Discussion
Heterogeneous environmental conditions across time and space can drive
adaptive population divergence among even populations that are only
partially reproductively isolated (Hereford, 2009). Here we assessed
population structure and functional divergence among six geographically
isolated breeding populations of Arctic-breeding snow buntings. Both our
neutral (microsatellite DNA loci) and functional (coding-gene SNPs)
genetic marker data show substantial population divergence among all
populations, likely indicative of some level of reproductive isolation.
Furthermore, we demonstrated that
the observed population differentiation patterns at selected
known-function SNPs likely resulted primarily from stabilizing, but also
divergent, selection at the candidate loci. The global divergence
analyses showed strong evidence of stabilizing selection across all
populations, which is not surprising given the expected canalization of
the vital functional gene loci chosen for this study. At the pairwise
population comparison level, our functional marker results show
signatures of both neutral drift and selection, with functional
divergent selection observed at some SNP loci. Such selection effects
likely reflect local adaptation of different snow bunting populations to
their local environments (e.g., both wintering and migratory route
selection pressures).
Although both of our marker types yielded broad spatial divergence
patterns separating resident and migratory populations, finer genetic
structure differed based on the marker type. A prime example is the lack
of divergence between the Alert & Mitivik Island and the Utqiagvik &
Svalbard populations, indicative of gene flow between both population
pairs. The Alert & Mitivik Island population pair exhibited similar
microsatellite and SNP divergence patterns; however, the Utqiagvik &
Svalbard population pair curiously exhibited significant SNP divergence,
but no divergence with the microsatellite markers. Such a pattern is
consistent with a strong selection signature at the SNP loci, despite
gene flow. Our observation of gene flow between the Alert and Mitivik
Island populations is a new finding, but supports previous work in this
species using stable hydrogen isotope analysis and light-level
geolocator tracking that suggested two parallel migratory systems for
the high and low Eastern Canadian Arctic with Hudson Bay as a migratory
divide (Macdonald et al., 2012). Thus, it is possible that the Alert
population follows the same migratory route as the Mitivik Island
population (i.e., to the West of Hudson Bay, NU, Canada; Macdonald et
al., 2012). Additionally, because the Mitivik Island population has been
shown to winter in the Canadian provinces of Saskatchewan and Alberta
(Macdonald et al., 2012), it is further possible the individuals in
these populations winter together, or even mix during spring migration
to the breeding grounds. On the other hand, the presence of potential
gene flow between the Utqiagvik and Svalbard populations is surprising,
given the geographic distance between the two sample sites. Although we
do not currently know where birds from the Utqiagvik breeding population
overwinter, recent tracking of the Svalbard buntings using light-level
geolocators (GLS) indicate they overwinter in the Asian Western Siberian
Steppe where they utilize the high abundance of grain croplands and face
very little interspecific competition (Snell et al., 2018). This could
also be true for individuals breeding at Utqiagvik, providing a
potential mechanism for gene flow between the two populations. If true,
Svalbard birds would be migrating west in the Fall, and Utqiagvik birds
east in the fall, to share wintering grounds in the Asian Western
Siberian Steppes. Such cross-hemisphere migration has been demonstrated
in a similarly-sized songbird, the northern wheatear (Oenanthe
oenanthe ), using GLS (Bairlein et al., 2012). Nevertheless, a detailed
migration study is needed for Utqiagvik snow buntings to empirically
test the possibility of a shared wintering ground.
While fairly spatially distant snow bunting populations showed genetic
connectivity, we surprisingly found significant differentiation between
the two non-migratory populations in Alaska (Aleutian and Pribilof
islands), based on microsatellite data. These populations exhibited
substantial divergence despite being geographically close (Figure 1).
Migratory life history is a critical component of genetic population
structure; high dispersal rates result in genetically homogeneous
populations, whereas restricted dispersal allows for development of
genetically differentiated populations (Milgroom, 2015) due to elevated
isolation and drift (Arguedas & Parker, 2000; Winker et al., 2000). Our
results support this pattern in snow buntings, with both microsatellite
and SNP data clustering resident and migratory populations separately.
Overall, in addition to identifying significant global population
differentiation, the genetic markers used in this study add to our
knowledge of migratory connectivity patterns among breeding snow bunting
populations. More importantly, our results shed light on the
vulnerability of common wintering grounds for some populations should
these sites face human-induced stressors such as habitat degradation.
Although local adaptation is predicted in populations experiencing
divergent local selection pressures, it is rarely directly demonstrated
empirically since it requires common-garden or reciprocal transplant
experiments (Kawecki & Ebert, 2004) which are not practical for many
wild populations (Blanquart et al., 2013). Avian species have been shown
to exhibit population-level patterns of variation in timing of migration
and brood initiation (Gu et al., 2021; Wanamaker et al., 2020), body
size and mass (Blondel et al., 2006), song (Badyaev et al., 2008),
personality (Mouchet et al., 2021), and plumage (Antoniazza et al.,
2010) that have been identified as possible locally adapted traits.
Although those studies provide strong indirect evidence of local
adaptation, they may reflect phenotypic plasticity or even flexibility.
In this study, we used SNP gene loci expected to be under selection
based on their putative gene function, and as such likely to reflect
environmental and ecological differences driving genetic variation among
populations (Wellband et al., 2018). Although more than a quarter
(28/101) of our SNP markers are missense variants, all were in very
strong linkage disequilibrium with the target known-function genes,
making our study unique from other SNP-based studies in birds that used
random SNPs located in both coding and non-coding regions of the genome
(e.g., Tiffin & Ross-Ibarra, 2014; Pardo-Diez et al., 2015). Oura priori choice of candidate gene function improves the
likelihood of detecting functional patterns of population
differentiation consistent with local adaptation in breeding snow
bunting populations. Local adaptation has implications for management
and conservation aimed at preserving local genetic diversity, especially
as Arctic-migratory species continue to face strong effects of climate
change and other anthropogenic stressors worldwide.
