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Molluscan Studies
Journal of Molluscan Studies (2014) 80: 291– 302. doi:10.1093/mollus/eyu014
Advance Access publication date: 27 March 2014
Nuclear and mitochondrial DNA variation within threatened species and
subspecies of the giant New Zealand land snail genus Powelliphanta:
implications for classification and conservation
Thomas R. Buckley 1,2,3 , Daniel J. White 1, Robyn Howitt 1 , Thomas Winstanley 1,
Ana Ramón-Laca 1 and Dianne Gleeson 1,4
1
Landcare Research, Private Bag 92170, Auckland, New Zealand;
School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand;
3
Allan Wilson Centre for Molecular Ecology and Evolution, Auckland, New Zealand; and
4
Institute for Applied Ecology, University of Canberra, ACT 2601, Australia
2
Correspondence: T. R. Buckley; e-mail: [email protected]
(Received 27 August 2013; accepted 20 January 2014)
ABSTRACT
We developed eight microsatellite markers using high-throughput pyrosequencing and screened these
in two species (82 individuals) of threatened New Zealand land snails from the genus Powelliphanta.
The taxa examined included five of the seven subspecies of P. lignaria, in addition to its sister species,
the newly described P. augusta. We also sequenced part of the cytochrome c oxidase subunit I gene for
these taxa. Powelliphanta augusta is differentiated from its sister species P. lignaria at both mitochondrial
DNA and microsatellite loci. Of the five P. lignaria subspecies we sampled, only one formed an exclusive genetic cluster based on Bayesian clustering of microsatellite data. None of the P. lignaria subspecies was monophyletic for mitochondrial DNA. We are unable to determine if the lack of genetic
differentiation is the result of hybridization, as hypothesized by previous authors, or very recent differentiation. Our data cast doubt on the current classification of subspecies within P. lignaria and
suggest that further scrutiny of the current morphological characters used to differentiate these subspecies is warranted. We recommend that conservation strategies be based on genetically defined
groups identified through analysis of multiple nuclear markers rather than the existing taxonomic
subspecies of P. lignaria.
INTRODUCTION
Giant land snails of the genus Powelliphanta O’Connor, 1945 are
a high-profile element of the New Zealand invertebrate fauna
due to their large size, striking colouration and patterning, and
predatory habits (Powell, 1979; Meads, Walker & Elliot, 1984;
Walker, Trewick & Barker, 2008; Boyer et al., 2011a, 2013).
Many species are, however, threatened or highly restricted geographically (Powell, 1979; Walker et al., 2008). The major
current threats include predation by introduced mammals and
habitat destruction (Meads et al. 1984; Walker et al., 2008). A
notable biogeographic feature of Powelliphanta is that a large
number of species and subspecies are found in the Westland and
Nelson regions of the South Island, a relatively small geographic
area. One genus of large New Zealand land snails, where high
numbers of putative subspecies are found in a small geographic
area, was recently revised, resulting in a large reduction in taxonomic diversity (Buckley et al., 2011a). Although recent revisions
in other New Zealand land-snail groups have shown that multiple congeneric species can be found within a single region
(Marshall & Barker, 2007; Climo & Mahlfeld, 2011), these
genera do not reach the taxonomic diversity of Powelliphanta on
such a small geographic scale and are much smaller in body size.
These observations have in part led to ongoing discussion and
debate over the actual number of species and subspecies within
Powelliphanta (e.g. Climo, 1978; Parkinson, 1979; Trewick,
Walker & Jordan, 2008; Walker et al., 2008).
The first described species of Powelliphanta was originally
placed in Helix Linnaeus, 1758 and later described species were
placed in Paryphanta Albers, 1850. O’Connor (1945) described
Powelliphanta as a subgenus of Paryphanta and Powelliphanta was
elevated to generic rank by Climo (1978). The most comprehensive taxonomic works published on the genus were those of
Powell (1932, 1936, 1946, 1949, 1979), who described a number
of species and subspecies from the Westland and Nelson regions
of the South Island. The most recent published and peerreviewed revision was that of Climo (1978), who questioned the
taxonomic arrangement of Powell (1949) and synonymized many
of these species and subspecies. However, the publication of
Climo (1978) and subsequent polemical critiques of Parkinson
# The Author 2014. Published by Oxford University Press on behalf of The Malacological Society of London, all rights reserved
T. R. BUCKLEY ET AL.
(1979) were not based on a formal analysis of characters. The
number of species and subspecies within Powelliphanta has
remained controversial ever since. A further issue is use of subspecies within Powelliphanta, a taxonomic rank that is used less
commonly in modern systematics (e.g. Burbrink et al., 2000; but
see Herbert & Moussalli, 2010; Braby, Eastwood & Murray,
2012). Trewick et al. (2008) and Walker et al. (2008) published a
mitochondrial DNA phylogeny of a subset of Powelliphanta
species and subspecies to provide data on species boundaries.
The publication of Walker et al. (2008) also included a morphometric study of shell character variation and internal morphology of exemplar taxa. Ongoing discoveries of populations from
new geographic localities and continued debate as to the
number of Powelliphanta species indicate that a taxonomic revision of the genus is urgently required.
Two groups of Powelliphanta are of particular interest due to
threats to their habitat. The recently described Powelliphanta
augusta Walker, Trewick & Barker, 2008 is known only from
Mount Augustus, on the Stockton –Denniston Plateau (Walker
et al., 2008). This species is of major conservation concern
because its distribution overlaps with a newly developed coal
mine and much of the population has had to be taken into captivity for later relocation. The second species of interest is
Powelliphanta lignaria Hutton, 1888, the sister species of P. augusta
(Trewick et al., 2008). Powelliphanta lignaria is distributed from
the Westport region of the South Island northwards to western
Nelson. Powell (1979) recognized seven subspecies, each subspecies with a relatively restricted distribution. The descriptions of
Powell (1932, 1936, 1946, 1949, 1979) differentiate these subspecies largely on the basis of shell pattern and colouration and
some slight differences in shell size. A year before Powell’s
(1979) book, Climo (1978) synonymized all these subspecies
within P. hochstetteri lignaria, due to their assumed hybrid
origin; however, Powell’s (1979) classification is still followed
by some authors (e.g. Trewick et al., 2008; Walker et al., 2008).
Many of these P. lignaria subspecies are distributed along the
Mokihinui River valley and associated catchments, which were
recently proposed for dam development. Although this proposal was cancelled in 2012, the habitat of P. lignaria is clearly
of interest.
In the absence of a taxonomic revision, mitochondrial DNA
(mtDNA) sequence data are being used to determine the distinctiveness of populations, evaluate species status and set conservation priorities (e.g. Trewick et al., 2008; Walker et al., 2008).
However, when genetic data are used to evaluate species status,
it is generally accepted that mitochondrial DNA in isolation can
yield misleading inferences, because a single genetic locus
reflects only one outcome of the gene coalescence process (e.g.
Tateno, Nei & Tajima, 1982; Pamilo & Nei, 1988; Maddison,
1997; Nichols, 2001). Mitochondrial DNA can be especially susceptible to introgression and selective sweeps due to its smaller
effective population size (e.g. Machado & Hey, 2003) and so it is
useful at least to verify patterns using nuclear loci.
For this reason we both sequenced mitochondrial DNA and
obtained nuclear microsatellite data to compare reconstructions
of population history and gene flow for both marker types
within Powelliphanta. We focus on two species with a high conservation profile: P. augusta and P. lignaria. We examine the following questions: (1) What degree of fine scale genetic variation
exists within Powelliphanta species? (2) Do patterns of nuclear
and mtDNA variation reflect the current classification within P.
lignaria? (3) Do genetic data support the previous evolutionary
hypotheses (Powell, 1949, 1979; Climo, 1978) on the origins of
diversity in P. lignaria? (4) Can we identify conservation units
within the sampled species?
