Network analysis for ecologists using R

[Melanie Colón]

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 oliverhooker@prstatistics.com or visit our
website www.prstatistics.com

Please feel free to distribute this material anywhere you feel is suitable

Upcoming courses - email for details oliverhooker@prstatistics.com

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)