The impact of Quaternary climate oscillations on divergence times and historical population sizes in...

The impact of Quaternary climate oscillations ondivergence times and historical population sizes inThylamys opossums from the Andes

THOMAS C. GIARLA*† and SHARON A. JANSA*†*Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN 55108, USA, †J.F. Bell Museum of

Natural History, University of Minnesota, St. Paul, MN 55108, USA

Abstract

Climate oscillations during the Quaternary altered the distributions of terrestrial ani-

mals at a global scale. In mountainous regions, temperature fluctuations may have led

to shifts in range size and population size as species tracked their shifting habitats

upslope or downslope. This creates the potential for both allopatric speciation and

population size fluctuations, as species are either constrained to smaller patches of

habitat at higher elevations or able to expand into broader areas at higher latitudes.

We considered the impact of climate oscillations on three pairs of marsupial species

from the Andes (Thylamys opossums) by inferring divergence times and demographic

changes. We compare four different divergence dating approaches, using anywhere

from one to 26 loci. Each pair comprises a northern (tropical) lineage and a southern

(subtropical to temperate) lineage. We predicted that divergences would have occurred

during the last interglacial (LIG) period approximately 125 000 years ago and that pop-

ulation sizes for northern and southern lineages would either contract or expand,

respectively. Our results suggest that all three north–south pairs diverged in the late

Pleistocene during or slightly after the LIG. The three northern lineages showed no

signs of population expansion, whereas two southern lineages exhibited dramatic,

recent expansions. We attribute the difference in responses between tropical and sub-

tropical lineages to the availability of ‘montane-like’ habitats at lower elevations in

regions at higher latitudes. We conclude that climate oscillations of the late Quaternary

had a powerful impact on the evolutionary history of some of these species, both pro-

moting speciation and leading to significant population size shifts.

Keywords: Andes, Didelphidae, divergence times, population sizes, Quaternary climate oscilla-

tions, Thylamys

Received 29 December 2014; revision received 3 March 2015; accepted 18 March 2015

Introduction

Cyclical climatic changes during the Quaternary altered

the spatial distribution of biomes worldwide, sparking

concomitant evolutionary changes among the organisms

that comprised those biomes (Bennett 1990; Dynesius &

Jansson 2000; Hewitt 2004). The magnitude and trajec-

tory of evolution in the face of shifting climates is

largely dependent on how tightly linked a species is to

its environment (i.e., the extent to which an organism’s

environmental niche is conserved). When a population’s

necessary environmental conditions shift in space—and

if the population does not become extinct—it might

respond by staying in place and coping with the new

environment through adaptive evolution or phenotypic

plasticity. Otherwise, it might track its preferred habitat

into the new area (Holt 1990). The latter scenario is

most likely if a species has a conserved environmental

niche, and mounting evidence suggests that recently

diverged species tend to retain conserved environmental

Correspondence: Thomas C. Giarla, Department of Biological

Sciences and Museum of Natural Science, Louisiana State

University, Baton Rouge, LA 70803, USA. Fax: +1 225 578 3075;

E-mail: [email protected]

© 2015 John Wiley & Sons Ltd

Molecular Ecology (2015) 24, 2495–2506 doi: 10.1111/mec.13173

niche characteristics (Mart�ınez Meyer et al. 2004; Wiens

et al. 2010; Peterson 2011; Quintero & Wiens 2013). If

that is the case, the joint effects of shifting climates and

stable niches can lead to distributional shifts with cas-

cading evolutionary outcomes. The potential outcomes

are especially dramatic for organisms that inhabit

mountainous areas.

Studies examining contemporary climate warming

have shown that some species adapted to live in rela-

tively cold montane habitats are being squeezed into

ever-smaller, patchy ranges at higher elevations, threat-

ening them with extinction (Beever et al. 2003; Parmesan

2006; Galbreath et al. 2009). However, other montane

species are expected to expand their ranges if similar

habitats are available and accessible at higher latitudes

(Guralnick 2007). Differences in the expected distribu-

tional shifts vary by global region. In the tropics, the

potentially cooler temperatures at higher latitudes are

far less accessible than the equivalent temperature

changes available by climbing higher in elevation. As

such, elevational range shifts are much more likely than

shifts in latitude among tropical species experiencing a

warming environment (Colwell et al. 2008). If a montane

species is unable to track its preferred climatic niche to

higher latitudes, the prevailing evolutionary pattern we

expect to observe under warming regimes is one of

demographic decline and population fragmentation.

Furthermore, if these fragmented populations do not go

extinct, population isolation may eventually lead to allo-

patric speciation (Wiens 2004; Wiens & Graham 2005).

As such, the most recent time period for recent montane

speciation (excluding the Earth’s current interglacial)

may be the last interglacial (LIG; 129 800–73 900 years

ago), when global temperatures were considerably war-

mer than today (Rohling et al. 2007).

Researchers have long debated the extent to which

Quaternary climate oscillations affected the timing of

species divergence (Haffer 1974; Fjelds�a 1994; Klicka &

Zink 1997; Avise et al. 1998; Rull 2008, 2011), and there

has been a growing effort to examine their role in shap-

ing species diversity, phylogeographic structure and

demographic shifts in montane systems (Roy 1997; Ko-

zak & Wiens 2006; Ribas et al. 2007; Galbreath et al.

2009, 2010; Shepard & Burbrink 2009). The Andes of

South America are among the most biodiverse moun-

tain ranges in the world (Myers et al. 2000), and numer-

ous studies have implicated Quaternary climate

oscillations in driving this remarkable diversity

(reviewed by Turchetto-Zolet et al. 2013). During the

last glacial maximum (LGM) approximately 26 000–21 000 years before present, temperatures in the tropical

Andes were 5–10 °C cooler than present (Baker et al.

2001; Mourguiart & Ledru 2003) and vegetation com-

munities were shifted downslope relative to their con-

temporary distributions (van der Hammen 1985;

Hooghiemstra et al. 1993). Elevation bands occupy more

surface area at lower elevations in the mountains.

Therefore, at a regional scale, biomes currently

restricted to small areas at high elevations would have

expanded in area when pushed into lower elevations

during previous glacial periods (Urrego et al. 2005).

When climates warmed during interglacial periods,

those same vegetation communities were again forced

farther upslope. This contraction of relatively cool, mon-

tane habitats may have led to a simultaneous reduction

in population size among the terrestrial animal species

that utilize those habitats. In the southern half of the

mountain range, Andean species may have been able to

escape this reduction in available habitat by expanding

into areas with montanelike climates at lower elevations

in the subtropical and temperate zones (e.g. Patagonia

or the lowland Monte desert).

