dna-based approaches for evaluating historical demography in terrestrial vertebrates

REVIEW ARTICLE

DNA-based approaches for evaluating historicaldemography in terrestrial vertebrates

ANNA L. FAHEY1*, ROBERT E. RICKLEFS2 and J. ANDREW DEWOODY1,3

1Departments of Forestry and Natural Resource, Purdue University, West Lafayette, Indiana 47907,USA2Department of Biology, University of Missouri at St Louis, St Louis, MO 63121, USA3Biological Sciences, Purdue University, West Lafayette, Indiana 47907, USA

Received 5 December 2013; revised 27 January 2014; accepted for publication 27 January 2014

Contemporary DNA sequences can provide information about the historical demography of a species. However,different molecular markers are informative under different circumstances. In particular, mitochondrial (mt)DNAis uniparentally inherited and haploid in most vertebrates and thus has a smaller effective population size thandiploid, biparentally inherited nuclear (n)DNA. Here, we review the characteristics of mtDNA and nDNA in thecontext of historical demography. In particular, we address how their contrasting rates of evolution and sex-biaseddispersal can lead to different demographic inferences. We do so in the context of an extensive review of thevertebrate literature that describes the use of mtDNA and nDNA sequence data in demographic reconstruction. Wediscuss the effects of coalescence, effective population size, substitution rates, and sex-biased dispersal oninformative timeframes and expected patterns of genetic differentiation. We argue that mtDNA variationin specieswith male-biased dispersal can imply deviations from neutrality that do not reflect actual population expansion orselection. By contrast, mtDNA can be more informative when coalescence has occurred within the recent past,which appears to be the case with many vertebrates. We also compare the application and interpretation ofdemographic and neutrality test statistics in historical demography studies. © 2014 The Linnean Society ofLondon, Biological Journal of the Linnean Society, 2014, ••, ••–••.

ADDITIONAL KEYWORDS: amphibians – birds – coalescence – mammals – mitochondrial genes – neu-trality test statistics – nuclear genes – reptiles – sex-biased dispersal – substitution rates.

INTRODUCTION

In contemporary populations, ecologists use data onbirths, deaths, and migration rates to predict demo-graphic trends. These data are generally unavailablefor historic populations. Rogers & Harpending (1992)first realized that ‘episodes of population growth anddecline leave characteristic signatures in the distri-bution of nucleotide differences between pairs ofindividuals’. Accordingly, evolutionary biologists canuse DNA sequences to test demographic hypothesesconcerning historical populations (e.g. exponentialgrowth, bottlenecks).

For most animal species, mitochondrial (mt)DNAsequences have been the easiest to generate and

analyze. Conserved regions of the mtDNA genomeprovide locations where primers can be anchoredto amplify orthologous genes in diverse taxa,making amplification and sequencing relativelystraightforward (Moritz, Dowlin & Brown, 1987). Themitochondrial genome is usually inherited maternallyas a single, nonrecombining unit and its haploidnature reduces the complications associated withmultiple-site haplotype reconstruction (Clark, 1990;Stephens, Smith & Donnelly, 2001). Clonal inheritanceand haploidy mean that the effective population size(Ne) of mtDNA is only one-quarter that of nuclear(n)DNA (Brown, George & Wilson, 1979; Moritz et al.,1987; Palumbi, Cipriano & Hare, 2001). Mitochondrialgene sequences also tend to be more variable thannuclear gene sequences because of the relativelyhigher nucleotide substitution rates of mtDNA (Brown*Corresponding author. E-mail: [email protected]

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et al., 1979). Thus, mtDNA can be used to discerndemographic signals over shorter time periods. Moreo-ver, because mtDNA achieves monophyly faster thannDNA, mtDNA genes are more likely to be congruentwith a species tree than any singlenuclear gene(Moore, 1995).

Despite these positive attributes, mtDNA also haslimitations for demographic reconstruction. Forexample, selection on a given mitochondrial gene, orany part of the nuclear genome that interacts withmtDNA, will impact the entire mtDNA moleculebecause of its nonrecombining nature (Galtier et al.,2009; Karl et al., 2012). In the case of historicaldemography, this will lead to inferences that reflectonly the most recent selective sweep, effectivelyerasing any prior demographic history of the species.The haploid and nonrecombining aspects of mtDNAare therefore a double-edged sword because theyallow convenient haplotypeinference and the genera-tion of resolved evolutionary trees, although selectivesweeps or introgression can blur demographic signals(Ballard & Whitlock, 2004).

To offset issues with mtDNA and add a complemen-tary perspective from nDNA, many researchers arenow pushing for the use of both sets of markers indemographic studies (Edwards et al., 2005; Bazin,Glemin & Galtier, 2006; Galtier et al., 2009; Dupuis,Roe & Sperling, 2012). The use of both genomesprovides multiple independent samples of geneticdiversity from both sets of parents. However, nDNAtends to be less polymorphic than mtDNA, and somore sampling (i.e. nucleotides and loci) is required toprovide statistical power equivalent to that often pro-vided by a single mitochondrial gene. Furthermore,nDNA substitution rates among genes vary more thanmtDNA rates (Yang & Nielsen, 1998) and thus diver-gence estimates based on nDNA data are oftensubject to much wider confidence intervals.

In this review, we consider the effectiveness ofmtDNA and nDNA gene sequences in reconstructingthe demographic histories of terrestrial vertebrates(mammals, birds, amphibians, and reptiles). Forexample, variation in dispersal tendencies amongvertebrates, notably male-biased in mammals andfemale-biased in birds, has the potential to makeuniparentally inherited markers (e.g. mtDNA) moreor less effective than nDNA in different taxonomicgroups. We also review the efficacy of differentmethods for inferring historical demography.

COALESCENCE AND EFFECTIVEPOPULATION SIZE

Accurate estimates of demographic history depend onthe coalescent; all neutral genes ultimately reachmonophyly, although nuclear and mitochondrialgenes drift at different rates because of the four-folddifference in Ne (Fig. 1) (Nei, 1987). With randommating, no selection, and no sex-biased dispersal,autosomal genes (on average) have a four-fold largerNe (and sex-linked loci a two-fold larger Ne) thanmtDNA (Moore, 1995). Conversely because of itssmaller effective population size, mtDNA completeslineage sorting sooner (after Ne generations underneutrality) and is more effective in demographicanalyses of the more recent past, whereas nucleargenes are more informative of the distant past (Zink& Barrowclough, 2008). Looking back 4Ne genera-tions, almost all mtDNA gene trees should bemonophyletic, whereas only 60% of nDNA gene treeswill have reached monophyly (Moore, 1995; Palumbiet al., 2001). For example, Moore (1995) calculatedthat, when the probability that a mtDNA gene treeresembles the true species tree is 0.95, the probabilityof congruence between any given nuclear gene tree

Figure 1. Illustration of coalescence versus divergence. The dotted line represents a contemporary barrier to gene flowbetween populations 1 and 2. Coalescence is the time since the most recent common ancestor and can vary depending onthe locus. In (A), the time since divergence and the coalescent are temporally consistent. This scenario will happen morerapidly in loci that reach monophyly after Ne versus 4Ne. By contrast, (B) illustrates temporal discord between divergenceand coalescence. In such cases, many genes will still be polyphyletic as a result of incomplete lineage sorting.

2 A. L. FAHEY ET AL.

© 2014 The Linnean Society of London, Biological Journal of the Linnean Society, 2014, ••, ••–••

and the species tree would be only 0.62. When fivenuclear genes are considered, the probability that allare congruent with the true species tree is then only0.625 (or 0.09) (i.e. less than 1 in 10). Moore (1995)also found that depending on the method, confidencein congruence requires from 16 to upwards of 40nuclear genes.

