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Phylogeography of Peromyscus schmidlyi: an endemic of the Sierra MadreOccidental, MexicoAuthor(s): Celia López-González , Miguel M. Correa-Ramírez , and Diego F. García-MendozaSource: Journal of Mammalogy, 95(2):254-268. 2014.Published By: American Society of MammalogistsDOI: http://dx.doi.org/10.1644/13-MAMM-A-166URL: http://www.bioone.org/doi/full/10.1644/13-MAMM-A-166
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Journal of Mammalogy, 95(2):254–268, 2014
Phylogeography of Peromyscus schmidlyi: an endemic of the SierraMadre Occidental, Mexico
CELIA LOPEZ-GONZALEZ,* MIGUEL M. CORREA-RAMIREZ, AND DIEGO F. GARCIA-MENDOZA
Centro de Interdisciplinario de Investigacion para el Desarrollo Integral Regional (CIIDIR) Unidad Durango, InstitutoPolitecnico Nacional, Sigma 119 Fraccionamiento 20 de Noviembre II Durango, Durango 34220, Mexico
* Correspondent: [email protected]
Peromyscus schmidlyi is an endemic rodent from the forested highlands of the Sierra Madre Occidental (SMO)
in Mexico. Using 2 genetic markers (cytochrome-b and D-loop) we explored the possible relationship between a
recently proposed division of pine–oak forests of the SMO into specific regional communities and patterns of
genetic and morphometric variation in P. schmidlyi. We found no genetic structure or significant relationships
between either marker and ecological or morphometric variation. Phylogenetic and haplotypic network analyses
revealed no geographically structured clusters; phylogenetic trees were shallow and networks were star-shaped.
No signal of selection was detected for either marker at the local level. All available evidence suggests that the
current distribution of P. schmidlyi is the result of dispersal into the SMO followed by rapid population
expansion throughout the area in the late Pleistocene, following the glacial cooling of the SMO highlands.
Key words: gene flow, historic demography, mitochondrial DNA, phylogeny, pine–oak forests, Schmidly’s deer mouse
� 2014 American Society of Mammalogists
DOI: 10.1644/13-MAMM-A-166
Schmidly’s deermouse (Peromyscus schmidlyi) is a common
rodent distributed throughout the highlands of the Sierra Madre
Occidental (SMO) in Mexico. Its description, based on
molecular and karyotypic data (Bradley et al. 2004), and
further morphological characterization (Lopez-Gonzalez et al.
2013), resolved a number of issues regarding the identity of
several specimens previously collected from the western slope
of the SMO, and explained some of the observed patterns of
variation in ‘‘Peromyscus boylii’’ from the SMO (e.g., Drake
1958; Baker and Greer 1962; Lee et al. 1972; Schmidly and
Schroeter 1974; Alvarez and Polaco 1984; Bradley et al. 2004).
The currently known distribution of this species spans from
north-central Chihuahua and eastern Sonora to Zacatecas and
northern Jalisco (Ordonez-Garza and Bradley 2011; Lopez-
Gonzalez et al. 2013; Fig. 1).
The SMO spans more than 1,200 km from northwest to
southeast, covering approximately 108 of latitude (roughly
from 218N to 30.58N), and more than 200 km in width. The
highlands are covered with an assortment of plant associations
where the physiognomically dominant elements are conifers,
mainly pines (Pinus), and oaks (Quercus). In general, Mexican
pine–oak communities are extremely diverse and exhibit a very
high degree of endemism. They are home to approximately
44% and 30% of the world’s pine and oak species, respectively
(Little 1971, 1976, 1977). In particular, the SMO harbors at
least 24 species of pines (Pinus, 46% of the Mexican species),
54 oaks (Quercus, 34%), and 7 madrones (Arbutus, 100%—
Gonzalez-Elizondo et al. 2012). These forests also contain the
highest floristic diversity in Mexico (Rzedowski 1978; Bye
1995), in part because of the large size of the mountain range,
and in part because it is located at the confluence of tropical
and semiarid biomes (Gonzalez-Elizondo et al. 2012).
Recently, based on vegetation data, Gonzalez-Elizondo et al.
(2013) classified the SMO into 7 major ecological regions (Fig.
1). P. schmidlyi occurs in the highlands, in the 3 regions where
pine and oak associations dominate. The 1st and northernmost
region is the Madrean-North region, including eastern Sonora
and western Chihuahua south to the Urique canyon (between
278N and 288N). With a mean elevation of 2,350 m, this region
has colder and more continental climates than the rest of the
SMO. The pine Pinus yecorensis and the berry Vacciniumchihuahuense are characteristic of the zone. This region also
shares a number of species with the Madrean Archipelago to
the north (e.g., Juniperus scopulorum and Quercus gambelii).The 2nd region is the Madrean-Central, which runs from
southwestern Chihuahua to southern Durango and eastern
Sinaloa, to the Mezquital-San Pedro River. In this region,
climates are more mesic and mean elevation is 2,650 m,
w w w . m a m m a l o g y . o r g
254
although it includes peaks above 3,200 m. Many plant species
and a genus (Megacorax) are endemic to this area. The 3rd
region is the Madrean-South, comprising southern Durango
southeast of the Mezquital Basin, western Zacatecas, and
northern Jalisco (Fig. 1). This is a rugged zone with deep and
wide canyons through which the tropical and xerophylous
zones converge. It also harbors a high diversity of endemic
plants (e.g., Pinus maximartinezii), as well as of madrones
(Arbutus) and agaves (Agave). This division of the highlands is
at least partially consistent with the findings of phylogeo-
graphic analyses on some vertebrates (Bryson and Riddle
2011; Wood et al. 2011; Bryson et al. 2012a, 2012b) and plants
(Jaramillo-Correa et al. 2006; Rodrıguez-Banderas et al. 2009;
Gugger et al. 2011). Similarly, a morphometric analysis of
geographic variation across the distributional range of P.
schmidlyi (Lopez-Gonzalez et al. 2013) showed significant
morphological variation between Madrean-North individuals
and the remaining populations, which suggests a certain degree
of population differentiation between ecological zones.
Historically, the origin of the distinct regional pine–oak
communities in North America is primarily attributed to the
fragmentation of the once continentally distributed Tertiary
forests, resulting from late Miocene and Pliocene (14–2.5
million years ago) orogeny and expansion of the Basin-and-
Range region in western North America (Graham 1999). In
contrast, it has been hypothesized that Quaternary climatic
FIG. 1.—Ecoregions of the Sierra Madre Occidental according to Gonzalez-Elizondo et al. (2013) and sampling localities of Peromyscusschmidlyi analyzed for this study. The currently known distribution of this mouse includes the Madrean-North, Madrean-Central, and Madrean-
South regions (Lopez-Gonzalez et al. 2013). Locality codes as in Table 1.
