PUBLICATIONS
Paleoceanography
RESEARCH ARTICLE
10.1002/2016PA002977
Key Points:
• Cryptic species of Globorotalia
inflata can be detected based on
morphology in sediments
• It can be used to reconstruct the shifts
of the Brazil-Malvinas Confluence in
the South Atlantic
• Cryptic diversity represents a source of
information for paleoceanography
Supporting Information:
• Supporting Information S1
• Data Set S1
Correspondence to:
R. Morard,
[email protected]
Citation:
Morard, R., M. Reinelt, C. M. Chiessi,
J. Groeneveld, and M. Kucera (2016),
Tracing shifts of oceanic fronts using the
cryptic diversity of the planktonic
foraminifera Globorotalia inflata,
Paleoceanography, 31, doi:10.1002/
2016PA002977.
Received 17 MAY 2016
Accepted 14 AUG 2016
Accepted article online 22 AUG 2016
Tracing shifts of oceanic fronts using the cryptic
diversity of the planktonic foraminifera
Globorotalia inflata
Raphaël Morard1, Melanie Reinelt1, Cristiano M. Chiessi2, Jeroen Groeneveld1, and Michal Kucera1
1
MARUM Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany, 2School of Arts, Sciences and
Humanities, University of São Paulo, São Paulo, Brazil
Abstract The use of planktonic foraminifera in paleoceanographic studies relies on the assumption that
morphospecies represent biological species with ecological preferences that are stable through time and
space. However, genetic surveys unveiled a considerable level of diversity in most morphospecies of
planktonic foraminifera. This diversity is significant for paleoceanographic applications because cryptic
species were shown to display distinct ecological preferences that could potentially help refine
paleoceanographic proxies. Subtle morphological differences between cryptic species of planktonic
foraminifera have been reported, but so far, their applicability within paleoceanographic studies remains
largely unexplored. Here we show how information on genetic diversity can be transferred to
paleoceanography using Globorotalia inflata as a case study. The two cryptic species of G. inflata are
separated by the Brazil-Malvinas Confluence (BMC), a major oceanographic feature in the South Atlantic.
Based on this observation, we developed a morphological model of cryptic species detection in core top
material. The application of the cryptic species detection model to Holocene samples implies latitudinal
oscillations in the position of the confluence that are largely consistent with reconstructions obtained from
stable isotope data. We show that the occurrence of cryptic species in G. inflata can be detected in the fossil
record and used to trace the migration of the BMC. Since a similar degree of morphological separation as in
G. inflata has been reported from other species of planktonic foraminifera, the approach presented in this
study can potentially yield a wealth of new paleoceanographical proxies.
1. Introduction
Investigations of biodiversity, biogeography, and ecological processes rely on the identification of “species”
as biologically significant, natural units of evolution. In the vast majority of cases the definition and identification of species are based on morphological characters. However, such morphotaxonomy only provides an
adequate level of resolution where biological species are morphologically distinct. In many groups of organisms, morphologically defined species mask considerable genetic diversity, which may be indicative of the
existence of cryptic species. This is the case with planktonic foraminifera, where cryptic diversity has been
shown to be a prevalent pattern and where cryptic species often display distinct ecological and/or biogeographical distribution patterns [e.g., Darling and Wade, 2008].
The prevalence of cryptic diversity in this group is significant, because the fossil record of planktonic foraminifera constitutes one of the most informative archives used in paleoceanographic studies. In addition to
quantitative empirical calibrations of sea surface temperature (SST) and the relative abundance of morphospecies in surface sediments [Imbrie and Kipp, 1971; Bé and Hutson, 1977; Malmgren et al., 2001], calcite shells
of planktonic foraminifera are the major substrate for an increasingly diverse set of geochemical proxies
[Henderson, 2002; Katz et al., 2010]. The application of both transfer functions and geochemical proxies relies
on the assumption that the sampled morphospecies of planktonic foraminifera occupy a niche that is stable
through time and space, although potential shifts in depth habitat can be traced when the target species can
be compared to an independent indicator [Hodell and Vayavananda, 1993; Ando et al., 2010; Matsui et al.,
2016]. In this context, the presence of cryptic diversity represents a large source of uncertainty in their use
as paleoceanographic proxies.
©2016. American Geophysical Union.
All Rights Reserved.
MORARD ET AL.
