DOI: 10.5061/dryad.stqjq2c4b
title: Data from: Feeding habits of the Middle Triassic pseudosuchian Batrachotomus kupferzellensis from Germany and palaeoecological implications for archosaurs

description: Bite traces on fossil bones are key to deciphering feeding ecology and
 trophic interactions of vertebrate past ecosystems. However, similarities
 between traces produced by different carnivorous taxa with similar
 dentitions and misidentifications due to equifinality hinder confident
 identifications of the bite makers. Here, we correlate bite traces with
 macroscopic wear and microanatomy of the teeth of the pseudosuchian
 archosaur Batrachotomus kupferzellensis from the Triassic Lower Keuper
 fossil lagerstätten (southern Germany), untangling its feeding habits and
 shedding light on the bite traces generated by ziphodont teeth (teeth with
 serrated carinae). Individually, bite traces reflect tooth morphology,
 whereas composite bite traces and their frequency are related to feeding
 behaviour and explain tooth macroscopic wear and microanatomy. Therefore
 the identification of the bite maker is possible by analysing composite
 bite traces, their location on bones, and their relative abundance. In
 addition, tooth macroscopic wear and microanatomy are proven as
 independent lines of evidence of feeding ecology. Comparing bite traces on
 fossil and present-day bone assemblages, we observe that bone
 modifications by the crocodylomorph lineage (from Triassic pseudosuchian
 archosaurs to extinct and extant crocodylians) are strikingly similar,
 including taxa with and without ziphodont teeth. Such a set of features
 differs from bone modification assemblages produced by taxa with similar
 ziphodont teeth outside the pseudosuchian lineage (such as theropod
 dinosaurs and the Komodo monitor), suggesting a phylogenetic signal in
 feeding ecology among saurian reptiles. --
description: Data S1: All prepared fossil material from the
 Kupferzell and Vellberg-Eschenau fossil lagerstätten (southwestern
 Germany) housed in the SMNS and MHI collections was examined
 using a magnifying glass, a dissecting microscope, and lighting
 in different orientations in order to identify bite traces (e.g.,
 Blumenschine <em>et al.</em> 1996; D’Amore &
 Blumenschine 2009; Drumheller & Brochu 2014; Drumheller
 <em>et al.</em> 2020). Bite traces were systematically
 classified according to their morphology (ichnotaxonomy) following Mikuláš
 <em>et al.</em> (2006), Jacobsen & Bromley (2009) and
 Pirrone <em>et al.</em> (2014). In addition, equivalences of
 the identified ichnotaxa with the taphonomic terms coined by Binford
 (1981) and extensively used in the literature (e.g., Pobiner 2008; D’Amore
 & Blumenschine 2009; Njau & Gilbert 2016; Drumheller
 <em>et al.</em> 2020) are provided. Associations between bite
 traces and their orientation and location on bones were also
 surveyed Data S2:
 Tooth crowns of <em>Batrachotomus</em> from
 Kupferzell (N = 258) and Vellberg-Eschenau (N = 56), all from the UGM,
 were analysed accounting for: (1) basic morphological classification by
 measuring (with a digital calliper) the apicobasal height of the crown (or
 crown height, CH), the mesiodistal depth and the labiolingual width of the
 crown at its base (or crown base length, CBL, and crown base width, CBW,
 respectively; for tooth parameters, see: Smith <em>et al.</em>
 2005; Hendrickx <em>et al.</em> 2015); (2)
 identification of the position within the jaw,
 distinguishing premaxillary from non-premaxillary teeth (also
 distinguishing teeth from the dentaries and maxillae, when
 possible). 
 Tables S1-S9 
 In order to investigate potential statistical
 differences regarding bite trace patterns on different bitten taxa
 (<em>Mastodonsaurus</em> vs. <em>Plagiosuchus</em>
 vs. <em>Nothosaurus</em> vs.
 <em>Batrachotomus</em>), body regions (skull vs. teeth vs.
 free vertebrae vs. sacrum vs. ribs vs. pectoral girdle vs. pelvic girdle
 vs. limbs) and bone regions (proximal end vs. distal end vs. side of the
 shaft vs. edge of the shaft vs. bone edge vs. bone centre), we converted
 the data collected in the bite trace database (Mujal <em>et
 al.</em> 2021, data S1) into three separate datasets, scoring the
 absence or presence of each bite trace morphotype for all bones within
 each group. A small fraction of bones was not included in the analyses
 (Mujal <em>et al.</em> 2021, data S1) because the elements
 could not be identified due to their fragmentary nature or preservation.
 These datasets were analysed using a permutational multivariate analysis
 of variance (perMANOVA). This method estimates the potential overlap
 between two or more groups by testing the significance of their
 distribution on the basis of permutation (9999 iterations) and the
 Euclidean distance as distance measures. In contrast to parametric tests,
 perMANOVA does not require normal distribution of the data (Anderson 2001;
 Hammer & Harper 2006). The spatial relationship between groups is
 expressed by an <em>F</em>-value and a Bonferroni-corrected
 <em>p</em>-value. Following Wills <em>et al.</em>
 (1994), the three datasets were further transformed into Euclidean
 distance matrices and subjected to a principal coordinate analysis (PCOA).
 This method reduces multivariate data down to a new set of independent
 variables (principal coordinates) that are linear combinations of the
 original set with zero covariance (Hammer & Harper 2006),
 providing a ‘biting space’. Afterwards, we ran a broken-stick method for
 each PCOA to obtain the number of principal coordinates (PCO) that contain
 the relevant amount of total variation (DeVita 1979; Jackson 1993). For
 these PCOs we applied a linear discriminant analysis (LDA), which reduces
 the number of PCOs to a smaller set of dimensions by maximizing the
 separation between the given groups using the Mahalanobis distance. This
 distance measure is estimated from the pooled within-group covariance
 matrix, resulting in a linear discriminant classifier and an estimated
 group assignment for each species. These results were cross-validated
 using Jackknife resampling (Hammer & Harper 2006; Hammer 2020).
 Based on the confusion matrix (Stehman 1997), we
 estimated the error of correct identification.