Survivorship.

This chapter describes the use of statistical models to determine the factors affecting the survival of killdeer (Charadrius vociferus) nests at gravelled oil pads and on native grass cover in western Oklahoma, USA.

Mapping and parsing text input for data.

Increasingly, data are becoming available about the distributions of organisms around the world and are being collated as freely available online resources in various formats. This chapter introduces the maptools library and plot distributions of taxa at country level on maps.

Analysis of variance (ANOVA).

Analysis of variance is used to analyze the differences between group means in a sample, when the response variable is numeric (real numbers) and the explanatory variable(s) are all categorical. Each explanatory variable may have two or more factor levels, but if there is only one explanatory variable and it has only two factor levels, one should use Student's t-test and the result will be identical. Basically an ANOVA fits an intercept and slopes for one or more of the categorical explanatory variables. ANOVA is usually performed using the linear model function lm, or the more specific function aov, but there is a special function oneway.test when there is only a single explanatory variable. For a one-way ANOVA the non-parametric equivalent (if variance assumptions are not met) is the kruskal.test.

Very basic R syntax.

R is a programming language that has a huge range of inbuilt statistical and graphical functions. Firstly, this chapter shows how R works by talking you through a number of exercises, often producing graphical output, so you will get to know how to write simple code and become familiar with some of the most commonly used R functions for manipulating data and doing simple calculations. For ease, the chapter will firstly use a non-biological type of example. Thereafter, it will enter, display and analyse a number of real biological or medical datasets as might be obtained in student class experiments or fieldwork projects. Further on, it will present an outline of statistical tests appropriate to various types of data that you will come across.

Monte Carlo tests and randomization.

This chapter focuses on Monte Carlo tests and randomization. It involves randomizing the observed numbers many times and comparing the randomized results with the original observed data. It is shown how randomization can be used in experimental design and sampling.

More on manipulating text.

This chapter provides more information on manipulating text, presenting two examples. Example 1 focuses on standardizing names in a phylogenetic tree description, using R to reformat taxon names, create lists, sort data and use wildcards for when some things you are interested in don't have exactly the same length. The example tree description concerns parasitoids of caterpillars at a study site that have been DNA barcoded and their possible taxonomic identities added automatically. Example 2 deals with substrings of unknown length. This example search for a numeric substring of unknown length but with a standard prefix, using data of some DNA sequences from a set of Aleiodes wasps. The trimming of white spaces and/or tabs, use of wildcards to locate internal letter strings, finding of suffixes, prefixes and specifying of letters, numbers and punctuation, manipulation of character case, ignoring of character case, and specifying of particular and modifiable character classes are briefly described.

Food webs and simple graphics.

Food webs are fundamental in much of ecology and there has been a steady increase in studying their structure and properties over the past 50 years, nowadays often utilizing molecular methods too. First, this chapter will create code to draw a food web, then it will introduce the package cheddar. The reason for learning how to produce your own is not just to improve programming skill and logical thinking, it also means you are in a position to customize your diagrams in ways that perhaps are not available in pre-written packages. A parasitoid foodweb example is given. In this example from Thailand, 22 braconid parasitoid wasps, representing a total of 9 species were associated with 22 lepidopteran hosts representing a total of 11 species using DNA barcoding.

Sets and Venn diagrams.

This chapter focuses on sets and Venn diagrams. Venn diagrams, also known as set diagrams, are commonly used to represent the overlap between sets. However, there is no in-built Venn diagram function in R so packages are used.

Survivorship.

This chapter describes the use of statistical models to determine the factors affecting the survival of killdeer (Charadrius vociferus) nests at gravelled oil pads and on native grass cover in western Oklahoma, USA.

More generalized linear modelling.

This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.