Heathland are costal habitats shaped by humans hundreds of years ago. They were burned to clear the land for their animals. Burning the heathland was continued to improves the fodder quality and prevent shrubs and trees to grow in the heathland.

In 2014 an intensive drought led to heather death along the coast of Norway. Landpress tests whether heather burning is an effective measure to prevent drought damage and restore damaged moorland.

Last week, one of the sites, close to Bergen was burned.

Creating presentations with RMarkdown in R is a useful alternative to using Power Point. It is very useful for code loaded presentations. But many people produce the results (figures) they present in R. And whenever something is changed in the data or code each figure has to be remade, save again and exchanged in the presentation. RMarkdown will do all that automatically.

But let’s start at the first page. Most presentations (that I have seen) have a picture on the title page, because pictures say more than words. So how do you put a picture on your RMarkdown presentation?

It took me a while to figure out, but it’s quiet easy. I assume you know how to get started with your presentation. Otherwise check out this link.

The first thing you need to fix are the first couple of lines in your RMarkdown file. After output you write: ioslides_presentation: css: styles.css

Make sure the spacing is right! This will tell your RMardown to look for a css file.

The next step is to create a css file. Where you then can define all sorts of things, for example to put a picture on your title page.

• open a new R Script
• type the following code in it, including the url and name of the picture you want in the presentation.

• save it as style.css (make sure that it has .css as ending and nothing else)

The new sign for the FunCaB field sites have arrived. Will be put out this week!

Save and load a R object can be very useful. A common way to store your results in R is to create a txt, csv or xls file. The advantage is that you can open the tables with many programs on your computer. But as soon as you want to do something with it in R it becomes complicated. You need to import the csv file back into R. You need to define header, how the data is separated. And you can never be sure if a factor is still a factor or a date is still a date. Usually, there are some complications that need to be fixed.

An easier solution is to use the function “save” to store your R object. The advantage here is that all the properties of the object are retained. For example a data frame will be a data frame again, a list will be opened as a list.

Let’s create a data set.

# create a data set
petal.length <- rnorm(100, mean = 2.5, sd = 1)
petal.width <- rnorm(100, mean = 1.75, sd = 1)
species <- rep(letters[1:10], each = 10)
floral.traits <- data.frame(species, petal.length, petal.width)

# here we have specis as a factor and two numeric variables
str(floral.traits)

## 'data.frame':    100 obs. of  3 variables:
##  $ species     : Factor w/ 10 levels "a","b","c","d",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ petal.length: num  2.4 3.08 3.5 2.75 2.92 ...
##  $ petal.width : num  1.525 2.138 2.397 2.148 0.838 ...

Now let us do somthing with this data, for example calculate the mean petal length and petal width for each species.

# calculate mean petal length and withd for each species
mean.pl <- tapply(floral.traits$petal.length, floral.traits$species, mean)
mean.pw <- tapply(floral.traits$petal.width, floral.traits$species, mean)
# transpose the data to make the table easier to handle and combine the means to one data set.
means <- data.frame(t(rbind(mean.pl, mean.pw)))
means$species <- factor(rownames(means)) # add speceis column

We have calculated the mean petal length and width for each species. And now we want to store this result. Maybe we want to use this result later to plot or calculate something. But let’s save it for now with the function “save”.
The first argument is the name of the object you want to save or a list of objects. File is the name of the file with RData at the end. There are more options, but you will mostly use the two first arguments.

# save the R object
save(means, file = "MeanPetalLengthAndWidth.RData")
# let's remove the data set to make sure this is working.
rm(means)

Your R object is now stored in the working directory you are using. We also removed the object from the environment to make sure the object is saved and we can reload it again.

Now we want to load the object back again to our environment to use it further. For this we use the “load” function. The first argument here is the file name. With envir you define where the data should be loaded to. And with verbose you can decide if you want the item name to be printed or not. This can be useful if you have forgotten what it was called.

# load the R object
load("MeanPetalLengthAndWidth.RData", verbose = TRUE)

## Loading objects:
##   means

# check the properties of the object.
str(means)

## 'data.frame':    10 obs. of  3 variables:
##  $ mean.pl: num  2.56 2.34 2.68 2.32 3.23 ...
##  $ mean.pw: num  1.82 1.76 1.75 1.74 2.34 ...
##  $ species: Factor w/ 10 levels "a","b","c","d",..: 1 2 3 4 5 6 7 8 9 10

Now you can check if the object has reapeard in the environment. And then you can . Have fun saving and loading!

# Barplot of petal length
barplot(means$mean.pl, names.arg = means$species, ylab = "Petal length", xlab = "Species")

The 2016 International Plant Functional Traits Course will offer a hands-on, field-based exploration of plant functional traits, along with experience in the usage of plant traits data in climate-change research and ecosystem ecology. Trait-based ecology offers an important set of methods and new approaches that enables a more powerful approach to predict how climate and biotic interactions shape the diversity of communities and functioning of ecosystems. This course will provide students with the essential background and skills needed for trait-based ecology.