Multivariate Analysis of Ecological Data

Multivariate Analysis of Ecological Data
Author: Michael Greenacre
Publisher: Fundacion BBVA
Total Pages: 336
Release: 2014-01-09
Genre: Ecology
ISBN: 8492937505

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.

The Interpretation of Ecological Data

The Interpretation of Ecological Data
Author: E. C. Pielou
Publisher: John Wiley & Sons
Total Pages: 278
Release: 1984-09-06
Genre: Science
ISBN: 9780471889502

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.

Analyzing Ecological Data

Analyzing Ecological Data
Author: Alain Zuur
Publisher: Springer
Total Pages: 686
Release: 2007-08-29
Genre: Science
ISBN: 0387459723

This book provides a practical introduction to analyzing ecological data using real data sets. The first part gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series analysis, and spatial statistics. The second part provides 17 case studies. The case studies include topics ranging from terrestrial ecology to marine biology and can be used as a template for a reader’s own data analysis. Data from all case studies are available from www.highstat.com. Guidance on software is provided in the book.

Data Analysis in Community and Landscape Ecology

Data Analysis in Community and Landscape Ecology
Author: R. H. Jongman
Publisher: Cambridge University Press
Total Pages: 325
Release: 1995-03-02
Genre: Mathematics
ISBN: 0521475740

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.

Ecological Models and Data in R

Ecological Models and Data in R
Author: Benjamin M. Bolker
Publisher: Princeton University Press
Total Pages: 408
Release: 2008-07-21
Genre: Computers
ISBN: 0691125228

Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.

Multivariate Analysis of Ecological Data with ade4

Multivariate Analysis of Ecological Data with ade4
Author: Jean Thioulouse
Publisher: Springer
Total Pages: 334
Release: 2018-11-08
Genre: Medical
ISBN: 1493988506

This book introduces the ade4 package for R which provides multivariate methods for the analysis of ecological data. It is implemented around the mathematical concept of the duality diagram, and provides a unified framework for multivariate analysis. The authors offer a detailed presentation of the theoretical framework of the duality diagram and also of its application to real-world ecological problems. These two goals may seem contradictory, as they concern two separate groups of scientists, namely statisticians and ecologists. However, statistical ecology has become a scientific discipline of its own, and the good use of multivariate data analysis methods by ecologists implies a fair knowledge of the mathematical properties of these methods. The organization of the book is based on ecological questions, but these questions correspond to particular classes of data analysis methods. The first chapters present both usual and multiway data analysis methods. Further chapters are dedicated for example to the analysis of spatial data, of phylogenetic structures, and of biodiversity patterns. One chapter deals with multivariate data analysis graphs. In each chapter, the basic mathematical definitions of the methods and the outputs of the R functions available in ade4 are detailed in two different boxes. The text of the book itself can be read independently from these boxes. Thus the book offers the opportunity to find information about the ecological situation from which a question raises alongside the mathematical properties of methods that can be applied to answer this question, as well as the details of software outputs. Each example and all the graphs in this book come with executable R code.

Ecological Data

Ecological Data
Author: William K. Michener
Publisher: John Wiley & Sons
Total Pages: 194
Release: 2009-04-01
Genre: Science
ISBN: 1444311395

Ecologists are increasingly tackling difficult issues like global change, loss of biodiversity and sustainability of ecosystem services. These and related topics are enormously challenging, requiring unprecedented multidisciplinary collaboration and rapid synthesis of large amounts of diverse data into information and ultimately knowledge. New sensors, computers, data collection and storage devices and analytical and statistical methods provide a powerful tool kit to support analyses, graphics and visualizations that were unthinkable even a few years ago. New and increased emphasis on accessibility, management, processing and sharing of high-quality, well-maintained and understandable data represents a significant change in how scientists view and treat data. These issues are complex and despite their importance, are typically not addressed in database, ecological and statistical textbooks. This book addresses these issues, providing a much needed resource for those involved in designing and implementing ecological research, as well as students who are entering the environmental sciences. Chapters focus on the design of ecological studies, data management principles, scientific databases, data quality assurance, data documentation, archiving ecological data and information and processing data into information and knowledge. The book stops short of a detailed treatment of data analysis, but does provide pointers to the relevant literature in graphics, statistics and knowledge discovery. The central thesis of the book is that high quality data management systems are critical for addressing future environmental challenges. This requires a new approach to how we conduct ecological research, that views data as a resource and promotes stewardship, recycling and sharing of data. Ecological Data will be particularly useful to those ecologists and information specialists that actively design, manage and analyze environmental databases. However, it will also benefit a wider audience of scientists and students in the ecological and environmental sciences.