Adaptive distance sampling

We investigate mechanisms to improve efficiency for line and point transect surveys of clustered populations by combining the distance methods with adaptive sampling. In adaptive sampling, survey effort is increased when areas of high animal density are located, thereby increasing the number of obse...

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Main Author: Pollard, John
Other Authors: Buckland, S. T. (Stephen T.), Hammond, Philip S.
Format: Doctoral or Postdoctoral Thesis
Language:English
Published: University of St Andrews 2018
Subjects:
Online Access:http://hdl.handle.net/10023/15176
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spelling ftstandrewserep:oai:research-repository.st-andrews.ac.uk:10023/15176 2023-07-02T03:32:29+02:00 Adaptive distance sampling Pollard, John Buckland, S. T. (Stephen T.) Hammond, Philip S. 205 p. 2018-07-10T11:27:06Z application/pdf http://hdl.handle.net/10023/15176 en eng University of St Andrews The University of St Andrews http://hdl.handle.net/10023/15176 QA276.6P7 Animal populations--Statistical methods Sampling (Statistics) Thesis Doctoral PhD Doctor of Philosophy 2018 ftstandrewserep 2023-06-13T18:25:59Z We investigate mechanisms to improve efficiency for line and point transect surveys of clustered populations by combining the distance methods with adaptive sampling. In adaptive sampling, survey effort is increased when areas of high animal density are located, thereby increasing the number of observations. We begin by building on existing adaptive sampling techniques, to create both point and line transect adaptive estimators, these are then extended to allow the inclusion of covariates in the detection function estimator. However, the methods are limited, as the total effort required cannot be forecast at the start of a survey, and so a new fixed total effort adaptive approach is developed. A key difference in the new method is that it does not require the calculation of the inclusion probabilities typically used by existing adaptive estimators. The fixed effort method is primarily aimed at line transect sampling, but point transect derivations are also provided. We evaluate the new methodology by computer simulation, and report on surveys of harbour porpoise in the Gulf of Maine, in which the approach was compared with conventional line transect sampling. Line transect simulation results for a clustered population showed up to a 6% improvement in the adaptive density variance estimate over the conventional, whilst when there was no clustering the adaptive estimate was 1% less efficient than the conventional. For the harbour porpoise survey, the adaptive density estimate cvs showed improvements of 8% for individual porpoise density and 14% for school density over the conventional estimates. The primary benefit of the fixed effort method is the potential to improve survey coverage, allowing a survey to complete within a fixed time and effort; an important feature if expensive survey resources are involved, such as an aircraft, crew and observers. Doctoral or Postdoctoral Thesis Harbour porpoise University of St Andrews: Digital Research Repository
institution Open Polar
collection University of St Andrews: Digital Research Repository
op_collection_id ftstandrewserep
language English
topic QA276.6P7
Animal populations--Statistical methods
Sampling (Statistics)
spellingShingle QA276.6P7
Animal populations--Statistical methods
Sampling (Statistics)
Pollard, John
Adaptive distance sampling
topic_facet QA276.6P7
Animal populations--Statistical methods
Sampling (Statistics)
description We investigate mechanisms to improve efficiency for line and point transect surveys of clustered populations by combining the distance methods with adaptive sampling. In adaptive sampling, survey effort is increased when areas of high animal density are located, thereby increasing the number of observations. We begin by building on existing adaptive sampling techniques, to create both point and line transect adaptive estimators, these are then extended to allow the inclusion of covariates in the detection function estimator. However, the methods are limited, as the total effort required cannot be forecast at the start of a survey, and so a new fixed total effort adaptive approach is developed. A key difference in the new method is that it does not require the calculation of the inclusion probabilities typically used by existing adaptive estimators. The fixed effort method is primarily aimed at line transect sampling, but point transect derivations are also provided. We evaluate the new methodology by computer simulation, and report on surveys of harbour porpoise in the Gulf of Maine, in which the approach was compared with conventional line transect sampling. Line transect simulation results for a clustered population showed up to a 6% improvement in the adaptive density variance estimate over the conventional, whilst when there was no clustering the adaptive estimate was 1% less efficient than the conventional. For the harbour porpoise survey, the adaptive density estimate cvs showed improvements of 8% for individual porpoise density and 14% for school density over the conventional estimates. The primary benefit of the fixed effort method is the potential to improve survey coverage, allowing a survey to complete within a fixed time and effort; an important feature if expensive survey resources are involved, such as an aircraft, crew and observers.
author2 Buckland, S. T. (Stephen T.)
Hammond, Philip S.
format Doctoral or Postdoctoral Thesis
author Pollard, John
author_facet Pollard, John
author_sort Pollard, John
title Adaptive distance sampling
title_short Adaptive distance sampling
title_full Adaptive distance sampling
title_fullStr Adaptive distance sampling
title_full_unstemmed Adaptive distance sampling
title_sort adaptive distance sampling
publisher University of St Andrews
publishDate 2018
url http://hdl.handle.net/10023/15176
op_coverage 205 p.
genre Harbour porpoise
genre_facet Harbour porpoise
op_relation http://hdl.handle.net/10023/15176
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