1. science
2. medicine
3. genetics

Sample Size Effects on Estimates of Population
Genetic Structure: Implications for Ecological
Restoration
Elizabeth A. Sinclair1,2,3 and Richard J. Hobbs1
Abstract
The field of ecological restoration is growing rapidly, and
the sourcing of suitable seed is a major issue. Information on the population genetic structure of a species can
provide valuable information to aid in defining seed collection zones. For a practical contribution from genetics,
a rapid approach to delineating seed collection zones
using genetic markers (amplified fragment length polymorphisms [AFLPs]) has been developed. Here, we test
the effects of sampling regime on the efficacy of this
method. Genetic data were collected for an outcrossing
seeder, Daviesia divaricata ssp. divaricata, an important
species in urban bushland restoration in Perth, Western
Australia. The effect of sample size and number of
AFLP markers on estimates of genetic variation and
population structure was examined in relation to implications for sourcing material for restoration. Three differ-
ent sample sizes were used (n ¼ 8, 15, and 30) from six
urban bushland remnants. High levels of genetic diversity were observed in D. divaricata (87.4% polymorphic
markers), with significant population differentiation
detected among sampled populations (QB ¼ 0.1386,
p < 0.001). Although sample size does not appear to affect the spatial pattern in principle co-ordinates analysis
(PCA) plots, the number of polymorphic loci increased
with sample size and estimates of population subdivision (FST and QB) and associated confidence intervals
decreased with increasing sample size. We recommend
using a minimum of 30 plants for sourcing seed for restoration projects.
Introduction
Fragmentation of natural habitats is increasingly one of
the most important factors influencing the long-term survival of many species of plants and animals. Developing
an understanding of how fragmentation affects plant and
animal populations is essential to meet the pressing need
for guidelines for the management of fragmented systems
(Hobbs & Yates 2003). In urban areas, remnant bushland
is a significant natural resource, providing natural habitat
for native flora and fauna, as well as cultural, educational,
and recreational opportunities for the local community. In
plants, gene flow among isolated habitat remnants can be
maintained through the movement of pollen and seeds by
vectors such as wind, insects, birds, and small marsupials.
Successful gene flow will depend largely on the ability of
these vectors to move between remnants. These vectors
may now be completely absent. For example, the Honey
possum, Tarsipes rostratus, is an important plant pollina-
tor but has disappeared from many areas of its former
range in southwestern Australia (Dell & Banyard 2000).
The small size and physical isolation of many remnants
means they may be unable to sustain current species diversity, as well as intraspecific genetic diversity.
Much of the literature on ecological restoration pertains
to the choice of species to be used, but intraspecific
genetic differentiation in relation to site ecology is also
important, particularly when high levels of genetic differentiation are found among populations (Coates 2000).
Therefore, decisions must be made in relation to the
source of new genetic material required to augment existing population(s) or reintroduce a species where it has
been lost. Natural gene flow is highly skewed, with most
pollen and seed dispersal typically occurring only over
a few meters (Sackville Hamilton 2001). The introduction
of genotypes from different populations is another form
of gene flow. Deciding where this material should come
from has been the subject of recent debates, with well-supported theory and empirical evidence for and against the
movement of genotypes (Lesica & Allendorf 1999; Keller
et al. 2000; Moore 2000; Sackville Hamilton 2001;
Wilkinson 2001). Collecting locally has generally been
accepted as the conservative ‘‘norm’’ in the absence of any
ecological and genetic data, as seen in more recent guidelines for restoration (e.g., Mortlock 2000), although it may
1
School of Environmental Science, Murdoch University, Murdoch, Western
Australia 6150, Australia
2
Address correspondence to E. A. Sinclair, email [email protected]
3
Botanic Gardens and Parks Authority, Fraser Avenue, West Perth, Western
Australia 6005, Australia
2008 Society for Ecological Restoration International
doi: 10.1111/j.1526-100X.2008.00420.x
Restoration Ecology
Key words: AFLP, Daviesia divaricata, population
genetic structure, sampling size, urban bushland
restoration.
