IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v278y2014icp74-84.html
   My bibliography  Save this article

Predicting the seed shadows of a Neotropical tree species dispersed by primates using an agent-based model with internal decision making for movements

Author

Listed:
  • Bialozyt, Ronald
  • Flinkerbusch, Sebastian
  • Niggemann, Marc
  • Heymann, Eckhard W.

Abstract

The spatial pattern of endozoochorous seed dispersal depends strongly on the movement patterns of the disperser and the gut transit times of the seeds. In this study, we developed an individual-based simulation model for seed dispersal in the tropical tree Parkia panurensis carried out via two primate species (Saguinus mystax and Saguinus nigrifrons) using data collected at the Estación Biológica Quebrada Blanco in northeastern Peruvian Amazonia. From field data, we identified factors determining the movement patterns of the primates. We assumed that the need for energy (food) is the driving force for movement and that other activities are scheduled accordingly. The final movement pattern is therefore an interplay between directional travel toward fruit trees, semi-directional searching for prey and stationary resting phases.

Suggested Citation

  • Bialozyt, Ronald & Flinkerbusch, Sebastian & Niggemann, Marc & Heymann, Eckhard W., 2014. "Predicting the seed shadows of a Neotropical tree species dispersed by primates using an agent-based model with internal decision making for movements," Ecological Modelling, Elsevier, vol. 278(C), pages 74-84.
    • Handle: RePEc:eee:ecomod:v:278:y:2014:i:c:p:74-84
    DOI: 10.1016/j.ecolmodel.2014.02.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030438001400088X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2014.02.004?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. Guy Pe'er & Klaus Henle & Claudia Dislich & Karin Frank, 2011. "Breaking Functional Connectivity into Components: A Novel Approach Using an Individual-Based Model, and First Outcomes," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-18, August.
    2. Dylan C. Kesler & Jeffrey R. Walters & John J. Kappes, 2010. "Social influences on dispersal and the fat-tailed dispersal distribution in red-cockaded woodpeckers," Behavioral Ecology, International Society for Behavioral Ecology, vol. 21(6), pages 1337-1343.
    3. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    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. Tang, Yi & Liu, Mingyu & Sun, Zhanli, 2020. "Indirect effects of grazing on wind-dispersed elm seeds in sparse woodlands of Northern China," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 9(12).

    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. Katherine A. Zeller & David W. Wattles & Javan M. Bauder & Stephen DeStefano, 2020. "Forecasting Seasonal Habitat Connectivity in a Developing Landscape," Land, MDPI, vol. 9(7), pages 1-20, July.
    2. Langhammer, Maria & Thober, Jule & Lange, Martin & Frank, Karin & Grimm, Volker, 2019. "Agricultural landscape generators for simulation models: A review of existing solutions and an outline of future directions," Ecological Modelling, Elsevier, vol. 393(C), pages 135-151.
    3. Allen, Corrie & Gonzales, Rodolphe & Parrott, Lael, 2020. "Modelling the contribution of ephemeral wetlands to landscape connectivity," Ecological Modelling, Elsevier, vol. 419(C).
    4. Douglas J Bruggeman, 2015. "The Value of Learning about Natural History in Biodiversity Markets," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-21, December.
    5. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    6. Vimercati, Giovanni & Hui, Cang & Davies, Sarah J. & Measey, G. John, 2017. "Integrating age structured and landscape resistance models to disentangle invasion dynamics of a pond-breeding anuran," Ecological Modelling, Elsevier, vol. 356(C), pages 104-116.
    7. Jagadish, Arundhati & Dwivedi, Puneet & McEntire, Kira D. & Chandar, Mamta, 2019. "Agent-based modeling of “cleaner” cookstove adoption and woodfuel use: An integrative empirical approach," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    8. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    9. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    10. Jascha-Alexander Koch & Jens Lausen & Moritz Kohlhase, 2021. "Internalizing the externalities of overfunding: an agent-based model approach for analyzing the market dynamics on crowdfunding platforms," Journal of Business Economics, Springer, vol. 91(9), pages 1387-1430, November.
    11. Crevier, Lucas Phillip & Salkeld, Joseph H & Marley, Jessa & Parrott, Lael, 2021. "Making the best possible choice: Using agent-based modelling to inform wildlife management in small communities," Ecological Modelling, Elsevier, vol. 446(C).
    12. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.
    13. David, Viviane & Joachim, Sandrine & Tebby, Cleo & Porcher, Jean-Marc & Beaudouin, Rémy, 2019. "Modelling population dynamics in mesocosms using an individual-based model coupled to a bioenergetics model," Ecological Modelling, Elsevier, vol. 398(C), pages 55-66.
    14. Lorscheid, Iris & Meyer, Matthias, 2016. "Divide and conquer: Configuring submodels for valid and efficient analyses of complex simulation models," Ecological Modelling, Elsevier, vol. 326(C), pages 152-161.
    15. Moritz Kersting & Andreas Bossert & Leif Sörensen & Benjamin Wacker & Jan Chr. Schlüter, 2021. "Predicting effectiveness of countermeasures during the COVID-19 outbreak in South Africa using agent-based simulation," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    16. Meli, Mattia & Auclerc, Apolline & Palmqvist, Annemette & Forbes, Valery E. & Grimm, Volker, 2013. "Population-level consequences of spatially heterogeneous exposure to heavy metals in soil: An individual-based model of springtails," Ecological Modelling, Elsevier, vol. 250(C), pages 338-351.
    17. Groeneveld, Jürgen & Johst, Karin & Kawaguchi, So & Meyer, Bettina & Teschke, Mathias & Grimm, Volker, 2015. "How biological clocks and changing environmental conditions determine local population growth and species distribution in Antarctic krill (Euphausia superba): a conceptual model," Ecological Modelling, Elsevier, vol. 303(C), pages 78-86.
    18. Henzler, Julia & Weise, Hanna & Enright, Neal J. & Zander, Susanne & Tietjen, Britta, 2018. "A squeeze in the suitable fire interval: Simulating the persistence of fire-killed plants in a Mediterranean-type ecosystem under drier conditions," Ecological Modelling, Elsevier, vol. 389(C), pages 41-49.
    19. Kanapaux, William & Kiker, Gregory A., 2013. "Development and testing of an object-oriented model for adaptively managing human disturbance of least tern (Sternula antillarum) nesting habitat," Ecological Modelling, Elsevier, vol. 268(C), pages 64-77.
    20. Fenintsoa Andriamasinoro & Raphael Danino-Perraud, 2021. "Use of artificial intelligence to assess mineral substance criticality in the French market: the example of cobalt," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 34(1), pages 19-37, April.

    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:278:y:2014:i:c:p:74-84. 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.