April 2014 255LOPEZ-GONZALEZ ET AL.—PHYLOGEOGRAPHY OF P. SCHMIDLYI
cycles had a different effect on the Madrean pine–oak areas
(McDonald 1993; Spellman and Klicka 2007), where the
cooling associated with glacial advances did not result in the
contraction of pine–oak areas, but instead they remained
relatively stable and in some cases may have expanded in
response to the cooler temperatures (McDonald 1993). A
consequence of these changes would have been the division of
pine–oak forests into specific regional communities with (at
least partially) separate histories (Spellman and Klicka 2007).
Despite the size and complexity of the Madrean mountain
range, relatively little is known about the biogeography of the
region. Most studies to date have focused on the ‘‘sky islands’’
or Madrean Archipelago of the northwestern end (see DeBano
and Ffolliott [2005] for a summary). Other than that, the SMO
usually is analyzed as a barrier between Mexican Plateau and
Pacific Plateau populations (e.g., Riddle et al. 2000; Jaeger et
al. 2005), or as a part of larger-scale studies (e.g., Morrone
2005; McCormack et al. 2008; Bryson et al. 2011; Gugger et
al. 2011), but few analyses deal with the SMO as a biographic
unit in itself. This might be the result of a paucity of data that
results in lack of understanding of its complexity and history.
Particularly lacking is basic knowledge of the spatial and
temporal scale of population differentiation, which might
provide insights into the roles of mountain-building, climatic
shifts, or geographic isolation in divergence and speciation
(McCormack et al. 2008). A phylogeographic approach also
might help discriminate between competing models of
diversification, such as those that emphasize geological
processes such as mountain uplift (Morafka 1977; Jaeger et
al. 2005) versus those that focus on habitat fluctuations caused
by Pleistocene climate change (Mengel 1970; Hewitt 1996;
Lessa et al. 2003; Johnson and Cicero 2004).
In this study we examined 2 mitochondrial DNA fragments
(cytochrome-b [Cytb] and D-loop) and morphometric variation
(from Lopez-Gonzalez et al. 2013) in P. schmidlyi to provide
insights into the evolutionary history of the species on the
highlands of the SMO. We explore the possible correspon-
dence between genetic and morphological variation and the
proposed ecological division of the SMO highlands. We would
expect that if P. schmidlyi evolved in these forests while
floristic differentiation occurred, we would observe divergent
lineages with clear phenotypic and genetic breaks coincident
with ecological breaks, rather than continuous or clinal
variation.
MATERIALS AND METHODS
Sample collection and DNA extraction.—Samples of P.schmidlyi from most of its distribution range were included
(Fig. 1). For Cytb, 26 individuals of P. schmidlyi from 10
populations (Table 1), and 1 P. gratus and 1 P. eremicus used
as outgroups in the phylogenetic analysis, were collected
(Table 1). Samples consisted of tissue preserved in 95%
ethanol, frozen tissue, and skin clips from individuals prepared
as museum specimens. Eighteen more sequences of the Cytbgene of P. schmidlyi published elsewhere, including the type
material (Bradley et al. 2004; Cabrera et al. 2007) were used as
well, for a total of 44. For D-loop we sequenced the 23
individuals reported above plus 12 specimens previously
reported in the literature, for a total of 35 plus the 2
outgroup specimens. Vouchers are deposited at the Mammal
Collection, CIIDIR Unidad Durango, Instituto Politecnco
Nacional, Durango, Mexico (CRD); the Natural Sciences
Research Laboratory at The Museum of Texas Tech
University, Lubbock, Texas (TTU); or the Mammal
Collection, Centro de Investigaciones Biologicas del
Noroeste, La Paz, Baja California, Mexico (CIB). Specimens
collected by us and deposited at CRD were captured and
processed following guidelines of the American Society of
Mammalogists (Sikes et al. 2011).
Laboratory procedures.—The DNA was extracted from
approximately 10 mg of skeletal muscle or skin clip using a
Promega kit (Promega, Madison, Wisconsin) and the
methodology described in Correa-Ramırez et al. (2010). We
amplified a 1,051–base-pair (bp) fragment of the Cytb gene
using forward primers L-14115 (50-GAT ATG AAA AAC CAT
CGT TG-30—Palumbi 1996), H-15288 (50-ACA AGA CCA
GAG TAA TGT TTA TAC TAT C-30), MVZ16 (50-AAA TAG
GAA RTA TCA YTC TGG TTT RAT-30—Smith and Patton
1999; Martin et al. 2000), or CBPrmF (50-CCC ATC CAA CAT
CTC ATC-30); and CBPrmR (50-GTA GCT GAT GGA GGC
TAG TT-30) as reverse primer. The primer pair for the D-loop
fragment (PrmsRCF [50-TTA GGG CAT CAA GAA GGA AG-
30] forward, and PrmsR D-loop 1 [50-TTG CTT TTG GGG TTT
GTC AA-30] reverse) was newly generated for this study and
allowed for amplification and sequencing of most of the D-loop
gene (750 bp).
Polymerase chain reactions were performed in a Labnet
9600 thermal cycler (Labnet International, Inc., Edison, New
Jersey) using 30–50 ng of DNA, 0.40 lM of each primer, 2.5
mM of MgCl2, 0.2 mM of each deoxynucleoside triphosphate
(Promega, Madison Wisconsin), 1x of polymerase chain
reaction buffer, and 1 U of Taq polymerase (Promega) in a
volume of 50 ll of sterile water. The polymerase chain reaction
was run with an initial denaturation step at 958C for 2.5 min,
followed by 45 cycles of 30 s of initial denaturation at 958C, 60
s of annealing at 508C (Cytb)/528C (D-loop), 60 s of extension
at 728C, and a final step of extension at 728C for 5 min.
Double-stranded products were purified using the Wizard SV
Gel and PCR Clean-Up System (Promega). Both strands of the
purified polymerase chain reaction products were sequenced in
an Applied Biosystems 310 Genetic Analyzer sequencer using
Big Dye chain terminators (Applied Biosystems Inc., Foster
City, California). Sequence files were edited and aligned using
Sequencher 4.1 (Gene Codes Corporation 2010). The resulting
Cytb sequences were translated into proteins to confirm the
identity of the fragments (Wernersson and Pedersen 2003).
Multiple alignments were performed in individual gene
fragments using Clustal X (Thompson et al. 1997), with gap
opening costs ¼ 50, gap extension ¼ 6.6, delay divergent
sequences¼ 30%, and DNA transition weight¼ 0.5 (Harrison
and Langdale 2006).
256 Vol. 95, No. 2JOURNAL OF MAMMALOGY
Population analyses.—Unless otherwise specified,
population analyses were performed for each fragment and
sampling locality using Arlequin version 3.5.1.21 (Excoffier
and Lischer 2010) or DnaSP version 5.1 (Librado and Rozas
2009). Genetic diversity within populations was measured as
haplotype diversity (h), number of private haplotypes (P), and
nucleotide diversity (p). For each fragment, population
structure was explored via analysis of molecular variance
(AMOVA) using Arlequin version 3.5.1.21 (Excoffier and
Lischer 2010). We tested for genetic structure among
ecoregions and among populations within ecoregions. A
spatial analysis of molecular variance (SAMOVA),
TABLE 1.—Sampling sites and GenBank accession numbers for 46 specimens examined in this work. Specimens belong to Peromyscusschmidlyi unless otherwise stated. ID refers to locality codes in Fig. 1. Museum numbers of vouchers are provided as well. Museum acronyms are
defined in the ‘‘Materials and Methods.’’ Cytb ¼ cytochrome-b.
