Computational Paleontology

Computational Paleontology . Ashraf M.T. Elewa Editor Computational Paleontology Editor Prof.Dr. Ashraf M.T. Elewa Minia University Fac. Science Dept. of Geology Minia Egypt [email protected] [email protected] ISBN 978-3-642-16270-1 e-ISBN 978-3-642-16271-8 DOI 10.1007/978-3-642-16271-8 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011923531 # Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: deblik, Berlin Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Foreword Computers and quantitative methods are fundamental tools in all branches of modern science, and paleontology is no exception. It has not always been this way, however. Quantitative approaches were of course always used by paleontologists, but the mainstream literature used to focus on qualitative description. In general, paleontology was surprisingly slow in adopting quantitative methods, compared with geology and particularly biology. One reason could be the idea that the fossil record is too incomplete for statistical treatment. What is the point of using sophisticated methods on such poor data? This is a misunderstanding – in fact the opposite is the case. It is precisely when the data are incomplete that we need the machinery of statistics to assess the effects of sampling. On the subject of mathematical modelling, a common objection is that the complexities of biological systems cannot be captured in a simple model. Again I would argue otherwise, that exactly when the system is complex beyond the capabilities of the human brain, a reduced model can lead to fundamental understanding by virtue of its very simplicity. After all, the purpose of modelling in paleontology is insight, not prediction. A spectacular, early application of computers in paleontology was Raup’s modelling of shell coiling. Another pioneer was Richard Reyment, who contributes to this volume. Now, computers are used almost everywhere in the paleontological work flow, from field work, data collection and visualization (Mallison; Poza-Rey; Stoinski; this volume) to morphometrics (Reyment) and data m