Impact of host sex and group composition on parasite dynamics in experimental populations
Bibliographic record
Abstract
To better understand the spread of disease in nature, it is fundamentally important to have broadly applicable model systems with readily available species which can be replicated and controlled in the laboratory. Here we used an experimental model system of fish hosts and monogenean parasites to determine whether host sex, group size and group composition (single-sex or mixed-sex) influenced host-parasite dynamics at an individual and group level. Parasite populations reached higher densities and persisted longer in groups of fish compared with isolated hosts and reached higher densities on isolated females than on isolated males. However, individual fish within groups had similar burdens to isolated males regardless of sex, indicating that females may benefit more than males by being in a group. Relative condition was positively associated with high parasite loads for isolated males, but not for isolated females or grouped fish. No difference in parasite dynamics between mixed-sex groups and single-sex groups was detected. Overall, these findings suggest that while host sex influences dynamics on isolated fish, individual fish in groups have similar parasite burdens, regardless of sex. We believe our experimental results contribute to a mechanistic understanding of host-parasite dynamics, although we are cautious about directly extrapolating these results to other systems.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".