Mapping Genetic Diversity of Cherimoya (Annona
cherimola Mill.): Application of Spatial Analysis for
Conservation and Use of Plant Genetic Resources
Maarten van Zonneveld1,2*, Xavier Scheldeman1, Pilar Escribano3, Marı́a A. Viruel3, Patrick Van
Damme2,4, Willman Garcia5, César Tapia6, José Romero7, Manuel Sigueñas8, José I. Hormaza3
1 Bioversity International, Regional Office for the Americas, Cali, Colombia, 2 Ghent University, Faculty of Bioscience Engineering, Gent, Belgium, 3 Instituto de
Hortofruticultura Subtropical y Mediterránea, (IHSM-UMA-CSIC), Estación Experimental La Mayora, Algarrobo-Costa, Málaga, Spain, 4 World Agroforestry Centre (ICRAF),
GRP1 - Domestication, Nairobi, Kenya, 5 PROINPA, Oficina Regional Valle Norte, Cochabamba, Bolivia, 6 Instituto Nacional Autónomo de Investigaciones Agropecuarias
(INIAP) Panamericana sur km1, Quito, Ecuador, 7 Naturaleza y Cultura Internacional (NCI), Loja, Ecuador, 8 Instituto Nacional de Innovación Agrı́cola (INIA), La Molina, Lima,
Peru
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.
Citation: van Zonneveld M, Scheldeman X, Escribano P, Viruel MA, Van Damme P, et al. (2012) Mapping Genetic Diversity of Cherimoya (Annona cherimola Mill.):
Application of Spatial Analysis for Conservation and Use of Plant Genetic Resources. PLoS ONE 7(1): e29845. doi:10.1371/journal.pone.0029845
Editor: Pär K. Ingvarsson, University of Umeå, Sweden
Received July 6, 2011; Accepted December 6, 2011; Published January 9, 2012
Copyright:  2012 van Zonneveld et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study has been carried out within the context of the CHERLA project funded by the International Cooperation with Developing Countries (INCO-
DEV) Sixth Framework Programme (Contract 015100) of the European Commission. Additional financial support was provided by the Spanish Ministry of
Education (Project Grants AGL2010-15140), the Instituto Nacional de Investigación y Tecnologı́a Agraria y Alimentaria (INIA) from Spain (RF2009-00010), Junta de
Andalucı́a (FEDER AGR2742) and by the INIA-Spain financed project ‘Strengthening Regional Collaboration in Conservation and Sustainable Use of Forest Genetic
Resources in Latin America and Sub-Saharan Africa.’ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of
the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: mvanzonneveld@cgiar.org
Introduction decreased population sizes resulting from land use changes and
land degradation, and due to changes in local climate that may
Many useful tropical and subtropical tree species, even those select against some genotypes [3]. Therefore, there is a growing
commonly cultivated, are still in incipient stages of domestication, call to assess the conservation status of the genetic resources of tree
with their genetic resources often principally or exclusively, present species [4].
in situ, i.e. on farm in home gardens or orchards and/or in natural The formulation of effective and efficient conservation strategies
populations. The local diversity of these tree species could offer requires a thorough understanding of spatial patterns of genetic
yet-unknown traits of high value to further domestication [1]. For diversity [5]. A better knowledge of areas of high genetic diversity
many outcrossing species, such as most tropical tree species, this is also important in optimizing the use of genetic resources, as the
genetic diversity is important to sustain local production as many likelihood to find interesting materials for breeding is higher where
of these species are vulnerable to inbreeding depression [2]. levels of genetic diversity are maximal [6], [7]. Initiatives to
Diversity is also a key factor for adaption to environmental prioritize research on global plant genetic resources, such as those
changes [2]. However, tree species are increasingly vulnerable to lead by the Food and Agriculture Organization of the United
losses of genetic diversity, referred to as genetic erosion, due to Nations (FAO), include calls for more inventories and surveys to
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Spatial Analysis of Plant Genetic Resources
increase understanding of variation in plant genetic resources, created opportunities for new applications of genetic diversity
explicitly referring to the application of molecular tools in such analyses [28]–[30]. Whereas neutral molecular markers are
assessments [8], [9]. considered a sound tool to measure patterns and trends in the
This study focuses on cherimoya (Annona cherimola Mill.), an use and conservation of plant genetic resources [31], Geographic
underutilized fruit tree species that belongs to the Annonaceae, a Information Systems (GIS) provide opportunities to carry out
family included within the Magnoliales in the Eumagnoliid clade spatial analyses of genetic diversity patterns identified with these
among the early-divergent angiosperms [10]. This Neotropical tree markers [32]. GIS can be used to interpolate genetic parameters
species still is in its initial stages of domestication [11] and it is between sampled populations (e.g. [33]–[35]), to apply re-
considered at high risk of losing valuable genetic material from its sampling of georeferenced samples within a defined buffer zone
genepool [12]. Cherimoya fruits are widely praised for their excellent [36], [37], or to develop grid-based genetic distance models [38],
organoleptic characteristics, and the species is therefore considered [39]. GIS are also an acknowledged tool to prioritize areas for
to have a high potential for commercial production and income conservation of plant genetic resources [40]. Several studies have
generation for both small and large-scale producers in subtropical used spatial analysis to develop conservation strategies for plant
climates [13]. Cherimoya presents protogynous dichogamy, i.e. it genetic resources based on molecular marker data (e.g. [36], [41]).
