Climate-driven increase in transmission of wildlife malaria parasite over the last quarter century
Bibliographic record
Abstract
Abstract Climate warming is expected to influence the prevalence of vector-transmitted parasites. Understanding the extent to which this is ongoing, or has already occurred, requires empirical data from populations monitored over long periods of time, but these studies are sparse. Further, vector-disease research involving human health is often influenced by disease control efforts that supersede natural trends. By screening for malaria parasite infections in a wildlife population of blue tits ( Cyanistes caeruleus ) in Northern Europe, over a 26-year period, we tested whether observed prevalence and transmission changes were climate-driven and show that all three malaria parasite genera have increased significantly in their prevalence and transmission over time. The most common parasite in the study, Haemoproteus majoris , increased in prevalence from 47% (1996) to 92% (2021), and this is a direct consequence of warmer temperatures elevating transmission. Climate window analyses reveal that elevated temperatures between May 9 th and June 24 th , a time period that overlaps with the host nestling period, are strongly positively correlated with H. majoris transmission in one-year-old birds. Warmer climate during this narrow timeframe has a demonstrable impact on parasite transmission, and this permeates into the overall prevalence in the host population. We now have empirical support that climate warming can drive a rapid rise in vector-transmitted parasites, and this has implications for other host-parasite systems. Given that we now know the exact time of year when climate warming is most influential on a common vector-transmitted parasite in this system, it is possible to investigate the evolutionary and environmental mechanisms that underly how these infections ultimately manifest. While more challenging to measure, similar implications of climate warming on human vector-disease systems might be occurring.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".