Palaeodistribution modelling and genetic evidence highlight differential post-glacial range shifts of a rain forest conifer distributed across a latitudinal gradient

ORIGINALARTICLE

Palaeodistribution modelling and geneticevidence highlight differentialpost-glacial range shifts of a rain forestconifer distributed across a latitudinalgradientRohan Mellick1,2*, Andrew Lowe2, Chris Allen1, Robert S. Hill3

and Maurizio Rossetto1

1National Herbarium of NSW, The Royal

Botanic Gardens and Domain Trust, Sydney,

NSW 2000, Australia, 2Australian Centre for

Evolutionary Biology and Biodiversity, School

of Earth and Environmental Sciences,

University of Adelaide, SA 5005, Australia,3Faculty of Sciences, University of Adelaide, SA

5005, Australia.

*Correspondence: Rohan Mellick, NationalHerbarium of NSW, The Royal Botanic Gardensand Domain Trust, Mrs Macquaries Road,Sydney, NSW 2000, Australia.E-mail: [email protected]

ABSTRACT

Aim We examine the range expansion/contraction dynamics during the last glacialcycle of the late-successional tropical rain forest conifer Podocarpus elatus using a

combination of modelling and molecular marker analyses. Specifically, we test

whether distributional changes predicted by environmental niche modelling are inagreement with (1) the glacial maximum contractions inferred from the southern

fossil record, and (2) population genetic-based estimates of range disjunctions and

demographic dynamics. In addition, we test whether northern and southern rangesare likely to have experienced similar expansion/contraction dynamics.

Location Eastern Australian tropical and subtropical rain forests.

Methods Environmental niche modelling was completed for three time periodsduring the last glacial cycle and was interpreted in light of the known palynology.

We collected 109 samples from 32 populations across the entire range of P. elatus.

Six microsatellite loci and Bayesian coalescence analysis were used to inferpopulation expansion/contraction dynamics, and five sequenced loci (one plastid

and four nuclear) were used to quantify genetic structure/diversity.

Results Environmental niche modelling suggested that the northern and southern

ranges of P. elatus experienced different expansion/contraction dynamics. In the

northern range, the habitat suitable for P. elatus persisted in a small refugial area duringthe Last Glacial Maximum (LGM, 21 ka) and then expanded during the post-glacial

period. Conversely, in the south suitable habitat was widespread during the LGM butsubsequently contracted. These differential dynamics were supported by Bayesian

analyses of the population genetic data (northern dispersal) and are consistent with the

greater genetic diversity in the south compared with the north. A contact zone betweenthe two genetically divergent groups (corresponding to the Macleay Overlap Zone) was

supported by environmental niche modelling and molecular analyses.

Main conclusions The climatic fluctuations of the Quaternary have

differentially impacted the northern and southern ranges of a broadly

distributed rain forest tree in Australia. Recurrent contraction/expansion cyclescontributed to the genetic distinction between northern and southern

distributions of P. elatus. By combining molecular and environmental niche

modelling evidence, this unique study undermines the general assumption thatbroadly distributed species respond in a uniform way to climate change.

KeywordsClarence River Corridor, environmental niche modelling, haplotypic diversity,

isolation by distance, Last Glacial Maximum, Macleay Overlap Zone, palaeo-distribution, Podocarpus elatus, rain forest conifer, range shifts.

Journal of Biogeography (J. Biogeogr.) (2012) 39, 2292–2302

2292 http://wileyonlinelibrary.com/journal/jbi ª 2012 Blackwell Publishing Ltddoi:10.1111/j.1365-2699.2012.02747.x

INTRODUCTION

Climatic changes are widely regarded as among the main

determinants of range shifts in plant species (Hill & Brodribb,

1999; Hughes, 2000; Williams et al., 2003; Pennington et al.,

2004). Although other drivers may influence distribution

ranges, for example fire frequency and intensity (Bowman,

2000; Dale et al., 2001; Mooney et al., 2010), indigenous

practices (Black & Mooney, 2007), the distribution of mega-

herbivores (Lehmann et al., 2011; Rule et al., 2012) and

modern agriculture (Archer & Pyke, 1991), all are in turn

influenced by climate. In recent geological time, the Quater-

nary glacial cycles have precipitated dramatic climatic change,

with the most recent cycle, which included the Last Glacial

Maximum (LGM, 21 ka), having had significant effects on the

present-day distribution of species (Hewitt, 2000).

