IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v272y2014icp116-128.html
   My bibliography  Save this article

Eelgrass re-establishment in shallow estuaries is affected by drifting macroalgae – Evaluated by agent-based modeling

Author

Listed:
  • Canal-Vergés, Paula
  • Potthoff, Michael
  • Hansen, Flemming Thorbjørn
  • Holmboe, Nikolaj
  • Rasmussen, Erik Kock
  • Flindt, Mogens R.

Abstract

It has been suggested that bedload transport of macroalgae in shallow lagoons and estuaries may negatively impact eelgrass through increased turbidity and physical stress. Increased turbidity and reduced benthic light availability for eelgrass occur when bedload transport of macroalgae erode surface sediment. Furthermore, drifting macroalgae ballistically damage eelgrass beds and increase seedling mortality. The frequency and impact of drifting macroalgae in Odense Fjord was evaluated with an agent-based model. The aims of this model were to understand and predict the mobility of opportunistic (Chaetomorpha linum) and non-ephemeral (Fucus vesiculosus) macroalgae and to describe and quantify the intensity and spatial distribution of bottom substrate physically affected by drifting macroalgae. The longest simulated movement by macroalgae was found to be 270 and 170km for brown and green algae respectively; while the macroalgae losses (export) out of the fjord were up to 11% of the total biomass; the simulated area impacted by macroalgae drift varied between 16% and 96.5% of the total fjord area; finally the degree on physically impacted area varied from 0.01 to 28.5m of algae trackm−2. The simulated pattern of drift distribution and hot spots for both brown and green algae fitted the geographical locations in which the algae community was observed on the field. Such high values for sea bed disturbances will have a major impact on the light availability due to sediment resuspension in bare bottoms and on rooted vegetation due to ballistic impacts in areas affected by algae drift.

Suggested Citation

  • Canal-Vergés, Paula & Potthoff, Michael & Hansen, Flemming Thorbjørn & Holmboe, Nikolaj & Rasmussen, Erik Kock & Flindt, Mogens R., 2014. "Eelgrass re-establishment in shallow estuaries is affected by drifting macroalgae – Evaluated by agent-based modeling," Ecological Modelling, Elsevier, vol. 272(C), pages 116-128.
    • Handle: RePEc:eee:ecomod:v:272:y:2014:i:c:p:116-128
    DOI: 10.1016/j.ecolmodel.2013.09.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380013004377
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2013.09.008?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. 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.
    2. Yñiguez, Aletta T. & McManus, John W. & DeAngelis, Donald L., 2008. "Allowing macroalgae growth forms to emerge: Use of an agent-based model to understand the growth and spread of macroalgae in Florida coral reefs, with emphasis on Halimeda tuna," Ecological Modelling, Elsevier, vol. 216(1), pages 60-74.
    3. Brush, Mark J. & Nixon, Scott W., 2010. "Modeling the role of macroalgae in a shallow sub-estuary of Narragansett Bay, RI (USA)," Ecological Modelling, Elsevier, vol. 221(7), pages 1065-1079.
    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. Kuusemäe, Kadri & Rasmussen, Erik Kock & Canal-Vergés, Paula & Flindt, Mogens R., 2016. "Modelling stressors on the eelgrass recovery process in two Danish estuaries," Ecological Modelling, Elsevier, vol. 333(C), pages 11-42.
    2. Canal-Vergés, Paula & Petersen, Jens K. & Rasmussen, Erik K. & Erichsen, Anders & Flindt, Mogens R., 2016. "Validating GIS tool to assess eelgrass potential recovery in the Limfjorden (Denmark)," Ecological Modelling, Elsevier, vol. 338(C), pages 135-148.
    3. Kuusemäe, Kadri & von Thenen, Miriam & Lange, Troels & Rasmussen, Erik Kock & Pothoff, Michael & Sousa, Ana I. & Flindt, Mogens R., 2018. "Agent Based Modelling (ABM) of eelgrass (Zostera marina) seedbank dynamics in a shallow Danish estuary," Ecological Modelling, Elsevier, vol. 371(C), pages 60-75.

    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. Yurek, Simeon & Eaton, Mitchell J. & Lavaud, Romain & Laney, R. Wilson & DeAngelis, Donald L. & Pine, William E. & La Peyre, Megan & Martin, Julien & Frederick, Peter & Wang, Hongqing & Lowe, Michael , 2021. "Modeling structural mechanics of oyster reef self-organization including environmental constraints and community interactions," Ecological Modelling, Elsevier, vol. 440(C).
    2. Glynn, Peter J & Glynn, Peter W & Maté, Juan & Riegl, Bernhard, 2020. "Agent-based model of Eastern Pacific damselfish and sea urchin interactions shows increased coral reef erosion under post-ENSO conditions," Ecological Modelling, Elsevier, vol. 423(C).
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Claudia Dislich & Elisabeth Hettig & Jan Salecker & Johannes Heinonen & Jann Lay & Katrin M Meyer & Kerstin Wiegand & Suria Tarigan, 2018. "Land-use change in oil palm dominated tropical landscapes—An agent-based model to explore ecological and socio-economic trade-offs," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-20, January.
    20. Dur, Gaël & Won, Eun-Ji & Han, Jeonghoon & Lee, Jae-Seong & Souissi, Sami, 2021. "An individual-based model for evaluating post-exposure effects of UV-B radiation on zooplankton reproduction," Ecological Modelling, Elsevier, vol. 441(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:ecomod:v:272:y:2014:i:c:p:116-128. 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.