Strona Giovanni, Lafferty Kevin D
Department of Biotechnology and Biosciences, University of Milano Bicocca, Piazza della Scienza 2, 20126 Milan, Italy.
J Parasitol. 2013 Feb;99(1):6-10. doi: 10.1645/GE-3147.1. Epub 2012 Aug 27.
Fish pathologists are often interested in which parasites would likely be present in a particular host. Parasite Co-occurrence Modeler (PaCo) is a tool for identifying a list of parasites known from fish species that are similar ecologically, phylogenetically, and geographically to the host of interest. PaCo uses data from FishBase (maximum length, growth rate, life span, age at maturity, trophic level, phylogeny, and biogeography) to estimate compatibility between a target host and parasite species-genera from the major helminth groups (Acanthocephala, Cestoda, Monogenea, Nematoda, and Trematoda). Users can include any combination of host attributes in a model. These unique features make PaCo an innovative tool for addressing both theoretical and applied questions in parasitology. In addition to predicting the occurrence of parasites, PaCo can be used to investigate how host characteristics shape parasite communities. To test the performance of the PaCo algorithm, we created 12,400 parasite lists by applying any possible combination of model parameters (248) to 50 fish hosts. We then measured the relative importance of each parameter by assessing their frequency in the best models for each host. Host phylogeny and host geography were identified as the most important factors, with both present in 88% of the best models. Habitat (64%) was identified in more than half of the best models. Among ecological parameters, trophic level (41%) was the most relevant while life span (34%), growth rate (32%), maximum length (28%), and age at maturity (20%) were less commonly linked to best models. PaCo is free to use at www.purl.oclc.org/fishpest.
鱼类病理学家常常对特定宿主可能感染哪些寄生虫感兴趣。寄生虫共现建模工具(PaCo)是一种用于识别已知寄生于鱼类物种的寄生虫列表的工具,这些鱼类在生态、系统发育和地理方面与目标宿主相似。PaCo利用鱼类数据库(最大体长、生长速率、寿命、成熟年龄、营养级、系统发育和生物地理学)的数据来估计目标宿主与主要蠕虫类群(棘头虫纲、绦虫纲、单殖吸虫纲、线虫纲和吸虫纲)的寄生虫物种属之间的兼容性。用户可以在模型中纳入宿主属性的任何组合。这些独特特性使PaCo成为解决寄生虫学中理论和应用问题的创新工具。除了预测寄生虫的出现,PaCo还可用于研究宿主特征如何塑造寄生虫群落。为测试PaCo算法的性能,我们通过将模型参数(248个)的任何可能组合应用于50种鱼类宿主,创建了12400个寄生虫列表。然后,我们通过评估每个参数在每个宿主的最佳模型中的出现频率来衡量其相对重要性。宿主系统发育和宿主地理被确定为最重要的因素,在88%的最佳模型中都存在。超过一半的最佳模型中包含栖息地(64%)。在生态参数中,营养级(41%)最为相关,而寿命(34%)、生长速率(32%)、最大体长(28%)和成熟年龄(20%)与最佳模型的关联较少。可在www.purl.oclc.org/fishpest免费使用PaCo。