Three-dimensional analysis of inter-and intraspecific variation in ontogenetic growth trajectories of planktonic foraminifera

Journal Pre-proof Three-dimensional analysis of inter-and intraspecific variation in ontogenetic growth trajectories of planktonic foraminifera

Janet E. Burke, Willem Renema, Ralf Schiebel, Pincelli M. Hull PII:

S0377-8398(19)30037-4

DOI:

https://doi.org/10.1016/j.marmicro.2019.101794

Reference:

MARMIC 101794

To appear in:

Marine Micropaleontology

Received date:

15 March 2019

Revised date:

15 October 2019

Accepted date:

31 October 2019

Please cite this article as: J.E. Burke, W. Renema, R. Schiebel, et al., Three-dimensional analysis of inter-and intraspecific variation in ontogenetic growth trajectories of planktonic foraminifera, Marine Micropaleontology(2019), https://doi.org/10.1016/ j.marmicro.2019.101794

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Journal Pre-proof THREE-DIMENSIONAL ANALYSIS OF INTER- AND INTRASPECIFIC VARIATION IN ONTOGENETIC GROWTH TRAJECTORIES OF PLANKTONIC FORAMINIFERA Janet E. Burke 1 *, Willem Renema2 , Ralf Schiebel3 , Pincelli M. Hull1 1

Department of Geology and Geophysics, Yale University, 210 Whitney Avenue, New Haven, CT 06511, USA 2 Naturalis Biodiversity Center, P.O. Box 91517, 2300 RA Leiden, the Netherlands
 3 Max Planck Institute for Chemistry, Hahn-Meitner-Weg 1, 55128 Mainz, Germany *Corresponding author

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ABSTRACT

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Planktonic foraminifera grow by adding chambers onto their tests, modifying their surface area, volume, porosity, and ornamentation with each addition. This process results in a wide variety of adult morphologies and preserves a record of the early morphology. With high-resolution threedimensional imaging techniques, precise measurements can be made on previously inaccessible parameters like surface area and volume of the test interior. Here, we use three-dimensional images (microCT scans) of extant planktonic foraminifera from plankton tows and sediment traps to measure and compare the variation in chamber and whole test volume within and between seven morphospecies. By incorporating data from Caromel et al., (2015), we were able to explore the extent of interspecific variation in growth (i.e., total volume) with each chamber addition from a total of 12 extant species. All species measured approximately adhered to loglinear growth trajectories through the majority of their ontogeny. Species exhibited characteristic deviations from the log- linear growth trajectory and were grouped into three categories on the basis thereon. The volume of the first chamber—the proloculus—varied by more than two orders of magnitude. However, subtle differences in relative growth rates could make up for the initial variation proloculus size, such that the correlation between proloculus size and biovolume decreased through ontogeny. Intraspecific variation in proloculus size, cavity volume at the final five chamber stages, and terminal calcite volume was pronounced in the seven morphospecies investigated and attributed environmental and ontogenetic differences amongst individuals. Finally, linear models are generated from our data for estimating cell volume and calcium carbonate test volume from linear measurements (major and minor axis) that are more accurate than the spherical equivalents used previously. Keywords: Planktonic Foraminifera; Computed Tomography; Ontogenetic Growth; Morphology

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Journal Pre-proof 1. INTRODUCTION The calcareous tests of planktonic foraminifera are archives of ocean chemistry, ecological dynamics, and evolution, and have helped to shape our understanding of the Earth’s ocean and climate. The morphology of planktonic foraminiferal tests is integral to these inquiries and can vary widely within named species, to the point of intergrading between closely related morphospecies, thereby complicating taxonomy (Kennett and Srinivasan, 1983; Schiebel and Hemleben, 2017). Knowledge of the extent and causes of morphological variation within planktonic foraminifera was revolutionized by the discovery of extensive genetic diversity and

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differentiation within species named on the basis of morphology alone (i.e., morphospecies;

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(Darling and Wade, 2008; de Vargas et al., 1999; de Vargas et al., 2001; Kucera and Darling, 2002; Quillévéré et al., 2013; Renaud and Schmidt, 2003). These genetically unique, (pseudo-)

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cryptic species, often correspond to known morphological variants within or between morphospecies (André et al., 2013; de Vargas et al., 2001; Huber et al., 1997; Morard et al.,

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2011; Morard et al., 2016; Quillévéré et al., 2013), differing in features such as color (Aurahs et

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al., 2011), pore size and amount (Huber et al., 1997), wall thickness (Marshall et al., 2015) and/or coiling direction (Darling et al., 2006). However, even within genetic species, planktonic foraminifera vary morphologically, sometimes as much as among genetic species (André et al.,

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2018; André et al., 2013). Further complicating the matter, cryptic genetic species may show no relationship between genotype and morphology at all (André et al., 2018). As such,

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understanding the determinants of inter and intraspecific morphological variation is critical to

record.

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applying species concepts to fossil taxa and to interpreting morphological patterns in the fossil

Although morphological variation within a morphospecies can be associated with the presence of cryptic species, it can also be caused by interactions with the environment (plasticity) or standing variation within the population (i.e., heritable genetic variation). Morphological plasticity describes the non- heritable phenotypic variation within a population induced by the environment or other organisms. Non-heritable phenotypic variation is often referred to as ‘ecophenotypy’. Several traits of planktonic foraminiferal morphology have been shown to vary in response to changes in temperature, light levels, or feeding frequency in laboratory cultures, sedimentary assemblages including pore size and pore density, test size, and the timing of gametogenesis (Bé, 1982, 1968; Bijma et al., 1990; Burke et al., 2018; Caron et al.,

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Journal Pre-proof 1987a, b; Hemleben et al., 1989; Malmgren and Healy-Williams, 1978; Mojtahid et al., 2015; Schiebel and Hemleben, 2017; Spero, 1988; Wiles, 1965). This type of variation can be useful for paleontological studies, as it can help in environmental reconstructions if the relationship between morphology and environment is known (Fisher et al., 2003; Wade and Olsson, 2009; Wiles, 1965). In contrast, phenotypic variation due to genetic factors is the variation in traits that can be passed from generation to generation. Heritable intraspecific variation is the standing variation upon which natural selection acts, but is remarkably poorly quantified in planktonic foraminifera.

