Molecular Ecology (2009) 18, 3020–3035
doi: 10.1111/j.1365-294X.2009.04254.x
Comparison of quantitative and molecular genetic
variation of native vs. invasive populations of purple
loosestrife (Lythrum salicaria L., Lythraceae)
Y O U N G J I N C H U N , * J O H N D . N A S O N † and K I R K A . M O L O N E Y †
*INRA, UMR 1210 Biologie et Gestion des Adventices, 17 rue Sully, BP 86510, F-21065 Dijon Cedex, France, †Department of
Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
Abstract
Study of adaptive evolutionary changes in populations of invasive species can be
advanced through the joint application of quantitative and population genetic methods.
Using purple loosestrife as a model system, we investigated the relative roles of natural
selection, genetic drift and gene flow in the invasive process by contrasting phenotypical
and neutral genetic differentiation among native European and invasive North American
populations (QST ) FST analysis). Our results indicate that invasive and native populations harbour comparable levels of amplified fragment length polymorphism variation, a
pattern consistent with multiple independent introductions from a diverse European
gene pool. However, it was observed that the genetic variation reduced during
subsequent invasion, perhaps by founder effects and genetic drift. Comparison of
genetically based quantitative trait differentiation (QST) with its expectation under
neutrality (FST) revealed no evidence of disruptive selection (QST > FST) or stabilizing
selection (QST < FST). One exception was found for only one trait (the number of stems)
showing significant sign of stabilizing selection across all populations. This suggests
that there are difficulties in distinguishing the effects of nonadaptive population
processes and natural selection. Multiple introductions of purple loosestrife may have
created a genetic mixture from diverse source populations and increased population
genetic diversity, but its link to the adaptive differentiation of invasive North American
populations needs further research.
Keywords: AFLP, FST and QST, genetic variation, invasive species, Lythrum salicaria, quantitative
trait
Received 5 January 2009; revision received 8 April 2009; accepted 15 April 2009
Introduction
Elucidating mechanisms by which populations differentiate at different geographical and temporal scales is a
primary theme in evolutionary biology. Of special interest are processes influencing levels of genetic variation
and the role of natural selection in shaping heritable
variation into adaptive differences among populations.
Although phenotypical differences between populations
are often assumed to be adaptive, both molecular and
quantitative variation can be influenced by nonadaptive
processes involving random genetic drift and gene flow
Correspondence: John D. Nason, Fax: +1 515 294 1337;
E-mail: jnason@iastate.edu
(Wright 1931; Hartl & Clark 1989; Falconer & Mackay
1996). Founder effects, bottlenecks and admixture, in
particular, can have strong impacts on the distribution
of phenotypical variation within and among populations (Lande 1980; Lynch et al. 1999). Under these conditions, it can be especially challenging and interesting
to determine whether heritable phenotypical differences
between populations represent the outcomes of adaptive or nonadaptive processes.
Invasive species provide excellent systems for investigating the relative roles of natural selection vs. genetic
drift and gene flow in population differentiation. Invasive populations of non-native species are having major
negative impacts on natural and human-managed
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QUANTITATIVE AND MOLECULAR GENETIC VARIATION 3021
landscapes (Vitousek et al. 1996; Clavero & GarciaBerthou 2005). These populations are expected to be
under strong selection to adapt to environmental conditions of the invasive range and, though often relatively
recently introduced, these have frequently been found to
possess genetically based phenotypical differences from
populations in the ancestral range (Baker & Stebbins
1965; Baker 1974; Sakai et al. 2001), indicative of rapid
evolutionary change (Lee 2002). While changes in size
and fecundity may be adaptive, they may also reflect
high rates of genetic drift associated with founding
events and postcolonization bottlenecks (Brown &
Marshall 1981; Le Page et al. 2000; Lee 2002; DeWalt &
Hamrick 2004). Multiple independent introductions from
genetically diverse sources and subsequent admixture
among them can also occur (Warwick et al. 1987; Novak
& Mack 1993; Neuffer & Hurka 1999; Pappert et al. 2000;
Meekins et al. 2001; Bartlett et al. 2002; Maron et al.
2004; Durka et al. 2005). Such admixture may increase
genetic diversity (Novak & Mack 1993; Amsellem et al.
2000; DeWalt & Hamrick 2004) and so enhance the
ability of populations to adapt to and potentially to
radiate into novel environments (e.g. Sakai et al. 2001),
directly contributing to significant phenotypical differentiation between populations from the invasive and native
range by spurring the formation of novel recombinant
genotypes (e.g. Hedge et al. 2006).
A research programme combining ecological and
genetic perspectives can potentially distinguish the relative contributions of adaptive and nonadaptive processes to population differentiation. Population genetic
methods can be effective for identifying influential
features of invasion history, including bottlenecks,
independent introduction events and subsequent intermingling of populations during invasive spread
(Cruzan 1998; Durka et al. 2005). The adaptive nature
of phenotypical differentiation among populations, in
turn, can be tested relative to a neutral model of quantitative genetic divergence, where neutral genetic variation is used as the null expectation for the amount of
differentiation among populations in quantitative variance (Spitze 1993; Whitlock 1999). This can be
achieved by contrasting neutral genetic variation
among populations (FST) with its analogue for quantitative genetic traits (QST) (Spitze 1993; Whitlock 1999;
McKay & Latta 2002). FST reflects population differentiation due to nonadaptive processes, including genetic
drift, migration and mutation, while QST may reflect
the action of natural selection, as well as these nonadaptive processes (Spitze 1993). If a quantitative trait is
selectively neutral and exhibits a purely additive
genetic basis and linkage equilibrium among underlying
loci, QST has the same expectation as FST as the trait is
affected only by random genetic drift, migration and
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mutation (Merilä & Crnokrak 2001). Alternatively, if
trait variation is not neutral, then QST „ FST with the
direction of the difference depending on the form of
selection (Reed & Frankham 2001). Stabilizing selection
for a common optimal phenotype can homogenize variation across populations resulting in QST < FST, whereas
disruptive selection in response to different local optima
results in QST > FST (Crnokrak & Merilä 2002; McKay &
Latta 2002).
The analysis of the QST ) FST contrast thus provides a
useful tool for investigating the role of adaptive vs.
nonadaptive processes in the spread of invasive species.
When introduced populations experience new local
selection pressures favouring alternative genotypes that
are appropriate to the different local environments, they
are expected to diverge and produce genetically distinct, locally specialized ecotypes, an important mechanism for rapid spread into novel environments (Sakai
et al. 2001; Sexton et al. 2002). Invasion may be further
promoted by multiple, genetically differentiated introductions and increased genetic diversity resulting from
recombination between them (Ellstrand & Schierenbeck
2000), genetic reorganization following bottlenecks (Suarez & Tsutsui 2008) and release from native predators,
parasites and competitors (Maron & Vilá 2001; Mitchell
& Power 2003; Reinhart & Callaway 2006).
