On this page find details about changes to the support material for *Community Ecology*. These changes will include:

- alterations and improvements to custom R commands
- new custom R commands
- additional data
- extra exercises

The changes are listed below in date order (newest entries at the top). Alterations will be reflected in the existing support pages, essentially:

- Any altered or additional R commands will be added to the RData support file and the ZIP Archive found on the Support Files page.
- Any extra data will be added to the Zip Archive (and RData file if in R-format) found on the Support Files page. Notes about the custom commands will be updated in the Custom Commands page.
- Any extra exercises will be presented on the website (I will make new pages as required).

## July 2021

### Importing data with character variables

Since R version 4, data imported using `read.table()`

or `read.csv()`

functions do NOT have `character`

columns converted to `factor`

variables.

This means that examples where a `factor`

variable is assumed will not work as expected, and you will need to alter `character`

variables to `factor`

.

You can do this individually for each variable required for example:

names(pd) [1] "Site" "Species" pd$Site <- as.factor(pd$Site)

You can also convert all `character`

variables to `factor`

using the `rapply()`

function like so:

pd <- rapply(pd, f = as.factor, classes = "character", how = "replace")

In this case any columns that were originally `character`

would be converted to a `factor`

.

## June 2015

### New command to carry out Polar Ordination (aka Bray-Curtis ordination):

polar.ord()

This is a **new** command that conducts polar ordination (also called Bray-Curtis ordination). The book shows polar ordination and presents a method to carry it out using Excel (Chapter 14). This is labour intensive but instructive for students, as it shows some of the basic principles behind ordination in general. However, I thought it would be useful to have an R command to carry out the procedure.

The command works out polar ordination site scores and works out axes as follows: Axis 1 is the combination of sites that gives the maximum separation (not necessarily the maximum variance explained). Axis 2 is the combination of sites that maximises the orthogonality to the 1st axis.

There are `print`

, `plot`

and `summary`

methods and an `identify`

method for use with plots.

### Usage

polar.ord(diss) print.polar.ord(ord, digits = getOption("digits")) summary.polar.ord(ord, k = 2, cor = TRUE, var.exp = TRUE, digits = getOption("digits")) plot.polar.ord(ord, x.axis = 1, y.axis = 2, type = c("t", "p", "n"), ...) identify.plot.polar.ord(pord, labels = pord$labels, ...)

`diss` |
A dissimilarity `matrix` e.g. as produced by `dist()` or `vegdist()` . |

`digits` |
The number of digits to display, defaults to current settings. |

`ord` |
The result of a `polar.ord` command. |

`k` |
The number of axes to display. |

`cor` |
If `TRUE` , shows the correlation between ordination axes. |

`var.exp` |
If `TRUE` , shows the variance explained. |

`x.axis` |
Which ordination axis to plot on the x-axis. |

`y.axis` |
Which ordination axis to plot on the y-axis. |

`type` |
The type of plot, `"t"` text, `"p"` points or `"n"` none. |

`pord` |
The result of a `plot.polar.ord` command i.e. a polar ordination plot result. |

`labels` |
A `vector` of labels to use for the sites. |

`...` |
Other plotting commands to pass to `plot` or `identify` . |

### Examples

See examples in the Custom R Commands page.

### Get the file

You can get a copy of the .R file here. Click the link to open the text. Right-click and “Save As” to download the file.

### Use the file

The routines are in the main data file. However, if you get the Polar Ordination.R file separately, it opens in any text editor. To install the commands in R, use something like:

source(file.choose())

Then select the file. In Linux you’ll need to specify the filename explicitly.

## February 2015

### Modified command:

plot_H()

This formerly computed bootstrap confidence intervals only (for “shannon” or “simpson”). Now you can specify `"shannon"`

, `"simpson"`

, or `"invsimpson"`

, which use `H_CI()`

and methods using variance estimates. The command calculates the appropriate diversity index and CI, then plots the result.

NOTE: returns an error if you specify `boot = TRUE`

and `index = "invsimpson"`

.

### New command:

Iapp()

This compliments the functions `Happ()`

and `Dapp()`

. It calculates bootstrap CI for Inverse Simpson’s index (`"invsimpson"`

). Designed for use with `apply()`

and others as an “internal” command.

### New command:

aic()

This is a wrapper for `add1()`

that allows display of potential variables in order of AIC. You can specify order (default: `decreasing = FALSE`

) and number of “results” to display. Specify `n = "all"`

to show all results (the default), or integer to display n results. This is useful when your pool of potential variables (the `scope`

parameter) is large.

### Imported & modified commands from vegan 1.17.12:

Some changes in recent versions of the vegan R-package prompted a few alterations.

### Fisher's log series:

The `vegan`

team removed Std Err calculations from `fisherfit()`

. There are sensible reasons for doing so as CI are somewhat “dodgy”. However, to get the same results as the exercises in the book I restored the behaviours. If you want to get the same output as the book then use `fisher_alpha()`

and `fisher_fit()`

in place of `fisher.alpha()`

and `fisherfit()`

commands.

fisher_alpha()

Renamed from `fisher.aplha()`

and restores calculation of se and allows profiling.

fisher_fit()

Renamed from `fisherfit()`

, which allows se calculations and profiling using the following commands restored from vegan 1.17.12.

confint.fisherfit() plot.profile.fisherfit() profile.fisherfit()

## My Publications

I have written several books on ecology and data analysis

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