A macrophyte bioassessment approach linking taxon-specific tolerance and abundance in north temperate lakes

Mikulyuk, Alison; Barton, Martha; Hauxwell, Jennifer; Hein, Catherine L.; Kujawa, Ellen Ruth; Minahan, Kristi; Nault, Michelle; Oele, Daniel L.; Wagner, Kelly I.

doi:10.1016/j.jenvman.2017.05.012

Cited by 8 publications

(1 citation statement)

References 47 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Visualization and Importance Ratio Analysis of Multivariate Polynomial Regression Ecological Models Based on River Hydromorphology and Water Quality

Shah

Jagupilla

Vaccari

et al. 2021

Water

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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.

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