Relationships between Riparian Forest Fragmentation and Biological Indicators of Streams

Interactive impact of landscape composition and configuration on river water quality under different spatial and seasonal scales

Currently, the comprehensive effect of the landscape pattern on river water quality has been widely studied. However, the interactive influences of landscape type, namely composition (COM) and configuration (CON) on water quality variations, as well as the specific landscape driving types affecting water quality variations under different spatial and seasonal scales remain unclear. To further improve the effectiveness of landscape planning and water quality protection, this study collected monthly water samples from the Fengyu River Watershed in southwestern China from 2018 to 2021, the Biota-Environment Matching Analysis (Bioenv) was used to identify key metrics representing landscape COM and CON, respectively. Then, the multiple regression (MLR) and redundancy analysis (RDA) were used to explore the relationship between these landscape metrics and water quality. In addition, this study used a variation partitioning analysis (VPA) to quantify the interactive and independent influence of landscape COM and CON on water quality. Results revealed that construction land and the Shannon's diversity index (SHDI) were the key metrics of landscape COM and CON, respectively, for predicting water pollution concentrations. The interactive contribution was particularly sensitive to seasonal changes in riparian buffer areas (27.66 % to 48.73 %), while it remained relatively stable at the sub-watershed scale (38.22 % to 40.51 %). Moreover, landscape CON had a higher independent contribution to variations on water quality across most spatio-temporal scales. Overall, identifying and managing key landscape type and consequential metrics, matching with the spatio-temporal scale, holds promise for enhancing water quality conservation. Furthermore, this study provides valuable insights into the identification and selection of core landscape metrics.

Estimating thresholds of natural vegetation for the protection of water quality in South African catchments

Many of South Africa's current water quality problems have been attributed to diffuse pollution derived from poorly regulated land use/land cover (LULC) transformations. To mitigate these impacts, the preservation of an adequate amount of natural vegetation within catchment areas is an important management strategy. However, it is not clear how much natural vegetation cover is required to provide adequate levels of protection, nor at which scale(s) this strategy would be most effective. To investigate the possibility of estimating minimum thresholds of natural vegetation required to protect water resources, regression analysis was used to model relationships between water quality (measured using Nemerow's Pollution Index) and metrics of natural vegetation at multiple scales across a sample of sub-catchments located along the western, southern, and south-eastern coast of South Africa. With conspicuous outliers removed, the models were able to explain up to 82 % of the variability in the relationship between land use and water quality. Moreover, a statistically significant, nonlinear, and inverse relationship was found between proportions of natural vegetation cover and pollution levels. This relationship was strongest when measured (1) across the whole catchment and (2) within a 200 m riparian buffer zone. The models further indicated that approximately 80 to 90 % natural vegetation cover was necessary at these scales to maintain water quality at ecologically acceptable levels. Additional nonlinear thresholds estimated using breakpoint analysis suggested that if proportions of natural vegetation fall below 45 % (across the whole catchment) and 60 % (within a 200 m riparian buffer zone) a dramatic increase in pollution levels can be expected. The estimated thresholds are recommended as guidelines that can be used to inform integrated land and water resources management strategies aimed at protecting water quality in the study area. Likewise, the methods described are recommended for the estimation of similar thresholds in other regions.

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.

Regression Tree Analysis for Stream Biological Indicators Considering Spatial Autocorrelation

Multiple studies have been conducted to identify the complex and diverse relationships between stream ecosystems and land cover. However, these studies did not consider spatial dependency inherent from the systemic structure of streams. Therefore, the present study aimed to analyze the relationship between green/urban areas and topographical variables with biological indicators using regression tree analysis, which considered spatial autocorrelation at two different scales. The results of the principal components analysis suggested that the topographical variables exhibited the highest weights among all components, including biological indicators. Moran′s I values verified spatial autocorrelation of biological indicators; additionally, trophic diatom index, benthic macroinvertebrate index, and fish assessment index values were greater than 0.7. The results of spatial autocorrelation analysis suggested that a significant spatial dependency existed between environmental and biological indicators. Regression tree analysis was conducted for each indicator to compensate for the occurrence of autocorrelation; subsequently, the slope in riparian areas was the first criterion of differentiation for biological condition datasets in all regression trees. These findings suggest that considering spatial autocorrelation for statistical analyses of stream ecosystems, riparian proximity, and topographical characteristics for land use planning around the streams is essential to maintain the healthy biological conditions of streams.

