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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; 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

Issue? : N/A

Keyword: gene flow, Mexican oaks, Ecological Niche Modeling, Last Glacial Maximum

https://mc06.manuscriptcentral.com/botany-pubs

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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 Ecosistemas 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:

[email protected]

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 evaluate 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 divided 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 continuous 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 high 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 sometimes 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 combined 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 alleles (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

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 analysis 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 partial 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 WorlClim (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

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 explained 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.


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 model 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 historical 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 observed 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).


Two morphologically differentiated population 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.

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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). 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

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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 probably 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

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México (UNAM). This project was partially supported by DGAPA-PAPIIT IV201015

and CONACYT 240136 grants.

REFERENCES

Aguirre-Planter, E., Jaramillo-Correa, J.P., Gómez-Acevedo, S., Khasa, D.P., Bousquet,

J., and Eguiarte, L.E. 2012. Phylogeny, diversification rates and species boundaries of

Mesoamerican firs (Abies, Pinaceae) in a genus-wide context. Mol. Phylogenet. Evol.

62(1): 263–274. Available from http://dx.doi.org/10.1016/j.ympev.2011.09.021

Aiello-Lammens, M. E., Boria, R.A., Radosavljevic, A., Vilela, B., and Anderson, R. P.

2015. spThin: an R package for spatial thinning of species occurrence records for use in

ecological niche models. Ecography, 38(5): 541-545.

Bandelt, H.J, Forster, P., and Röhl, A. 1999. Median-joining networks for inferring

intraspecic phylogenies. Mol. Biol. Evol. 16(1): 37-48. Available from

https://doi.org/10.1093/oxfordjournals.molbev.a026036

Barve, N., Barve, V., Jiménez-Valverde, A., Lira-Noriega, A., Maher, S. P., Peterson, A.

T., Soberón, J., and Villalobos, F. 2011. The crucial role of the accessible area in

ecological niche modeling and species distribution modeling. Ecol. Model. 222(11):

1810-1819. doi.org/10.1016/j.ecolmodel.2011.02.011

Page 22 of 48


Botany

Draft

23

Boria, R.A., Olson, L.E., Goodman, S.M. and Anderson, R.P. 2014. Spatial filtering to

reduce sampling bias can improve the performance of ecological niche model.

Ecol. Model. 275:73-77. doi.org/10.1016/j.ecolmodel.2013.12.012

Braconnot, P., Otto-Bliesner, B., Harrison, S., et al. 2007. Results of PMIP2 coupled

simulations of the Mid-Holocene and Last Glacial Maximum – Part 2: feedbacks with

emphasis on the location of the ITCZ and mid- and high latitudes heat budget. Clim. Past.

3: 279–296. doi:10.5194/cp-3-279-2007

Caballero, M., Lozano-García, S., Vázquez-Selem, L., and Ortega, B. 2010. Evidencias

de cambio climático y ambiental en registros glaciales y en cuencas lacustres del centro

de México durante el último máximo glacial. Bol. Soc. Geol. Mex. 62(3): 359–377.

Available from http://www.scielo.org.mx/scielo.php?pid=S1405-

33222010000300005&script=sci_arttext&tlng=en

Cavender-Bares, J., González-Rodríguez, A., Eaton, D. A., Hipp, A. A., Beulke, A., and

Manos, P. S. 2015. Phylogeny and biogeography of the American live oaks (Quercus

subsection Virentes): A genomic and population genetics approach. Mol. Ecol. 24(14):

3668-3687. doi:10.1111/mec.13269

Cavender-Bares, J., González-Rodríguez, A., Pahlich, A., Koehler, K., and Deacon, N.

2011. Phylogeography and climatic niche evolution in live oaks (Quercus series Virentes)

Page 23 of 48


Botany

Draft

24

from the tropics to the temperate zone. J. Biogeogr. 38(5): 962-981. doi:10.1111/j.1365-

2699.2010.02451.x

Collins, W. D., Bitz, C., Blackmon, M., Bonan, G., Bretherton, C. S., et al. 2006. The

community climate system model Version 3 (CCSM3). J. Climate, 19, 2122–2143.