Moore’s example assumed neutrality, panmixia,and random mating, although departures from theseassumptions can influence estimates of Ne, of coales-cent events, and, ultimately, of historical demography.For example, the Ne of a nuclear gene can be equiva-lent to, or smaller than, that of a mitochondrial genewhen a small proportion of males sire the next gen-eration, as in populations with highly polygynousmating systems (Hoelzer, 1997). Additionally, popula-tion genetic structure can bias estimates of Ne ifdifferentiation is more pronounced in one sex, increas-ing Ne for the philopatric sex relative to the dispers-ing sex (Hoelzer, 1997; Prugnolle & de Meeus, 2002).Finally, selection can also confound the reconstructionof evolutionary history by fixing selected genes,thereby creating the appearance of rapid coalescence.This could be interpreted as population expansion,although it represents the demographic expansiononly of those lineages bearing the beneficial allele(Slatkin & Hudson, 1991). Fortunately, selectivesweeps of mtDNA appear to be rare and actualempirical evidence for mtDNA selection is surpris-ingly sparse (Karl et al., 2012).

THE CENTRAL ROLE OF SUBSTITUTIONRATES IN HISTORICAL INFERENCE

Accurate reconstruction of population demographyrequires that the markers used for inference provideinsights into the appropriate historical timeframe(e.g. genes that evolve slowly are poor choices forinvestigations into the Pleistocene epoch). Further-more, accurate estimates of molecular evolutionaryrates require measures of the sequence divergencebetween two species, conversion of divergence esti-mates into nucleotide substitutions, and estimates ofthe time since divergence for the species in question(Bromham & Penny, 2003; Ellegren, 2007; Ho, 2007).Unfortunately, molecular evolutionary rates varyacross genomes and among species (Arbogast et al.,2002; Bromham & Penny, 2003; Smith & Donoghue,2008) and can also be time-dependent (Ho et al.,2011). Additionally, fossil-calibrated species diver-gence estimates are difficult to obtain for taxonomicgroups that are underrepresented in the fossil record(e.g. birds; Ellegren, 2007). An extensive study com-paring nuclear gene substitution rates betweenchicken and turkey (Axelsson et al., 2005) found the

mean intronic divergence to be 10.7%, which trans-lates to a mean neutral mutation rate of 0.12–0.15%per site per million years (Ellegren, 2007). This isapproximately twice the mean rate of 21 orthologousgenes compared between ducks and chickens (vanTuinen & Hedges, 2001; Ellegren, 2007). Further-more, these estimates are based on mean substitutionrates across the genome; in mammals, substitutionrates vary considerably across chromosomes and indifferent regions along a chromosome (MGSC, 2002;CSAC, 2005). Thus, the uncertainties associated withmolecular evolutionary rates can undermine efforts toaccurately date historical demographies.

Brown et al. (1979) provided one of the first esti-mates of mtDNA evolutionary rates: they determinedthat there were 0.02 substitutions per base pair permillion years (approximately 10 times the rate esti-mated for the nDNA genome) in a sample of primatespecies. Subsequently, a substitution estimate of 2%mtDNA per million years has become a general rule ofthumb for birds (Lovette, 2004; Weir & Schluter,2008). However, this rate varies considerably amongtaxa (Ballard & Whitlock, 2004; Galtier et al., 2009;Eo & DeWoody, 2010). For example, Nabholz, Glémin& Galtier (2008) measured lineage-specific mutationrates based on more than 1500 species (using fossilsand synonymous versus nonsynonymous substitutiondata) and determined that mtDNA substitution ratescan vary100-fold among mammalian species and30-fold among birds.

It logically follows that, if accurate substitutionrates are required for accurate clocks, and if substi-tution rates vary dramatically among genes andspecies, then independent rate estimates may berequired for different genes in each species (Fig. 1).Because this is a major challenge in studies of non-model species (Ellegren, 2007; Ho, 2007), the nextbest option is to incorporate data from a closely-related species (Lovette, 2004) and/or to incorporateuncertainty (i.e. a range of substitution rates) intoanalyses of divergence and Ne (Bos et al., 2008). Manystudies now use a range of substitution rates to morerealistically bracket estimated time intervals (Lopes,Miño & Del Lama, 2007; Townsend et al., 2007;Bellemain, Bermingham & Ricklefs, 2008; Faheyet al., 2012).

Once reasonable substitution rate estimates are tohand, the divergence and expansion times for aspecies or population can be computed. The substitu-tion rate per se does not affect the ability to computecoalescent times, although the substitution rateand divergence time determine the density ofpolymorphisms in a sample and thus the power of agiven marker. For neutral markers, Ne determinesthe range of coalescent times that may be estimatedfrom standing levels of genetic diversity. Thus, the

EVALUATING HISTORICAL DEMOGRAPHY 3


shallower perspective of mtDNA should provideinsights into recent (Quaternary) processes comparedto the deeper evolutionary perspective provided bynDNA sequences (Zink & Barrowclough, 2008).

What have we learned about the divergence andexpansion of historical vertebrate populations usingmolecular data from contemporary populations? Wereviewed 36 published nDNA and mtDNA studies ofvertebrate demography for estimates of divergenceand expansion. These studies were chosen becausethey reported both mtDNA and nDNA sequence data,employed one or more of the neutrality test statisticsaddressed below, and inferred the historical demog-raphy of a terrestrial vertebrate. Among studies ofmammals and birds, we found that, across markers,all but one coalescence point occurred within thePleistocene epoch (Table 1). These findings are con-sistent with the findings of Turchetto-Zolet et al.(2012), who also found that lineage divergence timesfor South American mammals and birds werepredominately Pleistocene (only three of 38 had esti-mates > 2.6 Mya). By contrast, our review of diver-gence times for amphibian species revealed that onlyapproximately half of the amphibian divergence timeestimates fall within the Pleistocene (reptile data aretoo sparse for inference). Thus, Table 1 suggests thatmost contemporary mammalian and avian lineagescoalesced within the ‘recent’ evolutionary past (i.e.within the last approximately two million years),whereas genetic variation in reptile and amphibian(herpetofauna; herps) populations tends to coalescedeeper in time.

It could be argued that mtDNA sequences are themost appropriate markers for mammals and birdsbecause most nDNA genes will not have coalesced (i.e.achieved monophyly; Zink & Barrowclough, 2008).The divergence estimates in Table 1 are based pre-dominantly on mtDNA and thus may not properlyrepresent nDNA (Turchetto-Zolet et al., 2012). Unfor-tunately, extensive calibrations with fossil data andnDNA gene sequences are required to evaluate poten-tial bias of nDNA relative to mtDNA estimates. Therapid development of whole genome DNA sequencingwill undoubtedly help with this problem in the future(Baird et al., 2008; Meyer, Stenzel & Hofreiter, 2008;Hohenlohe et al., 2010; O’Neill et al., 2012; Puritz,Addison & Toonen, 2012; Zhan et al., 2013), although,even then, uncertainties remain that can cloud demo-graphic inference. These include an incomplete fossilrecord (which leads to imprecise estimates of diver-gence time) and the variation in nucleotide substitu-tion rate (between genomes within a species, as wellas within a genome between taxonomic lineages).Finally, variation in the categorical assignment oflineages to species can further confound comparativestudies (e.g. the taxonomic disparity known to exist

among the vertebrates; reptiles and amphibians areoften ‘lumped’ into far fewer taxa than are mammalsand birds) (Johns & Avise, 1998).