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Recent advances computed x-ray tomography (CT scanning) technology has made three-

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dimensional scans of foraminiferal tests at sub-micron resolution more accessible than ever (Caromel et al., 2016; Caromel et al., 2017; Schmidt et al., 2013; Speijer et al., 2008). With

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micro-CT scans, precise measurements of surface area and volume can be made on the test calcite and the cell cavity rather than estimated based on linear measurements. Three-

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dimensional models built from micro-CT scans can be manipulated and segmented to remove

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chambers and approximately reconstruct the test at stages in the ontogenetic growth series (as in Caromel et al., 2016; 2018). From these models, the dimensions of the calcareous test and the internal cavity (and thus the cell) can be reconstructed through from the earliest stages of

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development, allowing for thorough comparison of patterns of shape, calcification, and ontogenetic growth between morphologically, genetically, and ecologically distinct specimens

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that was previously impossible.

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Variation in chamber shape, size, and placement (i.e., the coiling) during ontogeny accounts for some of the largest morphological differences amongst planktonic foraminiferal species (Berger, 1969; Tyszka, 2006). Previous studies using hand dissections, TEM imaging, and live caught specimens outlined the stages of planktonic foraminiferal ontogenetic growth from the first chamber or “proloculus” to the last chambers added before gametogenesis and death (defined by Brummer et al., 1986; 1987). Ontogenetic stage transitions are characterized by external morphological features such as gross chamber morphology, aperture number and shape, and microstructure of the test wall as well as the rate at which chamber size increases with each addition (growth rates). Through this work, it was concluded that transitions from one ontogenetic stage to the next were not found to be associated with a specific number of chambers, but rather with the achievement of a threshold cumulative size (Brummer et al., 1987;

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Journal Pre-proof Wei 1992) perhaps driven by the ability to capture more nutritious prey or to meet higher calcification demands (Brummer et al., 1987; Caromel et al., 2017). While many features of external morphology of the adult test are visible using traditional light and scanning electron microscopy, the dimensions of the internal test cavity and morphological changes during ontogeny are only measurable using 3D reconstructions. In diffusion limited single-celled organisms such as planktonic foraminifera, 3D measurements of cell surface are important because the cell surface is the locus for transport of gases, nutrients, and waste in and out of the cytoplasm. Growing to maximize volume can lead to higher

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reproductive output, but metabolic processes are often limited by exchange across the cell

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membrane, such that growth optimizing surface area to volume ratios might be favored in some species or growth stages. For instance, Caromel et al. (2016) generated three-dimensional growth

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trajectories from eight specimens from seven planktonic foraminiferal species and found that the growth trajectories of the globigerinoid species such as Globigerinoides sacculifer, differed from

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those of globorotalid species like Globorotalia menardii, with the latter exhibiting

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ontogenetically delayed increases in growth rates. Across species, gross morphologies can range from spherical—where volume is maximized—to dorso-ventrally flattened and disk- like—a shape that maximizes surface area over volume. Using micro-CT scans, the actual relationship

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between gross morphology and cell surface area and volume can be characterized across the suite of morphogroups in modern planktonic foraminifera.

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Here, we explore variation in the patterns of foraminiferal cell growth within and among

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species of extant planktonic foraminifera. Specifically, we focus on cumulative internal volumes, surface area to volume ratio of the cell cavity and whole-test calcite volumes from CT scan of 45 individual foraminifera from seven species to investigate the extent of variation associated with morphospecies identity, gross morphology, ecology, and environmental conditions. This work builds on that of Brummer et al. (1987), Wei et al. (1992), and Caromel et al. (2016; 2017), by increasing the number of species and specimens ontogenetically sequenced by three-dimensional methods, expanding the total number of morphospecies with a full ontogenetic sequence described from CT scans from 7 to 12. We also provide a three-dimensional dataset of intraspecific variation in modern planktonic foraminifera for future reference and use. Further, we test the accuracy of proxy measurements for cell volume and calcium carbonate test volume

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Journal Pre-proof based on linear measurements of text length to improve estimates of calcium carbonate export, foraminiferal biomass, and size trends.

2. METHODS Specimens used in this study were obtained from North Atlantic plankton tows and sediment traps (Table 1). Seven species were measured for this study: Neogloboquadrina pachyderma, Neogloboquadrina incompta, Globorotalia inflata, Globorotalia menardii, Globigerinoides ruber (pink), Globigerina bulloides, and Turborotalita quinqueloba. In order to examine more

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species, we also incorporated three-dimensional measurements of volume through ontogeny from the Caromel et al., 2015 dataset (described in Caromel et al., 2016). Species obtained from

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Caromel et al., 2015 are Globigerina bulloides, Globigerinoides sacculifer (‘trilobus’ and

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‘sacculifer’ morphologies), Globigerinella siphonifera and Globigerinella radians (once considered ectomorphs of the same species but now known to be genetically distinct species),

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Globorotalia truncatulinoides, Globorotalia tumida, and Globorotalia menardii. Globorotalia

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menardii and Globigerina bulloides measurements are included from both this study and Caromel et al. (2015) to allow for comparison and confirmation of observed patterns. Ontogenetic data for G. ruber, G. siphonifera, and G. inflata presented in Brummer et al, 1987

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and Wei et al., 1992 using one- and two-dimensional measurements are also incorporated in our analysis of growth rate patterns. Plankton tow samples from the northern North Atlantic (Table

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1, M10-3) were collected with 100- µm nets, stored in pH-8.2 buffered formalin, removed from

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the liquid after the expedition, and dry-stored until analysis (Schiebel et al., 1995). Sediment trap samples were fixed with 1-%-Na-azide, cool-stored at 0-4 °C, split, and dry-stored until analysis (Lundgreen, 1996). Tow samples from Bermuda were obtained using a Reeve net with a 125- µm mesh, sieved from the liquid, rinsed in deionized water, and dried (Table 1). Bermuda specimens have been accessioned to the Yale Peabody Museum (G. ruber) and the University of Colorado Museum (G. menardii; see Supplemental Table 1 identification numbers). Specimens were identified to the species level using taxonomic concepts from Schiebel and Hemleben (2017). Prior to imaging, specimens were mounted in stacks of 3-6 in plastic pipet tips closed with floral foam. All specimens with the exception of the Globorotalia menardii samples were imaged using a Zeiss Xradia 520 Versa micro-CT scanner at resolutions of 0.5-0.85μm/voxel (Figure 1). Globorotalia menardii specimens imaged for this study were scanned at the Advanced Photon

