IDEAS home Printed from https://ideas.repec.org/a/eee/ecoser/v58y2022ics2212041622000791.html
   My bibliography  Save this article

Towards a sustainable nature reserve management: Using Bayesian network to quantify the threat of disturbance to ecosystem services

Author

Listed:
  • Lyu, Rongfang
  • Zhao, Wenpeng
  • Pang, Jili
  • Tian, Xiaolei
  • Zhang, Jianming
  • Wang, Naiang

Abstract

Ecosystem services (ESs) of mountain areas may be impacted by cumulative and interactive effects of multiple disturbances including insect infestations, wildfires, timber harvesting, and building construction. However, the impacts of natural and human disturbances on ESs and the trade-offs/synergies among ESs are poorly known, particularly in mountain ecosystems with diverse landscapes. Here, we used the Qilian Nature Reserve in northwestern China as a case study, for which we quantified mountain disturbances with a BEAST algorithm and three critical ESs (carbon sequestration, water yield, and habitat quality) with the CASA and InVEST models. We then simulated ESs using the BN model, and estimated the impacts of disturbances on ESs and their trade-offs in different environment conditions through multi-scenario analysis. Our results suggested that BEAST could effectively capture the patterns and dynamics of small-scale disturbances, which were previously difficult to predict with normal land use/cover products. The established BN model could simulate the spatio-temporal dynamics of carbon sequestration, water yield, and habitat quality with an average classification error of 17.8, 12.7 and 4.5% for each ES, respectively. Significant synergy existed between carbon sequestration and habitat quality at the regional scale, while trade-off existed between water yield and the other two ESs. Specifically, these trade-offs/synergies among ESs tended to be weak at medium value of ESs, but stronger at higher and lower states. Thus, significant differences existed in the “win-lose†solutions between water yield and the other two ESs, further resulting the limited space to simultaneously improve three ESs. Disturbances at medium frequency and low-medium intensity were beneficial for the maintenance and improvement of three ESs. The BN model is a promising decision support tool to integrate small-scale disturbances into ES evaluation and identify the most suitable management solutions for mountain ecosystems; this could provide critical information for decision-makers and guidance for sustainable development.

