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Identifying sources of variation in parasite aggregation
André Morrill1, Ólafur K Nielsen2, Karl Skírnisson3
1Biology Department, Carleton University, Ottawa, Ontario, Canada.
Peerj
|August 30, 2022
Summary
Macroparasite aggregation in hosts is common. This study found parasite species predictability in aggregation levels, with ectoparasites showing an inverse relationship between abundance and aggregation.
Area of Science:
- Ecology
- Parasitology
- Statistical Modeling
Background:
- Macroparasite aggregation among hosts is a widespread phenomenon with significant implications for host-parasite dynamics and disease stability.
- Identifying drivers of parasite aggregation is challenging, especially in observational studies.
Purpose of the Study:
- To identify predictors of macroparasite aggregation in Icelandic Rock Ptarmigan (Lagopus muta).
- To quantify aggregation using Poulin's index of discrepancy (D) and test parasite species, taxonomic group, and parasite location (ecto- vs. endoparasite) as predictors.
Main Methods:
- Applied beta regressions within a Bayesian framework to analyze aggregation patterns.
- Utilized data from 1,140 ptarmigan sampled over 12 years (2006-2017).
- Tested parasite species, taxonomic group, and parasite location as predictors of aggregation, considering mean abundance.
Main Results:
- Parasite species was a significant predictor of aggregation, with consistent patterns observed for specific host-parasite associations.
- Mean parasite abundance was inversely related to aggregation for ectoparasites but not for endoparasites.
- Aggregation patterns were not consistent across broader taxonomic groups when accounting for mean abundance.
Conclusions:
- Parasite aggregation is predictable and distinguishable among infecting species, influenced by factors like parasite location and host-parasite association.
- Beta regression is a valuable tool for analyzing symbiont distributions and identifying drivers of aggregation.
- Further re-analysis of symbiont distribution data using beta regression is recommended to uncover broad and specific aggregation drivers.
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