Ecological niche modelling using R (ENMR01)
Delivered by Dr. Neftali Sillero
This course will run from 16th – 20th October 2017 at SCENE field
station, Loch Lomond national park, Scotland
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
Course content is as follows
Monday 16th – 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
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
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 17th – Classes from 09:00 to 17:00
Prepare environmental variables and run ecological niche models with
Module 6: Preparing variables. Choosing environmental data sources,
Downloading variables, Clipping variables, Aggregating variables,
Checking pixel size, Checking raster limits, Checking NoData,
Module 7: Dismo practice. How to run an ENM using the R package dismo.
Wednesday 18th – 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
Module 9: Maxent practice. How to run an ENM using the R packages dismo
and Biomod2 as well as Maxent software.
Thursday 19th – 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 20th – Classes from 09:00 to 17:00
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.
Please email any inquiries to [hidden email] or visit our
Please feel free to distribute this material anywhere you feel is
Our other courses
1. ADVANCED PYTHON FOR BIOLOGISTS (February 2017) #APYB
2. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R
(February 2017) #SIMM
3. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March 2017) #NTWA
4. ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April
5. INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017) #IRFB
6. ADVANCING IN STATISTICAL MODELLING USING R (April 2017) #ADVR
7. GEOMETRIC MORPHOMETRICS USING R (June 2017) #GMMR
8. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA (June 2017) #MASE
9. TIME SERIES MODELS FOR ECOLOGISTS USING R (JUNE 2017 (#TSME)
10. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017) #BIGB
11. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (August 2017) #SPAE
12. ECOLOGICAL NICHE MODELLING (October 2017) #ENMR
13. INTRODUCTION TO BIOINFORMATICS USING LINUX (October 2017) #IBUL
14. STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY
BIOLOGISTS (October 2017) #SEMR
15. GENETIC DATA ANALYSIS USING R (October 2017 TBC) #GDAR
16. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (November 2017
17. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS
(November 2017) #ABME
18. INTRODUCTION TO METHODS FOR REMOTE SENSING (November 2017) #IRMS
19. INTRODUCTION TO PYTHON FOR BIOLOGISTS (November 2017) #IPYB
20. DATA VISUALISATION AND MANIPULATION USING PYTHON (December 2017)
21. ADVANCING IN STATISTICAL MODELLING USING R (December 2017) #ADVR
22. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (January 2018) #IBHM
23. PHYLOGENETIC DATA ANALYSIS USING R (TBC) #PHYL
Oliver Hooker PhD.
128 Brunswick Street
+44 (0) 7966500340
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