Available from http://dx.doi.org/10.1175/JCLI3761.1

Corander, J., Marttinen, P., Siren, J., and Tang, J. 2008. Enhanced Bayesian modelling in

BAPS software for learning genetic structures of populations. BMC Bioinformatics, 9:

539. doi.org/10.1186/1471-2105-9-539

Deguilloux, M.F., Dumolin-Lapègue, S., Gielly, L., Grivet, D., and Petit, R. 2003. A set

of primers for the amplification of chloroplast microsatellites in Quercus. Mol. Ecol.

Notes, 3(1): 24–27. doi:10.1046/j.1471-8286.2003.00339.x

Eliades, N-GH., and Eliades, D.G. 2009. HAPLOTYPE ANALYSIS: Software for

analysis of haplotype data. Distributed by the authors. Forest Genetics and Forest Tree

Breeding, Georg-August University Goettingen, Germany. Available from

https://www.uni-goettingen.de/en/134935.html (accessed 25 October 2016)

Excoffier, L., and Lischer, H.E.L. 2010. Arlequin suite ver 3.5: a new series of programs

to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour.

10(3): 564–567. doi:10.1111/j.1755-0998.2010.02847.x

Page 24 of 48


Botany

Draft

25

Ferrari, L., Orozco-Esquivel, T., Manea, V., and Manea, M. 2012. The dynamic history

of the Trans-Mexican Volcanic Belt and the Mexico subduction zone. Tectonophysics,

522: 122–149. doi:https://doi.org/10.1016/j.tecto.2011.09.018

Fu, Y.X. 1997. Statistical tests of neutrality against population growth, hitchhiking and

background selection. Genetics, 147: 915-925. Available from

http://www.genetics.org/content/genetics/147/2/915.full.pdf

Gámez, N., Escalante, T., Rodríguez, G., Linaje, M., and Morrone, J.J. 2012.

Caracterización biogeográfica de la Faja Volcánica Transmexicana y análisis de los

patrones de distribución de su mastofauna. Rev. Mex. Biodiv. 83(1): 258-272. Available

from http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1870-

34532012000100028

Gándara, E., and Sosa, V. 2014. Spatio-temporal evolution of Leucophyllum pringlei and

allies (Scrophulariaceae): A group endemic to North American xeric regions. Mol.

Phylogenet. Evol. 76: 93-101. Available from

http://dx.doi.org/10.1016/j.ympev.2014.02.027

Gómez-Tuena, A., Orozco-Esquivel, M.T., and Ferrari, L. 2005. Petrogénesis ígnea de la

Faja Volcánica Transmexicana. Bol. Soc. Geol. Mex. 3(57): 227-283. Available from

http://s3.amazonaws.com/academia.edu.documents/30924814/94320266003.pdf?AWSAc

Page 25 of 48


Botany

Draft

26

cessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1488566256&Signature=XJIOqvp

7ljbQaFZzCeVc%2F7dVZKg%3D&response-content-

disposition=inline%3B%20filename%3DPetrogenesis_ignea_de_la_faja_Volcanica.pdf

Gómez-Tuena, A., Orozco-Esquivel, M.T., and Ferrari, L. 2007. Igneous petrogenesis of

the Trans-Mexican Volcanic Belt. GSA Special Papers, 422: 129–181.

doi1:0.1130/2007.2422(05)

González-Rodríguez, A., Arias, D.M., S. Valencia, S., and Oyama, K. 2004.

