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

Modelling relationships between land use and water quality using statistical methods: A critical and applied review

Land use/land cover (LULC) can have significant impacts on water quality and the health of aquatic ecosystems. Consequently, understanding and quantifying the nature of these impacts is essential for the development of effective catchment management strategies. This article provides a critical review of the literature in which the use of statistical methods to model the impacts of LULC on water quality is demonstrated. A survey of these publications, which included hundreds of original research and review articles, revealed several common themes and findings. However, there are also several persistent knowledge gaps, areas of methodological uncertainty, and questions of application that require further study and clarification. These relate primarily to appropriate analytical scales, the significance of landscape configuration, the estimation and application of thresholds, as well as the potentially confounding influence of extraneous variables. Moreover, geographical bias in the published literature means that there is a need for further research in ecologically and climatically disparate regions, including in less developed countries of the Global South. The focus of this article is not to provide a technical review of statistical techniques themselves, but to examine important practical and methodological considerations in their application in modelling the impacts of LULC on water quality.

Effectiveness of landscape indicators for explaining the variability of benthic macro-invertebrates in urban streams

Landscape indicators - measures of land use and land cover - are widely used as proxies for monitoring urban stream conditions, particularly for benthic invertebrates which are often negatively impacted by watershed urbanization. However, multi-scale and nonlinear relationships between benthic macroinvertebrates and landscape configuration derived from fine spatial resolution land cover are not well explored. Here, we developed a series of landscape indicators and assessed their effectiveness in explaining the variability of benthic macroinvertebrate communities in 63 streams across the Greater Vancouver Region in British Columbia, Canada. We asked: 1) How effective are multi-scale landscape indicators in explaining the variability of instream benthic macroinvertebrates? 2) Does the explanatory power of landscape indicators vary at different spatial scales? 3) Do different urban forest classification schemes and their spatial configurations impact the explanatory power of landscape indicators? We developed high spatial resolution (5-m) landscape indicators and evaluated their utility in statistical models explaining taxa richness, instream benthic indices of biological integrity (B-IBI), % Ephemeroptera, Plecoptera, and Trichoptera (EPT), and % Oligochaetes. For all benthic responses, landscape indicators measured at the watershed scale explained 5-25% more variation than riparian-based indicators. Combining indicators mapped at multiple scales further improved the explanatory power of landscape indicators for % EPT and % Oligochaetes, ultimately explaining over 70% of the variability of benthic macro-invertebrates in streams. Distinguishing deciduous and coniferous forest types improved the explanatory power of landscape indicators in a riparian model for B-IBI by 10%. When considering the spatial arrangement of land cover, patch density of forests in the surrounding watersheds of stream explained as much as 47% of the variability in % Oligochaetes. Our results highlight the importance of investigating nonlinear relationships between benthic macroinvertebrates and landscape configuration. This monitoring approach is transferable across cities interested in maintaining the ecological health of urban streams while supporting urban expansion and growth.

Managing landscape patterns at the riparian zone and sub-basin scale is equally important for water quality protection

Widespread attention has been given to understanding the effect of the landscape pattern on river water quality. However, which spatial scale (riparian zone versus sub-basin) has the greater impact on water quality has long been controversial, since the key metrics that affect water quality varied with spatial scale. Thus, quantifying the spatial scale effects of key landscape metrics on water quality is critical to clarifying which scale of landscape pattern is more conducive to water quality conservation. Here, we adopted variation partitioning analysis (VPA) and random forest models to quantify the landscape pattern impact on water quality at northern Erhai Lake during the 2019 rainy season (early, mid, and late), and comprehensively analyze the key landscape metrics on different scales. The results revealed that the riparian zone and sub-basin scale landscape patterns explained similar water quality variations (difference only 0.9%) in the mid (August) and late rainy season (October), but exhibited a large difference (24.1%) during the early rainy season (June). Furthermore, rivers were primarily stressed by nitrogen pollution. Maintaining the Grassland_ED > 27.99 m/ha, Grassland_LPI > 4.19%, Farmland_LSI < 3.2 in the riparian zone, and Construction_ED < 1.69 m/ha, Construction_LSI < 2.46, Farmland_PLADJ < 89.0% at the sub-basin scale could significantly reduce the TN concentration in the stream. Meanwhile, managing of these metrics can effectively prevent rapid increases of TN in rivers. Moreover, due to the low phosphorus concentration in the rivers, none of the landscape metrics significantly explained the variation in TP. This study explored the spatial scale effect of landscape patterns on water quality and revealed the driving factors of nutrient variation. This study will provide a scientific basis for aquatic environmental management in plateau watersheds.