Differential levels of genetic diversity and divergence among populations of an ancient Australian...

ORIGINAL ARTICLE

Differential levels of genetic diversity and divergenceamong populations of an ancient Australian rainforest conifer,Araucaria cunninghamii

Matthew G. Pye Æ Murray J. Henwood ÆPaul A. Gadek

Received: 27 February 2008 / Accepted: 29 October 2008 / Published online: 13 January 2009

� Springer-Verlag 2009

Abstract Historical fragmentation and subsequent isola-

tion has affected the levels of genetic diversity in many

lineages of ancient plant taxa. This study investigated the

effects of historical processes on Araucaria cunninghamii

(Hoop Pine), Araucariaceae, using both RAPD and ISSR

markers. While most populations maintain moderate levels

of diversity, there is some evidence for reduced genetic

variation. Most sampled populations are significantly dif-

ferentiated from each other, thereby emphasizing the high

degree of population structuring in A. cunninghamii.

Increased divergence among the northern Queensland

populations relative to southern Queensland populations

suggests historical pressures have impacted differently on

extant population distribution. More research is specifically

required into the biology and population demographics of

Australian Araucariaceae.

Keywords Araucaria cunninghamii � Hoop Pine �Araucariaceae � RAPDs � ISSRs � Conifers �Southern Hemisphere � Population genetics

Introduction

Historical fragmentation is well known to affect the levels

of genetic diversity and divergence of ancient plant taxa

through the combination of range contractions and popu-

lation isolation (Schaal and Leverich 1996). One such

group of plants particularly prone to these deleterious

processes is the conifers of the Southern Hemisphere.

Australia offers a unique opportunity to investigate the

effects of historical processes on this group as it contains

a number of conifer lineages which are known to have

previously been more geographically widespread. For

example, Araucariaceae are now principally confined to

southern latitudes, despite the earliest fossil evidence of the

family stemming from the Northern Hemisphere during the

Jurassic (Stockey 1994). Araucaria is now present only in

Irian Jaya/Papua New Guinea, Australasia and South

America, and is represented in Australia by two extant

species, A. bidwillii Hook. and A. cunninghamii Aiton ex

A.Cunn. The latter appears to be the oldest diverging

lineage within the genus, which is supported by the fossil

record (Pole 2000) and molecular sequence data (Setoguchi

et al. 1998). This lineage, known collectively as Section

Eutacta (Hill 1998), has undergone an extensive radiation

in New Caledonia where 13 of the 19 extant species of

Araucaria are found.

The current latitudinal distribution of Araucaria cun-

ninghamii is the largest of all extant araucarian species,

covering a range from 1�S (PNG/Irian Jaya) to 31�S

(northern NSW, Australia). This distribution is, however,

by no means even in terms of abundance, nor is it con-

tinuous in terms of occurrence. The areas of maximum

abundance centre on southeastern Queensland, within

300 km of Brisbane, and the Bulolu/Wau region of PNG

(Dieters et al. 2002). The distribution of the species within

Queensland is disjunct, but does not mirror the broad north/

south disjunction of A. bidwillii (Pye and Gadek 2004).

There are intervening populations of A. cunninghamii

throughout central Queensland, and it is frequently

M. G. Pye (&) � P. A. Gadek

School of Marine and Tropical Biology,

James Cook University, Cairns, QLD 4870, Australia

e-mail: [email protected]

Present Address:M. G. Pye � M. J. Henwood

School of Biological Sciences, University of Sydney,

Sydney, NSW 2006, Australia

123

Plant Syst Evol (2009) 277:173–185

DOI 10.1007/s00606-008-0120-1

dominant species of offshore islands, possibly indicating

a broader ecological tolerance than A. bidwilllii. The

majority of mainland populations of A. cunninghamii

appear to be isolated and/or associated with broader, eco-

logical refugia in northern and southern Queensland.

The patchy geographic distribution of A. cunninghamii

is the product of historical range contractions that presum-

ably occurred under the same, or similar, environmental

fluctuations that led to the extant distribution of A. bidwillii

(Pye and Gadek 2004). Contemporary exploitation of

A. cunninghamii for timber products has further fragmented

and dramatically reduced its abundance within extant pop-

ulations. The broader ecological tolerance and, potentially,

differences in the biology of A. cunninghamii has resulted

in a greater number of extant populations than its Australian

congener.

Natural populations of A. cunninghamii are now mostly

protected from logging, but the history of exploitation has

had a dramatic effect on the geographic distribution and

abundance of this species. Despite a history of genetic

improvement of plantation Hoop Pine since the 1940s,

there has been no effort to quantify the direct underlying

genetic properties of differing provenances. This is despite

the species being Australia’s greatest softwood timber asset

and gross differences being noted in plantation perfor-

mance among natural provenances (Dieters et al. 2002).

For example, one particular population on the Gillies

Range, Cairns, Queensland has shown growth character-

istics which are highly sought after in the timber industry

(Dieters et al. 2002).

Information on the genetic integrity of A. cunninghamii

is now urgently required to facilitate our understanding of

not only the impacts of past regimes of exploitation, but,

also, to inform forest managers of the differences existing

within and between provenances which have been selected

for plantation timber. Specifically, we need to quantify

the historical signal of genetic variation and divergence

so that we may interpret the effects of contemporary

processes.

The species is wind pollinated, passively dispersed and

extremely long-lived, all of which should generally con-

tribute to a low degree of population differentiation

(Nybom and Bartish 2000). Information on the genetic

architecture of species has also recently been incorporated

into management strategies for the conservation of frag-

mented populations and to assess which populations should

require differential management strategies (e.g. Bekessy

et al. 2002, Allnutt et al. 2003). This study aims to assess

the genetic variation and divergence within and among

representative populations of A. cunninghamii in eastern

Australia using both RAPD and ISSR molecular markers.

