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3200
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Molecular Ecology. 2023;32:3200–3219.wileyonlinelibrary.com/journal/mec
Received: 17 August 2022
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Revised: 30 January 2023
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Accepted: 16 March 2023
DOI : 10.1111/mec.16931
ORIGINAL ARTICLE
Genome- wide phylogeography reveals cryptic speciation in the
circumglobal planktonic calcifier Limacina bulimoides
L. Q. Choo1,2,3 | G. Spagliardi1 | M. Malinsky4,5 | M. Choquet3 | E. Goetze6 |
G. Hoarau3 | K. T. C. A. Peijnenburg1,2
This is an open access article under the terms of the Creative Commons Attribution License, which permit s use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2023 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.
1Plankton Diversity and Evolution,
Naturalis Biodiversity Center, Leiden, The
Netherlands
2Depar tment of Freshwater and Marine
Ecology, Institute for Biodiversity and
Ecosystem Dynamics, University of
Amsterdam, Amsterdam, The Netherlands
3Faculty of Biosciences and Aquaculture,
Nord Universit y, Bodø, Norway
4Institute of Ecology and Evolution,
University of Bern, Bern, Swit zerland
5Depar tment of Fish Ecology and
Evolution, EAWAG Swiss Federal Institute
of Aquatic Science and Technology,
Kastanienbaum, Switzerland
6Depar tment of Oceanography, Universit y
of Hawaiʻi at Mānoa, Honolulu, Hawaii,
USA
Correspondence
L. Q. Choo, School of Biosciences,
University of Sheffield, Sheffield, UK .
Emails: leqin.choo@naturalis.nl
K. T. C. A. Peijnenburg, Plankton Diversity
and Evolution, Naturalis Biodiversity
Center, Leiden, The Netherlands.
Email: K.T.C.A.Peijnenburg@uva.nl
Present address
M. Choquet, Natural History Museum,
University of Oslo, Oslo, Norway; and
Depar tment of Medical Biochemistry
and Microbiology, Uppsala Univer sity,
Uppsala, Sweden
Funding information
Nederlandse Organisatie voor
Wetenschappelijk Onderzoek, Grant/
Award Number: 016.161.351
Handling Editor: Michael M. Hansen
Abstract
Little is known about when and how planktonic species arise and persist in the open
ocean without apparent dispersal barriers. Pteropods are planktonic snails with thin
shells susceptible to dissolution that are used as bio- indicators of ocean acidifica-
tion. However, distinct evolutionary units respond to acidification differently, and
defining species boundaries is therefore crucial for predicting the impact of changing
ocean conditions. In this global population genomic study of the shelled pteropod
Limacina bulimoides, we combined genetic (759,000 single nucleotide polymorphisms)
and morphometric data from 161 individuals, revealing three major genetic lineages
(FST = 0.29– 0.41): an “Atlantic lineage” sampled across the Atlantic, an “Indo- Pacific
lineage” sampled in the North Pacific and Indian Ocean, and a “Pacific lineage” sam-
pled in the North and South Pacific. A time- calibrated phylogeny suggests that the
lineages diverged about 1 million years ago, with estimated effective population size
remaining high (~10 million) throughout Pleistocene glacial cycles. We do not observe
any signatures of recent hybridization, even in areas of sympatry in the North Pacific.
While the lineages are reproductively isolated, they are morphologically cryptic, with
overlapping shell shape and shell colour distributions. Despite showing that the cir-
cumglobal L. bulimoides consists of multiple species with smaller ranges than initially
thought, we found that these pteropods still possess high levels of genetic variabil-
ity. Our study adds to the growing evidence that speciation is often overlooked in
the open ocean, and suggests the presence of distinct biological species within many
other currently defined circumglobal planktonic species.
KEYWORDS
genome- wide SNPs, marine zooplankton, phylogeography, population genomics
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1 | INTRODUC TION
We still know little about how species arise and persist in the open
ocean, a seemingly interconnected habitat with few apparent bar-
riers to dispersal. Although allopatric speciation has been accepted
as the dominant mode of speciation (Bowen et al., 2016; Coyne &
Or r, 2004; Mayr, 1954; Norris, 2000), it has been well documented
that speciation can occur in the presence of high dispersal and
gene flow, including in marine systems (Bendif et al., 2019; Bierne
et al., 2003; De Vargas et al., 1999; Endler, 1977; Johannesson
et al., 2010; Potkamp & Fransen, 2019; Schluter, 2009). To gain
insight into the mechanisms of speciation in marine habitats, it
is important to understand the factors that facilitate or constrain
divergence, rather than categorizing case studies into allopatric,
parapatric or sympatric speciation (Butlin et al., 2008; Fitzpatrick
et al., 2009). While marine species have a wide variety of life his-
tory traits, dispersal potentials and extent of gene flow among pop-
ulations, holoplanktonic species typically represent an extreme end
of this scale with high fecundities, enormous population sizes, and
huge potential for dispersal and gene flow.
Many marine zooplankton species rank among the most abun-
dant multicellular eukaryotes in the world. With their unique life
history traits, such species present an interesting system to assess
the impact of large population size and high dispersal potential on
speciation and divergence (Bucklin et al., 2021; Faria et al., 2021).
The large effective population sizes in marine zooplankton can
have varying effects on evolution depending on the relative ef-
fects of selection and stochastic demographic events (Peijnenburg
& Goetze, 2013). There can be higher levels of genetic diversity for
selection to act upon and more effective selection due to the re-
duced effects of stochastic processes such as genetic drift (Barrio
et al., 2016; Peijnenburg & Goetze, 2013). However, the relationship
between adaptation and population size may not be straightforward
(Galtier, 2016), and the reduction in genetic drift slows down the
accumulation of genetic differentiation between (partially) isolated
populations, which might also have profound effects on species
divergence, for example under mutation- order speciation models
(Mani & Clarke, 1990; Schluter, 2009).
Species with widespread geographical distributions can have
slight morphological variation across their range, but this varia-
tion alone may be considered insufficient to delimit sibling species
(Fleminger & Hulsemann, 1987; Knowlton, 19 93). In other cases,
well- characterized morphological differences are deemed to be
the result of a highly variable morphospecies (e.g., Apolônio Silva
De Oliveira et al., 2017). Such practices have resulted in an under-
estimation of ecologically relevant divergence with conventional
morphology- based taxonomy in many cases (Bongaerts et al., 2021).
With the increased availability of powerful genetic tools,
there has been growing evidence for cryptic species complexes
in the open ocean, contrary to historical expectations that many
pelagic species would have circumglobal, panmictic populations
(Norris, 2000; van der Spoel & Heyman, 1983). The majority of
circumglobal planktonic species examined with genetic data have
been found to harbour cryptic diversity, with examples ranging
from diatoms (Casteleyn et al., 2010; Whittaker & Rynearson, 2017)
to copepods (Andrews et al., 2014; Cornils et al., 2017; Halbert
et al., 2013; Hirai et al., 2015) and other calcifying plankton, in-
cluding gastropods (Burridge et al., 2 019; Wall- Palmer et al., 2018),
foraminifers (Darling et al., 2004; De Vargas et al., 1999; Kucera
& Darling, 2002) and coccolithophores (Filatov et al., 2021; Sáez
et al., 2003). Therefore, an important step for understanding the
capacity of plankton to adapt to future environmental change is
to assess the spatial distribution of genetic variation and poten-
tial for gene flow across their species ranges (Bell, 2013; Harvey
et al., 2014; Manno et al., 2017; Munday et al., 2013; Poloczanska
et al., 2016; Sunday et al., 2 014). In addition, the use of an integra-
tive taxonomy framework (Burridge et al., 2019; Padial et al., 2010)
would be beneficial to identify any consistent morphological dif-
ferences between these lineages and support the current genetic
findings (McManus & Katz, 2009). Also, within pteropods, the
traditional use of shell shape to classify and identify species (Bé
& Gilmer, 1977; Lalli & Gilmer, 1989; van der Spoel et al., 1997),
while advantageous due to the traceable morphology of shells pre-
served in fossils (Janssen & Peijnenburg, 2017 ), has been shown to
be inadequate on it s own, following the identification of genetic ally
distinct cryptic species within several described morphospecies
(Hunt et al., 2010 ; Jennings et al., 2010). More recent case stud-
ies in pteropods have also successfully combined genetic data from
barcoding genes with geometric morphometrics of the shell shape
to assess species boundaries (e.g., Burridge et al., 2015; Burridge
et al., 2019; Choo et al., 2021; Shimizu et al., 2021).
Pteropods play important ecological and biogeochemical roles
globally (Buitenhuis et al., 2019; Fabry et al., 2009; Hunt et al., 2008;
Manno et al., 2010; Sulpis et al., 2021) and are commonly used as
bioindicators for the effects of ocean acidification on marine calci-
fiers (Bednaršek et al., 2016; Fabry et al., 2009; Manno et al., 2017).
However, little is known about their adaptive potential for future
environmental changes, including their levels of genetic diversity
and functional genomic variation. Pteropods possess thin arago-
nitic shells that are vulnerable to dissolution and more difficult
to produce in acidified waters, which are predicted for the future
(Busch et al., 2 014; Mekkes, Sepúlveda- Rodríguez, et al., 2021) and
also observed along contemporary gradients of ocean acidifica-
tion (Bednaršek et al., 2018; Manno et al., 2018; Mekkes, Renema,
et al., 2021; Niemi et al., 2021). Shelled pteropods feed on phyto-
plankton and particulate matter, including bacteria and small protists,
by trapping them in external mucous webs and ingesting the webs
(Conley et al., 2018; Hunt et al., 2008; Lalli & Gilmer, 1989). Shelled
pteropods also constitute a significant part of the diet of their un-
shelled relatives (gymnosomes), salmon and other predators from
higher trophic levels in the pelagic food web (Groot & Margolis, 1991;
Hunt et al., 2008; Lalli & Gilmer, 1989). Like other shelled pteropods,
species of the genus Limacina are protandrous hermaphrodites, de-
velop ing from lar vae into male s firs t, befo re tra nsi t ion ing into fema les
that lay free- floating egg strings from which free- swimming larvae
hatch (Lalli & Wells, 1978). Limacina bulimoides is the most common
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CHOO e t al.
warm- water Limacina species worldwide, and is found in all tropical
and subtropical oceans from ~45° N to ~40° S (Bé & Gilmer, 1977 ).
They inhabit depths of up to 200 m and exhibit diel vertical migration,
where they are abundant in the upper 100 m at night, and retreat to
deeper waters during the day (Wormuth, 1981). This species is esti-
mated to complete its life cycle in about 1 year (Wells, 1976).
