Archiv der Kategorie: Allgemein

SoilTemp – a global soil temperature database

SoilTemp is a new project initiated by Jonas Lambrechts and collegues to create a global soil temperature database. The goal is to make soil temperature data available to scientist, increase and facilitate collaborations across projects and synthesise microclimate data on a global scale to answer key ecological questions.

SoilTemp has recently launched a webpage, where information regarding the data, project updates and future publications can be found. So far they have collected For 1867 temperature sensors from 11 countries, from sea level till 6194 meter above the ocean, and covering more than a decade. And the collection is ongoing.

SeedClim has already provided their long-term (10 years) of soil temperature data. TransPlant, our Chinese Collaborators will follow.

Photo: Jonas Lambrechts

PhyloPic

I just discoverd the coolest thing ever! PhyloPic, a database with reusable silhouette images of organisms. Each image is available under a Creative Commons license and can be reused (for non-commercial work), some need to be attributed.

Medusozoa by Walker Pett

 

Fragaria, unknown creator.

 

Caprealla by Collin Gross, see license agreement

 

And here instructions by @TrevorABranch how to insert a silhouette of an animal or plant into your R plot!

ggplot with colour, shape and colour depending fill

Everything is possible with ggplot in R. I realized that again today when plotting some climate data with different colour, shapes and fill. Color showed different precipitation levels, shape showed different temperature levels and I wanted filled symbols for the short term data and filled symbols for the long term data set. The complication was that the filled symbol depended as well on the precipitation level.

My solution was to manually fit different colours for fill, but this messed up the legend. So here comes trick number 2 to manually change the legend.

Let’s have a look at the plots and create a data set. There are 2 climate variables: temperature and precipitation. A factor for temperature and precipitaion with each 2 levels to define color and shape. And the source of the data (short or long term data).

# create a data set
Data <- data_frame(Temperature = c(8.77, 8.67, 7.47, 7.58, 9.1, 8.9, 7.5, 7.7),
                   Precipitation = c(1848, 3029, 1925, 2725, 1900, 3100, 
                                     2000, 2800),
                   Temperature_level = as.factor(c(rep("subalpine", 2), 
                                                   rep("alpine", 2), 
                                                   rep("subalpine", 2), 
                                                   rep("alpine", 2))),
                   Precipitation_level = as.factor(c(rep(c(1,2),4))),
                   Source = c(rep("long term", 4), rep("short term", 4)))

Let’s plot the data using ggplot. We want the filled symbol to be according to the precipitation level. So we use a ifelse statement for fill. If the source is the short term data, then use the precipitation colours, otherwise not. And manually we define the two blue colours and white for the symbols we do not want to have filled.

p <- ggplot(Data, aes(x = Precipitation, y = Temperature, 
                      color = Precipitation_level, 
                      shape = Temperature_level, 
                      fill = factor(ifelse(Source == "short term", 
                                           Precipitation_level, Source)))) +
  scale_color_manual(name = "Precipitation level", 
                     values = c("skyblue1", "steelblue3")) +
  scale_shape_manual(name = "Temperature level", values = c(24, 21)) +
  # manually define the fill colours
  scale_fill_manual(name = "Source", 
                    values = c("skyblue1", "steelblue3", "white")) +
  theme_minimal()
p + geom_point(size = 3)

plot of chunk unnamed-chunk-3

The colours, shape and fill was plotted correctly, but this trick messed up the legend for the data source. The reason is that fill has 3 levels: 2 precipitation levels and one level for the long term data, which we coloured white.

We need another trick to fix this. We will use another factor with 2 levels and then replace the fill legend. First, we add different size for Source. It can be marginally different or have exacly the same value. This seems silly, but it’s useful to change the legend for fill. For changing the legend “guides” is a useful function. First we remove the fill legend. Then we use size which only has 2 levels and use override to draw different shapes for the two levels. And these shapes represent the filled and unfilled symbols.

p + 
  # add size for Source
  geom_point(aes(size = Source)) +
  # defining size with 2 marginally different values
  scale_size_manual(name = "Source", values = c(3, 3.01)) +
  # Remove fill legend and replace the fill legend using the newly created size
  guides(fill = "none", 
         size = guide_legend(override.aes = list(shape = c(1, 16))))

plot of chunk unnamed-chunk-4

So, everything is possible in ggplot. It’s not straight forward code and needed a few tricks to make it work. If you know a quicker way to draw this plot, please let me know!

Thanks, Richard for helping with trick nr. 2!

Landpress project

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.