Comparing species richness and abundance of bumble bees between urban and natural areas using a photographic survey approach
Bibliographic record
Abstract
Bumble bees are essential pollinators that provide critical ecosystem services yet, studies are documenting global species declines while recognizing those declines may be understated due to insufficient baseline data. This study investigates bumble bee species richness and abundance across urban and natural sites in Prince Edward Island, Canada, focusing on Charlottetown (urban) and Prince Edward Island National Park (natural). We conducted fieldwork in August and September 2019 using a non-invasive photographic survey technique. We used published keys and sought feedback from citizen science platforms like iNaturalist and Bumble Bee Watch to verify species identification. Our results revealed nine bumble bee species, with Bombus impatiens being the most abundant and Bombus perplexus the rarest. Species richness was higher in natural sites, while urban sites demonstrated moderate levels of bumble bee diversity. Additionally, our findings suggest that sites containing a mix of natural and human-cultivated plant types, predominantly found in our urban study sites, may support higher diversity and evenness levels than those of homogenized plant types (natural or human-cultivated). This research illustrates the practicality of photographic surveys to document the species richness and diversity of bumble bees while avoiding disturbance to populations in urban and natural habitats of Prince Edward Island.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".