Our prior work on L. bulimoides was limited to a subset of the
species' range in the Atlantic Ocean and used two barcoding genes
(Choo et al., 2021). Here we investigate patterns and evolution-
ary origins of divergence across the entire species' range and with
hundreds of thousands of genome- wide single nucleotide polymor-
phisms (SNPs) that derive from the target capture loci characterized
in Choo et al. (2020). We also measured phenotypic differences and
examined these within the geographical and genetic context of L.
bulimoides, and explored the historical origins of speciation in the
tropical and subtropical ocean. Overall, the work presented here
represents the first global population genomics study of a marine
zooplankton species, revealing the evolutionary history and extent
of reproductive isolation among multiple independently evolving lin-
eages that are morphologically cryptic.
2 | MATERIALS AND METHODS
2.1 | Sample collection
Bulk plankton samples were collected in the Atlantic, Pacific and
Indian Oceans during several cruises ( Table 1). Specimens were col-
lected using either oblique or vertical tows with ring nets or bongo
nets (mesh size 200– 505 μm). Tows were conducted for 20 min to
1 h, at approximate maximum depths ranging between 60 and 459 m.
Samples were immediately preserved in 96% ethanol and stored at
−20°C. The ethanol was replaced after 24 h of preservation. Limacina
bulimoides specimens were subsequently sorted in the laboratory
and photographed in a standard orientation prior to destructive ge-
netic work. Thus, the same set of individuals was used to obtain both
genetic and phenotypic data (Table S1).
2.2 | Library preparation and sequencing
Specimens were prepared for sequencing as described in Choo
et al. (2020). Briefly, genomic DNA was extracted from each indi-
vidual with either the E.Z.N.A mollusc or insect DNA extraction kit
(Omega Bio- Tek). The DNA was sheared by sonication to attain a
peak length of 300 bp with a Covaris S220 or ME220 focused ultra-
sonicator. After sonication, the fragmented DNA was prepared into
individual libraries using the NEXTflex Rapid Pre- Capture Combo Kit
(Bioo Scientific). Individually barcoded libraries were subsequently
pooled at equimolar concentrations with 26– 27 libraries per pool.
The target capture reaction was then performed on each pool using
the myBaits Custom Target Capture kit (Arbor Biosciences), with
a capture probe set specifically designed for L. bulimoides, as de-
scribed in Choo et al. (2020). The baits target 2890 nuclear regions
as well as the mitochondrial cytochrome c oxidase subunit I (COI)
and nine other mitochondrial gene regions. To maximize the speci-
ficity of the capture reaction, the hybridization time of the probes
with the libraries was extended to 3 days, and the capture was
TAB LE 1 Collection details of specimens of Limacina bulimoides from the global ocean.
Cruise and station Latitude Longitude
Date
collected Ocean basin Max. Depth (m) Target capture Morphometrics
NIC 2_S1C 3 21°16′ N 21°01′ W 31/12/2017 N. Atlantic 60 11 11
NIC8_ S5C3 38°45′ N 56°18′ W 14/4/2018 N. Atlantic 120 11 11
AMT24_17 7°28′ S 25°07′ W 15/10/2014 Eq. Atlantic 266 11 11
NIC2_S9C3 5°58′ N 47°50′ W 13/1/2018 Eq. Atlantic 80 10 10
AMT24_22 24°27′ S 25°03′ W 21 /10/2 014 S. Atlantic 323 10 10
AMT24_23 27°46′ S 25°01′ W 22/10/2014 S. Atlantic 260 10 10
SN105_08 4°23′ E 67°00′ E 10/12/2015 Indian 67 11 10
KOK1703_03 22°39′ N 157°41′ W 3/7/2017 N. Pacific 200 10 10
KH1110_02 23°00′ N 160°00′ E 7/12 / 2 011 N. Pacific 370 11 11
KH1110_05 23°00′ N 179°59′ E 14/1 2/2 011 N. Pacific n.a. 11 11
KH1110_08 22°47′ N 158°06′ W 19/12 /20 11 N. Pacific 411 11 10
KH1110_15 23°00′ S 119°15′ W 8/1/2012 S. Pacific 396 11 11
KH1110_18 29°59′ S 107°00′ W 13/1/2012 S. Pacific 459 11 11
KH1110_21 23°00′ S 100°0 0′ W 18/1/2012 S. Pacific 376 11 11
SO255_143 32°52′ S 179°46′ W 3/4/2017 S. Pacific 87 11 11
Tot a l 161 159
Note: Number of specimens per sampling location used in target capture and geometric morphometric analyses are indicated. The collection dates
are in the format DD/MM/YY YY. See also map in Figure 1a.
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CHOO et al.
performed twice, with 4 and 1.5 μL of probe mix, respectively (Choo
et al., 2020). Captured library pools were sequenced in paired- end
mode on the Illumina NextSeq 500 platform using three high- output
v2 chips (150 cycles).
2.3 | Alignment and variant calling
Raw sequences (a mean of 10,122,331 raw reads per individual;
GenBank accessions: SAMN11131477- 79, SAMN11131480- 82,
SAMN20293115- 269) were demultiplexed and then mapped with
bwa mem 0.7.12 (Li, 2013) to a re du ce d co nt i g se t of th e ge no mi c as se m-
bly from Choo et al. (2020) (GenBank accession: SWLX00000000).
The reduced genomic assembly contained only the relevant contigs
(i.e., those contigs that were used for the capture probe design).
The resulting alignments were cleaned and filtered with samto ols
version 1.4.1 (Li et al., 2009) to retain only properly and uniquely
mapped pairs (excluding SAM flag 3332 and reads with XA and SA
tags; including SAM flag 3). On average, 3,961,477 reads (39.1%) per
individual were then available for variant calling, 94.4% of which
were mapped within the target capture regions (see Table S1 for
per- individual statistics). Duplicates were marked and removed with
picard version 2.18.5 (http://broad insti tute.github.io/picard). Variant
calling was done using gatk 4.1.7.0, following the Variant Discovery
Pipeline (Auwera et al., 2013; Depristo et al., 2011) with gnu para llel
utility (Tange, 2011). Variants were first called per- individual using
HaplotypeCaller and the resulting gVCF files were combined with
CombineGVCFs. The combined gVCF file was then genotyped jointly
across all samples using the GenotypeGVCFs tool of gatk. Finally,
SNPs were extracted from the total variants using SelectVariants
(– SelectType SNP). The total number of SNPs obtained for all 161
individuals was 1,790,328.
2.4 | Quality filtering
SNPs were hard- filtered with VariantFiltration using QualByDepth
(QD) <2.0, FisherStrand (FS) >40.0, StandOddsRatio (SOR) >3.0,
MappingQuality (MQ) <40, MQRankSumTest (MQRankSum) <−5.0,
MQRankSumTest (MQRankSum) >5.0, and ReadPositionRankSum
(ReadPosRankSum) <−4.0, INFO/DP >100,000, guided by the best-
practice suggestions on the gatk websi te, wh ic h re duce d the number
of SNPs only slightly to 1,629,382. The distribution of these SNPs
across the different types of target capture probes included in our
data set is shown in Table S3.
For the majority of analyses, SNPs were further filtered in
bcftools version 1.7 (Danecek et al., 2021) to retain only SNPs with
Phred quality score >20 and with genotype calls based on at least
three reads present in at least 80% of the individuals. This resulted in
759,613 nuclear SNPs ready for further analyses. A summary of the
data processing steps, setting and data sets used for each analysis in
this study can be found in Figure S1.
2.5 | Population structure
For all population structure analyses, we excluded rare alleles.
Specifically, SNPs with minor allele frequency (MAF) of <1% were
FIGURE 1 (a) Geographical locations of the 15 sampling sites for Limacina bulimoides, coloured according to ocean basin: Atlantic, Indian,
North (N.) and South (S.) Pacific Ocean (see key). (b) Principal component analysis (PCA) plot based on 107,214 nuclear SNPs from 161
individuals, illustrating the global genetic structure within this species. The three main genetic lineages are indicated with coloured circles:
Atlantic (blue), Indo- Pacific (green), Pacific (orange). (c) An admixture plot showing three genetic clusters (K = 3, which had the lowest cross-
validation error). Each line represents one individual, while colour represents admixture proportion corresponding to each genetic cluster
(Atlantic lineage in blue, Indo- Pacific lineage in green and Pacific lineage in orange). The fine white lines represent boundaries between the
different sampling stations, while the thick black lines represent the different ocean basins.
−50
0
50
−100 0 100
Longitude
Latitude
(a)
−0.10
−0.05
0.00
0.05
0.10
−0.050.000.050.10
PC1 (23%)
PC2 (16.7%)
Ocean basin
Atlantic
Indian
N. Pacific
S. Pacific
(b)
0.00
0.25
0.50
0.75
1.00
AtlanticcificaP.NnaidnIS. Pacific
(c)
Indo-Pacific lineage
Atlantic lineage
Pacific lineage
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CHOO e t al.
removed. This resulted in a data set containing 235,290 nuclear
SNPs. We conducted a principal component analysis (PCA) in plink
version 1.90b6.17 (Chang et al., 2015) and population clustering
using the admixture soft ware (Alexander et al., 2009). We used the
in- built cross- validation procedure in admixture to estimate an ap-
propriate value of K with the lowest cross- validation error among
the values of K tested, between 2 and 8. Higher values of K we re
not tested as values of K between 3 and 8 showed an increasing
trend (Table S4). This was also supported by the admixture plots
which became increasingly noisy for the higher values of K (7 and
8) (Figure S3). For these two analyses, we used a subset of inde-
pendent SNPs, with linkage pruning settings of window size of 50
genetic variants, window shift of 10 variant counts and r2 of .2
in plink version 1.90b6.17 (Chang et al., 2015), which resulted in
107,214 SNPs.
To explore population structure in finer detail, we also calculated
nearest neighbour haplotype co- ancestry across all individuals with
fineradstructure version 0.3.2 (Malinsky et al., 2018). We used the
hapsFromVCF function of radpainter to convert all biallelic SNPs
(96,355 nuclear SNPs; no linkage disequilibrium [LD] pruning) into
an input format where SNPs from each of the 2609 contigs are as-
signed together into haplotypes. Because fineradstructure is sensi-
tive to variation in missing data proportion among individuals, we
controlled for this in the following way. First, for each individual,
we assessed the proportion of missing SNPs within each locus (i.e.,
contig). Where this proportion was more than 50%, the entire locus
was considered “missing” in that individual. Second, we excluded
five individuals that had an unusually high proportion (>4%) of such
missing loci. For the remaining 156 individuals, we used the paint
function of radpainter to calculate a co- ancestry matrix, which sum-
marizes the nearest neighbour haplotype relationships in the data
set. Next, a clustering dendrogram of shared ancestry was inferred
from the co- ancestry matrix using the finestructure Markov chain
Monte Carlo (MCMC) clustering algorithm, with 100,000 burn- in
iterations, 100,000 sample iterations and thinning of 1000. The in-
ferred clusters were arranged with a simple tree building algorithm
in finestructure with 10,000 hill- climbing iterations.
