Regression Tree Analysis for Stream Biological Indicators Considering Spatial Autocorrelation

Kim, Miyoung; Lee, Sang‐Woo

doi:10.3390/ijerph18105150

Cited by 4 publications

(3 citation statements)

References 109 publications

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“…The correlation analysis values of the flow metrics and BMI present a positive correlation in the watersheds with low imperviousness. This suggests that the imperviousness of the watershed has an indirect effect on the flow and the ecological index BMI [12]. The SN watershed exhibits negative correlations for all 67 flow metrics.…”

Section: Correlation Analysis Between Flow Metrics and Bmimentioning

confidence: 93%

“…Lee et al [11] examined the mediating effects of streamline geometry on the relationships between urban land use and the index of biological integrity (IBI) of the Nakdong River in Korea. Kim et al [12] analyzed 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 benthic macroinvertebrate index (BMI) is a type of ecological index obtained from measurements of benthic macroinvertebrates [13].…”

Section: Introductionmentioning

confidence: 99%

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Correlation Analysis between Hydrologic Flow Metrics and Benthic Macroinvertebrates Index (BMI) in the Han River Basin, South Korea

et al. 2021

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In aquatic ecosystems, flow is one of the most essential elements of aquatic species. It is necessary to explore the correlation with ecological indices for the management guidelines of aquatic ecosystems using flow because aquatic ecosystem data are limited. This study calculated the flow metrics using the flow and analyzed the correlation between the flow metrics and the ecological index. This study attempted to understand the correlation between the ecologic index and flow metrics. Flow metrics were quantified flow in various ways, depending on the size, frequency, and design of the flow. The characteristics of flow metrics were identified and the correlation with the ecological index was studied. The Pearson correlation coefficient values for 22 watersheds were compared using the flow data from 2008 to 2015 and the ecological index data from the BMI. In watersheds with high imperviousness, the Pearson correlation coefficient was negative, which indicated that the correlation in this study provides basic data for the quantitative evaluation of the river ecosystem by identifying the relationship between imperviousness and BMI. As a result, the highest Pearson correlation coefficient values of flow metrics were related to the flow coefficient of variation (MACV13-16; MHCV; MLCV).

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Section: Correlation Analysis Between Flow Metrics and Bmimentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Correlation Analysis between Hydrologic Flow Metrics and Benthic Macroinvertebrates Index (BMI) in the Han River Basin, South Korea

et al. 2021

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“…Spatial autocorrelation is an important indicator that reflects the correlation between a certain geographic phenomenon or a certain attribute value on a regional unit and the same phenomenon or attribute value on adjacent regional units. It measures the degree of value aggregation in the spatial domain [48]. Spatial autocorrelation analysis can reveal deep geographic information and highlight the characteristics of spatial data distribution.…”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Vulnerability Analysis of Coastal Zone Based on InVEST Model in Jiaozhou Bay, China

Tian

Wang

et al. 2022

Sustainability

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The coastal zone plays an essential part in maintaining the balance of the ecosystem and promoting the development of human society and economy. It is significant to assess the extent to which the Jiaozhou Bay coastal zone withstands floods and coastal erosion during storms. The coastal exposure index (CEI) of the Jiaozhou Bay in 1984, 2000 and 2019 was obtained by the coastal vulnerability model based on data including coastline, bathymetry and coastal terrain elevation. The spatial distribution and aggregation characteristics of CEI in Jiaozhou Bay were analyzed through spatial autocorrelation analysis. The results show that the north coast of Jiaozhou Bay is highly vulnerable, that is, prone to coastal erosion, while the south and east are less vulnerable, meaning that they can basically withstand natural disasters such as storm surges and floods. The CEI shows significant spatial autocorrelation, with little spatial heterogeneity. The type of coastline, elevation, distance to continental shelf and socio-economic development are the main factors that cause the north–south vulnerability differences in the Jiaozhou Bay coast. The results can identify the districts along the Jiaozhou Bay that are at greater risk of marine disasters, and provide scientific theoretical support for the coastal protection and sustainable development of the Jiaozhou Bay.

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Biomass and diversity of epiphytic liverworts along environmental gradients in a cloud forest in Mexico

Juárez-López,

Sánchez-González,

Pavón

et al. 2023

Journal of Bryology

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Regression Tree Analysis for Stream Biological Indicators Considering Spatial Autocorrelation

Cited by 4 publications

References 109 publications

Correlation Analysis between Hydrologic Flow Metrics and Benthic Macroinvertebrates Index (BMI) in the Han River Basin, South Korea

Correlation Analysis between Hydrologic Flow Metrics and Benthic Macroinvertebrates Index (BMI) in the Han River Basin, South Korea

Vulnerability Analysis of Coastal Zone Based on InVEST Model in Jiaozhou Bay, China

Biomass and diversity of epiphytic liverworts along environmental gradients in a cloud forest in Mexico

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