The rapid climate fluctuations of the Pleistocene also had a

considerable impact on the intraspecific genetic variation of

many species (Carstens & Knowles, 2007), and studies

integrating inferred palaeodistributions with population

genetic data have the potential to advance our understanding

of climate-induced diversification. Indeed, an understanding

of how intraspecific diversity varies both temporally and

spatially across broadly distributed species can shed light on

how climatic gradients contribute to species diversification and

evolution.

Distributional changes during the LGM, including the

regional extirpation of species, have been particularly well

documented in northern latitudes (Brewer et al., 2002; Petit

et al., 2002, 2003). However, the smaller amount of palaeo-

ecological evidence available for southern temperate zones, and

Australia in particular, suggests exposure to less extreme glacial

processes that did not involve the advance of extensive ice

sheets (Broccoli & Manabe, 1987; Clapperton, 1990; Velichko

et al., 1997). The available evidence for Australia also indicates

contrasting broad-scale temporal and spatial patterns between

wet rain forests (containing Nothofagus spp.) and drier rain

forests (containing Araucaria spp.) (Kershaw et al., 1994).

The persistence of rain forest in eastern Australia has been

facilitated by increased precipitation caused by the orographic

uplift along the Great Dividing Range (GDR), and precipitation

patterns contribute to the current fragmented distribution of the

eastern Australian rain forests (Bowman, 2000). Where there is

less relief (e.g. in the Hunter and Clarence river valleys), or where

the orientation of these highlands is parallel to the prevailing

south-easterly wind direction (e.g. Townsville rain shadow),

precipitation is lower and rain forest is absent. At the intraspe-

cific level, morphological and genetic variation has been

significant enough over small distances to result in clearly visible

divergence (Barnes et al., 2000; Rossetto et al., 2007, 2009),

illustrating the variation and complexity of the evolutionary

processes at work in these rain forests (Worth et al., 2009).

The extent to which a species’ distribution can respond to

changing climatic conditions is affected by niche breadth,

while the ability of a species to establish in new climatically

suitable areas is dependent on numerous ecological factors,

including dispersal ability and competitive advantage. The use

of environmental niche modelling (ENM) to investigate

historical environmental suitability may supplement the poor

fossil record and, in combination with molecular data, can

enhance our understanding of temporal changes in population

dynamics (Scoble & Lowe, 2010).

Podocarpus elatus R.Br. ex Endl. (Podocarpaceae) is a late

successional, mature-phase conifer, with a wide latitudinal

distribution (2500 km, 20! of latitude) in east Australian rain

forest, and is a good model species with which to examine east

coast Australian vegetation dynamics in relation to post-glacial

climatic changes. This species is associated with drier rain

forest types (Harden et al., 2006) and is commonly found with

Araucaria spp. both currently and in the fossil record

(Shimeld, 1995, 2004; Longmore, 1997; Black et al., 2006).

The palynological record suggests that in the Australian Wet

Tropics, gymnosperms expanded during glacial maxima (Ker-

shaw et al., 2007), and it is likely that the cool, dry conditions

of the LGM also favoured P. elatus. However, while southern

fossil records support a decline in abundance for P. elatus since

the LGM (Shimeld, 1995, 2004; Black et al., 2006; Williams

et al., 2006), in the north the co-occurrence of a number of

Podocarpus species and the classification of pollen to generic-

level reduce the interpretative power of the limited deposits

available. A previous study across the entire distribution of P.

elatus found agreement between genetic and contemporary

ENM disjunctions across the Clarence River Corridor (Mellick

et al., 2011).

In this study we combine sequence data from nuclear and

chloroplast loci, Bayesian inference on allelic data and ENM to

further explore the impact of glacial cycle climatic changes on

the distribution of this rain forest-dependent species. In

particular we address the following questions.

1. Are the distributional changes predicted by ENM in

agreement with the available fossil record?

2. Are ENM and population genetic-based estimates of range

contractions/expansions and disjunctions in agreement?

3. In the context of the broad latitudinal and climatic

distribution of the species, are the northern and southern

ranges likely to have experienced similar expansion/contrac-

tion dynamics?

MATERIALS AND METHODS

Study species and sampling strategy

Podocarpus elatus is a medium to large, dioecious and

anemophilous tree growing to 40 m. Its fruit are fleshy,

purple-black and consumed by a range of frugivorous

vertebrates. Podocarpus elatus has a broad, fragmented distri-

bution along the east coast of the Australian mainland, and

grows preferentially in subtropical, gallery and littoral rain

forest communities (Harden, 1990; Harden et al., 2006),

including in transitional ecotonal communities that have a

Differential response to post-glacial warming in Podocarpus

Journal of Biogeography 39, 2292–2302 2293ª 2012 Blackwell Publishing Ltd

drier northern aspect along a break in the rain forest canopy.