For the calculation of FST we us ed the We i r an d Co c kerham (1984)
estimator, implemented in vcftools version 0.1.15 (Danecek
et al., 2011) via the - - weir- fst- pop option. We started from the same
MAF- filtered data set as for PCA and admixture and, to avoid bias due
to genetic linkage of some loci, these estimates were obtained with
thinned SNP data sets, where one SNP was randomly chosen per
contig and FST was calculated for each thinning iteration, for 1000
iterations (achieved using a custom script from Choquet et al., 2019).
The resulting distributions for each lineage and pairwise compari-
sons were plotted in r version 4.0.3 (R Core Team, 2 017).
Genetic signals from the targeted mitochondrial COI fragments
were separately analysed in a haplotype network after mapping and
de novo assembly in geneious prime 20 21.1.1 (https://www.genei
ous.com). First, all raw Illumina NextSeq reads of each of the 161
individuals were mapped to a reference database containing 355
unique L. bulimoides COI sequences of 564 bp sequenced from the
Atlantic and Pacific Ocean (NCBI: MN952611- MN952965), using
the geneious assembler with medium– low sensitivity. The mapped
reads were extracted and de novo assembled per individual using
the geneious assembler with medium sensitivity. The resulting con-
tigs were annotated for the COI region, and the COI annotation
(564 bp) with the highest coverage for each individual was translated
to check that there were no stop codons or frameshift mutations
before being extracted. The 161 COI annotations (Genbank acces-
sions: MZ542566– MZ542726) were combined with one represen-
tative from each known haplogroup from Choo et al. (2021): the
Atlantic haplogroups 1 and 2, and the Pacific haplogroup (GenBank
accessions: MN952611, MN952937, MN952944), and used to create
a multiple sequence alignment using mafft version 7.222 (Katoh &
Standley, 2013). A minimum spanning network was calculated from
the alignment and visualized in popart (Leigh & Bryant, 2015).
2.6 | Genetic diversity
To estimate genetic diversity within the major lineages, we calcu-
lated heterozygosity (average proportion of heterozygous sites in a
sequence), nucleotide diversity (
𝜋
) and inbreeding coefficient within
the target capture probe regions for nuclear contigs. This was based
on the hard- filtered SNP data set (1,629,382 SNPs; see Figure S1)
divided into three VCFs, one for each major genetic lineage. We used
the command RegionsPiGeneral from the evo toolkit (https://github.
com/milla nek/evo; v.0.1 r27) to perform the calculations, specify-
ing the location of the target capture probe regions on each contig
by supplying a BED- format file. We excluded results for loci where
the target capture was not entirely successful, which we defined as
<80% of the probe having <15× average coverage per individual, in
line with Choo et al. (2020), and removed probes mapping to known
mitochondrial contigs. For the remaining nuclear probes (2265
probes; 78% of the total) both heterozygosity and
𝜋
are based on
dividing the number of differences between/among sequences by
the length of each probe region. Using the same set of 2265 capture
probe sequences, we also calculated per- individual estimates of the
inbreeding coefficient F using vcftools 0.1.15 (Danecek et al., 20 11)
with the setting - - het for the lineage- specific VCFs. Distributions
of heterozygosity and nucleotide diversity for each genetic lineage
were plotted in r version 4.0.3 (R Core Team, 2017).
2.7 | Phylogenetic and demographic analyses
To infer a time- calibrated Bayesian tree, we generated a new SNP
data set, including the sister species L. trochiformis. SNP calling was
performed with all 161 L. bulimoides individuals and 11 L. trochi-
formis individuals (NCBI: SAMN111131501– 09, SAMN20293270,
SAMN20293271 from Choo et al., 2020). The L. trochiformis indi-
viduals were included to root the phylogeny and calibrate the age of
divergence. SNP calling was followed by quality filtering using the
previously mentioned settings to obtain a data set of biallelic nuclear
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CHOO et al.
SNPs. Since coalescent analyses do not require a large number of
individuals (Felsenstein, 2006), to reduce computational effort, we
randomly selec ted two L. trochiformis individuals and 10 individu-
als from each of the L. bulimoides genetic lineages to include in our
analyses. SNPs corresponding to these 32 selected individuals were
refiltered to retain only biallelic nuclear SNPs. This resulting SNP
data set was thinned with vcftools 0.1.15 (Danecek et al., 2011) to
select one SNP every 100 bp to reduce linkage between the SNPs,
resulting in a final data set of 19,669 thinned, biallelic nuclear SNPs.
Finally, we selected SNPs that were shared across all four taxa, in-
cluding the outgroup, resulting in 1279 SNPs suitable for the snapp
analysis described below.
A snapp (Bryant et al., 2012) species tree was inferred in beast2
version 2.6.2 (Bouckaer t et al., 2019), using the approach demon-
stra te d in Stan ge et al. (2018). We us ed a custom ruby script (h t tps://
github.com/mmats chine r/snapp_prep/blob/maste r/snapp_prep.rb),
with a strict clock substitution rate calibrated with an a priori age
constraint of divergence between L. trochiformis and L. bulimoides
(Peijnenburg et al., 2020). The script was modified to allow for
unlinked population sizes between the three lineages. The diver-
gence time between L. trochiformis and L. bulimoides was estimated
at 13.57 million years ago (95% confidence interval [CI] = 9.2804–
20.9182 million years ago), which was approximated to a lognormal
distribution of (0, 13.57, 0.2), with a 95% CI of 8.99– 19.7. The gener-
ation time of L. bulimoides was assumed to be 1 year (Wells, 1976) in
order to calibrate the estimated effective population size. We used
tracer (Rambaut et al., 2018) to observe that all effective sample size
(ESS) parameters converged to stationarity with values above 200
after 1,500,000 MCMC iterations, for three independent trials. The
maximum clade credibility trees with mean heights were produced
in treeannotator version 2.6.0 (Bouckaert et al., 2014). The effective
population size (Ne) and their 95% highest posterior density (HPD)
confidence intervals for each of the three genetic lineages were
calculated with θ from the snapp analysis, according to the follow-
ing formula: Ne = θ/4 μ, where the mutation rate μ was given as the
log_clock_rate divided by 1 × 106 with a generation time of 1 year.
To assess support for the lineages as different species, we ran snapp
together with bfd* (Leaché et al., 2014), an approach for Bayes fac-
tor delimitation (BFD), for the following models of species delimi-
tation: all lineages being separate species, Indo- Pacific and Pacific
lumped as one species, and all three lineages as one species. We
ran the path- sampling for 48 steps, with chain length of 100,000
and pre- burnin of 10,000. Support for the different models was as-
sessed using marginal likelihood estimates and Bayes factors (Kass
& Rafter y, 1995).
To estimate levels of gene flow between the Atlantic and other
two lineages, we ran an ABBA– BABA test for all 161 individuals from
the three lineages of L. bulimoides and 11 individuals from their sis-
ter species L. trochiformis, which we used as an outgroup. We used
the SNP data set that was prepared for the snapp analysis above, but
retained all 172 individuals and did not thin the SNPs. Therefore,
a total of 383,675 biallelic SNPs were used as input for the analy-
sis in dsuite version 0.5 r44 (Malinsky et al., 2021) using the Dtrios
command with the tree topology from the previously inferred snapp
phylogeny.
Demographic changes for each lineage were reconstructed using
stairway plot version 2.1.1 (Liu & Fu, 2020). We used biallelic SNPs
from nuclear probes that passed the coverage requirement for the
genetic diversity estimates (2265 probes; 78% of the total), with a
total of 1,448,243 sites (polymorphic + fixed) across these probes.
Using vcftools 0.1.15 (Danecek et al., 20 11), we removed sites that
showed a heterozygosity excess across all individuals (P_HET_
EXCESS <0.01) as it could be indicative of poor mapping. The result-
ing VCF file was used to calculate the site frequency spectrum (SFS)
for each lineage with easysfs (https://github.com/isaac overc ast/
easySFS). For each SFS, 200 subsampling iterations were conducted,
as in the default recommendations. Population size estimates were
truncated at 1000 years as there were insufficient samples to recon-
struct demographic changes in more recent time. To put the popula-
tion size estimates in the context of climatic changes, we compared
the timing of demographic changes with the Marine Isotope Stages
(MIS) derived from Lisiecki and Raymo (2005).
The mutation rate estimated by snapp is based only on the vari-
able sites provided to this software and, therefore, is not meaningful
outside of the snapp analysis. To obtain a mutation rate estimate suit-
able for use in the stairway plot anal ysis ab ove, we used th e same da t a
set with 2265 probes, and calculated “net sequence divergence” (i.e.,
da of Cruickshank & Hahn, 2014), between the lineages: Atlantic vs.
Pacific (da = 0.005352), Atlantic vs. IndoPacific (da = 0.007948) and
IndoPacific vs. Pacific (da = 0.004463). Dividing these values by the
snapp- estimated divergence times between these pairs of lineages,
and, as elsewhere, assuming 1 year per generation, we obtained the
estimate μ = 5.22 × 10−9.
The mode of speciation between the three lineages was inferred
through testing various demographic scenarios with fastsimcoal2
(fsc27093) (Excoffier et al., 2013; Excoffier et al., 2021). We used
the biallelic nuclear SNP data set described earlier for the stairway
plot analysis and generated a multidimensional SFS file comprising
all three lineages using easysfs. With the phylogeny and mean di-
vergence times derived from the snapp analysis, and the previously
estimated mutation rate of 5.22 × 10−9, we evaluated the following
evolutionary models: (i) no gene flow, (ii) constant gene flow be-
tween all lineages, (iii) recent gene flow between the Indo- Pacific
and Pacific lineages, to test for a secondary contact model, and (iv)
ancient gene flow between the Indo- Pacific and Pacific lineages, to
test for a sympatric/parapatric model of evolution. The start of gene
flow was set to 100 thousand years from the present for the recent
gene flow model, and 100 thousand years after lineages split for the
parapatric model, while the amount of gene flow was allowed to
vary. All gene flow was assumed to be symmetrical. Each model was
run 100 times to infer the most likely parameters for effective pop-
ulation size and amount of gene flow. We then assessed the models
based on the likelihood distribution from the 100 runs, as well as the
Akaike Information Criterion (AIC), which takes into account model
complexity, using the script https://github.com/speci ation genom ics/
scrip ts/blob/maste r/calcu lateA IC.sh.
