The efficiency of biodiversity assessments and biomonitoring studies is commonly challenged by limitations in taxonomic identification and quantification approaches. In this study, we assessed the effects of different taxonomic and numerical resolutions on a range of community structure metrics in invertebrate compositional data sets from six regions distributed across North and South America. We specifically assessed the degree of similarity in the metrics (richness, equitability, beta diversity, heterogeneity in community composition and congruence) for data sets identified to a coarse resolution (usually family level) and the finest taxonomic resolution practical (usually genus level, sometimes species or morphospecies) and by presence-absence and relative abundance numerical resolutions. Spearman correlations showed highly significant and positive associations between univariate metrics (richness and equitability) calculated for coarse- and finest-resolution datasets. Procrustes analysis detected significant congruence between composition datasets. Higher correlation coefficients were found for datasets with the same numerical resolutions regardless of the taxonomic level (about 90%), while the correlations for comparisons across numerical resolutions were consistently lower. Our findings indicate that family-level resolution can be used as a surrogate of finer taxonomic resolutions to calculate a range of biodiversity metrics commonly used to describe invertebrate community structure patterns in New World freshwater wetlands without significant loss of information. However, conclusions on biodiversity patterns derived from datasets with different numerical resolutions should be critically considered in studies on wetland invertebrates.

生物多样性评估和生物监测研究的效率通常受到生物分类鉴定和量化方法的局限性的挑战。在这项研究中，我们评估了北美和南美六个地区无脊椎动物组成数据集中不同分类和数值分辨率对一系列群落结构指标的影响。我们针对评估为粗略分辨率（通常是家庭级别）和最佳分类标准（通常是属级别）的数据集，专门评估了指标（丰富性，公平性，β多样性，社区组成的异质性和一致性）的相似程度。物种或形态物种），并根据存在与否和相对丰度进行数值解析。Spearman相关性显示了为最高分辨率和最高分辨率数据集计算的单变量指标（丰富度和公平性）之间的高度显着正相关。前倾分析检测到组成数据集之间的显着一致性。不论分类水平如何（约90％），具有相同数值分辨率的数据集都具有较高的相关系数，而跨数值分辨率进行比较的相关性始终较低。我们的发现表明，家庭一级的分辨率可以用作更好的生物分类分辨率的替代品，以计算一系列生物多样性指标，这些指标通常用于描述新世界淡水湿地中的无脊椎动物群落结构模式，而不会造成重大信息损失。然而，