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Linking parasitism to network centrality and the impact of sampling bias in its interpretationuse asterix (*) to get italics
Zhihong Xu, Andrew J. J. MacIntosh, Alba Castellano-Navarro, Emilio Macanás-Martínez, Takafumi Suzumura, Julie DuboscqPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
2022
<p style="text-align: justify;">Group living is beneficial for individuals, but also comes with costs. One such cost is the increased possibility of pathogen transmission because increased numbers or frequencies of social contacts are often associated with increased parasite abundance or diversity. The social structure of a group or population is paramount to patterns of infection and transmission. Yet, for various reasons, studies investigating the links between sociality and parasitism in animals, especially in primates, have only accounted for parts of the group (e.g., only adults), which is likely to impact the interpretation of results. Here, we investigated the relationship between social network centrality and an estimate of gastrointestinal helminth infection intensity in a whole group of Japanese macaques (<em>Macaca fuscata</em>). We then tested the impact of omitting parts of the group on this relationship. We aimed to test: (1) whether social network centrality - in terms of the number of partners (degree), frequency of interactions (strength), and level of social integration (eigenvector) - was linked to parasite infection intensity (estimated by eggs per gram of faeces, EPG); and, (2) to what extent excluding portions of individuals within the group might influence the observed relationship. We conducted social network analysis on data collected from one group of Japanese macaques over three months on Koshima Island, Japan. We then ran a series of knock-out simulations. General linear mixed models showed that, at the whole-group level, network centrality was positively associated with geohelminth infection intensity. However, in partial networks with only adult females, only juveniles, or random subsets of the group, the strength of this relationship - albeit still generally positive - lost statistical significance. Furthermore, knock-out simulations where individuals were removed but network metrics were retained from the original whole-group network showed that these changes are partly a power issue and partly an effect of sampling the incomplete network. Our study indicates that sampling bias can thus hamper our ability to detect real network effects involving social interaction and parasitism. In addition to supporting earlier results linking geohelminth infection to Japanese macaque social networks, this work introduces important methodological considerations for research into the dynamics of social transmission, with implications for infectious disease epidemiology, population management, and health interventions.</p>
https://doi.org/10.5281/zenodo.6825009You should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
https://doi.org/10.5281/zenodo.6825009You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
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sociality, social network, geohelminth, Knock-out simulation, parasite transmission
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Animal networks, Biological Networks, Contact networks
No need for them to be recommenders of PCI Network Sci. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe [john@doe.com]
2021-06-20 16:29:07
Matthew Silk