Species Distribution Modeling using R (SDMRPR)
1st January 2030
£450.00
Course Format
Pre Recorded
About This Course
Using a combination of lectures, coding exercises in R, and case studies, participants will learn to:
- Understand background theory and model assumptions
- Identify, manipulate and prepare spatial datasets for SDMs
- Fit, interpret, and evaluate SDMs using several statistical methods (e.g., Maxent, Mahalanobis distance, generalized linear models, boosted regression trees)
- Project SDMs to predict climate change impacts, etc.
The course is entirely R-based and while techniques of working with spatial data in R will be covered in detail, prior experience with R is highly recommended. If you are new to R, this course will be of most use to you if you work through a few tutorials to understand the basics of R programming before the start of the course. Students are highly encouraged to bring their own data sets, but this is not required for participation.
Course material will be presented by Matt Fitzpatrick who has published broadly in the use of SDMs for applied and basic science.
Intended Audiences
Any researchers (PhD and MSc students, postdocs, primary investigators) and management and environmental professionals in government and industry interested in the use of SDMs for conservation, biogeography, spatial ecology, or related disciplines.
Course Details
Last Up-Dated – 05:05:2023
Duration – Approx. 32 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
There will be a combination of lectures and hands-on practicals. Afternoon practicals will be based on the topics covered in the morning lectures. Data sets for computer practicals will be provided, but participants are highly encouraged to bring their own data.
Assumed quantitative knowledge
Familiarity with GIS and geospatial data (i.e., rasters and point occurrence data). A basic understanding of statistical modeling concepts and inference, including regression methods and model validation.
Assumed computer background
Basic proficiency with R, including an ability to import/export and manipulate tabular data, fit basic statistical models, and generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact oliverhooker@prstatistics.com.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
COURSE PROGRAMME
Day 1 – approx. 8 hours
1) Overview on modeling and mapping species distributions: Theory, Data, Applications
2) Key steps and concepts in developing SDMs
3) Theory of niches, species distributions, and model assumptions / uncertainties
– Range equilibrium
– Niche conservatism
– Autocorrelation
– Sample size & bias
– Correlation of predictor variables
– Defining the study area
– Model thresholds, validation, and projections
4) Applications of SDMs
5) Data for SDMs
– Biological data
– Predictor variables
6) Practical: Working with spatial data in R
Day 2 – approx. 8 hours
Methods for fitting SDMs I – Overview
1) Overview of methods for fittings SDMs
2) Presence-absence vs. presence-only
– Distance-based
– Regression
– Machine Learning
– Boosting & Bagging
– Maximum entropy / point-process
3) Overview of R packages for SDM
4) Variable selection
5) Practical: Getting your data ready for SDMs
Day 3 – approx. 8 hours
Methods for fitting SDMs II – Presence-absence modeling
1) GLMs and GAMs
2) How to evaluate models
3) Model discrimination
4) Model calibration
5) Model complexity / simplicity
6) Boosted regression trees
7) Practical: Fitting presence-absence SDMs using ‘dismo’ and ‘biomod2’
Day 4 – approx. 8 hours
Methods for fitting SDMs III – Presence-only / background modeling, Projecting SDMs
1) Creating background data
2) Maxent
3) Evaluating presence-only models
4) Dealing with biases species data
5) Projecting / extrapolating models
6) Working with climate change data
7) Practical: Fitting maxent models using R
8) Practical: Projecting models to new places / times
Course Instructor
Dr. Matt Fitzpartrick
Works at –
Teaches –