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

Author

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  • 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
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    File URL: http://repec.deps.unisi.it/quaderni/854.pdf
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    References listed on IDEAS

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

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