Limits to gene flow in a cosmopolitan marine
planktonic diatom
Griet Casteleyna,1, Frederik Leliaertb,1, Thierry Backeljauc,d, Ann-Eline Debeera, Yuichi Kotakie, Lesley Rhodesf,
Nina Lundholmg, Koen Sabbea, and Wim Vyvermana,2
a
Laboratory of Protistology and Aquatic Ecology and bPhycology Research Group, Biology Department, Ghent University, B-9000 Ghent, Belgium; cRoyal
Belgian Institute of Natural Sciences, B-1000 Brussels, Belgium; dEvolutionary Ecology Group, Department of Biology, University of Antwerp, B-2020 Antwerp,
Belgium; eSchool of Marine Biosciences, Kitasato University, Sanriku, Iwate 022-0101, Japan; fCawthron Institute 7042, Nelson, New Zealand; and gThe Natural
History Museum of Denmark, DK-1307 Copenhagen, Denmark
Edited by Paul G. Falkowski, Rutgers, The State University of New Jersey, New Brunswick, Brunswick, NJ, and approved June 10, 2010 (received for review
February 3, 2010)
The role of geographic isolation in marine microbial speciation is
hotly debated because of the high dispersal potential and large
population sizes of planktonic microorganisms and the apparent
lack of strong dispersal barriers in the open sea. Here, we show that
gene flow between distant populations of the globally distributed,
bloom-forming diatom species Pseudo-nitzschia pungens (clade I) is
limited and follows a strong isolation by distance pattern. Furthermore, phylogenetic analysis implies that under appropriate geographic and environmental circumstances, like the pronounced
climatic changes in the Pleistocene, population structuring may lead
to speciation and hence may play an important role in diversification of marine planktonic microorganisms. A better understanding
of the factors that control population structuring is thus essential to
reveal the role of allopatric speciation in marine microorganisms.
|
|
allopatric speciation dispersal marine cosmopolitan planktonic
microorganisms population structure microsatellites
|
|
A
llopatric speciation models are challenging for marine planktonic microorganisms because the huge population sizes and
high passive dispersal potential of these organisms, as well as the
lack of apparent dispersal barriers in the ocean, may allow extensive gene flow at large geographic scales (1–3). Therefore,
marine environments are believed to sustain only a limited number of cosmopolitan planktonic microbial species that lack spatial
population genetic structuring. However, accumulating molecular data provide evidence for extensive genetic diversity and the
prevalence of cryptic species in many alleged cosmopolitan planktonic microorganisms (4–8). The concept of planktonic superspecies
has been introduced to describe these constrained morphological
monophyletic entities, which comprise multiple closely related species that may be geographically restricted or adapted to different
ecological niches (9). The apparent paradox of high cryptic microbial
species diversity in the absence of opportunities for allopatric speciation, in combination with fossil evidence for gradual patterns of
evolution in sympatry (reviewed in ref. 10) has lead to the paradigm
that sympatric processes and/or genomic changes are the prevalent
mode of speciation in planktonic marine microbial organisms (11–
14). On the other hand, based on the restricted geographic distributions of several cryptic species, it has been hypothesized that isolation by physical and/or ecological barriers is an important driver in
allopatric processes, even in high dispersal marine microbes (15–18).
Allopatric speciation results from geographic partitioning of
genetic diversity due to historical (e.g., dispersal limitation) and/or
contemporary environmental processes (e.g., divergence resulting
from local selection) (19). The relative importance of these factors
in structuring microbial systems is, however, still poorly understood (3). To gain insight in the mechanisms of microbial speciation
in the sea, it is essential to identify the factors that affect population
structuring at the different spatiotemporal scales at which gene
flow may operate. Population genetic surveys using high-resolution
markers that allow differentiation among individuals, are greatly
improving our understanding of the mechanisms that cause genetic
12952–12957 | PNAS | July 20, 2010 | vol. 107 | no. 29
diversity within and gene flow between populations (20). To date,
population genetic studies of high-dispersal organisms in the marine environment have largely focused on pelagic or benthic animals with planktonic larval stages (21–24). Population genetic
structuring of planktonic microorganisms has been much less explored due to difficulties with species delineation and lack of finescale genetic markers for these organisms (25–28).
Diatoms form a major component of the plankton and despite
their relatively young age (≤240 Ma), they are the most diverse
group of marine phytoplankton (29, 30). We used the obligate
sexual planktonic pennate diatom Pseudo-nitzschia pungens sensu
lato to study gene flow on a global scale because (i) the species
complex has been reported worldwide (31); (ii) it is known to
form extensive blooms (32, 33) and thus large population sizes
that could facilitate gene flow at macrogeographic scales; and (iii)
its life history, taxonomy, and biogeography have been well
studied (34, 35). Within P. pungens, both nuclear rDNA internal
transcribed spacer (ITS) and plastid-encoded rbcL sequences
have revealed the existence of three distinct clades (I–III), which
are also distinguishable by subtle but clear differences in frustule
ultrastructure. Although these clades are currently recognized as
subspecific taxa (varieties), the observed genetic and morphological
discontinuities suggest that these clades behave as independent
evolutionary lineages (36), hence justifying their recognition
as discrete species-lineages within a planktonic superspecies
P. pungens (35, 37). To avoid confusion, we refer to these three taxa
as clades I–III. The clades differ markedly in their geographical
distribution (35) (Fig. 1). Clade I (P. pungens var. pungens) has
a cosmopolitan distribution in temperate waters of the Atlantic and
Pacific Oceans. Clade II (P. pungens var. cingulata) has only been
found in the northeastern Pacific, where it cooccurs with clade
I. Clade III (P. pungens var. aveirensis) occurs in the tropical to
warm-temperate waters of the Atlantic and Pacific Oceans. Although clades I and II are genetically well-differentiated, occasional
hybrids between both clades have been recorded in the northeastern
Pacific (38–40), suggesting incomplete reproductive isolation.
