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USING CONONICAL CORRELATION TO DETECT ASSOCIATION OF LANDSCAPE METRICS WITH WATER BIOLOGICAL AND CHEMICAL PROPERTIES IN SAVANNAH RIVER BASIN
Citation:
Chaloud, D J. AND M S. Nash. USING CONONICAL CORRELATION TO DETECT ASSOCIATION OF LANDSCAPE METRICS WITH WATER BIOLOGICAL AND CHEMICAL PROPERTIES IN SAVANNAH RIVER BASIN. Presented at Above & Beyond 2001, An EPA Remote Sensing Conference, Las Vegas, NV, March 20-21, 2001.
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:
Surface water quality is related to conditions in the surrounding geophysical environment, including soils, landcover, and anthropogenic activities. For example, clearing vegetation exposes soil to increased water/wind erosion, resulting in increased sediment loads to surface waters. Nutrients, from crops and animal feedlots, may be transported to surface waters by runoff. Man's activities, from dam building and road construction to growth of cities. and industrial pollution, can profoundly impact surface water chemical, physical, and biological qualities. A number of statistical methods may be used to analyze and explore relationships among variables. Single- and multiple-regression analysis has frequently been used to relate water nutrient concentrations to selected landscape rnetrics (Jones et. al., In Press; Mehaffey et. al.,In Press). Canonical correlation, however, is better suited to exploring the relationships among two or more distinct data sets to describe their association and connection to the physical environment. In this study, three distinct data sets were used: water chemistry (Chem) from point sites, water biology (Bio) from stream reaches centered around the point sites, and landscape metrics (LS) generated for the drainage areas to the point sites. The Chem and Bio data sets were produced under the Regional Environmental Monitoring and Assessment Program (Remap) and were provided by U.S. EPA Region IV. The strength of the linear relationship between the first canonical variates for the LS&Bio was 0.69, LS&Chem was 0.75 and Bio&Chem was 0.60. Total amount of variance that shared and predicted by data sets of LS&Bio was 60%, LS&Chem was 68%, and Bio&Chem was 61%. Of that total amount, the fitted model of LS&Bio accounted for 78%, LS&Chem accounted for 92% and Bio&Chem accounted for 98%. The landscape-biota model indicated three major contributing variables: the LS variable Slope greater than 3 percent (Slope3), the Bio variable EPT (an indicator of three rnicroinvertebrate genera), and the Bio variable NE-richness (an index of microinvertebrate species richness). Within this model, the LS variable percent row crop was the second highest LS contributor, with a negative relationship to the Bio variables. Slope3 was also the major contributing LS variable in the landscape-chemistry model; the major Chem contributing variable in this model was dissolved oxygen (DO). In the chemistry-biota model, EPT and M -richness were again the major contributing Bio variables, while conductivity and pH were the major contributing Chem variables, with conductivity negatively related to biota. These analyses indicated increased slope (indicating complex topography, generally occurring in the mountainous areas of the Savannah River Basin) is associated with increased microinvertebrate quality and higher DO concentrations, while the percentage of landeover in row crops is associated with increased conductivity and declines in aquatic biota quality.