Application of Spatial Autocorrelation for Analysis of Spatial Distribution Characteristics of Birds Observed in Namdaecheon River, Muju-gun, Jeollabuk-do, Korea

Abstract

This study was conducted to find out characterization of spatial distribution of birds observed in river areas. Our bird survey was carried out 4 times at 31 sites from January to September in 2011. A total of 1,609 accumulated individuals belonging to 59 species, 28 families and 11 orders were observed. In the result of spatial autocorrelation analysis using the richness index of the maximum counts of each sites, we confirmed that the distribution of birds in Namdaecheon river was clustered and the tendency of spatial autocorrelation was apparent. The area of each sites within a 200m radius was classified in four biotope categories such as agricultural land, forest, residential area and water area, and the spatial autocorrelation was analysed about four types. In the result of spatial autocorrelation analysis for four biotope categories, all types were showed the positive spatial autocorrelation, but the type of water area was higher than other types. The positive correlation was found between the water area and water birds in statistical significance. However, the forest birds had non-significance values. Therefore, it is appropriate to focus on water birds except for forest birds, when researches of bird distribution in river ecosystem is conducted. The number of bird species and individuals increased as the riverside of water area was to widen. Thus, if the areas of riverside offering the feeding and roosting area increase, it will be accommodated many birds. Also, the areas of riverside should be maintained naturally because it is an important habitats of birds. Our study area is on the outskirts the city of higher rates of

Quality of life assessment and its spatial correlation in impoverished districts and counties: A case study of Guizhou Province

“…At present, spatial autocorrelation analysis in urban research is mainly applied in the understanding of topics related to urban environmental change, such as spatiotemporal change patterns of urban heat islands ( 41 ), spatial characteristics of urban atmospheric environmental efficiency ( 42 , 43 ), spatial characteristics of urban ecosystem services ( 44 ), and urban and rural animal habitats. ( 45 ). More notably, the usefulness of spatial autocorrelation analysis in urban management research has also received recognition.…”

A Forecasting Method for Harmful Algal Bloom(HAB)-Prone Regions Allowing Preemptive Countermeasures Based only on Acoustic Doppler Current Profiler Measurements in a Large River

“…Similarly [37] quantitatively analyzed the changes in the spatial distribution patterns in marine environments through spatial autocorrelation to understand the spatio-temporal patterns in marine environments. [38] utilized spatial autocorrelation to relate the spatial correlation of algae observed along a stream in Namdaecheon. Currently, the K-means algorithm has been widely used to predict and classify water quality in rivers and oceans [39][40][41].…”