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Thursday, August 6, 2020 | History

4 edition of Models for ecological data found in the catalog.

Models for ecological data

James Samuel Clark

Models for ecological data

an introduction

by James Samuel Clark

  • 114 Want to read
  • 15 Currently reading

Published by Princeton University Press in Princeton, NJ .
Written in English


Edition Notes

StatementJames S. Clark.
Classifications
LC ClassificationsQH
The Physical Object
Paginationxiii, 617 p. :
Number of Pages617
ID Numbers
Open LibraryOL22754761M
ISBN 109780691121789

Clark Models for Ecological Data [BMB] thorough introduction to Bayesian methods, much more coverage of population dynamic models than I give in EMD, but so comprehensive as to be a little daunting for first-time readers; Zuur et al books from Highland Statistics; advanced. Harrell "Regression modeling strategies" [BMB] biomedically oriented. a gap that can isolate those who analyze ecological models from those who collect and draw inference from data. This gap makes it difficult to test theory with data and to model data in appropriate ways (e.g., Oreskes et al. ). The goal of this book is to describe methods that can help to bridge the gap.

As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout. The first part of the book is a largely non-mathematical introduction to linear mixed effects modelling, GLM and GAM, zero inflated models, GEE, GLMM and : Springer-Verlag New York. The book serves as a comprehensive reference for the types of statistical models used to study individual-based animal movement. Animal Movement is an essential reference for wildlife biologists, quantitative ecologists, and statisticians who seek a deeper understanding of modern animal movement models. A wide variety of modeling approaches are.

This book provides data on long term monetary values of environmental impacts from human activities. It discusses the choice of system boundaries, and how to use monetary values in sustainable development. A large part of the book describes impact models in terms of the relation between emissions and natural goods and services. This book discusses the change in use of statistics in ecology—especially the increased use (over the last two decades) of more sophisticated statistical and computational methods. This book also highlights the contribution of statistical modeling to knowledge acquisition as an important way of abstracting ecological questions into mathematical models, and its role in the .


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Models for ecological data by James Samuel Clark Download PDF EPUB FB2

Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R.

Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to.

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language Cited by: Key features: Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study Adds new material on generalized additive models, point pattern analysis, and new methods of.

out a step-by-step process for building models of ecological systems. If you’re impatient with philosophical discussion, you can read Sec-tion and the R supplement at the end of the chapter and move on to Chapter 2.

INTRODUCTION This book is about combining models with data to answer ecological ques-tions. Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology.

In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language /5(16).

The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to understand and exploit powerful new statistical tools for making sense of ecological processes/5(9).

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language.

The book is based on a class I taught several times at the University of Florida, covering the nitty-gritty of constructing and fitting simple (statistical) ecological models to real data sets.

Here is the material for the labs I used in that class; more lab material can be found on the wiki, and I will be likely to update more of it there. Book Description.

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the.

This book provides a practical introduction to analysing ecological data using real data sets collected as part of postgraduate ecological studies or research projects. The first part of the book gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modelling techniques), multivariate.

This book provides a practical introduction to analyzing ecological data using real data sets collected as part of postgraduate ecological studies or research projects. The first part of the book gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate.

Get this from a library. Models for ecological data: an introduction. [James Samuel Clark] -- "The environmental sciences are undergoing a revolution in the use of models and data. Facing ecological data sets of unprecedented size and complexity, environmental scientists are struggling to.

This book is certain to be a benchmark in the science of ecological forecasting for decades to come.”–William H. Schlesinger, president emeritus of the Cary Institute of Ecosystem Studies “Dietze’s subject is a really important one, and his focus on forecasting and its implementation is novel.”–Alan Hastings, University of.

The book is not a set of pat statistical procedures but rather an approach. The Ecological Detective makes liberal use of computer programming for the generation of hypotheses, exploration of data, and the comparison of different models. The authors' attitude is one of exploration, both statistical and graphical.5/5(2).

Through an available lab manual, the book introduces readers to the practical work of data modeling and computation in the language R.

Based on a successful course at Duke University and National Science Foundation-funded institutes on hierarchical modeling, Models for Ecological Data will enable ecologists and other environmental scientists to /5(3). About this book. Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology.

In step-by-step detail, Ecological Models and Data in R teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data.

Its integration of modern statistical methods and ecological theory and data is fundamentally new. The book will train ecologists and other quantitative scientists in the 'new modeling techniques' that are becoming ever more prevalent in their field.

In particular, the book describes how one should deal with complicated problems in which there. Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology.

In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the /5. Ecological modelling can assist in the implementation of sustainable development, mathematical models, and systems analysis that describe how ecological processes can support the sustainable management of resources (Park et al., ).Sustainability, defined as the maintenance of natural capital and resources (Goodland, ), is an increasingly used term.

"Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language.

Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language /5(33).As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout.

The first part of the book is a largely non-mathematical introduction to linear mixed effects modeling, GLM and GAM, zero-inflated models, GEE, GLMM and GAMM.State-of-the-Art in Ecological Modelling covers the proceedings of the Conference on Ecological Modeling, held in Copenhagen, Denmark from August 28 to September 2, The book focuses on ecological modeling, particularly prey-predator models, lake and river models, toxic substances models, and holistic approaches to ecological modeling.