Biology Department, Carleton University, Ottawa, Ontario, Canada.
Icelandic Institute of Natural History, Garðabær, Iceland.
PeerJ. 2022 Aug 24;10:e13763. doi: 10.7717/peerj.13763. eCollection 2022.
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 (), for 13 species of parasites infecting Icelandic Rock Ptarmigan () 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 . Parasite species was an important predictor of aggregation in models. Despite variation in 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.
寄生虫在宿主间的聚集是一种近乎普遍的模式，对宿主-寄生虫关系的稳定性和疾病的影响有重要意义。确定哪些潜在的驱动因素导致了在寄生虫-宿主关系中观察到的聚集水平是具有挑战性的，特别是对于观察性研究。我们在贝叶斯框架中应用β回归来确定聚集的预测因子，使用 Poulin 的差异指数（）来量化，该指数用于 2006-2017 年 12 年间感染冰岛石鸡（）的 13 种寄生虫。使用最大限度地保持样本大小一致性以及代表不同年份的宿主年龄和性别组成的采样协议，共收集了 1140 只石鸡。寄生虫物种、分类群（昆虫、螨虫、球虫或线虫）以及寄生虫是外寄生虫还是内寄生虫，作为聚集的预测因子，单独或通过调节寄生虫平均丰度对的影响进行测试。寄生虫物种是模型中聚集的重要预测因子。尽管在样本和年份之间存在差异，但在考虑样本平均丰度后，每个特定的宿主-寄生虫关联都表现出相对一致的聚集，而不是更广泛的分类群。此外，在九个外寄生虫中，样本平均丰度与聚集之间始终呈负相关，但在四个内寄生虫中，没有观察到平均丰度与聚集之间的关系。我们讨论了观察到的聚集变化的来源，包括统计和生物学来源，并表明聚集是可预测的，并且在感染物种之间是可区分的。我们提出了观察到的模式的解释，并呼吁使用β回归审查和重新分析寄生虫和其他共生体的分布，以确定聚集的重要驱动因素——无论是广泛的还是特定于关联的。
