IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v419y2020ics0304380019304375.html
   My bibliography  Save this article

A spatial bayesian-network approach as a decision-making tool for ecological-risk prevention in land ecosystems

Author

Listed:
  • Guo, Kai
  • Zhang, Xinchang
  • Kuai, Xi
  • Wu, Zhifeng
  • Chen, Yiyun
  • Liu, Yi

Abstract

Prevention of ecological risks in land ecosystems is crucial for environmental protection and sustainable land use. With increasingly severe land degradation, new and effective methods must be developed for the management of ecological risks. In this study, a conceptual decision-making model in ecological risk prevention was developed using the Bayesian belief network with a geographic information system (GIS) for the regional-scale land ecosystem in the traditional mining city of Daye in Central China. Based on the results of a sensitivity analysis, the variable of eco-resilience reduction was identified as the most sensitive to habitat removal with the highest mutual information at 0.71. The two variables of soil pollution and water-quality deterioration were selected for a cross-validation analysis, and the changes in both the calibration and validation performance were very small. The scenarios we considered based on the interests of various stakeholders presented the spatial distribution of the following regulative effects of various management measures on a regional scale: (1) the variable of urbanisation showed that the probability of 11.5 % of all the grids decreased at a high state over an area of 177 km2; (2) the variable of mining showed that the probability of 35.5 % of the all the grids at a high state decreased, over an area of 554 km2; (3) the variable of habitat removal showed that the probability of 6.7 % of all the grids at a high state decreased, over an area of 87 km2; and (4) the variable of health threats showed that the probability of 8.4 % of all the grids at a high state decreased, over an area of 135 km2. The Bayesian-network-GIS based tools can support the decision-making process used for ecological-risk prevention in land ecosystems.

