Diversity and community structure of mosquitoes (Diptera: Culicidae) in suburban, field, and forest habitats in Montréal, Québec, Canada
Bibliographic record
Abstract
Abstract Understanding the medical and economic impacts of mosquitoes (Diptera: Culicidae) begins with knowing their natural history and distribution, including their association with habitat types, particularly those in which human activity is high. The effects of habitat on shaping the community structure of mosquitoes were studied across periurban habitats on the island of Montréal, Québec, Canada in 2014 and 2015. Mosquitoes were collected from 20 fixed sampling locations in suburban backyards, fields, and forests, using CO2-baited light-emitting diode encephalitis vector survey traps. A total of 184 607 mosquitoes were collected, representing eight genera and 35 species. Suburban, field, and forest sites had different communities of mosquitoes, but differences were not apparent among sites within similar habitat types in nonmetric multidimensional scaling ordinations and permutational multivariate analysis of variance. In both years, the greatest abundance of mosquitoes was collected from field habitat, and the highest species richness, from forests. Suburban sites consistently generated the lowest abundance and diversity. Nearly 75% of the total individuals collected were from three species:Aedes vexans(Meigen), 39%;Coquillettidia perturbans(Walker), 18%; andAedes canadensis(Theobald), 16%. This research shows that diverse communities of mosquitoes can be found in forests, fields, and backyards, yet the communities between forests differ from more open habitats. Our community analysis reveals that medically important species (e.g.,Culexsp.) are more commonly encountered in suburban backyards, yet overall mosquito nuisance potential is greater in forest and field habitats. This information highlights important patterns of mosquito abundance and species occurrence, vital for the development of management programmes.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".