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.
Highlights
Wadeable stream habitat is monitored and studied across stream size, from ephemeral to many meters wide, to manage for various aspects of stream ecology including fish population dynamics and species occurrence [1] [2] [3]
Weighted regression (GWR) may provide avenues for efficiency and needed insight into stream habitat data by addressing issues caused by moving between scales
This study examined the ability of Geographically weighted regression (GWR) to consistently model stream substrate on both large and small wadeable streams at an equivalent resolution
Summary
Wadeable stream habitat is monitored and studied across stream size, from ephemeral to many meters wide, to manage for various aspects of stream ecology including fish population dynamics and species occurrence [1] [2] [3]. Because of ties between habitat and population dynamics, wadeable stream assessment and monitoring protocols focus on quantifying key abiotic variables, such as substrate and depth. Their assessments are used to create maps, monitor change, and categorize streams based on the information from those assessments. Weighted regression (GWR) is a spatial modeling technique which may address issues of scale compatibility for important variables in stream habitat models
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