Ecological niche modelling using R (ENMR02)

https://www.prstatistics.com/course/ecological-niche-modelling-using-r-
enmr02/

12th March 2018 - 16th March 2018

Course Overview:
The course will cover the base theory of ecological niche modelling and its 
main methodologies. By the end of this 5-day practical course, attendees 
will have the capacity to perform ecological niche models and understand 
their results, as well as to choose and apply the correct methodology 
depending on the aim of their type of study and data.

Ecological niche, species distribution, habitat distribution, or climatic 
envelope models are different names for similar mechanistic or correlative 
models, empirical or mathematical approaches to the ecological niche of a 
species, where different types of ecogeographical variables (environmental, 
topographical, human) are related with a species physiological data or 
geographical locations, in order to identify the factors limiting and 
defining the species’ niche. ENMs have become popular due to the need for 
efficiency in the design and implementation of conservation management.
The course will be mainly practical, with some theoretical lectures. All 
modelling processes and calculations will be performed with R, the free 
software environment for statistical computing and graphics 
(http://www.r-project.org/). Attendees will learn to use modelling 
algorithms like Maxent, Bioclim, Domain, and logistic regressions, and R 
packages for computing ENMs like Dismo and Biomod2. Also, students will 
learn to compare different ecological niche models using the Ecospat 
package.

Monday 12th – Classes from 09:00 to 17:00
Elementary concepts on Ecological Niche Modelling
Module 1: Introduction to ENM theory. Definition of ecological niche model; 
introduction to species ecological niche theory, types of ecological 
niches, types of ENM, diagram BAM, ENMs as approximations to species’ 
niches.
Module 2: Problems and limitations on ENM. Assumptions and uncertainties, 
equilibrium concept, niche conservatism, autocorrelation and intensity, 
sample size, correlation of environmental variables, size and form of study 
area, thresholds, model validation, model projections.
Module 3: Methods on ENM. Mechanistic and correlative models. Overlap 
Analysis, Biomod, Domain, Habitat, Distance of Mahalanobis, ENFA, GARP, 
Maxent, Logistic regression, Generalised Linear Models, Generalised 
Additive Models, Generalised Boosted Regression Models, Random Forest, 
Support Vector Machines, Artificial Neural Network.
Module 4: Conceptual and practice steps to calculate ENM. How to make an 
ENM step-by-step.
Module 5: Applications of ENM. Ecological niche identification, 
Identification of contact zones, Integration with genetical data, Species 
expansions, Species invasions, Dispersion hypotheses, Species conservation 
status, Prediction of future conservation problems, Projection to future 
and past climate change scenarios, Modelling past species, Modelling 
species richness, Road-kills, Diseases, Windmills, Location of protected 
areas.

Tuesday 13th – Classes from 09:00 to 17:00
Prepare environmental variables and run ecological niche models with dismo 
package.
Module 6: Preparing variables. Choosing environmental data sources, 
Downloading variables, Clipping variables, Aggregating variables, Checking 
pixel size, Checking raster limits, Checking NoData, Correlating variables.
Module 7: Dismo practice. How to run an ENM using the R package dismo.

Wednesday 14th – Classes from 09:00 to 17:00
Run ecological niche models with Biomod2 package and Maxent.
Module 8: Biomod2 practice. How to run an ENM using the R package Biomod2.
Module 9: Maxent practice. How to run an ENM using the R packages dismo and 
Biomod2 as well as Maxent software.

Thursday 15th – Classes from 09:00 to 17:00
Compare ecological niche models with ecospat.
Module 10: Ecospat practice. Compare statistically two different ecological 
niche models using the R package Ecospat.
Module 11: Students’ talks. Attendees will have the opportunity to present 
their own data and analyse which is the best way to successfully obtain an 
ENM.

Friday 16th – Classes from 09:00 to 16:00
Run ecological niche models with your own data.
Module 12: Final practical. In this practical, the students will run ENM 
with their own data or with a new dataset, applying all the methods showed 
during the previous days.


1.      January 29t – February 2nd 2018
INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (IBHM02)
SCENE, Scotland, Dr. Andrew Parnell
http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-
modelling-using-r-ibhm02/

2.      January 29th – February 2nd 2018
PHYLOGENETIC DATA ANALYSIS USING R (PHYL02)
SCENE, Scotland, Dr. Emmanuel Paradis
https://www.prstatistics.com/course/introduction-to-phylogenetic-analysis-
with-r-phyg-phyl02/
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3.      February 19th – 23rd 2018
MOVEMENT ECOLOGY (MOVE01)
Margam Discovery Centre, Wales, Dr Luca Borger, Dr Ronny Wilson, Dr 
Jonathan Potts
https://www.prstatistics.com/course/movement-ecology-move01/

4.      February 19th – 23rd 2018
GEOMETRIC MORPHOMETRICS USING R (GMMR01)
Margam Discovery Centre, Wales, Prof. Dean Adams, Prof. Michael Collyer, 
Dr. Antigoni Kaliontzopoulou
http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/
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5.      March 5th - 9th 2018
SPATIAL PRIORITIZATION USING MARXAN (MRXN01)
Margam Discovery Centre, Wales, Jennifer McGowan   
https://www.prstatistics.com/course/introduction-to-marxan-mrxn01/

6.      March 12th - 16th 2018
ECOLOGICAL NICHE MODELLING USING R (ENMR02)
Glasgow, Scotland, Dr. Neftali Sillero
http://www.prstatistics.com/course/ecological-niche-modelling-using-r-
enmr02/