In this study, we investigate the impact of Quaternary

climate oscillations on the evolution and population his-

tory of three pairs of closely related Andean marsupial

lineages in the genus Thylamys. These six lineages have

mostly nonoverlapping distributions extending from

high-elevation habitats in tropical Peru to temperate

Argentina (Fig. 1, Fig. S1, Supporting information; Gia-

rla et al. 2010; Giarla & Jansa 2014; Giarla et al. 2014).

Here, we test two sets of hypotheses concerning the

impact of late-Quaternary climate oscillations on An-

dean Thylamys species: (i) that sister species diverged

due to allopatric speciation during the Quaternary

when the warm LIG forced species into isolated high-

land refugia; and (ii) that montane lineages experienced

population size changes in response to warming and

cooling cycles, modulated by their ability to expand

into more extensive areas at higher latitudes. For the six

Andean lineages studied here, we predict that the more

northerly ‘A’ lineages within each pair would see popu-

lation expansion during cool stages and population

decline during warm stages. In contrast, the southern

‘B’ lineages would see the opposite pattern because

they could expand their ranges during warm stages by

‘escaping’ into montanelike habitats farther south.

Materials and methods

Estimating divergence times

To examine the impact that underlying assumptions

and data types might have on our conclusions, we

employ four analytical approaches for the estimation of

speciation times across cryptic species pairs within Thy-

lamys pallidior, Thylamys sponsorius and Thylamys venu-

stus: (i) a relaxed clock, fossil-calibrated analysis of one

mitochondrial gene; (ii) a relaxed clock, fossil-calibrated,


2496 T. C. GIARLA and S. A. JANSA

multilocus super-matrix analysis; (iii) a mutation-rate-

scaled, multilocus species tree analysis; and (iv) a series

of three mutation-rate-scaled, multilocus estimates of

divergence times for each cryptic lineage pair under a

two-species coalescent model. The number of loci and

number of individuals included vary depending on the

analysis. Details on sampling strategies and GenBank

accession numbers are provided in Table S1 and

Appendix S1 (Supporting information). Giarla et al.

(2010) identified three mitochondrial haplogroups

within T. venustus (designated A, B, and C), but mul-

tilocus species delimitation tests suggested that haplo-

groups B and C actually represent just one cryptic

species (Giarla et al. 2014). We retain those labels here,

combining B and C into one taxon referred to as

T. venustus B. All of the sequence data used for these

analyses are published: three mitochondrial loci from

Giarla et al. (2010), 14 anonymous nuclear loci and one

X-linked intron from Giarla et al. (2014), and 8 addi-

tional nuclear loci (both exons and introns) from Voss

& Jansa (2009) and Giarla & Jansa (2014). Sequences

from each locus were aligned in GENEIOUS v5.4 (Kearse

et al. 2012) using the MUSCLE algorithm (Edgar 2004)

and adjusted by eye. We assessed nucleotide substitu-

tion models in JMODELTEST v2.1.3 (Darriba et al. 2012) for

each locus and applied the best fitting model to that

subset of the data if it was included in a given analysis.

Fossil-calibrated analysis of mitochondrial sequences. In BEAST

v1.7.5 (Drummond et al. 2012), we inferred an ultrametric,

fossil-calibrated tree of 49 marsupial species, including the

six Andean Thylamys lineages that are the subject of this

ARGENTINA

PARAGUAY

BOLIVIAPERU

CHILE

BRAZIL

Tropic of capricorn

–20º

–25º

–30º

–35º

–15º

–40º

–75º –70º –65º –60º

Pallidior B (10)

Venustus A (7)

Venustus B (20)

Sponsorius A (10)

Sponsorius B (9)

Pallidior A (4)

Elevation (m)0–600601–12001201–19001901–26502651–33003301–37003701–40004001–44004401–48004800–6700

Pallidior A

Pallidior B

Venustus A

Venustus B

Sponsorius A

Sponsorius B

Fig. 1 Map of species distributions for

the six Thylamys lineages included in this

study, with inset species tree from (Gia-

rla et al. 2014). The inset species tree does

not fully sample all Thylamys species;

T. venustus and T. sponsorius are sister

species, but Thylamys pallidior is more

closely related other Thylamys species not

sampled here (Giarla et al. 2010). Shapes

were drawn around collecting localities

for samples sequenced, and the number

of individuals sampled per species is

included in parentheses. Precise locality

information is uploaded to Dryad and

summarized in Table S1 (Supporting

information). Collecting localities used to

draw the shapes are illustrated in Fig. S1

(Supporting information).


PHYLOGEOGRAPHY OF ANDEAN MARSUPIALS 2497

study, using sequences from the mitochondrial gene cyto-

chrome b (CYTB). We calibrated the tree with two didelp-

hid fossils and one Australian marsupial fossil. These

fossils were used to constrain the minimum age of the split

between Didelphis and Philander (3.3 Ma), the minimum

age of the clade that comprises Monodelphis, Marmosa and

Tlacuatzin (12.1 Ma), and the minimum age of the split

between Dasyuromorphia and Peramelemorphia

(24.7 Ma). Detailed information about the assignment of

fossils to extant clades can be found in Jansa et al. (2014).

The minimum age of the clade containing the fossil was

constrained by a lognormal prior (mean = 1.0) offset by

the estimated age of the fossil. A Yule speciation prior and

a lognormal clock prior were both applied, and all remain-

ing priors were left at their default values. We ran the

MCMC chain for 25 million generations, sampling every

2500 steps. We ensured that effective samples sizes (ESS)

for each estimated parameter exceeded 200 in TRACER v1.6

(Rambaut et al. 2014). After removing the first 10% of trees

as burn-in, we assembled a maximum clade credibility tree

from the post-burn-in sample of trees using TREEANNOTATOR

v1.7.5 (Drummond et al. 2012).

Fossil-calibrated super-matrix analysis. Separate estimates

of divergence dates were extracted from a phylogenetic

tree inferred as part of a previous study examining the

biogeographic history of Thylamys (Giarla & Jansa 2014).

That analysis was performed in BEAST following the

same procedure as described above for the fossil-cali-

brated CYTB analysis, except a super-matrix of 26 con-

catenated loci was used. That super-matrix comprised 3

mitochondrial genes, 22 autosomal loci and 1 X-linked

locus. Details on the BEAST analysis, including the selec-

tion and implementation of partitioning strategies, are

described in Giarla & Jansa (2014).

