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Comparative analysis of subsampling methods for large mosquito samples

BACKGROUND: The analysis of large mosquito samples is expensive and time-consuming, delaying the efficient timing of vector control measurements. Processing a fraction of a sample using a subsampling method can significantly reduce the processing effort. However, a comprehensive evaluation of the re...

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Autores principales: Jaworski, Linda, Jansen, Stephanie, Pfitzner, Wolf Peter, Beck, Matthias, Becker, Norbert, Schmidt-Chanasit, Jonas, Kiel, Ellen, Lühken, Renke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6636137/
https://www.ncbi.nlm.nih.gov/pubmed/31311590
http://dx.doi.org/10.1186/s13071-019-3606-5
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author Jaworski, Linda
Jansen, Stephanie
Pfitzner, Wolf Peter
Beck, Matthias
Becker, Norbert
Schmidt-Chanasit, Jonas
Kiel, Ellen
Lühken, Renke
author_facet Jaworski, Linda
Jansen, Stephanie
Pfitzner, Wolf Peter
Beck, Matthias
Becker, Norbert
Schmidt-Chanasit, Jonas
Kiel, Ellen
Lühken, Renke
author_sort Jaworski, Linda
collection PubMed
description BACKGROUND: The analysis of large mosquito samples is expensive and time-consuming, delaying the efficient timing of vector control measurements. Processing a fraction of a sample using a subsampling method can significantly reduce the processing effort. However, a comprehensive evaluation of the reliability of different subsampling methods is missing. METHODS: A total of 23 large mosquito samples (397–4713 specimens per sample) were compared in order to evaluate five subsampling methods for the estimation of the number of specimens and species: area, volume, weight, selection of 200 random specimens and analyses with an image processing software. Each sample was distributed over a grid paper (21.0 × 29.7 cm; 25 grid cells of 4.2 × 5.9 cm) with 200 randomly distributed points. After taking pictures, mosquito specimens closest to each of the 200 points on the paper were selected. All mosquitoes per grid cell were identified by morphology and transferred to scaled tubes to estimate the volume. Finally, the fresh and dry weights were determined. RESULTS: The estimated number of specimens and species did not differ between the area-, volume- and weight-based method. Subsampling 20% of the sample gave an error rate of approximately 12% for the number of specimens, 6% for the proportion of the most abundant species and between 6–40% for the number of species per sample. The error for the estimated number of specimens using the picture processing software ImageJ gave a similar error rate when analyzing 15–20% of the total sample. By using 200 randomly selected specimens it was possible to give a precise estimation of the proportion of the most abundant species (r = 0.97, P < 0.001), but the number of species per sample was underestimated by 28% on average. Selecting adjacent grid cells instead of sampling randomly chosen grid cells and using dry weight instead of wet weight did not increase the accuracy of estimates. CONCLUSIONS: Different subsampling methods have various advantages and disadvantages. However, the area-based analysis of 20% of the sample is probably the most suitable approach for most kinds of mosquito studies, giving sufficiently precise estimations of the number of specimens and species, which is slightly less laborious compared to the other methods tested. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-019-3606-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-66361372019-07-25 Comparative analysis of subsampling methods for large mosquito samples Jaworski, Linda Jansen, Stephanie Pfitzner, Wolf Peter Beck, Matthias Becker, Norbert Schmidt-Chanasit, Jonas Kiel, Ellen Lühken, Renke Parasit Vectors Research BACKGROUND: The analysis of large mosquito samples is expensive and time-consuming, delaying the efficient timing of vector control measurements. Processing a fraction of a sample using a subsampling method can significantly reduce the processing effort. However, a comprehensive evaluation of the reliability of different subsampling methods is missing. METHODS: A total of 23 large mosquito samples (397–4713 specimens per sample) were compared in order to evaluate five subsampling methods for the estimation of the number of specimens and species: area, volume, weight, selection of 200 random specimens and analyses with an image processing software. Each sample was distributed over a grid paper (21.0 × 29.7 cm; 25 grid cells of 4.2 × 5.9 cm) with 200 randomly distributed points. After taking pictures, mosquito specimens closest to each of the 200 points on the paper were selected. All mosquitoes per grid cell were identified by morphology and transferred to scaled tubes to estimate the volume. Finally, the fresh and dry weights were determined. RESULTS: The estimated number of specimens and species did not differ between the area-, volume- and weight-based method. Subsampling 20% of the sample gave an error rate of approximately 12% for the number of specimens, 6% for the proportion of the most abundant species and between 6–40% for the number of species per sample. The error for the estimated number of specimens using the picture processing software ImageJ gave a similar error rate when analyzing 15–20% of the total sample. By using 200 randomly selected specimens it was possible to give a precise estimation of the proportion of the most abundant species (r = 0.97, P < 0.001), but the number of species per sample was underestimated by 28% on average. Selecting adjacent grid cells instead of sampling randomly chosen grid cells and using dry weight instead of wet weight did not increase the accuracy of estimates. CONCLUSIONS: Different subsampling methods have various advantages and disadvantages. However, the area-based analysis of 20% of the sample is probably the most suitable approach for most kinds of mosquito studies, giving sufficiently precise estimations of the number of specimens and species, which is slightly less laborious compared to the other methods tested. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13071-019-3606-5) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-16 /pmc/articles/PMC6636137/ /pubmed/31311590 http://dx.doi.org/10.1186/s13071-019-3606-5 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Jaworski, Linda
Jansen, Stephanie
Pfitzner, Wolf Peter
Beck, Matthias
Becker, Norbert
Schmidt-Chanasit, Jonas
Kiel, Ellen
Lühken, Renke
Comparative analysis of subsampling methods for large mosquito samples
title Comparative analysis of subsampling methods for large mosquito samples
title_full Comparative analysis of subsampling methods for large mosquito samples
title_fullStr Comparative analysis of subsampling methods for large mosquito samples
title_full_unstemmed Comparative analysis of subsampling methods for large mosquito samples
title_short Comparative analysis of subsampling methods for large mosquito samples
title_sort comparative analysis of subsampling methods for large mosquito samples
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6636137/
https://www.ncbi.nlm.nih.gov/pubmed/31311590
http://dx.doi.org/10.1186/s13071-019-3606-5
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