Redundancy analysis: disentangling patterns of isolation by
distance, environment, and barrier
We implemented a set of redundancy analyses (RDA) to disentangle the
relative contribution of alternative hypotheses for explaining the
genetic differentiation in A. cephalotes : IBD, IBE, and IBB. RDA
is a canonical extension of PCA, in which the principal components are
constrained to be linear combinations of a set of predictors (Bradburd,
Ralph, & Coop, 2013; Legendre & Fortin, 2010; Meirmans, 2015). The
goal of RDA here was to identify the best ordination model that
describes genetic differentiation (James et al., 2011) in order to
better understand how spatial heterogeneity across the Andes in the
Colombian Pacific and Andean regions affects patterns of gene flow inA. cephalotes .
For the microsatellite data, a conventional RDA analysis was performed
using population allele frequencies as a dependent matrix since the
Euclidean distance estimates involved in the analysis are directly
related to F ST values (as long as the frequencies
are not scaled) (De Queiroz et al., 2017; Noguerales et al., 2016). ThemtCOI data were analyzed through a distance-based RDA, making
direct use of a Φ ST-based genetic distance
matrix. In both cases, space (geographic distances matrix), environment
(climate variables), and topography (barriers) were considered
explanatory variables. These explanatory variables were grouped into
three classes according to their resulting patterns of isolation: 1)
IBD, with variables representing the geographic distance between
populations (space); 2) IBE, with variables determining environmental
differences between populations; and 3) IBB, based on the western range
as a geographic barrier that splits populations in an allopatric manner.
Since space was the only explanatory variable initially expressed as a
distance matrix, it was transformed into a vector format (Oksanen et
al., 2019) by PCA, using the ‘pcnm’ function in the package
VEGAN. Only the best explanatory PCNM components were retained. The
significance of predictors was assessed using multivariateF -statistics with 10000 permutations using the ‘anova.cca ’
function included in the package VEGAN. All explanatory variables were
scaled using the ‘scale’ function in the package VEGAN. The
allele frequencies used were not scaled in order to keep their
inter-population relation with the Φ ST.
Spatial explanatory variables were tested through an IBD analysis based
on RDA, followed by sequential elimination of environmental variables,
in which only variables with | r | < 0.80
were retained (Dormann et al. 2013; Supplementary figure SF2). The
remaining variables were tested through a model selection approach,
comparing all possible combinations or marginal tests to be finally
included in the IBE analysis. Each marginal test was compared to the
null model (intercept; AIC: 1.41). These combinations of environmental
variables were used to identify the best model based on the Akaike
information criteria (AIC). We selected the best model for significant
predictors with the ‘ordistep’ function, using the package VEGAN
to prevent overfitting. IBB was tested using the classification of
sampled populations represented by a dummy variable (see sampling). Once
all three models of genetic isolation were defined and tested, both IBE
and IBB were tested while controlling for spatial autocorrelation
through a partial RDA (conditional test). Finally, the relative
contribution of IBD, IBE, and IBB to the explanation of gene flow
patterns in A. cephalotes was evaluated from the final complete
model, including all mechanisms of genetic isolation, using a variation
partitioning analysis with the function ‘varpart ’ implemented in
the package VEGAN. This analysis allowed us to disentangle total genetic
variance according to the underlying mechanisms of isolation imposed by
space, environment, and barriers, as well as their relative
contributions when considered together.
RESULTS
Genetic diversity
All microsatellite loci followed expectations from the HWE or LD tests.
Most loci were highly variable, with the number of alleles ranging from
2 to 20 per locus (Table 2, Supplementary table ST2). The Pacific region
showed the highest allelic richness, largely due to 17 private alleles,
while the Andean 1 region had the lowest allelic richness, with four
private alleles. Most genetic diversity parameters were significantly
higher for the Pacific region, except for the Heestimates (Figure 2A, Supplementary table ST4). An excess of
heterozygotes was detected in the PPY population (IAM model: T= 3.40,P < 0.01; SSM model: T= 1.92, P = 0.03), while
the allele frequencies differed from the L-shaped distribution. A
putative bottleneck was supported by the low number of alleles per locus
detected in this population (range: 1 to 6). Inbreeding coefficients did
not differ significantly from zero, suggesting random mating in all
populations (Table 2).
