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Spatial analysis of ecological data using R (SPAE04)
21st November 2016 - 26th November 2016£630 - £1080
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 two packages
• COURSE ONLY – Includes lunch and refreshments.
• ALL INCLUSIVE – Includes breakfast, lunch, dinner, refreshments, minibus to and from meeting point and accommodation. Accommodation is multiple occupancy (max 3 people) single sex en-suite rooms. Arrival Sunday 20th November and departure Saturday 26th November PM.
To book ‘COURSE ONLY’ or ‘ALL INCLUSIVE’ pleases scroll to the bottom of this page.
Other payment options are available please email firstname.lastname@example.org
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 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.
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 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
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Sunday 20thMeet at the Tullie Inn, Balloch at approximately 18:30 before being taken by minibus to SCENE (Download directions PDF)
Monday 21st – 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 22nd – 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 23rd – 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 24th – 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 25th – 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 26th – Classes from 09:00 to 16:00
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.