DEMOGRAPHIC AND NEUTRALITYTEST STATISTICS

Several tests have been developed to estimate the sizeand growth rate of historic populations. Many of thesecompare the observed sequence variation with thevariation expected under a neutral model. Threeclasses of statistics can detect changes in populationsize over time (Ramos-Onsins & Rozas, 2002;Ramírez-Soriano et al., 2008). Class I statistics arethose that use segregating site (mutation) frequenciesand the excess of rare mutations to determine popu-lation history. These statistics include: Tajima’s D(Tajima, 1989), which evaluates the observed distri-bution of polymorphisms compared with the meanpairwise difference expected under neutrality; Fu andLi’s D* and F* (Fu & Li, 1993), which compare thenumber of derived singleton mutations with the totalor mean number of derived nucleotide variants; andR2, which compares the mean number of nucleotidedifferences with the difference in nucleotide muta-tions between singletons (Ramos-Onsins & Rozas,2002). Class II statistics, such as Fu’s FS (Fu, 1997),use composite haplotypes to determine populationdemography. Finally, Class III statistics use the dis-tribution of pairwise sequence differences, also knownas the mismatch distribution, to test for deviationsfrom the null model of population expansion (Slatkin& Hudson, 1991; Rogers & Harpending, 1992;Harpending et al., 1993). Demographic expansion fol-lowing a population bottleneck often produces a star-like gene tree, where the majority of coalescenceevents occur near the root (Slatkin & Hudson, 1991).This means that a polymorphism at a singlenucleotide position will consist of a ‘new’ mutationand all other nucleotide sites will have the ‘ancestral’mutation; this can occur over the length of thesequence, and neutrality tests uncover significantexcess of this pattern (Slatkin & Hudson, 1991).

The neutrality test statistics (D, D*, F*, R2, and FS)test for departures from neutral expectations (Fu,1997; Ptak & Przeworski, 2002). Significantly nega-tive values of these statistics signify populationexpansion or selection. Selection must be consideredbecause it has the potential to influence gene gene-alogies and compromise demographic inferences. Thisis not to say that selection necessarily poisons adataset because neutrality test statistics can helpquantify the extent of selection (if any) and its poten-tial impact on historical inference.

FS and R2 are the most powerful statisticsfor testing population expansion (Fu, 1997;



Tab

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Div

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and

expa

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ones

tim

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from

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Spe

cies

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erge

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e(M

ya)

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sion

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e(M

ya)

(con

fide

nce

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itat

ion

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Mam

mal

sA

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oun

dsq

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(Spe

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yii)

0.2–

1.7

0.01

6–0.

374

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albr

eath

etal

.(2

011)

Pac

ific

wh

ite-

side

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hin

(Lag

enor

hyn

chu

sob

liqu

iden

san

dL

agen

orh

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s)1.

05(0

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1.36

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are

etal

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002)

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arct

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(Sor

extu

nd

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0.19

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0.27

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126

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011)

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(201

2)



Ramos-Onsins & Rozas, 2002; Ramírez-Soriano et al.,2008); R2 is more powerful with small sample sizesand FS is more powerful with large sample sizes.Although FS and R2 are generally the most suitablefor detecting population expansion, they do not workas well with recombination, which interconvertshaplotypes and, in this case, D, D* or F* are better.Fu and Li’s D* and F* have the most power when theelapsed time since expansion has been relativelyshort (Ramírez-Soriano et al., 2008); thus, these sta-tistics are most applicable to genes that coalescerapidly (e.g. mtDNA). Fu and Li’s D* and F* are morepowerful at distinguishing background selection thanTajima’s D, and thus if D* and F* are significant butD is not, this points more to selection than to popu-lation expansion (Fu, 1997).

Ptak & Przeworski (2002) found that Tajima’s D ismore negative when few individuals from multiplelocations are sampled compared to many individualsfrom only a few localities because more rare allelesare likely to be included in samples across the entiredistribution of a population. Similarly, X-linked locishow lower values of D than autosomal loci becauseX-linked loci have smaller effective population sizes(Ptak & Przeworski, 2002). These findings indicatethat neutrality tests (i.e. Tajima’s D) are better ableto detect population expansion when complete lineagesorting has occurred because they depend on thecoalescence of genes.

A significantly negative neutrality statistic implieseither selection on a given locus or population expan-sion. Because DNA studies of historical demographyshould employ multiple neutral markers, statisticalconcordance across loci is more likely to imply demo-graphic processes (which should act in a genome-widemanner) than selection (which is typically more locusspecific). Thus, population expansion is probably amore parsimonious explanation than selection acrossan entire genome when several loci have significantlynegative D. This conclusion would be further substan-tiated if one of the markers was mtDNA in origin;selection pressures differ between mtDNA and nDNA,whereas both are affected similarly by demographicprocesses such as expansion or contraction.

How often are neutrality statistics concordantbetween nDNA and mtDNA genomes? We reviewed 26studies (Table 1), many with more than one species orpopulation, which surveyed both mtDNA and nDNA.Of the 40 populations surveyed, only one had signifi-cantly negative Tajima’s D values for both nDNA andmtDNA. In all other studies, either both categories ofmarkers were nonsignificant or only one was signifi-cant (Fig. 2A). In some cases, significantly negativemtDNA neutrality test statistics were not corrobo-rated with the nDNA genes (Bensch et al., 2006; Lee& Edwards, 2008; Lim & Sheldon, 2011). This was the

case for two rainforest passerines (Lim & Sheldon,2011), where significantly negative values of Tajima’sD or R2 in the mtDNA data contrasted with nonsig-nificant values at > 70% of the nuclear genes. Asimilar pattern (significant mtDNA neutrality teststatistics and nonsignificant nDNA test statistics)was also found in our study of the historical demog-raphy of avian populations on the island of Hispan-iola (Fahey et al., 2012; A. L. Fahey, unpubl. data).There, more than 50% of D, D* and F* values formtDNA were significantly negative, whereas fewerthan 10% of D, D*, and F* nDNA values were signifi-cantly negative (N = 4–6 nuclear loci investigated perspecies). In the Australian Red-backed Fairywren

(A)

(B)

0

5

10

15

20

25

only mtDNA significant

only nDNA significant

both were significant

neither was significant

# of

stud

ies

Rep les

Amphibians

Birds

Mammals

0

1

2

3

4

5

6

7

8

only mtDNA significant

only nDNA significant

both were significant

neither was significant

# of

stud

ies

Rep les

Amphibians

Birds

Mammals

Tajima’s D

Fu’s F

Figure 2. The distribution of significant values of (A)Tajima’s D and (B) Fu’s F compared between nuclear(n)DNA and mitochondrial (mt)DNA in terrestrial verte-brates. When such neutrality test statistics are significant,it can indicate population expansion. For each studyanalyzed, these test statistics were either concordant ordisparate between the markers. The distribution of sig-nificant values is shown (both nDNA and mDNA aresignificant; only nDNA is significant; only mDNA is sig-nificant; neither marker is significant), as well as thenumber of studies conducted for mammals, birds, reptiles,and amphibians.



(Malurus melanocephalus), the population with thehighest mean Tajima’s D at nDNA loci had the lowest(and significantly negative) mtDNA Tajima’s D, andthe population with the lowest mean Tajima’s D innDNA had the highest mtDNA statistics (Lee &Edwards, 2008). Although no values of D, D* and F*were significantly negative in a study of arctic groundsquirrels (Spermophilus parryii; Galbreath et al.,2011), the means of the values for nDNA genes werenegative and the mtDNA gene was positive for allthree statistics. Thus, the literature on Tajima’s Dhighlights the discordance between mtDNA andnDNA. In most cases, this conflict probably resultsbecause the species/populations surveyed originatedwithin the last approximately two million years, andthis timeframe captures the coalescence of mtDNAgenes but not nDNA genes (as a result of their four-fold greater Ne).

In our review, Fu’s F values were not as widelyreported as Tajima’s D but generally were more con-cordant between mtDNA and nDNA than wereTajima’s D values (Fig. 2B, Table 2). Over all taxa, themean F was negative (−23.6 for mtDNA; −6.82 fornDNA) and half of the values were significant. Therewere more significantly negative values of Fu’s F thanTajima’s D, perhaps because Fu’s F has greater sen-sitivity for detecting population expansion (Fu, 1997;Ramos-Onsins & Rozas, 2002; Ramírez-Soriano et al.,2008).

