Island biogeography theory applied to the microbiomes of
parasites
Habitat fragment area and protection status, but not degree of isolation
or distance to source, have a measurable but lesser effect on the
microbiome of bat flies compared to parasite species (Tables 2 and 3).
As bat flies travel with their host bat, it is not surprising that
isolation and distance to source do not explain bat fly microbiome
variation in these narrowly-separated habitat patches. These stretches
of inhospitable agricultural landscape between forest patches may be
more of a barrier for parasites of other vertebrates that cannot fly.
Mean and median bacterial ASV richness does not decrease with decreasing
habitat fragment area, increasing isolation, and increasing distance
from a source, contradicting expectations of the island biogeography
model (Figure 5). Given the host-specificity of the bat fly microbiome,
it is unrealistic to expect ASV richness to change with environment,
especially if bacterial ASVs in a bat fly are selected for or maternally
transmitted. Instead, variation of the microbiome in response to
environment may be better reflected by variation in relative abundance
of ASVs. Examining both relative abundance and diversity of bacteria
using ecological interaction networks provided a clear statistical
signal that habitat fragment area impacts microbiome composition
(Figures 6 and 7).
Habitat fragmentation resulting in decreasing habitat patch area was
associated with the loss of nodes that act as bridges between modules in
bacterial interaction networks (Figures 6, 7A). There were fewer ASVs
with high betweenness centrality in small fragments and more ASVs with
small betweenness centrality, consistent with the loss of connectivity
between disparate portions of the network (Figure 6). Large fragments
had lower raw modularity scores than small fragments, indicating more
connectivity between neighborhoods than in small fragments (Figure 7A).
ASVs that acted as bridge nodes in large sites, are present in smaller
sites (Figure 5), but these ASVs no longer perform the function of
connecting modules in the network in smaller fragments.
Site F4 is an outlier to this pattern in that it is intermediate in
area, isolation, and distance to a source, but has similar modularity
and betweenness centrality to the sites within REGUA and the largest
site (F10). This is likely driven by the high sample size at F4 compared
to other fragments of similar area, isolation, and distance to a source.
While we standardized the field collecting method across sampling sites,
we sequenced bat fly microbiomes only from the most prevalent bat fly
species, corresponding to the most prevalent bat species, across all of
the sampling sites, which may have introduced sampling bias into
measures of network size. Generally, sites with higher parasite
microbiome sample sizes are also the sites with a higher number of
captured bat fly species. Considering only the bat fly species that were
selected for sequencing, fragments F10 (the largest fragment outside of
REGUA, 9 bat fly species) and F4 (8 bat fly species) have the greatest
parasite richness outside of REGUA. The three REGUA sites, F10, and F4
are the only sampling sites where the bat species Artibeus
lituratus and Desmodus rotundus and their respectively
associated bat flies Paratrichobius longicrus and Strebla
wiedemanni were collected; all other sites are missing one or more of
these species. Bat species and bat fly species responses to habitat
disturbance are highly species specific, and may not always be negative
(Hiller et al., 2020; Pilosof et al., 2012; Saldaña-Vázquez et al.,
2013). Anecdotally, site F4 had many large trees, suitable for roosts,
compared to other patches of similar size. It is possible that factors
of the habitat at F4 which we did not evaluate, such as availability of
permanent tree roosts (Patterson et al. 2007) or access to particular
foraging resources (Pilosof et al. 2012), may mediate the effects of
habitat fragment size in some cases, allowing relatively smaller
fragments to support more bat species, and hence more parasite species,
which in turn may affect structure and connectivity of parasite
microbiomes.
The loss of bridge nodes as sampling sites decrease in area and parasite
species richness is driven by network size (i.e., number of
interactions), which corresponds to sampling size at each site (Figures
6 and 7). When we attempted to control for variation in sample size by
using subsampling, SPIEC-EASI networks built from subsampling sites with
large sample sizes never reached stability. Stability is assessed by
subsampling, so that when we provided SPIEC-EASI with a reduced dataset,
it was not able to infer the more complex networks from the REGUA sites,
F10, and F4. This may indicate that higher network size in these sites
is not an artifact of sample size. PCoA between correlations of orbit
incidences were used as a size-independent examination of the topology
of each network. There is no evidence that the environment alters the
underlying topology of the network, but it may drive network size
through increasing parasite richness (Figures 7). Regardless, sample
size cannot be controlled for when examining network size, so the
correlation may be spurious.
The finding that large fragments have higher null-adjusted modularity
scores than small fragments may appear contradictory to our conclusion
that bridge nodes are absent from fragmented habitats (Figure 7B).
However, this pattern is consistent with our finding that habitat
fragmentation causes changes in network size and not topology.
Randomizations of edges to create null distributions preserves the
degree distribution of the measured network, so measured networks with
many nodes of high degree will have null networks that have low
modularity. Large fragments have many ASVs with high degree (i.e., many
connections) and these are mostly distinct from ASVs with high
betweenness centrality (i.e., more shortest paths between nodes traverse
through these ASVs). The ASVs with high degree form dense subgraphs
rather than nodes connecting many modules in large, near to source
fragments, leading to greater disparity in modularity between the null
and measured networks than in small, distant fragments. More samples in
a site lead to more detected connections (i.e., larger network size),
but do not change the shape of the network by detecting more connections
outside of densely connected neighborhoods.