Genetics and Molecular Biology, 46, 3(suppl 1), e20230159 (2023)
Copyright © Sociedade Brasileira de Genética.
DOI: https://doi.org/10.1590/1678-4685-GMB-2023-0159
Research Article
60 years of the PPGBM UFRGS – Special Issue
Disentangling the causes of high polymorphism sharing in sympatric Petunia
species from subtropical highland grasslands: insights from nuclear diversity
Luize Simon1*, Luana S. Soares1* and Loreta B. Freitas1
1
Universidade Federal do Rio Grande do Sul, Departamento de Genética, Porto Alegre, RS, Brazil.
Abstract
Genetic polymorphism sharing between closely related and sympatric plant species could result from common ancestry,
ancient or recent hybridization. Here we analyzed four Petunia species from the subtropical highland grasslands in
southern South America based on nuclear diversity to disentangle the causes of high polymorphism sharing between
them. We genotyped microsatellite loci, employed population genetic methods to estimate variability, species limits,
and ancient and recent gene flow, and assigned individuals to genetic and taxonomic groups. Finally, we modeled
evolutionary processes to determine the impact of Quaternary climate changes on species phylogenetic relationships.
Our results indicated that genetic diversity was strongly influenced by expansion and habitat fragmentation during
the Quaternary cycles. The extensive polymorphism sharing is mainly due to species’ common ancestry, and we did
not discard ancient hybridization. Nowadays, niche differentiation is the primary driver for maintaining genetic and
morphological limits between the four analysed Petunia species and there is no recent gene flow between them.
Keywords: Petunia, Solanaceae, genetic diversity, speciation, subtropical highland grassland.
Received: June 09, 2023; Accepted: September 26, 2023.
Introduction
Closely related and recently diverged plant species
usually share many features, such as morphological traits,
ecological requirements, and genetic polymorphisms. The
degree of divergence between these species depends on the
time since separation, the intensity and direction of remaining
gene flow, if any, and the strength of stochastic and directed
evolutionary processes (e.g., Ley and Hardy, 2014). Thus,
polymorphism sharing might be an indication of a shared
recent common ancestor, selection pressure, or hybridization
(Comes and Abbott, 2001).
Hybridization, defined as the gene flow between different
lineages or species (Gompert and Buerkle, 2016), can have
contrasting consequences. On the one hand, hybridization
can lead to the extinction of the lineages as they are known
(e.g., Seehausen, 2013), on the other hand, it may produce
genetic combinations able to colonize new environments
(e.g., Burgarella et al., 2019). Independently of its effects,
hybridization is more probable as the lineages and species
are evolutionarily closer (Abbott et al., 2013). Changes in the
landscape can be a driver for species range expansion and
isolation due to fragmentation throughout their distribution.
For example, expansion may put different species or lineages in
contact, which favors hybridization under suitable conditions
such as weak reproductive barriers and pollinator sharing
(Schley et al., 2022).
A recent review (Turchetto et al., 2022) highlighted the
role of biotic and abiotic drivers for hybridization in plants from
Send correspondence to Loreta Brandão de Freitas. Universidade
Federal do Rio Grande do Sul, Departamento de Genética, Av. Bento
Gonçalves 9500, Cx. Postal 15053, 91501-970, Porto Alegre, RS,
Brazil. E-mail: loreta.freitas@ufrgs.br.
*These authors have equally contributed to this work.
the Neotropics. Pleistocene climate changes were involved
in most of the revised examples, whereas some pollinators’
behaviors were pivotal as biotic drivers. The past climate
changes during the Pleistocene affected plant distribution
(Behling, 2002), diversity, and population structure in the
southern hemisphere (e.g., Barros et al., 2015, 2020; Backes
et al., 2019; John et al., 2019). The Quaternary climate shifts
influenced plant range due to the forest contraction and
grassland expansion during the glacial periods and migration
in a converse sense at interglacial phases (Carnaval et al.,
2009), affecting several plant groups (e.g., Lorenz-Lemke et
al., 2010; Barros et al., 2015). The climate cycle alternation
produced landscape fragmentation and, in some areas, resulted
in vegetation mosaics among grasslands and forests (Behling
and Pillar, 2007).
