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Quantitative geographic ecology using R: modelling genomes, niches, and communities (QGER01)
30th April 2018 - 4th May 2018£275.00 - £540.00
Spatial modelling is increasingly being used in ecology and evolutionary biology for both basic and applied research questions. While emphasis traditionally has been on species-level niche modelling, the increasing availability of genomic and community-level data has increased interest in modelling biodiversity patterns above and below the species level. This 5-day course will provide a thorough introduction to different spatial modelling techniques for quantifying and visualizing patterns of biodiversity across scales of biological organization – from population-level genetic variation, to species ecological niches, to communities. Students will learn about theory, common data types, and statistical techniques used in these different applications.
The course will include introductory lectures, guided computer coding in R, and exercises for the participants, with an emphasis on visualization and reproducible workflows. All modelling and data manipulation will be performed with R. Attendees will learn to use niche modelling algorithms including Maxent, GLM, GAM, and others, and will learn both new and existing methods for conducting comparative studies using ENMs in the new ENMTools R package. Generalized Dissimilarity Modelling (GDM) and Gradient Forest (GF) will be taught for modelling genomic and community-level data. The course is intended for intermediate R users with interest in quantitative geographical ecology.
After successfully completing this course students will:
- Understand the theory underlying ENMs and the critical assumptions necessary to the modelling process.
- Be able to develop, evaluate, and apply ENMs both in the context of conservation-oriented studies and to study niche evolution.
- Understand the statistical underpinnings of GDM and GF
- Be able to develop, evaluate and apply GDM and GF for quantifying and mapping spatial genetic patterns and community-level compositional variation
- Assess population- and community-level vulnerability to climate change
Any researchers (from postgraduate students to senior investigators) interested in the use of spatial modelling for quantifying and visualizing patterns of biodiversity, including those in applied fields and basic science.
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 form the PR statistics head office. Accommodation is multiple occupancy (max 3-4 people) single sex en-suite rooms. Arrival Sunday 29th April (after 5pm) and departure Friday 4th May (accommodation must be vacated by 9am). An additional nights accommodation can be purchased, departure 9am Saturday morning email for details.
To book ‘COURSE ONLY’ with the option to add the additional ‘ACCOMMODATION PACKAGE’ please scroll to the bottom of this page.
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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 PR~statistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PR~statistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
There will be a combination of lectures and practicals. Practicals will be based on the topics covered in the preceding lectures. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
Familiarity with geospatial data. A basic understanding of statistical modeling concepts and inference, including regression methods and model validation. Basic familiarity in working with species, community, or genetic data.
Assumed computer background
Basic proficiency with R, including an ability to import/export and manipulate data, including spatial data such as rasters and point occurrence data, fit basic statistical models, and generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop/personal computer with a working version of R or RStudio and the following packages (and dependencies) installed:
• The ENMTools R package (https://github.com/danlwarren/ENMTools)
R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links:
R – https://cran.r-project.org/
RStudio – https://www.rstudio.com/products/rstudio/download/
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) as internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK email@example.com
Meet at 43 Cook Street, Glasgow G5 8JN at approx. 17:00 onwards
Monday 30th – Classes from 09:00 to 17:00
Organisation and Introductions.
Spatial data in R.
Point data, vector data, and raster data.
GBIF, the Global Biodiversity Information Facility.
Interacting with Google Maps.
Working with raster and vector data.
Tuesday 1st – Classes from 09:00 to 17:00
Ecological vs. historical biogeography.
ENM / SDM concepts and assumptions
Conceptual and practical issues with ecological inferences from distribution data.
Simulating species occurrence data.
Wednesday 2nd – Classes from 09:00 to 17:00
Testing ecological and evolutionary hypotheses via Monte Carlo methods.
ENMTools R package.
Questions of taxonomic scale.
Incorporating niche conservatism into the modelling process.
Thursday 3rd – Classes from 09:00 to 17:00
Introduction to community-level modeling
Background on GDM and GF
Review of data formats and data preparation
• Community-level data
• Genomic data
Model fitting and testing
Interpreting model results, including turnover functions
Model testing / validation / variable selection
Friday 4th – Classes from 09:00 to 16:30
Predictions & Applications of GDM / GF
Visualizing spatial variation in community / genetic composition
• Dissimilarity between locations
• Projecting patterns under climate change
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)