Double-Observer Line Transect Methods: Levels of Independence

Double-observer line transect methods are becoming increasingly widespread, especially for the estimation of marine mammal abundance from aerial and shipboard surveys when detection of animals on the line is uncertain. The resulting data supplement conventional distance sampling data with two-sample...

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Main Authors: Buckland, Stephen T., Laake, Jeffrey L., Borchers, David L.
Format: Text
Language:unknown
Published: DigitalCommons@University of Nebraska - Lincoln 2010
Subjects:
Online Access:https://digitalcommons.unl.edu/usdeptcommercepub/199
https://digitalcommons.unl.edu/context/usdeptcommercepub/article/1185/viewcontent/Laake_BIOMETRICS_2010_Double_observer.pdf
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spelling ftunivnebraskali:oai:digitalcommons.unl.edu:usdeptcommercepub-1185 2023-11-12T04:21:02+01:00 Double-Observer Line Transect Methods: Levels of Independence Buckland, Stephen T. Laake, Jeffrey L. Borchers, David L. 2010-03-01T08:00:00Z application/pdf https://digitalcommons.unl.edu/usdeptcommercepub/199 https://digitalcommons.unl.edu/context/usdeptcommercepub/article/1185/viewcontent/Laake_BIOMETRICS_2010_Double_observer.pdf unknown DigitalCommons@University of Nebraska - Lincoln https://digitalcommons.unl.edu/usdeptcommercepub/199 https://digitalcommons.unl.edu/context/usdeptcommercepub/article/1185/viewcontent/Laake_BIOMETRICS_2010_Double_observer.pdf Publications, Agencies and Staff of the U.S. Department of Commerce Environmental Sciences text 2010 ftunivnebraskali 2023-10-30T09:43:17Z Double-observer line transect methods are becoming increasingly widespread, especially for the estimation of marine mammal abundance from aerial and shipboard surveys when detection of animals on the line is uncertain. The resulting data supplement conventional distance sampling data with two-sample mark–recapture data. Like conventional mark–recapture data, these have inherent problems for estimating abundance in the presence of heterogeneity. Unlike conventional mark–recapture methods, line transect methods use knowledge of the distribution of a covariate, which affects detection probability (namely, distance from the transect line) in inference. This knowledge can be used to diagnose unmodeled heterogeneity in the mark–recapture component of the data. By modeling the covariance in detection probabilities with distance, we show how the estimation problem can be formulated in terms of different levels of independence. At one extreme, full independence is assumed, as in the Petersen estimator (which does not use distance data); at the other extreme, independence only occurs in the limit as detection probability tends to one. Between the two extremes, there is a range of models, including those currently in common use, which have intermediate levels of independence. We show how this framework can be used to provide more reliable analysis of double-observer line transect data. We test the methods by simulation, and by analysis of a dataset for which true abundance is known. We illustrate the approach through analysis of minke whale sightings data from the North Sea and adjacent waters. Text minke whale University of Nebraska-Lincoln: DigitalCommons@UNL Petersen ENVELOPE(-101.250,-101.250,-71.917,-71.917)
institution Open Polar
collection University of Nebraska-Lincoln: DigitalCommons@UNL
op_collection_id ftunivnebraskali
language unknown
topic Environmental Sciences
spellingShingle Environmental Sciences
Buckland, Stephen T.
Laake, Jeffrey L.
Borchers, David L.
Double-Observer Line Transect Methods: Levels of Independence
topic_facet Environmental Sciences
description Double-observer line transect methods are becoming increasingly widespread, especially for the estimation of marine mammal abundance from aerial and shipboard surveys when detection of animals on the line is uncertain. The resulting data supplement conventional distance sampling data with two-sample mark–recapture data. Like conventional mark–recapture data, these have inherent problems for estimating abundance in the presence of heterogeneity. Unlike conventional mark–recapture methods, line transect methods use knowledge of the distribution of a covariate, which affects detection probability (namely, distance from the transect line) in inference. This knowledge can be used to diagnose unmodeled heterogeneity in the mark–recapture component of the data. By modeling the covariance in detection probabilities with distance, we show how the estimation problem can be formulated in terms of different levels of independence. At one extreme, full independence is assumed, as in the Petersen estimator (which does not use distance data); at the other extreme, independence only occurs in the limit as detection probability tends to one. Between the two extremes, there is a range of models, including those currently in common use, which have intermediate levels of independence. We show how this framework can be used to provide more reliable analysis of double-observer line transect data. We test the methods by simulation, and by analysis of a dataset for which true abundance is known. We illustrate the approach through analysis of minke whale sightings data from the North Sea and adjacent waters.
format Text
author Buckland, Stephen T.
Laake, Jeffrey L.
Borchers, David L.
author_facet Buckland, Stephen T.
Laake, Jeffrey L.
Borchers, David L.
author_sort Buckland, Stephen T.
title Double-Observer Line Transect Methods: Levels of Independence
title_short Double-Observer Line Transect Methods: Levels of Independence
title_full Double-Observer Line Transect Methods: Levels of Independence
title_fullStr Double-Observer Line Transect Methods: Levels of Independence
title_full_unstemmed Double-Observer Line Transect Methods: Levels of Independence
title_sort double-observer line transect methods: levels of independence
publisher DigitalCommons@University of Nebraska - Lincoln
publishDate 2010
url https://digitalcommons.unl.edu/usdeptcommercepub/199
https://digitalcommons.unl.edu/context/usdeptcommercepub/article/1185/viewcontent/Laake_BIOMETRICS_2010_Double_observer.pdf
long_lat ENVELOPE(-101.250,-101.250,-71.917,-71.917)
geographic Petersen
geographic_facet Petersen
genre minke whale
genre_facet minke whale
op_source Publications, Agencies and Staff of the U.S. Department of Commerce
op_relation https://digitalcommons.unl.edu/usdeptcommercepub/199
https://digitalcommons.unl.edu/context/usdeptcommercepub/article/1185/viewcontent/Laake_BIOMETRICS_2010_Double_observer.pdf
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