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溪流生态系统生物监测方案的快捷方式:评估浮游植物、周丛生物、浮游动物和鱼类组合的分类学、数值和跨分类群一致性。

Shortcuts for biomonitoring programs of stream ecosystems: Evaluating the taxonomic, numeric, and cross-taxa congruence in phytoplankton, periphyton, zooplankton, and fish assemblages.

机构信息

Câmpus Anápolis de Ciências Exatas e Tecnológicas-Henrique Santillo, Universidade Estadual de Goiás, Anápolis, Goiás, Brazil.

Departamento de Ecologia, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, Goiás, Brazil.

出版信息

PLoS One. 2021 Oct 14;16(10):e0258342. doi: 10.1371/journal.pone.0258342. eCollection 2021.

Abstract

Different biological groups can be used for monitoring aquatic ecosystems because they can respond to variations in the environment. However, the evaluation of different bioindicators may demand multiple financial resources and time, especially when abundance quantification and species-level identification are required. In this study, we evaluated whether taxonomic, numerical resolution and cross-taxa can be used to optimize costs and time for stream biomonitoring in Central Brazil (Cerrado biome). For this, we sampled different biological groups (fish, zooplankton, phytoplankton, and periphyton) in stream stretches distributed in a gradient of land conversion dominated by agriculture and livestock. We used the Mantel and Procrustes analyses to test the association among different taxonomic levels (species to class), the association between incidence and abundance data (numerical resolution), and biological groups. We also assessed the relative effect of local environmental and spatial predictors on different groups. The taxonomic levels and numerical resolutions were strongly correlated in all taxonomic groups (r > 0.70). We found no correlations among biological groups. Different sets of environmental variables were the most important to explain the variability in species composition of distinct biological groups. Thus, we conclude that monitoring the streams in this region using bioindicators is more informative through higher taxonomic levels with occurrence data than abundance. However, different biological groups provide complementary information, reinforcing the need for a multi-taxa approach in biomonitoring.

摘要

不同的生物群落在监测水生生态系统时都可以发挥作用,因为它们可以对环境变化做出响应。然而,评估不同的生物指标可能需要大量的财力和时间,特别是在需要进行丰度量化和物种水平鉴定时。在本研究中,我们评估了在巴西中部(塞拉多生物群)的溪流生物监测中,分类、数量分辨率和跨分类群是否可以用于优化成本和时间。为此,我们在由农业和畜牧业主导的土地转化梯度上,对分布在不同溪流地段的不同生物群(鱼类、浮游动物、浮游植物和周丛生物)进行了采样。我们使用 Mantel 和 Procrustes 分析来检验不同分类水平(从物种到纲)之间的关联、发生率和丰度数据(数量分辨率)之间的关联以及生物群之间的关联。我们还评估了局部环境和空间预测因子对不同群体的相对影响。在所有分类群中,分类水平和数量分辨率之间都存在很强的相关性(r > 0.70)。我们没有发现生物群之间的相关性。不同的环境变量集对于解释不同生物群物种组成的变化具有重要意义。因此,我们得出结论,在该地区使用生物指标监测溪流时,与丰度相比,通过具有发生数据的更高分类水平进行监测更具信息量。然而,不同的生物群提供了互补的信息,强化了在生物监测中采用多分类群方法的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8220/8516258/237c0e0fcd94/pone.0258342.g001.jpg

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