IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v329y2016icp5-17.html
   My bibliography  Save this article

A cellular automaton model for grasshopper population dynamics in Inner Mongolia steppe habitats

Author

Listed:
  • Zhang, Na
  • Jing, Yong-Cai
  • Liu, Cheng-Yu
  • Li, Yao
  • Shen, Jing

Abstract

We developed a cellular automaton model for the spatiotemporal dynamics of grasshopper (Oedaleus decorus asiaticus) populations with a density of 3–12indm−2 in a typical semiarid steppe. The model simulates grasshopper dynamics within each cell and dispersal between adjacent cells at each time step from the completion of hatching to ovipositing. After the model parameters were obtained or calibrated, the natural growth of grasses (the preferred food of O. d. asiaticus), and their variation after being fed on by cattle and grasshoppers, were modeled to explore grasshopper–plant interactions. Dispersal rules were formulated by considering the abundance of grasshoppers and grasses and their interactions within both potential source and sink cells, habitat conditions of sink cells, and spatial isolation (or connectedness) between source and sink cells. A case study was conducted at a sample landscape in Xianghuangqi County, Inner Mongolia, China. The temporal variations of the modeled grass biomass and grasshopper density were consistent with actual variations. The modeled densities were within the measured ranges for most of the cells. The estimation accuracy was higher for the 4th and 5th instars than for adults, and was higher for cells with higher initial densities (not lower than 3indm−2). The modeled results showed that the main factors influencing grasshopper density differed for different instars. During the 2nd and 3rd instar, grasshopper density depended on the number of hatched nymphs and their mortality. During the 4th instar, habitat suitability determined the density threshold that caused grasshoppers to disperse and the potential sink or source cells. This resulted in different density variations between the cells with initial densities >6.0indm−2 and those with densities <6.0indm−2. During and after the 5th instar, grasshopper density was very stable due to the slightly decreasing habitat suitability. The modeled results are expected to provide a scientific basis for predicting grasshopper dynamics and controlling grasshopper plagues in heterogeneous spaces.

Suggested Citation

  • Zhang, Na & Jing, Yong-Cai & Liu, Cheng-Yu & Li, Yao & Shen, Jing, 2016. "A cellular automaton model for grasshopper population dynamics in Inner Mongolia steppe habitats," Ecological Modelling, Elsevier, vol. 329(C), pages 5-17.
    • Handle: RePEc:eee:ecomod:v:329:y:2016:i:c:p:5-17
    DOI: 10.1016/j.ecolmodel.2016.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380016300618
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2016.03.002?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. Perry, George L.W. & Enright, Neal J., 2007. "Contrasting outcomes of spatially implicit and spatially explicit models of vegetation dynamics in a forest-shrubland mosaic," Ecological Modelling, Elsevier, vol. 207(2), pages 327-338.
    2. Blackwell, P.G., 2007. "Heterogeneity, patchiness and correlation of resources," Ecological Modelling, Elsevier, vol. 207(2), pages 349-355.
    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. Lili Wang & Qiulin Xiong & Gaofeng Wu & Atul Gautam & Jianfang Jiang & Shuang Liu & Wenji Zhao & Hongliang Guan, 2019. "Spatio-Temporal Variation Characteristics of PM 2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018," IJERPH, MDPI, vol. 16(21), pages 1-20, November.
    2. Caiqing Liu & Huifeng Pan & Yurong Wei, 2023. "Spatial Distribution Characteristics and Influential Factors of Major Towns in Guizhou Province Analyzed with ArcGIS," Sustainability, MDPI, vol. 15(14), pages 1-14, July.
    3. Xiaojian Wei & Li Zhao & Penggen Cheng & Mingrui Xie & Huimin Wang, 2022. "Spatial-Temporal Dynamic Evaluation of Ecosystem Service Value and Its Driving Mechanisms in China," Land, MDPI, vol. 11(7), pages 1-20, June.
    4. Hao Wu & Tongtong Shan & Hassan Saif Khan & Lin Dong & Hua Li, 2024. "The Spatiotemporal Differentiation Characteristics and Driving Forces of Carbon Emissions from Major Livestock Farming in the Shaanxi–Gansu–Ningxia Region," Agriculture, MDPI, vol. 14(10), pages 1-16, October.
    5. Sidong Zhao & Weiwei Li & Kaixu Zhao & Ping Zhang, 2021. "Change Characteristics and Multilevel Influencing Factors of Real Estate Inventory—Case Studies from 35 Key Cities in China," Land, MDPI, vol. 10(9), pages 1-29, September.
    6. Qiurong Xu & Xinqi Zheng & Chunxiao Zhang, 2018. "Quantitative Analysis of the Determinants Influencing Urban Expansion: A Case Study in Beijing, China," Sustainability, MDPI, vol. 10(5), pages 1-16, May.

    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. Ferrarini, Alessandro & Tomaselli, Marcello, 2010. "A new approach to the analysis of adjacencies: Potentials for landscape insights," Ecological Modelling, Elsevier, vol. 221(16), pages 1889-1896.
    2. Bennie, Jonathan & Huntley, Brian & Wiltshire, Andrew & Hill, Mark O. & Baxter, Robert, 2008. "Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland," Ecological Modelling, Elsevier, vol. 216(1), pages 47-59.
    3. Mitchell, Michael S. & Powell, Roger A., 2008. "Estimated home ranges can misrepresent habitat relationships on patchy landscapes," Ecological Modelling, Elsevier, vol. 216(3), pages 409-414.
    4. Sadchatheeswaran, Saachi & Branch, George M. & Shannon, Lynne J. & Coll, Marta & Steenbeek, Jeroen, 2021. "A novel approach to explicitly model the spatiotemporal impacts of structural complexity created by alien ecosystem engineers in a marine benthic environment," Ecological Modelling, Elsevier, vol. 459(C).
    5. Hyytiäinen, Kari & Lehtiniemi, Maiju & Niemi, Jarkko K. & Tikka, Kimmo, 2013. "An optimization framework for addressing aquatic invasive species," Ecological Economics, Elsevier, vol. 91(C), pages 69-79.

    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:329:y:2016:i:c:p:5-17. 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.