ChapterPDF Available

Genomics of Plant Genetic Resources

Chapter

Genomics of Plant Genetic Resources

Abstract and Figures

There is a growing recognition of the need to evaluate the diversity status and trends of plant genetic resources’ use and maintenance in natural populations, farmers’ fields, home gardens and in other in situ settings to prioritize and optimize conservation actions and link these effectively with ex situ preservation approaches. The recent development of new powerful molecular tools that reveal many genome-wide polymorphisms has created novel opportunities for assessing genetic diversity, especially when these markers can be linked to key adaptive traits and are employed in combination with new geo-spatial methods of geographic and environmental analysis. New methods to prioritize varieties, populations and geographic areas for in situ conservation, and to enable monitoring of genetic diversity over time and space, are now available to support in situ germplasm management of annual crop and tree genetic resources. We will discuss concepts and examples of application of molecular markers and spatial analysis to optimize in situ conservation. We present a case study on the distribution and genetic diversity of the underutilized new world fruit tree crop cherimoya (Annona cherimola Mill.) in its Andean distribution range to exemplify the usefulness of combining molecular marker and spatial data to inform in situ conservation decisions.
Content may be subject to copyright.
Chapter 4
Application of Molecular Markers in Spatial
Analysis to Optimize In Situ Conservation of
Plant Genetic Resources
Maarten van Zonneveld, Ian Dawson, Evert Thomas, Xavier Scheldeman,
Jacob van Etten, Judy Loo and José I Hormaza
Contents
4.1 Introduction .............................................................. 68
4.2 Application of Molecular Markers to Optimize In Situ Conservation ............... 70
4.3 Geospatial Analysis Techniques for Mapping Molecular Genetic Diversity .......... 72
4.4 Case Study: Climate Change Impact on Cherimoya: Microsatellite Diversity and its
Distribution Currently and in the Future ....................................... 76
4.4.1 Introduction ....................................................... 76
4.4.2 Methods .......................................................... 78
4.4.3 Results and Discussion .............................................. 80
References .................................................................... 86
Abstract There is a growing recognition of the need to evaluate the diversity status
and trends of plant genetic resources’ use and maintenance in natural populations,
farmers’ fields, home gardens and in other in situ settings to prioritize and optimize
conservation actions and link these effectively with ex situ preservation approaches.
M. van Zonneveld ()·J. van Etten
Bioversity International, Turrialba office, Costa Rica
e-mail: m.vanzonneveld@cgiar.org
E. Thomas ·X. Scheldeman
Bioversity International, Regional Office for theAmericas, Cali, Colombia
Ghent University, Faculty of Bioscience Engineering, Gent, Belgium
I. Dawson
The World Agroforestry Centre, Headquarters, Nairobi, Kenya
J. Loo
Bioversity International, Headquarters, Rome, Italy
J. I. Hormaza
Instituto de Hortofruticultura Subtropical y Mediterránea, (IHSM-UMA-CSIC),
Estación Experimental La Mayora, Algarrobo-Costa, Málaga, Spain
R. Tuberosa et al. (eds.), Genomics of Plant Genetic Resources, 67
DOI 10.1007/978-94-007-7572-5_4,
© Springer Science+Business Media Dordrecht 2014
68 M. van Zonneveld et al.
The recent development of new powerful molecular tools that reveal many genome-
wide polymorphisms has created novel opportunities for assessing genetic diversity,
especially when these markers can be linked to key adaptive traits and are employed
in combination with new geo-spatial methods of geographic and environmental anal-
ysis. New methods to prioritize varieties, populations and geographic areas for in
situ conservation, and to enable monitoring of genetic diversity over time and space,
are now available to support in situ germplasm management of annual crop and tree
genetic resources. We will discuss concepts and examples of application of molecu-
lar markers and spatial analysis to optimize in situ conservation. We present a case
study on the distribution and genetic diversity of the underutilized new world fruit
tree crop cherimoya (Annona cherimola Mill.) in its Andean distribution range to
exemplify the usefulness of combining molecular marker and spatial data to inform
in situ conservation decisions.
Keywords Annona cherimola Mill (Cherimoya) ·Biogeography ·Climate Change ·
Conservation genetics ·Conservation genomics ·Domestication ·Geographic Infor-
mation Systems ·Germplasm collection ·Microsatellite markers ·Spatial genetics ·
Simple sequence repeat
4.1 Introduction
There is an increasing recognition of the need to assess in situ diversity status and
dynamics of plant genetic resources (PGR) (e.g. in wild populations and on farm)
to prioritize and optimize conservation actions and link these effectively with ex situ
preservation approaches (Palmberge-Lerche 2008;FAO2010,2011). In situ PGR are
often threatened by the modernization and expansion of agriculture, which involves
clearance of more land, replacement of landraces by advanced crop varieties, and
new management approaches that exclude diversity from the agricultural landscape,
leading to genetic erosion (van de Wouw et al. 2010a).
In situ conservation is considered to be important because it provides dynamism,
the potential for continued evolution to natural and human selection pressures. The
latter include the requirement for new and better crop varieties and improved trees
to meet evolving farmer and market preferences, and anthropogenic climate change
(Reed and Frankham 2003; Cleveland and Soleri 2007; Mercer and Perales 2010).
This makes in situ conservation areas potential sources of untapped and new diversity
for the development of new crop varieties for local and wider use.
In situ conservation is also the method of choice for many plants with recalci-
trant seed that cannot be stored in seed banks and for plants whose biology (e.g.
long period to maturity, large size) otherwise makes human-managed regeneration
costly or difficult; these criteria apply to thousands of locally or globally important
tree species. In the case of non-timber forest products, genetic resources are often
principally maintained in wild stands or, depending on the level of domestication, in
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 69
smallholders’ fields and home gardens. On farm conservation of tree species within
their native distributions is often referred to as circa situm rather than in situ con-
servation to make a distinction from preservation in natural populations [Boshier
et al. 2004], but in this chapter, we use the term in situ to refer to all plant genetic
resources - including trees, crops and crop progenitors - in farm and natural settings.
The diversity of annual crops and tree species maintained in farms and in the wild
is a treasure trove for as yet uncharacterized resources for local people and breeders
(Scheldeman et al. 2003; Ræbild et al. 2011). However, trees in modified natural
habitats and farmland may be susceptible to particular pressures such as inbreeding
depression (Dawson et al. 2009,2011; Vranckx et al. 2011).
The formulation of in situ conservation strategies can be optimized by an un-
derstanding of spatial patterns of genetic diversity (Petit et al. 1998). Areas of high
genetic diversity may be targets for in situ conservation as they may be more likely to
contain interesting materials for crop and tree improvement. Measuring genetic diver-
sity in situ is also a means for prioritising accessions for ex situ collections (Frankel
et al. 1995a; Tanksley and McCouch 1997; Odong et al. 2011). Genetic charac-
terization is increasingly being used to optimize in situ conservation approaches in
combination with new geospatial methods for presenting results (Samuel et al. 2013;
Thomas et al. 2012; van Zonneveld et al. 2012). Comparing the genetic diversity
that is present in situ with what is maintained ex situ provides guidance in devising
sampling strategies to fill ex situ collection gaps (Samuel et al. 2013; van Zonneveld
et al. 2012). Similarly, comparisons of farm stands with wild plant populations can
demonstrate the relative effectiveness of cultivated and natural landscapes for con-
servation (e.g. Hollingsworth et al. 2005; Miller and Schaal 2005). At the same time,
knowledge on patterns of genetic diversity in the wild and in farmland allows us to
better understand the evolutionary processes in the development of current species
distributions and, where relevant, in domestication (e.g. Russell et al. 2011). Of
course, monitoring activities are also required to measure the effectiveness of in situ
conservation programs over time, and to account for dynamic processes in the use
and management of natural and agricultural landscapes and the transitions between
them.
Various initiatives that promote the conservation and sustainable use of plant
genetic resources draw attention to the need for more assessments of genetic variation
with molecular markers (FAO 2010, 2011). The recent development of new powerful
molecular tools that reveal many genome-wide polymorphisms has created novel
opportunities for assessing genetic diversity. This is especially the case when these
markers can be linked to key adaptive traits and are employed in combination with
new geospatial methods of geographic and environmental analysis (e.g. Escudero
et al. 2003; Manel et al. 2003; Holderegger et al. 2010; Chan et al. 2011; Tuberosa
et al. 2011). New methods to prioritize varieties, populations and geographic areas
for in situ conservation, and to enable monitoring of genetic diversity over time
and space, are now available and can and should be exploited to improve in situ
germplasm management.
70 M. van Zonneveld et al.
4.2 Application of Molecular Markers to Optimize In Situ
Conservation
In situ conservation programs should seek to conserve functional genetic variation
that is important to foster future adaptive responses in agricultural and natural land-
scapes and to support human needs such as food security and agricultural productivity
in managed ones. Often, though, the variation that will be important in the future is
not known currently. As a result, some practitioners have taken the view that simply
as much variation as possible, whether of known value or not, should be conserved
(e.g. van Zonneveld et al. 2012). In this situation, ‘neutral’ molecular markers, which
are not linked to any particular trait but presumably provide a representation of the
‘underlying’ diversity in an organism, are appropriate. Although such markers are not
associated with fitness or adaptive potential (Avise 2010; Ouborg et al. 2010), they
contribute to a good understanding of many of the evolutionary processes involved in
the development of contemporary patterns of variation, including in the contraction
and expansion of populations and the development of refugia. They are also ideal for
understanding mating systems, the level of inbreeding and other key biological fea-
tures of importance for PGR management (Brown and Hodgkin 2008). Such markers
also reveal the level of kinship between different crop landraces and the degree of
the genetic contribution of different ancestors to cultivars (Eaton et al. 2006). This
has for example been used to prioritize livestock breeds for in situ conservation on
the basis of their genetic distinctiveness (Eding et al. 2002). These methods are now
also being applied to crop genetic resources (Samuel et al. 2013).
