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Advancing evaluation of bioassessment methods: A reply to Liu and Cao

机译:推进生物评估方法的评估:对刘和曹的答复

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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 MI 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). (c) 2018 Elsevier B.V. All rights reserved.
机译:有关由美国环境保护署进行的跨大陆调查的河流,溪流和湖泊中的硅藻使用硅藻开发多指标指数(MMI)的系列论文共三篇。史蒂文森等。 (2013年)使用2007年国家湖泊评估中的地表沉积物硅藻数据开发了针对MMI的国家级特定地点模型,以解释地点之间状况的自然变化。 Liu和Stevenson(2017)还使用2007年湖泊数据通过按生态区域或类型(通过硅藻物种组成的相似性定义的自然相似类型的湖泊)对地点进行分组,并使用针对具体地点的度量模型(SSMM)来调整MMI的绩效。网站之间的自然变异。 Tang等。 (2016年)使用来自2008-2009年国家河流和溪流评估的底栖硅藻数据,按生态区域和类型学开发了SSMM和MMI。所有这三项研究表明,SSMM通过考虑站点之间的自然差异来提高硅藻MMI的性能。没有一项研究提供一致的证据,表明按类型分组的站点比按生态区域分组的站点产生更好的MMI性能。 Liu和Cao(2018)批评了Tang等人。 (2016年)关于使用均值和标准误评估站点组规模的MMI计算方法相对性能的论文,但是,他们的批评是错误的。实际上,唐等人。 (2016年)仅使用手段总结和报告了本文正文中MMI计算方法的相对性能。 Tang等。 (2016年)适当地使用了非参数秩和方法来评估针对不同区域组的多个MMI计算对于生态区域(n = 9)和类型学(n = 7)站点组是相同的概率。 Liu和Stevenson(2017)使用相同的非参数方法来测试硅藻MMI。 Liu和Cao(2018)的担忧可以通过在国家和站点组规模上区分用于测试和评估MMI计算方法的目标和方法来解决。 Tang等。 (2016)并未汇总跨站点组的数据以在全国范围内测试MMI性能,因为它们遵循的是标准EPA方法,即为每个站点组开发单独的MMI。总之,Liu和Cao(2018)在Tang等人中误解了MI评估。 (2016)并没有为此工作增加任何新信息,因为在Liu和Stevenson(2017)中讨论了他们提出的所有担忧。 (c)2018 Elsevier B.V.保留所有权利。

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