IDEAS home Printed from
   My bibliography  Save this article

Autocorrelation offsets zero-inflation in models of tropical saplings density


  • Flores, O.
  • Rossi, V.
  • Mortier, F.


Modelling the local density of tropical saplings can provide insights into the ecological processes that drive species regeneration and thereby help predict population recovery after disturbance. Yet, few studies have addressed the challenging issues in autocorrelation and zero-inflation of local density. This paper presents Hierarchical Bayesian Modelling (HBM) of sapling density that includes these two features. Special attention is devoted to variable selection, model estimation and comparison.

Suggested Citation

  • Flores, O. & Rossi, V. & Mortier, F., 2009. "Autocorrelation offsets zero-inflation in models of tropical saplings density," Ecological Modelling, Elsevier, vol. 220(15), pages 1797-1809.
  • Handle: RePEc:eee:ecomod:v:220:y:2009:i:15:p:1797-1809
    DOI: 10.1016/j.ecolmodel.2009.01.030

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL:
    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

    1. Miller, Jennifer & Franklin, Janet & Aspinall, Richard, 2007. "Incorporating spatial dependence in predictive vegetation models," Ecological Modelling, Elsevier, vol. 202(3), pages 225-242.
    2. Angers, Jean-Francois & Biswas, Atanu, 2003. "A Bayesian analysis of zero-inflated generalized Poisson model," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 37-46, February.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Lambert, Dayton M. & Brown, Jason P. & Florax, Raymond J.G.M., 2010. "A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application," Regional Science and Urban Economics, Elsevier, vol. 40(4), pages 241-252, July.

    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. Santika, Truly & Hutchinson, Michael F., 2009. "The effect of species response form on species distribution model prediction and inference," Ecological Modelling, Elsevier, vol. 220(19), pages 2365-2379.
    2. Susanne Gschlößl & Claudia Czado, 2008. "Modelling count data with overdispersion and spatial effects," Statistical Papers, Springer, vol. 49(3), pages 531-552, July.
    3. Feng-Chang Xie & Jin-Guan Lin & Bo-Cheng Wei, 2014. "Bayesian zero-inflated generalized Poisson regression model: estimation and case influence diagnostics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1383-1392, June.
    4. Liviano Solís, Daniel & Arauzo Carod, Josep Maria, 2011. "Industrial Location and Space: New Insights," Working Papers 2072/152137, Universitat Rovira i Virgili, Department of Economics.
    5. Platts, Philip J. & McClean, Colin J. & Lovett, Jon C. & Marchant, Rob, 2008. "Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty," Ecological Modelling, Elsevier, vol. 218(1), pages 121-134.
    6. Tommi Härkänen & Anna But & Jari Haukka, 2017. "Non-parametric Bayesian Intensity Model: Exploring Time-to-Event Data on Two Time Scales," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 798-814, September.
    7. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    8. Jesse Elliott & Zemin Bai & Shu-Ching Hsieh & Shannon E Kelly & Li Chen & Becky Skidmore & Said Yousef & Carine Zheng & David J Stewart & George A Wells, 2020. "ALK inhibitors for non-small cell lung cancer: A systematic review and network meta-analysis," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-18, February.
    9. Christina Leuker & Thorsten Pachur & Ralph Hertwig & Timothy J. Pleskac, 2019. "Do people exploit risk–reward structures to simplify information processing in risky choice?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 76-94, August.
    10. Francois Olivier & Laval Guillaume, 2011. "Deviance Information Criteria for Model Selection in Approximate Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-25, July.
    11. Ferraz, V.R.S. & Moura, F.A.S., 2012. "Small area estimation using skew normal models," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2864-2874.
    12. Yirigui Yirigui & Sang-Woo Lee & A. Pouyan Nejadhashemi & Matthew R. Herman & Jong-Won Lee, 2019. "Relationships between Riparian Forest Fragmentation and Biological Indicators of Streams," Sustainability, MDPI, Open Access Journal, vol. 11(10), pages 1-24, May.
    13. Ander Wilson & Jessica Tryner & Christian L'Orange & John Volckens, 2020. "Bayesian nonparametric monotone regression," Environmetrics, John Wiley & Sons, Ltd., vol. 31(8), December.
    14. Douglas J Shinneman & Robert E Means & Kevin M Potter & Valerie D Hipkins, 2016. "Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-24, March.
    15. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    16. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    17. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    18. Padilla, Juan L. & Azevedo, Caio L.N. & Lachos, Victor H., 2018. "Multidimensional multiple group IRT models with skew normal latent trait distributions," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 250-268.
    19. David Macro & Jeroen Weesie, 2016. "Inequalities between Others Do Matter: Evidence from Multiplayer Dictator Games," Games, MDPI, Open Access Journal, vol. 7(2), pages 1-23, April.
    20. Peng Wei & Wei Pan, 2010. "Network‐based genomic discovery: application and comparison of Markov random‐field models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 105-125, January.


    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:220:y:2009:i:15:p:1797-1809. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    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: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.