IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v265y2013icp74-84.html
   My bibliography  Save this article

Modeling the habitat associations and spatial distribution of benthic macroinvertebrates: A hierarchical Bayesian model for zero-inflated biomass data

Author

Listed:
  • Lecomte, J.B.
  • Benoît, H.P.
  • Etienne, M.P.
  • Bel, L.
  • Parent, E.

Abstract

Biomass samples from marine scientific surveys are commonly used to investigate spatial and temporal variations in stock abundances. Biomass records are often characterized by a high proportion of zeros on the one hand, and occasional large catches on the other. These features induce a modeling challenge when trying to understand the state of populations and their ecological associations with one another and with habitat. We develop a hierarchical Bayesian model to represent the spatial structure of biomass and analyze the spatial distribution and habitat associations of three species of macro-invertebrates sampled in the southern Gulf of St. Lawrence (Canada). A zero-inflated distribution based on a compound Poisson with Gamma marks is used for the observation layer, and a linear model with spatial correlated errors accounts for the role of habitat variables (temperature, depth and sediment type) in the process layer. Maps of quantities of interest (e.g. probability of presence, quantity of biomass) are produced, taking into account the uncertainty of the estimated parameters and observation errors. This hierarchical Bayesian modeling approach provides a useful tool for spatial management of human activities that may affect living resources that may affect living resources, such as marine protected areas.

Suggested Citation

  • Lecomte, J.B. & Benoît, H.P. & Etienne, M.P. & Bel, L. & Parent, E., 2013. "Modeling the habitat associations and spatial distribution of benthic macroinvertebrates: A hierarchical Bayesian model for zero-inflated biomass data," Ecological Modelling, Elsevier, vol. 265(C), pages 74-84.
    • Handle: RePEc:eee:ecomod:v:265:y:2013:i:c:p:74-84
    DOI: 10.1016/j.ecolmodel.2013.06.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380013003013
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2013.06.017?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. Sileshi, Gudeta & Hailu, Girma & Nyadzi, Gerson I., 2009. "Traditional occupancy–abundance models are inadequate for zero-inflated ecological count data," Ecological Modelling, Elsevier, vol. 220(15), pages 1764-1775.
    2. Zhang, Hao, 2004. "Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 250-261, January.
    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. Rosa Fernández Ropero & María Julia Flores & Rafael Rumí, 2022. "Bayesian Networks for Preprocessing Water Management Data," Mathematics, MDPI, vol. 10(10), pages 1-18, May.
    2. Jakubowski, Wojciech & Szulczewski, Wiesław & Żyromski, Andrzej & Biniak-Pieróg, Małgorzata, 2016. "The estimation of basket willow (Salix viminalis) yield – New approach, Part II: Theoretical model and its practical application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 843-851.

    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. Rajala, T. & Penttinen, A., 2014. "Bayesian analysis of a Gibbs hard-core point pattern model with varying repulsion range," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 530-541.
    2. Zhang, Tonglin, 2017. "An example of inconsistent MLE of spatial covariance parameters under increasing domain asymptotics," Statistics & Probability Letters, Elsevier, vol. 120(C), pages 108-113.
    3. Girard, Didier A., 2016. "Asymptotic near-efficiency of the “Gibbs-energy and empirical-variance” estimating functions for fitting Matérn models — I: Densely sampled processes," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 191-197.
    4. Sierra Pugh & Matthew J. Heaton & Jeff Svedin & Neil Hansen, 2019. "Spatiotemporal Lagged Models for Variable Rate Irrigation in Agriculture," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 634-650, December.
    5. Monterrubio-Gómez, Karla & Roininen, Lassi & Wade, Sara & Damoulas, Theodoros & Girolami, Mark, 2020. "Posterior inference for sparse hierarchical non-stationary models," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
    6. Philip A. White & Alan E. Gelfand, 2021. "Multivariate functional data modeling with time-varying clustering," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 586-602, September.
    7. Reihaneh Entezari & Patrick E. Brown & Jeffrey S. Rosenthal, 2020. "Bayesian spatial analysis of hardwood tree counts in forests via MCMC," Environmetrics, John Wiley & Sons, Ltd., vol. 31(4), June.
    8. Jaewoo Park & Sangwan Lee, 2022. "A projection‐based Laplace approximation for spatial latent variable models," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
    9. Matthew J. Heaton & Stephan R. Sain & Andrew J. Monaghan & Olga V. Wilhelmi & Mary H. Hayden, 2015. "An Analysis of an Incomplete Marked Point Pattern of Heat-Related 911 Calls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 123-135, March.
    10. Bachoc, François & Lagnoux, Agnès & Nguyen, Thi Mong Ngoc, 2017. "Cross-validation estimation of covariance parameters under fixed-domain asymptotics," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 42-67.
    11. Anderes, Ethan B. & Stein, Michael L., 2011. "Local likelihood estimation for nonstationary random fields," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 506-520, March.
    12. David L. Miller & Richard Glennie & Andrew E. Seaton, 2020. "Understanding the Stochastic Partial Differential Equation Approach to Smoothing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 1-16, March.
    13. De Oliveira, Victor & Kone, Bazoumana, 2015. "Prediction intervals for integrals of Gaussian random fields," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 37-51.
    14. Jorge Castillo-Mateo & Miguel Lafuente & Jesús Asín & Ana C. Cebrián & Alan E. Gelfand & Jesús Abaurrea, 2022. "Spatial Modeling of Day-Within-Year Temperature Time Series: An Examination of Daily Maximum Temperatures in Aragón, Spain," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(3), pages 487-505, September.
    15. Philip A. White & Durban G. Keeler & Daniel Sheanshang & Summer Rupper, 2022. "Improving piecewise linear snow density models through hierarchical spatial and orthogonal functional smoothing," Environmetrics, John Wiley & Sons, Ltd., vol. 33(5), August.
    16. May, Paul & Biesecker, Matthew & Rekabdarkolaee, Hossein Moradi, 2022. "Response envelopes for linear coregionalization models," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    17. Varin, Cristiano & Host, Gudmund & Skare, Oivind, 2005. "Pairwise likelihood inference in spatial generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1173-1191, June.
    18. Jonas Wallin & David Bolin, 2015. "Geostatistical Modelling Using Non-Gaussian Matérn Fields," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(3), pages 872-890, September.
    19. Mailu, Stephen & Kuloba, Bernard & Ruto, Eric & Nyangena, Wilfred, 2010. "Effect of cropping policy on landowner reactions towards wildlife: a case of Naivasha area, Kenya," MPRA Paper 21308, University Library of Munich, Germany.
    20. Mailu, Stephen & Lukibisi, Barasa & Waithaka, Michael, 2011. "Application of various count models: Sahiwal demand from Naivasha," MPRA Paper 32074, University Library of Munich, Germany, revised 06 Jul 2011.

    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:265:y:2013:i:c:p:74-84. 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.