Mutation-rate-scaled species tree analysis. We conducted a

multilocus species tree analysis of the six focal lineages

using the *BEAST algorithm (Heled & Drummond 2010)

implemented in BEAST. Unlike the first two approaches

described above, this analysis involves only the six focal

Andean lineages and, for each species, includes multi-

ple sequences from 15 nuclear loci and CYTB (Table S1,

Supporting information). Individuals were assigned to

species in *BEAST according to previous species delimita-

tion efforts (Giarla et al. 2010, 2014). We assigned the

appropriate ploidy levels to each gene, and we applied

a Yule species tree prior with a piecewise-linear and

constant root population size model. Rate variation

among lineages was incorporated by applying uncorre-

lated lognormal clock models.

To calibrate the species tree analysis, the clock model

for CYTB was assigned a uniform rate prior ranging

from 0.0172 to 0.0668 substitutions per site per mil-

lion-years (Table S2, Supporting information). This

estimate of the CYTB rate range is based on the 95%

highest posterior density interval (HPDI) estimated for

the CYTB rate for the clade that includes Thylamys and

its sister-genus Lestodelphys from the 26-locus, fossil-cali-

brated super-matrix analysis. All other clock models

received exponential rate priors (mean = 1.0). The

remaining priors were kept at their default values, and

we started two MCMC chains of 125 million genera-

tions, sampling each every 12 500 steps. After assessing

convergence and removing 10% of trees as burn-in, we

combined the two runs in LOGCOMBINER v1.7.5 (Drum-

mond et al. 2012) and assembled a maximum clade

credibility tree from the post-burn-in sample of species

trees using TREEANNOTATOR.

Two-species coalescent models. For the final divergence

time estimation approach, we divided the data set used

for estimating the species tree into three subsets, one

for each pair of cryptic Andean lineages. Each of these

three data sets was analysed independently in BPP v2.2

(Yang & Rannala 2010) with a fixed, two-taxon guide

tree. Priors on effective population sizes (h) and diver-

gence times (s) were assigned diffuse gamma distribu-

tions of Γ(2, 500) and Γ(2, 3000), respectively (Prior

Scheme 1 from Giarla et al. 2014). Interlocus rate hetero-

geneity was allowed under the default Dirichlet prior,

and the heredity scalars for the X-linked locus and

mitochondrial locus were set to their respective values

of 0.75 and 0.25.

We ran the MCMC chain for 2.5 million generations,

sampling every five steps, for a posterior distribution of

500 000 samples. Of these, 50 000 samples were

removed as burn-in, and we assessed chain mixing by

examining ESS values in TRACER. For each pair, the 95%

HPDI BPP estimate for population splitting time (the sparameter) was time-calibrated using the mean substi-

tution rate across the 16 loci used here that were also

used in the 26-locus, fossil-calibrated super-matrix

analysis. To do this, we extracted mean rate estimates

from the maximum clade credibility tree for the clade

that includes Thylamys and its sister-genus Lestodelphys

for each of the 16 loci overlapping with the larger set

from Giarla & Jansa (2014) (Table S2, Supporting infor-

mation).

Modelling demographic fluctuations and rangeexpansions

Extended Bayesian skyline plots. We modelled demo-

graphic changes in each of the six cryptic lineages with

extended Bayesian Skyline plots (EBSPs; Heled &

Drummond 2008) in BEAST. We extracted six separate,

lineage-specific alignments from each of the 16 loci used



in the species tree analysis described above. After

dividing the data set into lineage-specific alignments, 13

(of 96) were invariant and excluded from further analy-

sis (Table S3, Supporting information). To set up the six

independent analyses, we applied strict clocks, unlinked

all site models and clock models and assigned the best

fitting available substitution model to each alignment.

For the tree priors, we chose the extended Bayesian sky-

line plot model with a piecewise-linear population size

function and assigned the appropriate ploidy levels to

the X-linked and mitochondrial loci. We assigned expo-

nential priors (mean = 1.0) to all of the ucld.mean

priors, kept all other priors at their default values, and

ran the MCMC chain for 400 million generations (T. pal-

lidior B and T. sponsorius B) or 300 million generations

(all other lineages).

Demographic model comparisons. We compared constant-

size and exponential growth coalescent models for each

lineage using marginal likelihood estimates calculated

via path sampling and stepping stone sampling (PS/

SS), two approaches that have been demonstrated to

provide accurate and reliable marginal likelihood esti-

mates for model comparisons (Baele et al. 2012). Over-

all, we set up 12 analyses in BEAST, one constant-size

coalescent model and one exponential growth coales-

cent model for each of the six lineages (using the same

data sets as for the EBSP analysis). Site models and

clock models were treated the same as for the EBSP

analyses, and either the constant-size or exponential

growth tree prior was chosen. We used the default

Laplace prior on the exponential growth rate, which

allows the MCMC chain to sample both positive and

negative rates. Because PS/SS marginal likelihood esti-

mates can be biased by priors with probability distribu-

tions that do not integrate to 1 (Baele et al. 2013), we

changed the default ‘1/x’ priors for the population size

parameters to lognormal priors (mean = 1.0, SD = 2.0).

The PS/SS calculations rely on well-mixed posterior

distribution to start, so we initialized each marginal

likelihood calculation with 50 million MCMC genera-

tions, sampling every 5000 steps. For all analyses, we

used PS/SS calculations with a chain length of 100 000

generations and 1000 path steps.

Results

Divergence times

Stationarity and high ESS values were attained for all of

the analyses across each of the analytical approaches

used to estimate divergence times (BEAST, *BEAST and

BPP). For the BEAST analysis of CYTB alone, 95% HPD

intervals for divergence estimates between haplogroup

pairs—T. pallidior A vs. B, T. sponsorius A vs. B and

T. venustus A vs. B—extend across a relatively wide

range between approximately 0.25 and 2.0 Ma (Fig. 2,

Fig. S2, Supporting information). The super-matrix

analysis of 26 loci resulted in younger and tighter HPD

intervals for the timing of the three splits, between

approximately 200 000 and 500 000 years ago (Fig. 2,

Fig. S3, Supporting information). Divergence times esti-

mated using the coalescent-based *BEAST and BPP

approaches resulted in congruent, considerably more

Single-Locus: BEAST mtDNA-only

Concatenated: BEAST supermatrix

Multispecies coalescent: *BEAST

Multispecies coalescent: BPP Pal

lidio

rA

vs.

