Are you working on sea bird trophic ecology using either stable isotopes or network analysis to construct and understand food webs? PR statistics has two courses scheduled for early 2017 aimed specifically at understanding and building food webs using stable isotopes and/or stomach contents.
Stable Isotope Mixing Models using SIAR, SIBER and MixSIAR (SIMM03) Delivered by Dr. Andrew Parnell and Dr. Andrew Jackson http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm03/
Network analysis for ecologists (NTWA01) Delivered by Dr.Marco Scotti http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/
Both courses will take place 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). SIMM03 is a 4 day course and will run from 28th -3rd March 2017 and NTWA01 is a 5 day course will run from 6th – 10th March 2017.
A COMBINED COURSE PACKAGE IS AVAILABLE
SIMM03 This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. This course will cover the concepts, technical background and use of stable isotope mixing models (SIMMs) with a particular focus on running them in R. Recently SIMMs have become a very popular tool for quantifying food webs and thus the diet of predators and prey in an ecosystem. Starting with only basic understanding of statistical models, we will cover the do’s and don’ts of using SIMMs with a particular focus on the widely used package SIAR and the new, more advanced MixSIAR. Participants will be taught some of the advanced features of these packages, which will enable them to produce a richer class of output, and are encouraged to bring their own data sets and problems to study during the round-table discussions.
Course content is as follows Tuesday 28th – Classes from 09:00 to 17:00 Basic concepts. Module 1: Introduction; why use a SIMM? Module 2: An introduction to bayesian statistics. Module 3: Differences between regression models and SIMMs. Practical: Revision on using R to load data, create plots and fit statistical models. Round table discussion: Understanding the output from a Bayesian model.
Wednesday 1st – Classes from 09:00 to 17:00 Understanding and using SIAR. Module 4: Do’s and Don’ts of using SIAR. Module 5: The statistical model behind SIAR. Practical: Using SIAR for real-world data sets; reporting output; creating richer summaries and plots. Round table discussion: Issues when using simple SIMMs.
Thursday 2nd – Classes from 09:00 to 17:00 SIBER and MixSIAR. Module 6: Creating and understanding Stable Isotope Bayesian Ellipses (SIBER). Module 7: What are the differences between SIAR and MixSIAR? Practical: Using MixSIAR on real world data sets; benefits over SIAR. Round table discussion: When to use which type of SIMM.
Friday 3rd – Classes from 09:00 to 17:00 Advanced SIMMs. Module 8: Using MixSIAR for complex data sets: time series and mixed effects models. Module 9: Source grouping: when and how? Module 10: Building your own SIMM with JAGS. Practical: Running advanced SIMMs with JAGS. Round table discussion: Bring your own data set.
NTWA01 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).
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Upcoming courses - email for details firstname.lastname@example.org 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/
ADVANCED PYTHON FOR BIOLOGISTS (February 2017) #APYB http://www.prstatistics.com/course/advanced-python-biologists-apyb01/
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/
NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March 2017) #NTWA http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/
ADVANCES IN MULTIVAIRAITE 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/
INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017) #IRFB http://www.prstatistics.com/course/introduction-to-statistics-and-r-for-biologists-irfb02/
ADVANCING IN STATISTICAL MODELLING USING R (April 2017) #ADVR http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-advr05/
INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (May 2017) #IBHM http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm02/
GEOMETRIC MORPHOMETRICS USING R (June) #GMMR http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/
MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (June 2017) #MASE http://www.prstatistics.com/course/multivariate-analysis-of-spatial-ecological-data-using-r-mase01/
BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017) #BIGB http://www.prstatistics.com/course/bioinformatics-for-geneticists-and-biologists-bigb02/
SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017) #SPAE http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r-spae05/
ECOLOGICAL NICHE MODELLING (October 2017) #ENMR http://www.prstatistics.com/course/ecological-niche-modelling-using-r-enmr01/
APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (November 2017) http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists-epidemiologists-abme03/
GENETIC DATA ANALYSIS USING R (October TBC)
- INTRODUCTION TO BIOINFORMATICS USING LINUX (October TBC)
- LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (November TBC)
- PHYLOGENETIC DATA ANALYSIS USING R (November TBC)
- INTRODUCTION TO METHODS FOR REMOTE SENSING (December 2017 TBC)
- ADVANCING IN STATISTICAL MODELLING USING R (December 2017 TBC)
- INTRODUCTION TO PYTHON FOR BIOLOGISTS (December 2017 TBC)
- DATA VISUALISATION AND MANIPULATION USING PYTHON (December 2017 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/