Automated Taxon Identification in Systematics: Theory, Approaches and Applications

Norman MacLeod

July 23, 2007 by CRC Press
Reference - 368 Pages - 10 Color & 143 B/W Illustrations
ISBN 9780849382055 - CAT# 8205
Series: Systematics Association Special Volumes

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Features

  • Demonstrates how automated identification can be applied to various organismal groups and in a range of contexts
  • Presents current trends in quantitative approaches to the group-recognition problem in biology
  • Provides an introduction to neural nets written in an easily accessible, non-mathematical manner
  • Illustrates the capabilities of various software systems currently available for identifying systematic objects/groups
  • Includes descriptions of highly developed applications for achieving image-based automated group identification
  • Contains accessible descriptions of ABIS, DAISY, and SPIDA
  • Summary

    The automated identification of biological objects or groups has been a dream among taxonomists and systematists for centuries. However, progress in designing and implementing practical systems for fully automated taxon identification has been frustratingly slow. Regardless, the dream has never died. Recent developments in computer architectures and innovations in software design have placed the tools needed to realize this vision in the hands of the systematics community, not several years hence, but now. And not just for DNA barcodes or other molecular data, but for digital images of organisms, digital sounds, digitized chemical data - essentially any type of digital data.

    Based on evidence accumulated over the last decade and written by applied researchers, Automated Taxon Identification in Systematics explores contemporary applications of quantitative approaches to the problem of taxon recognition. The book begins by reviewing the current state of systematics and placing automated taxon identification in the context of contemporary trends, needs, and opportunities. The chapters present and evaluate different aspects of current automated system designs. They then provide descriptions of case studies in which different theoretical and practical aspects of the overall group-identification problem are identified, analyzed, and discussed.

    A recurring theme through the chapters is the relationship between taxonomic identification, automated group identification, and morphometrics. This collection provides a bridge between these communities and between them and the wider world of applied taxonomy. The only book-length treatment that explores automated group identification in systematic context, this text also includes introductions to basic aspects of the fields of contemporary artificial intelligence and mathematical group recognition for the entire biological community.