The goal of this study was to investigate the relative
importance of different population genetic processes
and phenotypical adaptation in the invasive spread of
purple loosestrife (Lythrum salicaria L., Lythraceae) in
North America. Introduced from Europe in the early
1800s (Thompson et al. 1987), this aggressive invader of
wetlands is having significant negative impacts, including reduction in plant biodiversity and quality of wildlife habitat as well as alteration of wetland function
(Blossey et al. 2001). Research on this species has documented phenotypical differences between invasive
North American and native European populations (e.g.
Edwards et al. 1998; Bastlová & Květ 2002) with some
differences shown to have a significant genetic basis
(Chun et al. 2007). Nevertheless, the relative contributions of adaptive and nonadaptive processes to this differentiation have not been tested. Towards this end,
this study specifically addresses the following questions: (i) Do purple loosestrife populations from the
invasive provenance exhibit spatial patterns and levels
of neutral variation that, relative to native populations,
are indicative of single or multiple founding events,
bottlenecks or population admixture? (ii) For which fitness-related traits do invasive populations exhibit significant genetically based differentiation from native
populations? (iii) For which of these traits is the differentiation between invasive and native provenances
adaptive or neutral? (iv) For adaptive differences, is the
3022 Y . J . C H U N , J . D . N A S O N and K . A . M O L O N E Y
overall pattern of phenotypical variation indicative of
disruptive or stabilizing selection?
Materials and methods
Seed collection and germination
To contrast native vs. invasive populations, we examined three European regions (native provenance) and
three North American regions (invasive provenance).
The three study regions in North America were chosen
to reflect different stages in the invasion history of purple loosestrife, with New Jersey populations as the oldest (many dated to before 1900), Michigan populations
being of intermediate age (many appearing from the
1900s through the 1940s) and Iowa populations being
the youngest (established after the 1940s) (Stuckey 1980;
Edwards et al. 1995). Three populations from each
region (18 populations in total) were chosen to represent a range of environmental (nutrient and water) conditions in the natural field (Table 1). From each
population, we collected seeds from 20 plants separated
by at least 3 m to ensure they were distinct genets.
Seeds were obtained from at least 1000 capsules per
plant and pooled into maternal families at the individual plant level. Given that plants are obligately
outcrossing (trystylous; Ågren & Ericson 1996) and
sample populations were large, seeds within maternal
families were considered to be half-sibs. Seeds from
each half-sib family were planted in the Bessey greenhouse at Iowa State University on 2 May 2006. After
28 days of growth, eight half-sib families from each
population were selected at random for amplified fragment length polymorphism (AFLP) analysis, five of
which were used for a common garden experiment.
DNA extraction and AFLP analysis
Amplified fragment length polymorphism markers are
presumably neutral and have found wide application in
the analysis of genetic variation below the species level,
particularly in investigations of population structure
and differentiation (Mueller & Wolfenbarger 1999).
Leaves were obtained from one young plant sampled
from each of the eight half-sib families from the 18
study populations. We extracted genomic DNA from
loosestrife using the CTAB method (Doyle & Doyle
1990) modified to minimize polysaccharides and other
contaminating materials.
Amplified fragment length polymorphism procedures
were performed essentially following the protocol
described by Vos et al. (1995) with only minor modifi-
Table 1 Geographical position of sampling sites with polymorphism information content in parentheses for each hierarchical
geographical level. n, number of individuals after sample reduction
Provenance
Region
Population
Acronym
Latitude
Longitude
n
Number of total ⁄
private loci
North America
(0.174)
Iowa
(0.139)
Boone Folks (0.175)
Little South Storm Lake
(0.181)
Manly (0.060)
Kellogg Biological Station
(0.197)
Lake Lansing (0.188)
Rifle Range near Pittsford
(0.140)
Beaver Run (0.217)
Hainville County Store
(0.195)
Walkill River (0.210)
Golm (0.182)
Grube (0.194)
Potsdam (0.189)
Altmatt (0.221)
North Sihl Lake (0.188)
Steinbode (0.193)
Hagelloch Tobel (0.183)
Reusten ‘Hinterer See’
(0.198)
Unterjesingen ‘Wiesbrunn’
(0.181)
IABF
IALS
4217¢N
4238¢N
9356¢W
9514¢W
3
3
915 ⁄ 3
915 ⁄ 5
IAMA
MIKB
4316¢N
4221¢N
9307¢W
8521¢W
1
5
542 ⁄ 0
1045 ⁄ 3
MILL
MIRR
4246¢N
4151¢N
8423¢W
8430¢W
8
2
1015 ⁄ 9
810 ⁄ 1
NJBR
NJHV
4109¢N
4115¢N
7436¢W
7448¢W
8
8
1131 ⁄ 14
1102 ⁄ 5
NJWR
PDGO
PDGR
PDPO
SWAL
SWNO
SWST
TUHA
TURE
4103¢N
5225¢N
5227¢N
5229¢N
4720¢N
4736¢N
4709¢N
4832¢N
4833¢N
7437¢W
1257¢E
1257¢E
1257¢E
0752¢E
0813¢E
0843¢E
0901¢E
0855¢E
8
8
8
8
6
5
8
8
8
1110 ⁄ 7
1032 ⁄ 6
1113 ⁄ 2
1096 ⁄ 2
1141 ⁄ 16
1037 ⁄ 0
1068 ⁄ 10
1065 ⁄ 6
1106 ⁄ 15
TUUN
4831¢N
0858¢E
8
1036 ⁄ 4
Michigan
(0.175)
New Jersey
(0.207)
Europe (0.192)
Potsdam
(0.188)
Switzerland
(0.201)
Tübingen
(0.188)
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QUANTITATIVE AND MOLECULAR GENETIC VARIATION 3023
cations. Genomic DNA (200 ng) was digested with
10 units each of EcoRI and MseI, incubating at 37 C for
3 h. Double-stranded adaptors were prepared from the
following complementary single-stranded oligonucleotides: 5¢-CTCGTATACTGCGTACC-3¢ (forward) and
5¢-AATTGGTACGCAGTA-3¢ (reverse) for the EcoRI
adapter pair, and 5¢-GACGATGAGTCCTGAG-3¢ (forward) and 5¢-CTACTCAGGACTCAT-3¢ (reverse) for the
MseI adapter pair. Ligation reactions were performed
by adding 75 pmol each of the EcoRI adapter and MseI
adapter, and 20 units of T4 DNA ligase with its buffer
to the digested product, and incubating overnight at
16 C. For the preselective polymerase chain reaction
(PCR), we added 10 lL of the ligation product to 40 lL
of a preselective PCR mix consisting of: 13 lL dH2O,
5 lL 10X PCR buffer, 1.5 lL MgCl2 (50 mM), 4 lL dNTP
(2.5 mM), 8 mL (5 pmol ⁄ lL) of each preselective primer
and 0.5 lL of Taq DNA polymerase (5 U ⁄ lL). Sequences
of preselective primers are: EcoRI + A: 5¢-TACTGCGTACCAATTCA-3¢ and MseI + C: 5¢-GACGATGAGTCCTGAGTAAC-3¢. Preselective PCR conditions were a
