Improving estimates of bird density using multiple covariate distance sampling

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211 Citations (Scopus)


Inferences based on counts adjusted for delectability represent a marked improvement over unadjusted counts, which provide no information about true population density and rely on untestable and unrealistic assumptions about constant delectability for inferring differences in density over time or space. Distance sampling is a widely used method to estimate delectability and therefore density. In the standard method, we model the probability of detecting a bird as a function of distance alone. Here, we describe methods that allow us to model probability of detection as a function of additional covariates-an approach available in DISTANCE, version 5.0 (Thomas et al. 2005) but still not widely applied. The main use of these methods is to increase the reliability of density estimates made on subsets of the whole data (e.g., estimates for different habitats, treatments, periods, or species), to increase precision of density estimates or to allow inferences about the covariates themselves. We present a case study of the use of multiple covariates in an analysis of a point-transect survey of Hawaii Amakihi (Hemignathus virens).

Original languageEnglish
Pages (from-to)1229-1243
Number of pages15
JournalThe Auk
Issue number4
Publication statusPublished - Oct 2007


  • covariates
  • delectability
  • detection function
  • distance sampling
  • line transects
  • point transects


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