This course has no required text, but the following books will be useful to you. In each of the sections below the references are listed in order from basic to more advanced.

## General biostatistics books

Whitlock, M. and D. Schluter. 2015. 2^{nd} edition. The analysis of biological data. Greenwood Village, Colo., Roberts and Co. Publishers.

A readable introduction to analyzing data in biology. A good place to refresh your skills. Biol 501 assumes that you have already taken an undergraduate course in statistics and are familiar with basic principles covered in Chapters 1 through 17. Some of the material in the later chapters will be used in this course.

R commands to analyze the data for all examples presented in this 2^{nd} edition are *here*.

Quinn, G. P. and M. J. Keough. 2002. Experimental design and data analysis for biologists. Cambridge, UK; New York, Cambridge University Press.

*An excellent second course in statistics and biological data analysis. Emphasis of examples is ecology, especially marine intertidal ecology. Full of practical information on the best approaches to use in particular circumstances and the reasons why*.

## R books

Borcard, D., F. Gillet, and P. Legendre. 2011. Numerical ecology with R. Springer.

Based on the standard reference for ordination and multivariate methods in ecology.

UBC Online.

Crawley, M. J. 2012, The R book. 2^{nd} edition. Chichester, England; Hoboken, N.J.,Wiley.

A helpful reference for methods in R,including linear mixed modeling.Explanations not always straightforward. His approach to model simplification is outdated. We’ll be discussing alternative model selection approaches in class.

Dalgaard, P. 2008, Introductory statistics with R. 2nd. ed. New York,Springer.

A clear introduction to the basics of R and how to carry out the standard methods for analyzing data. Better for starters than Crawley’s but less comprehensive.

UBC Online.

Galecki, Andrzej T. 2013. Linear mixed-effects models using R: a step-by-step approach. Springer New York

Takes you through linear models and gls as well as linear mixed models.

UBC Online.

Paradis, E. 2006. Analysis of phylogenetics and evolution with R. New York, Springer.

Explains how to carry out phylogenetic comparative analysis using the ape package.

UBC online.

Pinheiro, J. C. and D. M. Bates. 2000. Mixed-effects models in S and S-PLUS. New York, Springer.

The standard reference for linear mixed effects modeling using R. Aimed at an advanced level. At least two of the chapters are essential reading if you use the `nlme`

library in R to analyze your data.

UBC online

Stevens, M. H. H. 2009. A primer of ecology with R. New York,Springer.

Using R to analyze models and data in population and community ecology.

UBC Online.

Sarkar, D. 2008. Lattice: Multivariate data visualization with R. New York, Springer.

The creator of the lattice package explains all.

UBC online.

Venables, W. N. and B. D. Ripley. 2002. Modern applied statistics with S-PLUS. 4th. New York, Springer.

The standard reference for statistical analysis of data using R and S. Covers most of the methods you will every need.

UBC online.

Wickam, H. 2016. ggplot2: Elegant graphics for data analysis. 2^{nd} edition.

How to create graphs using the increasingly popular ggplot2.

UBC online.

Zuur, A. F., E. N. Ieno and E. Meesters. 2009. A beginner’s guide to R. New York, Springer.

A readable, detailed introduction to data manipulation and plotting in R. Doesn’t get much farther with data analysis than tables and graphs.

UBC Online.

Zuur, A. F., E. N. Ieno, N. J. Walker, A. A. Saveliev and G. M.Smith. 2009. Mixed effects models and extensions in ecology with R. New York, Springer.

A useful guide to advanced methods of data analysis in ecology as well as to carrying them out in R. Topics including nonlinear regression, additive modeling, mixed-effects models, nonindependent data, generalized least squares and generalized additive models.

UBC Online.

## More specialized references

Bolker, B. M. 2008. Ecological models and data in R. Princeton, NJ,Princeton University Press.

A good complement to Hilborn and Mangel, with the added practical information on how to implement the general approach using R. Has a good overview chapter on likelihood.

Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd.New York, Springer.

The authoritative treatise on modern approaches to model selection.

UBC Online

Efron, B. and R. Tibshirani. 1998. An introduction to the bootstrap. Boca Raton, FL, Chapman & Hall/CRC Press.

An accessible introduction, at least the first few chapters.

Felsenstein, J. 2004. Inferring phylogenies. Sunderland,Mass, Sinauer.

The master’s voice. Chapter 25 is a clear and compact summary of comparative methods.

Gotelli, N. J. and A. M. Ellison. 2004. A primer of ecological statistics. Sunderland, Mass., Sinauer Associates Publishers.

A clear overview of basic principles and methods in analyzing ecological data. Not particularly rich with data or examples. The overview of multivariate methods is excellent.

Hilborn, R. and M. Mangel. 1997. The ecological detective: confronting models with data. Princeton, NJ, Princeton University Press.

A great overview of howto fit models to data using a likelihood and model selection approach.