ID Locality (by state) Latitude Longitude Museum no. Cytb D-loop
Durango
O 12 km E Ojitos 25.09431 �106.14033 TTU81603 AY322519a KC403915
O 12 km E Ojitos 25.09431 –106.14033 TTU81605 AY322522a KC403913
O 30 km SW Ojitos 24.96856 –106.29367 TTU81703 AY322524a KC403903
O 30 km SW Ojitos 24.96856 –106.29367 CRD1806 AY322514a KC403907
O 30 km SW Ojitos 24.96856 –106.29367 TTU81638 AY322523a KC403912
O 30 km SW Ojitos 24.96856 –106.29367 TTU81635 AY322520a KC403918
O 30 km SW Ojitos 24.96856 –106.29367 TTU81634 AY322513a KC403919
O 30 km SW Ojitos 24.96856 –106.29367 TTU115640 AY322515a KC403921
T 10.63 km N, 20.3 km W Otinapa 24.15473 –105.20687 CRD7938 KC403897 KC403928
T 10.63 km N, 20.3 km W Otinapa 24.15473 –105.20687 CRD7940 KC403896 KC403931
T 10.63 km N, 20.3 km W Otinapa 24.15473 –105.20687 CRD7939 KC403888 KC403922
T 10.63 km N, 20.3 km W Otinapa 24.15473 –105.20687 CRD7943 KC403894 KC403925
S 20 km SW San Miguel de Cruces 24.28583 –105.97967 CRD6136 KC403882 KC403904
S 20 km SW San Miguel de Cruces 24.28583 –105.97967 CRD6063 KC403887 KC403908
S 20 km SW San Miguel de Cruces 24.28583 –105.97967 CRD6062 KC403895 KC403920
S 20 km SW San Miguel de Cruces 24.28583 –105.97967 CRD6142 KC403891 KC403910
S 7.5 km S, 8 km W San Miguel de Cruces 24.35433 –105.92333 CRD6061 KC403889 KC403929
L Las Ramadas 23.03133 –104.67817 CRD8541 KC403883 KC403906
L Las Ramadas 23.03133 –104.67817 CRD8540 KC403884 KC403905
L Las Ramadas 23.03133 –104.67817 CRD8547 KC403886 KC403923
L Las Ramadas 23.03133 –104.67817 CRD8551 KC403890 KC403909
H 3.8 mi (6.1 km) W Coyotes, Hacienda Coyotes 23.82085 –105.33472 TTU81610 AY322517a KC403914
H 3.8 mi (6.1 km) W Coyotes, Hacienda Coyotes 23.82085 –105.33472 TTU81643 AY322521a KC403916
H 3.8 mi (6.1 km) W Coyotes, Hacienda Coyotes 23.82085 –105.33472 TTU81611 AY322518a KC403911
H 3.8 mi (6.1 km) W Coyotes, Hacienda Coyotes 23.82085 –105.33472 TTU81607 AY322516a KC403917
H 3.8 mi (6.1 km) W Coyotes, Hacienda Coyotes 23.82085 –105.33472 TTU81617b AY370610a —
Sonora
Y 0.8 km N, 1.4 km E Yecora 28.37722 –108.90889 CIB10888 EU234539c —
Y 0.8 km N, 1.4 km E Yecora 28.37722 –108.90889 CIB10889 EU234540c —
Y 0.8 km N, 1.4 km E Yecora 28.37722 –108.90889 CIB10887 EU234538c —
Y 0.8 km N, 1.4 km E Yecora 28.37722 –108.90889 CIB10886 EU234537c —
Y 0.8 km N, 1.4 km E Yecora 28.37722 –108.90889 CIB10885 EU234536c —
Chihuahua
N 2 km S Norogachi 27.26033 –107.13300 CRD4025 KC403885 KC403930
N 2 km S Norogachi 27.26033 –107.13300 CRD4036 KC403899 KC403933
N 2 km S Norogachi 27.26033 –107.13300 CRD4050 KF322235 —
N 2 km S Norogachi 27.26033 –107.13300 CRD3614 KF322233 KF322240
B 1.5 km S, 5.6 km W Basıhuare 27.45383 –107.54383 CRD4937 KC403892 KC403926
B 1.5 km S, 5.6 km W Basıhuare 27.45383 –107.54383 CRD4938 KC403893 KC403927
C 2.1 km NW Choguita 27.46900 –107.29617 CRD3812 KC403900 KC403932
C 2.75 km N, 3.6 km W Choguita 27.48017 –107.31900 CRD3807 KF322236 —
C 2.75 km N, 3.6 km W Choguita 27.48017 –107.31900 CRD3811 KF322234 KF322237
C 5 km S, 2 km W Rejogochi 27.37450 –107.50017 CRD3463 KF322231 —
V 3.2 km S, 0.8 km E Hueleyvo 27.23633 –107.39917 CRD4001 KC403898 KC403924
V 3.2 km S, 0.8 km W Hueleyvo 27.23633 –107.39917 CRD4000 KF322230 KF322238
V 3.7 km S, 0.9 km W Hueleyvo 27.23283 –107.40083 CRD4005 KF322232 KF322239
— 2 km S Norogachi (P. gratus, outgroup) 27.26033 –107.133 CRD3595 KC403901 KC403934
Sinaloa
— 0.75m km E Camotete (P. eremicus, outgroup) 25.26760 –107.57345 CRD3512 KC403902 KC403935
a Sequences from Bradley et al. (2004).b Type specimen.c Sequences from Cabrera et al. (2007).
April 2014 257LOPEZ-GONZALEZ ET AL.—PHYLOGEOGRAPHY OF P. SCHMIDLYI
implemented in the program SAMOVA 1.0 (Dupanloup et al.
2002; de Thoisy et al. 2010), was used to find combinations of
sampling sites that are maximally differentiated from each
other without a priori assumption about population structure.
This analysis allowed us to explore genetic structures other
than the one proposed initially. The method is based on a
simulated annealing procedure that maximizes the proportion
of genetic variance that can be explained by differences
between groups of populations (de Thoisy et al. 2010). Genetic
variance is the among-group genetic variation (hCT) coefficient
as estimated by AMOVA (Excoffier et al. 1992). We repeated
the SAMOVA analyses with different numbers of groups, from
K¼ 2 to K¼ 9 samples for Cytb and from K¼ 2 to K¼ 8 (same
samples except the Yecora population) samples for D-loop
(Saeki et al. 2011).
