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. 2020 May 8;11(1):2260.
doi: 10.1038/s41467-020-16153-4.

Predicting the global mammalian viral sharing network using phylogeography

Gregory F Albery  1   2   3 , Evan A Eskew  4 , Noam Ross  4 , Kevin J Olival  5
Affiliations

Predicting the global mammalian viral sharing network using phylogeography

Gregory F Albery et al. Nat Commun. .

Abstract

Understanding interspecific viral transmission is key to understanding viral ecology and evolution, disease spillover into humans, and the consequences of global change. Prior studies have uncovered macroecological drivers of viral sharing, but analyses have never attempted to predict viral sharing in a pan-mammalian context. Using a conservative modelling framework, we confirm that host phylogenetic similarity and geographic range overlap are strong, nonlinear predictors of viral sharing among species across the entire mammal class. Using these traits, we predict global viral sharing patterns of 4196 mammal species and show that our simulated network successfully predicts viral sharing and reservoir host status using internal validation and an external dataset. We predict high rates of mammalian viral sharing in the tropics, particularly among rodents and bats, and within- and between-order sharing differed geographically and taxonomically. Our results emphasize the importance of ecological and phylogenetic factors in shaping mammalian viral communities, and provide a robust, general model to predict viral host range and guide pathogen surveillance and conservation efforts.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Viral sharing GAMM outputs and data distribution.
a Predicted viral sharing probability increases with increasing phylogenetic relatedness; the different coloured lines represent different geographic overlap values. b Predicted viral sharing probability increases with increasing geographic overlap; the different coloured lines represent different phylogenetic relatedness values. c The geographic overlap:phylogenetic similarity interaction surface, where the darker colours represent increased probability of viral sharing. White contour lines denote 10% increments of sharing probability. Labels have been removed from some contours to avoid overplotting. d Hexagonal bin chart displaying the data distribution, which was highly aggregated at low values of phylogenetic similarity and especially of geographic overlap.
Fig. 2
Fig. 2. The predicted viral sharing network predicts observed trends in an independent dataset.
In all figures, points are jittered along the x-axis according to a density function; the black points and associated error bars are means ± standard errors. a Species pairs with higher predicted viral sharing probability from our model were more likely to be observed sharing a virus in the independent EID2 dataset. This comparison excludes species pairs that were also present in our training data. b Species that hosted a zoonotic virus in our dataset had more viral sharing links in the predicted all-mammal network than those without zoonotic viruses. c Species that had never been observed with a virus have fewer links in the predicted network than species that were known to host viruses in the EID2 dataset only, in our training data only, or in both. The y-axis represents viral sharing link number, scaled to have a mean of 0 and a standard deviation of 1 within each order for clarity. Black points represent means; error bars represent standard errors. Supplementary Figure 5 displays these same data without the within-order scaling.
Fig. 3
Fig. 3. Taxonomic and geographic patterns of mean predicted viral sharing link numbers (degree centrality).
Top row: all viral sharing links; middle row: viral sharing links with species in the same order; bottom row: viral sharing links with species in another order. a, c, e Average species-level viral sharing link numbers for mammalian orders in our dataset. Bars represent means; error bars represent standard errors. b, d, f Geographic distributions of mean viral sharing link numbers. Distributions were derived by summing the viral sharing link numbers of all species inhabiting a 25 km2 grid square and dividing them by the number of species inhabiting the grid square, giving mean degree number at the grid level.
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