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A Primer of Ecological Statistics, Second Edition explains fundamental material in probability theory, experimental design, and parameter estimation for ecologists and environmental scientists. The book emphasizes a general introduction to probability theory and provides a detailed discussion of specific designs and analyses that are typically encountered in ecology and environmental science. Appropriate for use as either a stand-alone or supplementary text for upper-division undergraduate or graduate courses in ecological and environmental statistics, ecology, environmental science, environmental studies, or experimental design, the Primer also serves as a resource for environmental professionals who need to use and interpret statistics daily but have little or no formal training in the subject. The book is divided into four parts. Part I discusses the fundamentals of probability and statistical thinking. It introduces the logic and language of probability (Chapter 1), explains common statistical distributions used in ecology (Chapter 2) and important measures of central tendency and spread (Chapter 3), explains P-values, hypothesis testing, and statistical errors (Chapter 4), and introduces frequentist, Bayesian, and Monte Carlo methods of analysis (Chapter 5). Part II discusses how to successfully design and execute field experiments and sampling studies. Topics include design strategies (Chapter 6), a 'bestiary' of experimental designs (Chapter 7), and transformations and data management (Chapter 8). Part III discusses specific analyses, and covers the material that is the main core of most statistics texts. Topics include regression (Chapter 9), analysis of variance (Chapter 10), categorical data analysis (Chapter 11), and multivariate analysis (Chapter 12). Part IV—new to this edition—discusses two central topics in estimating important ecological metrics. Topics include quantification of biological diversity (Chapter 13) and estimating occupancy, detection probability, and population sizes from marked and unmarked populations (Chapter 14). The book includes a comprehensive glossary, a mathematical appendix on matrix algebra, and extensively annotated tables and figures. Footnotes introduce advanced and ancillary material: some are purely historical, others cover mathematical/statistical proofs or details, and still others address current topics in the ecological literature. Data files and code used for some of the examples, as well as errata, are available online.
A detailed exposition of the most common mathematical models in population and community ecology, covering exponential and logistic population growth, age-structured demography, metapopulation dynamics, competition, predation, and island biogeography. Intended to demystify ecological models and the math behind them by deriving the models from first principles. The primer may be used as a self-teaching tutorial, as a primary textbook, or as a supplemental text to a general ecology textbook. Annotation copyright by Book News, Inc., Portland, OR
Provides simple explanations of the important concepts in population and community ecology. Provides R code throughout, to illustrate model development and analysis, as well as appendix introducing the R language. Interweaves ecological content and code so that either stands alone. Supplemental web site for additional code.
Ecological community data. Spatial pattern analysis. Species-abundance relations. Species affinity. Community classification. Community ordination. Community interpretation.
A detailed introduction to the methods used by ecologists--classification and ordination--to clarify and interpret large, unwieldy masses of multivariate field data. Permits ecologists to understand, not just mechanically use, pre-packaged programs for multivariate analysis. Demonstrates these techniques using artificial data simple enough for every analytical step to be understood.
This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.
Fundamentals of Ecosystem Science, Second Edition provides a comprehensive introduction to modern ecosystem science covering land, freshwater and marine ecosystems. Featuring full color images to support learning and written by a group of experts, this updated edition covers major concepts of ecosystem science, biogeochemistry, and energetics. Case studies of important environmental problems offer personal insights into how adopting an ecosystem approach has helped solve important intellectual and practical problems. For those choosing to use the book in a classroom environment, or who want to enrich further their reading experience, teaching and learning assets are available at Elsevier.com. - Covers both aquatic (freshwater and marine) and terrestrial ecosystems with updated information - Includes a new chapter on microbial biogeochemistry - Features vignettes throughout the book with real examples of how an ecosystem approach has led to important change in policy, management, and ecological understanding - Demonstrates the application of an ecosystem approach in synthesis chapters and case studies - Contains new coverage of human-environment interactions
This book is the first user-friendly regional guide devoted to ants—the “little things that run the world.” Lavishly illustrated with more than 500 line drawings, 300-plus photographs, and regional distribution maps as composite illustrations for every species, this guide will introduce amateur and professional naturalists and biologists, teachers and students, and environmental managers and pest-control professionals to more than 140 ant species found in the northeastern United States and eastern Canada. The detailed drawings and species descriptions, together with the high-magnification photographs, will allow anyone to identify and learn about ants and their diversity, ecology, life histories, and beauty. In addition, the book includes sections on collecting ants, ant ecology and evolution, natural history, and patterns of geographic distribution and diversity to help readers gain a greater understanding and appreciation of ants.
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more Deemphasizes computer coding in favor of basic principles Explains how to write out properly factored statistical expressions representing Bayesian models
'We are experiencing the beginning of an energy revolution in these early years of the 21st century.' Water, Energy, and Environment - A Primer provides an introduction to, and explanation of, this revolution.