IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v344y2017icp87-94.html
   My bibliography  Save this article

The integration of mark re-encounter and tracking data to quantify migratory connectivity

Author

Listed:
  • Korner-Nievergelt, Fränzi
  • Prévot, Céline
  • Hahn, Steffen
  • Jenni, Lukas
  • Liechti, Felix

Abstract

Animals which spend subsequent seasons in different areas connect geographical regions. The connection between breeding and non-breeding grounds is defined as migratory connectivity. The quantification of such connectivity is important, because movements between different locations can have strong consequences for the moving animal as well as the encountered habitats or ecosystems. Connectivity is usually investigated either on the basis of (few unsystematic) re-encounters of (often large numbers of) marked individuals or by observations of a few individuals tracked by remote sensing techniques, i.e. GPS or geolocation. The combination of qualitatively different data sets can reduce the limitations of each type of data and thus improve the accuracy of the estimated connectivity parameters considerably.

Suggested Citation

  • Korner-Nievergelt, Fränzi & Prévot, Céline & Hahn, Steffen & Jenni, Lukas & Liechti, Felix, 2017. "The integration of mark re-encounter and tracking data to quantify migratory connectivity," Ecological Modelling, Elsevier, vol. 344(C), pages 87-94.
  • Handle: RePEc:eee:ecomod:v:344:y:2017:i:c:p:87-94
    DOI: 10.1016/j.ecolmodel.2016.11.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380016306925
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2016.11.009?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. Elizabeth S. Garrett & Scott L. Zeger, 2000. "Latent Class Model Diagnosis," Biometrics, The International Biometric Society, vol. 56(4), pages 1055-1067, December.
    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. Yan Feng & Erpeng Liu & Zhang Yue & Qilin Zhang & Tiankuo Han, 2019. "The Evolutionary Trends of Health Behaviors in Chinese Elderly and the Influencing Factors of These Trends: 2005–2014," IJERPH, MDPI, vol. 16(10), pages 1-17, May.
    2. Hwan Chung & Brian P. Flaherty & Joseph L. Schafer, 2006. "Latent class logistic regression: application to marijuana use and attitudes among high school seniors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 723-743, October.
    3. Sarrias, Mauricio, 2021. "A two recursive equation model to correct for endogeneity in latent class binary probit models," Journal of choice modelling, Elsevier, vol. 40(C).
    4. Gioacchino Fazio & Francesca Giambona & Erasmo Vassallo & Elli Vassiliadis, 2018. "A Measure of Trust: The Italian Regional Divide in a Latent Class Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(1), pages 209-242, November.
    5. Subtil, Ana & de Oliveira, M. Rosário & Gonçalves, Luzia, 2012. "Conditional dependence diagnostic in the latent class model: A simulation study," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1407-1412.
    6. Kenneth W. Griffin & Lawrence M. Scheier & Bianca Acevedo & Jerry L. Grenard & Gilbert J. Botvin, 2011. "Long-Term Effects of Self-Control on Alcohol Use and Sexual Behavior among Urban Minority Young Women," IJERPH, MDPI, vol. 9(1), pages 1-23, December.
    7. Chia-Yi Chiu & Yan Sun & Yanhong Bian, 2018. "Cognitive Diagnosis for Small Educational Programs: The General Nonparametric Classification Method," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 355-375, June.
    8. Brian Neelon & A. James O'Malley & Sharon-Lise T. Normand, 2011. "A Bayesian Two-Part Latent Class Model for Longitudinal Medical Expenditure Data: Assessing the Impact of Mental Health and Substance Abuse Parity," Biometrics, The International Biometric Society, vol. 67(1), pages 280-289, March.
    9. Beth A. Reboussin & Nicholas S. Ialongo, 2010. "Latent transition models with latent class predictors: attention deficit hyperactivity disorder subtypes and high school marijuana use," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 145-164, January.
    10. Zhenke Wu & Livia Casciola‐Rosen & Antony Rosen & Scott L. Zeger, 2021. "A Bayesian approach to restricted latent class models for scientifically structured clustering of multivariate binary outcomes," Biometrics, The International Biometric Society, vol. 77(4), pages 1431-1444, December.
    11. Nir Billfeld & Moshe Kim, 2019. "Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision," Papers 1902.06286, arXiv.org.
    12. Labbe Aurelie & Bureau Alexandre & Merette Chantal, 2009. "Integration of Genetic Familial Dependence Structure in Latent Class Models," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-30, January.
    13. Benjamin E. Leiby & Mary D. Sammel & Thomas R. Ten Have & Kevin G. Lynch, 2009. "Identification of multivariate responders and non‐responders by using Bayesian growth curve latent class models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 505-524, September.
    14. Siqueira, Jose Ribamar & ter Horst, Enrique & Molina, German & Losada, Mauricio & Mateus, Marelby Amado, 2020. "A Bayesian examination of the relationship of internal and external touchpoints in the customer experience process across various service environments," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    15. Marcus E. Berzofsky & Paul P. Biemer & William D. Kalsbeek, 2014. "Local Dependence in Latent Class Analysis of Rare and Sensitive Events," Sociological Methods & Research, , vol. 43(1), pages 137-170, February.
    16. Diana L. Miglioretti, 2003. "Latent Transition Regression for Mixed Outcomes," Biometrics, The International Biometric Society, vol. 59(3), pages 710-720, September.
    17. Julia Y. Lin & Thomas R. Ten Have & Michael R. Elliott, 2009. "Nested Markov Compliance Class Model in the Presence of Time-Varying Noncompliance," Biometrics, The International Biometric Society, vol. 65(2), pages 505-513, June.
    18. Hwan Chung & Theodore Walls & Yousung Park, 2007. "A Latent Transition Model With Logistic Regression," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 413-435, September.
    19. Beth A. Reboussin & Edward H. Ip & Mark Wolfson, 2008. "Locally dependent latent class models with covariates: an application to under‐age drinking in the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 877-897, October.
    20. Yun Li & Jeremy M.G. Taylor & Michael R. Elliott, 2010. "A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials," Biometrics, The International Biometric Society, vol. 66(2), pages 523-531, 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:344:y:2017:i:c:p:87-94. 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.