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Spatial autocorrelation and sampling design in plant ecology

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Abstract

Using spatial analysis methods such as spatial autocorrelation coefficients (Moran's I and Geary's c) and kriging, we compare the capacity of different sampling designs and sample sizes to detect the spatial structure of a sugar-maple (Acer saccharum L.) tree density data set gathered from a secondary growth forest of southwestern Québec. Three different types of subsampling designs (random, systematic and systematic-cluster) with small sample sizes (50 and 64 points), obtained from this larger data set (200 points), are evaluated. The sensitivity of the spatial methods in the detection and the reconstruction of spatial patterns following the application of the various subsampling designs is discussed. We find that the type of sampling design plays an important role in the capacity of autocorrelation coefficients to detect significant spatial autocorrelation, and in the ability to accurately reconstruct spatial patterns by kriging. Sampling designs that contain varying sampling steps, like random and systematic-cluster designs, seem more capable of detecting spatial structures than a systematic design.

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Abbreviations

UPGMA =:

Unweighted Pair-Group Method using Arithmetic Averages

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Authors and Affiliations

  1. Department of Ecology and Evolution, State University of New York, 11794-5245, Stony Brook, New York, USA

    Marie-Josée Fortin

  2. Département de sciences biologiques, Université de Montréal, C.P. 6128, Succursale A, H3C 317, Montréal, Québec, Canada

    Pierre Drapeau & Pierre Legendre

Authors
  1. Marie-Josée Fortin
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  2. Pierre Drapeau
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  3. Pierre Legendre
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Fortin, MJ., Drapeau, P. & Legendre, P. Spatial autocorrelation and sampling design in plant ecology. Vegetatio 83, 209–222 (1989). https://doi.org/10.1007/BF00031693

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