Two additional methods for determining historicaldemographic parameters are the mismatch distribu-tion (Slatkin & Hudson, 1991) and the Bayesianskyline plot (Pybus, Rambaut & Harvey, 2000;Drummond et al., 2005). Both of these tests not onlydetect population expansion or decline, but alsoprovide information on the timing of these eventsand historic population sizes. Mismatch distributionsare based on coalescent theory and require aparameterized demographic model to test for popula-tion growth (Slatkin & Hudson, 1991). The mismatchdistribution only shows a single demographic event(Rogers, 1995; Lloyd, 2003; Lim & Sheldon, 2011)because an initial expansion will overshadow anysubsequent expansions or minor bottlenecks. Mis-match distributions tend to have large confidenceintervals because they are based on the polymorphicsites contained within only a few haplotypes. They arequite conservative, meaning that it is often difficult toreject the null model of population expansion(Felsenstein, 1992; Schneider & Excoffier, 1999). Thus,mismatch distributions are best used in conjunctionwith the demographic statistics discussed above.

The Bayesian skyline plot, unlike the mismatchdistribution, does not require any a priori parametersand, instead, is a piecewise model (different points intime) of a constant population that uses genealogies

to determine historical population sizes (Pybus et al.,2000; Drummond et al., 2005; Ho & Shapiro, 2011).Therefore, the skyline plot tracks population sizechanges across time and can detect multiple signifi-cant demographic events. Although the skyline plotcan provide a more nuanced (and presumably morenatural) demographic history, it cannot detect popu-lation size changes between intervals, unless inter-vals are pooled, which in turn may diminish theability to detect population size changes (Strimmer &Pybus, 2001). A newer method for determining his-torical population sizes is the pairwise sequentialMarkovian coalescent (PSMC) analysis (Li & Durbin,2011). The PSMC is used to find the most recentcommon ancestor from diploid whole-genomesamples, enabling population estimates from thou-sands of independent loci. Prado-Martinez et al.(2013) used PSMC to analyze genome-wide singlenucleotide polymorphisms (SNPs) and determine theeffective population sizes of great ape species overtime. PSMC may be a more informative test thaneither the mismatch distribution or skyline plotbecause of its ability to analyze genomic data asopposed to single locus data.

Published mtDNA mismatch distributions andBayesian skyline plots are not always concordant. Forexample, Lim & Sheldon (2011) examined the histori-cal demography of two rainforest bird species insouth-east Asia and, in one of the populations, themtDNA based mismatch distribution (and neutralitytest statistics) showed signs of population expansion,whereas the extended Bayesian skyline plot using thecombined mtDNA and nDNA did not. They attributedthis discordance to the skyline plot’s inability toaccount for population structure (subpopulations)after a population has split. Any deviation from equi-librium (e.g. selection or sex-biased dispersal), incom-plete lineage sorting or incorrect substitution rateestimates can cause misinterpretations in both mis-match distribution and skyline plots (Ho & Shapiro,2011; Lim & Sheldon, 2011). In our view, the myriadcaveats associated with mismatch distributions andskyline plots mean that strong inferences are moreplausible when the two methods produce concordantresults.

SEX-BIASED DISPERSAL

The theory of coalescence for a particular gene isbased on the complete divergence of species/populations from one another. However, prior to theevolution of reproductive barriers, individuals dis-persing between populations may create anastomosesin gene trees. Thus, organismal dispersal tendenciesinfluence coalescence within gene trees. In particular,sex-biased dispersal (whereby one sex disperses and



Tab

le2.

His

tori

cal

dem

ogra

phic

sum

mar

ies

ofst

udi

esu

sin

gbo

thm

tDN

Aan

dn

DN

Am

arke

rsin

mam

mal

s,bi

rds,

amph

ibia

ns,

and

rept

iles

Nu

mbe

rS

peci

es/

popu

lati

onM

arke

rN

JH

bpH

Dπ

Taji

ma’

sD

Fu

’sF

Ne

(104 )

Hap

loty

pen

etw

ork

Ref

eren

ceM

ean

/MD

Ran

ge%

Mea

n/M

DR

ange

%

Mam

mal

s1

Arc

tic

grou

nd

squ

irre

l(S

perm

oph

ilu

spa

rryi

i)

mtD

NA

(Cyt

b)20

059

1140

0.97

60.

0252

0.51

–0

––

––

Non

ran

dom

Gal

brea

thet

al.

(201

1)8

anon

ymou

slo

ci30

370

7485

0.73

10.

0026

−0.5

7/−0

.51

−1.4

5to

0.51

0–

––

–L

ess

obvi

ous

geog

raph

ical

stru

ctu

re

2P

acifi

cw

hit

e-si

ded

dolp

hin

s(L

agen

orh

ynch

us

obli

quid

ens)

mtD

NA

(Cyt

b)6

349

6–

0.00

25−0

.45

–0

––

–5.

09H

aplo

type

net

wor

kfo

rtw

on

DN

Aw

ith

both

spec

ies;

one

show

edpa

raph

yly,

one

look

spo

lyph

yly

Har

eet

al.

(200

2)4

nD

NA

loci

4220

7118

–0.

0011

−0.0

9/−0

.46

−0.7

7to

1.32

0–

––

3D

usk

ydo

lph

in(L

agen

orh

ynch

us

obsc

uru

s)

mtD

NA

(Cyt

b)5

449

6–

0.00

740.

08–

0–

––

10.9

4n

DN

Alo

ci36

2571

18–

0.00

160.

29/0

.12

−0.3

5to

1.28

0–

––

4H

olar

ctic

shre

w(S

orex

tun

dre

nsi

s)

mtD

NA

(Cyt

b)20

310

211

400.

985

0.01

13−1

.71

–1

−86.

1–

1–

NA

Hop

eet

al.

(201

1)2

nu

clea

rlo

ci17

217

1277

0.44

30.

0012

−1.2

6−2

.27

to−0

.22

0.5

−6.9

0−2

.0to

−11.

80.

5–

5S

orex

cin

ereu

sco

mpl

exof

shre

ws

(Ber

ingi

ancl

ade)

mtD

NA

(Cyt

b)12

946

947

0.96

00.

0137

−0.7

3–

0−7

.76

–0

–N

AH

ope

etal

.(2

012)

4n

ucl

ear

loci

208

6315

680.

606

0.00

54−1

.46/

−1.4

3−2

.27

to−0

.71

0.5

−7.2

9/−7

.85

−11.

25to

−2.2

10.

75–

6S

orex

cin

ereu

sco

mpl

exof

shre

ws

(Sou

ther

ncl

ade)

mtD

NA

(Cyt

b)23

211

985

30.

977

0.01

33−1

.72

–1

−107

.57

–1

–N

A

4n

ucl

ear

loci

392

103

1568

0.88

70.

0096

−0.8

2/−0

.85

−1.0

7to

−0.5

20

−11.

04/−

10.4

4−1

9.26

to−4

.03

1–

7F

ield

vole

(Mic

rotu

sag

rest

is)

mtD

NA

(Cyt

b)16

324

711

430.

990

0.02

98−0

.78

−2.0

3to

−0.7

80.

33−1

76.2

8−2

86.5

to−2

.96

0.67

–T

hre

ecl

ades

fou

nd

wit

hm

tDN

A.

Th

ese

are

also

fou

nd

inth

ese

x-li

nke

dan

dn

DN

A,

alth

ough

the

supp

ort

isn

otas

stro

ng

Pau

péri

oet

al.

(201

2)2

nD

NA

5525

330.

645

0.00

21−0

.21

−1.8

7to

0.41

0.25

−1.3

0−3

.95

to0.

920.

25–

2X

-lin

ked

nD

NA

1964

90.

369

0.00

291.