Allelic richness at neutral loci is often regarded as an indicator of effective pop-
ulation size (Widmer and Lexer 2001; Leberg 2002), which expresses the rate of
historic gene flow and bottleneck events. The measure has been used to target wild
tree populations for in situ conservation (Petit et al. 1998) and to monitor erosion
in crop gene pools (van de Wouw et al. 2010a). The number of locally common
alleles (alleles that only occur in a limited area of a species’ distribution but reach
relatively high frequencies in these areas) has been identified as a particularly useful
measure of richness. The maintenance of such alleles at high frequency in particular
geographic areas may reflect long processes of selection and adaptation (Frankel
et al. 1995b; van de Wouw et al. 2010a). The identification of areas where geograph-
ically restricted alleles occur in high frequency can also be determined by Allelic
Aggregation IndexAnalysis (AAIA) (Miller 2005). This calculates for each sampled
individual the average proximity of its alleles to similar alleles in other individuals,
in comparison to the average distance based on the distribution of all individuals
(Miller 2005). When only alleles with a frequency higher than 5 % are included in
AAIA, the function can be appropriate for calculating locally common alleles.
Although ‘neutral’ markers do not directly relate to function, their heterozygosity
can correspond with population fitness, especially for out-breeding species (Reed
and Frankham 2003; Vranckx et al. 2011).
At first sight counter-intuitively, increases in morphological variation in key fea-
tures that are selected by humans in the domestication process of annual crops (e.g.
Brassica, maize, chilli peppers: Capsicum spp., potato: Solanum tuberosum L.) and
trees (e.g. cacao: Theobroma cacao L., apple: Malus domestica Borkh.) are often
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 71
accompanied by decreases in genetic variation in the wider genome. This apparent
paradox has fascinated students of domestication for many years, and it may be due
to bottlenecks induced by human transport of germplasm and/or phenotypic selec-
tion events. With human selection, the range of variation at traits of interest becomes
wider, but elsewhere bottlenecks are introduced (e.g. de Haan et al. 2009a).
Different types of characterization thus provide us different information and in-
sights. Different characterization approaches may be used simultaneously to target
areas for in situ conservation because each method reveals different features about
populations. While some approaches may specifically reveal the results of recent
gene flows, other methods may shed light on ancient evolutionary processes that re-
late to climatic fluctuations over tens or hundreds of thousands of years (Newton et al.
1999). Increasingly, molecular markers are being identified that are linked to genes
associated with adaptive traits, which bridges the gap to function. Allelic shifts at loci
linked to adaptive traits under selection pressure can be evaluated against changes at
neutral reference loci to distinguish ‘real’ adaptive genetic changes from migration
and drift, and to separate plastic from genetic responses (Hansen et al. 2012). We
return to this topic later.
The use of molecular tools to target areas for in situ conservation has a number
of practical advantages compared with morphological characterization. First, it is
relatively easy to collect the samples needed for molecular analysis in the field and
to transport them to the laboratory for testing (easier, e.g. to sample leaves than
collect seeds that may be recalcitrant or difficult to germinate). Second, samples can
be analysed in a laboratory in another country.Assuming the necessary permissions
to exchange material have been obtained. This is particularly usefull when exam-
ining species’ diversity patterns across extensive distribution ranges covering many
countries to ensure consistency in analytical methods. Third, markers are neutral
to environmental ‘noise’ that is always present when contrasting the morphological
traits of plants grown in different locations. This can lead to plants that look different
from each other even when they are genetically very similar. An alternative is to char-
acterize plants in environment-controlled field trials, but these are often expensive
and a certain amount of environmental noise may remain. Fourth, modern molecular
marker methods are generally repeatable over time and location, which provides
opportunities to add data from extra, freshly sampled populations to existing data
sets. This is important when monitoring the dynamics of diversity in populations
over time, for example, when assessing genetic erosion. The molecular diversity of a
‘historic’ collection from a specific area can be compared with a new one, such as de
Haan et al. (2009a) did to assess possible allelic loss over time in local potato vari-
eties grown in PeruvianAndean villages. In this particular case, no loss of molecular
diversity was observed over a 25-year period, suggesting in situ conservation with
farmers was effective. When improved varieties cross with local landraces and are
taken up into informal seed systems they may however, reduce in situ diversity, as
shown for maize in southern Mexico (van Heerwaarden et al. 2009).
Despite examples of reductions of in situ crop genetic diversity due to replace-
ment and hybridization with new varieties, levels of newly introduced variation may
increase. For example, a meta-analysis of molecular diversity studies of eight food
72 M. van Zonneveld et al.
crops suggested that in the last decades breeders have increased the use of crop
diversity in the development of improved varieties (van de Wouw et al. 2010b).
As mentioned in the introduction, on farm conservation can complement the con-
servation of wild populations increasingly menaced by natural habitat loss. This
is relevant for many socio-economically important tree species that are incipient
domesticates. Further research is needed on the ecological and socio-economic
circumstances under which on farm conservation is an effective approach for the
sustainable management of tree genetic resources (Dawson et al. 2013). In the case
of the Amazonian tree species Inga edulis Mart. (ice-cream bean tree),for example,
molecular marker diversity is lower in farms than in wild populations, although allelic
variation remains relatively high in agricultural landscapes that are still important
sites for conservation (Hollingsworth et al. 2005). In another example, cultivated
populations of the Mesoamerican fruit tree Spondias purpurea L. (jocote) contained
unique chloroplast alleles that were not found in wild populations, supporting the
complementary roles of on farm- and wild stand-conservation approaches (Miller
and Schaal 2005).
4.3 Geospatial Analysis Techniques for Mapping Molecular
Genetic Diversity
Just as molecular marker methods have advanced greatly over the last decade, so too
have approaches for geospatial analysis (Guarino et al. 2002; Miller 2005; Jombart
2008; van Etten and Hijmans 2010; Chan et al. 2011; van Zonneveld et al. 2011).
Advances in geographic information systems (GIS) are still however underutilised
in genetic diversity studies, perhaps because many scientists are unaware of the
newer methods available. Training materials have been developed to bridge this gap
(Scheldeman and van Zonneveld 2010).A great advantage of GIS-based approaches
is the clear graphic presentation of results through maps, which facilitates the in-
terpretation of findings and hence their incorporation into conservation strategies
(Jarvis et al. 2010). Geospatial analysis of genetic diversity has been undertaken
for a wide range of trees because the maintenance of their genetic resources often
depends largely on in situ conservation. For the Norway spruce (Picea abies [L.]
Karst.) in Austria, for example, a geographic grid-based gap analysis was carried
out to identify new conservation units that complemented mitochondrial and nuclear
molecular marker studies (Schueler et al. 2012).
One effective method to describe genetic diversity in geographic space is to use
circular neighbourhood-type analyses. This is especial effective when working with
individual geographically-referenced accessions rather than with populations (van
Zonneveld et al. 2012). The circular neighbourhood approach allows calculation with
confidence of genetic parameters per grid cell by grouping georeferenced individuals
within a user-defined radius of geographic distance around each cell (Scheldeman
and van Zonneveld 2010). The approach makes analyses less sensitive to grid origin
definition and enables the inclusion of isolated trees in the calculation of genetic
parameters.
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 73
In the approach uneven sampling densities among grid cells can be corrected by
establishing a level of Rarefaction (minimum sample size per grid cell to include
in analysis) or by carrying out re-sampling without replacement (see Leberg 2002;
Thomas et al. 2012; van Zonneveld et al. 2012). The final results of the corrected
diversity analysis then provide detailed and representative estimates of geographic
patterns of diversity. Scaling can be adjusted to the dimensions of particular countries
or regions so that results can be incorporated into national and regional conservation
plans, as appropriate. Such an approach has been used to identify genetic diver-
sity hotspots for the in situ conservation of a number of important perennial tree
crops, including cacao in its Latin-American centres of origin and domestication
(Thomas et al. 2012), cherimoya in the Andes (van Zonneveld et al. 2012), and bush
mango (Irvingia gabonensis [Aubry-Lecomte ex O’Rorke] Baill. and I. wombolu
Vermoesen) in Central Africa (Lowe et al. 2000).
These are examples of geospatial analyses to prioritize conservation efforts for
a few economically important trees. However, thousands of tree species have local
livelihood value and many of these are threatened. As the costs to carry out analyses
with molecular markers are continuously decreasing, it will become more feasible
to perform such studies on these species.
One approach to extrapolate patterns observed from these analyses and prior-
itize areas with as many tree genetic resources as possible for conservation is to
identify putative Pleistocene refugia and converging post-glacial migration routes.
These areas may harbour high inter- and intra-specific diversity (Petit et al. 2003).
Georeferenced observation points from herbaria and genebanks can be used to
predict Pleistocene distributions on the basis of extrapolated past climate data (Wal-
tari et al. 2007). Climate data are freely available from online platforms such as
PMIP2 (http://www.pmip2.cnrs-gif.fr) and WorldClim (www.worldclim.org). Geo-
referenced plant data are increasingly available through sites such as the Global
Biodiversity Information Facility (www.gbif.org). These data, when of reasonable
quality, can be used in Environmental Envelope Modelling (EEM) to predict past
distributions and reconstruct potential refugia (Waltari et al. 2007; Thomas et al.
2012; Vinceti et al. 2013). Molecular marker data, especially chloroplast DNA vari-
ation, can help to validate or refute potential refugia determined by EEM (Newton
et al. 1999; Petit et al. 2003). A major limitation, however, is that different sampling
methods and marker types have often been used for separate studies of the same
species in different parts of its distribution. This complicates clear identification of
distribution-wide diversity patterns, as observed for example for the new world trop-
ical palm Bactris gasipaes Kunth (peach palm) (Clement et al. 2010; Graefe et al.
2013). For most important food crops standardized molecular tool kits have been
proposed to improve comparability (Van Damme et al. 2010); however, for most
other plants, molecular standards still need to be developed.
