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Applications of spatial statistical network models to stream data
Author(s) -
Isaak Daniel J.,
Peterson Erin E.,
Ver Hoef Jay M.,
Wenger Seth J.,
Falke Jeffrey A.,
Torgersen Christian E.,
Sowder Colin,
Steel E. Ashley,
Fortin MarieJosee,
Jordan Chris E.,
Ruesch Aaron S.,
Som Nicholas,
Monestiez Pascal
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.413
H-Index - 24
ISSN - 2049-1948
DOI - 10.1002/wat2.1023
Subject(s) - spatial analysis , computer science , data mining , statistical inference , statistical model , inference , streams , data stream mining , poisson distribution , statistics , remote sensing , geography , machine learning , artificial intelligence , mathematics , computer network
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. This article is categorized under: Water and Life > Nature of Freshwater Ecosystems