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Functionally redundant communities do not show differences in the main environmental drivers of different diversity metrics

机译:功能冗余社区不会显示不同多样性度量的主要环境驱动程序的差异

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We investigated which environmental variables of floodplain lakes act as potential drivers of fish assemblages and how they explain the variation in taxonomic and functional diversity of the community. We evaluated the taxonomic richness, functional dispersion (the distribution of species abundances with different traits) and redundancy (how similar the species are) of fish communities from six floodplain lakes of the upper Parana River floodplain, evaluating whether they respond to the same set of predictor variables. We predict that the variation in taxonomic richness will be explained by limnological variables that express the characteristics of the water and the functional variation by variables that express the physical structure of the habitat. We sampled limnological and habitat structural variables and fish communities of each floodplain lake in a span of 14 years. Functional diversity was evaluated from six functional traits. The three diversity indices were used as response variables in Generalized Linear Mixed Models (GLMMs), while environmental variables were used as predictor variables. Taxonomic richness was best explained by total phosphorus, water transparency, depth and water level, while functional dispersion was explained by water level. Functional redundancy was explained by the water transparency variable. Our results show that taxonomic and functional diversity metrics may have environmental predictors in common, but the taxonomic richness is more predictable depending on the environmental gradient than the functional diversity. Besides, the floodplain lakes showed high functional redundancy. This may suggest that in functionally redundant communities, the main drivers of different diversity metrics do not differ.
机译:我们调查了洪泛湖湖泊的环境变量,作为鱼群的潜在驱动因素以及他们如何解释社区的分类和功能多样性的变化。我们评估了分类的丰富性,功能性分散(具有不同特征的物种分布)和冗余(种类如何相似的物种)鱼群从上部Parana河洪泛区的六个洪泛区湖泊,评估他们是否回应相同的一套预测变量。我们预测,分类学丰富性的变化将通过表达水的特征和表达栖息地物理结构的变量的变量和功能变化的植物变量来解释。我们在14年的跨度中取样了每个洪水湖泊的林木和栖息地结构变量和鱼群。从六个功能性状评估功能多样性。三个分集指数用作广义线性混合模型(GLMM)中的响应变量,而环境变量用作预测变量。最佳磷,水透明度,深度和水位最佳地解释分类法,水位解释了功能分散。水透明度变量解释了功能冗余。我们的研究结果表明,分类学和功能多样性度量可能具有共同的环境预测因子,但分类学丰富性根据环境梯度比功能多样性更为可预测。此外,洪泛湖湖泊表现出高稳定的冗余。这可能表明,在功能冗余的社区中,不同分集度量的主要驱动程序不会有所不同。

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