Department of Chemical and Pharmaceutical Sciences, University of Ferrara, Via Fossato di Mortara 17, 44121, Ferrara, Italy.
Department of Chemical and Pharmaceutical Sciences, University of Ferrara, Via Fossato di Mortara 17, 44121, Ferrara, Italy.
Mar Environ Res. 2019 Feb;144:84-91. doi: 10.1016/j.marenvres.2019.01.001. Epub 2019 Jan 3.
Taxonomic Sufficiency (TS), the use of coarser taxonomic resolution in monitoring plans, has been receiving increasing attention in last years. A comprehensive dataset of macrobenthos from 18 Italian lagoons in a range of different latitude, typology, salinity and surface area, was analysed in order to test the efficiency of TS, in terms of correlation between patterns at level of species and patterns resulting from different levels of taxonomic aggregation. First, TS was applied on a range of univariate indices, providing complementary information on macrobenthic community, in order to test the efficiency, in a contest of different taxonomic composition, and different number of lower taxa belonging to the same higher taxon in each lagoon. Then, TS was applied on multivariate analyses, in order to test whether the efficiency changes between two different scales: local (comparison of sites nested within each lagoon) and regional (comparison among lagoons), and with different data transformation. The patterns resulting from univariate indices and multivariate analyses, at both local and regional scales, were retained till family level, despite the different levels of taxonomic composition and different number of lower taxa belonging to the same higher taxon of different lagoons. Nevertheless, the correlation values among matrices and the effect of data transformation differed between regional and local scales. Our results support the efficiency of TS until family level, but at the same time underline the need of scale- and region-specific baseline knowledge prior application of TS in lagoons.
分类充足性(TS),即在监测计划中使用更粗糙的分类分辨率,近年来受到越来越多的关注。本研究分析了来自意大利 18 个不同纬度、类型、盐度和面积的泻湖的大型底栖动物综合数据集,以测试 TS 在物种水平模式与不同分类聚合水平产生的模式之间相关性方面的效率。首先,TS 应用于一系列单变量指数,提供有关大型底栖动物群落的补充信息,以测试在不同分类组成和每个泻湖中属于同一较高分类群的较低分类群数量不同的情况下,TS 的效率。然后,TS 应用于多变量分析,以测试效率在两个不同尺度之间的变化:局部(比较每个泻湖内嵌套的站点)和区域(比较泻湖之间),以及不同的数据转换。尽管不同泻湖的分类组成和属于同一较高分类群的较低分类群数量不同,但在局部和区域尺度上,单变量指数和多变量分析得出的模式都保留到科水平。然而,矩阵之间的相关值和数据转换的影响在区域和局部尺度之间存在差异。我们的研究结果支持 TS 一直到科水平的效率,但同时也强调了在 TS 应用于泻湖之前,需要针对特定区域的尺度和区域进行基线知识的具体研究。