TWO DIFFERENT MULTIVARIATE SPATIAL ECOLOGY COURSES IN TWO DIFFERENT COUNTRIES IN JUNE AND AUGUST
Multivariate analysis of spatial ecological data using R (MASE01)
19 June 2017 - 23 June 2017
Northwest Atlantic Fisheries Centre, Canada
http://www.prstatistics.com/course/multivariate-analysis-of-spatial-
ecological-data-u
Spatial analysis of ecological data using R (SPAE05)
7 August 2017 - 12 August 2017
SCENE, Loch Lomond, Rowardennan,
Glasgow, Scotland, G63 0AW, United Kingdom
http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r-
spae05/
MASE01 OVERVIEW
This 5-day course will cover the concepts, methods, and R tools that can be
used to analyse spatial data in ecology. The start of the course will cover
the basics of linear models and spatial data processing in R and provide a
common ground for more advanced techniques encountered later on the course.
We will cover spatial statistical techniques for both continuous and
discrete data response types. We will put special emphasis on understanding
mechanisms using visual tools, and quantifying uncertainty for model
parameters and predictions. We will introduce Markov chain Monte Carlo
methods for spatial hierarchical models and discuss special topics relevant
for study design and conservation and management applications. Modules will
consist of introductory lectures, guided computer coding, and exercises for
the participants, analysing real data.
Module 1. Introduction (types of response and predictor variables), data
exploration (summaries, visualization, classification and regression trees
[CART]).
Module 2. Linear, nonlinear, and multiple regression (use of lm and gam
functions in R, visual interpretation, model diagnostics, model selection,
generalized additive models [GAM]).
Module 3. Generalized linear models (use of glm and gam functions in R,
visual interpretation, model diagnostics, model selection, generalized
additive models [GAM]).
Module 4. Spatial data manipulation in R (raster and vector layers, GIS
operations, mapping).
Module 5. Statistical models for point processes (homogeneous and non-
homogeneous Poisson process, non-parametric measures of autocorrelation).
Continuous data on regular and irregular grid points in space
Module 6. Kriging and spatial smoothing for continuous data.
Module 7. Spatial generalizations of GLM: Markov random fields and
hierarchical models.
Linear and Generalized linear mixed models in the context of spatial data
Module 8: Markov Chain Monte Carlo methods with applications in spatial data
Module 9. Movement models, species distribution models, critical habitat
delineation.
Module 10. Principles of optimal design for regression, effects of sampling
design related biases on regression, measurement error in predictor
variables.
Module 11. Philosophical considerations and other related discussion.
SPAE05 OVERVIEW
The course will cover the concepts and R tools that can be used to analyse
spatial data in ecology covering elementary and advanced spatial analysis
techniques applicable to both plants and animals. We will investigate
analyses appropriate to transect (e.g. line surveys, trapping arrays), grid
(e.g. occupancy surveys) and point data (e.g. telemetry). The focal
questions will be on deriving species distributions, determining their
environmental drivers and quantifying different types of associated
uncertainty. Novel methodology for generating predictions will be
introduced. We will also address the challenges of applying the results of
these methods to wildlife conservation and resource management and
communicate the findings to non-experts.
Module 1: Introductory lectures and practical; this will cover the key
questions in spatial ecology, the main types of data on species
distributions, concepts and challenges and different types of environmental
data; useful concepts from statistics; Generalised Linear Models.
Module 2: GIS tools in R: Types and structure of spatial objects in R,
generating and manipulating spatial objects, projections and
transformations, cropping and masking spatial objects, extracting covariate
data and other simple GIS operations in R, optionally plotting simple maps.
Module 3: Density estimation, Spatial autocorrelation, Smoothing, Kernel
Smoothers, Kriging, Trend-fitting (linear, generalised linear, generalised
additive models).
Module 4: Habitat preference, Resource selection functions, MaxEnt: What’s
it all about? Overview and caveats related to Niche models
Module 5: Analysing grid data, Poisson processes, Occupancy models, Use-
availability designs.
Module 6: Analysing telemetry data, Presence-only data, Spatial and serial
autocorrelation, Partitioning variation by mixed effects models.
Module 7: Analysing transect data, Detection functions for point and line
transects, Using covariates in transect models. Afternoon for catch up
and/or excursion.
Module 8: Advanced methods, Generalised Estimation Equations for difficult
survey designs, Generalised additive models for habitat preference, Dealing
with boundary effects using soap smoothers, Spatial point processes with
INLA.
Module 9: Prediction, Validation by resampling, Generalised Functional
Responses for species distribution, Quantifying uncertainty, Dealing with
the effects of population density.
Module 10: Applications, Designing protected areas, thinking about critical
habitat, Representing uncertainty.
email [log in to unmask] for more details or visit the links to
book online.
Other up-coming courses;
1. ADVANCING IN STATISTICAL MODELLING FOR EVOLUTIONARY BIOLOGISTS AND
ECOLOGISTS USING R #ADVR
17th – 21st April 2017, Scotland, Dr. Luc Bussiere, Dr. Ane Timenes Laugen
http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-
advr06/
2. CODING, DATA MANAGEMENT AND SHINY APPLICATIONS USING RSTUDIO FOR
EVOLUTIONARY BIOLOGISTS AND ECOLOGISTS #CDSR
15th - 19th May, Scotland Dr. Aline Quadros
http://www.prstatistics.com/course/coding-data-management-and-shiny-
applications-using-rstudio-for-evolutionary-biologists-and-ecologists-
cdsr01/
3. GEOMETRIC MORPHOMETRICS USING R #GMMR
5th – 9th June 2017, Scotland, Prof. Dean Adams, Prof. Michael Collyer, Dr.
