Mixed Effects Models and Extensions in Ecology With R

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  • Format: Hardcover
  • Copyright: 2009-03-01
  • Publisher: Springer Nature
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Supplemental Materials

What is included with this book?


Building on the successful Analysing Ecological Data (2007) by Zuur, Ieno and Smith, the authors now provide an expanded introduction to using regression and its extensions in analysing ecological data. 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 GAMM. The second part provides ten case studies that range from koalas to deep sea research. These chapters provide an invaluable insight into analysing complex ecological datasets, including comparisons of different approaches to the same problem. By matching ecological questions and data structure to a case study, these chapters provide an excellent starting point to analysing your own data. Data and R code from all chapters are available from

Table of Contents

Limitations of linear regression applied on ecological data
Things are not always linear; additive modelling
Dealing with hetergeneity
Mixed modelling for nested data
Violation of independence temporal data
Violation of independence; spatial data
Generalised linear modelling and generalised additive modelling
Generalised estimation equations
Estimating trends for Antarctic birds in relation to climate change
Large-scale impacts of land-use change in a Scottish farming catchment
Negative binomial GAM and GAMM to analyse amphibian road killings
Additive mixed modelling applied on deep-sea plagic bioluminescent organisms
Additive mixed modelling applied on phyoplankton time series data
Mixed modelling applied on American Fouldbrood affecting honey bees larvae
Three-way nested data for age determination techniques applied to small cetaceans
GLMM applied on the spatial distribution of koalas in a fragmented landscape
GEE and GLMM applied on binomial Badger activity data
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