preliminary 75 C extension for 2 min followed by 20
cycles of 94 C for 30 s, 56 C for 30 s, 75 C for 2 min,
finishing with one cycle of 60 C for 30 min. Five microlitres of this PCR product was electrophoresed through
1% TAE-agarose gels and stained with ethidium bromide to verify adequate preselective amplification. The
remaining 45 lL was diluted with 180 lL dH2O. For
the selective PCR, we added 5 lL of diluted preselective PCR product to 20 lL of the selective PCR mix consisting of 11.5 lL dH2O, 2.5 lL 10X PCR buffer, 0.75 lL
MgCl2 (50 mM), 3 lL dNTP (2.5 mM), 0.75 lL
(5 pmol ⁄ lL) each of two EcoRI labelled (6-FAM and
HEX) selective primers, 0.5 lL (50 pmol ⁄ lL) of one
MseI unlabelled primer and 0.25 lL of Taq DNA polymerase (5 U ⁄ lL). We performed selective PCR with
four primer pairs: EcoRI + AGC (6-FAM), EcoRI + ACG
(HEX), EcoRI + ACA (6-FAM) and EcoRI + AAC (HEX),
each paired with MseI + CAA. The PCR profile was one
cycle of 94 C for 2 min, one annealing cycle of 94 C
for 30 s, 65 C for 30 s and 72 C for 2 min, followed by
nine cycles of a 1 C decrease in annealing temperature
per cycle, followed by 35 cycles of 94 C for 30 s, 56 C
for 30 s and 72 C for 2 min, and a final extension at
60 C for 30 min. For all samples, we performed duplicates of entire reactions to verify reproducibility such
that the total sample size for AFLP analysis was 18 populations · eight half-sibs per population · two replicates per plant = 288. Selective PCR products were
electrophoretically separated using automated sequencing gels on an ABI Prism 3100 Genetic Analyzer
(Applied Biosystems) at the DNA Sequencing and Synthesis Facility at Iowa State University. Coloured gel
images were analysed with ABI GeneScan Analysis 2.1
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(Applied Biosystems) software. To verify reproducibility, samples were run in a manner that permitted replicates to be visualized side by side on each gel.
AFLP data analysis
Of the 144 individuals, 31 yielded highly anomalous
AFLP banding patterns in both replicates, and following
re-extraction of DNA. As technical errors at the digestion and ⁄ or ligation step appeared to produce artefactual amplification (Mueller & Wolfenbarger 1999), they
were omitted from further analysis and all subsequent
analyses and results were restricted to the remaining
113 individuals (Table 1). AFLP error rates were calculated by following the procedure described by Bonin
et al. (2004). A preliminary neighbour-joining cluster
analysis of these individuals based on all AFLP fragments between 35 and 500 bp in length appropriately
clustered replicate individuals together and within their
source populations. Therefore, we used all fragments in
the above range in our analyses.
We estimated polymorphism information content
(PIC) as a surrogate for population genetic diversity
(Anderson et al. 1993): PIC = 2 f (1 ) f), where f is the
percentage of samples in which the fragment is present.
PIC was calculated separately for each polymorphic
fragment to estimate genetic diversity within population
level. The genetic diversity was compared between
provenances and among regions within provenance
using t-test. In addition, to test whether genetic diversity has decreased or increased through invasion history
in North America, regression was used to test the linear
relationship between the genetic diversity of invasive
populations and their geographical distances (in kilometres) from the easternmost population (New Jersey
Walkill River).
Given an a priori null population model based on
geography (native European vs. invasive North American provenances and regions within provenances), we
tested for variation at provenance and regional levels
using analysis of molecular variance (AMOVA; Excoffier
et al. 1992). To take into account subtle differences in
AFLP genotypes between replicates, for each individual
we constructed a vector of means for each locus
between replicates and calculated the squared Euclidean distance matrix among all individual vectors in the
manner of Smouse & Peakall (1999) using R 2.3.1 (R
Development Core Team 2006). The squared Euclidean
distance data matrix was then used to evaluate a hierarchically nested AMOVA model examining how total AFLP
molecular genetic variation is partitioned among provenances relative to the total populations (FRT), among
populations within provenances (FPR) and within populations (FPT) using GENALEX 6.1 (Peakall & Smouse
3024 Y . J . C H U N , J . D . N A S O N and K . A . M O L O N E Y
2006). We also conducted AMOVA for each provenance
separately to characterize genetic structure among
regions (FGT), among populations within regions (FPG)
and within populations (FPT). To test for isolation by
distance within each province, we calculated FPT for
each pair of populations and tested this statistic against
the log10-scaled Euclidean geographical distances (in
kilometres) between populations. We used Mantel
(1967) test to determine significance with 9999 permutations of the data using GENALEX 6.1.
We explored alternative models of the genetic relationships among sample populations using principal
coordinate analysis and a model-based Bayesian clustering procedure. In the principal coordinate analysis,
principal coordinates were extracted from the squared
Euclidean genetic distance matrix described above and
plotted using GENALEX 6.1. The model-based clustering
analysis utilized Structure 2.2 (Pritchard et al. 2000)
with 30 000 generations of burn-in and 100 000 Markov
Chain Monte Carlo simulations. The Markov Chain
Monte Carlo algorithm accounted for the genotypical
ambiguity in dominant marker by setting the option
RECESSIVEALLELES = 1 (Falush et al. 2007). We used
the admixture model in which the fraction of ancestry
from each cluster is estimated for each individual with
the assumption that allele frequencies are correlated
among populations, as suggested in Falush et al. (2003).
We ran this simulation program, increasing the number
of clusters (K) from 2 to 10, with the number of natural
clusters in the data inferred from a plateau in the estimated posterior probability of K (Falush et al. 2003).
For each successive value of K, the inferred clusters
were drawn as coloured box plots using the DISTRUCT
(Rosenberg 2004) program, while the increasing
decomposition of clusters was graphed in the form of a
tree.