A 307 bp mtCOI gene fragment was successfully sequenced in 146
individuals. Ten nucleotide sites (3.3%) were polymorphic and defined
10 different haplotypes that differ in 1-3 positions (0.4% sequence
divergence). The two most common haplotypes, Hap1 (frequency= 0.51) and
Hap4 (frequency = 0.27), were found in almost all sampling locations.
One haplotype was specific to the Pacific (Hap3), one to the Andean 1
(Hap6), and three to the Andean 2 (Hap8, Hap9, Hap10; Figure 2C,
Supplementary Table ST5) regions. Overall, haplotype and nucleotide
diversities were Hd= 66% (range: 0.0 – 76%) and π =
0.3% (range: 0.0 – 0.4%), respectively. In contrast to the
microsatellite data, the Andean 1 region presented the highest diversity
(Table 2, Figure 2B). However, the differences in mitochondrial
diversity across regions were not significant (Figure 2B, Supplementary
Table ST4).
Population structure for nuclear and mitochondrial
data
Two regional models were used in the hierarchical AMOVA, following IBB
and IBE scenarios (Table 3). For the microsatellite data, the results
were similar under IBB and IBE scenarios, showing substantial
differentiation among regions and populations within regions. Moreover,
genetic differentiation was higher among regions when compared with that
among populations, and was slightly higher under the IBE model when
compared to the IBB model (Table 3).
Pairwise F ST estimates between populations ranged
from 0.01 to 0.28 (mean = 0.10) and were significant following
Bonferroni corrections, except for the BVT-QBD comparison in the Pacific
region (Figure 3A). Pairwise comparisons between regions under the IBE
model were significant, showing the highest differentiation values for
comparisons involving the Andean 1 region (Pacific-Andean 1: 0.04;
Pacific-Andean 2: 0.02; and Andean 1-Andean 2: 0.10), consistent with
the DAPC analysis (Figure 4A). However, regional comparisons under the
IBB model were also significant (F ST = 0.04).
Hierarchical AMOVA for mtCOI data indicated that populations
within regions harbored 34% of the genetic variance, but no region
effect was detected under the IBB and IBE models (Table 3). The pairwiseΦ ST values estimated among populations ranged
from 0.00 to 0.86, and 56% of these comparisons were significant
(Figure 3B). Moreover, DAPC clustering analysis also failed to group
populations by regions (Supplementary figure SF3), supporting the AMOVA
results.
Clustering analyses
Evidence for hierarchical population structure in microsatellite data
was also found from the STRUCTURE runs. The ΔK ad hocstatistic by Evanno et al. (2005) indicated an optimal K = 3
[Pr(X / K : 2) = -5061.73] as the uppermost hierarchical level
(Supplementary figure SF1). These three genetic clusters correspond to
the individuals belonging to the three regions originally considered
under the IBE model, except for the PPY population from the Andean 2
region. The first group (blue in Figure 4B) clustered most samples from
the Pacific region. The second group (purple in Figure 4B) clustered
most samples from the Andean 1 region, as well as samples from Andean 2
region (PPY). The third group (orange in Figure 4B) clustered most
samples from Andean 2 region, apart from the PPY population. This
pattern suggests a hierarchical north-south structure in the Andean
region rather than a climate-related structure, as tested through the
IBE-based AMOVA (Figure 4C), and suggests that IBD is influencing
regional differentiation for the Andean regions rather than, or in
addition to, IBE.
To further investigate the role of IBD in the genetic structure ofA. cephalotes , we performed a new AMOVA by moving PPY from the
Andean 2 to the Andean 1 region. However, the results were similar to
those produced under the original IBE scenario (results not shown). We
also ran another STRUCTURE analysis, this time only including samples
from the Andean 1 region and the PPY population (southern Andean
cluster). This analysis revealed two genetic groups (K = 2), in which
PPY formed a sole cluster (Figure 4B).
The results for the IBE model were supported by the DAPC analysis, which
clustered defined regions based on environmental variables. The first
two principal components of the DAPC analysis explained 77.1% of the
variance in allele frequencies (50 PCs retained; Figure 4A). In this
case, the first principal component clustered the Pacific and Andean
regions, while the second principal component reflected the
differentiation between Andean 1 and Andean 2 regions (Figure 4A).