The specific aims of this study are to assess the utility

of neutral molecular markers (RAPDs and ISSRs) for

determining genetic variation and its partitioning in

selected Australian populations of A. cunninghamii, and to

assess the utility of these molecular markers in the ongoing

assessment of genetic structuring and the maintenance of

genetic diversity in future generations of both native and

plantation populations.

The following questions were addressed:

1. Given the dynamic history of Araucaria in Australia, as

revealed by the fossil record, what levels of diversity do

extant populations of A. cunninghamii maintain? Has the

evolutionary potential of this species, or of particular

populations, been compromised through historical range

reductions and fragmentation?

2. Can the level of genetic diversity within a population

be predicted from extant population size? Has popu-

lation size, itself, been an influence in the maintenance

or loss of genetic diversity through evolutionary

processes such as genetic bottlenecks induced, for

example, by drift, inbreeding or founder effects?

3. How is the genetic variation distributed among pop-

ulations? Do these populations form a cohesive unit

(metapopulation), at least within geographic regions,

or does significant genetic structuring exist?

4. If significant genetic structuring exists, does it corre-

spond with predictions from conifer population genetic

theory? This theory, based on genetic data from

northern hemisphere conifers, predicts low levels of

population differentiation and high levels of within

population variation (Nybom and Bartish 2000).

Materials and methods

Sampling

Leaf samples were collected from 147 individuals from

eight populations (Table 1) across a comparable geo-

graphic range to that sampled in a similar genetic analysis

of A. bidwillii (Pye and Gadek 2004). Collections were

made in two different regions, reflecting the sampling

strategy employed in the above study (Fig. 1).

For northern Queensland, populations were sampled at

Huntsbrook Creek on the Gillies Range (Gillies), Blencoe

Falls (Blencoe) and Little Crystal Creek, Paluma (Paluma)

(Fig. 1). Within southern Queensland, populations were

sampled at Festoon Falls in the Bunya Mountains

National Park (Bunya Mts) and Jimna State Forest

(Jimna) where the two native araucarian species co-occur.

Other populations sampled include Noosa National Park

(Noosa), Binna Burra section of Lamington National

Park (Lamington), and Jolly’s Lookout in Brisbane Forest

Park (Brisbane).

174 M. G. Pye et al.

123

Individual trees were selected arbitrarily from each

population, but were separated as far as possible within

each population to minimize the chance of sampling

individuals from the same reproductive event. All samples

were collected from mature reproductive individuals in

order to gain an historical (i.e. pre-European settlement)

perspective on genetic structuring. From 10 to 23

(n = 13) individuals from each population were sampled

(Table 1). Population sample sizes depended upon acces-

sibility and on the number of individuals present at each

population. Approximately 20 cm of branch, containing

several of the short, imbricate leaves, was collected from

each tree, assigned a unique identifier, and placed into

plastic sealable bags with a small amount of silica gel, and

stored until DNA could be extracted. Vouchers for all

specimens were retained and are stored at the Molecular

Biology Laboratory, James Cook University, Cairns,

Australia.

DNA isolation

The isolation of DNA followed a modified CTAB extrac-

tion protocol of Doyle and Doyle (1987) which has proven

to be a successful methodology for DNA extraction from

other Australian conifers (Pye et al. 2003; Pye and Gadek

2004).

Primer selection

Twelve 10-mer RAPD primers, from primer kit OPB

(Operon Technologies, Alameda, California), which were

used to investigate genetic structuring in A. bidwillii (Pye

and Gadek 2004), were tested for variability and repro-

ducibility in A. cunninghamii. From these, seven RAPD

primers displayed appropriate levels of variability

(Table 2). Forty-one ISSR primers were initially screened

for the ISSR analysis. From these, six ISSR primers

exhibited an appropriate amount of variability and repro-

ducibility and were used for the analysis (Table 3).

RAPD reactions

All RAPD reactions were optimized and carried out

according to the protocol described for A. bidwillii (Pye

and Gadek 2004). These reaction conditions resulted in

successful amplification products for A. cunninghamii, and

suggest that they may be useful for amplification of RAPD

products in other araucarian species. Along with negative

controls, which were run to identify contamination or the

presence of primer-dimers and/or artifactual bands, a ran-

dom sample of positive controls were run simultaneously

in different thermal cyclers to verify consistent amplifica-

tion profiles between them.

ISSR reactions

All reactions were performed simultaneously for each ISSR

primer using the same thermal cyclers as for the RAPD

reactions. After optimization, the following protocol was

employed for all ISSR reactions: 40 ng template DNA

(4 lL at 10 ng/lL), 1 lL 10 mM primer (UBC, Canada),

1.0 lL 40 mM dNTPs (Promega), and 0.3 lL 0.4% bovine

serum albumin, with 14.5 lL of double distilled H2O.

These constituents were then subjected to a 30 min soak at

60�C to remove excess proteins from the reaction. Enzyme

mix, containing 2.5 lL 109 PCR buffer (Life Technolo-

gies), 1.6 lL 50 mM MgCl2 (Life Technologies), and

0.1 unit of Taq polymerase (Life Technologies), was

then added, bringing the total reaction volume to 25 lL.

Various cycling regimes were tested, which varied in

number of cycles (30, 35, or 40), annealing temperatures

(45–55�C) and extension times (2–10 min), in order to

optimize reaction conditions across a range of primers

with different properties (e.g. G/C content, length, 50 or 30

anchoring).