has hermaphroditic flowers wherein female and male parts do not Moreover, results obtained using GIS can be presented in a clear
mature simultaneously, which favors outcrossing in its native range way on maps, which facilitates the incorporation of these findings
[14]. For commercial production, hand pollination with pollen and into the formulation of conservation strategies and the implemen-
stamens is common practice due to lack in overlap of the female and tation of conservation measures [42].
male stages and absence of pollinating agents outside its native range In this article we further explore the possibilities of incorporating
[14]. At present, advanced commercial production is found in Spain, molecular marker data into GIS to better visualize and understand
the world’s largest cherimoya producer, with around 3000 ha of spatial patterns of genetic diversity, as a key input to optimize
plantations, while small-scale cultivation occurs throughout the conservation and enhance use of local plant diversity, based on a
Andes, Central America and Mexico. case study of cherimoya. The specific objectives of this article are to
Most early chroniclers and scientists proposed the Andean (1) apply innovative spatial analysis to improve understanding of the
region, and more specifically, the valleys of southern Ecuador and geographic distribution of cherimoya ‘s genetic diversity in its
northern Peru, as cherimoya’s centre of origin [12], [15], [16]. putative native range, based on microsatellite molecular markers
The existence of natural cherimoya forest patches, which are (SSRs); and (2) formulate optimal conservation strategies by
scattered across the inter-Andean valleys in Ecuador and northern prioritizing areas for in situ conservation and identifying existing
Peru, supports this hypothesis. Nonetheless the possibility that diversity gaps in ex situ collections. Based on the outcomes, we
these are feral populations cannot be excluded. This phenomenon discuss how these spatial analyses can be used to define possible
has been observed in the case of several fruit tree species, such as strategies that guarantee the long term conservation of cherimoya
olives [17]. An alternative hypothesis for the centre of origin of genetic resources and how these analyses can be applied to improve
cherimoya is Central America [18], which would imply that the conservation and use of tree and crop genetic resources in general.
area of northern Peru and southern Ecuador is a secondary centre
of diversity. Most relatives of cherimoya are native to Central Results
America and southern Mexico, which is an argument in favor of
this alternate hypothesis (H. Rainer, Institute of Botany, University A total of 1504 trees were analyzed in this study, i.e. 395 from
of Vienna, 2011, pers. comm.). In any case, cherimoya fruits were Bolivia, 351 from Ecuador and 758 from Peru. Of those, 502 are
consumed in the Andean region in antiquity [12] and the currently conserved in ex situ collections (either in Ecuador, Peru or
movement of germplasm across Mesoamerica, southern Mexico Spain) whereas the remainder trees were sampled in situ. The
and the Andes probably took place in pre-Columbian times. molecular analysis included a core set of nine microsatellite loci
The conservation status of cherimoya genetic resources has [27] resulting in 71 different alleles. In all analyses of a-diversity
improved considerably in recent years. Due to increasing and b-diversity (also referred to as divergence) we applied circular
commercial prices for cherimoya at local markets, Andean farmers neighborhood re-sampling technique resulting in a total dataset of
are stimulated to conserve in situ the cherimoya trees growing in 48,128 trees (Figure 1). This technique facilitates analysis of
their backyards. Indeed, trees established in home gardens and patterns in genetic variation across extensive distribution ranges
orchards are common throughout the Andean region in Bolivia, while maintaining high-resolution grids.
Ecuador and Peru, which usually originate from planted local
seeds or chance seedlings [11], and among them some individuals Allelic richness
show promising traits for future breeding programs [19]. In Peru, Allelic richness is a straightforward measure of genetic diversity
the local cultivar ‘Cumbe’ is already fetching retail prices that is commonly used in studies based on molecular markers that
significantly above the prices of unselected cherimoya fruit types aim at selecting populations for conservation [5], [43]. Figure 2
[20]. In contrast to most tropical and subtropical underutilized presents the distribution of the average number of alleles per locus
fruit tree species, cherimoya genetic resources are also well found in the study area. It clearly shows that a higher number of
conserved ex situ. Several field collections have been established in alleles is present in the northern part of the study area, specifically
Spain, Peru and Ecuador, preserving over 500 different accessions in northern Peru, around Cajamarca Department, while other
[11], [21]. The Spanish collection based at la Estación areas of high diversity are located on the border zone between
Experimental La Mayora in Malaga, which holds over 300 Ecuador (Loja Province) and Peru (Piura Department), in the
accessions (190 of them collected in the Andean region), is northern part of Ecuador around its capital Quito and in the
currently used as a source of materials for the Spanish cherimoya northern part of the Lima Department in Peru.
