IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v293y2014icp139-149.html
   My bibliography  Save this article

Applying network methods to acoustic telemetry data: Modeling the movements of tropical marine fishes

Author

Listed:
  • Finn, J.T.
  • Brownscombe, J.W.
  • Haak, C.R.
  • Cooke, S.J.
  • Cormier, R.
  • Gagne, T.
  • Danylchuk, A.J.

Abstract

Modeling animal movements is fundamental to animal ecology as it provides the foundation for further exploration into mechanisms affecting individual and population-level processes. In the last few decades, biotelemetry has enabled scientists to track the movements of marine life across a variety of scales. However, the use of such technology is progressing faster than the analytical techniques for modeling movement patterns. In summer 2012, we deployed an acoustic telemetry array around Culebra, Puerto Rico, consisting of 48 remote receivers that can detect coded transmissions sent by tags implanted in fish. We surgically implanted transmitters in bonefish (n=28), great barracuda (n=2) and permit (n=1) as part of a multi-year study. In January 2013, we downloaded over 850,000 detections from 39 receivers for 31 fish (several receivers had zero fish detections, and two receivers were not downloaded), and used that six-month data set to explore how graph theory and network analysis can be used to model the movement ecology of the tagged fish. We analyzed this data as two types of graphs. First, a bipartite graph was constructed by linking each fish with an edge weighted by the number of detections of that fish by that receiver. Bipartite graphs are not explicitly spatial, but rather represent which fish associate with which receivers. Second, spatial movement graphs for individuals were built by linking receivers (nodes) by edges with the number of times each fish moved along that edge as weights. The bipartite graph identified groups of fish visiting the same sites, and groups of sites visited by the same fish. Of the six community detection algorithms used, Multilevel, Fast-Greedy, and Walk-Trap performed best, with similar module partitions and modularity scores. All three of these algorithms produced modules (groups) that appear to reflect working hypotheses related to the coastal bathymetry, habitat types, and associated movement ecology of the tagged species. Spatial movement graphs were very different for each fish examined and reflect behavioral differences. Fish exhibited various movement patterns, some showing the pattern of a central place forager (bonefish), while others cruised along a territory (great barracuda and permit).

Suggested Citation

  • Finn, J.T. & Brownscombe, J.W. & Haak, C.R. & Cooke, S.J. & Cormier, R. & Gagne, T. & Danylchuk, A.J., 2014. "Applying network methods to acoustic telemetry data: Modeling the movements of tropical marine fishes," Ecological Modelling, Elsevier, vol. 293(C), pages 139-149.
  • Handle: RePEc:eee:ecomod:v:293:y:2014:i:c:p:139-149
    DOI: 10.1016/j.ecolmodel.2013.12.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380013006017
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2013.12.014?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. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    2. Driezen, Kassandra & Adriaensen, Frank & Rondinini, Carlo & Doncaster, C. Patrick & Matthysen, Erik, 2007. "Evaluating least-cost model predictions with empirical dispersal data: A case-study using radiotracking data of hedgehogs (Erinaceus europaeus)," Ecological Modelling, Elsevier, vol. 209(2), pages 314-322.
    3. Peter D. Hoff, 2005. "Bilinear Mixed-Effects Models for Dyadic Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 286-295, March.
    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. Almpanidou, Vasiliki & Mazaris, Antonios D. & Mertzanis, Yorgos & Avraam, Ioannis & Antoniou, Ioannis & Pantis, John D. & Sgardelis, Stefanos P., 2014. "Providing insights on habitat connectivity for male brown bears: A combination of habitat suitability and landscape graph-based models," Ecological Modelling, Elsevier, vol. 286(C), pages 37-44.
    2. Borrett, Stuart R. & Moody, James & Edelmann, Achim, 2014. "The rise of Network Ecology: Maps of the topic diversity and scientific collaboration," Ecological Modelling, Elsevier, vol. 293(C), pages 111-127.

    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. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    2. Xiao-Li Meng, 2016. "Discussion: Should a Working Model Actually Work?," International Statistical Review, International Statistical Institute, vol. 84(3), pages 362-367, December.
    3. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    5. Koen Jochmans, 2018. "Semiparametric Analysis of Network Formation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 705-713, October.
    6. Eric A. Vance & Elizabeth A. Archie & Cynthia J. Moss, 2009. "Social networks in African elephants," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 273-293, December.
    7. Marchette, David J. & Priebe, Carey E., 2008. "Predicting unobserved links in incompletely observed networks," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1373-1386, January.
    8. repec:spo:wpecon:info:hdl:2441/dpido2upv86tqc7td18fd2mna is not listed on IDEAS
    9. repec:hal:wpspec:info:hdl:2441/dpido2upv86tqc7td18fd2mna is not listed on IDEAS
    10. Mark S. Handcock, 2017. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1537-1539, October.
    11. Montes-Rojas Gabriel, 2022. "Subgraph Network Random Effects Error Components Models: Specification and Testing," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 17-34, January.
    12. Adrian E. Raftery, 2017. "Comment: Extending the Latent Position Model for Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1531-1534, October.
    13. Yingda Lu & Kinshuk Jerath & Param Vir Singh, 2013. "The Emergence of Opinion Leaders in a Networked Online Community: A Dyadic Model with Time Dynamics and a Heuristic for Fast Estimation," Management Science, INFORMS, vol. 59(8), pages 1783-1799, August.
    14. Sosa, Juan & Betancourt, Brenda, 2022. "A latent space model for multilayer network data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    15. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    16. Tyler H. McCormick & Tian Zheng, 2015. "Latent Surface Models for Networks Using Aggregated Relational Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1684-1695, December.
    17. Patacchini, Eleonora & Hsieh, Chih-Sheng & Lin, Xu, 2019. "Social Interaction Methods," CEPR Discussion Papers 14141, C.E.P.R. Discussion Papers.
    18. Amanda M. Y. Chu & Thomas W. C. Chan & Mike K. P. So & Wing-Keung Wong, 2021. "Dynamic Network Analysis of COVID-19 with a Latent Pandemic Space Model," IJERPH, MDPI, vol. 18(6), pages 1-22, March.
    19. Peter D. Hoff, 2009. "Multiplicative latent factor models for description and prediction of social networks," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 261-272, December.
    20. Dass, Mayukh & Fox, Gavin L., 2011. "A holistic network model for supply chain analysis," International Journal of Production Economics, Elsevier, vol. 131(2), pages 587-594, June.
    21. Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1516-1530, October.
    22. ter Braak, Cajo J.F. & Kourmpetis, Yiannis & Kiers, Henk A.L. & Bink, Marco C.A.M., 2009. "Approximating a similarity matrix by a latent class model: A reappraisal of additive fuzzy clustering," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3183-3193, June.

    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:293:y:2014:i:c:p:139-149. 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.