For each data set, we calculated the sum of squares deviation
(SSD) and Harpending’s raggedness index (RI—Harpending
1994) to assess the fit of the observed data to a model of
sudden expansion. The mismatch distribution, based on the
number of observed nucleotide differences between pairs of
sequences, was compared to the distributions expected under
models of pure demographic expansion (Rogers and Harpend-
ing 1992) and sudden spatial expansion (Excoffier 2004; Rozas
2009). Model parameters (h0, h1, and s) were estimated using a
generalized nonlinear least-squares approach with confidence
intervals obtained through parametric bootstrapping with 1,000
replicates (Schneider and Excoffier 1999). The SSDs of
bootstrapped replicates were used to calculate the significance
of the fit between the observed and expected mismatch
distributions (Slatkin and Hudson 1991; Schneider and
Excoffier 1999).
To test for deviations from neutrality, Tajima’s D (Tajima
1983, 1989) and Fu’s FS (Fu 1997) statistics were calculated
for Cytb and D-loop sequences by population and for the
complete sample of P. schmidlyi. Significant deviations from
neutrality may be caused by selection or historic demographic
fluctuations such as population bottlenecks or population
expansion (Aris-Brosou and Excoffier 1996; Fu 1997).
Statistical significance was assessed by comparing the
observed statistic values to expected values based on 1,000
neutral coalescent simulations. We estimated gene flow by
calculating the number of migrants per generation (Nm) based
on FST-values, using the model of Hudson et al. (1992)
estimated from the formula proposed by Wright (1951). Nm is
an indirect measure of gene flow among populations under
Wright’s island model of population subdivision (Slatkin
1985). It assumes an infinite number of equal-sized populations
that exchange migrants at a constant rate (Wright 1951, 1965).
All population parameters were estimated using Arlequin
version 3.5.1.21 (Excoffier and Lischer 2010).
Correlation between genetic, geographic, and morphometricdistances.—For each marker, correlations between matrices of
genetic versus geographic and genetic versus morphometric
distances were assessed using Mantel’s test (Mantel 1967).
Using MEGA5 (Tamura et al. 2011) we calculated pairwise
genetic distances between populations using the Kimura 2-
parameter model. The model takes into account transitional and
transversional substitution rates, while assuming that the 4
nucleotide frequencies are the same and substitution rates do
not vary among sites. All ambiguous positions were removed
for each sequence pair (Tamura et al. 2011). Pairwise
geographic (Euclidian) distances (km) between sites were
calculated using Geographic Distance Matrix Generator
version 1.2.3 (Ersts 2012). Morphometric distances were
calculated using a subset (156 specimens) of the data from
the morphometric analysis by Lopez-Gonzalez et al. (2013)
that included only specimens corresponding to the localities
analyzed in this work (Table 1). The Yecora population was
not included because no specimens from that region were
examined by Lopez-Gonzalez et al. (2013). All cranial
morphometric variables used in that study were included. A
matrix of pair-wise Mahalanobis distances between localities
was built using PROC DISCRIM in SAS (SAS Institute Inc.
1995). Pooled within-group variance was used to estimate pair-
wise distances. Significance of correlations between matrices
was calculated by bootstrapping the samples 10,000 times in
Matlab for Windows, version 7.10.0.499 (R2010a—The
MathWorks Inc. 2010), using functions and scripts written
by the 1st author or Strauss (2013). An independent test for
significance of genetic versus geographic distance was run
using the MANTEL-STRUCT program from Alleles in Space
(AIS—Miller 2005). Unlike the tests described above, which
directly relate geographic and genetic distance, Miller’s test
compares within- versus among-population genetic distances
(Miller 1999), thus allowing comparisons with other studies.
Phylogenetic analysis.—For each fragment, a median-
joining network was constructed using NETWORK version
4.1.0.3 (Bandelt et al. 1999) to depict phylogeographic patterns
and potential ancestor–descendant relationships. To determine
the reliability of phylogenetic inferences, for each marker
sequences were tested for saturation (Hulsey et al. 2004).
To reconstruct phylogenetic relationships, we used maxi-
mum-likelihood and Bayesian inferences for the Cytb and D-
loop data sets separately. We used P. gratus and P. eremicus as
outgroups to polarize the characters within the populations of
P. schmidlyi. For maximum likelihood, a model of evolution
was selected using MODELTEST (Posada and Crandall 1998).
Maximum likelihood searches were conducted using
GTRGAMMA as model of nucleotide evolution and 10,000
bootstrap repetitions using RAxML version 7.0.4 (Stamatakis
2006; Stamatakis et al. 2008).
Bayesian analysis used the GTR model with invariant rate
heterogeneity. A posterior probability analysis (Rannala and
Yang 1996) was performed using the program MrBayes
version 3.0b4 (Ronquist and Huelsenbeck 2003). Bayesian
posterior probability calculations were implemented in a range
of ten million generations, sampling every 1,000 generations,
and discarding the first 1,000 trees sampled (as burn-in).
Support for nodes was determined by posterior probabilities
(Huelsenbeck et al. 2002; Ronquist and Huelsenbeck 2003).
For each method of reconstruction 3 independent runs were
conducted to get an impression of the robustness of the
258 Vol. 95, No. 2JOURNAL OF MAMMALOGY
phylogenetic reconstruction. To evaluate sufficient mixing,
stable convergence on a unimodal posterior probability curve,
and effective sample size (ESS) we used Tracer version 1.5
with EES . 200 (Rambaut and Drummond 2007). All null
hypotheses were rejected at a ¼ 0.05.
RESULTS
Genetic diversity.—Among the 44 individuals of P.schmidlyi examined for Cytb (Table 1), we detected 40
different haplotypes, 37 were private, 1 was shared among 3
localities geographically distant from each other (PsCB20 at
Hacienda Coyotes [H] and Las Ramadas [L], Durango, and
Basıhuare [B], Chihuahua), and 2 haplotypes were shared
between 2 localities (PsCB12 between Basıhuare [B] and
Otinapa [T], Durango, and PsCB14 between San Miguel de
Cruces [S], Durango, and Hueleyvo [V], Chihuahua), also
geographically distant from each other (Fig. 1).
Within-population haplotype diversity (Table 2) for the 10
sampling localities was 1.00 at each locality, that is, all
individual haplotypes within localities were different from each
other. Overall haplotype diversity was high (h ¼ 1), overall
nucleotide diversity was 0.0094. The AMOVA was significant
(Table 3) for differences among regions, but not for variation
among or within populations. Nucleotide diversity ranged from
0.0054 to 0.0119 (Table 2). The SAMOVA was significant for
combinations of populations in which Yecora (Y [Fig. 1]) was
considered as an independent group.
We detected 34 private D-loop haplotypes of P. schmidlyi(Table 2). Only 1 occurred in 2 localities geographically distant
(Hueleyvo [V] and Otinapa [T] [Fig. 2]). Nonetheless, for all
localities there is more than 1 haplotype per locality (Table 2).
Within-population haplotype diversity was 1.00 at each locality
(Table 2). Overall haplotype diversity also was high (h ¼ 1),
overall nucleotide diversity was 0.012. Unlike Cytb, the
AMOVA for D-loop (Table 3) was not significant, that is,
most variation was contained within populations. For this
analysis, significant results were obtained for all combinations
of populations, but no pattern was found.
