...
首页> 外文期刊>Aquatic Ecology >Current environmental conditions are weak predictors of fish community structure compared to community structure of the previous year
【24h】

Current environmental conditions are weak predictors of fish community structure compared to community structure of the previous year

机译:与前一年的社区结构相比,当前的环境条件是鱼群结构的弱预测因子

获取原文
获取原文并翻译 | 示例

摘要

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.
机译:预测溪流中的鱼群结构是考虑这些环境的强大动态的挑战。在这项研究中,我们测试了在以前的时间中获得的鱼数据集是否与随后的时间预测鱼群结构。我们还测试了鱼群的时间β多样性与环境变异性,流尺寸和订单相关。为了测试这些假设,我们在连续两次干旱期间收集了在RIO DAS Mortes Basin(Mato Grosso State,Barzil)的15个溪流中的鱼群,环境和空间变量的数据(2016年和2017年)。鱼丰富和丰富的梯度在几年之间相关。变异分区分析的结果表明,2016年的鱼群结构是2017年鱼群结构的主要解释性矩阵(与环境和空间变量相比)。仅基于环境和空间变量的变形分区分析显示了更高的剩余变化。我们没有发现鱼类时间β多样性与我们的解释变量之间的重要关系。我们的结果表明,通过使用过去的社区上的数据作为解释性变量,我们的预测力可能大大增加。这是一种可行的分析策略,因为长期研究变得更加频繁。社区数据的时间自相关分析也可能有助于评估优先效应。此外,这些分析可以帮助计划生物监测程序。然而,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。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号