In the subtropical highland grasslands in southern Brazil
(SHGs), vegetation is a mosaic between grassland-adapted
species and Araucaria Forest, with Araucaria angustifolia
as the dominant tree species. SHGs are located throughout
the Serra Geral, inserted in the Brazilian Atlantic Forest
domain, and display high diversity in geological substrates
and altitudinal variation (Behling, 2002). SHGs were strongly
affected by the Pleistocene climate cycles, which shaped the
current landscape and species structure and diversity, allowing
rapid speciation for different plant groups (Barros et al., 2015,
2020), mainly in allopatric processes (Lorenz-Lemke et al.,
2010; Fregonezi et al., 2013; Mäder and Freitas, 2019). The
climate shifts and vegetation contraction-expansion dynamic
are often pointed out as the primary driver of species richness
and endemism observed in SHGs (Iganci et al., 2011). In turn,
isolation can interrupt the gene flow and, if it occurs rapidly
and intensely, promotes speciation with low differentiation
(Binelli et al., 2020), intensifying the speciation rates (e.g.,
Lorenz-Lemke et al., 2010; Mäder and Freitas, 2019).
2
The genus Petunia (Solanaceae) is a young herbaceous
group that originated and diversified in lowland grasslands
in southern South America, migrating to highland grasslands
as Pleistocene climate cycles favored herbs to expand (ReckKortmann et al., 2014). Four Petunia species are restricted
to the SHG (Figure 1): P. altiplana has the most extensive
distribution, reaching 300-1,500 m above sea level (a.s.l.)
in open fields in Rio Grande do Sul and Santa Catarina
Brazilian states, can be found in open and sunny grasslands;
P. bonjardinensis that grows in open and disturbed areas, at
1,200-1,500 m a.s.l. in Santa Catarina; P. reitzii that occurs
at 800-1000 m a.s.l. growing on the walls of small cliffs
beside rivers, hanging freely in space in Santa Catarina; and
P. saxicola, which inhabits humid and shadowed rocks, in
the forest borders, at 800 m in elevation also only in Santa
Catarina (Stehmann et al., 2009; Lorenz-Lemke et al., 2010;
Souza et al., 2022; Soares et al., 2023).
Although the four species have a sympatric distribution
considering most of their occurrence area, they are never found
in the same site, occupying different micro-environments.
Simon et al.
These species are interfertile, as demonstrated by experimental
pollinations and bee-pollinated, with the same solitary bees’
genera already observed visiting their flowers (Stehmann
et al., 2009). Nuclear and plastid data were previously
published for these species revealing high polymorphism
sharing among them (Lorenz-Lemke et al., 2010; Souza et al.,
2022; Soares et al., 2023). Species share a common ancestor
(Reck-Kortmann et al., 2014), diverged recently, and evolved
under the Pleistocene influence in an allopatric process due
to landscape fragmentation (Lorenz-Lemke et al., 2010).
Moreover, has been suggested that the nuclear polymorphism
sharing could be due to hybridization with another Petunia
species widely distributed in the same area (Souza et al., 2022).
Testing such hypothesis regarding multiple hybridization
events under landscape changes served as starting point to
re-evaluate the microsatellite data.
The landscape fragmentation and range expansion,
mainly those resulting from climate changes in SHG, could
have put the four Petunia species in contact facilitating
hybridization, as there is incomplete reproductive isolation
Figure 1 – Sampling distribution and analyzed species. (a) All collection sites location: blue stars, P. altiplana; green dots, P. bonjardinensis; pink squares,
P. reitzii; orange triangles, P. saxicola. (b) to (e) flowers in frontal view of each species (photos by J.R. Stehmann, UFMG).
Plant diversity
among Petunia species (Watanabe et al., 2001). Such a scenario
could explain the polymorphism sharing due to hybridization
(Payseur and Rieseberg, 2016). Instead, the same landscape
changes could allow divergence under a sky-island model
(Oliveira et al., 2021) for species that were kept isolated
during the expansion and contraction cycles.
