Botany
Phylogeography and climate-associated morphological variation in the endemic white oak Quercus deserticola (Fagaceae) along the Trans-Mexican Volcanic Belt
Journal: Botany
Manuscript ID cjb-2017-0116.R1
Manuscript Type: Article
Date Submitted by the Author: 26-Oct-2017
Complete List of Authors: Rodríguez-Gómez, Flor; Universidad Nacional Autonoma de Mexico, Escuela Nacional de Estudios Superiores Morelia Oyama, Ken;Draft Escuela Nacional de Estudios Superiores (ENES) Unidad Morelia, UNAM, Laboratorio Nacional de Análisis y Síntesis Ecológica para la Conservación de los Recursos Genéticos Ochoa-Orozco, Magaly; Universidad Nacional Autonoma de Mexico, Instituto de Investigaciones en Ecosistemas y Sustentabilidad Mendoza-Cuenca, Luis; Universidad Michoacana de San Nicolas de Hidalgo, Facultad de Biología Gaytán-Legaria, Ricardo; Universidad Nacional Autonoma de Mexico, Instituto de Investigaciones en Ecosistemas y Sustentabilidad González-Rodríguez, Antonio; Universidad Nacional Autonoma de Mexico, Instituto de Investigaciones en Ecosistemas y Sustentabilidad
Is the invited manuscript for consideration in a Special N/A Issue? :
Keyword: gene flow, Mexican oaks, Ecological Niche Modeling, Last Glacial Maximum
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Phylogeography and climate-associated morphological variation in the endemic
white oak Quercus deserticola (Fagaceae) along the Trans-Mexican Volcanic Belt
Flor Rodríguez Gómez1, Ken Oyama1, Magaly Ochoa Orozco2, Luis Mendoza Cuenca3
Ricardo Gaytán Legaria2 and Antonio González Rodríguez2*
1Escuela Nacional de Estudios Superiores Unidad Morelia, UNAM, Antigua Carretera a
Pátzcuaro 8701, Col. Ex Hacienda de San José de la Huerta, 58190, Morelia, Michoacán,
México. FRG: [email protected]; KO: [email protected]
2Instituto de Investigaciones en EcosistemasDraft y Sustentabilidad, Universidad Nacional
Autónoma de México, Antigua Carretera a Pátzcuaro No. 8701, Col. Ex Hacienda de
San José de la Huerta, 58190 Morelia, Michoacán, México. MOO: [email protected];
RGL: [email protected]
*Correspondence: Antonio González Rodríguez, Fax: +52 (443) 322 2719; E mail:
3 Laboratorio de Ecología de la Conducta, Facultad de Biología, Universidad
Michoacana de San Nicolás de Hidalgo, Avenida Francisco J. Múgica S/N, Morelia,
Michoacán, México. LMC: [email protected]
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ABSTRACT
Mexico is a center of diversification for the genus Quercus, with an important number of
taxa occurring along the Trans Mexican Volcanic Belt (TMVB). However, the impact of
the interaction between historical and current climatic variation and geological
heterogeneity in the TMVB on the genetic and phenotypic diversification within oak
species has been scarcely investigated. We used chloroplast DNA microsatellites and a
geometric morphometrics analysis of leaf shape to understand differentiation between populations of Quercus deserticola, which inhabits dry highlands along the TMVB.
Ecological niche modeling (ENM) for present day conditions and projections into past
scenarios were performed to evaluateDraft the influence of environmental variables on the
evolutionary history of the species. Results showed high genetic diversity (hS =0.774) and
high genetic structure (RST =0.75) and the morphological subdivision of populations into
two clusters, corresponding to the west/south and east/north sectors of the Q. deserticola geographic distribution. ENM indicated that the potential distribution of the species has remained similar from the late Pleistocene to the present. Seemingly, the phylogeographic structure of the species has been shaped by low seed mediated gene flow and mostly local migration patterns. In turn, leaf shape is responding to climate differences either through phenotypic plasticity or local adaptation.
Keywords: gene flow, Mexican oaks, Ecological Niche Modeling, Last Glacial
Maximum.
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INTRODUCTION
The Trans Mexican Volcanic Belt (TMVB) is a center of diversification, endemism and
biogeographic transition of the Mexican biota (Halffter and Morrone 2017). The TMVB
is a volcanic mountain chain with nearly 8000 volcanic structures, extending about 1200
km west to east through central Mexico, from the Pacific coast to the Gulf of Mexico
coast. Furthermore, the TMVB has a large environmental heterogeneity and has
experienced important climatic changes from the Pliocene and Pleistocene to the present
(Gómez Tuena et al. 2007; Ferrari et al. 2012). Based on age, orogeny and tectonic
features, the TMVB has been dividedDraft into four sectors (western, central, eastern and
easternmost), each with its own characteristics (Gómez Tuena et al. 2005; Ferrari et al.