breeding program and has been thoroughly analyzed using Despite the effort to implement a similar sampling density
isozymes [22]–[24] and microsatellite markers [11], [25]–[27]. throughout the study area, some areas (often locations with a
The recent development of new molecular tools in combination higher abundance of traditionally managed cherimoya trees and
with new spatial methods and increased computer capacity has stands) have been sampled more intensively than others (Figure 1),
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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.
doi:10.1371/journal.pone.0029845.g001
generating a sampling bias [44]. The rarefaction methodology frequency in a limited area, and can indicate the presence of
corrects this sampling bias by recalculating allelic richness in each genotypes adapted to specific environments [43]. Figure 4 shows
grid cell to a minimum sample size [5]. Figure 3 shows only the the richness of locally common alleles per locus in the study area.
grid cells where 20 or more trees were present after applying a The high diversity levels found in the Cajamarca Department in
one-degree circular neighborhood, and for which allelic richness northern Peru are reconfirmed. Besides harboring the highest
was corrected following the rarefaction methodology to a number of different alleles, it also contains the highest number of
minimum sample size of 20 trees. The Cajamarca Department locally common alleles. This makes this area a priority for in situ
in northern Peru remains the area with the highest diversity, up to conservation, both of cultivated trees on farm and of natural
an average of 5.18 different alleles per locus. After correction by stands. The border region between Peru and Ecuador (Piura
rarefaction, diversity in Ecuador, especially around Quito, is Department and Loja Province) is another area where a high
reduced, whereas the same seems to happen in the northern part concentration of locally common alleles has been observed and
of the Lima Department, in Peru, indicating the presence of a may, therefore, be a second area to prioritize in situ conservation
sampling bias around the capitals of both countries. The area efforts. To a lesser extent, the area around Quito in Ecuador and
around the Peruvian capital Lima, an important commercial the northern part of the Lima Department in Peru also present
cherimoya cultivation area, shows the lowest allelic richness within locally common alleles.
Peru, probably due to the widespread cultivation of a vegetatively
propagated cultivar, ‘Cumbe’. Another striking result is that allelic Expected Heterozygosity (He), Fixation Index (F) and
richness in Bolivia, already low in the uncorrected analysis, is even Genetic Distance (GD)
lower with correction of sampling bias, resulting in an even higher In situ conservation should focus on viable populations, where
contrast between cherimoya genetic diversity in Bolivia and that inbreeding and subsequent loss of alleles are minimal. Parameters
found in Peru and Ecuador. that allow assessment of inbreeding are expected heterozygosity
(He) and the fixation index (F). The fixation index (F) was used to
Locally common alleles detect areas subjected to high inbreeding depression and, as the
Priority for conservation should be given to populations that inverse to that, excess in heterozygosity [45]. Figure 5 shows the
retain locally common alleles; these are alleles that occur in high values for He in the study area, again confirming Cajamarca
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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.
doi:10.1371/journal.pone.0029845.g002
Department in northern Peru as the area with the highest genetic cherimoya distribution area in our study, which is more likely to be
diversity. High He values, however, radiate towards the south (as a product of natural gene flow patterns.
opposed to the higher diversity towards the north found in the
allelic richness analyses) indicating higher levels of diversity in b-diversity (divergence)
terms of heterozygosity in central Peru compared to Ecuador. Besides a-diversity parameters, aimed at identifying those areas
Figure 6 shows the values for the fixation index, with F values close with highest allelic richness and balanced allele frequencies, in situ
to 0 in the Cajamarca Department indicating that natural and conservation also needs to take into account allelic composition
cultivated cherimoya tree stands in this area have not experienced (b-diversity or divergence) as it is possible that populations with
inbreeding. The highest values for F are observed in central low allelic richness possess unique allele compositions, different
Ecuador, suggesting that the level of inbreeding is highest in that from those of populations in other areas of the range, which
part of cherimoya’s Andean distribution range. would warrant their in situ conservation [5]. Applying the
The most important Peruvian commercial cherimoya cultiva- Structure software (see [46]) and using the statistic parameter
tion area, located near the Capital Lima, particularly shows DK [47] to define the number of clusters with genetically similar
negative F values, i.e. excess of heterozygosity. Most of the trees present in the study area, we differentiated two main
cherimoyas cultivated in this area are vegetatively propagated populations. Figure 8 shows the differentiation of the populations
clones of the cultivar ‘Cumbe’ which resulted in highly among distribution areas in cluster A and B, respectively. Cluster
heterozygous values from the molecular analysis, i.e. the ‘Cumbe’ A has the highest presence in the areas previously identified as
accession conserved in the Spanish genebank is heterozygote for those with the highest allelic richness (Cajamarca Department in
eight of the nine microsatellite loci analyzed in this study (Ho value northern Peru, border zone between Ecuador and Peru and the
of 0.89). An analysis of the average genetic distance, between the area around Quito in Ecuador), whereas cluster B is mainly
‘Cumbe’ accession and the genotypes in each grid cell with 20 or confined to southern Peru and Bolivia. Bolivian cherimoya trees
more re-sampled trees in the study area, clearly shows lowest are almost exclusively assigned to cluster B. Particular areas that
genetic distance values near the Peruvian capital, Lima, indicating did not show a strong linkage to either of the two clusters
that the cherimoya trees in this area are very similar to the cultivar included the surroundings of the city of Lima and Loja Province
‘Cumbe’ (Figure 7). This area clearly differs from the rest of the in southern Ecuador.
