|
Analysis of Ecological and Palaeoecological Data with R University of Maine, 8th-12th August 2013
Dr Steve Juggins,
School of Geography, Politics & Sociology, Newcastle University,
Newcastle upon Tyne, NE3 4LP.
Stephen.Juggins@ncl.ac.uk
The R
statistical language and environment has become increasingly popular in
recent years, in part because it is a free, open source application and
because it is incredibly powerful and easily extended via add-on
packages. This course is aimed at those with little or no experience in
R and will address both the essential numerical understanding and the R
skills required to handle, process and analyse palaeolimnological data.
Course content
The couse will comprise lectures and computer
sessions with time available in the evenings for students to work on
their own data and for discussions with the course leaders.
The workshop will begin on the evening of the 8th
August with a welcome and an introduction to R to revise basic
understanding. Participants will be expected to bring their own laptops
with R installed and we will provide a self-led tutorial to help with
this before the course. The following day we will cover exploratory data
analysis and graphics in R. Next we discuss regression, including the
use of modern regression methods involving smoothers. We will consider
how the temporal nature of palaeo data can be accommodated by relaxation
of the assumption of independence. On day 3 we will focus on cluster
analysis and ordination, techniques widely used to summarise patterns in
stratigraphic data. Appropriate hypothesis testing using permutations
for temporal data will be emphasised. Next we consider
palaeoenvironmental reconstruction and developing age models for
stratigraphic sequences. Chronological clustering, smoothing, and
interpolating stratigraphic data and calculating rates of change will
also be covered. Each topic will be presented using a 30-45-minute lecture and 1-hour practical. The lecture will introduce the theory of each set of methods and models, discuss their assumptions, and give participants the knowledge to enable them to identify the type of model appropriate for a particular data analytical problem. The following practical will reinforce the understanding of the lecture material as you apply the techniques to datasets to adress real palaeolimnological questions. You are particularly encouraged to bring your own data to discuss and work on during the course. Logistics and cost The course will be limited to 20 participants. Resources A
full reading list will be included with the course materials. In the
meantime we recommend the following two books:
Borcard, D., Gillet, F., & Legendre, P. (2011)
Numerical Ecology with R Springer. |
|