2012). The TMVB has also been considered as a complex biogeographic unit (i. e. it
shows a high degree of species endemism and diversity), with two sectors, west and east
(Gámez et al. 2012; Torres Miranda et al. 2013). Four main episodes of volcanic activity
of the TMVB have occurred during different periods from the early Miocene to the
present, affecting this region asynchronously, first the western and later the eastern
sectors (Gómez Tuena et al. 2005; Gámez et al. 2012; Ferrari et al. 2012).
Several studies have found that the physiographic context of the TMVB has been
important in the genetic structuring and phenotypic divergence of different species and
how climatic and geologic events have modified their distributions in various time
periods (Jaramillo Correa et al. 2008; Gámez et al. 2012; Ruiz Sánchez and Specht 2013;
Torres Miranda et al. 2013; Mastretta Yanes et al. 2015; Rodríguez Gómez and Ornelas
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2015). The TMVB has been shown to be a geographic barrier that limits the dispersion of plants and animals that inhabit to the north and south of the barrier (Aguirre Planter et al.
2000; McCormack et al. 2011; Parra Olea et al. 2012; Gándara and Sosa 2014; Moreno
Letelier et al. 2014; Grummer et al. 2015; Jiménez and Ornelas 2016). Also, climatic
oscillations and geological activity during the Miocene and Pleistocene promoted
alternating periods of connection and disconnection of habitats along the TMVB,
impacting the genetic diversity of the populations. In particular, the colder periods of the
Pleistocene may have promoted increased connectivity among temperate habitats,
allowing the expansion of associated plant and animal populations across the TMVB,
which could have acted repeatedly as a corridor for the migration and possible gene flow
of organisms and even as a continuousDraft connection between the Sierra Madre Occidental
(SMOc) and the Sierra Madre Oriental (SMOr) regions (González Rodríguez et al. 2004;
Ruiz Sánchez and Specht 2013; Mastretta Yanes et al. 2015; Rodríguez Gómez and
Ornelas 2015). Phylogeographic studies for species in this region have also shown that
historical isolation into multiple refugia played an important role in structuring genetic
diversity on the TMVB, sometimes followed by population expansion and increased
connectivity among habitats (Parra Olea et al. 2012; Ruiz Sánchez et al. 2012; Velo
Antón et al. 2013; Ornelas and González 2014).
Mexico is considered a center of species diversification for the genus Quercus
(Fagaceae) (Manos et al. 1999; Hipp et al. 2017). This genus is represented in Mexico by
approximately 161 species, and 109 of these are endemic (Valencia 2004). Oaks can be
found throughout the country, except for the Yucatan Peninsula. However, the highlands,
such as the SMOc, SMOr, TMVB, Sierra Madre del Sur (SMS) and Sierras de Chiapas,
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are the areas with the greatest oak species diversity (Rzedowski 1978; Rodríguez Correa
et al. 2015; Ramírez Toro et al. 2017). The TMVB, for example, contains 29% of the
total species of oaks in Mexico. A recent biogeographic study suggested an important
role of the TMVB on the distribution of oak species, identifying several areas of
endemism related to the presence of a wide variety of climatic zones, which may have
allowed the establishment of oak species with different climatic requirements
(Rodríguez Correa et al. 2015). However, evolutionary patterns and processes have been
studied for a few Mexican oak species, mainly from tropical lowlands (Cavender Bares et
al. 2011, 2015), cloud and temperate forests (González Rodríguez et al. 2004; Tovar
Sánchez et al. 2008; Ramos Ortiz et al. 2016), and disturbed areas with a xerophytic
scrub type of vegetation at mid to highDraft altitude (Valencia Cuevas et al. 2014).
Nevertheless, the great ecological diversity of the Mexican oaks represents an interesting
opportunity to evaluate and compare how geological and climatic factors affected
congeneric species differing in habitat affinities.
Here, we focused on the Mexican endemic white oak Quercus deserticola Trel.
that inhabits high, cold and dry regions, around the foothills of the mountains in
xerophytic areas (at altitudes between 2000 to 2800 m), with a geographical distribution
mostly spanning the TMVB. Few studies in Mexico have assessed the genetic structure
and population history in plants within xerophytic vegetation in the highlands (Sosa et al.
2009; Ruiz Sanchez et al. 2012; Gándara and Sosa 2014; Valencia Cuevas et al. 2014). In
this study, we used chloroplast DNA microsatellites (cpSSRs) to investigate
phylogeographic patterns in populations of Q. deserticola. We also used species
distribution models (SDMs) to evaluate if populations contracted (disconnected) or
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expanded (connected) during past climate fluctuations as observed in other plant species
inhabiting the highlands of the TMVB (e. g. Ruiz Sanchez and Specht 2014).
Disconnections should have led to restrictions in gene flow promoting genetic
differentiation. On the contrary, if populations expanded their distribution, this should
have led to contact and gene exchange.
On the other hand, in oaks it has been shown that much of the morphological variation in leaves between individuals of the same species results from the influence of ecological factors (e. g. temperature, precipitation and light conditions) (González
Rodríguez and Oyama 2005; Uribe Salas et al. 2008). Common garden experiments in some oak species have shown that variation in leaf size and shape has a genetic component (Nicoli et al. 2006) and sometimesDraft geographically matches the neutral genetic structure of the species (e. g. Cavender Bares et al. 2011).