Within clade I, all individuals have identical ITS and rbcL sequences
and isolates from geographically widely separated populations reproduce sexually under laboratory conditions, suggesting the potential for gene flow between distant, or even transoceanic,
populations (35, 39). Therefore, clade I is well-suited to explore
patterns of population genetic structuring and to evaluate gene flow
Author contributions: G.C., T.B., K.S., and W.V. designed research; G.C., A.-E.D. , Y.K., L.R.,
and N.L. performed research; G.C., F.L., and T.B. analyzed data; and G.C. and F.L. wrote
the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
1
G.C. and F.L. contributed equally to this work.
2
To whom correspondence should be addressed. E-mail: wim.vyverman@UGent.be.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1001380107/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1001380107
Denmark
Ireland
Belgium
USA
Japan
Canada
New Zealand
Recorded distribution of P. pungens
clade I
clade II
clade III
Results and Discussion
Our analysis refutes the hypothesis that P. pungens clade I constitutes a single globally panmictic population. Instead, it shows
significant geographical genetic structuring.
Allelic diversity was significantly higher on a global scale than
on a regional scale in the North Sea (41, 42). Globally, we
recorded 118 alleles in 242 isolates (19.7 alleles per locus on
average) (Tables S1 and S2), whereas in the North Sea a maximum of 77 alleles were found in almost twice as many isolates
(12.5 alleles per locus on average) (41, 42), indicating that the
higher global allelic richness is not a matter of sample size but
due to additional alleles outside the North Sea. Highest and
lowest allelic richness and heterozygosity (He 0.83 vs. 0.53) were
found on opposite sides of the northern Pacific, in Japan and the
Pacific United States respectively (Fig. 2). Other populations
showed intermediate He values that are comparable with those
of clade I in the North Sea [0.69 and 0.73 (41, 42)] and of the
marine planktonic diatom Ditylum brightwellii in the northwestern Pacific (0.71) (43).
There was a large overlap in allele size classes for the six loci,
although allele frequency distributions differed among predefined
populations (Fig. S1). Allelic and genotypic differentiation tests
(for each locus separately and over all loci) gave the same results
(Table 1). Pairwise FST values between populations were relatively
high (ranging from 0.06 to 0.76) and all, except Belgium vs.
Denmark, were significant (Table 1). In agreement with the results
based on the predefined populations, the STRUCTURE analyses
revealed six genetic clusters (Fig. 3, Fig. S2, and SI Text), indicating
that genetic structuring was strong enough to assign isolates to
different groups. Whereas northeastern Atlantic isolates showed
a more admixed background, northwestern Atlantic and Pacific
Casteleyn et al.
isolates clustered mainly according to their geographic origin,
suggesting that migration rates and/or gene flow between the
sampling locations is insufficient to make the sampling locations
act as a single panmictic population (44). This is in line with the
PCA and isolation by distance (IBD) analyses (Figs. 4 and 5). The
PCA of pairwise FST values separated Pacific and Atlantic populations along the first axis, and indicated that populations from
the Atlantic are less differentiated than those from the Pacific (Fig.
4). There was a strong IBD signal (Fig. 5) (Z = 16.8975, r =
−0.8707, one-sided P = 0.0044), although even at the largest
geographic scale, the STRUCTURE analysis detected some admixing (Fig. 3). This suggests that long-distance dispersal may
occur, but not frequently enough to counteract population differentiation. Whether this long-distance dispersal is natural and may
involve introductions of new genotypes through low-abundance
populations [eukaryotic “rare biosphere” (45)] or rather reflect anthropogenic introductions (e.g., via ballast water or translocation of
aquaculture stocks), cannot be deduced from our data. Historical
distribution records, data on divergence times between the populations and testing the observed geographic patterns with oceanic
current models (46, 47) may resolve this question. Whereas the age
of population differentiation in clade I is uncertain, it must be more
recent than the split between clades I and II, which has occurred in
the Middle to Lower Pleistocene (200–800 kya) (Fig. 6).
120
Japan
100
Allelic richness
over a range of geographical scales. For example, recent microsatellite analyses showed that at regional scale, North Sea populations
constitute a highly diverse genetic continuum with extensive gene
flow (41, 42).
This study assesses global patterns of gene flow in P. pungens
clade I by means of six highly polymorphic nuclear microsatellite
loci. We analyzed isolates from different localities in the Atlantic
and Pacific Ocean to test the assumption that clade I represents
a single globally panmictic population (35). Using a calibrated
phylogenetic analysis we estimated the maximal divergence times
between different populations of this clade, providing a historical
framework to interpret patterns of gene flow and population
differentiation.
Belgium
Denmark
Ireland
Atlantic Canada
New Zealand
80
60
40
Pacific USA
20
0
20
60
40
Number of individuals
80
100
Fig. 2. Comparison of allelic richness between P. pungens clade I predefined populations from different areas. Allelic richness was inferred from
multilocus genotypes (six microsatellite loci) and extrapolated beyond the
sample size using ARES.
