Melanie Colón Posted December 9, 2015 Share Posted December 9, 2015 "Spatial analysis of ecological data using R"Delivered by Prof. Jason MatthiopoulosThis course will run from 11th - 17th April 2016 at Millport Field Station,Isle of Cumbrae, ScotlandThe course will cover the concepts and R tools that can be used to analyse spatial dataspatial data in ecology covering elementary and advanced spatial analysis techniques aptechniques applicable to both plants and animals. We will investigate analyses appranalyses 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 wilquestions will be on deriving species distributions, determining their environmentalenvironmental drivers and quantifying different types of associated uncertainty.uncertainty. Novel methodology for generating predictions will be introduced. Wintroduced. We will also address the challenges of applying the results of these methodsthese methods to wildlife conservation and resource management and communicate tcommunicate the findings to non-experts.http://prstatistics.co.uk/spatial-analysis-in-R/index.htmlCourse content is as followsDay 1: Elementary concepts>Module 1 Introductory lectures and practical; this will cover the key questiquestions in spatial ecology, the main types of data on species distridistributions, concepts and challenges and different types of environmental data,data, concepts and challenges; useful concepts from statistics; Generalised LinearLinear Models>Module 2 GIS tools in R: Types and structure of spatial objects in R, genergenerating and manipulating spatial objects,projections and transformations, cropping and masking spatial objects, extrextracting covariate data and other simpleGIS operations in R, optionally plotting simple mapsDay 2: Overview of basic analyses>Module 3 Density estimation, Spatial autocorrelation,Smoothing, Kernel SSmoothers, Kriging, Trend-fitting (linear, generalised linear, generalised aadditive models)>Module 4 Habitat preference, Resource selection functions, MaxEnt: What’s it all about? Overview and cavit all about? Overview and caveats related to Niche modelsDay 3: Challenging problems>Module 5 Analysing grid data, Poisson processes, Occupancy models, Use-availability designs>Moduavailability designs>Module 6 Analysing telemetry data, Presence-only data, Spatial and serial autocorrelation, Partitionautocorrelation, Partitioning variation by mixed effects modelsDay 4: Challenging problems>Module 7 Analysing transect data, Detection functions for point and line transects, Using covaritransects, Using covariates in transect models. Afternoon for catch up and/or excursionDaand/or excursionDay 5: Challenging problems>Module 8 Advanced methods, Generalised Estimation Equations for difficult survey designs, Genesurvey designs, Generalised additivemodels for habitat preference, Dealing with boundary effects using soap smoothers, Spatialsmoothers, Spatial point processes with INLADay 6: Delivering advice>Module 9 Prediction, Validation by resampling, Generalised Functional Responses for spResponses for species distribution, Quantifyinguncertainty, Dealing with the effects of population density>Module 10 Applications, Designing protected areas, thinking about critical habitat, Reprehabitat, Representing uncertaintyDay 7: Hands-on problem solving>Module 11 Round table discussions, About 4 groups, each of 5-10 people working onworking on a particular problem.This 7 day course costs £630 for course only including lunch or £965 all inclusiveinclusive, including all accommodation and meals.Please email any inquiries to oliverhooker@prstatistics.co.ukPlease feel free to distribute this material anywhere you feel is suitableUpcoming courses; ADVANCING IN STATISTICAL MODELLING USING R; INTRODUCTION TOTO R AND STATISTICS FOR BIOLOGISTS; STABLE ISOTOPE MIXING MODELS USING SIASIAR, SIBER AND MIXSIAR; INTRODUCTION TO PYTHON FOR BIOLOGISTS; TIMES SERSERIES DATA ANALYSIS FOR ECOLOGISTS AND CLIMATOLOGISTS USING R; MODEL BASED MULMULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R; ADVANCES IN DNA TAXONOMY USIUSING R; GENETIC DATA ANALYSIS USING R; APPLIED BAYESIAN MODELLING FOR ECOECOLOGISTS AND EPIDEMIOLOGISTSOliver HookerPR~Statistics Link to comment Share on other sites More sharing options...
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