305
genetic structure of natural populations to a fine scale. This genetic structure may be
manifested among geographically distinct populations, within a local group of
plants, or even in the progeny of individuals. Ecological factors affecting reproduction and dispersal are likely to be particularly important in determining genetic
structure. Also, spatial and genetic patterns are often assumed to result from environmental heterogeneity and differential selection pressures (Loveless and
Hamrick 1984).
The spatial structure of the genomic variation among natural populations constitutes a central topic in evolutionary biology. The structure is primarily influenced by
the population density, breeding system and environmental heterogeneity, among
other factors. For plants, the ability to extend the geographical distribution and
maintain genetic variability within populations depends on the gene flow mediated
by seed movement and pollen dispersal (Peakall et al. 2003; Moran and Clark 2011).
These mechanisms influence the structuration of genetic diversity within and
between populations, which is usually referred to as spatial genetic structure (SGS)
(Vekemans and Hardy 2004).
A study of landscape genetic structure in the six varieties of A. caven by means
of AFLP, showed that the 15 populations analysed were significantly structured
using both the Wright’s approach (F ST = 0.315) and AMOVA (Φ ST = 0.315), despite
a significant proportion of genetic variation (about 68.5%) existed within populations (Pometti et al. 2012). When software STRUCTURE was applied, the optimal
number of K genetic clusters was 11, what is almost the same number of analysed
populations, which is indicative of a high differentiation (Fig. 11.5a). The populations analysed in this study were sited in five ecoregions of Argentina: Pampa, Puna,
Espinal, Wet Chaco and Dry Chaco. When regions were studied separately, Wet
Chaco showed the lowest values, while Puna showed the highest values of genetic
structure, both in terms of F ST and Φ ST (Pometti et al. 2012).
A similar study was done in A. visco with AFLP markers in seven populations
within two subregions of Argentina: Puna and Chaco. A significant amount of
genetic differentiation among populations was observed using both the Wright’s
approach (F ST = 0.126) and AMOVA (Φ ST = 0.23), and a large proportion of genetic
variation (about 77.4%) existed within populations. The analysis of molecular variance showed that the variance between subregions was relatively low (2.1%) but
highly significant. The analysis with STRUCTURE showed that the optimal number of K clusters was 6 (Fig. 11.5b). These results indicated that populations belonging to Puna subregion were more differentiated from the rest in comparison to those
belonging to Chaco (Pometti et al. 2016).
In a study of six natural populations of A. aroma in the Argentinean Chaco, the
analysis of population structure by means of Wright’s F ST statistic was high
(F ST = 0.42) and significant. The analysis of molecular variance indicated that the
largest component of genetic diversity (60.7%) was found within populations as
usual, but a great part of it (39.3%) was found between populations. The analysis
with STRUCTURE showed that the optimal number of clusters (K) was 3, what was
interpreted to be caused by the geographical proximity of some populations (Pometti
et al. 2018) (Fig. 11.5c). Significant SGS was detected in short to medium distances
11 Species Without Current Breeding Relevance But High Economic Value: Acaci
genetic structure of natural populations to a fine scale. This genetic structure may be
manifested among geographically distinct populations, within a local group of
plants, or even in the progeny of individuals. Ecological factors affecting reproduction and dispersal are likely to be particularly important in determining genetic
structure. Also, spatial and genetic patterns are often assumed to result from environmental heterogeneity and differential selection pressures (Loveless and
Hamrick 1984).
The spatial structure of the genomic variation among natural populations constitutes a central topic in evolutionary biology. The structure is primarily influenced by
the population density, breeding system and environmental heterogeneity, among
other factors. For plants, the ability to extend the geographical distribution and
maintain genetic variability within populations depends on the gene flow mediated
by seed movement and pollen dispersal (Peakall et al. 2003; Moran and Clark 2011).
These mechanisms influence the structuration of genetic diversity within and
between populations, which is usually referred to as spatial genetic structure (SGS)
(Vekemans and Hardy 2004).
A study of landscape genetic structure in the six varieties of A. caven by means
of AFLP, showed that the 15 populations analysed were significantly structured
using both the Wright’s approach (F ST = 0.315) and AMOVA (Φ ST = 0.315), despite
a significant proportion of genetic variation (about 68.5%) existed within populations (Pometti et al. 2012). When software STRUCTURE was applied, the optimal
number of K genetic clusters was 11, what is almost the same number of analysed
populations, which is indicative of a high differentiation (Fig. 11.5a). The populations analysed in this study were sited in five ecoregions of Argentina: Pampa, Puna,
Espinal, Wet Chaco and Dry Chaco. When regions were studied separately, Wet
Chaco showed the lowest values, while Puna showed the highest values of genetic
structure, both in terms of F ST and Φ ST (Pometti et al. 2012).
A similar study was done in A. visco with AFLP markers in seven populations
within two subregions of Argentina: Puna and Chaco. A significant amount of
genetic differentiation among populations was observed using both the Wright’s
approach (F ST = 0.126) and AMOVA (Φ ST = 0.23), and a large proportion of genetic
variation (about 77.4%) existed within populations. The analysis of molecular variance showed that the variance between subregions was relatively low (2.1%) but
highly significant. The analysis with STRUCTURE showed that the optimal number of K clusters was 6 (Fig. 11.5b). These results indicated that populations belonging to Puna subregion were more differentiated from the rest in comparison to those
belonging to Chaco (Pometti et al. 2016).
In a study of six natural populations of A. aroma in the Argentinean Chaco, the
analysis of population structure by means of Wright’s F ST statistic was high
(F ST = 0.42) and significant. The analysis of molecular variance indicated that the
largest component of genetic diversity (60.7%) was found within populations as
usual, but a great part of it (39.3%) was found between populations. The analysis
with STRUCTURE showed that the optimal number of clusters (K) was 3, what was
interpreted to be caused by the geographical proximity of some populations (Pometti
et al. 2018) (Fig. 11.5c). Significant SGS was detected in short to medium distances
11 Species Without Current Breeding Relevance But High Economic Value: Acaci