Historic demography.—Mismatch analysis for both
fragments exhibited unimodal distributions that did not differ
significantly from the distribution expected under population
expansion (Fig. 2; average values Cytb, RI¼0.33, P¼ 0.19; D-
loop, RI¼ 0.34, P¼ 0.49). Parameters of the expansion model
were h0 ¼ 0.78, h1 ¼ 60,022.7, and s ¼ 6.28 for the Cytbfragment; and h0¼ 1.47, h1¼ 66,679.3, and s¼ 7.4 for D-loop.
Maximum sequence divergence between any 2 haplotypes was
2.7% (TTU81610 versus TTU81635) for Cytb and 2.8%
(TTU81635 versus TTU81634) for D-loop. Average number of
migrants per generation (Nm) for Cytb was 5.4, whereas for D-
loop, Nm was 10.31.
Values of Tajima’s D-test for neutrality were nonsignificant
and negative by locality (average for Cytb,�1.25, P¼ 0.1; D-
loop, �0.93, P ¼ 0.21) and significant for P. schmidlyi as a
whole for Cytb (�1.95, P¼ 0.007), but not for D-loop (�1.69,
P ¼ 0.11). Fu’s FS-tests of neutrality were negative and
nonsignificant for both fragments when estimated by locality
(Cytb, �2.37, P ¼ 0.1; D-loop, 0.61, P ¼ 0.37; Table 4), and
significant for P. schmidlyi as a whole for both Cytb (�24.95, P¼ 0.0) and D-loop (�24.57, P ¼ 0.0).
Genetic versus geographic and morphometric distances.—
Matrices of geographic and genetic distances were not
significantly correlated for Cytb (r ¼ 0.11, P ¼ 0.24), but
they were significantly correlated for D-loop (r ¼ 0.205, P ¼0.04). Correlation between genetic and morphometric distances
was not significant for any fragment (Cytb, r¼ 0.16, P¼ 0.24;
D-loop, r¼�0.02, P¼ 0.534). The Mantel test for correlation
among geographic and genetic distances using AIS was
nonsignificant for both markers (Cytb, r ¼ 0.11, P ¼ 0.91; D-
loop, r¼ 0.08, P ¼ 0.13).
TABLE 2.—Basic statistics for cytochrome-b (Cytb) and D-loop DNA fragments in 10 samples of Peromyscus schmidlyi. n, sample size; S,
number of segregating sites; Ts, number of transitions; Tv, number of transversions; ID, number of insertions–deletions; h, haplotype diversity; p,
nucleotide diversity; PA, private alleles. Mean and SD are provided; locality codes as in Table 1.
Statistics O T S L Y H N B C V X SD
Cytb
n 8 4 5 4 5 5 4 2 4 3 3.7 2.10
S 32 12 10 12 17 19 16 7 17 7 14.9 6.96
Ts 24 12 9 11 17 14 12 7 13 5 12.4 5.06
Tv 8 0 1 1 0 5 4 0 4 2 2.5 2.54
ID 0 0 0 0 0 0 0 0 0 0 0.0 0.00
h 1 1 1 1 1 1 1 1 1 1 1 0
p 0.0118 0.0080 0.0054 0.0108 0.0093 0.0106 0.0108 0.0091 0.0119 0.0060 0.0094 0.0023
PA 8 4 3 4 5 4 3 2 4 3 3.80 1.98
D-loop
n 8 4 5 4 — 4 3 2 2 3 3.89 1.73
S 35 14 15 26 — 15 25 12 12 11 18.33 7.86
Ts 31 13 13 22 — 13 19 11 9 10 15.67 6.72
Tv 4 1 2 4 — 2 6 1 3 1 2.67 1.64
ID 2 0 0 1 — 1 0 0 0 1 0.56 0.68
h 1 1 1 1 — 1 1 1 1 1 1.00 0.00
p 0.0143 0.0083 0.0083 0.0167 — 0.0098 0.0098 0.0161 0.0147 0.0098 0.012 0.0034
PA 8 3 5 4 — 4 3 2 2 2 3.67 1.73
April 2014 259LOPEZ-GONZALEZ ET AL.—PHYLOGEOGRAPHY OF P. SCHMIDLYI
Phylogeny.—The median-joining networks of Cytb and D-
loop haplotypes (Fig. 3) did not reveal divergent clusters of
haplotypes by locality or ecoregion. Rather, the Cytb network
is star-shaped (Avise 2000), whereas the network for D-loop
shows no structure. The saturation test showed that Cytb (index
of substitution saturation [ISS] ¼ 0.097, critical value [ISS.C] ¼0.82, P ¼ 0.0) and D-loop (ISS ¼ 0.06, ISS.C ¼ 0.81, P ¼ 0.0)
fragments presented little substitution saturation (Xia et al.
2003; Xia and Lemey 2009), indicating that the phylogenetic
information given by these sequences is adequate for
phylogenetic inference. For the Cytb fragment there was no
geographic clustering in the phylogenetic analyses (Fig. 4; only
results of Bayesian analysis are shown). The tree generated by
the Bayesian analyses is mostly unresolved, and the clusters
that are formed may or may not contain sequences from
different localities. The maximum-likelihood tree is resolved
but weakly supported, and clusters do not reflect geographic
structure. The only geographic cluster is that of the Yecora
specimens, although 1 of the specimens from this locality
clusters elsewhere. Results for the D-loop data set are similar,
there is little structure, and where there is some, clades are
weakly supported and include localities that are geographically
apart (Fig. 4).
DISCUSSION
Both mitochondrial DNA fragments presented a high
amount of genetic diversity. For P. schmidlyi, haplotype
diversity and nucleotide diversity estimates (Table 2) are
relatively high compared to most terrestrial vertebrates, but are
similar to those of the desert lizard Uta stanburiana (h¼0.979,
p¼0.007 for Cytb—Micheletti et al. 2012), and mammals such
as the squirrels Sciurus niger (h¼ 0.985, p¼ 0.023—Moncrief
et al. 2012) and S. carolinensis (h ¼ 0.853, p ¼ 0.017—
Moncrief et al. 2012). The high genetic diversity found is
unlikely to be due to sequencing error or artifact because
sequences were run in both directions, and we resequenced 10
samples to verify consistency of the results. We did not detect
heterozygote base calling (double peaks in any sequence
direction) in either mitochondrial DNA fragments. Further-
more, the Cytb data set was checked by amino acid translation
and we found no stop codons. D-loop sequences were verified
by blasting our sequences against D-loop sequences deposited
at GenBank.
Notwithstanding these high levels of variation, we were
unable to detect any structure in the data. We expected that the
pattern of genetic variation would reflect that of morphological,
geographic, or ecological variation. We found no relationship
between geographic or morphological variation and genetic
variation except for D-loop, which had a significant relation-
ship with geography in the direct test, but showed no
significance using the AIS test.
