Making genetic biodiversity measurable : a review of statistical multivariate methods to study variability at gene level
Measures of agro-ecosystems genetic variability are essential to sustain scientific-based actions and policies tending to protect the ecosystem services they provide. To build the genetic variability datum it is necessary to deal with a large number and different types of variables. Molecular marker...
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| Autores principales: | , , , |
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| Formato: | article Artículo publishedVersion |
| Lenguaje: | Inglés |
| Publicado: |
Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias
2011
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| Materias: | |
| Acceso en línea: | http://bdigital.uncu.edu.ar/3936 |
| Aporte de: |
| Sumario: | Measures of agro-ecosystems genetic variability are essential to sustain scientific-based
actions and policies tending to protect the ecosystem services they provide. To build the genetic
variability datum it is necessary to deal with a large number and different types of variables.
Molecular marker data is highly dimensional by nature, and frequently additional types of
information are obtained, as morphological and physiological traits. This way, genetic variability
studies are usually associated with the measurement of several traits on each entity. Multivariate
methods are aimed at finding proximities between entities characterized by multiple traits by
summarizing information in few synthetic variables.
In this work we discuss and illustrate several multivariate methods used for different
purposes to build the datum of genetic variability. We include methods applied in studies for
exploring the spatial structure of genetic variability and the association of genetic data to
other sources of information. Multivariate techniques allow the pursuit of the genetic variability
datum, as a unifying notion that merges concepts of type, abundance and distribution of
variability at gene level.
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