Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS: Volume 1:Prelude and Static Models

Academic Press
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Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness

offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management.

This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields.

  • Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection
  • Presents models and methods for identifying unmarked individuals and species
  • Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses
  • Includes companion website containing data sets, code, solutions to exercises, and further information
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About the author

Dr Kery is a Population Ecologist with the Swiss Ornithological Institute and a courtesy professor ("Privatdozent") at the University of Zürich/Switzerland, from where he received his PhD in Ecology in 2000. He is an expert in the estimation and modeling of abundance, distribution and species richness in "metapopulation designs" (i.e., collections of replicate sites). For most of his work, he uses the Bayesian model fitting software BUGS and JAGS, about which he has published two books with Academic Press (2010 and 2012). He has authored/coauthored 70 peer-reviewed articles and four book chapters. Since 2007, and for a total of 103 days, he has taught 23 statistical modeling workshops about the methods in the proposed book at research institutes and universities all over the world.

Dr Royle is currently a Research Statistician at the U.S. Geological Survey's Patuxent Wildlife Research Center. His research is focused on the application of probability and statistics to ecological problems, especially those related to animal sampling and demographic modeling. Much of his research over the last 10 years has been devoted to the development of methods illustrated in our new book. He has authored or coauthored more than 100 journal articles, and co-authored the books Spatial Capture Recapture, Hierarchical Modeling and Inference in Ecology and Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, all published by Academic Press.

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Additional Information

Publisher
Academic Press
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Published on
Nov 14, 2015
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Pages
808
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ISBN
9780128014868
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Language
English
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Genres
Science / Environmental Science
Science / Life Sciences / Ecology
Technology & Engineering / Agriculture / Animal Husbandry
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Content Protection
This content is DRM protected.
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Available on Android devices
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Eligible for Family Library

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Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance.Introduction to the essential theories of key models used by ecologists Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGSProvides every detail of R and WinBUGS code required to conduct all analysesCompanion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)
A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods.

This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems.

The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures.
The authors apply principles of hierarchical modeling to ecological problems, including

* occurrence or occupancy models for estimating species distribution
* abundance models based on many sampling protocols, including distance sampling
* capture-recapture models with individual effects
* spatial capture-recapture models based on camera trapping and related methods
* population and metapopulation dynamic models
* models of biodiversity, community structure and dynamics

* Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants)

* Development of classical, likelihood-based procedures for inference, as well as
Bayesian methods of analysis

* Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS

* Computing support in technical appendices in an online companion web site
All life on earth occurs in natural assemblages called communities.Community ecology is the study of patterns and processes involvingthese collections of two or more species. Communities are typicallystudied using a diversity of techniques, including observations ofnatural history, statistical descriptions of natural patterns,laboratory and field experiments, and mathematical modelling.Community patterns arise from a complex assortment of processesincluding competition, predation, mutualism, indirect effects,habitat selection, which result in the most complex biologicalentities on earth – including iconic systems such as rainforests and coral reefs.

This book introduces the reader to a balanced coverage ofconcepts and theories central to community ecology, using examplesdrawn from terrestrial, freshwater, and marine systems, andfocusing on  animal, plant, and microbial species. Thehistorical development of key concepts is described usingdescriptions of classic studies, while examples of exciting newdevelopments in recent studies are used to point toward futureadvances in our understanding of community organization.Throughout, there is an emphasis on the crucial interplay betweenobservations, experiments, and mathematical models.

This second updated edition is a valuable resource for advancedundergraduates, graduate students, and  establishedscientists  who seek a broad overview of community ecology.The book has developed from a course in community ecology that hasbeen taught by the author since 1983.

Figures and tables can be downloaded for free from ahref="http://www.wiley.com/go/morin/communityecology"www.wiley.com/go/morin/communityecology/a

“We highly recommend it—not just for statisticallyterrified biology students and faculty, but also for those who areoccasionally anxious or uncertain. In addition to being a goodstarting point to learn statistics, it is a useful place to returnto refresh your memory.” –The Quarterly Review ofBiology, March 2009

"During the entire course of my Ph.D. I've been(embarrasingly) looking for a way to teach myself the fundamentalsof statistical analysis. At this point in my education, I've cometo realize that often times, simply knowing the basics is enoughfor you to properly apply even the most complex analytical methods.‘Statistics for Terrified Biologists’ has been justsuch a book - it was more than worth the $40 I spent on it, andwhile my 'book clubs' aren't meant to be reviews, I highlyrecommend the book to anyone who's in a similar predicament to myown." –Carlo Artieri's Blog Book Club

The typical biology student is “hardwired” to bewary of any tasks involving the application of mathematics andstatistical analyses, but the plain fact is much of biologyrequires interpretation of experimental data through the use ofstatistical methods.

This unique textbook aims to demystify statistical formulae forthe average biology student. Written in a lively and engagingstyle, Statistics for Terrified Biologists draws on theauthor’s 30 years of lecturing experience. One of theforemost entomologists of his generation, van Emden has anextensive track record for successfully teaching statisticalmethods to even the most guarded of biology students.

For the first time basic methods are presented usingstraightforward, jargon-free language. Students are taught to usesimple formulae accurately to interpret what is being measured witheach test and statistic, while at the same time learning torecognize overall patterns and guiding principles. Complemented bysimple illustrations and useful case studies, this is an idealstatistics resource tool for undergraduate biology andenvironmental science students who lack confidence in theirmathematical abilities.

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