Abstract
Multivariate polynomial regression (MPR) models were developed for five macrophyte indices. MPR models are able to capture complex interactions in the data while being tractable and transparent for further analysis. The performance of the MPR modeling approach was compared to previous work using artificial neural networks. The data were obtained from hydromorphologically modified Polish rivers with a widely varying water quality. The modeled indices were the Macrophyte Index for Rivers (MIR), the Macrophyte Biological Index for Rivers (IBMR), and the River Macrophyte Nutrient Index (RMNI). These indices measure the trophic and ecological status of the rivers. Additionally, two biological diversity indices, species richness (N) and the Simpson index (D), were modeled. The explanatory variables were physico-chemical properties depicting water quality and river hydromorphological status indices. In comparison to artificial neural networks, the MPR models performed similarly in terms of goodness of fit. However, the MPR models had advantages such as model simplicity and ability to be subject to effective visualization of complex nonlinear input–output relationships, as well as facilitating sensitivity analysis using importance ratios to identify effects of individual input variables.
Highlights
Several studies acknowledge the significance of accurately accounting for multiple sources of ambiguity when modeling ecological phenomena [1]
Many countries in Europe have started developing river monitoring systems in their national monitoring programs based on macrophytes [9,10], According to the Water Framework Directive (WFD) [11], assessment of freshwater is based on ecological status consisting of biological indicators, supported by water quality and physical conditions of ecosystems
The significance of macrophytes in biological river assessment is formally acknowledged under the WFD, and macrophytes are an essential element in the monitoring of ecological status and surface water quality
Summary
Several studies acknowledge the significance of accurately accounting for multiple sources of ambiguity when modeling ecological phenomena [1]. Several studies focus on the quality of groundwater and of water bodies such as rivers, watersheds, and coastal environments This plays an important role in determining its impact on public health and the environment [7,8]. Many countries in Europe have started developing river monitoring systems in their national monitoring programs based on macrophytes [9,10], According to the Water Framework Directive (WFD) [11], assessment of freshwater is based on ecological status consisting of biological indicators (fish, macroinvertebrates, phytoplankton, phytobenthos, and macrophytes), supported by water quality and physical conditions of ecosystems. The significance of macrophytes in biological river assessment is formally acknowledged under the WFD, and macrophytes are an essential element in the monitoring of ecological status and surface water quality. The increasing amount of monitoring data creates opportunities for even better understanding of the relationships between different elements of the aquatic ecosystems [16,17]
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