After optimization of these variables, the following

cycling regime was deemed appropriate for the majority of

primers and was subsequently employed for all ISSR

reactions: initial denaturation for 3 min at 94�C, followed

by 40 cycles of denaturation for 1 min at 94�C, annealing

Table 1 Localities and

descriptives of sampled

populations of A. cunninghamii

Population name Region Latitude Longitude Approx. population

size

No. individuals

sampled

Lamington South QLD 28�100 153�080 1,000 \ x [ 100 12

Blencoe North QLD 18�150 145�450 1,000 \ x [ 100 20

Brisbane South QLD 27�200 152�460 x \ 100 10

Bunya Mts South QLD 26�520 151�350 x [ 1,000 20

Gillies North QLD 17�110 145�110 x \ 100 18

Jimna South QLD 26�420 152�270 x [ 1,000 23

Noosa South QLD 26�250 153�070 1,000 \ x [ 100 22

Paluma North QLD 18�580 146�150 x \ 100 22

Genetic diversity and divergence in Araucaria cunninghamii 175

123

for 1 min at 50�C, and extension for 2 min at 72�C. The

amplification was completed by a final extension step of

10 min at 72�C, to allow complete extension of the PCR

products. Negative and positive controls were included in

the same manner described for the RAPD reactions.

DNA fragment visualization

RAPD and ISSR products were resolved on 2% agarose gels

by electrophoresis, with the addition of 1 lL/250 mL

ethidium bromide. Products were initially run at 100 V for

10 min to ensure even migration from the wells, and then run

over 5 h at 70 V, and then visualized and photographed over

UV light (GelDoc 1000 image system, BIORAD). Digital

images were captured and stored for later use in band scoring.

A 1 Kb ladder (Life Technologies) was run alongside

the PCR products to estimate fragment size and to facilitate

the scoring of bands within and between populations. To

further assist in band scoring and to ascertain inter-popu-

lation band homology, additional gels were run containing

Fig. 1 Localities of populations

of A. cunninghamii sampled for

the study


123

two individuals from each of the six populations sampled.

Random samples of reactions were duplicated, and cross-

checked with the original amplification products to ensure

reaction repeatability.

Data analysis

All RAPD or ISSR products were assumed to represent a

single locus and were scored as present (1) or absent (0)

and entered into a binary data matrix. Bands which dis-

played identical migration were considered as identical

fragments, regardless of intensity. Following the protocol

adopted in Pye and Gadek (2004) only bands that appeared

in size ranges between, or closely adjacent to, visible

monomorphic bands were scored. Once again this sub-

stantially reduced the overall number of bands that could

be scored, but eliminated scoring non-homologous bands.

Monomorphic loci were not scored, so as to maximize

comparability between estimates of diversity and diver-

gence in previous studies of Southern Hemisphere conifers.

Statistical assessment of genetic diversity

Shannon’s diversity estimates (Lewontin 1972) were cal-

culated from band frequencies obtained from the binary

matrices in PopGene Ver. 1.31 (Yeh et al. 1999). RAPD

and ISSR markers are dominant and, therefore, the detec-

tion of heterozygotes is precluded. The notation of

Shannon’s index as ‘S’ and not ‘H’ is used here in order to

avoid confusion with diversity measures such as hetero-

zygosity (Allnutt et al. 1999). ‘S’ was calculated using the

following formula:

S ¼ �X

pi log2 pi

where pi is the frequency of the presence or absence of

each RAPD band. Total diversity across the populations

was estimated using the mean value of S over all the

populations sampled, and the pooled species-level value

was also determined. An analysis of variance (ANOVA)

was used, after arcsine transformation of the data, to

examine differences in the levels of diversity across pop-

ulations. Arcsine transformation of estimates of genetic

diversity for each locus was necessary as proportional data,

with values encompassed by 0 and 1, are binomially rather

than normally distributed (Zar 1996). This transformation

means the resultant data will have an underlying distribu-

tion that is nearly normal. If significant differences were

found among the populations sampled, post hoc tests

employing least significant difference (LSD) were con-

ducted in SPSS v. 10 to identify which populations

accounted for the difference. The percentage of polymor-

phic loci (%P), the mean value and pooled species-level

values were calculated in PopGene.

Statistical assessment of genetic differentiation

A Euclidean distance matrix was calculated in Arlequin

ver. 2.0 (Schneider et al. 2000), from the binary matrix, and

used as the input file. Pairwise UST values among all

populations were calculated in Arlequin from the Euclidean

distance matrix, and their significance tested through

10,000 random permutations of the data set. These values

were then used to conduct a principal coordinates analysis

(PCA) in GenAlEx Ver. 5.04 (Peakall and Smouse 2001) to

spatially examine the variation among populations and to

highlight the resolving power of the ordination (data not

shown). Correlation between the two pairwise population

matrices produced from RAPD and ISSR analyses was

estimated using a Mantel matrix correspondence test,

with 1,000 random permutations to determine significance

values. This was done to examine congruence between the

analytical methods in determining population structure.

Unrooted UPGMA trees were calculated in PopGene, from

Nei’s unbiased measure of genetic distance (Nei 1973), in

order to graphically display the relative divergence and

relationships among populations sampled.

