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Trop Life Sci Res ; 27(1): 115-33, 2016 Feb.
Article in English | MEDLINE | ID: mdl-27019685

ABSTRACT

This study analyses the sampling performance of three benthic sampling tools commonly used to collect freshwater macroinvertebrates. Efficiency of qualitative D-frame and square aquatic nets were compared to a quantitative Surber sampler in tropical Malaysian streams. The abundance and diversity of macroinvertebrates collected using each tool evaluated along with their relative variations (RVs). Each tool was used to sample macroinvertebrates from three streams draining different areas: a vegetable farm, a tea plantation and a forest reserve. High macroinvertebrate diversities were recorded using the square net and Surber sampler at the forested stream site; however, very low species abundance was recorded by the Surber sampler. Relatively large variations in the Surber sampler collections (RVs of 36% and 28%) were observed for the vegetable farm and tea plantation streams, respectively. Of the three sampling methods, the square net was the most efficient, collecting a greater diversity of macroinvertebrate taxa and a greater number of specimens (i.e., abundance) overall, particularly from the vegetable farm and the tea plantation streams (RV<25%). Fewer square net sample passes (<8 samples) were sufficient to perform a biological assessment of water quality, but each sample required a slightly longer processing time (±20 min) compared with those gathered via the other samplers. In conclusion, all three apparatuses were suitable for macroinvertebrate collection in Malaysian streams and gathered assemblages that resulted in the determination of similar biological water quality classes using the Family Biotic Index (FBI) and the Biological Monitoring Working Party (BMWP). However, despite a slightly longer processing time, the square net was more efficient (lowest RV) at collecting samples and more suitable for the collection of macroinvertebrates from deep, fast flowing, wadeable streams with coarse substrates.

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