A comparison of blue vane trap, timed targeted netting, and timed photographic collection methods for evaluating Canadian bumble bee diversity
Bibliographic record
Abstract
Bumble bee (genus Bombus) populations across Canada are experiencing increases and decreases in abundance; some species are becoming more common while others are at risk of extirpation or extinction. It is important to monitor population changes so that extirpation and extinction can be prevented. Current population assessments for bumble bees, when conducted, use many different collection methods, but this limits our ability to compare across studies and understand trends. There is a call within the scientific community to create a national standard method for collecting bees. The goal of this research was to provide a recommendation for which collection methods could be used across Canada for bumble bee assessments, including assessments of species at risk. Three collection methods, blue vane traps (BVTs), timed targeted netting, and timed targeted photography, were compared with the objective of determining which method provided good diversity information, detected at-risk species, and required low sampling effort. To assess the universality of method performances across the country, surveys were conducted in three different regions of Canada, the Carolinian portion of the Mixedwood Plains Ecozone (southern Ontario), the Prairies Ecozone (Saskatchewan), and the Boreal Shield Ecozone (Newfoundland and Labrador). With some exceptions, the general structure of surveys was that BVTs were deployed for 1 week at a time, and multiple 30-minute netting and photographic surveys were conducted during each collection week. Regional differences were apparent. In the Prairies Ecozone BVTs collected the most specimens while in the other regions BVTs collected the fewest. BVTs detected the most species in the Carolinian and Prairies Ecozones, but netting detected the most species in the Boreal Shield Ecozone. For all regions, BVTs were the most efficient method at low sample sizes when compared using rarefaction. BVTs also detected the most species at risk. Distinct species compositions produced by BVTs compared to netting and photos demonstrated complementarity between these methods. Netting and photo species distributions also differed from each other in most regions. The overall recommendation when assessing Canadian bumble bee populations is to use BVTs in week-long durations with either netting or photo surveys to complement them.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".