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Spatial regression techniques for inter-population data: studying the relationships between morphological and environmental variation

  1. S. I. PEREZ1,
  2. J. A. F. DINIZ-FILHO2,
  3. V. BERNAL1,
  4. P. N. GONZALEZ1

Article first published online: 26 NOV 2009

DOI: 10.1111/j.1420-9101.2009.01905.x

Issue

Journal of Evolutionary Biology

Journal of Evolutionary Biology

Volume 23, Issue 2, pages 237–248, February 2010

Additional Information

How to Cite

PEREZ, S. I. , DINIZ-FILHO, J. A. F., BERNAL, V. and GONZALEZ, P. N. (2010), Spatial regression techniques for inter-population data: studying the relationships between morphological and environmental variation. Journal of Evolutionary Biology, 23: 237–248. doi: 10.1111/j.1420-9101.2009.01905.x

Author Information

  1. 1

    División Antropología, Museo de La Plata, Universidad Nacional de La Plata, La Plata, Argentina

  2. 2

    Departamento de Ecologia, ICB, Universidade Federal de Goiás, CP 131, 74001-970, Goiânia, Goiás, Brazil

*S. Ivan Perez, División Antropología, Museo de La Plata, Universidad Nacional de La Plata, Paseo del Bosque s/n, La Plata 1900, Argentina. Tel.: +54 221 4215184; fax: +54 221 4257744; e-mail: iperez@fcnym.unlp.edu.ar

Publication History

  1. Issue published online: 13 JAN 2010
  2. Article first published online: 26 NOV 2009
  3. Received 24 June 2009; accepted 23 October 2009

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Keywords:

  • autocorrelation;
  • evolutionary anthropology;
  • morphometric techniques;
  • spatial statistical techniques

Abstract

Understanding the importance of environmental dimensions behind the morphological variation among populations has long been a central goal of evolutionary biology. The main objective of this study was to review the spatial regression techniques employed to test the association between morphological and environmental variables. In addition, we show empirically how spatial regression techniques can be used to test the association of cranial form variation among worldwide human populations with a set of ecological variables, taking into account the spatial autocorrelation in data. We suggest that spatial autocorrelation must be studied to explore the spatial structure underlying morphological variation and incorporated in regression models to provide more accurate statistical estimates of the relationships between morphological and ecological variables. Finally, we discuss the statistical properties of these techniques and the underlying reasons for using the spatial approach in population studies.

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