ONLINE COURSE – Species distribution modelling with Bayesian statistics in R (SDMB02) This course will be delivered live
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 firstname.lastname@example.org for full details or to discuss how we can accommodate you).
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.
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