RDA analysis – Alternative scenarios of genetic
differentiation: IBD, IBE, and
IBB
Significant IBD was detected from the RDA analysis for microsatellite
data, in which the geographic distance expressed as the first axis of
the PCNM analysis (PCNM1) contributed to the genetic structure ofA. cephalotes , based on model selection. This suggests that
distance is an important determinant of genetic structure in A.
cephalotes (Figure 5). These results agree with those obtained from
STRUCTURE, suggesting that genetic differentiation in the Andean regions
can also be explained by an IBD pattern (north to south) and not
exclusively by IBE across regions (Figure 4B). The IBD results followed
different patterns between nuclear and mitochondrial markers since IBD
was not detected from the mtCOI data.
Although alternative models used for regional differentiation suggested
the stronger influence of IBE, based on its higher regional genetic
differentiation when compared to IBB or IBD, all of the models were
significant (Table 3). This was also observed from the RDA results,
where two environmental variables (temperature and precipitation) and
the barrier variable (Andes classification) were significantly
associated with genetic divergence (Table 4, Figure 5). Moreover, a
significant contribution of alternative models of genetic
differentiation was only observed from the microsatellite data
(Supplementary table ST6). The optimal model, including all mechanisms
of genetic isolation as tested for IBD, IBE, and IBB, accumulated up to
33% of explained variation, which remained marginally significant after
accounting for IBD in conditional tests (Figure 5).
Variation partitioning for the full model, including all alternative
models of genetic isolation, showed that IBD and IBE explained the
highest proportion of genetic variation relative to IBB (Figure 5).
Interaction effects between major mechanisms of genetic isolation were
low for the triple interaction (IBD + IBE + IBB = 2%) and were not
significant for paired interactions (Figure 5). The higher fraction of
unexplained variation (residuals) might reflect the occurrence of
genetic drift within populations, which is not associated with the
explanatory variables.
DISCUSSION
The complex landscape across the Andean uplift is an important barrier,
isolating populations and increasing genetic divergence on both sides of
the mountains (Antonelli et al., 2009; Hoorn et al., 2010; Salgado-Roa
et al., 2018). The western range of the Andes in Colombia is one of the
most biodiverse ecosystems in the Neotropics (Kattan et al., 2004;
Salgado-Roa et al., 2018) and is the most complex landscape within the
distribution of the leaf-cutting ant A. cephalotes . By
integrating hierarchical population structure with models of isolation
by distance (IBD), environment (IBE), and barrier (IBB), we explored the
role of the western mountain range in the distribution of genetic
variation of A. cephalotes . Here, we demonstrated that the
environmental heterogeneity imposed by the Andean uplift has highly
influenced the population structure of A. cephalotes .
Genetic diversity
Our results indicate that the populations of A. cephalotes are
highly variable at nuclear markers, with significant genetic
differentiation at both region and population levels. In contrast,
populations are highly differentiated for the mtCOI gene, but no
region effect was detected with this marker. Ten mtCOI haplotypes
were detected in 146 nests analyzed, showing low nucleotide and moderate
haplotype diversities, with most populations dominated by the same
haplotype (Hap1). As expected considering the common biogeographical
history, similar mtCOI patterns have been detected in the
leaf-cutting ant A. colombica (Helmkampf, Gadau, & Feldhaar,
2008), where six mtCOI haplotypes and relatively low levels of
nucleotide diversity (0.1% population-wide) were detected across 20
colonies, with most specimens sharing the same haplotype. However, these
results were obtained from a small sample size in a small area. Despite
the fact that several other genetic studies have been reported in Attini
(e.g., Acromyrmex ) (Cantagalli, Mangolin, &
Ruvolo-Takasusuki, 2013; Diehl, Cavalli-molina, & Mellender de Araujo,
2002; Diehl, de Araújo, & Cavalli-Molina, 2001; Pinheiro dos Reis,
Fernandes Salomão, de Oliveira Campos, & Garcia Tavares, 2014), the
nature of the markers and sampling employed prevents us from drawing
meaningful comparisons with these studies regarding diversity estimates.
Spatial differentiation at regional and local
levels
We detected significant genetic differentiation across regions from the
nuclear data, as evidenced from the AMOVA analyses and clustering
methods, suggesting the potential role of the Andean landscape in
restricting gene flow in A. cephalotes . We tested alternative
scenarios causing regional differentiation, considering IBE as a model
of divergence, as well as the geographic barrier imposed by the western
range under an IBB model.
