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| Pometti,Carolina L.; Bessega,Cecilia F.; Saidman,Beatriz O.; Vilardi,Juan C.. |
| Bayesian clustering as implemented in STRUCTURE or GENELAND software is widely used to form genetic groups of populations or individuals. On the other hand, in order to satisfy the need for less computer-intensive approaches, multivariate analyses are specifically devoted to extracting information from large datasets. In this paper, we report the use of a dataset of AFLP markers belonging to 15 sampling sites of Acacia caven for studying the genetic structure and comparing the consistency of three methods: STRUCTURE, GENELAND and DAPC. Of these methods, DAPC was the fastest one and showed accuracy in inferring the K number of populations (K = 12 using the find.clusters option and K = 15 with a priori information of populations). GENELAND in turn, provides... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Acacia caven; AFLP; GENELAND; DAPC. |
| Ano: 2014 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572014000100012 |
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| Muñoz,Cristina; Ovalle,Carlos; Zagal,Erick. |
| The overexploitation of the natural resources in the Mediterranean-type climate region of Chile, has resulted in natural forest fragmentation and expansion of Acacia caven (Mol), forming the Espinal ecosystem (EE) that includes two million of hectares in the Central part of Chile. The effect of the anthropogenic intervention over the soil organic carbon (SOC) in these ecosystems is unknown. The aim of this study was to quantify the SOC stocks and it profile distribution in the EE. This was achieved by collecting soils samples according to soil coverage percentage canopy from: well-preserved Espinal (WPE), 51-80 %; good-preserved (GE), 26-50 %; degraded (DE), 11-25 % and very degraded (VDE), 0-10 %. We also included a native forest (NF), 80-98 % of soil... |
| Tipo: Journal article |
Palavras-chave: Acacia caven; Degraded ecosystems; Soil use intensity. |
| Ano: 2007 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-27912007000100002 |
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