The species thrives after small-scale disturbance or around

topographic features such as rivers and ridgelines that produce

an opening in the canopy, as observed for other shade-tolerant

members of the Podocarpaceae (Brodribb & Hill, 2003).

A total of 109 individuals from 32 populations were sampled

to represent the full distribution range of the species (gaps

between sampling sites represent natural distribution gaps).

The rarity of populations and variable population size meant

that sampling was not always balanced among populations.

One to seven mature individuals were sampled from each

population for sequence analysis (Table 1).

Environmental niche modelling

ENM was used to predict the historical geographic range and to

infer past contraction and expansion events in P. elatus. The

method identifies historical areas of climatic suitability (likely

refugia) based on present-day distributions and abundances.

Mellick et al. (2011) used ENM to show that habitat character-

istics correspond to genetic divergence between two groups of

P. elatus populations, situated north and south of the Clarence

River Corridor. Although the additional genetic evidence

reported in this study suggests the existence of two northern

subgroups, owing to the paucity of available distributional

records and lack of strong statistical significance on which to

train our data we developed only a single model for the

northern palaeodistribution.

The variables used in these models were trained on the

Model for Interdisciplinary Research on Climate (MIROC 3.2)

pre-industrial (PI) (0 ka) layer and were projected onto past

climatic estimates to identify areas of climatic suitability for

the species (http://pmip2.lsce.ipsl.fr/). The machine-learning

maximum entropy application, Maxent 3.3.3 (Phillips et al.,

Table 1 The 32 Podocarpus elatus populations used in the study, including the number of individuals sampled (N) in each population andtheir location in eastern Australia (decimal latitude and longitude). The Clarence River Corridor (Mellick et al., 2011), separating popu-lations 1–12 from 13–32 (microsatellite data; K = 2), and the division between Macleay Overlap Zone and the northern population group(between populations 25 and 26) are indicated by solid lines. Sequence data estimates of Shannon’s information index (I) and unbiaseddiversity (uh) with standard error are provided for each population exceeding one individual.

