Network analysis for ecologists using R (NTWA01)
Delivered by Dr. Marco Scotti http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/ This 5 day course will run from 6th – 10th March 2017 at Millport field centre, Isle of Cumbrae, Scotland (please note that although the filed centre in on an island it is extremely easy and uncomplicated to reach by public transport form both within and outside the UK) (PLEASE NOT THIS COURSE IS PRECEDED BY ‘STABLE ISOTOPE MIXING MODELS USING SIAR, MixSIAR AND SIBER – this course concentrates a lot of food webs and therefore may also be of interest. A COMBINED COURSE PACKAGE IS AVAILABLE) http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm03/ The first graphical representation of a food web dates back to 1880, with the pioneering works of Lorenzo Camerano. Since then, research on ecological networks has further developed and ecology is one of the fields that contributed the most to the growth of network science. Nowadays, ecologists routinely apply network analysis with a diverse set of objectives that range from studying the stability of ecological communities to quantifying energy flows in ecosystems. The course is intended to provide the participants theoretical knowledge and practical skills for the study of food webs. First, lessons and exercises will introduce basic principles of network theory. Second, ecological examples will be focused on binary food webs, networks depicting who eats whom in ecosystems. Algorithms quantifying either global food web properties or single species features within the trophic network will be introduced. Third, we will study how the architecture of the food webs can be used to investigate robustness to biodiversity loss, thus helping to predict cascading extinction events. Then, ecosystem network analysis (ENA), a suite of matrix manipulation routines for the study of energy/matter circulation in ecosystems, will be presented. We will apply ENA to characterize the trophic structure of food webs and quantify the amount of cycling in ecosystems. Finally, we will learn how to visualize food web graphs to illustrate their features in an intuitive and fancy way. Course content is as follows Monday 6th – Classes from 09:00 to 17:00 Module 1: Introduction to graph theory and network science. Basic terminology for learning the language of networks: from nodes and links to degree distribution. Three types of mathematical graphs and their properties: random networks, small-world networks, and scale-free networks. Tuesday 7th – Classes from 09:00 to 17:00 Module 2: The use of graph theory in ecology: (1) networks representing various interactions in ecological communities (e.g., predator-prey and plant-pollinator networks); (2) networks illustrating interactions at different hierarchical levels (e.g., social networks at the population level and species dispersal in the landscape graph). Who eats whom in ecosystems and at which rate? Binary and weighted food web networks. Quantitative descriptors of food web networks (e.g., fraction of basal, intermediate and top species, connectance and link density). Wednesday 8th – Classes from 09:00 to 17:00 Module 3: The structural properties of food web networks. Biodiversity loss and food web network robustness. How to predict secondary extinctions using the information embedded in the network structure of the food webs. The relevance of bipartite networks in ecology for the description of various interaction types (e.g., plant-pollinator and plant-seed disperser relationships). Thursday 9th – Classes from 09:00 to 17:00 Module 4: Ecosystem network analysis (ENA): basic principles and algorithms. Input-output analysis: partial feeding and partial host matrices. Possible ways to trace indirect effects in ecosystems. Trophic considerations: the effective trophic position of species in acyclic food webs. Finn cycling index and the amount of cycling in ecosystems. Friday 10th – Classes from 09:00 to 16:00 Module 5: Can network analysis help to better understand possible consequences of global warming on ecological communities? Network visualization with Cytoscape: how to change the layout of graphs illustrating food web interactions (the Style interface to modify node, link and network properties). There will be a 15 minute morning coffee break, an hour for lunch, and a15 minute afternoon coffee break. We keep the timing of these flexible depending how the course advances. Breakfast is from 08:00-08:45 and dinner is at 18:00 each day. Please email any inquiries to [hidden email] or visit our website www.prstatistics.com Please feel free to distribute this material anywhere you feel is suitable Upcoming courses - email for details [hidden email] 1. MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (January 2017) #MBMV http://www.prstatistics.com/course/model-base-multivariate-analysis-of-abundance-data-using-r-mbmv01/ 2. ADVANCED PYTHON FOR BIOLOGISTS (February 2017) #APYB http://www.prstatistics.com/course/advanced-python-biologists-apyb01/ 3. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R (February 2017) #SIMM http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm03/ 4. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March 2017) #NTWA http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/ 5. ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April 2017) #MVSP http://www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice-mvsp02/ 6. INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017) #IRFB http://www.prstatistics.com/course/introduction-to-statistics-and-r-for-biologists-irfb02/ 7. ADVANCING IN STATISTICAL MODELLING USING R (April 2017) #ADVR http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr05/ 8. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (May 2017) #IBHM http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm02/ 9. GEOMETRIC MORPHOMETRICS USING R (June 2017) #GMMR http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/ 10. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (June 2017) #MASE http://www.prstatistics.com/course/multivariate-analysis-of-spatial-ecological-data-using-r-mase01/ 11. TIME SERIES MODELS FOR ECOLOGISTS USING R (JUNE 2017 (#TSME) 12. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017) #BIGB http://www.prstatistics.com/course/bioinformatics-for-geneticists-and-biologists-bigb02/ 13. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017) #SPAE http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r-spae05/ 14. ECOLOGICAL NICHE MODELLING (October 2017) #ENMR http://www.prstatistics.com/course/ecological-niche-modelling-using-r-enmr01/ 15. INTRODUCTION TO BIOINFORMATICS USING LINUX (October 2017) #IBUL 16. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (November 2017) #ABME http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists-epidemiologists-abme03/ 17. INTRODUCTION TO METHODS FOR REMOTE SENSING (November 2017) #IRMS 18. INTRODUCTION TO PYTHON FOR BIOLOGISTS (November 2017) #IPYB 19. DATA VISUALISATION AND MANIPULATION USING PYTHON (December 2017) #DVMP 20. ADVANCING IN STATISTICAL MODELLING USING R (December 2017) #ADVR 21. GENETIC DATA ANALYSIS USING R (October TBC) 22. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (November TBC) 23. PHYLOGENETIC DATA ANALYSIS USING R (November TBC) 24. STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS (TBC) -- Oliver Hooker PhD. PR statistics 3/1 128 Brunswick Street Glasgow G1 1TF +44 (0) 7966500340 www.prstatistics.com www.prstatistics.com/organiser/oliver-hooker/ _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
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