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Changes in barley (Hordeum vulgare
L. subsp. vulgare) genetic diversity and
structure in Jordan over a period of 31 years
I. Thormann
1
*, P. Reeves
2
, S. Thumm
3
, A. Reilley
2
, J. M. M. Engels
1
, C. M. Biradar
4
,
U. Lohwasser
3
, A. Börner
3
, K. Pillen
5
and C. M. Richards
2
1
Bioversity International, Via dei Tre Denari 472/A, 00057 Maccarese Rome, Italy,
2
United States Department
of Agriculture-Agricultural Research Service, National Center for Genetic Resources Preservation, 1111 South
Mason Street, Fort Collins, CO 80521, USA,
3
Genebank Department, Leibniz Institute of Plant Genetics and
Crop Plant Research, Corrensstr. 3, D-06466 Stadt Seeland, OT Gatersleben, Germany,
4
International
Center for Agricultural Research in Dry Areas (ICARDA), P.O. Box 950764, Amman 11195, Jordan and
5
Plant Breeding, Institute for Agricultural and Nutritional Science, Martin-Luther-University Halle-
Wittenberg, Betty-Heimann-Str. 3, 06120 Halle/Saale, Germany
Received 13 August 2016; Accepted 6 January 2017
Abstract
In many regions of the world, the cultivation of landraces is still common, in particular in centres of
crop diversity. Significant effort has been put into ex situ conservation of landraces but limited data
exist on the changes in genetic diversity that occur over time in farmers’fields. We assessed temporal
changes in barley landrace diversity in Jordan using seed samples collected in 1981 and 2012 from
the same locations. We did not observe significant changes in the amount of genetic diversity, but
samples collected in 2012 were more homogenous and less locally distinct. In two sites, we ob-
served replacement of the old material. We observed a change in phenotype, and phenotypes
were found to be more homogeneous among sites in 2012. Climate changed significantly over
the study period, becoming hotter and dryer, but we did not identify any correlation between the
changes in climate and genetic and phenotypic variations. While the amount of genetic diversity in
terms of allelic richness and number of multi-locus genotypes has been maintained, local distinct-
iveness among landrace barley populations in Jordan was reduced.
Keywords: genetic erosion, landrace, re-collection
Introduction
Landraces are defined as dynamic populations of a culti-
vated species associated with traditional farming systems
(Jarvis et al., 2000; Camacho Villa et al., 2005). While locally
adapted and genetically distinct, they usually lack a history
of formal crop improvement. Landraces are a critical elem-
ent of food security. In many regions of the world, the cul-
tivation of landraces is still common, in particular in the
centres of crop diversity (Ceccarelli, 1996; Mercer and
Perales, 2010; Jarvis et al., 2011). They are used in breeding
because they are an important source of unique variability,
in particular regarding adaptive traits (FAO, 2010; Bertoldo
et al., 2014; Dwivedi et al.,2016). Although their conserva-
tion in ex situ collections is quite extensive –nearly half of
the genebank material, for which the biological status of
accessions (as defined in Alercia et al., 2015) is known,
are landraces (FAO, 2010)–gaps in ex situ collections
are still being reported, and limited data exist on numbers
and diversity of landraces currently grown in farmers’fields
(FAO, 2010). On-farm management of landraces, i.e. main-
tenance of genetic diversity in production systems, is there-
fore an essential complement to ex situ conservation.
Importantly, it allows adaptive processes to continue,
which shape landrace genetic diversity and result in unique
*Corresponding author. E-mail: i.thormann@cgiar.org
©NIAB 2017 Plant Genetic Resources; 1–15
ISSN 1479-2621 doi:10.1017/S1479262117000028
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resources for farmers and breeders (Henry et al., 1991).
How landraces evolve under on-farm management and
under the pressure of environmental change requires fur-
ther research (Mercer and Perales, 2010; Dornelas et al.,
2013).
Barley is the fourth most important cereal crop world-
wide in terms of production, yield and area harvested. It
is adapted to marginal areas and stress-affected environ-
ments and is therefore important to small farmers in devel-
oping countries. In Jordan, barley is the predominant crop
in areas with <300 mm of annual rainfall. In these areas,
barley is mainly grown for animals; both grain and straw
are utilised (Al-Tabbal and Al-Fraihat, 2012). Given that
water scarcity is a major environmental challenge in
Jordan, a crop such as barley will likely remain an essential
component of the landscape, providing food security in
Jordan’s rain-fed agricultural system.
Genetic diversity in cultivated barley is known to be par-
ticularly high in the near East (Malysheva-Otto et al., 2006;
Varshney et al., 2008). To examine how landrace barley di-
versity has changed in Jordan over a period of 31 years, we
sourced landrace samples collected in Jordan in 1981 from
a genebank and re-collected contemporary samples from
the same sites in 2012. We examined genetic and pheno-
typic diversity at both time points to investigate the pattern
of temporal changes in diversity and tested whether these
changes were associated with geography or climate.