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Journal Pre-proof Source at the Argonne National Laboratories in Lemont, Illinois using Beamline 2-BM-A,B at a resolution of 0.65 μm/voxel. Resulting images were projected as three-dimensional volumes in VGStudioMAX 3.0 software and manually thresholded to remove extraneous particles and organic matter. Calcite (CaCO 3 ) test surface area, CaCO 3 volume, and linear dimensions (length, width, height) of the entire test were measured from the initial data. Chambers were segmented by hand and converted to three-dimensional meshes (.stl files) of the internal cavity of the chamber in VGStudioMAX by segmenting the chamber wall and creating a mesh of the negative space. The inner chamber

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meshes were then converted to solid volumes to quantify the internal volume of each chamber

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(see Table 2 for a glossary of terms used to describe different data types). For most specimens (38 of the 45), only the proloculus and the final five chambers in the whorl were measured to

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examine intraspecific variation in the proloculus size and in the final chambers of the whorl at the time of reproduction, death, or capture. This procedure was designed to increase sample size

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while still targeting the chambers where the variation would be most evident. The remaining

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seven individuals, one from each morphospecies included in this study, were fully measured (i.e., all chambers) to examine the full ontogenetic trajectory. Ontogenetic trajectories were assessed

given chamber number.

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as cumulative volume sequences by summing the internal volumes of all chambers in the test at a

External meshes of specimen tests were imported into Meshlab software, where they

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were resurfaced and replaced with watertight “wrap” mesh. This was done by selecting only the

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outer surface of the test and covering it with a screened poisson surface reconstruction (Kazhdan and Hoppe, 2013) which effectively closes all apertures and pores. This created a smoothed, solid object that could then be imported into VGStudioMAX, converted into a solid volume, and measured for surface area and volume. From these “wrap measurements” we then inferred the cavity volume of the entire test by subtracting the CaCO 3 volume from the wrap volume. This procedure allowed cavity volume to be measured quickly and without segmentation. To calculate the volume of the cell at different ontogenetic stages, we subtracted the volume of the segmented individual chambers in sequence, starting with the final chamber. These volume measurements are referred to as “cumulative volumes”, in contrast to the whole-specimen “cavity volumes” described above (see Table 2).

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Journal Pre-proof The resulting data set includes measurements of whole-test calcium carbonate volume and surface area, whole test wrap volume and surface area (representing the approximate size and surface area of the living organism), individual internal chamber volumes and surface areas, total internal cavity volume of the organism, and major and minor axis lengths (Figure 1, Table 2). Variation in growth rates of the fully segmented specimens were then compared by plotting the residuals of linear regressions between the log-scaled total cavity volume and total chamber count throughout ontogeny for each specimen (as in Brummer et al., 1987). Finally, we tested the efficacy of using major and minor axes as a proxy for cell and CaCO 3 volume using linear

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regressions.

3. RESULTS

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Ontogenetic variation between species in cumulative volume, proloculus size, growth rate, and surface area to volume ratio (SA:V) was assessed for the seven fully segmented specimens

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unique to this study and compared to the eight specimens from Caromel et al. (2015) (Figures 2-

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5). The cumulative volume of each specimen increases approximately logarithmically with each chamber addition in all species examined with the exception of the second chamber or deuterconch, which is always smaller than the proloculus (Figure 2), as previously described

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(Caromel et al., 2015). The volume of the initial chamber, the proloculus, can vary by a factor of two amongst species (Figure 2). However, due to different rates of chamber size increase, the

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total internal volume of at the end of the individual life varied less than the proloculus size

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despite a wide spread in the total number of chambers (Figure 2). Species with the largest proloculus volumes were generally larger at a given chamber count than those with the smallest proloculus volumes (Figure 2), but this relationship weakened as more chambers were added (Figure 3). The diminishing correlation between proloculus volume and cumulative volume with ontogeny (Figure 3) is due to variation in growth trajectories, as is clear when the growth sequences are normalized by proloculus size (Figures 2b, 2e). Proloculus-normalization was achieved by scaling the proloculus volumes to the average size and all subsequent volumes in the series by the same amount as the initial offset. For instance, the disparity in the volumes of Globoconella inflata and Globorotalia tumida (Caromel et al., 2015) at any ontogenetic step is not driven by the initial offset in proloculus volume alone (17948 μm3 vs 5333 μm3 ), but is also

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Journal Pre-proof due to the more rapid increase in volume of G. inflata with each subsequent chamber addition (i.e., a growth curve slope of 0.24 vs. m = 0.20; Figure 2b). Because the ratio of surface area to volume (SA:V) of the entire cell is tied to metabolic efficiency and reproductive output in diffusion-dependent organisms such as planktonic foraminifera (Caromel et al., 2015), the cumulative surface area to volume ratio (normalized to proloculus size) was also explored (Fig. 2c). We find that SA:V initially rises with the addition of the second chamber (deuteroconch) when the shape of the organism changes from roughly spherical (i.e., the shape with the lowest possible SA:V ratio) to a more elongated ellipsoid. For

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G. menardii, G. inflata, and N. incompta, the SA:V continues to rise for the subsequent 1-2

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chambers, but in all other specimens the SA:V continues to decrease after the formation of the deuteroconch. For the remainder of the series, chamber additions are accompanied by decreasing

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SA:V, although there are pronounced differences in the slope of these declines. The SA:V of G. bulloides declines most rapidly throughout its ontogeny, reflecting its high rate of cumulative

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volume increase (Figure 2a). The SA:V of the spherical model is most closely approached by G.

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bulloides by the 9th chamber, followed by G. ruber at the 14th chamber. Conversely, the G. menardii specimen maintains a relatively high surface-area to volume ratio throughout its ontogeny.

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To examine the variation in the shape of the growth curves (i.e. deviations from loglinear growth), we plotted the residual cumulative volumes for each chamber, as per Brummer et

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al., 1987. Residual volumes were obtained from a linear regression of cumulative volume and

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chamber number for each fully segmented individual from this study and from Caromel et al., 2015 (Figure 4). From these plots, general patterns in growth dynamics are evident from the magnitude and sign of the residuals (Figure 4-5). In the globose specimens, Globigerina bulloides, Globigerinoides ruber, and Globigerinoides sacculifer (Caromel et al., 2015), there is a deep “U” shape to the residual curves as the growth rate steadily drops until the approximate onset of the neanic stage after which the growth rate increases. This growth pattern, herein referred to as “Type-U”, was also observed by Brummer et al. (1987) in Globigerinoides ruber and Globigerinoides sacculifer, with the nadir of the curve coinciding with the onset of the neanic stage, and by Caromel et al. (2015) for G. bulloides, G. sacculifer, G. siphonifera, G. radians, and Globorotalia menardii. The two genetic species long-thought to be members of the same morphospecies, Globigerinella siphonifera and Globigerinella radians (from Caromel et

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Journal Pre-proof al., 2015), have Type-U growth trajectories of much smaller magnitude than those observed in G. bulloides, G. ruber, and G. sacculifer (Figure 5). The Globorotalia menardii measured by Caromel et al., (2015) has more typical Type-U growth pattern than the specimen measured in this study, but both display drops in the growth rate early in ontogeny and increase in growth rate later in ontogeny. Globorotalia tumida, Neogloboquadrina incompta, and Neogloboquadrina pachyderma have a roughly sinusoidal growth trajectory, with two inflection points, dubbed “Type-S” growth (Figure 4). Growth rate decreases steadily before first inflection point at the fifth chamber in N. pachyderma and N. incompta and at the tenth chamber in G. tumida (where

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Caromel et al., 2015 placed the juvenile-neanic transition) and increases until the second

tumida).