Most crops were domesticated in the last 12,000 years and the current distribution
of their diversity is marked by relatively recent human dispersal. More inter- and intra-
specific diversity can be expected to be found in and around centres of domestication
such as the Andes, Mesoamerica and the Amazon in the Americas, and the Fertile
74 M. van Zonneveld et al.
Crescent in the Middle East. Just as high tree genetic diversity is expected in post-
Pleistocene converging migration routes, so high crop diversity can be expected in
converging human dispersal routes.
An example is cultivated chili pepper in Peru. The diversity of cultivated Capsicum
encountered there is probably the highest in the world. It is an important area of
diversification and varieties from the five cultivated species have been grown there
since pre-Colombian times (Perry 2012). However, Peru is probably not the centre
of origin of these five species. Rather, they were likely transported to their current
locations in Peru from putative centres of domestication elsewhere (Eshbaugh 2012).
In such situations, molecular markers can help distinguish between centres of origin
and centres of diversity.
Studies in human genetics show that relatively simple models of diffusion can
be used to predict global genetic diversity patterns (Ramachandran et al. 2005).
Diffusion models have been used to model the spread of agriculture generally and
of particular crops (Pinhasi et al. 2005). van Etten and Hijmans (2010) showed
that, for crops, spatial diffusion models and genetic diversity models can be linked.
Such combined models could eventually be used to predict levels of diversity and
complementarity between locations, including of un-sampled locations.
Spatial studies with a more local scope can be important for deciding in detail the
most appropriate on farm PGR management strategies in traditional rural communi-
ties. Such studies can, for example, help to better understand how farmers manage
and conserve crop diversity within the landscape over time (Worthington et al. 2012).
This can help identify the geographic and social levels at which in situ conservation
should be implemented and crop diversity monitored (Barry et al. 2007).
PGR management in traditional rural communities differs by crop species, by
social context and by environment. For example, molecular markers have shown
that farmers in southern Mexico maintain bean diversity (Phaseolus coccineus L.,
P. dumosus Macfad., P. vulgaris L.) in clearly separated fields along a topographical
climate gradient (Worthington et al. 2012). A simple sequence repeat (SSR) analysis
of the genetic structure of rice (Oryza sativa L., O. glaberrima Steud.) in the Re-
public of Guinea revealed genetic differences between lowland coastal and upland
savannah areas, but no differentiation between villages or farms within contrasting
agro-ecosystems (Barry et al. 2007). Although within each rice variety high genetic
diversity was found, most of this diversity could be conserved within just a few farms
of a village. The high diversity within farms and the low genetic structure between
farms observed by Barry et al. (2007) may be explained by active human seed ex-
change and high varietal turnover. Likewise, in potato growing areas in the Peruvian
Andes, most variation in SSRs in the potato (Solanum spp.) crop was observed within
farms (de Haan et al. 2009a). The same study of de Haan et al. (2009a) indicated
that the level of diversity maintained by farm families can however vary greatly. The
conservation of high potato varietal diversity by many farmers can be explained by
preferences for specific cultivars for home consumption and the desire to spread risk
through varietal diversification (de Haan et al. 2009a,b).
As some of the above examples illustrate, GIS can be used to overlay different
information types on to genetic data to make more informed management decisions.
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 75
An understanding of the drivers of genetic erosion of natural populations, threats to
ecosystems and their relative vulnerability, can be gained (e.g. Jarvis et al. 2010;
Hirota et al. 2011).
Threats to ecosystems need to be interpreted carefully when applied to particular
species, since individual taxa, and the populations within them, will respond differ-
ently. Nevertheless, areas of important genetic diversity under threat can be identified
for urgent conservation, such as natural populations with high allelic richness located
in areas of agricultural encroachment and/or in locations where future climate will
likely not support regeneration and survival.
Recent studies have begun to explore further how to incorporate spatially-defined
threat information to prioritize for in situ conservation. Optimal solutions for conser-
vation considering the costs to conserve unique genetic diversity can be calculated
(Samuel et al. 2013). Information on the probability of variety replacement can be
included, based on variables such as the geographic distance to areas of high human
population density (Samuel et al. 2013).
EEM of species distributions within current and projected future climates to assess
changes over time can be used in combination with genetic analysis to identify
hotspots of diversity that are particularly vulnerable to change. This has for example
been done for cacao (Thomas et al. 2012) and a further example (cherimoya) is
described below. The comparison of current and future modelled distributions for
cacao revealed several areas of low climate change threat within the Amazonian
area with high genetic diversity; these areas could be targets for in situ conservation
(Thomas et al. 2012). Tree species are good candidates for studying climate change
impacts on landscapes because of their longevity, which means signatures of past
events are retained for longer (Petit et al. 2008). For trees, the available molecular
data in combination with pollen cores and other data sets would suggest that natural
dispersal will not be able to keep up with climate change in many parts of the
world. Whole forest ecosystems that are crucial for the in situ conservation of trees
and associated flora (including the wild populations of some crucial crops and their
relatives) and fauna may therefore be threatened (Malhi et al. 2009).
For annual crops, a good example of application of current and Past climate EEM
is with wild barley (Hordeum vulgare L. subsp. spontaneum [K. Koch] Thell.) in the
Fertile Crescent and Central Asia (Russell et al. 2013). In this case, contemporary
patterns of molecular marker diversity expressed using the circular neighbourhood
method corresponded with EEM for the Last Glacial Maximum. Both analyses indi-
cated that the eastern Mediterranean was likely to have been a Pleistocene refuge for
wild barley, as the highest levels of genetic diversity were located here and habitat
was indicated to be in common. This area should therefore be a focus for conservation
activities.
Most interestingly in this case, geographic point location data of wild barley
accessions were used to identify the environments in which the taxon grows in its by
extracting values for the 19 bioclimatic variables of WorldClim. The advantage of
studying barley compared to many lesser-researched plants is that the chromosome
positions of many molecular markers are known. This allows associations between
environmental data and genetic markers to be located within the genome. This has
76 M. van Zonneveld et al.
the potential to be very useful in crop breeding and in monitoring responses to
environmental change (Hansen et al. 2012). In the case of wild barley, for example,
it was possible to identify regions of the genome that are candidates for adaptive genes
to the environment. This type of analysis may prove especially useful for plants for
which comparatively little phenotypic data are available (Neale and Kremer 2011).
Phenotypic data taken directly from wild stands rather than collected from field trials
will also become more important.
The study of genetic and plastic responses of plant populations to climate change
is especially relevant when migration to more suitable locations may be restricted
due to habitat fragmentation and/or by the rapid pace of change (Hoffmann and
Sgró 2011). Distribution range shifts may also cause reduced fitness of populations
due to founder effects (Cobben et al. 2011). Molecular data modelled in geographic
space can help determine potential migration rates and adaptation at current loca-
tions can be monitored through allele shifts at important genes (as described in the
barley example above). The latter approach is becoming increasingly feasible as
chromosome-mapped markers are linked to adaptive traits.
Conservation genomics (i.e. combining conservation genetic principles with func-
tional genomics approaches) is in Ouborg’s (2010) opinion both necessary and
feasible to understand the effects of genetic diversity losses on fitness.Avise (2010)
noted that the ‘genomics revolution’allows scientists to examine sequence variation
at unprecedented numbers of loci for unprecedented numbers of individuals. Al-
though most genomic advances are currently associated with well-studied crops and
some other model species, rapid developments will allow for genome-wide mapping
in most plant species in the near future (Ingvarsson and Street 2011).
A major challenge for mapping diversity at gene sequences of adaptive signifi-
cance is that important traits may be controlled by many loci. If drought tolerance
is influenced by more than 200 loci, for example, what is the value of choosing
only a handful of these, to study? Such concerns will be less relevant with the ap-
plication of ‘exome capture’ methods that assess variation among individuals at all
expressed genes and with new statistical approaches to assess genome-environment
associations (Mascher et al. 2013).
4.4 Case Study: Climate Change Impact on Cherimoya:
Microsatellite Diversity and its Distribution Currently
and in the Future
4.4.1 Introduction
In this section we present a case study on the distribution and genetic diversity of
cherimoya in its Andean range to exemplify the usefulness of combining molecu-
lar marker and spatial data to inform in situ conservation decisions. Northern Peru
(Cajamarca) and southern Ecuador (Loja) were identified as areas of high conserva-
tion priority for cherimoya and as important areas for further germplasm exploration
on the basis that areas of high neutral genetic diversity have a high likelihood of
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 77
containing unknown traits of interest for domestication (van Zonneveld et al. 2012).
Here, we compare a climate change impact study of cherimoya with the spatial dis-
tribution of its genetic diversity in the Andes and discuss the potential implications
for conservation. Temperature and rainfall variations are likely to occur in high ge-
netic diversity areas of cherimoya’s distribution due to climate change, potentially
threatening genetic resources.
Cherimoya is an underutilized new world tropical fruit tree belonging to the
Annonaceae, a family included within the Magnoliales in the Eumagnoliid clade
among the early-divergent angiosperms (Bremer et al. 2009). It is still in the ini-
tial stages of domestication (Escribano et al. 2007) and is considered at high risk
of genetic erosion (Popenoe et al. 1989). Cherimoya fruits are widely praised for
their excellent organoleptic characteristics, and the species is therefore considered
to have high potential for commercial production and income generation for both
small and large-scale growers in subtropical climates (Van Damme and Schelde-
man 1999). Cherimoya presents protogynous dichogamy, i.e. it has hermaphroditic
flowers wherein female and male parts do not mature simultaneously, favouring
outcrossing in its native range (Lora et al. 2010). For commercial production out-
side of the tree’s native range, hand pollination with pollen and stamens is common
practice due to a lack in overlap of female and male stages and the absence of pol-
linating agents (Lora et al. 2010). At present, large-scale commercial production is
concentrated in Spain, the world’s largest cherimoya producer with around 3000 ha
of plantations, while small-scale cultivation occurs throughout the Andes, Central
America and Mexico. Cherimoya is commonly grown inAndean home gardens and
orchards, and trees from these environments may contain promising traits for future
breeding programs (Scheldeman et al. 2003). In Peru, the local cultivar ‘Cumbe’ is
already sold for retail prices significantly above the prices of unselected cherimoya
fruit types (Vanhove and Van Damme 2009,2013).