Generally, locally adapted populations are predicted to exhibit
significantly higher (for divergent selection) or lower (for stabilizing
selection) genetic differentiation than expected under neutral processes
(Schlötterer, 2002; Hoban et al., 2016). Consistent with this idea, a
high proportion of our candidate loci exhibited evidence of being under
selection at candidate functional loci. Only a handful of previous
studies have assessed patterns of divergence at both coding (i.e.,
functional) and non-coding (i.e., presumed neutral) marker loci, to
interpret selection patterns in migratory bird species. Furthermore, the
majority of those studies used randomly selected genome-wide SNPs and
they inferred divergent selection at functional loci based on presumed
linkage disequilibrium. For example, Zhan et al. (2015) used a targeted
approach comparing thirteen wild populations of saker falcon
(Falco cherrug ) across Eurasia using SNP data and inferred that
the MHC genes were under directional selection
(FST > 0.5), with the remaining
candidate SNPs showing signatures of stabilizing selection or drift.
Although SNP-based selection studies are becoming more common in
migratory bird species (e.g., Ruegg et al., 2014; Bay et al., 2021;
Larison et al., 2021; Ruegg et al., 2021), there have only been two such
studies on Arctic-breeding migratory birds, both of which employed a
random SNP approach and reported no or low levels of selection. For
example, Colston-Nepali et al. (2020) used RAD-seq to genotype six
breeding colonies of northern fulmar (Fulmarus glacialis ) at
6,614 genome-wide SNPs; however, no outlier loci were identified.
Similarly, Tigano et al. (2017) used 2220 genome-wide SNPs across five
colonies of Arctic-breeding thick-billed murres and found
~6% outlier SNPs (only 28% of which showed divergent
selection). However, random SNP surveys do not have a priori SNP
gene function and often it is difficult to assign function. For example,
Tigano et al. (2017) found only 6 of their 111 identified outlier SNP
loci could be assigned a putative function (GO term). In contrast, we
detected strong signatures of stabilizing selection at known-function
SNP loci, with some showing evidence of divergent selection in pairwise
population comparisons. The high level of stabilizing selection likely
results from canalization of the genes associated with our SNP loci as
they were selected to reflect critical organismal and cellular
functions.
Our candidate SNP loci that exhibited consistent patterns of divergent
selection may reflect local adaptation. For example, ACVR2A (divergent
between Utqiagvik and Mitivik, and between Utqiagvik and Svalbard
population pairs) codes for a receptor that is involved in the induction
of adipogenesis and growth (Donaldson et al., 1992). It has been shown
that fat reserves aid in thermogenesis, cold endurance (Vézina et al.,
2012; Montgomerie & Lyon, 2020) and modulate the adrenocortical
response to environmental stress (Wingfield et al., 2004). Those
functions not only facilitate successful breeding in Arctic conditions,
but also help snow buntings survive challenging conditions (i.e., scarce
food resources and cold temperatures) on arrival as they prepare for
breeding (Le Pogam et al., 2021). PTPRZ1 (divergent between Pribilof
Islands and Svalbard, and between Utqiagvik and Svalbard population
pairs) is mainly involved in development of myelinating oligodendrocytes
and is thought to play a role in the establishment of contextual memory
and learning (The UniProt Consortium, 2015). The role of spatial memory
and learning has been explored in passerines for behaviours associated
with food hoarding (Hitchcock & Sherry, 1990; Brodin, 1994; Healy &
Krebs, 1996; Smulders & DeVoogd, 2000) and vocal communication
(Nottebohm, 1999; Zeigler & Marler, 2004). Our candidate gene loci
implicated in divergent selection warrant further examination of allelic
variation at the loci under selection in snow buntings and possibly
other migratory avian species.
Arctic-breeding migratory bird species utilize diverse breeding and
over-wintering habitats, resulting in a substantial variation in
experienced local abiotic factors such as temperature, wind,
precipitation, and snow cover (among others; Martin & Wiebe, 2004;
Wingfield et al., 2004). Variation likely drives selection pressures on
Arctic-breeding birds that have short breeding times and high energetic
demands (Le Pogam et al., 2021), which may contribute to local
adaptation (Macdonald et al., 2012; Tigano et al., 2017; Snell et al.,
2018). To our knowledge, this is the first study to investigate global
population structure and genetic divergence consistent with local
adaptation in a circum-polar arctic-breeding songbird. Consistent with
our predictions, we observed strong evidence of genetic isolation
coupled with some SNP loci showing divergent selection signatures.
However, we observed stabilizing selection signatures across most SNP
loci, and while high levels of stabilizing selection are reasonable
given the nature of our candidate genes, we did not expect such a
dominant role for apparent stabilizing selection among our SNP loci.
Identifying population genetic structure at a pan-Arctic scale in snow
buntings is especially important for their conservation as they face
severe effects of climate change in their breeding areas coupled with
other anthropogenic stressors in their overwintering areas (Walker et
al., 2015). Our analyses of population divergence using both neutral and
functional markers provide conservation-related data particularly
valuable for species such as the snow bunting, which, because of its
migratory life history, experiences diverse management jurisdictions,
habitat degradation, and survival challenges.