Here, we aimed to answer the following questions:
(1) Did hybridization occur among these Petunia species
explaining the polymorphism sharing? (2) Was hybridization
an ancient or recent event? (3) If hybridization does not explain
the polymorphism sharing, which alternative evolutionary
driver does?
Material and Methods
Plant material and genetic information
In this work, we included 38 individuals of P. altiplana,
30 of P. bonjardinensis, 20 of P. reitzii, and 22 of P. saxicola
(Table S1). Sampling size was proportional to the species
density and range. We retrieved genetic information based
on eight nuclear microsatellites, amplified with previously
published primers (Bossolini et al., 2011; Table S2). We reevaluated the original data (Souza et al., 2022; Soares et al.,
2023) by visualizing and scoring the alleles with GeneMarker
v.1.97 software (Softgenetics LLC, State College, USA). We
used Micro-Checker (van Oosterhout et al., 2004) to identify
possible null alleles, significant allele dropout, and scoring
errors due to stutter peaks.
We evaluated basic genetic diversity indices such as the
number of alleles per locus (A), allele richness (AR), gene
diversity (GD), and inbreeding coefficient (FIS) per locus
using Fstat v.2.9.4 (Goudet, 1995). The levels of observed
(HO), expected (HE), and total (HT) heterozygosity, and any
significant deviations from the Hardy–Weinberg equilibrium
(HWE) after Bonferroni correction were estimated in Arlequin
v.3.5.2.2 (Excoffier and Lischer, 2010). Finally, we computed
the genetic differentiation using a hierarchical analysis of
molecular variance (AMOVA; Excoffier et al., 1992) in
Arlequin to estimate genetic variation between taxa.
Individual identity and gene flow
We estimated the individual identity based on genetic
structure among species using the discriminant analysis of
principal components (DAPC; Jombart et al., 2010) performed
in Adegenet (Jombart, 2008). We performed the analysis
without including any geographical location or taxonomic
priors and used the find.clusters option to identify the clusters
under a Bayesian information criterion (BIC) and the function
optim.a.score to find the best number of principal components
to keep. The group membership probabilities were visualized
using the compoplot function.
We also ran Structure v.2.3 (Pritchard et al., 2000) with
no previous information about each sample’s taxonomy or
geographical origin. We estimated the best K value through
∆K (Evanno et al., 2005) in Harvester v.0.6.93 (Earl and von
Holdt, 2012). We performed Structure using 106 Markov chain
Monte Carlo (MCMC) repetitions after a 2.5 x 105 burn-in
period and ten iterations per K value, evaluating different
numbers of clusters (1 to 5). The resulting bar plot from the
3
summarized iterations for the best K was generated using
Pophelper (Francis, 2017).
We used NewHybrids v.1.1 (Anderson and Thompson,
2002) software to estimate the probability of individuals
belonging to distinct hybrid or purebred classes (F1, F2,
backcrosses with each considered parental species). We ran
two independent analyses using Jeffrey’s priors with uniform
priors that included 105 steps as burn-in followed by 106
MCMC interactions to ensure the chains’ convergence and
homogeneity across runs.
We performed historical and contemporary estimates
of gene flow between the species using the coalescent-based
method implemented in Migrate-N v.3.6.11 (Beerli and
Felsenstein, 2001; Beerli, 2006) and the disequilibrium-based
method implemented in BayesAss v.3.0.4 (Wilson and Rannala,
2003). In Migrate-N, we estimated ancient migration (m) and
effective population size (Ne) between the species using the
Brownian motion model, with starting conditions based on
10% of the prior and uniform prior distribution to estimate
theta (θ) and M, which is the migration rate in θ scale. We
included one long chain, with 1,000 sampling steps, 200,000
recorded genealogies, and 500 chains as burn-in. In addition,
we ran four independent chains with different temperatures
(1.0, 1.5, 3.0, and 1 x 106) in a Metropolis-coupled MCMC
(MCMCMC) procedure (Geyer and Thompson, 1995) to
ensure better mixing along the chains. We calculated the
estimates per locus and summarized them as weighted values
for all loci. Finally, we assessed the MCMC stationarity by
checking the posterior distribution over all loci and running
the analysis several times with different starting points. To
obtain Ne, we used θ values with SSR mean mutation rate
(μ) uniformly ranging from 10-4 to 10-2 (Zhang and Hewitt,
2003); to estimate the number of migrants, we used M with
recipient population θ value [(Ne = θ / 4μ) and (m = M x θ)].
