Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.

Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of in...

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Autores principales:	Mohammad Salehi, David R Smith
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/84a567a58ef14a899a5e585b4b0476a6
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spelling	oai:doaj.org-article:84a567a58ef14a899a5e585b4b0476a62021-12-02T20:14:59ZAdaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.1932-620310.1371/journal.pone.0255256https://doaj.org/article/84a567a58ef14a899a5e585b4b0476a62021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255256https://doaj.org/toc/1932-6203Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.Mohammad SalehiDavid R SmithPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255256 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammad Salehi David R Smith Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.
description	Sampling rare and clustered populations is challenging because of the effort required to find rare units. Heuristically, a practitioner would prefer to discontinue sampling in areas where rare units of interest are apparently extremely sparse or absent. We take advantage of the characteristics of inverse sampling to adaptively inform practitioners when it is efficient to move on to sample new areas. We introduce Adaptive Two-stage Inverse Sampling (ATIS), which is designed to leave a selected area after observation of an a priori number of only non-rare units and to continue sampling in the area when rare units are observed. ATIS is efficient in many cases and yields more rare units than conventional sampling for a rare and clustered population. We derive unbiased estimators of population total and variance. We also introduce an easy-to-compute estimator, which is nearly as efficient as the unbiased estimator. A simulation study on a rare plant population of buttercups (Ranunculus) shows that ATIS even with the easy-to-compute estimator is more efficient than its conventional sampling counterparts and is more efficient than Two-stage Adaptive Cluster Sampling (TACS) for small and moderate final sample sizes. Additional simulations reveal that ATIS is efficient for binary data (e.g., presence or absence) whereas TACS is inefficient for binary data. The overall results indicate that ATIS is consistently efficient compared to conventional sampling and to adaptive cluster sampling in some important cases.
format	article
author	Mohammad Salehi David R Smith
author_facet	Mohammad Salehi David R Smith
author_sort	Mohammad Salehi
title	Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.
title_short	Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.
title_full	Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.
title_fullStr	Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.
title_full_unstemmed	Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.
title_sort	adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/84a567a58ef14a899a5e585b4b0476a6
work_keys_str_mv	AT mohammadsalehi adaptivetwostageinversesamplingdesigntoestimatedensityabundanceandoccupancyofrareandclusteredpopulations AT davidrsmith adaptivetwostageinversesamplingdesigntoestimatedensityabundanceandoccupancyofrareandclusteredpopulations
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Adaptive two-stage inverse sampling design to estimate density, abundance, and occupancy of rare and clustered populations.

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