Analysis of molecular variance (AMOVA) (Excoffier

et al. 1992) was used to further investigate the genetic

structuring of the sampled populations. Variance compo-

nents were estimated for (1) within populations, (2)

amongst populations. Population structure was investigated

Table 2 RAPD primer sequences used in the genetic analysis of

A. cunninghamii

Name Sequence 50-30 # loci scored

OPB 20 GGACCCTTAC 4

OPB17 AGGGAACGAG 4

OPB12 CCTTGACGCA 11

OPB10 CTGCTGGGAC 16

OPB9 TGGGGGACTC 10

OPB8 GTCCACACGG 13

OPB3 CATCCCCCTG 17

Table 3 ISSR primer sequences used in the genetic analysis of

A. cunninghamii


807 AGAGAGAGAGAGAGAGT 11

808 AGAGAGAGAGAGAGAGC 7

809 AGAGAGAGAGAGAGAGG 9

810 GAGAGAGAGAGAGAGAT 9

811 GAGAGAGAGAGAGAGAC 11

857 ACACACACACACACACYG 12


123

using all populations, southern populations only and

northern populations only to determine differences in dif-

ferentiation between the two regions. Probability values

were calculated within Arlequin for each of the AMOVA

parameters using 10,000 random permutations. Signifi-

cance is tested by permutating populations among regions

and individuals among populations.

Results

RAPDs: genetic diversity

Seven RAPD primers scored from the twelve screened

primers produced a total of 75 polymorphic loci (Table 4).

All products scored were between 300 and 2,000 bp. A

minimum of two monomorphic markers was produced

from each primer, but these were not included in the

analysis. No fixed population-specific bands were recorded.

Shannon’s diversity index (S) varied from 0.28 for the

Gillies range population, to 0.46 for the population at

Paluma (Table 4). An analysis of variance (ANOVA)

revealed a significant difference in the levels of diversity

detected among populations (F = 2.27; P = 0.03). Sub-

sequent post hoc tests identified the diversity within the

Gillies population as being significantly different from all

other populations sampled. All other populations con-

tained similar levels of diversity, however, the variation

detected in the Gillies population was significantly lower

when compared to all other populations (P B 0.05). The

mean population diversity was 0.41 and the pooled spe-

cies-level estimate was 0.53 (Table 4). The percentage of

polymorphic loci varied from 53% for the Gillies popu-

lation to 85% for Jimna (Table 4). The mean percentage of

polymorphic loci across all populations was 74%. Once

again, as only polymorphic bands were scored for this

analysis the pooled-species level of polymorphism was

100%.

RAPDs: genetic differentiation

All but three UST values between individual pairs of pop-

ulations were significant (Table 5). Non-significant values

were found between Brisbane and the Bunya Mountains,

Jimna and the Bunya Mountains, and between the Bunya

Mountains and Noosa. The maximum value of 42% diver-

gence was recorded between the Gillies range population

and the Lamington population (Table 5). Surprisingly, the

Gillies Range population showed less divergence from

the population at Paluma (28%) than it did from the

geographically closer Blencoe Falls population (30%). The

divergence between the populations at Paluma and Blencoe

was 14%, which is above that recorded among all southern

populations (Table 5).

The lowest significant value of differentiation was

recorded between the Bunya Mountains and Noosa popu-

lations (4%). The highest values of divergence among the

Table 5 Matrix of pairwise differences between sampled populations of A. cunninghamii (lower diagonal) with significance values (above

diagonal) calculated from AMOVA derived UST values from analysis of the RAPD data set

Lamington Blencoe Brisbane Bunya Mts Gillies Jimna Noosa Paluma

Lamington – 0.000 0.002 0.002 0.000 0.000 0.000 0.000

Blencoe 0.27475 – 0.000 0.000 0.000 0.000 0.000 0.000

Brisbane 0.09941 0.21699 – 0.817 0.000 0.000 0.001 0.000

Bunya Mts 0.08053 0.18213 20.02424 – 0.000 0.951 0.159 0.000

Gillies 0.41462 0.29814 0.34262 0.37068 – 0.000 0.000 0.000

Jimna 0.10700 0.24341 0.06325 20.02445 0.31793 – 0.000 0.000

Noosa 0.11575 0.25739 0.09473 0.03670 0.33047 0.04447 – 0.000

Paluma 0.26412 0.14038 0.20849 0.17526 0.27624 0.20937 0.21369 –

Non-significant associations are highlighted in bold

Table 4 Shannon’s diversity index (S) and percent polymorphic

RAPD loci (%P) for all populations of A. cunninghamii sampled

as well as mean values averaged over all populations and pooled

species-level values

Population S RAPDs (±standard errors) %P

Lamington 0.4134 (0.2812) 71

Blencoe 0.3883 (0.2835) 71

Brisbane 0.4043 (0.2810) 72

Bunya Mts 0.4081 (0.2694) 75

Gillies 0.2822 (0.2971) 53

Jimna 0.4516 (0.2328) 85

Noosa 0.4568 (0.2409) 84

Paluma 0.4582 (0.2488) 84

Mean, Spop 0.4079 74

Species level 0.5266 (0.1565) 100

Standard errors are presented in parentheses


123

southern Queensland populations occurred between Noosa

and Lamington (12%). In addition, there was less signifi-

cant divergence between the Paluma and Blencoe Falls

populations and any other southern Queensland population

(18–26%) than there was between each of these two

northern Queensland populations and the population

slightly to the north of them at the Gillies Range (28 and

30%, respectively).

The unrooted UPGMA tree derived from the RAPD data

(Fig. 2), graphically displays these groupings. The northern

Queensland populations are clearly separated from all

southern Queensland populations. However, that separation

appears to be as great among the northern Queensland

populations as it is between the northern Queensland and

southern Queensland populations. The branch length

leading to the Gillies population appears to be as long as

that separating the northern Queensland and southern

Queensland populations. Its affinity with the northern

Queensland populations is evident from the much shorter

branch length leading to the grouping of Paluma and

Blencoe populations.