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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.
doi:10.1371/journal.pone.0029845.g003
Ex situ conservation status Cross-validation, to evaluate the quality of the distribution
Of the 1504 trees included in this study, 502 genotypes are model, returned an Area Under Curve (AUC) value of 0.9, which
currently conserved in ex situ collections (either in Ecuador, Peru or indicates good model performance [48]. AUC is a commonly used
Spain). Only eight alleles, corresponding to 11% of the total of 71 parameter in the validation of distribution models. Another
alleles that have been found in the study area, are not represented measure of validation, the Kappa value, returned a value of
in any accession of these collections. Figure 9 shows the 0.799 indicating the model performed even excellent [49].
distribution of the missing alleles. There is only a small area with In general, sampling covered most of the cherimoya-modeled
a significant portion of missing alleles (3 in total), i.e. in southern distribution (Figure 10); 46% of the modeled distribution area is
Ecuador (Azuay Province). Natural cherimoya forest patches and covered by grid cells with 20 or more re-sampled trees (Figure 10,
areas of traditional cherimoya cultivation in this province should dark blue areas). In 24.5% of the potential area of cherimoya
be prioritized for future cherimoya collection missions. With occurrence less than 20 trees were re-sampled (light blue areas)
almost 90% of alleles found to be present in ex situ collections, it whereas 29.5% of the modeled range was not sampled (red areas)
can be concluded that, in general, cherimoya diversity from the and are considered sample gaps. The largest sample gaps are
countries analyzed is fairly well conserved ex situ. located in northern Peru in the transition zone between the
Peruvian Andes and the Amazon (in the Departments of San
Distribution range of cherimoya in the Andes Martin and Amazonas) and in southern Peru (in the Departments
The above results and subsequent conclusions are obviously of Junı́n, Pasco, Huancavelica, Ayacucho and Puno). The Andean-
only of practical use if the sampling performed was indeed Amazon transition zone should be priority for future complemen-
representative for the distribution of cherimoya in the study area. tary cherimoya collection trips because it is adjacent to an area
Maxent species distribution modeling software was applied to where already high levels of diversity have been found, i.e.
model cherimoya’s distribution range in Ecuador, Peru and Cajamarca Department in northern Peru.
Bolivia based on the climatic niche in which the 1504 sampled Cherimoya was predicted absent by the distribution model in a
trees of our study were located. The modeled distribution was then significant area of southern Peru, indicating that the environmen-
compared with the sampled areas in these countries. tal conditions in substantial parts of that region are not suitable for
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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.
doi:10.1371/journal.pone.0029845.g004
cherimoya cultivation (Figure 10). This explains why no trees have leads to genetic erosion [50]. Our results do not allow us to
been sampled in that area. determine how much genetic erosion has taken place in Ecuador
in comparison to Peru and Bolivia, but the high inbreeding values
Discussion in Ecuador could explain why currently allelic richness is lower in
this country than in northern Peru.
Areas of high diversity in the cherimoya centre of origin At the population level, significant differences can be observed
Our results are in line with a previous genetic study of the between the cherimoya germplasm present in the area with highest
Spanish cherimoya collection that also distinguished populations diversity (where genotypes belonging to cluster A are predominant)
in Ecuador and northern Peru from those in southern Peru [11], and genotypes found in areas with lower diversity, i.e. in southern
and corroborate with results from isozyme markers that showed Peru and Bolivia (represented by cluster B). Cluster A seems likely
high genetic variation present in Peru and Ecuador [24]. to represent material that is genetically closer to the ‘‘wild’’
However, our study is based on a much higher number of samples cherimoya type. No natural cherimoya stands have been observed
and, therefore, provides much more detail for prioritizing areas for in Bolivia, and this probably explains why no genotypes pertaining
in situ conservation and germplasm collection. to cluster A have been recorded there. Cluster B probably
At the allele level, our analysis confirms that, within our study corresponds to a genepool that is genetically different from most of
area, the highest allelic richness as well as the highest number of the wild or semi-domesticated cherimoya found in northern Peru
locally common alleles are found in the area of southern Ecuador and Ecuador and that could have formed the basis for Bolivian
and northern Peru, i.e. the putative centre of origin of cherimoya. cherimoya cultivation. Looking at the areas of high cluster B
Northern Peru, and more specifically the Cajamarca Department, dominance, Bolivian germplasm probably originates from south-
shows the highest levels of genetic diversity. ern Peru.
