Melanie Colón Posted January 2, 2017 Share Posted January 2, 2017 Network analysis for ecologists using R (NTWA01)Delivered by Dr. Marco Scottihttp://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/This 5 day course will run from 6th – 10th March 2017 at Millport fieldcentre, Isle of Cumbrae, Scotland (please note that although the filedcentre in on an island it is extremely easy and uncomplicated to reach bypublic transport form both within and outside the UK)(PLEASE NOT THIS COURSE IS PRECEDED BY ‘STABLE ISOTOPE MIXING MODELS USINGSIAR, MixSIAR AND SIBER – this course concentrates a lot of food webs andtherefore 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, withthe pioneering works of Lorenzo Camerano. Since then, research onecological networks has further developed and ecology is one of the fieldsthat contributed the most to the growth of network science. Nowadays,ecologists routinely apply network analysis with a diverse set ofobjectives that range from studying the stability of ecological communitiesto quantifying energy flows in ecosystems.The course is intended to provide the participants theoretical knowledgeand practical skills for the study of food webs. First, lessons andexercises will introduce basic principles of network theory. Second,ecological examples will be focused on binary food webs, networks depictingwho eats whom in ecosystems. Algorithms quantifying either global food webproperties or single species features within the trophic network will beintroduced. Third, we will study how the architecture of the food webs canbe used to investigate robustness to biodiversity loss, thus helping topredict cascading extinction events. Then, ecosystem network analysis(ENA), a suite of matrix manipulation routines for the study ofenergy/matter circulation in ecosystems, will be presented. We will applyENA to characterize the trophic structure of food webs and quantify theamount of cycling in ecosystems. Finally, we will learn how to visualizefood web graphs to illustrate their features in an intuitive and fancy way.Course content is as followsMonday 6th – Classes from 09:00 to 17:00Module 1: Introduction to graph theory and network science.Basic terminology for learning the language of networks: from nodes andlinks 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 17:00Module 2: The use of graph theory in ecology: (1) networks representingvarious interactions in ecological communities (e.g., predator-prey andplant-pollinator networks); (2) networks illustrating interactions atdifferent hierarchical levels (e.g., social networks at the populationlevel and species dispersal in the landscape graph).Who eats whom in ecosystems and at which rate? Binary and weighted food webnetworks.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 17:00Module 3: The structural properties of food web networks.Biodiversity loss and food web network robustness. How to predict secondaryextinctions using the information embedded in the network structure of thefood webs.The relevance of bipartite networks in ecology for the description ofvarious interaction types (e.g., plant-pollinator and plant-seed disperserrelationships).Thursday 9th – Classes from 09:00 to 17:00Module 4: Ecosystem network analysis (ENA): basic principles and algorithms.Input-output analysis: partial feeding and partial host matrices. Possibleways to trace indirect effects in ecosystems.Trophic considerations: the effective trophic position of species inacyclic food webs.Finn cycling index and the amount of cycling in ecosystems.Friday 10th – Classes from 09:00 to 16:00Module 5: Can network analysis help to better understand possibleconsequences of global warming on ecological communities?Network visualization with Cytoscape: how to change the layout of graphsillustrating 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 a15minute afternoon coffee break. We keep the timing of these flexibledepending how the course advances. Breakfast is from 08:00-08:45 and dinneris at 18:00 each day.Please email any inquiries to oliverhooker@prstatistics.com or visit ourwebsite www.prstatistics.comPlease feel free to distribute this material anywhere you feel is suitableUpcoming courses - email for details oliverhooker@prstatistics.com1. MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R(January 2017) #MBMVhttp://www.prstatistics.com/course/model-base-multivariate-analysis-of-abundance-data-using-r-mbmv01/2. ADVANCED PYTHON FOR BIOLOGISTS (February 2017) #APYBhttp://www.prstatistics.com/course/advanced-python-biologists-apyb01/3. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R(February 2017) #SIMMhttp://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm03/4. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March 2017) #NTWAhttp://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/5. ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April2017) #MVSPhttp://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) #IRFBhttp://www.prstatistics.com/course/introduction-to-statistics-and-r-for-biologists-irfb02/7. ADVANCING IN STATISTICAL MODELLING USING R (April 2017) #ADVRhttp://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr05/8. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (May 2017) #IBHMhttp://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm02/9. GEOMETRIC MORPHOMETRICS USING R (June 2017) #GMMRhttp://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/10. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (June 2017) #MASEhttp://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) #BIGBhttp://www.prstatistics.com/course/bioinformatics-for-geneticists-and-biologists-bigb02/13. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017) #SPAEhttp://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r-spae05/14. ECOLOGICAL NICHE MODELLING (October 2017) #ENMRhttp://www.prstatistics.com/course/ecological-niche-modelling-using-r-enmr01/15. INTRODUCTION TO BIOINFORMATICS USING LINUX (October 2017) #IBUL16. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS(November 2017) #ABMEhttp://www.prstatistics.com/course/applied-bayesian-modelling-ecologists-epidemiologists-abme03/17. INTRODUCTION TO METHODS FOR REMOTE SENSING (November 2017) #IRMS18. INTRODUCTION TO PYTHON FOR BIOLOGISTS (November 2017) #IPYB19. DATA VISUALISATION AND MANIPULATION USING PYTHON (December 2017)#DVMP20. ADVANCING IN STATISTICAL MODELLING USING R (December 2017) #ADVR21. 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 EVOLUTIONARYBIOLOGISTS (TBC) Link to comment Share on other sites More sharing options...

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