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

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.

Identifying Key Watershed Characteristics That Affect the Biological Integrity of Streams in the Han River Watershed, Korea

Understanding the complex human and natural processes that occur in watersheds and stream ecosystems is critical for decision makers and planners to ensure healthy stream ecosystems. This study aims to characterize the Han River watershed in Korea and extract key relationships among watershed attributes and biological indicators of streams using principal component analysis (PCA) and self-organizing maps (SOM). This study integrated watershed attributes and biological indicators of streams to delineate the watershed and stream biological status. Results from PCA strongly suggested that the proportions of watershed and riparian land use are key factors that explain the total variance in the datasets. Forest land in the watershed appeared to be the most significant factor. Furthermore, SOM planes showed that the biological indicators of streams have strong positive relationships with forest land, well-drained soil, and slope, whereas they have inverse relationships with urban areas, agricultural areas, and poorly drained soil. Hierarchical clustering classified the watersheds into three clusters, exclusively located in the study areas depending on the degree of forest, urban, and agricultural areas. The findings of this study suggest that different management strategies should be established depending on the characteristics of a cluster to improve the biological condition of streams.

Spatial scale effects of landscape metrics on stream water quality and their seasonal changes

Physiography and land use patterns influence streams water quality by affecting non-point source (NPS) pollution process. However, each landscape factor may affect the NPS pollution process differently with the variations of the spatial scale and season. Thus, quantitative analysis of each landscape metrics scale effect and determination of the abrupt change-point in the relationship between stream water quality and the metrics is very helpful for landscape planning of water quality protection. Based on water quality monitoring data for four years in 12 sub-watersheds of a typical headwater watershed in Eastern China, we adopted regular and partial redundancy methods to quantify the spatial scale effects and seasonal dependence of various landscape metrics impact on stream water quality, and then to identify the abrupt change-point of the water quality along the gradient of landscape metrics. Results revealed that the pure effects of different categories of landscape metrics on stream water quality were in the following order: landscape configuration metrics (20.5-31.6%) > physiographic metrics (4.0-15.9%) >landscape composition metrics (3.2-7.5%). The spatial scale effect of physiography impact on stream water quality was the most significant, while the impact of landscape configuration on water quality had the highest seasonal sensitivity. The overall water quality variation was better explained by buffer zone scale than by catchment scale landscape characteristics, and this phenomenon was more obvious during the wet season than during the dry season. In the studied watershed, we identified the largest patch index of farmland (LPI_far) and the landscape shape index of forest (LSI_for) as the key landscape metrics at sub-watershed scale and buffer zone scale, respectively. The LPI_far > 7.0% at the sub-watershed scale and LSI_for < 5.5 at the buffer zone scale were suggested as the preferred landscape planning parameters to protect the stream water quality efficiently. Results indicated that, to protect water quality, landscape regulation should follow the scale-adaptability measures and consider the landscape thresholds, which cause abrupt changes in water quality.

Spatiotemporal Evolution Analysis of Habitat Quality under High-Speed Urbanization: A Case Study of Urban Core Area of China Lin-Gang Free Trade Zone (2002–2019)

This paper, examining the Pilot Free Trade Zone Lin-Gang Special Area in China (Shanghai), identifies the relationship between urban expansion and habitat change and analyzes the influence mechanism of habitat quality (HQ) on spatiotemporal distribution. The results show the following: (1) From 2002 to 2019, the HQ in the study area decreased significantly, and the spatial differences gradually expanded over time. The HQ was low in the southwest and high in the northeast, and low-level habitats gradually moved to the southwest. This spatiotemporal evolutionary law was consistent with the local government’s 2003–2020 plans, which are composed of the joint development of a logistics park in the north, the Lin-Gang industrial zone in the west, and Shanghai port in the south. (2) Due to the interspersed distribution of high and low habitats caused by urban development and expansion, Moran’s index in spatial autocorrelation decreased over time, which means the spatial agglomeration of HQ decreased and that homogeneity increased. (3) The spatial distribution of HQ was quantified by landscape analysis. The results showed that the fragmentation degree in high-level habitat areas increased with time, while the middle-level habitat areas first increased and then decreased, and the low-level habitat areas displayed the opposite change in trend to that of the middle habitat.

