You are here:
MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.
Citation:
Nash, M S. AND D J. Chaloud. MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA. U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-02/091 (NTIS PB2003-106619), 2003.
Impact/Purpose:
The primary objectives of this research are to:
Develop methodologies so that landscape indicator values generated from different sensors on different dates (but in the same areas) are comparable; differences in metric values result from landscape changes and not differences in the sensors;
Quantify relationships between landscape metrics generated from wall-to-wall spatial data and (1) specific parameters related to water resource conditions in different environmental settings across the US, including but not limited to nutrients, sediment, and benthic communities, and (2) multi-species habitat suitability;
Develop and validate multivariate models based on quantification studies;
Develop GIS/model assessment protocols and tools to characterize risk of nutrient and sediment TMDL exceedence;
Complete an initial draft (potentially web based) of a national landscape condition assessment.
This research directly supports long-term goals established in ORDs multiyear plans related to GPRA Goal 2 (Water) and GPRA Goal 4 (Healthy Communities and Ecosystems), although funding for this task comes from Goal 4. Relative to the GRPA Goal 2 multiyear plan, this research is intended to "provide tools to assess and diagnose impairment in aquatic systems and the sources of associated stressors." Relative to the Goal 4 Multiyear Plan this research is intended to (1) provide states and tribes with an ability to assess the condition of waterbodies in a scientifically defensible and representative way, while allowing for aggregation and assessment of trends at multiple scales, (2) assist Federal, State and Local managers in diagnosing the probable cause and forecasting future conditions in a scientifically defensible manner to protect and restore ecosystems, and (3) provide Federal, State and Local managers with a scientifically defensible way to assess current and future ecological conditions, and probable causes of impairments, and a way to evaluate alternative future management scenarios.
Description:
Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical (classes, levels). For example, relating nitrogen, phosphorous and fecal coliform to presence/absence of urban development, farm, soil types, geological formations, etc, (nitrogen + phosphorous + fecal coliform = type of farm, urban development, geology, soil, ...). In analysis of variance (ANOVA), a dependent (response) variable is related to many independent variables that are categorical. For example, determining the response of an ant species to grazing level (severe, medium, low) in an area (ant abundance = grazing levels). In multiple discriminant analysis the dependent variable (Y) is categorical (groups or classes) and related to the independent variables (x's). For example, presence/absence of amphibians in an area relates to many environmental variables (pres/abs = percent bedrock substrate cover + water depth + percent vegetation cover + ...). In multiple regression the dependent variable (Y) is related to many independent variables (x's). For example nitrogen loading relates to landscape metrics such as percent forest, percent crops, percent of wetiand, percent of urban development. In canonical correlation, two sets of variables are related and these variables may or may not be categorical. So it is a generalized multivariate statistical technique in respect to that described above, and is directly related to principal components-type factor analytic models. In canonical analysis method, a number of composite associations between sets of multiple dependent and independent variables are performed. Consequently, a number of independent canonical functions that maximize the correlation between the linear composites of sets of dependent and independent variables are developed. The main goal of the canonical correlation analysis is to develop these linear composites (canonical variate), derive a set of weights for each variate, thereby explaining the nature of relationships that exist between the sets of response and predictor variables that is measured by the relative contribution of each variable to the canonical functions (relationships) that exist. The results of applying canonical correlation is a measure of the strength of the relationship between two sets of multiple variables. This measure is expressed as a canonical correlation coefficient (r) between the two sets.