If the occurrence of cryptic species could be detected based on morphological characteristics, they would
represent a potential additional source of paleoceanographic information. By detection we do not necessarily
mean the discovery of consistent morphological discontinuity within a morphospecies that would justify
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10.1002/2016PA002977
taxonomic revision, as it has been done in the case of Neogloboquadrina incompta [Darling et al., 2006],
Globigerinoides elongatus [Aurahs et al., 2011], and Globigerinella radians [Weiner et al., 2015]. To make use
of cryptic species, it should be theoretically sufficient if it is shown that they occupy different portions of a
continuous morphospace [Morard et al., 2009]. In this case, changes in morphospace occupancy within a
morphospecies in the fossil record can be used to detect shifts in the proportions of the constituent cryptic
species. However, morphological diversity within a given taxonomic unit does not necessarily equate for
genetic diversity, as it has been showed that the four morphotypes occurring within the plexus Trilobatus sacculifer
are genetically identical [André et al., 2013; Spezzaferri et al., 2015].
Theoretical ecological modeling showed that the integration of cryptic diversity will increase the accuracy of
transfer functions [Kucera and Darling, 2002; Morard et al., 2013]. Despite the obvious potential, such integration has been so far limited by the lack of suitable morphological models for the recognition or detection of
cryptic diversity. The development of such models requires the collection of preserved samples covering
extensive areas allowing genetic and morphometric analyses that capture the complete living range of a given
morphospecies. These conditions are rarely met. In particular, collections of living specimens are often limited
in space and time and may not be representative for the full range of variability within the morphospecies.
A way to bypass such difficulties is to project the known biogeography of cryptic species derived from the
plankton onto surface sediments and use subfossil material from areas where a distinct cryptic species should
dominate to characterize its full range of morphological variability. Assuming the genetic identity only from
fossil populations is most useful when the biogeography of the cryptic species is sufficiently constrained and
implies a limited overlap between the cryptic species. An attempt to this end has been presented by Renaud
and Schmidt [2003], but the geographical overlap in the analyzed cryptic species of Globorotalia
truncatulinoides was too large to make the results directly applicable for paleoceanographic reconstructions.
Thus, the main conclusion of that study remained that the data on shell size and shape variation could only
be correctly interpreted when the genetic diversity was considered.
A potentially more powerful example is given by the case of Globorotalia inflata, which is constituted of two
cryptic species with mutually exclusive spatial distribution [Morard et al., 2011]. Type I of G. inflata is found in
transitional and subtropical waters of the Northern and Southern Hemispheres, whereas Type II is only
found in the subpolar waters of the Southern Hemisphere, i.e., to the south of the Subantarctic Front. This
distribution pattern makes the cryptic diversity of G. inflata a potential paleoproxy to track the past
migration of the Subantarctic Front. In addition, subtle morphological differentiation has been described
between the cryptic species suggesting that their presence could be inferred from morphological features
[Morard et al., 2011].
This study makes use of this example. It aims to demonstrate the feasibility of using the cryptic diversity of
G. inflata in the frame of paleoceanography. To this end, we explored the morphological variability of the
morphospecies in surface sediment samples collected in the western South Atlantic across the Subantarctic
Front along the eastern South American continental margin. Based on isotopic values [Chiessi et al., 2007],
we constrained the origin of sedimentary populations with respect to the position of the front and used this
information to infer their genetic identity. Analyses of the sedimentary populations were used to establish a
morphological model of cryptic species detection, which was then applied to fossil populations from three
sediment cores covering the Holocene for which extensive isotopic data are also available [Voigt et al.,
2015]. The results allow us to evaluate the use of cryptic diversity for the detection of latitudinal oscillations
of the Subantarctic Front.
2. Regional Setting and Cryptic Species Distribution
The study area encompasses the western South Atlantic, which is dominated by the encounter between the
southward flowing Brazil Current (BC) and the northward flowing Malvinas Current (MC), expressed as the
Brazil-Malvinas Confluence (BMC, Figure 1a [Peterson and Stramma, 1991]). This configuration forms one of
the most vigorous exchange areas of the world ocean, where the confluence of the two currents forms sharp
temperature and salinity gradients (Figure 1b). The BMC is approximately located at 38°S along the shelf
break and can vary by 900 km latitude between seasonal extremes (Figures 1a and 1b) [Olson et al., 1988].
At the confluence between the two currents the BC flows eastward as the South Atlantic Current and the
MC flow eastward as the northernmost branch of the Antarctic Circumpolar Current.
