Ecology Spatial Pattern Analysis
by
Michael Rosenberg, Corey Anderson
  • LAST MODIFIED: 31 March 2016
  • DOI: 10.1093/obo/9780199830060-0144

Introduction

When one looks around the world, spatial pattern appears to be ubiquitous; little appears to be random. The analysis of spatial pattern has become a fundamental part of science and a major tool of geographical and ecological analysis; its popularity has rapidly accelerated as computational resources for conducting spatial analysis have become widely available. Broadly speaking, spatial pattern analysis is focused on (1) describing the observed pattern of data in space, (2) testing whether the observed pattern differs from an expected null model (such as Complete Spatial Randomness), and (3) fitting observed distributions to theoretical models for the sake of prediction or significance testing. Recognition of spatial pattern is extremely important for the statistical analysis of ecological data because most statistical tests assume independence of data observations: this may not be a reliable assumption if the data are spatially structured. More generally, basic description of spatial pattern can provide important clues about underlying ecological and evolutionary processes, which can then be further vetted via additional hypothesis testing and/or modeling.

General Overview

Methods for analyzing spatial pattern have been developed independently in a wide variety of disciplines. Studies in ecology and statistical geography traditionally focused on the description of spatial pattern and on testing whether observed patterns are statistically significant. Plant ecologists, in particular, made a large contribution of methods based on the analysis of Point Patterns, as well as many methods for Transect and Surface Analysis. The landmark monograph Cliff and Ord 1973 detailed a suite of methods to test for Spatial Autocorrelation that were quickly assimilated into the ecological sciences. Geologists focused on interpolation, fitting empirical data to theoretical models to estimate variables at unsampled locations. Despite differing objectives, the methods used to measure spatial structure show remarkable convergence across disciplines. Liebhold and Gurevitch 2002 describes an effort in ecology to compare, contrast, and synthesize these various approaches to spatial pattern analysis into a single paradigm, and integration of these disciplines has continued to advance spatial analysis in ecology. For ecologists new to spatial analytics, the review paper Fortin, et al. 2002 provides a brief but thorough overview of the basic concepts and methods relevant to ecological studies.

  • Cliff, Andrew D., and J. Keith Ord. 1973. Spatial autocorrelation. London: Pion.

    E-mail Citation »

    This monograph ushered in a new age of statistical geography by thoroughly describing a set of methods (i.e., join count analysis, Moran’s I, and Geary’s c) for measuring and testing for spatial autocorrelation between variate values of geographic units and regression residuals.

  • Fortin, Marie-Josée, Mark R. T. Dale, and Jay ver Hoef. 2002. Spatial analysis in ecology. In Encylopedia of enviromatics. Edited by Abdel H. El-Shaarawi and Walter W. Piegorsch, 2051–2058. Chichester, UK: John Wiley.

    E-mail Citation »

    Surveys key concepts and popular methods of spatial pattern analysis for ecologists.

  • Liebhold, Andrew M., and Jessica Gurevitch. 2002. Integrating the statistical analysis of spatial data in ecology. Ecography 25:553–557.

    DOI: 10.1034/j.1600-0587.2002.250505.xE-mail Citation »

    Describes the interdisciplinary attempt to bridge the disparate methods of spatial pattern analysis found in ecology, geography, and geology; five companion papers are introduced that delve into the integration in more detail.

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