Tracing shifts of oceanic fronts using the crypticdiversity of the planktonic foraminiferaGloborotalia inflataRaphaël Morard1, Melanie Reinelt1, Cristiano M. Chiessi2, Jeroen Groeneveld1, and Michal Kucera1

1MARUM Center for Marine Environmental Sciences, University of Bremen, Bremen, Germany, 2School of Arts, Sciences andHumanities, University of São Paulo, São Paulo, Brazil

Abstract The use of planktonic foraminifera in paleoceanographic studies relies on the assumption thatmorphospecies represent biological species with ecological preferences that are stable through time andspace. However, genetic surveys unveiled a considerable level of diversity in most morphospecies ofplanktonic foraminifera. This diversity is significant for paleoceanographic applications because crypticspecies were shown to display distinct ecological preferences that could potentially help refinepaleoceanographic proxies. Subtle morphological differences between cryptic species of planktonicforaminifera have been reported, but so far, their applicability within paleoceanographic studies remainslargely unexplored. Here we show how information on genetic diversity can be transferred topaleoceanography using Globorotalia inflata as a case study. The two cryptic species of G. inflata areseparated 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 topmaterial. The application of the cryptic species detection model to Holocene samples implies latitudinaloscillations in the position of the confluence that are largely consistent with reconstructions obtained fromstable isotope data. We show that the occurrence of cryptic species in G. inflata can be detected in the fossilrecord and used to trace the migration of the BMC. Since a similar degree of morphological separation as inG. inflata has been reported from other species of planktonic foraminifera, the approach presented in thisstudy 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 identifica-tion of species are based on morphological characters. However, such morphotaxonomy only provides anadequate level of resolution where biological species are morphologically distinct. In many groups of organ-isms, morphologically defined species mask considerable genetic diversity, which may be indicative of theexistence of cryptic species. This is the case with planktonic foraminifera, where cryptic diversity has beenshown to be a prevalent pattern and where cryptic species often display distinct ecological and/or biogeo-graphical distribution patterns [e.g., Darling and Wade, 2008].

The prevalence of cryptic diversity in this group is significant, because the fossil record of planktonic forami-nifera constitutes one of the most informative archives used in paleoceanographic studies. In addition toquantitative empirical calibrations of sea surface temperature (SST) and the relative abundance of morphos-pecies in surface sediments [Imbrie and Kipp, 1971; Bé and Hutson, 1977; Malmgren et al., 2001], calcite shellsof 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 relieson the assumption that the sampled morphospecies of planktonic foraminifera occupy a niche that is stablethrough time and space, although potential shifts in depth habitat can be traced when the target species canbe 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 useas paleoceanographic proxies.

If the occurrence of cryptic species could be detected based on morphological characteristics, they wouldrepresent a potential additional source of paleoceanographic information. By detection we do not necessarilymean the discovery of consistent morphological discontinuity within a morphospecies that would justify

MORARD ET AL. CRYPTIC DIVERSITY IN PALEOCEANOGRAPHY 1

PUBLICATIONSPaleoceanography

RESEARCH ARTICLE10.1002/2016PA002977

Key Points:• Cryptic species of Globorotaliainflata can be detected based onmorphology in sediments

• It can be used to reconstruct the shiftsof the Brazil-Malvinas Confluence inthe South Atlantic

• Cryptic diversity represents a source ofinformation for paleoceanography

Supporting Information:• Supporting Information S1• Data Set S1

Correspondence to:R. Morard,rmorard@marum.de

Citation:Morard, R., M. Reinelt, C. M. Chiessi,J. Groeneveld, and M. Kucera (2016),Tracing shifts of oceanic fronts using thecryptic diversity of the planktonicforaminifera Globorotalia inflata,Paleoceanography, 31, doi:10.1002/2016PA002977.

Received 17 MAY 2016Accepted 14 AUG 2016Accepted article online 22 AUG 2016

©2016. American Geophysical Union.All Rights Reserved.

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 useof cryptic species, it should be theoretically sufficient if it is shown that they occupy different portions of acontinuous morphospace [Morard et al., 2009]. In this case, changes in morphospace occupancy within amorphospecies in the fossil record can be used to detect shifts in the proportions of the constituent crypticspecies. However, morphological diversity within a given taxonomic unit does not necessarily equate forgenetic diversity, as it has been showed that the fourmorphotypes occurringwithin the plexus Trilobatus sacculiferare genetically identical [André et al., 2013; Spezzaferri et al., 2015].

