Effect of Climate Warming on Mosquito Population Dynamics in Newfoundland
Bibliographic record
Abstract
Abstract Mosquitoes are key vectors of several infectious diseases affecting humans and animals. In North America, Culex mosquitoes are primary vectors of West Nile virus, St. Louis encephalitis, and Japanese encephalitis, as well as pathogens that impact birds and horses. The Culex life cycle consists of four stages, eggs, larvae, pupae, and adults, each with distinct development and mortality rates. Only active (non-diapausing) adults can reproduce, and environmental factors such as temperature, photoperiod, and rainfall influence population dynamics and stage-specific abundances. We develop a stage-structured model that integrates historical climate data and available experimental data to describe how key climate variables regulate life history parameters. Specifically, oviposition rates depend on temperature, maturation and survival are influenced by both temperature and rainfall, mortality is modeled as temperature-dependent, and diapause induction and termination are driven by photoperiod. Unlike many previous models focused on tropical mosquitoes, our framework explicitly incorporates diapause, a critical adaptation for temperate Culex populations. Simulations reveal strong nonlinear responses to warming, moderate temperature shifts amplify the differences in mosquito abundance across rainfall regimes, whereas higher warming leads to convergence at consistently high densities. Rainfall amounts determine whether populations remain suppressed, variable, or strongly amplified, whereas warming extends the active season and elevates population abundance. These results underscore the importance of jointly considering temperature, rainfall, and photoperiod in predicting mosquito dynamics. Our findings suggest that climate change may expand the seasonal window of mosquito activity and raise vector abundance, thereby increasing opportunities for pathogen transmission. The model provides a mechanistic framework for exploring how interacting climate drivers shape vector ecology and highlights priorities for data collection and adaptive control strategies under environmental change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".