Most early chroniclers and scientists have proposed the Andean region, more
precisely the valleys of southern Ecuador and northern Peru, as cherimoya’s centre of
origin (Popenoe 1921; Popenoe et al. 1989). The existence of isolated putatively wild
cherimoya forest patches in the inter-Andean valleys of Ecuador and northern Peru
supports this hypothesis. Nonetheless, the possibility that these are feral populations
cannot be immediately discounted, because this phenomenon has been observed for
several fruit trees including olives (Gepts 2003). An alternative hypothesis for the
centre of origin of cherimoya is Central America, considering that most relatives
of cherimoya are native to that region and southern Mexico (H. Rainer, Institute of
Botany, University of Vienna, 2011, pers. comm.), and that high genetic diversity is
found in cherimoya genotypes there (Hormaza et al., unpublished data). In any case,
cherimoya fruits have been consumed in theAndean region since antiquity (Popenoe
et al. 1989) and movement of germplasm across southern Mexico, Mesoamerica and
the Andes probably took place in pre-Columbian times. Wolters (1999) indicated that
the ceramic cherimoya-shaped vases found in the remains to the EcuadorianValdivia
culture (3,500–1,600 A.C.) may testify to the important role this early culture played
in the exchange of cherimoya germplasm (as well as other crops) between theAndean
region and Mesoamerica.
78 M. van Zonneveld et al.
In contrast to most tropical and subtropical underutilized fruit trees, cherimoya
genetic resources are well represented in ex situ germplasm collections. Several field
collections have been established in Spain, Peru and Ecuador, preserving over 500
different accessions (Escribano et al. 2007; CHERLA 2008). The Spanish collection,
based at la Estación Experimental La Mayora in Malaga, holds over 300 accessions
(190 collected from the Andean region) and is currently used as source material
for the Spanish cherimoya breeding program. This collection has been thoroughly
analyzed using isozymes (Pascual et al. 1993; Perfectti and Pascual 1998,2005) and
Microsatellite markers (Escribano et al. 2007,2008).
4.4.2 Methods
4.4.2.1 Sampling and SSR Analysis
A total of 1,506 cherimoya accessions were sampled (395 from Bolivia, 351 from
Ecuador and 760 from Peru) and tested with nine highly polymorphic nuclear SSR
markers (Escribano et al. 2008). DNA was extracted from young leaves according
to Viruel et al. (2004). Details of the methods of SSR selection, amplification and
PCR product analysis are given in van Zonneveld et al. (2012). The coordinates of
tree locations were verified with DIVA-GIS (www.diva-gis.org) discarding erroneous
points, i.e., (1) points showing inconsistencies between the location mentioned in
passport data and map projections at department and province level, applying a buffer
of 20 minutes (approximately 30 km); and (2) outliers based on current climate data
derived from WorldClim (Hijmans et al. 2005) (for two or more of the 19 bioclimatic
variables according to the reverse jackknife method implemented in DIVA-GIS;
Chapman 2005). Based on these checks, only two points were excluded from further
analysis. The cleaned dataset thus included microsatellite data of 1,504 georeferenced
trees.
4.4.2.2 Spatial Analysis
Similar to van Zonneveld et al. (2012), we constructed 10-minute grid layers (which
corresponds to approximately 18 km in the study area) for all genetic parameters,
applying a circular neighbourhood with a diameter of one degree (corresponding to
approximately 111 km), using R program version 2.15.1 with the packages Raster
(Hijmans and van Etten 2012) andAdegenet (Jombart et al. 2013). We performed grid
cell-based calculations of allelic richness and the number of locally common alleles
as measures of alpha genetic diversity. To establish comparability of these parameters
between cells, we corrected sample-bias through re-sampling without replacement
after Leberg (2002) to a sample size of 20 trees. Per parameter, we calculated the
average value from 1,000 subsamples following the bootstrap method developed by
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 79
Thomas et al. (2012). Re-sampling without replacement provides similar results to
the rarefaction approach applied by van Zonneveld et al. (2012) with the advantage
that it can be used to correct other genetic parameters in addition to allelic richness
(Thomas et al. 2012). As a measure of beta diversity, a spatial principal component
analysis (sPCA) was carried out with Adegenet using the neighbourhood-by-distance
connection network as explained by Jombart (2013) with a minimum distance of
zero and maximum distance of one degree. For each tree the projection score on
the first axis was visualized with a 10-minute resolution raster applying a circular
neighbourhood of one degree.
We carried out EEM to assess potential impacts of climate change across cheri-
moya’s Andean range by comparing distributions under current and future climate.
We used the EEM software Maxent version 3.3.3k (Phillips et al. 2006; Elith et al.
2011) implemented in the R package Dismo (Hijmans et al. 2013), on the basis
that it performs very well in comparison to other EEM software (Elith et al. 2006;
Aguirre-Gutiérrez et al. 2013). We trained our model on the basis of the 1,504 re-
tained georeferenced cherimoya trees and the 19 WorldClim bioclimatic variables at
a resolution of 2.5 minutes (Hijmans et al. 2005). For characterising future climate we
used 19 downscaled climate models for 2050 based on theA2 scenario of greenhouse
gas emissions (available at http://ccafs-climate.org). To improve model performance
(cf. Acevedo et al. 2012), we limited the extraction of background points to the area
enclosed by the convex hull polygon constructed based on all records and extended
with a buffer corresponding to 10 % of the polygon’s longest axis (Willis et al. 2003).
Apart from this pre-selection of background points, we used Maxent default settings.
To compare unbiased cAUC values and hence the performance of models con-
structed with Maxent with a geographical null model (see Hijmans 2012), we (1)
randomly partitioned both presence and background points in five groups, (2) re-
moved spatial sorting bias; and (3) ran both models for each of the five data subsets
(each time using 80% of the points as test data and 20% as training data) using rel-
evant functions implemented in the Dismo package. The Maxent models performed
significantly better (mean cAUC =0.70) than the geographical null models (mean
cAUC =0.51) (Mann–Whitney P=0.01), justifying the use of Maxent. We projected
the Maxent model both to (1) the average of the 19 downscaled general circulation
climate models for 2050; and (2) to each of the 19 models separately. The modelled
distribution based on (1) was slightly more conservative than the distribution ob-
tained by cell-based averaging of the logistic values of (2), and we therefore used (1)
for comparison with the present-day modelled distribution. At the same time, the re-
sults for each separately run general circulation climate model were used to examine
the level of agreement between models in predicting suitable areas for cherimoya in
the 2050s. We restricted the potential distributions generated by Maxent using the
maximum of the sum of sensitivity and specificity as a threshold value (here 0.15
for the logistic threshold). To reduce the risk of including areas within the modelled
distribution where the species does not in reality occur (e.g. due to dispersal limi-
tations), we limited the potential distributal under current climatic conditions to the
area enclosed by the convex hull polygon created on the basis of the species’presence
80 M. van Zonneveld et al.
Fig. 4.1 This map shows the average number of alleles per locus in 10-minute grid cells applying
a one-degree circular neighbourhood
points and extended with a buffer around it corresponding to 10% of the polygon’s
longest axis.
All maps were edited in ArcMap.
4.4.3 Results and Discussion
The cherimoya populations in northern Peru, around the Cajamarca Department, con-
tain the highest allelic richness across the Andean distribution range (Fig.4.1). Other
areas of high Alpha diversity are located on the border zone between Ecuador (Loja
Province) and Peru (the Piura Department), in the northern part of Ecuador around
the capital Quito and in the northern part of the Lima Department in Peru. However,
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 81
Fig. 4.2 This map shows the average number of alleles per locus in 10-minute grid cells applying
a one-degree circular neighbourhood and resampling without replacement to a minimum sample
size of 20 trees. The value per grid cell is the average of 1,000 bootstrapped subsamples
when we observe allelic richness corrected by re-sampling without replacement,
clearly most diversity is found in northern Peru, around the Cajamarca Department
(Fig. 4.2). This parameter is highly correlated with allelic richness corrected by
rarefaction to a minimum sample size of 20 trees, as calculated by comparing rar-
efaction and bootstrapped subsample values for 654 grid cells (van Zonneveld et al.
2012;r=0.98, P<0.0001). These results are confirmed by the high number of
Locally common alleles after resampling without replacement found in the Caja-
marca department (Fig. 4.3), indicating that the tree stands in this region have likely
been subjected to long processes of selection and local adaptation, resulting in the
accumulation of diversity.
Beta diversity explained by projection scores on the first axis of the sPCA reveals
a clear global genetic structure and little local structure (Fig. 4.4), distinguishing
82 M. van Zonneveld et al.
Fig. 4.3 This map shows the average number of alleles per locus in 10-minute grid cells that are
relatively common (occurring with a frequency higher than 5%) in a limited area (in 25 % or less
of grid cells), applying a one-degree circular neighbourhood and resampling without replacement
to a minimum sample size of 20 trees. The value per grid cell is the average of 1,000 bootstrapped
subsamples
between two genetic populations, one comprising northern Peru and Ecuador and
the other consisting of southern Peru and Bolivia (Fig. 4.5). This is consistent with
the genetic structure found by van Zonneveld et al. (2012) applying Bayesian cluster
analysis. The proportion of variance explained by the first sPCA axis by respectively
spatial autocorrelation and genetic variance is 0.64 and 0.40 (Fig. 4.4). This indicates
clear spatial and genetic structure. The low genetic diversity in Bolivia compared
to Peru (and to a lesser degree compared to Ecuador), suggests that populations in
Bolivia have been established more recently. Our results from the sPCA suggest
that plant material in Bolivia most likely has been introduced from southern Peru,
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 83
Fig. 4.4 This graph shows the
Eigenvalues (λ) of the spatial
principle component analysis
for each component according
to the genetic variance
explained (x-axis) and
Moran×s Index for spatial
autocorrelation (I) (y-axis).
