Streams and rivers provide essential habitat for many freshwater and terrestrial organisms, but this habitat is frequently fragmented by human-induced alterations, such as dams or near-stream land use. Moreover, freshwater organisms are sensitive to changes in water temperature, which may make them particularly vulnerable to alterations associated with elevated temperatures and global warming. The ability to accurately predict patterns in chemicals, fish abundance, and temperature within streams and to understand the ecological processes that drive these patterns is critical if these environments are to be sustainably managed. New models using spatial statistics in stream networks can account for the unique spatial configuration, connectivity, flow volume, and flow direction in a stream network. These models have practical applications for ecological research and the monitoring of physical, chemical, and biological stream characteristics. For example, a spatial statistical approach can be used to identify and quantify patterns of habitat at multiple scales, which may provide additional information about ecosystem structure and function. It may also be used as part of broad-scale monitoring programs, where the number of observations is often limited by money, but we can make predictions, with estimates of uncertainty, at every location within the stream network. The goals of our proposed working group are to 1) identify the most pressing needs in terms of analytical capabilities (i.e., what would be most useful for informing science and management), with possible extensions to include space-time models, generalized linear mixed models, computing for massive datasets, and others as identified by the working group, 2) assess the current state of software and its functionality and determine whether it is sufficient to meet those needs, and 3) analyze a large, nationally important, multivariate stream dataset collected across the Northwestern (NW) United States (US) to gain ecological insights, evaluate methods, and demonstrate new spatial statistical modeling capabilities.
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