Finally,

Turborotalita

quinqueloba,

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inflection point before the final 2-4 chambers (i.e., the neanic to adult stage transition in G. Globoconella

inflata,

and

Globorotalia

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truncatulinoides (Caromel et al., 2015) all produce relatively flat growth trajectories with low residual variance (Figure 4-5) from a log- linear growth trajectory and are referred to as “Type-F”

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growth (Fig. 4d, 4g and 4h). Although multiple specimens have kummerform final chambers, the

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patterns were consistent even when the final chamber was removed from analysis. The plots in Figures 2 and 4 also allow other stage transitions described by Brummer et al., (1986; 1987) based on one and two-dimensional measurements of ontogenetic trajectories for

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four extant morphospecies (Globigerinoides sacculifer, Globigerinoides ruber, Globigerinella siphonifera (synonymous to Globigerinella aequilateralis), and Globoconella inflata). The

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transition from the prolocular stage to the juvenile stage with the addition of the second chamber,

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or deuteroconch, is always accompanied by an increase in the SA:V and a reduction in the volumetric growth rate. This is due to the fact that the seco nd chamber is always smaller than the proloculus and lobate rather than ellipsoid or spherical. The transition between the adult and terminal stages is also evident in sharp reductions in the growth rate due to the addition of diminutive kummerform or gametogenic chambers (Figure 4). Such chambers are present in both specimens of G. bulloides, the ‘sac’ type specimen of G. sacculifer (from Caromel et al., 2015), T. quinqueloba, G. truncatulinoides (Caromel et al., 2015), and one of the specimens of G. menardii presented here (Caromel et al., 2015). The fully segmented specimens in Figures 2-5, differ in proloculus size, growth rate dynamics, and surface area to volume ratios through ontogeny. However, the extent to which this variation is unique to each species cannot be concluded from single specimens. To compare

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Journal Pre-proof ontogenetic variation within (interspecific variation) as well as between species (intraspecific variation), we imaged and measured multiple specimens from each species group, including some from different sample locations or collection method (See Table 1 for details). In Figure 6, convex hulls envelope the cumulative internal volume measurements for all specimens from each species. This figure illustrates the overlap between the species groups in the rate of internal volume increase. Within these samples, the variation between all of the species in terms of terminal size and total number of chambers is generally less variable within a group than between them. The largest convex hulls (indicating the largest variation in total number of

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chambers and/or size) are those with specimens from multiple locations, suggesting that greater

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sample sizes from a wider range of localities are needed to resolve the extent of intraspecific variation.

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Some of the intraspecific variation observed coincides with differences in sampling location or date (Figure 7). For example, specimens of N. pachyderma from two different tows at

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the same location in the northern North Atlantic (Tab. 1, M10-3) differ in chamber and cavity

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volume and growth rate dynamics. For instance, the specimens from M10-3-393 from June, 1989, have smaller proloculi, more chambers, and larger final sizes than those from M10-3-4571 towed in July 1989 (Figure 7b-1, Figure 8). Neogloboquadrina incompta from the sediment

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trap L292B collected in June and July of 1989 attained larger sizes in fewer chambers then the samples from the same trap towed in July and August of the same year (Figure 7a-1). In G.

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bulloides, specimens from different sample collections can also differ in the shape of their

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growth trajectories. G. bulloides specimens from M10-3-457-1 (plankton tow samples) have shallower growth curves than those from the other two samples in this study (sediment trap samples) and from the specimen from a core top sample from Walvis Ridge in the southern Atlantic, despite having larger proloculi than most other specimens (Figure 7c-1, Figure 8; Caromel et al., 2015). One-dimensional linear measurements of foraminiferal tests are fast and easy to make using simple equipment. Because of this, these measurements have been used as proxies for other aspects of foraminiferal size, specifically the volume of the cell (Lombard et al., 2009; Michaels et al., 1995; Movellan et al., 2012). Generally, these estimates assume a sphere with a radius that is half the length of the major axis (Michaels et al., 1995; Lombard et al., 2009), minor axis (Movellan et al., 2012), or mean diameter (Beer et al., 2010). We tested the strength

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Journal Pre-proof and accuracy of the relationship between CT scan based estimates of cavity volume, calcium carbonate test volume, and the wrap volume, relative to traditional sphere-based estimates from major and minor axis measurements. Major axis and minor axis lengths are significantly correlated with all three whole-test volume types analyzed in this study (Figure 9). Major axis is the strongest predictor of wrap volume (r2 =0.80) and internal volume (r2 =0.78). Major and minor axes explain less variance in test CaCO 3 content (r2 =0.48), although major axis length is still significantly correlated. We tested the relative accuracy of the spherical estimates (also shown in Fig. 7, pink lines) based on both major and minor axis lengths by comparing the root mean

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squared error (RMSE) of the linear regressions based on the observed relationships of axis

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lengths to volumes, and the predicted values based on spherical estimates (Table 3). In all cases, the linear regression approach outperformed the spherical estimates, although the spherical

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estimates of CaCO 3 volume based on the minor axis length performed nearly as well as linear

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regressions based on direct measurements (RMSE=23.58 vs 20.52).