Theoretical ecological modeling showed that the integration of cryptic diversity will increase the accuracy oftransfer functions [Kucera and Darling, 2002;Morard et al., 2013]. Despite the obvious potential, such integra-tion has been so far limited by the lack of suitable morphological models for the recognition or detection ofcryptic diversity. The development of such models requires the collection of preserved samples coveringextensive areas allowing genetic andmorphometric analyses that capture the complete living range of a givenmorphospecies. These conditions are rarely met. In particular, collections of living specimens are often limitedin 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 theplankton onto surface sediments and use subfossil material from areas where a distinct cryptic species shoulddominate to characterize its full range of morphological variability. Assuming the genetic identity only fromfossil populations is most useful when the biogeography of the cryptic species is sufficiently constrained andimplies a limited overlap between the cryptic species. An attempt to this end has been presented by Renaudand Schmidt [2003], but the geographical overlap in the analyzed cryptic species of Globorotaliatruncatulinoides 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 onlybe 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 twocryptic species with mutually exclusive spatial distribution [Morard et al., 2011]. Type I of G. inflata is found intransitional and subtropical waters of the Northern and Southern Hemispheres, whereas Type II is onlyfound in the subpolar waters of the Southern Hemisphere, i.e., to the south of the Subantarctic Front. Thisdistribution pattern makes the cryptic diversity of G. inflata a potential paleoproxy to track the pastmigration of the Subantarctic Front. In addition, subtle morphological differentiation has been describedbetween 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 ofG. inflata in the frame of paleoceanography. To this end, we explored the morphological variability of themorphospecies in surface sediment samples collected in the western South Atlantic across the SubantarcticFront 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 thisinformation to infer their genetic identity. Analyses of the sedimentary populations were used to establish amorphological model of cryptic species detection, which was then applied to fossil populations from threesediment 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 oscillationsof 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 thesouthward flowing Brazil Current (BC) and the northward flowing Malvinas Current (MC), expressed as theBrazil-Malvinas Confluence (BMC, Figure 1a [Peterson and Stramma, 1991]). This configuration forms one ofthe most vigorous exchange areas of the world ocean, where the confluence of the two currents forms sharptemperature and salinity gradients (Figure 1b). The BMC is approximately located at 38°S along the shelfbreak 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 theMC flow eastward as the northernmost branch of the Antarctic Circumpolar Current.

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Living specimens of G. inflata were collected in both the MC and the BC during the austral spring during theAMT-5 cruise [Aiken et al., 1998]. Specimens belonging to the cryptic species Type I were found in the warmwaters of the BC, whereas the specimens belonging to the cryptic species Type II were found only in the coldwaters of the MC (Figure 1a). The same biogeographic pattern has also been observed in the southern IndianOcean [Morard et al., 2011]. Because they have never been observed co-occurring, the two cryptic species canbe considered allopatric. In the western South Atlantic, the two cryptic species are considered to be sepa-rated by the BMC and therefore could be used to track its past latitudinal migration.

3. Material and Methods3.1. Sample Collections

We investigated the morphological variability of G. inflata in 13 core tops, 14 downcore samples, and 5plankton tow samples (Figure 1 and Table 1). The core top samples were taken during R/V Meteor cruisesM23/2 [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

Figure 1. Location of samples and environmental setting. (a) Location of the investigated samples in the western SouthAtlantic. 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], thesquares correspond to the plankton tow samples genetically and morphometrically analyzed in Morard et al. [2011], andthe stars correspond to the downcore samples isotopically analyzed by Voigt et al. [2015]. The symbols colored in redcorrespond to the samples where only Type I Globorotalia inflata genotype occurs, the blue symbols correspond to thesamples where only Type II G. inflata genotype occurs, and the grey star corresponds to the sample where the two G. inflatagenotypes are expected to co-occur. The samples represented by the black symbols have not been analyzed in the presentstudy. Black arrows represent main annual surface ocean currents redrawn from Chiessi et al. [2007]. (b) SST variability atregional scale during austral summer (January) and winter (July) plotted against latitude [Locarnini et al., 2013]. The shadingin the background corresponds to the monthly averaged SST values extracted from the World Ocean Atlas 2013 withthe 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 topsplotted against latitude [Chiessi et al., 2007]. Colors as in Figure 1a.

