Joint Modeling of Distances and Times in Point-Count Surveys

dc.contributor.author Dixon, Philip
dc.contributor.author Martin-Schwarze, Adam
dc.contributor.author Niemi, Jarad
dc.contributor.author Dixon, Philip
dc.contributor.author Niemi, Jarad
dc.contributor.department Statistics
dc.date 2021-03-18T07:50:15.000
dc.date.accessioned 2021-04-30T12:17:56Z
dc.date.available 2021-04-30T12:17:56Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2021
dc.date.issued 2021-01-01
dc.description.abstract <p>Removal and distance modeling are two common methods to adjust counts for imperfect detection in point-count surveys. Several recent articles have formulated models to combine them into a distance-removal framework. We observe that these models fall into two groups building from different assumptions about the joint distribution of observed distances and first times to detection. One approach assumes the joint distribution results from a Poisson process (PP). The other assumes an independent joint (IJ) distribution with its joint density being the product of its marginal densities. We compose an IJ+PP model that more flexibly models the joint distribution and accommodates both existing approaches as special cases. The IJ+PP model matches the bias and coverage of the true model for data simulated from either PP or IJ models. In contrast, PP models underestimate abundance from IJ simulations, while IJ models overestimate abundance from PP simulations. We apply all three models to surveys of golden-crowned sparrows in Alaska. Only the IJ+PP model reasonably fits the joint distribution of observed distances and first times to detection. Model choice affects estimates of abundance and detection but has little impact on the magnitude of estimated covariate effects on availability and perceptibility.</p>
dc.description.comments <p>This article is published as Martin-Schwarze, Adam, Jarad Niemi, and Philip Dixon. "Joint Modeling of Distances and Times in Point-Count Surveys." <em>Journal of Agricultural, Biological and Environmental Statistics</em> (2021). doi:<a href="https://doi.org/10.1007/s13253-021-00437-3">10.1007/s13253-021-00437-3</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/319/
dc.identifier.articleid 1321
dc.identifier.contextkey 22088494
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/319
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/105174
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/319/2021_Niemi_JointModeling.pdf|||Fri Jan 14 23:33:05 UTC 2022
dc.source.uri 10.1007/s13253-021-00437-3
dc.subject.disciplines Design of Experiments and Sample Surveys
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords abundance estimation
dc.subject.keywords detectability
dc.subject.keywords distance sampling
dc.subject.keywords distance-removal modeling
dc.subject.keywords perceptibility
dc.subject.keywords removal sampling
dc.title Joint Modeling of Distances and Times in Point-Count Surveys
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 7b3eb8d2-a569-4aba-87a1-5d9c2d99fade
relation.isAuthorOfPublication 31b412ec-d498-4926-901e-2cb5c2b5a31d
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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