Applications of spatial statistical network models to stream data
Applications of spatial statistical network models to stream data
复制标题
空间统计网络模型在流数据中的应用
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
P. Monestiez
中科院分区：
文献类型：
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作者：
D. Isaak;E. Peterson;J. V. Ver Hoef;S. Wenger;Jeffrey A. Falke;C. Torgersen;Colin D. Sowder;E. Steel;M. Fortin;Chris E. Jordan;A. Ruesch;Nicholas A. Som;P. Monestiez
溪流和河流承载着地球生物多样性的很大一部分，并为人类提供重要的生态系统服务。有关河流资源状况和趋势的准确信息对于有效保护和管理至关重要。大多数应用于流网络测量数据的统计技术都是为地面应用而开发的，并未针对流进行优化。一类新的空间统计模型基于流网络的有效协方差结构，通过应用适当的分布（例如高斯分布、二项分布、泊松分布），可以与许多常见类型的流数据（例如水质属性、栖息地条件、生物调查）一起使用。空间统计网络模型考虑了测量之间的空间自相关（即非独立性），这使得它们能够应用于具有聚集测量位置的数据库。许多领域都存在大量流数据，空间统计分析可用于开发新颖的见解、改进未采样地点的预测，并帮助以相对较低的成本设计有效的监测策略。我们回顾了空间自相关主题及其对统计推断的影响，展示了空间统计与常见研究和管理问题相关的流数据集的使用，并讨论了流网络上空间统计的其他应用和发展潜力。用于实现空间统计网络模型的免费软件已经开发出来，可以使用许多流数据库实现自定义应用程序。
Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream data (e.g., water quality attributes, habitat conditions, biological surveys) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson). The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered measurement locations. Large amounts of stream data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.
影响因子：
11.4
作者：
Money, Eric S.;Carter, Gail P.;Serre, Marc L.
通讯作者：
Serre, Marc L.
影响因子：
12.8
作者：
Money, Eric;Carter, Gail P.;Serre, Marc L.
通讯作者：
Serre, Marc L.