Eco-Stats - Data Analysis in Ecology : From t-tests to Multivariate Abundances, Hardback Book

Eco-Stats - Data Analysis in Ecology : From t-tests to Multivariate Abundances Hardback

Part of the Methods in Statistical Ecology series


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This book introduces ecologists to the wonderful world of modern tools for data analysis, especially multivariate analysis. Assuming only a vague recollection of an introductory statistics course, the book begins by reviewing some core principles in statistics, and relates common methods to the linear model, a general framework for modeling data where the response is continuous. This is then extended to discrete data using generalized linear models, to designs with multiple sampling levels via mixed models, and to situations where there are multiple response variables via model-based approaches to multivariate analysis. Along the way there is an introduction to: important principles in model selection; adaptations of the model to handle non-linearity and cyclical variables; dependence due to structured correlation in time, space or phylogeny; and design-based techniques for inference that can relax some of the modelling assumptions. Examples span a variety of applications including environmental monitoring, species distribution modeling, global-scale surveys of plant traits, and small field experiments on biological controls. Maths Boxes throughout the book explain some of the core ideas mathematically for readers who want to delve deeper, and R code is used throughout. Accompanying code and data can be found in the ecostats R package on CRAN. For biologists with relatively little prior knowledge of statistics - introducing a modern, advanced approach to data analysis in an intuitive and accessible wayIntroduces modern methods of multivariate analysis to ecology - as direct extensions of univariate techniques Introduces a range of advanced statistics topics relevant to the modern ecologist - including mixed models, model selection, design-based inference, spatial statistics, latent variable models.