Evaluating the Relationships between Riparian Land Cover Characteristics and Biological Integrity of Streams Using Random Forest Algorithms

Abstract

The relationships between land cover characteristics in riparian areas and the biological integrity of rivers and streams are critical in riparian area management decision-making. This study aims to evaluate such relationships using the Trophic Diatom Index (TDI), Benthic Macroinvertebrate Index (BMI), Fish Assessment Index (FAI), and random forest regression, which can capture nonlinear and complex relationships with limited training datasets. Our results indicate that the proportions of land cover types in riparian areas, including urban, agricultural, and forested areas, have greater impacts on the biological communities in streams than those offered by land cover spatial patterns. The proportion of forests in riparian areas has the greatest influence on the biological integrity of streams. Partial dependence plots indicate that the biological integrity of streams gradually improves until the proportion of riparian forest areas reach about 60%; it rapidly decreases until riparian urban areas reach 25%, and declines significantly when the riparian agricultural area ranges from 20% to 40%. Overall, this study highlights the importance of riparian forests in the planning, restoration, and management of streams, and suggests that partial dependence plots may serve to provide insightful quantitative criteria for defining specific objectives that managers and decision-makers can use to improve stream conditions.

An Evaluation of Indices of Biotic Integrity for Algal and BMI Assemblages in Streams of the Los Angeles Region

“…2019; Park et al. 2021). Here, we evaluate the performances of the D_ASCI, H_ASCI, compared to the CSCI, as indices of biological water quality in the Los Angeles region.…”

“…Random forest was chosen as our modeling technique due to prior evidence of its low sensitivity to skewed data, relatively low risk of overfitting to data, and robust performance with large numbers of variables (Evans et al 2011). It has also been found to be useful in assessing the response of assemblages of BMIs to changes in the abiotic environment (Maloney et al 2009;Waite et al 2010;Desrosiers et al 2019;Park et al 2021). Here, we evaluate the performances of the D_ASCI, H_ASCI, compared to the CSCI, as indices of biological water quality in the Los Angeles region.…”

Effect of Land Use on Stream Water Quality and Biological Conditions in Multi-Scale Watersheds

“…Runoff with high concentrations of pollutants from industrial facilities or sewage treatment plants also acts as a stressor for aquatic organisms. The influx of these artificial pollutants destabilizes the physical and chemical runoff processes and disrupts the stream ecosystem [6][7][8]. For example, although nutrients can benefit aquatic organisms, heavy metals can have negative effects, and the combined effects of these stressors can disrupt ecosystem mechanisms [9].…”

“…Recently, ecosystem research has used machine learning models to replace traditional statistical models that were previously built as description-driven limited models [24]. Machine learning, a branch of artificial intelligence, effectively overcomes the limitations of data-dependent bivariate and multivariate statistical methods, enabling predictiondriven models to estimate highly predictive models [8,25]. Unlike the general linear model, which traditionally predicts variables, tree-based random forest (RF) models make no assumptions about data distribution.…”

“…Another advantage of these RF models is the possibility of revealing complex nonlinear relations between land cover characteristics and stream water quality. Park et al [8] found that these models accurately depict the complex nonlinear relation between landscape characteristics and stream water quality. Further benefits include the accuracy of machine learning models tested on new datasets and their application in predicting characteristics of aquatic ecosystems, such as the stream water quality.…”