In BayesAss, we estimated current gene flow by
performing the analysis in triplicate using different seeds.
We ran each round for 1 x 107 iterations, sampling every 100
iterations, with a burn-in of 1 x 106 iterations. To achieve
acceptance rates between 20 and 40 %, we set the mixing
parameters for migration rate (0.2), inbreeding (0.5), and
allele frequency (0.5). Finally, we assessed the convergence
of the MCMC using Tracer v.1.7.1 (Rambaut et al., 2018) and
chose the lowest Bayesian deviance value per run (Faubet et
al., 2007; Meirmans, 2014).
To understand the evolutionary history of these four
Petunia species, inferring the divergence time and ancestral
population effective size, we analyzed four alternative
scenarios (Figure 2a) for taxa divergence using an approximate
Bayesian computation (ABC) approach in DIYABC v.2.0
(Cornuet et al., 2014). The scenarios changed in how the
species diverged and the different phylogenetic relationships
between species. The divergence time in all scenarios included
the last glacial maximum, when grasslands dominated the
SHG, indicating a cold and dry climate. These scenarios
also differed in the number and order of divergence events,
and we did not consider any change in effective population
size. Scenario 1 involved the ancestral population diverging
into two subpopulations, one giving rise to P. altiplana and
P. bonjardinensis and another to P. reitzii and P. saxicola
4
group. In scenario 2, the ancestral population diverged into
three subpopulations, one corresponding to P. reitzii, the
second to P. saxicola independently, and the last grouped P.
altiplana and P. bonjardinensis. In scenario 3, the ancestral
population diverged independently into four lineages, one for
each species. Scenario 4 involved a successional divergence,
first P. altiplana, second P. bonjardinensis, and, finally, P.
reitzii and P. saxicola group. We estimated the divergence
time for each scenario using priors uniformly distributed
between the minimum and maximum values (Figure 2D).
We assumed generation time as one year, as Petunia species
are annual herbs (Stehmann et al., 2009).
We simulated one million data sets for each scenario.
We used 30 summary statistics to represent the observed
data per species separately (mean number of alleles, gene
diversity, allele size, and size variance) and for species pairs
Simon et al.
(mean number of alleles, gene diversity, and FST). Each
scenario’s posterior probability (PP) was assessed based on
logistic regression and direct estimate approaches using the
10,000 and 100 best simulations. For the best scenario, the
parameters’ posterior distribution was estimated using the logit
transformation and considering the 10,000 best simulations.
We used several strategies to assess the confidence in scenario
choice and parameter estimation. First, we perform a posterior
model checking based on 10,000 simulations. The Type I
error was estimated as the proportion of incorrect exclusion
of the true model among the datasets simulated under a given
scenario. The Type II error was calculated as the recovery rate
of a given scenario when it was not the true model (Cornuet
et al., 2010). We also determined the bias and precision of
parameter estimation based on 1,000 pseudo-observed datasets
with parameters sampled from the posterior distribution.
Figure 2 – Evolutionary modeling analysis using ABC approach in DYABC software for four Petunia species from Subtropical Highland Grasslands. (a)
four tested scenarios, highlighting the best-model (scenario 1); (b) scenarios’ posterior probability assessed based on logistic regression; (c) scenarios’
posterior probability assessed based on logistic approach; (d) parameters’ priors for all scenarios and estimated parameters for the best-model (scenario 1).
Scenarios 3 and 4 had zero posterior probability in both approaches.
5
Plant diversity
Results
Genetic diversity
All loci exhibited a clear single band per allele. All
individuals displayed one or two alleles per locus, consistent
with the diploid condition of Petunia species and with the
expected sizes based on loci description. Null alleles were
present in low frequency (< 0.5%), and no significant allele
dropout and scoring errors due to stutter peaks were observed
considering the eight analyzed loci. All pairs of loci were in
linkage equilibrium after Bonferroni’s correction (P < 0.001),
and levels of polymorphism and diversity were high for most
of the eight microsatellite loci (Table S3). The combined
probability of identity (PID) for the eight loci was almost zero
for all taxa, indicating that two unrelated individuals would
not share the same multilocus genotype.