Antigoni Kaliontzopoulou
http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/
4. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA #MASE
19th – 23rd June, Canada, Prof. Subhash Lele, Dr. Peter Solymos
http://www.prstatistics.com/course/multivariate-analysis-of-spatial-
ecological-data-using-r-mase01/
5. TIME SERIES MODELS FOR ECOLOGISTS USING R (JUNE 2017 #TSME
26th – 30th June, Canada, Dr. Andrew Parnell
http://www.prstatistics.com/course/time-series-models-foe-ecologists-tsme01/
6. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS #BIGB
3rd – 7th July 2017, Scotland, Dr. Nic Blouin, Dr. Ian Misner
http://www.prstatistics.com/course/bioinformatics-for-geneticists-and-
biologists-bigb02/
7. META-ANALYSIS IN ECOLOGY, EVOLUTION AND ENVIRONMENTAL SCIENCES
#METR01
24th – 28th July, Scotland, Prof. Julia Koricheva, Prof. Elena Kulinskaya
http://www.prstatistics.com/course/meta-analysis-in-ecology-evolution-and-
environmental-sciences-metr01/
8. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R #SPAE
7th – 12th August 2017, Scotland, Prof. Jason Matthiopoulos, Dr. James
Grecian
http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r-
spae05/
9. ECOLOGICAL NICHE MODELLING USING R #ENMR
16th – 20th October 2017, Scotland, Dr. Neftali Sillero
http://www.prstatistics.com/course/ecological-niche-modelling-using-r-
enmr01/
10. INTRODUCTION TO BIOINFORMATICS USING LINUX #IBUL
16th – 20th October, Scotland, Dr. Martin Jones
http://www.prstatistics.com/course/introduction-to-bioinformatics-using-
linux-ibul02/
11. GENETIC DATA ANALYSIS AND EXPLORATION USING R #GDAR
23rd – 27th October, Wales, Dr. Thibaut Jombart, Zhian Kavar
http://www.prstatistics.com/course/genetic-data-analysis-exploration-using-
r-gdar03/
12. STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY
BIOLOGISTS USING R #SEMR
23rd – 27th October, Wales, Prof Jarrett Byrnes, Dr. Jon Lefcheck
http://www.prstatistics.com/course/structural-equation-modelling-for-
ecologists-and-evolutionary-biologists-semr01/
13. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R #LNDG
6th – 10th November, Wales, Prof. Rodney Dyer
http://www.prstatistics.com/course/landscape-genetic-data-analysis-using-r-
lndg02/
14. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS #ABME
20th - 25th November 2017, Scotland, Prof. Jason Matthiopoulos, Dr. Matt
Denwood
http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists-
epidemiologists-abme03/
15. INTRODUCTION REMOTE SENSING AND GIS APPLICATIONS FOR ECOLOGISTS
#IRMS
27th Nov – 1st Dec, Wales, Dr Duccio Rocchini, Dr. Luca Delucchi
http://www.prstatistics.com/course/introduction-to-remote-sensing-and-gis-
for-ecological-applications-irms01/
16. INTRODUCTION TO PYTHON FOR BIOLOGISTS #IPYB
27th Nov – 1st Dec, Wales, Dr. Martin Jones
http://www.prstatistics.com/course/introduction-to-python-for-biologists-
ipyb04/
17. DATA VISUALISATION AND MANIPULATION USING PYTHON #DVMP
11th – 15th December 2017, Wales, Dr. Martin Jones
http://www.prstatistics.com/course/data-visualisation-and-manipulation-
using-python-dvmp01/
18. ADVANCING IN STATISTICAL MODELLING USING R #ADVR
11th – 15th December 2017, Wales, Dr. Luc Bussiere, Dr. Tom Houslay, Dr.
Ane Timenes Laugen,
http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-
advr07/
19. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING #IBHM
29th Jan – 2nd Feb 2018, Scotland, Dr. Andrew Parnell
http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-
modelling-using-r-ibhm02/
20. ANIMAL MOVEMENT ECOLOGY (February 2018) #ANME
??th - ??th February 2018, Wales, Dr Luca Borger, Dr. John Fieberg
21. AQUATIC TELEMENTRY DATA ANALYSIS USIR R (TBC) #ATDAR
??th - ??th February 2018, Wales,
22. FUNCTIONAL ECOLOGY FROM ORGANISM TO ECOSYSTEM: THEORY AND
COMPUTATION #FEER
5th – 9th March 2018, Scotland, Dr. Francesco de Bello, Dr. Lars
Götzenberger, Dr. Carlos Carmona
http://www.prstatistics.com/course/functional-ecology-from-organism-to-
ecosystem-theory-and-computation-feer01/
23. ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA USING
R #MVSP
Prof. Pierre Legendre, Dr. Olivier Gauthier - Date and location to be
confirmed
24. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR #SIMM
Dr. Andrew Parnell, Dr. Andrew Jackson – Date and location to be confirmed
25. NETWORK ANAYLSIS FOR ECOLOGISTS USING R #NTWA
Dr. Marco Scotti - Date and location to be confirmed
26. MODEL BASE MULTIVARIATE ANALYSIS OF ABUNDANCE DATA USING R #MBMV0
Prof David Warton - Date and location to be confirmed
27. ADVANCED PYTHON FOR BIOLOGISTS #APYB
Dr. Martin Jones - Date and location to be confirmed
28. PHYLOGENETIC DATA ANALYSIS USING R (TBC) #PHYL
Dr. Emmanuel Paradis – Date and location to be confirmed
Oliver Hooker PhD.
PR statistics
2017 publications -
Ecosystem size predicts eco-morphological variability in post-glacial
diversification. Ecology and Evolution. In press.
The physiological costs of prey switching reinforce foraging
specialization. Journal of animal ecology.
prstatistics.com
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