Therefore, the specific questions addressed in this study were: 1) What are the phylogeographic and historical demographic patterns in Q. deserticola populations spanning the TMVB? 2) Are there variations in leaf morphology among Q. deserticola populations along their geographical distribution? 3) How are morphological and genetic variations in Q. deserticola populations associated to geographic and climatic factors?
MATERIALS AND METHODS
Sampling, DNA extraction and PCR amplification
We collected 144 individuals from 13 populations of Q. deserticola throughout its
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geographic distribution (Table 1, Fig. 1) for genetic and morphological analyses. Total
genomic DNA was extracted from 100 mg of leaf material using the Qiagen DNeasy
Plant Mini Kit (Qiagen, Valencia, CA, USA). Seven chloroplast DNA microsatellite loci
previously developed for the Fagaceae family were used in polymerase chain reactions
(PCR): the primer pairs UDT3 and UKK4 (Deguilloux et al. 2003), and CMCS4,
CMCS5, CMCS6, CMCS7 and CMCS12 (Sebastiani et al. 2004). PCRs were performed
using the Qiagen Multiplex PCR kit in a volume of 5 µl containing 1X Multiplex PCR
Master Mix, 2 µМ each primer, dH20, and 20 ng template DNA. The thermal cycling
conditions consisted of 35 cycles, each at 94°C for 30 s, annealing at 45°C for 30 s, and
extension at 72°C for 2 min. A final extension at 72 °C for 10 min was included.
Multiplex PCR products were combinedDraft with a GeneScan 500 LIZ size standard and then
run in an ABI PRISM 3100 Avant sequencer (Applied Biosystems). Fragments were
analyzed and sized with the Peak Scanner program 1.0 (Applied Biosystems).
Genetic diversity and phylogeographic structure
Genetic variation in each population was measured by calculating the number of
haplotypes (H), the number of private haplotypes (P), allelic richness (Ar ) and haplotype
diversity (hs) with the software HAPLOTYPE ANALYSIS Version 1.05 (Eliades and
Eliades 2009) and SPAGeDi v1.1 (Hardy and Vekemans 2002). To depict genealogical
relationships among haplotypes, we constructed a haplotype network using the Median
joining algorithm in NETWORK v4.51.6 (Bandelt et al. 1999).
Genetic differentiation among populations was assessed by performing two
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hierarchal analyses of molecular variance (AMOVA), the first with the FST statistic
(based on the infinite alleles mutation model, IAM), and the second with RST (based on
the stepwise mutation model, SMM). Pairwise RST comparisons among populations were
also estimated in Arlequin v3.5 (Excoffier and Lischer 2010). The pairwise comparisons
and AMOVA significance tests were run with 10,000 permutations. The pattern of
genetic structure was further evaluated with the clustering analysis with linked loci
implemented in the BAPS v0.6 software (Corander et al. 2008). We ran the model four
times for K = 1 8 to ensure convergence and chose the K with the highest log likelihood
(Corander et al. 2008).
Phylogeographic structure was assessed by calculating genetic differentiation with
unordered alleles (GST) and ordered Draftalleles (NST) in SPAGeDi v1.1 (Hardy and Vekemans
2002). SPAGeDi implements a permutation test to evaluate if the values of GST and NST are significantly different. A higher value of NST than GST indicates phylogeographic
structure among populations, resulting from the presence of closely related haplotypes
within the same populations (Pons and Petit 1996). Significance of NST and GST values
was determined by 10,000 random permutations of individuals among populations
(Hardy and Vekemans 2002).
Demographic history
The demographic history of Q. deserticola populations was investigated by means of
mismatch distributions and neutrality tests carried out in Arlequin v3.5 (Excoffier and
Lischer 2010). The mismatch distribution (Harpending 1994) can be used to determine
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whether a population has undergone a sudden population expansion. We tested for
deviations of the observed mismatch distributions from those expected under the model
of Schneider and Excoffier (1999) with 1000 bootstrap replicates. The validity of the
sudden expansion assumption was determined using the sum of squares differences
(SSD). We also used the Tajima´s D statistic (Tajima 1989) and Fu´s FS test (Fu 1997) to
assess demographic expansions. Both tests were run with 1000 bootstrap replicates. For
all these tests, the cpSSR data were binary coded following Navascués et al. (2009) with
the number of repeats coded as ‘1’ and shorter alleles being coded filling the differences
in repeats as ‘0’.
Morphological variation Draft
A geometric morphometrics approach was used for the analysis of morphological
variation. Analyses were performed on photographs (abaxial side) of ten randomly
chosen, fully extended, undamaged leaves from each individual. Coordinates ‘x, y’ of 29
unambiguous and repeatable anatomical marks (i. e. 12 landmarks and 17 semi
landmarks) were registered along the border of each leaf image using the program
TpsDig (Rohlf 2005). We constructed a ‘‘fan’’ (radial guidelines with equal angular
spacing on images) with 20 radial guidelines covering the whole leaf contour, which was
used to digitalize the 17 semi landmarks. Two additional marks were placed on the ruler
for size reference. The MakeFan6 program from the ‘‘Integrated Morphometrics
Package’’ IMP series (http://www.canisius.edu/~sheets/morphsoft.html) was used for this
procedure.