BSp

onso

rius

A v

s. B

Ven

ustu

sA

vs.

B

Last interglacial period(LIG)

0.0 0.5 1.0 1.5 2.0 0.25 0.75 1.25 1.75 2.25

Divergence time (millions of years ago)

Fig. 2 Divergence time estimates for three pairs of montane Thylamys lineages using four different approaches. Bars represent 95%

highest posterior density intervals. Vertical lines within the bars are mean divergence time estimates. A shaded vertical bar illustrates

the duration of the last interglacial (LIG) period (Rohling et al. 2007).



recent, and tighter HPD intervals (Fig. 2, Table 1). For

T. pallidior A/B and T. sponsorius A/B, these estimates

overlap with the LIG. For T. venustus A/B, these esti-

mates are more recent still, with HPD intervals for the

timing of the split extending between approximately

20 000 and 75 000 years ago, during the cooling period

between the LIG and the LGM.

Historical demography

Each of the six EBSP runs reached stationarity and

exhibited high ESS values. The x-axes on the EBSPs

were scaled using the mean substitution rate for all loci

in Table S2 (Supporting information). For sister species

not exchanging genes, gene coalescence events are

always older (sometimes considerably so) than the

actual population splitting event that precipitated speci-

ation (Edwards & Beerli 2000), which our coalescent-

based divergence estimates (Fig. 2) attempted to infer.

The long tails on the whole EBSPs (not shown) reflect

the oldest modelled coalescence event across all of the

genes sampled within a species. As such, we illustrate

only the portion of the EBSP that represents a species’

independent coalescent history, separate from its sister

(Fig. 3). For T. pallidior A, the effective population size

gradually increases over the course of its coalescent his-

tory (Fig. 3a). For T. pallidior B, the population size

remains relatively constant until approximately

60 000 years ago, when the population undergoes an

over sixfold increase (Fig. 3b). Thylamys sponsorius A

(Fig. 3c), T. venustus A (Fig. 3e) and T. venustus B

(Fig. 3f) appear to remain approximately the same size

over the course of their demographic history, with

slight declines towards the present. Thylamys sponsorius

B has a complex demographic history, showing a five-

fold population size decline until approximately

40 000 years ago, when it reverses and rapidly expands

to its former population size (Fig. 3d).

Constant-size and exponential growth coalescent tree

priors were compared for each lineage using PS and SS

marginal likelihood estimates (Table 2). In each case,

both estimators gave identical or nearly identical

results. The exponential growth model received a signif-

icantly higher marginal likelihood score as measured by

Bayes Factors (Kass & Raftery 1995) for T. pallidior A

and B, and for T. venustus B. The constant-size model

received a significantly higher marginal likelihood for

T. sponsorius A and T. venustus A, and the constant-size

model could not be rejected for T. sponsorius B. There

was extensive variation in the estimates for the expo-

nential growth rate across runs (Table 2). Thylamys pal-

lidior B, for which the exponential growth model fit the

data best, is estimated to have grown at a rate of 623%

per million-years over its coalescent history, whereas

T. pallidior A and T. venustus B increased at rates of

208% and 143% per million-years, respectively.

Although the exponential growth model did not pro-

vide a significantly better fit to the data than the con-

stant growth model for T. sponsorius B, the overall rate

is negative. However, if the more parameter-rich sky-

line plot model for T. sponsorius B is correct (Fig. 3d), a

simple one-parameter model will not be able to explain

the shift from population decline to population expan-

sion.

Discussion

A major goal of phylogeography is to understand how

ancient earth dynamics shaped the structure of genetic

diversity across landscapes. In montane areas, where

late-Quaternary climate oscillations may have repeat-

edly isolated and reconnected populations, the relation-

ship between population histories and present-day

phylogeographic structure is complex. Late-Quaternary

climate oscillations have been implicated as drivers of

phylogeographic patterns in a number of Andean taxa

(reviewed by Turchetto-Zolet et al. 2013), but the impre-

cision of parameter estimates and lack of statistical tests

have hampered the ability of researchers to tie inferred

evolutionary changes to geological or climatological

events in the earth’s history. The results of this study

offer a significant improvement in the precision of such

estimates among six Andean marsupials.

Divergence during the late pleistocene

Theory indicates that divergence dates estimated from

gene trees (or trees inferred from concatenated alignments

Table 1 Divergence time estimates for three pairs of Andean Thylamys species based on results from *BEAST and BPP analysis of 15

nuclear loci and one mitochondrial gene

pallidior A vs. B sponsorius A vs. B venustus A vs. B

*BEAST BPP *BEAST BPP *BEAST BPP

Mean divergence (years ago) 133 000 105 843 85 500 94 481 50 600 33 584

95% HPD minimum (years ago) 83 000 67 081 56 100 62 985 27 100 19 912

95% HPD maximum (years ago) 192 500 143 624 118 100 126 959 76 300 49 004



of multiple loci) will be biased towards older splitting

times relative to those estimated from species trees. This

is because gene tree divergences must predate species tree

divergences in the absence of post-divergence gene flow

(Edwards & Beerli 2000). Despite these known shortcom-

ings, some studies still rely on single-gene and concate-

nated approaches to infer divergence times, including a

recent study that inferred the timing of speciation across

the entire radiation of Thylamys opossums using only a

single mitochondrial marker (Palma et al. 2014). By com-

paring four different divergence dating approaches

(Fig. 2), we illustrate how such analytical decisions can

mislead subsequent evolutionary inferences. Across the

four approaches, results vary in both their mean estimates

and their precision (as measured by 95% HPD intervals).

