4 | Discussion
Accounting for spatial processes in our system was integral to
explaining observed patterns of microbiome variation. Longitude
explained variation in almost every microbiome diversity measure
considered. Alpha diversity declined from west to east, and longitude
was correlated with both Euclidean and weighted UniFrac measures of
β-diversity. Unmeasured environmental variables may account for these
patterns; however, plant communities representing the Sable Island
horses’ primary forage were present in our analyses. Furthermore, if
environmental selective pressures acting on the microbiome were
spatially autocorrelated, we would have expected co-occurring horse
microbiomes to be more phylogenetically similar, and distant horses to
be more phylogenetically disparate, than expected by chance. Contrary to
these expectations, we did not observe a positive correlation between
βMNTDses and the distance separating horses. Further,
the microbiomes of horses in the west and towards the longitudinal
extremes of the island tended to be further from the population average.
The significance of spatial terms in PERMANOVA analysis of Euclidean
distances may therefore derive partly from differences in community
variance rather than differences in average community structure.
Host genetics, and thus indirectly the physiological environment with
which microbes directly interact, might explain some of the spatial
variation in microbiome variance. Based on microsatellite data, Sable
Island horse heterozygosity is higher in the east (Lucas, McLoughlin,
Coltman, & Barber, 2009) which is where we also observed lower
microbiome alpha diversity and beta-dispersion when compared to horses
in the west. Evidence from captive and wild mammalian systems has shown
microbiome alpha diversity to be negatively correlated with host
heterozygosity (Grosser et al., 2019; Wadud Khan, Zac Stephens,
Mohammed, Round, & Kubinak, 2019). Similarly, an effect of
population-level heterozygosity has been reported on the bacterial
microbiome of free-living bighorn sheep (Couch et al., 2020). The
homozygosity implicit of inbred hosts might restrict their immunological
complexity (Potts & Wakeland, 1993; Reid, Arcese, & Keller, 2003),
thereby also restricting the dexterity with which host’s recruit and
“leash” (see Foster, Schluter, Coyte, & Rakoff-Nahoum, 2017) their
microbial communities, perhaps allowing for greater stochastic variation
between individuals. Alternatively, Fst values suggest
population sub-structuring between the east and west (Lucas et al.,
2009), therefore genetic differences between horses might explain why
the microbiome differs across the island’s length. For example, among
free-living house mice genetic similarity along a latitudinal gradient
was a better predictor of microbiome similarity than spatial proximity
(Suzuki et al., 2019). Genetic variation among Sable Island horses
expressed as phenotypic variation could therefore drive microbiome
variance across the island’s longitude (Alberdi, Aizpurua, Bohmann,
Zepeda-Mendoza, & Gilbert, 2016).
While we cannot rule out a role for host genetics, in the present
absence of data informative for testing this, bacterial dispersal
limitation between horses provides the most parsimonious explanation of
observed patterns. For example, we observed an apparent positive
correlation between the proximity of horses and the similarity of their
microbiome in Euclidean space (independent of similarities in local
environment). A similar relationship was observed with respect to
weighted UniFrac distances, however no positive relationship was
observed among phylogeny-informed null modeling approaches
(βMNTDses). Assuming bacterial niche space and phylogeny
are non-independent, these patterns suggest that the decrease in
microbiome similarity with spatial separation was not due primarily to
differences in selective pressures across space. Conversely, the
positive relationship between spatial separation and
RCbray values suggest dispersal limitation may occur
over relatively short spatial scales. Evidence for dispersal limitation
may be unsurprising given a zero-inflated ASV count table; of 3767 ASVs,
only 2 were detected in all horses and only 441 were present in at least
half of the horses.
In addition to a positive correlation with spatial separation,
RCbray values were negatively correlated with average
longitude, suggesting greater dispersal limitation among horses in the
west than the east. This was unexpected since horse population density,
which could facilitate bacterial dispersal between individuals,
decreases from west to east (Marjamäki et al., 2013). However, while
multiple above-ground ponds can be found in the west, horses in the east
must crater through sand to access freshwater (Contasti et al., 2012).
Horse-excavated wells are semi-permanent within a season and visited by
multiple social bands but are only accessible to 1–2 horse at a time
(Figure 2D). Prolonged occupancy of an area of social band overlap, and
bottlenecked access to a communal consumable resource, could catalyze
bacterial dispersal despite low population densities in the east.
Similar host aggregation due to patchy resource distribution on urban
landscapes facilitates disease transmission in wildlife (Bradley &
Altizer, 2007); the same aggregative effect could as easily facilitate
transmission of commensal and mutualistic microbiota.
