Objectives

This is an introduction to using the DISTANCE software package for the analysis of line transect data. Download and installation details for DISTANCE are here.

The main role of DISTANCE in dung density estimation is to fit a detection function to the data, which consist of distances from the line transect to the dung piles detected. The estimated detection probability is then combined with the encounter rate (number of dung piles detected per unit transect length) to calculate a density for dung. DISTANCE can combine this estimate with dung production rate and disappearance time to give an estimate of elephant density with appropriate confidence intervals and CV.

You should be familiar with the concepts of distance sampling before looking at this unit. DISTANCE uses maximum likelihood estimators and AIC: check the units "Frogs in ponds - maximum likelihood estimators" and "More frogs in ponds - AIC and likelihood" if necessary.

More details of estimating animal density from surveys of signs are here.

Working through the analysis

In 2001, WCS Indonesia Program estimated the elephant population of Bukit Barisan Selatan National Park (BBSNP) in Sumatra, Indonesia, from dung surveys (Hedges et al, 2005).

The field research comprised three components:

• dung production rate estimation based on observations of 12 captive elephants ranging freely in the nearby Way Kambas National Park;
   
• dung disappearance time based on monitoring 1302 dung piles in BBSNP for 18 months prior to the survey of dung density; and
   
• dung pile density estimated by line transect surveys.

Here we will focus on the line transect surveys.

Download the file containing the line transect data: "BBSNP_dung_distances.csv".

Download the lab guide "BBSN_dung_DISTANCE.pdf". You will probably want to print out the lab guide and have it next to your computer while you work through the instructions.

Work through the lab guide before going on.

Summary

DISTANCE enables us to carry out a very sophisticated analysis without worrying about the computations involved. Nevertheless, you do need to understand the principles involved and you need to find an optimum model on the basis of the software's output. Simply loading the data into DISTANCE and running the default analysis will certainly give you an answer, but it is unlikely to be optimal.

With surveys of dung (or other signs) it is quite easy to ensure that the assumptions underpinning distance analysis are met:

1. objects on the transect line are detected with certainty;
2. objects are detected at their original location;
3. distances are measured exactly.

Distance sampling is therefore an appropriate method for dung density estimation.