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2.8 | Shell morphology
All 161 L. bulimoides individuals used for genetic analyses were
photographed in a standardized apertural orientation using a Zeiss
V20 stacking stereomicroscope with Axiovision software prior to
destructive DNA extraction. The shell images were digitized at 11
(semi- )landmarks (Figure S2) using tpsutil and tpsdig (Rohlf, 2015). In
total, 159 of the 161 L. bulimoides individuals had the complete set
of landmarks and could be included in the geometric morphomet-
ric analysis. Coordinates of the (semi- )landmarks were analysed in
tpsrelw (Rohlf, 2015), using a generalized least- squares Procrustes
superimposition (Rohlf & Slice, 1990; Zelditch et al., 2004).
A repeatability analysis was conducted with a subset of 30 in-
dividuals, comprising two individuals from each of the 15 locations.
Images of these individuals were landmarked at the 11 (semi- )land-
marks by two independent researchers. Centroid size and relative
warp (RW) scores between the pairs of images per specimen after
Procrustes fitting were compared using intraclass coefficient (ICC) in
past3.0 (Hammer et al., 2001), and ICC values >0.75 were considered
sufficiently repeatable.
We tested for significant variation in shell shape across genetic
lineages with a nonparametric permutational multivariate analy-
sis of variance (one- way PERMANOVA) using Euclidean distances
and 9999 permutations with the vegan package in r (Oksanen
et al., 2019). Only the six repeatable RWs were used in the one-
way PERMANOVA. The first two RW axes were plotted to visualize
shell shape variation for different genetic lineages of L. bulimoides.
Additionally, a canonical variates analysis (CVA) was conducted in r
(R Core Team, 2017) to discriminate shell morphometric differences
between genetic lineages. A one- way ANOVA with a post hoc Tukey
HSD test was also conducted in r to test if the means of the canon-
ical variate for each genetic lineage were different from the other
groups. We also examined the effect of sampling location on shell
shape, by conducting the above analyses with the four ocean basins
(Atlantic, Indian, N. Pacific and S. Pacific) instead of the three genetic
lineages.
We also assessed shell colour variation by qualitatively scoring
aperture colour as either transparent, pink, tan or red- brown. We
chose to score the colour of the aperture as it was not obscured
by the tissue, which could interfere with colour identification. Also,
the shell was thicker at the aperture, making it easier and more re-
liable to observe the colour. Scoring was done by two independent
researchers to ensure repeatability of the colour scores. We tested
then whether aperture colour was randomly distributed across loca-
tion using a Fisher's exact test of independence.
3 | RESULTS
3.1 | Global genetic structure
Overall, we observed three highly divergent genetic lineages that
exhibited no evidence of recent admixture, namely the Atlantic,
Indo- Pacific and Pacific lineages (Figure 1). The Atlantic lineage
comprised all 63 individuals sampled in the Atlantic basin, the Indo-
Pacific lineage comprised 38 individuals sampled from the North
Pacific and the Indian Ocean site, and the Pacific lineage comprised
60 individuals sampled from both the North and South Pacific. The
Indo- Pacific and Pacific lineages were sympatric at three sites in
the North Pacific where they appeared equally common; across the
three sites we sampled 16 individuals representing the Indo- Pacific
lineage and 16 individuals representing the Pacific lineage (Table S2).
The PCA plot revealed three genetic clusters with no intermedi-
ates between them (Figure 1b). The first two principal component
(PC) axes comprised 39.7% of the total genetic variation; PC1 (23%)
shows the Atlantic as a clearly distinct lineage, whereas PC2 (16.7%)
separates all three lineages. Congruently, three genetic clusters were
the best supported by the admixture analysis (Figure 1c), as K = 3 gave
the lowest cross- validation error of 0.232.
The same three genetic clusters were also recovered from the
nearest neighbour haplotype co- ancestry matrix generated by
fineRADstructure (Figure 2). In addition, we saw that the Atlantic
lineage comprised three subclusters corresponding geographically
to North, Equatorial and South Atlantic sampling sites (Table 1),
consistent with previous findings based on barcoding genes (Choo
et al., 2021). Within the Indo- Pacific and Pacific lineages, there were
also smaller subsets of individuals with relatively higher shared co-
ancestry consistent with geographical clustering. These subclusters
corresponded to individuals from the Indian Ocean site within the
Indo- Pacific lineage, and individuals from North Pacific sites within
the Pacific lineage (Figure 2).
Pairwise FST comparisons indicate substantial allele frequency
divergence among the major lineages. Interestingly, mean FST was
highest between the Atlantic and Indo- Pacific lineages (FST = 0.410),
while the mean FST values between Indo- Pacific and Pacific lineages
(FST = 0.300), and the Atlantic and Pacific lineages (FST = 0.293) were
similar (Figure 3a). Consistent with the fineRADstructure results,
within- lineage mean FST values also indicate genetic substruc-
ture within all major lineages, the most within the Atlantic lineage
(FST = 0.127), but also within the Indo- Pacific (FST = 0.0321) and
Pacific (FST = 0.0429) (Figure 3a). All three within- lineage FST values
were significantly greater than zero (p < 2.2 × 10−16 , one- tailed t
test). We also calculated the inbreeding coefficient FIS for each indi-
vidual, which was on average slightly positive in all lineages: 7.8% in
Atlantic (p < 2.2 × 10−1 6; one- tailed t test), 2.9% in Pacific (p = .005),
and 1.5% Indo- Pacific (p = .12). These slightly positive FIS values are
consistent with the within- lineage substructuring reported above.
At the same time, the fact that the FIS valu es ar e onl y sligh tly positi ve
indicates that outcrossing appears to be the rule in natural popula-
tions, despite Limacina bulimoides being hermaphrodites.
The mitochondrial COI haplotype network (Figure S4) showed
that the COI sequences were unique across all individuals, with the
exception of one shared sequence between two individuals from
the Atlantic Ocean. Specimens of the Atlantic lineage could be dis-
tinguished readily from the Indo- Pacific and Pacific lineages, with
at least 62 nucleotide substitutions. Congruent with our previous
|
3207
CHOO et al.
study using barcoding genes (Choo et al., 2021), specimens from
the North and Equatorial Atlantic (stations NIC2_S1C3, NIC2_S9C3,
NIC8_S5C3 and AMT24_17) could be separated from specimens
from the South Atlantic (stations AMT24_22 and AMT24_23) by
17 nucleotide substitutions. However, there are exceptions, namely
five individuals that were sampled in the South Atlantic grouped
with the North and Equatorial haplotype cluster in the mitochon-
drial network. Interestingly, these individuals appeared on the
periphery of the haplotype cluster comprising specimens from a
different sampling location (Figure S4) but grouped according to
their geographical sampling location using the nuclear SNP data
set (Figure 2). This indicates that these individuals are not recent
migrants or expatriates, transported across dispersal barriers in the
Atlantic Ocean, but could represent examples of introgression of
mitochondrial DNA. The haploid mitochondrial genome is expected
to sort faster, with shorter coalescence times as compared to the
nuclear genome, so this incongruence is likely to be introgression
rather than incomplete lineage sorting. The Indo- Pacific and Pacific
lineages could not be clearly distinguished in the mitochondrial COI
network. We obser ve a central cluster of individuals belonging to
the Pacific lineage, composed mostly of individuals sampled from
the Sou th Pa cif ic. From this cen tral clus ter, ther e are diff ere nt ha plo -
type groups consisting of individuals belonging to either the Pacific
or the Indo- Pacific lineage.
FIGURE 2 Co- ancestry matrix for 156 individuals, coloured according to the number of loci (contigs) at which two individuals are each
other's closest relatives (see key: black/blue colours indicate greater relatedness while yellow indicates lower relatedness). The three main
genetic lineages (Atlantic, Indo- Pacific and Pacific) can be identified, along with finer scale structure within each lineage. The Atlantic lineage
(blue) can be further subdivided into three geographical regions with higher co- ancestry: North, Equatorial and South Atlantic. Within the
Indo- Pacific lineage (green), there is higher co- ancestry within the Indian Ocean locality compared to the North Pacific sampling sites.
Within the Pacific lineage (orange), the North Pacific individuals have a higher co- ancestry than with the South Pacific lineages.