Three southern Queensland groups are identified. These

groups separate the Lamington population from the other

southern Queensland populations by a relatively short

branch length. The Brisbane population groups with the

Bunya Mountains population which, together, are equi-

distant from the Lamington population and the grouping of

the Jimna and Noosa populations (Fig. 2).

Genetic structuring in the RAPD data set of A. cun-

ninghamii was investigated by AMOVA. When no regional

hierarchy is invoked, 20.25% of the variation is partitioned

among all sampled populations, with the remaining

79.75% being contained within populations (P \ 0.00001;

Table 6).

To further investigate the structuring of the southern

Queensland populations, all northern Queensland popu-

lations were excluded from the analysis. Invoking

no hierarchical structure gave a UST of 5.67%, with

94.33% of the variation residing within populations

(P \ 0.00001; Table 7). Similarly, to investigate the

structuring of northern Queensland populations alone, all

southern Queensland populations were excluded from the

analysis. Invoking no hierarchical structure gave a UST of

23.42%, with 76.58% of the variation residing within

populations (P \ 0.00001; Table 7). This represents an

approximate fourfold increase in the divergence among

northern populations relative to that among the southern

populations.

Table 6 AMOVA analysis of RAPD variation for all 8 Araucariacunninghamii populations

df a Variance

component

% of total

variance

Pb

All populations

Among populations 7 2.19531 20.25 0.00000

Within populations 137 8.64525 79.75 0.00000

a Degrees of freedomb Significance of the variance components

Table 7 AMOVA analysis of RAPD variation for different regional

groupings of Araucaria cunninghamii populations in northern and

southern Queensland

df a Variance

component

% of total

variance

Pb

Southern populations only



Northern populations only




1

Brisbane

Bunya MtsJimna

Noosa

Lamington

Gillies

Blencoe

Paluma

Fig. 2 UPGMA tree, unrooted, displaying the relative divergence

among sampled populations of A. cunninghamii as determined from

the RAPD data set


123

ISSRs: genetic diversity

The six ISSR primers chosen from the screened primers

(Table 8) produced a total of 59 polymorphic loci. All

products scored were between 300 and 2,000 bp in length.

From one to five monomorphic markers were produced

from each ISSR primer, but these were not included in the

analysis. No population-specific bands were recorded,

although a number of loci reached fixation in several

populations.

Shannon’s index (S) indicated that the genetic diversity

harboured in the sampled populations of A. cunninghamii

varied from 0.29 for the Gillies population to 0.45 for the

Jimna population (Table 9; cf. 0.28 to 0.46 from RAPDs).

An ANOVA indicated a significant difference among the

populations in the diversity they contained (F = 3.29;

P \ 0.005). Subsequent post hoc multiple comparison

tests, employing the test of LSD, identified the Gillies

population as the sole divergent population (P \ 0.05). The

Gillies population displayed significantly lower levels of

genetic diversity when compared to all other populations.

All other populations contained a similar amount of

diversity. The mean population diversity was 0.40 and the

pooled species-level estimate was 0.50 (Table 9; cf. 0.41

and 0.53, respectively for RAPDs). The percentage of

polymorphic loci varied from 63% for the Gillies popula-

tion to 93% for Jimna (Table 9). The mean percentage of

polymorphic loci across all populations was 81% (cf. 74%

from RAPDs). As only polymorphic bands were scored for

both analyses the pooled-species level of polymorphism

was 100%. Subsequent Mantel tests indicated a high and

significant degree of correlation between the ISSR and

RAPD data sets (r = 0.9, P \ 0.05).

ISSRs: genetic differentiation

All pairwise population UST values were significant except

between the Brisbane and Bunya Mountains populations

(Table 10). A maximum value of divergence (34%) was

recorded between the Gillies population and both the

Brisbane and Lamington populations (Table 10). The

smallest significant amount of divergence, 7%, was

detected between the Jimna and the Bunya Mountains

populations. The Gillies population is as divergent from the

Paluma population as it is from the Blencoe Falls popula-

tion (23%; Table 10).

The unrooted UPGMA tree (Fig. 3) indicated that the

northern Queensland populations are separated from all

southern populations. The divergence of the Gillies popu-

lation is evident as a long branch, arising equidistant from

the northern Queensland and southern Queensland popu-

lations, which leads away from the much shorter branches

separating all other populations. The divergence of the

Lamington population is also clear, being separated from

the two southern Queensland groupings of the Jimna and

Noosa populations, and Brisbane and the Bunya Mountains

populations. These results confirm the significant regional

structuring of A. cunninghamii as determined by AMOVA.

Genetic structuring was further investigated in the

ISSR data set by AMOVA. When all populations were

included a significant UST value of 18.12% was obtained,

with 81.88% of the variation contained within populations

(P \ 0.00001; Table 11; cf. UST = 20% from RAPDs).

The structuring of both northern Queensland and south-

ern Queensland populations, identified in the UPGMA tree,

was further investigated by alternately excluding each

group from the analysis. Analysing the southern Queens-

land populations separately gave a UST value of 10.90%

(P \ 0.000001; Table 12; cf. 6% from RAPDs), with the

remainder, 89.10%, contained within populations. The

same analysis of only northern Queensland populations

gave a UST value of 20.01% (P \ 0.000001, Table 12; cf.

23% from RAPDs), with 79.99% of the variation contained

within populations. This represents a twofold increase in

divergence among northern populations relative to the

southern populations.

