IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v221y2010i20p2425-2434.html
   My bibliography  Save this article

Imposed and inherent scales in cellular automata models of habitat

Author

Listed:
  • Craig, Peter D.

Abstract

Both observational and modelling studies of the natural environment are characterised by their ‘grain’ and ‘extent’, the smallest and largest scales represented in time and space. These are imposed scales that should be chosen to ensure that the natural scales of the system are captured in the study. A simple cellular automata model of habitat represents only the presence or absence of vegetation, with global and local interactions described by four empirical parameters. Such a model can be formulated as a nonlinear Markov equation for the habitat probability. The equation produces inherent space and time scales that may be considered as transition scales or the scales for recovery from disturbance. However, if the resolution of the model is changed, the empirical parameters must be changed to preserve the properties of the system. Further, changes in the spatial resolution lead to different interpretations of the spatial structure. In particular, as the resolution is reduced, the apparent dominance of one habitat type over the other increases. The model provides an ability to compare both field and model investigations conducted at different resolutions in time and space.

Suggested Citation

  • Craig, Peter D., 2010. "Imposed and inherent scales in cellular automata models of habitat," Ecological Modelling, Elsevier, vol. 221(20), pages 2425-2434.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:20:p:2425-2434
    DOI: 10.1016/j.ecolmodel.2010.07.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380010003510
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2010.07.011?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. Colasanti, R.L. & Hunt, R. & Watrud, L., 2007. "A simple cellular automaton model for high-level vegetation dynamics," Ecological Modelling, Elsevier, vol. 203(3), pages 363-374.
    2. Pawlowski, Christopher W. & McCord, Christopher, 2009. "A Markov model for assessing ecological stability properties," Ecological Modelling, Elsevier, vol. 220(2), pages 86-95.
    3. J. Timothy Wootton, 2001. "Local interactions predict large-scale pattern in empirically derived cellular automata," Nature, Nature, vol. 413(6858), pages 841-844, October.
    4. Mathey, Anne-Hélène & Krcmar, Emina & Dragicevic, Suzana & Vertinsky, Ilan, 2008. "An object-oriented cellular automata model for forest planning problems," Ecological Modelling, Elsevier, vol. 212(3), pages 359-371.
    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. Hui, Cang, 2011. "Forecasting population trend from the scaling pattern of occupancy," Ecological Modelling, Elsevier, vol. 222(3), pages 442-446.

    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. Gong, Jian-zhou & Liu, Yan-sui & Xia, Bei-cheng & Zhao, Guan-wei, 2009. "Urban ecological security assessment and forecasting, based on a cellular automata model: A case study of Guangzhou, China," Ecological Modelling, Elsevier, vol. 220(24), pages 3612-3620.
    2. Wickramasuriya, Rohan Chandralal & Bregt, Arnold K. & van Delden, Hedwig & Hagen-Zanker, Alex, 2009. "The dynamics of shifting cultivation captured in an extended Constrained Cellular Automata land use model," Ecological Modelling, Elsevier, vol. 220(18), pages 2302-2309.
    3. Li, Hong & Arias, Mijail & Blauw, Anouk & Los, Hans & Mynett, Arthur E. & Peters, Steef, 2010. "Enhancing generic ecological model for short-term prediction of Southern North Sea algal dynamics with remote sensing images," Ecological Modelling, Elsevier, vol. 221(20), pages 2435-2446.
    4. Mouissie, A. Maarten & Apol, M. Emile F. & Heil, Gerrit W. & van Diggelen, Rudy, 2008. "Creation and preservation of vegetation patterns by grazing," Ecological Modelling, Elsevier, vol. 218(1), pages 60-72.
    5. Augustynczik, Andrey Lessa Derci & Arce, Julio Eduardo & Yousefpour, Rasoul & da Silva, Arinei Carlos Lindbeck, 2016. "Promoting harvesting stands connectivity and its economic implications in Brazilian forest plantations applying integer linear programming and simulated annealing," Forest Policy and Economics, Elsevier, vol. 73(C), pages 120-129.
    6. Fotakis, Dimitris G. & Sidiropoulos, Epameinondas & Myronidis, Dimitriοs & Ioannou, Kostas, 2012. "Spatial genetic algorithm for multi-objective forest planning," Forest Policy and Economics, Elsevier, vol. 21(C), pages 12-19.
    7. Bittebiere, A.-K. & Mony, C. & Clément, B. & Garbey, M., 2012. "Modeling competition between plants using an Individual Based Model: Methods and effects on the growth of two species with contrasted growth forms," Ecological Modelling, Elsevier, vol. 234(C), pages 38-50.
    8. Hualin Xie & Chih-Chun Kung & Yanting Zhang & Xiubin Li, 2012. "Simulation of Regionally Ecological Land Based on a Cellular Automation Model: A Case Study of Beijing, China," IJERPH, MDPI, vol. 9(8), pages 1-16, August.
    9. Kilburn, R. & Gregory, A. & Murray, A.G., 2012. "Using a Markov-Chain Monte-Carlo modelling approach to identify the relative risk to farmed Scottish Rainbow trout (Oncorhynchus mykiss) in a multi-sector industry of Viral Haemorrhagic Septicaemia Vi," Ecological Modelling, Elsevier, vol. 237, pages 34-42.
    10. Huan Cao & Tian Li & Shuxia Li & Tijun Fan, 2017. "An integrated emergency response model for toxic gas release accidents based on cellular automata," Annals of Operations Research, Springer, vol. 255(1), pages 617-638, August.
    11. Clancy, Damian & Tanner, Jason E. & McWilliam, Stephen & Spencer, Matthew, 2010. "Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo," Ecological Modelling, Elsevier, vol. 221(10), pages 1337-1347.
    12. 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.
    13. Convertino, M., 2011. "Neutral metacommunity clustering and SAR: River basin vs. 2-D landscape biodiversity patterns," Ecological Modelling, Elsevier, vol. 222(11), pages 1863-1879.
    14. Molyneaux, Lynette & Brown, Colin & Wagner, Liam & Foster, John, 2016. "Measuring resilience in energy systems: Insights from a range of disciplines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1068-1079.
    15. Correa, Renata Naoko & Scarpin, Cassius Tadeu & Ferrari, Linamara Smaniotto & Arce, Julio Eduardo, 2020. "Application of relax-and-fix heuristic in the aggregation of stands for tactical forest scheduling," Forest Policy and Economics, Elsevier, vol. 119(C).
    16. Mathey, Anne-Hélène & Krcmar, Emina & Dragicevic, Suzana & Vertinsky, Ilan, 2008. "An object-oriented cellular automata model for forest planning problems," Ecological Modelling, Elsevier, vol. 212(3), pages 359-371.
    17. Qian Wang & Chun-Jing Wang & Ji-Zhong Wan, 2022. "A Model-Based Assessment for the Ability of National Nature Reserves to Conserve the Picea Species in China under Predicted Climate Conditions," Sustainability, MDPI, vol. 14(12), pages 1-13, June.
    18. J Timothy Wootton & James D Forester, 2013. "Complex Population Dynamics in Mussels Arising from Density-Linked Stochasticity," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    19. Perez, Liliana & Dragicevic, Suzana, 2012. "Landscape-level simulation of forest insect disturbance: Coupling swarm intelligent agents with GIS-based cellular automata model," Ecological Modelling, Elsevier, vol. 231(C), pages 53-64.

    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:221:y:2010:i:20:p:2425-2434. 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.