METHODS
Classification and tissue sample collection
We follow the classification presented in Powell (1979) with
modifications by Trewick et al. (2008) and Walker et al. (2008),
but note that not all authors follow Powell’s classification (e.g.
Climo, 1978; Spencer et al., 2009). For reasons discussed by
Leschen, Buckley & Hoare (2009) we do not use ‘tag names’ for
populations of undetermined taxonomic status that have been
proposed in previous publications or grey literature. We have
avoided latinized tag names as these have the potential to introduce nomina nuda into the literature (Leschen et al., 2009). We
were unable to collect whole animals because of conservation
concerns, so specimens were identified in the field on the basis of
characters described by Powell (1979) and from known localities
of various taxa.
Biopsies were taken from field-captured Powelliphanta lignaria
specimens from populations shown in Figure 1. We were able to
sample five of the seven P. lignaria subspecies in addition to several
individuals not assigned to a subspecies. Some live specimens
were collected by Kath Walker (Department of Conservation)
and provided to Gary Barker (Landcare Research) for dissection.
Snails were found mainly at night by hand collecting and a small
(, 5 mm) biopsy was taken from the foot, using a clean scalpel
blade, and placed into 100% ethanol. Following rehydration,
snails were returned to their habitat. Samples of P. augusta were
taken from whole animals that had died in a captive population
held by the Department of Conservation, Hokitika. Each of these
snails had originally been collected from the wild and their exact
or approximate locality recorded by GPS (Supplementary material, Table S1). DNA was extracted from tissue samples using the
Qiagen QIAxtractor robot and reagents following the manufacturer’s instructions. Biopsy samples and DNA extractions are
stored in the Ecological Genetics Laboratory, Landcare
Research, Auckland, New Zealand.
Generation of DNA sequence data
For sequencing of mitochondrial DNA we used the universal
cytochrome c oxidase subunit I (COI) primers LCO1490 and
HCO2198 of Folmer et al. (1994). PCR amplifications were
carried out in a 20-ml volume and consisted of 10 pmol of each
primer, 10 mM Tris-HCl pH 8.3, 1.5 mM MgCl2, 50 mM KCl
and 0.2 mM of each dNTP. The addition of 2 units of Taq polymerase (Boehringer Mannheim) followed an initial 2-min denaturation step at 948C. The remaining cycling conditions
consisted of denaturation at 948C for 1 min, annealing at 508C
for 1 min and extension at 728C for 1 min 30 s for 35 cycles. A
final cycle included a 5-min extension at 728C. The resulting
PCR products were purified using the Qiagen PCR direct purification kit, following the procedure outlined by the manufacturer. Purified PCR products were sequenced using Big Dye TM
Terminator Cycle Sequencing Ready reaction Mix v. 3.1 kit
(Applied Biosystems, USA). Cycle sequencing products were
analysed on an ABI 3130xl Avant genetic analyser (Applied
Biosystems, USA).
Generation of microsatellite data
We followed the method of Abdelkrim et al. (2009) for obtaining microsatellite markers for Powelliphanta. A DNA extract
from a single individual of P. augusta (X1778) was sequenced
on 1/16 of the plate of a Roche GS FLX sequencer. The resulting genomic DNA sequences were scanned for microsatellite
repeats using MSATCOMMANDER v. 0.8.2 (Faircloth,
2008). Primers were designed for sequences containing repeats
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NUCLEAR AND MITOCHONDRIAL DNA VARIATION WITHIN POWELLIPHANTA
Figure 1. Map of the Stockton—Denniston plateau and Mohikinui River areas showing distribution of sampled Powelliphanta. Dots represent
individual snails (some obscured because of overlying sampling) and colours show optimal assignment to populations as inferred by the
STRUCTURE analysis (cf. Fig. 4A for P. augusta population colour, and Fig. 4B for all other colours). Letters refer to inset maps.
Table 1. Microsatellite loci, PCR primers, dye labels and associated thermal cycling conditions. The microsatellite sequence motif below corresponds
to that from the genomic DNA from Powelliphanta augusta individual x1778.
Marker
Powell-6
Motif
agat(4)gat(1)agat(23)
Primer sequence (5′ –3′ )
F: CAGGAAAGACAGACAAATGATAGAG
5′ Fluorescent
Melting
Allele size
label
temperature (8C)
range (bp)
NED
60
214 – 263
6-FAM
60
148 – 152
NED
60
142 – 162
PET
60
238 – 252
6-FAM
60
164 – 174
VIC
60
170 – 172
NED
60
170 – 175
PET
60
158 – 170
R: AGCACACGGTTTGAGAGATG
Powell-8
atg(7) . . . ga(4)
F: GCCAACGTCTTTACTCTAAGTTCC
R: ATGGGATCATTGTTTTAGCCC
Powell-9
ttc(4) . . . ttc(13)
F: CTCCTCTTCTTCATTTTCTTCTTCATC
R: CAAAGACCAACGGGGACG
Powell-14
aag(6)
F: ACGGGGACGAAAAGAGACG
R: GGATGTCTTCAGGCCCTTTG
Powell-20
tc(12)
F: GGGGACTTCTGTATCTTTATTGC
R: ACAATAGAATGCCGGTCAAAG
Powell-21
ag(7)cg(1)ag(10)
F: GCAGCAGTTTTCCCCAAAAG
R: AAGTCGTCTGGTATGTTGTTTC
Powell-26
tc(10)
F: ACGGAACAGGGTAACCACC
R: TGTTCAATATAGCCACAAGAGCG
Powell-28
ag(13)
F: GCATTGGGTCGTCAGGAATAG
R: TGCAGCATATCAATGACTACAG
of appropriate lengths (di-, tri- and tetra-nucleotides) using
the PRIMER3 software (Rozen & Skaletsky, 2000) bundled
within MSATCOMMANDER. M13 tails (Boutin-Ganache
et al., 2001; Schuelke, 2000) were appended to either the
forward or reverse primers depending on the optimal primer
design. Primer pairs were then screened on a subset of the
DNA extractions, including the individual used in the high
throughput sequencing. PCR conditions for the selected microsatellite markers are described in Table 1.
Population genetic and phylogenetic analysis of DNA
sequence data
We downloaded the COI sequences of Trewick et al. (2008) from
Genbank and combined these with our data. We aligned the
DNA sequences using GENEIOUS v. 5.3.6 (created by
Biomatters, available from http://www.geneious.com), which
was trivial due to the absence of indels among the sampled
individuals. We calculated nucleotide and haplotype diversity
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T. R. BUCKLEY ET AL.
(Nei, 1987) in DNASP v. 5.10.01 (Librado & Rozas, 2009).
Model selection was performed with the Akaike Information
Criterion (AIC) using PAUP* v. 4.0b10 (Swofford, 2002) and
MODELTEST v. 3.7 (Posada & Crandall, 1998). Maximum
likelihood phylogenetic analysis was run in PAUP* v. 4.0b10
(Swofford, 2002) under the best-fit substitution model. We
implemented a heuristic search with TBR branch swapping, the
initial tree found by stepwise addition and 10 random addition
sequence replicates. We disabled the MULTREES option
because of large amounts of branch swapping on different trees
with zero length internal branches, which are common in phylogeographic data sets (e.g. Sullivan, Arellano & Rogers, 2000).