Eigenvalues that explain
global spatial structure have a
positive I, Eigenvalues that
contribute to local spatial
structure have a negative I
particularly from the regions of Cuzco, Huancavelica and/or Junín, because trees
from these Peruvian departments are genetically closely related to Bolivian stands.
Modelling of expected climate change impacts on the cherimoya distribution by
the 2050s reveals that with little of its Andean range is expected to be seriously
affected, with the exception of a few lower-altitude zones (Fig. 4.6). This suggests
that climate change is not a significant threat to cherimoya genetic resources in the
region in the next four decades, including in the diversity hotspot in northern Peru.
Of course, this analysis includes a certain amount of imprecision because climate
prediction models may not capture all local dynamics in inter-Andean valleys and
the A2 scenario may be exceeded. Progressive climate change is, of course, expected
to continue after the 2050s, and modelling of long-term climate change impacts
on cherimoya tree stands is required. Temperatures above 30 C usually result in
pollination problems and can cause the drop of recently set fruit; humidity changes
also influence the reproductive process (Lora et al. 2009,2011,2012).
Other threats such as replacement by currently more profitable crops such as avo-
cado (Persea americana Mill.) may be more important drivers of cherimoya genetic
erosion than climate change (personal observation of X. Scheldeman). Possible in
situ conservation interventions to reduce genetic erosion include high-value market
development for traditional cultivars and the organization of seed fairs to promote
seed exchange among farmers (for further discussion see van Zonneveld et al. 2012).
Several new geographic areas are predicted to have a suitable climate for cherimoya
in the 2050s, such as in the high Andes around Lake Titicaca (Fig. 4.7). The future
expansion of suitable habitat in the Andes shows the potential for increasing che-
rimoya cultivation in the region. This could be a good alternative for commercial
84 M. van Zonneveld et al.
Fig. 4.5 This map shows the average Eigenvector score of trees on the first axis of the spatial
principal component analysis for each 10-minute grid cell with 20 or more trees, applying a one-
degree circular neighbourhood
cherimoya cultivation, to the Mediterranean countries where commercial production
is now centered, but where climate may become too warm and dry in the future.
Our case study shows that hotspots of genetic diversity can be clearly identified
with the use of spatial analysis tools, and threats to diversity can be assessed when
such analysis is combined with other types of geographic information. In our exam-
ple, we assessed the impact of climate change on cherimoya’s spatial genetic diversity
pattern in the Andes. Our results suggest that for cherimoya in itsAndean distribution
range, climate change impacts may be positive because of an extension of habitat
(and reflecting the wide habitat range of the species). Several high-elevation areas
are, for example, expected to become newly climatically suitable for cherimoya cul-
tivation in the 2050s. Cherimoya cultivation under a shifting climate may however
require the realignment of cherimoya ecotypes adapted to specific climates.
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 85
Fig. 4.6 This map provides an overlay of the modelled distributions of cherimoya under current and
future (2050s) climatic conditions based on an average of 19 general circulation climate models and
emission scenario A2 for future modelling. The map shows where new potential habitat is expected,
which current-habitat are expected to remain climatically areas are expected to remain climatically
suitable (low impact), and which current areas are expected to become climatically unsuitable (high
impact)
Acknowledgements We thank Patrick Van Damme for his comments on an early version of this
chapter. Maarten van Zonneveld thanks the CGIAR research programs Forest, Trees and Agro-
forestry (FTA) and Climate Change for Agriculture and Food Security (CCAFS) for financial
support.
86 M. van Zonneveld et al.
Fig. 4.7 This map shows the number of general circulation global climate models predicting suitable
cherimoya habitat in the 2050s under emission scenario A2, based on separate modelling with
Maxent for each of the 19 models. The higher the number of models that agree on suitable climate
in the 2050s, the more confident the predictions
References
Acevedo P, Jiménez-ValverdeA, Lobo JM, Real R (2012) Delimiting the geographical background
in species distribution modelling. J Biogeogr 39:1383–1390
Aguirre-Gutiérrez J, Carvalheiro LG, Polce C et al (2013) Fit-for purpose: species distribution
model performance depends on evaluation criteria – Dutch hoverflies as a case study. PLoS
ONE 8: e63708
Aguirre-Gutiérrez J, Carvalheiro LG, Polce C et al (2013) Fit-for purpose: species distribution
model performance depends on evaluation criteria—Dutch hoverflies as a case study. PLoS
ONE 8:e63708
Avise JC (2010) Perspective: conservation genetics enters the genomics era. Conserv Genet 11:665–
669
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 87
Barry MB, Pham JL, Courtois B et al (2007) Rice genetic diversity at farm and village levels and
genetic structure of local varieties reveal need for in situ conservation. Genet Resour Crop Ev
54:1675–1690
Bremer B, Bremer K, Chase MW et al (2009) An update of the angiosperm phylogeny group
classification for the orders and families of flowering plants: APG III. Bot J Linn Soc 161:105–
121
Boshier DH, Gordon JE, Barrance AJ (2004) Prospects for circa situm tree conservation in
Mesoamerican dry-forest agro-ecosystems. In: Frankie GW, Mata A, Vinson SB (eds)
Biodiversity conservation in Costa Rica. University of California Press, Berkeley, pp 210–226
Brown AHD, Hodgkin T (2008) Measuring, managing and maintaining crop genetic diversity
on farm. In: Jarvis DI, Padoch C, Cooper HD (eds) Managing biodiversity in agricultural
ecosystems, Columbia University Press, pp 13–33
Chan LM, Brown JL,Yoder AD (2011) Integrating statistical genetic and geospatial methods brings
new power to phylogeography. Mol Phylogenet Evol 59:523–537
Chapman AD (2005) Principles and methods of data cleaning—primary species and species-
occurrence data, version 1.0. Report for the Global Biodiversity Information Facility,
Copenhagen
CHERLA (2008) Inventory of current ex situ germplasm collections. Deliverable 7, Project no.
015100, INCO sixth framework programme
Clement CR, De Cristo-Araújo M, Coppens D’EeckenbruggeGetal (2010) Origin and domestication
of native Amazonian crops. Diversity 2010 2:72–106
Cleveland DA, Soleri D (2007) Extending Darwin’s analogy: bridging differences in concepts of
selection between farmers, biologists, and plant breeders. Econ Bot 61:121–136
Cobben MMP, Verboom J, Opdam PFM et al (2011) Projected climate change causes loss and redis-
tribution of genetic diversity in a model metapopulation of a medium-good disperser. Ecography
34:920–932
Dawson IK, Lengkeek A, Weber JC, Jamnadass R (2009) Managing genetic variation in tropical
trees: linking knowledge with action in agroforestry ecosystems for improved conservation and
enhanced livelihoods. Biodivers Conserv 18:969–986
Dawson IK, Vinceti B, Weber JC et al (2011) Climate change and tree genetic resource manage-
ment: maintaining and enhancing the productivity and value of smallholder tropical agroforestry
landscapes. A review. Agroforest Syst 81:67–78
Dawson IK, Guariguata MR, Loo J et al (2013) What is the relevance of smallholders’ agroforestry
systems for conserving tropical tree species and genetic diversity in circa situm,in situ and ex
situ settings? A review. Biodivers Conserv 22:301–324
de Haan S, Núez J, Bonierbale M, Ghislain M (2009a) Species, morphological and molecular
diversity of Andean potatoes in Huancavelica, central Peru. In: de Haan S (ed) Potato diversity
at height: multiple dimensions of farmer-driven in-situ conservation in the Andes, PhD thesis,
Wageningen University, The Netherlands, pp 35–58
de Haan S, Bonierbale M, Juárez H et al (2009b) Annual spatial management of potato diversity
in Peru’s central Andes. In: de Haan S (ed) Potato diversity at height: multiple dimensions
of farmer-driven in-situ conservation in the Andes, PhD thesis, Wageningen University, The
Netherlands, pp 91–115
Eaton D, Windig J, Hiemstra SJ et al (2006) Indicators for livestock and crop biodiversity centre for
genetic resources, CGN report 2006/05. Centre for Genetic Resources, CGN/DLO Foundation,
Wageningen, The Netherlands
Eding H, Crooijmans R (2002) Assessing the contribution of breeds to genetic diversity in
conservation schemes. Genet Sel Evol 34:613–633
Elith J, Phillips SJ, Hastie T et al (2011) A statistical explanation of MaxEnt for ecologists. Divers
Distrib 17:43–57
Elith J, Graham CH, Anderson RP et al (2006) Novel methods improve prediction of species’
distributions from occurrence data. Ecography 29:129–151
Escribano P, Viruel MA, Hormaza JI (2007) Molecular analysis of genetic diversity and geographic
origin within an ex situ germplasm collection of cherimoya by using SSRs. J Am Soc Hortic
