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

By L. Thomas, S. T. Buckland, E. Rexstad, Jeffrey L. Laake, S. Strindberg, S. Hedley, Jonathan R. B. Bishop, Tiago A. Marques, K. Burnham. 2009

2,208 citations recorded by the source corpus.

Summary 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.

Subfields: Ecology, Statistics and Probability

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Citations in the corpus

Defaunation and biomass collapse of mammals in the largest Atlantic forest remnant (2017, methodology): “The densities of species were estimated using the Distance 6.0 Software (Thomas et al., 2010).”
Rise of the generalists: evidence for climate driven homogenization in avian communities (2012, methodology): “For each species, we fitted half-normal distributions to data 133 from the first two distance bands only (see Newson et al., 2008) using the Mark-Recapture 134 Distance Sampling (MRDS) package (Thomas et al., 2010) for R (R Development Core Team, 135 2009).”
Energy Development Affects Populations of Sagebrush Songbirds in Wyoming (2011, methodology): “We used Program DISTANCE (Thomas et al. 2010) to fit detection-probability functions for species with sufficient detections at each study area.”
An update to the methods in Endangered Species Research 2011 paper "Estimating North Pacific right whale Eubalaena japonica density using passive acoustic cue counting" (2011, methodology): “The software Distance (Thomas et al. 2010) was used to fit these models to data.”
A simple method for estimating the effective detection distance of camera traps (2017, methodology): “The fitting of detection probability functions to distance data can be done with several software packages such as DISTANCE (Thomas et al. 2010) or the R package mrds (Laake et al. 2015).”
Rare Species Support Vulnerable Functions in High-Diversity Ecosystems (2013, methodology): “Reef fish were sampled along 50 m underwater visual transects using a distance sampling method [49].”
Long-term trends in carnivore abundance using distance sampling in Serengeti National Park, Tanzania (2011, methodology): “Calculations were performed in Distance 6.0 Release 2 (Thomas et al. 2010), except where noted.”
High Bird Species Diversity in Structurally Heterogeneous Farmland in Western Kenya (2012, methodology): “Density estimates based on distance sampling account for different detection probabilities in different habitats by fitting a detection function for the relationship between detection and distance from the observer (Thomas et al. 2010).”
Distribution, abundance, and individual movements of Indo-Pacific humpback dolphins (Sousa chinensis) in the Pearl River Estuary, China (2010, methodology): “The following formulae were used to estimate density, abundance, and their associated coefficient of variation with the computer program DISTANCE Version 3.5 (Thomas et al. 2010): ˆ ˆn f 0 E sŽ .”
Quantifying the sensitivity of camera traps: an adapted distance sampling approach (2011, background): “If logistic mixture models are not required, models can also readily be fitted in programDISTANCE (Thomas et al. 2010).”
Cetacean abundance and distribution in European Atlantic shelf waters to inform conservation and management (2013, methodology): “Analyses were undertaken using software DISTANCE (Thomas et al., 2010).”
Altered activity patterns and reduced abundance of native mammals in sites with feral dogs in the high Andes (2016, methodology): “When dogs were observed in groups, estimation of distance to the geometric center was difficult, so perpendicular distance to all individuals detected was recorded, and existence of the group was ignored (Buckland et al., 2010; Thomas et al., 2010).”
Estimating Population Density of Amazonian Titi Monkeys (Callicebus discolor) via Playback Point Counts (2011, methodology): “0 (Thomas et al. 2010) was used to estimate population density via statistical modeling of the generalized ‘detection function’ g(r), which estimates the probability of an object being detected as a function of its distance r from the sample point.”
Modelling changes in species’ abundance in response to projected climate change (2012, methodology): “To control for detectability, we used a distance-sampling approach and fitted half normal distributions to count data (early and late visit data) for each species (Buckland et al., 2001) using the ‘mrds’ package (Thomas et al., 2010) for R (R Development Core Team, 2009).”
Distance sampling and the challenge of monitoring butterfly populations (2011, positive): “To test whether this approach compromised our conclusions, we repeated our analysis on the factors associated with detectability (Question 3) using the multiple covariate distance sampling (MCDS) engine (Thomas et al. 2010).”