Bibliographic record
Abstract
In the maritime provinces of Canada, rising temperatures, more variable precipitation, increased humidity, and shorter, milder winters are altering the environmental conditions that shape mosquito communities. Because mosquitoes act as pollinators, occupy a key position at lower trophic levels, and can vector disease, it is essential to monitor shifts in their distribution and abundance. The last comprehensive mosquito surveys in New Brunswick and Prince Edward Island were conducted over 20 years ago, leaving crucial gaps in our understanding of both native and invasive species. To establish a modern baseline for ongoing mosquito surveillance, we sampled 297 sites across New Brunswick, Prince Edward Island, and Nova Scotia from May to October 2023 and May to September 2024. Using larval collections from ephemeral ponds, roadside ditches, bogs, and artificial containers, adult collections using CDC light traps baited with CO₂, updraft gravid traps and human landing captures, we collected over 50,000 specimens. Our results include eight new species records for New Brunswick, seven for PEI, and two for Nova Scotia, as well as one new subspecies record. Notably, we report the first Maritime occurrence of <em>Anopheles quadrimaculatus</em> and document the expansion of the invasive <em>Aedes japonicus</em> into PEI. We observed pronounced interannual variation in community composition between 2023 and 2024, likely driven by year-to-year weather fluctuations. When compared to the 2004 New Brunswick survey, there is a long-term trend finding that the majority of mosquito communities in the Maritimes are becoming dominated by fewer species. Whether climate change is driving this long-term change remains uncertain. as the infrequent surveillance in the Maritimes offers us only small snapshots of a much larger picture. In order to gain a better understanding of long-term mosquito community change and to detect invasive species soon after arrival routine proactive mosquito surveillance must be implemented in the Maritimes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".