Up-coming events
Short Course:
Practical Computational Statistics for Field Ecologists
(Kampala, January 16th – 18th, 2012)
Background
An understanding of statistical principles and methods is essential for any scientist but is
particularly important for those in the life sciences. The field biologist faces very
particular problems and challenges with statistics as "real-life" situations can hardly be
expected to be as reliable or controllable as a laboratory-based experiment.
Acknowledging the peculiarities of field-based data and its interpretation, this training
provides a superb introduction to statistical analysis helping participants relate to their
particular and often diverse data with confidence and ease. This course will provide an
excellent introduction to participants on the principles and methods of statistical analysis
in the life sciences, helping them choose and analyze statistical tests for their own
problems and present their findings. To enhance the usefulness of the training materials,
the course will incorporate the more advanced method of multivariate analysis,
introducing the nature of multivariate problems and describing their respective
techniques. It will be extremely useful for students in ecology, biology, and earth and
environmental sciences and of interest to postgraduates who are not familiar with the
application of multivariate and diversity techniques and practicing field biologists
working in these areas.
Course Components
This course will cover most topics needed to develop a broad and thorough working
knowledge of modern ecological statistical computing and biodiversity statistics. We
seek to develop a practical understanding of how and why existing methods work,
enabling participants to use modern statistical methods effectively. Since many new
methods are built from components of existing techniques, our ultimate goal is to provide
scientists with the tools they need to contribute new ideas to their fields of work.
Achieving these goals requires familiarity with diverse topics in statistical computing,
computational statistics, and numerical analysis. We will pragmatically assign priority to
topics that can be of the most benefit to students and researchers most quickly.
Consequently, this course will provide a comprehensive overview of how to measure
biodiversity. The course content will highlight recent developments, including innovative
approaches to measuring taxonomic distinctness and estimating species richness, and
evaluate these alongside traditional methods such as species abundance distributions, and
diversity and evenness statistics. This way, the participants will be able to quantify and
interpret patterns of ecological diversity, focusing on the measurement and estimation of
species richness and abundance. Further, the course will explore the concept of ecological
diversity, bringing new perspectives to a field beset by contradictory views and advice. In
this way, issues such as the meaning of community, windows, points and land use types
in the context of ecological diversity, scales of diversity and distribution of diversity
among taxa will be demystified. Finally, the course will place interest on advances in
measurement with emphasis on new techniques such as species richness estimation,
application of measures of diversity to conservation and environmental management and
addressing sampling issues.
Software to be Covered
1. ade4: This is software developed in the Biometry and Evolutionary Biology
Laboratory - University Lyon. It contains Data Analysis functions to analyse
Ecological and Environmental data in the framework of Euclidean Exploratory
methods, hence the name ade4.
2. BiodiversityR: This package provides a GUI (Graphical User Interface, via the RCommander) and some utility functions (often based on the vegan package) for
statistical analysis of biodiversity and ecological communities, including species
accumulation curves, diversity indices, Renyi profiles, GLMs for analysis of
species abundance and presence-absence, distance matrices, Mantel tests, and
cluster, constrained and unconstrained ordination analysis.
3. PAST is an easy-to-use data analysis package originally aimed at paleontology
but now also popular in many other fields. It includes features for computing all
diversity and similarity indices, statistical, plotting and modelling functions.
Course Fee:
One Hundred Thousand (100,000=) Ugandan Shillings (Course fee includes the
instruction fee, three software packages, meals and Certification. The course fee for non
Ugandans is US$ 100.
Start and location
The course starts on January 16th and ends on 18th, 2012. The course will be held at the
National Banana Resources Centre at the National Agricultural Research Laboratories
(formerly Kawanda Agriculture Research Institute). Precise details will be sent on
confirmation of your booking.
Who Should Attend
This course is designed for under and post graduate students, researchers, managers,
lecturers, and decision makers.
Merit Award
At the end of the course, successful participants will be awarded a “Certificate in
Ecological Computational Statistics”.
To Book Your Place
Please complete the registration form and email it to brianisabirye@yahoo.com. Once
this is received, your place will be confirmed on the course.
For details about the course, Please contact the Course Coordinator: Brian E. Isabirye
(brianisabirye@yahoo.com or +256772352739)