CREATING, ACCESSING AND ANALYSING BIODIVERSITY DATA FOR AFRICA

This course is taylored to postgraduate students, researchers, and professionals who are interested in the interface between biodiversity and modelling (biodiversity informatics) for decision making. Students should have a background in one or more of the following disciplines: biodiversity and conservation, mathematics, bio-mathematics, ecology, ecological modelling, or their related disciplines.

The course aims to build the next generation of interdisciplinary professionals in biodiversity informatics, with the ability to make an informed decision to pursue a career path in biodiversity or bio-mathematics research.

Learning Objectives
Proposed course outcomes:

  1. Awareness of key mathematical concepts applied to analysis of biodiversity data.
  2. Understanding of key ecological concepts such as competition, mutualism, stability.
  3. Awareness and understanding of different kinds of biodiversity data (as classified in the Essential Biodiversity Variables concept).
  4. Knowledge of availability of data on Essential Biodiversity Variables (EBVs) from freely available and restricted databases
  5. Ability to synthesise data to create a biodiversity database.
  6. Ability to apply R programming skills to process and analyse biodiversity data, in particular ecological network analyses and species distribution modelling.

Lectures will be paired with practical exercises in data handling, analysis, and visualisation to cover the majority of EBV classes (https://geobon.org/ebvs/what-are-ebvs/). Several seminar-style presentations will be included in which PhD students, postdocs, and alumni highlight their own biodiversity research. Recordings of lectures will be available to participants. Students will also be able to work on practicals in their own time if they desire, and four practicals are allocated as 'catch-up sessions' to provide give further assistance to students who have worked on practical exercises in their own time. Practicals will progressively develop skills in r-programming over the two-week course. Theory lectures will be approximately 45 minutes long and practicals will be 1-2 hours long. The course will take two weeks and participants can expect to spend up to 4-6 hours per day on the course.