Ecological Network Analysis

TheGlobal Ecology Laboratorysits within theEcology and EvolutionResearch Section of theCollege of Science and EngineeringatFlinders Universityin South Australia.We have a wide range of interests in ecologicalresearch, with a particular focus on palaeo-ecological systems and forecasting future ecosystems. We are also the main Modelling Node of theAustralian Research CouncilCentre of Excellence for Australian Biodiversity and Heritage.

Global Ecology Laboratory, Flinders University

We are most fortunate that Dr <A HREF="https://scholar.google.it/citations?user=yD6KDroAAAAJ&hl=en" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Giovanni Strona</A> of the <A HREF="https://ec.europa.eu/jrc/en" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">EU Joint Research Centre</A> in Ispra, Italy, will be visiting <A HREF="http://www.flinders.edu.au/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Flinders University</A> for most of April. Dr Strona has kindly agreed to give a day-long (and hands-on) workshop in network modelling.
What is network analysis? Well, anything that is connected to other things is ostensibly a 'network' — think social-media users, neurones, electric elements in a circuit, or species in an ecological community. It doesn't really matter what the 'nodes' of a network actually represent, because all networks more or less share the same properties. The analysis of network structure and behaviour is what Dr Strona will focus on for the workshop.
Being ecologists, we will of course be primarily interested in ecological networks, but maths and coding is essentially the same for all types of networks. 
The outline of the workshop is described in more detail below.
1. Brief introduction to theory
<UL>
<LI>background history</LI>
<LI>basic definitions</LI>
<LI>examples of applications (ecology and beyond)</LI>
<LI>major defining network properties, with real-world examples (e.g., edge directionality and weight, unimode versus bipartite networks)</LI>
<LI>formal network representation (e.g., edge list versus adjacency matrix);</LI>
<LI>basic local and global properties (node degree – degree distribution; path length – diameter; clustering; k-coreness; edge betweenness; PageRank ...;</LI>
<LI>different kinds of networks (regular, random, power law)</LI>
<LI>network visualisation</LI>
</UL>
2. Programming training
Attendants will learn to:
<UL>
<LI>download ecological network data from various online datasets</LI>
<LI>load those data in network format</LI>
<LI>generate random network with given properties</LI>
<LI>measure local and global network properties (and rank nodes according to different criteria of importance)</LI>
<LI>manipulate/modify networks (add and remove nodes and edges; extract sub-networks; randomise links)</LI>
<LI>evaluate network robustness/simulate network collapse following the loss of nodes</LI>
<LI>visualise networks (also using 3D-animation techniques)</LI>
</UL>
Most of the training will be done in the <A HREF="https://www.r-project.org" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">R programming language</A>. Basic knowledge of R (using a terminal) is therefore desirable. Other software tools will be introduced as well.

2030 CML Computer Lab

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Ecological Network Analysis

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