DOI: 10.5061/dryad.qz612jmb6
title: Data from: Patterns of intraspecific variation through ontogeny–a case study of the Cretaceous nautilid Eutrephoceras dekayi and modern Nautilus pompilius

description: The magnitude and ontogenetic patterns of intraspecific variation can
 provide important insights into the evolution and development of
 organisms. Understanding the intraspecific variation of organisms is a key
 to correctly pursuing studies in major fields of palaeontology. However,
 intraspecific variation has been largely overlooked in ectocochleate
 cephalopods, particularly nautilids. Furthermore, little is known
 regarding the evolutionary pattern. Here, we present morphological data
 for the Cretaceous nautilid Eutrephoceras dekayi (Morton, 1834) and the
 modern nautilid Nautilus pompilius Linnaeus, 1758 through ontogeny. The
 data are used to describe the change of conch morphology and to elucidate
 the evolutionary patterns of intraspecific variation. We discovered the
 presence of morphological changes at hatching and maturity, which is
 shared in every conch parameter in E. dekayi and N. pompilius. We also
 found that intraspecific variation is high in early ontogeny in both taxa,
 followed by various patterns in later ontogeny. The high variation in
 early ontogeny may imply their flexibility of changing the timing of
 growth events, which may have played an important role in nautilid
 evolutioin. We assume that the decrease in variation in later ontogeny may
 indicate developmental constraints. Lastly, we compared the
 similarity/dissimilarity of ontogenetic patterns of variation between
 taxa. Results reveal that the similarity/dissimilarity of the ontogenetic
 pattern differs between E. dekayi and N. pompilius. We conclude that this
 shift in the ontogenetic pattern of variation may be rooted in changes in
 the developmental program of nautilids through time. We propose that
 studying ontogenetic patterns of intraspecific variation can provide new
 insights into the evolution and development of
 organisms.     
    
description: We produced polished cross-sections of
 <i>Eutrephoceras</i> <i>dekayi </i>along the plane
 of symmetry (i.e., along the siphuncle). These sections were photographed
 using a Nikon D2X with Micro Nikkor 60 mm lens. The photographs were used
 to count the number of septa per 180° (0.5 whorl) through ontogeny to
 calculate the septal spacing index (SSI; Fig. 1C). Because nepionic
 constictions, which indicate the point of hatching cannot be observed on
 cross-sections in nautlids, embryonic size in <i>E. dekayi</i>
 was estimated based on the following assumptions: 1) septal crowding in
 early ontogeny formed at hatching, 2) <i>Eutrephoceras</i>
 hatched with a body chamber angle of 130°. We also polished specimens to
 produce cross-sections perpendicular to the plane of symmetry. We
 photographed the cross-sections in order to measure the following conch
 parameters: diameter (dm), whorl height (wh), whorl width (ww), imprint
 zone (iz), apertural height (ah), and distance between the ventral edge of
 the siphuncle and the ventral edge of the conch (vd), which are commonly
 used for cephalopod morphological descriptions. On the basis of the
 parameters measured above, we calculated the following ratios: whorl
 expansion rate (WER) =
 (dm<sub>1</sub>/dm<sub>2</sub>)<sup>2</sup>, whorl width index (WWI) = ww/dm, siphuncle position index (SPI) = vd/wh, and imprint zone rate (IZR) = iz/wh. Regarding modern <i>Nautilus pompilius</i>, we used the data of WER and WWI published by Tajika<i> et al.</i> (2018). It should be noted that WER and WWI in their study were calculated every 45°. In addition, we produced new data for SSI and SPI from CT-images provided by Tajika<i> et al.</i> (2018). Equidistant points were calculated for each parameter with a distance of 0.5 mm using linear interpolation. The resultant equidistant points in the same size classes permitted the calculation of intraspecific variation through ontogeny. In this study we use standard deviation to represent intraspecific variation. We also examined similarity/dissimilarity in the pattern of intraspecific variation through ontogeny for several various conch parameters. To evaluate similarity/dissimilarity, we carried out a cluster analysis using the Euclidean distance and Ward’s method. Intraspecific variation (standard deviation) of each conch parameter was normalized in the cluster analysis so that the parameters share a common scale. Since SPI is measurable only within the phragmocone, the cluster analysis was performed at two different size classes (diameter of the shell at the end of the phragmocone and adult). A two-sample t-test between each parameter of <i>E. dekayi</i> and <i>N. pompilius</i> was also conducted to test if intraspecific variation shows a significant difference between taxa. These statistical tests were conducted using the Statistics and Machine Learning Toolbox of MATLAB R2019a (MathWorks).             We applied a geometric morphometric approach to reconstruct intraspecific variation through ontogeny. We designated a total of 11 points of reference (“landmarks”) with equidistant intervals along the shell on one half of the whorl cross section using the Image Processing Toolbox of MATLAB R2019a (MathWorks). The landmarks for each specimen were slid along the original outline to minimize the bending energy from a reference individual (sliding semi-landmark method). Since these landmarks represent only the left/right side of the whorl section, we constructed a new dataset covering the entire whorl section by mirroring the landmarks, which resulted in 20 landmarks representing the whole whorl section (i.e., the 2<sup>nd</sup> to 10<sup>th</sup> landmarks were mirrored along the plain of symmetry of 1<sup>st</sup> and 11<sup>th</sup> landmarks; Fig. 1F). This procedure was carried out through ontogeny (on whorl cross-sections in various ontogenetic stages). The landmark coordinates of each whorl section were normalized by centroid size and registered using the Generalized Procrustes Method. The Procrustes residuals were then analyzed by principal components analysis (PCA) to reduce the dimension of data. To obtain an estimate of the average value for each principal component through ontogeny, we applied the Locally Estimated Scatterplot Smoothing (LOESS) using PAST.