125
In preparation for submission
Genetic structure and diversity of Phaeocystis antarctica Karsten as
Abstract:
Strong ocean current systems characterize the Southern Ocean. The genetic structure of phytoplankton species is believed to be influenced mainly by these currents. Genetic estimates on the relatedness of these specimens therefore should provide a proxy on how strong geographical regions are interconnected by the ocean current systems. In this study, genetic diversity and distribution of genetic polymorphisms in the circumpolar prymnesiophyte Phaeocystis antarctica were studied using eight fast-evolving nuclear microsatellite loci.
Analyses were conducted for 110 P. antarctica isolates representative of five major oceanic regions in the Southern Ocean. These results reveal unexpected strong differentiation among several populations, whereas some geographically very distant populations are more closely related. The data are mainly in agreement with the Antarctic Circumpolar Current as well as the Antarctic costal current acting as major transport systems.
Keywords: Southern Ocean, Phaeocystis antarctica, population genetics, microsatellites, current patterns
127 Introduction
The Southern Ocean (SO) has a unique current system and is the only ocean that connects the Atlantic, Pacific and Indian Oceans. The Antarctic Circumpolar Current (ACC) has no existing continental barriers and therefore forms a continuous, circumpolar current system (Thompson 2008). The ACC is driven partly by the vigorous mid-latitude westerly winds and affected by adjacent landmasses and submarine topography. It is the main current around the Antarctic, transporting a water volume of 130 Sv (1 Sverdrup = 1 Sv = 1x106 m3 s-1) along a 24 000 km path, varying in depth and width (Rintoul et al. 2001, Boning et al. 2008, Thompson 2008). Close to the Antarctic continent, the Antarctic Coastal Current, a counter current to the ACC, flows westward, parallel to the Antarctic coastline (Gyory et al 2003).
Between these current systems, hydrographical circulations form well-defined gyres, the two largest being the Weddell Gyre and the Ross Gyre. Within the nanoflagellates, the prymnesiophyte Phaeocystis antarctica, dominates the phytoplankton standing stock in certain regions of the SO and thus contributes significantly to primary produced biomass in the Antarctic pelagic system (e.g. Arrigo et al. 1999, Nöthig et al. 2009).
P. antarctica is one of the six species belonging to the genus Phaeocystis. The morphology of this genus has led to much taxonomic confusion assigning a species name to the similar colony stages in the past (Sournia 1988). Molecular techniques have provided the possibility to identify phytoplankton down to the species level, regardless of their sizes and developmental stages, therefore the global distribution of the genus Phaeocystis was unravelled taking advantage of three genetic markers, the nuclear-encoded 18S rDNA genes and two non-coding regions – the internal transcribed spacer 1 (ITS1) and the plastid ribulose-1,5-bisphosphate carboxylase/oxygenase (RUBISCO). Only 18S and ITS1 data were informative (Lange et al. 2002a, 2000b). The species Phaeocystis antarctica Karsten was resurrected for Antarctic isolates and the epithet P. pouchetii was reserved only for the Arctic species. Similarity among ITS sequences from Antarctic isolates indicated that P. antarctica could be a single species in contrast to both other colonial species, P. globosa and P.
pouchetii which are likely species complexes.
Blooms of P. antarctica have been shown to dominate both in deep (Arrigo et al. 1999) and shallow mixed layers in the SO (Bodungen et al. 1986). Only relatively few species play such fundamental roles in the trophic structure and biogeochemical cycles of the SO (Smetacek et al. 2004). Its peculiar physiology can profoundly influence tropho-dynamics, community composition and biogeochemical carbon and sulphur cycles in the SO (Davidson & Marchant 1992). The distribution of P. antarctica varies throughout different regions of the SO.
Populations of P. antarctica within Antarctic continental boundary water masses appear to be well-mixed because currents move huge water masses around the Antarctic continent that may prevent a defined population structure (Medlin et al. 2007). Ocean boundaries in the SO caused by the current patterns delimit specific oceanic regimes, such as the ACC and the gyres. These regimes could also act as potential barriers to gene flow between members of species living in different biological provinces and therefore maintain genetic differentiation.
In addition, these various oceanic regimes differ in their ecophysiological niche parameters and therefore, P. antarctica may be regionally different and diverse as a result of adaptations (local) of subpopulations to these respective ecological conditions. A very strong microevolutionary force on genetic diversity is usually gene flow, which represents the movement of genes from one population to another. It can be low for species with limited dispersal capabilities but otherwise increases overall genetic diversity because of the high number of local polymorphisms in mutation-drift equilibrium populations (Bagley et al. 2002).