Morphological and RAPD analysis of hibridization between Quercus affinis and Q.

laurina (Fagaceae), two Mexican red oaks. Am. J. Bot. 91(3): 401-409.

doi:10.3732/ajb.91.3.401

González-Rodríguez, A., and Oyama, K. 2005. Leaf morphometric variation in Quercus

affinis and Q. laurina (Fagaceae), two hybridizing Mexican red oaks. Bot. J. Linnean

Soc. 147(4): 427-435. doi: 10.1111/j.1095-8339.2004.00394.x

Grivet, D., Deguilloux, M. F., Petit, R. J., and Sork, V. L. 2006. Contrasting patterns of

historical colonization in white oaks (Quercus spp.) in California and Europe. Mol. Ecol.

15(13): 4085-4093. doi:10.1111/j.1365-294X.2006.03083.x

Grummer, J. A., Calderón�Espinosa, M. L., Nieto-Montes de Oca, A., Smith, E. N.,

Méndez de la Cruz, F. R., and Leaché, A. D. 2015. Estimating the temporal and spatial

Page 26 of 48


Botany

Draft

27

extent of gene flow among sympatric lizard populations (genus Sceloporus) in the

southern Mexican highlands. Mol. Ecol. 24(7): 1523-1542. doi:10.1111/mec.13122

Gugger, P. F., Ikegami, M., and Sork, V. L. 2013. Influence of late Quaternary climate

change on present patterns of genetic variation in valley oak, Quercus lobata Née. Mol.

Ecol. 22(13): 3598-3612. doi: 0.1111/mec.12317

Halffter, G., and Morrone, J. J. 2017. An analytical review of Halffter's Mexican

transition zone, and its relevance for evolutionary biogeography, ecology and

biogeographical regionalization. Zootaxa, 4226(1): 1-46. Available from

http://mapress.com/j/zt/article/view/zootaxa.4226.1.1

Hardy, O. J., and Vekemans, X. 2002. SPAGeDi: a versatile computer program to

analyse spatial genetic structure at the individual or population levels. Mol. Ecol. Notes,

2(4): 618–620. doi:10.1046/j.1471-8286.2002.00305.x

Harpending, H. C. 1994. Signature of ancient population growth in a low-resolution

mitochondrial DNA mismatch distribution. Hum. Biol. 66: 591-600. Available from

http://www.jstor.org/stable/41465371

Hasumi, H., and Emori, D. 2004. K-1 coupled GCM (MIROC) description. Center for

Climate System Research, University of Tokyo, Tokyo, Japan.

Page 27 of 48


Botany

Draft

28

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A. 2005. Very high

resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25(15):

1965–1978. doi:10.1002/joc.1276

Hipp, A. L., Manos, P. S., González-Rodríguez, A., Hahn, M., Kaproth, M., McVay, J.

D., Valencia Avalos, S., and Cavender�Bares, J. 2017. Sympatric parallel diversification

of major oak clades in the Americas and the origins of Mexican species diversity. New

Phytol. doi: 10.1111/nph.14773

Jaramillo-Correa, J. P., Aguirre-Planter, E., Khasa, D. P., Eguiarte, L. E., Piñero, D.,

Furnier, G. R., and Bousquet, J. 2008. Ancestry and divergence of subtropical montane

forest isolates: molecular biogeography of the genus Abies (Pinaceae) in southern Mexico

and Guatemala. Mol. Ecol. 17(10): 2476–2490. doi:10.1111/j.1365-294X.2008.03762.x

Jensen, J. L., Bohonak, A. J., and Kelley, S. T. 2005. Isolation by distance, web service.

BMC Genetics, 6:13. v.3.23. Available from http://ibdws.sdsu.edu/ (accessed 22

September 2016)

Jiménez, R. A. and Ornelas, J. F. 2016. Historical and current introgression in a

Mesoamerican hummingbird species complex: a biogeographic perspective. PeerJ, 4:

e1556. Available from https://doi.org/10.7717/peerj.1556

Page 28 of 48


Botany

Draft

29

Magri, D., Fineschi, S., Bellarosa, R., Buonamici, A., Sebastiani, F., Schirone, B.,

