The global decline of pollinator populations poses a significant threat to terrestrial biodiversity and human food security. This crisis is largely driven by the intensification of agriculture, which replaces diverse, resource-rich landscapes with simplified monocultures. This change creates a basic contradiction: farming, which often relies on pollination services, is also damaging the ecological foundations needed to support healthy pollinator communities. This issue is particularly evident in the Canadian Prairies, where the conversion of native grasslands into one of the world's largest canola-producing regions has been extensive. These agroecosystems offer a massive but short-lived floral blooms that are insufficient to sustain pollinators throughout their life cycles. Despite the region's economic reliance on pollination, there is a critical knowledge gap regarding the structure of wild bee communities and the floral resources available to them within these highly modified landscapes. To further investigate, this thesis established two interconnected primary objectives. The first was to quantify the abundance, species richness, and community composition of wild bees across the Saskatchewan canola belt and to determine their relationship with the surrounding landscape structure, particularly the proportion of semi-natural habitat (SNH). The second, complementary objective was to use managed honey bees (Apis mellifera) as landscape-level bio-samplers to identify the key floral resources sustaining the entire pollinator assemblage. This integrated study was conducted at ten agricultural sites across Saskatchewan during the 2024 season. Wild bees were collected monthly (June-August) using a combination of pan and vane traps, while corbicular pollen was simultaneously collected from honey bee colonies at the same locations. The taxonomic identity of pollen was determined using DNA metabarcoding. Landscape composition was quantified from satellite imagery, and the data were analyzed using Generalized Linear Models (GLMs) and multivariate methods to assess the influence of landscape and seasonality. We identified 54 species of wild bees. We found a strong positive correlation between SNH and wild bee abundance and species richness. Populations declined precipitously in landscapes with less than 10% SNH. These findings provide a consistent picture: pollen analysis revealed that approximately 80% of the floral resources collected by honey bees came from non-crop forbs and shrubs within these SNH patches. The overall pollen diet was dominated by Brassica, Melilotus, and Syringa but showed significant seasonal variation. This confirms that canola alone is insufficient for season-long nutrition. Consequently, pollen diversity was significantly higher in more heterogeneous landscapes. This research provides clear, actionable evidence that even small, remnant patches of semi-natural habitat are not marginal lands but critical life-support systems within intensive agroecosystems. They provide the essential nesting sites and continuous floral nutrition required to maintain both wild and managed bees’ populations. Therefore, the conservation and restoration of SNH should be considered a fundamental strategy for building agricultural resilience, ensuring sustainable crop pollination, and safeguarding biodiversity in the Canadian Prairies and similar systems worldwide.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".