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


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.


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



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

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Supplementary material 1 (DOC 615 kb)


  1. Ahern J (2013) Urban landscape sustainability and resilience: the promise and challenges of integrating ecology with urban planning and design. Landscape Ecol 28:1203–1212CrossRefGoogle Scholar
  2. Andersson E, Bodin O (2009) Practical tool for landscape planning? An empirical investigation network based models of habitat fragmentation. Ecography 32:123–132CrossRefGoogle Scholar
  3. Baggio JA, Salau K, Janssen MA, Schoon ML, Bodin O (2011) Landscape connectivity and predator-prey population dynamics. Landscape Ecol 26:33–45CrossRefGoogle Scholar
  4. Brooks CP (2006) Quantifying population substructure: extending the graph-theoretic approach. Ecology 87:864–872PubMedCrossRefGoogle Scholar
  5. Cushman SA, Raphael MG, Ruggiero LF, Shirk AS, Wasserman TN, O’Doherty EC (2011) Limiting factors and landscape connectivity: the American marten in the Rocky Mountains. Landscape Ecol 26:1137–1149CrossRefGoogle Scholar
  6. Decout S, Manel S, Miaud C, Luque S (2012) Integrative approach for landscape-based graph connectivity analysis: a case study with the common frog (Ranatemporaria) in human-dominated landscapes. Landscape Ecol 27:267–279CrossRefGoogle Scholar
  7. Devi BSS, Murthy MSR, Debnath B, Jha CS (2013) Forest patch connectivity diagnostics and prioritization using graph theory. Ecol Model 251:279–287CrossRefGoogle Scholar
  8. Ferrari JR, Lookingbill TR, Neel M (2007) Two measures of landscape-graph connectivity: assessment across gradients in area and configuration. Landscape Ecol 22:1315–1323CrossRefGoogle Scholar
  9. Freudenberger L, Hobson PR, Rupic S, Pe’er G, Schluck M, Sauermann J, Kreft S, Selva N, Ibisch PL (2013) Spatial road disturbance index (SPROADI) for conservation planning: a novel landscape index, demonstrated for the State of Brandenburg, Germany. Landscape Ecol 28:1353–1369CrossRefGoogle Scholar
  10. Fu W, Liu SL, Degloria SD, Dong SK, Beazley R (2010) Characterizing the “fragmentation-barrier” effect of road networks on landscape connectivity: a case study in Xishuangbanna, Southwest China. Landscape Urban Plan 95:122–129CrossRefGoogle Scholar
  11. Galpern P, Manseau M, Fall A (2011) Patch-based graphs of landscape connectivity: a guide to construction, analysis and application for conservation. Biol Conserv 144:44–55CrossRefGoogle Scholar
  12. Garcia-Feced C, Saura S, Elena-Rossello R (2011) Improving landscape connectivity in forest districts: a two-stage process for prioritizing agricultural patches for reforestation. For Ecol Manag 261:154–161CrossRefGoogle Scholar
  13. Gledhill DG, James P, Davies DH (2008) Pond density as a determinant of aquatic species richness in an urban landscape. Landscape Ecol 23:1219–1230CrossRefGoogle Scholar
  14. Goodwin BJ (2003) Is landscape connectivity a dependent or independent variable? Landscape Ecol 18:687–699CrossRefGoogle Scholar
  15. Janin A, Lena JP, Ray N, Delacourt C, Allenmand P, Joly P (2009) Assessing landscape connectivity with calibrated cost-distance modelling: predicting common toad distribution in a context of spreading agriculture. J Appl Ecol 46:833–841CrossRefGoogle Scholar
  16. Joshi PK, Kumar M, Paliwal A, Midha N, Dash PP (2009) Assessing impact of industrialization in terms of LULC in a dry tropical region (Chhattisgarh), India using remote sensing data and GIS over a period of 30 years. Environ Monit Assess 149:371–376PubMedCrossRefGoogle Scholar
  17. Li XW, Xie YF, Wang JF, Christakos G, Si JL, Zhao HN, Ding YQ, Li J (2013) Influence of planting pattern on fluoroquinolone residues in the soil of an intensive vegetable cultivation area in northern China. Sci Total Environ 458–460:63–69PubMedCrossRefGoogle Scholar
  18. Liu JX, Liu SG, Loveland TR (2006) Temporal evolution of carbon budgets of the Appalachian forests in the U.S. from 1972 to 2000. For Ecol Manag 222:191–201CrossRefGoogle Scholar
  19. Liu SL, Dong YH, Deng L, Liu Q, Zhao HD, Dong SK (2014) Forest fragmentation and landscape connectivity change associated with road network extension and city expansion: a case study in the lancing River Valley. Ecol Indic 36:160–168CrossRefGoogle Scholar
  20. Lookingbill TR, Gardner RH, Ferrari JR, Keller CE (2010) Combing a dispersal model with network theory to assess habitat connectivity. Ecol Appl 20:427–441PubMedCrossRefGoogle Scholar
  21. Lü N, Ni J (2013) Natural succession of vegetation in Tiantong National Forest Park, Zhejiang Province of East China: a simulation study. Chinese J Appl Ecol 24:161–169Google Scholar
  22. Luque S, Saura S, Fortin MJ (2012) Landscape connectivity analysis for conservation: insights from combining new methods with ecological and genetic data. Landscape Ecol 27:153–157CrossRefGoogle Scholar
  23. Martin-Martin C, Bunce RGH, Saura S, Elena-Rossello R (2013) Changes and interactions between forest landscape connectivity and burnt area in Spain. Ecol Indic 33:129–138CrossRefGoogle Scholar
