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 Other upcoming courses FREE 1 DAY INTRO TO R AND R STUDIO (FIRR01) https://www.prstatistics.com/course/free-1-day-intro-to-r-and-r-studio-firr01/ 20 October 2021 Introduction to generalised linear models using R and Rstudio (IGLM04) https://www.prstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm04/ 3 November 2021 - 4 November 2021 Introduction to mixed models using R and Rstudio (IMMR05) https://www.prstatistics.com/course/introduction-to-mixed-models-using-r-and-rstudio-immr05/ 10 November 2021 - 11 November 2021 Introduction to Machine Learning and Deep Learning using R (IMDL02) https://www.prstatistics.com/course/introduction-to-machine-learning-and-deep-learning-using-r-imdl02/ 17 November 2021 - 18 November 2021 Model selection and model simplification (MSMS02) https://www.prstatistics.com/course/model-selection-and-model-simplification-msms02/ 24 November 2021 - 25 November 2021 Species distribution modelling with Bayesian statistics in R (SDMB03) www.prstatistics.com/course/species-distribution-modelling-with-bayesian-statistics-in-r-sdmb03/ 6 December 2021 - 10 December 2021 Introduction to Hidden Markov and State Space models (HMSS01) https://www.prstatistics.com/course/introduction-to-hidden-markov-and-state-space-models-hmss01/ 8 December 2021 - 9 December 2021 Time Series Data Analysis (TSDA01) https://www.prstatistics.com/course/time-series-data-analysis-tsda01/ 14 December 2021 - 17 December 2021 Bayesian Data Analysis (BADA01) https://www.prstatistics.com/course/bayesian-data-analysis-bada01/ 10th January 2022 - 14th January 2022 Introduction to Stan for Bayesian Data Analysis (ISBD01) https://www.prstatistics.com/course/introduction-to-stan-for-bayesian-data-analysis-isbd01/ 18th January 2022 - 20th January 2022 Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM08) https://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm08/ 1st February 2022 - 4th February 2022 Species Distribution Modeling using R (SDMR04) www.prstatistics.com/course/species-distribution-modeling-using-r-sdmr04/ 21 September 2021 - 30 September 2021 Introduction to eco-phylogenetics and comparative analyses using R (ECPH01) This course will be delivered live https://www.prstatistics.com/course/introduction-to-eco-phylogenetics-and-comparative-analyses-using-r-ecph01/ 22 September 2021 - 28 September 2021 Functional ecology from organism to ecosystem: theory and computation (FEER02) https://www.prstatistics.com/course/functional-ecology-from-organism-to-ecosystem-theory-and-computation-feer02/ 5th September 2022 - 9th September 2022
Oliver Hooker PhD. PR statistics Species Distribution Modeling using R 21 September 2021 - 30 September Introduction to eco-phylogenetics and comparative analyses using R 22 September 2021 - 28 September 2021 Multivariate analysis of ecological communities in R with the VEGAN package 4 October 2021 - 8 October Introduction to Data Wrangling and Data Visualization using R 4 October 2021 - 8 October 2021 Introduction to Bayesian modelling with INLA 4 October 2021 - 8 October 2021 Landscape genetic data analysis using R 18 October 2021 - 27 October 2021 FREE 1 DAY INTRO TO R AND R STUDIO 20 October 2021 Introduction to generalised linear models using R and Rstudio 3 November 2021 - 4 November 2021 Introduction to mixed models using R and Rstudio 10 November 2021 - 11 November 2021 Introduction to Machine Learning and Deep Learning using R 17 November 2021 - 18 November 2021 Model selection and model simplification 24 November 2021 - 25 November 2021 Species distribution modelling with Bayesian statistics in R 6 December 2021 - 10 December 2021 [[alternative HTML version deleted]]