Population N Lat. Long. I uh

1 Target Beach, Beecroft Peninsula 4 )35.0505 150.785 0.164 ± 0.055 0.123 ± 0.042

2 Foxground Road, Foxground 3 )34.732 150.769 0.099 ± 0.047 0.079 ± 0.038

3 Porrots Brush, Jamberoo 6 )34.6548 150.813 0.265 ± 0.063 0.194 ± 0.050

4 Bass Point, Shellharbour 4 )34.5965 150.898 0.207 ± 0.061 0.159 ± 0.047

5 Katandra Reserve, Gosford 1 )33.4101 151.394

6 Wallarah National Park 3 )33.1256 151.635 0.128 ± 0.052 0.103 ± 0.043

7 Ash Island, Newcastle 1 )32.85 151.717

8 Mungo Brush, Tea Gardens 4 )32.5181 152.325 0.183 ± 0.056 0.136 ± 0.043

9 Pacific Palms, Booti Booti NP 3 )32.2482 152.536 0.099 ± 0.047 0.079 ± 0.038

10 Wards River, Gloucester 4 )32.178 151.968 0.177 ± 0.059 0.136 ± 0.046

11 Harrington (NNE of Taree) 2 )31.8598 152.69 0.177 ± 0.064 0.167 ± 0.061

12 Bundagen, Raleigh 4 )30.4315 153.075 0.317 ± 0.065 0.247 ± 0.052

13 Victoria Park, Alstonville 2 )28.9016 153.414 0.146 ± 0.059 0.136 ± 0.056

14 Alstonville 1 )28.8422 153.441

15 Rotary Park, Lismore 2 )28.8105 153.298 0.134 ± 0.054 0.121 ± 0.049

16 Inner Pocket NP 1 )28.4895 153.415

17 Moore Park, Kyogle 4 )28.3961 152.881 0.289 ± 0.064 0.224 ± 0.051

18 Mooball NP 1 )28.3956 153.467

19 Mt Glorious, Maiala NP 5 )27.323 152.757 0.264 ± 0.065 0.200 ± 0.050

20 Neurum Creek, Mt Delaney 3 )27.0447 152.694 0.198 ± 0.058 0.158 ± 0.047

21 Buderim Forest Park 5 )26.6783 153.048 0.198 ± 0.057 0.143 ± 0.043

22 Burumba Env. Edu. Centre 3 )26.3668 152.343 0.250 ± 0.062 0.200 ± 0.050

23 Wrattens Camp, Beauty spot 50 7 )26.356 152.342 0.196 ± 0.057 0.138 ± 0.042

24 Rainbow Beach, Tin Can Bay 3 )25.9602 153.114 0.140 ± 0.051 0.109 ± 0.040

25 Mary River Heads 4 )25.4301 152.924 0.216 ± 0.063 0.169 ± 0.050

26 Bulburin State Forest 4 )24.5676 151.574 0.188 ± 0.060 0.146 ± 0.047

27 Eurimbula National Park 6 )24.2009 151.795 0.199 ± 0.059 0.145 ± 0.044

28 Water Creek Park 4 )22.9173 150.727 0.203 ± 0.065 0.162 ± 0.052

29 Upper Stoney Creek 4 )22.9095 150.631 0.000 ± 0.000 0.000 ± 0.000

30 Flanders Road, Byfield 1 )22.8629 150.636

31 Crediton, Mackay 3 )21.1969 148.545 0.209 ± 0.062 0.170 ± 0.051

32 Bakers Blue, Mt Molloy 7 )16.6746 145.33 0.119 ± 0.049 0.084 ± 0.035

R. Mellick et al.

2294 Journal of Biogeography 39, 2292–2302ª 2012 Blackwell Publishing Ltd

2006), was used as it has been shown to outperform other

modelling methods when generating predictions of species

ranges (Elith et al., 2006). Maxent is based on a probabilistic

structure that relies on the hypothesis that the incomplete

observed probability distribution (based on the species’

occurrences) can be approximated with a probability distri-

bution of maximum entropy to a species’ potential geographic

range (Phillips et al., 2006; Saatchi et al., 2008).

Data included 11 environmental variables from the WorldC-

lim 1.4 database (Hijmans et al., 2005) and 405 occurrence

records (224 southern and 181 northern records) compiled and

verified from all Australian herbaria, the Office of Environment

and Heritage’s vegetation survey database (YETI) (http://

www.environment.nsw.gov.au/research/VISplot.htm), and

the Atlas of NSW Wildlife databases (http://wildlifeatlas.

nationalparks.nsw.gov.au/wildlifeatlas/watlas.jsp). The meth-

odology used to tune and assess the current model (training

data) is outlined in Mellick et al. (2011). The 11 environmental

variables were: annual mean temperature, minimum temper-

ature of the coldest month, mean temperature of the wettest

quarter, mean temperature of the driest quarter, annual

precipitation, precipitation of the driest month, precipitation

seasonality (coefficient of variation), precipitation of the

wettest quarter, precipitation of the driest quarter, precipitation

of the warmest quarter, and precipitation of the coldest quarter.

The climatic variables found to be significant (Mellick et al.,

2011) in the contemporary refined models (WorldClim 1.4, 1960–

1990) were projected onto three time periods of climatic signifi-

cance: 0 ka (pre-industrial), 6 ka (Holocene Climatic Optimum,

HCO) and 21 ka (LGM). These scenarios indicate, respectively, a

pre-industrial climate, a warm wet interglacial climate and a cool

dry glacial climate. The HCO and LGM time periods were selected

to represent the climatic extremes of the last Quaternary glacial

cycle. Each of the LGM environmental variables was correlated

with latitude and a bathymetric grid and extrapolated down to the

lower sea level of the LGM (http://pmip2.lsce.ipsl.fr/).

DNA extraction, sequencing and sequence analyses

Total genomic DNA was extracted from fresh leaf tissue and

silica-dried leaf tissue using DNeasy plant kits (QIAGEN,

Venlo, the Netherlands). Five markers (four nucleic loci and

one plastid locus) from a total of 75 sequenced markers tested

were found to be reliably polymorphic (and non-paralogous)

and were used in the study. The loci used in this study

represent microsatellite flanking regions from PeA16BGT,

PeA45BGT, PeB37BGT, PeC26BGT and PeD13BGT (Almany

et al., 2009). All loci were checked using the web-based

sequence blast software (Madden et al., 1996), with no

significant return except for locus PeB37BGT (Fig. 1c), for

which 14% of the forward flanking sequence was 94% identical

to Passiflora RNA polymerase RPO subunit (chloroplast).

Single-haplotype amplification, characteristic of a plastid locus

in a diploid species, was also observed for PeB37BGT across all

populations. Methodology regarding polymerase chain reac-

tion (PCR) conditions, optimization procedures and cross-

transferability results are outlined in Almany et al. (2009).

Sequencing methodology is outlined in Rossetto et al. (2009).

