Species Distribution Modeling using R (SDMR04)

oliver-hookerOliver Hooker
  • 27 Jul '21

Species Distribution Modeling using R (SDMR04)


21 September 2021 - 30 September 2021

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Course Overview:
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 8 half 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:

Understand background theory and model assumptions
Identify, manipulate and prepare spatial datasets for SDMs
Fit, interpret, and evaluate SDMs using several statistical methods (e.g., Maxent, Mahalanobis distance, generalized linear models, boosted regression trees)
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.

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 oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).

Tuesday 21st – Classes from 09:30 to 17:30

1) Overview on modeling and mapping species distributions: Theory, Data, Applications
2) Key steps and concepts in developing SDMs
3) Theory of niches, species distributions, and model assumptions / uncertainties
– Range equilibrium
– Niche conservatism
– Autocorrelation
– Sample size & bias
– Correlation of predictor variables
– Defining the study area
– Model thresholds, validation, and projections
4) Applications of SDMs
5) Data for SDMs
– Biological data
– Predictor variables
6) Practical: Working with spatial data in R

Thursday 23rd – Classes from 09:30 to 17:30

Methods for fitting SDMs I – Overview
1) Overview of methods for fittings SDMs
2) Presence-absence vs. presence-only
– Distance-based
– Regression
– Machine Learning
– Boosting & Bagging
– Maximum entropy / point-process
3) Overview of R packages for SDM
4) Variable selection
5) Practical: Getting your data ready for SDMs

Tuesday 28th – Classes from 09:30 to 17:30

Methods for fitting SDMs II – Presence-absence modeling
1) GLMs and GAMs
2) How to evaluate models
3) Model discrimination
4) Model calibration
5) Model complexity / simplicity
6) Boosted regression trees
7) Practical: Fitting presence-absence SDMs using ‘dismo’ and ‘biomod2’

Thursday 30th – Classes from 09:30 to 17:30

Methods for fitting SDMs III – Presence-only / background modeling, Projecting SDMs
1) Creating background data
2) Maxent
3) Evaluating presence-only models
4) Dealing with biases species data
5) Projecting / extrapolating models
6) Working with climate change data
7) Practical: Fitting maxent models using R
8) Practical: Projecting models to new places / times

Email oliverhooker@prstatistics.com

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