Department of Integrative Biology, Michigan State University, East Lansing, MI 48824, USA.
Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China.
Sci Total Environ. 2018 Dec 15;645:895-900. doi: 10.1016/j.scitotenv.2018.07.129. Epub 2018 Jul 24.
A series of three papers was written about the development of multimetric indices (MMIs) using diatoms in rivers, streams and lakes for transcontinental surveys conducted by the United States Environmental Protection Agency. Stevenson et al. (2013) used the surface sediment diatom data from the 2007 National Lake Assessment to develop national scale site specific models for MMIs to account for natural variation in condition among sites. Liu and Stevenson (2017) also used the 2007 lakes data to evaluate performance of MMIs by grouping sites by ecoregions or typologies (naturally similar types of lakes defined by similarity in diatom species composition) with site specific metric models (SSMMs) that adjust metrics for natural variability among sites. Tang et al. (2016) used benthic diatom data from the 2008-2009 National River and Stream Assessment to develop SSMMs and MMIs by ecoregion and typology. All three studies showed that SSMMs improved performance of diatom MMIs by accounting for natural variation among sites. None of the studies provided consistent evidence that grouping sites by typologies produced better MMI performance than grouping sites by ecoregions. Liu and Cao (2018) criticized the Tang et al. (2016) paper for using means and standard errors to evaluate relative performance of MMI calculation methods at the site group scale, however, their criticism is incorrect. Actually, Tang et al. (2016) only used means to summarize and report relative performance of MMI calculation methods in the body of the paper. Tang et al. (2016) appropriately used non-parametric rank sum approaches to evaluate the probability that the multiple MMI calculations for separate site groups were the same for ecoregion (n = 9) and typology (n = 7) site groups. Liu and Stevenson (2017) used this same non-parametric approach for tests of lake diatom MMIs. Liu and Cao's (2018) concerns can be addressed by distinguishing between the goals and methods used for testing and evaluation of MMI calculation methods at the national and site-group scales. Tang et al. (2016) did not aggregate data across site groups to test MMI performance at the national scale because they were following standard EPA methods that develop separate MMIs for each site group. In conclusion, Liu and Cao (2018) misunderstood the MMI evaluation in Tang et al. (2016) and added no new information to this body of work, because all the concerns they raised were discussed in Liu and Stevenson (2017).
这一系列的三篇论文是关于使用河流、溪流和湖泊中的硅藻开发多指标指数（MMI）的，这些论文是由美国环境保护署进行的跨国调查。Stevenson 等人（2013）使用了 2007 年国家湖泊评估的表层沉积物硅藻数据，为 MMI 开发了国家尺度的特定地点模型，以解释各地点之间条件的自然变化。Liu 和 Stevenson（2017）还使用 2007 年湖泊数据，通过按生态区或类型（通过硅藻物种组成的相似性定义的自然相似的湖泊类型）对站点进行分组，并用特定于站点的度量模型（SSMM）对 MMI 的性能进行评估，该模型可调整站点之间自然变异性的度量值。Tang 等人（2016）使用 2008-2009 年国家河流和溪流评估的底栖硅藻数据，通过生态区和类型开发 SSMM 和 MMI。这三项研究都表明，SSMM 通过解释站点之间的自然变化，提高了硅藻 MMI 的性能。没有一项研究提供一致的证据表明，按类型对站点进行分组比按生态区对站点进行分组能产生更好的 MMI 性能。Liu 和 Cao（2018）批评 Tang 等人（2016）的论文使用均值和标准误差来评估站点组尺度上 MMI 计算方法的相对性能，但他们的批评是不正确的。实际上，Tang 等人（2016）仅使用均值来总结和报告论文中 MMI 计算方法的相对性能。Tang 等人（2016）适当地使用非参数秩和方法来评估生态区（n=9）和类型（n=7）站点组的多个站点组的多个 MMI 计算结果相同的概率。Liu 和 Stevenson（2017）对湖泊硅藻 MMI 也使用了这种相同的非参数方法。Liu 和 Cao（2018）的担忧可以通过区分国家和站点组尺度上 MMI 计算方法的测试和评估的目标和方法来解决。Tang 等人（2016）没有将数据汇总到站点组中，以测试全国范围内的 MMI 性能，因为他们遵循的是为每个站点组开发单独的 MMI 的标准 EPA 方法。总之，Liu 和 Cao（2018）误解了 Tang 等人（2016）中的 MMI 评估，并且没有为这项工作增加新的信息，因为他们提出的所有担忧都在 Liu 和 Stevenson（2017）中进行了讨论。