Genetic structure can be maintained chiefly by the current systems and thus should provide corroborating evidence for the direction and interconnection of the different currents appearing in the SO (Leese et al. in prep.). Genetic diversity is most commonly measured by fingerprinting techniques, of which Amplified Fragment Length Polymorphisms (AFLPs) and microsatellites (MS) are those most often used (Chavarriaga-Aguirre et al. 1999, Mariette et al. 2001, De Bruin et al. 2003, Lamote et al. 2005).
In a first genetic fingerprinting analysis using AFLPs, a high genetic diversity within P.
antarctica populations from the Scotia Sea, Weddell Sea, Prydz Bay and areas in the ACC was observed, suggesting that there was gene flow between regions (Gäbler-Schwarz et al.
subm.). However, AFLPs suffer from several technical limitations because no species-specific primers are employed. Thus it is particularly sensitive to genomic contaminations of other biota. Therefore, independent and species-specific DNA markers are necessary. In order to gain deeper insights into P. antarctica’s life cycle and population structure highly polymorphic nuclear genetic markers, such as microsatellites (MS), are particularly useful (see Held and Leese 2007 for detailed discussion). These markers have the potential to provide intraspecific information and to resolve rates of migration or gene flow. MS can also estimate the relatedness of individuals within a population.
Microsatellites have been applied to population genetic studies in a wide range of organisms (Weber & May 1989, Hughes and Queller 1993, Lagercrantz 1993; Ostrander et al. 1993, Schlotterer and Pemberton, 1994; Jarne and Lagoda, 1996; Roy et al. 1994, Scribner et al.
1994, Wattier et al., 1997, Goldstein and Schlotterer 1999, Procaccini et al., 2001) and are
129
codominantly inherited markers consisting of repeating units (simple sequence repeats).
Because of the large number of alleles per locus, MS show very high levels of heterozygosity.
Heterozygosity is the proportion of individuals with two different alleles at a locus (for a diploid specimen) and is a common measure of genetic diversity. Also, heterozygous individuals might have a selective advantage in a changing environment. A high degree of heterozygosity means high genetic diversity. In contrast, if heterozygosity is low, then it indicates low genetic diversity. Microsatellite analysis involves the assessment of the observed (HO) and the expected (HE) level of heterozygosity based on the different allele frequencies observed and scored for the individuals. If HO > HE, then there might have been an “isolate-breaking effect” i.e., excess heterozygosity from the recent mixing of two previously isolated populations. If HO < HE, then inbreeding or selfing might have had a strong impact on the structure of the sampled populations, or the population consists, in fact, of two or more cryptic populations (Hamilton 2009). Because microsatellites are not universally applicable and must be developed new for each species of interest, we developed eight markers for P. antarctica (Gäbler-Schwarz, in prep).
This study focussed on the estimation of the genetic diversity of P. antarctica addressing the following research questions:
1. Are bloom-forming populations composed mainly of one clone or do we find genetically different individuals?
2. Are the distinct water masses around the Antarctic continent isolated from one another or does the ACC provide a vehicle for dispersal among populations in all other oceanic regimes of the SO?
3. If so, what is the magnitude of gene flow around the Antarctic continent?
Answers to these questions can provide valuable insight into the population structure of one of the major keystone species in Antarctic pelagic ecosystem functioning and the general structuring oceanographic forces in the SO on the genetic structure of its microalgal populations.
Material and Methods Culture sampling
P. antarctica were sampled with a phytoplankton hand net (55μm) or skimming the surface water with a bucket on several RV Polarstern cruises (ANT XXII-4, ANT XXIII-2, ANT XXIII-4, ANT XXIII-9) and Antarctic research stations between 2005 and 2007. About 586 cultures (x3=1758) of P. antarctica were isolated and after many re-isolation steps because of culturing drawbacks, we now maintain a culture collection of 230 (x at least 5 backups = ca.
1150) clonal cultures originating from various regions in the SO. In this study we analyzed 110 clonal isolates among five defined populations (Fig. 6.1, Table 6.1).
Cultures and Culture Conditions
110 unialgal cultures of the prymnesiophyte P. antarctica were maintained and grown for harvesting at 0°C in sterile filtered GP5 media (33 psu, Loeblich & Smith, 1968, Loeblich 1975) (Table 6.1, Fig. 6.1). Cultures contained both flagellate and colonial stages of P.
antarctica and in each case, isolates from the same region originated from the same bloom and even the same bucket of water.