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4. DISCUSSION

Our goal was to explore interspecific and intraspecific variation in ontogenetic growth patterns in planktonic foraminifera using three dimensional models in order to re-evaluate observations

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made using one- and two-dimensional measurement techniques, and to add to the growing archive of CT-scanned foraminiferal specimens. To achieve these aims, we investigated growth

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patterns from volume and surface area measurements of 12 morphospecies from CT scans (seven

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unique to this study and seven from Caromel et al., 2015, with two over lapping species, Globigerina bulloides and Globorotalia menardii) and examined intraspecific variation in seven of those species. When the volumetric growth series of a single specimen from each species are plotted on a logarithmic scale (Fig. 2), a known overall growth trajectory common across planktonic foraminifer species is apparent (Brummer et al., 1987). With the addition of each new chamber, the total volume of the foraminiferal cell increases roughly exponentially to create a log-linear trajectory. However, there is considerable variation between specimens in terms of the size of the proloculus, the slope of the growth curve, and the dynamics of the growth throughout the ontogenetic series. These differences result in disparities in cumulative volume at a given chamber number/ontogenetic step, even within a single species (Figures 2, 7).

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Journal Pre-proof Highly variable proloculus sizes like those seen in our sample are known from even larger samples of a single species towed from a single location (Brummer et al., 1987; Wei, 1992). The causes or correlates of variation in proloculus size are unknown for planktonic foraminifera. Brummer et al. (1987) hypothesized that transition from one ontogenetic stage to the next was closely associated with organismal size and thus concluded larger proloculi led to earlier ontogenetic shifts. By comparing the proloculus sizes of specimens that underwent gametogenesis in culture with natural populations, Brummer et al. (1987) found the size distribution to be similar, and inferred that smaller proloculus sizes do not seem to prevent

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individuals from reaching sexual maturity. Instead, individuals with smaller proloculi would

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have to accrete more chambers, or increase in volume more rapidly with each chamber addition, than those individuals with larger proloculi.

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Our observations on N. incompta, N. pachyderma, and G. bulloides from traps suggest that the habitat could play a role in shaping size variation throughout ontogeny (Figure 7a-c).

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Terminal size in planktonic foraminifera has been associated with latitude, temperature, food

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availability (Bijma et al., 1990; Bijma et al., 1992; Caromel et al., 2016; Caron et al., 1987a, b; Hemleben et al., 1989; Mojtahid et al., 2015; Schmidt et al., 2004) and small-scale patchiness in these parameters could drive the variation in size within populations. Specimens with smaller

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proloculi and a sustained pattern of lower volume at a given chamber may be those that encounter less food at each stage and thus grow longer and reproduce later (Mojtahid et al.

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2015). Our data (from all 45 specimens) show that calcite volume and total number of chambers

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are not correlated (r2 =0.002, p=0.768), therefore having more chambers is not necessarily more demanding in terms of calcification. Further, local variation in proloculus size may be beneficial in causing the temporal reproductive window to be extended in local populations. While specimens with larger proloculi generally attained larger sizes in fewer chambers than those with smaller proloculi (Figure 3, Figure 8), our analysis shows that this can be offset eventually by faster growth rates (Figure 2). For example, both fully segmented specimens of Globigerina bulloides were eventually larger (i.e., greater cumulative volume) than a number of species with larger proloculi including Globigerinoides ruber, Turborotalita quinqueloba, and Neogloboquadrina incompta. Our data further reveals a greater diversity of ontogenetic growth trajectories than the two trajectories previously described (Brummer et al., 1987; Caromel et al., 2016), with more closely related species often exhibiting similar growth. First, the globigerinoid

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Journal Pre-proof species G. ruber and G. sacculifer; the globose taxa G. bulloides, G. siphonifera, and G. radians; and the globorotalid taxa G. menardii exhibit Type-U growth. Growth rates decline relative to a log- linear trajectory throughout early ontogeny before increasing steadily in late ontogeny (i.e., in the neanic stage). Species exhibiting Type-U growth vary in the magnitude of growth rate change, with G. ruber, G. sacculifer, and G. bulloides exhibiting the greatest changes in growth rate through ontogeny. The second growth mode, Type-S growth, seen in Neogloboquadrina incompta, Neogloboquadrina pachyderma, and Globorotalia tumida, is characterized by two changes in growth rate. The pattern of two changes is initially similar to Type U growth, except

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that the onset of increasing growth rates begins much earlier (between 5 a nd 10 chambers) and

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slows to the point of reversal by the final whorl of chambers. The species in the Type-S group are symbiont barren (Takagi et al., 2019), and inhabit cooler water either at depth or at higher

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latitudes. The deceleration in volume late in ontogeny in the Type-S group might therefore relate to resource limitation or the need to maintain relatively large surface:volume ratios in deeper

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dwelling species as compared to the accelerated growth rates in the primarily mixed layer,

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symbiont hosting Type-U group species. Interestingly, the mixed- layer species G. menardii had growth trajectories more similar to those in the Type-U category, with the growth rate accelerating (residual value of 0.20) in later ontogeny. Globorotalia menardii is both

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evolutionarily and ecologically related to and resembles G. tumida in having dorso-ventrally flattened chambers in later ontogeny and a keel structure on the periphery of the test. Finally,

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Turborotalita quinqueloba, Globorotalia inflata, and Globorotalia truncatulinoides have no

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persistent trends in growth rate dynamics through ontogeny (i.e., Type-F growth). All three of these species are symbiont barren and G. inflata and G. truncatulinoides may live below the mixed layer during most of their life. Although more specimens are needed to examine how representative these patterns observed in 13- individuals are of their species, we compared our curves to those in shown in Brummer et al. (1987) for G. sacculifer, G. ruber, and G. siphonifera. The trajectories shown in of Brummer et al. (1987) differ in being based on averages of linear measurements from multiple individuals (Figure 4). Although the general shape of the curves is similar to our data, there are some deviations that we suspect are due to the differences in data collection method and sample size. First, Brummer et al. (1987) show an initial increase in growth rate between the first and second chambers as the test goes from spherical to bilobed, whereas our measurements utilizing

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Journal Pre-proof three-dimensional reconstructions do not. This is likely due to the fact that the spherical to bilobed transition results in a large linear increase in maximum diameter (what Brummer et al. 1987 measured) but is accompanied by a decrease in rate of volume increase (what we measure) as the test goes from a spherical cell shape (the most voluminous shape) to an elongate one (See Supplemental Figure 5). Second, the amplitude of the growth trajectory curves in Brummer et al. (1987) are more muted than ours (Figure 4). This may arise because the curves of Brummer et al. (1987) were constructed from averages of several specimens in contrast to our single-specimen curves.

