The influence of macroinvertebrate abundance on the assessment of freshwater quality in The Netherlands
The influence of macroinvertebrate abundance on the assessment of freshwater quality in The Netherlands
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DOI：
10.3897/mbmg.2.26744
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发表时间：
2018-01-01
影响因子：
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通讯作者：
van der Hoorn, Berry B.
中科院分区：
文献类型：
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作者：
Beentjes, Kevin K.;Speksnijder, Arjen G. C. L.;van der Hoorn, Berry B.
使用分子工具检测和识别无脊椎动物物种，能够为水质评估制定更容易标准化的生物元素清单，因为它避免了物种识别中的人为偏见和错误。然而，目前的生态质量比（EQR）评估方法往往依赖于丰度数据。将元条形码序列数据翻译成生物量或样本丰度已被证明是困难的，因为由于引物错配导致的PCR扩增偏倚通常提供了读段丰度的偏斜比例。虽然在以前的研究中已经提出了一些潜在的解决方案，但我们研究了EQR评估中丰度数据的必要性。在这项研究中，我们使用的历史监测数据，从自然（湖泊，河流和溪流）和人工（沟渠和运河）水体评估的影响，物种丰度的EQR评分的大型无脊椎动物在水框架指令（WFD）监测计划的荷兰。通过删除所有的丰度数据从分类观察，我们模拟存在/缺席的监测，EQR计算根据传统方法。我们的研究结果表明，基于丰度和存在/不存在的EQR之间有很强的相关性。EQR分数一般较高，没有丰度（75.8%的所有样品），这导致9.1%的样品被分配到一个更高的质量类。在两种情况下，大多数样品（89.7%）被分配到相同的质量类别。这些结果是有价值的纳入存在/不存在的metabarcoding数据到水质评估方法，潜在地消除了需要将metabarcoding数据转化为生物量或绝对标本计数EQR评估。
The use of molecular tools for the detection and identification of invertebrate species enables the development of more easily standardisable inventories of biological elements for water quality assessments, as it circumvents human-based bias and errors in species identifications. Current Ecological Quality Ratio (EQR) assessments methods, however, often rely on abundance data. Translating metabarcoding sequence data into biomass or specimen abundances has proven difficult, as PCR amplification bias due to primer mismatching often provides skewed proportions of read abundances. While some potential solutions have been proposed in previous research, we instead looked at the necessity of abundance data in EQR assessments. In this study, we used historical monitoring data from natural (lakes, rivers and streams) and artificial (ditches and canals) water bodies to assess the impact of species abundances on the EQR scores for macroinvertebrates in the Water Framework Directive (WFD) monitoring programme of The Netherlands. By removing all the abundance data from the taxon observations, we simulated presence/absence-based monitoring, for which EQRs were calculated according to traditional methods. Our results showed a strong correlation between abundance-based and presence/absence-based EQRs. EQR scores were generally higher without abundances (75.8% of all samples), which resulted in 9.1% of samples being assigned to a higher quality class. The majority of the samples (89.7%) were assigned to the same quality class in both cases. These results are valuable for the incorporation of presence/absence metabarcoding data into water quality assessment methodology, potentially eliminating the need to translate metabarcoding data into biomass or absolute specimen counts for EQR assessments.