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A Procrustes superimposition analysis for the configuration of landmarks and semi
landmarks was developed with the CoordGen6 program in the IMP series
(http://www.canisius.edu/~sheets/morphsoft.html). This analysis allows the calculation of
leaf shape variation without the effect of the size. The first step of superimposing
configurations of landmarks in two dimensional shapes (x1, y1, x2, y2 . . .) is a
generalized least squares Procrustes superimposition that minimizes differences between
landmark configurations by translation, scaling, and rotation to remove all information
unrelated to shape and to obtain shape variables (Procrustes distances; Rohlf 1990). After
the superimposition, resulting Procrustes coordinates were averaged across all ten leaves by individual. Shape variables (Procrustes distances average) for all individuals were
used for a discriminant function analysisDraft to determine the variation in leaf shape among populations using SPSS 22.0 (IBM Corp. Released 2013).
Ecological niche modeling
An Ecological Niche Model (ENM) was constructed in order to compare the extent of the potential distribution of Quercus deserticola through time, i.e. between the present, the
Last Glacial Maximum (LGM, 21 ka) and the Last Interglacial (LIG, 120 ka). Presence
records of Q. deserticola were obtained from the Global Biodiversity Information Facility
(GBIF, http://data.gbif.org/species/browse/taxon/), and filtered according to the
geographical distribution of Q. deserticola proposed by Valencia (2004). To avoid
overfitting due to the spatial correlation of the occurrences resulting from unequal
collection effort across the species’ range (Boria et al. 2014), records were thinned to be
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spaced at least 20 km from each other with the spThin package (Aiello Lammens et al.
2015) in R (R Core Team 2014). After these procedures, the potential distribution of Q.
deserticola was modeled with a total of 58 unique records. The area of accessibility (M)
was defined as the biogeographic provinces proposed by Morrone (2005), where there are
records of the species, that is, the TMVB, the SMOr, the SMOc, the Balsas Depression
(BD) and the SMS. We used M as a mask to reduce the overprediction of area suitability
(habitat suitability) as well as to perform a better model validation (Barve et al. 2011).
To construct the ENM, 19 bioclimatic variables were obtained from the WorldClim
Global Climate Data V. 1.4 (http://www.worldclim.org/version1) with a resolution of 30
arcsec (1km2). To reduce redundancy among variables, we simultaneously considered
those variables with the highest partialDraft contribution to the first two principal components
from a Principal Components Analysis (PCA) and with pairwise Spearman’s rank
correlations lower than 0.9. These analyses were performed with the JMP v13 software
(SAS Institute). The final dataset included 11 variables: mean diurnal range of the
temperature (BIO2), isothermality (BIO3), temperature seasonality (BIO4), maximum
temperature of the warmest month (BIO5), temperature annual range (BIO7), mean
temperature of the coldest quartet (BIO11), annual precipitation (BIO12), precipitation
seasonality (BIO15), precipitation of the driest quartet (BIO17), precipitation of the
warmest quartet (BIO18) and precipitation of the coldest quartet (BIO19).
The algorithm of maximum entropy implemented in MAXENT v3.3.3 (Phillips and
Dudik 2008) was used to construct the ENM using the eleven bioclimatic variables. To
evaluate the performance of the model, a random subset of 20% of the total unique
records was set aside, and the area under the curve (AUC) of the receiver operating
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characteristic (ROC) was measured. We ran 100 replicates of the model with a
convergence threshold of 10 5 and 500 iterations. In the settings, we disabled the extrapolation and clamping options to avoid overprediction. To compare present and past potential distribution of Q. deserticola, the ENM was projected into two general global circulation models (GCM) used as past climate scenarios for the LGM: the Community
Climate System Model (CCSM; Collins 2006) and the Model for Interdisciplinary
Research on Climate (MIROC; Hasumi and Emori 2004). Both models simulate climatic conditions as they are calculated to have been for the LGM, with a stronger temperature decrease assumed in CCSM compared to MIROC (Otto Bliesner et al. 2007). Also, we projected the ENM into the LIG climate scenario. The GCM data for the LGM and the
LIG were downloaded from WorlClimDraft (http://www.worldclim.org/paleo climate1) (Otto
Bliesner et al. 2006; Braconnot et al. 2007).
Association among genetic structure, morphological variation and environmental
distances
We examined the relationship of genetic and morphological differentiation with
environmental conditions and with geographic distances (Euclidean distances) across all
13 sampling localities. First, morphological and environmental distances were calculated
as dissimilarity matrices with Euclidean distances scaling to values between 0 and 1. We
obtained the environmental distance matrix from the values of the 19 climate variables
from WorldClim (Hijmans et al. 2005) for each locality. For the morphological distance
matrix, we performed a discriminant function analysis (DFA) using the locality as the
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grouping variable, and the distance between localities was estimated as the Euclidean
distance between group centroids for the first discriminant function. These dissimilarity
matrices and DFA were estimated in SPSS 22.0 (IBM Corp. Released 2013). For genetic
distances we used the pairwise RST matrix. We used partial Mantel test to assess if the
genetic composition of the populations and morphological dissimilarity are associated
with environmental variables while controlling for the potential effects of geographic
distance (Mantel 1967). Mantel tests were performed in IBD (Jensen et al. 2005) with
1,000 randomizations.