Simeone, M. C., and Vendramin, G. G. 2007. The distribution of Quercus suber

chloroplast haplotypes matches the palaeogeographical history of the western

Mediterranean. Mol. Ecol. 16: 5259-5266. doi:10.1111/j.1365-294X.2007.03587.x

Magni, C. R., Ducousso, A., Caron, H., Petit, R. J., and Kremer, A. 2005. Chloroplast

DNA variation of Quercus rubra L. in North America and comparison with other

Fagaceae. Mol. Ecol. 14(2): 513-524. doi:10.1111/j.1365-294X.2005.02400.x

Manos, P. S., Doyle, J. J., and Nixon, K. C. 1999. Phylogeny, biogeography, and

processes of molecular differentiation in Quercus subgenus Quercus (Fagaceae). Mol.

Phylogenet. Evol. 12(3): 333-349. Available from

http://dx.doi.org/10.1006/mpev.1999.0614

Mantel, N. 1967. The detection of disease clustering and a generalized regression

approach. Cancer Res. 27: 209–220. Available from

http://cancerres.aacrjournals.org/content/27/2_Part_1/209.full-text.pdf

Marsico, T. D., Hellman, J. J., Romero-Severson, J. 2009. Patterns of seed dispersal and

pollen flow in Quercus garryana (Fagaceae) following post-glacial climatic changes. J.

Biogeogr. 36:929-941. doi:10.1111/j.1365-2699.2008.02049.x

Page 29 of 48


Botany

Draft

30

Mastretta-Yanes, A., Moreno-Letelier, A., Pinero, D., Jorgensen, T. H., and Emerson,

B.C. 2015. Biodiversity in the Mexican highlands and the interaction of geology,

geography and climate within the Trans�Mexican Volcanic Belt. J. Biogeogr. 42(9):

1586-1600. doi:10.1111/jbi.12546

McCormack, J. E., Heled, J., Delaney, K. S., Peterson, A. T., and Knowles, L. L. 2011.

Calibrating divergence times on species trees versus gene trees: implications of

speciation history of Aphelocoma jays. Evolution, 65(1): 184–202. doi:10.1111/j.1558-

5646.2010.01097.x

Metcalfe, S. E. 2006. Late Quaternary environments of the northern deserts and central

Transvolcanic Belt of Mexico. Ann. Mo. Bot. Gard. 93(2): 258-273. Available from

http://dx.doi.org/10.3417/0026-6493(2006)93[258:LQEOTN]2.0.CO;2

Moreno-Letelier, A., Mastretta-Yanes, A., and Barraclough, T.G. 2014. Late Miocene

lineage divergence and ecological differentiation of rare endemic Juniperus blancoi:

clues for the diversification of North American conifers. New Phytol. 203(1): 335-347.

doi:10.1111/nph.12761

Morrone, J. J. 2005. Hacia una síntesis biogeográfica de México. Rev. Mex.

Biodiv. 76:(2) 207-252. Available from http://scielo.unam.mx/scielo.php?pid=s1870-

34532005000200006&script=sci_arttext

Page 30 of 48


Botany

Draft

31

Navascués, M., Hardy, O.J., and Burgarella, C. 2009. Characterization of demographic

expansions from pairwise comparisons of linked microsatellite haplotypes. Genetics,

181:1013–1019. doi: https:/doi.org/10.1534/genetics.108.098194

Nicoli, N. P., Krstic, B. D., Pajevic, S. P., and Orlovic, S. S. 2006. Variability of leaf

characteristics in different pedunculate oak genotypes (Quercus robur L.). Proc. Nat. Sci.

Matica Srpska Novi Sad. 111: 95–105. doi:10.2298/ZMWSPN0611095N

Ornelas, J. F., and González, C. 2014. Interglacial genetic diversification of Moussonia

deppeana (Gesneriaceae), a hummingbird-pollinated, cloud forest shrub in northern

Mesoamerica. Mol. Ecol. 23(16): 4119–4136. doi:10.1111/mec.12841

Otto-Bliesner, B. L., Hewitt, C.D., Marchitto, T. M., Brady, E., Abe-Ouchi, A., Crucifix,

M., Murakami, S., and Weber, S.L. 2007. Last Glacial Maximum ocean thermohaline

circulation: PMIP2 model intercomparisons and data constraints. Geophys. Res. Lett.