Harvesting and DNA extraction
To yield the maximum DNA concentration at least 250 mL culture (aged three weeks) were harvested. Harvesting was done by several centrifugation steps at 2500 rpm to 3100 rpm for 4 min (depending on the properties of the sample) and washing the received pellet with cold (0°-5°C) sterile filtered Antarctic water. Samples were kept on ice or at 4°C during the procedures. After a final centrifugation at 4000 rpm for 6 min and discarding the supernatant, the pellet was frozen in liquid nitrogen and stored at -20°C until further processing. Total DNA of these 110 clonal P. antarctica isolates was isolated using the E.Z.N.A. Plant DNA Kit (Peqlab Biotechnologie GmbH) following a slightly modified protocol 1) using Qiagen RNase instead of the E.Z.N.A. RNase, because of better results, 2) additional washing steps were conducted and 3) 50 μL elution buffer was used to increase concentration.
PCR and genotyping
Eight microsatellite loci for P. antarctica developed by Gäbler-Schwarz et al. (in prep. subm.) were applied to assess the genetic diversity of this species around Antarctica. The 20ȝL PCR reactions contained the components listed in Table 6.2 and 10–20 ng DNA template.
131
Cycling conditions on a epgradient thermocycler (Eppendorf) were an initial denaturation at 94°C for 1 min followed by 50 cycles of 94°C for 15 sec, annealing (Ta) for 20 sec and extension of 70°C for 30 sec; followed by a final extension of 70°C for 10 min and 4°C on hold (Gäbler-Schwarz et al. in prep.). PCR products were controlled on 2% TBE agarose gels, diluted 1-10 fold with Fluka water (Sigma) and then 1 μL of the diluted product was denatured of 14,7 μL HI-DI formamide with 0,3 μL GeneScan ROX 500 size standard (Applied Biosystems). Fragments were analysed on an ABI 3130xl sequencer and genotyping was performed with GENEMAPPER 4.0 (Applied Biosystems). Allele length scoring was performed manually using the software GENEMAPPER 4.0 (Applied Biosystems).
Data analysis
Several different microsatellite (MS) data sets were tested, because of the appearance of non-amplifying DNA samples at some loci. The high number of non-non-amplifying DNA samples from isolates from Scotia Sea (12) at some loci might be because of non-amplifying alleles, the so called null alleles. Six MS data sets were generated, the smaller sets rejecting loci or individuals because of missing data (Table 6.3).
Tests for Hardy Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were performed using GENALEX v6.2 and GENEPOP version 4.0.6 (Rousset 2007). Bonferroni adjusted significance levels were calculated according to Rice (1989). Size range, allele number and the number of private alleles per population were retrieved for all MS loci by using GENALEX v6.2 (Peakall & Smouse 2007).
Genetic differentiation/variability. To assess partitioning of genetic variability within populations and regions, we performed hierarchical Analyses of MOlecular VAriance (AMOVA) using GENALEX v6.2. The procedure follows the methods of Excoffier et al.
(1992), Huff et al. (1993), Peakall et al. (1995), and Michalakis & Excoffier (1996). In addition pairwise population coancestry coefficients FST values (based on the infinite allele model) were estimated with the GENALEX v6.2 program testing for significance using 9999 permutations (Weir and Cockerham 1984, Peakall & Smouse 2007). An estimate of the stepwise mutation model analogue of FST, RST (Slatkin 1995) was also calculated, testing for significance using 9999 permutations.
Population assignment test was performed with different microsatellite data sets using the program GENALEX v6.2 (Peakall & Smouse 2007). Microsatellite data sets (number of isolates/number of loci) (a) 110/8, (b) 110/6, (c) 110/5, d), (e) 97/6 and (f) 97/5 were used to test if severe differences would occur, rejecting either loci producing a high number of null
alleles or isolates having null alleles/missing data, and a combination of all. The frequency-based assignment test of Paetkau (1995, 2004) was calculated. Here the program calculates for each sample, the expected genotype frequency at each locus, assuming random mating in the population in question, multiplied across loci and log-transformed to give a log likelihood value. For each sample, a log likelihood value is calculated for each population, using the allele frequencies of the respective population. If an allele frequency value of zero is encountered for a given allele (i.e., if the allele is absent from one of the represented populations), it used the value 0.01. A sample is assigned to the population with the highest log likelihood (i.e., the population with the least negative log-likelihood value) (Peakall &
Smouse 2007).
