A Unimodal Model for Double Observer Distance Sampling Surveys

PloS One
Earl F Becker, Aaron M Christ

Abstract

Distance sampling is a widely used method to estimate animal population size. Most distance sampling models utilize a monotonically decreasing detection function such as a half-normal. Recent advances in distance sampling modeling allow for the incorporation of covariates into the distance model, and the elimination of the assumption of perfect detection at some fixed distance (usually the transect line) with the use of double-observer models. The assumption of full observer independence in the double-observer model is problematic, but can be addressed by using the point independence assumption which assumes there is one distance, the apex of the detection function, where the 2 observers are assumed independent. Aerially collected distance sampling data can have a unimodal shape and have been successfully modeled with a gamma detection function. Covariates in gamma detection models cause the apex of detection to shift depending upon covariate levels, making this model incompatible with the point independence assumption when using double-observer data. This paper reports a unimodal detection model based on a two-piece normal distribution that allows covariates, has only one apex, and is consistent with the point independence ass...Continue Reading

References

Sep 12, 2001·Biometrics·L Hiby, M B Krishna
Feb 19, 2004·Biometrics·Fernanda F C Marques, Stephen T Buckland
May 13, 2009·Biometrics·Ying Qing Chen
May 13, 2009·Biometrics·Stephen T BucklandDavid L Borchers
Aug 21, 2012·PloS One·Paul B ConnDevin S Johnson

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Citations

Jun 5, 2019·Ecology and Evolution·Earl Becker, Aaron Christ

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Software Mentioned

package mrds
PlosOneMRDSAnalysis
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SkwetnaBlackBearAnalysis
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