Comparative analysis of subsampling methods for large mosquito samples

• Main Authors: Linda Jaworski, Stephanie Jansen, Wolf Peter Pfitzner, Matthias Beck, Norbert Becker, Jonas Schmidt-Chanasit, Ellen Kiel, Renke Lühken
• Format: Article
• Language: English
• Published: BMC 2019-07-01
• Series: Parasites & Vectors
• Subjects: Mosquito surveillance; Large mosquito samples; Subsampling
• Online Access: http://link.springer.com/article/10.1186/s13071-019-3606-5

Abstract 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.