Predicting fish community structure in streams is a challenge considering the strong dynamics of these environments. In this study, we tested whether using a fish dataset obtained in a previous time was relevant to predict fish community structure in a subsequent time. We also tested whether temporal beta diversity of fish communities was correlated with environmental variability, stream size and order. To test these hypotheses, we collected data on fish communities, environmental and spatial variables from 15 streams in the Rio das Mortes Basin (Mato Grosso State, Brazil) in two consecutive drought periods (in 2016 and 2017). The gradients in fish richness and abundance were correlated between years. The results of a variation partitioning analysis indicated that the fish community structure in 2016 was the main explanatory matrix of the fish community structure in 2017 (when compared to environmental and spatial variables). A variation partitioning analysis, based only on environmental and spatial variables, showed a much higher residual variation. We did not detect significant relationships between fish temporal beta diversity and our explanatory variables. Our results indicate that our predictive power may be substantially increased by using data on past communities as explanatory variables. This is a viable analytical strategy because long-term studies are becoming more frequent. Temporal autocorrelation analyses of community data can also be useful to evaluate priority effects. In addition, these analyses can help plan biomonitoring programs. The second part of the results indicates, however, that our ability to predict temporal beta diversity is still limited.

中文翻译：

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与前一年的群落结构相比，当前的环境条件是鱼类群落结构的弱预测指标

考虑到这些环境的强大动态，预测河流中的鱼类群落结构是一项挑战。在这项研究中，我们测试了使用先前获得的鱼类数据集是否与预测随后一段时间的鱼类群落结构相关。我们还测试了鱼类群落的时间β多样性是否与环境变异性，溪流大小和次序相关。为了检验这些假设，我们在连续两个干旱时期（2016年和2017年）从里约das Mortes盆地（巴西马托格罗索州）的15条溪流中收集了鱼类群落，环境和空间变量的数据。鱼类丰富度和丰度的梯度与年份之间相关。变异分区分析的结果表明，2016年的鱼类群落结构是2017年鱼类群落结构的主要解释矩阵（与环境和空间变量相比）。仅基于环境和空间变量的变异分区分析显示出更高的残留变异。我们没有发现鱼类的时间β多样性与我们的解释变量之间的显着关系。我们的结果表明，通过使用过去社区的数据作为解释变量，我们的预测能力可能会大大提高。这是一种可行的分析策略，因为长期研究变得越来越频繁。社区数据的时间自相关分析也可以用于评估优先级影响。此外，这些分析可以帮助计划生物监测计划。结果的第二部分表明，我们预测时间β多样性的能力仍然有限。