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Chiessi et al. [2007] and Voigt et al. [2015], and the specimens collected with plankton tows were morphome-trically 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 signatureof 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 to3.06‰ [Chiessi et al., 2007]. Based on these criteria, populations of G. inflata from six core top and five down-core samples located in the BC were assigned to Type I, and populations of G. inflata in six core top and fivedowncore samples located in the MCwere assigned to Type II. One core top and four downcore samples fromthe core GeoB13862-1 located at the confluence of both currents (i.e., BMC) could not be unambiguouslyclassified 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 characterizingmorphological 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 wasanalyzed. These cores are presently not affected by the seasonal migration of the BMC (Figure 1 andTable 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 inmorphospace occupancy between the two populations, we tested several combinations of morphological

Table 1. Location and Related Information of the Investigated Samples

Code Location Cruise DeviceLatitude

(°S)Longitude

(°W) Analyzed inDepth

SampledAgeModel δ18O Broken Kummerform

Number ofAnalyzedSpecimens

GeneticType

AMT5 st-24 AMT-5 Simple Net 35.29 48.52 Morard et al. [2011] Plankton N/A N/A - - 28 IAMT5 st-25 AMT-5 Simple Net 38.5 51.55 Morard et al. [2011] Plankton N/A N/A - - 28 IAMT5 st-26 AMT-5 Simple Net 42.14 54.27 Morard et al. [2011] Plankton N/A N/A - - 28 IIAMT5 st-28 AMT-5 Simple Net 46.03 56.42 Morard et al. [2011] Plankton N/A N/A - - 28 IIAMT5 st-30 AMT-5 Simple Net 49.79 57.62 Morard et al. [2011] Plankton N/A N/A - - 28 IIGeoB13862 M78/3B Multi-Corer 38.01 53.74 Voigt et al. [2015] 0–1 cm Recent 1.34 - - 31 MixtureGeoB13862-1 M78/3B Gravity Core 38.01 53.74 Voigt et al. [2015] 132 cm 1250 2.47 - - 28 MixtureGeoB13862-1 M78/3B Gravity Core 38.01 53.74 Voigt et al. [2015] 254 cm 3900 1.66 - - 28 MixtureGeoB13862-1 M78/3B Gravity Core 38.01 53.74 Voigt et al. [2015] 470 cm 6140 1.18 - - 27 MixtureGeoB13862-1 M78/3B Gravity Core 38.01 53.74 Voigt et al. [2015] 642 cm 8530 2.43 - - 30 MixtureGeoB2722-1 M29-1 Multi-Corer 47.33 58.62 Chiessi et al. [2007] 0–1 cm Recent 2.93 4 - 29 IIGeoB6209-2 M32-2 Multi-Corer 31.76 48.15 Chiessi et al. [2007] 0–1 cm Recent N/A - - 55 IGeoB6211-1 M46-2 Multi-Corer 32.5 50.24 Voigt et al. [2015] 0–1 cm Recent 0.83 - - 29 IGeoB6211-2 M46-2 Gravity Core 32.5 50.24 Voigt et al. [2015] 28 cm 2260 0.93 - - 27 IGeoB6211-2 M46-2 Gravity Core 32.5 50.24 Voigt et al. [2015] 38 cm 3240 0.88 - - 27 IGeoB6211-2 M46-2 Gravity Core 32.5 50.24 Voigt et al. [2015] 48 cm 4190 1.04 - - 27 IGeoB6211-2 M46-2 Gravity Core 32.5 50.24 Voigt et al. [2015] 58 cm 5310 0.92 - - 29 IGeoB6211-2 M46-2 Gravity Core 32.5 50.24 Voigt et al. [2015] 68 cm 6840 1.22 - - 27 IGeoB6217-2 M46-2 Multi-Corer 34.72 51 Chiessi et al. [2007] 0–1 cm Recent 1.54 - 1 28 IGeoB6218-1 M46-2 Multi-Corer 35.05 50.78 Chiessi et al. [2007] 0–1 cm Recent 1.1 - 1 31 IGeoB6220-1 M46-2 Multi-Corer 33.36 49.39 Chiessi et al. [2007] 0–1 cm Recent 0.75 - 5 28 IGeoB6222-2 M46-2 Multi-Corer 34.08 48.62 Chiessi et al. [2007] 0–1 cm Recent 1.08 3 2 27 IGeoB6308-3 M46-3 Gravity Core 39.3 53.96 Voigt et al. [2015] 1–2 cm 330 2.87 - - 27 IIGeoB6308-3 M46-3 Gravity Core 39.3 53.96 Voigt et al. [2015] 6.50 cm 590 2.9 - - 32 IIGeoB6308-3 M46-3 Gravity Core 39.3 53.96 Voigt et al. [2015] 26.5 cm 2620 2.8 - - 32 IIGeoB6308-3 M46-3 Gravity Core 39.3 53.96 Voigt et al. [2015] 59.5 cm 5490 2.92 - - 29 IIGeoB6308-3 M46-3 Gravity Core 39.3 53.96 Voigt et al. [2015] 86.5 cm 8010 2.74 - - 31 IIGeoB6308-3 M46-3 Multi-Corer 39.17 54.14 Chiessi et al. [2007] 0–1 cm Recent 2.9 - - 31 IIGeoB6311-2 M46-3 Multi-Corer 38.81 54.63 Chiessi et al. [2007] 0–1 cm Recent 2.73 3 1 29 IIGeoB6314-2 M46-3 Multi-Corer 39.64 55.15 Chiessi et al. [2007] 0–1 cm Recent 2.65 - - 32 IIGeoB6334-2 M46-3 Multi-Corer 46.09 58.52 Chiessi et al. [2007] 0–1 cm Recent 2.88 2 - 70 IIGeoB6336-2 M46-3 Multi-Corer 46.14 57.85 Chiessi et al. [2007] 0–1 cm Recent 2.91 - - 36 II

