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Journal of The Malacological Society of London 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 292 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 293 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 294 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 295 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. 296 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. 297 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 298 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. 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