The mutational variations identified in the flanking

sequence of nuclear loci were located in close proximity to

hyper-variable microsatellite regions. Geneious 4.8.5 (Drum-

mond et al., 2009) was used to edit and align nuclear DNA

sequence data (which included ambiguous codes for hetero-

zygous bases). DnaSP 5 (Librado & Rozas, 2009) was used to

phase diploid sequence data, and each individual was repre-

sented by two haplotypes. Network 4.6.0.0 (http://www.

fluxus-engineering.com) was used to generate haplotype net-

work diagrams from the phased sequence data. Reduced median

and median joining returned the same network configurations.

Genetic diversity and structure using DNA sequencedata

We used sequence data from the five nuclear (microsatellite-

flanking) loci to estimate genetic diversity within each

population group via DnaSP 5 based on seven metrics: the

number of polymorphic sites (S), the number of distinct

haplotypes (h), the number of unique haplotypes (u), haplo-

typic diversity (HD), the mean number of nucleotide differ-

ences between haplotypes (k), the mean number of pairwise

differences per nucleotide site (p), and Watterson’s estimator

of the site-level proportion of segregating polymorphic sites

(hW; Watterson, 1975). Shannon’s information index and

unbiased genetic diversity (with standard error) indices were

generated for each population exceeding one individual in

GenAlEx 6.4.1 (Peakall & Smouse, 2006).

Two-way and three-way analyses of molecular variance (AMO-

VAs; Excoffier et al., 1992) were used to compare variance

components and significance between population group divisions

in GenAlEx 6.4.1. Two population groups were used in the

analyses: the first included the north/south aggregates previously

identified (Mellick et al., 2011); the second introduced a third

population group, the Macleay Overlap Zone, as suggested by the

haplotype distribution. Current habitat (Bowman, 2000) and

floristic distribution (Burbidge, 1960; Booth, 1978; Crisp et al.,

2001) also support the aggregation of populations into the

population groups used in this study. Linearized UPT pairwise

matrices were generated using 10,000 permutations for use as

genetic distance in later analyses. Mantel tests were used to explore

the association between geographic distance and genetic differen-

tiation among populations (the isolation-by-distance model; IBD),

using natural log distance and linearized FST transformations

(Rousset, 1997) in GenAlEx 6.4.1. Separate analyses were com-

pleted within the whole species and within each population group.

Bayesian inference of gene flow using multi-locusgenotypic data

To understand the genetic connectivity between groups of

populations, and to infer population expansion and contraction,

we employed a multi-locus coalescence Bayesian approach

implemented in Migrate-n 3.2.16 (Beerli & Felsenstein, 2001;



Beerli, 2006). We used six microsatellite loci and Markov chain

Monte Carlo (MCMC) runs for estimating bidirectional gene flow

rates and effective population sizes among the three predeter-

mined population groups. All runs used 10 concurrent chains

(replicates) and two long chains of 10,000 recorded genealogies

with a sampling increment of 500 and a burn-in of 20,000. An

unweighted pair-group method based on FST using arithmetic

averages (UPGMA) as a starting tree and static heating with

default temperature were used. A Brownian motion approxima-

tion of a stepwise mutation model (SMM) based on nuclear

microsatellite data was assumed. Runs were replicated at least four

times with different random seed values to achieve convergence.

RESULTS

Post-LGM changes in modelled habitat suitabilityfor Podocarpus elatus

Analysis of variable contribution indicated that for the

southern population group: mean temperature of the wettest

(a)

(d) (e)

(b) (c)

Figure 1 Podocarpus elatus sequence haplotype distribution and haplotype networks for each of the five loci (a–e) used in the study. TheClarence River Corridor (dashed curve) is a microsatellite genetic boundary (K = 2), with the north including both Macleay Overlap Zoneand northern populations. Pie charts are coloured according to the haplotypes present in each population and are proportional to thesample size, with the smallest circles representing n = 1 and the largest n = 7. The networks are shaded according to region and are sizedrelative to the abundance of each haplotype. The proportion of each haplotype present in the southern populations (1–12) is white, in theMacleay Overlap Zone (13–25) it is grey and in the northern populations (26–32) it is black. Only two haplotypes were found at locus A45(b), and therefore no network was generated. Locus B37 (c) is a chloroplast locus and the remaining loci are nuclear.

