Effets combinés des changements climatiques et des changements d’utilisation des sols sur les populations de moustiques en Ontario
Bibliographic record
Abstract
The entomological context in Canada is undergoing significant transformations, marked by an increase in mosquito species over the last decade, bringing the total to approximately 80. Among these species, a minority pose a significant risk to public health due to their ability to transmit disease agents, primarily arboviruses in the Canadian context. In recent years, we have witnessed the emergence and re-emergence of human mosquito-borne diseases (MBD), caused by West Nile virus (WNV) and viruses from the California serogroup (CSV), including those of Snowshoe hare (SSHV) and Jamestown Canyon (JCV). Additionally, some diseases that affect mainly animals, like the Eastern equine encephalitis, also remain a concern for public health. These phenomena of emergence and re-emergence are often linked to climate change, which is the most studied and best-documented factor to date. However, other elements, such as land use, though less popularized, have also had scientific interest in recent years, highlighting the complexity of the issue and the importance of considering a broader range of variables in modeling mosquito populations and MBDs. Existing literature abounds with studies analyzing the separate impacts of climate or land use on mosquito populations, but the joint consideration of these two factors has rarely been addressed, particularly in the context of Canada. This gap underscores the need for investigations that explore the combination of climatic conditions and land use for a more comprehensive understanding of their influence on mosquito population dynamics. Our study aims to bridge this gap by examining specifically Eastern Ontario. First, our research used entomological data collected in the Greater Ottawa area between 2017 and 2018 to assess whether integrating land use improves understanding of observed variations in mosquito species occurrence and abundance. This involved correlating entomological data with Daymet weather data and annual crop inventory information for the same period. We analyzed two types of models: one considering only weather variables, and other including both weather and land use variables. Results highlighted the importance of considering land use, and its impact varied by species, underscoring the need to adjust models according to the species being studied. Our results have also highlighted unexplained variance, indicating that factors not considered in our study could influence the outcomes. We then extended our study area to Eastern Ontario, where we developed and modeled land use scenarios suited to the ecology of mosquitoes. The Dyna-CLUE model was used to simulate these land use scenarios until 2070, based on historical trends. We proposed five scenarios reflecting different levels of urbanization, agricultural expansion, and the conservation of natural areas. The results showed a good match between predicted and observed maps in 2020, predicting significant changes by 2050, and notable deforestation by 2070. These scenarios, developed to be combined with climate projections, were used to estimate mosquito populations. Finally, our focus shifted to Culex pipiens-restuans abundance, the main vector for WNV in the region, and to mosquito diversity, as a more general indicator of species. For this phase, the study period covered 2002 to 2020, and the study area was Eastern Ontario. The objective was to determine how Cx. pipiens-restuans abundance and mosquito diversity would evolve in the future in response to climate and land use changes. For this purpose, the previously developed and modeled land use scenarios were integrated with climate scenarios to carry out projections. Results demonstrated that land use is a relevant factor for predicting Cx. pipiens-restuans abundance. As for mosquito diversity, only the impact of climate changes proved to be decisive. Our analyses also revealed a characteristic spatial pattern of the regional landscape, showing stability in western natural areas compared to more pronounced changes in the east, more affected by human activities. Our results suggest that incorporating land use enhances our understanding of the spatio-temporal dynamics of mosquito populations. They also shed light on human activities as a factor facilitating changes in mosquito populations, thereby impacting disease transmission. This study underscores the significant role of land use and climatic conditions in the dynamics of mosquito populations, highlighting the impact of human activities on MBD transmission. Our findings emphasize that preserving natural habitats could be an effective strategy to mitigate the effects of climate change and reduce the risks associated with vector-borne diseases, adapting to changing environmental realities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".