"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 [email protected] 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