  24. Martin-Queller E, Saura S (2013) Landscape species pools and connectivity patterns influence tree species richness in both managed and unmanaged stands. For Ecol Manag 289:123–132CrossRefGoogle Scholar
  25. Moilanen A (2011) On the limitations of graph-theoretic connectivity in spatial ecology and conservation. J Appl Ecol 48:1543–1547CrossRefGoogle Scholar
  26. O’Brien D, Manseau M, Fall A, Fortin MJ (2006) Testing the importance of spatial configuration of winter habitat for woodland caribou: an application of graph theory. Biol Conserv 130:70–83CrossRefGoogle Scholar
  27. Partel M, Helm A, Reitalu T, Liira J (2007) Grassland diversity related to the Late Iron Age human population density. J Ecol 95:574–582CrossRefGoogle Scholar
  28. Pascual-Hortal L, Saura S (2006) Comparison and development of new graph-based landscape connectivity indices: towards the priorization of habitat patches and corridors for conservation. Landscape Ecol 21:959–967CrossRefGoogle Scholar
  29. Ren Y, Wei X, Wei XH, Pan JZ, Xie PP, Song XD, Peng D, Zhao J (2011a) Relationship between vegetation carbon storage and urbanization: a case study of Xiamen, China. For Ecol Manag 261:1214–1223CrossRefGoogle Scholar
  30. Ren Y, Wei XH, Zhang L, Cui SH, Chen F, Xiong YZ, Xie PP (2011b) Potential for forest vegetation carbon storage in Fujian Province, China, determined from forest inventories. Plant Soil 345:125–140CrossRefGoogle Scholar
  31. Ren Y, Yan J, Wei XH, Wang YJ, Yang YS, Hua LZ, Xiong YZ, Niu X, Song XD (2012) Effects of rapid urban sprawl on urban forest carbon stocks: integrating remotely sensed, GIS and forest inventory data. J Environ Manag 113:447–455CrossRefGoogle Scholar
  32. Richard Y, Armstrong DP (2010) Cost distance modelling of landscape connectivity and gap-crossing ability using radio-tracking data. J Appl Ecol 47:603–610CrossRefGoogle Scholar
  33. Royle JA, Chandler RB, Gazenski KD, Graves TA (2013) Spatial capture-recapture models for jointly estimating population density and landscape connectivity. Ecology 94:287–294PubMedCrossRefGoogle Scholar
  34. Saura S, Pascual-Hortal L (2007) A new habitat availability index to integrate connectivity in landscape conservation planning: comparison with existing indices and application to a case study. Landsc Urban Plan 83:91–103CrossRefGoogle Scholar
  35. Saura S, Torné J (2009) ConeforSensinode 2.2: a software package for quantifying the importance of habitat patches for landscape connectivity. Environ Model Softw 24:135–139CrossRefGoogle Scholar
  36. Saura S, Estreguil C, Mouton C, Rodriguez-Freire M (2011) Network analysis to assess landscape connectivity trends: application to European forests (1990–2000). Ecol Indic 11:407–416CrossRefGoogle Scholar
  37. Schweiger O, Maelfait JP, Wingeren WV, Hendrickx F, Billeter R, Speelmans M, Augenstein I, Aukema B, Aviron S, Bailey D, Bukacek R, Burel F, Diekotter T, Dirksen J, Frenzel M, Herzog F, Liira J, Roubalova M, Bugter R (2005) Quantifying the impact of environmental factors on arthropod communities in agricultural landscapes across organizational levels and spatial scales. J Appl Ecol 42:1129–1139CrossRefGoogle Scholar
  38. Soga M, Kaike S (2013) Large forest patches promote breeding success of a terrestrial mammal in urban landscapes. PLoS One 8:1–3Google Scholar
  39. Tang LN, Shao GF, Piao ZJ, Dai LM, Jenkins MA, Wang SX, Wu G, Wu JG, Zhao J (2010) Forest degradation deepens around and within protected areas in East Asia. Biol Conserv 143:1295–1298CrossRefGoogle Scholar
  40. Tang LN, Zhao Y, Yin K, Zhao JZ (2013) Xiamen. Cities 31:615–624CrossRefGoogle Scholar
  41. Urban D, Keitt T (2001) Landscape connectivity: a graph-theoretic perspective. Ecology 82:1205–1218CrossRefGoogle Scholar
  42. Urban MC, Skelly DK, Burchsted D, Price W, Lowry S (2006) Stream communities across a rural-urban landscape gradient. Divers Distrib 12:337–350CrossRefGoogle Scholar
  43. Wang JF, Hu Y (2012) Environmental health risk detection with GeogDetector. Environ Model Softw 33:114–115CrossRefGoogle Scholar
  44. Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, Zheng XY (2010) Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. Int J Geogr Inf Sci 24:107–127CrossRefGoogle Scholar
  45. Wu JG (2013a) Key concepts and research topics in landscape ecology revisited: 30 years after the Allerton Park workshop. Landscape Ecol 28:1–11CrossRefGoogle Scholar
  46. Wu JG (2013b) Landscape sustainability science: ecosystem services and human well-being in changing landscapes. Landscape Ecol 28:999–1023CrossRefGoogle Scholar
  47. Yang DW, Kao WTM, Zhang GQ, Zhang NY (2014) Evaluating spatiotemporal differences and sustainability of Xiamen urban metabolism using energy synthesis. Ecol Model 272:40–48CrossRefGoogle Scholar
  48. Zhao J, Zheng XC, Dong RC, Shao GF (2013) The planning, construction, and management toward sustainable cities in China needs the Environmental Internet of Things. Int J Sus Dev World 20:195–198CrossRefGoogle Scholar

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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