PNAS | July 20, 2010 | vol. 107 | no. 29 | 12953
EVOLUTION
Fig. 1. Geographic distribution of P. pungens. Morphology-based records are indicated by open squares and verified isolates based on DNA sequencing are
shown as filled squares. Sampling locations for the microsatellite study of the P. pungens clade I are indicated by arrows. Redrawn and updated from Trainer
et al. (32).
Table 1. Estimates of standardized multilocus Fst values among pairs of predefined populations (above diagonal);
the loci that were significantly different among populations in the single locus tests (allelic and genotypic tests) are
given below the diagonal
Belgium
Belgium
Denmark
Ireland
Canada
USA
Japan
New Zealand
6
1, 2, 3, 4, 6
1, 2, 3, 4, 6
all 6
all 6
all 6
Denmark
Ireland
Atlantic Canada
0.06
0.18
0.06
0.31
0.18
0.23
1, 4
1, 2, 3, 4, 6
all 6
all 6
all 6
1, 2, 3, 4, 6
all 6
all 6
all 6
Pacific
United States
0.74
0.73
0.70
0.57
all 6
all 6
all 6
all 6
all 6
Japan
New Zealand
0.59
0.49
0.47
0.50
0.66
0.73
0.74
0.76
0.74
0.65
0.57
all 6
Significant values (after sequential Bonferroni correction) are shown in bold.
In contrast to the panmictic clade I population in the North
Sea (41, 42), our results suggest that at larger geographic scales
gene flow is more restricted. The limited population mixing at
global scale may be due to pre- or postcolonization barriers (49).
Precolonization barriers are a result of restricted dispersal abilities and generally scale with distance. In contrast, postcolonization barriers act in a distance-independent manner and
reduce population admixing if new colonizers fail to become
established because of different local environmental factors and
selection pressures. Our PCA and IBD analyses indicate that
there are limits to the dispersal ability of clade I so that geographic genetic differentiation is prevalent in this taxon. Hence,
our data strongly suggest that isolation by large distances allows
for genetic divergence, even in high-dispersal organisms like
planktonic diatoms. This is in line with other marine organisms,
where similar patterns of significant IBD were observed from
regional to global scales (23, 50–52).
Apart from distance, various other isolating mechanisms, including present-day ecological and physical barriers, may be at
work in the supposedly homogeneous marine environment.
Clade I is hitherto only known from temperate coastal regions.
Its apparently antitropical distribution suggests that geographic
variation in seawater temperature may be an ecological barrier
for transequatorial gene flow, as has been shown in some coolwater fishes and foraminifers (16, 53–55). This hypothesis
remains to be tested, particularly in the light of the possibility
that seasonal upwelling of cold waters along the western margins
of the American and African continents may act as a corridor for
transequatorial dispersal, as has been suggested for anti-tropical
and bipolar planktonic foraminifers (54, 56). Overall, transoceanic dispersal of clade I seems less plausible than previously
thought because coastal and open sea environments have very
different physical and biological characteristics (53, 57). In addition, several key features of the life history of Pseudo-nitzschia
appear to preclude easy dispersal over the open ocean. Firstly, it
does not produce cysts needed to survive inhospitable open
ocean conditions. Secondly, its obligate sexual life cycle is controlled by specific environmental conditions during seasonal
Belgium
Denmark
NE Atlantic
Ireland
blooms that are promoted by high levels of inorganic nutrients
introduced by coastal upwelling. Importantly, in the onset of reproduction, mating cells attach to dense colonies of surf-zone
diatoms (40). Together, these findings imply that P. pungens requires specific coastal conditions to complete its life cycle, which
probably constrain transoceanic dispersal. Similar conservatism in
reproductive habit has been invoked to explain restricted dispersal
in planktonic foraminifers (55).
Flow patterns of tidal or oceanic currents may also contribute
to the genetic isolation among global populations by hindering
dispersal (22, 23, 28, 58, 59). For example, there were no significant FST values between populations from the North Sea and
a connected Danish inland fjord, yet populations from the Irish
Sea and the adjacent North Sea showed significant differentiation. The Irish Sea and the North Sea are separated by sea fronts
that limit exchange between both areas (60). At a macrogeographic scale, ocean circulation and continental land masses may
act as effective barriers to gene flow among oceanic basins and
among oceanic circulation centers (61). Such barriers may explain the reduced gene flow of clade I between Atlantic and
Pacific populations, and between the northern and southwestern
Pacific Ocean. Similarly, oceanic gyres and continental land
masses are barriers to gene flow between two globally distributed
oceanic copepod sister species (62), even if the impact of specific
barriers on population genetic structuring varied between the
two species. In the same sense, ecological and geographic barriers to gene flow are species-specific in marine foraminifers (17),
reflecting the unique evolutionary histories and ecological requirements of even closely related species (17, 62, 63).
Given enough time and appropriate geographic and environmental circumstances, the population structure in clade I may
provoke allopatric speciation. This is illustrated by a phylogenetic analysis, which shows that the split of P. pungens into three
distinct clades probably occurred during the Pleistocene (Fig. 6),
a period characterized by repeated glacial cycles. To date we can
only speculate on how glacial cycles have promoted divergence
within this superspecies, but a number of possible scenarios can
be forwarded. During periods of glacially lowered sea level when
Canada
USA
Japan
NW Atlantic NE Pacific NW Pacific
New Zealand
SW Pacific
Fig. 3. Structure plot for K = 6. Each individual is depicted by a vertical line that is partitioned into K colored sections, with the length of each section
proportional to the estimated membership coefficient (qind) of the isolate to each cluster.
