[ECOLOG-L] ESA 2016: The mgcv package as a one-stop-shop for fitting non-linear ecological models (WK2)
We will be giving a one day course on using the popular R package mgcv to build complex (and simple!) ecological models. Our aim is to take participants with an understanding of the glm() function in R to the point where they can fit generalized additive and random effects models using mgcv. We'll be taking data from a number of different ecological sources, showing how mgcv can be used in a variety of modelling situations. Saturday August 6th 8am-5pm 204, Ft Lauderdale Convention Center Instructors: Eric J Pedersen, Gavin Simpson, David L Miller, Noam Ross Course website: https://eric-pedersen.github.io/mgcv-esa-workshop/ Registration is $25. === Detailed workshop description: To address the increase in both quantity and complexity of available data, ecologists require flexible, robust statistical models, as well as software to perform such analyses. This workshop will focus on how a single tool, the mgcv package for the R language, can be used to fit models to data from a wide range of sources. mgcv is one of the most popular packages for modelling non-linear relationships. However, many users do not know how versatile and powerful a tool it can be. This workshop will focus on teaching participants how to use mgcv in a wide variety of situations (including spatio-temporal, zero-inflated, heavy-tailed, time series, and survival data) and advanced use of mgcv (fitting smooth interactions, seasonal effects, spatial effects, Markov random fields and varying-coefficient models). The workshop will give paricipants an understanding of: - practical elements of smoothing theory, with a focus on why they would choose to use different types of smoothers - model checking and selection - the range of modelling possibilities using mgcv. Participants will be assumed to be familiar with the basics of R (loading/manipulating data, functions, and plotting) and regression in R (lm() and glm()). The organizers have extensive practical experience with ecological statistics and modelling using mgcv. Participants should ensure that they are running the latest version of R (http://www.r-project.org), mgcv and ggplot2 (running update.packages() in R) on their laptops before they arrive.
[ECOLOG-L] Reminder: Workshop: Spatial models for distance sampling data using R and ArcGIS, October 2015, Duke University
There are still some seats available for our upcoming workshop: On 27-30 October 2015 Duke Environmental Leadership program in collaboration with the University of St Andrews will offer a workshop on spatial modelling methods for distance sampling line transect data. The workshop will cover: the basics of organizing survey data in ArcGIS using the MGET toolbox, developed at the Marine Geospatial Ecology Lab, Duke; followed by building and evaluating spatially explicit models of abundance (using environmental predictors) in R, using packages developed at the Centre for Research into Ecological and Environmental Modelling, St Andrews. The aim will be to demonstrate an end-to-end workflow for spatial modelling of distance sampling data, and illustrate the underlying theory behind both spatial modelling using generalized additive models and distance sampling (we will focus on the "density surface modelling" approach). The course is intended for graduate students/postdocs/faculty in applied ecology, government/industry scientists and wildlife managers interested in developing spatially explicit models of abundance. Prior knowledge of R and ArcGIS will be highly advantageous, as is a quantitative background. The course will be taught by David L Miller (St Andrews) and Jason J Roberts (Duke), at the Duke campus in Durham, NC, USA. The deadline for registration is 12 October 2015. Detailed information on the course content and registration can be found at https://nicholas.duke.edu/del/distance and http://distancesampling.org/workshops/duke-spatial-2015/. Please feel free to contact me if you have any questions not covered there.
[ECOLOG-L] Workshop: Spatial models for distance sampling data using R and ArcGIS, October 2015, Duke Univ.
On 27-30 October 2015 Duke Environmental Leadership program in collaboration with the University of St Andrews will offer a workshop on spatial modelling methods for distance sampling line transect data. The workshop will cover: the basics of organizing survey data in ArcGIS using the MGET toolbox, developed at the Marine Geospatial Ecology Lab, Duke; followed by building and evaluating spatially explicit models of abundance (using environmental predictors) in R, using packages developed at the Centre for Research into Ecological and Environmental Modelling, St Andrews. The aim will be to demonstrate an end-to-end workflow for spatial modelling of distance sampling data, and illustrate the underlying theory behind both spatial modelling using generalized additive models and distance sampling (we will focus on the "density surface modelling" approach). The course is intended for graduate students/postdocs/faculty in applied ecology, government/industry scientists and wildlife managers interested in developing spatially explicit models of abundance. Prior knowledge of R and ArcGIS will be highly advantageous, as is a quantitative background. The course will be taught by David L Miller (St Andrews), Jason J Roberts (Duke) and Eric Rexstad (St Andrews), at the Duke campus in Durham, NC. The deadline for registration is 12 October 2015. Detailed information on the course content and registration can be found at https://nicholas.duke.edu/del/distance.
[ECOLOG-L] Distance sampling user survey
The Distance Development Team would like to hear how we can improve our software for distance sampling analysis and design. If you have ever conducted any kind of distance sampling analyses we would love to hear from you. We have put together a short questionnaire to find out what developments would be of most use to you. Please submit your responses by March 31st 2015 and forward this e-mail to any others who may find it interesting. Link to the survey: https://docs.google.com/forms/d/1v89Aaju40yBxF5s6GlpXcM7RTW3nmSRKMeTeg6x6qqs/viewform Thanks from the Distance Development Team!