Table 9 Shannon’s diversity index (S) and percent polymorphic

ISSR loci (%P) for all sampled A. cunninghamii populations and

mean values averaged over all populations and pooled species-level

values

Population S ISSRs %P

Lamington 0.4475 (0.2281) 86

Blencoe 0.3816 (0.2764) 71

Brisbane 0.3851 (0.2540) 78

Bunya Mts 0.4079 (0.2314) 88

Gillies 0.2937 (0.2705) 63

Jimna 0.4511 (0.2028) 93

Noosa 0.4190 (0.2345) 88

Paluma 0.3987 (0.2568) 78

Mean, Spop 0.3947 81

Species level 0.4971 (0.1618) 100

Standard errors are presented in parentheses

Table 8 ISSR primer sequences used in the genetic analysis of

A. cunninghamii


807 AGAGAGAGAGAGAGAGT 11

808 AGAGAGAGAGAGAGAGC 7

809 AGAGAGAGAGAGAGAGG 9

810 GAGAGAGAGAGAGAGAT 9

811 GAGAGAGAGAGAGAGAC 11

857 ACACACACACACACACYG 12


123

Discussion

Substantial levels of genetic variation and population

divergence were detected among the populations of

A. cunninghamii sampled for this study. The degree of

genetic structuring in this species contrasts with the

majority of plant population genetic studies, which sug-

gests that genetic diversity within plant populations is

negatively correlated with the degree of population diver-

gence (Nybom and Bartish 2000).

Genetic erosion has been cited as a likely outcome of

small population size in species that formerly had more

extensive distributions (Pluess and Stocklin 2004). How-

ever, the reduction of genetic diversity in A. cunninghamii

is moderate and only slightly lower than those reported in

RAPD studies for other Southern Hemisphere conifers with

similar life-history traits: Fitzroya cupressoides, S = 0.54

(Allnutt et al. 1999); Araucaria araucana, S = 0.65

(Bekessy et al. 2002). The estimates of genetic diversity

from both RAPD and ISSR analyses in A. cunninghamii

are in very close agreement (S [ISSR] = 0.40, S [RAPD] =

0.41) as indicated by the high correlation assessed by the

Mantel test (r = 0.9, P \ 0.05).

The ANOVA from both data sets detected significantly

lower levels of diversity in the Gillies Range population

(northern Queensland). All other populations contained

similar levels of diversity, which suggests that the Gillies

population may represent an anomaly among extant Hoop

Pine populations, and, indeed, among Australian Araucaria

Table 11 AMOVA analysis of ISSR variation for 8 Araucaria cun-ninghamii populations

df a Variance

component

% of total

variance

Pb

All populations




1

Lamington

Brisbane

Bunya Mts

Jimna

Noosa

Blencoe

Paluma

Gillies

Fig. 3 UPGMA tree, unrooted, displaying the relative divergence

among sampled populations of A. cunninghamii as determined from

the ISSR data set

Table 10 Matrix of pairwise differences between sampled populations of A. cunninghamii (lower diagonal) with significance values (above

diagonal) calculated from AMOVA derived UST values from analysis of the ISSR data set

Lamington Blencoe Brisbane Bunya Mts Gillies Jimna Noosa Paluma

Lamington – 0.000 0.002 0.000 0.000 0.000 0.000 0.000

Blencoe 0.19928 – 0.000 0.000 0.000 0.000 0.000 0.000

Brisbane 0.09683 0.15865 – 0.25988 0.000 0.000 0.001 0.000

Bunya Mts 0.08655 0.14861 0.01259 – 0.000 0.000 0.000 0.000

Gillies 0.34262 0.23441 0.34157 0.29591 – 0.000 0.000 0.000

Jimna 0.08645 0.19087 0.11527 0.07178 0.28692 – 0.000 0.000

Noosa 0.14661 0.23795 0.22952 0.16498 0.32788 0.07661 – 0.000

Paluma 0.22626 0.14622 0.16420 0.15922 0.22996 0.11492 0.14751 –

Non-significant values are highlighted in bold

Table 12 AMOVA analysis of RAPD variation for different regional

groupings of Araucaria cunninghamii populations in northern and

southern Queensland

df a Variance

component

% of total

variance

Pb

Southern populations



Northern populations





123

populations. Other populations show similar degrees of

geographical isolation (e.g. Blencoe, Bunya Mountains,

Noosa) or similar extant populations sizes (e.g. Brisbane,

Paluma) to the Gillies population, yet appear to have

maintained similar levels of genetic diversity to compara-

tively more extensive and connected populations.

Low genetic diversity may be brought about by a

number of evolutionary processes associated with small

population size. In A. cunninghamii, small population size

does not seem to be the most parsimonious explanation for

the distribution of genetic diversity in the Gillies popula-

tion, since all northern Queensland populations are

effectively small and isolated, were not heavily logged,

and, therefore, should have experienced the same histori-

cal, evolutionary pressures. It is possible, then, that the

significantly lower diversity in the Gillies population is a

result of a genetic bottleneck caused by a founder effect

with subsequent high levels of inbreeding and/or restricted

gene flow. As the use of dominant markers precludes

the quantification of heterozygosity, the hypothesis of

inbreeding requires further investigation with suitable co-

dominant molecular markers, such as microsatellites. If this

low diversity in the Gillies population is a result of a

founder effect then this population should display a close

genetic association with its source population. The long

branch lengths detected for this population from both data

sets, however, suggest it does not.

The results from Forestry growth performance trials

indicate that this small, isolated, genetically depauperate,

population may represent a substantial resource for the

future of Australia’s forestry industry (Dieters et al. 2002).