025

−1.3

8to

1.44

01.

89−2

.89

to3.

810

–

2Y-

lin

ked

nD

NA

1110

570.

608

0.00

271.

61−0

.37

to1.

980

3.30

−0.3

4to

4.73

0–

8Ja

mu

nap

ari

goat

(Cap

raae

gagr

ush

ircu

s)br

eed

inIn

dia

mtD

NA

(HV

R1)

5034

457

0.98

4–

––

–−1

5.53

–1

<0.

8N

AR

out

etal

.(2

012)

Y-li

nke

dn

DN

A12

–58

4–

0.13

50.

68–

02.

54–

0

9F

inle

sspo

rpoi

ses

(Neo

phoc

aen

aph

ocae

noi

des

)

4n

DN

A14

416

318

110.

760

0.00

68−2

.27

−2.6

7to

−1.9

91

−20.

46−2

6to

−6.0

51

–N

etw

ork

was

not

clea

rly

divi

ded

into

the

thre

ere

gion

s.O

vera

ll,

star

-lik

en

etw

ork

(in

mos

tm

arke

rs)

Juet

al.

(201

2)



Bir

ds

10P

ine

gros

beak

(Pin

icol

aen

ucl

eato

r)

mtD

NA

(ND

2)74

5110

410.

951

0.00

37–

––

−10.

19/−

8.61

−17

to−4

.94

1–

NA

Dro

vets

kiet

al.

(201

0n

DN

A(A

CO

19)

7312

096

80.

731

0.00

39–

––

2.34

/0.4

1−0

.68

to7.

280

–

11L

ittl

esp

ider

hu

nte

r(A

rach

not

her

alo

ngi

rost

ra)

mtD

NA

(ND

2+C

ytb)

7333

1850

–0.

0025

−1.4

9−2

.4to

−0.5

70.

5–

––

–N

AL

im&

Sh

eldo

n(2

011)

10n

ucl

ear

loci

280

–42

31–

0.00

73−0

.24/

−0.0

2−1

.89

to1.

130.

11–

––

–

1Z

-lin

ked

24–

400

–0.

0042

−1.0

6−1

.59

to−0

.52

0–

––

–

12S

hor

t-ta

iled

babb

ler

(Mal

acoc

incl

am

alac

cen

sis)

mtD

NA

(ND

2+C

ytb)

4636

1915

–0.

0047

−0.5

5/−1

.29

−1.3

3to

0.97

0–

––

–N

A

10n

ucl

ear

loci

420

–43

16–

0.00

52−0

.54/

−0.5

4−1

.93

to1.

140.

04–

––

–

1Z

-lin

ked

37–

404

–0.

0038

−0.1

9/−0

.63

−0.7

8to

0.84

0–

––

–

13H

awai

iA

kepa

(Lox

ops

cocc

ineu

sco

ccin

eus)

mtD

NA

(ND

2+C

R)

186

9721

590.

901

0.00

42−0

.56/

−0.6

7−1

.38

to0.

440

−4.2

8/−2

.57

−16.

19to

0.35

0.5

5–14

Hap

loty

pen

etw

orks

did

not

show

popu

lati

onst

ruct

ure

Red

ing

etal

.(2

010)

2n

ucl

ear

loci

158

1053

50.

284

0.00

13−1

.22

−1.7

3to

−0.7

10.

5−3

.48

−5.7

7to

−1.1

90.

5–

14R

ed-b

acke

dfa

iry

wre

n(M

alu

rus

mel

anoc

eph

alu

s)–

top

end

mtD

NA

(ND

2)14

–46

7–

0.00

70−0

.90

–0

––

–42

No

hap

loty

pes

shar

edbe

twee

nw

hit

e-w

inge

dan

dre

d-ba

cked

fair

yw

ren

sin

mtD

NA

,al

thou

ghth

ere

was

inn

DN

A

Lee

&E

dwar

ds(2

008)

35n

ucl

ear

loci

–15

004

–0.

0143

−0.4

8/−0

.66

−1.7

1to

0.94

0.06

––

–

15R

ed-b

acke

dfa

iry

wre

n(M

alu

rus

mel

anoc

eph

alu

s)–

Cap

eYo

rk

mtD

NA

(ND

2)8

–46

7–

0.00

90−1

.00

–0

––

–26

35n

ucl

ear

loci

–15

004

–0.

0127

−0.2

0/−0

.37

−1.2

9to

1.38

0–

––

16R

ed-b

acke

dfa

iry

wre

n(M

alu

rus

mel

anoc

eph

alu

s)–

Eas

tern

For

est

mtD

NA

(ND

2)8

–46

7–

0.00

80−1

.8–

1–

––

27.5

35n

ucl

ear

loci

–15

004

–0.

0139

0.29

/0.3

4−0

.99

to2.

280

––

–

17W

illo

ww

arbl

er(P

hyl

losc

opu

str

och

ilu

s)

mtD

NA

(Cyt

b)33

–10

41–

0.00

21−2

.28

–1

––

–20

.7N

AB

ensc

het

al.

(200

6)4

nu

clea

rlo

ci15

0–

1513

––

−1.1

4/−1

.03

−2.1

1to

−0.2

70.

33–

––

205.

7

18C

hif

fch

aff

(Ph

yllo

scop

us

coll

ybit

a)

mtD

NA

(Cyt

b)13

–10

41–

0.00

14−1

.95

–1

––

–10

5.9

4n

ucl

ear

loci

32–

1513

––

−1.0

1/−1

.01

−1.3

6to

−0.6

70

––

–23

2.5

19R

ufo

us-

brea

sted

leaf

toss

er(S

cler

uru

ssc

anso

r)

mtD

NA

(ND

2)85

2510

410.

871

0.01

240.

59–

0−0

.54

–0

–H

aplo

type

net

wor

kssh

owed

geog

raph

ical

stru

ctu

rin

g

D’H

orta

etal

.(2

011)

nD

NA

(Fib

7)44

3095

40.

875

0.00

40−0

.41

–0

−22.

04–

1–

Hap

loty

pes

wer

esh

ared

b/w

the

diff

eren

tpo

pula

tion

s

20C

inn

amon

teal

-lo

wla

nd

(An

ascy

anop

tera

cyan

opte

ra)

mtD

NA

(CR

)52

1612

72–

0.00

34−0

.75

–0

−3.4

3–

09.

8H

aplo

type

net

wor

ksh

adm

ixin

gof

A.c

.cy

anop

tera

and

A.c

.or

inom

us

hap

loty

pes

(exc

ept

Hem

oglo

bin

Asu

bun

it)

Wil

son

etal

.(2

012)

7n

DN

A74

3843

–0.

0081

0.73

/0.4

−0.7

5to

2.05

01.

63/2

.95

−7.1

5to

9.64

0.14

21C

inn

amon

teal

-h

igh

lan

d(A

nas

cyan

opte

raor

inom

us)

mtD

NA

(CR

)50

1312

72–

0.00

19−1

.47

–0

−3.3

3–

05.

0

7n

DN

A50

3843

–0.

0060

0.34

/0.3

8−1

.94

to2.

770.

141.

74/1

.92

−2.8

4to

5.99

0.14

22M

arbl

edm

urr

elet

s(B

rach

yram

phu

sm

arm

orat

us)

9n

DN

A12

061

>39

28–

–−0

.01

–0

––

––

Sta

r-li

keh

aplo

type

rela

tion

ship

amon

gm

ost

intr

ons

(dat

an

otpu

blis

hed

)

Con

gdon

etal

.(2

000)



Tab

le2.

Con

tin

ued N

um

ber

Spe

cies

/po

pula

tion

Mar

ker

NJ

Hbp

HD

π

Taji

ma’

sD

Fu

’sF

Ne

(104 )

Hap

loty

pen

etw

ork

Ref

eren

ceM

ean

/MD

Ran

ge%

Mea

n/M

DR

ange

%

AM

PH

IBIA

NS

23S

moo

thn

ewt

(Lis

sotr

iton

vula

ris

vulg

aris

)

mtD

NA

(ND

2)22

640

1224

0.91

00.