1
Sample Sizes for Sourcing Seed
not always be the best choice, e.g., when the local population is very small and has limited genetic variation. The
selection of genetic material for habitat restoration needs
to be defined in terms of ecological, geographic, and
genetic proximity (Sackville Hamilton 2001).
The large and rapidly growing restoration industry
urgently requires accurate seed sourcing guidelines for
a wide range of species. In the highly diverse southwest
Australian floristic region (Hopper & Gioia 2004),
where a large number of species require restoration
activities, a rapid approach using minimal sampling and
amplified fragment length polymorphism (AFLP) has
been developed to collect genetic information for
a large number of species (e.g., Krauss & Koch 2004;
Bussell et al. 2006; Krauss & He 2006). However, such
minimal sampling may not provide an accurate reflection of genetic parameters. If the sampling is inadequate, the advantages of the rapidity of the technique
may be overshadowed by the potential for the collection and interpretation of such datasets leading to poor
management decisions and actions, as well as poor
long-term outcomes. Here, we examine the effects of
sample size, using a species of interest in urban bushland restoration, in order to test and, if necessary,
improve sampling strategies for estimating genetic
parameters for identifying source population(s) for ecological restoration.
Methods
Study Area and Species
Bold Park is a large (437 ha) and significant coastal bushland remnant in the western suburbs of the Perth metropolitan area (Western Australia). As part of a long-term
integrated restoration project (BGPA 2000, 2006), the
genetic profiles of many of the native plant species are
being collected, providing genetic data to contribute information for sourcing suitable seed and green stock material
for restoration. Daviesia divaricata, commonly known as
the Marno, is a member of the legume family (Fabaceae).
There are two currently recognized subspecies: D. divaricata ssp. lanulosa (north of Geraldton) and D. divaricata
ssp. divaricata (south of Geraldton), with disjunct, nonoverlapping distributions (Fig. 1). This study focuses on
populations of D. divaricata from the Perth metropolitan
area because populations within this area were deemed
most likely to provide suitable material for the ecological
restoration of Bold Park due to their geographic proximity
and similar soil type. Daviesia divaricata is an outcrossing
seeder. It ranges in size from a small to large shrub
approximately 3 m high, tending to do well in the deeper
sandy soils, and often common in disturbed areas, such as
roadside verges. The flowering season is between June
and December, with pollination mostly likely performed
by bees. It sets seeds that are wind dispersed but, because
of their size, are unlikely to disperse far from the parent
2
Figure 1. Enlarged map of the Perth metropolitan area showing the
sampling localities for this study for D. divaricata with sample sizes
in parentheses. Inset: map showing the distribution of Daviesia divaricata in Western Australia (modified from FloraBase, http://florabase.
dec.wa.gov.au/).
plant. It is relatively common in Bold Park at present.
However, the combination of low seed set, few new plants
germinating since a major fire in 2000, and regular fires
(10 in the past 40 years; J. Fisher 2005, UWA, personal
communication) are probably contributing to a reduced
soil seed bank, leading to consideration of other potential
seed source populations.
Sampling
Fresh stem material was collected from six sampling locations within the Perth metropolitan area between July and
November 2004: Bold Park (n ¼ 29), Yanchep National
Park (n ¼ 31), Whitfords (n ¼ 29), Wireless Hill (n ¼ 28),
Kings Park (n ¼ 30), and Yalgorup National Park
(n ¼ 12) (Fig. 1). The sampling strategy was to collect
from the larger populations across the Perth metropolitan
area, which were more likely to provide good seed collections for restoration projects, without compromising the
Restoration Ecology
Sample Sizes for Sourcing Seed
source population. Samples were collected widely within
locations so as to maximize the genetic diversity sampled
within each location. All sampling locations were in the
Spearwood dune system in which sands were derived from
Tamala limestone (Dell & Banyard 2000). The precise
location of each plant was recorded using a global positioning system (GPS) (AGD84). All plants were mature,
with the exception of several younger plants in Bold Park.
between 50 and 450 base pairs with the aid of Genotyper
software (Applied Biosystems). Replicate samples were
run across each gel so as to permit consistent scoring of
bands. Multiple extractions and PCR were also carried out
to identify those bands that were not reproducible (and
hence were excluded from the dataset prior to analysis).
