IDEAS home Printed from https://ideas.repec.org/p/usi/wpaper/854.html
   My bibliography  Save this paper

Design-based mapping of plant species presence, association and richness by nearest-neighbor interpolation

Author

Listed:
  • Alice Bartolini
  • Rosa Maria Di Biase
  • Lorenzo Fattorini
  • Sara Franceschi
  • Agnese Marcelli

Abstract

The difference between potential and actual distribution of species is emphasized, pointing out the ecological importance of maps depicting the actual species presence on the study region. Owing to the impossibility of performing complete surveys over large areas, the presence/absence of species at a pre-fixed spatial grain is estimated for any location of the study region from the presences/absences recorded within plots centered at sample locations and having the same grain. Estimation is performed in a design-based framework by means of the well-known nearestneighbor interpolator. Association maps and species richness maps are obtained as products and sum of the presence maps of single species. The design-based asymptotic unbiasedness and consistency of these maps are theoretically proven and pseudo-population bootstrap estimators of their precision are proposed and discussed. A simulation study is performed on a real community of 302 tree species settled in a 50-ha rectangle in the lowland tropical moist forest of Barro Colorado Island (BCI), central Panama, to check the finite-sample performance of the proposal. A case study for estimating the presence map and the association of holly oak and white violet in the Montagnola Senese (Central Italy) is reported. Technical details are contained in the appendices.

Suggested Citation

  • Alice Bartolini & Rosa Maria Di Biase & Lorenzo Fattorini & Sara Franceschi & Agnese Marcelli, 2021. "Design-based mapping of plant species presence, association and richness by nearest-neighbor interpolation," Department of Economics University of Siena 854, Department of Economics, University of Siena.
  • Handle: RePEc:usi:wpaper:854
    as

    Download full text from publisher

    File URL: http://repec.deps.unisi.it/quaderni/854.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. L. Fattorini & M. Marcheselli & L. Pratelli, 2018. "Design-Based Maps for Finite Populations of Spatial Units," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 686-697, April.
    2. Lucio Barabesi, 2003. "A Monte Carlo integration approach to Horvitz-Thompson estimation in replicated environmental designs," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 355-374.
    3. Little R.J., 2004. "To Model or Not To Model? Competing Modes of Inference for Finite Population Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 546-556, January.
    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. Rosa Maria Di Biase & Lorenzo Fattorini & Sara Franceschi & Mirko Grotti & Nicola Puletti & Piermaria Corona, 2022. "From model selection to maps: A completely design‐based data‐driven inference for mapping forest resources," Environmetrics, John Wiley & Sons, Ltd., vol. 33(7), November.
    2. Little Roderick J., 2013. "Discussion," Journal of Official Statistics, Sciendo, vol. 29(3), pages 363-366, June.
    3. Kunihama, T. & Herring, A.H. & Halpern, C.T. & Dunson, D.B., 2016. "Nonparametric Bayes modeling with sample survey weights," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 41-48.
    4. Marivoet, Wim & De Herdt, Tom, 2017. "From figures to facts: making sense of socio-economic surveys in the Democratic Republic of the Congo (DRC)," IOB Analyses & Policy Briefs 23, Universiteit Antwerpen, Institute of Development Policy (IOB).
    5. Geoffrey Jones & Wesley O. Johnson, 2016. "A Bayesian Superpopulation Approach to Inference for Finite Populations Based on Imperfect Diagnostic Outcomes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 314-327, June.
    6. J. Andrew Royle, 2009. "Analysis of Capture–Recapture Models with Individual Covariates Using Data Augmentation," Biometrics, The International Biometric Society, vol. 65(1), pages 267-274, March.
    7. David Kaplan & Chansoon Lee, 2018. "Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments," Evaluation Review, , vol. 42(4), pages 423-457, August.
    8. Bijak Jakub & Bryant Johan & Gołata Elżbieta & Smallwood Steve, 2021. "Preface," Journal of Official Statistics, Sciendo, vol. 37(3), pages 533-541, September.
    9. Parcel Joshua D. & Schroeter John R. & Azzam Azzeddine M, 2017. "A Re-Examination of Multistage Economies in Hog Farming," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 15(2), pages 1-15, December.
    10. Ralf Münnich & Siegfried Gabler & Christian Bruch & Jan Pablo Burgard & Tobias Enderle & Jan-Philipp Kolb & Thomas Zimmermann, 2015. "Tabellenauswertungen im Zensus unter Berücksichtigung fehlender Werte," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 9(3), pages 269-304, December.
    11. Sahar Z. Zangeneh & Roderick J. Little, 2022. "Likelihood‐Based Inference for the Finite Population Mean with Post‐Stratification Information Under Non‐Ignorable Non‐Response," International Statistical Review, International Statistical Institute, vol. 90(S1), pages 17-36, December.
    12. Shira Mitchell & Andrew Gelman & Rebecca Ross & Joyce Chen & Sehrish Bari & Uyen Kim Huynh & Matthew W. Harris & Sonia Ehrlich Sachs & Elizabeth A. Stuart & Avi Feller & Susanna Makela & Alan M. Zasla, "undated". "The Millennium Villages Project: A Retrospective, Observational, Endline Evaluation," Mathematica Policy Research Reports 8376cf28448b40f69543be760, Mathematica Policy Research.
    13. Tenan, Simone & Rotger Vallespir, Andreu & Igual, José Manuel & Moya, Óscar & Royle, J. Andrew & Tavecchia, Giacomo, 2013. "Population abundance, size structure and sex-ratio in an insular lizard," Ecological Modelling, Elsevier, vol. 267(C), pages 39-47.
    14. Ying Yuan & Roderick J. A. Little, 2007. "Parametric and Semiparametric Model-Based Estimates of the Finite Population Mean for Two-Stage Cluster Samples with Item Nonresponse," Biometrics, The International Biometric Society, vol. 63(4), pages 1172-1180, December.
    15. Lorenzo Fattorini & Marzia Marcheselli & Caterina Pisani & Luca Pratelli, 2022. "Design‐based properties of the nearest neighbor spatial interpolator and its bootstrap mean squared error estimator," Biometrics, The International Biometric Society, vol. 78(4), pages 1454-1463, December.
    16. West Brady T. & Sakshaug Joseph W. & Aurelien Guy Alain S., 2018. "Accounting for Complex Sampling in Survey Estimation: A Review of Current Software Tools," Journal of Official Statistics, Sciendo, vol. 34(3), pages 721-752, September.
    17. Shixiao Zhang & Peisong Han & Changbao Wu, 2023. "Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference," International Statistical Review, International Statistical Institute, vol. 91(2), pages 165-192, August.
    18. Hwanhee Hong & Kara E. Rudolph & Elizabeth A. Stuart, 2017. "Bayesian Approach for Addressing Differential Covariate Measurement Error in Propensity Score Methods," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1078-1096, December.
    19. Geoffrey Jones & Wesley O. Johnson, 2014. "Prior Elicitation: Interactive Spreadsheet Graphics With Sliders Can Be Fun, and Informative," The American Statistician, Taylor & Francis Journals, vol. 68(1), pages 42-51, February.
    20. Robert M. Groves & Steven G. Heeringa, 2006. "Responsive design for household surveys: tools for actively controlling survey errors and costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 439-457, July.

    More about this item

    Keywords

    species distribution; asymptotic unbiasedness; consistency; pseudo-population bootstrap; simulation study; case study.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:usi:wpaper:854. 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: Fabrizio Becatti (email available below). General contact details of provider: https://edirc.repec.org/data/desieit.html .

    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.