RESULTS
Draft
Genetic diversity and phylogeographic structure
Fifty four haplotypes were found in the 13 sampled populations (Table 2, Fig. 1), with
most of the localities exhibiting more than one haplotype. The haplotype network (Fig.
1b) showed several closed loops and no clear haplogroups could be distinguished. The
most frequent haplotypes were H12 (12 individuals, 8.3%) and H20 (11 individuals,
7.6%) and the most widespread haplotype was H22, found in four populations (30.7%).
The number of haplotypes per population ranged from two to nine. The Amealco
population was the one that shared more haplotypes (four) with other populations (Table
2, Fig. 1a). Sierra de Agustinos and Chiluca populations did not share haplotypes with
other populations. Allelic richness (Ar) and haplotype diversity (hS) were higher for the
Amealco (3.73 and 0.955) and the Huichapan (3.73 and 0.956) populations and Sierra
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Agustinos showed the lowest values (1.8 and 0.378, respectively) (Table 2). The BAPS
clustering analysis for linked loci supported two main groups (K = 2, log likelihood =
666.47) (Fig. 2). One of the clusters is more frequent (blue in Fig. 2) and is present in all populations except in the Pablo Ixtayoc population (13). The second group (in red in Fig.
2) was found in nine populations. The two groups did not show a clear geographical structuring, but the red group seems to be in a higher frequency towards the east and in the northernmost population than in western/southern populations.
The mean of within population genetic diversity (hS, 0.774) and the total genetic
diversity (hT, 0.984) were high. The analysis of molecular variance (AMOVA) revealed significant genetic differentiation among populations; for the analysis under the IAM,
42.33% of the variation was explainedDraft by differences among populations and the 57.67% by differences within populations, and for the SMM, 75.97% was explained by
differences among populations and 24.03% within populations (Table 3) and
corresponding ΦST values were highly significant (0.42, P < 0.0001 and 0.75, P <0.0001, respectively for IAM and SMM) (Table 3). Pairwise RST values were higher and
significant for Sierra Agustinos, Chiluca and Pablo Ixtayoc (Table S1), as expected from
the result that Sierra Agustinos and Chiluca populations did not share any haplotype with
other populations, while the Pablo Ixtayoc population only shared one haplotype (H33)
with Xihuingo.
Values of both NST (0.75) and GST (0.235) indicated overall genetic differentiation
across populations. The permutation test for the comparison of NST and GST was significant (P< 0.003), indicating phylogeographic structure or that more closely related haplotypes tend to occur together in the same populations.
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Demographic history
In the mismatch distribution analysis, the null model of population expansion was not
rejected except for the Sierra Agustinos and Tecajete populations, suggesting a
demographic expansion for the rest of the populations (Table 4). The values of Tajima´s
D statistic did not depart significantly from neutrality in any population, while Fu’s FS
test detected the signal of demographic expansion in the Amealco, Cerro los Pitos,
Chiluca and Huichapan populations (Table 4), all of them located in the eastern part of
the TMVB.
Draft
Morphological variation
The first and second axes of the discriminant function derived from the morphometric
data analysis explained 62% of total variation in leaf shape and significantly
discriminated between two morphological clusters (for F1, Wilks’ lambda = 0.002, d. f. =
312, P < 0.0001; for F2, Wilks’ lambda = 0.011, d. f. = 275, P < 0.0001). One cluster was
formed by populations on the west and south of the TMVB (Santa Fe, Volcán de Colima,
Agustinos, Chiluca and Pablo Ixtayoc) and the other cluster was formed by populations
on the east and north of the TMVB (Matehuala, Amealco, Cerro Pitos, Huichapan, Mesa
Burro, Pachuca, Tecajetes and Xihuingo) (Figs. 1 and 3).
Ecological niche modeling
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The ENM yielded a good fit to the current distribution of Q. deserticola (AUC > 0.96)
(Fig. 4a, b). The potential distribution predicted for the LGM scenarios suggest the presence of the species in roughly the same regions where it currently occurs (Figs. 4c, d), with some differences between the CCSM and the MIROC scenarios, particularly regarding the eastern and southern parts of the distribution, which appear to have higher suitability values under the CCSM scenario. In contrast, the potential distribution in the
LIG model appears contracted and shifted to the south, with some areas in the Sierra
Madre del Sur showing the highest habitat suitability values, and a decreased prediction of habitat suitability in the northernmost areas of the TMVB (Fig. 4e). The analysis of the contribution of each variable to the modelDraft indicated that the variables that contributed the most were temperature seasonality (BIO4) with 36.7% (permutation importance 52.7%), mean temperature of coldest quarter (BIO11) with 25% (permutation importance 27.5%) and the maximum temperature of warmest month (BIO5) with 19.9% (permutation importance 5.7%).
