Edge Effects and Pitfall Trap Design Influence Spider Diversity and Assemblages in Canola Agroecosystems on the Canadian Prairies
Bibliographic record
Abstract
L.). However, their diversity and community structure remain understudied in the Canadian Prairies. To address this knowledge gap, we surveyed spider assemblages in the Aspen Parkland region of Alberta, Canada, using pitfall traps placed at field edges and interiors during the 2021 and 2022 growing seasons. We collected 968 spiders in 74 species across 14 families, with Lycosidae and Linyphiidae being the most abundant. Spider abundance was consistently greater at field edges, although family-level composition varied by year and location. In 2023, we tested pitfall trap modifications to improve spider retention, evaluating (1) trap diameter, (2) preservative substrate (glass beads vs. propylene glycol), and (3) polytetrafluoroethylene (PTFE; Fluon, a non-stick fluoropolymer) coated trap interiors (to reduce friction and prevent escape). This was tested across canola habitats adjacent to grass- or tree-dominated non-crop habitat edges. Trap catches, and resulting richness and diversity, were significantly higher in large PTFE-treated traps, particularly in treed-edge habitats compared to other treatment combinations. These results underscore the importance of non-crop field margins in maintaining spider diversity and highlight design improvements to optimize passive sampling. Our findings provide a regional baseline for spider assemblages in canola systems and offer methodological advances to support future ecological monitoring and conservation biological control efforts in Prairie agroecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".