Discrimination of Picea chihuahuana Martinez populations on the basis of numerous dendrometric, climatic and edaphic traits and genetic diversity
- Published
- Accepted
- Subject Areas
- Biodiversity, Conservation Biology, Ecology, Genetics, Plant Science
- Keywords
- conservation, cross-validation, tree species, linear discriminant function analysis
- Copyright
- © 2017 Dominguez-Guerrero et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Discrimination of Picea chihuahuana Martinez populations on the basis of numerous dendrometric, climatic and edaphic traits and genetic diversity. PeerJ Preprints 5:e2677v1 https://doi.org/10.7287/peerj.preprints.2677v1
Abstract
Background. Picea chihuahuana, which is endemic to Mexico, is currently listed as “Endangered” on the Red List. Chihuahua spruce is only found in the Sierra Madre Occidental (SMO), Mexico. About 42,600 individuals are distributed in forty populations. The populations are fragmented and can be classified into three distinct clusters in the SMO of the two States (south, center and north), each group separated by a distance of about 300 km. The total area covered P. chihuahuana trees is less than 300 ha. A recent study suggested assisted migration as an alternative to the ex situ conservation of P. chihuahuana, taking into consideration the genetic structure and diversity of the populations and also predictions regarding the future climate of the habitat. However, detailed background information is required to enable development of plans for protecting and conserving species and for successful assisted migration. Thus, it is important to identify differences between populations in relation to environmental conditions. The vitality and genetic diversity of populations, which affect vigour, evolution and adaptability of the species, must also be considered. In this study, we examined the P. chihuahuana tree community growing in fourteen different locations, with the overall aim of discriminating the populations and clusters of this species using 22 climatic, 27 edaphic and 15 dasometric variables and three genetic diversity indices. Methods. Each location was represented by one 50 x 50 m plot established in the center of the location in which was measured the climate, soil, dasometric and genetic variables. The putative neutral and adaptive AFLP were used to calculate genetic diversity. Multivariate discriminant analysis including cross-validation was considered to test for significant differences in variables in the southern, central and northern populations and locations of the P. chihuahuana tree community. Spearman's correlation test was used to analyze the relationships between genetic diversity, population size, and the climatic, soil and dasometric variables. Results. The discriminant analysis revealed 22 highly significant variables, which separated the southern, central and northern populations. The mean genetic diversity of P. chihuahuana was significantly correlated with the mean temperature in the warmest month. Genetic diversity of P. chihuahuana calculated with putative adaptive AFLP was not statistically significantly correlated with any environmental factor. Finally, no significant correlations were observed between any of the three genetic diversity indices and population size. Discussion. At least three different ecotypes of P. chihuahuana probably exist, as local adaptation may take place because of the different environmental conditions. Therefore, future reforestation programs should take into account these different ecotypes and environmental conditions.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Raw data
Raw data for the multivariate discriminant analysis including cross-validation.