We used the clustering program STRUCTURE to assess the genetic structure of the populations using a different, Bayesian clustering algorithmic approach. This algorithmic approach has the prerequisite to analyse no or only weakly linked multilocus data and it tries to optimise HWE within subpopulations. By this approach, it computes the most likely number of populations (K) for a data set either including or excluding population sampling prior information STRUCTURE assigns individuals in the data set probabilistically to populations, or jointly to two or more populations if genotypic data of individuals indicates that they are admixed (indicated by differently coloured proportions of the individual’s genotypes). We applied the population assignment method implemented in the program STRUCTURE Version 2.3.1 (Pritchard et al. 2000, Falush et al. 2003) to infer simultaneously population structure and assign individuals to populations. For the P. antarctica data set, the most likely number of populations was inferred with and without prior information on geographic origin of individuals using STRUCTURE.
Nei´s unbiased minimum distance among populations was calculated and to visualize genetic distances (Nei 1972) between populations, an unweighted pair-grouping method using an, arithmetic averages (UPGMA), dendrogram was constructed using R (R Development Core Team, 2008).
Results
Microsatellites were highly polymorphic in all five populations. Total number of isolates scored for each population and locus, number of alleles, number of effective alleles, Information Index, observed heterozygosity, expected heterozygosity (genetic diversity), unbiased expected heterozygosity and fixation index (F) are given in Table 6.4. The number of alleles per microsatellite locus for all isolates screened [MS data set (a)], ranged from Na=5
133
(LOC7) to Na=25 (LOC2) [MS data set (f): Na=6 (LOC1) to Na=25 (LOC2)]. Observed heterozygosity (HO) (Table 6.4) ranged from 0.250 (LOC1, Pop4) to 1.000 (LOC2, Pop3;
LOC5, Pop2&5; LOC8, Pop4) [MS data set (f): HO ranged from 0.250 (LOC1, Pop4) to 1.000 (LOC2, Pop3; LOC5, Pop2&5)]. The length range of alleles was extremely broad for locus LOC7 and locus LOC8 (70 bp to 404 bp, Fig. 6.2) indicating that other mutations than simple repeat unit extensions have contributed to the extension. An overview of the allele frequencies detected for MS data set (a) is given in Fig. 6.2. Loci 2, 7, and 8 had the most even distributions of alleles, whereas loci 1,3,4,5, had several regions where the alleles differed by only a single repeat unit, which would suggest that these alleles are more recently derived by the step-wise mutation model assumed for microsatellites. Private alleles are present in all populations, but this can not be taken into account, because a limiting factor is the amount of isolates sampled per population in this study. Private alleles and unbalanced (approximate) frequency distributions of the alleles are normally mainly responsible for their pronounced genetic differentiation.
There was no evidence observed for linkage disequilibrium among loci. Tests for departure from HWE were highly significant in all loci.
Genetic differentiation/variability. Results of the AMOVA indicate that most genetic variation is distributed within individuals, whereas the values indicate that most variation is among individuals for both data sets used (a & f). Φ were significantly positive for all populations. Estimates of FST (Wright 1965) and RST (Slatkin 1995) values at the various polymorphic loci in all the geographic populations are compared in Table 6.5. Wright’s FST and Slatkins’s RST for MS data set (a) as well as for (f) reveal different estimates of differentiation among population pairs (from low to high). Highly significance levels (P<0.001) of FST/RST were found for pairwise comparisons of the five populations in both data sets. Only two less significant levels (P<0.05) were detected among both MS data set (a) and (f). This suggests that genetic variation is not distributed homogeneously among populations but that moderate to strong barriers to gene flow exist.
Population assignment using GENALEX v6.2. Different microsatellite data sets (isolates/loci) were used to show if severe differences would occur if we rejected loci from the analysis if they contained a high number of missing data. The outcome is that the only limiting factor was the number of loci used. Number of isolates did not have a systematic influence on changing the assignments but did affect the absolute likelihood values.
Populations 1 (Prydz Bay) and 2 (ACC) are similar to each other, population 2 being very similar to population 3 (Amundsen Sea/Ross Sea) in all MS data set combinations used.
Populations 1 and 3 are only sharing some genotypes. Genotypes for population 4 (Weddell Sea/Scotia Sea) most likely have originated from all other populations [except in MS data set (a) no similarity to population 3]. The Weddell Sea/Scotia Sea population does not seem to really have its own identity being a hodgepodge from all the areas. For population 5 (Coastal region) is only slightly similar to population 1 and it overlaps with population 4.
