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Authors: Kevin Nichols, Elena Trevino, Calvin Pham, Jane Giamporcaro, Anthony Rambone, Atousa Karimi, Kali Chowdhury
Title: [download]
(1677)
Regression of Algae Biomass over Variables with Disjoint Spatial Support
Reference: Vol. 7, Issue 5, Sep 2015
Submitted 2014-08-04, Accepted 2015-06-18
Type: Article
Abstract:

Algae biomass in California watersheds are impacted by several covariates including atmospheric nitrogen, watershed nutrients, land-use variables and physical-habitat variables. However, with several different agencies collecting and reporting data on both biomass response variables and subsets of all potential predictors, the result is several variables contained within multiple datasets, each compiled with data disjoint both in space and time from the other datasets. In this paper, the authors discuss a spatial statistical technique for forecasting values for each response and predictor over a shared spatial support and then using weighted standardized regression to identify which predictive variables are most important in explaining variability in algae biomass levels. Results will indicate that algae biomass levels are consistently correlated with the following: N03, N0x, Total Nitrogen and some land-use variables, while physical-habitat variables and atmospheric nitrogen are less successful predictors of algae biomass population density.

Paper: [download]
(1677)
Regression of Algae Biomass over Variables with Disjoint Spatial Support
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Code: Commons GNU General Public License License
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