Host species exploitation and discrimination by animal parasites
Bibliographic record
Abstract
Parasite species often show differential fitness on different host species. We developed an equation-based model to explore conditions favouring host species exploitation and discrimination. In our model, diploid infective stages randomly encountered hosts of two species; the parasite's relative fitness in exploiting each host species, and its ability to discriminate between them, was determined by the parasite's genotype at two independent diallelic loci. Relative host species frequency determined allele frequencies at the exploitation locus, whereas differential fitness and combined host density determined frequency of discrimination alleles. The model predicts instances where populations contain mixes of discriminatory and non-discriminatory infective stages. Also, non-discriminatory parasites should evolve when differential fitness is low to moderate and when combined host densities are low, but not so low as to cause parasite extinction. A corollary is that parasite discrimination (and host-specificity) increases with higher combined host densities. Instances in nature where parasites fail to discriminate when differential fitness is extreme could be explained by one host species evolving resistance, following from earlier selection for parasite non-discrimination. Similar results overall were obtained for haploid extensions of the model. Our model emulates multi-host associations and has implications for understanding broadening of host species ranges by parasites.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".