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The efficiency of Taxonomic Sufficiency for identification of spatial patterns at different scales in transitional waters

机译:分类学充分性在过渡水域不同尺度上识别空间格局的效率

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摘要

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.
机译:近年来,生物分类充足性(TS)在监视计划中使用了较粗的生物分类分辨率,受到了越来越多的关注。为了测试TS的效率,分析了来自18个意大利泻湖的大型底栖动物的完整数据集,这些物种在不同的纬度,类型,盐度和表面积范围内,从而在物种水平上的模式与不同水平下的模式之间具有相关性分类聚集。首先,将TS应用于一系列单变量指标,以提供有关大型底栖动物群落的补充信息,以测试效率,在不同分类学组成的竞赛中,以及每个泻湖中属于同一较高分类群的不同较低分类群的数量。然后,将TS应用于多变量分析,以检验效率是否在两个不同的尺度之间变化:局部(每个泻湖中嵌套的站点的比较)和区域(泻湖之间的比较),以及不同的数据转换。尽管分类结构的不同水平和属于不同泻湖的同一较高分类群的较低分类群的数量不同,但在地方和区域范围内,单变量指数和多元分析所产生的模式一直保持到家庭水平。但是,区域尺度和局部尺度之间矩阵之间的相关值和数据转换的效果有所不同。我们的结果支持直到家庭水平的TS效率,但同时强调了在泻湖中应用TS之前需要特定规模和特定区域的基线知识。

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