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Figure 1. Location of samples and environmental setting. (a) Location of the investigated samples in the western South
Atlantic. The colors in the background correspond to the mean annual sea surface temperature (SST) [Locarnini et al.,
2013]. The circles correspond to the locations of the core top samples isotopically analyzed in Chiessi et al. [2007], the
squares correspond to the plankton tow samples genetically and morphometrically analyzed in Morard et al. [2011], and
the stars correspond to the downcore samples isotopically analyzed by Voigt et al. [2015]. The symbols colored in red
correspond to the samples where only Type I Globorotalia inflata genotype occurs, the blue symbols correspond to the
samples where only Type II G. inflata genotype occurs, and the grey star corresponds to the sample where the two G. inflata
genotypes are expected to co-occur. The samples represented by the black symbols have not been analyzed in the present
study. Black arrows represent main annual surface ocean currents redrawn from Chiessi et al. [2007]. (b) SST variability at
regional scale during austral summer (January) and winter (July) plotted against latitude [Locarnini et al., 2013]. The shading
in the background corresponds to the monthly averaged SST values extracted from the World Ocean Atlas 2013 with
the 0.25° grid, and the thick curve corresponds to the smoothed averaged calculated using ggplot2 [Wickham, 2009]
implemented in R [R Development Core Team, 2014]. (c) Stable oxygen isotopic values of G. inflata observed from core tops
plotted against latitude [Chiessi et al., 2007]. Colors as in Figure 1a.
Living specimens of G. inflata were collected in both the MC and the BC during the austral spring during the
AMT-5 cruise [Aiken et al., 1998]. Specimens belonging to the cryptic species Type I were found in the warm
waters of the BC, whereas the specimens belonging to the cryptic species Type II were found only in the cold
waters of the MC (Figure 1a). The same biogeographic pattern has also been observed in the southern Indian
Ocean [Morard et al., 2011]. Because they have never been observed co-occurring, the two cryptic species can
be considered allopatric. In the western South Atlantic, the two cryptic species are considered to be separated by the BMC and therefore could be used to track its past latitudinal migration.
3. Material and Methods
3.1. Sample Collections
We investigated the morphological variability of G. inflata in 13 core tops, 14 downcore samples, and 5
plankton tow samples (Figure 1 and Table 1). The core top samples were taken during R/V Meteor cruises
M23/2 [Bleil et al., 1993], M29/1 [Segl et al., 1994], M46/2 [Schulz et al., 2001], M46/3 [Bleil et al., 2001a],
and M49/3 [Bleil et al., 2001b], and the downcore samples were taken during R/V Meteor cruises M46/2,
M46/3, and M78/3 [Krastel et al., 2012]. Plankton tow samples were recovered during the AMT-5 cruise
[Aiken et al., 1998]. The core top and downcore samples were analyzed for stable oxygen isotopes by
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Table 1. Location and Related Information of the Investigated Samples
Code Location
Cruise
Device
AMT5 st-24
AMT5 st-25
AMT5 st-26
AMT5 st-28
AMT5 st-30
GeoB13862
GeoB13862-1
GeoB13862-1
GeoB13862-1
GeoB13862-1
GeoB2722-1
GeoB6209-2
GeoB6211-1
GeoB6211-2
GeoB6211-2
GeoB6211-2
GeoB6211-2
GeoB6211-2
GeoB6217-2
GeoB6218-1
GeoB6220-1
GeoB6222-2
GeoB6308-3
GeoB6308-3
GeoB6308-3
GeoB6308-3
GeoB6308-3
GeoB6308-3
GeoB6311-2
GeoB6314-2
GeoB6334-2
GeoB6336-2
AMT-5
AMT-5
AMT-5
AMT-5
AMT-5
M78/3B
M78/3B
M78/3B
M78/3B
M78/3B
M29-1
M32-2
M46-2
M46-2
M46-2
M46-2
M46-2
M46-2
M46-2
M46-2
M46-2
M46-2
M46-3
M46-3
M46-3
M46-3
M46-3
M46-3
M46-3
M46-3
M46-3
M46-3
Simple Net
Simple Net
Simple Net
Simple Net
Simple Net
Multi-Corer
Gravity Core
Gravity Core
Gravity Core
Gravity Core
Multi-Corer
Multi-Corer
Multi-Corer
Gravity Core
Gravity Core
Gravity Core
Gravity Core
Gravity Core
Multi-Corer
Multi-Corer
Multi-Corer
Multi-Corer
Gravity Core
Gravity Core
Gravity Core
Gravity Core
Gravity Core
Multi-Corer
Multi-Corer
Multi-Corer
