[R-sig-eco] Species Distribution Modelling With Bayesian Statistics Using R (SDMB05)

[Oliver Hooker]

ONLINE COURSE – Species Distribution Modelling With Bayesian Statistics
Using R (SDMB05)

https://www.prstatistics.com/course/online-course-species-distribution-modelling-with-bayesian-statistics-in-r-sdmb05/
<https://www.prstatistics.com/course/online-course-species-distribution-modelling-with-bayesian-statistics-in-r-sdmb04/>

4th - 8th September 2023

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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.
Please feel free to contact oliverhooker using prstatistics.com with any
questions.

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Oliver Hooker PhD.
PR statistics

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