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Landscape Ecology

, Volume 29, Issue 10, pp 1741–1758 | Cite as

Geographical modeling of spatial interaction between human activity and forest connectivity in an urban landscape of southeast China

  • Yin Ren
  • Luying Deng
  • Shudi Zuo
  • Yunjian Luo
  • Guofan Shao
  • Xiaohua Wei
  • Lizhong Hua
  • Yusheng Yang
Research Article

Abstract

Geographical detector models provide a quantitative approach for evaluating spatial correlations among ecological factors, population density and landscape connectivity. Here, we used a geographical model to assess the influence of different gradients of urbanization, human activities and various environmental factors on the connectivity of urban forest landscapes in Xiamen, China from 1996 to 2006. Our overarching hypothesis is that human activity has modified certain ecological factors in a way that has affected the connectivity of urban forest landscapes. Therefore, spatiotemporal distributions of landscape connectivity should be similar to those of ecological factors and can be represented quantitatively. Integral indices of connectivity and population density were employed to represent urban forest landscape connectivity and human activity, respectively. We then simulated the spatial relationship between forest patches and population density with Conefor 2.6 software. A geographical detector model was used to identify the dominant factors that affect urban forest landscape connectivity. The results showed that a distance of 600 m was the threshold of node importance. Mean annual temperature, mean annual precipitation, elevation, patch area, population density and dominant species had significant effects on the node importance. Mean annual temperature was more significant than population density in controlling the spatial pattern of the delta of the integral index of connectivity (dIIC). The spatial interaction between population density and various ecological factors as well as their linearly enhanced or nonlinearity enhanced urban forest landscape connectivity. In conclusion, a combination of graph theory and geographical detector models is effective for quantitatively evaluating interactive relationships among ecological factors, population density and landscape connectivity.

Keywords

Geographical detector model Graph theory analysis Human activity Landscape connectivity Subtropical monsoon Asia Urban forests 

Notes

Acknowledgments

This work was supported by National Science Foundation of China (31470578, 31200363), CAS/SAFEA International Partnership Program for Creative Research Teams (KZCX2-YW-T08), Knowledge Innovation Project of the Chinese Academy of Sciences (KZCX-2-YW-453), National Forestry Public Welfare Foundation of China (201304205 and 201204604), National Key Technology Program (2010BAE00739), Fujian Provincial S&T Project (2013YZ0001-1, 2013Y0083 and 2014J05044), Xiamen Municipal Department of Science and Technology (3502Z20142016), and Knowledge Innovation Program of the CAS (IUEQN-2012-01). We are grateful to Drs. Xinhu Li and Yilan Liao for their constructive suggestions.

Supplementary material

10980_2014_94_MOESM1_ESM.doc (616 kb)
Supplementary material 1 (DOC 615 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Yin Ren
    • 1
  • Luying Deng
    • 1
  • Shudi Zuo
    • 1
  • Yunjian Luo
    • 1
  • Guofan Shao
    • 2
  • Xiaohua Wei
    • 3
  • Lizhong Hua
    • 4
  • Yusheng Yang
    • 5
    • 6
  1. 1.Key Laboratory of Urban Environment and Health, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban EnvironmentChinese Academy of SciencesXiamenChina
  2. 2.Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteUSA
  3. 3.Department of Earth and Environmental SciencesUniversity of British Columbia(Okanagan Campus)KelownaCanada
  4. 4.Department of Spatial Information Science and EngineeringXiamen University of TechnologyXiamenChina
  5. 5.Key Laboratory of Humid Subtropical Eco-Geographical Processes, Ministry of EducationFujian Normal UniversityFuzhouChina
  6. 6.Forestry CollegeFujian Agriculture and Forestry UniversityFuzhouChina

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