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

[Oliver Hooker]

ONLINE COURSE – Species distribution modelling with Bayesian
statistics in R (SDMB02) This course will be delivered live

https://www.prstatistics.com/course/species-distribution-modelling-with-bayesian-statistics-in-r-sdmb02/

17 May 2021 - 21 May 2021

TIME ZONE – GMT+1 – however all sessions will be recorded and made
available allowing attendees from different time zones to follow a day
behind with an additional 1/2 days support after the official course
finish date (please email oliverhooker using prstatistics.com for full
details or to discuss how we can accommodate you).

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.

Email oliverhooker using prstatistics.com with any questions

-- 
Oliver Hooker PhD.
PR statistics

2020 publications;
Parallelism in eco-morphology and gene expression despite variable
evolutionary and genomic backgrounds in a Holarctic fish. PLOS
GENETICS (2020). IN PRESS

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