Multi-Corer
Multi-Corer
Latitude Longitude
(°S)
(°W)
35.29
38.5
42.14
46.03
49.79
38.01
38.01
38.01
38.01
38.01
47.33
31.76
32.5
32.5
32.5
32.5
32.5
32.5
34.72
35.05
33.36
34.08
39.3
39.3
39.3
39.3
39.3
39.17
38.81
39.64
46.09
46.14
48.52
51.55
54.27
56.42
57.62
53.74
53.74
53.74
53.74
53.74
58.62
48.15
50.24
50.24
50.24
50.24
50.24
50.24
51
50.78
49.39
48.62
53.96
53.96
53.96
53.96
53.96
54.14
54.63
55.15
58.52
57.85
Analyzed in
Depth
Sampled
Age
Model
Number of
Analyzed Genetic
18
Type
δ O Broken Kummerform Specimens
Morard et al. [2011]
Morard et al. [2011]
Morard et al. [2011]
Morard et al. [2011]
Morard et al. [2011]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Chiessi et al. [2007]
Chiessi et al. [2007]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Chiessi et al. [2007]
Chiessi et al. [2007]
Chiessi et al. [2007]
Chiessi et al. [2007]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Voigt et al. [2015]
Chiessi et al. [2007]
Chiessi et al. [2007]
Chiessi et al. [2007]
Chiessi et al. [2007]
Chiessi et al. [2007]
Plankton
Plankton
Plankton
Plankton
Plankton
0–1 cm
132 cm
254 cm
470 cm
642 cm
0–1 cm
0–1 cm
0–1 cm
28 cm
38 cm
48 cm
58 cm
68 cm
0–1 cm
0–1 cm
0–1 cm
0–1 cm
1–2 cm
6.50 cm
26.5 cm
59.5 cm
86.5 cm
0–1 cm
0–1 cm
0–1 cm
0–1 cm
0–1 cm
N/A
N/A
N/A
N/A
N/A
Recent
1250
3900
6140
8530
Recent
Recent
Recent
2260
3240
4190
5310
6840
Recent
Recent
Recent
Recent
330
590
2620
5490
8010
Recent
Recent
Recent
Recent
Recent
N/A
N/A
N/A
N/A
N/A
1.34
2.47
1.66
1.18
2.43
2.93
N/A
0.83
0.93
0.88
1.04
0.92
1.22
1.54
1.1
0.75
1.08
2.87
2.9
2.8
2.92
2.74
2.9
2.73
2.65
2.88
2.91
4
3
3
2
-
1
1
5
2
1
-
28
28
28
28
28
31
28
28
27
30
29
55
29
27
27
27
29
27
28
31
28
27
27
32
32
29
31
31
29
32
70
36
I
I
II
II
II
Mixture
Mixture
Mixture
Mixture
Mixture
II
I
I
I
I
I
I
I
I
I
I
I
II
II
II
II
II
II
II
II
II
II
Chiessi et al. [2007] and Voigt et al. [2015], and the specimens collected with plankton tows were morphometrically analyzed in Morard et al. [2011].
We used the modern biogeographic occurrence of the two genotypes of G. inflata based on Morard et al.
[2011] coupled with oxygen isotopic signatures in specimens from surface sediment samples [Chiessi et al.,
2007] to estimate the putative taxonomic composition of the sampled cores (Figure 1). In the modern ocean,
Type I occurs to the north of the BMC and the Type II the south [Morard et al., 2011]. The mean δ18O signature
of G. inflata to the north of the BMC is 0.61 to 1.70‰, whereas values to the south of the BMC are 2.33 to
3.06‰ [Chiessi et al., 2007]. Based on these criteria, populations of G. inflata from six core top and five downcore samples located in the BC were assigned to Type I, and populations of G. inflata in six core top and five
downcore samples located in the MC were assigned to Type II. One core top and four downcore samples from
the core GeoB13862-1 located at the confluence of both currents (i.e., BMC) could not be unambiguously
classified by these criteria and were therefore considered to potentially contain a mixture of both Types
(Table 1 and Figure 1).
3.2. Selection of Morphological Variables
To select optimal variables for characterizing morphological variability among the cryptic species of G. inflata,
an initial set of 112 specimens from the uppermost centimeter of cores GeoB6209-2 and GeoB6334-2 was
analyzed. These cores are presently not affected by the seasonal migration of the BMC (Figure 1 and
Table 1), and the analyzed specimens should thus represent pure populations of the two cryptic species.
To determine the minimum number of measurements needed to effectively characterize the difference in
morphospace occupancy between the two populations, we tested several combinations of morphological
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Figure 2. Appraisal of the morphological variability in Globorotalia inflata. (a1) Principal component analysis of the 997
specimens morphometrically analyzed. A sketch of a specimen is provided with the position of the landmarks. The
correlation between the (a2 and a3) two first components and size and (a4 and a5) oxygen isotopic values are also shown.