Comparing the four species, P. altiplana showed the
highest diversity values considering allele richness, and gene
diversity, whereas P. saxicola had the lowest indices. Observed
heterozygosity was lower than expected under Hardy-Weinberg
equilibrium (P < 0.05), except for P. saxicola. The highest
heterozygotes deficit was observed in P. altiplana. All species
had significant FIS values, negative only for P. saxicola. The
four species displayed private alleles that were more frequent
in P. altiplana (Table 1). Comparing the four species based
on genetic structure, AMOVA showed that most variation is
observed within populations (83%), whereas FST between
species did not reveal differences between P. bonjardinensis
and P. reitzii (FST = 0.06; P < 0.01).
Individual identity and gene flow
The DAPC analysis (Figure 3A) identified three
independent groups. Individuals classified as P. altiplana or
P. reitzii formed separate clusters, whereas P. bonjardinensis and
P. saxicola formed the third group. We retained seven principal
components (PCs) and three discriminant eigenvalues, resulting
in three genetic components with more than one (q ≥ 0.2) per
species (Figure 3B). Structure analysis also revealed three
genetic components (best K = 3). Still, contrarily to DAPC,
one component predominated among P. altiplana individuals,
another was associated with P. bonjardinensis, and the third was
shared by P. reitzii and P. saxicola. Several individuals displayed
mixed genetic constitutions, and none had only the genetic
component of different species (Figure 3C). All individuals
displaying improper membership probability in DAPC presented
q ≥ 0.2 in Structure, although several other individuals have
been assigned as admixed. All admixed individuals showed the
P. bonjardinensis genetic component. Just a few individuals
display polymorphism of the three clusters.
The NewHybrids analysis (Figure S1) revealed that no
individual, even those with admixed ancestry in Structure,
was classified as F1 hybrid and, overall, most were purebred.
We find individuals classified as P. saxicola purebred among
P. bonjardinensis (one) or P. reitzii (three) individuals.
The Migrate-N estimation for the ancestral population
size (Table 2) varied between species. The highest Ne
value was observed for P. saxicola and the lowest for P.
bonjardinensis. We observed ancient bidirectional gene flow
among all species. In contrast, BayesAss revealed most of
Table 1 – Genetic diversity median values for four Petunia species based on nuclear microsatellites.
Species
N
A
AR
P
GD
HE
HO
FIS
P. altiplana
38
76
7.03
21
0.66
0.66
0.48
0.27
P. bonjardinensis
30
60
6.16
12
0.58
0.61
0.51
0.14
P. reitzii
20
40
4.62
4
0.50
0.58
0.50
0.16
P. saxicola
22
46
4.06
4
0.47
0.53
0.61
-0.26
N – number of analyzed individuals; A – total number of alleles per species; P – number of private alleles; AR – allele
richness (min. sample size of 14 individuals); GD – gene diversity per locus; HE – expected heterozygosity; HO – observed
heterozygosity; FIS – inbreeding coefficient. Bold values indicate significant HWE deviation (P < 0.05) after Bonferroni’s
correction.
Table 2 – Effective population size and migration among the highland species estimated by Migrate-N and BayesAss.
M
P. alti
100.33
P. alti
Migrate-n
BayesAss
P. bonj
P. reit
P. saxi
Θ
Ne 10-4
Ne 10-2
99.00
98.33
1.30
3250.00
32.50
91.67
100.33
1.10
2750.00
27.50
103.00
1.37
3416.68
34.17
1.50
3750.00
37.50
P. bonj
96.33
P. reit
94.33
98.33
P. saxi
96.33
101.67
100.33
P. alti
0.97(0.02)
0.01(0.01)
0.01(0.01)
0.01(0.01)
P. bonj
0.01(0.01)
0.95(0.03)
0.02(0.02)
0.02(0.02)
P. reit
0.02(0.02)
0.03(0.02)
0.93(0.03)
0.03(0.02)
P. saxi
0.01(0.01)
0.02(0.02)
0.02(0.01)
0.95(0.02)
M – Migration rate; θ – theta; Ne – effective size of founder population. Bold values indicate intraspecific gene flow
in BayesAss analysis; values in parenthesis correspond to the standard deviation. Cells in blue, migration from – to; in
orange, opposite direction
6
the individuals were residents of each species (M ≥ 0.93
± 0.03), and a low or no recent gene flow was observed
between species (Table 2).
