A balanced view of scale in spatial statistical analysis

Authors

  • J. L. Dungan,

  • J. N. Perry,

  • M. R. T. Dale,

  • P. Legendre,

  • S. Citron-Pousty,

  • M.-J. Fortin,

  • A. Jakomulska,

  • M. Miriti,

  • M. S. Rosenberg


J. L. Dungan (jdungan@gaia.arc.nasa.gov ), MS 242-2, NASA Ames Research Center, Moffett Field, CA 94035-1000, USA. – J. N. Perry, Plant and Invertebrate Ecology Div., Rothamsted Experimental Station, Harpenden, Herts, U.K. AL5 2JQ. – M. R. T. Dale, Dept of Biological Sciences, Univ. of Alberta, Edmonton, AB, Canada T6G 2E9. – P. Legendre, Dépt de Sciences Biol., Univ. de Montréal, C.P. 6128 succ. A, Montréal, QC, Canada H3C 3J7. – S. Citron-Pousty, Social Science Statlab, Yale Univ., 140 Prospect St., P.O. Box 208208, New Haven, CT 06520-8208, USA. – M.-J. Fortin, Dept of Zoology, 25 Harbord St., Univ. of Toronto, Toronto, ON, Canada M5S 3G5. – A. Jakomulska, Remote Sensing of Environment Lab., Fac. of Geography and Regional Studies, Univ. of Warsaw, 26/28, PL-00-927 Warsaw, Poland. – M. Miriti, Dept of Evolution, Ecology and Organismal Biology, The Ohio State Univ., 1735 Neil Ave., Columbus, OH 43210-1293, USA. – M. S. Rosenberg, Dept of Biology, Arizona State Univ., P.O. Box 871501, Tempe, AZ 85287-1501, USA.

Abstract

Concepts of spatial scale, such as extent, grain, resolution, range, footprint, support and cartographic ratio are not interchangeable. Because of the potential confusion among the definitions of these terms, we suggest that authors avoid the term “scale” and instead refer to specific concepts. In particular, we are careful to discriminate between observation scales, scales of ecological phenomena and scales used in spatial statistical analysis. When scales of observation or analysis change, that is, when the unit size, shape, spacing or extent are altered, statistical results are expected to change. The kinds of results that may change include estimates of the population mean and variance, the strength and character of spatial autocorrelation and spatial anisotropy, patch and gap sizes and multivariate relationships. The first three of these results (precision of the mean, variance and spatial autocorrelation) can sometimes be estimated using geostatistical support-effect models. We present four case studies of organism abundance and cover illustrating some of these changes and how conclusions about ecological phenomena (process and structure) may be affected. We identify the influence of observational scale on statistical results as a subset of what geographers call the Modifiable Area Unit Problem (MAUP). The way to avoid the MAUP is by careful construction of sampling design and analysis. We recommend a set of considerations for sampling design to allow useful tests for specific scales of a phenomenon under study. We further recommend that ecological studies completely report all components of observation and analysis scales to increase the possibility of cross-study comparisons.

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