We estimated a Bayesian phylogeny using BEAST v. 1.6.1
(Drummond & Rambaut, 2007). Because the data are a mixture
of intra- and interspecific sampling we analysed them under a
Yule tree-topology prior (appropriate for among-species data) and
a constant population-size prior (appropriate for within-species
data). We used a strict clock, because there was not sufficient variation in the data to obtain stable and reliable posterior distributions under complex relaxed-clock models. The prior distributions
were gamma priors (0.05) on the rate matrix and a shape parameter for among-site rate variation. Empirical base frequencies were
used. MCMC analyses were run for 10 million cycles with a thinning interval of 1,000. Appropriate burnin lengths were determined postrun by inspection of parameter value plots through
time and comparison of nodal posterior probabilities between
runs. The runs were repeated 10 times to assess convergence.
chain. To estimate K, four replicate runs at each value of K
from 2 to 8 were performed, and the most likely value was estimated from the plot of ln Pr (XjK) vs K, and also from Evanno’s
method (Evanno, Regnaut & Goudet, 2005), which plots DK (a
second order rate of change of ln Pr (XjK)) vs K, using
STRUCTURE HARVESTER v. 0.6.92 (Earl & von Holdt,
2011). Only individuals with complete scoring of microsatellites
for all loci were included in the analyses as individuals with
missing data tended to have ambiguous population assignments.
All STRUCTURE figures were produced using DISTRUCT
v. 1.1 (Rosenberg, 2004).
To describe genetic variation within P. lignaria in more detail,
STRUCTURE analysis was run on P. lignaria samples alone.
We selected samples with no missing data, used an admixture
model and assumed allele frequencies to be correlated between
the populations. Two scenarios were tested: (1) not using any
prior information of subspecies classification, and (2) using the
subspecies classification as a prior probability for inferring populations; individuals that were not assigned to a subspecies were
given a separate prior cluster. Eight replicate runs at each value
of K from 2 to 8 were performed. After preliminary assessment of
convergence times for the Monte Carlo Markov chain, a burnin
period of 100,000 steps was chosen, followed by 1,000,000 steps of
the chain. The optimal values of K were determined as described
above. To explore any hierarchical partitioning of genetic variation among the subspecies within P. lignaria, an AMOVA analysis was conducted in ARLEQUIN v. 3.1 (Excoffier, Laval &
Schneider, 2005) and P values were estimated using the nonparametric procedure described by Excoffier, Smouse & Quattro
(1992), with 16,000 permutations.
Genetic distances between subspecies, as taxonomically defined
here, were estimated by computing pairwise FST between all subspecies with more than one individual, also in ARLEQUIN.
Only individuals with no missing data were used, and the four
individuals (M7, M14, M19 and M20) that could not be assigned
to any subspecies removed. P values were estimated from 10,100
permutations of the data.
Finally, to determine whether clustering of P. lignaria individuals is explained better by geographic sampling location or historical taxonomic classification, principal coordinate analyses
(PCoAs) were run in GENALEX v. 6.41 (Peakall & Smouse,
2006).
Analysis of microsatellite data
Evidence for allelic dropout, scoring error due to stutter and
presence and frequency of any null alleles was assessed with
MICRO-CHECKER v. 2.2.3 (Van Oosterhout et al., 2004)
using a standard Bonferroni-adjusted 95% confidence interval
and 10,000 repetitions. We determined the mean number of
individuals per marker, mean number of alleles per marker, and
observed and expected heterozygosities in GENALEX v. 6.41
(Peakall & Smouse, 2006). Allelic richness, a measure of allelic
diversity corrected for variable sample size, was estimated in
FSTAT v. 2.9.3.2 (Goudet, 2001). For each locus, Weir &
Cockerham’s (1984) estimate of the FIS statistic, also known as
the inbreeding coefficient, and the exact test of Hardy –
Weinberg equilibrium were implemented in GENEPOP
v. 4.1.4, along with tests of linkage disequilibrium (Rousset,
2008). For markers with less than five alleles, a complete enumeration algorithm was used to estimate the exact P value, and
for markers with five or more alleles the Markov chain algorithm
of Guo & Thompson (1992) was used to generate an unbiased
estimate of the exact P value.
We used the program STRUCTURE v. 2.3.3 (Pritchard,
Stephens & Donnelly, 2000) to estimate the number of genetically distinct populations in our samples, to assign individuals to
populations, and to estimate the amount of admixture between
these populations. STRUCTURE uses a Bayesian clustering
method to assign individuals to one of K populations and to estimate the degree of interpopulation admixture. STRUCTURE
analysis was done in a hierarchical fashion. Initially all individuals were included in analyses. Our model assumed admixture
and independence of allele frequencies between groups. We also
implemented the ‘locprior’ option in STRUCTURE by using
the historical taxonomic classification as prior information for
clustering (Hubisz et al., 2009). Under this model, only if the
data strongly contradict the assignment of individuals to taxonomic category will the prior information be ignored during
clustering. We used two prior clusters representing each of the
Powelliphanta species. After preliminary assessment of convergence times for the Monte Carlo Markov chain, a burnin period
of 50,000 steps was chosen, followed by 100,0000 steps of the
RESULTS
Patterns of mitochondrial DNA variation
The alignment had a length of 634 bp and contained 116 DNA
sequences from 11 described taxa and four specimens not assigned
to species and/or subspecies by Trewick et al. (2008). All new DNA
sequences have been submitted to Genbank and accession
numbers are given in Supplementary material, Table S1. We
rooted the phylogeny at the midpoint, which fell on the branch
leading to Powelliphanta fiordlandica Climo, 1971 and this was the
same root location with the highest posterior probability (1.0)
under the Bayesian molecular clock. Within P. augusta, two haplotypes differed by a single third-position A/G transition. The A/G
transition was the most frequent substitution type under the GTR
rate matrix (data not shown). The nucleotide diversity was
0.00028. The two other taxa with some intraspecific sampling were
P. lignaria and P. rossiana patrickensis Powell, 1949, which had nucleotide diversities of 0.00582 and 0.00295 respectively.
Powelliphanta lignaria had 18 haplotypes with 27 polymorphic sites
and P. r. patrickensis had 5 haplotypes with 5 polymorphic sites.
The highest uncorrected distances within P. lignaria, P. r. patrickensis
and P. augusta were 1.93%, 0.87% and 0.35% respectively.
The topology of the Bayesian phylogeny (data not shown) was
very similar to the maximum likelihood topology (Fig. 2). The
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NUCLEAR AND MITOCHONDRIAL DNA VARIATION WITHIN POWELLIPHANTA
Figure 2. Maximum-likelihood gene tree showing relationships among mitochondrial COI haplotypes. Branch lengths are drawn proportional to the
number of substitutions per site following the scale bar. Numbers above nodes are bootstrap percentages followed by Bayesian posterior probabilities
(expressed as percentages) and only values greater than 50% for the bootstrap are marked. The tree is rooted using Powelliphanta fiordlandica, which was
the same root location as under the Bayesian molecular clock model.
root height was 0.133 substitutions/site (0.0945–0.1779, 0.95 credible intervals) under the Yule model and 0.146 (0.0829–0.2147)
under the constant-population coalescent model. The two species
P. augusta and P. lignaria were reciprocally monophyletic (posterior
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T. R. BUCKLEY ET AL.
probability PP ¼ 1.0, 1.0) for COI. The node supporting the
sister group relationship between these two species was well supported (PP ¼ 1.0). However, none of the five P. lignaria subspecies
for which we sampled multiple individuals, P. l. ruforadiata Powell,
1949, P. l. unicolorata Powell, 1930, P. l. rotella Powell, 1939, P. l.
johnstoni Powell, 1949, and P. l. lignaria was reciprocally monophyletic. Powelliphanta lignaria rotella possessed three haplotypes, two of
which were shared with P. l. johnstoni and one was unique.
