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Multivariate Condition Assessment of Watersheds with Linked Micromaps
Author(s) -
McManus Michael G.,
Pond Gregory J.,
Reynolds Lou,
Griffith Michael B.
Publication year - 2016
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12399
Subject(s) - environmental science , habitat , biotic component , watershed , water quality , abiotic component , principal component analysis , hydrology (agriculture) , ecology , biology , geology , statistics , mathematics , geotechnical engineering , machine learning , computer science
A challenge for statewide stream monitoring is visualizing the spatial and statistical characteristics of such data to compare the biotic condition of watersheds and relate that condition to watershed‐level estimates of instream variables. We used linked micromaps on stream survey data of 25 subbasins (766‐5,982 km 2 ) for biotic condition, nine water quality, and two habitat variables. Subbasin biotic condition was negatively correlated with conductivity, magnesium and sulfate concentrations, and weakly positively correlated with habitat scores of sedimentation and embeddedness, with higher scores indicating better habitat. Positive spatial autocorrelation was detected among the subbasins in both habitat variables, iron concentration, pH , and exceedances of fecal coliform criteria as shown in linked micromaps. A spatial principal components analysis reduced the 11 environmental variables to two principal axes. The first axis synthesized a gradient of water quality and habitat scores among the subbasins. Subbasin biotic condition regressed on first axis subbasin scores had a significant, negative slope and accounted for 55% of the variation. Subbasins in degraded biotic condition had elevated conductivities and ion concentrations in northern and southern subbasins, and low habitat scores in western subbasins. Through linked micromaps, we compared the biotic condition among subbasins and identified stressors prevalent among subbasins that affected biotic condition.