Crown Volume Calculation

The UHURU experiment in Kenya has conducted a survey of Acacia and other tree species in ungulate exclosure treatments. Data for the tree data is available here in a tab delimited ("\t") format. Each of the individuals surveyed were measured for tree height (HEIGHT) and canopy size in two directions (AXIS_1 and AXIS_2). Read these data in using the following code:

tree_data <- read.csv("http://www.esapubs.org/archive/ecol/E095/064/TREE_SURVEYS.txt",
                 sep = '\t',
                 na.strings = c("dead", "missing", "MISSING",
                                "NA", "?", "3.3."),
                 stringsAsFactors = FALSE)

You want to estimate the crown volumes for the different species and have developed equations for species in the Acacia genus:

volume = 0.16 * HEIGHT^0.8 * pi * AXIS_1 * AXIS_2

and the Balanites genus:

volume = 1.2 * HEIGHT^0.26 * pi * AXIS_1 * AXIS_2

For all other genera you’ll use a general equation developed for trees:

volume = 0.5 * HEIGHT^0.6 * pi * AXIS_1 * AXIS_2
  1. Write a function called tree_volume_calc that calculates the canopy volume for the Acacia species in the dataset. To do so, use an if statement in combination with the str_detect() function from the stringr R package. The code str_detect(SPECIES, "Acacia") will return TRUE if the string stored in this variable contains the word “Acacia” and FALSE if it does not. This function will have to take the following arguments as input: SPECIES, HEIGHT, AXIS_1, AXIS_2. Then run the following line:

    tree_volume_calc("Acacia_brevispica", 2.2, 3.5, 1.12)

  2. Expand this function to additionally calculate canopy volumes for other types of trees in this dataset by adding if/else statements and including the volume equations for the Balanites genus and other genera. Then run the following lines:

    tree_volume_calc("Balanites", 2.2, 3.5, 1.12) tree_volume_calc("Croton", 2.2, 3.5, 1.12)

  3. Now run the completed tree canopy function on the entire tree_data dataframe using mapply(). Save the output of the mapply() as an object, then turn this object into a dataframe.

[click here for output] [click here for output] [click here for output]