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.

We extracted data from the Global Primate Parasite Database (parasites.nunn-lab.org), which is part of the Global Mammal Parasite Database (Nunn & Altizer 2005; Stephens et al. 2017). The version used in this study was downloaded on February 25, 2019, and included over 7,900 lines of host-parasite records, improving on the ~6,300 records in the previous compilation (Gómez et al. 2013). We culled this dataset to include only hosts identified at the species level, and parasites identified at the species or genus level. For host species, the database generally followed a standardized mammalian taxonomy (Wilson & Reeder 2005), though for more recently added taxa that were in the database, the species-level taxonomy was taken from the authors of the paper and matched to the species in the phylogeny, described below. For most parasites, the species level could be accurately recorded, but for cases in which species-level parasite data were available for a parasite genus on a host, the records listed as ‘sp.’ were omitted. This resulted in data for 213 primate hosts and 763 parasites, compared to 140 priates and 300 parasites in recent work (Gómez et al. 2013).

Node- and dyad-level variables: We searched the literature for data on the host and parasite phenotypes and ecology. We included characters that are specific to the nodes (node-level attributes), as well as dyad-level attributes, or pair-wise associations among nodes. All data are available in the supplementary information Appendix S1-4. Continuous variables were z-score transformed before analyses.

Datasets are saved as .csv files and can be opened with any text reader. 'R' code to run analysis is a '.R' file, requiring the software 'R' and the associated packages.