Genetic Analyses
Laboratory Methods
Genomic DNA was extracted from freshly collected or
fresh frozen material using plant Qiagen kits (Qiagen,
Inc., Doncaster, Victoria, Australia), with all plant material ground in liquid nitrogen prior to extraction. AFLP
profiles (Vos et al. 1995; Mueller & Wolfenbarger 1999)
were generated using the restriction enzymes PstI and
MseI and associated primers as in Muluvi et al. (1999).
AFLP involved three steps: (1) restriction–ligation:
restriction of genomic DNA was done at 37C for 2 hours
in a 20 lL volume containing approximately 250 ng of
DNA, 2.5 U of Mse1 and 5.2 U Pst1, 2.0 lL NE buffer 2
(supplied with Mse1 enzyme), 2.0 lL 0.1% BSA, and
DNA-free water. Next, 5 lL of a solution containing 0.5
lL T4 ligase, 0.5 lL ligation buffer (supplied with T4
ligase), and 4.0 lL Mse1/Pst1-adapter solution was added
to the samples and further incubated at 20C overnight and
then diluted 1/10 in TE buffer; (2) preselective polymerase
chain reaction (PCR) amplification: performed in a 20 lL
total volume containing 4.0 lL 5X PCR buffer containing
dNTPs, 0.6 lL MgCl2 (50 mM), 0.5 lL each of Pst1 (5 lM)
and Mse1 (5 lM) primer, 0.825 U Taq DNA polymerase
(Fisher Biotech), 4.0 lL restricted/diluted DNA template,
and DNA-free water. The PCR was performed using a
GeneAmp 9700 PCR System (Applied Biosystems, Foster
City, CA, U.S.A.) for 20 cycles each at 94C for 30 seconds,
56C for 2 minutes, and 72C for 2 minutes. A final extension step at 72C for 5 minutes was performed. PCR products were diluted 1/20 with TE buffer for subsequent,
selective amplification; and (3) selective PCR amplification:
selective PCR was done in a 10 lL total volume containing
2.0 lL 5X PCR buffer containing dNTPs, 0.3 lL MgCl2
(50 mM), 0.25 lL fluorescently labeled Pst1 primer (1 lM),
0.5 lL Mse1 primer (5 lM) (GeneWorks, Hindmarsh,
South Australia, Australia), 0.25 U Taq DNA polymerase,
2.5 lL of diluted preselective PCR product, and DNA-free
water. The selective PCR cycle consisted of a touchdown
cycle for 13 cycles at 94C for 30 seconds, 65–53C for 30
seconds, and 72C for 1 minute, followed by 25 cycles at
94C for 30 seconds, 56C for 2 minutes, and 72C for 2
minutes, and a final extension at 72C for 2 minutes. Two
primer pair combinations were used: mCTT and Pst-AC (6Fam label) and mCTT and Pst-CT (Tet label). Bands were
visualized using an ABI 377 sequencer and Genescan software (Applied Biosystems) with internal size standard
(GS-500 TAMRA; Applied Biosystems). The presence (1)
or absence (0) of fragments was scored unambiguously
Restoration Ecology
The number of polymorphic bands was assessed by population and across all populations. Analysis of molecular
variance (AMOVA) (Excoffier et al. 1992; Huff et al.