R. Mellick et al.


quarter explained 25% of the current distribution, precipita-

tion of the driest month explained 20%, minimum temper-

ature of the coldest month explained 17%, annual

precipitation explained 14%, and precipitation seasonality

(coefficient of variation) explained 11%. Analysis of variable

contribution indicated that for the northern population group:

precipitation of the warmest quarter explained 30% of the

current distribution, precipitation seasonality explained 28%,

minimum temperature of the coldest month explained 15%,

and precipitation of the driest quarter explained 12%. Tested

omission rate and predicted area as a function of the

cumulative threshold (a statistical measure of model perfor-

mance) (Phillips et al., 2004) averaged over the replicate runs

and were closely aligned to the predicted omission for both

groups. For the southern group, the average test (area under

the curve, AUC) for the replicate runs was 0.980 (SD ± 0.006).

For the northern group, the AUC for the replicate runs was

0.958 (SD ± 0.016).

There are discrepancies with respect to climate-driven

habitat expansion and contraction between the northern and

the southern genetic clusters. The environmental niche

models (Fig. 2) predict that the climatic envelope of P. elatus

populations south of the Clarence River Corridor (CRC) has

consistently contracted since the LGM (21 ka). The climatic

envelope supporting northern populations, after a significant

expansion between the LGM and the HCO (6 ka), has also

since contracted. The distance between the two modelled

ranges appears to be at its broadest at the LGM. During the

HCO, the northern predicted range substantially overlaps the

southern predicted range, with both ranges contracting to

their current sizes north and south of the CRC in pre-

industrial times (PI, 0 ka). Modelling indicated that the

climatic suitability of the Clarence River valley (an important

genetic disjunction; Mellick et al., 2011) has gradually

decreased since the LGM, with areas of high suitability

currently existing north and south of the valley.

Distribution of haplotype diversity

Sequencing of five loci produced a total of 22 haplotypes

(Fig. 1). The southern group had 17 haplotypes (39 individuals

across 4.6! of latitude), the Macleay Overlap Zone (MOZ) had

16 (41 individuals across 3.5! of latitude) and the northern

group had 11 (29 individuals across 7.9! of latitude; Table 2).

The southern group had the highest haplotype diversity (HD),

and the northern the lowest (Table 2). Six haplotypes were

unique to the south, four to the MOZ and none to the north.

Two haplotypes (Fig. 1d) were shared between (1) the south

and the MOZ, and (2) the north and the MOZ. Ten haplotypes

were widely distributed throughout the entire range and

represented the majority of individuals in the study. Popula-

tion 12, located south of the CRC, contained 13 (59%) of all

haplotypes, including three of the four chloroplast DNA

(cpDNA) haplotypes and two exclusive nuclear DNA (nDNA)

haplotypes. Furthermore, population 12 showed the highest

population-level diversity (Table 1).

Group-level disjunctions and dynamics

The sequence-based two-way AMOVA run over all popula-

tions showed that among-population variance explained 25%

(P < 0.01) of the total molecular variance (Table 3). The

three-way AMOVA showed that among-group variance

explained 8% (P < 0.01) of the total molecular variance for

two groups (north versus south separated by the CRC), and

6% (P = 0.01) when including the MOZ as a distinct

population group (Table 3). A Mantel test across the whole

species returned R2 = 0.05 (P = 0.02), suggesting weak but

significant isolation by distance, but within-group tests were

not significant.

Migrate-n results based on the genotypic dataset inferred

that the majority of current gene flow is towards the north

(Fig. 3). The mutation rate could not be estimated accurately

because the sister species (Podocarpus grayae) has a divergence

estimate of between 5 and 12 Ma (Edward Biffin, University of

Adelaide, Australia, pers. comm.). For this reason, the number

of migrants per generation could not be calculated. Instead,

percentage gene flow estimates of total observed gene flow for

each parameter were obtained (Fig. 3). The largest inferred

effective population size h = 4Nel (effective population size

multiplied by mutation rate per site; Kuhner et al., 1995) is for

the southern population group, followed closely by the MOZ,

with the northern population group being considerably

smaller.

DISCUSSION

We investigated the impact of Quaternary climatic cycles on

the species-wide distributional dynamics of a rain forest-

dependent conifer, P. elatus, in eastern Australia. Overall, the

combination of ENM and molecular data (microsatellites and

sequenced loci) suggests a differential response to post-glacial

climatic warming in the northern and southern ranges of the

species.