Bacterial dispersal between horses undoubtedly occurs; however, it may
be largely restricted to between individuals within the same, or closely
interacting, social bands. Social band membership was correlated with
both Euclidean and weighted UniFrac β-diversity; however, microbiome
phylogenetic diversity (βMNTDses) was no more similar
between members of the same band than between members of different bands
when compared to null expectations, offering little support for
homogenizing selection as the mechanism for the effect of band
membership on the microbiome. RCbray values, which were
lower between members of the same band than between horses of different
bands, suggests bacterial dispersal limitation as a primary cause for
the observed effect of social band. This interpretation is consistent
with Antwis, Lea, Unwin, & Shultz (2018) who report an effect of band
identity and inter-band connectivity on microbiome β-diversity among
three large social bands of feral Welsh ponies. Similar differences in
band connectivity might explain why, above and beyond parameterized
environmental terms, distance from the population’s centre was
correlated with Euclidean β-diversity and β-dispersion. No relationship
was observed with respect to βMNTDses but
RCbray values were positively correlated with average
distance from the centre of the population. Horses on the edges of the
population—those more poorly connected within the population’s
microbiome meta-community (Miller et al., 2018)—might be vulnerable to
erosion of microbiome diversity through microbial extinctions and
exacerbated ecological drift. Together these results support recent
suggestions of the importance of inter-host dispersal in maintaining the
‘social microbiome’ in free-living populations (Sarkar et al., 2020).
Phylogeny-informed measures of diversity were often better explained by
local plant community composition than spatial terms. Horses with
sandwort in their 150-m radius buffer had lower alpha diversity and
differed in both phylogeny-informed (Euclidean) and
phylogeny-independent (weighted UniFrac) β-diversity measures. The
intuitive explanation is that plant community availability reflects
dietary composition, and different dietary components differ in the
functions required to metabolize component polysaccharides (David et
al., 2014; Julliand & Grimm, 2017). However, among pairwise comparisons
in which sandwort was present for at least one horse, microbiomes were
no more phylogenetically disparate than expected by chance (higher
βMNTDses). By comparison, the microbiomes of horses
without access to sandwort tended to be more phylogenetically similar.
Conversely, grassland and beach pea dominated habitat classes were
negatively correlated with βMNTDses, while heath (only
present where sandwort was absent) appeared positively correlated with
βMNTDses. Therefore, phylogenetic patterns most
consistent with homogenizing selection on the microbiome were observed
when sandwort and heath were absent, but beach pea and marram grass were
abundant; with reversed conditions, phylogenetic similarities did not
deviate far from stochastic expectations.
Increased evidence for stochasticity may stem from the fact that
sandwort, forbs and small graminoids—the primary horse forage in
heathland habitat—possess lower neutral detergent fibre (NDF) when
compared to beach pea and marram grass (personal communication K.
Johnsen; Lee, 2018). NDF is a coarse measure of plant lignin,
hemicellulose, and cellulose (Mongeau & Brassard, 1982); compounds
which many herbivores are obligately reliant on their gastrointestinal
microbiota to metabolize (Costa & Weese, 2012). The low NDF observed in
sandwort and heathland forbs may alleviate the horses’ reliance on their
intestinal microbiota, allowing them to directly absorb nutrients from a
relatively labile diet. Loss of dietary complexity constrains fibrolytic
and cellulolytic niche-space in the microbiome which can manifest as
reductions in bacterial gene richness (Cotillard et al., 2013) or alpha
diversity (Schnorr et al., 2014). Conversely, high fibre forage (e.g.
marram grass and beach pea) can facilitate complex microbial symbioses
in which different species specialize on metabolizing different
biochemical compounds, and in doing so, create by-products to be
absorbed by the host or further metabolized by other microbiota
(Oliphant & Allen-Vercoe, 2019). The reduction in alpha diversity
observed in horses with access to sandwort mirror the effects of low
dietary fibre manipulations in domestic horses (Julliand & Grimm,
2017). When compared to with marram grass and beach pea, sandwort might
represent a reduction in the carbon source complexity accessible to the
microbiome, a property thought to have a stabilizing effect on the
microbiome (Coyte et al., 2015). A diet containing sandwort might not
select for different microbial functions, so much as fail to support
fibrolytic niche-space supported by high fibre diets, leading to species
extirpation and greater ecological drift within individual host
microbiomes (Deehan & Walter, 2016). This could also explain the
greater variability in weighted UniFrac β-diversity among horses with
access to sandwort and the decrease in dispersion in response to beach
pea availability. These results highlight how dietary derived microbiome
variation might not always be the result of strong differential
selective pressures between communities; the relationship between
dietary complexity and ecological drift must also be considered (Adair
& Douglas, 2017; Zhou & Ning, 2017).
