"Spatial analysis of ecological data using R"

Delivered by Prof. Jason Matthiopoulos, Dr. Jana Jeglinski, Dr. James Grecian

http://prstatistics.com/course/spatial-analysis-of-ecological-data-using-r-spae-2/

This course will run from 21st – 26th November 2016 at SCENE field station, Loch Lomond national park, Scotland
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.

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 

Please email any inquiries to [log in to unmask] or visit our website www.prstatistics.com

Please feel free to distribute this material anywhere you feel is suitable

Upcoming courses - email for details [log in to unmask]
1.	INTRODUCTION TO PYTHON FOR BIOLOGISTS (October)
2.	LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (October)
3.	APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (October)
4.	SPATIAL ANALYSIS OF ECOLOGIC AL DATA USING R (November)
5.	ADVANCING IN STATISTICAL MODELLING USING R (December) 
6.	MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (January)
7.	ADVANCED PYTHON FOR BIOLOGISTS (February)
8.	NETWORK ANALYSIS FOR ECOLOGISTS (March)
9.	INTRODUCTION TO GEOMETRIC MORPHOMETRICS USING R (June)

Dates still to be confirmed - email for details [log in to unmask]
•	STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R
•	INTRODUCTION TO R AND STATISTICS FOR BIOLOGISTS
•	BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS
•	ADVANCES IN SPATIAL ANALYSIS OF MULTIVARIATE ECOLOGICAL DATA
•	INTRODUCTION TO BIOINFORMATICS USING LINUX
•	GENETIC DATA ANALYSIS / EXPLORATION USING R
•	INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING

Oliver Hooker
PR statistics
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www.prstatistics.com
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