ONLINE COURSE – Species Distribution Modeling using R (SDMR03) This course will be delivered live
25 January 2021 - 29 January 2021
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE –Eastern Daylight Time – 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).
If you are interested in gaining the introductory knowledge required to work with SDMs, whether you be a student, postdoc, or practicing scientist, this course is for you.This four-day course will provide participants with the background knowledge and skills needed to get started in the use of species distribution models (SDMs) for applied and basic research. The course will focus on (1) the preparation of required spatial datasets (biological observations and environmental predictors); (2) practical considerations in the development, application, and interpretation of SDMs; and (3) fitting and evaluating SDMs using different statistical approaches – all using R.
Using a combination of lectures, coding exercises in R, and case studies, participants will learn to:
1) Understand background theory and model assumptions
2) Identify, manipulate and prepare spatial datasets for SDMs
3) Fit, interpret, and evaluate SDMs using several statistical methods (e.g., Maxent, Mahalanobis distance, generalized linear models, boosted regression trees)
4) Project SDMs to predict climate change impacts, etc.
The course is entirely R-based and while techniques of working with spatial data in R will be covered in detail, prior experience with R is highly recommended. If you are new to R, this course will be of most use to you if you work through a few tutorials to understand the basics of R programming before the start of the course. Students are highly encouraged to bring their own data sets, but this is not required for participation.
Course material will be presented by Matt Fitzpatrick who has published broadly in the use of SDMs for applied and basic science.
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