1993) was performed using the computer program GenAlEx 5.1 (Peakall & Smouse 2001) to partition genetic variance within and among the sampled populations. A
distance matrix was generated using Huff et al. (1993)
for dominant markers. We estimated population subdivision based on FST using Tools For Population Genetic
Analysis (TFPGA) (Miller 1997), available at http://www.
marksgeneticsoftware.net/, and theta-B (B), using the
software Hickory available at http://darwin.eeb.uconn.
edu/hickory/software.html. Hickory uses a Bayesian approach to estimate an FST analog for dominant markers,
which does not assume Hardy–Weinberg proportions
(Holsinger et al. 2002). Although the estimation of FST is
widely used in population genetics and more powerful
methods can infer much more from data (Pearse & Crandall 2004), it does provide a basic descriptor of population
structure (Neigel 2002). FST (and B) estimates range
from 0 to 1 (panmixia to no gene flow). Pairwise Fisher’s
exact tests (Raymond & Rousset 1995) were performed
using TFPGA to determine the significance of FST values.
A principle co-ordinates analysis (PCA) was performed
using the computer program GenAlEx to visually represent
the relative degree of genetic similarity among individuals
and the distinction of populations. The combination of discretely clustered populations together with high levels of
population subdivision and significant pairwise exact tests
will be taken as evidence for population genetic differentiation and hence determine which population(s) are genetically similar to the Bold Park population and therefore
a potential seed source for ecological restoration.
To address the effect of sample size on estimates of
genetic diversity and population differentiation, the
data were analyzed using three different sample sizes:
n ¼ 8, 15, and 28–31 samples per population, with the
exception of the Yalgorup population (n ¼ 12). The
sample sizes selected are representative of the range of
recently published datasets addressing genetic provenance issues (e.g., Wells et al. 2003; Broadhurst et al.
2006; Bussell et al. 2006). The smaller datasets were
generated by randomly selecting individuals, by population, from within the complete dataset. The two primer
pairs were also analyzed separately for the full set of
samples to (1) see if each marker gave a similar result
and (2) look at the effect of the number of markers on
genetic diversity and population differentiation.
3
Sample Sizes for Sourcing Seed
Results
Population Structure in Daviesia divaricata
One hundred and seventeen bands were scored from two
primer combinations, 59 bands from mCTT/Pst-AC and
58 bands from mCTT/Pst-CT, for 159 plants from six sampled locations. There was a high overall level of variation
in Daviesia divaricata, with 87.4% of all bands polymorphic (Table 1). Levels of variation within populations were
considerably lower (range 43.6–60.7% polymorphic), with
the lowest level detected in Bold Park. Only two bands
were unique to individual populations (one each to Bold
Park and Kings Park) and these were not fixed, with frequencies of 0.10 and 0.43, respectively. AMOVA partitioned the majority of genetic variation to within (82%)
relative to among (18%) populations (Table 2). The
primer pair mCTT/Pst-AC showed slightly higher variation within populations (86%) than mCTT/Pst-CT (79%)
(AMOVA, not shown). The overall B value was 0.1386
(Table 3), with exact tests indicating significant population
differentiation (p < 0.001; Table 4). FST values were higher
than the B values in all cases, suggesting that non-Bayesian FST is overestimating population subdivision. Pairwise
population comparisons showed that Bold Park and Yanchep populations were not significantly differentiated
from each other, but were so from all other sampled
populations (Table 4). The PCA showed spatial overlap
between Bold Park and Yanchep (Fig. 2a, 2b, & 2d), but
considerably less with other populations. A hierarchical
AMOVA, based on the two clusters observed in the PCA
(and significant pairwise exact tests), was highly significant
(p ¼ 0.001; Table 2), accounting for 14% of the total
genetic variation.
Effect of Sample Size
Estimates of genetic variation, as measured by the number
of polymorphic bands, increased with sample size for
every population (Table 1). Similar levels of genetic variation were attributed to variation within relative to among
populations across all sample sizes (AMOVA, not shown).
The amount of subdivision (FST, B, and confidence intervals) decreased with increasing sample size (Table 3),
showing small sample sizes overestimate population subdivision. FST values were less affected by the number of
markers (58/59 vs. 117) but showed that different markers
will show different levels of variation. Significant differentiation among pairs of populations was not detected with
the smaller sample sizes (n ¼ 8 and 15; Table 4). Significant
pairwise population differentiation was detected at n ¼ 28–
31. This was mostly consistent with the complete dataset
and the pairwise tests for each primer pair, suggesting that
the sample size was more important than the number of
markers used here (58/59 vs. 117 AFLP bands). The PCA
showed a similar spatial arrangement with respect to overlap of sampling location across the size classes (Fig. 2a–c)
and the whole dataset (Fig. 2d). However, the two primer
pairs give different spatial arrangements (Fig. 2e–f).