The highest values of the fixation index, which is an indication Although most early chroniclers and scientists proposed
of inbreeding, were found in Ecuador. Inbreeding may occur southern Ecuador and northern Peru to be cherimoya’s centre
because of reduction and fragmentation of natural stands and of origin [12], [15], [16], [51], the possibility of that area being a
cultivated areas, increasing the risk of allele loss, which eventually secondary centre of origin cannot be discarded. A diversity study
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Spatial Analysis of Plant Genetic Resources
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.
doi:10.1371/journal.pone.0029845.g005
similar to the one described in this study, but including cherimoya A priority for in situ conservation should be the Cajamarca
genotypes from Central America and Mexico, would shed light on Department, the area with the highest levels of genetic diversity. A
the genetic variation across the complete pre-Columbian distri- second area of priority should be the Loja Province in southern
bution range of cherimoya and provide additional clues on the Ecuador, an area with a high number of locally common alleles.
primary centre of origin and diversification of this species. Both areas are assigned mostly to cluster A. Since trees pre-
dominantly assigned to cluster B have a particular allelic
Ex situ and in situ conservation of cherimoya genetic composition in comparison to trees predominantly grouped in
resources in the Andean region cluster A, genotypes of cluster B should also be considered in
Most alleles identified in our study are represented in one or conservation activities. The part of Lima Department north of the
more of the existing ex situ collections in Ecuador, Peru and Spain. Peruvian capital, which is assigned mostly to cluster B, could be
The results obtained suggest that the highest priority for further prioritized for in situ conservation of genotypes from this cluster. In
collection should be the Azuay Province in Ecuador, since contrast to the low levels of allelic richness around Lima city in the
cherimoya stands in this area harbor most alleles not yet included southern part of the Lima Department, the northern part of this
in genebanks. It is also one of the areas with the highest risk of Department contains a fair number of locally common alleles.
allele loss because of the high observed levels of inbreeding, The long-term conservation of cherimoya genetic resources is
compared to other parts of the study area. An additional priority far from guaranteed. As commercial prices for fruits can fluctuate,
area for germplasm collection is the transition zone from the short-term incentives for farmers to maintain cherimoya as a
Andes to the Amazon in Peru (in the higher elevation areas of the profitable crop are reduced and a decline in commercial interest
Departments of San Martin and Amazonas), which was not may lead to the replacement of cherimoya trees by other crops,
sampled in this study. According to the distribution model there is increasing the risks of genetic erosion. Around Quito, for example,
a high probability of finding cherimoya stands in this region, most of the traditional cherimoya cultivation is being replaced by
which probably is also high in genetic diversity, because it is avocado plantations, which are commercially more attractive (X.
adjacent to the area with the highest diversity found in this study, Scheldeman, pers. obs.). An increase in commercial prices for
i.e. the Cajamarca Department in northern Peru. cherimoya products will not necessarily promote the conservation
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Spatial Analysis of Plant Genetic Resources
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.
doi:10.1371/journal.pone.0029845.g006
of the existing genetic diversity. Indeed, in our study we found low germplasm (which currently is the case for cherimoya), and be
levels of genetic diversity around the Peruvian capital, Lima where monitored periodically to assess the dynamics in diversity use and
the clonally propagated cultivar ‘Cumbe’ is widely cultivated, risks of genetic erosion. Ex situ collections of fruit tree species often
because it fetches higher prices in the market. consist of living trees, such as the cherimoya collections. This
A promising strategy to enhance in situ conservation on farm is allows conservation of superior combination of alleles that can be
through the promotion of seed or bud-for-grafting exchange propagated vegetatively through grafting. Additional reasons
between farmers [52]. During the CHERLA project, cherimoya include the following: (1) many tropical and subtropical trees
fairs, which facilitate exchange of plant material, were organized (including cherimoya) have seeds with recalcitrant or intermediate
in different areas of this study, including the Cajamarca and Piura behavior, which cannot be stored for long-term conservation; and
Departments in Peru, Loja Province in Ecuador and various (2) pollen, fruits and seeds can be collected continuously for
departments in Bolivia. Seed and bud exchange can also be a way characterization, evaluation and genetic improvement once trees
to conserve local races from unfavorable alterations in the local have reached the reproductive stage. Nevertheless, the high costs
environment due to climate change, by re-distributing them in for research institutions to maintain field genebanks of woody
new areas with suitable climate conditions [53]. Another way to perennial species, can be a reason to minimize ex situ collections
ensure conservation of genetic resources of tree species while their and focus especially on in situ conservation [55]. In that case, it is
use is stimulated could be the establishment of local clonal seed important to screen the existing accessions through morphological,
orchards if and when adequate propagation techniques, to enable biochemical and/or molecular characterization to maximize the
the multiplication of clones, are made available as well [1], [54]. conservation of genetic diversity and potentially interesting
This is the case for cherimoya, as demonstrated by the successful functional attributes in a reduced collection [6]. This approach
clonal propagation of the cultivar ‘Cumbe’ around the city of has already successfully been used in the cherimoya collection la
Lima. Mayora, Malaga, Spain [27]. Ex situ conservation may particularly
Ideally, each area targeted for in situ conservation - where the be important for areas that suffer from inbreeding -an indicator for
existing cherimoya stands and forest patches can evolve within the high rates of allelic loss and genetic erosion- such as central
local environment - should be backed up by ex situ conservation of Ecuador in the case of cherimoya, whereas in situ conservation
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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.