Population assignment with STRUCTURE. Only the five loci with fewest number of missing data were used to avoid any bias from this aspect. Individuals, missing data for three or more loci, were also excluded, resulting in a final data set of 97 specimens for the structure analysis [MS data set (f)].
The likelihood of the population assignment in STRUCTURE was highest for K=5 both with and without using a population prior (Fig. 6.3). Without a population prior, the assignment revealed several admixed genotypes within most populations, in particular for population 4, which has a recurring dominant genotype (indicated by the red proportion in Fig. 6.3a);
however, almost all other genotype proportions are found as well. Individuals of population 3 (Amundsen Sea/Ross Sea) appeared to be genetically relatively homogeneous and distinct. In population 1 (Prydz Bay) and 2 (ACC), several individuals showed the characteristic genotype of population 3 (Amundsen Sea/Ross Sea). Population 4 (Weddell Sea/Scotia Sea) and 5 (Coastal Region) showed several specimens with admixed genotypes. Genotypes for population 4 (Weddell Sea/Scotia Sea) show a dominating genotype (red proportion), however, almost all other genotype proportions can be found as well. Most individuals contained admixed genotypes that originate from almost all other populations in principle (Fig.
6.3a). For population 5, only a major input from population 4 could be traced (red proportions).
Using a population site prior for analysis in general supported the same view (Fig. 6.3b).
Populations 1 (Prydz Bay) and 2 (ACC) appeared to be genetically very similar, and most closely related to population 3 (Amundsen Sea/Ross Sea). Populations 4 (Weddell Sea/Scotia Sea) and 5 (Coastal Region) were relatively distinct, but showed similarities in genotype proportions to another.
Nei´s unbiased minimum distance depicted the relatedness among populations 1 (Prydz Bay), 2 (ACC) and 3 (Amundsen Sea/Ross Sea) by UPGMA dendrograms and also between populations 4 (Weddell Sea/Scotia Sea) and 5 (Coastal Region). The dendrograms showed a correlation between genetic and geographic distances (Fig. 6.4). The arrangement of the populations in the dendrogram was not remarkably influenced by using the smaller MS data
135
set (f) (97 isolates/5 loci); only populations 1 and 2 shifted their position, because of their close relatedness (Fig. 6.4b).
Discussion
Data on the geographic partitioning of genetic polymorphisms as performed in our study reveal that the genetic variability of P. antarctica is much more complex and restricted among several sampling sites as expected before. Our data indicated that the ocean currents do not allow genetic admixture among all populations and possibly even act as an isolating barrier between several sites (Fig. 6.5).
As all phytoplankton, P. antarctica is under the control of the current patterns in the Southern Ocean. Its distribution is largely shaped by water circulation, which should allow gene flow from one region to another (Patarnello et al.1996). Primer pairs for eight previously described microsatellite loci from P. antarctica were used to assess genetic variation within this species.
Analysis of microsatellite length variants (alleles) was conducted for 110 P. antarctica isolates representative of five oceanic regimes in the Southern Ocean. The diversity and distribution of this polar prymnesiophyte are much more complex than was ever estimated.
These results revealed high genetic differentiation among and within P. antarctica populations with the currents playing an important role in their structure. The major current in the Southern Ocean, the ACC, is driven partly by the vigorous mid-latitude westerly winds and affected by adjacent landmasses and submarine topography (Rintoul et al. 2001, Boning et al. 2008, Thompson 2008). Close to the Antarctic continent, the Antarctic Coastal Current, acting as counter-current of the ACC, flows westward, parallel to the Antarctic coastline (Gyory et al 2003). Within this westward flow, smaller hydrographic regimes form two well defined gyres: the Weddell Gyre and the Ross Gyre. The three populations originating from Prydz Bay (Pop1), ACC (Pop2) and Amundsen Sea/Ross Sea (Pop3) are genetically less differentiated than the two populations originating from Weddell Sea/Scotia Sea (Pop4) and Coastal region (Pop5, transect from Neumayer to Prydz Bay within the Polar Current).. This is indicated by their admixed genotypes in both assignment methods applied. Even when applying STRUCTURE using no population prior, individuals of Amundsen Sea/Ross Sea appeared rather isolated, genetically very similar to each other and correctly assigned to one population. Populations of Weddell Sea/Scotia Sea and the coastal region showed several specimens with admixed genotypes. As in the populations from the Weddell Sea/Scotia Sea, these most likely originated from all other populations. For the population of the coastal region, only a major input from this Weddell Sea/Scotia Sea population could be found. This
could be explained by the current patterns, because while the ACC is encircling the Antarctic continent eastward the Antarctic Coastal Current flows westwards near to the Antarctic continent. Populations of Amundsen Sea/Ross Sea, Weddell Sea/Scotia Sea and Prydz Bay are steadily entrained into the ACC, which is why we find genetic similarities. Thus, the ACC is acting as a vehicle for dispersing specimens and must be regarded as a central means for why genetically closely related individuals can be found in geographically distant populations.