The coefficient of correlation and associated p values are given within each box. (b1) Distribution of specimens attributed
to the Type I and Type II exclusively from core top samples. Sketch of specimen analyzed is provided to represent the
morphological variability. Distribution of the two types for (b2) size, (b3) PC1, and (b4) PC2.
characters. Rather than simple metrics, as used originally by Morard et al. [2011], we chose to characterize the
shape of the shells as a whole by a set of comparable points (landmarks) positioned on the spiral and lateral
views of the shell. Because of the lack of anatomical landmarks (biologically strictly homologous points) in
Globorotalia inflata, we resorted to the use of geometrically homologous landmarks (extremal points or
points of maximum curvature) to characterize the morphology of each specimen. All measurements were
done by a single person (i.e., Melanie Reinelt) to reduce noise due to data acquisition. The optimal model only
requires the identification of 11 landmarks (Figure 2a1) located on the terminal chamber of the specimens
oriented in the lateral view. A more detailed discussion of the methodology development can be found in
the supporting information S1 [Hammer et al., 2001; Rohlf, 2005; Cardini and Elton, 2007; Klingenberg, 2011;
Morard et al., 2011].
3.3. Morphological Model of Recognition and Mixture Assessment
For the analysis of the morphological variability in space and through time, the >0.150 mm residues from 11
additional core top samples (in addition to the two samples used to select the morphological variables
described above) and 14 downcore samples were split to obtain random samples of at least 26 specimens,
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which was identified as the minimum
amount to confidently represent the
morphological variability of each population (see supporting information S1).
Kummerform and broken specimens
were excluded. The retained specimens
were mounted on glass slides, oriented
and photographed with a Canon EOS
600D mounted on a Zeiss Stereo
Discovery V8 microscope using a magnification of 6.3 (1 pixel = 0.344 μm),
and the same set of 11 landmarks was
digitized for every specimen with TPS v
2.17 [Rohlf, 2005]. The major axis of each
specimen in the lateral view was also
measured. In addition, photographs of
G. inflata collected by plankton tows
and analyzed in Morard et al. [2011]
were reanalyzed in the same way to
allow intercomparison between both
studies. Coordinates of all specimens
were exported to MorphoJ, and the
Procrustes fits were calculated.
The morphological variability among
the specimens was appraised by performing a principal component analysis
(PCA) on the Procrustes coordinates
with Past 2.17 [Hammer et al., 2001].
The morphological variation along the
two first principal components was
compared with size (Figures 2a2 and
2a3) and literature-derived δ18O for
the core top and downcore samples
(Table 1 and Figures 2a4 and 2a5). This
was done in order to explore the ontogenetic and ecophenotypic effects on
the morphology. We chose to test the
correlation with the δ18O instead of
abiotic factors such as temperature
and salinity because the potential living
depth range of the species is large
(0–500 m [Wilke et al., 2006]) and the isotopic values reflect the conditions of the
water mass where most of the tests of
Figure 3. Discrimination of genotypes. (a)
Morphological model of recognition of the
two genotypes of Globorotalia inflata based
on core top samples and subsequent
application on (b) the southern core
assumed to be composed of Type II only, (c)
the northern core assumed to be composed
of the Type I only, and (d) the central core
composed of a mixture of both genotypes.
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the specimens have been secreted
[Lončarić et al., 2006] and are therefore
a direct proxy for the conditions in the
habitat of the specimens [Groeneveld
and Chiessi, 2011].
The degree of separation between the
two genotypes was determined by
linear discriminant analysis of the
Procrustes coordinates of specimens
from all core top samples that show
stable isotopic values indicative for a
consistent position on one side of the
BMC (Figure 3a). The discriminant function was then applied on specimens
from the downcore samples of core
GeoB6308-3, assumed to contain only
Type II specimens (Figure 3b), of core
GeoB6211-2, assumed to contain only
specimens of the Type I (Figure 3c),
and of core GeoB13862-1, assumed to
contain a mixture of both genotypes
(Figure 3d).
Next, we designed a simple model
to predict the relative proportion of
both genotypes in fossil assemblages
(Figure 4). We first produced artificial
assemblages by randomly sampling
the scores of the discriminant function
of specimens from cores GeoB6308-3
(Type II) and GeoB6211-2 (Type I), generating artificial assemblages of 30
specimens with relative proportions of
100% to 0% of Type I and 0% to 100%
of Type II with increments of 10%
(Figure 4a), with 1000 random iterations
for each of the 11 relative proportions of
genotypes. This produced averaged
theoretical distributions of the discrimi-
Figure 4. Mixture model. (a) Average profiles
of the mixed populations based on 1000
random sampling of the discriminant score
of the downcore samples (Figures 4b and 4c)
of the northern core (Type I) and southern
core (Type II) for a total of 30 specimens. The
relative proportion of the two genotypes is
given by the circular diagram in the left
upper corner of each biplot where the proportion of Type I is in red and the proportion
of Type II in blue (dark part: 1st–3rd quartiles = 50% confidence interval; gray part:
5th–95th percentiles = 90% confidence
interval; black dot: average). (b) Matching
scores of 10,000 random mixed populations
with the averaged profiles.