Table 1. Number and percentage of polymorphic loci (PPL) by population and sample size for 117 AFLP bands scored in Daviesia divaricata.
n¼8
Sample Location
Yanchep
Whitfords
Bold Park
Kings Park
Wireless Hill
Yalgorup
Total
n ¼ 15
n ¼ 28–31*
Complete Dataset
Polymorphic
Bands
PPL
Polymorphic
Bands
PPL
n
Polymorphic
Bands
PPL
n
Polymorphic
Bands
PPL
47
37
36
48
46
47
90
40.2
31.6
30.8
41.0
39.3
40.2
76.9
59
56
42
58
51
53
91
50.4
47.9
35.9
49.6
43.6
45.3
77.8
31
29
29
30
28
—
147
68
64
51
71
60
—
97
58.1
54.7
43.6
60.7
51.3
—
82.9
31
29
29
30
28
12
159
68
64
51
71
60
53
97
58.1
54.7
43.6
60.7
51.3
45.3
87.4
* Yalgorup not included due to small sample size.
Table 2. AMOVA for 159 individuals of Daviesia divaricata from six populations using 117 AFLP markers.
Source
df
SSD
One region
Among populations/within regions
5
269.075
Within populations
153
1,240.573
Two regions—Bold Park/Yanchep vs. all other populations
Among regions
1
144.523
Among populations/regions
4
124.552
Individuals/within populations
153
1,240.573
MSD
Variance Component
% Total Variation
p Value
53.815
8.108
1.746
8.108
82.0
0.001
144.523
31.138
8.108
1.477
0.903
8.108
14.0
9.0
77.0
0.001
0.001
0.001
Statistics include SSD, MSD, variance component estimates, percentage of the total variance (%), and the probability of obtaining a more extreme component estimate by chance alone (estimated from 999 sampling realizations). SSD, sums of squared deviations; mean squared deviations; df, degrees of freedom.
4
Restoration Ecology
Sample Sizes for Sourcing Seed
Table 3. Estimates for population subdivision, FST and theta-B,
among all sampling locations for different sample sizes.
Sample Size
n¼8
n ¼ 15
n ¼ 28–31*
mCTT/Pst-AC
mCTT/Pst-CT
Complete
FST
95% CI
Theta-B
95% CI
0.2709
0.2047
0.1606
0.1807
0.1636
0.1724
0.2219–0.3192
0.1603–0.2462
0.1278–0.1959
0.1229–0.2470
0.1248–0.2080
0.1372–0.2132
0.1946
0.1491
0.1335
0.1266
0.1614
0.1386
0.1454–0.2452
0.1125–0.1923
0.1076–0.1648
0.0917–0.1717
0.1226–0.2091
0.1137–0.1690
95% CIs are given. CI, confidence interval.
* Yalgorup not included due to small sample size.
Discussion
Management Implications for Daviesia divaricata
High levels of genetic diversity were detected using
AFLPs, with the majority of variation being detected
within populations (82%). This is consistent with high levels of variation (using allozymes) detected in two congeners Daviesia suaveolens and D. mimisoides (Young &
Brown 1996). There was evidence for population structuring across the sampled locations, as supported by the FST
(and B) values, significant pairwise exact tests, and PCA
showing two clusters (of populations): Bold Park and
Yanchep and Whitfords, Wireless Hill, Kings Park, and
Yalgorup. Of the populations sampled, Yanchep was
genetically the most similar to Bold Park and is recommended as the genetically most appropriate seed source
population for ecological restoration in Bold Park.