7.      March 19th – 23rd 2018
BEHAVIOURAL DATA ANALYSIS USING MAXIMUM LIKLIHOOD IN R (BDML01)
Glasgow, Scotland, Dr William Hoppitt
http://www.psstatistics.com/course/behavioural-data-analysis-using-maximum-
likelihood-bdml01/
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8.      April 9th – 13th 2018 
NETWORK ANAYLSIS FOR ECOLOGISTS USING R (NTWA02
Glasgow, Scotland, Dr. Marco Scotti   
https://www.prstatistics.com/course/network-analysis-ecologists-ntwa02/

9.      April 16th – 20th 2018
INTRODUCTION TO STATISTICAL MODELLING FOR PSYCHOLOGISTS USING R (IPSY01)
Glasgow, Scotland, Dr. Dale Barr, Dr Luc Bussierre   
http://www.psstatistics.com/course/introduction-to-statistics-using-r-for-
psychologists-ipsy01/

10.     April 23rd – 27th 2018
MULTIVARIATE ANALYSIS OF ECOLOGICAL COMMUNITIES USING THE VEGAN PACKAGE 
(VGNR01)
Glasgow, Scotland, Dr. Peter Solymos, Dr. Guillaume Blanchet             
https://www.prstatistics.com/course/multivariate-analysis-of-ecological-
communities-in-r-with-the-vegan-package-vgnr01/

11.     April 30th – 4th May 2018
QUANTITATIVE GEOGRAPHIC ECOLOGY: MODELING GENOMES, NICHES, AND COMMUNITIES 
(QGER01)
Glasgow, Scotland, Dr. Dan Warren, Dr. Matt Fitzpatrick
https://www.prstatistics.com/course/quantitative-geographic-ecology-using-r-
modelling-genomes-niches-and-communities-qger01/
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12.     May 7th – 11th 2018 ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL 
ECOLOGICAL DATA USING R (MVSP02)
CANADA (QUEBEC), Prof. Pierre Legendre, Dr. Guillaume Blanchet
https://www.prstatistics.com/course/advances-in-spatial-analysis-of-
multivariate-ecological-data-theory-and-practice-mvsp03/
13.     May 14th - 18th 2018
INTRODUCTION TO MIXED (HIERARCHICAL) MODELS FOR BIOLOGISTS (IMBR01)
CANADA (QUEBEC), Prof Subhash Lele 
https://www.prstatistics.com/course/introduction-to-mixed-hierarchical-
models-for-biologists-using-r-imbr01/

14.     May 21st - 25th 2018
INTRODUCTION TO PYTHON FOR BIOLOGISTS (IPYB05)
SCENE, Scotland, Dr. Martin Jones
http://www.prinformatics.com/course/introduction-to-python-for-biologists-
ipyb05/

15.     May 21st - 25th 2018
INTRODUCTION TO REMOTE SENISNG AND GIS FOR ECOLOGICAL APPLICATIONS (IRMS01)
Glasgow, Scotland, Prof. Duccio Rocchini, Dr. Luca Delucchi
https://www.prinformatics.com/course/introduction-to-remote-sensing-and-gis-
for-ecological-applications-irms01/

16.     May 28th – 31st 2018
STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR (SIMM04)
CANADA (QUEBEC) Dr. Andrew Parnell, Dr. Andrew Jackson 
https://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-
simm04/

17.     May 28th – June 1st 2018
ADVANCED PYTHON FOR BIOLOGISTS (APYB02)
SCENE, Scotland, Dr. Martin Jones
https://www.prinformatics.com/course/advanced-python-biologists-apyb02/
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18.     June 12th -0 15th 2018
SPECIES DISTRIBUTION MODELLING (DBMR01)
Myuna Bay sport and recreation, Australia, TBC
COMING SOON  www.PRstatistics.com

19.     November 6th – 10th 2017
LANDSCAPE GENETIC DATA ANALYSIS USING R (LNDG03)
Myuna Bay sport and recreation, Australia, TBC
COMING SOON  www.PRstatistics.com

20.     June 18th – 22nd 2018
STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS 
USING R (SEMR02)
Myuna Bay sport and recreation, Australia, TBC
COMING SOON  www.PRstatistics.com
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21.     July 2nd - 5th 2018
SOCIAL NETWORK ANALYSIS FOR BEHAVIOURAL SCIENTISTS USING R (SNAR01)
Glasgow, Scotland, Prof James Curley
http://www.psstatistics.com/course/social-network-analysis-for-behavioral-
scientists-snar01/

22.     July 8th – 12th 2018
MODEL BASE MULTIVARIATE ANALYSIS OF ABUNDANCE DATA USING R (MBMV02)
Glasgow, Scotland, Prof David Warton
https://www.prstatistics.com/course/model-base-multivariate-analysis-of-
abundance-data-using-r-mbmv02/

23.     July 16th – 20th 2018
PRECISION MEDICINE BIOINFORMATICS: FROM RAW GENOME AND TRANSCRIPTOME DATA 
TO CLINICAL INTERPRETATION (PMBI01)
Glasgow, Scotland, Dr Malachi Griffith, Dr. Obi Griffith
https://www.prinformatics.com/course/precision-medicine-bioinformatics-from-
raw-genome-and-transcriptome-data-to-clinical-interpretation-pmbi01/

24.     July 23rd – 27th 2018
EUKARYOTIC METABARCODING (EUKB01)
Glasgow, Scotland, Dr. Owen Wangensteen
http://www.prinformatics.com/course/eukaryotic-metabarcoding-eukb01/


-- 
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.

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