Resources and Recommendations for Experimental Ecologists and Biologists

Quantitative Ecology
This is my blog on quantitative ecology and population biology.  There will be considerable thoughts (and links) on experimental design, statistical analysis, inference, philosophy of science, and programming in R, WinBUGS, and potentially other programs such as SAS, JAGS, and Matlab.

Recommended Quantitative Coursework for Students
Undergraduate Courses
Calculus I & II
Linear Algebra
Introductory Statistics
Quantitative Ecology
Population Biology/Dynamics
Theoretical Ecology

Graduate Courses
Probability and Mathematical Statistics (1-2 semesters)
Experimental Design and Analysis

Additional Specific Courses (pick 1-3 based on your research in grad school)
Analysis of Variance (easy to learn on your own with background in Prob and Stats)
Regression (easy to learn on your own with background in Prob and Stats)
Multivariate Statistics
Survival Analysis
Time Series Analysis
Spatial Statistics/Analysis
Bayesian Statistics - good addition to other courses based in classical frequentist statistics

List of UNH Quantitative Courses (pdf)

Papers
Zuur et al. (2010) - excellent paper on the first step in the process of analyzing data pdf

Ellison and Dennis (2010). Paths to statistical fluency for ecologists. Frontiers in Ecology and the Environment. - a must read for all ecologists and especially upper level undergrads and new graduate students.

Cottingham et al. (2005). Knowing when to draw the line: designing more informative ecological experiments. Frontiers in Ecology and the Environment. - a good review of inference from ANOVA vs. Regression and how to design experiments with this in mind.

Anderson et al. (2001). Suggestions for presenting the results of data analyses. Journal of Wildlife Management. 65(3): 373-378


Books
Models for Ecological Data: An Introduction (Clark) - While I like aspects of other ecological modeling books (Bolker, Zuur), I find Clark's book to be the best (but with huge emphasis on Bayesian analysis). It gives lots background on modeling, statistical inference, and reviews probability theory plus matrix algebra, probability density functions, and some relevant calculus. The author uses a variety of understandable examples. Some books hold so tightly to a few examples that you have to read the textbook from the start to make sense of a particular example late in the book. This can cause a text to be of less use as a desk reference. Clark does a good job with the examples and the book can truly be used as a reference or can be read from cover to cover with equal utility. I recommend this book as essential reading for all ecologists. It includes an excellent introduction to Bayesian Statistical Inference and compares Bayesian and Frequentist approaches. An indispensable reference for ecologists. I also second the notion of purchasing the lab manual , if nothing else then for the examples of R code.

Statistical Computation for Environmental Sciences in R: Lab Manual for Models for Ecological Data (Lab Manual)  - lab manual of exercises and R/WinBUGS code associated with Clark's textbook (above).

Ecological Models and Data in R -  Bolker gives better descriptions of the statistics and their use in R than most authors of R books.  He provides excellent descriptions and diagrams/flow charts for determining what types of models to use (see especially page 301 figure 9.2).  He also provides readily understandable and useful examples when discussing each model.  Like most statistics books and virtually all programming books the code and text builds upon itself throughout the book making it slightly challenging to jump ahead to later chapters without extensive previous knowledge of statistics and R programming.  The book covers a large range of modeling options and therefore omits some details on the statistics and testing the necessary assumptions.  This is necessary for a book of this breadth.  The reader should use this book to determine what models to use for their dataset and how to write the code in R but should consult a more detailed statistics book written about that specific approach.  I would recommend this book for any ecologists interested in using R for data analysis.

Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach - This book by Burnham and Anderson is a must have for ecologists and conservation biologists.

Mixed Effects Models and Extensions in Ecology with R (Statistics for Biology and Health) - Zuur and colleagues do a good job with this book and I recommend it for any field biologists/ecologists will to take time to analyze their data well.  There are good, understandable examples.  It is not a statistics book but it has sufficient, clear descriptions of the statistical methods and why one model would be used rather than another.

Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses - Kery does an excellent job introducing ecologists to the Bayesian framework and leading the reader step-by-step through WinBUGS and R2WinBUGS (used to interface R and WinBUGS which is WAY more convenient).

Bayesian Methods for Ecology - McCarthy presents a nice introduction to Bayesian Analysis for ecologists.  There is a bit more on Bayesian theory than in Kery's book (see above) and there are examples of WinBUGS code for basic analyses.  I own both, but I would just get Kery's book if I had to choose one.  Neither book proceeds to the complex models where Bayesian analysis are most useful but both provide very well-writen, understandable introductions to Bayesian analysis and the associated software packages.  I recommend Clark's book on ecological models for more on the modeling aspect of the analysis (remember: model development and statistical inference are two very different parts of the data analysis process).

Electronic Resources
R Bloggers
Gallery of R Graphs
Avoid Dynamite Plots: From Bolker, Resource with some R code, Vanderbilt Wiki (definitely view linked poster)
Understanding How R Works (environments, packages, objects, etc.)