Research ArticleECOLOGY

Host susceptibility to snake fungal disease is highly dispersed across phylogenetic and functional trait space

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Science Advances  20 Dec 2017:
Vol. 3, no. 12, e1701387
DOI: 10.1126/sciadv.1701387
  • Fig. 1

    (A) Traitgram of snakes found in the EUS, which projects the topology of the molecular phylogeny related to traits (on the x axis), showing SFD-infected taxa in red. (B) PSV density distribution of 1000 null communities (n = 23) assembled from the random samples taken from the 92 sampled taxa in EUS. The distribution colored blue indicates the proportion of the distribution above the actual PSV value (0.633) for the real SFD-infected community.

  • Fig. 2

    (A) Multivariate clusters showing the three trait/ecological groups for snakes found in the EUS. Taxa infected with O. ophidiodiicola are bold within each group. (B) Distributions of P values showing the probability of having a Euclidian distance from each infected taxon to their group medoid are greater than those distances among all other uninfected taxa and medoid per group for all groups (see text). Infected taxa are generally not ecological outliers in any particular cluster.

  • Fig. 3 FPDist for SFD-infected taxa, over all 20 values of the tuning parameter α, showing a conserved pattern where trait differences increase with phylogenetic differences.

    Here, α values closer to 0 indicate a strong contribution of traits, whereas values closer to 1.0 indicate a strong contribution of phylogeny, with intermediate values showing combined trait/phylogeny contributions. The shaded area is formed from 1000 null simulations with the average represented by the dark red solid line and the real SFD community represented by the black dashed line.

  • Fig. 4

    (A) The preferred artifical NN model showing the following groups of input neurons (trait variables), the first layer of hidden neurons (H1 to H3), and the output variables predicting presence or absence of the diease (0/1). In addition, this NN diagram shows both intercept terms (bias), B1 and B2, which increases NN effeciency, and synaptic weights indicated by the thickness and bolding of lines connecting the neurons. (B) Density distributions and means (dashed lines) for the two NN metrics taken from 100 randomly chosen replicate data sets: (i) area under the receiver operator characteristic curve (AUC), where values >0.70 are considered reliable predictors; and (ii) difference between real and randomly shuffled accuracy (ΔA; see text), where values centered on zero indicate poor NN-predictive function.

  • Fig. 5 Beeswarm plots showing the accuracy of the NN for 100 data simulations per category, where the percentage of trait variables were randomized by 0, 20, 40, 60, 80, and 100%, and the remaining traits were randomized by 100% (random noise).

    For example, in the upper left hand graph, 26% of variables were randomized by 0 to 100%, whereas the remaining 74% of variables were randomized completely by 100%.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/12/e1701387/DC1

    table S1. Identity of infected snake taxa in the EUS used in the study of Burbrink et al.

    table S2. Traits used to examine ecological diversity in the study of Burbrink et al.

    text S1. Phylogenetic trees for taxa from both the US and globally used to examine phylogenetic diversity in the study of Burbrink et al.

    text S2. Identity of SFD wild-infected taxa from the EUS in the study of Burbrink et al.

    text S3. Identity of SFD wild-infected taxa globally in the study of Burbrink et al.

    text S4. Identity of SFD wild- and laboratory-infected globally in the study of Burbrink et al.

    text S5. Code for running NN analyses in the study of Burbrink et al.

    text S6. Code for running NN simulations in the study of Burbrink et al.

    text S7. Machine learning data

  • Supplementary Materials

    Other Supplementary Material for this manuscript includes the following:

    • table S1 (.txt format). Identity of infected snake taxa in the EUS used in the study of Burbrink et al.
    • table S2 (.txt format). Traits used to examine ecological diversity in the study of Burbrink et al.
    • text S1 (.txt format). Phylogenetic trees for taxa from both the US and globally used to examine phylogenetic diversity in the study of Burbrink et al.
    • text S2 (.txt format). Identity of SFD wild-infected taxa from the EUS in the study of Burbrink et al.
    • text S3 (.txt format). Identity of SFD wild-infected taxa globally in the study of Burbrink et al.
    • text S4 (.txt format). Identity of SFD wild- and laboratory-infected globally in the study of Burbrink et al.
    • text S5 (.R format). Code for running NN analyses in the study of Burbrink et al.
    • text S6 (.txt format). Code for running NN simulations in the study of Burbrink et al.
    • text S7 (.R format). Machine learning data

    Files in this Data Supplement:

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