0080

−0.4

5–

0–

––

36&

29.8

Nad

ach

owsk

a&

Bab

ik(2

009)

8n

DN

A86

6–

4749

0.66

00.

0058

−0.6

1/−0

.82

−1.9

6to

2.14

0.25

––

–

24S

moo

thn

ewt

(Lis

sotr

iton

vula

ris

koss

wig

i)

mtD

NA

(ND

2)21

612

240.

670

0.00

230.

39–

0–

––

8n

DN

A30

4–

4749

0.77

00.

0119

1.20

/1.3

6−1

.72

to2.

40.

38–

––

25C

ane

toad

(Rh

inel

lam

arin

a)

mtD

NA

(Cyt

b)65

1432

7–

0.62

10.

420.

02to

0.81

0−0

.37

−0.7

4to

0.01

026

.9m

tDN

Ash

owed

two

grou

ps(o

ne

wit

hR

.sch

nei

der

ian

dR

AB

R.m

arin

apo

pula

tion

and

the

oth

erL

AB

R.m

arin

a).

nD

NA

show

edtw

ogr

oups

(on

ew

ith

R.m

arin

aR

AB

and

LA

Ban

dth

eot

her

R.s

chn

eid

eri)

Seq

uei

raet

al.

(201

1)3

nD

NA

196

4817

67–

0.47

4−0

.99/

−0.7

6−1

.6to

−0.6

20.

33−5

.39/

−5.1

4−6

.33

to−4

.69

0.67

26C

uru

ruto

ad(R

hin

ella

sch

nei

der

i)

mtD

NA

(Cyt

b)27

532

7–

0.13

1−1

.56

–1

−3.0

7–

114

.3

3n

DN

A84

2717

67–

0.41

7−0

.05/

−0.2

8−0

.63

to0.

760

−2.1

9/−2

.47

−2.6

9to

−1.4

0

27P

uer

toR

ican

frog

–L

uqu

illo

(Ele

uth

erod

acty

lus

port

oric

ensi

s)

2m

tDN

A(C

ytb/

CR

)91

4312

110.

963

0.00

95–

––

−14.

92−2

4.23

to−5

.62

1–

mtD

NA

show

edtw

om

ain

clu

ster

sco

rres

pon

din

gto

LU

Qan

dC

AY

sepa

rate

dby

15m

uta

tion

s.In

nD

NA

,L

UQ

and

CA

Ysh

ared

no

hap

loty

pes

and

ther

ew

asa

divi

sion

base

don

regi

on

Bar

ker

etal

.(2

011)

nD

NA

182

596

0.50

00.

0010

––

–1.

79–

0

28P

uer

toR

ican

frog

–C

ayey

(Ele

uth

erod

acty

lus

port

oric

ensi

s)

2m

tDN

A(C

ytb/

CR

)89

3312

110.

882

0.00

80–

––

−7.6

5−1

2.26

to−3

.03

1–

nD

NA

1810

596

0.79

80.

0030

––

–−2

.83

–1

29M

alag

asy

reed

frog

(Het

erix

alu

sbe

tsil

eo)

mtD

NA

(Cyt

b)17

962

316

0.93

0–

0.40

–0

−12.

91–

1–

Indi

vidu

alcl

ust

ers

wer

est

ar-l

ike

inm

tDN

A.

nD

NA

had

shal

low

diff

eren

tiat

ion

,an

dso

me

hap

loty

pesh

arin

g

Geh

rin

get

al.

(201

2)n

DN

A(R

ag-1

)15

350

1298

0.76

2–

−1.9

1–

1−2

7.67

–1

30M

alag

asy

reed

frog

(Het

erix

alu

sm

adag

asca

rien

sis)

mtD

NA

(Cyt

b)61

2531

60.

904

–0.

15–

0−2

.93

–0

–In

divi

dual

clu

ster

sw

ere

star

-lik

ein

mtD

NA

.n

DN

Ah

adso

me

geog

raph

ical

stru

ctu

re,

alth

ough

som

eh

aplo

type

shar

ing

nD

NA

(Rag

-1)

4310

1298

0.57

9–

−1.7

6–

1−3

.23

–1



AM

PH

IBIA

NS

31M

iddl

eE

aste

rntr

eefr

og(H

yla

savi

gnyi

)

mtD

NA

116

4089

30.

948

0.01

02−0

.02

–0

−8.8

6–

0–

mtD

NA

and

nD

NA

wer

eco

nco

rdan

tin

that

they

had

the

sam

em

ain

grou

ps/s

peci

es.

Hap

loty

pes

wer

ege

ogra

phic

ally

stru

ctu

red

inm

tDN

A.

mtD

NA

clad

esw

ere

not

mon

oph

ylet

icat

nD

NA

Gvo

ždík

etal

.(2

010)

2n

DN

A10

418

772

0.66

70.

0028

−0.4

4−1

.11

to0.

230

−3.6

9−7

.79

to0.

400.

5

32M

iddl

eE

aste

rntr

eefr

og(H

yla

feli

xara

bica

)

mtD

NA

3611

896

0.84

80.

0070

1.21

–0

1.18

–0

–

2n

DN

A56

877

20.

393

0.00

19−0

.46

−1.1

5to

0.24

0−0

.70

−1.1

6to

−0.2

40

33M

iddl

eE

aste

rntr

eefr

og(H

yla

orie

nta

li)

mtD

NA

4421

895

0.87

60.

0042

−1.1

5–

0−9

.95

–1

–

2n

DN

A64

1577

20.

544

0.00

42−0

.7−1

.74

to0.

350.

5−2

.56

−3.8

7to

−1.2

50.

5

34D

irt

frog

s(E

leu

ther

odac

tylu

sst

ejn

eger

ian

us)

mtD

NA

(ND

2)29

1151

0–

0.00

40−0

.37/

−0.2

4−1

.49

to0.

490

––

–10

(for

1po

p.)

Cra

wfo

rd(2

003)

nD

NA

(c-m

yc)

7622

351

–0.

0028

−0.3

0/−0

.65

−1.5

2to

1.26

0–

––

35S

tou

tn

ewts

(Pac

hyt

rito

nar

chos

potu

s)

mtD

NA

(ND

2+C

ytb)

10–

1200

––

−0.4

7–

0–

––

–W

uet

al.

(201

3)

2n

DN

A–

1800

––

0.45

−0.4

3to

1.33

0–

––

–

36S

tou

tn

ewts

(Pac

hyt

rito

nbr

evip

es)

mtD

NA

(ND

2+C

ytb)

18–

1200

––

0.38

–0

––

––

2n

DN

A–

1800

––

−0.1

5−0

.18

to−0

.11

0–

––

–

37S

tou

tn

ewts

(Pac

hyt

rito

ngr

anu

losu

s)

mtD

NA

(ND

2+C

ytb)

15–

1200

––

0.69

–0

––

––

2n

DN

A–

1800

––

−0.9

2−1

.49

to−0

.36

0–

––

–

38S

tou

tn

ewts

(Pac

hyt

rito

nin

expe

ctat

us)

mtD

NA

(ND

2+C

ytb)

15–

1200

––

0.63

–0

––

––

2n

DN

A–

1800

––

−0.3

5−0

.98

to0.

280

––

––

39G

arde

nsl

ende

rsa

lam

ande

r(B

atra

chos

eps

maj

or)

mtD

NA

(Cyt

b)12

6–

715

0.98

30.

0520

ns

–0

––

––

Mar

tín

ez-

Sol

ano

etal

.(2

012)

2n

DN

A30

2–

1431

0.87

90.

