Data from: Predicting primate-parasite associations with exponential random graph models

Ecological associations between hosts and parasites are influenced by host
exposure and susceptibility to parasites, and by parasite traits, such as
transmission mode. Advances in network analysis allow us to answer
questions about the causes and consequences of traits in ecological
networks in ways that could not be addressed in the past. We used a
network-based framework (exponential random graph models, or ERGMs) to
investigate the biogeographic, phylogenetic, and ecological
characteristics of hosts and parasites characteristics that affect the
probability of interactions among nonhuman primates and their parasites.
Parasites included arthropods, bacteria, fungi, protozoa, viruses, and
helminths. We investigated existing hypotheses, along with new predictors
and an expanded host-parasite database that included 213 primate nodes,
763 parasite nodes, and 2,319 edges among them. Analyses also investigated
phylogenetic relatedness, sampling effort, and spatial overlap among
hosts. In addition to supporting some previous findings, our ERGM approach
demonstrated that more threatened hosts had fewer parasites, and notably,
that this effect was independent of threatened hosts also having a smaller
geographic range. Despite having fewer parasites, threatened host species
shared more parasites with other hosts, consistent with the loss of
specialist parasites and threats arising from generalist parasites that
can be maintained in other, non-threatened hosts. Viruses, protozoa, and
helminths had broader host ranges than bacteria or fungi, and parasites
that infect non-primates had a higher probability of infecting more
primate species. The value of the ERGM approach for investigating the
processes structuring host-parasite networks provided a more complete view
of the biogeographic, phylogenetic, and ecological traits that influence
parasite species richness and parasite sharing among hosts. The results
supported some previous analyses and revealed new associations that
warrant future research, thus revealing how hosts and parasites interact
to form ecological networks.

宿主与寄生虫的生态关联，受宿主对寄生虫的暴露程度、易感性，以及寄生虫自身特征（如传播模式）的共同影响。网络分析技术的进步，使得我们能够以过往无法实现的方式，解答生态网络中各类特征的成因与生态后果相关的问题。本研究采用基于网络的分析框架——指数随机图模型（exponential random graph models，ERGMs），探究影响非人灵长类动物与其寄生虫之间互作概率的宿主与寄生虫的生物地理、系统发育及生态特征。本次研究涉及的寄生虫类群涵盖节肢动物、细菌、真菌、原生动物、病毒与蠕虫。我们对现有研究假说进行验证，同时引入全新的预测变量，并使用扩展后的宿主-寄生虫数据库：该数据库包含213个宿主节点、763个寄生虫节点，以及其间的2319条互作边。分析过程同时考量了宿主间的系统发育相关性、采样工作量以及空间重叠情况。除验证部分既往研究结论外，本研究的ERGM分析还发现，受威胁程度更高的宿主拥有的寄生虫种类更少，且值得注意的是，这一效应与受威胁宿主地理分布范围更小这一因素相互独立。尽管受威胁宿主的寄生虫种类更少，但它们与其他宿主共享的寄生虫种类更多，这一结果与特化寄生虫的丢失，以及可在非受威胁宿主中存续的泛化寄生虫所带来的威胁这一现象相符。病毒、原生动物与蠕虫的宿主范围较细菌或真菌更广；而能够感染非灵长类动物的寄生虫，其感染更多灵长类物种的概率也更高。ERGM分析方法在解析宿主-寄生虫网络构建过程中的应用价值，为我们提供了更为全面的视角，用以理解影响寄生虫物种丰富度以及宿主间寄生虫共享情况的生物地理、系统发育与生态特征。本研究结果既支撑了部分既往分析结论，也揭示了值得后续深入探究的全新关联，从而阐明了宿主与寄生虫如何相互作用形成生态网络。