Role of parasite transmission in promoting inbreeding: I. Infection intensities drive individual parasite selfing rates
Bibliographic record
Abstract
Among parasitic organisms, inbreeding has been implicated as a potential driver of host-parasite co-evolution, drug-resistance evolution and parasite diversification. Yet, fundamental topics about how parasite life histories impact inbreeding remain to be addressed. In particular, there are no direct selfing-rate estimates for hermaphroditic parasites in nature. Our objectives were to elucidate the mating system of a parasitic flatworm in nature and to understand how aspects of parasite transmission could influence the selfing rates of individual parasites. If there is random mating within hosts, the selfing rates of individual parasites would be an inverse power function of their infection intensities. We tested whether selfing rates deviated from within-host random mating expectations with the tapeworm Oochoristica javaensis. In doing so, we generated, for the first time in nature, individual selfing-rate estimates of a hermaphroditic flatworm parasite. There was a mixed-mating system where tapeworms self-mated more than expected with random mating. Nevertheless, individual selfing rates still had a significant inverse power relationship to infection intensities. The significance of this finding is that the distribution of parasite infection intensities among hosts, an emergent property of the transmission process, can be a key driver in shaping the primary mating system, and hence the level of inbreeding in the parasite population. Moreover, we demonstrated how potential population selfing rates can be estimated using the predicted relationship of individual selfing rates to intensities and showed how the distribution of parasites among hosts can indirectly influence the primary mating system when there is density-dependent fecundity.
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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 | 0.000 | 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".