We will be using the data from the following study (Döbert et al. 2017):
Döbert, T.F., Webber, B.L., Sugau, J.B., Dickinson, K.J.M. and
Didham, R.K. (2017), Logging increases the functional and phylogenetic
dispersion of understorey plant communities in tropical lowland rain
forest. J Ecol, 105: 1235-1245. https://doi.org/10.1111/1365-2745.12794
Logging is the major cause of forest degradation in the Tropics. The
effect of logging on taxonomic diversity is well known but more rarely
studied on other facets of biodiversity such as functional diversity and
phylogenetic diversity. Functional diversity and phylogenetic diversity
should better reflect the impact of logging on ecosystem. For example
logging can decrease the functional “redundancy” observed in ecosystems,
meaning that some functional traits could be lost.
The tropical lowland rain forests on the island of Borneo are
floristically among the most diverse systems on the planet, yet
large-scale timber extraction and conversion to commercial tree
plantations continue to drive their rapid degradation and loss. As is
the case for the majority of tropical forests, the effects of logging on
habitat quality in these forests have rarely been assessed, despite the
critical implications for biodiversity conservation. Moreover, studies
investigating the effects of logging on plant community dynamics across
both tropical and temperate forest ecosystems have rarely focused on the
understorey, despite its crucial relevance for successional
trajectories.
Our goal with this tutorial is to reproduce the analyses from the
paper and analyze how logging impacts the different facets of the
diversity of the understorey vegetation, and to reveal to what extent
similar facets give similar answers. The general goal is to familiarize
yourself with the data and functions needed to compute diversity
indices. As well the general principles behind them.
Associated slides
This practical session comes with some slides that cover the general
context of the study as well as some basic facts regarding functional
diversity indices.
Fortunately for us, the authors of the study have shared openly the
data they used in their article (Döbert et al.
2018). They are available through the Dryad platform at the
following link: https://doi.org/10.5061/dryad.f77p7
Döbert, Timm F. et al. (2018), Data from: Logging increases the
functional and phylogenetic dispersion of understorey plant communities
in tropical lowland rainforest, Dryad, Dataset, https://doi.org/10.5061/dryad.f77p7
The fact that these data researchers provided the full dataset
including all data and meta-data will help us reproduce the exact same
analyses as well as additional analyses not in their paper.
Getting the data
To get the data you can follow the above-mentioned link https://doi.org/10.5061/dryad.f77p7 and click on the
“Download Dataset” button available on the top right of the webpage. It
will download a .zip file that you can unzip in the folder you created
for the project. This will create a folder named
doi_10.5061_dryad.f77p7__v1 that contains all needed data
files.
Summarizing the data
The zip file contains 4 files (also available in the
data/doi_10.5061_dryad.f77p7__v1/ folder):
README.txt which is a text file that describes the
content of the other files with great precision. It details all the
columns available in the other files.
PlotData.csv is a comma-separated file that describes
characteristics for each of the sampled vegetation plots including
logging metrics, environmental variables as well as taxonomic,
functional and phylogenetic diversity indices (to which we’ll compare
the indices we compute ourselves).
PlotSpeciesData.csv is a comma-separated file that
contains a matrix of biomass values for the plant taxa sampled across
the sampled vegetation plots.
SpeciesTraitData.csv contains the complete list of
species sampled across all vegetation plots, with their associated
traits both continuous and discrete.
We will load each of the file (apart from the phylogenetic tree) in
your workspace now with the read.csv() function:
To describe the data we will use the str(),
summary(), and dim() functions.
str(plot_data)
summary(plot_data)
str(plot_species_data[, 1:5])
summary(head(plot_species_data)[,1:5])
dim(plot_species_data)
# Transform one column for further analyses
species_traits$seed = ordered(species_traits$seed)
str(species_traits)
summary(species_traits)
Questions to you
Q1: How many plots were sampled?
Q2: How many species are there in the dataset?
Q3: How many traits are available?
Q4: How many of them are continuous? How many of
them are discrete?
Q5: What is the most numerous family among all
observed species?
Q6: What is the most numerous genus?
Environment variables
Forest loss proportion is one of the main driver variable. The data
has been acquired across different block with different proportion of
logging and compared to unlogged forest.
boxplot(forestloss17 ~ block, data = plot_data,
xlab = "Block of plot", ylab = "Forest loss (%)",
main = "Forest loss in funciton of block of data")
References
Döbert, Timm F., Bruce L. Webber, John B. Sugau, Katharine J. M.
Dickinson, and Raphael K. Didham. 2017. “Logging Increases the
Functional and Phylogenetic Dispersion of Understorey Plant Communities
in Tropical Lowland Rain Forest.”Journal of Ecology 105
(5): 1235–45. https://doi.org/10.1111/1365-2745.12794.
———. 2018. “Data from: Logging Increases the
Functional and Phylogenetic Dispersion of Understorey Plant Communities
in Tropical Lowland Rainforest.”Dryad. https://doi.org/10.5061/DRYAD.F77P7.