Monferran et al.—Chemical taphonomy of Jurassic spinicaudatans from Patagonia, Argentina 92(6):1054–1065 1057 EDS measurements must be performed on well-polished


specimens so that surface roughness does not affect the results. However, the particular features of the sample carapaces preclude polishing, thus yielding semiquantitative results (see the following section). These results have sufficient precision for multivariate statistical analyses. Point measurements by EDS were used to monitor trends in


the elemental distributions (major and minor components). The locations of measurement points were chosen at preset intervals. However, in some cases, point positions were manually modified to an adjacent area to avoid fossil or rock pore spaces. This process ensures a sufficiently flat surface area for the electron beam. At each point, the weight percentages of the following elements were obtained: O, F, Na, Mg, Al, Si, P, S, K, Ca, Ti, and Fe. The estimated reproducibility was within 10%.


X-ray diffraction analyses.—X-ray diffraction (XRD) analyses were carried out using a BrukerD8AdvanceX-ray diffractometer at the Center of Technology forMineral Resources and Ceramics (CETMIC), Buenos Aires, Argentina. The spectra were acquired from a copper anode (Cu-Kα=1.54) operated at 30kV and 10mA. The scanning of the samples was carried out from 3° to 70° (2theta) with a step size of 0.04°, a collection time of 2.5 sec- onds per frame, and fixed divergence slit. The sample carapaces andmaterial from the surrounding rockmatrix were pulverized in an agate mortar and mounted on fixed sample holders for further analysis. The analysis of the diffractograms was performed using DIFFRACPLUSEVA software (Bruker AXS).


Semiquantitative determinations.—The use of semiquantitative EDS analysis to study fossil materials has been demonstrated to be reliable and has provided reproducible results (e.g., Klug et al., 2009; Lin and Briggs, 2010; Thomas et al., 2012; Previtera et al., 2013; Benavente et al., 2014). The absolute method (as opposed to a relative method) used here does not employ standards (e.g., Vázquez et al., 1988, 1990; Barrea and Mainardi, 1998; D’Angelo et al., 2002). The samples provide all the experimental information required since the physical-chemical processes involved are well known. The standardless, or absolute, analysis is based on the fundamental parametricmethod, inwhich all parameters are derived from the fundamental parameter data- base (theoretical equations) aswell as precisemodels of theX-ray tube, detector, and geometry. As mentioned, and because of the sample features, obtaining highly polished (smooth) surfaces for a quantitative analysis is not possible. The surface roughness results in inconsistencies in takeoff angles between the measured zones and between samples (Goldstein et al., 1992, 2007), yielding semiquantitative results. Thus, semiquantitative elemental con- centrations were obtained from the measurements of X-ray fluor- escence intensities for each element in the specimen. The characteristic X-ray fluorescence intensity emitted by each ele- ment in a sample is recorded and then compared with the corre- sponding intensity I(j) emitted by a standard of concentrationC(j). As a first approximation, the intensity Ij may be considered as proportional to the mass concentration Cj of element j:


Ij = Ij ðÞ=Cj =Cj ðÞ (1)


Comparisons with the standards permitted eliminating physical and geometrical factors, which are very difficult to


determine (Goldstein et al., 1992, 2007). Matrix effects must be taken into account using correction factors (i.e., ZAF). Z and A factors represent generation, scattering, and absorption effects, whereas the F factor involves secondary fluorescence enhance- ment (e.g., Philibert, 1963). Considering that these effects may differ between the sample and the standard, the ZAF correction is necessary to accurately relate the sample composition with the measured, characteristic X-ray fluorescence intensities (Reed, 1993). The magnitudes of the Z, A, and F correction factors are strongly dependent on the experimental conditions, mainly the X-ray energy of the incident beam, takeoff angle, and composition differences of the standards used for comparisons. All of the elements in the sample contribute to the matrix effects, which are related to elemental concentrations. The ZAF terms are calculated from suitable, long-accepted, and well- established physical models. Corrections for matrix effects are routinely carried out by applying the phi-rho-Z matrix correc- tion algorithm (i.e., PROZA; e.g., Bastin et al., 1986; Bastin and Heijligers, 1990). The results presented here are from semiquantitative


determinations of major and minor components by means of EDS. The estimated statistical errors (not shown) in elemental concentrations can be as large 10%. The concentration data were recalculated to 100%.


Multivariate statistical analysis.—The semiquantitative data were analyzed by PCA, which is a nonparametric, pattern- recognizing method. The main idea behind this method is to reduce the dimensionality of a data set consisting of a large number of variables while retaining as much of the variance of the original data set as possible. This goal is achieved by transforming the original group of variables to a new set of variables, that is, the principal components (PCs; Hammer et al., 2001). PCs are linear combinations of the original variables, uncorrelated (orthogonal), and are ordered so that the first few components capture most of the variation present in all the ori- ginal variables (e.g., Jolliffe, 2002; Lattin et al., 2002; Rencher, 2002; Anderson, 2003; Johnson and Wichern, 2007; Izenman, 2008). The main utility of PCA is to detect the formation of groups, systematic separation, or the presence of outliers (Jol- liffe, 2002). Detecting which variables (or groups of variables) most influence separations in the data is also possible by ana- lyzing the loading plots, that is, coefficients of the linear com- binations associated with each PC (Jolliffe, 2002). We retained the number of components with an explained


cumulative variance of approximately 80% (Kaiser, 1960; see Kendall, 1965 for other methods). Our aim was to determine a set of data groupings to evaluate the set as a function of the EDS-derived data (elemental composition). PCA was per- formed using STATISTICA (StatSoft Inc., 2004) on raw data consisting of eight variables.


Repository and institutional abbreviation.—The materials stu- died here were collected near the Lahuincó creek. This location is on the west side of the Chubut river, near provincial road No. 12 and 12km south of the Cerro Condor village (43.30572°S, 69.8267°W). The original samples include a fossil association of conchostracans, bivalve mollusks, and ostracods. The mate- rial is deposited in the repository of ‘Dr. Rafael Herbst’


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