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Spatial analysis of ecological data using R (SPAE05)
7th August 2017 - 12th August 2017£300.00 - £600.00
The course will cover the concepts and R tools that can be used to analyse spatial data in ecology covering elementary and advanced spatial analysis techniques applicable to both plants and animals. We will investigate analyses appropriate to transect (e.g. line surveys, trapping arrays), grid (e.g. occupancy surveys) and point data (e.g. telemetry). The focal questions will be on deriving species distributions, determining their environmental drivers and quantifying different types of associated uncertainty. Novel methodology for generating predictions will be introduced. We will also address the challenges of applying the results of these methods to wildlife conservation and resource management and communicate the findings to non-experts.
Research postgraduates, practicing academics and primary investigators in spatial ecology and management and environmental professionals in government and industry.
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, dinner, refreshments, accommodation and minibus collection from the meeting point on Sunday. Accommodation is multiple occupancy (max 3 people) single sex en-suite rooms. Arrival Sunday 6th August and departure Saturday 12th August PM.
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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 email@example.com 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.
Introductory lectures on the concepts and refreshers on R usage. Intermediate-level lectures interspersed with hands-on mini practicals and longer projects. Round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A good understanding of statistical concepts, statistical significance and hypothesis testing. Ability to perform linear and generalised linear regression models and interpret when to use them.
Assumed computer background
R experience is essential, attendees should be able to import/export data, manipulate data frames within R, fit basic statistical models such a LM’s and GLM’s, generate simple exploratory and diagnostic plots, be able to write basic functions.
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
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Meet at the Tullie Inn, Balloch at approximately 18:30 before being taken by minibus to SCENE (Download directions PDF)
Monday 7th – Classes from 09:00 to 17:00
Module 1: Introductory lectures and practical; this will cover the key questions in spatial ecology, the main types of data on species distributions, concepts and challenges and different types of environmental data; useful concepts from statistics; Generalised Linear Models.
Module 2: GIS tools in R: Types and structure of spatial objects in R, generating and manipulating spatial objects, projections and transformations, cropping and masking spatial objects, extracting covariate data and other simple GIS operations in R, optionally plotting simple maps.
Tuesday 8th – Classes from 09:00 to 17:00
Overview of basic analyses.
Module 3: Density estimation, Spatial autocorrelation, Smoothing, Kernel Smoothers, Kriging, Trend-fitting (linear, generalised linear, generalised additive models).
Module 4: Habitat preference, Resource selection functions, MaxEnt: What’s it all about? Overview and caveats related to Niche models
Wednesday 9th – Classes from 09:00 to 17:00
Module 5: Analysing grid data, Poisson processes, Occupancy models, Use-availability designs.
Module 6: Analysing telemetry data, Presence-only data, Spatial and serial autocorrelation, Partitioning variation by mixed effects models.
Thursday 10th – Classes from 09:00 to 17:00
Module 7: Analysing transect data, Detection functions for point and line transects, Using covariates in transect models. Afternoon for catch up and/or excursion.
Friday 11th – Classes from 09:00 to 17:00
Module 8: Advanced methods, Generalised Estimation Equations for difficult survey designs, Generalised additive models for habitat preference, Dealing with boundary effects using soap smoothers, Spatial point processes with INLA.
Saturday 12th – Classes from 09:00 to 14:00
Predictions and applications.
Module 9: Prediction, Validation by resampling, Generalised Functional Responses for species distribution, Quantifying uncertainty, Dealing with the effects of population density.
Module 10: Applications, Designing protected areas, thinking about critical habitat, Representing uncertainty.