IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v438y2020ics0304380020304075.html
   My bibliography  Save this article

Combining disciplines: Dealing with observed and cryptic animal residencies in passive telemetry data by applying econometric decision-making models

Author

Listed:
  • Bruneel, Stijn
  • Verhelst, Pieterjan
  • Reubens, Jan
  • Luca, Stijn
  • Coeck, Johan
  • Moens, Tom
  • Goethals, Peter

Abstract

Migratory species do not necessarily behave migratory continuously. An important aspect of studying migratory species is therefore to distinguish between movement and resident behavior. Telemetry is a rapidly evolving technique to study animal movement, but the number of data processing techniques to account for resident behavior remains limited. In this study we describe how models that were initially developed to predict human customer behavior, i.e. two-part and three-part models, provide new insights in the movement of migrating eel by accounting for resident behavior apparent from telemetry data sets. In econometrics, two-part models take into account that the decision of a customer to purchase an item and the decision of the customer on the purchase quantity of the concerning product, might be affected by different factors. Similarly, the factors that affect the decision of a fish to migrate or to stay resident might be different from the factors that affect the swimming speed of the fish. Telemetry data of eel movement in the Permanent Belgian Acoustic Receiver Network (PBARN) of the Scheldt Estuary was used. This network with high detection probabilities allowed residencies to be recognized, defined, and introduced as zero values in a movement-residency data set. Two-part models, which consider movement decision, i.e. residency or movement, and movement intensity, i.e. swimming speed, as two different processes or parts of one larger model, outperformed one-part models that do not make that distinction. This underlines the complex migration behavior eels exhibit. These two-part models in turn were outperformed by three-part models that also accounted for cryptic (i.e. unobserved) residencies. While the one-part model identified the tides and the distance from the most upstream gate as most important for movement, the three-part models identified the tides as most important for the movement decision and the distance from the most upstream gate as most important for the movement intensity. Considering movement decisions, cryptic residencies and movement intensity in modeling efforts increased model performance by 9.8%, underlining the importance of acknowledging the potentially complex behavior animals exhibit.

Suggested Citation

  • Bruneel, Stijn & Verhelst, Pieterjan & Reubens, Jan & Luca, Stijn & Coeck, Johan & Moens, Tom & Goethals, Peter, 2020. "Combining disciplines: Dealing with observed and cryptic animal residencies in passive telemetry data by applying econometric decision-making models," Ecological Modelling, Elsevier, vol. 438(C).
  • Handle: RePEc:eee:ecomod:v:438:y:2020:i:c:s0304380020304075
    DOI: 10.1016/j.ecolmodel.2020.109340
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380020304075
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2020.109340?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. Madden, David, 2008. "Sample selection versus two-part models revisited: The case of female smoking and drinking," Journal of Health Economics, Elsevier, vol. 27(2), pages 300-307, March.
    2. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    3. Winfried Pohlmeier & Volker Ulrich, 1995. "An Econometric Model of the Two-Part Decisionmaking Process in the Demand for Health Care," Journal of Human Resources, University of Wisconsin Press, vol. 30(2), pages 339-361.
    4. Federico Belotti & Partha Deb & Willard G. Manning & Edward C. Norton, 2015. "twopm: Two-part models," Stata Journal, StataCorp LP, vol. 15(1), pages 3-20, March.
    5. Murray Smith, 2003. "On dependency in double-hurdle models," Statistical Papers, Springer, vol. 44(4), pages 581-595, October.
    Full references (including those not matched with items on IDEAS)