Lbul_KH1110_15_05
Lbul_KH1110_15_12
Lbul_KH1110_21_14
Lbul_KH1110_18_09
Lbul_KH1110_18_03
Lbul_KH1110_18_10
Lbul_KH1110_18_08
Lbul_KH1110_15_03
Lbul_KH1110_15_06
Lbul_KH1110_18_12
Lbul_KH1110_21_04
Lbul_KH1110_15_14
Lbul_KH1110_18_15
Lbul_KH1110_21_08
Lbul_SO255_143_07
Lbul_KH1110_21_15
Lbul_SO255_143_02
Lbul_SO255_143_15
Lbul_SO255_143_08
Lbul_KH1110_15_15
Lbul_KH1110_15_01
Lbul_KH1110_18_11
Lbul_KH1110_15_08
Lbul_KH1110_15_13
Lbul_KH1110_21_10
Lbul_KH1110_18_13
Lbul_KH1110_18_04
Lbul_KH1110_21_01
Lbul_KH1110_21_05
Lbul_KH1110_21_13
Lbul_SO255_143_10
Lbul_SO255_143_06
Lbul_SO255_143_12
Lbul_SO255_143_13
Lbul_SO255_143_14
Lbul_KH1110_02_05
Lbul_KH1110_02_11
Lbul_KH1110_02_06
Lbul_KH1110_02_03
Lbul_KH1110_02_14
Lbul_KH1110_05_09
Lbul_KH1110_05_11
Lbul_KH1110_02_07
Lbul_KH1110_02_12
Lbul_KOK1703_03_05
Lbul_KOK1703_03_07
Lbul_KOK1703_03_06
Lbul_KOK1703_03_11
Lbul_KOK1703_03_14
Lbul_SO255_143_05
Lbul_SO255_143_09
Lbul_KH1110_18_07
Lbul_KH1110_21_09
Lbul_KH1110_21_11
Lbul_KH1110_15_07
Lbul_KH1110_18_14
Lbul_KH1110_15_09
Lbul_KH1110_21_07
Lbul_KH1110_02_13
Lbul_KH1110_05_02
Lbul_KH1110_05_08
Lbul_SN105_08_11
Lbul_SN105_08_12
Lbul_SN105_08_01
Lbul_SN105_08_06
Lbul_SN105_08_07
Lbul_SN105_08_08
Lbul_SN105_08_02
Lbul_SN105_08_04
Lbul_SN105_08_10
Lbul_KH1110_02_10
Lbul_KH1110_05_06
Lbul_KH1110_05_10
Lbul_KH1110_08_05
Lbul_KH1110_08_10
Lbul_KH1110_08_15
Lbul_SN105_08_03
Lbul_KH1110_08_08
Lbul_KH1110_02_09
Lbul_KH1110_05_04
Lbul_KH1110_05_07
Lbul_KH1110_05_12
Lbul_KH1110_08_02
Lbul_KOK1703_03_08
Lbul_KH1110_08_09
Lbul_KH1110_05_05
Lbul_KOK1703_03_10
Lbul_KH1110_08_13
Lbul_KH1110_08_04
Lbul_KH1110_08_14
Lbul_KH1110_08_03
Lbul_KOK1703_03_13
Lbul_KOK1703_03_15
Lbul_NIC2_S9C3_02
Lbul_NIC2_S9C3_08
Lbul_NIC2_S9C3_03
Lbul_NIC2_S9C3_04
Lbul_NIC2_S9C3_01
Lbul_NIC2_S9C3_05
Lbul_AMT24_17_12
Lbul_NIC2_S9C3_06
Lbul_AMT24_17_07
Lbul_AMT24_17_14
Lbul_AMT24_17_13
Lbul_AMT24_17_03
Lbul_AMT24_17_06
Lbul_AMT24_17_08
Lbul_AMT24_17_05
Lbul_AMT24_17_09
Lbul_AMT24_17_10
Lbul_AMT24_17_15
Lbul_NIC2_S9C3_09
Lbul_NIC2_S9C3_07
Lbul_NIC2_S9C3_10
Lbul_AMT24_22_08
Lbul_AMT24_22_06
Lbul_AMT24_22_11
Lbul_AMT24_22_12
Lbul_AMT24_23_02
Lbul_AMT24_23_06
Lbul_AMT24_22_13
Lbul_AMT24_22_04
Lbul_AMT24_22_07
Lbul_AMT24_22_09
Lbul_AMT24_22_14
Lbul_AMT24_22_15
Lbul_AMT24_23_07
Lbul_AMT24_23_11
Lbul_AMT24_23_10
Lbul_AMT24_23_09
Lbul_AMT24_23_08
Lbul_AMT24_23_05
Lbul_AMT24_23_12
Lbul_AMT24_23_13
Lbul_NIC2_S1C3_08
Lbul_NIC2_S1C3_01
Lbul_NIC2_S1C3_02
Lbul_NIC2_S1C3_10
Lbul_NIC8_S5C3_06
Lbul_NIC8_S5C3_04
Lbul_NIC2_S1C3_15
Lbul_NIC8_S5C3_07
Lbul_NIC8_S5C3_12
Lbul_NIC8_S5C3_08
Lbul_NIC2_S1C3_03
Lbul_NIC2_S1C3_12
Lbul_NIC8_S5C3_03
Lbul_NIC8_S5C3_14
Lbul_NIC2_S1C3_07
Lbul_NIC2_S1C3_13
Lbul_NIC8_S5C3_11
Lbul_NIC8_S5C3_09
Lbul_NIC2_S1C3_11
Lbul_NIC2_S1C3_14
Lbul_NIC8_S5C3_01
Lbul_NIC8_S5C3_13
Lbul_KH1110_15_05
Lbul_KH1110_15_12
Lbul_KH1110_21_14
Lbul_KH1110_18_09
Lbul_KH1110_18_03
Lbul_KH1110_18_10
Lbul_KH1110_18_08
Lbul_KH1110_15_03
Lbul_KH1110_15_06
Lbul_KH1110_18_12
Lbul_KH1110_21_04
Lbul_KH1110_15_14
Lbul_KH1110_18_15
Lbul_KH1110_21_08
Lbul_SO255_143_07
Lbul_KH1110_21_15
Lbul_SO255_143_02
Lbul_SO255_143_15
Lbul_SO255_143_08
Lbul_KH1110_15_15
Lbul_KH1110_15_01
Lbul_KH1110_18_11
Lbul_KH1110_15_08
Lbul_KH1110_15_13
Lbul_KH1110_21_10
Lbul_KH1110_18_13
Lbul_KH1110_18_04
Lbul_KH1110_21_01
Lbul_KH1110_21_05
Lbul_KH1110_21_13
Lbul_SO255_143_10
Lbul_SO255_143_06
Lbul_SO255_143_12
Lbul_SO255_143_13
Lbul_SO255_143_14
Lbul_KH1110_02_05
Lbul_KH1110_02_11
Lbul_KH1110_02_06
Lbul_KH1110_02_03
Lbul_KH1110_02_14
Lbul_KH1110_05_09
Lbul_KH1110_05_11
Lbul_KH1110_02_07
Lbul_KH1110_02_12
Lb
ul_KOK1703_03_05
Lb
ul_KOK1703_03_07
Lb
ul_KOK1703_03_06
Lb
ul_KOK1703_03_11
Lb
ul_KOK1703_03_14
Lbul_SO255_143_05
Lbul_SO255_143_09
Lbul_KH1110_18_07
Lbul_KH1110_21_09
Lbul_KH1110_21_11
Lbul_KH1110_15_07
Lbul_KH1110_18_14
Lbul_KH1110_15_09
Lbul_KH1110_21_07
Lbul_KH1110_02_13
Lbul_KH1110_05_02
Lbul_KH1110_05_08
Lbul_SN105_08_11
Lbul_SN105_08_12
Lbul_SN105_08_01
Lbul_SN105_08_06
Lbul_SN105_08_07
Lbul_SN105_08_08
Lbul_SN105_08_02
Lbul_SN105_08_04
Lbul_SN105_08_10
Lbul_KH1110_02_10
Lbul_KH1110_05_06
Lbul_KH1110_05_10
Lbul_KH1110_08_05
Lbul_KH1110_08_10
Lbul_KH1110_08_15
Lbul_SN105_08_03
Lbul_KH1110_08_08
Lbul_KH1110_02_09
Lbul_KH1110_05_04
Lbul_KH1110_05_07
Lbul_KH1110_05_12
Lbul_KH1110_08_02
Lb
ul_KOK1703_03_08
Lbul_KH1110_08_09
Lbul_KH1110_05_05
Lb
ul_KOK1703_03_10
Lbul_KH1110_08_13
Lbul_KH1110_08_04
Lbul_KH1110_08_14
Lbul_KH1110_08_03
Lb
ul_KOK1703_03_13
Lb
ul_KOK1703_03_15
Lbul_NIC2_S9C3_02
Lbul_NIC2_S9C3_08
Lbul_NIC2_S9C3_03
Lbul_NIC2_S9C3_04
Lbul_NIC2_S9C3_01
Lbul_NIC2_S9C3_05
Lbul_AMT24_17_12
Lbul_NIC2_S9C3_06
Lbul_AMT24_17_07
Lbul_AMT24_17_14
Lbul_AMT24_17_13
Lbul_AMT24_17_03
Lbul_AMT24_17_06
Lbul_AMT24_17_08
Lbul_AMT24_17_05
Lbul_AMT24_17_09
Lbul_AMT24_17_10
Lbul_AMT24_17_15
Lbul_NIC2_S9C3_09
Lbul_NIC2_S9C3_07
Lbul_NIC2_S9C3_10
Lbul_AMT24_22_08
Lbul_AMT24_22_06
Lbul_AMT24_22_11
Lbul_AMT24_22_12
Lbul_AMT24_23_02
Lbul_AMT24_23_06
Lbul_AMT24_22_13
Lbul_AMT24_22_04
Lbul_AMT24_22_07
Lbul_AMT24_22_09
Lbul_AMT24_22_14
Lbul_AMT24_22_15
Lbul_AMT24_23_07
Lbul_AMT24_23_11
Lbul_AMT24_23_10
Lbul_AMT24_23_09
Lbul_AMT24_23_08
Lbul_AMT24_23_05
Lbul_AMT24_23_12
Lbul_AMT24_23_13
Lbul_NIC2_S1C3_08
Lbul_NIC2_S1C3_01
Lbul_NIC2_S1C3_02
Lbul_NIC2_S1C3_10
Lbul_NIC8_S5C3_06
Lbul_NIC8_S5C3_04
Lbul_NIC2_S1C3_15
Lbul_NIC8_S5C3_07
Lbul_NIC8_S5C3_12
Lbul_NIC8_S5C3_08
Lbul_NIC2_S1C3_03
Lbul_NIC2_S1C3_12
Lbul_NIC8_S5C3_03
Lbul_NIC8_S5C3_14
Lbul_NIC2_S1C3_07
Lbul_NIC2_S1C3_13
Lbul_NIC8_S5C3_11
Lbul_NIC8_S5C3_09
Lbul_NIC2_S1C3_11
Lbul_NIC2_S1C3_14
Lbul_NIC8_S5C3_01
Lbul_NIC8_S5C3_13
Pacific Indo-Pacific
North
South
Equatorial
Atlantic
Indian
N. Pacific
Estimated co-ancestry
1.87
14.2
26.5
38.7
51
63.3
75.6
87.9
100
112
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3.2 | Genetic diversity
To further assess the amount of within- lineage genetic diversity
within our data set, we calculated nucleotide diversity (π) and the
proportion of heterozygous sites (heterozygosity) for each indi-
vidual at each of the target capture loci (Figure 3b). Both of these
measures were highest within the Pacific lineage, followed by the
Atlantic, and with the Indo- Pacific lineage showing the lowest ge-
netic diversity. The values of π varied from zero to 7.2% across the
target capture loci and the average values of π per lineage were
0.8% in Indo- Pacific, 1.0% in Atlantic and 1.6% in the Pacific line-
age. Heterozygosity was in all cases lower than π (Figure 3b: one-
tailed t test, Atlantic: t(4341.5) = 8.54 p < 2.2 × 10−1 6, Indo- Pacific:
t(4498.7) = 5.25, p = 8.02 × 10−8, Pacific: t(4280.5) = 9.62, p < 2.2 × 10−16 ).
The values of π are only slightly above average when compared with
other animals (Leffler et al., 2012; Romiguier et al., 2014), which may
seem surprising given the large population sizes of planktonic spe-
cies, including L. bulimoides. However, it is worth keeping in mind
that our target capture data consist of a large proportion of cod-
ing, including nonsynonymous, sites (Choo et al., 2020) and are af-
fected by purifying and background selection, which reduce genetic
diversity.