Association among genetic, morphological and environmental distances
We observed a positive correlation between morphological and environmental distances while controlling for geographic distance (r = 0.32, P = 0.031). However, we found no correlation between genetic (pairwise RST) and morphological distances (r = 0.10, P =
0.22), nor a relationship between genetic and environmental distances (r = 0.17, P =
0.77) while controlling for geographic distance. These analyses suggest that
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environmental conditions are contributing in maintaining the morphological differences
between geographical regions.
DISCUSSION
Phylogeographic structure and demographic history
The populations of Q. deserticola were characterized by high genetic diversity and
considerable differentiation, as well as significant phylogeographic structure. Most
previous studies of oak species using cpDNA markers have found similar high
differentiation levels and a clear geographic segregation of haplotypes lineages, which
are usually explained by the historicalDraft migration patterns of the populations and the very
low dispersal capacity of acorns (Magni et al. 2005; Grivet et al. 2006; Magri et al. 2007;
Marsico et al. 2009). However, in Q. deserticola the distribution of haplotypes was rather
patchy, displaying strong local genetic structure but without obvious phylogeographic
breaks, contrasting with some previous studies in the TMVB that have found a west east
pattern of lineage differentiation (e. g. Parra Olea et al. 2012; Ruiz Sánchez and Specht
2014; Pérez Crespo et al. 2017) consistent with the idea that climatic oscillations and
geological activity during the Miocene and Pleistocene promoted alternating periods of
connection and disconnection between different habitats along this mountain range
(Jaramillo Correa et al. 2008; Ornelas and González 2014; Mastretta Yanes et al. 2015).
In the case of Q. deserticola, the observed palaeodistribution obtained from
ecological niche modeling supports the scenario in which populations of this species on
the TMVB probably expanded northwards from the LIG to the LGM, but were largely
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stable from the LGM to the present. However, changes in the altitudinal distribution,
resulting in the expansion and contraction of temperate forests might have occurred, as it
is suggested for different taxa and by palynological records that indicate abundance of
temperate forests in valleys and lowlands during the LGM (Metcalfe 2006; Caballero et
al. 2010; Ramírez Barahona and Eguiarte 2013; Villanueva Amadoz et al 2014;
Mastretta Yanes et al. 2015). The relative historical range stability of Q. deserticola from the LGM to the present, in combination with the complex topography, the scattered distribution of suitable sites for the colonization of Q. deserticola and the limited dispersal capacity of acorns are factors that probably enhanced genetic drift as a predominant force shaping the distribution of cpDNA haplotypes in this species. These
results are similar to those observedDraft for Q. lobata in California, which also showed a
mosaic distribution of haplotypes and signs of range stability and local migration patterns
from the late Pleistocene to the present (Gugger et al. 2013).
The historical demography tests gave contrasting results. The mismatch
distribution analyses suggested demographic expansions for most populations, while Fu’s
Fs tests detected expansions only in four populations from the eastern part of the TMVB
and the values of Tajima’s D were not significant for any population. This might be
explained by the different sensitivity of the tests to demographic expansions and to
sample size (Ramos Onsins and Rozas 2002). As previously mentioned, the species
distribution modelling suggested a northwards range expansion from the LIG to the
LGM, and the MIROC model suggested a modest eastwards expansion from the LGM to
the present along the TMVB. Therefore, it is possible that the populations to the north
and the east of the TMVB were more recently colonized than the populations to the south
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and the west. Interestingly, a recent phylogeographic study of the mistletoe Psittacanthus
calyculatus (Loranthaceae) which frequently uses Q. deserticola as a host (Pérez Crespo
et al. 2017), found evidence of a south to north expansion and colonization of the TMVB
from the Sierra Madre del Sur during the late Pleistocene; but afterwards the direction of
the expansion within the TMVB was from east to west. However, this parasitic plant has
a broad host range and seems to follow its own ecological niche rather than closely
tracking its hosts (Ramírez Barahona et al. 2017).
Morphological variation
Two morphologically differentiated Draftpopulation groups were identified within Q.
deserticola. The first grouped populations from the west/south of the TMVB and the
second was formed by populations from the east/north. The two morphological groups
are partially concordant with the previous regionalization of the TMVB into western and
eastern biogeographic sectors (Gámez et al. 2012; Torres Miranda et al. 2013), and with
genetic subdivisions found in other organisms (Parra Olea et al. 2012; Ruiz Sánchez and
Specht 2014; Pérez Crespo et al. 2017). However, according to the partial Mantel tests, in
Q. deserticola the association between morphological and genetic distances among
populations was not significant while controlling for the geographic distance. While a
correlation between phenotypic variation and neutral genetic markers may occur if the
same neutral processes (e. g. gene flow and genetic drift) have historically affected the
spatial structure of both types of variation (e. g. Cavender Bares et al. 2011), in this case
it seems like the variation patterns in cpDNA markers and leaf morphology of Q.
