Role of parasite transmission in promoting inbreeding: <scp>II</scp>. Pedigree reconstruction reveals sib‐transmission and consequent kin‐mating
Bibliographic record
Abstract
Even though parasitic flatworms are one of the most species-rich groups of hermaphroditic organisms, we know virtually nothing of their mating systems (selfing or kin-mating rates) in nature. Hence, we lack an understanding of the role of inbreeding in parasite evolution. The natural mating systems of parasitic flatworms have remained elusive due to the inherent difficulty in generating progeny-array data in many parasite systems. New developments in pedigree reconstruction allow direct inference of realized selfing rates in nature by simply using a sample of genotyped individuals. We built upon this advancement by utilizing the closed mating systems, that is, individual hosts, of endoparasites. In particular, we created a novel means to use pedigree reconstruction data to estimate potential kin-mating rates. With data from natural populations of a tapeworm, we demonstrated how our newly developed methods can be used to test for cosibling transmission and inbreeding depression. We then showed how independent estimates of the two mating system components, selfing and kin-mating rates, account for the observed levels of inbreeding in the populations. Thus, our results suggest that these natural parasite populations are in inbreeding equilibrium. Pedigree reconstruction analyses along with the new companion methods we developed will be broadly applicable across a myriad of parasite species. As such, we foresee that a new frontier will emerge wherein the diverse life histories of flatworm parasites could be utilized in comparative evolutionary studies to broadly address ecological factors or life history traits that drive mating systems and hence inbreeding in natural populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".