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The level of intraspecific variation in the timing and magnitude of growth through

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ontogeny was sometimes as great or greater than that seen between species (Figures 6-8). There is some support for the environment in driving some of the variation via phenotypic plasticity. In

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Figure 7, some grouping of individuals within a species by sampling method (tow, trap, or sediment core) and timing of the sample event is observed. For example, although specimens of

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Neogloboquadrina pachyderma from the tow sample M10-3-457-1 collected in July of 1989 are

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smaller overall and have fewer chambers than those from tow M10-3-393-1, collected in June 1989 (Fig. 6a-2), the former shows signs of reaching the terminal growth stage (i.e. a decrease in size of the final chamber compared to the penultimate chamber). Although N. pachyderma

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individuals from both M10-3 samples may have reached maturity, the former (M10-3-457-1) individuals were sampled earlier in their life cycle (day 23 of the synodic lunar cycle), and were

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therefore ontogenetically younger and smaller than the latter sample (M10-3-393-1) from lunar

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day (LD) one (i.e. one day after the full moon) (Volkmann 2000; Schiebel et al. 2017). Similarly to N. pachyderma, the Neogloboquadrina incompta specimens from the sediment trap L2-92 collected in May 1992 reach smaller terminal sizes but more total chambers (slower growth) than those collected from the same locality in June/July1992, which are larger with fewer chambers. This difference in sediment trap individuals (for which we cannot know the exact timing of death) may be related to environmental conditions with rapid growth fostered by late springbloom condition in May, and slow growth fostered by food limitation in July (Schiebel and Hemleben, 2000). Specimens of Globigerina bulloides analyzed in this study come from three different sources. Five specimens were collected from sediment traps (L2, Tab. 1), three were obtained from tows (M10-3), and one was from a core-top sample (Caromel et al., 2016). The trap and

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Journal Pre-proof core-top samples are presumably mature, post-gametogenic adult specimens and show comparable trajectories and terminal sizes, albeit different chamber numbers at those sizes. This type of variation in size at a given chamber number has previously been observed in samples from the same time and place (Brummer et al., 1987; Wei et al., 1992). The towed samples of Globigerina bulloides have shallower growth curves and much smaller final sizes than the trap samples, indicating (1) that they might have been immature at the time of capture, and / or (2) that the subpolar specimen from the tow samples (M10-3) grew faster and reached maturity earlier than the mid- latitude specimen (L2 traps), and / or (3) that the subpolar and transitional

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specimen represent different genotypes IIa and IIb, respectively (Darling and Wade, 2008). We

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also observed broad variation in specimens of T. quinqueloba, and these were collected from a single tow and had kummerform final chambers indicative of maturity at the time of death (M10-

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3-457-1, Tab. 1). The T. quinqueloba specimens thus suggest that ontogenetic stage is unlikely to account for the entirety of intraspecific variation observed.

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Our sample size used is too small to make general conclusions about the causes of

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intraspecific variation in planktonic foraminifera. More sub-thermocline sediment trap samples, which generally represent post-gametogenic specimens from a known time interval, are needed to explore the causes of variation in terminal volume and a broader environmental gradient of

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sample sites is needed to test correlates of terminal volume, proloculus size, and relative growth rates. The potential presence of cryptic species is an additional complication that could not be

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addressed in the specimens used here, but would be important to explore in future studies. The

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effect of dissolution of test carbonate during sampling (tows samples were pH buffered at 8.2) and sedimentation traps and surface samples is difficult to assess, and is assumed to be similar in all samples analyzed here. Whereas differential dissolution may cause up to 20 % loss of test weight (Schiebel et al., 2007) and cause higher cavity volume and chamber volume (Fig. 6a), the growth trajectories (i.e., relative changes) and chamber surface area to volume ratios (Fig. 6a) would not be affected. The size and composition of our dataset does allow us to test the efficacy of a common method employed in our community for estimating size: the use of 2D measurements for volume estimates. 2D measurements—which are quick and easy to obtain non-destructively—can be informative for inferring the overall sizes of the test and cell (i.e. Brombacher et al., 2017), but are an approximation of volume. Here we find that major axis, measured from the external

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Journal Pre-proof surface of the test, is correlated with cavity volume (r2 =0.77, p<0.001; Figure 9), which reflects the volume of the cell in globose specimens. Major axis is also correlated with wrap volume (r2 =0.8, p<0.001; Figure 9), which represents the volume of the whole living organism (cell and test). The correlation we observe is similar to that in Brombacher et al. (2017) for volume measurements obtained from z-stacked light microscopic images (general linearized model regression, r2 = 0.88). Relatively little of the variation in CaCO 3 volume is explained by major or minor axis length (major axis: r2 =0.48, p<0.001; minor axis: r2 =0.37, p<0.001; Figure 9). Globorotalia menardii specimens were excluded from these analyses as they deviate from the

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general pattern seen in the other species (see Supplemental Figure 6). With only three specimens,

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a separate equation for dorso-ventrally flattened or discoidal forms cannot be presented here, but spherical estimates are likely to be especially inappropriate for these morphologies. We find

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linear regressions are more accurate than spherical estimates of cavity volume, wrap volume and carbonate volume for all of the other gross morphologies examined (Table 3). However,

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spherical estimates based on the minor axis do account for a similar portion of the variance in

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CaCO3 volume as linear regressions (RMSE=23.58 versus 20.52). Previous studies (Lombard et al., 2009; Michaels et al., 1995; Movellan et al., 2012) have calculated cell volumes and protein biomass based on spherical equivalents using major or minor axis length. For non-globorotalid

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taxa (with the probable exception of Globorotalia inflata) the linear regression equations provided here (Figure 9) are more accurate than spherical estimates and should be used in future

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volume estimates based from major (or minor) axis length (Table 3).

5. CONCLUSION

The calcareous tests of planktonic foraminifera are readily available for study from sedimentary archives, but the living cell is much harder to access and measure. Fortunately, the growth of planktonic foraminiferal cell is preserved in their tests and provides a record of biomass accumulation through ontogeny. Using new micro-CT scans and data from previous work (Caromel et al., 2015), we compared the ontogenetic trajectories of 12 species of extant planktonic foraminifera and examined intraspecific variation within seven of those species. We found evidence for three growth trajectories characterized by differences in the pattern of relative growth rates through ontogeny. Specifically, we found that growth rates through ontogeny could be characterized by the number of marked transitions in the rate of volume accumulation (0, 1, or 16

Journal Pre-proof 2), and that these rate dynamics were generally consistent among closely related or ecologically similar species. We also observed inter- and intraspecific variation in proloculus size and size at a given ontogenetic step—especially in temporally or spatially disparate populations. While more data is needed to resolve the extent and drivers of intraspecific variation in planktonic foraminiferal ontogeny, our data suggest that environmental factors may play a role shaping ontogenetic trajectories from the earliest stages of test growth. Building on these findings can improve our understanding of the evolution and ecology of planktonic foraminifera in order to

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better interpret their vast fossil record.