Regional differentiation from microsatellite data was higher under the
IBE than the IBB model, suggesting the more prominent role of climate
compared to the geographic barrier in restricting gene flow in A.
cephalotes . On the other hand, the STRUCTURE results support a regional
differentiation with mixed effects between IBE and IBD, evidencing a
north-south spatial pattern for Andean populations rather than a
regional climate model. However, this IBD pattern was likely due to a
single population. PPY was initially classified within the Andean 2
region, but clustered mostly (Q > 0.95) with the
geographically closer Andean 1 region. Nevertheless, this pattern occurs
only in the Andean region, suggesting that population differentiation in
this particular case results from dispersal distance rather than
environmental conditions or a geographic barrier imposed by the western
range. Medium to long dispersal distances have been reported forA. cephalotes , with queens flying up to 50 km during nuptial
flights (Cherrett, 1968; Helms, 2018). This distance is at least one
order of magnitude smaller than the inter-population distance used in
this study, but it is also probably an underestimate since it was
originally measured on a small island (Helms, 2018). Moreover, only a
small number of individuals are required in order to homogenize
population allele frequencies, and gene flow can also be conveyed
stepwise. Although the genetic variance at the region level suggested
IBE, all of the models of hierarchical differentiation were significant
for the RDA analysis, suggesting the likelihood that all three leading
causes of genetic isolation are contributing to the observed patterns of
gene flow.
Significant nuclear differentiation at the population level suggests
that other features besides proximity, environment, and major dispersal
barriers act to restrict gene flow. The markedly increased variance in
the PPY population is consistent with a recent population expansion
signal. Whether the bottleneck signal is a product of a founder effect
(Barton & Turelli, 2004; Matute, 2013; Parisod, Trippi, & Galland,
2005) or a recent change in effective population size cannot be
determined with the current data. However, the proximity and clustering
of PPY to Andean 1 populations rather than the Andean 2 region in the
STRUCTURE results, suggests a recent founder effect or an admixture
between the two Andean regions.
In contrast to the nuclear data, there was no regional differentiation
in the mtCOI data, but populations were significantly
differentiated within the regions in both the IBE and IBB models.
Muñoz-Valencia et al. (2021) used mtCOI data to study the genetic
structure of A. cephalotes from a larger geographic range
covering most of the distribution of the species in South and Central
America. These authors found significant genetic differentiation, both
at the regional scale and among populations within major regions.
Together, these results show that the eastern range of the Andes appears
to be a major dispersal barrier driving regional differentiation, while
the western range forms a relatively homogenous biogeographic area.
Disentangling the patterns of IBD, and IBE,
IBB
Our RDA framework provides strong evidence for restricted gene flow inA. cephalotes across the western mountain range of the Colombian
Andes by all of the mechanisms tested. Furthermore, we found that the
explained genetic variation was maximized when all spatial and
environmental variables associated with these mechanisms were included
in the model. These multifactorial patterns are often expected in
complex and heterogeneous environments (Sexton et al., 2014; Shafer &
Wolf, 2013), such as in the Colombian Andes (Kattan et al., 2004;
Pérez-Escobar et al., 2017; Salgado-Roa et al., 2018). Regions
classified according to climate variation and geographic barriers follow
IBE but also include IBB, supporting the higher differentiation estimate
(F CT) obtained under the IBE model and the
significance when both models are included in the RDA (IBE + IBB).
Migration through low elevation passes in the western mountain range
(Hernández-Camacho, 1992; Kattan et al., 2004) would allow gene flow
between regions, explaining why such a major geographic barrier did not
have a more pronounced effect. Spatial and environmental variables
presumably associated with restriction to gene flow often suffer from
spatial autocorrelation, challenging the differentiation of their
underlying causes (Crispo et al., 2006; Edwards et al., 2012; Wang et
al., 2013). Our RDA framework accounted for this issue by demonstrating
strong IBE even after controlling for IBD; while IBB was not
significant, even when ignoring IBD. Temperature and precipitation
therefore appear to be the leading causes of IBE in A.
cephalotes .
The significant effect of the environment on the genetic structure ofA. cephalotes populations indicates the potential of this species
to adapt to local environmental conditions. This may occur when
processes such as sex-biased dispersal (Edelaar & Bolnick, 2012) or
selection against migrants (Hendry, 2004; Weber, Bradburd, Stuart,
Stutz, & Bolnick, 2016; Wright, 1943) decrease the rate of gene flow,
as has been observed in several other species (De Queiroz et al., 2017;
Lee & Mitchell-Olds, 2011; Wang et al., 2013). Moreover, geographic
distance and barriers in between can restrict gene flow by reducing
dispersal efficiency, which is more associated with local genetic drift
(Clémencet, Viginier, & Doums, 2005; Cross, Naugle, Carlson, &
Schwartz, 2016; De Queiroz et al., 2017; Noguerales et al., 2016; Smith
et al., 2018), leading to combined patterns of IBD and IBB. These
complex patterns have been detected, for example, in the Amazonian
common sardine Triportheus albus (De Queiroz et al., 2017).
