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.
Table of Contents
Introduction. The Need for Automated Approaches to Species Identification. Is Automated Species Identification Feasible? Natural Object Recognition: Machines Vs. Humans. Homology and Morphometrics: An Old Theme Revisited. Plastic Self-Organizing Maps. Decision Trees: A Machine-learning Methodology to Analyze the Relationship between Skeletal Morphology and Ecological Adaptations. DAISY: A Practical Computer Based Tool for Semi-Automated Species Identification. Introducing SPIDA-web: An Automated Identification System for Biological Species. Automated Extraction and Analysis of Morphological Features for Species Identification. Pattern Recognition for Ecological Science and Environmental Monitoring. Identification of Botanical Taxa using Artificial Neural Networks. Use of Neural Nets in Identification of Spheniscid Species. Drawing the Line: the Differentiation Between Morphological Plasticity and Interspecific Variation. Summary and Prospectus.