Analysis of phenotypical traits
We conducted a common garden experiment in an open
field site at the Bruner Farm, Boone, IA, USA (4200¢N,
9343¢W). We chose four seedlings of 2 cm in height
from each of five half-sib families per population (18 populations · five half-sib families · four seedlings = 360)
to reduce environmental variation in traits related to
germination and pre- ⁄ post-transplant growth (e.g.
maternal effects). On 30 May 2006, seedlings were
individually transplanted into plastic pots (30 cm diameter · 25 cm depth) and placed into plastic wading
pools (1.4 m diameter · 30 cm depth). The pots were
filled with Sunshine LC1 potting soil (Sun Gro Horticulture Canada). As the initial nutrient charge is highly
water soluble, providing the equivalent of approximately one application of a liquid fertilizer, nutrients
were supplemented by regular fertilizer treatments as
described below.
Because the expression of heritable phenotypical variation can be environmentally dependent (Falconer &
Mackay 1996), and purple loosestrife populations exhibit significant differences in reaction norms across environments (Chun et al. 2007), an experimental design
growing half-sib families in a single environment would
confound measurement of quantitative trait variation
with environmental effects. To avoid this, we grew purple loosestrife plants in four environments, ultimately
using restricted maximum-likelihood approach to
decompose total quantitative trait variation into genetic
and environmental components. Plants were arranged
in a split-plot design consisting of five complete blocks,
with four subplots within each block. Each subplot consisted of three plastic wading pools, which contained
one of four environmental treatment combinations:
(i) low water ⁄ low nutrient (WLNL), (ii) low water ⁄ high
nutrient (WLNH), (iii) high water ⁄ low nutrient (WHNL),
and (iv) high water ⁄ high nutrient (WHNH). Three plants
from each provenance were put in each wading pool
(six plants per pool). Three populations from each
region were distributed among three pools at random,
with the restriction that only one population from each
region can appear in a pool. Five half-sib families from
each population were also randomized across five
blocks. Four individuals from a half-sib family cannot
occur within the same block, but were distributed
evenly among all four environmental treatment combinations. The total number of experimental units for this
experiment was five blocks · four treatment combinations = 20 units, with six regions · three populations = 18 observations per experimental unit. Pots in
the high water treatments were kept at saturation, similar to standing water conditions in lakes and ponds.
Low water treatments are comparable with drier,
upland conditions, where plants were watered to the
extent of letting the soil soak for a few hours, after
which the soil was allowed to dry. Due to drought conditions at the common garden site, plants in the low
water treatments were watered every day until 27 June,
and then watered three times a week. In addition, 52
seedlings were killed from summer heat between 2 and
22 June and were replaced with seedlings from the
same half-sib family. After that period, six plants died
and were not considered in the data analysis.
In the low nutrient treatment, no fertilizer was
applied, whereas in the high nutrient treatment, 100 g
of slow-release 14:14:14 N:P:K Osmocote (The Scotts
Company) was applied once to a pot at the beginning
of the experiment (N, 2481 mg ⁄ kg soil; P, 563 mg ⁄ kg
soil; K, 1320 mg ⁄ kg soil). The Osmocote applied in the
high nutrient treatment followed the manufacturer’s
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QUANTITATIVE AND MOLECULAR GENETIC VARIATION 3025
recommendation for amount and rate (one application
for a 3- to 4-month growing period) to be used in producing high fertilization levels (The Scotts Company).
As a consequence, the low and high nutrient treatments
were designed to be a reasonable approximation of low
and high soil nutrient levels, respectively, that would
be encountered under natural field conditions (cf.,
Bridgham et al. 1996; Thormann & Bayley 1997; Bedford
et al. 1999).
On 31 August 2006, after 97% of the plants had initiated flowering, we harvested the aboveground part of
the plants. Belowground parts were left for a subsequent
study. We randomly chose wading pools across blocks
for harvesting to minimize experimental variation due
to growth during the harvesting period. The following
eight traits were measured for each experimental individual: (i) height, (ii) number of secondary branches,
(iii) number of stems originating from the rootstock, (iv)
leaf area of largest leaf, (v) number of days from sowing
to the first flowering, (vi) aboveground biomass, (vii)
ratio of reproductive (flowering) part to the aboveground biomass, and (viii) total number of flowers per
plant. To determine (iv), we chose the largest leaf from
each plant and calculated leaf area by multiplying the
maximum length by width of the leaf. To determine
(viii), we haphazardly sampled six short sections (5 cm)
of flower stalk from each plant (two each from apical,
middle and basal part of the stalk) to count the number
of flowers and measure biomass, then the number of
flowers per unit biomass of flower stalk was calculated.
We also measured total biomass of flower stalks per
plant for each individual. Total number of flowers per
plant was then calculated by multiplying the number of
flowers per unit biomass by total biomass of flower stalk
per plant, for each individual. To determine aboveground biomass and flower stalk biomass, plants were
divided into the respective parts and dried in an oven
for 24 h at 60 C to constant weight.
Analysis of quantitative phenotypical data
QST measures quantitative genetic variation in a manner
analogous to FST (McKay & Latta 2002). A correct estimation of QST requires one to disentangle genetic variation among populations from environmental variation
(Latta 2003; Pujol et al. 2008); thus, a common garden
system is an excellent research design to study QST.
Assuming that the inbreeding coefficient was zero, we
estimated QST as: QST ¼ r2BP =ðr2BP þ 2r2WP Þ, where r2BP
and r2WP are the additive genetic variance among and
within population respectively. As our experimental
design involves multiple half-sib families nested within
population, the additive genetic variance within population is four times the among-family variance (Lynch &
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Walsh 1998). Quantitative trait data were log10-transformed, except for one trait (the ratio of reproductive
biomass to aboveground biomass) which was arcsinetransformed, to create variables and their residuals that
were more normally distributed to satisfy assumptions
of our analytical methods. We calculated QST for each
of the eight quantitative traits using the variances
obtained from a restricted maximum-likelihood analysis
conducted using PROC MIXED in SAS 9.1 (SAS Institute
Inc.) while accounting for environmental effects. To
determine which traits were neutral or under disruptive ⁄ stabilizing selection, QST was contrasted with FST
for each quantitative trait. FST was estimated as h(II)
based on Weir & Cockerham’s (1984) method using the
Bayesian approach implemented in the program HICKORY
v1.1 (Holsinger & Lewis 2006). To account for the sampling error and predict the true estimation of FST and
QST, we calculated 1000 values of mean FST- and QSTvalues by generating bootstrap replicates (Whitlock
2008), and tested if mean QST-values are greater than,
smaller than or not significantly different with mean
FST using Lewontin & Krakauer’s (1973) approach. For
each iteration of bootstrap, we computed the probability
of getting a QST-value greater or smaller than the predicted FST-distribution (two-tailed test), then the average probability was reported before and after applying
sequential Bonferroni correction (Rice 1989) to account
for multiple tests. We conducted the QST ) FST contrast
for four population models in which VBP was the variance between (i) all 18 populations, (ii) the two provenances, (iii) the nine invasive populations, and (iv) the
nine native populations.