34(12): L12706. doi:10.1029/2007GL029475

Otto-Bliesner, B. L., Marshall, S. J., Overpeck, J. T., Miller, G. H. and Hu, A. X. and

CAPE Last Interglacial Project members. 2006. Simulating Arctic climate warmth and

icefield retreat in the Last Interglaciation. Science, 311(5768): 1751–1753.

doi:10.1126/science.1120808

Page 31 of 48


Botany

Draft

32

Parra-Olea, G., Windfield, J. C., Velo- Antón, G., and Zamudio, K. R. 2012. Isolation in

habitat refugia promotes rapid diversification in a montane tropical salamander. J.

Biogeogr. 39(2): 353–370. doi:10.1111/j.1365-2699.2011.02593.x

Pérez-Crespo, M. J., Ornelas, J. F., González�Rodríguez, A., Ruiz-Sánchez, E.,

Vásquez-Aguilar, A. A., and Ramírez-Barahona, S. 2017. Phylogeography and

population differentiation in the Psittacanthus calyculatus (Loranthaceae) mistletoe: a

complex scenario of climate–volcanism interaction along the Trans�Mexican Volcanic

Belt. J. Biogeogr. doi 10.1111/jbi.13070

Phillips, S. J., and Dudik, M. 2008. Modeling of species distributions with Maxent: new

extensions and a comprehensive evaluation. Ecography, 31(2): 161–175.

doi:10.1111/j.0906-7590.2008.5203.x

Pons, O., and Petit, R. J. 1996. Measuring and testing genetic differentiation with ordered

versus unordered alleles. Genetics, 144(3): 1237–1245. Available from

http://www.genetics.org/content/144/3/1237.article-info

Ramírez-Barahona, S., and Eguiarte, L. E. 2013. The role of glacial cycles in promoting

genetic diversity in the Neotropics: the case of cloud forests during the Last Glacial

Maximum. Ecol. Evol. 3(31): 725–738. doi:10.1111/1442-1984.12109

Page 32 of 48


Botany

Draft

33

Ramírez-Barahona, S., González, C., González-Rodríguez, A., and Ornelas, J. F. 2017.

The influence of climatic niche preferences on the population genetic structure of a

mistletoe species complex. New Phytol. 214(4): 1751-1761. doi 10.1111/nph.14471

Ramírez-Toro, W., Torres-Miranda, A., González-Rodríguez, A., Ruiz-Sánchez, E.,

Luna-Vega, I., and Oyama, K. 2017. A Multicriteria Analysis for Prioritizing Areas for

Conservation of Oaks (Fagaceae: Quercus) in Oaxaca, Southern Mexico. Trop. Conserv.

Sci. 10, 1940082917714227. doi: 10.1177/1940082917714227

Ramos-Onsins, S. E., and Rozas, J. 2002. Statistical properties of new neutrality tests

against population growth. Mol. Biol. Evol. 19(12):2092–2100.

https://doi.org/10.1093/oxfordjournals.molbev.a004034

Ramos-Ortiz, S., Oyama, K., Rodríguez-Correa, H., and González-Rodríguez, A. 2016.

Geographic structure of genetic and phenotypic variation in the hybrid zone between

Quercus affinis and Q. laurina in Mexico. Plant Spec. Biol. 31(3): 219-232.

doi:10.1111/1442-1984.12109

Riordan, E. C., Gugger, P. F., Ortego, J., Smith, C., Gaddis, K., Thompson, P., and Sork,

V. L. 2016. Association of genetic and phenotypic variability with geography and climate

in three southern California oaks. Am. J. Bot. 103(1): 73-85. doi:10.3732/ajb.1500135

Page 33 of 48


Botany

Draft

34

Rodríguez-Correa, H., Oyama, K., MacGregor-Fors, I., and González-Rodríguez, A.

