The maternal effects of haemosporidian infection in red-winged blackbirds
Bibliographic record
Abstract
Vector borne diseases present a substantial selective pressure to many animals, as they can impose direct fitness costs and intensify trade-offs between immune function and other life history traits, like reproduction. Haemosporidian parasites, a family of blood-borne parasites that include the causative agent of malaria (Plasmodium spp), are spread by insect vectors. In populations with endemic malaria, chronic infection can have a significant impact on individuals through changes to physiological condition, reproductive success, and survival. In addition to the individual effects on host physiology and success, there can also be associated impacts on the offspring of the host, where maternal infection can affect offspring in both negative and adaptive ways. The maternal effects of malaria on wild populations have not been studied extensively through experimental means, though they could play an important role in offspring success and phenotypic expression in future generations. We estimated the maternal effects of chronic haemosporidian infection in free-ranging red-winged blackbirds by medicating females with antimalarial medication or a control solution before reproduction. We measured egg mass, hatching success, and fledging success for each female, and a suite of measures to determine nestling condition. We found that females who received antimalarial medication laid heavier eggs, and fledged more young per nest, but we did not detect differences in hatching success between groups. We also found that nestlings from medicated mothers were heavier, and had higher haematocrit and lower plasma glucose when females were in good condition before treatment. This is one of the first studies to experimentally quantify the maternal effects of chronic malaria infection on offspring condition in birds, and one of few studies to describe fitness effects of chronic maternal infection in a population with endemic haemosporidian infection. This study demonstrates that the maternal effects of infection have the potential to shift individual and population phenotypes through changes to offspring size and physiological condition as a result of chronic maternal infection. Understanding the effects of maternal infection on offspring condition and fitness furthers our knowledge of the maternal effects of disease, which will continue to be important as pathogens colonize new areas and naïve hosts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".