Sci 132:357–367
88 M. van Zonneveld et al.
Escribano P, Viruel MA, Hormaza JI (2008) Development of 52 new polymorphic SSR markers
from cherimoya (; Mill.). Transferability to related taxa and selection of a reduced set for DNA
fingerprinting and diversity studies. Mol Ecol Resour 8:317–321
Escudero A, Iriondo JM, Torres ME (2003) Spatial analysis of genetic diversity as a tool for plant
conservation. Biol Conserv 113:351–365
Eshbaugh WH (2012) The taxonomy of the genus Capsicum. In: Russo VM (ed) Peppers, production
and uses, CABI, pp 1–13
FAO (2010) The second report on the state of the world’s plant genetic resources for food and
agriculture. Rome
FAO (2011) Draft updated global plan of action for the conservation and sustainable utilization of
plant genetic resources for food and agriculture. Fifth session of the Intergovernmental Technical
Working Group on Plant Genetic Resources for Food and Agriculture, Rome, 27–29 April 2011
Frankel OH, Brown AHD, Burdon J (1995a) The conservation of cultivated plants. In: Frankel
OH, Brown AHD, Burdon J (eds) The conservation of plant biodiversity, 1st edn. Cambridge
University Press, UK, pp 79–117
Frankel OH, Brown AHD, Burdon J (1995b) The genetic diversity of wild plants. In: Frankel
OH, Brown AHD, Burdon J (eds) The conservation of plant biodiversity, 1st edn. Cambridge
University Press, UK, pp 10–38
Gepts P (2003) Crop domestication as a long-term selection experiment. In: Janick J (ed) Plant
breeding reviews 24 Part 2: Long-term selection: crops, animals, and bacteria, pp 1–44
Graefe S, Dufour D, van Zonneveld M. (2013) Peach palm (Bactris gasipaes) in tropical Latin
America: implications for biodiversity conservation, natural resource management and human
nutrition. Biodivers Conserv. doi:10.1007/s10531-012-0402-3
Guarino L, Jarvis A, Hijmans RJ, Maxted N (2002) Geographic information systems (GIS) and
the conservation and use of plant genetic resources. In: Engels JMM, Ramanatha RV, Brown
AHD, Jackson MT (eds) Managing plant genetic diversity. International Plant Genetic Resources
Institute (IPGRI), Rome, pp 387–404
Hansen MM, Olivieri I, Waller DM et al (2012) Monitoring adaptive genetic responses to
environmental change. Mol Ecol 21:1311–1329
Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces
for global land areas. Int J Climatol 25:1965–1978
Hijmans RJ (2012) Cross-validation of species distribution models: removing spatial sorting bias
and calibration with a null model. Ecology 93:679–688
Hijmans RJ, van Etten J (2012) Geographic analysis and modeling with raster data. R package
“Raster.” (http://cran.r-project.org/web/packages/raster/raster.pdf)
Hijmans RJ, Phillips S, Leathwick J, Elith J (2013) Species distribution modelling with R. R package
“Dismo.” (http://cran.r-project.org/web/packages/dismo/dismo.pdf)
Hirota M, Holmgren M, van Nes EH, Scheffer M (2011) Global resilience of tropical forest and
savanna to critical transitions. Science 334:232–235
Hoffmann AA, Sgró CN (2011) Climate change and evolutionary adaptation. Nature 479:479–485
Holderegger R, Buehler D, Gugerli F, Manel S (2010) Landscape genetics of plants. Trends Plant
Sci 15:675–683
Hollingsworth PM, Dawson IK, Goodall-Copestake WP et al (2005) Do farmers reduce genetic
diversity when they domesticate tropical trees?A case study from Amazonia. Mol Ecol 14:497–
501
Ingvarsson PK, Street NR (2011) Association genetics of complex traits in plants. New Phytol
189:909–922
Jarvis A, Touval JL, Castro SM (2010) Assessment of threats to ecosystems in South America. J
Nat Conserv 18:180–188
Jombart T (2008) Adegenet: a R package for the multivariate analysis of genetic markers.
Bioinformatics 24:1403–1405
Jombart T (2013) A tutorial for the spatial analysis of principal components (sPCA) using adegenet
1.3–6. R vignette. http://cran.r-project.org/web/packages/adegenet/vignettes/adegenet-spca.pdf
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 89
Jombart T, Ahmed I, Cori A. (2013)Adegenet: an R package for the exploratory analysis of genetic
and genomic data. R package “Adegenet.” http://cran.r-project.org/web/packages/adegenet/
adegenet.pdf
Leberg PL (2002) Estimating allelic richness: Effects of sample size and bottlenecks. Mol Ecol
11:2445–2449
Lora J, Herrero M, Hormaza JI (2009) The coexistence of bicellular and tricellular pollen in Annona
cherimola Mill. (Annonaceae): Implications for pollen evolution.Am J Bot 96:802–808
Lora J, Hormaza JI, Herrero M (2010) The progamic phase of an early-divergent angiosperm,
Annona cherimola (Annonaceae). Ann Bot 105:221–231
Lora J, Herrero M, Hormaza JI (2011) Stigmatic receptivity in a dichogamous early-divergent
angiosperm species, Annona cherimola Mill. (Annonaceae). Influence of temperature and
humidity.Am J Bot 98:265–274
Lora J, Herrero M, Hormaza JI (2012) Pollen performance, cell number, and physiological
state in the early-divergent angiosperm Annona cherimola Mill. (Annonaceae) are related to
environmental conditions during the final stages of pollen development. Sex Plant Reprod
25:157–167
Lowe AJ, Gillies ACM, Wilson J, Dawson IK (2000) Conservation genetics of bush mango from
central/west Africa: implications from random amplified polymorphic DNA analysis. Mol Ecol
9:831–841
Malhi Y, Aragao LEOC, Galbraith D et al (2009) Exploring the likelihood and mechanism of a
climate-change-induced dieback of the Amazon rainforest. Proc NatlAcad Sci U S A 106:20610–
20615
Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape
ecology and population genetics. Trends Ecol Evol 18:189–197
Mascher M, Richmond TA, Gerhardt DJ et al (2013) Barley whole exome capture: a tool for genomic
research in the genus Hordeum and beyond. Plant J (in press)
Mercer KL, Perales HR (2010) Evolutionary response of landraces to climate change in centers of
crop diversity. Evol Appl 2010 3:480–493
Miller MP (2005) Alleles in space (AIS): computer software for the joint analysis of interindividual
spatial and genetic information. J Hered 96:722–724
Miller A, Schaal B (2005) Domestication of a Mesoamerican cultivated fruit tree, Spondias purpurea.
Proc Natl Acad Sci U S A 102:12801–12806
Neale DB, Kremer A (2011) Forest tree genomics: growing resources and applications. Nat Rev
Genet 12:111–122
Newton AC, Allnut TR, GilliesACM et al (1999) Molecular phylogeography, intraspecific variation
and the conservation of tree species. Trends Ecol Evol 14:140–145
Odong TL, van Heerwaarden J, Jansen J et al (2011) Statistical techniques for defining reference
sets of accessions and microsatellite markers. Crop Sc 51 doi:10.2135/cropsci2011.02.0095.
Ouborg NJ, Pertoldi C, Loeschcke V et al (2010) Conservation genetics in transition to conservation
genomics. Trends Genet 26:177–187
Pascual L, Perfectti F, Gutierrez M, Vargas AM (1993) Characterizing isozymes of Spanish
cherimoya cultivars. HortScience 28:845–847
Palmberg-Lerche C (2008) Thoughts on the conservation of forest biological diversity and forest
tree and shrub genetic resources. J Trop For Sci 20:300–312
Perfectti F, Pascual L (1998) Characterization of cherimoya germplasm by isozyme markers. Fruit
Varieties J 52:53–62
Perfectti F, Pascual L (2005) Genetic diversity in a worldwide collection of cherimoya cultivars.
Genet Resour Crop Ev 52:959–966
Perry L (2012) Ethnobotany. In: RussoVM (ed) Peppers, production and uses, CABI, pp 1–13
Petit RJ, El Mousadik A, Pons O (1998) Identifying populations for conservation on the basis of
genetic markers. Conserv Biol 12:844–855
Petit RJ, Aguinagalde I, de Beaulieu JL, Bittkau C (2003) Glacial refugia: hotspots but not melting
pots of genetic diversity. Science 300:1563–1565
90 M. van Zonneveld et al.
Petit RJ, Hu FS, Dicks CW (2008) Forests of the past: a window to future changes. Science
320:1450–1452
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic
distributions. Ecol Model 190:231–259
Pinhasi R, Fort J, Ammerman AJ (2005) Tracing the origin and spread of agriculture in Europe.
PLoS Biol 3:2220–2228
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus
genotype data. Genetics 155:945–959
Popenoe W (1921) The native home of the cherimoya. J Hered 12:331–336
Popenoe H, King SR, León J et al (1989) Cherimoya. In: Lost crops of the Incas: little-known plants
of the Andes with promise for worldwide cultivation. National Academy Press, Washington, DC,
pp 228–239
Ræbild A, LarsenAS, Jensen JS et al (2011) Advances in domestication of indigenous fruit trees in
the West African Sahel. New Forest 41:297–315
Ramachandran S, Deshpande O, Roseman CC et al (2005) Support from the relationship of genetic
and geographic distance in human populations for a serial founder effect originating in Africa.
Proc Natl Acad Sci U S A 102:15942–15947
Reed DH, Frankham R (2003) Correlation between fitness and genetic diversity. Conserv Biol
17:230–237
Russell J, Dawson IK, FlavellAJ et al (2011) Analysis of >1000 single nucleotide polymorphisms
in geographically matched samples of landrace and wild barley indicates secondary contact and
chromosome-level differences in diversity around domestication genes. New Phytol 191:564–
578
Russell J, van Zonneveld M, Dawson IK et al (2013) Genetic diversity and ecological niche mod-
elling of wild barley: refugia, large-scale post-lgm range expansion and limited mid-future
climate threats. PloS ONE under review
Samuel AF, Drucker AG, Andersen SB et al (2013) Development of a cost-effective diversity-
maximizing decision-support tool for in situ crop genetic resources conservation: The case of
Cacao. Ecol Econ under review
Scheldeman X, Van Damme P, UreaAlvarez JV, Romero Motoche JP (2003) Horticultural potential
of Andean fruit crops exploring their centre of origin.Acta Hortic 598:97–102
Scheldeman X, van Zonneveld M (2010) Training manual on spatial analysis of plant diversity and
distribution. Bioversity International, Rome, Italy
Schueler S, Kapeller S, Konrad H et al (2012)Adaptive genetic diversity of forest trees: promise for
future forests and a threatened resource—a case study on Norway spruce in Austria. Biodivers
Conserv. doi:10.1007/s10531-012-0313-3
Tanksley SD, McCouch SR (1997) Seed banks and molecular maps: unlocking genetic potential
from the wild. Science 227:1063–1066
Thomas E, van Zonneveld M, Loo J et al (2012) Present spatial diversity patterns of Theobroma
cacao L. in the Neotropics reflect genetic differentiation in Pleistocene refugia followed by
human-influenced dispersal. PLoS One 7:e47676
Tuberosa R, Graner A, Varshney RK (2011) Genomics of plant genetic resources: an introduction.