19 https://mc06.manuscriptcentral.com/botany-pubs Botany Page 20 of 48
deserticola are the result of different processes. As previously mentioned, the limited
dispersal capacity of acorns, coupled with the heterogeneous landscape, the sparse
distribution of suitable sites for the colonization of Q. deserticola and the relative historical range stability from the LGM to the present, have promoted a patchy distribution of haplotypes with a strong local genetic structure. In contrast, the geographical subdivision of leaf morphological variation was significantly associated to climate variation, while controlling for geographical or genetic distances among populations. For example, the sites that constitute the western group are characterized by a higher mean annual precipitation (mean ± standard error = 829 ± 55.3 mm) and a higher precipitation seasonality (93.7 ± 3.89), than the sites that constitute the eastern group
(537.2 ± 74.2 mm and 76.2 ± 5.22, respectively).Draft Similarly, previous studies on
California oaks (Riordan et al. 2016) and Q. rugosa in Mexico (Uribe Salas et al. 2008) have also indicated strong and significant effects of climate on leaf morphology when controlling for geographical distances among populations. However, whether the association of leaf morphological variation with climate in Q. deserticola reflects phenotypic plasticity, common ancestry or adaptive differentiation among populations is
unclear. The incongruence between leaf morphology and genetic differentiation can be
explained because phenotypic plasticity as a response to environmental variation can
modify morphological characters without a corresponding genetic differentiation pattern.
Also, phenotypic differences could be due to local adaptation as a result of selection pressures at loci not linked to neutral genetic markers. This is particularly likely in our
case since such selected loci are expected to be in the nuclear DNA and largely
independent of cpDNA haplotype variation. In any case, the two morphological groups
20 https://mc06.manuscriptcentral.com/botany-pubs Page 21 of 48 Botany
identified in Q. deserticola are clearly reflecting the climatic differences between the
east/south and west/north portions of its distribution along the TMVB, but an
understanding of the causes of this relationship would require common garden
experiments or a landscape genomics approach (e. g. Sork et al. 2016).
CONCLUSIONS
In this study, we suggest that different evolutionary processes are likely to be involved in
shaping the genetic structure and phenotypic variation of Q. deserticola populations
distributed along of the TMVB. First, a pattern supported by local sharing of haplotypes
and a high genetic differentiation is Draftprobably due to low seed dispersal and restricted
range displacements during recent historical periods. Second, morphological variation is
also found between populations to the west/south and east/north sectors of the Q.
deserticola geographic distribution, as a response to environmental variables. The
genetic, morphological and environmental patterns found in Q. deserticola are significant
to continue understanding the evolutionary history of the species that inhabit the TMVB
region.
ACKNOWLEDGMENTS
We thank V. Rocha, H. Rodríguez Correa and J. Llanderal Mendoza for technical
support. FRG thanks the postdoctoral scholarship provided by Dirección General de
Asuntos del Personal Académico (DGAPA) of the Universidad Nacional Autónoma de
21 https://mc06.manuscriptcentral.com/botany-pubs Botany Page 22 of 48
México (UNAM). This project was partially supported by DGAPA PAPIIT IV201015 and CONACYT 240136 grants.
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Table 1. Geographical localization of thirteen studied Q. deserticola populations.
Locality State Elevation (masl) Latitude Longitude
1. Nevado de Colima Jalisco 2870 19.47 103.56
2. Santa Fe Jalisco 2546 19.27 102.50
3. Matehuala San Luis Potosí 2640 22.58 101.50
4. Sierra Agustinos Guanajuato 2600 20.23 100.67
5. Amealco Querétaro 2595 20.43 100.27
6. Xihuingo Hidalgo 2702 20.72 99.86
7. Huichapan Hidalgo 2337 20.23 99.51
8. Chiluca Estado de México 2565 19.54 99.31
9. Mesa del Burro Hidalgo Draft2400 20.10 99.03
10. Tecajete Hidalgo 2582 19.93 98.91
11. Cerro de los Pitos Hidalgo 2700 19.48 98.79
12. Pachuca Hidalgo 2649 19.95 98.60
13. Pablo Ixtayoc Estado de México 2670 19.28 98.55
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hs 39
, H31 = Allelic richness, richness, = Ar Allelic H32, H41, H22 H41, H32,
H14, H19, H18 H18 H19, H14, , H49, H50, H10 H10 H50, , H49, H28, H45, H53, H25, H27 H25, H53, H45, H28, , H37, H46, H47, H48, H51 H48, H47, H46, , H37,
