Inconsistency in taxonomic identification and analyst bias impede the effective use of diatom data in regional and national stream and lake surveys. In this study, we evaluated the effect of existing protocols and a revised protocol on the precision of diatom species counts. The revised protocol adjusts four elements of sample preparation, taxon identification and enumeration, and quality control (QC). We used six independent data sets to assess the effect of the adjustments on analytical outcomes. The first data set was produced by three laboratories with a total of five analysts following established protocols (Charles et al., Protocols for the analysis of algal samples collected as part of the U.S. Geological Survey National Water‐Quality Assessment, 2002) or their slight variations. The remaining data sets were produced by one to three laboratories with a total of two to three analysts following a revised protocol. The revised protocol included the following modifications: (1) development of coordinated precount voucher floras based on morphological operational taxonomic units, (2) random assignment of samples to analysts, (3) postcount identification and documentation of taxa (as opposed to an approach in which analysts assign names while they enumerate), and (4) increased use of QC samples. The revised protocol reduced taxonomic bias, as measured by reduction in analyst signal, and improved similarity among QC samples. Reduced taxonomic bias improves the performance of biological assessments, facilitates transparency across studies, and refines estimates of diatom species distributions.

中文翻译：

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减少硅藻物种数据中的分类偏差

分类学识别和分析师偏见的不一致阻碍了硅藻数据在区域和国家河流和湖泊调查中的有效利用。在这项研究中，我们评估了现有协议和修订后的协议对硅藻物种计数精度的影响。修订后的协议调整了样品制备，分类单元识别和枚举以及质量控制（QC）的四个要素。我们使用六个独立的数据集来评估调整对分析结果的影响。第一个数据集是由三个实验室按照既定规程（Charles等人，作为《美国地质调查局国家水质评估》的一部分，对收集的藻类样品进行分析的规程，2002年）建立的，总共有五名分析人员提供。变化。其余数据集由一到三个实验室按照修订后的协议生成，总共有两到三名分析师。修订后的协议包括以下修改：（1）基于形态学操作分类单位开发协调的计票凭证菌群；（2）将样品随机分配给分析人员；（3）记数后的识别和分类单位的记录（与之相反的是分析人员在枚举时分配名称），以及（4）增加使用质量控制样本。修订后的协议减少了分类偏差（通过分析人员信号的减少来衡量），并改善了QC样品之间的相似性。减少分类学偏见可提高生物学评估的性能，促进研究的透明度，并改善硅藻物种分布的估计。