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characters. Rather than simple metrics, as used originally byMorard et al. [2011], we chose to characterize theshape of the shells as a whole by a set of comparable points (landmarks) positioned on the spiral and lateralviews of the shell. Because of the lack of anatomical landmarks (biologically strictly homologous points) inGloborotalia inflata, we resorted to the use of geometrically homologous landmarks (extremal points orpoints of maximum curvature) to characterize the morphology of each specimen. All measurements weredone by a single person (i.e., Melanie Reinelt) to reduce noise due to data acquisition. The optimal model onlyrequires the identification of 11 landmarks (Figure 2a1) located on the terminal chamber of the specimensoriented in the lateral view. A more detailed discussion of the methodology development can be found inthe 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 >0.150mm residues from 11additional core top samples (in addition to the two samples used to select the morphological variablesdescribed above) and 14 downcore samples were split to obtain random samples of at least 26 specimens,

Figure 2. Appraisal of the morphological variability in Globorotalia inflata. (a1) Principal component analysis of the 997specimens morphometrically analyzed. A sketch of a specimen is provided with the position of the landmarks. Thecorrelation 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 attributedto the Type I and Type II exclusively from core top samples. Sketch of specimen analyzed is provided to represent themorphological variability. Distribution of the two types for (b2) size, (b3) PC1, and (b4) PC2.

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which was identified as the minimumamount to confidently represent themorphological variability of each popu-lation (see supporting information S1).Kummerform and broken specimenswere excluded. The retained specimenswere mounted on glass slides, orientedand photographed with a Canon EOS600D mounted on a Zeiss StereoDiscovery V8 microscope using a mag-nification of 6.3 (1 pixel = 0.344μm),and the same set of 11 landmarks wasdigitized for every specimen with TPS v2.17 [Rohlf, 2005]. The major axis of eachspecimen in the lateral view was alsomeasured. In addition, photographs ofG. inflata collected by plankton towsand analyzed in Morard et al. [2011]were reanalyzed in the same way toallow intercomparison between bothstudies. Coordinates of all specimenswere exported to MorphoJ, and theProcrustes fits were calculated.