Powelliphanta lignaria johnstoni had two haplotypes, both shared
with P. l. rotella. Powelliphanta lignaria unicolorata had seven haplotypes, six were unique and one shared with a specimen that could
not be assigned to a subspecies (M14). However, the unique haplotypes did not form a monophyletic group. Powelliphanta lignaria
ruforadiata possessed three haplotypes, two were unique and one
shared with two specimens of unknown identification (M19,
M7). Finally, P. l. lignaria had five haplotypes, four were unique
and one was shared with a specimen collected from Millerton
(EU265756) that was not placed in a described subspecies by
Trewick et al. (2008).
Development and screening of microsatellite markers
The pyrosequencing of the total genomic DNA library on the 454
FLX yielded 35,196 sequences with a mean length of 186 bp.
Searching these data for repeats with MSATCOMMANDER
yielded 1013, 333 and 515 sequences containing di-, tri- and
tetra-repeats with a length of at least six repeats. We screened
primer pairs for 18 of these sequences which contained 16 tri- and
two tetra-repeats. Of these 18 primer pairs, we were able to
obtain consistent results for eight primer pairs that flanked
Table 2. Summary statistics for eight microsatellite markers in two species of Powelliphanta.
N
NA
AR (n ¼ 36)
HO
HE
FIS (s.d.)
HWE
P. augusta
53.5
3.9
3.7
0.44
0.52
0.137 (+0.121)
3/8*
P. lignaria
36.4
10.6
10.6
0.49
0.73
0.331 (+0.149)
8/8*
Abbreviations: N, mean number of individuals genotyped per marker; NA, mean number of alleles per marker; AR, allelic richness; HO, observed heterozygosity;
HE, expected heterozygosity; FIS: mean inbreeding coefficient across markers; s.d., standard deviation; HWE, the number of markers that deviate from Hardy–
Weinberg equilibrium; *, global P value ,,0.001.
Figure 3. A. Plot of ln Pr (XjK) vs K for all Powelliphanta individuals, using species classification as prior probability for population assignment. B. Plot
of DK vs K for all Powelliphanta individuals, using species classification as prior probability for population assignment. C. Plot of ln Pr (XjK) vs K for P.
lignaria individuals using subspecies classification as prior probability for population assignment. D. Plot of ln Pr (XjK) vs K for P. lignaria individuals
excluding this prior information.
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NUCLEAR AND MITOCHONDRIAL DNA VARIATION WITHIN POWELLIPHANTA
polymorphic microsatellites (Table 1). We were not able to
obtain genotype data from several of the samples, due to their
decomposed nature.
Summary statistics for genetic diversity within the two species
are shown in Table 2 (information for each marker is presented
in Supplementary material, Table S2). We calculated statistics
by pooling all individuals within a species, because individual
populations and subspecies lacked sufficient sample size for estimation of meaningful results. All loci were polymorphic within
P. augusta and P. lignaria. Observed heterozygosities were
significantly lower than expected under Hardy– Weinberg equilibrium for three loci within P. augusta and eight loci within P.
lignaria. While there is some evidence of null alleles for the
Powell-9 locus in P. lignaria, removal of the locus and repetition
of the STRUCTURE analyses yielded the same population
assignments.
STRUCTURE analyses on all individuals revealed the most
likely K to be 4 (Fig. 3A, B). Population assignment with K ¼ 4
clearly placed all P. augusta individuals into a distinct genetic
cluster (Fig. 4A: purple); all individuals show a PP of at least
Figure 4. A. Plot of assignment of all individuals to populations and coancestry coefficients (K ¼ 4) from STRUCTURE. B. Plot of assignment of
Powelliphanta lignaria individuals only to populations and coancestry coefficients (K ¼ 4). The height of each shaded bar is proportional to the posterior
mean estimate of the proportion of that individual’s microsatellite genotype derived from that population.
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T. R. BUCKLEY ET AL.
Table 3. AMOVA results describing genetic variation in four subspecies (P. l. johnstoni, P. l. rotella, P. l. unicolorata, P. l. lignaria) of Powelliphanta lignaria.
Source of Variation
Among subspecies
d.f.
Sum of squares
Variance components
Percentage of variation
Fixation indices
P
,0.001
3
36.896
0.62356
20.41
0.2041 (FST)
Among individuals, within subspecies
27
76.443
0.39948
13.07
0.1643 (FIS)
,0.001
Within individuals
31
63.000
2.03226
66.52
0.3348 (FIT)
,0.001
0.994. The second cluster (Fig. 4A: orange) consisted of
P. l. rotella and P. l. johnstoni individuals, and one P. l. lignaria
individual (Sn46) whose genome is a mixture of cluster two
(86.4%), cluster four (12.9%) and cluster three (0.7%). The
third cluster (Fig. 4A: blue) consists mainly of P. l. unicolorata
and P. l. lignaria individuals. The fourth cluster (Fig. 4A:
green) groups together the four P. lignaria individuals that
could not be assigned taxonomically and two P. l. lignaria individuals. Interestingly, P. l. ruforadiata shows a mixed genome
from clusters 2, 3 and 4. Overall, when all individuals are
considered, P. lignaria subspecies do not cluster according to
taxonomic classification and some individuals show genomes of
mixed origin.
STRUCTURE analysis run on P. lignaria alone revealed
structure within this species. Considering allele frequencies to
be correlated between populations, the most probable K was
estimated to be 5, with both the inclusion and exclusion of prior
information of subspecies classification (Fig. 3A and B, respectively). Interestingly, when historical subspecies classification was
included in the clustering analysis, overall probability levels
were lower and variation about means was greater, suggesting
less resolution. Figure 4B shows population assignments for K ¼
5 when no prior probabilities were included in clustering analysis. Powelliphanta lignaria rotella individuals all cluster together
(blue cluster), as do P. l. johnstoni (yellow). Powelliphanta lignaria
unicolorata individuals are split between the green cluster and the
orange cluster, the latter of which is shared with P. l. lignaria
individuals and one individual whose subspecies classification
was not determined (M14). The fifth cluster (red) groups the
other three individuals with undetermined subspecies classification. The one P. l. ruforadiata individual shows a mixture from all
five clusters, an expected result for a population with only one
sample. No individual was assigned with 100% identity to any
one cluster, and maximum PP ranged from 0.560 to 0.986.
Each STRUCTURE cluster had a relatively distinct geographic distribution, as shown in Figure 1. The P. augusta cluster (from
Fig. 4A: purple) was restricted to Mount Augusta. The second
cluster (from Fig. 4B: yellow) was found to the north of Mokihinui
River and south of the river mouth, near the coast at Ngakawau.
The third cluster (from Fig. 4B: green) was restricted to the south
branch of Mokihinui River. The fourth cluster (from Fig. 4B:
orange) was spread to the north and south of Mokihinui River.
The fifth cluster (from Fig. 4B: blue) was found at Seddonville.
The sixth cluster (from Fig. 4B: red) contained unclassified
samples collected only from south of Mount O’Connor.
AMOVA helped reveal how genetic variation is partitioned
within P. lignaria and showed that 20.4% of genetic variation can
be explained by the subspecies taxonomy hypothesis (Table 3).