Results
From the four selective primer pairs and 226 samples,
1864 fragments were scored, of which 284 (15.2%) were
monomorphic. The number of fragments per sample
ranged from 439 to 621, with an average of 546 fragments. Among the 113 individuals analysed, no clone
was detected. The error rate per fragment was 0.6–
2.4%, with an average of 1.1%. Across our data set of
18 populations, observed genetic diversity was significantly smaller in the invasive provenance than that in
the native provenance (Table 1, t-test: P < 0.001).
Within the invasive provenance, genetic diversity
decreased from the ancestral region (New Jersey, 0.207)
through the intermediate region (Michigan, 0.175) to the
region where purple loosestrife established most
recently (Iowa, 0.139) (pairwise t-tests: P < 0.001 for all
pairs of regions). However, the regression of genetic
diversity on geographical distance from the easternmost
population (New Jersey Walkill River) was not significant (R2 = 0.331, P = 0.105).
3026 Y . J . C H U N , J . D . N A S O N and K . A . M O L O N E Y
In the principal coordinate analysis, principal coordinate axes PC1 and PC2 (Fig. 1a) together explained
57.5% of the total genetic variation, while PC2 and
PC3 (Fig. 1b) together explained 38.2%. PC1 alone
accounted for 34% of the total genetic variation. Populations from invasive and native provenances formed
two separate groups, with invasive populations having
positive PC1 scores and native populations having negative PC1 scores (Fig. 1a). Exceptionally, one population from each provenance (Potsdam Golm and
Michigan Lake Lansing) was in contact with each other
in the middle. PC2 and PC3 generally reflected the
regional structures within each provenance (Fig. 1b).
For example, populations from Michigan and Iowa had
positive PC2 scores and negative PC3 scores, while
European populations (Switzerland Steinbode and Potsdam Golm) had positive PC2 scores and positive PC3
scores.
In evaluating population relationships inferred using
Structure (Fig. 2), we observed a plateau of estimated
(a)
(b)
Fig. 1 Principal coordinate plots displaying population structure (a) between PC1 and 2, (b) between PC2 and 3.
posterior probability at K = 7 (Falush et al. 2003). In
general, the pattern of clustering hierarchically divided
population structure into invasive and native provenances and the regions within each. A primary exception, however, was the native Potsdam Golm
population. This population clustered with populations
from the invasive provenance at K = 3 before becoming
a distinct cluster at K ‡ 4, a pattern consistent with the
principal coordinate plot in which the Potsdam Golm
population was in contact with an invasive population
(Michigan Lake Lansing; Fig. 1a).
From our geographical null model, the nested AMOVA
for both provenances reflected significant genetic structure of provenances and populations, allowing 8% of
the variation (P < 0.0001) to be accounted for among
provenances and 19% of the variation (P < 0.0001)
among populations within provenance (Table 2). Analysing provenances separately revealed almost no difference between the invasive and native provenances in
population divergence: 23% and 22% of the genetic
variation was partitioned among populations in the
invasive and native provenances respectively. However,
regional differentiation was greater in the introduced
provenance (11%) than in the native provenance (5%).
Population differentiation within regions was greater in
the native provenance (17%) than that in the introduced
provenance (12%). Population structure was comparable between invasive and native populations in that the
majority of genetic variation was retained within populations. Pairwise FPT between populations were significant with a few exceptions (Table 3). The nonsignificant
or less significant values involved the smallest populations after sample reduction (Iowa Boone Folks, Iowa
Little South Storm Lake, Michigan Rifle Range near
Pittsford, Switzerland Altmatt and Switzerland North
Sihl Lake; Table 1), possibly indicating a sample effect.
The correlation between FPT and geographical distance
indicated significant pattern of isolation by distance for
both invasive and native provenances (RXY = 0.480,
P = 0.006; RXY = 0.279, P = 0.008 respectively).
The analysis of quantitative traits resolved significant
genetic differences among provenances and among
populations within each provenance. Across all environmental treatments, invasive plants were generally
taller, possessing greater leaf area and flowering later
than native plants (Fig. 3a, d, e). Populations from
Switzerland differed from other populations in native
provenance by less number of branches, stems, flowers,
leaf area, more reproductive: aboveground biomass
ratio and early flowering (Tukey–Kramer test, P < 0.05).
In the invasive provenance, populations from Michigan
showed early flowering and more reproductive: aboveground biomass ratio than other populations (Tukey–
Kramer test, P < 0.001). This indicates that Switzerland
2009 Blackwell Publishing Ltd
QUANTITATIVE AND MOLECULAR GENETIC VARIATION 3027
Fig. 2 Estimated population structure from model-based clustering analysis. (Left) Each sample is represented by a thin vertical line,
which is partitioned into K-coloured segments that represent the sample’s estimated membership fractions in K clusters. Black lines
separate different populations. Populations are labelled below the figure. (Right) Clustering pattern diagram based on the highest
percentage of population membership to each cluster.
Table 2 Hierarchical nested analysis of molecular variance (AMOVA; Excoffier et al. 1992)
Source
Invasive and native provenances
Among provenances
Among populations within provenances
Within populations
Total
Invasive provenance
Among regions
Among populations within regions
Within populations
Total
Native provenance
Among regions
Among populations within regions
Within populations
Total
d.f.
SS
MS
Variance
% Var
Statistic
P-value
1
15
95
111
1176.202
5319.506
12462.950
18958.658
1176.202
354.634
131.189
1662.025
14.897
34.316
131.189
180.402
8
19
73
FRT = 0.083
FPR = 0.207
FPT = 0.273
<0.0001
<0.0001
<0.0001
2
5
37
44
1066.884
1322.573
5227.388
7616.844
533.442
264.515
141.281
939.237
20.922
22.164
141.281
184.367
11
12
77
FGT = 0.113
FPG = 0.136
FPT = 0.234
<0.0001
<0.0001
<0.0001
2
6
58
66
983.542
1946.508
7235.563
10165.612
491.771
324.418
124.751
940.940
7.429
26.969
124.751
159.149
5
17
78
FGT = 0.047
FPG = 0.178
FPT = 0.216
<0.0001
<0.0001
<0.0001
and Michigan populations cease vegetative growth earlier to produce more flowers than other populations in
their respective provenance. The univariate ANOVA analysis indicated that purple loosestrife populations are
genetically differentiated between invasive and native
provenances for six out of eight traits (Table 4). Within
each provenance, populations differed for all eight
traits.