2015. How are oaks distributed in the neotropics? A perspective from species turnover,

areas of endemism, and climatic niches. Int. J. Plant. Sci. 176(3): 222-231.

doi:10.1086/679904

Rodríguez-Gómez, F., and Ornelas, J. F. 2015. At the passing gate: past introgression in

the process of species formation between Amazilia violiceps and A. viridifrons

hummingbirds along the Mexican Transition Zone. J. Biogeogr. 42(7): 1305-1318.

doi:10.1111/jbi.12506

Rohlf, F.J. 1990. Rotational fit (Procrustes) methods. In Proceedings of the Michigan

Morphometrics Workshop. Edited by F. J. Rohlf, F. Bookstein. University of Michigan

Museums of Zoology, Ann Arbor. pp. 227–236.

Rohlf, F. J. 2005. tpsDig, digitize landmarks and outlines, ver 2.04. Department of

Ecology and Evolution, State University of New York at Stony Brook.

Ruiz-Sánchez, E., Rodríguez-Gómez, F., and Sosa, V. 2012. Refugia and geographic

barriers of populations of the desert poppy, Hunnemannia fumariifolia (Papaveraceae).

Org. Divers. Evol. 12(2): 133-143. doi:10.1007/s13127-012-0089-z

Ruiz-Sánchez, E., and Specht, C.D. 2013. Influence of the geological history of the

Trans-Mexican Volcanic Belt on the diversification of Nolina parviflora (Asparagaceae:

Nolinoideae). J. Biogeogr. 40(7): 1336-1347. doi:10.1111/jbi.12073

Page 34 of 48


Botany

Draft

35

Ruiz-Sánchez, E., and Specht, C.D. 2014. Ecological speciation in Nolina parviflora

(Asparagaceae): lacking spatial connectivity along of the Trans-Mexican Volcanic

Belt. PloS one, 9(6): e98754. Available from

http://dx.doi.org/10.1371/journal.pone.00987544

Rzedowski, J. 1978. La vegetación de México, Limusa, México

Schneider, S., and Excoffier, L. 1999. Estimation of demographic parameters from the

distribution of pairwise differences when the mutation rates vary among sites:

Application to human mitochondrial DNA. Genetics, 152:1079–1089. Available from

http://www.genetics.org/content/152/3/1079.short

Sebastiani, F., Carnevale, S., and Vendramin G. G. 2004. A new set of mono- and

dinucleotide chloroplast microsatellites in Fagaceae. Mol. Ecol. Notes. 4(2): 259–261.

doi:10.1111/j.1471-8286.2004.00635.

Sork, V. L., Squire, K., Gugger, P. F., Steele, S. E., Levy, E. D., Eckert, A. J. 2016.

Landscape genomic analysis of candidate genes for climate adaptation in a California

endemic oak, Quercus lobate. Am. J. Bot. 103(1): 33-46. doi: 10.3732/ajb.1500162

Sosa, V., Ruiz-Sánchez, E., and Rodríguez-Gómez, F. C. 2009. Hidden phylogeographic

complexity in the Sierra Madre Oriental: the case of the Mexican tulip poppy

Hunnemannia fumariifolia (Papaveraceae). J. Biogeogr. 36(1): 18-27.

doi:10.1111/j.1365-2699.2008.01957.x

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Tajima, F. 1989. Statistical method for testing the neutral mutation hypothesis by DNA

polymorphism. Genetics, 123: 585–595. Available from

http://www.genetics.org/content/genetics/123/3/585.full.pdf

Torres-Miranda, A., Luna-Vega, I., and Oyama, K. 2013. New approaches to the

biogeography and areas of endemism of red oaks (Quercus L., Section Lobatae). Syst.