doi:10.1371/journal.pone.0029845.g007
may be most successful in areas of high diversity where still low they share the same seed system, and/or breed with each other.
rates of inbreeding are observed such as in the cherimoya stands Based on this concept, trees have been sampled in this study
from northern Peru. following a scattered distribution to calculate, across the Andean
distribution range of cherimoya, several diversity estimates
Use of GIS and molecular markers to enhance important to prioritize areas for conservation, including two
conservation and use of plant genetic resources recommended parameters: allelic richness [5] and the number of
Despite the advances in new computational applications and the locally common alleles [43]. Since the application of molecular tools
use of molecular tools, spatial analyses are still underutilized in is becoming cheaper, intraspecific diversity studies with large
efforts to conserve plant diversity [56]. With respect to targeting datasets will probably be more common in the near future, allowing
collection sites and prioritizing the conservation of plant genetic for studies of this sort on other tree species and annual crops.
resources, spatial analyses of diversity have been carried out mainly The size of the grid cells and width of the circular neighborhood
at the species level for crop genepools (e.g. [57]–[59]). Only a few for this type of spatial analysis depends on how many plant
studies have mapped intraspecific diversity to enhance the individuals have been collected across the landscape, and the
conservation of genetic resources of specific crops and trees (e.g. minimum number of plant individuals that is considered sufficient
[36], [41]). Kiambi et al. [41] grouped samples using a grid to to make confident estimates of genetic parameters per grid cell.
compare diversity between geographic areas of similar size, whereas Application of circular neighborhood provides an effective way to
Lowe et al. [36] applied re-sampling to enable the calculation of decrease grid cell size, which facilitates detection of spatial patterns
diversity estimates with high degrees of confidence. However, these in genetic variation across an extensive distribution range. By re-
studies were carried out with fewer than 100 individuals per species, sampling the trees in the landscape, it generates a high number of
which limits the type of spatial analysis that can be carried out over grid cells with a sufficient number of trees to make confident
the geographic distribution range of species. Our analysis combines calculations of genetic parameters per grid cell. It also makes
both techniques on a large dataset (1504 trees), which can be analyses less sensitive to grid origin definition and enables the
conceptualized as a continuous distribution of plant individuals, in inclusion of isolated trees in the calculation of the genetic
which each individual is connected to its neighboring trees because parameters, i.e. together with their closest neighboring trees.
PLoS ONE | www.plosone.org 9 January 2012 | Volume 7 | Issue 1 | e29845
Spatial Analysis of Plant Genetic Resources
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.
doi:10.1371/journal.pone.0029845.g008
Ideally, the sampling strategy for this type of analysis should be across the landscape, larger grid cells and/or a larger width of
identified based on a pre-defined grid, aiming at measuring the circular neighborhood could be applied, always assuring a
same number trees per grid cell. However, due to logistical sufficient number of trees per grid cell. The overall resolution of
constraints and because a species simply may be more abundant in the study will obviously be lower.
some areas than in others, in practice, sampling will always be sub- Following Frankel et al. [6], we hypothesized that areas with
optimal to a certain degree. Of all the genetic parameters, allelic high diversity measured by neutral molecular markers (like our
richness is most sensitive to uneven sampling and, accordingly, we microsatellite loci) have a high probability to contain genetic
have corrected sample size by rarefaction [5]. Repeated material that will also show diversity in functional traits, including
subsampling of a minimum number of tree individuals per grid traits of agronomic interest. Molecular markers are considered an
cell is another possibility to correct for sampling bias [60]. This appropriate indicator to quantify patterns and trends in the use
technique could also be used to correct other genetic parameters and conservation of plant genetic resources [31]. However, while
than allelic richness for sampling bias, such as expected neutral molecular marker surveys are suitable for diversity studies,
heterozygosity, although these are less sensitive to uneven direct measurement of traits in field trials may be more desirable to
sampling [61]. evaluate the genetic health and adaptive capacity of tree
Given the sampling distribution in our study area and the fact populations [50]. Nevertheless, molecular marker studies repre-
that for the calculation of most genetic parameters, we maintained sentative of the whole genome provide a less expensive and
a minimum of 20 re-sampled trees per grid cell, we defined a cell scientifically sound alternative to assess the genetic resource status
size of 10 minutes and a circular neighborhood with a diameter of of tree species, for which, in comparison to annual crops, field
one degree, which enabled us to detect spatial patterns of genetic trials are particularly expensive because of the long generation
variation at administrative level one in Ecuador, Peru and Bolivia times [62]. Markers of DNA sequences related to phenotypic
(provinces and departments). For studies of plant species, in which traits, including expressed sequence tagged (EST) markers and
individuals are sampled in a more clumped distribution compared markers in specific genes, could be of interest to include in spatial
to our scattered sampling distribution and/or in lower densities analysis of patterns and trends in plant genetic resources. More
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Spatial Analysis of Plant Genetic Resources
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.