Similar observations have been made for macroalgal species or specimens that can raft on such species (Waters 2008, Leese et al. in prep.).
The population of the Coastal region is genetically distinct from the ACC and closer to the Weddell Sea/Scotia Sea, because it is entrained in the Antarctic Coastal Current that flows westwards towards the Weddell Sea directly dispersing P. antarctica into this region. It is interesting to note that our data do not support a closer relation between the Prydz Bay samples and the Coastal population, which might be expected because of the coastal current systems (Fig. 6.5).
Concerning the three questions addressed in the introduction it can be concluded that based on our data, bloom forming populations are composed of genetically different individuals. In no case, did we find the same genotype for all loci tested. In each case, isolates from the same region originated from the same bloom (or bucket of water) and we suggested that these isolates were genetically closely related. P. antarctica populations are not strictly isolated in the oceanic regimes in which it inhabits and the ACC provides a dispersal mechanism among populations to all other oceanographic regimes of the Southern Ocean. But these regimes still maintain “their own genetically distinct” populations. However this genetic diversity is not restricted to only isolated population originating from different oceanic regimes because the different populations are related to each other via gene flow. Gene flow was not evenly dispersed among all populations. A highest gene flow from population 5 (Coastal region) to population 4 (Weddell Sea/Scotia Sea) was found, which can be explained by the Polar counter current flowing westwards. So we would propose that the magnitude of gene flow among populations is highly dependent on the current system.
Conclusion
Our results indicate genetic interconnections among populations possibly via the ACC.
Geographic barriers, such as oceanic boundaries seem to be limiting and forcing factors for greater gene flow between populations, because in this study however gene flow was not unlimited but regionally restricted (possibly a physiological phenomena).
137 Acknowledgements
We thank S. Strieben and V. Levkov for their help with cultures of P. antarctica and the German Science Foundation (DFG ME 1480) for financial support.
References
Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitocondrial DNA restriction sites. Genetics, 131, 479-491
Huff DR, Peakall R, Smouse PE (1993) RAPD variation within and among natural populations of outcrossing buffalograss Buchloe dactyloides (Nutt) Engelm. Theoretical and Applied Genetics, 86, 927-934.
Michalakis Y, Excoffier L (1996) A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. Genetics, 142, 1061-1064.
Peakall R, Smouse PE, Huff DR (1995) Evolutionary implications of allozyme and RAPD Variation in diploid populations of dioecious buffalograss Buchloe dactyloides. Molecular Ecology, 4, 135-147.
Paetkau D, Calvert W, Stirling I, Strobeck C (1995) Microsatellite analysis of population structure in canadian polar bears. Molecular Ecology, 4, 347-354.
Paetkau D, Slade R, Burdens M, Estoup A (2004) Genetic assignment methods for the direct, real-time estimation of migration rate: a simulationbased exploration of accuracy and power.
Molecular Ecology, 13, 55-65.
Peakall, R., and P. E. Smouse. (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6:288-295.
Peakall, R and Smouse PE (2007) GenAlEx 6.1 – Appendix 1 – Methods and Statistics
Slatkin M (1995) A measure of population subdivision based on microsatellite allele frequencies. Genetics, 139, 1463.
Waters JM (2008). Driven by the West Wind Drift? A synthesis of southern temperate marine biogeography, with new directions for dispersalism. Journal of Biogeography 35: 417-427 Weir BS (1990) Genetic Data Analysis, Sunderland, Massachusetts: Sinauer Associates, Inc.
The Evaluation of Forensic DNA Evidence. Washington, DC: National Academy Press.
Wright S (1965) The interpretation of population structure by F-Statistics with special regard to systems of mating. Evol, 19, 395-420.
