*Looking for a course on Species Distribution Modelling (SDMs)?* PR Statistics has the right course for you—whether you’re just starting out, building on existing skills, or ready to explore Bayesian methods for SDMs.
*For beginners:* Start with our introduction to SDMs and ENMs (SDMR06). This course requires no prior SDM experience—just a basic understanding of R. If you don’t yet have the basics, check out our free recorded courses first. SDMR06 covers the *core foundations* of SDM: how to calculate and interpret niche models, choose appropriate modelling approaches, run standard algorithms, compare model outcomes, build ensemble predictions, and apply models to your own data. 🔗 Learn more about SDMR06 <https://www.prstats.org/course/species-distribution-modelling-sdmr06/?utm_source=chatgpt.com> *For intermediate users:* Take your modelling further with our advanced course (ASDM01). This course goes beyond baseline workflows into *model refinement, accuracy improvement, and the integration of physiological and environmental realism* through mechanistic and simulated species models. 🔗 Learn more about ASDM01 <https://www.prstats.org/course/advanced-species-distribution-modelling-using-r-asdm01/?utm_source=chatgpt.com> *For those looking to improve model accuracy with Bayesian methods:* Explore our Bayesian SDM course (SDMB07). This course covers the *entire Bayesian modelling pipeline*: from data preparation and model fitting, to cross-validation, performance evaluation, and interpreting variable importance with tools like partial dependence plots. SDMB07 introduces *Bayesian Additive Regression Trees (BART)*—a modern approach that produces robust predictions, reduces overfitting, and explicitly quantifies uncertainty. Unlike traditional or mechanistic SDMs, Bayesian SDMs allow for a clear representation of uncertainty, providing posterior means, credible intervals, and uncertainty surfaces for deeper insight into prediction confidence. 🔗 Learn more about SDMB07 <https://www.prstats.org/course/species-distribution-modelling-with-bayesian-statistics-sdmb07/?utm_source=chatgpt.com> -- Oliver Hooker PhD. PR stats [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