3.3 | Divergence- time and population size
The snapp tree including three L. bulimoides genetic lineages and
the L. trochiformis outgroup supported a split of the ancestral L.
bulimoides population into the Atlantic and the Pacific/Indo- Pacific
lineages (Figure 4). The divergence of the Atlantic lineage and the
Indo- Pacific/Pacific lineage was estimated at 1.20 million years
ago (Ma) (95% highest posterior density [HPD] = 0.737– 1.76 Ma).
The Indo- Pacific and Pacific lineages split from one another with
a mean age of 0.978 Ma (95% HPD = 0.603– 1.43 Ma). Consistent
with the estimates of genetic diversity π, the snapp analyses indi-
cate that the average estimated effective population size (Ne) was
highest for the Pacific lineage (Ne = 6.36 × 106, 95% HPD = 4.17–
8.56 × 106), followed by the Atlantic lineage (Ne = 3.02 × 106, 95%
HPD = 2.12– 3.86 × 106) and was lowest for the Indo- Pacific lineage
(Ne = 1.61 × 106, 95% HPD = 1.23– 2.02 × 106). The three lineages
were best supported as separate species based on the BFD analy-
sis (BF > 10), in comparison to the other models lumping all three
FIGURE 3 Characterizing genetic
structuring and diversity of Limacina
bulimoides based on nuclear SNPs. (a) Weir
and Cockerham estimates of FST within
and between the major genetic lineages:
Atlantic (N = 63), Indo- Pacific (N = 38)
and Pacific (N = 60). The distribution
is over 1000 random subsamples of
unlinked SNPs (see Methods for details).
(b) Estimates of heterozygosity and
nucleotide diversity (π) within each
of the major lineages. Heterozygosity
was estimated as the proportion of
heterozygous sites averaged across
individuals. Each data point corresponds
to the estimate for one target capture
region.
Heterozygosity and π
Major lineage
Atlantic Indo-PacificPacific
πππhethet het
0.0%
2.5%
5.0%
7.5%
(b)
F
ST
within
lineages
between
lineages
0.0
0.1
0.2
0.3
0.4
0.5
Atlantic (A)
Indo-Pacific (IP)
A vs. IP
Pacific (P)
A vs. P
P vs. IP
(a)
FIGURE 4 Maximum clade credibility tree of the snapp
phylogeny tracing the divergence age of three genetic lineages
of Limacina bulimoides to ~1 Ma. The phylogeny of the Atlantic,
Indo- Pacific and Pacific L. bulimoides and L. trochiformis was based
on 1279 thinned, biallelic nuclear SNPs. Two individuals of L.
trochiformis and 10 individuals per lineage of L. bulimoides were
included in this tree. The long branches at the root were truncated
to allow for better visualization of the recent divergences. The
mean age of each node is labelled above the node, and the bars
indicate the 95% highest posterior densities. The scale at the
bottom represents time in million years ago.
0
Atlantic
L. trochiformi
s
Pacific
Indo-Pacific
2.01.5 1.00.5
0.978
1.20
13.0
0
Atlantic
L. trochiformi
s
Pacific
Indo-Pacific
2.01.5 1.00.5
0.978
1.20
13.0
Mya
|
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CHOO et al.
lineages as one species, or lumping the Indo- Pacific and Pacific line-
ages as one species (Table S5).
Based on the ABBA– BABA test with the topology (((Indo-
Pacific, Pacific) Atlantic) L. trochiformis), there was tentative ev-
idence for excess allele sharing between the Atlantic and Pacific
lineages compared to the Atlantic and Indo- Pacific lineages (D-
statistic = 0.0557, Z = 2.31, p = .0208) (Figure S5 and Table S6).
Furthermore, fastsi mcoal2 analyses, based on a joint site al-
lele frequency spectrum, suggest very limited recent gene flow
(m = 1.17 × 10−6) between the sympatric Indo- Pacific and Pacific
lineages, as it was the best- supported model based on likelihood
distributions and AIC among the four models tested (Figure S6
and Table S7).
To estimate historical changes in Ne in the three major lin-
eages, we used stairway plot analyses (Methods). The stairway plot
approach indicates that all three lineages had similar demographic
histories, with Ne increase followed by a long period of stable high
Ne and a recent Ne decrease (Figure 5). The steep increase in Ne
to ~10 million is estimated about 900 thousand years ago (ka) for
the Pacific lineage, corresponding approximately to MIS23, while
similar increases were estimated to be more recent in the Atlantic
(500 ka; MIS12) and Indo- Pacific (400 ka; MIS10). The analysis then
indicates that Ne in all lineages remained stable through repeated
glaciations and interglacial periods (400– 15 ka; MIS9 to MIS2). From
MIS1 (14 ka), the start of the Holocene interglacial at 10,000 years
ago, Ne across the three lineages decreased. While the timing of
many of the reconstructed Ne changes corresponding to MIS transi-
tions is intriguing, we note that purifying and background selection
in our data set is likely to have a considerable impact on the Ne
estimates presented above, and could especially explain the recent
Ne decrease. It will therefore be very interesting to see if similar
demographic patterns are recovered in future studies using neu-
tral markers. In addition, the evolutionary scenarios tested support
the large effective population sizes (at least 4 million) of the three
present- day and ancestral populations, which correspond with our
results in the snapp and stairway plot analyses, and rule out popula-
tion bottlenecks.
3.4 | Morphological variation
Based on the repeatability analysis with 30 individuals landmarked
independently by two observers, RWs 1, 2, 3, 5, 10 and 11 were se-
lected as repeatable parameters to be used in the geometric morpho-
metric analyses of shell shape. Of the 161 L. bulimoides individuals, 159
had the complete set of 11 (semi- )landmarks and were included in the
anal ysis. The six re peatable RWs exp lained 83.75% of sh ell sha pe vari-
ation (Table S8). Most of the geometric morphometric variation was
due to changes in shell width as shown in RW1 accounting for 51.05%
of the total variation, and relative size of the aperture as shown in
RW2 explaining 18.16% of the total variation (Figure 6a). Though
the shell shapes of the three genetic lineages overlapped to a large
extent, the distributions were significantly different (PERMANOVA:
F(2,156) = 18.99, R2 = .196, p = 1 × 10−4) (Table 2). Likewise, shell shape
was significantly different between the three genetic lineages
in a canonical variate analysis (one- way ANOVA: F(2,156) = 41.16,
p = 4.42 × 10−15 ) (Figure S7; Table 2). Grouping specimens according to
their sampling location also resulted in significant differences in shell
shape (Figures S8 and S9), but this is to be expected because genetic
lineages are not randomly distributed in space.
We observed large variability in shell colour within our samples.
The L. bulimoides specim ens ra nge d in colo ur fro m almo st com plet ely
white to beige to reddish- brown (Figure 6b; Figure S10). In most co-
loured individuals, pale beige or red pigmentation was found on the
inner aperture, lower half of the aperture and sometimes on the su-
tures of the shell. Tissue pigmentation also varied widely, with dark
FIGURE 5 Effective population size (Ne) of the three Limacina bulimoides lineages through time as reconstructed by stairway plot. The
x- axis is the number of years before present, assuming a generation time of 1 year. Estimates of median Ne are coloured per lineage. Axes are
log- scaled for clearer visualization of recent histories. The plot was truncated from 0 to 1000 years ago as sample sizes are not sufficient for
reconstructing very recent events as in Liu and Fu (2020). Dotted lines indicate the temporal boundaries between Marine Isotope Stages
corresponding to timing of the reconstructed Ne changes. Odd MIS numbers refer to interglacial stages, while even numbers refer to glacial
stages, with MIS1 marking the start of the Holocene and MIS12 being one of the strongest glacials of the Quaternary period.
11
01223
1,000
100,000
10,000,000
1,000 10,000 100,000
Time (years ago)
Effective population size
Lineage
Atlantic
Indo−P
acific
Pacific
1,000,000
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CHOO e t al.
grey tissue that is visible through the somewhat transparent shell
observed in specimens from the North Atlantic and Indian Ocean,
while almost completely white tissue (and shell) was mostly recorded
from the Sout h Pacif ic (Figure S10). Since the colours of the shell and
tissue are confounded with each other and difficult to distinguish,
we limited the statistical analyses to shell aperture colour, which is
not affected by tissue pigmentation.
Aperture colour was not randomly distributed with respect
to genetic lineage (p = .0005, two- sided) or sampling location
(p = .0005, two- sided) (Figure S10, Tables S10 and S11). Atlantic
specimens had mainly pink and red- brown apertures while North
Pacific specimens were highly pigmented with mainly tan and
red- brown apertures. Specimens from the South Pacific had less
pigmented apertures, with either transparent or pink apertures,
and Indian Ocean specimens had highly pigmented shells with
red- brown apertures. Aperture colour was often consistent within
sampling station (six of the 15 stations) but could also vary (e.g.,
KH1110_05 from the North Pacific had all four aperture colours
represented; Table S9).
3.5 | Lineages in the North Pacific appear
morphologically cryptic
We closely examined morphological variation in individuals from
the two lineages that co- occur in the North Pacific, namely the
Indo- Pacific lineage (n = 16) and Pacific lineage (n = 16) (Figure 7).
We found that there were no significant differences in shell shape
between the two lineages despite their distinct genetic back-
grounds (Figure 7b; PERMANOVA: F(1,30 ) = 3.75, R2 = .111, p = .0612).
However, 81% of Pacific lineage individuals (13 out of 16) had dark
pigmented spots on their tissue, which was visible through their
transparent shell, and none of the Indo- Pacific lineage individuals
had such spots (Figure 7c). Spots were not observed on the pho-
tographs of three Pacific individuals, but these spots could have
been on the opposite side that was not photographed. Dissection
of additional individuals from the same samples showed that these
pigmented spots were localized on the margin of their “wing- feet”
or parapodia (Figure S11). Interestingly, these pigmented spots were
not obser ved in the photographs of other individuals belonging to
FIGURE 6 Variation in shell shape and aperture colour across the three lineages of Limacina bulimoides. (a) Shell shape variation of 159 L.
bulimoides specimens, categorized into the three genetic lineages (Atlantic, Indo- Pacific and Pacific). Shell shape variation is visualized with
relative warp axes 1 and 2, explaining 51.05% and 18.16% of the total shell shape variation, respectively. Extremes of both relative warp axes
are shown with the thin plate spline images, with each black dot corresponding to a shell landmark. Line drawings of example individuals
from each genetic lineage are shown: Atlantic (Lbul_NIC2_S9C3_04), Indo- Pacific (Lbul_SN105_08_02) and Pacific (Lbul_KH1110_18_03). (b)
Illustration of the intensity of aperture colour observed, arranged from light, medium to dark apertures in each of the three lineages (see also
Figure S10).
