Data from: Patterns of intraspecific variation through ontogeny–a case study of the Cretaceous nautilid Eutrephoceras dekayi and modern Nautilus pompilius
Cite this dataset
Tajika, Amane et al. (2020). Data from: Patterns of intraspecific variation through ontogeny–a case study of the Cretaceous nautilid Eutrephoceras dekayi and modern Nautilus pompilius [Dataset]. Dryad. https://doi.org/10.5061/dryad.qz612jmb6
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.
We produced polished cross-sections of Eutrephoceras dekayi 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 E. dekayi was estimated based on the following assumptions: 1) septal crowding in early ontogeny formed at hatching, 2) Eutrephoceras 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) = (dm1/dm2)2, whorl width index (WWI) = ww/dm, siphuncle position index (SPI) = vd/wh, and imprint zone rate (IZR) = iz/wh. Regarding modern Nautilus pompilius, we used the data of WER and WWI published by Tajika et al. (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 et al. (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 E. dekayi and N. pompilius 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 2nd to 10th landmarks were mirrored along the plain of symmetry of 1st and 11th 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.
Fukada Geological Institute
Japan Society for the Promotion of Science