0075

P<

0.05

–1

––

––

40E

ast

Asi

anbr

own

frog

s(R

ana

chen

sin

ensi

ssp

ecie

sco

mpl

ex)

mtD

NA

(Cyt

b)39

412

680

0–

–−0

.41/

−0.3

1−1

.97

to0.

960.

5−6

.30/

−0.9

2−2

5.69

to2.

340.

25–

nD

NA

net

wor

ksw

ere

not

com

plet

ely

com

pati

ble,

nor

wh

ere

they

com

pati

ble

toth

em

tDN

A

Zh

ouet

al.

(201

2)5

nD

NA

131

7021

26–

–−0

.17/

−0.1

5−1

.84

to1.

890.

05−1

.08/

−0.5

7−9

.01

to2.

180.

1–



Tab

le2.

Con

tin

ued

Nu

mbe

rS

peci

es/

popu

lati

onM

arke

rN

JH

bpH

Dπ

Taji

ma’

sD

Fu

’sF

Ne

(104 )

Hap

loty

pen

etw

ork

Ref

eren

ceM

ean

/MD

Ran

ge%

Mea

n/M

DR

ange

%

Rep

tile

s41

Wes

tern

rock

skin

k(T

rach

ylep

issu

lcat

a)–

Nor

ther

n

mtD

NA

(ND

2)86

–10

56–

0.04

291.

66–

0−0

.22

–0

–n

DN

Ah

aplo

type

net

wor

kssh

owsh

arin

gof

alle

les

betw

een

Cen

tral

and

Sou

ther

nre

gion

s,an

dN

orth

ern

regi

onth

eal

lele

sar

ety

pica

lly

sepa

rate

dfr

omth

eot

her

two

regi

ons

(an

dar

eth

ean

cest

ral

alle

les)

.T

his

issi

mil

arto

the

mtD

NA

(San

dC

shar

eso

me

alle

les,

wh

ile

Nal

lele

sar

edi

stin

ctly

sepa

rate

)

Por

tik

etal

.(2

011)

nD

NA

-co

mbi

ned

–26

44–

0.00

73−0

.31

–0

−6.3

–1

–

42W

este

rnro

cksk

ink

(Tra

chyl

epis

sulc

ata)

–C

entr

al

mtD

NA

(ND

2)–

1056

–0.

0071

−2.2

6–

1−2

5.03

–0

–

nD

NA

-co

mbi

ned

–26

44–

0.00

33−0

.42

–0

−24.

74–

0–

43W

este

rnro

cksk

ink

(Tra

chyl

epis

sulc

ata)

–S

outh

ern

mtD

NA

(ND

2)–

1056

–0.

0045

−2.2

–1

−25.

94–

0–

nD

NA

-co

mbi

ned

–26

44–

0.00

15−0

.13

–0

−26

–0

–

44M

edit

erra

nea

nh

ouse

geck

o(H

emid

acty

lus

turc

icu

s)

mtD

NA

(Cyt

b+12

S)

100

2367

9–

0.00

18−1

.46/

−1.5

1−1

.24

to−1

.64

0.67

––

––

mtD

NA

net

wor

kssh

owed

two

sepa

rate

lin

eage

s,n

DN

Adi

dn

ot(n

oph

ylog

eogr

aph

ical

patt

ern

)

Rat

oet

al.

(201

1)

2n

DN

A12

2–

1174

–0.

0018

−0.1

6/−0

.30

−0.4

0to

0.32

0–

––

–

45C

omm

ongr

oun

dsk

ink

(Sci

nce

lla

late

rali

s)

mtD

NA

(Cyt

b)36

631

411

770.

999

0.05

08−0

.02

–0

−23.

75–

158

–760

nD

NA

net

wor

kh

adla

rge

amou

nt

ofh

aplo

type

shar

ing

Jack

son

&A

ust

in(2

009)

8n

DN

A63

285

4673

0.77

00.

0055

−1.8

7/−1

.89

−2.4

5to

−1.0

30.

88−1

7.28

/−19

.11

−25.

0to

−6.2

0.88

46G

reen

anol

e(A

nol

isca

roli

nen

sis)

mtD

NA

(ND

2)19

112

811

720.

987

0.02

34−1

.52

–0

−34.

54–

0–

Toll

iset

al.

(201

2)10

nD

NA

915

107

6218

0.51

50.

0023

−0.7

0/−0

.65

−1.8

3to

0.67

0−4

.04/

−1.7

9−1

2.54

to−0

.60

–

Spe

cies

nam

e,to

tal

nu

mbe

rof

indi

vidu

als

orse

quen

ces

exam

ined

(N),

tota

ln

um

ber

ofh

aplo

type

sor

alle

les

(H),

tota

lle

ngt

hof

base

pair

s(b

p),

mea

nn

ucl

eoti

dedi

vers

ity

(π),

mea

nh

aplo

type

dive

rsit

y(H

D),

mea

nan

dm

edia

n(M

D)

ofn

eutr

alit

yte

stst

atis

tics

,ra

nge

inn

eutr

alit

yte

stst

atis

tics

,pe

rcen

tage

ofn

eutr

alit

yst

atis

tics

that

wer

esi

gnifi

can

tly

neg

ativ

e(%

),an

def

fect

ive

popu

lati

onsi

ze(N

e).

NA

,n

otav

aila

ble.



the other is philopatric) can change the evolutionaryhistory of sex-specific chromosomes (e.g. mtDNA, Y,and Z chromosomes) relative to autosomes. Thus,investigators must carefully consider the use andinterpretation of these markers when studyingspecies with sex-biased dispersal (Prugnolle & deMeeus, 2002).

Dispersal behaviour in mammals and birds differ-entially impact the evolutionary history of theirnuclear and mitochondrial genomes. In mammals,natal and breeding dispersal is typical of males,although the pattern is reversed in birds (Greenwood,1980; Liberg & von Schantz, 1985; Prugnolle & deMeeus, 2002). Thus, in both mammals and birds, theheterogametic sex is usually the dispersing sex. Inmammals and other species with female philopatry,mtDNA genetic differentiation between populationswill be higher than with nDNA markers (Hoelzer,1997; Prugnolle & de Meeus, 2002). Accordingly, morepopulation structure is likely in mtDNA because thefemales remain near their natal site, whereas nDNAmay show admixture between populations (Palumbi &Baker, 1994; Firestone et al., 1999; Hare, Cipriano &Palumbi, 2002; Tchaicka et al., 2007). The oppositeoccurs in birds, where female dispersal can reducepopulation differentiation in mtDNA (Lee & Edwards,2008). Because exceptions to the general sex-biasedpatterns in mammals and birds occur (Greenwood,1980; Anser erythropus: Ruokonen et al., 2010),knowing the ecology of the study species is important.Dispersal tendencies are not well-characterized inreptiles and amphibians (Smith & Green, 2006; Mooreet al., 2008), although some reptile species are knownto exhibit male-biased dispersal (Crocodylus johnstoni:Tucker et al., 1998; Anolis roquet: Johansson,Surget-Groba & Thorpe, 2008; Hofmann et al., 2012),and so their patterns of genetic differentiation shouldparallel that of most mammals. However, in otherspecies, sex-biased dispersal can vary among popula-tions of the same species (laticaudine sea kraits: Lane& Shine, 2011; Odorrana schmackeri: Wang, Lane &Ding, 2012). Regardless of the direction of bias, thesesex-specific behavioural tendencies of individualorganisms can obscure molecular insights into thehistorical demography of populations.

Population expansion following a bottleneck shouldproduce an excess of low-frequency/rare mutationsand thus gene trees should exhibit a star-like topol-ogy whereby rare alleles will typically differ by onenucleotide from an ancestral sequence (Slatkin &Hudson, 1991). Dispersal, however, will dampen sig-natures of population expansion by introducing diver-gent alleles from outside the local population; thisskews neutrality test statistics towards positivevalues. With male-biased dispersal, mtDNA should bemore structured and thus have more negative neu-

trality test statistics in a population experienc-ing expansion than would nDNA alleles (which expe-rience more mixing between populations/subpopulations). With female-biased dispersal, bothmtDNA and nDNA genes are dispersed and thusthere should be more similarity in neutrality statis-tics between nDNA and mtDNA.