doi:10.1371/journal.pone.0029845.g009
and more are becoming available, especially for important crops intraspecific level, to ensure the conservation of priority populations
where sequencing programs have been performed or will be of specific crops and useful tree species. Spatial information on the
carried out in the near future. An example in cherimoya is a patterns and characteristics of human societies can be used to
recently described gene involved in seedlessness in a sister species, understand the drivers behind threats. In a study on changes in
Annona squamosa [63]. However these markers are less polymorphic cassava diversity in the Peruvian Amazon, GIS was used to correlate
than neutral ones, such as those that have been used in our study, cassava diversity data with biotic and socio-economic spatial data to
so the use of neutral markers to study spatial patterns of genetic identify possible drivers behind diversity and genetic erosion [66].
diversity is still necessary. This can be useful information in the development of adequate
It is difficult to compare our results with those of Lowe et al. policies and measures to promote in situ conservation of plant
[36] and Kiambi et al. [41] because of the differences in genetic resources on farms and in natural populations.
methodology used. To examine molecular marker studies on the
same species, minimum standard sets of markers have already Materials and Methods
been suggested [64]. Standardization of methodologies in studies
on different species would improve comparability of results and Sampling and SSR analysis: A total of 1504 cherimoya
also would facilitate Meta-analyses, for example to better accessions have been analyzed in this study, 395 from Bolivia, 351
understand how well genetic diversity of tropical and subtropical from Ecuador and 758 from Peru. DNA was extracted from young
tree species is protected on farm and in protected areas. leaves after [67]. Based on polymorphism, a set of nine SSRs has
In our study we only examined spatial patterns of genetic been selected from those previously developed in cherimoya [26].
variation without relating them to other spatial attributes. GIS can A 15 mL of reaction solution containing 16 mM (NH4)2SO4,
also be used to link genetic data to available spatial information 67 mM Tris-ClH pH 8.8, 0.01% Tween20, 2 mM MgCl2,
relevant to conservation of plant genetic resources, for instance to 0.1 mM each dNTP, 0.4 mM each primer, 25 ng genomic DNA
reveal short-term threats such as accessibility and long-term threats and 0.5 units of BioTaq
TM DNA polymerase (Bioline, London,
such as climate change. With this type of analysis, hotspots of UK) was used for amplification on an I-cycler (Bio-Rad
diversity under threat could be identified following Myers et al. [65] Laboratories, Hercules, CA, USA) thermocycler using the
but instead of looking at species level, this could be done at the following temperature profile: an initial step of 1 min at 94uC,
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Spatial Analysis of Plant Genetic Resources
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.
doi:10.1371/journal.pone.0029845.g010
35 cycles of 30 s at 94uC, 30 s at 45uC–55uC and 1 min at 72uC, approximate 18 km in the study area) applying a circular
and a final step of 5 min at 72uC. Forward primers were labeled neighborhood with a diameter of one degree (corresponding to
with a fluorescent dye on the 59 end. The PCR products were approximate 111 km) constructed in Excel. The circular neigh-
analyzed by capillary electrophoresis in a CEQTM 8000 capillary borhood is used to re-sample the allelic composition of a single tree
DNA analysis system (Beckman Coulter, Fullerton, CA, USA). to all surrounding grid cells, in this case, 32 cells with a size of
Samples were denaturalized at 90uC during 120 s, injected at 10 minutes, within a diameter of one degree around its location.
2.0 kV, 30 s and separated at 6.0 kV during 35 min. Each In this way, the allelic composition of each sampled tree is
reaction was repeated twice and the Spanish cultivar Fino de Jete representative for the area within the defined buffer zone.
was used as control in each run to ensure size accuracy and to Applying the circular neighborhood re-sampling technique
minimize run-to-run variation. resulted in a total dataset of 48,128 trees and 866,304 alleles.