12954 | www.pnas.org/cgi/doi/10.1073/pnas.1001380107
Casteleyn et al.
Ireland
Belgium
Denmark
Japan
New Zealand
Axis 1
Fig. 4. Principal component analysis of pairwise FST values among the seven
predefined populations of P. pungens clade I. The first and second principal
components account for 59.47% and 24.58% of the total variation, respectively.
coastal seas drained or large lagoons became landlocked (61),
populations of P. pungens may have experienced prolonged geographical isolation. In such environments, populations are likely
to experience demographic declines that will amplify the effects of
genetic drift, which in concert or interaction with local adaptation
to specific environmental conditions may lead to speciation (19,
64). In addition, creation and colonization of empty niches associated with Pleistocene cycles of sea level rise-and-fall (e.g., as
a consequence of local extinctions) likely accelerated speciation
rates. Extended periods of isolation or colonization events possibly led to the differentiation into the warm-water clade III and
the cool-water clades I and II. Increased cooling and upwelling
along the equatorial divergences in the Pacific and the Atlantic
possibly allowed the latter to disperse from one hemisphere to
the other, resulting in an antitropical distribution, similar to evolutionary scenarios proposed for bipolar mollusks and foraminifers (54–56, 65). The diversification and geographic distribution of clades I and II, and their regional cooccurrence in the
northeastern Pacific can be explained by two scenarios: (i) the two
clades evolved sympatrically along the northeastern Pacific coast,
or (ii) a secondary contact arose after para- or allopatric differentiation of the two clades, either along the same coast or in distant
geographic regions. Both scenarios are difficult to separate (66),
but a number of clues favor the second hypothesis in which clade II
originated separately in the northeastern Pacific and came later in
secondary contact with clade I, producing limited genetic admixture. First, individuals from both clades occur in the same samples (38), indicating limited ecological differentiation. This argues
against sympatric speciation through ecological mechanisms (19).
P. americana
.89
P. seriata f. obtusa
.99
1.00
1.00
1.00
P. seriata
1.00
P. australis
1.00
P. multistriata
1.00
P. multiseries
.69
Cn172
1.00
.99
1.00
Genetic similarity (M) (log)
1.4
5 My
1.1
0.8
US135
clade II
Viet
0.99
KBH2
0.5
1.00
0.2
Mex18
1.00 P24
- 0.1
Miocene
my 8
- 0.4
3.2
3.8
4.4
Geographic Distance (Log)
Fig. 5. Isolation by distance. Scatter plot of genetic similarity versus geographic distances (in km) among seven P. pungens clade I populations,
showing significant correlation between geographic and genetic distance.
Mantel test for matrix correlation between log(genetic similarity (M)) and
log(geographic distance): Z = 16.8975, r = −0.8707, one-sided P = 0.0044.
Regression analyses: r2 = 0.758, 95% CI: 0.450–0.889.
Casteleyn et al.
(global,
antitropical)
1.00 US115
1.00
7
5
4
3
(NE Pacific)
clade III
(tropical to
warmtemperate
Atlantic and
Pacific)
Pleistocene
Pliocene
6
clade I
V120
2
1
0
Fig. 6. Divergence time estimates (Mya) among Pseudo-nitzschia species,
based on a Bayesian relaxed molecular clock applied to a concatenated
alignment of rbcL, LSU rDNA and ITS sequences, and calibrated based on
Sorhannus et al. (48). Uncertainty of divergence times are indicated by gray
bars on internal nodes, corresponding to the 95% highest posterior density
of node ages. The chronogram shows that within P. pungens the clade III
diverged first (around 0.6 and 1.6 Mya), followed by the divergence of clades
I and II, between 200 and 800 kya.
PNAS | July 20, 2010 | vol. 107 | no. 29 | 12955
EVOLUTION
Axis 2
Atlantic Canada
Second, reproduction in both clades is induced by similar environmental cues and hybridization is common (38–40). This suggests incomplete reproductive isolation, which is indicative for
allopatric divergence where reproductive isolation in the absence
of gene flow is expected to arise more gradually than in sympatry
(67). Third, assuming that centers of origin show a higher genetic
diversity than newly invaded areas (68), the highest microsatellite
allelic richness and He in Japan suggests that clade I has spread
from the northwestern Pacific to other regions. A similar phylogeographic pattern, in which planktonic microorganisms have spread
from a NW Pacific center has been suggested for the dinoflagellate
Alexandrium catenella (26). The lowest allelic richness and He in the
northeastern Pacific may then be due to founder effects caused by
recent range expansions. This hypothesis is supported by the much
higher allelic richness and He of clade II in the northeastern Pacific
(comparable with clade I in the northwestern Pacific) than clade I
(38) (Fig. S3). The biogeographical asymmetry between clades I
and II (Fig. 1) may result from a wider ecological tolerance of clade
I, but may also be related to specific historical or environmental
conditions in the northeastern Pacific (69), as endemism there is
not unique to clade II but is also observed in other planktonic
groups (e.g., 14, 55). Finally, it should be noted that the possibility
of abrupt genomic changes (e.g., genomic duplications) as a mechanism for sympatric genetic diversification (14) remains to be tested
for P. pungens.
In conclusion, our results show that within the cosmopolitan
marine planktonic diatom P. pungens clade I, significant population differentiation exists at macrogeographic scales. Our data
indicate that dispersal limitation by geographic distance may be an
important factor in genetic differentiation, even in high dispersal
marine microorganisms. The globally distributed clade I therefore
behaves as an incipient planktonic superspecies (9) in which allopatric populations may eventually diverge into cryptic species.