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18
Figure 5. Downcore morphological variation. (a) Ice volume-corrected Globorotalia inflata δ O variation during the
Holocene performed for the three cores from Voigt et al. [2015], the 15 samples selected for morphometric measurements are highlighted by circled numbers (red = Type I; blue = Type II; and grey = mixture of both genotypes). (b) Matching
score with the mixture model (Figure 4) of each downcore sample.
nant scores for a given relative proportion of genotypes. To estimate the stability and reliability of the estimation of relative proportions of genotypes, the discriminant function score distribution of 10,000 artificial
assemblages generated in the same manner was compared with the average score distribution, and the similarity was expressed as the number of bins where the abundance matched the 50% range of the averaged
distribution for that bin (Figure 4b). The matching score of the assemblages of the fossil samples was calculated in the same way to estimate the relative proportion of genotypes in these samples and compared it to
the isotopic values given by Voigt et al. [2015] (Figure 5).
Finally, we estimated the potential impact of small sample size on isotopic measurements when the
sampled fossilized populations can incorporate specimens with divergent values, such as expected to be
the case for core GeoB13862-1. Again, we set a simple model by considering theoretical fossil assemblages
of G. inflata with relative proportions of 100% to 0% of Type I and 0% to 100% of Type II with increments of
10%. This time, we considered a random picking of 5 to 100 specimens from those theoretical populations
and attributed to these specimens randomized isotopic values ranging from 0.61 to 1.70‰ for Type I specimens and 2.33 to 3.06‰ for Type II specimens. The isotopic range was defined by the extreme values
observed in the northern and southern cores [Voigt et al., 2015] considered as constituted of pure assemblages of either type (Figure 6). We calculated the theoretical isotopic value obtained from the simulated
set of picked specimens by simple averaging of their isotopic values. We repeated the simulation for each
sample size 1000 times. We neglected the impact of the weight variability of the specimens in the simulation that will necessarily occur in a natural population due to variability in size and differential intensity of
calcification [Weinkauf et al., 2013].
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Figure 6. Theoretical modeling of the effect of small sampling on isotopic measurements. The variability in isotopic
measurements for a given sample size and various relative proportions of genotypes are represented by violin plots
(box plot associated with kernel density), in which variation in thickness represent a density of probability. The violin plots
are centered on the average isotopic value of each theoretical assemblage.
4. Results
4.1. Overall Morphological Variability
A total of 997 specimens of G. inflata were measured to appraise the morphological variability of the two
cryptic species in plankton, core top, and downcore samples (Figure 2). Joint PCA of all specimens revealed
that about half of the total morphological variability among the specimens can be expressed by the first
two components. The distribution of scores of specimens in the space of these two axes showed that morphological variability in the core top samples covers the entire morphospace, the downcore samples cover
a smaller portion of it, and the plankton samples capture the smallest portion of the variability (Figure 2a1).
The morphospace is continuous, confirming the cryptic status of the two genetic types. The scores of both
axes correlate with size, indicating an allometric component in the shape of the last chamber, but this relationship only explains a small part of the morphological variability (in both cases r2 < 0.1). Further multivariate linear regression performed between size and the score of all components returns similar results
(r2 = 0.044; p = 3.185E 73). Similarly, there is a weak but significant correlation between the first principal
component and δ18O (Figures 2a2–2a5). The scores along the first two PCA axes for specimens from the
12 surface sediment samples located unambiguously on either side of the BMC reveal a large degree of
overlap (Figure 2b). One-way analysis of variance (ANOVA) indicates no significant difference in size
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(F = 0.1127; df = 1; p = 0.7373), but a one-way PERMANOVA performed with Euclidian distance measure on
the principal components scores indicates that the populations on either side of the BMC are morphologically significantly different (F = 16.76; df = 1; p = 0.0001).
4.2. Morphological Model for the Detection of Cryptic Species
In order to develop a morphological model that emphasized the difference between the two cryptic
species of G. inflata, a discriminant analysis was applied on 425 specimens from the 12 core top samples
consistently recording conditions on either side of the BMC (Figure 3). The analysis confirms the existence
of a significant difference in shape between the two populations, allowing correct assignment to either
side of the BMC of ~80% of the specimens, with the leave-one-out returning essentially the same level
of efficiency (Hotteling's t2 = 341.71; F = 14.81; p(same) = 3.60.10 40). The application of this model on
downcore samples from the northern (Type I) and the southern cores (Type II) indicates the same level
of specificity with >80% of the classification of the downcore specimens corresponding to the position
of the core with respect to the BMC (Figures 3b and 3c). The classification accuracy given by the
discriminant function is comparable to classification models developed for other cryptic species of
planktonic foraminifera based on genotyped specimens from the plankton [Morard et al., 2009;
Quillévéré et al., 2013]. The accuracy is higher than the ~66% successful classification obtained for the
same species with simple biometric measurements during a previous study [Morard et al., 2011].