Material and methods
Barley germplasm collecting missions
Barley landrace samples were collected in Jordan in 1981
(18 May–2 June) during a collecting trip carried out under
a regional FAO project operated by the IBPGR
(International Board for Plant Genetic Resources)
(Witcombe et al., 1982) and were re-sampled from the
same sites in 2012 (21 May–3 June) in collaboration with
the Jordanian National Center for Agricultural Research
and Extension (NCARE). Using map coordinates and loca-
tion descriptions from the 1981 collecting reports, and in-
formation provided by the original collector, precise site
locations were determined for resampling in 2012. Seed
samples in 1981 were collected from farmers’fields by
walking a transect across the field, collecting up to 200
spikes, and trying to maximize variability expressed in phe-
notypes while collecting. After threshing, seeds of each
sample were divided at random between the three partici-
pating organizations (J. Witcombe, pers. commun.).
Samples of up to 100 spikes were randomly collected
from each site in 2012 and conserved at NCARE and the
German federal ex situ genebank of the Leibniz Institute
of Plant Genetics and Crop Plant Research (IPK).
Seeds from the 1981 collecting mission were obtained
from the Nordic Genetic Resource Center (NordGen),
where the original seed samples had been stored for the
long term in sealed aluminium foil bags, in standard house-
hold deep freezers at −18°C. The collecting years are re-
ferred to in the following also as time point t
1
(1981) and
time point t
2
(2012).
Field trial
Accessions were grown in a standard field trial at IPK dur-
ing the 2013 growing season. Seeds were sown on 19 April
2013. Each sample was sown in two rows of 1 m each,
separated by a row of wheat, in 1.0 × 1.5 m
2
plots. No irri-
gation was provided and hand weeding occurred as neces-
sary. Plants were bagged before flowering to avoid any
cross fertilization with adjacent wild barley (Hordeum
vulgare subsp. spontaneum (K. Koch) Thell.) plots. A
total of 40 accessions, 20 from each collecting year, were
used for the study (Fig. 1). Sixteen individual plants per
accession were randomly chosen for phenotypic and
genotypic data collection. Thinning was carried out be-
tween these plants to equalize plant density within plots.
Leaf tissue was collected from all labelled plants, dried at
37°C, and then frozen for later DNA extraction. Twenty
phenotypic traits were measured during the growing sea-
son, at harvest and post-harvest (online Supplementary
Table S1) to assess relative phenotypic variation.
DNA extraction and genotyping
DNA was purified using the Qiagen DNeasy
®
96 Plant Kit.
Thirty-eight EST-derived SSR primers were used for geno-
typing (Thiel et al., 2003; Stein et al., 2007; Varshney et al.,
2007) (online Supplementary Table S2). Loci were distrib-
uted across all seven barley chromosomes. DNA amplifi-
cation and fragment size analysis was completed by the
Genomics and Bioinformatics Research Group, USDA-ARS,
Stoneville, Mississippi. PCR was carried out in 5-μl reactions
consisting of 2–10 ng genomic DNA, 1 × Qiagen Multiplex
PCR Master Mix, 225 nM of each primer pair. Fragments
were amplified using the followingPCR profile: an initial de-
naturing step of 15 min at 95°C followed by 40 cycles with
denaturation at 94°C for 30 s, annealing at 55°C for 60 s
and extension at 72°C for 60 s. After 40 cycles, a final exten-
sion step was performed at 60°C for 20 min. Amplification
products were resolved by capillary electrophoresis on an
ABI 3730XL Genetic Analyzer. Fragment sizes were calcu-
lated using GeneScan 500 (ROX) internal size standards
and scored with GeneMapper Software (version 5.0) (Life
Technologies, Thermo Fisher Scientific Inc.).
Care was taken to mitigate scoring errors in the microsat-
ellite data. Out of the 45 loci examined, the 38 used were
I. Thormann et al.2
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chosen based on polymorphism, low drop-out rates and
scoring/amplification consistency. Every sample was
inspected manually for allele call fidelity. DNA from four
H. vulgare L. accessions from IPK (BCC844, BCC1500,
BCC1411 and BCC1390) were used as internal controls
for genotype scoring. Unique positioning of controls and
blanks on each 96-well plate provided checks for plate iden-
tification and orientation as well as for scoringconsistency.In
the rare case discrepancies were observed, the entire plate
was rerun. Samples with ambiguous peaks were also rerun.
Because barley is an inbreeding species, and exhibits ex-
treme heterozygote deficiency, deviations from Hardy–
Weinberg equilibrium could not be used to indicate scoring
errors. However, loci with a high incidence of heterozygotes
were suspect and were re-examined. There were 40 out of
24,320 (0.17%) missing data points in the SSR data set.
Statistical analyses
Genetic diversity, population differentiation and
structure
Summary statistics, such as number of alleles, sample ad-
justed allelic richness and observed heterozygosity, were
calculated with GDA (Lewis and Zaykin, 2001) and
FSTAT version 2.93.2 (Goudet, 2001). The PIC (poly-
morphism information content) and the number of multi-
locus (ML) genotypes was determined using GenAlEx
6.502 (Peakall and Smouse, 2006,2012). Population sub-
division was quantified using F
ST
and Jost’s D (Jost,
2008), which is an alternative to F
ST
that is unaffected by
within-population diversity (Meirmans and Hedrick,
2011). Both were calculated with the R package diveRsity
(Keenan et al., 2013). Differentiation among populations
within a collecting year as well as differentiation within
each site between collecting years was calculated.