Our results oppose earlier assumptions that uninterrupted gene
flow and range expansions of marine planktonic microbes produce
global, homogeneous populations and limit opportunities for
geographic speciation (1, 3, 13) and thus challenge the current
paradigm that sympatric speciation is the prevalent mode of spe-
P. pungens
Pacific USA
ciation in marine planktonic microorganisms (9, 14). Instead, we
suggest that persistent restricted gene flow between geographically
separated populations can, given the right geographic and environmental conditions, lead to population differentiation and high
diversity in planktonic organisms, despite the dampening effect of
large population sizes.
Materials and Methods
Isolation of P. pungens and Molecular Analysis. Sampling was performed in
seven areas in the Atlantic and Pacific Ocean (Fig. 1 and Table S1). P. pungens
cells were isolated and monoclonal cultures established as described in
Casteleyn et al. (35, 42). Identification of isolates (clades I−III) was based on
nrDNA ITS sequencing as described in Casteleyn et al. (35). For clade I isolates, six nuclear microsatellite loci (PP1, PP2, PP3, PP4, PP5, and PP6) were
amplified with primers developed by Evans and Hayes (70) (SI Text). Two
hundred and forty-two isolates were successfully genotyped, except for
a single Canadian isolate that had a missing genotype at locus PP4.
Microsatellite Data Analysis. Initial data analysis included identification of
matching multilocus genotypes (MLGs) and verification of possible scoring
errors due to stuttering or large allele dropout (SI Text). For measurement of
genetic diversity the seven sampling areas were arbitrarily regarded as
populations. Probability of identity (P(ID) = 3.75 × 10−9) and the probability of
identity for siblings (P(ID)sib = 1.95 × 10−3) calculated with GIMLET v1.3.3 (71)
were within the threshold values suggested by Waits et al. (72), indicating
that our six microsatellite markers had enough resolving power to distinguish
between individuals. Genetic diversity was measured by numbers of alleles,
allele frequencies, numbers of genotypes, observed heterozygosity (Ho), and
expected heterozygosity (He) per population and per locus (SI Text). Extrapolation of allele numbers beyond the sample size was done with ARES v1.2.2
(73). Departures from Hardy-Weinberg Equilibrium (HWE) at each locus and
across loci in every population, and linkage disequilibrium (LD) between all
pairs of loci per population were tested using exact tests (SI Text). Multiple
test problems were dealt with by calculating corrected P values using the
sequential Bonferroni technique.
ulations (FST) using GENALEX v6 (78). To infer population structure without
a predefined population subdivision, the Bayesian clustering program STRUCTURE v2.2 (44) was used (SI Text). The most likely number of populations was
estimated to be K = 6 following the recommendation of Pritchard et al. (79)
(Fig. S2). For the selected K value, we evaluated the individual membership
coefficient (qind) to the inferred clusters. CLUMPP v1.1.1 (80) was used to line
up the cluster labels across runs and to estimate the degree of congruence
between independent runs. Visualization of the results from the Bayesian
analyses was done with DISTRUCT v1.1 (81).
Isolation by Distance. Correlations between genetic and geographic distances
among populations (IBD) were evaluated with the program IBDWS v3.16 (82)
using nonparametric Mantel tests. Geographic distance between sampling
areas was measured as the shortest continuous water surface distance using
Google Earth, even if these sea connections do not necessarily reflect the
actual dispersal distance in complex oceanic circulations. Genetic similarities
among population pairs were computed using Slatkin’s (83) similarity measure, M = [(1/FST) − 1]/4. The significance of the major axis regression was
assessed by 10,000 permutations of the data.
Phylogenetic Analysis. A dated phylogeny of Pseudo-nitzschia clade I [as
presented in Lundholm et al. (6)] was based on Bayesian inference of rbcL,
large subunit rDNA and rDNA ITS sequence data (Table S3). Divergence
times were estimated under a relaxed molecular clock using an uncorrelated
lognormal model in BEAST v1.4.6 (84). The Markov chain Monte Carlo
analysis was run for 10 million generations, of which the last 5 million were
used for generating summary statistics and trees. The single calibration
point was the estimated split of P. pungens with its closest relative P. multiseries at 5 Mya (48). Of course, the chronogram has to be interpreted with
care, as different molecular clock studies have shown variation in divergence
times between major diatom lineages (48).