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ACKNOWLEDGEMENTS

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The authors would like to thank to Ulrich Lundgreen for providing sediment trap samples (L2); Christoph Hemleben for providing Meteor 10-3 and 26-1 samples; Leanne Elder for providing towed Globorotalia menardii specimens; Michael Henehan, Leanne Elder, and Amy Maas helping to collect the Bermuda-2016 samples; Dirk van der Marel for obtaining CT-scans; Francesco DeCarlo and Pavel Schevchenko from Argonne National Laboratories for obtaining scans and making reconstructions of Globorotalia menardii specimens; Elizabeth Clark and Adam Pritchard for advice on segmenting and ‘shrinkwrapping’ procedures; and members of the Hull Lab at Yale University for comments and feedback on an early draft of the manuscript. Janet E. Burke was supported by the National Science Foundation Graduate Research Fellowship under grant no. DGE-1122492 and Pincelli M. Hull by a Sloan Research Fellowship. Additional financial support for this work was provided by the Naturalis Biodiversity Center Martin Fellowship, the Bermuda Institute of Ocean Sciences Grants- in-Aid of Research program, and the Yale Peabody Museum. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.

Declarations of Interest: none Data Availability: CT-scan data, meshes of whole tests and individual chamber segments, and tables of measurements have been made publically available on the Zenodo data repository (https://doi.org/10.5281/zenodo.2593861)

17

Journal Pre-proof TABLES

Species

RS11-1 RS11-6, RS12-1 RS12-3, RS13-1 RS13-3, RS14-3 RS8-1 RS8-5

Water Depth (m)

T (°C)

Salinity

Synodic Lunar Day (0=FM)

10080

3.86

34.8

23

10080

35.59

7

72,329

10,632

July 10, 1989

M26-1843-1

Tow

47,790

19,758

Sept. 8, 1993

N. incompta G. inflata

11.81

M10-3393-1

Tow

72,351

10,661

June 18, 1989

G. bulloides N. pachyderma

RS6-1 RS6-4

10080

2.52

34.81

1

Bermuda2016

Tow

33.5

64.6

Sept. 16-19, 2016

G. ruber (pink)

T1, T4-2, T4-4

<30

27 +/0.5

36

0, 3

Bermuda 2017

Tow

33.5

64.6

G. menardii

B17, M2-10, M1-10

<30

25 +/0.3

36

L2-92A42-3

Trap

47.83

19.65

April 412, 1992

G. bulloides

RS9-1 RS9-4

1000

-

-

16-24

L2-92A42-8

Trap

47.83

19.65

May 17-22 1992

N. incompta

RS7-3RS7-6

1000

-

-

43471.00

L2-92B46-2

Trap

47.83

19.65

June 10-July 8, 1992

N. incompta

RS3-1, RS3-3

3530

-

-

24-FM23

L2-92B46-3

Trap

47.83

19.65

July 8 – Aug.. 8, 1992

N. incompta

RS4-2 RS4-6

3530

-

-

23-FM22

L2-92B46-4

Trap

47.83

19.65

Aug. 5 – Sept. 2, 1992

G. bulloides

RS5-1 RS5-2

3530

-

-

22-FM20

Nov. 6, 2017

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Tow

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M10-3457-1

N. pachyderma G. bulloides T. quinqueloba

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Date

Specimen Number

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Longitude (°W)

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Sample Latitude Type (°N)

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Sample Name

Table 1. Metadata and ecological data for analyzed samples. FM signifies the full moon, i.e. synodic lunar day zero. In situ temperature (T) and salinity are not available for the sediment trap data. 18

Journal Pre-proof

Table 2: Glossary of Terms The volume of the calcium carbonate material as output by VGStudioMAX 3.0.

Major Axis

The longest of the three linear dimensions output by VGStudioMAX 3.0 fro m the reconstructed foraminiferal test. The shortest of the three linear d imensions output by VGStudioMAX 3.0 fro m the reconstructed foraminiferal test. The volume of the shrinkwrap made in Meshlab that covers all pores and apertures in the test. This measure is an external estimate of volume as it includes the CaCO3 test and the empty cavity space enclosed therein. The volu me of the empty test cavity obtained by subtracting the CaCO3 volu me fro m the wrap volu me. This is an internal volu me estimate. To calculate cav ity volu me at a given chamber count, indiv idual chamber volu mes are subtracted fro m the total cavity volume.

Cavity Volume Chamber Volume Cumulative Volume

The volume of the internal cavity of a single chamber, obtained by segmenting the chamber in VGStudioMAX 3.0. The volume of a chamber co mb ined with all of the preceding chambersrepresentative of the full volume of a test cavity/cell at a given ontogenetic step. Distinct fro m cavity volume in that it is made by comb ining discrete chamber volumes rather than by subtracting the CaCO3 volume from the wrap volume.

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Wrap Volume

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Minor Axis

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CaCO3 Volume

The internal cavity surface area o f a single chamber as obtained by segmenting out individual chambers in VGStudioMAX 3.0.

Cumulative Surface Area

An approximat ion of the interior surface area calculated by summing up all chamber surface areas relevant to each ontogenetic step.

na

Table 2. Definitions of terms used.

Regression (Major Axis)

CaCO3 Volume

17.60

Cavity Volume

15.19

Jo 15.76

Sphere (Major Axis)

Regression (Minor Axis)

Sphere (Minor Axis)

46.80

20.52

23.58

23.06

21.62

63.04

48.80

23.05

93.10

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Table 3.

Wrap Volume

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Chamber Surface Area

Table 3. Root mean squared error (RMSE) for CaCO 3 volume, cavity volume, and wrap volume predictions based from major or minor axis based on the linear regressions and spherical equivalents, respectively.

19

Journal Pre-proof FIGURE CAPTIONS

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1. Three-dimensional imaging and measurement workflow. (a) Specimens were mounted in plastic pipette tips plugged with floral foam and (b) imaged using an Xradia 520 Versa Micro-CT at Naturalis Biodiversity Center in Leiden, Netherlands at resolutions ranging from 0.45 to 0.65 microns/voxel. (c) From the resulting image stack, three-dimensional volumes were computed, and (d) thresholded to removed non-test material before converting to .stl mesh files in VG StudioMax 3.0. (e) Whole-test mesh files were then imported into MeshLab and fitted with a watertight shrinkwrap, to create a solid object with the dimensions of the external surface of the test. (f) Chamber interiors were segmented by hand to isolate individual chambers, (g) rendered as a mesh of the internal test surface, and (h) converted to solid volume objects from which volume and surface area measurements could be taken in VG StudioMax 3.0.

