Host allometry influences the evolution of parasite host-generalism: theory and meta-analysis
Bibliographic record
Abstract
Parasites vary widely in the diversity of hosts they infect: some parasite species are specialists-infecting just a single host species, while others are generalists, capable of infecting many. Understanding the factors that drive parasite host-generalism is of basic biological interest, but also directly relevant to predicting disease emergence in new host species, identifying parasites that are likely to have unidentified additional hosts, and assessing transmission risk. Here, we use mathematical models to investigate how variation in host body size and environmental temperature affect the evolution of parasite host-generalism. We predict that parasites are more likely to evolve a generalist strategy when hosts are large-bodied, when variation in host body size is large, and in cooler environments. We then explore these predictions using a newly updated database of over 20 000 fish-macroparasite associations. Within the database we see some evidence supporting these predictions, but also highlight mismatches between theory and data. By combining these two approaches, we establish a theoretical basis for interpreting empirical data on parasites' host specificity and identify key areas for future work that will help untangle the drivers of parasite host-generalism.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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".