The morphological variability amongthe specimens was appraised by per-forming a principal component analysis(PCA) on the Procrustes coordinateswith Past 2.17 [Hammer et al., 2001].The morphological variation along thetwo first principal components wascompared with size (Figures 2a2 and2a3) and literature-derived δ18O forthe core top and downcore samples(Table 1 and Figures 2a4 and 2a5). Thiswas done in order to explore the onto-genetic and ecophenotypic effects onthe morphology. We chose to test thecorrelation with the δ18O instead ofabiotic factors such as temperatureand salinity because the potential livingdepth range of the species is large(0–500m [Wilke et al., 2006]) and the iso-topic values reflect the conditions of thewater mass where most of the tests of

Figure 3. Discrimination of genotypes. (a)Morphological model of recognition of thetwo genotypes of Globorotalia inflata basedon core top samples and subsequentapplication on (b) the southern coreassumed to be composed of Type II only, (c)the northern core assumed to be composedof the Type I only, and (d) the central corecomposed of a mixture of both genotypes.

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the specimens have been secreted[Lončarić et al., 2006] and are thereforea direct proxy for the conditions in thehabitat of the specimens [Groeneveldand Chiessi, 2011].

The degree of separation between thetwo genotypes was determined bylinear discriminant analysis of theProcrustes coordinates of specimensfrom all core top samples that showstable isotopic values indicative for aconsistent position on one side of theBMC (Figure 3a). The discriminant func-tion was then applied on specimensfrom the downcore samples of coreGeoB6308-3, assumed to contain onlyType II specimens (Figure 3b), of coreGeoB6211-2, assumed to contain onlyspecimens of the Type I (Figure 3c),and of core GeoB13862-1, assumed tocontain a mixture of both genotypes(Figure 3d).

Next, we designed a simple modelto predict the relative proportion ofboth genotypes in fossil assemblages(Figure 4). We first produced artificialassemblages by randomly samplingthe scores of the discriminant functionof specimens from cores GeoB6308-3(Type II) and GeoB6211-2 (Type I), gen-erating artificial assemblages of 30specimens with relative proportions of100% to 0% of Type I and 0% to 100%of Type II with increments of 10%(Figure 4a), with 1000 random iterationsfor each of the 11 relative proportions ofgenotypes. This produced averagedtheoretical distributions of the discrimi-

Figure 4. Mixture model. (a) Average profilesof the mixed populations based on 1000random sampling of the discriminant scoreof the downcore samples (Figures 4b and 4c)of the northern core (Type I) and southerncore (Type II) for a total of 30 specimens. Therelative proportion of the two genotypes isgiven by the circular diagram in the leftupper corner of each biplot where the pro-portion of Type I is in red and the proportionof Type II in blue (dark part: 1st–3rd quar-tiles = 50% confidence interval; gray part:5th–95th percentiles = 90% confidenceinterval; black dot: average). (b) Matchingscores of 10,000 random mixed populationswith the averaged profiles.

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nant scores for a given relative proportion of genotypes. To estimate the stability and reliability of the estima-tion of relative proportions of genotypes, the discriminant function score distribution of 10,000 artificialassemblages generated in the samemanner was compared with the average score distribution, and the simi-larity was expressed as the number of bins where the abundance matched the 50% range of the averageddistribution for that bin (Figure 4b). The matching score of the assemblages of the fossil samples was calcu-lated in the same way to estimate the relative proportion of genotypes in these samples and compared it tothe isotopic values given by Voigt et al. [2015] (Figure 5).

Finally, we estimated the potential impact of small sample size on isotopic measurements when thesampled fossilized populations can incorporate specimens with divergent values, such as expected to bethe case for core GeoB13862-1. Again, we set a simple model by considering theoretical fossil assemblagesof G. inflata with relative proportions of 100% to 0% of Type I and 0% to 100% of Type II with increments of10%. This time, we considered a random picking of 5 to 100 specimens from those theoretical populationsand attributed to these specimens randomized isotopic values ranging from 0.61 to 1.70‰ for Type I speci-mens and 2.33 to 3.06‰ for Type II specimens. The isotopic range was defined by the extreme valuesobserved in the northern and southern cores [Voigt et al., 2015] considered as constituted of pure assem-blages of either type (Figure 6). We calculated the theoretical isotopic value obtained from the simulatedset of picked specimens by simple averaging of their isotopic values. We repeated the simulation for eachsample size 1000 times. We neglected the impact of the weight variability of the specimens in the simula-tion that will necessarily occur in a natural population due to variability in size and differential intensity ofcalcification [Weinkauf et al., 2013].