Inter-provenance trials, which have hybridized the Gillies

population with individuals from Jimna State Forest, dis-

play increased vigour—indicated by earlier and more

sustained growth (Dieters et al. 2002). These results,

together with the detection of significantly lower levels of

genetic diversity, indicate that this population requires a

different model of management and conservation than is

applicable to other populations of A. cunninghamii sam-

pled in this study. The importance of the conservation of

this small population is emphasized when the number of

individuals (*20) responsible for the potential increase in

productivity, through inter-provenance hybridization, is

considered. These individuals are in an extremely fire-

prone area, which is burned almost annually, due to its

proximity to a highway and the surrounding sclerophyll

vegetation (Pye pers. obs.). Thus, unless appropriate con-

servation measures are undertaken the potential for

complete loss of this unique genetic resource is high. The

threat to this genetic reservoir was reinforced as recently as

2006 when a severe Category 4 tropical cyclone (Cyclone

Larry) devastated the area. The small population sustained

severe damage. The most cost-effective option to protect

this resource would be to establish a number of ex situ

plantations, derived from cuttings from the remaining

individuals, thereby ensuring the future of this potentially

massive economic resource. These plantations should, of

course, be situated as far away as possible from all extant

populations to minimize the potential for genetic pollution

from divergent provenances as determined in this study.

With the exception of the Gillies population, there

appears to be no evidence of widespread genetic depletion

in A. cunninghamii. Supporting this is the fact that the

percentage of polymorphic RAPD markers was high

regardless of the population size or degree of isolation.

Polymorphism was greater in the ISSR data set than in the

RAPD data set, which agrees with the few comparative

studies conducted using these methodologies (Nybom

2004). Across molecular markers, there was no trend of

increasing polymorphism with increasing population size,

as would be expected from theoretical population genetic

models (Hamrick and Godt 1996). The small, isolated

population at Noosa, for example, has similar (ISSRs) or

higher (RAPDs) values of polymorphism than the much

larger population in the Bunya Mountains (Tables 4 and 9).

For both data sets, Jimna recorded the highest levels of

polymorphism, which likely reflects the historical abun-

dance of the species in this area as opposed to its currently

restricted distribution in the area due to previous logging.

The results presented here, together with those reported in

Pye and Gadek (2004), indicate that these conifers can

maintain substantial amounts of genetic diversity in very

small populations.

In contrast to A. bidwillii (Pye and Gadek 2004),

effective population size in A. cunninghamii may be

independent of actual census size. A more likely explana-

tion is that current population sizes are of insufficient age

to allow a genetic equilibrium to be reached and, therefore,

any reduction in genetic variability due to the effects of

small contemporary population size remains undetectable

at this stage. This is especially likely for those populations

where recent exploitation has severely reduced the size of

the population (e.g. Brisbane). We can also say that his-

torical population size fluctuations, associated with

climatic variability (Kershaw and Wagstaff 2001), do not

appear to have dramatically affected the genetic diversity

of any of these populations, with the possible exception of

the Gillies population. Within-population diversity has

most likely been maintained by distributing the variation

temporally across a range of age classes in this long-lived

species. In doing so, small populations may be able to

maintain similar levels of genetic variation to larger pop-

ulations for substantial periods of time.

The long generation times, typical of Araucariaceae,

appear to distribute genetic variation temporally within

A. cunninghamii via the maintenance of a range of age


123

classes in most populations. The spatial distribution of this

genetic variation will be governed by a balance of popu-

lation divergence and gene flow and is discussed below.

Regional differentiation in A. cunninghamii

Regardless of the molecular marker used, pairwise differ-

ences between populations indicated that most sampled

populations are significantly differentiated from each other

(Tables 5 and 10), thereby emphasizing the high degree of

population structuring in A. cunninghamii. Significant dif-

ferentiation occurs between populations separated by as

much as 2,000 km (e.g. Blencoe to Lamington) or as little

as 50 km (Lamington to Brisbane) and is independent of

the degree of the current isolation of the populations.

RAPD and ISSR data sets identified a high degree of

differentiation among the populations of A. cunninghamii.

Both data sets provide evidence for six distinct groups:

Gillies, Blencoe, Paluma, Lamington, Brisbane plus Bunya

Mts, and Noosa plus Jimna. These congruent results mirror

the extant geographic locations of the populations and reflect

the historical association of these populations. For example,

the grouping of Brisbane plus Bunya Mts, and Noosa plus

Jimna, is also reflected in non-significant pairwise popula-

tion divergence values from the RAPD data set for both

groups, and the ISSR data set for the former group only.

Historical written records suggest that prior to European

settlement these populations were part of a continuous tract

of Araucarian forest (Petrie 1904), and, as such, their extant

isolation is the product of contemporary exploitation which

is not expected to be reflected in this historical genetic signal.

In order to better manage this resource, further research that

uses the progeny of these populations is required to detect

contemporaneous effects of small population size brought

about by previous exploitation.

The substantial differentiation of the Gillies population

in both data sets, as well as the aforementioned significant

reduction in diversity in this population, appears to repre-

sent an anomaly among the populations sampled. These

results suggest that the isolation of this population may

have preceded that of any other population sampled, and

most likely reflects the effects of small population size on

genetic integrity, as predicted by genetic theory. Given

enough time, all populations may be similarly affected, as

has been suggested for Wollemia nobilis (Peakall et al.

2003). The remaining northern Queensland populations

sampled show a greater affinity with the southern popula-

tions than they do with the Gillies population, which

suggests that low diversity along with genetic drift and,

potentially, inbreeding has lead to its extreme divergence.

Given the congruence in life-history traits, habitat and

environmental pressures that the northern populations have

experienced, this result is surprising and requires further

research with markers suitable for assessing levels of

homozygosity. It appears highly likely that this population

is suffering genetic erosion, along with subsequent genetic

drift, due to the effects of small population size, most likely

manifested through an older isolation event than all other

populations sampled. The broad regional differences

detected likely resulted from historical processes that

occurred from the Late Pliocene onwards, and have been

enforced by comparatively recent (Pleistocene/Holocene)

selective pressures and barriers to gene flow.