Population Structure. Population differentiation was evaluated with allelic
and genotypic exact tests in GENEPOP v4.0 (74) using default settings. Tests
were run for each locus between all pairs of populations and combinations of
P values across loci were obtained according to Fisher’s method. The degree
of genetic differentiation between pairs of populations was quantified using
Weir and Cockerham’s (75) estimate of FST in FSTAT v2.9.3 (76). Permutation
tests were used to determine whether FST values were significantly different
from zero. FST values depend on allelic diversities, therefore standardized FST
values were calculated using RECODEDATA v0.1 (77). In all cases, P values
were corrected using a sequential Bonferroni technique. A principal component analysis (PCA) was performed to visualize differentiation among pop-
ACKNOWLEDGMENTS. We thank Victor Chepurnov, (Ghent University, Ghent,
Belgium) Veronique Creach, (Centre for Environment, Fisheries & Aquaculture
Science, Lowestoft, UK) Stephen Bates, (Fisheries & Oceans Canada, Moncton,
Canada) Claude Léger, (Fisheries & Oceans Canada) Nick Adams, (NOAA-Fisheries
Service, Seattle) and the Flemish Marine Institute (VLIZ), for providing isolates. We
thank Andy Vierstraete (Ghent University) for allele fragment electrophoresis. We
thank two anonymous reviewers and the editor for insightful suggestions that
greatly improved the manuscript. Funding was provided by the Flanders agency
for Innovation by Science and Technology (IWT) (PhD fellowship to G.C.), Research
Foundation (FWO) Flanders (Grants G.0197.05, G.0208.08, G.0292.00, G.0404.07,
and G.0208.08, and a postdoctoral fellowship to F.L.) and Special Research Fund
(BOF) of Ghent University (GOA 12050398). Northeastern Pacific samples were
collected as part of the Ecology and Oceanography of Harmful Algal Blooms in
the Pacific Northwest (ECOHAB-PNW) project funded by National Science Foundation ECOHAB Project OCE-0234587 and National Oceanic and Atmospheric
Administration ECOHAB Grant NA16OP1450.
1. Norris RD (2000) Pelagic species diversity, biogeography, and evolution. Paleobiology
26:236–258.
2. Finlay BJ (2002) Global dispersal of free-living microbial eukaryote species. Science
296:1061–1063.
3. Cermeño P, Falkowski PG (2009) Controls on diatom biogeography in the ocean.
Science 325:1539–1541.
4. de Vargas C, Norris R, Zaninetti L, Gibb SW, Pawlowski J (1999) Molecular evidence of
cryptic speciation in planktonic foraminifers and their relation to oceanic provinces.
Proc Natl Acad Sci USA 96:2864–2868.
5. Saez AG, et al. (2003) Pseudo-cryptic speciation in coccolithophores. Proc Natl Acad Sci
USA 100:7163–7168.
6. Lundholm N, et al. (2006) Inter- and intraspecific variation of the Pseudo-nitzschia
delicatissima complex (Bacillariophyceae) illustrated by rRNA probes, morphological
data and phylogenetic analyses. J Phycol 42:464–481.
7. Šlapeta J, López-García P, Moreira D (2006) Global dispersal and ancient cryptic
species in the smallest marine eukaryotes. Mol Biol Evol 23:23–29.
8. Kooistra WH, et al. (2008) Global diversity and biogeography of Skeletonema species
(bacillariophyta). Protist 159:177–193.
9. de Vargas C, Sáez AG, Medlin LK, Thierstein HR (2004) Super-Species in the calcareous
plankton. Coccolithophores: From Molecular Processes to Global Impact, eds Thierstein HR,
Young JR (Springer-Verlag, Berlin), pp 271–298.
10. Benton MJ, Pearson PN (2001) Speciation in the fossil record. Trends Ecol Evol 16:
405–411.
11. Bierne N, Bonhomme F, David P (2003) Habitat preference and the marine-speciation
paradox. Proc R Soc B Biol Sci 270:1399–1406.
12. Palenik B, et al. (2007) The tiny eukaryote Ostreococcus provides genomic insights
into the paradox of plankton speciation. Proc Natl Acad Sci USA 104:7705–7710.
13. Sexton PF, Norris RD (2008) Dispersal and biogeography of marine plankton: Longdistance dispersal of the foraminifer Truncorotalia truncatulinoides. Geology 36:
899–902.
14. Koester JA, Swalwell JE, von Dassow P, Armbrust EV (2010) Genome size differentiates co-occurring populations of the planktonic diatom Ditylum brightwellii
(Bacillariophyta). BMC Evol Biol 10:1.
15. de Vargas C, et al. (2002) A molecular approach to biodiversity and biogeography in the
planktonic foraminifer Globigerinella siphonifera (d’Orbigny). Mar Micropaleontol 45:
101–116.
16. Darling KF, Kucera M, Pudsey CJ, Wade CM (2004) Molecular evidence links cryptic
diversification in polar planktonic protists to Quaternary climate dynamics. Proc Natl
Acad Sci USA 101:7657–7662.
17. Darling KF, Wade CA (2008) The genetic diversity of planktic foraminifera and the
global distribution of ribosomal RNA genotypes. Mar Micropaleontol 67:216–238.
18. Aurahs R, Grimm GW, Hemleben V, Hemleben C, Kucera M (2009) Geographical
distribution of cryptic genetic types in the planktonic foraminifer Globigerinoides
ruber. Mol Ecol 18:1692–1706.
19. Sobel JM, Chen GF, Watt LR, Schemske DW (2010) The biology of speciation.
Evolution 64:295–315.
20. Balloux F, Lugon-Moulin N (2002) The estimation of population differentiation with
microsatellite markers. Mol Ecol 11:155–165.
21. Knutsen H, et al. (2009) Bathymetric barriers promoting genetic structure in the
deepwater demersal fish tusk (Brosme brosme). Mol Ecol 18:3151–3162.
22. White TA, Stefanni S, Stamford J, Hoelzel AR (2009) Unexpected panmixia in a longlived, deep-sea fish with well-defined spawning habitat and relatively low fecundity.
Mol Ecol 18:2563–2573.
23. Yasuda N, et al. (2009) Gene flow of Acanthaster planci (L.) in relation to ocean
currents revealed by microsatellite analysis. Mol Ecol 18:1574–1590.
12956 | www.pnas.org/cgi/doi/10.1073/pnas.1001380107
Casteleyn et al.
Casteleyn et al.
