[R-sig-eco] Species distribution modelling with Bayesian statistics in R (SDMB03)

[Oliver Hooker]

Species distribution modelling with Bayesian statistics in R (SDMB03)

6th - 10th December


https://www.prstatistics.com/course/species-distribution-modelling-with-bayesian-statistics-in-r-sdmb03/



Course Overview:
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.



Monday 6th – Classes from 14:30 to 17:30
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

Tuesday 7th – Classes from 14:30 to 17:30
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

Wednesday 8th – Classes from 14:30 to 17:30
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

Thursday 9th – Classes from 14:30 to 17:30
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

Friday 10TH – Classes from 14:30 to 17:30
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


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-- 
Oliver Hooker PhD.
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