The fossil-calibrated analyses of CYTB alone offered the

Time (millions of years ago)L

og(p

opul

atio

n Si

ze x

µ)

T. pallidior A T. pallidior B

T. sponsorius A T. sponsorius B

T. venustus A T. venustus B

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

0.0001

0.001

0.01

0.1

1

0.001

0.01

0.1

1

0.0001

0.001

0.01

0.1

1

0.0001

0.001

0.01

0.1

1

0.0001

0.001

0.01

0.1

1

0.0001

0.001

0.01

0.1

1

0.0001

(a)

(c)

(e)

(b)

(d)

(f)

Fig. 3 Extended Bayesian skyline plots for (a) Thylamys pallidior A, (b) T. pallidior B, (c) Thylamys sponsorius A, (d) Thylamys sponsorius

B, (e) Thylamys venustus A and (f) T. venustus B. The middle lines denote median estimates for population size over time. Lines above

and below the median represent the 95% highest posterior density interval for population size estimates. Vertical dotted lines denote

the onset of the last glacial maximum. The x-axis is timescaled from substitutions/site/million-years to millions of years using the

average substitution rate from Table S2 (Supporting information).



least precision and the oldest divergence estimates,

matching theoretical expectations for divergences

estimated from a single-locus (Edwards & Beerli 2000; Ar-

bogast et al. 2002). Divergence time estimates from the

fossil-calibrated super-matrix approach are more precise

than CYTB alone. However, the increased precision is

likely an artifact of concatenation and its simplifying

assumptions (Edwards & Beerli 2000; Kubatko & Degnan

2007). Given the even larger increase in precision we

observed in our coalescent-based divergence time esti-

mates, we rely only on those results as a basis for further

discussion and interpretation. Results from those

approaches place the three splits between ‘A’ and ‘B’

lineages within the last 200 000 years in a narrow

window near the LIG, a time when global temperatures

were at least 2 °C higher than today (Rohling et al. 2007).

The LIG likely represents the most recent time in Earth

history when tropical montane populations were as

fragmented as they have ever been in the past several

million-years.

Our results are broadly concordant with population

divergences seen in other South American species.

Overall, most South American mammalian sister species

(for which data are available) diverged during the Pleis-

tocene, but only two studies pinpoint the divergences

to the period after the onset of the LIG (Turchetto-Zolet

et al. 2013). The paucity of divergence times estimated

to have occurred during the last part of the Pleistocene

might be due to the reliance on gene tree divergences

instead of species tree estimates, which, as discussed

above, tend to overestimate divergence times. In order

to accurately estimate divergence times and compare

them to codistributed taxa, future studies must rely on

methodologies that explicitly incorporate coalescent var-

iation among loci (Edwards et al. 2007; Heled & Drum-

mond 2010; Yang & Rannala 2010) and properly

account for rate variation among lineages (Hope et al.

2014). Otherwise, divergence estimates will be biased

towards older splitting times.

Palma et al. (2014) recently inferred a time tree for

Thylamys using CYTB sequences and two fossil-calibra-

tion points, estimating relatively old divergence times

among species. Their estimates suggested that Thylamys

diverged from its sister-genus Lestodelphys as early as

the Oligocene, with interspecific divergence dates 10

times as old as in our fossil-calibrated super-matrix

analysis (they did not attempt to infer splitting times

for any of the ‘A’ and ‘B’ lineages we study here). The

discrepancy between the results from Palma et al. (2014)

and our multilocus efforts (Fig. 2) is likely driven by

the imprecision inherent to single-locus studies coupled

with imprecise fossil calibrations and inappropriate

prior choices. Palma et al. (2014) calibrated two nodes

using priors with normal distributions, which can lead

to imprecise estimates because it is usually not an

appropriate representation of fossil information (Ho &

Phillips 2009). Moreover, their assignment of two fossil

taxa to crown groups in their tree is unsubstantiated

and does not follow best practices for justifying fossil

calibrations (Parham et al. 2012) because there is no dis-

cussion of how those assignments were made. We

argue that the fossils Palma et al. (2014) used—Thylamys

pinei (Goin et al. 2000) ‘with affinities to T. venustus’ and

Thylamys contrerasi (Deschamps et al. 2012) ‘with affini-

ties to T. pusillus’—should not be linked so tightly to

crown group ages without a more explicit morphologi-

cal study.

Demographic shifts depend on latitude

We predicted that the three northern ‘A’ lineages,

which are presently constrained to tropical montane

Table 2 Marginal likelihood estimates (MLEs) and exponential

growth rates from BEAST analyses using constant-size or expo-

nential change coalescent tree priors. MLEs are based on Step-

ping Stone Sampling (SS) and Path Sampling (PS). Exponential

growth rates are scaled to million-years�1 by assuming a muta-

tion rate of 0.0071 substitutions/site/million-years (Table S2,

Supporting information). 2ln(BF) scores higher than 2 are con-

sidered positive evidence against the null hypothesis (here,

constant size), and scores higher than 20 are considered very

strong evidence against the null hypothesis (Kass & Raftery

1995). The best fitting model (when significant) is marked by

an asterisk

Tree prior MLE (SS) MLE (PS) Growth rate

Thylamys pallidior A

Constant �10944.40 �10944.12 n/a

Exponential* �10941.31 �10941.03 207.96%

2ln(BF) 6.18 6.18

Thylamys sponsorius A

Constant* �14378.64 �14378.27 n/a

Exponential �14383.11 �14382.64 91.87%

2ln(BF) 8.94 8.74

Thylamys venustus A

Constant* �14957.52 �14957.20 n/a

Exponential �14958.55 �14958.23 89.96%

2ln(BF) 2.06 2.06

T. pallidior B

Constant �15730.06 �15729.69 n/a

Exponential* �15723.77 �15723.41 623.03%

2ln(BF) 12.58 12.56

T. sponsorius B

Constant �11999.79 �11999.56 n/a

Exponential �12000.33 �12000.07 �89.53%

2ln(BF) 1.06 1.02

T. venustus B

Constant �17833.98 �17832.79 n/a

Exponential* �17816.29 �17815.11 143.07%

2ln(BF) 35.38 35.36



habitats, would have experienced population declines

following post-LGM warming due to compression into

high-elevation habitats. Our results do not match this

prediction. Instead, each ‘A’ lineage had relatively sta-

ble population sizes over the past 200 000 years (Fig. 3),

and a constant-size model could not be rejected for two

of those lineages (T. sponsorius A, and T. venustus A;

Table 2). This unexpected stability suggests that late-

Quaternary climate oscillations had no discernable

effect on population sizes in these tropical Andean lin-

eages. Or, perhaps, the structure of the mountain sys-

tem in the tropical Central Andes is such that ample

habitat space exists at both mid- and high-elevation

areas. In contrast, we predicted that Andean lineages

with ancestral distributions adjacent to subtropical or

temperate areas would have been able to expand their

ranges into those areas during warm stages of the Qua-

ternary (and that such range expansions would trigger

demographic growth detectable in EBSPs). We find evi-

dence for substantial demographic expansion in the

skyline plots for both T. sponsorius B and T. pallidior B,

but not for T. venustus B (Fig. 3). The expansion appar-

ent in the T. pallidior B and T. sponsorius B EBSPs

appears to start before the onset of warming leading up

to the Holocene, but, given the relatively broad HPD

intervals surrounding the demographic functions, it is

impossible to reject our prediction that the expansion

occurred after the LGM. Regardless of the timing, we

suspect that the demographic growth was precipitated

by a southward range expansion into cooler climates at

higher latitudes.

