Parasite spread in experimental metapopulations: resistance, tolerance and host competence
Bibliographic record
Abstract
Host competence, defined as the likelihood that a host will transmit infection, may be affected by an individual's resistance to infection and its ability to withstand damage caused by infection (tolerance). Host competence may therefore be one of the most important factors to impact host–parasite dynamics, yet the relationships among resistance, tolerance and competence are poorly understood. The objective of the present study was to determine whether individual host resistance (ability to resist or minimize infection) and/or tolerance (ability to withstand or minimize reduction in fitness due to infection) contributed to the competence (ability to spread infection) of hosts using guppies infected with the ectoparasite, Gyrodactylus turnbulli . This individual‐fish level analysis used data collected from a previous metapopulation experiment that had tracked host–parasite dynamics at the metapopulation scale using individually marked guppies that were moved among experimental tanks within replicate metapopulations. Fish tolerance was measured as the residual from a fish's expected survival post‐infection for a given parasite burden. Fish resistance was measured as the peak parasite load (– log‐transformed). Host competence was measured as the incidence (number of new infections over two days after the arrival of a fish to a tank) weighted by the density of available uninfected fish in the tank. In contrast to the assumption of a trade‐off between resistance and tolerance, individual fish tolerance and resistance were both negatively associated with competence. Connectivity (the number of fish with which an individual came into contact) was not associated with competence. Our results indicate that resistance and tolerance are both important to disease spread. These findings highlight the importance of understanding how individual defence against parasites may contribute to its competence as a host, and therefore impact metapopulation‐level dynamics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".