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2. Cumulative volume and surface area of fully-segmented specimens. Data by from this study and from Caromel et al., 2015 shown as a) log cumulative volume (this study), b) log cumulative volumes normalized by proloculus size (this study), c) log cumulative chamber surface area to volume ratio (this study), d) log cumulative volume (Caromel et al., 2015), and e) log cumulative volumes normalized by proloculus size (Caromel et al., 2015).

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3. Decreasing correlation between proloculus volume and test volume through ontogeny. Proloculus volume as as a predictor for cumulative volume at the (a) 2nd, (b) 5th , and (c) 10th chamber in all fully- segmented specimens from this study and from Caromel et al. (2015) with corresponding linear regression statistics.

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4. Log linear regression residuals of cumulative volumes for all fully-segmented individuals. a) Blue curves indicate specimens from this study, red curves indicate specimens from Caromel et al., 2015, and the grey curves indicate data from Brummer et al., 1987 and Wei et al., 1992 as noted (curves approximated from Brummer el al. 1987 Figures 3-6 and from Wei 1992 Figure 7). Species vary in the magnitude of their residuals as shown in (b).

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5. Absolute magnitude of cumulative volume residuals at for each species, comparing the deviations from log-linearity, regardless of direction of number of inflections, for each species and growth rate type. 6. Cumulative volume variation within species. Convex hull plots encompassing all cumulative volume measurements at a given chamber number for a given species. 7. Final five chamber cavity volumes by species. Measurements arranged from (top) final chamber back and (bottom) by absolute chamber number, in order to consider terminal size and size at each ontogenetic step respectively. Sampling method (tow, trap, or core) and sample event name are indicated when specimens where obtained from multiple samples. For more information on samples, see Table 1. Core top samples in Figures 7c-1 through 7c-3 are from Caromel et al. (2015). 8. Plots of proloculus size by species and by sample for all specimens plotted in Figure 7.

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9. Major and minor axis in relation to cavity volume, wrap volume, and CaCO 3 volume. Volume measurements shown as cube root of the measured values. Linear regressions show in blue alone with r2 and p-values. Spherical predictions shown in red lines assuming a sphere with a radius of half the major (or minor axis) length.

21

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Aurahs, R., Treis, Y., Darling, K., and Kucera, M.: A revised taxonomic and phylogenetic concept for the planktonic foraminifer species Globigerinoides ruber based on molecular and morphometric evidence, Marine Micropaleontology, 79, 1-14, 2011.

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Bé, A. W.: Biology of planktonic foraminifera, Studies in Geology, Notes for a Short Course, 6, 51-89, 1982.

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Bé, A. W.: Shell porosity of Recent planktonic foraminifera as a climatic index, Science, 161, 881-884, 1968.

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Beer, C. J., Schiebel, R., and Wilson, P.: Technical Note: On methodologies for determining the size-normalised weight of planktic foraminifera, Biogeosciences, 7, 2193, 2010.

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Berger, W. H.: Planktonic foraminifera: basic morphology and ecologic implications, Journal of paleontology, 1969. 1369-1383, 1969.

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Bijma, J., Faber, W. W., and Hemleben, C.: Temperature and salinity limits for growth and survival of some planktonic foraminifers in laboratory cultures, Journal of Foraminiferal Research, 20, 95-116, 1990. Bijma, J., Hemleben, C., Oberhaensli, H., and Spindler, M.: The effects of increased water fertility on tropical spinose planktonic foraminifers in laboratory cultures, Journal of Foraminiferal Research, 22, 242-256, 1992. Brombacher, A., Wilson, P. A., and Ezard, T. H.: Calibration of the repeatability of foraminiferal test size and shape measures with recommendations for future use, Marine Micropaleontology, 133, 21-27, 2017. Brummer, G.-J. A., Hemleben, C., and Spindler, M.: Ontogeny of extant spinose planktonic foraminifera (Globigerinidae): A concept exemplified byGlobigerinoides sacculifer (Brady) andG. Ruber (d'Orbigny), Marine Micropaleontology, 12, 357-381, 1987. 22

Journal Pre-proof Burke, J. E., Renema, W., Henehan, M. J., Elder, L. E., Davis, C. V., Maas, A. E., Foster, G. L., Schiebel, R., and Hull, P. M.: Factors influencing test porosity in planktonic foraminifera, Biogeosciences, 15, 6607-6619, 2018. Caromel, A. G., Schmidt, D. N., Fletcher, I., and Rayfield, E. J.: Morphological change during the ontogeny of the planktic foraminifera, Journal of Micropalaeontology, 35, 2-19, 2016. Caromel, A. G., Schmidt, D. N., Fletcher, I., and Rayfield, E. J.: Morphometric measurements throughout ontogeny of selected planktic foraminiferal test species. PANGAEA, 2015.

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Caron, D. A., Faber, W. W., and Bé, A. W.: Growth of the spinose planktonic foraminifer Orbulina universa in laboratory culture and the effect of temperature on life processes, Journal of the marine biological association of the United Kingdom, 67, 343-358, 1987b.

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de Vargas, C., Norris, R., Zaninetti, L., Gibb, S. W., and Pawlowski, J.: Molecular evidence of cryptic speciation in planktonic foraminifers and their relation to oceanic provinces, Proceedings of the National Academy of Sciences, 96, 2864-2868, 1999. de Vargas, C., Renaud, S., Hilbrecht, H., and Pawlowski, J.: Pleistocene adaptive radiation in Globorotalia truncatulinoides: genetic, morphologic, and environmental evidence, Paleobiology, 27, 104-125, 2001. Fisher, C. G., Sageman, B. B., Asure, S. E., Acker, B., and Mahar, Z.: Planktic Foraminiferal Porosity Analysis as a Tool for Paleoceanographic Reconstruction, Mid-Cretaceous Western Interior Sea, Palaios, 18, 34-46, 2003. Hemleben, C., Spindler, M., and Anderson, O. R.: Modern planktonic foraminifera, Springer Science & Business Media, 1989.

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Lombard, F., Erez, J., Michel, E., and Labeyrie, L.: Temperature effect on respiration and photosynthesis of the symbiont‐bearing planktonic foraminifera Globigerinoides ruber, Orbulina universa, and Globigerinella siphonifera, Limnology and Oceanography, 54, 210-218, 2009.

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