This shows there is genetic differentiation between four subspecies
and the pairwise FST results revealed unequal distances between
them (Table 4). For example, P. l. unicolorata shows least genetic
similarity with P. l. johnstoni and P. l. rotella, whereas P. l. lignaria
shows substantially greater similarity with P. l. johnstoni and
P. l. unicolorata.
While the PCoAs (Fig. 5) support the results from
STRUCTURE, there is no obviously superior explanation for
the clustering of individuals, based on either existing taxonomic
Table 4. Estimates of pairwise FST values between four subspecies (P. l.
johnstoni, P. l. rotella, P. l. unicolorata, P. l. lignaria) of Powelliphanta lignaria.
Subspecies
P. l. johnstoni
P. l. johnstoni
P. l. rotella
0.0002
P. l. rotella
0.1957
P. l. unicolorata
0.2439
0.2667
P. l. lignaria
0.1595
0.2075
P. l. unicolorata
P. l. lignaria
0.0001
0.0024
0.0000
0.0003
0.0009
0.1295
Figures below the diagonal are pairwise FST values, figures above the diagonal
represent the proportion of 10,100 permutations that a pairwise FST greater or
equal to the estimated value was generated based on a null distribution of
haplotypes across populations.
classification or sampling location, although heterogeneity is
reduced slightly when sampling location is used.
DISCUSSION
Taxonomic boundaries and evolutionary processes within
Powelliphanta
Our data show that the newly described Powelliphanta augusta is
differentiated from its sister taxon P. lignaria in the mitochondrial genome, in agreement with Trewick et al. (2008) and
Walker et al. (2008). Our microsatellite data show that this differentiation is also evident in the nuclear genomes of these two
species. Further, STRUCTURE analyses reveal structure
within P. lignaria, although the genetic partitions are in only
partial agreement with the existing taxonomic classification.
The nominal subspecies of P. lignaria show a complex pattern of
haplotype sharing and nuclear-gene admixture between some
subspecies, and genetic differentiation between others. Only P.
lignaria rotella formed a differentiated genetic cluster when the P.
lignaria data were analysed in isolation. However, this subspecies
was not monophyletic for mitochondrial DNA haplotypes. None
of the other subspecies form an exclusive genetic cluster of
nuclear alleles or a clade of mitochondrial haplotypes, as predicted from their classification. For example, we revealed two
genetic clusters within P. l. unicolorata, and there is evidence for
admixture between one of these (Fig. 4B: orange) and
P. l. lignaria individuals. AMOVA analysis confirmed a hierarchical clustering of genetic variation within P. l. lignaria, and pairwise FST values revealed the genetic similarity between nominal
subspecies.
The interpretation of our results requires consideration, however, because (1) sample sizes were small (e.g. P. l. lignaria had
only five individuals and 80 alleles in total), and (2) it is not possible with the current sampling strategy to confirm the processes
underlying the observed structure. One possibility for structure
observed in a dataset is structured sampling, where the noncontiguous nature of a sampling strategy leads to a signal of population fragmentation due to differences in allele frequencies. While
our dataset is not contiguous, there is no clear association
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NUCLEAR AND MITOCHONDRIAL DNA VARIATION WITHIN POWELLIPHANTA
Figure 5. A. Principal coordinates analysis for Powelliphanta lignaria individuals labelled with subspecies classification. B. Principal coordinates analysis
for P. lignaria individuals labelled with sampling location. The first two axes explain 28.6% of the variation.
between geographic distance and genetic distance (Figs 1 and
5), and the impact of geographical barriers is well known to
cause high rates of microendemism in land snails (Thomaz,
Guiller & Clarke, 1996; Kokshoorn & Gittenberger, 2012). It is
likely, therefore, that the pattern of genetic variation detection
within P. lignaria reflects areas consistent with population clines
where routes of gene flow allow, interspersed with structured
populations separated by geographical or ecological barriers.
Both Powell (1949, 1979) and Climo (1978) predicted that
introgression was involved in generating the taxonomic diversity
within P. lignaria. Our data clearly show evidence for gene flow
among populations of P. lignaria and therefore our data are at
least consistent with introgression having occurred; however, the
patterns differ from those predicted by Powell (1949) and Climo
(1978). Climo (1978) criticized Powell’s (1949) classification of P.
lignaria on the basis that the large number of species and subspecies ‘merge clinally with one another.’ Climo (1978) considered P.
lignaria to be a complex hybrid mixture with genetic contributions
from several Powelliphanta species, including P. superba Powell,
1930, P. rossiana Powell, 1930, P. spendeni Powell, 1932 and P. hochstetteri (Pfeiffer, 1862). The mitochondrial data presented here
and by Trewick et al. (2008) do not support this hypothesis,
because P. lignaria is distinct from the parental species postulated
by Climo (1978). Powell (1949) argued that hybridization was occurring between many pairs of subspecies with parapatric distributions or distributions separated by short distances. For
example, Powell (1949) claimed that there was evidence of hybridization between P. l. johnstoni and P. l. rotella, and our data do
show the sharing of haplotypes between these two subspecies.
Furthermore, we have not sampled at the putative hybrid zones
described by Powell (1949), especially at the mouth of the
Mokihinui River. We have observed genetic admixture across the
Mokihinui River, where P. l. unicolorata and P. l. lignaria come into
contact, and this is consistent with the hypothesis of Powell (1949)
that floods facilitate gene flow across the river, although numerous
other explanations cannot be discounted.
The lack of nuclear-gene differentiation between some of the
subspecies and the sharing of mitochondrial haplotypes between
subspecies are inconsistent with their taxonomic distinction under Powell’s (1949) classification (see also Climo, 1978). Braby,
Eastwood & Murray (2012) reviewed the use of the subspecies
rank with special reference to invertebrates. They advocated
that subspecies be partially isolated lineages, allopatric, have at
least one fixed diagnosable character state and that these states
are correlated with the population genetic structure. We find
that not all of Powell’s (1949) subspecies meet all of these criteria. Not all of the subspecies are allopatric as they come into
contact within the Mokihinui River catchment area. Although
Powell (1949) provided morphological diagnoses, these do not
match the population-genetic structure as revealed through
microsatellites and mitochondrial DNA for some of the subspecies, calling into question the taxonomic reliability of these
characters. Although the occurrence of gene flow between
populations is inconsistent with the usual taxonomic usage
of the rank of subspecies, we have refrained from making
formal changes to the classification of P. lignaria, pending
thorough genetic and morphological analysis of all P. lignaria
subspecies and of populations of uncertain taxonomic status
(see Walker et al., 2008 for examples). As an interim measure
we recommend not basing management actions on Powell’s
(1949) classification within P. lignaria. Instead we suggest
basing conservation strategies on conservation units identified using suites of microsatellite markers, as developed here,
or single-nucleotide polymorphisms.
299
T. R. BUCKLEY ET AL.
Our results suggest that the characters of shell morphology
used by Powell (1949, 1979) to differentiate subspecies do not
reflect the underlying evolutionary patterns. It is clear that these
characters require reassessment in light of the genetic data presented here. We note that these same morphological characters
are used to differentiate subspecies within other species of
Powelliphanta, such as P. superba, P. rossiana, P. hochstetteri and P. gilliesi. Although our data do not bear directly on the status of subspecies within other Powelliphanta species, our results do strongly
suggest that analysis of nuclear-gene variation within other
Powelliphanta species complexes is warranted to assess how well
Powell’s (1949) characters perform for defining taxon boundaries.