Late-Quaternary range expansions in southern South

America are seen in several other terrestrial taxa,

including amphibians (Nu~nez et al. 2011), squamates

(Olave et al. 2011; Camargo et al. 2013), birds (Masello

et al. 2011) and other mammals (Mar�ın et al. 2007; Hi-

mes et al. 2008; Coss�ıos et al. 2009; Lessa et al. 2010),

suggesting that this pattern might be widespread. How-

ever, phylogeographic data from the open and montane

habitats of central and southern South America are

scarce (Turchetto-Zolet et al. 2013), and future work

should focus on clarifying demographic shifts in

response to climate change in these regions. Accurately

estimating the timing of such demographic shifts is a

challenging but essential part of testing phylogeograph-

ic hypotheses (Hope et al. 2014). The use of relatively

slowly evolving loci might not generate precise enough

estimates for productive investigations into recent evo-

lutionary events on the order of tens of thousands of

years. Phylogenomic-scale data sets incorporating mark-

ers like RAD-seq (Baird et al. 2008) or ultraconserved

elements (Faircloth et al. 2012; Smith et al. 2014) might

be large enough to overcome the sampling error inher-

ent to smaller data sets like ours.

Conclusions

We inferred divergence times and modelled the demo-

graphic histories of six Andean Thylamys lineages, and

our results support extremely recent divergence times

(all within the past 200 000 years) and two noteworthy

shifts in population size (as much as a sixfold expan-

sion in T. pallidior B). Taken as a whole, these results

suggest that late-Quaternary climate oscillations may

have played a role in both stimulating speciation and

(for southern lineages) demographic expansion. Diver-

gence time intervals for two pairs of cryptic lineages

(T. pallidior and T. sponsorius) overlap with the LIG, the

most recent time in Earth’s history when presently

montane species may have been most isolated. Recent

demographic expansions in two lineages with more

southerly contemporary ranges (T. pallidior B and

T. sponsorius B) fit our predictions for extra-tropical

range expansion. None of the three more northerly ‘A’

members of each cryptic pair showed the demographic

decline predicted for species with a more tropical

ancestral distribution, which we expected to have been

forced to contract in size during the post-LGM warm-

ing period. The lack of population size contraction can

be cautiously taken as positive news and suggests that

Central Andean small mammals like Thylamys species

might be more resilient to climate warming than mon-

tane species in other parts of the world (e.g. Rocky

Mountain pikas; Beever et al. 2003). Nonetheless,

although the warming observed since the LGM does

not appear to have reduced the effective population

size for any of the species studied here to date, ongoing

habitat destruction and more extreme warming could

still pose significant extinction risks for these and other

Andean species.

Acknowledgements

This work was supported by grants to T.C.G. from the

National Science Foundation (DEB-1110365), the University of

Minnesota, the Bell Museum of Natural History, the American

Society of Mammalogists and the Society of Systematic Biolo-

gists, and a grant to S.A.J from the National Science Founda-

tion (DEB-0743062). We are grateful to the many collectors and

curators who provided us with tissue and specimen loans as

part of this project. Andrew Simons, Keith Barker, Jake Essels-

tyn, Melissa DeBiasse, Jeremy Brown and three anonymous

reviewers provided helpful comments on earlier drafts of this

work.

References

Arbogast BS, Edwards SV, Wakeley J, Beerli P, Slowinski JB

(2002) Estimating divergence times from molecular data on

phylogenetic and population genetic timescales. Annual

Review of Ecology and Systematics, 33, 707–740.



Avise JC, Walker D, Johns GC (1998) Speciation durations and

Pleistocene effects on vertebrate phylogeography. Proceedings

of the Royal Society of London. Series B: Biological Sciences, 265,

1707–1712.Baele G, Lemey P, Bedford T, Rambaut A, Alekseyenko AV

(2012) Improving the accuracy of demographic and

molecular clock model comparison while accommodating

phylogenetic uncertainty. Molecular Biology and Evolution, 29,

2157–2167.Baele G, Li WLS, Drummond AJ, Suchard MA, Lemey P (2013)

Accurate model selection of relaxed molecular clocks in

Bayesian phylogenetics. Molecular Biology and Evolution, 30,

239–243.Baird NA, Etter PD, Atwood TS et al. (2008) Rapid SNP discov-

ery and genetic mapping using sequenced RAD markers.

PLoS One, 3, e3376.

Baker PA, Seltzer GO, Fritz SC et al. (2001) The history of

South American tropical precipitation for the past

25,000 years. Science, 291, 640–643.Beever EA, Brussard PF, Berger J (2003) Patterns of apparent

extirpation among isolated populations of pikas (Ochotona

princeps) in the great basin. Journal of Mammalogy, 84, 37–54.Bennett KD (1990)Milankovitch cycles and their effects on species

in ecological and evolutionary time. Paleobiology, 16, 11–21.Camargo A, Werneck FP, Morando M, Sites JW Jr, Avila LJ

(2013) Quaternary range and demographic expansion of Lio-

laemus darwinii (Squamata: Liolaemidae) in the Monte Desert

of Central Argentina using Bayesian phylogeography and

ecological niche modelling. Molecular Ecology, 22, 4038–4054.Colwell RK, Brehm G, Cardel�us CL, Gilman AC, Longino JT

(2008) Global warming, elevational range shifts, and lowland

biotic attrition in the wet tropics. Science, 322, 258–261.Coss�ıos D, Lucherini M, Ruiz-Garc�ıa M, Angers B (2009) Influ-

ence of ancient glacial periods on the Andean fauna: the case

of the pampas cat (Leopardus colocolo). BMC Evolutionary Biol-

ogy, 9, 68.

Darriba D, Taboada GL, Doallo R, Posada D (2012) JMODELTEST

2: more models, new heuristics and parallel computing. Nat-

ure Methods, 9, 772.

