"Spatial analysis of ecological data using R"
Delivered by Prof. Jason Matthiopoulos, Dr. Jana Jeglinski, Dr. James Grecian
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 relevant to both plants and animals and highly applicable to ornithological data. 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.
This course will run from 21st – 27th November 2016 at SCENE field station, Loch Lomond national park, Scotland
Course content is as follows
Day 1: Elementary concepts 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, concepts and challenges; 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
Day 2: 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
Day 3: Challenging problems 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
Day 4: Challenging problems Module 7 Analysing transect data, Detection functions for point and line transects, Using covariates in transect models. Afternoon for catch up and/or excursion
Day 5: Challenging problems 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
Day 6: Delivering advice 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
Day 7: Hands-on problem solving Module 11 Round table discussions, About 4 groups, each of 5-10 people working on a particular problem.
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Upcoming courses - email for details firstname.lastname@example.org 1. GENETIC DATA ANALYSIS USING R (August) 2. INTRODUCTION TO BIOINFORMATICS USING LINUX (August) 3. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING (August) 4. INTRODUCTION TO PYTHON FOR BIOLOGISTS (October) 5. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (October) 6. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (October) 7. PHYLOGENETIC DATA ANALYSIS USING R (October/November) 8. ADVANCING IN STATISTICAL MODELLING USING R (December) 9. MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (January) 10. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March) 11. INTRODUCTION TO GEOMETRIC MORPHOMETRICS USING R (June)
Dates still to be confirmed - email for details email@example.com • STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R • INTRODUCTION TO R AND STATISTICS FOR BIOLOGISTS • BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS
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