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Species Distribution Modelling With Bayesian Statistics In R (SDMB03R)

5th May 2025 - 9th May 2025

£400.00
Species Distribution Modelling With Bayesian Statistics In R (SDMB03R)

Event Date

Monday, May 23rd, 2022

Course Format

This course is not available in a recorded format.

Please email oliverhooker@prstatistics.com to be notified of the next edition.

About This Course

Bayesian Additive Regression Trees (BART) are a powerful machine learning technique with very promising potential applications in ecology and biogeography in general, and in species distribution modelling (SDM) in particular. Unlike most other SDM methods, BART models can generally provide a well-balanced performance regarding both main aspects of
predictive accuracy, namely discrimination (i.e. distinguishing presence from absence localities) and calibration (i.e., having predicted probabilities reflect the species' gradual occurrence frequencies). BART can generate accurate predictions without overfitting to noise or to particular cases in the data. As it is a cutting-edge technique in this field, BART
is not yet routinely included in SDM workflows or in ensemble modelling packages. This course will include 1) an introduction or refresher on the essentials of the R language; 2) an introduction or refresher on species distribution modelling; 3) an overview of SDM methods of different complexity, including regression-based and machine-learning (both Bayesian and non-Bayesian) methods; 4) SDM building and block cross-validation focused on different aspects of model performance, including discrimination and calibration or reliability. We will use R packages 'embarcadero', 'fuzzySim' and 'modEvA' to see how BART can perform well when all these aspects are equally important, as well as to identify
relevant predictors, map prediction uncertainty, plot partial dependence curves with Bayesian credible intervals, and map relative probability of presence regarding particular predictors. Students will apply all these techniques to their own species distribution data, or to example data that will be provided during the course.

Intended Audiences

Any researchers (PhD and MSc students, post-docs, primary investigators) and environmental professionals who are interested in implementing best practices and state-of-the-art methods for modelling species’ distributions or ecological niches, with applications to biogeography, spatial ecology, biodiversity conservation and relateddisciplines.

Course Details

Last Up-Dated – 10:12:2021

Duration Approx. 35 hours

ECT’s – Equal to 3 ECT’s

Language – English

Teaching Format

Each day will consist of approximately 3 hours of interactive live online sessions (at 14:30 GMT time), which will include theoretical lectures, discussion, and general practical guidance; and approximately 4 hours of practical’s that each participant will do on their own schedule / time zone, based on annotated self-explanatory R scripts. The instructor will be available for questions and help during Western European working hours and a bit beyond that, depending on the participants’ time zones. Data sets for the practicals will be provided, although participants are also encouraged to use their own species distribution data.

Assumed quantitative knowledge

A basic understanding of what species distribution / ecological niche models are.

Assumed computer background

Basic knowledge and experience with R is not strictly mandatory (as the basics will be provided), but it will make the practicals much less of a struggle.

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

Tickets

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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 3 hours
4 additional hours are needed each day for self-guided practicals, on hand support (via email and video if needed) is available from 08:00 to 22:00 to accommodate participants’ from different time zones.

An introduction / refresher on base R language
Species distribution modelling: basic concepts
Species distributions: data types and sources
Predictor variables: data types and sources
Defining the modelling region: extent and resolution
Discussion
Practicals

Day 2 – approx 3 hours
4 additional hours are needed each day for self-guided practicals, on hand support (via email and video if needed) is available from 08:00 to 22:00 to accommodate participants’ from different time zones.

Overview of methods and R packages for species distribution modelling
Presence-absence vs. presence-background modelling methods
Regression and machine-learning methods: GLM, GAM, Maxent, Random Forests, Bayesian Additive Regression Trees (BART)
Discussion
Practicals

Day 3 – approx 3 hours
4 additional hours are needed each day for self-guided practicals, on hand support (via email and video if needed) is available from 08:00 to 22:00 to accommodate participants’ from different time zones.

Model evaluation and validation: overview of performance metrics
Different facets of model performance: discrimination, classification, calibration
Splitting the study area for block-cross-validation
Comparing the performance of regression, machine-learning and Bayesian methods
Making predictions comparable across species, regions and time periods: probability and favourability
Discussion
Practicals

Day 4 – approx 3 hours
4 additional hours are needed each day for self-guided practicals, on hand support (via email and video if needed) is available from 08:00 to 22:00 to accommodate participants’ from different time zones.

Selecting relevant predictors with BART
Mapping prediction uncertainty with BART
Plotting partial dependence curves with Bayesian credible intervals
Mapping relative favourability regarding specific predictor variables
Discussion
Practicals

Day 5 – approx 3 hours
4 additional hours are needed each day for self-guided practicals, on hand support (via email and video if needed) is available from 08:00 to 22:00 to accommodate participants’ from different time zones.

Students’ presentations
Final discussion and outlook

 

Course Instructor

 
 
 
Dr. Marcia barbosa

Works at – 
Teaches –

Details

Start:
5th May 2025
End:
9th May 2025
Cost:
£400.00
Event Category:

Venue

Recorded
United Kingdom + Google Map

Tickets

The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.
SDMB03R NOT AVAILABLE
SDMB03R NOT AVAILABLE
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