Suggested Citation

  • Lyu, Rongfang & Zhao, Wenpeng & Pang, Jili & Tian, Xiaolei & Zhang, Jianming & Wang, Naiang, 2022. "Towards a sustainable nature reserve management: Using Bayesian network to quantify the threat of disturbance to ecosystem services," Ecosystem Services, Elsevier, vol. 58(C).
    • Handle: RePEc:eee:ecoser:v:58:y:2022:i:c:s2212041622000791
    DOI: 10.1016/j.ecoser.2022.101483
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2212041622000791
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecoser.2022.101483?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. Maldonado, A.D. & Aguilera, P.A. & Salmerón, A. & Nicholson, A.E., 2018. "Probabilistic modeling of the relationship between socioeconomy and ecosystem services in cultural landscapes," Ecosystem Services, Elsevier, vol. 33(PB), pages 146-164.
    2. Jiang, Chong & Nath, Reshmita & Labzovskii, Lev & Wang, Dewang, 2018. "Integrating ecosystem services into effectiveness assessment of ecological restoration program in northern China's arid areas: Insights from the Beijing-Tianjin Sandstorm Source Region," Land Use Policy, Elsevier, vol. 75(C), pages 201-214.
    3. Zeileis, Achim & Grothendieck, Gabor, 2005. "zoo: S3 Infrastructure for Regular and Irregular Time Series," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i06).
    4. W. A. Kurz & C. C. Dymond & G. Stinson & G. J. Rampley & E. T. Neilson & A. L. Carroll & T. Ebata & L. Safranyik, 2008. "Mountain pine beetle and forest carbon feedback to climate change," Nature, Nature, vol. 452(7190), pages 987-990, April.
    5. Forio, Marie Anne Eurie & Villa-Cox, Gonzalo & Van Echelpoel, Wout & Ryckebusch, Helena & Lock, Koen & Spanoghe, Pieter & Deknock, Arne & De Troyer, Niels & Nolivos-Alvarez, Indira & Dominguez-Granda,, 2020. "Bayesian Belief Network models as trade-off tools of ecosystem services in the Guayas River Basin in Ecuador," Ecosystem Services, Elsevier, vol. 44(C).
    6. Pham, Hung Vuong & Sperotto, Anna & Furlan, Elisa & Torresan, Silvia & Marcomini, Antonio & Critto, Andrea, 2021. "Integrating Bayesian Networks into ecosystem services assessment to support water management at the river basin scale," Ecosystem Services, Elsevier, vol. 50(C).
    7. Costanza, Robert & d'Arge, Ralph & de Groot, Rudolf & Farber, Stephen & Grasso, Monica & Hannon, Bruce & Limburg, Karin & Naeem, Shahid & O'Neill, Robert V. & Paruelo, Jose, 1998. "The value of the world's ecosystem services and natural capital," Ecological Economics, Elsevier, vol. 25(1), pages 3-15, April.
    8. Kubiszewski, Ida & Costanza, Robert & Anderson, Sharolyn & Sutton, Paul, 2017. "The future value of ecosystem services: Global scenarios and national implications," Ecosystem Services, Elsevier, vol. 26(PA), pages 289-301.
    9. Boix-Fayos, Carolina & Boerboom, Luc G.J. & Janssen, Ron & Martínez-Mena, María & Almagro, María & Pérez-Cutillas, Pedro & Eekhout, Joris P.C. & Castillo, Victor & de Vente, Joris, 2020. "Mountain ecosystem services affected by land use changes and hydrological control works in Mediterranean catchments," Ecosystem Services, Elsevier, vol. 44(C).
    10. Wei, Hejie & Liu, Huiming & Xu, Zihan & Ren, Jiahui & Lu, Nachuan & Fan, Weiguo & Zhang, Peng & Dong, Xiaobin, 2018. "Linking ecosystem services supply, social demand and human well-being in a typical mountain–oasis–desert area, Xinjiang, China," Ecosystem Services, Elsevier, vol. 31(PA), pages 44-57.
    11. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    12. Lyu, Rongfang & Zhang, Jianming & Xu, Mengqun & Li, Jijun, 2018. "Impacts of urbanization on ecosystem services and their temporal relations: A case study in Northern Ningxia, China," Land Use Policy, Elsevier, vol. 77(C), pages 163-173.
    13. Obiang Ndong, Gregory & Therond, Olivier & Cousin, Isabelle, 2020. "Analysis of relationships between ecosystem services: A generic classification and review of the literature," Ecosystem Services, Elsevier, vol. 43(C).
    14. Schirpke, Uta & Wang, Genxu & Padoa-Schioppa, Emilio, 2021. "Editorial: Mountain landscapes: Protected areas, ecosystem services, and future challenges," Ecosystem Services, Elsevier, vol. 49(C).
    Full references (including those not matched with items on IDEAS)

