A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.

Estimation of pest density is a basic requirement for integrated pest management in agriculture and forestry, and efficiency in density estimation is a common goal. Sequential sampling techniques promise efficient sampling, but their application can involve cumbersome mathematics and/or intensive wa...

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Autores principales:	R Drew Carleton, Stephen B Heard, Peter J Silk
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/09ba3115f1054c8f9d01bffe4cd0cbff
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spelling	oai:doaj.org-article:09ba3115f1054c8f9d01bffe4cd0cbff2021-11-18T08:40:43ZA simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.1932-620310.1371/journal.pone.0082618https://doaj.org/article/09ba3115f1054c8f9d01bffe4cd0cbff2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24376556/?tool=EBIhttps://doaj.org/toc/1932-6203Estimation of pest density is a basic requirement for integrated pest management in agriculture and forestry, and efficiency in density estimation is a common goal. Sequential sampling techniques promise efficient sampling, but their application can involve cumbersome mathematics and/or intensive warm-up sampling when pests have complex within- or between-site distributions. We provide tools for assessing the efficiency of sequential sampling and of alternative, simpler sampling plans, using computer simulation with "pre-sampling" data. We illustrate our approach using data for balsam gall midge (Paradiplosis tumifex) attack in Christmas tree farms. Paradiplosis tumifex proved recalcitrant to sequential sampling techniques. Midge distributions could not be fit by a common negative binomial distribution across sites. Local parameterization, using warm-up samples to estimate the clumping parameter k for each site, performed poorly: k estimates were unreliable even for samples of n ∼ 100 trees. These methods were further confounded by significant within-site spatial autocorrelation. Much simpler sampling schemes, involving random or belt-transect sampling to preset sample sizes, were effective and efficient for P. tumifex. Sampling via belt transects (through the longest dimension of a stand) was the most efficient, with sample means converging on true mean density for sample sizes of n ∼ 25-40 trees. Pre-sampling and simulation techniques provide a simple method for assessing sampling strategies for estimating insect infestation. We suspect that many pests will resemble P. tumifex in challenging the assumptions of sequential sampling methods. Our software will allow practitioners to optimize sampling strategies before they are brought to real-world applications, while potentially avoiding the need for the cumbersome calculations required for sequential sampling methods.R Drew CarletonStephen B HeardPeter J SilkPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e82618 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q R Drew Carleton Stephen B Heard Peter J Silk A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.
description	Estimation of pest density is a basic requirement for integrated pest management in agriculture and forestry, and efficiency in density estimation is a common goal. Sequential sampling techniques promise efficient sampling, but their application can involve cumbersome mathematics and/or intensive warm-up sampling when pests have complex within- or between-site distributions. We provide tools for assessing the efficiency of sequential sampling and of alternative, simpler sampling plans, using computer simulation with "pre-sampling" data. We illustrate our approach using data for balsam gall midge (Paradiplosis tumifex) attack in Christmas tree farms. Paradiplosis tumifex proved recalcitrant to sequential sampling techniques. Midge distributions could not be fit by a common negative binomial distribution across sites. Local parameterization, using warm-up samples to estimate the clumping parameter k for each site, performed poorly: k estimates were unreliable even for samples of n ∼ 100 trees. These methods were further confounded by significant within-site spatial autocorrelation. Much simpler sampling schemes, involving random or belt-transect sampling to preset sample sizes, were effective and efficient for P. tumifex. Sampling via belt transects (through the longest dimension of a stand) was the most efficient, with sample means converging on true mean density for sample sizes of n ∼ 25-40 trees. Pre-sampling and simulation techniques provide a simple method for assessing sampling strategies for estimating insect infestation. We suspect that many pests will resemble P. tumifex in challenging the assumptions of sequential sampling methods. Our software will allow practitioners to optimize sampling strategies before they are brought to real-world applications, while potentially avoiding the need for the cumbersome calculations required for sequential sampling methods.
format	article
author	R Drew Carleton Stephen B Heard Peter J Silk
author_facet	R Drew Carleton Stephen B Heard Peter J Silk
author_sort	R Drew Carleton
title	A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.
title_short	A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.
title_full	A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.
title_fullStr	A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.
title_full_unstemmed	A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.
title_sort	simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/09ba3115f1054c8f9d01bffe4cd0cbff
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A simulation approach to assessing sampling strategies for insect pests: an example with the balsam gall midge.

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