−0.05
0.00
0.05
0.10
−0.10−0.05 0.00 0.05 0.10
Relative Warp 1 (51.05%)
Relative Warp 2 (18.16%)
Lineage
Atlantic
Indo−Pacific
Pacific
(a) (b)
Light
Atlantic
Indo-Pacific
Pacific
Dark
Medium
TAB LE 2 Pairwise PERMANOVA and Tukey HSD of canonical variate results for comparisons of shell shape variation between genetic
lineages of Limacina bulimoides (Atlantic, Indo- Pacific and Pacific).
PERMANOVA Tukey HSD
Pairwise comparison Df F- value R2p- value Difference Lower Upper p- value
Atlantic— Indo- Pacific (1,97) 13.1 0.119 6.0 0 × 10−4 1. 35 0.647 2.05 3 .11 × 10−6
Atlantic— Pacific (1121) 6.49 0.0509 .0139 2.30 1.70 2.91 0
Indo- Pacific— Pacific (1,94) 59.6 0.388 1.0 0 × 10−4 0 .959 0.253 1.66 4.48 × 10−4
Note: All pairwise comparisons are significant, after strict Bonferroni corrections (α = .05, p < .0167). Significant p- values are indicated in bold.
|
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CHOO et al.
the Pacific lineage but sampled in a different location, in the South
Pacific (Figure S10).
4 | DISCUSSION
We found that the shelled pteropod Limacina bulimoides is com-
posed of three main genetic lineages (Atlantic, Indo- Pacific and
Pacific), the latter two of which were found in sympatry at three
sampling sites in the North Pacific. We did not find any evidence
for recent hybrids despite surveying genome- wide genetic varia-
tion. The evidence of very limited gene flow between the sympatric
Indo- Pacific and Pacific lineages (m = 1.17 × 10−6) is indic ative of the
presence of strong isolating barriers characteristic of virtually com-
pleted speciation. Thus, these lineages remain distinct and can be
considered as separate species. The Atlantic and (Indo- )Pacific line-
ages are allopatric , with sim ilar levels of divergence as found for the
sympatric lineages, and are also likely to be reproductively isolated.
The three lineages diverged ~1 Ma during the mid- Pleistocene, and
show substantial allele frequency divergence with FST up to 40%
between them. We also observed further genetic substructur-
ing within each of the three lineages, consistent with geography.
Morphological (shell shape) measurements revealed significant dif-
ferences among the major lineages. However, unlike genetic data,
which show three clear tight clusters, morphological data have
overlapping distributions. Interestingly, the biological species pair
in the North Pacific do not show any shell shape differences at the
sampling locations where they are in sympatry, although we found
dark pigmentation spots that were confined to the Pacific lineage
at those stations.
We discuss possible mechanisms of reproductive isolation that
are involved in maintaining the integrity of the sympatric lineages,
including: (i) prezygotic, such as habitat, temporal and behavioural
isolation, or (ii) postzygotic, such as gamete isolation and nonviabil-
ity of hybrids. Specimens from three stations in the North Pacific
(KH110_02, KH1110_05 and KOK1703_03) were collected by
oblique tows in sur face waters (maximum depth was 370 m); there-
fore, it is unclear if the two lineages may have distinct depth habitats
and/or corresponding diet preferences, and thus could experience
habitat or ecological isolation. L. bulimoides has internal fertilization
and it is likely that they have evolved species- specific mate recogni-
tion mechanisms in order to locate each other in the pelagic environ-
ment. While they are cryptic in terms of their shell shape, the two
sympatric lineages in the North Pacific could potentially be differ-
entiated by the presence of tissue pigmentation on their parapodia
(Figure S11), reminiscent of “pseudocryptic” species where distin-
guishing morphological traits are identified after initial genetic iden-
tification (Knowlton, 1993; Sáez et al., 2003). These “wing” spots
seem analogous to wing pigmentation found in butterflies and flies,
which could be a convergent distinguishing trait that mediates spe-
cies recognition (Gompel et al., 2005; Wiernasz & Kingsolver, 1992).
However, it is not known whether Limacina, although they are com-
monly referred to as “sea- butterflies,” can actually perceive such
spots. Limacina possess eyes and optic nerves (Laibl et al., 2019),
and are capable of detecting changes in light levels to trigger daily
vertical migration (Cohen & Forward Jr, 2016), but there is a lack
of information on the types of cues used by pteropods to recog-
nize conspecifics. In other planktonic species, such as copepods,
pheromone trails and swimming patterns are used for mate finding
(Goetze & Kiørboe, 2008; Kiørboe, 20 07), which may also ser ve to
FIGURE 7 Distribution (a) and
morphological variation (b,c) of the
sympatric genetic lineages of Limacina
bulimoides in the North Pacific with 16
individuals from the Indo- Pacific lineage
(green) and 16 individuals from the Pacific
lineage (orange). (b) Shell shape variation
as visualized with relative warps 1 and 2,
which explain 51.05% and 18.16% of the
total shell shape variation, respectively.
(c) Photographs of an example individual
from each genetic lineage are shown,
with dark pigmented spots on the tissue,
visible through the transparent shell, of
the specimen from the Pacific lineage (see
also Figures S10 and S11).
17.5
20.0
22.5
25.0
27.5
160 170 180 −170 −160 −150
Longitude
Latitude
(a)
−0.02
0.00
0.02
−0.03 0.00 0.03 0.06
Relative Warp 1 (51.05%)
Relative Warp 2 (18.16%)
Lineage
Indo−P
acific
Pacific
(b)
Hawaiʻi
North Pacific Ocean
(c) Indo−Pacific
Pacific
KH1110_05 KOK1703_03
KH1110_02
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CHOO e t al.
mediate the reproductive isolation between sibling species, as seen
in other marine organisms such as isopods, stomatopods and amphi-
pods (Palumbi, 1994).
We have insufficient evidence to conclude whether the Pacific
and Indo- Pacific lineages evolved in sympatr y or have arrived at their
present- day distribution through secondary contact. While we see
that the model incorporating recent gene flow between the Indo-
Pacific and Pacific lineages is better supported than the other mod-
els tested, we have insufficient information to discern whether gene
flow is still occurring or has occurred earlier in their evolutionary
history. Based on the comparisons between the recent and ancient
gene flow models (Figure S6), the lower likelihood in the ancient gene
flow model may be due to (i) gene flow after the initial split between
the Atlantic and ancestral Indo- Pacific and Pacific lineages, (ii) gene
flow after the split between the Indo- Pacific and Pacific lineages, or
(iii) a combination of both factors. The absence of the Pacific lineage
from the Indian Ocean, despite potential connectivity with the Nor th
Paci fic , where both lin eages are pres ent , co uld be th e resul t of, for ex-
ample, incomplete sampling, niche incumbency effects (as in Weiner
et al., 2014), recent extinction in the Indian Ocean or a combination
of factors. A recent extinction in the Indian Ocean has been re-
corded in the fossil record of another pteropod species (Wall- Palmer
et al., 2014). Mo re detai led samp lin g, in the Indi an an d Pac ifi c Oceans,
will be needed to resolve the geographical boundaries and extent of
overlap between the two lineages and to understand the causes be-
hind their modern- day distributions. Sampling of L. bulimoides with
depth- stratified collection techniques, metabarcoding of microbiome
and gut contents, examination of their radulae, transcriptome se-
quencing and observations of their reproductive timings will be also
be needed to gain more insight into the possible modes of current
reproductive isolation between the two lineages.
Morphological variations congruent with genetic clines can in-
dicate ecological selection or phenotypic plasticity, while the ab-
sence of morphological divergence between genetically distinct
populations can result from recent colonization, widespread gene
flow or stabilizing selection (Fišer et al., 2018; Milá et al., 2017).
We observe both situations. Among the three main lineages, there
are statistically significant differences in the (overlapping) distribu-
tions of shell shapes and aperture colours. These traits also vary
among sampling locations within lineages (Tables S 9 – S 1 1 ). At the
same time, when considering only the sampling locations where the
Indo- Pacific and Pacific lineages appear in sympatry there are no
statistical differences in shell shape. Shell shape is likely to have a
phenotypically plastic component linked to life history and envi-
ronmental conditions (Hoffman et al., 2010 ; Hollander et al., 2006;
Mariani et al., 2012; Zieritz et al., 2010). Pteropod shell shape is
an important trait that directly affects their sinking and swimming
speeds, manoeuvrability, and their resulting ability to navigate the
water column for food and evade predators (Karakas et al., 2020).
In other molluscs, shell shape has been shown to be correlated with
environment, either as genetically inherited differences or phe-
notypically plastic traits. Examples are found in various species of
intertidal snails with a “crab” or “wave” ecotype, such as Littorina
and Nucella (Guerra- Varela et al., 2009; Hollander & Butlin, 2010 ;
Johannesson, 2003; Rolán et al., 2004), or in My tilus exhibiting shell
shape plasticity as a response to environmental parameters such as
temperature and food (Telesca et al., 2018).
The nonrandom variation in shell colour across sampling loca-
tions (Figure S10) may be due to pigments incorporated from their
diet, which is composed of phytoplankton, microbes and particu-
late matter trapped within their mucous web (Conley et al., 2018).
Limacina can feed selectively by moving the cilia on their wings
and mantle lining to sort and reject unwanted food particles (Lalli
& Gilmer, 1989), and they can also control their vertical distribution
through diel vertical migration. These differences in food choice
and vertical habitat may lead to variable shell colour among indi-
viduals from the same sampling station. Production of shell colour
has been suggested to be energetically costly across molluscs
(Williams, 2017), but pteropods, with their unique planktonic life-
style, may be subject to other selective trade- offs such as being
transparent to remain inconspicuous to visual predators in the water
column (Johnsen, 2001) or possessing red pigment for protection
against UV radiation (Hansson, 2000).