Within-population genetic variation was much higher than
variation among ecoregions (Table 3). Although for both
markers analyzed there is high haplotype diversity within
populations, most haplotypes occur in a single locality, and
TABLE 3.—Results of AMOVA and SAMOVA on 9 (cytochrome-b [Cytb] and 6 (D-loop) populations of Peromyscus schmidlyi. UST, fixation
index among groups; UCT, fixation index within groups; K, number of comparisons. Results of each test are based on 20,000 permutations.
Population groupings in AMOVA reflect the ecoregion subdivision of the Sierra Madre Occidental by Gonzalez-Elizondo et al. (2013). Population
codes are as in Table 1. Significant results at a ¼ 0.05 are indicated in boldface type.
d.f.
Sum of
squares
Variance
components % variation UST/UCT P Partition
AMOVA Cytb
Among ecoregions 2 13.29 0.22 5.62 0.056 0.044 (YþCþVþNþB)þ (OþTþSþH)þ(L)
Among populations within ecoregions 7 26.84 0.03 0.81 0.064 0.083
Within populations 34 125.65 3.70 93.56 0.009 0.067
Total 43 165.78 3.95
SAMOVA Cytb
K ¼ 2 1 8.07 0.57 13.27 0.13 0.0958 (C)þ(OþTþSþLþNþBþHþVþY)
K ¼ 3 2 19.15 0.59 14.39 0.14 0.0020 (Y)þ(NþCþV)þ(OþTþSþHþB)
K ¼ 4 3 24.27 0.54 13.83 0.14 0.0000 (Y)þ(OþH)þ(NþCþV)þ(TþSþLþB)
K ¼ 5 4 27.17 0.57 14.64 0.15 0.001 (Y)þ(NþCþV)þ(O)þ(TþSþLþB)þ(H)
K ¼ 6 5 30.70 0.62 15.96 0.16 0.000 (Y)þ(SþV)þ(O)þ(NþC)þ(H)þ(TþLþB)
K ¼ 7 6 33.93 0.71 18.34 0.18 0.000 (Y)þ(L)þ(O)þ(SþV)þ(H)þ(TþB)þ(NþC)
K ¼ 8 7 36.48 0.87 22.63 0.23 0.001 (NþV)þ(O)þ(TþB)þ(H)þ(S)þ(Y)þ(L)þC
K ¼ 9 8 37.39 0.59 15.31 0.15 0.062 (NþV)þ(O)þ(T)þ(B)þ(H)þ(S)þ(Y)þ(L)þC
AMOVA D-loop
Among ecoregions 2 12.96 0.21 4.20 0.04 0.06 (CþVþNþB)þ(OþTþSþH)þ(L)
Among populations within ecoregions 6 26.65 �0.16 �3.19 0.01 0.81
Within populations 26 131.49 5.058 98.99 �0.03 0.51
Total 34 171.10 5.11
K ¼ 2 1 10.30 0.68 12.09 0.12 0.03 (NþB)þ(OþTþSþLþHþVþC)
K ¼ 3 2 17.03 0.63 11.49 0.11 0.002 (NþB)þ(L)þ(OþTþSþHþVþC)
K ¼ 4 3 21.01 0.58 10.76 0.11 0.012 (N)þ(L)þ(B)þ(OþTþSþHþVþC)
K ¼ 5 4 26.04 0.54 10.09 0.10 0.005 (N)þ(L)þ(B)þ(C)þ(OþTþSþHþV)
K ¼ 6 5 29.73 0.53 10.24 0.10 0.00 (HþVþT)þ(NþB)þ(O)þ(S)þ(L)þC
260 Vol. 95, No. 2JOURNAL OF MAMMALOGY
those that occur in more than 1 locality are shared by
populations separated by large geographic distances along the
3 ecoregions. For instance, Cytb haplotypes are shared among
Basıhuare (Madrean-North), Hacienda Coyotes (Madrean-
Central), and Las Ramadas (Madrean-South [Fig. 1]); even
though Basıhuare and Las Ramadas are separated by more than
500 km. Mismatch results for both markers also are consistent
with the prediction of Slatkin (1993) that genetic relationships
among localities did not resemble an equilibrium state caused
by isolation by distance, that is, geographically close localities
can be as different as those that are geographically distant.
These results are confirmed by the median-joining networks
and phylogenetic trees. The median-joining networks for both
markers (Fig. 3) showed no divergent clusters of haplotypes by
locality or ecoregion, which suggests genetic continuity among
localities. Similarly, there was no geographic clustering in the
phylogenetic analyses (Fig. 4). Trees were mostly unresolved,
and clades may or may not contain sequences from
geographically distant localities. In the tree built from Cytbdata, the clade formed by sequences from Basıhuare and
Otinapa had 85% statistical support (Fig. 4a) even though
Basıhuare is more than 400 km southeast of Otinapa (Fig. 1).
The exception is the Yecora population, where 4 of the 5
specimens included in the analysis clustered together in the
Bayesian phylogeny; they constitute a statistically significant
cluster in the SAMOVA under various combinations of
populations, and account for the significance found among
regions in the Cytb AMOVA. This population is nearly 180 km
northwest from the closest locality sampled (Basıhuare).
Nonetheless, the Yecora specimens are not genetically different
from those of Basıhuare, nor from any of the Chihuahuan
populations; thus, the Yecora clade has no support (Fig. 4) and
is not consistent in the maximum-likelihood phylogeny.
Furthermore, this cluster is not evident in the haplotypic
network, which shows that the most dissimilar specimens are
from southern Chihuahua (Fig. 3a). Also, the SAMOVA
results indicate that these specimens are significantly different
from everything else, with no geographic structure. Therefore,
based on this sample, it is not possible to consider the Yecora
specimens as a phylogeographic unit.
Results for the D-loop data set are similar, there is little
structure, and where there is some, clades include localities that
are geographically apart; for instance, the clade including
Basıhuare and Otinapa had 96% support (Fig. 4b). In the
maximum-likelihood analysis most branches are weakly
supported, and the few localities with well-supported clades
include samples that are geographically separated (Fig. 4b). In
general, the genetic patterns obtained did not reflect the
ecological regionalization of Gonzalez-Elizondo et al. (2013).
Nevertheless, these data provide insights into the population
history of P. schmidlyi. In phylogenetic trees (Fig. 4) poorly
supported nodes were left in place for the purpose of
comparing the concordance between the trees and the
networks, but if they were to collapse, there would be a single
clade nested within a polytomy. The observed trees and
networks fit in category IV of the phylogeographic patterns
proposed by Avise (2000): shallow phylogenetic trees and a
star-shaped network for Cytb, and no discernible structure for
D-loop. These patterns are consistent with an explosive
increase in effective population size from a few individuals,
followed by an expansion of the range of the species in recent
evolutionary times. They also suggest that the population is
nearly panmictic (Avise 2000).