139
Table 6.1 Sampling size details and Cruise information for the populations of P. antarctica Origin Cruise/Research
Stations
Nmsat Population
Prydz Bay AADa 27 Pop1
ACC ANT XXIII-9b 19 Pop2
Ross Sea VIMSc 2 Pop3
Amundsen Sea ANT XXIII-4d 9 Pop3 Scotia Sea ANT XXII-4d 24 Pop4 Weddell Sea ANT XXIII-2e 8 Pop4
Coastal Region
(Neymayr to Prydz Bay)
ANT XXIII-9b 21 Pop5
aAustralian Antarctic Division (AAD), CSIRO Division of Fisheries, Hobart, Tasmania, Australia cultures provided and isolated by A. Davidson (1982-2002); bCruise leg of RV Polarstern (2007) samples provided by J. Mondzech (University Hannover, Germany) and isolated by S. Gäbler-Schwarz; cVirginia Institut of Marine science, samples provided byA.
Shields (VIMS) and isolated by S. Gäbler-Schwarz; dCruise leg of RV Polarstern (2006), samples provided by S. Wickham (University Salzburg, Austria) and isolated by S. Gäbler-Schwarz; eCruise leg of RV Polarstern (2005/06), samples provided by B.A. Fach (IMS-METU, Turkey) and isolated by S. Gäbler-Schwarz.
Table 6.2 Microsatellites reaction Mastermix. *labelled fluorescently with FAM/HEX.
Microsatellites mix Volume
MilliQ 9.0 μL
10 x HotMaster Taq-buffer with 25 mM Mg2+
2.0 μL Eppendorf, Germany
F* primer (10 mM) 1.0 μL Thermo-elektron, Germany R primer (10 mM) 1.0 μL MWG, Germany
Betain (5 M) 2.0 μL Sigma, Germany dNTP´s (10 mM) 2.0 μL Eppendorf, Germany BSA (10x) 1.0 μL Biolabs, England HotMaster Taq-polymerase (5 U/μL) 0.15 μL Eppendorf, Germany
Table 6.3 MS data sets used for calculations
MS data sets Isolates Loci Rejected loci Rejected Isolates
(a) 110 8 0 0
(b) 110 6 LOC7, LOC8 0 (c) 110 5 LOC3, LOC7, LOC8 0
(d) 97 8 0 13
(e) 97 6 LOC7, LOC8 13 (f) 97 5 LOC3, LOC7, LOC8 13
141
Table 6.4 Total number of isolates scored for each population and locus (N), number of alleles (NA), number of effective alleles (NE), Information Index (I), observed heterozygosity (HO), expected heterozygosity (HE, genetic diversity), unbiased expected heterozygosity (UHE) and fixation index (F).
Prydz Bay ACC Amundsen Sea/
RossSea
Scotia Sea/
Weddell Sea
Coastal Region (Pop1) (Pop2) (Pop3) (Pop4) (Pop5) LOC1
N 24 18 11 16 20
Na 14 6 6 7 13
Ne 7,432 3,880 3,227 1,741 4,545 I 2,285 1,517 1,443 0,996 1,956 Ho 0,667 0,389 0,545 0,250 0,650 He 0,865 0,742 0,690 0,426 0,780 UHe 0,884 0,763 0,723 0,440 0,800 F 0,230 0,476 0,210 0,413 0,167 LOC2
N 21 18 10 9 20
Na 25 14 15 12 17
Ne 16,333 6,821 12,500 9,000 11,940 I 3,015 2,233 2,623 2,351 2,638 Ho 0,714 0,889 1,000 0,444 0,850 He 0,939 0,853 0,920 0,889 0,916 UHe 0,962 0,878 0,968 0,941 0,940 F 0,239 -0,042 -0,087 0,500 0,072 LOC3
N 19 14 11 5 18
Na 13 11 16 7 13
Ne 8,205 4,261 10,522 6,250 5,684 I 2,297 1,881 2,599 1,887 2,150 Ho 0,579 0,500 0,818 0,600 0,722 He 0,878 0,765 0,905 0,840 0,824 UHe 0,902 0,794 0,948 0,933 0,848 F 0,341 0,347 0,096 0,286 0,124 LOC4
N 24 18 10 31 20
Na 10 10 8 16 9
Ne 4,627 6,545 4,762 3,739 2,952 I 1,867 2,047 1,782 1,939 1,532 Ho 0,458 0,944 0,700 0,387 0,550 He 0,784 0,847 0,790 0,733 0,661 UHe 0,801 0,871 0,832 0,745 0,678 F 0,415 -0,115 0,114 0,472 0,168 LOC5
N 23 18 9 23 12
Na 12 13 12 11 13
