1 | Introduction
Research on the microbiome ecology of wild animals, where ecological and
evolutionary processes free from human intervention shape diversity, is
key to understanding the form and causes of observed microbiome
variation in nature. Exploration of new systems necessarily begins with
characterizing the form and putative causes of microbiome variation
observed among hosts, host populations, or host species. Following
methods of clinical research, aspects of host physiology (Amato et al.,
2014; Stothart, Palme, & Newman, 2019; Suzuki et al., 2019) and diet
(Kartzinel, Hsing, Musili, Brown, & Pringle, 2019; Teyssier et al.,
2020) remain heavily emphasized to the neglect of the fuller breadth of
ecological processes which shape community variation (selection,
ecological drift, and dispersal; Vellend et al., 2014). Niche-based
processes (i.e., selection) are sometimes assumed by default, with
stochastic processes such as ecological drift (chance demographic change
in a population) more rarely taken into account (Adair & Douglas, 2017;
Zhou & Ning, 2017). Similarly, microbiota dispersal (e.g. spatial,
temporal, or social isolation)—while generally ignored beyond
co-housing considerations in clinical research—is a natural process
integral to microbial ecology within free-living systems (Greyson-Gaito
et al., 2020). Yet, studies of wildlife microbiomes also overlook
dispersal in favour of putative mechanisms related to host diet and
physiology.
While host physiology and diet clearly shape wildlife-microbiome
variation, the ability of microbiota to disperse between host intestinal
tracts arguably supersede the importance of either in governing
microbiome diversity (Miller, Svanbäck, & Bohannan, 2018). Correlations
between microbiome composition and social networks in gregarious hosts
illustrate the importance of microbial community connectivity and
bacterial dispersal on microbiome diversity (savannah baboons
[Papio cynocephalus] , Tung et al., 2015; Sarkar et al.,
2020). Bacterial dispersal between hosts can occur through grooming
(rhesus macaques [Macaca mulatta ], Balasubramaniam et al.,
2018), coprophagy (Brandt’s vole [Lasiopodomys brandtii ], Bo
et al., 2020), shared environment (humans [Homo sapiens ],
Rothschild et al., 2018), or copulation (black legged kittiwakes
[Rissa tridactyla ], White et al., 2010). Therefore, we would
expect rates of bacterial dispersal to decrease as a function of the
time and space separating hosts. An effect of spatial separation has
been demonstrated at large spatial scales between (sub)populations of
red squirrels (Tamiasciurus hudsonicus ; ~7km; Ren
et al., 2017), bighorn sheep (~150 km; Couch et al.,
2020), house mice (Mus musculus ; ~1100 km;
Linnenbrink et al., 2013), American pika (Ochotona princeps ;
~1400 km; Kohl, Varner, Wilkening, & Dearing, 2018),
red colobus (Procolobus rufomitratus ; ~1100km;
Mccord et al., 2014), and between pairs of predator and prey species
(~12100 km; Moeller et al., 2017).
The affects of spatial separation on microbial dispersal between social
groups of host individuals within populations are more rarely
considered. One study of a single focal population of house mice found a
greater importance of fine-scale habitat heterogeneity than spatial
separation (Goertz et al., 2019). Conversely, spatial structuring of the
microbiome has been reported among a contiguous moose population
spanning 150 km (Fountain-Jones et al., 2020). Similar effects of
spatial proximity have been observed among semi-feral ponies (40
km2; Antwis, Lea, Unwin, & Shultz, 2018), but were
limited to comparisons between three large social groups (bands).
Regardless of the spatial scale considered, many studies do not control
for local environmental variation, which can be problematic given an
expectation of spatial autocorrelation in environmental conditions.
Conversely, studies which consider environmental terms often do not
consider spatial processes.
Although spatial proximity and social networks can facilitate dispersal
between hosts and can thereby drive microbiome similarity, strong
dispersal limitation can cause greater than expected divergence between
communities and unpredictable β-diversity patterns. In a meta-population
context, dispersal between communities are thought to stabilize
populations (Crowley, 1981), so long as dispersal is not so high as to
drive spatial synchrony (Yaari, Ben-Zion, Shnerb, & Vasseur, 2012).
Conversely, dispersal limitation among isolated biological communities
increases the strength of ecological drift and heightens the risk of
local extinctions (Vellend, 2010). Hosts disconnected from the broader
meta-community of conspecific microbiomes (Miller et al., 2018)—those
in low density populations, at the fringes of populations, or
experiencing social isolation—may be at greater risk of stochastic
microbiome dysregulation. Evidence for dispersal limitation induced
dysregulation of the microbiome has been demonstrated through
experimental prevention of coprophagy in Brandt’s voles (Bo et al.,
2020). While similar concerns have been raised with respect to wildlife
in captivity (McKenzie et al., 2017; Trevelline, Fontaine, Hartup, &
Kohl, 2019), this effect remains to be explicitly tested in free-living
settings.
Dispersal limitation can feed ecological drift but so too can dietary
and physiological factors which are often assumed to be deterministic.
For example, different diets can exert divergent selective pressures,
but can also differ in the energy made accessible to the microbiome and
the diversity of metabolic niche space supported. Labile high energy
diets may fail to support fibrolytic and cellulolytic niche-space in the
microbiome (Oliphant & Allen-Vercoe, 2019) and can destabilize
microbial communities in a process similar to the paradox of enrichment
(Coyte, Schluter, & Foster, 2015; Rosenzweig, 1971). Similarly, while
different host physiological states might select for different microbial
functions (Foster, Schluter, Coyte, & Rakoff-Nahoum, 2017), a loss of
host homeostatic control among physiologically stressed hosts might
result in community instability and greater stochastic variation
(Zaneveld, McMinds, & Vega Thurber, 2017). Microbiome β-dispersion, a
measure of microbiome variance, is one indication of the relative
strength of stochasticity. A second indication of stochasticity is the
failure of communities to deviate from the stochastic expectations as
predicted by null modelling approaches. Despite past misuse (for
overview see: Narwani, Matthews, Fox, & Venail, 2015), phylogenetic
null modelling methods are valuable to consider alongside conventional
β-diversity metrics, as traditional diversity metrics can be influenced
by system gamma diversity and imbalances in alpha diversity between
communities (Chase, Kraft, Smith, Vellend, & Inouye, 2011; Gering &
Crist, 2002; Zhou & Ning, 2017).
Here we directly contrast the ability of host physiology, habitat
heterogeneity, and spatial measures to explain variation in the faecal
bacterial microbiome of feral horses using 86 adult females from the
closed population of Sable Island (Nova Scotia, Canada). Building on a
comprehensive, long-term, detailed individual-based study of ecology and
evolution for this population (Richard, Simpson, Medill, & Mcloughlin,
2014), we apply a combination of conventional diversity analyses and
null modeling approaches to evaluate the evidence for drift, dispersal,
and niche-based processes. If environmental conditions and host
physiology—sources of selective pressures which shape the
microbiome—are more similar among populations than between populations
or between species, then we would predict microbial dispersal patterns
and ecological drift to play a comparably large role in shaping
inter-individual microbiome variation within populations. Specifically,
we predict that phylogeny-independent diversity measures will be most
strongly influenced by spatial and social variables, reflecting
microbial dispersal patterns. Conversely, we predicted that host
physiology and local habitat heterogeneity would better explain
variation in phylogeny-weighted metrics of microbiome diversity,
reflecting different selective pressures imposed on host-associated
microbiota between host physiological states or diets. Our study
represents one of the first direct comparisons between environmental and
spatial effects on host-associated microbiomes in the wild at a within
population scale, with consideration offered to alternative ecological
processes.