However, disentangling the individual environmental drivers was not
possible due to strong correlations across environmental variables
(Crispo et al., 2006; Edwards et al., 2012; Wang et al., 2013). The
underlying causes of genetic isolation of populations have been rarely
explored in leaf-cutting ants (Branstetter et al., 2017). In A.
sexdens rubipilosa , geographic separation of populations alone did not
explain population divergence (Cantagalli et al., 2013), and significant
IBD and biome fragmentation imposed physical barriers to gene flow inA. robusta (Pinheiro dos Reis et al., 2014). All of these results
together strongly suggest that the isolating mechanisms studied are not
mutually exclusive (Crispo et al., 2006; Edwards et al., 2012; Wang et
al., 2013) and are important factors in the evolution of Neotropical
species.
Agricultural activities have significantly contributed to the
colonization processes of leaf-cutting ants (de Carvalho Cabral, 2015).
Conversion of their natural habitat into cultivated lands may favor
population growth and dispersal of leaf-cutting ants (Montoya-Lerma,
Giraldo-Echeverri, Armbrecht, Farji-Brener, & Calle, 2012; Schowalter
& Ring, 2017). Queens of Atta laevigata have been shown to
prefer nesting in open areas rather than closed forests (Vasconcelos
1997). Our exceptional study population PPY is located in a large city
(Popayán). It is possible that the city infrastructure and constant
human interference, rather than natural dispersal associated with
nuptial flights, have influenced its genetic structure. This pattern is
more compatible with human-mediated dispersal (Zheng, Yang, Zeng, Vargo,
& Xu, 2018), where environmental changes associated with human
expansion appear to promote population growth in leaf-cutting ants
(Montoya-Lerma et al., 2012; Siqueira et al., 2017). However, PPY is
most likely an exception to a general large-scale migration process inA. cephalotes .
The Andean uplift appears to modulate the population structure ofA. cephalotes in a more complex manner than previously thought.
The eastern mountain range of the Andean uplift in Colombia plays a
major role as a geographic barrier to historical gene flow, restricting
the dispersion of A. cephalotes from north to south
(Muñoz-Valencia et al., 2021). By exploring a finer scale across the
western mountain range and incorporating neutral genetic markers with
environmental variables, we clearly show that the observed genetic
differentiation in A. cephalotes is mainly affected by the
combined multifactorial effect of different isolating mechanisms,
mediated by the landscape complexity. Together, these results suggest
that studies directed at exploring historical gene flow across the Andes
should be interpreted with caution, since the complexity and history of
this landscape can dramatically influence results at different scales.
ACKNOWLEDGEMENTS
We thank Kirsi Kähkönen, the ESB (Evolution, Sociality, and Behavior)
research group, and everyone in the Molecular Ecology and Systematics
laboratory (University of Helsinki) for helping to develop the DNA
microsatellite loci. We also thank Sandra Milena Valencia Giraldo,
Andrea López-Peña, and Glever Alexander Vélez-Martínez for helping with
the DNA extraction. This work was funded by the Vice-Rectoría de
Investigaciones, Universidad del Valle, Cali, Colombia (grant number:
CI71067); and COLCIENCIAS National Program of PhD (grant number:
617-2013).
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DATA ACCESSIBILITY
STATEMENT
The data that support the findings of this study will be openly
available in DataDryad after manuscript is accepted for publication.
AUTHORS’ CONTRIBUTIONS
VMV participated in the conceptualization, ant sampling, data analysis,
original draft, reviewing, and editing of the manuscript. JML
participated in conceptualization, original draft, reviewing, and
editing of the manuscript. PS participated in data analysis, reviewing,
and editing of the manuscript. FD participated in conceptualization,
data analysis, original draft, reviewing, and editing of the manuscript.
All authors have read and approved the final version of the manuscript.
TABLES AND FIGURES
Table 1. Sampled populations of A.
cephalotes. Locations with their regional classifications (Pacific,
Andean 1, and Andean 2 regions from Colombia) and the number of nests
are indicated for each population.