Data cleaning: The coordinates of the respective tree Spatial analysis – a-diversity: After applying circular
locations were checked in DIVA-GIS (www.diva-gis.org) on neighborhood to all trees, genetic parameters were calculated in
erroneous points based on passport data at administrative level GenAlEx per 10-minutes grid cell, for all trees present in each cell
one (e.g. departments, provinces) with a buffer of 20 minutes after re-sampling. Genetic parameters included the average
(approx 30 km), and outliers based on climate data derived from number of alleles per locus (Na), the number of locally common
the Worldclim data set [68] (two or more of the 19 bioclim alleles per locus (alleles occurring with a frequency higher than 5%
variables according the Reverse jackknife method [69]). Based on in 25% or less of the grid cells), average expected heterozygosity
these analyses, two points were excluded. The cleaned dataset thus per locus (He), fixation index (F) and genetic distance (GD) (see
included microsatellite data of 1504 georeferenced trees. Taking [45]). Na and the number of locally common alleles per locus were
into account that nine SSR markers were analyzed, this results in a presented for all grid cells with trees included. Na was corrected by
total of 27,072 georeferenced alleles. rarefaction to a minimum sample size of 20 trees per cell with the
Spatial analysis – Circular neighborhood: Grids for all HP-RARE software (see [70]); consequently, this parameter was
genetic parameters were generated in DIVA-GIS and are based only calculated for grid cells with 20 or more re-sampled trees.
on a grid with a cell size of 10 minutes (which corresponds to This minimum sample size was also used as a threshold of the
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Spatial Analysis of Plant Genetic Resources
number of trees per grid cell to get interpretable results for the presence points as well as layers of environmental variables
parameters He, F and GD. GD, which was used to calculate covering the study area. Maxent is a species distribution modeling
distance in allelic composition of each cherimoya genotype to the tool for which the applied algorithm has been evaluated as
commercial variety ‘Cumbe’, was calculated in GenAlEx using the performing very well, in comparison to other ecological niche
GD option for codominant markers (see [71]). Final GD value per modeling software [74], [75]. Therefore, it was selected for this
grid cell was the average GD for all re-sampled trees present in study’s distribution modeling analysis. The coordinates in the
each cell. The reference tree was the accession ‘Cumbe’ from the passport data of the sampled trees were used for the presence point
Spanish cherimoya genebank in Malaga. input. For environmental layer input, we used the 10-minutes grids
Spatial analysis - b-diversity: Population structure was of 19 bioclimatic variables (see [76]), derived from the Worldclim
defined by running the software Structure (see [46]) on all 1504 dataset [68]. The modeled distribution area was restricted using
samples applying a 10,000 burn-in period, 10,000 Markov chain the 10 percentile training presence threshold, which indicates the
Monte Carlo (MCMC) repetitions after burn-in, and 20 iterations. probability value at which 10% of the presence points falls outsides
Optimal K was selected after [47] by running Structure for K the potential area. The modeled distribution was generated in
values between one and 10 and defining the final number of Maxent with 80% of the points (training data) and was cross-
clusters where value of DK was highest. This was at K = 2, hence a validated in DIVA-GIS with 20% of the remaining tree
map was developed for these two clusters, which we named observations (test data). Besides 20% of the presence points, test
respectively A and B. We used the probabilities of each tree data included randomly generated points in 0.16 the bounding
belonging to cluster A and B to visualize the clusters on a map. box of the presence points as a proxy for absence points (5 times
Mapping of probabilities was done based on the average value of the number of presence points). Based on the cross-validation, the
all trees per 10-minutes cell for those grid cells with 20 or more re- Area Under Curve (AUC) and Kappa value were calculated in
sampled trees after applying the one-degree circular neighbor- DIVA-GIS as measures of model performance.
hood. All maps were edited in ArcMap.
Spatial analysis - Ex situ conservation status: The
private alleles function in GenAlEx (PAS) was used to identify the Acknowledgments
alleles exclusively found in trees that were sampled in situ. To
visualize patterns in these alleles that are not included in any We thank Jorge Rojas and his team from PROINPA for DNA extraction
and Bernardo Guzmán from PROINPA for field prospection and sampling
genebank, a point-to-grid richness analysis, using a 10-minutes
in Bolivia. We also thank the personnel from INIA for the DNA extraction
grid, was carried out in DIVA-GIS based on the one-degree and field prospection in Peru and from INIAP Ecuador. Doris
circular neighborhood re-sampled tree grid. Chalampuente, Fernando Paredes, Marcelo Tacán, Eddie Zambrano
Spatial analysis - distribution modeling: To identify how and Edwin Naranjo. Laura Snook and Evert Thomas from Bioversity, and
well the sampling covered the Andean distribution range of an anonymous reviewer provided useful comments on an early version of
cherimoya, and thus to identify potential collection gaps, we the manuscript.
modeled the distribution (presence only) of cherimoya in the study
area using the distribution modeling program Maxent (see [72], Author Contributions
[73]). With this technique, potential distribution areas are
Conceived and designed the experiments: XS JIH WG CT JR MS MAV.
identified as those areas where similar environmental conditions Performed the experiments: PE MV JIH. Analyzed the data: MvZ XS.
prevail as those at the sites where the species has already been Contributed reagents/materials/analysis tools: PE MAV JIH WG CT JR
observed. The data required to identify these areas include species MS MvZ XS. Wrote the paper: MvZ XS PVD JIH.
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