We also suggest examining the effects of substrate on shell phenotype, for which a relationship has been shown among New
Zealand land snails from the genus Placostylus (Buckley et al.,
2011a) and molluscs from other parts of the world (Estebenet &
Martı́n, 2003; Anderson, Weaver & Guralnick, 2007; Madec &
Bellido, 2007; Ozgo & Bogucki, 2011). This possibility is particularly relevant, given that different species and subspecies of
Powelliphanta inhabit quite different substrate types with different
mineral characteristics (Powell, 1949; Walker et al., 2008).
Many invertebrate species show high levels of genetic variation over small geographic scales (e.g. Garrick et al., 2004).
This is especially so in New Zealand, where many widespread
invertebrate species that have been densely sampled geographically have shown large amounts of genetic variation within populations, with little sharing of alleles or haplotypes among
populations (e.g. Trewick, Wallis & Morgan-Richards, 2000;
Morgan-Richards, Trewick & Wallis, 2001; Boyer, Baker &
Giribet, 2007; Leschen et al., 2008; Hill et al., 2009; McCulloch,
Wallis & Waters, 2009; O’Neill et al., 2009; Marske, Leschen &
Buckley, 2012). Globally, high levels of genetic variation and
microendemism are especially prevalent in land snails (e.g.
Thomaz et al., 1996; Ross, 1999; Watanabe & Chiba, 2001;
Haase & Misof, 2009; Kokshoorn & Gittenberger, 2012), including the Rhytididae (e.g. Moussalli, Herbert & Stuart-Fox,
2009). This variation has been attributed to various factors, including a high rate of nucleotide substitution, genetic admixture, balancing selection or population structure (Thomaz et al.,
1996), with the last of these receiving support from empirical
studies (e.g. Watanabe & Chiba, 2001). We recommend
population-genetic studies on other poorly dispersing invertebrate groups distributed across the Mokihinui River region, such
as earthworms (e.g. Boyer, Blakemore & Wratten, 2011b;
Buckley et al., 2011b) and other land-snail groups (e.g. Climo &
Mahlfeld, 2011), to determine how general are the biogeographic patterns observed here. Although land snails are clearly prone
to high rates of speciation over relatively small geographic areas
(Haase & Misof, 2009; Fiorentino et al., 2013; Stankowski, 2013),
the hypothesis of multiple taxa within P. lignaria across the
Mokihinui area does predict that geographically constrained
genetic units will be present in invertebrates with similar ecological
requirements, such as earthworms and other land-snail taxa.
Despite the lack of a strong signal between geography and
patterns of genetic diversity, the protection of multiple populations within a species is desirable. There is clearly differentiation
within P. lignaria in the nuclear genome. For this reason we recommend conservation of multiple populations north and south
of the Mokihinui River that would ensure preservation of all
genetic clusters we inferred within this species. Because P. lignaria is widespread through the Mokihinui area, we also recommend biopsy sampling from more localities to determine if they
represent novel genetic clusters or are genetically contiguous
with the populations sampled here.
SUPPLEMENTARY DATA
Supplementary material is available at Journal of Molluscan
Studies online.
ACKNOWLEDGEMENTS
We appreciate the efforts of Mark Hamilton and colleagues
(MBC Contracting) for collecting Powelliphanta biopsies. Other
material was provided by Gary Barker and the Department of
Conservation (West Coast Conservancy). 454 pyrosequencing
and data analyses were assisted by Jo-Ann Stanton, Jawad
Abdelkrim and Neil Gemmell. The manuscript was improved
after comments provided by Gary Houliston, Gary Barker,
Robyn Simcock, Anne Austin, Thierry Backeljau and two anonymous reviewers. This project was funded by core funding for
Crown Research Institutes from the Ministry of Business,
Innovation and Employment’s Science and Innovation Group
and Solid Energy New Zealand Ltd. Support for collecting
samples was also provided by Meridian Energy Ltd.
REFERENCES
ABDELKRIM, J., ROBERTSON, B.C., STANTON, J.A.L. &
GEMMELL, N.J. 2009. Fast, cost-effective development of
species-specific microsatellite markers by genomic sequencing.
Biotechniques, 46: 185 – 191.
ANDERSON, T.K., WEAVER, K.F. & GURALNICK, R.P. 2007.
Variation in adult shell morphology and life-history traits in the land
snail Oreohelix cooperi in relation to biotic and abiotic factors. Journal
of Molluscan Studies, 73: 129–137.
BOUTIN-GANACHE, I., RAPOSO, M., RAYMOND, M. &
DESCHEPPER, C.F. 2001. M13-tailed primers improve the
readability and usability of microsatellite analyses performed with
two different allele-sizing methods. Biotechniques, 31: 24–28.
BOYER, S., BLAKEMORE, R.J. & WRATTEN, S.D. 2011b. An
integrative taxonomic approach to the identification of three new
New Zealand endemic earthworm species (Acanthodrilidae,
Octochaetidae: Oligochaeta). Zootaxa, 2994: 21–32.
BOYER, S., WRATTEN, S.D., HOLYOAKE, A., ABDELKRIM, J.
& CRUICKSHANK, R.H. 2013. Using next-generation sequencing
to analyse the diet of a highly endangered land snail (Powelliphanta
augusta) feeding on endemic earthworms. PLoS One, 8: e75962.
BOYER, S., YEATES, G.W., WRATTEN, S.D., HOLYOAKE, A. &
CRUICKSHANK, R.H. 2011a. Molecular and morphological
analyses of faeces to investigate the diet of earthworm predators: example
of a carnivorous land snail endemic to New Zealand. Pedobiologia, 54S:
S153–S158.
BOYER, S.L., BAKER, J.M. & GIRIBET, G. 2007. Deep genetic
divergences in Aoraki denticulata (Arachnida, Opiliones, Cyphophthalmi):
a widespread ‘mite harvestman’ defies DNA taxonomy. Molecular
Ecology, 16: 4999–5016.
BRABY, M.F., EASTWOOD, R. & MURRAY, N. 2012. The
subspecies concept in butterflies: has its application in taxonomy and
conservation biology outlived its usefulness. Biological Journal of the
Linnean Society, 106: 699– 716.
Utility of mtDNA versus microsatellites for assessing population
structure and conservation recommendations
In some cases, the population-genetic inferences from mtDNA
were slightly different from those from our microsatellite data.
In other cases the two data sources were in agreement. For
example, both sources of data agreed on the differentiation of P.
augusta from its sister species P. lignaria. Likewise, both data
sources clearly showed sharing of alleles and haplotypes among
some subspecies of P. lignaria. However, the pattern of sharing of
microsatellite alleles differed from that of mtDNA haplotypes.
This demonstrates the more complex demographic and biogeographic histories typically detected with a combination of
nuclear and mitochondrial markers (e.g. Hare, 2001).
300
NUCLEAR AND MITOCHONDRIAL DNA VARIATION WITHIN POWELLIPHANTA
BUCKLEY, T.R., JAMES, S., ALLWOOD, J., BARTLAM, S.,
HOWITT, R. & PRADA, D. 2011b. Phylogenetic analysis of New
Zealand earthworms (Oligochaeta: Megascolecidae) reveals ancient
clades and cryptic taxonomic diversity. Molecular Phylogenetics and
Evolution, 58: 85–96.
BUCKLEY, T.R., STRINGER, I., GLEESON, D., HOWITT, R.,
ATTANAYAKE, D., PARISH, R., SHERLEY, G. & ROHAN, M.
2011a. A revision of the New Zealand Placostylus land snails using
mitochondrial DNA and shell morphometric analyses, with implications
for conservation. New Zealand Journal of Zoology, 38: 55–81.
