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Melanie Colón

SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R - STATS COURSE

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"Spatial analysis of ecological data using R"

Delivered by Prof. Jason Matthiopoulos

This course will run from 11th - 17th April 2016 at Millport Field Station,
Isle of Cumbrae, Scotland

The course will cover the concepts and R tools that can be used to analyse spatial data
spatial data in ecology covering elementary and advanced spatial analysis techniques ap
techniques applicable to both plants and animals. We will investigate analyses appr
analyses appropriate to transect (e.g. line surveys, trapping arrays), grid (e.g. occupan
(e.g. occupancy surveys) and point data (e.g. telemetry). The focal questions wil
questions will be on deriving species distributions, determining their environmental
environmental drivers and quantifying different types of associated uncertainty.
uncertainty. Novel methodology for generating predictions will be introduced. W
introduced. We will also address the challenges of applying the results of these methods
these methods to wildlife conservation and resource management and communicate t
communicate the findings to non-experts.

http://prstatistics.co.uk/spatial-analysis-in-R/index.html

Course content is as follows

Day 1: Elementary concepts
>Module 1 Introductory lectures and practical; this will cover the key questi
questions in spatial ecology, the main types of data on species distri
distributions, concepts and challenges and different types of environmental data,
data, concepts and challenges; useful concepts from statistics; Generalised Linear
Linear Models
>Module 2 GIS tools in R: Types and structure of spatial objects in R, gener
generating and manipulating spatial objects,
projections and transformations, cropping and masking spatial objects, extr
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 S
Smoothers, Kriging, Trend-fitting (linear, generalised linear, generalised a
additive models)
>Module 4 Habitat preference, Resource selection functions, MaxEnt: What’s it all about? Overview and cav
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
>Modu
availability designs
>Module 6 Analysing telemetry data, Presence-only data, Spatial and serial autocorrelation, Partition
autocorrelation, Partitioning variation by mixed effects models

Day 4: Challenging problems
>Module 7 Analysing transect data, Detection functions for point and line transects, Using covari
transects, Using covariates in transect models. Afternoon for catch up and/or excursion

Da
and/or excursion

Day 5: Challenging problems
>Module 8 Advanced methods, Generalised Estimation Equations for difficult survey designs, Gene
survey designs, Generalised additive
models for habitat preference, Dealing with boundary effects using soap smoothers, Spatial
smoothers, Spatial point processes with INLA

Day 6: Delivering advice
>Module 9 Prediction, Validation by resampling, Generalised Functional Responses for sp
Responses for species distribution, Quantifying
uncertainty, Dealing with the effects of population density
>Module 10 Applications, Designing protected areas, thinking about critical habitat, Repre
habitat, Representing uncertainty

Day 7: Hands-on problem solving
>Module 11 Round table discussions, About 4 groups, each of 5-10 people working on
working on a particular problem.

This 7 day course costs £630 for course only including lunch or £965 all inclusive
inclusive, including all accommodation and meals.

Please email any inquiries to oliverhooker@prstatistics.co.uk

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

Upcoming courses; ADVANCING IN STATISTICAL MODELLING USING R; INTRODUCTION TO
TO R AND STATISTICS FOR BIOLOGISTS; STABLE ISOTOPE MIXING MODELS USING SIA
SIAR, SIBER AND MIXSIAR; INTRODUCTION TO PYTHON FOR BIOLOGISTS; TIMES SER
SERIES DATA ANALYSIS FOR ECOLOGISTS AND CLIMATOLOGISTS USING R; MODEL BASED MUL
MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R; ADVANCES IN DNA TAXONOMY USI
USING R; GENETIC DATA ANALYSIS USING R; APPLIED BAYESIAN MODELLING FOR ECO
ECOLOGISTS AND EPIDEMIOLOGISTS

Oliver Hooker
PR~Statistics

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