Introduction
Overall, the genotypes and phenotypes of populations undergoing local
adaptation are expected to be spatially congruent, i.e. genetic
diversity is expected to follow similar differentiation patterns as
adaptive phenotypic diversity (Coop et al. 2010, Tigano and Friesen
2016, Fris et al. 2018, Simmonds et al. 2020). This positive correlation
is commonly known as ‘isolation by adaptation’, and it is usually
studied by estimating ecologically adaptive among-populations divergence
as a proxy of the divergent patterns of selection that cause local
adaptation (Schluter 2000, Nosil et al. 2008, Funk et al. 2011). In
theory, isolation by adaptation should be theoretically detectable in
the form of divergence peaks among locally selected loci (or among loci
genetically correlated with them), but if populations remain isolated
long enough, selectively neutral loci can also become differentiated,
which makes the detection of outlier loci empirically challenging (Krohn
et al. 2018, Llanos-Garrido et al. 2019). However, discordances may also
appear, whereby two populations may be genetically undifferentiated
while showing evident phenotypic divergence (Moody et al. 2015, Palmer
and Kronforst 2015, Shaner et al. 2015). This can happen, despite the
existence of processes that blur overall genomic divergence (e.g. gene
flow, recent divergence), only when natural (or sexual; Yang et al.
2018) selection is strong enough to overcome such processes, favoring
the presence of locally divergent regions with variants under selection
within an otherwise undifferentiated overall genomic background (Burri
2017, Wang et al. 2019).
While numerous studies have dealt with locally adapted populations
occupying different environments or isolated by ecological barriers
(Rosenblum 2006, Orsini et al 2013, Sexton et al. 2014, Zhao et al.
2020), only a few have focused on the lack of correlation between
isolation and adaptation (Feder et al. 2013). Moreover, it has been
suggested that there is a significant publication bias against such
studies (Krohn et al. 2018). This possible under-representation of
apparent negative results may lead to biased estimates of how frequently
local adaptation occurs without isolation, and this is precisely the
reason why replicated studies on species or populations with different
degrees of isolation are needed (Feder et al. 2013, Talla et al. 2017,
Sendell-Price et al. 2020). In fact, if studies that do not find any
effect of environmental gradients on genetic differentiation are rarely
published, examples of incipient ecological speciation and/or isolation
by environment may artificially be deemed frequent or even widespread
(Hendry 2009, Shafer and Wolf 2013, Sexton et al. 2014).
The aim of this study is to elucidate the patterns of genetic
differentiation that underlie phenotypic divergence between two
populations of the lacertid lizard Psammodromus algirus separated
by a 600-700 m altitudinal gradient. This gradient is associated with a
large number of habitat differences, including forest type (deciduous
vs. perennial) or average annual rainfall (1170 vs. 438 mm; see the
Methods section for a detailed explanation of these habitat
differences). The analysis of mitochondrial DNA sequences has shown that
these populations present very little genetic differentiation
(Verdú-Ricoy et al. 2010, Díaz et al. 2017), even though they differ in
a wide variety of adaptive phenotypic characteristics, including many
life history traits (Iraeta et al. 2006, 2010, 2011, and 2013,
Llanos-Garrido et al. 2017; Table 1). The evidence for such adaptations
is based on previous studies that have shown, through reciprocal
transplant and common garden experiments, that these adaptive phenotypic
differences are not sustained solely by environmental effects (Iraeta et
al. 2006, 2013). Therefore, there must be a genetic basis to determine
such phenotypic differences between these apparently undifferentiated
populations, even if such genetic basis does not lead to an
environmentally based isolation. To define the degree of genetic
differentiation between the two populations, we used a GBS genomic scan
based on 73,291 SNPs that allowed us to analyze the genetic structure
and distance between them. In addition, we used a Bayesian method of
detection of SNPs with genetic distances between populations greater
than expected given the degree of background genomic differentiation
(i.e. FST based outlier test; Bayescan: Foll and
Gaggiotti 2008). With this approach, we tried to define polymorphisms
possibly associated with the patterns of divergent selection that
promote the observed adaptive differences (Bonhomme et al 2010). Thus,
if local adaptation has led to isolated populations, we expect to find a
genome-wide differentiation standard that could hamper the detection of
divergence peaks at adaptive loci. Alternatively, we might find an
undifferentiated genomic background where such peaks should be easy to
detect, at least in theory.
We used an approach in which all genetic variants are used to infer the
basal level of genomic differentiation, defining outliers as SNPs with a
greater degree of divergence and with allelic frequencies deviated from
the expected under neutral selection (Lewontin and Krakauer 1975). The
reason why comparing differentiation peaks with adjacent regions should
facilitate this task is that the degree of differentiation is
heterogeneously distributed throughout the genome (Campagna et al 2015).
Therefore, detecting a divergence peak in a specially conserved region
is challenging using delocalized genetic variation, which may find such
degree of divergence even below the background genomic differentiation,
but which may actually be very divergent within its genomic location
(Lawson y Petren, 2017). This scenario is especially common in the
coding regions where the genetic basis of phenotypic diversity is
located, so that approaches with delocalized SNPs do not usually respond
to what is the genetic basis of a given phenotype, but describe the
general patterns of genetic differentiation that lay behind the process
of local adaptation (e.g. Tigano et al. 2017, Llanos-Garrido et al.
2019). Thus, while uncovering the specific genetic basis behind already
published adaptations between these populations would be specially
challenging given the methodology we used, it is still possible to
elucidate whether such local adaptation is accompanied by any degree of
isolation (or genome-wide differentiation) or not (Krohn et al. 2018).