Identifying sources of variation in parasite aggregation

Abstract

Aggregation of macroparasites among hosts is a near-universal pattern, and has important consequences for the stability of host-parasite associations and the impacts of disease. Identifying which potential drivers are contributing to levels of aggregation observed in parasite-host associations is challenging, particularly for observational studies. We apply beta regressions in a Bayesian framework to determine predictors of aggregation, quantified using Poulin’s index of discrepancy (D), for 13 species of parasites infecting Icelandic Rock Ptarmigan (Lagopus muta) collected over 12 years. 1,140 ptarmigan were collected using sampling protocols maximizing consistency of sample sizes and of composition of host ages and sexes represented across years from 2006–2017. Parasite species, taxonomic group (insect, mite, coccidian, or nematode), and whether the parasite was an ecto- or endoparasite were tested as predictors of aggregation, either alone or by modulating an effect of parasite mean abundance on D. Parasite species was an important predictor of aggregation in models. Despite variation in D across samples and years, relatively consistent aggregation was demonstrated for each specific host-parasite association, but not for broader taxonomic groups, after taking sample mean abundance into account. Furthermore, sample mean abundance was consistently and inversely related to aggregation among the nine ectoparasites, however no relationship between mean abundance and aggregation was observed among the four endoparasites. We discuss sources of variation in observed aggregation, sources both statistical and biological in nature, and show that aggregation is predictable, and distinguishable, among infecting species. We propose explanations for observed patterns and call for the review and re-analysis of parasite and other symbiont distributions using beta regression to identify important drivers of aggregation—both broad and association-specific.

Keywords

• Aggregation
• Bayesian analysis
• Lagopus muta
• Parasitism
• Rock ptarmigan