Deschamps CM, Vucetich MG, Verzi DH, Olivares AI (2012) Bio-

stratigraphy and correlation of the Monte Hermoso Formation

(early Pliocene, Argentina): the evidence from caviomorph

rodents. Journal of South American Earth Sciences, 35, 1–9.Drummond AJ, Suchard MA, Xie D, Rambaut A (2012) Bayes-

ian phylogenetics with BEAUTI and the BEAST 1.7. Molecular

Biology and Evolution, 29, 1969–1973.Dynesius M, Jansson R (2000) Evolutionary consequences of

changes in species’ geographical distributions driven by Mil-

ankovitch climate oscillations. Proceedings of the National

Academy of Sciences, USA, 97, 9115–9120.Edgar RC (2004) MUSCLE: multiple sequence alignment with

high accuracy and high throughput. Nucleic Acids Research,

32, 1792–1797.Edwards SV, Beerli P (2000) Perspective: gene divergence, pop-

ulation divergence, and the variance in coalescence time in

phylogeographic studies. Evolution, 54, 1839–1854.Edwards SV, Liu L, Pearl DK (2007) High-resolution species

trees without concatenation. Proceedings of the National Acad-

emy of Sciences, USA, 104, 5936–5941.Faircloth BC, McCormack JE, Crawford NG, Harvey MG,

Brumfield RT, Glenn TC (2012) Ultraconserved elements

anchor thousands of genetic markers spanning multiple

evolutionary timescales. Systematic Biology, 61, 717–726.Fjelds�a J (1994) Geographical patterns for relict and young species of

birds in Africa and South America and implications for conserva-

tion priorities. Biodiversity and Conservation, 3, 207–226.Galbreath KE, Hafner DJ, Zamudio KR (2009) When cold is

better: climate-driven elevation shifts yield complex patterns

of diversification and demography in an alpine specialist

(American pika, Ochotona princeps). Evolution, 63, 2848–2863.Galbreath KE, Hafner DJ, Zamudio KR, Agnew K (2010) Isola-

tion and introgression in the Intermountain West: contrasting

gene genealogies reveal the complex biogeographic history

of the American pika (Ochotona princeps). Journal of Biogeogra-

phy, 37, 344–362.Giarla TC, Jansa SA (2014) The role of physical geography and

habitat type in shaping the biogeographical history of a

recent radiation of Neotropical marsupials (Thylamys: Didel-

phidae). Journal of Biogeography, 41, 1547–1558.Giarla TC, Voss RS, Jansa SA (2010) Species limits and phylo-

genetic relationships in the didelphid marsupial genus Thyla-

mys based on mitochondrial DNA sequences and

morphology. Bulletin of the American Museum of Natural His-

tory, 346, 1–67.Giarla TC, Voss RS, Jansa SA (2014) Hidden diversity in the Andes:

comparison of species delimitation methods in montane marsupi-

als.Molecular Phylogenetics and Evolution, 70, 137–151.Goin FJ, Montalvo C, Visconti G (2000) Los marsupiales (Mammalia)

del Mioceno superior de la Formaci�on Cerro Azul (provincia de

La Pampa, Argentina). Estudios Geol�ogicos, 56, 101–126.Guralnick R (2007) Differential effects of past climate warming

on mountain and flatland species distributions: a multispe-

cies North American mammal assessment. Global Ecology and

Biogeography, 16, 14–23.Haffer J (1974) Avian speciation in Tropical South America,

with a systematic survey of the Toucans (Ramphastidae) and

Jacamars (Galbulidae). Publications of the Nuttall Ornithological

Club, no. 14.

van der Hammen T (1985) The Plio-Pleistocene climatic record of

the tropical Andes. Journal of the Geological Society, 142, 483–489.Heled J, Drummond AJ (2008) Bayesian inference of popula-

tion size history from multiple loci. BMC Evolutionary Biol-

ogy, 8, 289.

Heled J, Drummond AJ (2010) Bayesian inference of species

trees from multilocus data. Molecular Biology and Evolution,

27, 570–580.Hewitt GM (2004) Genetic consequences of climatic oscillations

in the Quaternary. Philosophical Transactions of the Royal Soci-

ety of London B: Biological Sciences, 359, 183–195.Himes CM, Gallardo MH, Kenagy GJ (2008) Historical biogeog-

raphy and post-glacial recolonization of South American

temperate rain forest by the relictual marsupial Dromiciops

gliroides. Journal of Biogeography, 35, 1415–1424.Ho SYW, Phillips MJ (2009) Accounting for calibration uncer-

tainty in phylogenetic estimation of evolutionary divergence

times. Systematic Biology, 58, 367–380.Holt RD (1990) The microevolutionary consequences of climate

change. Trends in Ecology & Evolution, 5, 311–315.Hooghiemstra H, Melice JL, Berger A, Shackleton NJ (1993)

Frequency spectra and paleoclimatic variability of the high-

resolution 30–1450 ka Funza I pollen record (Eastern Cordil-

lera, Colombia). Quaternary Science Reviews, 12, 141–156.



Hope AG, Ho SYW, Malaney JL, Cook JA, Talbot SL (2014)

Accounting for rate variation among lineages in comparative

demographic analyses. Evolution, 68, 2689–2700.Jansa SA, Barker FK, Voss RS (2014) The early diversification

history of didelphid marsupials: a window into South Amer-

ica’s “Splendid Isolation”. Evolution, 68, 684–695.Kass RE, Raftery AE (1995) Bayes factors. Journal of the Ameri-

can Statistical Association, 90, 773–795.Kearse M, Moir R, Wilson A et al. (2012) Geneious Basic: an

integrated and extendable desktop software platform for the

organization and analysis of sequence data. Bioinformatics,

28, 1647–1649.Klicka J, Zink RM (1997) The importance of recent ice ages in

speciation: a failed paradigm. Science, 277, 1666–1669.Kozak KH, Wiens JJ (2006) Does niche conservatism promote

speciation? A case study in North American salamanders.

Evolution, 60, 2604–2621.Kubatko LS, Degnan JH (2007) Inconsistency of phylogenetic

estimates from concatenated data under coalescence. System-

atic Biology, 56, 17–24.Lessa EP, D’El�ıa G, Pardi~nas UFJ (2010) Genetic footprints of

late Quaternary climate change in the diversity of Patago-

nian-Fueguian rodents. Molecular Ecology, 19, 3031–3037.Mar�ın JC, Casey CS, Kadwell M et al. (2007) Mitochondrial

phylogeography and demographic history of the Vicu~na:

implications for conservation. Heredity, 99, 70–80.Mart�ınez Meyer E, Townsend Peterson A, Hargrove WW

(2004) Ecological niches as stable distributional constraints

on mammal species, with implications for Pleistocene extinc-

tions and climate change projections for biodiversity. Global

Ecology and Biogeography, 13, 305–314.Masello JF, Quillfeldt P, Munimanda GK et al. (2011) The high

Andes, gene flow and a stable hybrid zone shape the genetic

structure of a wide-ranging South American parrot. Frontiers

in Zoology, 8, 16.

