The influence of weather on the population dynamics of common mosquito vector species in the Canadian Prairies
Bibliographic record
Abstract
BACKGROUND: Mosquito seasonal activity is largely driven by weather conditions, most notably temperature, precipitation, and relative humidity. The extent by which these weather variables influence activity is intertwined with the animal's biology and may differ by species. For mosquito vectors, changes in weather can also alter host-pathogen interactions thereby increasing or decreasing the burden of disease. METHODS: In this study, we performed weekly mosquito surveillance throughout the active season over a 2-year period in Manitoba, Canada. We then used Generalized Linear Mixed Models (GLMMs) to explore the relationships between weather variables over the preceding 2 weeks and mosquito trap counts for four of the most prevalent vector species in this region: Oc. dorsalis, Ae. vexans, Cx. tarsalis, and Cq. perturbans. RESULTS: More than 265,000 mosquitoes were collected from 17 sampling sites throughout Manitoba in 2020 and 2021, with Ae. vexans the most commonly collected species followed by Cx. tarsalis. Aedes vexans favored high humidity, intermediate degree days, and low precipitation. Coquillettidia perturbans and Oc. dorsalis activity increased with high humidity and high rainfall, respectively. Culex tarsalis favored high degree days, with the relationship between number of mosquitoes captured and precipitation showing contrasting patterns between years. Minimum trapping temperature only impacted Ae. vexans and Cq. perturbans trap counts. CONCLUSIONS: The activity of all four mosquito vectors was affected by weather conditions recorded in the 2 weeks prior to trapping, with each species favoring different conditions. Although some research has been done to explore the relationships between temperature/precipitation and Cx. tarsalis in the Canadian Prairies, to our knowledge this is the first study to investigate other commonly found vector species in this region. Overall, this study highlights how varying weather conditions can impact mosquito activity and in turn species-specific vector potential.
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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 | 0.002 |
| 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 | 0.001 | 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".