BURBRINK, F.T., LAWSON, R. & SLOWINSKI, J.B. 2000.
Mitochondrial DNA phylogeography of the polytypic North
American rat snake (Elaphe obsoleta): a critique of the subspecies
concept. Evolution, 54: 2107– 2118.
CLIMO, F.M. 1977. A new higher level classification of New Zealand
Rhytididae (Mollusca: Pulmonata). Journal of the Royal Society of New
Zealand, 7: 59–65.
CLIMO, F.M. 1978. The Powelliphanta gilliesi-traversi-hochstetteri-rossianalignaria-superba ring species (Mollusca: Pulmonata). New Zealand
Journal of Zoology, 5: 289– 294.
CLIMO, F.M. & MAHLFELD, K. 2011. Kokopapa, a new genus of land
snails (Pulmonata: Punctidae) with description of six new species
and discussion of South Island, New Zealand biogeography. Bulletin
of the Buffalo Society of Natural Sciences, 40: 29–48.
DRUMMOND, A.J. & RAMBAUT, A. 2007. BEAST: Bayesian
evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7:
214. doi:10.1186/1471-2148-7-214.
EARL, D.A. & VONHOLDT, B.M. 2011. STRUCTURE HARVESTER:
a website and program for visualizing STRUCTURE output and
implementing the Evanno method. Conservation Genetics Resources, 4:
359–361.
ESTEBENET, A.L. & MARTÍN, P.R. 2003. Shell interpopulation
variation and its origin in Pomacea canaliculata (Gastropoda:
Ampullariidae) from southern Pampas, Argentina. Journal of Molluscan
Studies, 69: 301–310.
EVANNO, G., REGNAUT, S. & GOUDET, J. 2005. Detecting the
number of clusters of individuals using the software STRUCTURE:
a simulation study. Molecular Ecology, 14: 2611–2620.
EXCOFFIER, L., LAVAL, G. & SCHNEIDER, S. 2005. Arlequin
(version 3.0): an integrated software package for population genetics
data analysis. Evolutionary Bioinformatics, 1: 47–50.
EXCOFFIER, L., SMOUSE, P.E. & QUATTRO, J.M. 1992. Analysis
of molecular variance inferred from metric distances among DNA
haplotypes: application to human mitochondrial-DNA restriction
data. Genetics, 131: 479 –491.
FAIRCLOTH, B.C. 2008. Msatcommander: detection of microsatellite
repeat arrays and automated, locus-specific primer design. Molecular
Ecology Resources, 8: 92–94.
FIORENTINO, V., MANGANELLI, G., GIUSTI, F., TIEDEMANN,
R. & KETMAIER, V. 2013. A question of time: the land snail
Murella muralis (Gastropoda: Pulmonata) reveals constraints on past
ecological speciation. Molecular Ecology, 22: 170–186.
FOLMER, O., BLACK, M., HOEN, W., LUTZ, R. & VRIJENHOEK,
R. 1994. DNA primers for amplification of mitochondrial cytochrome
c oxidase subunit I from diverse metazoan invertebrates. Molecular
Marine Biology and Biotechnology, 3: 294–299.
GARRICK, R.C., SANDS, C.J., ROWELL, D.M., TAIT, N.N.,
GREENSLADE, P. & SUNNUCKS, P. 2004. Phylogeography
recapitulates topography: very fine-scale local endemism of a
saproxylic ‘giant’ springtail at Tallaganda in the Great Dividing
Range of south-east Australia. Molecular Ecology, 13: 3329–3344.
GOUDET, J. 2001. FSTAT, a program to estimate and test gene
diversities and fixation indices (version 2.9.3). Available at http://
www.unil.ch/izea/softwares/fstat.html.
GUO, S.W. & THOMPSON, E.A. 1992. Performing the exact test of
Hardy-Weinberg proportion for multiple alleles. Biometrics, 48: 361–372.
HAASE, M. & MISOF, B. 2009. Dynamic gastropods: stable shell
polymorphism despite gene flow in the land snail Arianta arbustorum.
Journal of Zoological Systematics and Evolutionary Research, 47: 105–114.
HARE, M.P. 2001. Prospects for nuclear gene phylogeography. Trends
in Ecology and Evolution, 12: 700– 706.
HERBERT, D.G. & MOUSSALLI, A. 2010. Revision of the larger
cannibal snails (Natalina s. l.) of Southern Africa—Natalina s. s.,
Afrorhytida and Capitina (Mollusca: Gastropoda: Rhytididae). African
Invertebrates, 51: 1 –132.
HILL, K.B.R., SIMON, C., MARSHALL, D.C. & CHAMBERS,
G.K. 2009. Surviving glacial ages within the biotic gap:
phylogeography of the New Zealand cicada Maoricicada campbelli.
Journal of Biogeography, 36: 675– 692.
HUBISZ, M.J., FALUSH, D., STEPHENS, M. & PRITCHARD, J.K.
2009. Inferring weak population structure with the assistance of
sample group information. Molecular Ecology Resources, 9: 1322– 1332.
KOKSHOORN, B. & GITTENBERGER, E. 2012. Phylogeography of
the land snail Abida secale (Draparnaud) (Chondrinidae). Journal of
Molluscan Studies, 78: 128– 133.
LESCHEN, R.A.B., BUCKLEY, T.R., HARMAN, H.M. &
SHULMEISTER, J. 2008. Determining the origin and age of the
Westland Beech (Nothofagus) gap, New Zealand, using fungus beetle
genetics. Molecular Ecology, 17: 1256– 1276.
LESCHEN, R.A.B., BUCKLEY, T.R. & HOARE, R. 2009. The use of
tag names and New Zealand taxonomy. New Zealand Entomologist, 32:
85– 87.
LIBRADO, P. & ROZAS, J. 2009. DnaSP v5: a software for
comprehensive analysis of DNA polymorphism data. Bioinformatics,
25: 1451– 1452.
MCCULLOCH, G.A., WALLIS, G.P. & WATERS, J.M. 2009. Do
insects lose flight before they lose their wings? Population genetic
structure in subalpine stoneflies. Molecular Ecology, 18: 4073–4087.
MACHADO, C.A. & HEY, J. 2003. The causes of phylogenetic conflict
in a classic Drosophila species group. Proceedings of the Royal Society of
London: Series B, 270: 1193–1202.
MADDISON, W.P. 1997. Gene trees in species trees. Systematic Biology,
46: 523–536.
MADEC, L. & BELLIDO, A. 2007. Spatial variation of shell
morphometrics in the subantarctic land snail Notodiscus hookeri from
Crozet and Kerguelen Islands. Polar Biology, 30: 1571–1578.
MARSHALL, B.A. & BARKER, G.M. 2007. A revision of New
Zealand landsnails of the genus Cytora Kobelt & Möllendorff, 1897
(Mollusca: Gastropoda: Pupinidae). Tuhinga, 18: 49–113.
MARSKE, K.A., LESCHEN, R.A.B. & BUCKLEY, T.R. 2012.
Concerted versus independent evolution and the search for multiple
refugia: comparative phylogeography of four forest beetles. Evolution,
66: 1862– 1877.
MEADS, M.J., WALKER, K.J. & ELLIOT, G.P. 1984. Status,
conservation, and management of the land snails of the genus
Powelliphanta (Mollusca: Pulmonata). New Zealand Journal of Zoology,
11: 277–306.
MORGAN-RICHARDS, M., TREWICK, S.A. & WALLIS, G.P.
2001. Chromosome races with Pliocene origins: evidence from
mtDNA. Heredity, 86: 303–312.
