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

S. I. PEREZ,J. A. F. DINIZ‐FILHO,V. BERNAL,P. N. GONZALEZ

doi:10.1111/j.1420-9101.2009.01905.x

S. I. PEREZ, J. A. F. DINIZ‐FILHO + Show 2 more

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https://doi.org/10.1111/j.1420-9101.2009.01905.x

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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.

Highlights

Phenotypic diversification in the intra-specific level results from random and nonrandom factors (Reznick et al, 1997; Hendry & Kinnison, 1999; Carrollet al., 2007)
We argue that any study aimed at evaluating the environ mental influence on phenotypic evolution within a species ought to apply an adequate methodology that account for spatial autocorrelation in data
The other principal compo nents (PCs) scores represent important shape variation among human populations, and because the main objective of this paper was to review the statistics of spatial regression techniques, in the following analyses we restrict the tests to the first PC score to simplify the explanation

Summary

Introduction

Phenotypic diversification in the intra-specific level results from random and nonrandom factors (Reznick et al, 1997; Hendry & Kinnison, 1999; Carrollet al., 2007). Environmental variation can profoundly affect the phenotypic variation within and among populations yet the developmental and evolutionary mechanisms behind this correlation are poorly understood (Badyaev, 2005)-, and nonrandom factors such as selection and phenotypic plasticity can be of great importance to account for phenotypic diversity at this taxonomic level (Hendry & Kinnison, 1999; Carroll et al, 2007; Ezardet al., 2009; Perez & Monteiro, 2009).

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