Analysis

How to run basic distance sampling analyses in R

Import data, create a candidate model set, and learn about Information Theory

In this module we focus on two main aspects of distance sampling:

  1. How to get line transect data into R and run a basic distance sampling model, and
  2. The ideas behind using Information Theory to choose between models

Table of contents

Learning objectives

What you will learn in this module
By the end of this module you will be able to do the following

Set up R

Prepare your RStudio project
Install and load the unmarked package

Manage project files

Organise data and scripts into self-contained projects
Use the here package to retrieve datasets so code runs smoothly for you and your collaborators

A drawing of a Chinese water deer

Case study and dataset

Download our example dataset, or use your own
Work with Chinese water deer sightings to learn distance sampling analysis

Prepare your data

Import, inspect and manipulate your sightings
Get your data into the correct format for running a basic distance sampling analysis

Run a distance model

Create a null model that assumes density and detectability are stable across your study
Run your first distance sampling model and calculate density and detection probability

Truncate observations

Remove distant sightings to make modelling easier
Far away animals contribute little useful information for estimating density, and complicate our analysis

Models and reality

What is the connection between hypotheses, models and reality?
Models are mathematical representations of hypotheses, which are possible explanations for real-world observations

Information theory

Compare hypotheses with Akaike’s Information Criterion (AIC)
Information theory allows us to evaluate which of our models is closest to reality

Compare models

Understand how dAIC values allow you to compare models
Use Information Theory to compare hypotheses about the shape of the detection function

Assignment

Demonstrate your learning and earn a badge!
Submit work that showcases your new skills and a reflection on what you’re learning, plus feedback to others

A sketch of a confused cartoon tiger

Summary and reflection

A brief review of this module
Take a minute to reflect on what you’ve just learnt

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