Figure 5. Downcore morphological variation. (a) Ice volume-corrected Globorotalia inflata δ18O variation during theHolocene performed for the three cores from Voigt et al. [2015], the 15 samples selected for morphometric measure-ments are highlighted by circled numbers (red = Type I; blue = Type II; and grey =mixture of both genotypes). (b) Matchingscore with the mixture model (Figure 4) of each downcore sample.

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4. Results4.1. Overall Morphological Variability

A total of 997 specimens of G. inflata were measured to appraise the morphological variability of the twocryptic species in plankton, core top, and downcore samples (Figure 2). Joint PCA of all specimens revealedthat about half of the total morphological variability among the specimens can be expressed by the firsttwo components. The distribution of scores of specimens in the space of these two axes showed that mor-phological variability in the core top samples covers the entire morphospace, the downcore samples covera 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 bothaxes correlate with size, indicating an allometric component in the shape of the last chamber, but this rela-tionship only explains a small part of the morphological variability (in both cases r2< 0.1). Further multivari-ate 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 principalcomponent and δ18O (Figures 2a2–2a5). The scores along the first two PCA axes for specimens from the12 surface sediment samples located unambiguously on either side of the BMC reveal a large degree ofoverlap (Figure 2b). One-way analysis of variance (ANOVA) indicates no significant difference in size

Figure 6. Theoretical modeling of the effect of small sampling on isotopic measurements. The variability in isotopicmeasurements 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 plotsare centered on the average isotopic value of each theoretical assemblage.

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(F= 0.1127; df=1; p=0.7373), but a one-way PERMANOVA performed with Euclidian distance measure onthe principal components scores indicates that the populations on either side of the BMC are morphologi-cally 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 crypticspecies of G. inflata, a discriminant analysis was applied on 425 specimens from the 12 core top samplesconsistently recording conditions on either side of the BMC (Figure 3). The analysis confirms the existenceof a significant difference in shape between the two populations, allowing correct assignment to eitherside of the BMC of ~80% of the specimens, with the leave-one-out returning essentially the same levelof efficiency (Hotteling's t2 = 341.71; F=14.81; p(same) = 3.60.10�40). The application of this model ondowncore samples from the northern (Type I) and the southern cores (Type II) indicates the same levelof specificity with >80% of the classification of the downcore specimens corresponding to the positionof the core with respect to the BMC (Figures 3b and 3c). The classification accuracy given by thediscriminant function is comparable to classification models developed for other cryptic species ofplanktonic 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 thesame 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 likedimensionality reduction [Evin et al., 2013] could potentially further improve the classification accuracyof the discriminant analysis.

An application of the model on samples from the central core suggests a mixture of both genotypes butwith the dominance of Type II typical for the region to the south of the BMC (74% of classified specimensattributed 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 isexpected. To determine the likely composition of the assemblages in this central core, we applied the mix-ture model (Figure 4). The model reflects the limitations imposed by the large morphological overlapbetween the populations ascribed to the two types. However, it allows detection of dominance of eithertype, as an assemblage dominated by one type never produces high scores with the theoretical profile pro-duced by the other type. It should also allow to differentiate mixtures composed of equal or almost equalproportion of genotypes from pure or almost pure assemblages.

Being conscious of the potential limitation of our mixture model, we applied it on the downcore assem-blages of the three cores (Figure 5). As expected, the assemblages of the northern and southern coreswere identified as being composed dominantly by one type: samples in the northern core showedscores distribution indicative of 100–70% of Type I, while samples in the southern core indicated a com-position of 100–90% of Type II. The application of the mixture model on the central core indicated amore 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 domi-nant pattern of morphological variability even in a set of characters optimized to differentiate between thecryptic species is not reflecting the presence of the cryptic species (Figure 2). Not only is there no evidencefor a discontinuity in the morphospace, but the direction of variation along the first axis does not coincidewith the difference between the two cryptic species. This observation explains why the two genetic typesremained unnoticed by classical taxonomy and confirm their status as cryptic species. Nevertheless, theanalysis also confirms the observations from plankton samples presented by Morard et al. [2011] thatthe 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 thebimodal distribution of discriminant scores (Figure 3), indicates an incipient differentiation, which couldeventually lead to the characterization of morphologically distinguishable species. For now, the overlapis too large to be able to use the model to identify individual specimens, but the differentiation is largeenough 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 thespecies 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 mor-phological variability encountered in the plankton and downcore samples (Figure 2a). This suggests thatestablishing the morphological model of cryptic species detection from core tops covering a large environ-mental 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 fossilpopulation from the northern and southern cores, located on both sides of the BMC and assumed to becomposed 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 onthe 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 returnedpositively unambiguous results for the northern and southern cores, indicating that the model could beused to detect the dominance of either cryptic species in fossil samples, allowing interpretation of resultsfrom the central core, which has been hypothesized to record latitudinal migration of the BMC duringthe Holocene [Voigt et al., 2015].