    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. Luiz Flavio Andrade & Thomas Rapp & Christine Sevilla-Dedieu, 2016. "Exploring the determinants of endocrinologist visits by patients with diabetes," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(9), pages 1173-1184, December.
    2. Yaldız Hanedar, Elmas & Broccardo, Eleonora & Bazzana, Flavio, 2014. "Collateral requirements of SMEs: The evidence from less-developed countries," Journal of Banking & Finance, Elsevier, vol. 38(C), pages 106-121.
    3. Mark N. Harris & Xueyan Zhao, 2004. "Modelling Tobacco Consumption with a Zero-Inflated Ordered Probit Model," Monash Econometrics and Business Statistics Working Papers 14/04, Monash University, Department of Econometrics and Business Statistics.
    4. Harris, Mark N. & Zhao, Xueyan, 2007. "A zero-inflated ordered probit model, with an application to modelling tobacco consumption," Journal of Econometrics, Elsevier, vol. 141(2), pages 1073-1099, December.
    5. Elmas Yaldiz Hanedar & Eleonora Broccardo & Flavio Bazzana, 2012. "Collateral Requirements of SMEs:The Evidence from Less–Developed Countries," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0034, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    6. Myck, Michal & Nici?ska, Anna & Morawski, Leszek, 2009. "Count Your Hours: Returns to Education in Poland," IZA Discussion Papers 4332, Institute of Labor Economics (IZA).
    7. McGovern, Mark E. & Canning, David & Bärnighausen, Till, 2018. "Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers," Economics Letters, Elsevier, vol. 171(C), pages 239-244.
    8. García, Jaume & Suárez, María José, 2023. "The relevance of specification assumptions when analyzing the drivers of physical activity practice," Economic Modelling, Elsevier, vol. 119(C).
    9. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Comparisons of observed and unobserved parameter heterogeneity in modeling vehicle-miles driven," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    10. Angulo, Ana María & Barberán, Ramón & Egea, Pilar & Mur, Jesús, 2011. "An analysis of health expenditure on a microdata population basis," Economic Modelling, Elsevier, vol. 28(1), pages 169-180.
    11. Julieta Trías, 2004. "Determinantes de la Utilización de los Servicios de Salud: El caso de los niños en la Argentina," CEDLAS, Working Papers 0009, CEDLAS, Universidad Nacional de La Plata.
    12. Benjamin J. McMichael & W. Kip Viscusi, 2017. "The Punitive Damages Calculus: The Differential Incidence of State Punitive Damages Reforms," Southern Economic Journal, John Wiley & Sons, vol. 84(1), pages 82-97, July.
    13. Marwan Benali & Bernhard Brümmer & Victor Afari‐Sefa, 2018. "Smallholder participation in vegetable exports and age‐disaggregated labor allocation in Northern Tanzania," Agricultural Economics, International Association of Agricultural Economists, vol. 49(5), pages 549-562, September.
    14. Alfred K. Mukong & Ernest N. Tingum, 2018. "The Demand for Cigarettes: New Evidence from South Africa," Working Papers 745, Economic Research Southern Africa.
    15. Blackman, Allen & Qin, Ping & Yang, Jun, 2020. "How costly are driving restrictions? Contingent valuation evidence from Beijing," Journal of Environmental Economics and Management, Elsevier, vol. 104(C).
    16. Yujing Shen, 2008. "Choice of a regular physician and health care utilization: principal-agent issues for American veterans," Applied Economics, Taylor & Francis Journals, vol. 40(4), pages 449-463.
    17. Emily Schmidt & Paul Dorosh & Rachel Gilbert, 2021. "Impacts of COVID‐19 induced income and rice price shocks on household welfare in Papua New Guinea: Household model estimates," Agricultural Economics, International Association of Agricultural Economists, vol. 52(3), pages 391-406, May.
    18. Truong, Thao Duc & Bui, Phuong Cam, 2022. "The lasting effect of formalization on credit access: Evidence from Vietnamese private SMEs," Finance Research Letters, Elsevier, vol. 47(PB).
    19. Luca Grassetti & Laura Rizzi, 2019. "The determinants of individual health care expenditures in the Italian region of Friuli Venezia Giulia: evidence from a hierarchical spatial model estimation," Empirical Economics, Springer, vol. 56(3), pages 987-1009, March.
    20. Wilkinson, Lindsay R. & Schafer, Markus H. & Wilkinson, Renae, 2020. "How painful is a recession? An assessment of two future-oriented buffering mechanisms," Social Science & Medicine, Elsevier, vol. 255(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:438:y:2020:i:c:s0304380020304075. 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.