Taylor's Power Law: Order and Pattern in Nature is a broad synthesis of this ubiquitous property of natural and man-made phenomena. This stimulating and approachable work surveys the biological and non-biological empirical data, describes the statistical uses of Taylor's power law (TPL) and its relationship to statistical distributions, exposes the mathematical connections to other power laws, covers the competing explanatory models; and develops an argument for TPL's genesis. Taylor's power law relates the variability of a process or population to its average value. It was first described in relation to insect populations and then more broadly to other animal and plant populations. Subsequently it has been recognized in microbiology, genetics, economics, astronomy, physics, and computer science, and it is thought to be one of the few general laws in ecology where it is routinely used to describe the spatial and temporal distributions of populations. Biologists who know the law as Taylor's power law and physical scientists who know it as fluctuation scaling will be interested in the bigger picture on this fascinating subject. As the relationship between variance and mean is found in so wide a range of disciplines, it seems possible it is a deep property of number, not just a phenomenon in ecology as was thought originally. Although theories abound that purport to explain or predict TPL, none is entirely satisfactory either because it fails to be very predictive, or it does not account for all the available empirical data. To uncover such a property requires a synthesis across disciplines, an acute need that is approached by this exciting work. - Provides a single reference describing the properties, scope, and limitations of Taylor's power law - Reports the empirical, analytical, and theoretical work without opinion and ends with a critique of the work in order to develop a synthesis - Collects together thoughts and suggestions of the hundreds who have written and speculated about Taylor's power law in order to review examples (and counter-examples), as well as examine the various models developed to account for it

Handbook of Sampling Methods for Arthropods in Agriculture offers a comprehensive look at the principles and practicality of developing accurate sampling programs for arthropod pests and their arthropod enemies. The book examines developments in sampling populations and reviews sampling plans that produce accurate and affordable population estimates. The text stresses practicality, as well as the theoretical background of sampling. This book will be an indispensable reference for researchers, students, and practitioners in entomology and agriculture.

This book provides applied biologists and ecologists with the mathematical tools they need to understand the ever increasingly mathematical and complex area of population ecology.

Covering a wide range of disciplines, this book explains the formulae, techniques, and methods used in field ecology. By providing an awareness of the statistical foundation for existing methods, the book will make biologists more aware of the strengths and possible weaknesses of procedures employed, and statisticians more appreciative of the needs of the field ecologist. Unique to this book is a focus on ecological data for single-species populations, from sampling through modeling. Examples come from real situations in pest management, forestry, wildlife biology, plant protection, and environmental studies, as well as from classical ecology. All those using this book will acquire a strong foundation in the statistical methods of modern ecological research. This textbook is for late undergraduate and graduate students, and for professionals.

This book constitutes the refereed proceedings of the International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, held in Shanghai, China, on November 7-8, 2009. The 79 revised full papers presented in this volume were carefully reviewed and selected from 1203 submissions. The papers are organized in topical sections on support vector machine, rough set theory, particle swarm optimization, neural computation, intelligent agents and systems, information security, immune computation, genetic algorithms, fuzzy computation, biological computing, applications of computational intelligence, ant colony algorithm, robotics, pattern recognition, neural networks, natural language processing, machine vision, machine learning, logic reasoning and theorem-proving, knowledge representation and acquisition, intelligent signal processing, intelligent scheduling, intelligent information retrieval, intelligent information fusion, intelligent image processing, heuristic searching methods, fuzzy logic and soft computing, distributed AI and agents, data mining and knowledge discovering, applications of artificial intelligence, and others.