Tajima’s D and Fu’s F were all negative, but were not
significant for any marker when samples were considered. At
the species level, D-values were significant for Cytb, but not
for D-loop. Considering the associated probability of this test
(P ¼ 0.11), lack of significance might be a result of the small
population used to effect calculations. In general, Tajima’s Dand Fu’s F for the species, as well as mismatch distribution
analysis, high migration rate, lack of evidence of geographic
structuring, and presence of unique haplotypes in all localities
sampled support the idea that P. schmidlyi had a range
expansion in past times. Although both markers indicate recent
demographic expansion, the exact mode of growth (stepwise,
exponential, or logistic growth) cannot be distinguished
(Slatkin and Hudson 1991; Rogers and Harpending 1992;
Rogers 1995; Polanski et al. 1998; Schneider and Excoffier
1999). Nonetheless, there is a large difference between the
initial (Cytb, h0¼ 1.71; D-loop, h0¼ 1.47) and final (Cytb, h1¼
FIG. 2.—Mismatch frequency distribution for a) cytochrome-b(Cytb) and b) D-loop mitochondrial DNA haplotypes of Peromyscusschmidlyi. The black line indicates expected distribution based on a
model of exponential population growth; the gray line indicates
observed distribution.
April 2014 261LOPEZ-GONZALEZ ET AL.—PHYLOGEOGRAPHY OF P. SCHMIDLYI
60,022.7; D-loop, h1 ¼ 66,679.3; Table 3) values of the
estimates of effective population size. These values suggest an
explosive increase of population size across the species
distribution.
Nonsignificance of AMOVA and lack of correlations
between genetic and geographic distances in both data sets
(Cytb and D-loop), as well as good fit with an unimodal
distribution without elevated upper-tail probabilities (Fig. 2),
indicate that populations have high connectivity and that there
is gene flow across the species range, which is supported by the
Nm of both markers and high haplotype diversity. A
mechanism that may explain the observed pattern is long-
distance dispersal (Hewitt 1996). Under this model, a few
members of the pre-expansion population are successful long-
distance dispersers that establish new populations during range
expansion, producing the mismatch distribution for Cytb and
D-loop fragments observed in P. schmidlyi.High levels of gene flow might be thought to be inconsistent
with an animal such as P. schmidlyi. Drake (1958) estimated an
average home range of 688 square yards (575.25 m2) for males
and 533 square yards (445.6 m2) for females of this species.
Recent research, however, suggests that individuals of the
closely related species P. boylii increase their range propor-
tionally with population density (Abramson et al. 2013), and
that they can disperse more than 2,000 m at population density
peaks. If this is also true for P. schmidlyi, this behavior would
be expected in a period of expansion into new territories.
Furthermore, examination of the few data available indicates
that P. schmidlyi can have at least 3 reproductive periods a year
(Alvarez and Polaco 1984); thus, fecundity is high for this
rodent. Also, although little is known about its ecological
requirements, an analysis of stomach contents in a population
form southeastern Durango suggests that even though it is
primarily granivorous, P. schmidlyi is able to feed on insects or
fungi when availability of grasses is limited (Alvarez and
Polaco 1984). The ability to use a wide variety of available
resources to survive further supports the possibility of high
potential for dispersal even if individuals had restricted
movement during their lifetime.
We found no evidence of phenotypic and genetic differen-
tiation coincident with ecological breaks, but instead, a pattern
of sudden, relatively recent expansion and dispersal across the
SMO. We propose that the biogeographic history of P.schmidlyi is one of dispersal in the SMO followed by rapid
population expansion throughout the area in relatively recent
times, following the glacial cooling of the SMO highlands. The
most recent continental glaciation, the Wisconsinan (approx-
imately 0.11–0.85 thousand years ago), extended at its
maximum as far south as the Pacific northwestern coast of
the present-day United States. Southward into Mexico, and
eastward into the Great Plains, lowlands were colder than
today, but were not perpetually covered by ice. In Mexico, an
average decrease in temperature of 68C has been reported for
the Last Glacial Maximum, together with increased precipita-
tion in some areas (Bradbury 1997; Metcalfe 2006), and a
snow-line depression of 1,300 m (Lachniet and Vazquez-Selem
2005; Lozano-Garcıa and Vazquez-Selem 2005; Mark et al.
2005). There is strong evidence that this decrease in
TABLE 4.—Tajima’s D, Fu’s FS, mismatch analysis parameters (s, h0, h1), sum of squares deviations (SSDs), Harpending’s raggedness index
(RI) tests, and associated P-values for 10 populations of Peromyscus schmidlyi. Mean and SD are provided. Population codes are as in Table 1.
O T S L Y H N B C V X SD
Cytb
n 8 4 5 4 5 5 4 2 4 3 4.4 1.5
Tajima’s D �1.25 �0.58 �0.89 �0.33 �0.86 �0.74 �0.46 — �0.12 — �0.52 �0.39
P 0.14 0.49 0.28 0.49 0.28 0.31 0.41 — 0.64 — 0.14 0.49
Fu’s FS �2.37 �0.25 �1.63 �0.22 �0.79 �0.61 0.12 1.94 0.23 0.31 �2.37 1.11
P 0.10 0.23 0.06 0.20 0.12 0.17 0.26 0.54 0.39 0.32 0.10 0.23
s 4.33 5.21 4.75 6.33 8.43 10.98 9.82 — 7.95 5.06 6.28 3.00
h0 5.55 2.06 0.00 0.00 0.00 0.007 0.023 — 0.025 0.002 0.78 1.71
h1 1 3 105 1 3 105 1 3 105 1 3 105 45.47 26.46 160.85 — 1 3 105 1 3 105 60,022.7 48,960.8
SSD 0.046 0.11 0.11 0.22 0.05 0.05 0.12 — 0.14 0.33 0.12 0.09
P 0.23 0.34 0.11 0.18 0.73 0.79 0.33 — 0.44 0.10 0.23 0.34
RI 0.13 0.22 0.24 0.61 0.14 0.14 0.44 — 0.28 1.11 0.33 0.31
P 0.19 0.71 0.50 0.15 0.65 0.66 0.31 — 0.62 0.25 0.19 0.71
D-loop
n 8 4 5 4 — 4 3 2 2 3 3.89 1.73
Tajima’s D �0.93 �0.62 �0.41 �0.23 — �0.43 — — — — �0.29 0.33
P 0.21 0.43 0.45 0.59 — 0.43 — — — — 0.64 0.28
FS �1.89 �0.06 �0.87 0.71 — 0.12 1.65 2.48 2.48 0.90 0.61 1.39
P 0.11 0.29 0.23 0.32 — 0.35 0.45 0.59 0.50 0.44 0.37 0.16
s 5.67 7.45 9.34 8.48 — 9.18 18.06 — — 8.40 7.40 5.11
h0 7.11 0.01 0.002 6.06 — 0.00 0.0018 — — 0.0035 1.47 2.75
h1 1 3 105 1 3 105 120.16 1 3 105 — 1 3 105 1 3 105 — — 1 3 105 66,679.3 47,121.1
SSD 0.025 0.090 0.088 0.16 — 0.033 0.44 — — 0.39 0.13 0.16
P 0.60 0.60 0.17 0.31 — 0.88 0.03 — — 0.07 0.29 0.30
RI 0.046 0.22 0.18 0.28 — 0.11 1.11 — — 1.11 0.34 0.42
P 0.72 0.75 0.57 0.64 — 0.95 0.35 — — 0.31 0.49 0.32
262 Vol. 95, No. 2JOURNAL OF MAMMALOGY
temperature allowed temperate species of plants to migrate and
diversify southward into Mexico. Particularly in the highlands
of the Sierra Madre Occidental, the Douglas fir (Pseudotsuga
menziesii—Gugger et al. 2011), some species of pines
(Pinus—Moreno-Letelier and Pinero [2009] and references
therein), and their associated beetles (Dendroctonus—Andu-
cho-Reyes et al. 2008) show evidence of population expansion
in the late Pleistocene.
