Mechanisms and consequences of infection‐induced phenotypes
Bibliographic record
Abstract
Abstract Researchers studying animal phenotypes often overlook the potential influence of parasites hiding inside their study organisms. Yet, most wild animals host parasites, which can alter individual phenotypes (e.g. morphology, physiology, behaviour). Infection‐induced phenotypes stem from several nonmutually exclusive mechanisms (i.e. adaptive host defences, infection‐induced debilitation and host manipulation by parasites), driven by parasites and/or hosts. Changes in host phenotype can impact all levels of biological organization, from cells to communities. The nature and magnitude of these effects can vary depending on the biotic and abiotic environmental factors experienced by hosts and parasites. This special feature highlights recent insights into the ways parasites alter host phenotypes across a range of systems. Here, we contextualize how each contribution expands our knowledge of the role of parasites in driving individual variation in animal phenotypes. Looking to the future, we need to better understand how infection‐induced phenotypes fluctuate with natural variation in infection (e.g. infection intensity, coinfection) and whether studies in laboratory‐based environments provide strong proxies for host–parasite interactions in the wild. The time is ripe to acknowledge, critique and discuss the implications of infection on host phenotypes across taxonomic boundaries and biological levels of organization. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".