Evolutionary modeling through ABC analysis
(Figure 2A) revealed that the best model was scenario 1
Simon et al.
(Figure 2B, C), which stated the divergence of two sister
lineages that gave rise to a group with P. altiplana and P.
bonjardinensis and another clustering P. reitzii and P. saxicola.
The four species have diverged simultaneously during t1
(included in the Quaternary period).
Figure 3 – Clustering analyses for individuals of four Petunia species based on microsatellite variability. (a) DAPC Cartesian plane with individuals
colored according their taxonomic classification as lables at the top; (b) DAPC scatter plot indicating the membership probability; (c) Bar plot considering
the best number of genetic components obtained with Structure software (K = 3). In (b) and (c), each vertical bar represents individuals and colors indicate
the genetic components; solid black bars separate species; individuals with q ≥ 0.2 were considered mixed.
7
Plant diversity
Discussion
Here we investigated the potential drivers for
polymorphism sharing in four Petunia species from the
subtropical highland grasslands in southern Brazil, basing
our population diversity and structure analyses on nuclear
microsatellites. First, we hypothesized that the polymorphism
sharing among the four species was due to interspecific
hybridization, as hybridization is more likely between closely
related species (Whitney et al., 2010) for which barriers against
gene flow are incomplete or absent, and species are in contact
or nearly distributed (Schley et al., 2022). Alternatively, we
considered a recent common ancestry to explain the high
genetic similarity between species. Finally, we did not consider
common selective forces because at least three species have
nonoverlapping ecological niches (Souza et al., 2022).
The four SHG Petunia species displayed genetic diversity
(Table 1) compatible with their geographical range compared
with other Petunia species and similar markers (e.g., Turchetto
et al., 2015b; Silva-Arias et al., 2017; Backes et al., 2019)
and following the general statement for correlation between
the extent of distribution and diversity indices (Gitzendanner
and Soltis, 2000). The most diverse species was P. altiplana,
which has the largest distribution area (Soares et al., 2023).
The lowest variability values were observed for P. saxicola,
found in only one location (Souza et al., 2022).
Three in four species showed homozygote excess,
indicating significant inbreeding values probably due to
biparental inbreeding because these species are considered
self-incompatible (Robertson et al., 2011), and most Petunia
species have pollen dispersal at short distances (e.g., Turchetto
et al., 2015a). Moreover, populations of these three species
are distantly distributed, and just a few individuals compose
them (Souza et al., 2022; Soares et al., 2023). Only P. saxicola
showed an excess of heterozygotes that could be attributed
to the self-incompatibility in this species (Robertson et al.,
2011) and the massive presence of individuals as the species
currently occurs in only a known site (Souza et al., 2022).
The four SHG Petunia species are not syntopic (LorenzLemke et al., 2010; Souza et al., 2022; Soares et al., 2023),
each occurring in different microenvironments. All these
species are bee-pollinated, and the same generalist bee
genera have been observed visiting them (Stehmann et
al., 2009). Such bees have flight ranges compatible with
distances between species (Mouga et al., 2016), which makes
the interspecific gene flow between Petunia species from
SHG theoretically likely. However, current mating events
do not seem to contribute to the polymorphism sharing
between species, which, according to our results, has been
restricted to the past. The membership probability in DAPC
analysis indicated that a few individuals were assigned to
different species than expected according to their taxonomic
classification or provenience (Figure 3B). Most individuals
showed mixed ancestry in Structure analysis (Figure 3C),
especially involving the P. bonjardinensis genetic component.
