Are you working on Marine Mammal 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 understand 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/
AND
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).
Please email any inquiries to [email protected] or visit our
website www.prstatistics.com
Please feel free to distribute this material anywhere you feel is
suitable.
Upcoming courses - email for details [email protected]
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 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/
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) #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. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017) #BIGB
http://www.prstatistics.com/course/bioinformatics-for-geneticists-and-biologists-bigb02/
12. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017) #SPAE
http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r-spae05/
13. ECOLOGICAL NICHE MODELLING (October 2017) #ENMR
http://www.prstatistics.com/course/ecological-niche-modelling-using-r-enmr01/
14. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS
(November 2017)
http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists-epidemiologists-abme03/
15. GENETIC DATA ANALYSIS USING R (October TBC)
16. INTRODUCTION TO BIOINFORMATICS USING LINUX (October TBC)
17. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (November TBC)
18. PHYLOGENETIC DATA ANALYSIS USING R (November TBC)
19. INTRODUCTION TO METHODS FOR REMOTE SENSING (December 2017 TBC)
20. ADVANCING IN STATISTICAL MODELLING USING R (December 2017 TBC)
21. INTRODUCTION TO PYTHON FOR BIOLOGISTS (December 2017 TBC)
22. 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/
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