Parental status was more strongly correlated with measures of microbiome
variance, rather than mean community structure. Specifically, mares with
foals had microbiomes which were a) more diverse, b) marginally less
variable in weighted UniFrac space, c) less randomly phylogenetically
dispersed (greater |MNTDses|), and d)
further from phylogenetic null expectations of random community assembly
(higher |βMNTDses|) when compared to
mares without foals. Effects of parturition and maternal status on
microbiome alpha and β-diversity have been observed in livestock (Lima
et al., 2015) and wildlife (Amato et al., 2014). Although, to our
knowledge, a difference in β-dispersion between parental states has not
previously been reported. Myriad changes to maternal physiology during
pregnancy and parturition are likely partly responsible for microbiome
differences during birth and child-rearing (Huang et al., 2019;
Nuriel-Ohayon, Neuman, & Koren, 2016). In addition to these
physiological changes, maternal care among mammals, especially
lactation, saddles mothers with a heavy energetic burden (Dufour &
Sauther, 2002; Scantlebury, Russell, McIlrat, Speakman, &
Clutton-Brock, 2002). To meet higher energetic demands, hosts may become
increasingly reliant on their microbiomes (Amato et al., 2014);
especially in species, such as horses, which are obligately reliant on
their gut microbiomes for nutrient uptake (Costa & Weese, 2012).
Therefore, during periods of high energetic demand hosts might be forced
to enforce stronger control on the microbiome to maximize metabolic
efficiency. For example, in laboratory mice, postpartum dampening of
bi-directionality in the host-microbiome relationship is evidenced by
attenuated bacterial driven immunomodulation (Mu et al., 2019). We
suggest that hosts facing a high energetic burden might keep their
microbial constituents on a “tighter leash” than those with a lower
energetic demand (Foster et al., 2017). Within host species, host
physiological variation might in many cases act to facultatively
constrain β-dispersion, rather than drive changes in β-diversity,
although patterns of the former are often overlooked (Zaneveld et al.,
2017). The reverse causal relationship could also explain the patterns
observed, whereby a diverse microbiome under tight host control signals
better host health and therefore greater likelihood of carrying a foal
to term.
Overall, the bacterial microbiome of Sable Island horses is dominated by
clades of fibrolytic taxa, including Ruminococcaceae, Lachnospiraceae,
Prevotellaceae, and Fibrobacteraceae (Biddle, Stewart, Blanchard, &
Leschine, 2013; Esquivel-Elizondo, Ilhan, Garcia-Peña, &
Krajmalnik-Brown, 2017; Spain, Forsberg, & Krumholz, 2011).
Spirochaetaceae and Kiritimatiellae are also present at modest relative
abundances; however, their metabolic niches are currently less well
characterized. These results are consistent with findings from domestic,
feral, and wild horse systems (Antwis et al., 2018; Costa et al., 2015;
Metcalf et al., 2017) and a comprehensive comparison of wild and
domestic equid species (Edwards et al., 2020). Unlike previous studies,
however, we detected no effect of age, likely because we constrained
sampling to horses of at least 3 years of age, and the horse microbiome
appears to reach maturation after ~1 year (Antwis et
al., 2018; De La Torre et al., 2019; Metcalf et al., 2017).
We characterized the bacterial microbiome of 86 mares from the feral
horse population of Sable Island (Nova Scotia, Canada) and contrasted
the ability of spatiotemporal, physiological, and diet-linked
environmental variables to explain microbiome variation.
Phylogeny-independent measures of diversity were best explained by
spatial variables while phylogeny-informed measures were generally
better characterized by host physiology (parental status) and measures
of local habitat heterogeneity; however, despite statistical
significance, these variables explained only nominal variation in
overall β-diversity. Only the longitudinal distance separating horses
and social band membership explained what could be considered
substantive variation, and yet, much of the variation in the Sable
Island horse microbiome remained unexplained. In context, our results
suggest a predominant importance of bacterial dispersal and ecological
drift in shaping faecal microbiome variation among Sable Island horses.
Our findings are relevant to the study of wildlife microbiome variation:
clearly data on the spatial
distribution of hosts should be collected, even at the within-population
scale, alongside metrics of individual-based environmental variation.
Further, when a response of the microbiome to environmental or
physiological variation is observed, deterministic processes must not be
assumed as the sole causal process.