MOUSSALLI, A., HERBERT, D.G. & STUART-FOX, D. 2009. A
phylogeny of the cannibal snails of southern Africa, genus Natalina
sensu lato (Pulmonata: Rhytididae): assessing concordance between
morphology and molecular data. Molecular Phylogenetics and Evolution,
52: 167–182.
NEI, M. 1987. Molecular evolutionary genetics. Columbia University Press,
New York.
NICHOLS, R. 2001. Gene trees and species trees are not the same.
Trends in Ecology & Evolution, 16: 358– 364.
O’CONNOR, A.C. 1945. Notes on the eggs of New Zealand
Paryphantidae, with description of a new sub-genus. Transactions of
the Royal Society of New Zealand, 75: 54–59.
O’NEILL, S.B., BUCKLEY, T.R., JEWELL, T.R. & RITCHIE, P.R.
2009. Phylogeographic history of the New Zealand stick insect
Niveaphasma annulata (Phasmatodea) estimated from mitochondrial
and nuclear loci. Molecular Phylogenetics and Evolution, 5: 523– 536.
OZGO, M. & BOGUCKI, Z. 2011. Colonization, stability, and adaptation
in a transplant experiment of the polymorphic land snail Cepaea nemoralis
(Gastropoda: Pulmonata) at the edge of its geographical range. Biological
Journal of the Linnean Society, 104: 462–470.
301
T. R. BUCKLEY ET AL.
PAMILO, P. & NEI, M. 1988. Relationships between gene trees and
species trees. Molecular Biology and Evolution, 5: 568– 583.
PARKINSON, P.G. 1979. A consideration of the systematics of the New
Zealand Rhytididae (Mollusca: Pulmonata) with a repudiation of the
nonsensical ‘ring species’ speculation lately indulged in by Dr. Climo.
Pettifogging Press, Auckland.
PEAKALL, R. & SMOUSE, P.E. 2006. GENALEX 6: genetic analysis
in Excel. Population genetic software for teaching and research.
Molecular Ecology Notes, 6: 288 –295.
POSADA, D. & CRANDALL, K.A. 1998. Modeltest: testing the model
of DNA substitution. Bioinformatics, 14: 817– 818.
POWELL, A.W.B. 1930. The Paryphantidae of New Zealand: their
hypothetical ancestry, with descriptions of new species and a new
genus. Records of the Auckland Institute and Museum, 1: 17– 55.
POWELL, A.W.B. 1932. The Paryphantidae of New Zealand.
Descriptions of further new species. Records of the Auckland Institute and
Museum, 1: 155–162.
POWELL, A.W.B. 1936. The Paryphantidae of New Zealand. III.
Further new species of Paryphanta and Wainuia. Records of the Auckland
Institute and Museum, 2: 29– 41.
POWELL, A.W.B. 1946. The Paryphantidae of New Zealand.
No. V. Further new species of Paryphanta, Wainiua and Rhytida.
Records of the Auckland Institute and Museum, 3: 99– 136.
POWELL, A.W.B. 1949. The Paryphantidae of New Zealand. No. VI.
Distribution, hybrids and new species of Paryphanta, Rhytida and
Schizoglossa. Records of the Auckland Institute and Museum, 3: 347– 372.
POWELL, A.W.B. 1979. New Zealand Mollusca: marine, land and freshwater
shells. Collins, Auckland.
PRITCHARD, J.K., STEPHENS, M. & DONNELLY, P. 2000.
Inference of population structure using multilocus genotype data.
Genetics, 155: 945 –959.
ROSENBERG, N.A. 2004. Distruct: a program for the graphical
display of population structure. Molecular Ecology Notes, 4: 137 –138.
ROSS, T.K. 1999. Phylogeography and conservation genetics of the
Iowa Pleistocene snail. Molecular Ecology, 8: 1363– 1373.
ROUSSET, F. 2008. GENEPOP ’007: a complete re-implementation of
the GENEPOP software for Windows and Linux. Molecular Ecology
Resources, 8: 103–106.
ROZEN, S. & SKALETSKY, H.J. 2000. Primer3 on the WWW for
general users and for biologist programmers. In Bioinformatics methods
and protocols: methods in molecular biology (S. Krawetz & S. Misener,
eds), pp. 364 –386. Humana Press, Totowa, NJ.
SCHUELKE, M. 2000. An economic method for the fluorescent
labeling of PCR fragments. Nature Biotechnology, 18: 233–234.
SPENCER, H.G., MARSHALL, B.A., MAXWELL, P.A.,
GRANT-MACKIE, J.A., STILWELL, J.D., WILLAN, R.C.,
CAMPBELL, H.J., CRAMPTON, J.S., HENDERSON, R.A.,
BRADSHAW, M.A., WATERHOUSE, J.B. & POJETA, J. 2009.
Phylum Mollusca: chitons, clams, tusk shells, snails, squids, and kin.
In: New Zealand inventory of biodiversity. Vol. 1, Kingdom Animalia:
Radiata, Lophotrochozoa, Deuterostomia (D.P. Gordon, ed.), pp.
161–254. Canterbury University Press, Christchurch.
STANKOWSKI, S. 2013. Ecological speciation in an island snail:
evidence for the parallel evolution of a novel ecotype and maintenance
by ecologically dependent postzygotic isolation. Molecular Ecology, 10:
2726–2741.
SULLIVAN, J., ARELLANO, E. & ROGERS, D.E. 2000. Comparative
phylogeography of mesoamerican highland rodents: concerted versus
independent response to past climatic fluctuations. American Naturalist,
155: 755–768.
SWOFFORD, D.L. 2002. PAUP*. Phylogenetic analysis using parsimony (*
and other methods). Version 4. Sinauer Associates, Sunderland, MA.
TATENO, T., NEI, M. & TAJIMA, F. 1982. Accuracy of estimated
phylogenetic trees from molecular data. Journal of Molecular Evolution,
18: 387–404.
THOMAZ, D., GUILLER, A. & CLARKE, B. 1996. Extreme
divergence of mitochondrial DNA within species of pulmonate land
snails. Proceedings of the Royal Society of London: Series B, 263: 363–368.
TREWICK, S.A., WALKER, K.J. & JORDAN, C. 2008. Taxonomic
and conservation status of a newly discovered giant landsnail from
Mount Augustus, New Zealand. Conservation Genetics, 9: 1563–1575.
TREWICK, S.A., WALLIS, G.P. & MORGAN-RICHARDS, M.
2000. Phylogeographical pattern correlates with Pliocene mountain
building in the alpine scree weta (Orthoptera, Anostostomatidae).
Molecular Ecology, 9: 657– 666.
VAN OOSTERHOUT, C., HUTCHINSON, W.F., WILLS, D.P.M. &
SHIPLEY, P. 2004. MICRO-CHECKER: software for identifying
and correcting genotyping errors in microsatellite data. Molecular
Ecology Notes, 4: 535 –538.
WALKER, K.J., TREWICK, S.A. & BARKER, G.M. 2008.
Powelliphanta augusta, a new species of land snail, with a description of
its former habitat, Stockton coal plateau, New Zealand. Journal of the
Royal Society of New Zealand, 88: 163–186.
WATANABE, Y. & CHIBA, S. 2001. High within-population
mitochondrial DNA variation due to microvicariance and
population mixing in the land snail Euhadra quaesita (Pulmonata,
Bradybaenidae). Molecular Ecology, 10: 2635– 2645.
WEIR, B.S. & COCKERHAM, C.C. 1984. Estimating F-statistics for
the analysis of population structure. Evolution, 38: 1358– 1370.
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