The patterns of QST ) FST contrasts were shown from
the analysis with four data sets: (i) all 18 populations,
(ii) two provenances, (iii) nine invasive populations,
and (iv) nine native populations (Table 5). Whether QST
is similar, greater or smaller than FST varied depending
on the quantitative traits tested and the populations
included in the analysis. Before sequential Bonferroni
2009 Blackwell Publishing Ltd
corrections, QST was significantly smaller than FST for a
few traits—height (invasive and native populations),
number of branches (invasive populations), number of
stems (all populations, native populations) and leaf area
(invasive populations). Among these, only one case (the
number of stems for all populations) was still significant after sequential Bonferroni corrections.
Discussion
Little is known about how adaptive and nonadaptive
genetic processes influence the heritable phenotypical
differences among natural populations. Many researchers addressing this issue indicate that adaptive processes contribute to form populations with different
IABF
IALS
MIKB
MILL
MIRR
NJBR
NJHV
NJWR
PDGO
PDGR
PDPO
SWAL
SWNO
SWST
TUHA
TURE
TUUN
0.041
0.123
0.238
0.162
0.159
0.193
0.149
0.304
0.218
0.221
0.242
0.246
0.306
0.301
0.273
0.283
0.127
0.248
0.154
0.198
0.213
0.178
0.328
0.232
0.247
0.252
0.248
0.317
0.321
0.282
0.296
NS
0.156
0.157
0.230
0.238
0.200
0.294
0.187
0.204
0.209
0.226
0.282
0.278
0.252
0.257
*
*
0.274
0.304
0.334
0.270
0.304
0.247
0.250
0.290
0.286
0.273
0.323
0.308
0.322
**
**
***
NS, not significant. *P < 0.05, **P < 0.01, ***P < 0.001.
Native
Invasive
MILL
0.178
0.225
0.165
0.369
0.247
0.244
0.261
0.282
0.362
0.349
0.316
0.321
NS
NS
*
*
MIRR
0.129
0.125
0.320
0.221
0.218
0.221
0.234
0.309
0.290
0.263
0.271
**
**
***
***
*
NJBR
0.119
0.366
0.249
0.245
0.256
0.271
0.347
0.314
0.291
0.311
**
**
***
***
*
***
NJHV
0.306
0.207
0.207
0.221
0.217
0.291
0.274
0.245
0.263
**
**
***
***
*
***
***
NJWR
0.288
0.299
0.291
0.303
0.271
0.357
0.312
0.349
**
**
***
***
*
***
***
***
PDGO
MIKB
IABF
IALS
Native
Invasive
0.015
0.149
0.079
0.244
0.208
0.162
0.188
**
**
***
***
*
***
***
***
***
PDGR
0.143
0.094
0.241
0.199
0.161
0.181
**
**
***
***
*
***
***
***
***
NS
PDPO
0.108
0.219
0.196
0.143
0.191
*
*
**
***
*
***
***
***
***
***
***
SWAL
0.169
0.159
0.105
0.134
*
*
**
***
*
***
***
***
***
***
***
**
SWNO
0.239
0.181
0.254
**
**
***
***
*
***
***
***
***
***
***
***
**
SWST
0.129
0.164
**
***
***
***
*
***
***
***
***
***
***
***
**
***
TUHA
0.114
**
**
***
***
*
***
***
***
***
***
***
***
**
***
***
TURE
**
**
***
***
*
***
***
***
***
***
***
***
***
***
***
***
TUUN
Table 3 Matrix of pairwise comparisons of population genetic distance (FPT) within each provenance. Population acronyms are as in Table 1. Population IAMA was omitted
due to the insufficient number of samples after sample reduction
3028 Y . J . C H U N , J . D . N A S O N and K . A . M O L O N E Y
2009 Blackwell Publishing Ltd
QUANTITATIVE AND MOLECULAR GENETIC VARIATION 3029
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
optima (disruptive selection; Merilä & Crnokrak 2001;
Palo et al. 2003; Porcher et al. 2004; Le Corre 2005; Chapuis et al. 2007), while others suggest stabilizing selection (Stenøien et al. 2005; Evanno et al. 2006), or mixed
results depending on the quantitative trait (Zhan et al.
2005) or habitat fragmentation (Johansson et al. 2007).
This indicates that the role of nonadaptive processes is
actually considerable in nature and should be carefully
evaluated to assess the proportion of adaptive processes
contributing to natural selection. Empirical studies
examining this issue with native vs. invasive populations may allow us to evaluate the relative contribution
of adaptive and nonadaptive processes affecting the formation of invasive populations, to deepen our understandings of the process of invasion. Using purple
loosestrife as a model system, we first examined the
population genetic variation and diversity to infer the
population genetic processes involved in the process of
invasion; then, we contrasted the genetic variation with
quantitative trait variation to investigate how adaptive
2009 Blackwell Publishing Ltd
Fig. 3 Least square mean values (±SE)
of eight trait values across all treatments
for native (open circles) and invasive
(solid circle) plants from each region.
Region acronyms are as in Table 1.
and nonadaptive processes influenced the formation of
invasive vs. native populations.
Interestingly, our study indicates that purple loosestrife populations introduced into North America maintained
substantial
molecular
genetic
diversity,
comparable with native populations. The changes in
genetic diversity after introduction to a new range may
depend on the breeding system of invasive species. The
impact of genetic bottlenecks and founder effects
strongly limits the genetic diversity of plants relying on
selfing or asexual reproduction (Baker 1967; Brown &
Marshall 1981). Such species may be limited further by
reduced recombination among founding genotypes and
thereby maintain low genetic diversity (Lambrinos
2001). In contrast, outbreeding species often exhibit high
levels of genetic diversity in both native and introduced
ranges (Pappert et al. 2000). For outcrossing species in
general, most of the total genetic diversity is retained
within populations, while more is proportioned among
populations of selfing species (Hamrick & Godt 1990).
