Document Type

Article

Publication Date

1-1-2020

Abstract

© 2020 Georg Thieme Verlag. All rights reserved. Identifying the factors that structure host-parasite interactions is fundamental to understand the drivers of species distributions and to predict novel cross-species transmission events. More phylogenetically related host species tend to have more similar parasite associations, but parasite specificity may vary as a function of transmission mode, parasite taxonomy, or life history. Accordingly, analyses that attempt to infer host-parasite associations using combined data on different parasite groups may perform quite differently relative to analyses on each parasite subset. In essence, are more data always better when predicting host-parasite associations, or does parasite taxonomic resolution matter? Here, we explore how taxonomic resolution affects predictive models of host-parasite associations using the London Natural History Museum's database of host-helminth interactions. Using boosted regression trees, we demonstrate that taxon-specific models (i.e., of Acanthocephalans, Nematodes, and Platyhelminthes) consistently outperform full models in predicting mammal-helminth associations. At finer spatial resolutions, full and taxon-specific model performance do not vary, suggesting tradeoffs between phylogenetic and spatial scales of analysis. Although all models identify similar host and parasite covariates as important to such patterns, our results emphasize the importance of phylogenetic scale in the study of host-parasite interactions and suggest that using taxonomic subsets of data may improve predictions of parasite distributions and cross-species transmission. Predictive models of host-pathogen interactions should thus attempt to encompass the spatial resolution and phylogenetic scale desired for inference and prediction and potentially use model averaging or ensemble models to combine predictions from separately trained models.

Publication Source (Journal or Book title)

Parasitology

Download
COinS