52. Pogson GH, Taggart CT, Mesa KA, Boutilier RG (2001) Isolation by distance in the
Atlantic cod, Gadus morhua, at large and small geographic scales. Evolution 55:
131–146.
53. Bowen BW, Grant WS (1997) Phylogeography of the sardines (Sardinops spp):
Assessing biogeographic models and population histories in temperate upwelling
zones. Evolution 51:1601–1610.
54. Darling KF, et al. (2000) Molecular evidence for genetic mixing of Arctic and Antarctic
subpolar populations of planktonic foraminifers. Nature 405:43–47.
55. Darling KF, Kucera M, Wade CM (2007) Global molecular phylogeography reveals
persistent Arctic circumpolar isolation in a marine planktonic protist. Proc Natl Acad
Sci USA 104:5002–5007.
56. Norris RD, de Vargas C (2000) Evolution all at sea. Nature 405:23–24.
57. Nuwer M, Frost B, Armbrust EV (2008) Population structure of the planktonic
copepod Calanus pacificus in the North Pacific Ocean. Mar Biol 156:107–115.
58. Nagai S, et al. (2007) Microsatellite markers reveal population genetic structure of
the toxic dinoflagellate Alexandrium tamarense (Dinophyceae) in Japanese coastal
waters. J Phycol 43:43–54.
59. Peijnenburg KT, Fauvelot C, Breeuwer JA, Menken SBJ (2006) Spatial and temporal
genetic structure of the planktonic Sagitta setosa (Chaetognatha) in European seas as
revealed by mitochondrial and nuclear DNA markers. Mol Ecol 15:3319–3338.
60. Pingree RD, Griffiths DK (1978) Tidal fronts on shelf seas around British Isles. J Geophys
Res 83:4615–4622.
61. Palumbi SR (1994) Genetic divergence, reproductive isolation and marine speciation.
Annu Rev Ecol Syst 25:547–572.
62. Goetze E (2005) Global population genetic structure and biogeography of the oceanic
copepods Eucalanus hyalinus and E. spinifer.. Evolution 59:2378–2398.
63. Planes S, Fauvelot C (2002) Isolation by distance and vicariance drive genetic structure
of a coral reef fish in the Pacific Ocean. Evolution 56:378–399.
64. Dawson MN, Hamner WM (2005) Rapid evolutionary radiation of marine zooplankton in
peripheral environments. Proc Natl Acad Sci USA 102:9235–9240.
65. Crame JA (1993) Bipolar mollusks and their evolutionary implications. J Biogeogr 20:
145–161.
66. Savolainen V, et al. (2006) Sympatric speciation in palms on an oceanic island. Nature
441:210–213.
67. Coyne JA, Orr HA (2004) Speciation (Sinauer Associates, Sunderland, MA).
68. Dlugosch KM, Parker IM (2008) Founding events in species invasions: Genetic
variation, adaptive evolution, and the role of multiple introductions. Mol Ecol 17:
431–449.
69. Briggs JC (2003) Marine centres of origin as evolutionary engines. J Biogeogr 30:1–18.
70. Evans KM, Hayes PK (2004) Microsatellite markers for the cosmopolitan marine
diatom Pseudo-nitzschia pungens. Mol Ecol Notes 4:125–126.
71. Valiere N (2002) GIMLET: A computer program for analysing genetic individual
identification data. Mol Ecol Notes 2:377–379.
72. Waits LP, Luikart G, Taberlet P (2001) Estimating the probability of identity among
genotypes in natural populations: Cautions and guidelines. Mol Ecol 10:249–256.
73. Van Loon EE, Cleary DFR, Fauvelot C (2007) ARES: Software to compare allelic richness
between uneven samples. Mol Ecol Notes 7:579–582.
74. Rousset F (2008) GENEPOP’007: A complete re-implementation of the GENEPOP
software for Windows and Linux. Mol Ecol Resour 8:103–106.
75. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population
structure. Evolution 38:1358–1370.
76. Goudet J (2001) FSTAT, a program to estimate and test gene diversities and fixation
indices 2.9.3.
77. Meirmans PG (2006) Using the AMOVA framework to estimate a standardized
genetic differentiation measure. Evolution 60:2399–2402.
78. Peakall R, Smouse PE (2006) GENALEX 6: Genetic analysis in Excel. Population genetic
software for teaching and research. Mol Ecol Notes 6:288–295.
79. Pritchard JK, Wen X, Falush D (2009) STRUCTURE 2.2.
80. Jakobsson M, Rosenberg NA (2007) CLUMPP: A cluster matching and permutation
program for dealing with label switching and multimodality in analysis of population
structure. Bioinformatics 23:1801–1806.
81. Rosenberg NA (2004) DISTRUCT: A program for the graphical display of population
structure. Mol Ecol Notes 4:137–138.
82. Jensen JL, Bohonak AJ, Kelley ST (2005) Isolation by distance, web service. BMC Genet
6:13.
83. Slatkin M (1993) Isolation by distance in equilibrium and nonequilibrium populations.
Evolution 47:264–279.
84. Drummond AJ, Rambaut A (2007) BEAST: Bayesian evolutionary analysis by sampling
trees. BMC Evol Biol 7:214.
PNAS | July 20, 2010 | vol. 107 | no. 29 | 12957
EVOLUTION
24. Galarza JA, et al. (2009) The influence of oceanographic fronts and early-life-history
traits on connectivity among littoral fish species. Proc Natl Acad Sci USA 106:
1473–1478.