0.6903 (16, 254)
0.5311 (16, 254)
0.1972 (16, 254)
0.7469 (16, 262)
0.0221 (16, 262)
0.7534 (16, 262)
0.5306 (16, 257)
0.3542 (16, 257)
0.6332 (16, 257)
0.8613 (16, 264)
0.9605 (16, 264)
0.8345 (16, 264)
0.7505 (16, 255)
0.5661 (16, 255)
0.0195 (16, 255)
0.4022 (16, 246)
0.6948 (16, 246)
0.7418 (16, 246)
0.2299 (16, 261)
0.4909 (16, 261)
0.6915 (16, 261)
0.8919 (16, 259)
0.2384 (16, 259)
0.7456 (16, 259)
(1, 34.8)
(1, 34.8)
(1, 34.8)
(1, 219)
(1, 219)
(1, 219)
(1, 219)
(16, 254)
<0.0001
<0.0001
<0.0001
0.1973
0.2838
0.7583
0.7835
<0.0001
0.0002
<0.0001
<0.0001
<0.0001
0.5365
0.1996
0.6595
0.0004
Water, W
Nutrient, N
W·N
Provenance, R
R·W
R·N
R·W·N
Population within
provenance, P(R)
R · W(P)
R · N(P)
R · W · N(P)
(1, 42.5)
(1, 42.5)
(1, 42.5)
(1, 225)
(1, 225)
(1, 225)
(1, 225)
(16, 259)
0.7685
<0.0001
<0.0001
0.0058
0.7676
0.0132
0.0177
0.0002
(1, 43.6)
(1, 43.6)
(1, 43.6)
(1, 232)
(1, 232)
(1, 232)
(1, 232)
(16, 261)
0.0003
<0.0001
0.0632
0.2609
0.6305
0.4092
0.1598
0.0108
(1, 47.4)
(1, 47.4)
(1, 47.4)
(1, 228)
(1, 228)
(1, 228)
(1, 228)
(16, 246)
0.0012
<0.0001
<0.0001
<0.0001
0.2833
0.4031
0.0301
<0.0001
(1, 40.2)
(1, 40.2)
(1, 40.2)
(1, 221)
(1, 221)
(1, 221)
(1, 221)
(16, 255)
0.4304
0.2944
0.0511
<0.0001
0.0250
0.0338
0.2296
<0.0001
(1, 41.6)
(1, 41.6)
(1, 41.6)
(1, 231)
(1, 231)
(1, 231)
(1, 231)
(16, 264)
<0.0001
<0.0001
<0.0001
<0.0001
0.9605
0.1408
0.2656
<0.0001
(1, 38.3)
(1, 38.3)
(1, 38.3)
(1, 225)
(1, 225)
(1, 225)
(1, 225)
(16, 257)
<0.0001
0.2795
0.4125
<0.0001
0.0437
0.0569
0.9328
<0.0001
(1, 35.4)
(1, 35.4)
(1, 35.4)
(1, 223)
(1, 223)
(1, 223)
(1, 223)
(16, 262)
Flower
RatioRT
MassT
NDFF
LeafArea
Stem
Branch
Height
Source
Table 4 Summary of ANOVA with P-values of the corresponding F-tests for each quantitative trait. Numerator degrees of freedom (d.f.) and denominator d.f. are presented in
parentheses. Significant P-values are indicated in bold. Traits are: Height, final height at harvest; Branch, number of secondary branches; Stem, number of stems originating from
the rootstock; LeafArea, leaf area of the largest leaf; NDFF, number of days from sowing to the first flowering; MassT, aboveground biomass; RatioRT, ratio of reproductive
(flowering) part to the aboveground biomass; Flower, total number of flowers per plant
3030 Y . J . C H U N , J . D . N A S O N and K . A . M O L O N E Y
Our results indicate that in both the invasive and native
provenances, the genetic variation within populations
accounted for the majority of total genetic variation
(Table 2). It suggests that the founding populations
may be composed of diverse mixtures of purple loosestrife lineages and ⁄ or that multiple episodes of introduction from different sources of native populations
have produced genetically intermingled populations.
During the invasion process, population processes
such as founder effects and genetic drift may have
reduced the genetic diversity of introduced purple
loosestrife populations. Evidences exist that these nonadaptive processes resulted in biased frequencies of
flower morphs (Eckert & Barrett 1992; Mal & LovettDoust 1997), while equal frequencies of three flower
morphs are typical in native European populations
(Eckert et al. 1996). Our results suggest that genetic
diversity decreased during the course of invasion in
North America from New Jersey through Michigan to
Iowa. Rapid expansion of populations in North America
could have involved founder events and drifts that ultimately reduced the genetic variability during subsequent invasion process.
Population genetic structures of purple loosestrife populations appeared to be generally similar between invasive and native provenances. Distinct genetic entities of
purple loosestrife were revealed between invasive and
native provenances, with subsequent genetic subdivision
at the region-wide geographical scale (Table 2; Figs 1
and 2). The pattern of isolation by distance was significant for both invasive and native provenances, which
indicates that they are geographically structured and differentiated through migration and genetic drift. However, genetic variation among regions was twofold
greater in the invasive than in the native provenance
(Table 2). This may be due to the greater geographical
range of sampled populations in the invasive than in the
native provenance: mean pairwise geographical distances among populations were 624 and 297 km for invasive and native provenances respectively.
The QST ) FST contrast is useful to evaluate the relative
contribution of nonadaptive processes (genetic drift,
migration and mutation) and adaptive processes (disruptive or stabilizing selection) in shaping quantitative traits.
We did not find any evidence of significant differences
between QST and FST, except for only one case of
QST < FST for the number of stems in all populations
(Table 5). This suggests stabilizing selection for a common optimum for this particular trait; however, it should
be interpreted with care. Theoretical studies indicate that
quantitative traits related to fitness generally have a large
amount of dominance and epistatic genetic variance
(Crnokrak & Roff 1995), which may cause a general
decrease in QST (Whitlock 1999, 2008; López-Fanjul et al.
2009 Blackwell Publishing Ltd
QUANTITATIVE AND MOLECULAR GENETIC VARIATION 3031
Table 5 Mean and 95% confidence limits (CL) of FST and QST for eight quantitative traits and the average probability of Lewontin
& Krakauer (1973) test for (a) all populations, (b) between provenances, (c) invasive populations and (d) native populations. Bold
P-values indicate where significant differences between FST and QST occurred. P < 0.025 indicates QST > FST, while P > 0.975 indicates
QST < FST. Significant P-value after sequential Bonferroni correction is underlined. Trait acronyms are as in Table 4
(a) All populations
Estimates
Mean
2.5% CL
97.5% CL
P-value
Mean
2.5% CL
97.5% CL
P-value
Height
Branch
Stem
LeafArea
NDFF
MassT
RatioRT
Flower
0.2250
0.1127
0.0766
0.0296
0.1431
0.3874
0.1560
0.2357
0.1214
0.2201
0.0596
0.0137
0.0000
0.0771
0.2693
0.0798
0.1422
0.0546
0.2301
0.1922
0.1903
0.0864
0.2399
0.5364
0.2883
0.3686
0.2394
0.9270
0.9604
0.9971
0.8330
0.0609
0.7787
0.4416
0.8882
0.0832
0.1448
0.0162
0.0017
0.1394
0.2182
0.1526
0.0128
0.0011
0.0768
0.0854
0.0000
0.0000
0.0812
0.1669
0.0878
0.0009
0.0000
0.0899
0.2149
0.0535
0.0144
0.2134
0.2713
0.2513
0.0312
0.0087
0.1945
0.7050
0.9458
0.2041
0.1080
0.1863
0.7125
0.9574
FST
QST
(b) Between provenances
(c) Invasive populations
Estimates
Mean
2.5% CL
97.5% CL
P-value
Mean
2.5% CL
97.5% CL
P-value
Height
Branch
Stem
LeafArea
NDFF
MassT
RatioRT
Flower
0.2233
0.0311
0.0214
0.0670
0.0382
0.1807
0.0737
0.2087
0.1951
0.2154
0.0000
0.0000
0.0000
0.0000
0.0767
0.0012
0.0667
0.0235
0.2315
0.0944
0.1208
0.2405
0.1215
0.3899
0.2512
0.4955
0.5093
0.9886
0.9820
0.9282
0.9780
0.6181
0.9142
0.5543
0.7123
0.1823
0.0201
0.1622
0.0223
0.0662
0.2537
0.0451
0.2657
0.1238
0.1761
0.0000
0.0196
0.0000
0.0071
0.1060
0.0000
0.1501
0.0537
0.1889
0.0798
0.7349
0.1056
0.1878
0.4608
0.1546
0.4541
0.2560
0.9900
0.6395
0.9807
0.9017
0.2693
0.9467
0.2241
0.7089
FST
QST
(d) Native populations
2003; Goudet & Büchi 2006). The effects of nonadditive
genetic variation thus often lead to underestimation of
QST and difficulties in drawing inferences from QST < FST
that we observed.
