Epigenetic effects of parasites and pesticides on captive and wild nestling birds
Bibliographic record
Abstract
Abstract Anthropogenic changes to the environment challenge animal populations to adapt to new conditions and unique threats. While the study of adaptation has focused on genetic variation, epigenetic mechanisms may also be important. DNA methylation is sensitive to environmental stressors, such as parasites and pesticides, which may affect gene expression and phenotype. We studied the effects of an invasive ectoparasite, Philornis downsi, on DNA methylation of Galápagos mockingbirds (Mimus parvulus). We used the insecticide permethrin to manipulate P. downsi presence in nests of free‐living mockingbirds and tested for effects of parasitism on nestling mockingbirds using epiGBS, a reduced‐representation bisulfite sequencing (RRBS) approach. To distinguish the confounding effects of insecticide exposure, we conducted a matching experiment exposing captive nestling zebra finches (Taeniopygia guttata) to permethrin. We used zebra finches because they were the closest model organism to mockingbirds that we could breed in controlled conditions. We identified a limited number of differentially methylated cytosines (DMCs) in parasitized versus nonparasitized mockingbirds, but the number was not more than expected by chance. In contrast, we saw clear effects of permethrin on methylation in captive zebra finches. DMCs in zebra finches paralleled documented effects of permethrin exposure on vertebrate cellular signaling and endocrine function. Our results from captive birds indicate a role for epigenetic processes in mediating sublethal nontarget effects of pyrethroid exposure in vertebrates. Environmental conditions in the field were more variable than the laboratory, which may have made effects of both parasitism and permethrin harder to detect in mockingbirds. RRBS approaches such as epiGBS may be a cost‐effective way to characterize genome‐wide methylation profiles. However, our results indicate that ecological epigenetic studies in natural populations should consider the number of cytosines interrogated and the depth of sequencing in order to have adequate power to detect small and variable effects.
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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) | 0.001 | 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".