Therefore, we did not attempt to further optimize the discrimination, but we note that approaches like
dimensionality reduction [Evin et al., 2013] could potentially further improve the classification accuracy
of the discriminant analysis.
An application of the model on samples from the central core suggests a mixture of both genotypes but
with the dominance of Type II typical for the region to the south of the BMC (74% of classified specimens
attributed to Type II, Figure 3d). As shown by the isotopic measurements performed by Voigt et al. [2015],
the central core recorded the migration of the BMC and thus a potential mixture of both genotypes is
expected. To determine the likely composition of the assemblages in this central core, we applied the mixture model (Figure 4). The model reflects the limitations imposed by the large morphological overlap
between the populations ascribed to the two types. However, it allows detection of dominance of either
type, as an assemblage dominated by one type never produces high scores with the theoretical profile produced by the other type. It should also allow to differentiate mixtures composed of equal or almost equal
proportion of genotypes from pure or almost pure assemblages.
Being conscious of the potential limitation of our mixture model, we applied it on the downcore assemblages of the three cores (Figure 5). As expected, the assemblages of the northern and southern cores
were identified as being composed dominantly by one type: samples in the northern core showed
scores distribution indicative of 100–70% of Type I, while samples in the southern core indicated a composition of 100–90% of Type II. The application of the mixture model on the central core indicated a
more mixed assemblage, with sample composition varying between 70–50% of Type I and 100–90%
of Type II.
5. Discussion
Appraisal of the morphological variability of G. inflata in surface sediment samples revealed that the dominant pattern of morphological variability even in a set of characters optimized to differentiate between the
cryptic species is not reflecting the presence of the cryptic species (Figure 2). Not only is there no evidence
for a discontinuity in the morphospace, but the direction of variation along the first axis does not coincide
with the difference between the two cryptic species. This observation explains why the two genetic types
remained unnoticed by classical taxonomy and confirm their status as cryptic species. Nevertheless, the
analysis also confirms the observations from plankton samples presented by Morard et al. [2011] that
the two genetic types are not occupying the same part of the total morphospace of the morphospecies.
The existence of a differentiation among the populations from either side of the BMC, reflected in the
bimodal distribution of discriminant scores (Figure 3), indicates an incipient differentiation, which could
eventually lead to the characterization of morphologically distinguishable species. For now, the overlap
is too large to be able to use the model to identify individual specimens, but the differentiation is large
enough to detect the presence and mixture between the two populations (Figures 4 and 5).
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Because the set of core top samples covers the largest portion of the ecological gradient along which the
species occurs, we expect the largest morphological variability to occur among the core top specimens.
Indeed, the total morphological variability encountered in the surface sediments is higher than the morphological variability encountered in the plankton and downcore samples (Figure 2a). This suggests that
establishing the morphological model of cryptic species detection from core tops covering a large environmental gradient is most appropriate to cover the complete morphological range within the morphospecies.
The application of the morphological model of recognition based on the same core top samples onto fossil
population from the northern and southern cores, located on both sides of the BMC and assumed to be
composed only by Type I and Type II, respectively, returned the expected results of classification (~80%
of correct classification), confirming our working hypothesis (Figure 3). The application of the model on
the selected samples of the central core returned results consistent with the presence of both genotypes.
Our simple but robust model (Figure 4) used to estimate the relative proportion of genotypes returned
positively unambiguous results for the northern and southern cores, indicating that the model could be
used to detect the dominance of either cryptic species in fossil samples, allowing interpretation of results
from the central core, which has been hypothesized to record latitudinal migration of the BMC during
the Holocene [Voigt et al., 2015].
The application of the cryptic species detection model on samples from the central core indeed confirms the
occurrence of mixed populations (Figure 5). Based on the oxygen isotope signature in G. inflata, samples
labeled as numbers 2 and 5 should be dominated by Type II. This conjecture is confirmed by the scores of
the mixture model, interpreting the scores as best fitting samples containing 80–90% of Type II (Figure 5).
In the remaining three samples (numbers 1, 3, and 4), isotopic values suggested a significant contribution
of Type I. In these samples, the mixture model inferred a contribution of Type II as being only 70–30%
(Figure 5). These results imply that the population shifts between the two cryptic species are detectable both
by isotopic signatures and by morphology.