In the process of estimating QST, we detected generally low level of phenotypical variance among and
within populations. This may be due in part to the
strong environmental effects, which accounted for most
of the observed phenotypical variances. Indeed, phenotypical plasticity itself evolves and may be a part of
adaptive process (Whitlock 2008), as previously
reported for this species (Chun et al. 2007). The number
and geographical scale of study populations (O’Hara &
Merilä 2005; Volis et al. 2005), the choice of quantitative
traits and molecular marker (Merilä & Crnokrak 2001;
Whitlock 2008) may bias the outcome of QST ) FST analysis. Sampling error may also be attributable to the relatively large confidence intervals of QST compared with
those of FST. Our study design was practically limited
by lack of replication with only four plants per half-sib
family randomly distributed across five blocks. In addition, generally high levels of FST from our AFLP marker
may add more difficulties in detecting QST > FST (Hendry 2002; Goudet & Büchi 2006). In sum, although the
2009 Blackwell Publishing Ltd
QST ) FST analysis is an appropriate method to test the
adaptive phenotypical differentiation among populations, its safe interpretation is limited by many assumptions and factors potentially leading to biased results.
Our results indicate no significant differences between
FST and QST between invasive vs. native provenance and
within each provenance. However, this does not necessarily implicate the absence of adaptive genetic differentiation; instead, this suggests the difficulties in
distinguishing the effect of nonadaptive population processes and natural selection (Merilä & Crnokrak 2001).
Our quantitative trait data (Fig. 3) indicated significant
genetic variation between native and invasive provenances and among populations within each provenance
(Table 4). Previous studies also support that purple
loosestrife populations are known to be genetically differentiated along latitudinal gradient in both invasive
(Montague et al. 2008) and native (Olsson & Ågren 2002)
provenances. Chapuis et al. (2007) noted that previous
studies successfully detecting the evidences of disruptive
selection (QST > FST) often involved populations across
latitudinal or climatic gradient. In our study, the latitudinal range within invasive provenance was small in contrast to the large differences in longitude (Table 1), thus
3032 Y . J . C H U N , J . D . N A S O N and K . A . M O L O N E Y
suggesting the difficulties in detecting significant pattern
of local adaptation among populations in this scale. A
more effective way to test local adaptation would be to
transplant individuals reciprocally from populations
along climatic or latitudinal gradient, as well as between
invasive and native provenance (e.g. Maron et al. 2004).
There is still no explicit answer with regard to the
universal invasion success of purple loosestrife in North
America across a broad geographical range. Consistent
differences in phenotypical traits between invasive and
native plants have been reported, supporting Baker’s
(1974) ‘general purpose genotype’ hypothesis. Invasive
purple loosestrife populations are known to be taller
with greater total biomass in North America than in
Europe (Blossey & Nötzold 1995; Blossey & Kamil 1996;
Bastlová 2001; Bastlová & Květ 2002; Chun et al. 2007),
agreeing with our results (Fig. 3). Our results revealed
that invasive and native populations have comparable
level of genetic diversity and similar genetic structure.
It is also known that polyploidy increases genetic diversity (Soltis & Soltis 2000) and purple loosestrife populations vary in ploidy levels (Kubátová et al. 2008). Taken
together, multiple introductions may have created a
genetic mixture from diverse source populations in Europe and increased population genetic diversity facilitated by the auto-tetraploid nature of purple loosestrife
(Houghton-Thompson et al. 2005). During the second
half of the 19th century, purple loosestrife was introduced by European immigrants as a medicinal, horticultural and beekeeping plant (Thompson et al. 1987).
Thus, it may have been repeatedly introduced, involving various genotypes from different areas in Europe
(Edwards et al. 1995), and ultimately increasing genetic
variation in the introduced provenance. Although population admixture and intraspecific hybrids may confer
high level of genetic diversity and greater ability of
responding to local selection pressure, their relationship
with adaptive evolutionary changes or general increase
of fitness is still inconclusive (Johansen-Morris & Latta
2006; Wolfe et al. 2007). Further, adaptation and
increased fitness of genotypes may not fully explain the
ultimate demographical success of purple loosestrife
invasion, as the relationship with their predators and
competitors in the broader context of biotic community
may be also important (e.g. escape from natural enemies, Blossey & Nötzold 1995). More research is needed
to deepen our understanding on the evolutionary consequences of adaptive population genetic process in successful invasive species.
Acknowledgements
We thank J.F. Wendel for the use of the facilities in his laboratory. We are grateful to R. Percifield, M.L. Collyer, J. Hawkins, L.
Flagel, J. Triplett and L. Gutierrez for thoughtful comments and
suggestions. We would also like to thank H. Dietz, C. Holzapfel,
D. Landis, R. Prasse, F. Jeltsch and F. Schurr for providing seed
samples. This research was funded by EEOB Department at
Iowa State University and by CGRER Seed Grant Program at
University of Iowa to Y.J. Chun.
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This research is part of Y.J.C.’s doctoral research supervised by
J.D.N. and K.A.M. Y.J.C. is currently a postdoctoral associate
working with François Bretagnolle at INRA, France. His interests are in the evolutionary ecology of biological invasion with
particular emphasis in the population genetic and ecological
processes relating to the adaptation and establishment of invasive species. J.D.N. is interested in the population and conservation genetics of plants and their associated insect herbivores
and pollinators. K.A.M. focuses on the population dynamics of
invasive species, specifically on the identification of the crucial
demographic stages that predispose a species to be a successful
invader. This study reflects our shared interest in combining
ecological and genetic perspectives for investigating the adaptation of invasive species.