Biol. 62(4): 555-573. Available from https://doi.org/10.1093/sysbio/syt021

Tovar-Sánchez, E., Mussali-Galante, P., Esteban-Jiménez, R., Piñero, D., Arias, D.M.,

Dorado, O., and Oyama, K. 2008. Chloroplast DNA polymorphism reveals geographic

structure and introgression in Quercus crassifolia × Q. crassipes complex in México.

Botany, 86(3): 228–239. Available from http://www.nrc-cnrc.gc.ca/main_e.html

Uribe-Salas, D., Sáenz-Romero, C., González-Rodríguez, A., Téllez-Valdéz, O., and

Oyama, K. 2008. Foliar morphological variation in the white oak Quercus rugosa Née

(Fagaceae) along a latitudinal gradient in Mexico: Potential implications for management

and conservation. Forest Ecol. Manag. 256(12): 2121-2126.

doi:10.1016/j.foreco.2008.08.002

Valencia-Cuevas, L., Piñero, D., Mussali-Galante, P., Valencia-Ávalos, S., and Tovar-

Sánchez, E. 2014. Effect of a red oak species gradient on genetic structure and diversity

of Quercus castanea (Fagaceae) in Mexico. Tree Genet. Genomes, 10(3) 641-652. doi:

10.1007/s11295-014-0710-8

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Valencia, S. 2004. La diversidad del género Quercus (Fagaceae) en México. Bol. Soc.

Bot. Méx. 75: 33–53. Available from http://www.redalyc.org/articulo.oa?id=57707503

Velo-Antón, G., Parra, J. L., Parra-Olea, G., and Zamudio, K. R. 2013. Tracking climate

change in a dispersal-limited species: reduced spatial and genetic connectivity in a

montane salamander. Mol. Ecol. 22(12): 3261-3278. doi:10.1111/mec.12310

Villanueva-Amadoz, U., Calvillo-Canadell, L., and Cevallos-Ferriz, S. R. S. 2014.

Síntesis de los trabajos paleobotánicos del Cretácico en México. Bol. Soc. Geol.

Mex. 66(1): 97-121. Available from http://www.scielo.org.mx/scielo.php?pid=S1405-

33222014000100009&script=sci_arttext

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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 2400 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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Table 2. Population genetic variability of Q. deserticola. n = sample size, P = number of private haplotypes, Ar = Allelic richness, hs

= genetic diversity, H = number of haplotypes in each population. Shared haplotypes among populations are in bold.

Locality n P Ar hs H Haplotypes

1. Nevado de Colima 4 2 3 0.833 3 H6, H36, H7

2. Santa Fe 5 0 2 0.600 2 H7, H12

3. Matehuala 10 4 2.98 0.778 6 H31, H40, H29, H13, H16, H34

4. Sierra Agustinos 10 3 1.8 0.378 3 H1, H3, H15

5. Amealco 12 5 3.73 0.955 9 H22, H44, H40, H37, H46, H47, H48, H51, H31

6. Xihuingo 17 3 2.98 0.794 7 H12, H22, H33, H11, H49, H50, H10

7. Huichapan 10 5 3.73 0.956 8 H22, H44, H12, H28, H45, H53, H25, H27

8. Chiluca 12 6 2.88 0.758 6 H23, H38, H5, H42, H26, H2

9. Mesa del Burro 17 3 2.82 0.750 7 H32, H21, H20, H41, H14, H19, H18

10. Tecajete 11 2 2.83 0.764 5 H24, H39, H40, H31, H41

11. Cerro de los Pitos 16 4 3.38 0.883 9 H21, H20, H30, H17, H54, H52, H32, H41, H22

12. Pachuca 10 2 2.61 0.711 4 H11, H44, H9, H43

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13. Pablo Ixtayoc 10 3 2.46 0.644 4 H8, H4, H35, H33

Mean 11 3.2 2.86 0.754 5.6

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Table 3. Analysis of molecular variance (AMOVA) for the seven chloroplast

microsatellites of Q. deserticola.

Source of variation d. f. Sum of squares

Variance components

Percentage of variation

Fixation index 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 30719.5 223.82 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 -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 circles 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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