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, 7 (1), e29845

Mapping Genetic Diversity of Cherimoya (Annona Cherimola Mill.): Application of Spatial Analysis for Conservation and Use of Plant Genetic Resources

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Mapping Genetic Diversity of Cherimoya (Annona Cherimola Mill.): Application of Spatial Analysis for Conservation and Use of Plant Genetic Resources

Maarten van Zonneveld et al. PLoS One.

Abstract

There is a growing call for inventories that evaluate geographic patterns in diversity of plant genetic resources maintained on farm and in species' natural populations in order to enhance their use and conservation. Such evaluations are relevant for useful tropical and subtropical tree species, as many of these species are still undomesticated, or in incipient stages of domestication and local populations can offer yet-unknown traits of high value to further domestication. For many outcrossing species, such as most trees, inbreeding depression can be an issue, and genetic diversity is important to sustain local production. Diversity is also crucial for species to adapt to environmental changes. This paper explores the possibilities of incorporating molecular marker data into Geographic Information Systems (GIS) to allow visualization and better understanding of spatial patterns of genetic diversity as a key input to optimize conservation and use of plant genetic resources, based on a case study of cherimoya (Annona cherimola Mill.), a Neotropical fruit tree species. We present spatial analyses to (1) improve the understanding of spatial distribution of genetic diversity of cherimoya natural stands and cultivated trees in Ecuador, Bolivia and Peru based on microsatellite molecular markers (SSRs); and (2) formulate optimal conservation strategies by revealing priority areas for in situ conservation, and identifying existing diversity gaps in ex situ collections. We found high levels of allelic richness, locally common alleles and expected heterozygosity in cherimoya's putative centre of origin, southern Ecuador and northern Peru, whereas levels of diversity in southern Peru and especially in Bolivia were significantly lower. The application of GIS on a large microsatellite dataset allows a more detailed prioritization of areas for in situ conservation and targeted collection across the Andean distribution range of cherimoya than previous studies could do, i.e. at province and department level in Ecuador and Peru, respectively.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Number of trees per grid after re-sampling.
This map is made with a 10-minutes grid applying a one-degree circular neighborhood.
Figure 2
Figure 2. Allelic richness.
This map shows the average number of alleles per locus in all 10-minutes grid cells applying a one-degree circular neighborhood.
Figure 3
Figure 3. Allelic richness corrected for sample size by using rarefaction.
This map shows the average number of alleles per locus in the 10-minutes grid cells applying a one-degree circular neighborhood and a correction by rarefaction to a minimum sample size of 20 trees.
Figure 4
Figure 4. Locally common alleles.
This map shows the average number of alleles per locus in a 10-minutes grid cell that are relatively common (occurring with a frequency higher that 5%) in a limited area (in 25% or less of the grid cells) applying a one-degree circular neighborhood.
Figure 5
Figure 5. Expected heterozygosity (He).
This map shows the average He value in each 10-minutes grid cell with 20 or more trees applying a one-degree circular neighborhood.
Figure 6
Figure 6. Fixation index (F).
This map shows the average F value in each 10-minutes cell with 20 or more trees applying a one-degree circular neighborhood. Yellow areas indicate cherimoya stands where observed heterozygosity is as expected, red areas indicate stands where observed heterozygosity is lower than expected (indicating inbreeding) whereas observed heterozygosity is higher than expected in green areas.
Figure 7
Figure 7. Genetic distance to the Peruvian cultivar ‘Cumbe’.
This maps shows the average genetic distance (GD) to the cultivar ‘Cumbe’, in each 10-minutes cell with 20 or more trees applying a one-degree circular neighborhood. As reference of the cultivar, the ‘Cumbe’ accession from the collection la Mayora, Malaga, Spain, was used.
Figure 8
Figure 8. Genetic structure of Andean cherimoya distribution in Population clusters A and B.
This map shows in each 10-minutes cell with 20 or more trees applying a one-degree circular neighborhood, the average probability of finding a cherimoya tree belonging to cluster A or B. Dark blue areas show a higher probability of finding trees belonging to cluster A whereas dark green areas show a higher probability of finding trees belonging to cluster B. Light blue colored areas are not clearly assigned to any of the two clusters.
Figure 9
Figure 9. Gap analysis of alleles not found in ex situ collections.
Richness analysis of alleles (eight alleles out of the total of 71 observed alleles) that are not found in any ex situ collection based on 10-minutes grid with a one-degree circular neighborhood.
Figure 10
Figure 10. Modeled distribution of cherimoya.
Areas of the modeled distribution in dark blue are covered by the 10-minutes grid cells with 20 or more trees applying circular neighborhood. Light blue areas of modeled distribution coincide with grid cells that contain less than 20 trees after re-sampling. Red areas indicate potential areas for cherimoya occurrence and cultivation that have not been in sampled.

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