Table 2 shows that, for Tajima’s D, mammals,amphibians, and reptiles were similar in that noneindicated one marker type (nDNA or mtDNA) beingmore negative on average than the other (Fig. 3A). Inbirds, mtDNA values for Tajima’s D were more nega-tive than the corresponding nDNA values (Fig. 3A);this contradicts the prediction that female-biaseddispersal would reduce mtDNA structuring, and sug-gests that other evolutionary factors, such as incom-plete lineage sorting in nDNA, might have a greatereffect than sex-biased dispersal. Note that, regardlessof marker or taxon, mean Tajima’s D values werenegatively skewed and thus deviated from neutrality.This finding is consistent with the findings of Wares(2009), who reported that mitochondrial cytochromeoxidase I sequences departed from neutrality acrosstaxonomic groups (all had negative mean values ofTajima’s D).

The mean Fu’s F for mammals, birds, and amphib-ians (Fig. 3B) tended to have more negative mtDNAvalues (80–100% of the time) than nDNA values,perhaps because of greater mtDNA structure in specieswith male-biased dispersal (similar to most mammals).Thus, the expected pattern is not apparent with regardto Tajima’s D, although mammals exhibit more nega-tive values for Fu’s F, the most powerful statistic fortesting population expansion (Fu, 1997; Ramos-Onsins& Rozas, 2002; Ramírez-Soriano et al., 2008). Reptileswere evenly split between nDNA and mtDNA exhibit-ing more negative values, and thus did not display aclear pattern.

When contrasting mtDNA and nDNA data, incom-plete lineage sorting is more often associated withnDNA. In practice, this means that nDNA mayprovide little insight into recent historical demogra-phy, especially when Ne is high. The extent of disper-sal (i.e. the proportion of individuals that disperseand the mean dispersal distance) and sex-bias varyamong taxa, and such natural history data can helpdetermine the most appropriate molecular methodol-ogy for demographic inference (i.e. whether to usemtDNA, nDNA, sex-linked markers, or a combinationof multiple markers).

FUTURE DIRECTIONS

Next generation sequencing (NGS) will play a majorrole in future studies of historical demography by



making the use of both mtDNA and multiple nDNAgenes more feasible. Initially, NGS has been driven bythe desire to decode whole genomes. As whole genomesequencing becomes more commonplace, substitution

rates for nuclear genes will come into focus andnDNA will be of much greater utility in demographicstudies. The ability to sequence thousands of nDNAloci should lead to the discovery of loci with recent

(A)

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

1 2 3 4 5 6 7 11 12 13 14 15 16 17 18 19 20 21 23 24 25 26 29 30 31 32 33 34 35 36 37 38 40 41 42 43 44 45 46

Tajim

a's D mtDNA

nDNA

Rep lesMammals Birds Amphibians

(B)

-40

-35

-30

-25

-20

-15

-10

-5

0

5

4 5 6 7 8 10 13 19 20 21 25 26 27 28 29 30 31 32 33 40 41 42 43 45 46

Fu's F

mtDNA

nDNA

Mammals

Birds Amphibians Rep les

Figure 3. The means for (A) Tajima’s D and (B) Fu’s F in studies that used both nuclear (n)DNA and mitochondrial(mt)DNA markers. Numbers on the x-axis correspond to the species shown in Table 2.



coalescence that might better complement mDNA.Zhan et al. (2013) used whole genome sequences ofperegrine and saker falcons to model the demographichistory of both species. They used hundreds of thou-sands of polymorphisms to infer that both species hadincurred Pleistocene bottlenecks, although the per-egrine underwent a second bottleneck approximately20 000 years ago (presumably because of habitatdiminution).

NGS has also led to increased sample sizes; robustpopulation studies require the ability to sequencemultiple genes from many individuals. One way totag and multiplex multiple individuals with NGS isrestriction-site associated DNA (RAD) tags (Bairdet al., 2008; Hohenlohe et al., 2010). This methodallows for the identification of thousands of SNPsfrom multiple individuals for population genomicstudies, and has been beneficial in studies of quanti-tative trait loci. RAD tags allow investigators tosequence a random sample of the genome in multipleindividuals, although they do not enable researchersto target specific genes. Parallel tagged sequencingallows for the identification of double-stranded DNA,such as polymerase chain reaction products(amplicons), within a pooled NGS project (Meyeret al., 2008; O’Neill et al., 2012). O’Neill et al. (2012)used this approach for a large-scale tiger salamanderproject to sequence 95 nuclear loci from 93 individu-als; using a similar approach, Puritz et al. (2012)sequenced five nDNA loci for 20 individuals in each of16 different populations of two sea star species. Thesestudies illustrate the potential power of NGS forrevealing the evolutionary history of populations.

CONCLUDING REMARKS

Historical demographic analyses are generally basedon departures from the ideal population: one thatevolves neutrally, has no recombination or selection,and has equal effective population sizes between thesexes. In reality, we do not know the extent to whichmost species deviate from these ideal conditions.Adaptive potential and dispersal ability can factorinto the effects that climate and habitat changes willhave on a species. For example, the finding thatpopulations of butterflies lacked barriers to gene flowallowed investigators to determine whether geneticdrift or adaptive change created observed patterns ofgenetic variation (Norgate et al., 2009).

The accurate timing of population growth or declinecan help determine whether a particular geographicalbarrier has been the historical cause of differentiationbetween two populations or whether it simply limitscontemporary gene flow (Cheviron et al., 2005). Forexample, Qu et al. (2010) found that avian species onthe Tibetan plateau experienced population expan-

sion concomitant with the retreat of the glaciers,whereas species on the edge of the plateau were lessaffected by ice and therefore showed more stablepopulation sizes throughout the Pleistocene. Theattributes of a species (e.g. habitat preferences anddispersal ability), in addition to their evolutionaryhistory, determine contemporary population structureand future adaptation capabilities.

Knowledge of the level of sex-biased dispersal, rela-tive timeframes of species divergence, and effectivepopulation size are needed for informed decisions onthe best marker type for any given study where timeand/or resources are limited. For example, specieswith male-biased dispersal should have more mtDNAstructure and therefore the use of nDNA markers (inaddition to mtDNA) will help prevent biases whendetermining population structure. The timing ofdiversification is another important parameter thatinfluences marker choice. Whereas some sisterspecies separations date to the Pliocene, many verte-brate divergences occurred during the Pleistocene(Avise & Walker, 1998) and thus mtDNA genes shouldprovide more resolution than nDNA because of thelimited time for sorting of nDNA lineages, dependingon Ne.

The design, implementation, and interpretation ofdemographic studies on evolutionary timeframes arecomplex. Nevertheless, evolutionary biologists haveused contemporary DNA sequence data to providedetailed insights into recent historical demography.These studies are usually synthetic, requiring theassimilation of natural history, geology, climate, andevolutionary data. We expect that with the recent andimpending advances in molecular methodologies,novel analytical methods (particularly those that dealwith genome-level data) will emerge and provide evenmore comprehensive insight into historical processes.As we better understand organic evolution inresponse to historic environmental factors, we willacquire the capacity to better predict future demo-graphic trends in the face of a changing planet.

ACKNOWLEDGEMENTS

We thank the Provost’s Office at Purdue Universityfor financial support through the University FacultyScholar program (JAD), as well as the Curators of theUniversity of Missouri and the Alexander vonHumboldt Foundation (RER). We also thank threeanonymous reviewers for their insightful commentsthat helped improve this manuscript.

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