Genetic variation in D. divaricata in Bold Park was also
lower than in the other sampled populations. Low seed
production and dispersal distance, accentuated by the
highly fragmented state of the urban bushland, may be
responsible for some of the structure detected over what
is a relatively small geographic distance for an outcrossing
species. Observation of few new plants in the northern
edge of Bold Park following the bushfire in 2000 suggests
that there is low recruitment of plants into the population.
The reproductive strategy for individual species (selfing,
outcrossing, and pollen and seed dispersal vectors), size of
the remnant population, and length of time the bushland
fragment has been isolated are more likely to be important than introgression, inbreeding depression, and local
adaptation when looking at provenance-related issues
within small geographic areas, such as the Perth metropolitan area. Because data are gathered from an increasing
number of species, genetic patterns may develop across
species with common reproductive and/or dispersal strategies, such that in the absence of genetic data, informed
management strategies can be developed. However, it is
important that when genetic data are collected for species,
they are adequately sampled. We encourage the integration of genetic data for restoration (and rehabilitation)
programs and recommend that seed and/or green stock
collecting protocols reflect those that maximize genetic
variation from a source population(s).
Approach to Estimating Genetic Diversity and Subsequent
Seed Sourcing
Analyses using different sample sizes (n ¼ 8, 15, and
28–30) showed that as sample size increased, there was an
increase in genetic variation detected within sampling
locations and a decrease in the amount of population subdivision and associated confidence intervals. The relative
values, however, did not change, Bold Park always had
the lowest and Kings Park the highest value for percentage polymorphic loci. Significant pairwise population differentiation was not detected below the n ¼ 28–31 sample
size. From this, we conclude that genetic diversity will be
Table 4. p Values for pairwise exact tests for population differentiation (Raymond & Rousset 1995) over all loci.
Pairwise Population Comparisons
Bold Park vs. Yanchep
Bold Park vs. Whitfords
Bold Park vs. Wireless Hill
Bold Park vs. Kings Park
Bold Park vs. Yalgorup
Yanchep vs. Whitfords
Yanchep vs. Wireless Hill
Yanchep vs. Kings Park
Yanchep vs. Yalgorup
Whitfords vs. Wireless Hill
Whitfords vs. Kings Park
Whitfords vs. Yalgorup
Wireless Hill vs. Kings Park
Wireless Hill vs. Yalgorup
Kings Park vs. Yalgorup
Overall
n¼8
n ¼ 15
n ¼ 28–31*
Total
mCTT/Pst-AC
mCTT/Pst-CT
1.0000
1.0000
1.0000
0.9994
0.9993
1.0000
1.0000
1.0000
0.9999
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.0000
1.0000
0.5015
0.5877
0.0505
0.0000
0.9334
0.9429
0.7962
0.0204
1.0000
1.0000
0.3902
1.0000
0.9350
1.0000
0.0000
0.3554
0.0000
0.0000
0.0000
—
0.0000
0.0000
0.0000
—
0.9776
0.3229
—
0.9857
—
—
0.0000
0.4198
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.9839
0.3359
0.0119
0.9846
0.1063
0.9993
0.0000
0.1983
0.0707
0.0001
0.0000
0.0000
0.0342
0.0129
0.0007
0.0003
0.9998
0.2527
0.0023
0.6295
0.0345
0.9444
0.0000
0.7397
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.3942
0.4082
0.2805
0.9986
0.5064
0.9984
0.0000
Results are given for different sample sizes (both loci combined) and for all individuals by primer pair.
* Yalgorup not included due to small sample size.