25. Iglesias-Rodriguez MD, et al. (2006) Intraspecific genetic diversity in the marine
coccolithophore Emiliania huxleyi (Prymnesiophyceae): The use of microsatellite
analysis in marine phytoplankton population studies. J Phycol 42:526–536.
26. Masseret E, et al. (2009) Unexpected genetic diversity among and within populations of
the toxic dinoflagellate Alexandrium catenella as revealed by nuclear microsatellite
markers. Appl Environ Microbiol 75:2037–2045.
27. McCauley LAR, et al. (2009) Biogeographic analysis of the globally distributed harmful
algal bloom species Alexandrium minutum (Dinophyceae) based on rRNA gene
sequences and microsatellite markers. J Phycol 45:454–463.
28. Nagai S, et al. (2009) Genetic structuring and transfer of marine dinoflagellate
Cochlodinium polykrikoides in Japanese and Korean coastal waters revealed by
microsatellites. Mol Ecol 18:2337–2352.
29. Kooistra WHCF, Gersonden R, Medlin LK, Mann DG (2007) The origin and evolution of
the diatoms: Their adaptation to a planktonic existence. Evolution of Planktonic
Photoautotrophs, eds Falkowski PG, Knoll AH (Academic Press, Burlington), pp
207–249.
30. Armbrust EV (2009) The life of diatoms in the world’s oceans. Nature 459:185–192.
31. Hasle GR (2002) Are most of the domoic acid-producing species of the diatom genus
Pseudo-nitzschia cosmopolites? Harmful Algae 1:137–146.
32. Trainer VL, et al. (1998) Domoic acid production by Pseudo-nitzschia pungens.
Harmful Algae, eds Reguera B, Blanco J, Fernández ML, Wyatt T (Xunta de Galicia and
the IOC of UNESCO, Paris), pp 337–340.
33. Marchetti A, et al. (2009) Ferritin is used for iron storage in bloom-forming marine
pennate diatoms. Nature 457:467–470.
34. Chepurnov VA, et al. (2005) Sexual reproduction, mating system, chloroplast
dynamics and abrupt cell size reduction in Pseudo-nitzschia pungens from the North
Sea (Bacillariophyta). Eur J Phycol 40:379–395.
35. Casteleyn G, et al. (2008) Pseudo-nitzschia pungens (Bacillariophyceae): A cosmopolitan
diatom species? Harmful Algae 7:241–257.
36. De Queiroz K (2007) Species concepts and species delimitation. Syst Biol 56:879–886.
37. Churro CI, et al. (2009) Diversity and abundance of potentially toxic Pseudo-nitzschia
Peragallo in Aveiro coastal lagoon, Portugal and description of a new variety, P.
pungens var. aveirensis var. nov. Diatom Res 24:35–62.
38. Adams NG, et al. (2009) Genetic population structure of Pseudo-nitzschia pungens
(Bacillariophyceae) from the Pacific Northwest and the North Sea. J Phycol 45:
1037–1045.
39. Casteleyn G, et al. (2009) Natural hybrids in the marine diatom Pseudo-nitzschia
pungens (Bacillariophyceae): Genetic and morphological evidence. Protist 160:
343–354.
40. Holtermann KE, et al. (2010) Mass sexual reproduction in the toxigenic diatoms
Pseudo-nitzschia australis and P. pungens (Bacillariophyceae) on the Washington
coast, USA. J Phycol 46:41–52.
41. Evans KM, Kuhn SF, Hayes PK (2005) High levels of genetic diversity and low levels of
genetic differentiation in North Sea Pseudo-nitzschia pungens (Bacillariophyceae)
populations. J Phycol 41:506–514.
42. Casteleyn G, et al. (2009) Lack of population genetic structuring in the marine
planktonic diatom Pseudo-nitzschia pungens (Bacillariophyceae) in a heterogeneous
area in the Southern Bight of the North Sea. Mar Biol 156:1149–1158.
43. Rynearson TA, Armbrust EV (2005) Maintenance of clonal diversity during a spring
bloom of the centric diatom Ditylum brightwellii. Mol Ecol 14:1631–1640.
44. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using
multilocus genotype data. Genetics 155:945–959.
45. Stoeck T, et al. (2009) Massively parallel tag sequencing reveals the complexity of
anaerobic marine protistan communities. BMC Biol 7:72.
46. Dawson MN, Sen Gupta A, England MH (2005) Coupled biophysical global ocean
model and molecular genetic analyses identify multiple introductions of cryptogenic
species. Proc Natl Acad Sci USA 102:11968–11973.
47. Bolch CJS, de Salas MF (2007) A review of the molecular evidence for ballast water
introduction of the toxic dinoflagellates Gymnodinium catenatum and the Alexandrium
“tamarensis complex” to Australasia. Harmful Algae 6:465–485.
48. Sorhannus U (2007) A nuclear-encoded small-subunit ribosomal RNA timescale for
diatom evolution. Mar Micropaleontol 65:1–12.
49. Marshall DJ, Monro K, Bode M, Keough MJ, Swearer S (2010) Phenotype-environment
mismatches reduce connectivity in the sea. Ecol Lett 13:128–140.
50. Nishida M, Lucas JS (1988) Genetic differences between geographic populations of
the Crown-of-thorns starfish throughout the Pacific region. Mar Biol 98:359–368.
51. Palumbi SR, Metz EC (1991) Strong reproductive isolation between closely related
tropical sea urchins (genus Echinometra). Mol Biol Evol 8:227–239.