[R-sig-eco] Species Distribution Modeling using R (SDMR02) This course will be delivered live

[Oliver Hooker]

ONLINE COURSE – Species Distribution Modeling using R (SDMR02) This
course will be delivered live

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

https://www.prstatistics.com/course/species-distribution-modeling-using-r-sdmr02/

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 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:

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.

Monday 27th – 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

Tuesday 28th – 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

Wednesday 29th – 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 using prstatistics.com  with any questions.

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-- 
Oliver Hooker PhD.
PR statistics

2020 publications;
Parallelism in eco-morphology and gene expression despite variable
evolutionary and genomic backgrounds in a Holarctic fish. PLOS
GENETICS (2020). IN PRESS

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