[MARMAM] ONLINE COURSE Species distribution modelling with Bayesian statistics in R (SDMB01) This course will be delivered live

Oliver Hooker oliverhooker at prstatistics.com
Sat Oct 17 06:02:19 PDT 2020

ONLINE COURSE Species distribution modelling with Bayesian statistics
in R (SDMB01) This course will be delivered live


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. Becasue 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) they are an effective method for handling marine mammal
data. 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,
classification, 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 credible intervals, and map relative
favourability regarding combined or individual 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 at prstatistics.com with any enquiries or to request
different payment options

Introduction to statistics using R and Rstudio (IRRS02)
28 October 2020 - 29 October 2020

Species distribution modelling with Bayesian statistics in R (SDMB01)
9 November 2020 - 13 November 2020

Introduction to Bayesian modelling with INLA (BMIN01)
9 November 2020 - 13 November 2020

Introduction to generalised linear models using R and Rstudio (IGLM02)
18 November 2020 - 19 November 2020

Fundamentals of populations genetics using R (FOPG01)
18 November 2020 - 27 November 2020

Introduction to mixed models using R and Rstudio (IMMR03)
25 November 2020 - 26 November 2020

Introduction to Python (PYIN01)
25 November 2020 - 26 November 2020

Bayesian hierarchical modelling using R (IBHM05)
27 November 2020 - 11 December 2020

Meta-analysis in ecology, evolution and environmental sciences (METR01)
30 November 2020 - 4 December 2020

Introduction to Python for Scientific Computing (PYSC01)
2 December 2020 - 3 December 2020

Machine Learning and Deep Learning using Python (PYML01)
9 December 2020 - 10 December 2020

Structural Equation Modelling for Ecologists and Evolutionary
Biologists (SEMR03) This course will be delivered live
18th January 2021 - 22nd January 2021

Species Distribution Modeling using R (SDMR03)
25th January 2021 - 29th January 2021

Advanced Ecological Niche Modelling Using R (ANMR01)
25th January 2021 - 29th January 2021

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


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+44 (0) 7966500340
+44 (0) 7966500340

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