DOI: 10.13020/28r6-bq38
title: Watershed-based Stressors for the Great Lakes Basin

description: The Watershed-based Stressors for the Great Lakes Basin dataset includes component and aggregated measures of environmental stress to coastal ecosystems from watersheds of the Great Lakes Basin. Stressors include the amount of agricultural and developed land use, as well as road and population density. These summaries are based on a set of 5971 watersheds that cover the US and Canadian Great Lakes basin, derived using methods from Hollenhorst et al. (2007). Indices presented in this dataset include SumRel (Host et al. 2011) and the more recent combined Agriculture and Development - AgDev index (Host et al. 2019). These were developed as part of the Great Lakes Environmental Indicators II (GLEI-II) project, funded through the Great Lakes Restoration Initiative and used to quantify the response of biota (birds, fish, macroinvertebrates, diatoms and wetland vegetation) to varying degrees of watershed stress (Kovalenko et al. 2014). As of 2015, a more recent version of watersheds has been created by the Great Lakes Aquatic Habitat Framework and stressors recalculated based on those watersheds.
description: The Watershed-based Stressors for the Great Lakes Basin dataset includes component and aggregated measures of environmental stress to coastal ecosystems from watersheds of the Great Lakes Basin. Stressors include the amount of agricultural and developed land use, as well as road and population density. These summaries are based on a set of 5971 watersheds that cover the US and Canadian Great Lakes basin, derived using methods from Hollenhorst et al. (2007). Indices presented in this dataset include SumRel (Host et al. 2011) and the more recent combined Agriculture and Development - AgDev index (Host et al. 2019). A brief description of the AgDev "Euclidean Distance" stressor integration, with R code, is here: 

https://github.com/NRRI/stressor_lib/tree/master/euc_distance

Use agdevbasn in preference to sumrel. For historical reference, for SumRel: we evaluated a number of normalizing transformations for each variable, including log, ln, and arcsine transformations. The use of high-resolution watersheds resulted in a large number of zeros (i.e. non-occurrence of the stressor) for many of the variables. The best results were obtained using a log10 transformation of non-zero values. Each of the five variables data values (x) were transformed to log10 (x), using the minimum non-zero value of x to replace zero values. These transformed (x') values were then standardized, (x'-m)/s, with m and s being the mean and standard-deviation for all x', respectively. These standardized values (x'') were then normalized, (x''-min)/(max-min), with min and max being the minimum and maximum for all x'', respectively. Finally the five x'' values for each variable in each watershed were summed and the summed values normalized again to give a single number - SumRel - for each watershed. SumRel ranges from 0.0-1.0, with 1.0 representing the maximum composite stress within a geographic coverage of interest. Note that this design allows stressor scores to be calculated for any given spatial extent - from local watersheds to an ecoregion, lake, or basin.