Natural selection, adaptive plasticity, and avian malaria
Bibliographic record
Abstract
Plastic traits, such as variable behavior and physiology, are front‐line components of adaptive responses to dynamic challenges. For example, plastic responses to parasites and pathogens are essential to both resistance and tolerance of infection; yet, these responses can also be associated with high costs, including immunopathology, loss of homeostasis, and expended resources. Because of these costs and benefits, we expect that natural selection will continue to shape and refine plastic responses to parasites. Understanding the ways that selection is shaping plastic traits could help explain differences among individuals, populations, and species, but adaptive plasticity complicates the application of classical approaches to measuring natural selection. In this talk, I will describe our work aimed at understanding how selection acts on plastic responses to infection in a free‐ranging population of red‐winged blackbirds with a remarkably high prevalence (95%) of haemosporidian parasites (e.g., Plasmodium and Haemoproteus ). Overall, our findings point to fitness costs of chronic infection that blackbirds tolerate through induction of adaptive plasticity. Our work illustrates both the benefits and perils of applying classical evolutionary approaches to understanding the adaptive significance of individual variation in plastic traits. More broadly, with an evolutionary perspective in the study of plastic traits involved with resistance and tolerance of infection, we can better appreciate the dynamic nature of host‐parasite interactions. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".