The application of the cryptic species detection model on samples from the central core indeed confirms theoccurrence of mixed populations (Figure 5). Based on the oxygen isotope signature in G. inflata, sampleslabeled as numbers 2 and 5 should be dominated by Type II. This conjecture is confirmed by the scores ofthe 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 contributionof 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 bothby isotopic signatures and by morphology.

If the isotopic signatures in samples numbers 3 and 4 were interpreted strictly, they would imply that thepopulations in these samples should be dominated by Type I. Yet the mixture model only indicates a50–70% contribution by this Type (Figure 5). To explain this apparent conflict, we first eliminated potentialerrors linked to (i) the isotopic measurements (these are less than 0.07‰ [Voigt et al., 2015]), (ii) differencesin the development of crust between the two genotypes (both populations had similarly well-developedcrust, and postmortem processes are not expected to differentially affect the two types), (iii) error of the mor-phological 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 315–400μm 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 inves-tigated assemblages while picking for isotopic measurements. In other words, we quantified the odds of pro-ducing a representative average for a mixed assemblage when the measurement is based on a small numberof specimens (Figure 6).

In the study by Voigt et al. [2015], the stable isotope values are based on approximately 10 specimens. Theresults of our model for mixed population of both types (50:50) imply that it is very likely that a given valuewill deviate by 0.5‰ from the actual mean (Figure 6). Since 2‰ is the shift in isotopic values observed acrossthe BMC [Chiessi et al., 2007], measurements from the same mixed population based on 10 specimens as wasthe case by Voigt et al. [2015] can be expected to vary by as much as one half of the range. This potential arti-fact could not only explain why the morphological detection results are in absolute values not entirely con-gruent with the isotopic data but also provide a potential explanation for the high variability in the isotopicmeasurements from the central core (Figure 5). To reduce the scattering to an acceptable range, we suggestto (i) pick a large number of specimens (>50), crush them, homogenize the obtained calcite powder, and per-form the analysis on it; (ii) make replicate measurements to assess the reliability of the picking; or (iii) onlyinterpret 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 interpre-tation about the latitudinal migration of the BMC remains robust, as demonstrated by the favorable compar-ison to the transient run of a state-of-the-art comprehensive coupled atmosphere-ocean general circulationmodel [Voigt et al., 2015]. However, this third option masks potential informative short-term meridional shiftsof 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 finerknowledge on the ecology of foraminifera which will necessarily include the distribution of their constituentcryptic species in space and time. The approach shown here would potentially allow selection of singlespecimens 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 torefine our knowledge of past oceans. Morphometric measurements can potentially be automated, anduser-friendly freeware exists to build and apply fast morphological models of detection. The morphometricapproach shown in the present study represents only one possibility to express the shape of a foraminiferalshell and shall be seen as a “proof of concept” on how the transfer of knowledge of cryptic diversity ontofossil record can be done. Alternative morphometric methods could be applied to find the best compromisebetween measurement effort and classification success prior to large-scale application. The only limit tobridge the gap between cryptic diversity and paleoceanography is the availability of data on the occurrenceof 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 onplanktonic foraminifera by gathering all existing data into the Planktonic Foraminifera Ribosomal Referencedatabase (PFR2, http://pfr2.sb-roscoff.fr/ [Morard et al., 2015]) and proposing a standardized nomenclatureof the known cryptic species [Morard et al., 2016]. This will be further strengthened by the availability ofmetagenomic data sets [de Vargas et al., 2015] that will make accessible the distribution of all cryptic speciesin planktonic foraminifera, allowing a comprehensive characterization of constraints on their ecology. Since itcan be expected that many of the cryptic species are ecologically distinct, this progress will open newavenues for paleoceanography.

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