The distributional decline in P. elatus suggested by the

southern fossil records (Shimeld, 1995, 2004; Black et al., 2006;

Williams et al., 2006) is supported by the palaeodistribution

modelling (Fig. 2d–f), with a gradual contraction of southern

climatic suitability from a maximum extent during the LGM

(21 ka). While the northern fossil record from the Australian

Wet Tropics (AWT) also suggests that the cool, dry conditions

of the glacial periods favoured slower-growing gymnosperms

and the hot, wet conditions of the interglacial periods favoured

faster-growing angiosperms (Kershaw et al., 2007), the

palaeodistribution modelling produced the opposite trend

for northern P. elatus populations (Fig. 2a–c). These contrast-

ing results could be because the AWT fossil pollen data was

collected at the generic level, not taking into consideration

existing variation in the habitat preference of the four local, co-

occurring Podocarpus species (Andrew Ford, CSIRO, Atherton,

Australia, pers. comm.). In northern Queensland, P. elatus is

only known to occur in dry rain forest at high elevations, and

Bakers Blue (population 32) is the only P. elatus population



(e) (f)

(a)

(d)

(b) (c)

Figure 2 Environmental niche models for Podocarpus elatus north (a, b and c) and south (d, e and f) of the Clarence River Corridorbiogeographic barrier in eastern Australia based on the global climatic Model for Interdisciplinary Research on Climate (MIROC) for the21 ka Last Glacial Maximum (a and d), the 6 ka Holocene Climatic Optimum (b and e) and the 0 ka pre-industrial (c and f) time periods.Dark blue indicates a low probability of occurrence, and warmer colours indicate a high probability of occurrence.

R. Mellick et al.


confirmed to occur in the AWT (Andrew Ford, pers. comm.).

Two of the other local taxa do not overlap in habitat as they

occur only in wet rain forest, either at low to mid-elevations

(P. dispermus) or at high elevations (P. smithii). Finally,

P. grayae co-occurs with P. elatus, and, although it is

phenotypically similar, it is genetically differentiated (Almany

et al., 2009). These habitat differences and suggested differen-

tial community-level climatic responses (Hilbert et al., 2007;

VanDerWal et al., 2009) are likely to account for the discrep-

ancy between the generic-level palynological record and the

northern ENM for P. elatus.

Thus, although factors other than climate affect distribution

(Beatty & Provan, 2011), the different climatic drivers

operating in southern subtropical versus northern tropical

rain forests may account for the contrasting range-shift

patterns across the latitudinal distribution of P. elatus. For

instance, communities distributed at lower latitudes are less

exposed to seasonality, and consequently are expected to be

more sensitive to climatic change (Hughes, 2000), while

increases in summer precipitation since the LGM could have

impacted on the southern distribution of P. elatus.

The study species, P. elatus, is observed to be more abundant

along ecotonal gradients bordering dry rain forest, and could be

considered an ecotonal specialist. This trait may have allowed

the species to colonize areas of lower climatic suitability. The

micro-environmental character of these ecotonal areas may

encapsulate certain ecological requirements for P. elatus that

the bordering rain forest communities lack (e.g. frequent

burning and increased light availability). The southern popu-

lations of P. elatus are regularly burnt, and considerable

recruitment is observed post-burning (Chris Quinn, The Royal

Botanic Gardens and Domain Trust, Sydney, Australia, pers.

comm.). Possibly, the species’ thick bark, waxy leaf cuticle, high

Table 2 Summary statistics of basic haplotype data for easternAustralian Podocarpus elatus obtained from all sequenced loci in allsamples and each population group.

P. elatus 2N S h u HD p k hW

All samples 218 15 22 10 0.437 0.0720 0.8672 0.0440

Southern 78 13 17 6 0.428 0.0832 0.9234 0.0672

Macleay 82 13 16 4 0.420 0.0580 0.7878 0.0379

Northern 58 10 11 0 0.383 0.0591 0.7278 0.0488

2N, number of haplotypes sampled; S, number of polymorphic sites; h,

number of distinct haplotypes; u, number of unique haplotypes; HD,

haplotype diversity; p, average number of pairwise differences per

nucleotide site; k, average number of nucleotide differences between

haplotypes; hW, proportion of segregating polymorphic sites.

Table 3 Analysis of molecular variance (AMOVA) for all eastern Australian Podocarpus elatus populations sampled using sequence data: (a)analysis pools all populations into one group, (b) analysis includes all populations in two groups (north and south of the Clarence RiverCorridor), and (c) analysis includes all populations in three groups, namely north, south and Macleay Overlap Zone.