Mourguiart P, Ledru M-P (2003) Last glacial maximum in an

Andean cloud forest environment (Eastern Cordillera, Boli-

via). Geology, 31, 195–198.Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB,

Kent J (2000) Biodiversity hotspots for conservation priori-

ties. Nature, 403, 853–858.Nu~nez JJ, Wood NK, Rabanal FE, Fontanella FM, Sites JW Jr

(2011) Amphibian phylogeography in the Antipodes: refugia

and postglacial colonization explain mitochondrial haplotype

distribution in the Patagonian frog Eupsophus calcaratus

(Cycloramphidae). Molecular Phylogenetics and Evolution, 58,

343–352.Olave M, Martinez LE, Avila LJ, Sites JW Jr, Morando M (2011)

Evidence of hybridization in the Argentinean lizards Liolae-

mus gracilis and Liolaemus bibronii (Iguania: Liolaemini): an

integrative approach based on genes and morphology. Molec-

ular Phylogenetics and Evolution, 61, 381–391.Palma RE, Boric-Bargetto D, Jayat JP et al. (2014) Molecular

phylogenetics of mouse opossums: new findings on the

phylogeny of Thylamys (Didelphimorphia, Didelphidae). Zoo-

logica Scripta, 43, 217–234.Parham JF, Donoghue PCJ, Bell CJ et al. (2012) Best practices for

justifying fossil calibrations. Systematic Biology, 61, 346–359.Parmesan C (2006) Ecological and evolutionary responses to

recent climate change. Annual Review of Ecology, Evolution,

and Systematics, 37, 637–669.

Peterson AT (2011) Ecological niche conservatism: a time-struc-

tured review of evidence. Journal of Biogeography, 38, 817–827.Quintero I, Wiens JJ (2013) Rates of projected climate change

dramatically exceed past rates of climatic niche evolution

among vertebrate species. Ecology Letters, 16, 1095–1103.Rambaut A, Suchard MA, Xie D, Drummond AJ (2014) Tracer

v1.6. Available from http://beast.bio.ed.ac.uk/TRACER.

Ribas CC, Moyle RG, Miyaki CY, Cracraft J (2007) The assem-

bly of montane biotas: linking Andean tectonics and climatic

oscillations to independent regimes of diversification in Pi-

onus parrots. Proceedings of the Royal Society of London. Series

B: Biological Sciences, 274, 2399–2408.Rohling EJ, Grant K, Hemleben C et al. (2007) High rates of

sea-level rise during the last interglacial period. Nature Geo-

science, 1, 38–42.Roy MS (1997) Recent diversification in African greenbuls (Py-

cnonotidae: Andropadus) supports a montane speciation

model. Proceedings of the Royal Society of London. Series B: Bio-

logical Sciences, 264, 1337–1344.Rull V (2008) Speciation timing and Neotropical biodiversity:

the Tertiary-Quaternary debate in the light of molecular phy-

logenetic evidence. Molecular Ecology, 17, 2722–2729.Rull V (2011) Neotropical biodiversity: timing and potential

drivers. Trends in Ecology & Evolution, 26, 508–513.Shepard DB, Burbrink FT (2009) Phylogeographic and demo-

graphic effects of Pleistocene climatic fluctuations in a mon-

tane salamander, Plethodon fourchensis. Molecular Ecology, 18,

2243–2262.Smith BT, Harvey MG, Faircloth BC, Glenn TC, Brumfield RT

(2014) Target capture and massively parallel sequencing of

ultraconserved elements for comparative studies at shallow

evolutionary time scales. Systematic Biology, 63, 83–95.Turchetto-Zolet AC, Pinheiro F, Salgueiro F, Palma-Silva C

(2013) Phylogeographical patterns shed light on evolutionary

process in South America. Molecular Ecology, 22, 1193–1213.Urrego DH, Silman MR, Bush MB (2005) The last glacial maxi-

mum: stability and change in a western Amazonian cloud

forest. Journal of Quaternary Science, 20, 693–701.Voss RS, Jansa SA (2009) Phylogenetic relationships and classi-

fication of didelphid marsupials, an extant radiation of New

World metatherian mammals. Bulletin of the American

Museum of Natural History, 322, 1–177.Wiens JJ (2004) Speciation and ecology revisited: phylogenetic niche

conservatism and the origin of species. Evolution, 58, 193–197.Wiens JJ, Graham CH (2005) Niche conservatism: integrating

evolution, ecology, and conservation biology. Annual Review

of Ecology, Evolution, and Systematics, 36, 519–539.Wiens JJ, Ackerly DD, Allen AP et al. (2010) Niche conserva-

tism as an emerging principle in ecology and conservation

biology. Ecology Letters, 13, 1310–1324.Yang Z, Rannala B (2010) Bayesian species delimitation using

multilocus sequence data. Proceedings of the National Academy

of Sciences, USA, 107, 9264–9269.

T.C.G. conceived the study and performed the analyses.

S.A.J. and T.C.G. synthesized the results and wrote the

manuscript.



http://beast.bio.ed.ac.uk/Tracer

Data accessibility

GenBank numbers of the sequences used in this study

are provided in Appendix S1 (Supporting information).

All DNA alignments, BEAST and BPP input files, resulting

tree files and geographic coordinates of collecting locali-

ties for specimens included in this study have been

uploaded to Dryad (doi:10.5061/dryad.n5s02).

Supporting information

Additional supporting information may be found in the online ver-

sion of this article.

Fig. S1 Map of collecting localities for specimens used in this

study, taken from Giarla et al. (2014).

Fig. S2 Ultrametric CYTB tree inferred in BEAST using three

fossil calibrations.

Fig. S3 Ultrametric tree inferred in BEAST using a multilocus

super-matrix and three fossil calibrations.

Table S1 Individuals sequenced for four fifteen nuclear loci as

part of Giarla et al. (2014).

Table S2 Evolutionary rates per locus for Thylamys + Lestodel-

phys.

Table S3 Table of alignments used in the six extended Bayes-

ian skyline plot runs.

Appendix S1 Table of GenBank accession numbers for

sequences used in this study.



http://dx.doi.org/10.5061/dryad.n5s02

The impact of Quaternary climate oscillations on divergence times and historical population sizes in...

Documents

Transcript of The impact of Quaternary climate oscillations on divergence times and historical population sizes in...