Suggested Citation

  • Guo, Kai & Zhang, Xinchang & Kuai, Xi & Wu, Zhifeng & Chen, Yiyun & Liu, Yi, 2020. "A spatial bayesian-network approach as a decision-making tool for ecological-risk prevention in land ecosystems," Ecological Modelling, Elsevier, vol. 419(C).
  • Handle: RePEc:eee:ecomod:v:419:y:2020:i:c:s0304380019304375
    DOI: 10.1016/j.ecolmodel.2019.108929
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380019304375
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2019.108929?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
    2. Pollino, Carmel A. & White, Andrea K. & Hart, Barry T., 2007. "Examination of conflicts and improved strategies for the management of an endangered Eucalypt species using Bayesian networks," Ecological Modelling, Elsevier, vol. 201(1), pages 37-59.
    3. Kininmonth, Stuart & van Oppen, Madeleine J.H. & Possingham, Hugh P., 2010. "Determining the community structure of the coral Seriatopora hystrix from hydrodynamic and genetic networks," Ecological Modelling, Elsevier, vol. 221(24), pages 2870-2880.
    4. Forio, Marie Anne Eurie & Landuyt, Dries & Bennetsen, Elina & Lock, Koen & Nguyen, Thi Hanh Tien & Ambarita, Minar Naomi Damanik & Musonge, Peace Liz Sasha & Boets, Pieter & Everaert, Gert & Dominguez, 2015. "Bayesian belief network models to analyse and predict ecological water quality in rivers," Ecological Modelling, Elsevier, vol. 312(C), pages 222-238.
    5. Dick, Jan & Turkelboom, Francis & Woods, Helen & Iniesta-Arandia, Irene & Primmer, Eeva & Saarela, Sanna-Riikka & Bezák, Peter & Mederly, Peter & Leone, Michael & Verheyden, Wim & Kelemen, Eszter & H, 2018. "Stakeholders’ perspectives on the operationalisation of the ecosystem service concept: Results from 27 case studies," Ecosystem Services, Elsevier, vol. 29(PC), pages 552-565.
    6. Iniesta-Arandia, Irene & García-Llorente, Marina & Aguilera, Pedro A. & Montes, Carlos & Martín-López, Berta, 2014. "Socio-cultural valuation of ecosystem services: uncovering the links between values, drivers of change, and human well-being," Ecological Economics, Elsevier, vol. 108(C), pages 36-48.
    7. Adam Pártl & David Vačkář & Blanka Loučková & Eliška Krkoška Lorencová, 2017. "A spatial analysis of integrated risk: vulnerability of ecosystem services provisioning to different hazards in the Czech Republic," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 1185-1204, December.
    8. Gallego, Aurea & Calafat, Consuelo & Segura, Marina & Quintanilla, Israel, 2019. "Land planning and risk assessment for livestock production based on an outranking approach and GIS," Land Use Policy, Elsevier, vol. 83(C), pages 606-621.
    9. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    10. Johnson, Sandra & Mengersen, Kerrie & de Waal, Alta & Marnewick, Kelly & Cilliers, Deon & Houser, Ann Marie & Boast, Lorraine, 2010. "Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle," Ecological Modelling, Elsevier, vol. 221(4), pages 641-651.
    11. Turner, Katrine Grace & Anderson, Sharolyn & Gonzales-Chang, Mauricio & Costanza, Robert & Courville, Sasha & Dalgaard, Tommy & Dominati, Estelle & Kubiszewski, Ida & Ogilvy, Sue & Porfirio, Luciana &, 2016. "A review of methods, data, and models to assess changes in the value of ecosystem services from land degradation and restoration," Ecological Modelling, Elsevier, vol. 319(C), pages 190-207.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sayed Mohammad Mousavi & Yazdan Shahin Rad, 2023. "Challenges and Legal Aspects of Financing Projects Through Cryptocurrencies in Iran," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 13(4), pages 127-151.
    2. Xinchang Zhang & Min Chen & Kai Guo & Yang Liu & Yi Liu & Weinan Cai & Hua Wu & Zeyi Chen & Yiyun Chen & Jianguo Zhang, 2021. "Regional Land Eco-Security Evaluation for the Mining City of Daye in China Using the GIS-Based Grey TOPSIS Method," Land, MDPI, vol. 10(2), pages 1-18, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcot, Bruce G., 2017. "Common quandaries and their practical solutions in Bayesian network modeling," Ecological Modelling, Elsevier, vol. 358(C), pages 1-9.
    2. Liedloff, Adam C. & Smith, Carl S., 2010. "Predicting a ‘tree change’ in Australia's tropical savannas: Combining different types of models to understand complex ecosystem behaviour," Ecological Modelling, Elsevier, vol. 221(21), pages 2565-2575.
    3. Vilizzi, L. & Price, A. & Beesley, L. & Gawne, B. & King, A.J. & Koehn, J.D. & Meredith, S.N. & Nielsen, D.L. & Sharpe, C.P., 2012. "The belief index: An empirical measure for evaluating outcomes in Bayesian belief network modelling," Ecological Modelling, Elsevier, vol. 228(C), pages 123-129.
    4. Jim Lewis & Kerrie Mengersen & Laurie Buys & Desley Vine & John Bell & Peter Morris & Gerard Ledwich, 2015. "Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-21, July.
    5. Moe, S. Jannicke & Haande, Sigrid & Couture, Raoul-Marie, 2016. "Climate change, cyanobacteria blooms and ecological status of lakes: A Bayesian network approach," Ecological Modelling, Elsevier, vol. 337(C), pages 330-347.
    6. Leonel Lara-Estrada & Livia Rasche & L. Enrique Sucar & Uwe A. Schneider, 2018. "Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks," Land, MDPI, vol. 7(1), pages 1-13, January.
    7. Meyer, Spencer R. & Johnson, Michelle L. & Lilieholm, Robert J. & Cronan, Christopher S., 2014. "Development of a stakeholder-driven spatial modeling framework for strategic landscape planning using Bayesian networks across two urban-rural gradients in Maine, USA," Ecological Modelling, Elsevier, vol. 291(C), pages 42-57.
    8. Anna Sperotto & Josè Luis Molina & Silvia Torresan & Andrea Critto & Manuel Pulido-Velazquez & Antonio Marcomini, 2019. "Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks," Sustainability, MDPI, vol. 11(17), pages 1-34, August.
    9. Bruce G. Marcot & Anca M. Hanea, 2021. "What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?," Computational Statistics, Springer, vol. 36(3), pages 2009-2031, September.
    10. Valencia Torres, Angélica & Tiwari, Chetan & Atkinson, Samuel F., 2021. "Progress in ecosystem services research: A guide for scholars and practitioners," Ecosystem Services, Elsevier, vol. 49(C).
    11. Alessandro Pagano & Irene Pluchinotta & Raffaele Giordano & Anna Bruna Petrangeli & Umberto Fratino & Michele Vurro, 2018. "Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(6), pages 2131-2145, April.
    12. Barton, David N. & Benjamin, Tamara & Cerdán, Carlos R. & DeClerck, Fabrice & Madsen, Anders L. & Rusch, Graciela M. & Salazar, à lvaro G. & Sanchez, Dalia & Villanueva, Cristóbal, 2016. "Assessing ecosystem services from multifunctional trees in pastures using Bayesian belief networks," Ecosystem Services, Elsevier, vol. 18(C), pages 165-174.
    13. Lima, Flávia Pereira & Bastos, Rogério Pereira, 2019. "Perceiving the invisible: Formal education affects the perception of ecosystem services provided by native areas," Ecosystem Services, Elsevier, vol. 40(C).
    14. Gieder, Katherina D. & Karpanty, Sarah M. & Fraser, James D. & Catlin, Daniel H. & Gutierrez, Benjamin T. & Plant, Nathaniel G. & Turecek, Aaron M. & Robert Thieler, E., 2014. "A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features," Ecological Modelling, Elsevier, vol. 276(C), pages 38-50.
    15. Turkelboom, Francis & Leone, Michael & Jacobs, Sander & Kelemen, Eszter & García-Llorente, Marina & Baró, Francesc & Termansen, Mette & Barton, David N. & Berry, Pam & Stange, Erik & Thoonen, Marijk, 2018. "When we cannot have it all: Ecosystem services trade-offs in the context of spatial planning," Ecosystem Services, Elsevier, vol. 29(PC), pages 566-578.
    16. Ropero, R.F. & Aguilera, P.A. & Rumí, R., 2015. "Analysis of the socioecological structure and dynamics of the territory using a hybrid Bayesian network classifier," Ecological Modelling, Elsevier, vol. 311(C), pages 73-87.
    17. Johnson, Sandra & Mengersen, Kerrie & de Waal, Alta & Marnewick, Kelly & Cilliers, Deon & Houser, Ann Marie & Boast, Lorraine, 2010. "Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle," Ecological Modelling, Elsevier, vol. 221(4), pages 641-651.
    18. Junquera, Victoria & Meyfroidt, Patrick & Sun, Zhanli & Latthachack, Phokham & Grêt-Regamey, Adrienne, 2020. "From global drivers to local land-use change: Understanding the northern Laos rubber boom," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 109, pages 103-115.
    19. Le, Hai Dinh & Smith, Carl & Herbohn, John, 2015. "Identifying interactions among reforestation success drivers: A case study from the Philippines," Ecological Modelling, Elsevier, vol. 316(C), pages 62-77.
    20. Ropero, R.F. & Renooij, S. & van der Gaag, L.C., 2018. "Discretizing environmental data for learning Bayesian-network classifiers," Ecological Modelling, Elsevier, vol. 368(C), pages 391-403.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:419:y:2020:i:c:s0304380019304375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.