Plant Genet Resour 9:151–154
Van Damme P, Scheldeman X (1999) Promoting cultivation of cherimoya in Latin America.
Unasylva 198:43–47
Van Damme V, Gómez-Paniagua H, De Vicente MC (2010) The GCP molecular marker toolkit, an
instrument for use in breeding food security crops. Mol Breeding 28:597–610
van de Wouw M, Kik C, van Hintum T et al (2010a) Genetic erosion in crops: concept, research
results and challenges. Plant Genet Resour 8:1–15
van de Wouw M, van Hintum T, Kik C et al (2010b) Genetic diversity trends in twentieth century
crop cultivars: a meta analysis. TheorAppl Genet 120:1241–1252
van Etten J, Hijmans RJ (2010) A geospatial modelling approach integrating archaeobotany and
genetics to trace the origin and dispersal of domesticated plants. PLoS One 5:e12060
van Heerwaarden J, Hellin J, Visser RF, Eeuwijk FA van (2009) Estimating maize genetic erosion
in modernized smallholder agriculture. Theor Appl Genet 119:875–888
4 Application of Molecular Markers in Spatial Analysis to Optimize ... 91
Vanhove W, Van Damme P (2009) Marketing of cherimoya in the Andes for the benefit of the rural
poor and as a tool for agrobiodiversity conservation.Acta Hortic 806:497–504
Vanhove W, Van Damme P (2013) On-farm conservation of cherimoya (Annona cherimola Mill.)
germplasm diversity. A value chain perspective. Trop Conserv Sci 6: 158–310
van Zonneveld M, Thomas E, Galluzzi G, Scheldeman X (2011) Chapter 15/16: Map-
ping the ecogeographic distribution of biodiversity and GIS tools for plant germplasm
collectors. In: Guarino L, Ramanatha RV, Goldberg E (eds) Collecting Plant Genetic
Diversity: Technical Guidelines—2011 Update, Bioversity International, Rome, Italy
(http://cropgenebank.sgrp.cgiar.org/index.php?option=com_content&view=article&id=662)
van Zonneveld M, Scheldeman X, Escribano 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:e29845
Viruel MA, Hormaza JI (2004) Development, characterization and variability analysis of microsatel-
lites in lychee (; Sonn., Sapindaceae). Theor Appl Genet 108:896–902
Vinceti B, Loo J, Gaisberger H, et al (2013) Conservation priorities for Prunus africana defined
with the aid of spatial analysis of genetic data and climatic variables. PLoS ONE 8: e59987
Vranckx G, Jacquemyn H, Muys B, Honnay O (2011) Meta-analysis of susceptibility of woody
plants to loss of genetic diversity through habitat fragmentation. Conserv Biol 26:228–237
Waltari E, Hijmans RJ, Peterson AT et al (2007) Locating Pleistocene refugia: comparing
phylogeographic and ecological niche model predictions. PLoS One 2:e563
WidmerA, Lexer C (2001) Glacial refugia: sanctuaries for allelic richness, but not for gene diversity.
Trends Ecol Evol 16:267–269
Willis F, Moat J, Paton A (2003) Defining a role for herbarium data in Red List assessments:
a case study of Plectranthus from eastern and southern tropical Africa. Biodivers Conserv
12:1537–1552
Wolters B (1999) Zur Verbreitungsgeschichte und Ethnobotanik indianischer Kultursplanfzen,
insbesondere des Kakaobaumes. Angew Bot 73:128–137
Worthington M, Soleri D, Aragón-Cuevas F, Gepts P (2012) Genetic composition and spatial dis-
tribution of farmer-managed Phaseolus bean planting: an example from a village in Oaxaca,
Mexico. Crop Sc 52:1721–1735
... Although the usefulness of species distribution modelling for guiding conservation and management decision is increasingly being recognized (Van Zonneveld et al. 2014), some caution is indeed important when using it to guide decision making. These models have been designed for predicting habitat suitability only, and it is much less clear how suitability scores relate to population stability and persistence under rapid environmental change (Guisan et al. 2013;Oliver et al. 2012). ...
Article
Full-text available
We investigated the impact of past changes in habitat suitability on the current patterns of genetic diversity of two southern beeches (Nothofagus nervosa and Nothofagus obliqua) in their eastern fragmented range in Patagonian Argentina, and model likely future threats to their population genetic structure. Our goal was to develop a spatially-explicit strategy for guiding conservation and management interventions in light of climate change. We combined suitability modelling under current, past (Last Glacial Maximum ~ 21,000 bp), and future (2050s) climatic conditions with genetic characterization data based on chloroplast DNA, isozymes, and microsatellites. We show the complementary usefulness of the distribution of chloroplast haplotypes and locally common allelic richness calculated from microsatellite data for identifying the locations of putative glacial refugia. Our findings suggest that contemporary hotspots of genetic diversity correspond to convergence zones of different expansion routes, most likely as a consequence of admixture processes. Future suitability predictions suggest that climate change might differentially affect both species. All genetically most diverse populations of N. nervosa and several of N. obliqua are located in areas that may be most severely impacted by climate change, calling for forward-looking conservation interventions. We propose a practical spatially- explicit strategy to target conservation interventions distinguishing priority populations for (1) in situ conservation (hotspots of genetic diversity likely to remain suitable under climate change), (2) ex situ conservation in areas where high genetic diversity overlaps with high likelihood of drastic climate change, (3) vulnerable populations (areas expected to be negatively affected by climate change), and (4) potential expansion areas under climate change.
Article
Annona cherimola is a woody perennial species in the Annonaceae family that produces edible fruits and has economic importance in several regions of the world with subtropical climates. Together with other 10‐12 species, A. cherimola belongs to the section Atta of the Annona genus with a center of origin in Central America and the Caribbean. Species of the section Atta produce soft skin ripe fruits with raised areoles bounded by recessed furrows. Annona cherimola is the only species of the section naturally found in the Andean region of South America. Currently, no information is available at the molecular level on the phylogenetic relationships of most of the species of Atta and closely related sections in Annona. In order to fill this gap, in this work a phylogenetic approach was performed using five coding and non‐coding plastid DNA regions, to determine the phylogenetic relationships between A. cherimola and other related species included in Atta and other sections of the genus. The results obtained support recent studies that demonstrated the likely Mesoamerican origin of A. cherimola based on biogeographical analysis with SSR markers, rather than the previously considered South American origin hypothesis. In addition, the species belonging to the Atta section did not show monophyly. Finally, A. cherimola and A. pruinosa seem to be phylogenetically close species and additional studies are needed to discern the relations between them.
Article
Knowledge on the structure and distribution of genetic diversity is a key aspect in order to plan and execute an efficient conservation and utilization of the genetic resources of any crop as well as for determining historical demographic inferences. In this work, a large data set of 1765 accessions of cherimoya (Annona cherimola Mill, Annonaceae), an underutilized fruit tree crop native to the neotropics and used as a food source by pre-Columbian cultures, was collected from 6 different countries across the American continent and amplified with 9 highly informative microsatellite markers. The structure analyses, fine representation of the genetic diversity and an ABC approach suggest a Mesoamerican origin of the crop, contrary to previous reports, with clear implications for the dispersion of plant germplasm between Central and South America in pre-Columbian times. These results together with the potential distribution of the species in a climatic change context using two different climate models provide new insights for the history and conservation of extant genetic resources of cherimoya that can be applied to other currently underutilized woody perennial crops.
Chapter
DNA is the most elemental level of biodiversity, drives the process of speciation, and underpins other levels of biodiversity, including functional traits, species and ecosystems. Until recently biodiversity indicators have largely overlooked data from the molecular tools that are available for measuring variation at the DNA level. More direct analysis of trends in genetic diversity are now feasible and are ready to be incorporated into biodiversity monitoring. This chapter explores the current state-of-the-art in genetic monitoring, with an emphasis on new molecular tools and the richness of data they provide to supplement existing approaches. We also briefly consider proxy approaches that may be useful for many-species, global scale monitoring cases.
Article
Full-text available
Safeguarding sustainability of forest ecosystems with their habitat variability and all their functions is of highest priority. Therefore, the long-term adaptability of forest ecosystems to a changing environment must be secured, e.g., through sustainable forest management. High adaptability is based on biological variation starting at the genetic level. Thus, the ultimate goal of the Convention on Biological Diversity (CBD) to halt the ongoing erosion of biological variation is of utmost importance for forest ecosystem functioning and sustainability. Monitoring of biological diversity over time is needed to detect changes that threaten these biological resources. Genetic variation, as an integral part of biological diversity, needs special attention, and its monitoring can ensure its effective conservation. We compare forest genetic monitoring to other biodiversity monitoring concepts. Forest genetic monitoring (FGM) enables early detection of potentially harmful changes of forest adaptability before these appear at higher biodiversity levels (e.g., species or ecosystem diversity) and can improve the sustainability of applied forest management practices and direct further research. Theoretical genetic monitoring concepts developed up to now need to be evaluated before being implemented on a national and international scale. This article provides an overview of FGM concepts and definitions, discusses their advantages and disadvantages, and provides a flow chart of the steps needed for the optimization and implementation of FGM. FGM is an important module of biodiversity monitoring, and we define an effective FGM scheme as consisting of an assessment of a forest population’s capacity to survive, reproduce, and persist under rapid environmental changes on a long-term scale.