Source of variation d.f. SS MS Est. var. % P-value

(a) All populations

Among populations 25 157.037 6.281 0.552 25 UPT 0.248**

Within populations 192 321.248 1.673 1.673 75

(b) All populations in two groups

Among groups 1 24.743 24.743 0.189 8 UGT 0.082**

Among populations in groups 24 132.294 5.512 0.461 20 UPG 0.216**

Within populations 192 321.248 1.673 1.673 72 UPT 0.280**

(c) All populations in three groups

Among groups 2 30.393 15.197 0.127 6 UGT 0.056**

Among populations in groups 23 126.643 5.506 0.464 20 UPG 0.217**

Within populations 192 321.248 1.673 1.673 74 UPT 0.261**

SS, sum of squares; MS, mean of squares; Est. var., estimated variance; P-value, probability of corresponding U parameter and significance

[UGT = AG/total variance; UPG = AP/(WP + AP); UPT = (AP + AG)/total variance; AG, estimated variance among groups; AP, estimated variance

among populations; WP, estimated variance within populations]. **P < 0.01.

Figure 3 Migrate-n results for Podocarpus elatus from micro-satellite data showing population group parameters, includingpercentage of total gene flow (indicated by percentage values andweighted arrows) and h = 4Nel (effective population size multi-plied by the mutation rate per site) with 95% confidence intervals.All estimates of gene flow lay within the 95% confidence intervalreturned.



fertility and fast propagation of seed allow clumped popula-

tions to form shortly after fire and to persist in areas inhabited

by more fire-tolerant species, such as P. spinulosus, with which

it co-occurs in its southern distribution.

Bayesian analysis of gene flow and effective population size

parameters (Fig. 3) infer, in concordance with ENM, a post-

LGM expansion for the northern population group. The low

northern habitat suitability during the LGM, the lack of

unique northern haplotypes and the directional prevalence of

gene flow towards the north suggest that much of the

current distribution and low diversity at lower latitudes

could be the result of northern expansion (post-LGM) and

founder effects.

The combination of ENM and sequence data (as shown by

AMOVA and IBD) supports the significance of the CRC as a

biogeographic barrier (Mellick et al., 2011). An additional

genetic discontinuity, coinciding with the MOZ, was observed

in the haplotype distribution and corresponds to previously

inferred habitat transitions (Bowman, 2000) and floristic

distributional changes (Burbidge, 1960; Booth, 1978; Crisp

et al., 2001). The fact that the AMOVAs partitioned variance

similarly when grouping all populations in either two or three

groups (Table 3) suggests that the MOZ is genetically discrete

owing to the additive effects of a north/south genetic overlap,

although the occurrence of unique MOZ haplotypes could

suggest genetic drift within localized refugia.

CONCLUSIONS

In this study we combined molecular (cpDNA and nDNA) and

ENM data to compare the distribution histories of two

genetically differentiated groups of the widespread Australian

rain forest tree P. elatus. Molecular and ENM results were

congruent in suggesting that north/south divergence can be

explained by differential range-shift responses between these

two genetic clusters separated by the CRC. The differential

range-shift response of the species in northern versus southern

distributions is likely to be illustrative of differing climatic

drivers between the southern subtropical and northern tropical

rain forests. Stronger seasonality in the south might have

contributed to maintaining this divergence, and although the

species occupies such a broad environmental envelope, the

degree of gene flow between the population groups has been

sufficient to maintain the species’ integrity.

ACKNOWLEDGEMENTS

This research was funded by the Australian Research Council

Discovery Grant (DP0665859). The authors thank the Uni-

versity of Adelaide and The Royal Botanic Gardens and

Domain Trust, Sydney. They also particularly thank Andrew

Ford (CSIRO), Rebecca Johnson, Bob Coveny, Phillip Green-

wood, NSW National Parks and Wildlife, and Robert Kooy-

man for their assistance in undertaking collections and field

observations; Simon Ho (University of Sydney) and Paul

Rymer (University of Western Sydney) for their support with

regard to multi-locus coalescence-based analysis; and three

anonymous referees for their comments.

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BIOSKETCH

Rohan Mellick is a PhD candidate within the Evolutionary

Ecology Unit at the National Herbarium of NSW, the Royal

Botanic Garden and Domain Trust. He is interested in

understanding the evolutionary roles that climate change

and intraspecific divergence have played as mechanisms of

speciation along latitudinal gradients, especially in the absence

of physical barriers to gene flow.

The Evolutionary Ecology Unit has interests in combining

molecular data, functional ecology and environmental mod-

elling to investigate the distribution and assemblage of native

species, with particular focus on responses to temporal change

and associated patterns along wide environmental gradients.

Author contributions: R.M., A.L. and M.R. conceived the

ideas; R.M. collected the data; R.M. and C.A. analysed the

data; A.L., R.H. and M.R. funded the project; and R.M. led the

writing with help from all authors.

Editor: Jack Williams

R. Mellick et al.


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Palaeodistribution modelling and genetic evidence highlight differential post-glacial range shifts of a rain forest conifer distributed across a latitudinal gradient