The degree of sensitivity of the markers differed in

detecting population differentiation. This difference, how-

ever, occurred over large geographic ranges and was

predominantly confined to the discrepancy in values

between northern and all southern populations. RAPDs

detected substantially more differentiation between north-

ern Queensland and the southern Queensland population

(18–42%) than did the ISSRs (12–34%). Pairwise values

from the different markers among the southern Queensland

populations were broadly congruent, and ranged from 4 to

12% for RAPDs (Table 5) and 7 to 17% for ISSRs

(Table 10). The utility of ISSRs in studies of widely dis-

tributed native plant taxa remains to be fully assessed.

As was suggested for A. bidwillii (Pye and Gadek 2004),

loss of intervening habitat and intensive, broad-scale

selective logging over the past 200 years may have left

in situ genotypes of A. cunninghamii that are more closely

related than present geographic ‘isolation’ of these popu-

lations would suggest. This effect is more exaggerated for

A. cunninghamii than for A. bidwillii as the former species

was far more extensively exploited in the early settlement

of southeastern Queensland (Petrie 1904).

The present ‘isolation’ of populations of A. cunning-

hamii appears to be the product of a range of different

temporal and spatial events which is reflected in the higher

values of divergence amongst the northern Queensland

populations relative to the southern Queensland popula-

tions. There was no significant difference in the variation

detected between these populations (with the exception of

the Gillies population), which adds further support to the

hypothesis of the temporal maintenance of genetic diversity

in small populations of long-lived southern conifers (Pye

and Gadek 2004). These results suggest that A. cunning-

hamii is buffered from the deleterious effects of small

population size for substantial periods, as long as a range of

age classes is maintained. Furthermore, the contemporary

isolation of the northern Queensland populations is reflected

in values of population divergence, which suggests these

populations have been isolated for a substantially longer

period of time relative to southern Queensland populations.

When AMOVA analyses of RAPD data were restricted

to southern Queensland populations, the value of popula-

tion differentiation (UST) was 6% (Table 7). This value is


123

lower than those for other Southern Hemisphere conifers

with similar pollination and dispersal mechanisms (e.g.

Araucaria araucana, UST = 12%, Bekessy et al. 2002)

and likely reflects the different historical distributions of

these populations, with A. cunninghamii being widely

distributed throughout southeastern Queensland. The sig-

nificant genetic structuring, however, found between most

populations in southern Queensland, regardless of their size

or degree of isolation further contradicts expectations from

genetic theory. This pattern was detected over most spatial

scales, from as few as 50 km to as much as 2,000 km.

Wind-pollinated conifers are expected to show low popu-

lation differentiation, especially over shorter distances, as

enhanced gene flow acts to negate differentiation. The

possibility remains, then, that seed dispersal is restricted

and is the main factor in determining genetic differentiation

of these populations, through fewer opportunities for inter-

population dispersal. Recent studies, reviewed in Koenig

and Ashley (2003), suggest that further research on the

dispersal limitations of wind-pollinated trees is required.

Studies of pollen dispersal in Araucaria are few, but

Sousa and Hattemer (2003) showed that the large non-

saccate pollen of Araucaria angustifolia does not travel

great distances (Sousa and Hattemer 2003). The pollen

grains of this species, and possibly the genus, are signifi-

cantly larger than other anemophilous species (*61.5 lm).

The emergent habit is obviously advantageous in facili-

tating pollen distribution in an otherwise closed canopy

habitat, although in a forest dominated by this species only

particularly old specimens may emerge above younger

cohorts. Given the logging history of the sites used in the

current study, there is also the possibility that each popu-

lation was preferentially pollinated by a few emergent

‘donor’ individuals, which would act to increase local

divergence. As only mature individuals were sampled for

this analysis this signal is historical and will necessarily

reflect the pre-European structure of the forests.

The current study has revealed moderate levels of

genetic diversity as well as significant genetic structuring

among the majority of sampled populations of A. cun-

ninghamii. Increased divergence among the northern

Queensland populations relative to southern Queensland

populations suggests different historical pressures have

impacted on extant population distribution. More research

is required into the biology and population demographics

of Australian Araucariaceae. Specifically, the characteris-

tics of Australian Araucaria pollen and its dispersal,

together with studies on seed dispersal and the implications

of these on extant genetic structuring should be explored.

This is imperative for species such as A. cunninghamii

which has experienced intensive logging regimes that have

shaped its current distribution. This is all the more

important given the surprisingly high degree of genetic

divergence revealed in this study and the fact that many of

these divergent populations are now islands in a sea of

plantation Hoop Pine, most of which are of mixed or

unknown provenance, and which place native populations

at risk of outbreeding depression.

Acknowledgments We are extremely grateful for the assistance

provided by the Environmental Protection Agency of Queensland,

especially Bunya Mountains National Park staff, during M. Pye’s stay

for sample collections. Michelle Waycott provided valuable assis-

tance for the optimisation of the RAPD procedure and the formation

of ISSR protocols. We thank Adella Edwards for the preparation of

the figure of sampled Hoop Pine localities. We also thank Dr. Garth

Nikles for insightful discussions regarding the history of Hoop Pine

plantations in Australia. This research was supported by funds from

the Australian Postgraduate Award, Rainforest CRC, Cairns and the

School of Tropical Biology, James Cook University, Cairns. This

forms part of a larger project examining the response of Australian

Araucaria spp. to historical climate change.

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