IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v222y2011i10p1657-1665.html
   My bibliography  Save this article

Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms

Author

Listed:
  • Pontin, D.R.
  • Schliebs, S.
  • Worner, S.P.
  • Watts, M.J.

Abstract

Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it is important to predict how individuals, particularly passive dispersers are transported and dispersed in the ocean as well as in coastal regions so that new incursions of potential invasive species are rapidly detected and origins identified. Such predictions also support strategic monitoring, containment and/or eradication programs. To determine factors influencing a passive disperser, around coastal New Zealand, data from the genus Physalia (Cnidaria: Siphonophora) were used. Oceanographic data on wave height and wind direction and records of occurrences of Physalia on swimming beaches throughout the summer season were used to create models using artificial neural networks (ANNs) and Naϊve Bayesian Classifier (NBC). First, however, redundant and irrelevant data were removed using feature selection of a subset of variables. Two methods for feature selection were compared, one based on the multilayer perceptron and another based on an evolutionary algorithm. The models indicated that New Zealand appears to have two independent systems driven by currents and oceanographic variables that are responsible for the redistribution of Physalia from north of New Zealand and from the Tasman Sea to their subsequent presence in coastal waters. One system is centred in the east coast of northern New Zealand and the other involves a dynamic system that encompasses four other regions on both coasts of the country. Interestingly, the models confirm, molecular data obtained from Physalia in a previous study that identified a similar distribution of systems around New Zealand coastal waters. Additionally, this study demonstrates that the modelling methods used could generate valid hypotheses from noisy and complicated data in a system about which there is little previous knowledge.

Suggested Citation

  • Pontin, D.R. & Schliebs, S. & Worner, S.P. & Watts, M.J., 2011. "Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms," Ecological Modelling, Elsevier, vol. 222(10), pages 1657-1665.
  • Handle: RePEc:eee:ecomod:v:222:y:2011:i:10:p:1657-1665
    DOI: 10.1016/j.ecolmodel.2011.03.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380011001098
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2011.03.002?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. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(5), pages 777-788, October.
    2. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(1), pages 151-160, February.
    3. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(4), pages 629-637, August.
    4. Ready, Jonathan & Kaschner, Kristin & South, Andy B. & Eastwood, Paul D. & Rees, Tony & Rius, Josephine & Agbayani, Eli & Kullander, Sven & Froese, Rainer, 2010. "Predicting the distributions of marine organisms at the global scale," Ecological Modelling, Elsevier, vol. 221(3), pages 467-478.
    5. ,, 1999. "Problems And Solutions," Econometric Theory, Cambridge University Press, vol. 15(3), pages 427-432, June.
    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. Crisci, C. & Ghattas, B. & Perera, G., 2012. "A review of supervised machine learning algorithms and their applications to ecological data," Ecological Modelling, Elsevier, vol. 240(C), pages 113-122.

