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DATE: 2017-12-01
AUTHOR: John L. Godlee
I have been attempting to write some scripts that will hopefully serve me well as I progress through writing my PhD. As I'm looking at the spatial distribution of trees and their canopy properties in dry tropical woodlands/savannahs, I thought it would be good to learn how to effectively plot tree distributions in plots. So I got hold of some data which contains the lat-long coordinates of trees in two 25 ha permanent savannah plots, one of which is known to be home to elephants, and one which isn't. As always, the code I used is in R.
The data and an example script which I used can be found here[1].
1: /files/elephants/elephant_data.zip
First load the necessary packages:
# Packages ---- library(ggplot2) # Plotting library(dplyr) # Manipulating data library(readr) # Importing data library(viridis) # Colour schemes
Then load in the data:
# Set working directory ---- setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) # Import data ---- # Tree locations and diameters tree_loc_diam_1_2_summ <- read.csv("tree_loc_diam_1_2.csv") # Plot bounding boxes plot_bbox_1 <- read_csv("plot_bb_1.csv") plot_bbox_2 <- read_csv("plot_bb_2.csv")
First I want to plot the trees according to their DBH (Diameter at Breast Height, ~1.3 m), to see if there is a difference between plots. I'll make a map, and a box plot:
# Plot tree DBH as point size ---- ggplot(tree_loc_diam_1_2_summ, aes(x = dec_lon, y = dec_lat, size = dbh_cm, colour = species)) + geom_point(alpha = 0.5) + theme(aspect.ratio = 1) + # coord_map() doesn't work with facet_wrap() facet_wrap(~ plot, scales = "free") + theme(legend.position = "bottom") # DBH boxplots ---- ggplot(tree_loc_diam_1_2_summ, aes(x = plot, y = dbh_cm)) + geom_boxplot()
Now I want to create a map that shows how clustered together the trees are in any given plot. For this I use the handy stat_density2d geom from ggplot2, and fill according to ..level... Also I found I had to manually increase the size of the plot, otherwise the density shading polygons get clipped and look really messy.
# Is there an effect of elephants on spatial clustering of trees? ---- # Split data frames tree_loc_diam_1_summ <- tree_loc_diam_1_2_summ %>% filter(plot == "elephants") tree_loc_diam_2_summ <- tree_loc_diam_1_2_summ %>% filter(plot == "no_elephants") # Create heatmaps based on density of trees elephant_plot <- ggplot(tree_loc_diam_1_summ, aes(x = dec_lon, y = dec_lat)) + stat_density2d(aes(fill = ..level..), geom = "polygon") + scale_fill_viridis() + geom_polygon(data = plot_bbox_1, aes(x = dec_lon, y = dec_lat), fill = NA, colour = "black") + geom_point(size = 0.8) + xlim(min(plot_bbox_1$dec_lon) - 0.001, max(plot_bbox_1$dec_lon) + 0.001) + ylim(min(plot_bbox_1$dec_lat) - 0.001, max(plot_bbox_1$dec_lat) + 0.001) + xlab("Decimal Longitude") + ylab("Decimal Latitude") + theme_classic() + theme(legend.text = element_text(size = 10), axis.text = element_text(size = 10)) + labs(fill = "Tree Density") no_elephant_plot <- ggplot(tree_loc_diam_2_summ, aes(x = dec_lon, y = dec_lat)) + stat_density2d(aes(fill = ..level..), geom = "polygon") + scale_fill_viridis() + geom_polygon(data = plot_bbox_2, aes(x = dec_lon, y = dec_lat), fill = NA, colour = "black") + geom_point(size = 0.8) + xlim(min(plot_bbox_2$dec_lon) - 0.001, max(plot_bbox_2$dec_lon) + 0.001) + ylim(min(plot_bbox_2$dec_lat) - 0.001, max(plot_bbox_2$dec_lat) + 0.001) + xlab("Decimal Longitude") + ylab("Decimal Latitude") + theme_classic() + theme(legend.text = element_text(size = 10), axis.text = element_text(size = 10)) + labs(fill = "Tree Density")
Heatmap of trees with elephants
Heatmap of trees without elephants
The spatial clustering of trees in a plot with elephant activity (left) and without elephant activity (right). Elephants clearly have caused spatial clustering of trees.