The degree of genetic differentiation between P. schmidlyi
and its closest living relative (Peromyscus levipes) is only
3.25% (Bradley et al. 2007), which supports the idea of a
recent speciation event. Examination of our data suggests that
it is possible that as temperate species expanded southward
with the cooling, P. schmidlyi expanded northward and upward
into the SMO from a small founding population with a
previously restricted distribution. Similar arguments have been
FIG. 3.—Median-joining haplotypic network constructed for a) cytochrome-b (Cytb) and b) D-loop mitochondrial DNA haplotypes of
Peromyscus schmidlyi. Circles represent haplotypes; area of circle is proportional to haplotypic frequency. Letters represent sampling localities as
in Table 1. Shading indicates the ecoregion of the locality according to Gonzalez-Elizondo et al. (2013): no shading¼Madrean-North (MN); light
gray ¼Madrean-Central (MC); dark gray ¼Madrean-South (MS).
April 2014 263LOPEZ-GONZALEZ ET AL.—PHYLOGEOGRAPHY OF P. SCHMIDLYI
proposed to explain the current distribution of species of the
Peromyscus aztecus group (Sullivan et al. 1997). Although
beyond the scope of this paper, it is reasonable to speculate that
an analogous process occurred in the Sierra Madre Occidental
with the populations that eventually would become P. levipes.
The Holocene postglacial heating of North America probably
restricted the distribution of P. schmidlyi to its current refuge in
the highlands of the SMO, as has been suggested for other
species of plants (Jaramillo-Correa et al. 2006; Gugger et al.
2011; Mastretta-Yanes et al. 2012) and animals (Anducho-
Reyes et al. 2008; McCormack et al. 2008; Bryson et al. 2011;
Wood et al. 2011). This hypothesis is further supported by
evidence indicating that at least part of the Mexican Plateau
and Chihuahuan Desert were covered by woodlands at this
time (Toledo 1982; Van Devender et al. 1987; Edwards and
Bradley 2002).
In summary, we propose that P. schmidlyi could have
expanded throughout the SMO in the late Pleistocene without
any significant or prolonged bottleneck-derived loss of genetic
diversity. The abundance of suitable pine and pine–oak forest
habitat in the SMO might have exerted little novel selective
pressures during the range expansion of P. schmidlyi, which
would explain the absence of any signal of selection. Although
P. schmidlyi seems to have a high potential for dispersal and
FIG. 4.—Bayesian consensus trees for a) a portion of the cytochrome-b (Cytb) gene and b) D-loop gene in Peromyscus schmidlyi. Bayesian
posterior probabilities are given for each clade with . 50% support. Trees were outgroup-rooted on P. gratus and P. eremicus. Distribution of
localities across ecoregions is as follows: Madrean-North (MN), Madrean-Central (MC), Madrean-South (MS).
264 Vol. 95, No. 2JOURNAL OF MAMMALOGY
use of a variety of resources, it is still restricted to the highlands
of the SMO. This suggests dependence, or at least preference,
for some forest-associated resource or small tolerance to the
higher temperatures of the lowlands. Unfortunately, very little
is known about the ecology and life histories of the mammals
of the highlands, even though at least 9 species are endemic to
this region (Lopez-Gonzalez and Garcıa-Mendoza 2012;
Garcıa-Mendoza and Lopez-Gonzalez 2013). The current rate
of deforestation and climatic change occurring on the highlands
of the SMO pose a significant threat to the populations of P.schmidlyi and other species of small mammals with similar
requirements, but very little is being done for the preservation
of these ecosystems. Six officially protected areas exist in the
region: La Michilıa, Basaseachic Falls, Campo Verde,
Papigochic, Tutuaca, and Janos (Comision Nacional de Areas
Naturales Protegidas 2013a). Although they cover a reasonable
portion of the SMO (about 1,216,400 ha all together—
Comision Nacional de Areas Naturales Protegidas 2013a),
there seem to be no effective conservation efforts and only
Janos has a management plan (Comision Nacional de Areas
Naturales Protegidas 2013b). A sound policy for management
and conservation is urgent if we expect at least to have a
general knowledge of the diversity of this complex mountain
system.
RESUMEN
Peromyscus schmidlyi es un roedor endemico de los bosques
de pino–encino de las partes altas de La Sierra Madre
Occidental (SMO), Mexico. Utilizando dos marcadores
moleculares (Citocromo B y D-loop), se evaluaron las posibles
relaciones entre la variacion genetica y morfometrica de las
poblaciones de P. schmidlyi y la recientemente propuesta
regionalizacion de las comunidades vegetales de las partes altas
de la sierra. No encontramos estructura genetica, ni relaciones
significativas entre la variacion genetica de ningun marcador y
la variacion ecologica o morfometrica. El analisis de filogenias
y redes haplotıpicas no revelo grupos significativos geo-
graficamente estructurados, mas bien, lo que se obtuvo fueron
arboles con poca profundidad y redes en forma de estrella.
Asimismo, las pruebas de neutralidad por localidad fueron no
significativas y no se encontro evidencia de seleccion en
ninguno de los marcadores. La evidencia disponible sugiere
que la distribucion actual de P. schmidlyi es resultado de
dispersion seguida de una rapida explosion de la poblacion
sobre las partes altas de la SMO a finales del Pleistoceno, como
resultado del enfriamiento glacial de las mismas.
ACKNOWLEDGMENTS
We thank R. J. Baker and H. Gardner of The Museum, Texas Tech
University, for allowing access to the specimens under their care.
Funding for this research was provided by Secretarıa de Inves-
tigacion y Posgrado, Instituto Politecnico Nacional (SIP 2010-0434,
2011-0349, 2012-1104, and 2013-0840 to CL-G), Comision
Nacional para el conocimiento y uso de la Biodiversidad (CON-
ABIO) (X011 and GT015 to CL-G), and Consejo Nacional de
Ciencia y Tecnologıa (CONACYT) (290662-IPN to MMC-R). We
thank S. Gonzalez-Elizondo and colleagues for allowing access to
the digital version of their vegetation and ecoregion maps for the
SMO, as well as L. Ruacho-Gonzalez for making the necessary
transformations for us to be able to use them. Specimens were
collected under Secretarıa de Medio Ambiente y Recursos Naturales
(SEMARNAT) permit FAUT-0085 to CL-G. Two anonymous
reviewers provided useful comments and suggestions that helped
improve the manuscript.
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