This book introduces ecologists to the wonderful world of modern tools
for data analysis, especially multivariate analysis.
For biologists with relatively little prior knowledge of statistics, it
introduces a modern, advanced approach to data analysis in an intuitive
and accessible way. 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. It concludes with a range of advanced topics in model-based
multivariate analysis relevant to the modern ecologist, including fourth
corner, latent variable and copula models.
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. Math 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, data, and solutions to exercises can be found in the
ecostats R package on CRAN.