    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. Bello, Carolina & Culot, Laurence & Ruiz Agudelo, Cesar Augusto & Galetti, Mauro, 2021. "Valuing the economic impacts of seed dispersal loss on voluntary carbon markets," Ecosystem Services, Elsevier, vol. 52(C).
    2. Xiaoyan Li & Gulinaer Suoerdahan & Zhenyu Shi & Zihan Xing & Yongxing Ren & Ran Yang, 2021. "Spatial–Temporal Impacts of Urban Sprawl on Ecosystem Services: Implications for Urban Planning in the Process of Rapid Urbanization," Land, MDPI, vol. 10(11), pages 1-17, November.
    3. Yue Wang & Qi Fu & Tinghui Wang & Mengfan Gao & Jinhua Chen, 2022. "Multiscale Characteristics and Drivers of the Bundles of Ecosystem Service Budgets in the Su-Xi-Chang Region, China," IJERPH, MDPI, vol. 19(19), pages 1-26, October.
    4. Gaglio, M. & Lanzoni, M. & Goggi, F. & Fano, E.A. & Castaldelli, G., 2023. "Integrating payment for ecosystem services in protected areas governance: The case of the Po Delta Park," Ecosystem Services, Elsevier, vol. 60(C).
    5. Chen, Haojie & Costanza, Robert & Kubiszewski, Ida, 2022. "Legitimacy and limitations of valuing the oxygen production of ecosystems," Ecosystem Services, Elsevier, vol. 58(C).
    6. 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).
    7. Wang, Jinsong & Gao, Dongdong & Shi, Wei & Du, Jiayan & Huang, Zhuo & Liu, Buyuan, 2023. "Spatio-temporal changes in ecosystem service value: Evidence from the economic development of urbanised regions," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    8. Lyu, Rongfang & Clarke, Keith C. & Zhang, Jianming & Feng, Junli & Jia, Xuehui & Li, Jijun, 2021. "Dynamics of spatial relationships among ecosystem services and their determinants: Implications for land use system reform in Northwestern China," Land Use Policy, Elsevier, vol. 102(C).
    9. Schibalski, Anett & Kleyer, Michael & Maier, Martin & Schröder, Boris, 2022. "Spatiotemporally explicit prediction of future ecosystem service provisioning in response to climate change, sea level rise, and adaptation strategies," Ecosystem Services, Elsevier, vol. 54(C).
    10. Aryal, Kishor & Maraseni, Tek & Apan, Armando, 2023. "Examining policy−institution−program (PIP) responses against the drivers of ecosystem dynamics. A chronological review (1960–2020) from Nepal," Land Use Policy, Elsevier, vol. 132(C).
    11. Zhang, Pengyan & Yang, Dan & Qin, Mingzhou & Jing, Wenlong, 2020. "Spatial heterogeneity analysis and driving forces exploring of built-up land development intensity in Chinese prefecture-level cities and implications for future Urban Land intensive use," Land Use Policy, Elsevier, vol. 99(C).
    12. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    13. Ana D. Maldonado & Darío Ramos-López & Pedro A. Aguilera, 2019. "The Role of Cultural Landscapes in the Delivery of Provisioning Ecosystem Services in Protected Areas," Sustainability, MDPI, vol. 11(9), pages 1-18, April.
    14. Hendrawan, Dienda C P & Musshoff, Oliver, 2022. "Oil Palm Smallholder Farmers' Livelihood Resilience and Decision Making in Replanting," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322441, Agricultural and Applied Economics Association.
    15. Metsaranta, J.M. & Kurz, W.A., 2012. "Inter-annual variability of ecosystem production in boreal jack pine forests (1975–2004) estimated from tree-ring data using CBM-CFS3," Ecological Modelling, Elsevier, vol. 224(1), pages 111-123.
    16. van der Hoff, Richard & Nascimento, Nathália & Fabrício-Neto, Ailton & Jaramillo-Giraldo, Carolina & Ambrosio, Geanderson & Arieira, Julia & Afonso Nobre, Carlos & Rajão, Raoni, 2022. "Policy-oriented ecosystem services research on tropical forests in South America: A systematic literature review," Ecosystem Services, Elsevier, vol. 56(C).
    17. Roland R. Ramsahai, 2020. "Connecting actuarial judgment to probabilistic learning techniques with graph theory," Papers 2007.15475, arXiv.org.
    18. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    19. Myriam Patricia Cifuentes & Clara Mercedes Suarez & Ricardo Cifuentes & Noel Malod-Dognin & Sam Windels & Jose Fernando Valderrama & Paul D. Juarez & R. Burciaga Valdez & Cynthia Colen & Charles Phill, 2022. "Big Data to Knowledge Analytics Reveals the Zika Virus Epidemic as Only One of Multiple Factors Contributing to a Year-Over-Year 28-Fold Increase in Microcephaly Incidence," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
    20. Evans, Nicole M. & Carrozzino-Lyon, Amy L. & Galbraith, Betsy & Noordyk, Julia & Peroff, Deidre M. & Stoll, John & Thompson, Aaron & Winden, Matthew W. & Davis, Mark A., 2019. "Integrated ecosystem service assessment for landscape conservation design in the Green Bay watershed, Wisconsin," Ecosystem Services, Elsevier, vol. 39(C).

    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:ecoser:v:58:y:2022:i:c:s2212041622000791. 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: https://www.journals.elsevier.com/ecosystem-services .

    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.