Distance software: design and analysis of distance sampling surveys for estimating population size

Abstract

1.Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance.2.We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use.3.Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated.4.A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance.5.All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap.6.Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software.7.Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of-the-art software that implements these methods is described that makes the methods accessible to practising ecologists.

Comparative woodland caribou population surveys in Slate Islands Provincial Park, Ontario

“…Perpendicular distances between caribou locations and transect lines were determined in ArcGIS 9.2 (ESRI, 2006). A caribou population estimate and associated confidence intervals from the FLIR survey were then calculated using Distance 6.0 release 2 (Thomas et al, 2010). The population estimate, assuming 100% sightability along transect lines, was based on a half-normal detection model with simple polynomial adjustment that was chosen by minimisation of Akaike's information criterion (AIC c ) from a variety of hazard rate and half-normal models examined (Buckland et al, 2001).…”

“…Caribou density and associated confidence intervals from the ground transect survey were calculated using Distance 6.0 release 2 (Thomas et al, 2010).…”

“…In the use of models examining sampling covariates where three sampling windows were considered, the Mo, Mt and Mh models, alternate population estimates were calculated (Tables 1, 3 and 4). The Mh, heterogeneity jackknife estimator, 40-85 (Thomas et al, 2010) 71-255 (Thomas et al, 2010) 80-349 (Arnason et al, 1991) 85-122 (White et al,1982, Otis et al, 1978 …”

Estimating densities of large herbivores in tropical forests: Rigorous evaluation of a dung‐based method

“…By combining the detection function estimates with the number of clusters ( n ) and their respective sizes ( C s ), we were able to estimate the abundance parameters of interest: encounter rate ( n/l ; l  = length of sampled transect line), species cluster density ( C D ), and species density (S D ). These parameters of interest were estimated using the DISTANCE software, the most widely used analytical tool for distance sampling of animals (Thomas et al., 2010). …”

“…DISTANCE helps select the detection probability model based on the lowest Akaike information criteria (AIC) value, as well as on other model selection criteria like the p ‐value of Kolmogorov–Smirnov test and the precision of parameter estimates (Buckland et al., 2001; Thomas et al., 2010). This final analytical step, however, was executed only after an initial exploratory analysis that assessed potential assumption violations and identified optimal strip widths to exclude outliers, which in turn helped choose the best‐fit detection probability function for a species.…”