Research@StAndrews
 
The University of St Andrews

Research@StAndrews:FullText >
Mathematics & Statistics (School of) >
Statistics >
Statistics Research >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10023/817
This item has been viewed 51 times in the last year. View Statistics

Files in This Item:

File Description SizeFormat
ThomasJAE2010.pdf251.3 kBAdobe PDFView/Open
Title: Distance software: design and analysis of distance sampling surveys for estimating population size
Authors: Thomas, Len
Buckland, Stephen Terrence
Rexstad, Eric
Laake, J L
Strindberg, S
Hedley, S L
Bishop, J R B
Marques, Tiago Andre Lamas Oliveira
Keywords: distance sampling
line transect sampling
point transect sampling
population density
population abundance
sighting surveys
survey design
wildlife surveys
Issue Date: 2010
Citation: Journal of Applied Ecology 47 (1): 5-14 2010
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 pre-requisite 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: CDS (conventional distance sampling), which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; MCDS (multiple covariate distance sampling), which allows covariates in addition to distance; and MRDS (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 DSM (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 practicing ecologists.
Version: Publisher PDF
URI: http://dx.doi.org/10.1111/j.1365-2664.2009.01737.x
http://hdl.handle.net/10023/817
ISSN: 0021-8901
Type: Journal article
Rights: Published as a Wiley InterScience OnlineOpen research article
Publication Status: Published
Status: Peer reviewed
Appears in Collections:Centre for Research into Ecological & Environmental Modelling (CREEM) Research
Statistics Research



This item is protected by original copyright

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

DSpace Software Copyright © 2002-2012  Duraspace - Feedback
For help contact: Digital-Repository@st-andrews.ac.uk | Copyright for this page belongs to St Andrews University Library | Terms and Conditions (Cookies)