Impact of area, isolation, and distance from source on bacterial
interaction networks
SPIEC-EASI networks of bacterial interactions changed in betweenness
centrality and modularity associated with environment (Figures 6 and 7).
In REGUA sampling sites, low-abundance bacteria and bacteria in the
genera Arsenophonus , Wolbachia , and Bartonella had
higher betweenness centrality than other bacteria in these networks,
acting to connect graph neighborhoods (Figure 6). As networks became
more modular (i.e. decreasing habitat fragment area), there were fewer
bridge nodes with greater extremes of high and low betweenness
centrality. In smaller habitat patches, low-abundance bacteria andMycoplasma had much larger betweenness centrality compared to
other bacteria within each network. Arsenophonus ,Wolbachia , and Bartonella did not have high betweenness
centrality in the networks of smaller habitat fragments. The exception
to this pattern is fragment F4, where betweenness centrality of bridge
nodes was not extremely large compared to the betweenness centrality of
the other bacteria in the graph and Arsenophonus ,Wolbachia , and Bartonella had high betweenness centrality.
While fragment F4 had small habitat area, it was the only fragment
outside of REGUA that supported all streblid bat fly species.
The raw modularity of each network was higher in fragments outside REGUA
than in sites within the protected area, although this pattern was not
statistically significant (Figure 7A; Wilcoxon Rank Sum test,
p-value= 0.133). This is largely due to fragment F9, which showed
much lower modularity than any other network. When we remove fragment
F9, the difference in modularity between REGUA sites and fragments is
significant (Wilcoxon Rank Sum test, p-value=0.033). Null
distribution-centered modularity measures, which are standardized to
account for variation in network size and shape, are highest in REGUA,
the largest fragment (F10), and the fragment with the second highest bat
fly diversity after the largest fragment (F4; Figure 7B). Smaller
fragments and those distant from a source had low centered modularity,
and fragment F9 was no longer an outlier. Standardizing modularity by
comparison with the mean and standard deviation of the set of measured
networks did not control for network size and shape variation, and
mimicked the pattern exhibited by raw modularity. Calculation of the
Z-score modularity using the null distribution for each site indicated
that all measured networks have significantly higher modularity than a
randomly connected network of the same size and shape.
Sample size (i.e., number of parasite individuals) was lowest in small
fragments and highest in large fragments, with the exception of F4 which
has intermediate area, isolation, and distance to source measurements.
While there was no impact of sample size on ASV richness in each
network, a greater number of samples may allow detection of more edges
between nodes (more interactions between ASVs). Efforts to reconstruct
SPIEC-EASI networks using subsampling of the sites with the largest
sample sizes failed because SPIEC-EASI networks on subsamples never
reached stability. To examine the topology of the network independent of
size (number of connections), we decomposed the graph into 4-node
graphlets and examined correlations between the incidences of graphlet
orbits and ordinated the networks using PCoA (Mahana et al., 2016; Ruiz
et al., 2017; Yaveroğlu et al., 2014). The position of networks in the
ordination differed from what we would expect if landscape variables
were the primary driver of network topology (Figure 7D). The REGUA2
sampling site was found to be quite different from the other REGUA
sampling sites which were instead closest to fragment F9, the second
largest fragment. Outside of the REGUA sites, there was no pattern of
differentiation with decreasing area, increasing isolation, and
increasing distance to source.
Discussion