Intermediate-level Distance Sampling Workshop

This is the site for the Intermediate-level Distance Sampling Workshop given in St Andrews, 31st July to 4th August 2017.

Course materials

Course description

The first day of the workshop will review fundamental principles of distance sampling, analyses involving conventional distance sampling and survey design. Subsequently, attention will turn to simulation of distance sampling surveys for design purposes, and to survey and analysis methods for dealing with imperfect detection on the trackline (double-observer methods). Slightly more than two days will be devoted to spatial modelling of distance sampling data. A blend of the latest version Distance 7 and the R computing language will be employed. Throughout the workshop, there will be unstructured time, with instructors working with participants on their specific problems.


Day Purpose 0900-1030 1045-1215 1345-1515 1530-1700
Sunday R refresher/tutorial     Start 1400 Practical 1
Monday Detection functions, distance sampling simulation Welcome and review of assumptions Practicals 2 & 3: Analyse simple simulated and awkward data sets Distance sampling simulation: automated survey design Practical 4: Simulation
Tuesday Sperm whale data analysis What is a Density Surface Model (DSM)? Intro to generalized additive models (GAMs) Practicals 5, 6 & 7: Process data, fit detection function, fit GAMs DSM: Model checking
Wednesday Add covariates, produce predictions DSM: Multiple smooths and model selection Practical 8: Fit and check multiple smooth DSMs DSM: Prediction & variance estimation Practicals 9 & 10: DSM: Prediction and variance
Thursday Double platform detection functions and advanced topics DSM wrapup Mark-recapture distance sampling Practical 11: mrds DSM: Modelling advice and advanced topics
Friday Research talks and unstructured time Research talks: acoustics, SCR, inlabru, HMMs, movement Special topics Open Open


Participants are encouraged to bring their own laptops to use during the workshop practicals. R and Distance for Windows will be used.

To ensure your computer is setup correctly prior to the workshop please check the instructions below.

Distance for Windows

Distance can be downloaded from the distance sampling website. Distance 7.1 was released 10 July 2017, if you already have Distance on your computer please ensure you have the latest version.


The course will use R, RStudio and various R packages to be installed from CRAN. The following steps should setup your computer for the workshop:

  1. Install R from the R website
  2. Install RStudio from the RStudio website
  3. Open RStudio and install R packages using the following command (cut and paste into the Editor window and submit):
install.packages(c("mrds", "Distance", "dsm", "DSsim", "ggplot2", "rgdal", "knitr",
                   "plyr", "raster", "reshape2", "viridis", "htmltools",
                   "caTools", "bitops", "rmarkdown", "tweedie", "shapefiles"))

There may be quite a lot of packages downloaded, including many not listed here because the packages listed depend upon many other packages.

Several participants have noted they are unfamiliar with R and RStudio. There are three things that can be done to increase your familiarity.

  • For practice with the R language
  • Experience using the RStudio interface with R
  • Tutorial prior to the distance sampling workshop
    • we will spend Sunday (30 August) afternoon 1400-1700 working through a tutorial that will be available as part of the workshop practical exercises


As a collaboration tool, there exists an online scratchpad. This can be used to share questions, solutions, code snippets, request special topics to be covered on Friday and chat between participants and instructors. Feel free to bookmark this URL and use as you wish:


Participants may be interested in the MGET toolbox for ArcGIS for use in data formatting, importing etc. Information on installing the software can be found here, if you already have ArcGIS installed. A brief tutorial is also available on the MGET website.