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Rastetter, E.B., King, A.W., Cosby, B.J., Hornberger, G.M., O’Neill, R.V. and Hobbie, J.E. (1992) Aggregating Fine-Scale Ecological Knowledge to Model Coarser-Scale Attributes of Ecosystems. Ecological Applications, 2, 55-70.
https://doi.org/10.2307/1941889

has been cited by the following article:

• TITLE: Comparison of Geographically Weighted Regression of Benthic Substrate Modeling Accuracy on Large and Small Wadeable Streams

  AUTHORS: Ken R. Sheehan, Stuart A. Welsh

  KEYWORDS: Stream Habitat Modeling, Geographically Weighted Regression, Spatial Scale, Habitat Interpolation, Geographic Information System

  JOURNAL NAME: Journal of Geographic Information System, Vol.13 No.2, April 1, 2021

  ABSTRACT: Aquatic habitat assessments encompass large and small wadeable streams which vary from many meters wide to ephemeral. Differences in stream sizes within or across watersheds, however, may lead to incompatibility of data at varying spatial scales. Specifically, issues caused by moving between scales on large and small streams are not typically addressed by many forms of statistical analysis, making the comparison of large (>30 m wetted width) and small stream (2 values of large and small stream streams. Results also provided a much needed method for comparison of large and small wadeable streams. Our results have merit for aquatic resource managers, because they demonstrate ability to spatially model and compare substrate on large and small streams. Using depth to guide substrate modeling by geographically weighted regression has a variety of applications which may help manage, monitor stream health, and interpret substrate change over time.