The divergence of L. bulimoides lineages at around 1 Ma (Figure 4)
and increase in Pacific lineage Ne (Figure 5), indicating population
expansion, coincides with the timing of divergence in a mesopelagic
copepod species (Andrews et al., 2014) and several coccolithophore
speciation events (Filatov et al., 2021) during the mid- Pleistocene
transition (0.6– 1.2 Ma, between MIS22 and 24). This period was
characterized by global cooling, lengthening of glacial cycles from
41,000 to 100,000 years, changing ocean circulation and produc-
tivity, and the evolution of many terrestrial and marine biota (Clark
et al., 2006; Elderfield et al., 2012; Kender et al., 2016; McClymont
et al., 2013). Changes in ocean circulation during the mid- Pleistocene
transition could have facilitated the physical separation and subse-
quent divergence of the three lineages across the various ocean
basins. These changes include the reduced exchange between
the Indian and Atlantic Oceans via the Agulhas leakage due to the
northward migration of the Subtropical Front towards the Agulhas
Plateau (Caley et al., 2012; Cartagena- Sierra et al., 2021), and the
connection between the Pacific and Indian Ocean due to the weak-
ening of the Indonesian Throughflow (Petrick et al., 2019). Similar
geographical structuring across ocean basins for other circumglobal
warm- water plankton species has been attributed to both physi-
cal (e.g., ocean currents or continental landmasses) and ecological
(species- specific interactions with oceanographic gradients) barriers
(e.g., Bendif et al., 2019; Burridge et al., 2 019; Filatov et al., 2021;
Goetze et al., 2015; Hirai et al., 2015), although it is unknown if these
structured populations arose at the same time. Within the Pacific
and Atlantic basins, habitat discontinuities such as the mesotro-
phic equatorial upwelling waters, have been described as ecological
dispersal barriers for subtropical copepods (Goetze, 2005; Goetze
et al., 2017) and the pteropod genus Cuvierina (Burridge et al., 2015).
Congruently, fossil evidence of coccolithophore species distribu-
tions point to the emergence of new species at equatorial latitudes
(Filatov et al., 2021).
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CHOO et al.
We found substantial levels of genetic variation in all lineages
(e.g., π ranged from 0.8% to 1.6% per lineage), despite the fact that
most of our targeted regions are coding and thus affected by pu-
rifying and background selection. These levels are comparable to
those reported for two species of planktonic copepods (π ranging
from 0.6% to 0.8%) based on a similar target capture approach but
with more restricted geographical sampling (Choquet et al., 2019).
This level of π, coupled with the stairway plot and snapp estimates
of Ne for L. bulimoides, indicates an absence of any pronounced pop-
ulation bottlenecks across multiple Pleistocene glacial– interglacial
transitions. This suggests that this species complex is likely to have
been resilient to ocean changes associated with glacial cycles. In
the stairway plot, the timing of the rapid rise of Ne in the Atlantic
and Indo- Pacific lineage between MIS10 and 12 is coincident with
MIS11, the warmest interglacial interval of the last 500,000 years,
with high levels of atmospheric CO2 (Siegenthaler et al., 2005),
warm sea- surface temperatures (SSTs) and atypical blooms of cal-
careous plankton in the high latitudes (Howard, 1997; McManus
et al., 2003). Stable population sizes in L. bulimoides were associated
with the repeated glacials and interglacials from MIS2 to 9, includ-
ing the Last Glacial Period (MIS2– 4), even though pH levels fluc-
tuated up to 0.2 ± 0.1 pH between glacial and interglacial periods
(Hönisch & Hemming, 2005; Sanyal et al., 1995). Stable abundances
of L. bulimoides were also recorded within sediment cores in the
Caribbean Sea, which had low variation in SST across both glacial
and interglacial periods (Wall- Palmer et al., 2014). At the beginning
of the Holocene, between 19,000 and 7000 years ago (boundary of
MIS1/2), temperatures increased and global sea levels rose rapidly
(Lambeck & Chappell, 2001), while atmospheric CO2 increased by
30% (Hönisch et al., 2012; Monnin et al., 20 01). These environmen-
tal changes have been linked to decreasing shell mass in other plank-
tonic calcifiers such as foraminifers and coccolithophores (Barker
& Elderfield, 2002; Beaufor t et al., 2 011) and are associated with a
decrease in Ne in our stairway plot reconstructions. However, cau-
tion in interpreting these correlations is warranted because our de-
mographic inferences are affected by background selection (Ewing
& Jensen, 2016), which is expected to result in a signal of recent Ne
decrease. Another caveat is that the timings of divergence and de-
mographic changes are a function of generation time, which was set
to 1 year based on our knowledge of L. bulimoides biology. A more
accurate estimate of generation time may affect these conclusions
to some degree.
The ver y high levels of genetic diversity (heterozygosity and π)
and large repetitive genomes of many marine zooplankton species
hinder the adoption of genomic approaches in evolutionar y stud-
ies of these organisms. By focusing on transcribed regions of the
genome, the target capture approach has enabled us to greatly
reduce these challenges and conduct a global genomic study in a
marine zooplankton species. We note the usefulness of genome-
wide data, compared to mitochondrial DNA or barcoding genes, in
detecting population structure and assessing species boundaries.
Unlike a single locus (e.g., the mitochondrial COI barcoding region),
whose evolutionary history represents a single genealogy or a gene
tree, ou r set of hundreds of thou san ds of genom e- wide SNP s all ows
us to unambiguously apportion the genetic structure within L. buli-
moides. Evolutionary histories of individual genes (gene trees) and
histories of populations or species (population/species trees) are
seldom identical (e.g., Nichols, 2001). Moreover, in some studies
using barcode regions, species delimitations have been made based
on taxon- specific thresholds of levels of sequence divergence be-
yond which reproductive isolation is expected (Krug et al., 2013;
Lefébure et al., 2006; Young et al., 2017). This method is highly
effective for detecting new putative species in broad- scale bar-
coding studies without prior information; however, divergence dis-
tances do not necessarily indicate reproductive isolation or lead
to further understanding of the process of speciation (Freeman
& Pennell, 2021). The genome- wide data set we present here will
underpin future investigations into the nature of selection, adapta-
tion, divergence and speciation in the open ocean. We expect that
the predominance of coding loci in our data and the ability to sepa-
rate synonymous and nonsynonymous sites will be major assets in
these future studies.
Despite the potential for global dispersal in L. bulimoides, we ob-
served diversification of lineages across ocean basins that probably
originated from the mid- Pleistocene transition. These lineages are
probably distinct species with strong reproductive isolation among
them, and have survived through periods of glacial– interglacial tran-
sitions representing a wide range of oceanographic conditions, in-
cluding ocean alkalinity. There are slight differences in shell shape
between the lineages, but shell shape alone cannot be used as a tax-
onomic character, and it is unclear whether environmental or genetic
factors have a greater impact on shell morphology. Even though their
effective population sizes may have decreased since the start of the
Holocene, the three lineages still possess high levels of standing ge-
netic variation and nucleotide diversity, upon which selection could
act to drive further adaptation in the future (Bernatchez, 2016; Bit ter
et al., 2019; Schluter & Conte, 2009). However, it is unclear whether
pteropods and other planktonic calcifiers can cope with the rate of
ongoing ocean changes, including anthropogenic carbon emission
rates that are unprecedented over at least the last 66 million years,
leading to increasing ocean acidification (Zeebe et al., 2016). While
there are no visibly obvious dispersal barriers in the open ocean, we
have found genome- wide evidence for speciation and divergence in
L. bulimoides, and there is probably more diversity in planktonic spe-
cies than meets the eye.
AUTHOR CONTRIBUTIONS
LQC, GH and KTCAP designed the study. EG and KTCAP contrib-
uted samples used in this study. LQC collected the molecular data
while GS collected the morphometric data. LQC, MM, MC, GH and
KTCAP contributed to the bioinformatic analyses while LQC and
GS analysed the morphometric data. LQC, MM, MC, GH, EG and
KTCAP contributed to the manuscript writing. All authors provided
feedback and approved of the manuscript.
3214
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CHOO e t al.
ACKNO WLE DGE MENTS
We thank L. Mekkes, A. Burridge and M. Jungbluth for assistance
at sea, and all captains and crews of the ocean expeditions for
their suppor t and assistance. We thank D. Wall- Palmer for provid-
ing Indian Ocean and South Pacific samples from the SN105 and
SO255 cruises and A. Tsuda for providing the Pacific samples from
the 2011/2012 R/V Hakuho- Maru KH- 11- 10 cruise. We warmly ac-
knowledge M. Matschiner, E. Trucchi and R.K. Butlin for their ad-
vice on data analyses, W. Renema and D. Wall- Palmer for helpful
discussions regarding the manuscript, and M. Kopp for guidance
in the laboratory. We also thank the four anonymous reviewers
whose comments improved the manuscript. This research was sup-
ported by the Netherlands Organization for Scientific Research
(NWO) Vidi grant 016.161.351 to K.T.C.A.P. The Netherlands
Initiative Changing Oceans (NICO) expedition on R/V Pelagia was
also funded by NWO and the Royal Netherlands Institute for Sea
Research (NIOZ). L.Q.C. was partly supported by a Nord University
Internalization Grant during the writing of the manuscript. Further
fieldwork was supported by NSF grants OCE- 1029478 and OCE-
1338959 to E.G. The R/V Sonne cruise SO255 was funded by the
German Federal Ministry of Education and Research (BMBF; grant
03G0255A), and the SN105 cruise on board the ORV Sagar Nidhi
was funded by the Indian National Centre for Ocean Information
Services (INCOIS), Ministry of Earth Sciences, India, as the first
cruise of the second International Indian Ocean Expedition (IIOE-
2). The Atlantic Meridional Transect is funded by the UK Natural
Environment Research Council through its National Capability
Long- term Single Centre Science Programme, Climate Linked
Atlantic Sector Science (grant no. NE/R015953/1). This study con-
tributes to the international IMBeR project and is contribution no.
368 of the AMT programme.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
DATA AVAIL AB ILI T Y STAT EME N T
The raw sequence data for Limacina bulimoides were archived on
NCBI GenBank with the following accessions: SAMN11131477- 79,
SAMN11131480- 82 and SAMN20293115- 269. The assembled COI
sequences for L. bulimoides can be found on NCBI GenBank with
the following accessions: MZ542566– MZ542726. Shell images for
the specimens, the methods protocol for DNA extraction and tar-
get capture, and hard- filtered vcf files used for this study can be ac-
cessed on Mendeley Data using the doi: https://doi.org/10.17632/
7 m 8 6 t w n z p 2 . 1 .
ORCID
L. Q. Choo https://orcid.org/0000-0001-8383-2926
E. Goetze https://orcid.org/0000-0002-7273-4359
M. Malinksy https://orcid.org/0000-0002-1462-6317
M. Choquet https://orcid.org/0000-0001-6719-2332
K. T. C. A. Peijnenburg https://orcid.org/0000-0001-7544-1079
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