Table of contents

An accessible introduction to the theory and practice of multivariate analysis for graduates, researchers and professionals dealing with ecological problems.

This revised and updated edition focuses on constrained ordination (RDA, CCA), variation partitioning and the use of permutation tests of statistical hypotheses about multivariate data. Both classification and modern regression methods (GLM, GAM, loess) are reviewed and species functional traits and spatial structures analysed. Nine case studies of varying difficulty help to illustrate the suggested analytical methods, using the latest version of Canoco 5. All studies utilise descriptive and manipulative approaches, and are supported by data sets and project files available from the book website: http://regent.prf.jcu.cz/maed2/. Written primarily for community ecologists needing to analyse data resulting from field observations and experiments, this book is a valuable resource to students and researchers dealing with both simple and complex ecological problems, such as the variation of biotic communities with environmental conditions or their response to experimental manipulation.

La diversidad biológica es fruto de la interacción entre numerosas especies, ya sean marinas, vegetales o animales, a la par que de los muchos factores limitantes que caracterizan el medio que habitan. El análisis multivariante utiliza las relaciones entre diferentes variables para ordenar los objetos de estudio según sus propiedades colectivas y luego clasificarlos; es decir, agrupar especies o ecosistemas en distintas clases compuestas cada una por entidades con propiedades parecidas. El fin último es relacionar la variabilidad biológica observada con las correspondientes características medioambientales. Multivariate Analysis of Ecological Data explica de manera completa y estructurada cómo analizar e interpretar los datos ecológicos observados sobre múltiples variables, tanto biológicos como medioambientales. Tras una introducción general a los datos ecológicos multivariantes y la metodología estadística, se abordan en capítulos específicos, métodos como aglomeración (clustering), regresión, biplots, escalado multidimensional, análisis de correspondencias (simple y canónico) y análisis log-ratio, con atención también a sus problemas de modelado y aspectos inferenciales. El libro plantea una serie de aplicaciones a datos reales derivados de investigaciones ecológicas, además de dos casos detallados que llevan al lector a apreciar los retos de análisis, interpretación y comunicación inherentes a los estudios a gran escala y los diseños complejos.

Ecological data has several special properties: the presence or absence of species on a semi-quantitative abundance scale; non-linear relationships between species and environmental factors; and high inter-correlations among species and among environmental variables. The analysis of such data is important to the interpretation of relationships within plant and animal communities and with their environments. In this corrected version of Data Analysis in Community and Landscape Ecology, without using complex mathematics, the contributors demonstrate the methods that have proven most useful, with examples, exercises and case-studies. Chapters explain in an elementary way powerful data analysis techniques such as logic regression, canonical correspondence analysis, and kriging.

A straightforward introduction to a wide range of statistical methods for field biologists, using thoroughly explained R code.

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and comprehensive approach. These methods are derived from the fields of mathematical physics, parametric and nonparametric statistics, information theory, numerical taxonomy, archaeology, psychometry, sociometry, econometry and others. Compared to the first edition of Numerical Ecology, this second edition includes three new chapters, dealing with the analysis of semiquantitative data, canonical analysis and spatial analysis. New sections have been added to almost all other chapters. There are sections listing available computer programs and packages at the end of several chapters. As in the previous English and French editions, there are numerous examples from the ecological literature, and the choice of methods is facilitated by several synoptic tables.

The 3rd edition of this popular textbook introduces the reader to the investigation of vegetation systems with an emphasis on data analysis. The book succinctly illustrates the various paths leading to high quality data suitable for pattern recognition, pattern testing, static and dynamic modelling and model testing including spatial and temporal aspects of ecosystems. Step-by-step introductions using small examples lead to more demanding approaches illustrated by real world examples aimed at explaining interpretations. All data sets and examples described in the book are available online and are written using the freely available statistical package R. This book will be of particular value to beginning graduate students and postdoctoral researchers of vegetation ecology, ecological data analysis, and ecological modelling, and experienced researchers needing a guide to new methods. A completely revised and updated edition of this popular introduction to data analysis in vegetation ecology. Includes practical step-by-step examples using the freely available statistical package R. Complex concepts and operations are explained using clear illustrations and case studies relating to real world phenomena. Emphasizes method selection rather than just giving a set of recipes.