H40, H31, H41 H31, H40, H29, H13, H16, H34 H34 H16, H13, H29, , H30, H17, H54, H52, H52, H54, H17, , H30, H43 , H9, H7 number of private haplotypes, haplotypes, private of number
H20 P = P , H31, H40, H31, H40 H44, H22, H11 H33, H22, H12, H12, H44, H22, H41, H20, H21, H32, H21 H44 H11, Haplotypes Haplotypes H7, H12 H12 H7, Botany
= sample size, sample = Draft https://mc06.manuscriptcentral.com/botany-pubs Q. deserticola.Q. n 10 10 4 2.98 0.778 6 12 12 6 2.88 0.758 6 H26, H2 H42, H5, H38, H23, 5 5 0 2 0.600 2 n n P Ar hs H number of haplotypes in each population. Shared haplotypes among populations are in bold. in populations are among Shared population. haplotypes in haplotypes each of number H H = 11. Cerro de los Pitos de Cerro 11. 16 4 3.38 0.883 9 4. Sierra Agustinos Agustinos Sierra 4. Amealco 5. 10 3 1.8 12 0.378 5 3 3.73 H15 H3, H1, 0.955 9 7. Huichapan Huichapan 7. 10 5 3.73 0.956 8 6. Xihuingo Xihuingo 6. 17 3 2.98 0.794 7 = genetic diversity, diversity, genetic = Locality 12. Pachuca Pachuca 12. 10 2 2.61 0.711 4 3. Matehuala Matehuala 3. 1. Nevado de Colima de Nevado 1. Fe Santa 2. 4 2 3 0.833 3 H36, H6, Table 2. Population genetic variability of variability genetic Population 2. Table 10. Tecajete Tecajete 10. 11 2 2.83 0.764 5 H39, H24, 8. Chiluca Chiluca 8. del Burro Mesa 9. 17 3 2.82 0.750 7 Page 39 of 48 Page 40 of 48 40
H33 Botany Draft https://mc06.manuscriptcentral.com/botany-pubs 11 11 3.2 2.86 0.754 5.6 Mean Mean 13. Pablo Ixtayoc Pablo 13. Ixtayoc 10 3 2.46 0.644 4 H35, H4, H8, Page 41 of 48 Botany
Table 3. Analysis of molecular variance (AMOVA) for the seven chloroplast microsatellites of Q. deserticola.
Source of variation d. f. Sum of Variance Percentage Fixation index squares components of variation ΦST FST (IAM)
Among populations 12 95.44 0.63 42.33 0.42*
Within populations 133 115.46 0.86 57.67
Total 145 210.11 1.50
RST (SMM) Among populations 12 30719.5Draft 223.82 75.97 0.75* Within populations 32 9414.2 70.78 24.03
Total 145 40133.7 294.61
*P < 0.00001
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Table 4. Summary statistics of historical demography analyses of Quercus deserticola. D
= Tajima’s D and FS = Fu’s Fs. Significant negative values (**P < 0.01 and *P < 0.05 in bold) indicate historical demographic expansion events. SSD = differences in the sum of squares or mismatch distribution. Significant (P ≤ 0.05 in bold) SSD values indicate deviations from the sudden expansion model.
Locality D Fs SSD 1. Nevado de Colima 0.2124 0.5563 0.1248 2. Santa Fe 1.2247 0.6261 0.0542 3. Matehuala 0.4331 0.9254 0.0204 4. Sierra Agustinos 0.6909 0.5938 0.2143 5. Amealco 0.4224 -3.6791* 0.0019 6. Xihuingo 0.5035 Draft 1.5221 0.0224 7. Huichapan 0.0248 -4.2247** 0.0223 8. Chiluca 0.4219 -2.9799** 0.0129 9. Mesa del Burro 1.0558 1.7538 0.0286 10. Tecajete 1.2177 0.8131 0.2257 11. Cerro los Pitos 0.0020 -3.9531** 0.0018 12. Pachuca 0.1542 0.8067 0.0584 13. Pablo Ixtayoc 1.1494 0.9275 0.0082
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Figure legends.
Figure 1. (a) Frequency and geographic distribution of cpDNA haplotypes in 13 localities
of Q. deserticola in the TMVB, the number correspond to those in Table 1. The pie charts
represent the frequency of occurrence of haplotypes in each population, and colors
correspond to those shown in the haplotype network below. The black line encircles the
populations belonging to cluster one (west/south) of the morphological analysis (triangles
in Fig 2). (b) Haplotype statistical parsimony network. Each circle indicates an individual
haplotype and its size is proportional to the frequency of the haplotype. Lines represent a
single mutational change and black Draftcircles correspond to unsampled haplotypes (loops
are showed in the network).
Figure 2. Distribution map of genetic clusters inferred from BAPS based on chloroplast
microsatellites (cpSSRs) from Q. deserticola. The population numbers correspond to
those in the Table 1. Two clusters (K = 2) were retrieved, cluster 1 in blue and cluster 2 in
red.
Figure 3. Morphological differentiation between Q. deserticola populations. The first and
second discriminant functions are shown in x and y axes. Colors represent the different
populations. Triangles correspond to western populations and circles to eastern of the
TMVB. Locality numbers correspond those in Table 1.
Figure 4. MAXENT analyses showing distribution points (a), and species distribution
models for Q. deserticola, at present (b), Last Glacial Maximum (LGM, CCSM, 21 ka)
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(c), Last Glacial Maximum (LGM, MIROC, 21 ka) (d) and Last Interglacial (120 ka) (e).
Light gray indicates areas with low suitability values for the occurrence of the species and dark gray indicates high suitability values.
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