If the isotopic signatures in samples numbers 3 and 4 were interpreted strictly, they would imply that the
populations in these samples should be dominated by Type I. Yet the mixture model only indicates a
50–70% contribution by this Type (Figure 5). To explain this apparent conflict, we first eliminated potential
errors linked to (i) the isotopic measurements (these are less than 0.07‰ [Voigt et al., 2015]), (ii) differences
in the development of crust between the two genotypes (both populations had similarly well-developed
crust, and postmortem processes are not expected to differentially affect the two types), (iii) error of the morphological model of recognition (unlikely because of consistent results for the northern and southern cores),
and (iv) difference in the size fractions used for isotope data (unlikely as a relatively narrow fraction 315–
400 μm was used for isotopic measurements and both genotypes cover similar size range, Figure 2b2).
Then, we modeled the effect of a small sample size coupled with the presence of two genotypes in the investigated assemblages while picking for isotopic measurements. In other words, we quantified the odds of producing a representative average for a mixed assemblage when the measurement is based on a small number
of specimens (Figure 6).
In the study by Voigt et al. [2015], the stable isotope values are based on approximately 10 specimens. The
results of our model for mixed population of both types (50:50) imply that it is very likely that a given value
will deviate by 0.5‰ from the actual mean (Figure 6). Since 2‰ is the shift in isotopic values observed across
the BMC [Chiessi et al., 2007], measurements from the same mixed population based on 10 specimens as was
the case by Voigt et al. [2015] can be expected to vary by as much as one half of the range. This potential artifact could not only explain why the morphological detection results are in absolute values not entirely congruent with the isotopic data but also provide a potential explanation for the high variability in the isotopic
measurements from the central core (Figure 5). To reduce the scattering to an acceptable range, we suggest
to (i) pick a large number of specimens (>50), crush them, homogenize the obtained calcite powder, and perform the analysis on it; (ii) make replicate measurements to assess the reliability of the picking; or (iii) only
interpret trends resulting from averaged observation of adjacent samples. Indeed, because Voigt et al.
[2015] only interpreted trends defined by 10 or more adjacent samples (i.e., >100 specimens), their interpretation about the latitudinal migration of the BMC remains robust, as demonstrated by the favorable comparison to the transient run of a state-of-the-art comprehensive coupled atmosphere-ocean general circulation
model [Voigt et al., 2015]. However, this third option masks potential informative short-term meridional shifts
of the BMC.
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Reliable single-specimen isotopic measurements are now feasible [Feldmeijer et al., 2015; Metcalfe et al., 2015],
as well as measurements of single chambers [Vetter et al., 2013]. Interpreting these data will require a finer
knowledge on the ecology of foraminifera which will necessarily include the distribution of their constituent
cryptic species in space and time. The approach shown here would potentially allow selection of single
specimens from fossil samples that are most likely to represent a given cryptic species.
6. Conclusion
We conclude that the existence of cryptic diversity in planktonic foraminifera can be seen as a chance to
refine our knowledge of past oceans. Morphometric measurements can potentially be automated, and
user-friendly freeware exists to build and apply fast morphological models of detection. The morphometric
approach shown in the present study represents only one possibility to express the shape of a foraminiferal
shell and shall be seen as a “proof of concept” on how the transfer of knowledge of cryptic diversity onto
fossil record can be done. Alternative morphometric methods could be applied to find the best compromise
between measurement effort and classification success prior to large-scale application. The only limit to
bridge the gap between cryptic diversity and paleoceanography is the availability of data on the occurrence
of cryptic species in the modern ocean and the existence of morphological differences (no matter how fine)
among them. To this end, the SCOR WG138 has initiated an effort to synthesize the available knowledge on
planktonic foraminifera by gathering all existing data into the Planktonic Foraminifera Ribosomal Reference
database (PFR2, http://pfr2.sb-roscoff.fr/ [Morard et al., 2015]) and proposing a standardized nomenclature
of the known cryptic species [Morard et al., 2016]. This will be further strengthened by the availability of
metagenomic data sets [de Vargas et al., 2015] that will make accessible the distribution of all cryptic species
in planktonic foraminifera, allowing a comprehensive characterization of constraints on their ecology. Since it
can be expected that many of the cryptic species are ecologically distinct, this progress will open new
avenues for paleoceanography.
Acknowledgments
The authors are thankful to Brian
T. Huber and one anonymous reviewer
for their constructive comments. This
work was supported by DFG-Research
Center/Cluster of Excellence “The Ocean
in the Earth System.” C.M. Chiessi
acknowledges the financial support
from FAPESP (grant 2012/17517-3) and
CAPES (grants 1976/2014 and
564/2015). The authors are thankful to
the GeoB Core repository at the
MARUM, Center for Marine
Environmental Sciences, University of
Bremen and Ines Voigt for providing the
samples. The data reported in this paper
will be archived in Pangaea
(www.pangaea.de).
MORARD ET AL.
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