Restoration Ecology
5
Sample Sizes for Sourcing Seed
Figure 2. PCA for different sample sizes: (a) n ¼ 8, (b) n ¼ 15, (c) n ¼ 28–31 plants per population, (d) complete dataset and two different primer
combinations, (e) mCTT/Pst-AC, and (f) mCTT/Pst-CT. Population symbols: Bold Park ( ), Yanchep National Park (¤), Whitfords (:), Wireless Hill (d), Kings Park (h), and Yalgorup National Park (s).
underestimated with small sample sizes. This may be particularly evident when samples are collected from only
a small area within a sampling location. Population subdivision will be overestimated with small sample sizes and
fail to obtain statistical significance for pairwise differentiation. Our results indicate that a minimum of 30 individuals per population is required to achieve good statistically
supported estimates of genetic diversity and population
subdivision. This result is consistent with recent simulation
(Krutovskii et al. 1999; Cavers et al. 2005) and empirical
studies (He submitted) using dominant markers, which
also recommend larger sample sizes for better estimates
of genetic diversity and detecting significant structure
among populations. A total of 150 individuals for 100 polymorphic AFLP markers were necessary for detecting spatial
genetic structure in a neotropical tree, Symphonia globuli-
6
fera (Cavers et al. 2005). Our results here show that the
number of samples collected was more important than the
number of AFLP markers scored by comparing results for
the complete dataset (117 bands) with those from individual
primer pairs (58 and 59 bands). However, this will depend
on how variable the markers are (97/117 markers were polymorphic in D. divaricata). Interestingly, patterns of spatial
separation among populations were similar across all three
sample sizes, suggesting that small sample sizes may still
provide a good approximation of spatial overlap among
populations for identifying potential source populations.
Restoration practitioners tend to focus on limited collection from local or large single populations because
this is usually the most practical, cost-effective option.
However, in doing so, the collected material may not be
representative of the genetic diversity within the source
Restoration Ecology
Sample Sizes for Sourcing Seed
population. Good sampling (numbers and distribution)
similar to those collections made to estimate population
genetic parameters here will provide better representative
material, that is collecting seed (or cuttings) from a larger
number of plants. We have demonstrated here that sample
size was extremely important for obtaining reliable estimates of genetic variation and statistically significant population structure. So while a PCA generated from smaller
sample numbers may indicate which source population(s)
to use, material should be sourced from a larger number of
plants to obtain more genetic diversity for introduction
into the restoration site. Smulders et al. (2000) showed that
despite collecting seed from 100 mother plants for a reintroduction experiment, there was still a slight loss of
polymorphic AFLP bands and significant differentiation
(based on an FST analog) between source and reintroduced
populations observed in the first generation. Estimates of
genetic diversity and population subdivision generated
from small sample numbers should not be used as species
estimates. The sampling strategy will be relevant to most
studies being undertaken, although the specific results for
D. divaricata identify the most closely related (genetically)
potential source population; this pattern will be compared
as further datasets are accumulated for other plant species
in the Swan Coastal Plain. When selecting populations to
sample and identify possible source populations for restoration activities, populations with larger plant numbers
(and potentially higher genetic diversity and seed numbers) should be targeted to increase collecting efficiency
and reduce the chance of negative impacts on the existing
population. Although sampling was good within the focal
area (Perth metropolitan area) for D. divaricata, we recognize that the sampling range was limited in relation to the
total geographic distribution for this species. This type of
geographically restricted sampling will not give an overall
estimate of genetic variation in the species and limits the
ability to detect population structure across the species
range. We have focused on how to select and sample
source population(s) for ecological restoration of a single
site. However, this information compliments those strategies designed to conserve species in trouble across their
whole range that may require ex situ conservation programs (Brown & Briggs 1991; Guerrant et al. 2004).
Implications for Practice
Population genetic diversity will be underestimated
with small sample sizes, whereas population subdivision will be overestimated.
d The spatial arrangement of populations using PCA
was not affected by sample size.
d A minimum sample size of 30 is recommended to
make seed or green stock collections for sourcing restoration/rehabilitation sites.
d The restored population should contain similar levels
of genetic diversity to the source population.
d
Restoration Ecology
Acknowledgments
Thank you to R. Barrett for initial field identifications; M.
Drobel, G. Zawko, N. Du Cros, and T. He for help in the
field; and J. Fisher, R. Taylor, and G. Zawko for useful
discussions, advice, and technical support. All plant collections were made under a valid collecting license
(SW009725). This research was funded by an Australian
Research Council linkage grant (LP0348958).
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