Based on the plastid haplotypes (Lorenz-Lemke et al.,
2010), P. bonjardinensis shares haplotypes only with P.
altiplana, whereas P. altiplana shares with the other three,
and P. reitzii and P. saxicola share all their haplotypes. In
the molecular phylogenetic tree (Reck-Kortmann et al.,
2014), P. reitzii and P. saxicola have the most recent common
ancestor, whereas P. bonjardinensis is basal to them and P.
altiplana that belongs to the same subclade has a species
from tropical grassland as the sister group, occupying a more
basal position to the other three SHG species. The strong
phylogenetic signal was confirmed here mainly regarding
P. reitzii and P. saxicola, which shared the same genetic
component (Figure 2A and Figure 3C) and occupied the
same genospace in DAPC Cartesian plane with several
superimposed individuals (Figure 3A).
The Petunia ancestor originated in lowland grasslands
(Reck-Kortmann et al., 2014) ca. 3.0 million years ago
(Mya; Särkine et al., 2013). Based on the coalescence of
plastid haplotypes, the SHG lineage split from sister group
ca. 1.3 Mya, still in low to middle elevation grasslands,
diversifying during the Quaternary in highland fields ca.
1.0 Mya (Lorenz-Lemke et al., 2010) under a sky-island
model that would explain SHG Petunia endemism, range
limits, and species richness. Our results supported such an
evolutionary model where the ancestor lineage expanded its
range during cold and dry periods and contracted it in warmer
and wetter cycles (Figure 2). Such demographic processes
could promote recurrent ancient connections and fragmentation
between SHG precursor lineages or even themselves at primary
diversification stages allowing hybridization that, summed to
the common origin, would explain the polymorphism sharing
without current hybridization (Table 2).
The divergence from a common ancestor expanding
its range is primarily based on stochastic processes such
as drift, mutation, and recombination. Ancestral gene flow
detected between Petunia species from the SHG is consistent
with phylogenetic radiation without strict reproductive
isolation, a property of the mechanisms of maintenance of
genetic integrity in species complexes as observed in other
Neotropical species (e.g., Binelli et al., 2020). Four endemic
Calibrachoa species also occur in SHG. The genetic variability
and population structure of these Calibrachoa species (John et
al., 2019) revealed a similar differentiation process. Moreover,
Quaternary cycles are the main driver for phylogenetic niche
conservatism in Calibrachoa (Mäder and Freitas, 2019),
including the SHG clade.
In conclusion, our results supported the idea that
polymorphism sharing between the four SHG Petunia species
is mainly due to a common ancestral as different clustering
analyses combined individuals differently (Figure 3), and only
ancestral migration events were detected (Table 2; Figure S1).
Despite that, we cannot discard an ancient gene flow between
ancestral lineages of these four species. Therefore, a more
detailed genomic evaluation should be implemented to
disentangle such pending questions.
Acknowledgements
This work was supported by the Conselho Nacional
de Desenvolvimento Científico e Tecnológico (CNPq),
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
(CAPES), and Programa de Pós-Graduação em Genética e
Biologia Molecular da Universidade Federal do Rio Grande
do Sul (PPGBM-UFRGS). PIBIC-CNPq/UFRGS supports
LS, and LSS is a PPGBM-UFRGS Ph.D. student.
8
Simon et al.
Conflict of Interest
The authors declare that they have no conflict of interest.
Author Contributions
LBF led the project; LS and LSS obtained data and ran
analyses. All authors have commented on and approved the
final manuscript.
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Supplementary material
The following online material is available for this article:
Table S1 – Sampling information for four Petunia species
from the Subtropical Highland Grasslands.
Table S2 – Nuclear microsatellite markers used to genotype
four Petunia species.
Table S3 – Genetic diversity per locus of nuclear microsatellite
considering four Petunia species.
Figure S1 – Posterior probabilities of Newhybrids of four
Petunia species individuals (pure parental species, F1 or
F2 hybrids, and backcrosses); classes follow label colors.
Vertical dotted black lines separate individuals according
their taxonomic classification in each comparison.
Associate Editor: Lavínia Schüler-Faccini
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