Spatial Chemometric Analyses of Essential Oil Variability inEugenia dysenterica

Eliane C Vilela,Ronaldo V Naves,José C Seraphin,Suzana C Santos,Pedro H Ferri,Alessandra R Duarte

doi:10.5935/0103-5053.20130099

Eliane C Vilela, Ronaldo V Naves + Show 4 more

Open Access

https://doi.org/10.5935/0103-5053.20130099

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Abstract

Chemovariations in essential oils were used for studying the spatial chemical structure of eight E. dysenterica populations in Central Brazilian Cerrado. Previously, multivariate Mantel autocorrelogram and chemical matrix variation partitioning, using the spatial and environmental data sets as predictors, have suggested a highly significant spatial variation in essential oils. In the present study, spatial chemometric methods using variograms and probability maps detected and characterized the spatial chemical structure among populations, as well as the environmental factors responsible for them. All these strategies indicated that the populations differ chemically whenever the geographical distance exceeds 120 km, an indicator of the minimal distance between samples required for conserving the genetic diversity of populations. Although being scarcely used with secondary metabolites, these methodologies may be used in a wide range of applications in species management and may lead to an effective integration of genetic, chemical and ecological perspectives.

Highlights

Phenotypic variation patterns in secondary plant metabolites have strong ecological significance and are an important factor in understanding the evolutionary history of natural populations, as they affect both intra and interspecific interactions.[1]
To identify the existence of spatial structures and describe the spatial variability of response variables, variograms of redundancy analysis (RDA) first axes from each oil data set, percentage values of oxygenated terpenes, oxygenated monoterpene and sesquiterpene hydrocarbons were computed for sampling populations
The study of variograms is important in natural product chemistry, as its nugget effect provides information on the error made by the measuring instruments and by chemovariations undetected in the sampling neighborhood

Summary

Introduction

Phenotypic variation patterns in secondary plant metabolites have strong ecological significance and are an important factor in understanding the evolutionary history of natural populations, as they affect both intra and interspecific interactions.[1]. Computed at each variable to compare the distance in which a high spatial dependence occurred in the variogram.[20] If the model shows only the nugget, there is no spatial structure in the data.

Results

Conclusion