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Net type, tow duration, and day or night sampling effects on the composition of marine zooplankton, derived from metabarcoding

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NIAID Data Ecosystem2026-05-02 收录
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https://www.ncbi.nlm.nih.gov/sra/SRP509440
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DNA metabarcoding requires only a single DNA fragment to detect a species in mixed zooplankton samples, compared to morphology-based methods that rely on the presence of intact specimens. However, metabarcoding protocols have not yet been fully standardized, thus hindering data comparability between studies. To converge on standardized metabarcoding protocols, we used an experimental field-sampling approach to compare the effects of sampling gear (ring, Manta and WP2 nets), day and night (DN) sampling, and tow duration (5, 10 and 15 minute tows) on marine zooplankton composition. High-throughput sequencing of the cytochrome c oxidase subunit I (COI) gene region with different primers and taxonomic assignment of amplicon sequence variants at 97 percent similarity to barcode records were used to identify species. Metabarcoding detected a total of 224 species, of which 92 percent matched prior occurrence records from the region. Malacostraca (crabs, hermit crabs, lobsters, prawns, and shrimps) was the best-represented class (49 percent) followed by Copepoda (21 percent), Actinopterygii (ray-finned fishes; 21 percent), and Gastropoda (9 percent). Species counts ranged from 9 to 61 species per tow, with high proportions of unique species in replicate tows. Mean species counts did not differ significantly between net types, DN samples, or tow durations, respectively. Proportionate representation among taxonomic classes remained within a narrow range, except when sampling deeper habitats with a smaller mesh size. DN samples showed no evidence of daily vertical migration of zooplankton. Consistent inferred species composition across net types, tow duration and DN sampling treatments reflects high detection sensitivity of individual-based sampling, allowing for greater flexibility in planning of zooplankton sampling regimes.
创建时间:
2024-05-25
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