Effects of urbanization and environmental heterogeneity on fish assemblages in small streams

Abstract The structure of freshwater assemblages may be driven directly by urbanization or indirectly by a reduction in environmental heterogeneity (EH). Disentangling the effects of urbanization and EH requires uncorrelated proxies of each of these factors. We assessed the effects of the degree of urbanization and EH on the structure of fish assemblages. We sampled fish in 45 streams located in the urban area of Cuiabá. We assessed the effects of urbanization and EH on rarefied fish species richness (Srarefied), the local contribution to beta diversity (LCBD), and composition with linear models and distance-based redundancy analysis. Our indexes of urbanization and EH were not correlated. We found that both Srarefied and the LCBD decreased with an increasing degree of urbanization, but were not associated with EH. We also noted that few native fish species abundances were associated with the EH. Serrapinnus microdon, S. calliurus, Hemigrammus tridens, and Astyanax lacustris were abundant in streams with a lower degree of urbanization. The non-native Poecilia reticulata was more abundant in streams with a higher degree of urbanization. Our results highlight that urbanization leads in negative impacts on fish assemblages, such as decreases in diversity and the dominance of non-native species.

Spatially Varying and Scale-Dependent Relationships of Land Use Types with Stream Water Quality

Understanding the complex relationships between land use and stream water quality is critical for water pollution control and watershed management. This study aimed to investigate the relationship between land use types and water quality indicators at multiple spatial scales, namely, the watershed and riparian scales, using the ordinary least squares (OLS) and geographically weighted regression (GWR) models. GWR extended traditional regression models, such as OLS to address the spatial variations among variables. Our results indicated that the water quality indicators were significantly affected by agricultural and forested areas at both scales. We found that extensive agricultural land use had negative effects on water quality indicators, whereas, forested areas had positive effects on these indicators. The results also indicated that the watershed scale is effective for management and regulation of watershed land use, as the predictive power of the models is much greater at the watershed scale. The maps of estimated local parameters and local R² in GWR models showcased the spatially varying relationships and indicated that the effects of land use on water quality varied over space. The results of this study reinforced the importance of watershed management in the planning, restoration, and management of stream water quality. It is also suggested that planners and managers may need to adopt different strategies, considering watershed characteristics—such as topographic features and meteorological conditions—and the source of pollutants, in managing stream water quality.

Multi-Scale Assessment of Relationships between Fragmentation of Riparian Forests and Biological Conditions in Streams

Due to anthropogenic activities within watersheds and riparian areas, stream water quality and ecological communities have been significantly affected by degradation of watershed and stream environments. One critical indicator of anthropogenic activities within watersheds and riparian areas is forest fragmentation, which has been directly linked to poor water quality and ecosystem health in streams. However, the true nature of the relationship between forest fragmentation and stream ecosystem health has not been fully elucidated due to its complex underlying mechanism. The purpose of this study was to examine the relationships of riparian fragmented forest with biological indicators including diatoms, macroinvertebrates, and fish. In addition, we investigated variations in these relationships over multiple riparian scales. Fragmentation metrics, including the number of forest patches (NP), proportion of riparian forest (PLAND), largest riparian forest patch ratio (LPI), and spatial proximity of riparian forest patches (DIVISION), were used to quantify the degree of fragmentation of riparian forests, and the trophic diatom index (TDI), benthic macroinvertebrates index (BMI), and fish assessment index (FAI) were used to represent the biological condition of diatoms, macroinvertebrates, and fish in streams. PLAND and LPI showed positive relationships with TDI, BMI, and FAI, whereas NP and DIVISION were negatively associated with biological indicators at multiple scales. Biological conditions in streams were clearly better when riparian forests were less fragmented. The relationships of NP and PLAND with biological indicators were stronger at a larger riparian scale, whereas relationships of LPI and DIVISION with biological indicators were weaker at a large scale. These results suggest that a much larger spatial range of riparian forests should be considered in forest management and restoration to enhance the biological condition of streams.