Article
Full-text available
Phaedranassa schizantha (Amaryllidaceae) is an endangered species endemic to Ecuador and two varieties have been described: P. schizantha var. schizantha and P. schizantha var. ignea. We assessed population genetic structure and demographic patterns in 11 populations across the range of the species using 13 microsatellite loci. Our data show that genetic diversity was generally lower in the southern part of the range and was especially low in populations closest to cities. We found significant population differentiation (FST = 0.14, DEST = 0.34) and evidence of a genetic bottleneck. Genetic variation did not show isolation by distance. Instead, results suggest genetic barriers around two main cities. Bayesian analysis identified two genetic groups, neither of which represents either of the two varieties previously recognized. Coalescent analysis indicates a relatively recent colonization pattern between the two genetic groups (< 3000 generations). Conservation efforts need to be taken to facilitate genetic exchange between the groups, especially between locations that seem to be genetically isolated.
Article
Full-text available
Maize rough dwarf disease (MRDD) is a destructive viral disease in China, which results in 20-30% of the maize yield losses in affected areas and even as high as 100% in severely infected fields. Understanding the genetic basis of resistance will provide important insights for maize breeding program. In this study, a diverse maize population comprising of 527 inbred lines was evaluated in four environments and a genome-wide association study (GWAS) was undertaken with over 556000 SNP markers. Fifteen candidate genes associated with MRDD resistance were identified, including ten genes with annotated protein encoding functions. The homologous of nine candidate genes were predicted to relate to plant defense in different species based on published results. Significant correlation (R2 = 0.79) between the MRDD severity and the number of resistance alleles was observed. Consequently, we have broadened the resistant germplasm to MRDD and identified a number of resistance alleles by GWAS. The results in present study also imply the candidate genes in defense pathway play an important role in resistance to MRDD in maize.
Article
Full-text available
• We studied the organisation of the genetic variation of the common bean (Phaseolus vulgaris) in its centres of domestication. • We used 131 single nucleotide polymorphisms to investigate 417 wild common bean accessions, including Mesoamerican and Andean genotypes, and we compared these to a representative sample of 160 domesticated genotypes, for a total of 577 accessions. • By analysing the genetic spatial patterns of wild common bean, we have documented the existence of several genetic groups and the occurrence of variable levels of diversity in Mesoamerica and the Andes. Moreover, using a landscape genetics approach, we demonstrate that both demographic processes and selection for adaptation are responsible for the observed genetic structure. • We show that the study of correlations between markers and ecological variables at a continental scale can help in the identification of genes involved in local adaptation. Also, we located the putative area of common bean domestication in Mesoamerica, in the Oaxaca Valley, and in the Andes, in southern Bolivia-northern Argentina. These observations are of paramount importance for the conservation and exploitation of the genetic diversity preserved within this species and other plant genetic resources.
Article
Full-text available
Isozymes have been used as genetic markers to characterize seven Spanish cherimoya (Annona cherimola Mill.) cultivars. Fifteen enzyme systems were analyzed. Ten varied [aconitase (ACO, EC 4.2.1.3), alcohol dehydrogenase (ADH, EC 1.1.1.1), glutamate oxalacetate transaminase (GOT, EC 2.6.1.1), isocitrate dehydrogenase (IDH, EC 1.1.1.42), leucine aminopeptidase (LAP, EC 3.4.11.1), malate dehydrogenase (MDH, EC 1.1.1.37), phosphoglucose isomerase (PGI, EC 5.3.1.9), phosphoghtcomutase (PGM, EC 2.7.5.1), shikimate dehydrogenase (SKDH, EC 1.1.1.25), and triose phosphate isomerase (TPI, EC 5.3.1.1)] and five did not [acid phosphatase (ACPH, EC 3.1.3.2), diaphorase (DIA, EC 1.6.4.3), malic enzyme (ME, EC 1.1.1.40), 6-phosphogluconic dehydrogenase (6PGDH, EC 1.1.1.44), and superoxide dismutase (SOD, EC 1.15.1.1)]. Two cultivars, Campa and Campa Mejorada, had identical banding patterns for all enzymes tested. All others were identified as distinct cultivars because of isozyme differences. The identical isozyme profiles of `Campa' and `Campa Mejorada' probably indicate that they are the same cultivar. A cluster analysis of isozyme profiles showed that Spanish cultivars were clearly different from Californian cultivars.
Article
Full-text available
Isozymes have been used as genetic markers to characterize more than 200 cherimoya and atemoya (A. cherimola x A. squamosa) accessions from the worldwide collection of cherimoya (Annona cherimola Mill) germplasm at the C.S.I.C. Estacion Experimental 'La Mayora' (Spain). These accessions have been incorporated into this collection from both the original species range (Peru and Ecuador) and the main producing regions (Bolivia, California, Chile, Israel, Madeira, Spain). We studied 13 enzyme systems encoded by 23 loci. Fifteen loci displayed polymorphism. The allozymes identified allowed us to genotype the cultivars, to differentiate 95% of them, and to address the possible origins of those cultivars with identical isozyme profiles. The atemoya and cherimoya cultivars showed clear isozyme differences based on alleles specific to atemoya.
Article
The package adegenet for the R software is dedicated to the multivariate analysis of genetic markers. It extends the ade4 package of multivariate methods by implementing formal classes and functions to manipulate and analyse genetic markers. Data can be imported from common population genetics software and exported to other software and R packages. adegenet also implements standard population genetics tools along with more original approaches for spatial genetics and hybridization. Availability: Stable version is available from CRAN: http://cran.r-project.org/mirrors.html. Development version is available from adegenet website: http://adegenet.r-forge.r-project.org/. Both versions can be installed directly from R. adegenet is distributed under the GNU General Public Licence (v.2). Contact:jombart@biomserv.univ-lyon1.fr Supplementary information:Supplementary data are available at Bioinformatics online.
Article
We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.
Article
Southern Ecuador is generally acknowledged to be located in a biodiversity hot spot. Several species of agricultural and horticultural crops can be found there with a huge genetic diversity and can be found in wild or in semi-cultivated status as backyard crops. Cherimoya (Annona cherimola Mill.) and several species of highland papayas (Vasconcella spp.) have their centre of origin in southern Ecuador and are cultivated as minor fruit crops in several subtropical zones worldwide. Little research work has been carried out so far in exploring the crops' centre of origin. Germplasm collection and characterisation can give valuable information to local and international crop breeding programmes whereas a detailed study of the ecology of wild stands, may provide useful information on climate and soil preferences of the crop and can-combined with a GIS analyses-be used to locate suitable cropping areas. Collection and characterisation of 137 wild and semi-cultivated cherimoya accessions was carried out in southern Ecuador between January 1996 and March 1998. A tremendous variability of pomological characteristics was encountered, typical situation of a main centre of biodiversity. The best accessions collected during these trips can easily withstand preliminary comparison with commercial cherimoya cultivars ('Fino de Jete', 'Bays', 'White', 'Bronceada' and 'Concha Lisa'). The collection of Vasconcella germplasm was carried out between August 1997 and April 2000. A total of 211 accessions of the common species Vasconcella cundinamarcensis (Solms-Laub.) Badillo, V. stipulata (Badillo) Badillo and V. x heilbornii (Badillo) Badillo were collected together with some accessions of the rarer species V. candicans (A. Gray) A.DC., V. microcarpa (Jacq.) A.DC., V. monoica (Desf.) A.DC., V. palandensis (Badillo et al.) Badillo, V. parviflora A.DC. and V. weberbaueri (Harms) Badillo. These accessions showed great variability and raised several questions about their exact taxonomic position. Preliminary studies of the papain content of some of these accessions showed papain activities up to 20 times higher than those of papaya (Carica papaya L.). Edapho-climatological conditions in the collection areas indicated the preferences of cherimoya and highland papayas in their natural environment, and were used, combined with GIS studies, to determine the potential cultivation zones.
Article
Cherimoya (Annona cherimola Mill.) is an exquisite subtropical fruit, intensively cultivated in Spain, but generally underutilized in the inter-Andean valleys where the species shows high botanical diversity. Economic use of Andean cherimoyas currently remains far below potential levels. In 2006-2007, a cherimoya value chain analysis was performed through a market survey in which structured interviews were made with 172 cherimoya producers and 346 cherimoya traders in Ecuador, Peru, and Bolivia. Two main flows of cherimoya were observed in all markets studied. The first flow pertains to locally produced cherimoya fruits that have irregular quality due to high infestation levels of fruit flies. These fruits are inadequately packed and transported, and consequently have a low economic value to both producers and traders. The other flow consists of cherimoyas produced in the Huarochir province (Lima Department, Peru), commonly known as 'Cumbe', a collective trademark registered by producers from the Cumbe Valley. This trademark which serves as a geographical indication currently lacks appropriate legal protection. Consequently, illegal application of the 'Cumbe'-label is widespread. 'Cumbe' fruits are intensively selected and graded on wholesaler markets in the Lima metropolis and distributed in wooden crates to main markets both within Peru and to its neighboring countries Ecuador and Bolivia. Fruit characterization data show that 'Cumbe' fruits are partly distinctive from other Andean cherimoyas. The value of the collective trademark 'Cumbe' should thus be attributed not only to elite germplasm and appropriate cultivation practices, but also to the intensive selection, grading, and packaging process. As a result of higher product quality, 'Cumbe' cherimoya market prices are up to twice or more of the prices for local cherimoyas. More denominations of origin, locally managed by producer organizations, are valuable tools for both conserving on-farm cherimoya diversity and additional income generation for the rural Andean poor.
Article
The cherimoya (Annona cherimola Mill.) is one of the so-called 'lost crops of the Incas' (Vietmeyer in Popenoe et al., 1989) that has come to us from the Andean heights. Also called chirimoya, chirimolla, or the custard apple in English, it is well-known to indigenous populations in Latin America, familiar to only a limited group of consumers outside the region and largely ignored by mainstream agricultural science. In Latin America and particularly in Ecuador, the cherimoya has the potential to become a commercial subtropical crop for both resource-poor farmers and commercial farmers who serve international markets (George, Nissen and Brown, 1987; Sanewski, 1991; Rasai, George and Kantharajah, 1995). This discussion focuses on the challenges involved in developing the crop, particularly those related to infrastructure, institutional support and market factors.