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TWO DIFFERENT MULTIVARIATE SPATIAL ECOLOGY COURSES IN TWO DIFFERENT COUNTRIES IN JUNE AND AUGUST Multivariate analysis of spatial ecological data using R (MASE01) 19 June 2017 - 23 June 2017 Northwest Atlantic Fisheries Centre, Canada http://www.prstatistics.com/course/multivariate-analysis-of-spatial- ecological-data-u Spatial analysis of ecological data using R (SPAE05) 7 August 2017 - 12 August 2017 SCENE, Loch Lomond, Rowardennan, Glasgow, Scotland, G63 0AW, United Kingdom http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r- spae05/ MASE01 OVERVIEW This 5-day course will cover the concepts, methods, and R tools that can be used to analyse spatial data in ecology. The start of the course will cover the basics of linear models and spatial data processing in R and provide a common ground for more advanced techniques encountered later on the course. We will cover spatial statistical techniques for both continuous and discrete data response types. We will put special emphasis on understanding mechanisms using visual tools, and quantifying uncertainty for model parameters and predictions. We will introduce Markov chain Monte Carlo methods for spatial hierarchical models and discuss special topics relevant for study design and conservation and management applications. Modules will consist of introductory lectures, guided computer coding, and exercises for the participants, analysing real data. Module 1. Introduction (types of response and predictor variables), data exploration (summaries, visualization, classification and regression trees [CART]). Module 2. Linear, nonlinear, and multiple regression (use of lm and gam functions in R, visual interpretation, model diagnostics, model selection, generalized additive models [GAM]). Module 3. Generalized linear models (use of glm and gam functions in R, visual interpretation, model diagnostics, model selection, generalized additive models [GAM]). Module 4. Spatial data manipulation in R (raster and vector layers, GIS operations, mapping). Module 5. Statistical models for point processes (homogeneous and non- homogeneous Poisson process, non-parametric measures of autocorrelation). Continuous data on regular and irregular grid points in space Module 6. Kriging and spatial smoothing for continuous data. Module 7. Spatial generalizations of GLM: Markov random fields and hierarchical models. Linear and Generalized linear mixed models in the context of spatial data Module 8: Markov Chain Monte Carlo methods with applications in spatial data Module 9. Movement models, species distribution models, critical habitat delineation. Module 10. Principles of optimal design for regression, effects of sampling design related biases on regression, measurement error in predictor variables. Module 11. Philosophical considerations and other related discussion. SPAE05 OVERVIEW 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. 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; 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. 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 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. Module 7: Analysing transect data, Detection functions for point and line transects, Using covariates in transect models. Afternoon for catch up and/or excursion. 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. 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. email [log in to unmask] for more details or visit the links to book online. Other up-coming courses; 1. ADVANCING IN STATISTICAL MODELLING FOR EVOLUTIONARY BIOLOGISTS AND ECOLOGISTS USING R #ADVR 17th – 21st April 2017, Scotland, Dr. Luc Bussiere, Dr. Ane Timenes Laugen http://www.prstatistics.com/course/advancing-statistical-modelling-using-r- advr06/ 2. CODING, DATA MANAGEMENT AND SHINY APPLICATIONS USING RSTUDIO FOR EVOLUTIONARY BIOLOGISTS AND ECOLOGISTS #CDSR 15th - 19th May, Scotland Dr. Aline Quadros http://www.prstatistics.com/course/coding-data-management-and-shiny- applications-using-rstudio-for-evolutionary-biologists-and-ecologists- cdsr01/ 3. GEOMETRIC MORPHOMETRICS USING R #GMMR 5th – 9th June 2017, Scotland, Prof. Dean Adams, Prof. Michael Collyer, Dr. Antigoni Kaliontzopoulou http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/ 4. MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA #MASE 19th – 23rd June, Canada, Prof. Subhash Lele, Dr. Peter Solymos http://www.prstatistics.com/course/multivariate-analysis-of-spatial- ecological-data-using-r-mase01/ 5. TIME SERIES MODELS FOR ECOLOGISTS USING R (JUNE 2017 #TSME 26th – 30th June, Canada, Dr. Andrew Parnell http://www.prstatistics.com/course/time-series-models-foe-ecologists-tsme01/ 6. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS #BIGB 3rd – 7th July 2017, Scotland, Dr. Nic Blouin, Dr. Ian Misner http://www.prstatistics.com/course/bioinformatics-for-geneticists-and- biologists-bigb02/ 7. META-ANALYSIS IN ECOLOGY, EVOLUTION AND ENVIRONMENTAL SCIENCES #METR01 24th – 28th July, Scotland, Prof. Julia Koricheva, Prof. Elena Kulinskaya http://www.prstatistics.com/course/meta-analysis-in-ecology-evolution-and- environmental-sciences-metr01/ 8. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R #SPAE 7th – 12th August 2017, Scotland, Prof. Jason Matthiopoulos, Dr. James Grecian http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r- spae05/ 9. ECOLOGICAL NICHE MODELLING USING R #ENMR 16th – 20th October 2017, Scotland, Dr. Neftali Sillero http://www.prstatistics.com/course/ecological-niche-modelling-using-r- enmr01/ 10. INTRODUCTION TO BIOINFORMATICS USING LINUX #IBUL 16th – 20th October, Scotland, Dr. Martin Jones http://www.prstatistics.com/course/introduction-to-bioinformatics-using- linux-ibul02/ 11. GENETIC DATA ANALYSIS AND EXPLORATION USING R #GDAR 23rd – 27th October, Wales, Dr. Thibaut Jombart, Zhian Kavar http://www.prstatistics.com/course/genetic-data-analysis-exploration-using- r-gdar03/ 12. STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS USING R #SEMR 23rd – 27th October, Wales, Prof Jarrett Byrnes, Dr. Jon Lefcheck http://www.prstatistics.com/course/structural-equation-modelling-for- ecologists-and-evolutionary-biologists-semr01/ 13. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R #LNDG 6th – 10th November, Wales, Prof. Rodney Dyer http://www.prstatistics.com/course/landscape-genetic-data-analysis-using-r- lndg02/ 14. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS #ABME 20th - 25th November 2017, Scotland, Prof. Jason Matthiopoulos, Dr. Matt Denwood http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists- epidemiologists-abme03/ 15. INTRODUCTION REMOTE SENSING AND GIS APPLICATIONS FOR ECOLOGISTS #IRMS 27th Nov – 1st Dec, Wales, Dr Duccio Rocchini, Dr. Luca Delucchi http://www.prstatistics.com/course/introduction-to-remote-sensing-and-gis- for-ecological-applications-irms01/ 16. INTRODUCTION TO PYTHON FOR BIOLOGISTS #IPYB 27th Nov – 1st Dec, Wales, Dr. Martin Jones http://www.prstatistics.com/course/introduction-to-python-for-biologists- ipyb04/ 17. DATA VISUALISATION AND MANIPULATION USING PYTHON #DVMP 11th – 15th December 2017, Wales, Dr. Martin Jones http://www.prstatistics.com/course/data-visualisation-and-manipulation- using-python-dvmp01/ 18. ADVANCING IN STATISTICAL MODELLING USING R #ADVR 11th – 15th December 2017, Wales, Dr. Luc Bussiere, Dr. Tom Houslay, Dr. Ane Timenes Laugen, http://www.prstatistics.com/course/advancing-statistical-modelling-using-r- advr07/ 19. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING #IBHM 29th Jan – 2nd Feb 2018, Scotland, Dr. Andrew Parnell http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical- modelling-using-r-ibhm02/ 20. ANIMAL MOVEMENT ECOLOGY (February 2018) #ANME ??th - ??th February 2018, Wales, Dr Luca Borger, Dr. John Fieberg 21. AQUATIC TELEMENTRY DATA ANALYSIS USIR R (TBC) #ATDAR ??th - ??th February 2018, Wales, 22. FUNCTIONAL ECOLOGY FROM ORGANISM TO ECOSYSTEM: THEORY AND COMPUTATION #FEER 5th – 9th March 2018, Scotland, Dr. Francesco de Bello, Dr. Lars Götzenberger, Dr. Carlos Carmona http://www.prstatistics.com/course/functional-ecology-from-organism-to- ecosystem-theory-and-computation-feer01/ 23. ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL ECOLOGICAL DATA USING R #MVSP Prof. Pierre Legendre, Dr. Olivier Gauthier - Date and location to be confirmed 24. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR #SIMM Dr. Andrew Parnell, Dr. Andrew Jackson – Date and location to be confirmed 25. NETWORK ANAYLSIS FOR ECOLOGISTS USING R #NTWA Dr. Marco Scotti - Date and location to be confirmed 26. MODEL BASE MULTIVARIATE ANALYSIS OF ABUNDANCE DATA USING R #MBMV0 Prof David Warton - Date and location to be confirmed 27. ADVANCED PYTHON FOR BIOLOGISTS #APYB Dr. Martin Jones - Date and location to be confirmed 28. PHYLOGENETIC DATA ANALYSIS USING R (TBC) #PHYL Dr. Emmanuel Paradis – Date and location to be confirmed Oliver Hooker PhD. PR statistics 2017 publications - Ecosystem size predicts eco-morphological variability in post-glacial diversification. Ecology and Evolution. In press. The physiological costs of prey switching reinforce foraging specialization. Journal of animal ecology. prstatistics.com facebook.com/prstatistics/ twitter.com/PRstatistics groups.google.com/d/forum/pr-statistics-post-course-forum prstatistics.com/organiser/oliver-hooker/ 3/1, 128 Brunswick Street Glasgow G1 1TF +44 (0) 7966500340