Ne 5,343 5,400 8,526 5,426 9,600 I 2,046 2,062 2,322 1,960 2,417 Ho 0,609 1,000 0,667 0,826 1,000 He 0,813 0,815 0,883 0,816 0,896 UHe 0,831 0,838 0,935 0,834 0,935 F 0,251 -0,227 0,245 -0,013 -0,116 LOC6
N 17 17 11 16 19
Na 14 17 16 13 15
Ne 7,811 4,129 12,737 5,447 8,914 I 2,336 2,125 2,665 2,128 2,422 Ho 0,647 0,824 0,909 0,313 0,947 He 0,872 0,758 0,921 0,816 0,888 UHe 0,898 0,781 0,965 0,843 0,912 F 0,258 -0,087 0,013 0,617 -0,067 LOC7
N 9 17 9 5 20
Na 12 21 13 5 19
Ne 10,125 14,098 9,529 5,000 11,765 I 2,399 2,869 2,428 1,609 2,692 Ho 0,444 0,588 0,667 0,000 0,900 He 0,901 0,929 0,895 0,800 0,915 UHe 0,954 0,957 0,948 0,889 0,938 F 0,507 0,367 0,255 1,000 0,016 LOC8
N 15 16 11 6 19
Na 18 19 16 10 20
Ne 12,857 8,533 13,444 9,000 11,460 I 2,719 2,588 2,689 2,254 2,714 Ho 0,800 0,688 0,727 1,000 0,737 He 0,922 0,883 0,926 0,889 0,913 UHe 0,954 0,911 0,970 0,970 0,937 F 0,133 0,221 0,214 -0,125 0,193
143
Table 6.5 Pairwise FST/RST values among the five P. antarctica populations for MS data set a).
ACC Am. Sea/Ross Sea Weddell/Scotia Sea Coastal Region
(Pop2) (Pop3) (Pop4) (Pop5)
Prydz Bay (Pop1) 0,056***/0,131*** 0,053***/0,106*** 0,092***/0,212*** 0,074***/0,078***
ACC (Pop2) 0,028***/0,057* 0,210***/0,485*** 0,069***/0,483***
Am. Sea/Ross Sea (Pop3)
0,233***/0,151*** 0,044***/0,087* Weddell/Scotia Sea
(Pop4)
0,208***/0,435***
***P<0.001; **P<0.01; *P<0.05; ns not significantly different from zero.
Table 6.6 Pairwise FST/RST values between the five P. antarctica populations for MS data set f).
ACC Am. Sea/Ross Sea Weddell/Scotia Sea Coastal Region
(Pop2) (Pop3) (Pop4) (Pop5)
Prydz Bay (Pop1) 0,046***/0,136*** 0,023**/0,064* 0,058***/0,133*** 0,052***/0,126***
ACC (Pop2) 0,033**/0,054* 0,142***/0,396*** 0,089***/0,294***
Am. Sea/Ross Sea (Pop3)
0,102***/0,251*** 0,048***/0,131**
Weddell/Scotia Sea (Pop4)
0,085***/0,315***
***P<0.001; **P<0.01; *P<0.05; ns not significantly different from zero.
Fig. 6.1 Sampling sites of P. antarctica in the Southern Ocean (Prydz Bay, diamond; ACC, triangle; Amundsen Sea/Ross Sea, star; Scotia Sea/Weddell Sea, circle; Transit to Prydz Bay, square). Polar front systems according to Orsi et al. (1995) and Stewart (2007).
STF=Subtropical Front, ACC=Antarctic Circumpolar Current, PF=Polar Front, SAF=Subantarctic Front. Arrowheads indicate the direction of the current.
145
Fig. 6.2 Allele length frequencies of eight microsatellite loci genotyped for the populations of P. antarctica from Prydz Bay (Pop1 dark blue); ACC (Pop2 red); Amundsen Sea/Ross Sea (Pop3 green); Scotia Sea/Weddell Sea (Pop4 purple) and Transit to Prydz Bay (Pop5 light blue)
147
.
Fig. 6.3 Structure program calculation on MS data set (f) a) without prior, b) with prior.
1=Pop1 (Prydz Bay), 2=Pop2 (ACC), 3=Pop3 (Amundsen Sea/Ross Sea), 4=Pop4 (Weddell Sea/Scotia Sea) and 5=Pop5 (Coastal Region)
149 a
b
Fig. 6.4 UPGMA dendrogram of the five populations based on pairwise population matrix of Nei unbiased genetic distance (Nei’s 1972) for a) MS data set (a) and b) MS data set (f).
151
a b
Fig. 6.5 Current system in the Southern Ocean (redrawn from Rintoul et al. 2001). White arrows indicate the Antarctic Coastal Current, acting as counter-current of the ACC.
153
155
Submitted August 29th 2009 to Marine Ecology Progress Series