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Species distribution models using R (SDMR01)
12th June 2018 - 15th June 2018
The aim of this four-day course is to work towards an understanding of, and practical ability to fit, species distribution models (SDMs). It will be useful if you plan to use SDMs, or if you just want to understand them better. We will focus on statistical models of species distributions – those that combine observed species records with environmental data. Using a mixture of lectures, computer exercises and case studies, participants will learn to:
- identify relevant data, and prepare it for modelling;
- fit models using several modelling methods (including Maxent, generalized linear models and their extensions, and boosted regression trees);
- consider how to model species if detection is imperfect;
- evaluate models and interpret them;
- understand the range of practical issues that arise in typical applications of SDMs.
Practical sessions will use the free statistical software, R – prior experience (even if some practice before you come) will be useful. Example data will be provided but participants may also bring their own data.
Presenters include Jane Elith and Gurutzeta Guillera-Arroita, who are highly experienced in SDMs.
Research postgraduates, practicing academics and primary investigators in spatial ecology particularly communities (bacteria, plants, animals) sampled along transects, regular grids or irregularly distributed sites. The skills learnt can also be applied by management and environmental professionals in government and industry.
Venue – PR statistics head office, 53 Morrison Street, Glasgow, G5 8LB – Google map
Availability – 30 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, welcome dinner Monday evening, farewell dinner Thursday evening, refreshments and accommodation. Self-catering facilities are available in the accommodation. Accommodation is approximately a 6-minute walk from the PR statistics head office. Accommodation is multiple occupancy (max 3-4 people) single sex en-suite rooms. Arrival Sunday 11th June (after 5pm) and departure Friday 15th June (accommodation must be vacated by 9am).
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Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact firstname.lastname@example.org Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PRstatistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PRstatistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practical’s based on the topics covered that morning. Data sets for computer practical’s will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.
Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
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Meet at Myuna Bay Sport and |recreation at approx. 18:30
Tuesday 12th – Classes from 09:00 to 17:00
Overview of modelling distributions; niches and theory
The modelling process – key concepts
Practical: start working with supplied data
Methods for presence-absence data; introduction to use of regression models for species modelling
Wednesday 13th – Classes from 09:00 to 17:00
Generalised linear models (GLMs) and GLMMS
Practical using GLMs with data
How to evaluate models – lecture and practical
Occupancy-detection models – lecture and practical
Thursday 14th – Classes from 09:00 to 17:00
What if no absence data? – introduction to presence-only and background data
Practical: GLM with background data
Relative probabilities, point processes
Maxent – lecture and practical
Evaluation for presence-background models
How to deal with biased data
Friday 15th – Classes from 09:00 to 16:00
Boosted regression trees – lecture and practical
Complexity vs simplicity in models
Using models for extrapolation
Hot topics: where is species modelling heading?