    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. Coro, Gianpaolo & Pagano, Pasquale & Ellenbroek, Anton, 2013. "Combining simulated expert knowledge with Neural Networks to produce Ecological Niche Models for Latimeria chalumnae," Ecological Modelling, Elsevier, vol. 268(C), pages 55-63.
    2. Coro, Gianpaolo & Vilas, Luis Gonzalez & Magliozzi, Chiara & Ellenbroek, Anton & Scarponi, Paolo & Pagano, Pasquale, 2018. "Forecasting the ongoing invasion of Lagocephalus sceleratus in the Mediterranean Sea," Ecological Modelling, Elsevier, vol. 371(C), pages 37-49.
    3. Coro, Gianpaolo & Magliozzi, Chiara & Vanden Berghe, Edward & Bailly, Nicolas & Ellenbroek, Anton & Pagano, Pasquale, 2016. "Estimating absence locations of marine species from data of scientific surveys in OBIS," Ecological Modelling, Elsevier, vol. 323(C), pages 61-76.
    4. Krzysztof S. Targiel & Maciej Nowak & Tadeusz Trzaskalik, 2018. "Scheduling non-critical activities using multicriteria approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(3), pages 585-598, September.
    5. F. Castro-Llanos & G. Hyman & J. Rubiano & J. Ramirez-Villegas & H. Achicanoy, 2019. "Climate change favors rice production at higher elevations in Colombia," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(8), pages 1401-1430, December.
    6. Okitonyumbe Y.F., Joseph & Ulungu, Berthold E.-L., 2013. "Nouvelle caractérisation des solutions efficaces des problèmes d’optimisation combinatoire multi-objectif [New characterization of efficient solution in multi-objective combinatorial optimization]," MPRA Paper 66123, University Library of Munich, Germany.
    7. Amit Kumar & Anila Gupta, 2013. "Mehar’s methods for fuzzy assignment problems with restrictions," Fuzzy Information and Engineering, Springer, vol. 5(1), pages 27-44, March.
    8. Monica Motta & Caterina Sartori, 2020. "Normality and Nondegeneracy of the Maximum Principle in Optimal Impulsive Control Under State Constraints," Journal of Optimization Theory and Applications, Springer, vol. 185(1), pages 44-71, April.
    9. Zhang, Quanzhong & Wei, Haiyan & Liu, Jing & Zhao, Zefang & Ran, Qiao & Gu, Wei, 2021. "A Bayesian network with fuzzy mathematics for species habitat suitability analysis: A case with limited Angelica sinensis (Oliv.) Diels data," Ecological Modelling, Elsevier, vol. 450(C).
    10. Chenchen Wu & Dachuan Xu & Donglei Du & Wenqing Xu, 2016. "An approximation algorithm for the balanced Max-3-Uncut problem using complex semidefinite programming rounding," Journal of Combinatorial Optimization, Springer, vol. 32(4), pages 1017-1035, November.
    11. Gengping Zhu & Matthew J Petersen & Wenjun Bu, 2012. "Selecting Biological Meaningful Environmental Dimensions of Low Discrepancy among Ranges to Predict Potential Distribution of Bean Plataspid Invasion," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
    12. Uzma Ashraf & Hassan Ali & Muhammad Nawaz Chaudry & Irfan Ashraf & Adila Batool & Zafeer Saqib, 2016. "Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model," Sustainability, MDPI, vol. 8(8), pages 1-11, July.
    13. Ernst Althaus & Felix Rauterberg & Sarah Ziegler, 2020. "Computing Euclidean Steiner trees over segments," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 309-325, October.
    14. World Bank, 2003. "Argentina : Reforming Policies and Institutions for Efficiency and Equity of Public Expenditures," World Bank Publications - Reports 14637, The World Bank Group.
    15. Ceretani, Andrea N. & Salva, Natalia N. & Tarzia, Domingo A., 2018. "Approximation of the modified error function," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 607-617.
    16. Parihar, Amit Kumar Singh & Hammer, Thomas & Sridhar, G., 2015. "Development and testing of tube type wet ESP for the removal of particulate matter and tar from producer gas," Renewable Energy, Elsevier, vol. 74(C), pages 875-883.
    17. Liang, Wanwan & Papeş, Monica & Tran, Liem & Grant, Jerome & Washington-Allen, Robert & Stewart, Scott & Wiggins, Gregory, 2018. "The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift," Ecological Modelling, Elsevier, vol. 388(C), pages 1-9.
    18. Brown, Jeffrey R., 2001. "Private pensions, mortality risk, and the decision to annuitize," Journal of Public Economics, Elsevier, vol. 82(1), pages 29-62, October.
    19. Mark Christensen, 2007. "What We Might Know (But Aren't Sure) About Public-Sector Accrual Accounting," Australian Accounting Review, CPA Australia, vol. 17(41), pages 51-65, March.
    20. Wong, Patricia J.Y., 2015. "Eigenvalues of a general class of boundary value problem with derivative-dependent nonlinearity," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 908-930.

    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:222:y:2011:i:10:p:1657-1665. 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.