Population structure was inferred using InStruct (Gao
et al., 2007). InStruct extends the algorithm used in
STRUCTURE (Pritchard et al., 2000) to account for self-
pollination and inbreeding, common in barley. InStruct
was run in mode v= 2 (infer population structure and
population selfing rates) for K=1–10. For each K, five
chains were run, with 200,000 Markov chain Monte Carlo
iterations, a burn-in of 100,000 and a thinning interval of
10 steps. Results from independent chains were summar-
ized using CLUMPP (Jakobsson and Rosenberg, 2007)
and graphical representations of cluster assignments were
Fig. 1. Collecting sites of barley landraces for temporal comparison.
Changes in barley (Hordeum vulgare L. subsp. vulgare) genetic diversity and structure in Jordan over a period of 31 years 3
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rendered with DISTRUCT (Rosenberg, 2004). ΔK (Evanno
et al., 2005) was calculated to identify the appropriate num-
ber of clusters.
IBD (Isolation by distance) was estimated using the R
package ecodist. Geographic distances were calculated as
straight-line distances with the GeographicDistanceMatrix
Generator version 1.2.3 (Ersts, Internet,http://biodiversity
informatics.amnh.org/open_source/gdmg/) and log trans-
formed. Genetic distances were calculated as F
ST
/(1–F
ST
)
(Rousset, 1997). A two-tailed Mantel test was carried out
with 10
5
permutations.
Climate data analysis
Long-term high-resolution daily and monthly climate data
sets were generated by the GIS unit of the International
Center for Agricultural Research in Dry Areas (ICARDA),
using ECMWF (European Centre for Medium-Range
Weather Forecasts, ERA-40) with bias correction through
use of the WRF (Weather Research and Forecasting)
model. Spatial downscaling was done in ArcGIS to generate
1 km surfaces. These high-resolution 1 km time series grid-
ded data sets were used to extract monthly precipitation
and mean temperature values for all collecting sites for
1980–2013 for further analysis.
In our analysis, temperature and precipitation trends
over the 34-year period from 1980 to 2013 were consid-
ered. Annual and monthly precipitation and mean tem-
peratures were tabulated for all years. Precipitation of the
driest and wettest month and quarter, and mean tempera-
ture of the hottest and coldest month and quarter were cal-
culated. Temporal trends were analysed using least squares
regression. These data were compared with precipitation
and temperature data from Jordanian weather stations for
the years 1978–2008.
Phenotypic analysis
All phenotypic measurements were tabulated and the 10
traits marked with * in Table 3 were used for phenotypic
analysis. Given that the field trial was limited to a single sea-
son, and was carried out in a location outside the study
area, our analysis was limited to relative changes in pheno-
types and did not focus on single trait values. For this rea-
son we summarized the individual phenotypes as a
multivariate statistic through principal component analysis
(PCA). We used the first principal component to describe
these multi-trait phenotypes through a single value. We
then used a two-way analysis of variance (ANOVA) to as-
sess the influence of collecting year and site on the pheno-
type. The phenotype was described by the first principle
component and treated as the dependant variable. The
interaction effects between the independent variables site
number and collecting year on phenotype were tested.
Additionally, we used one-way ANOVAs to test the
significance of phenotypic variation among sites within
each collecting year. The first principle component describ-
ing phenotype was the dependant variable, and site was
used as the independent variable for all phenotypes of
the respective collecting year. PCA and ANOVAs were car-
ried out in JMP
®
12.0.1.
Correlation analyses
Correlations between climatic, phenotypic and genetic
change, as well as correlations between geographical vari-
ables (latitude and elevation) and climatic, phenotypic and
genetic change were tested.
Genetic change was measured using the metric F
ST
and Δ
allelic richness. F
ST
estimated the differentiation within
each site between the t
1
and t
2
population. Change in allelic
richness was expressed as the difference in allelic richness
estimated in each site for both collecting years. A composite
measure of phenotypic change was generated through a
PCA on average trait differences between collecting
years. The average trait value for each of the 10 traits was
calculated at each site and for each collecting year. The
differences within each site between average trait values
from both collecting years (t
2
–t
1
) were subjected to PCA
and the first principal component was used for regression
to climatic and genetic change measures.
Similarly, PCA was used to generate a composite meas-
ure of climatic change. Annual, quarterly and monthly pre-
cipitation and temperature differences between collecting
years were calculated based on values averaged over the
collecting year and its preceding year, i.e. values for
1980–1981 were used for t
1
and values for 2011–2012 for
t
2
. The differences between the five precipitation and five
temperature variables within each site were subjected to
PCA and the first principal component was used for regres-
sion to phenotypic and genetic change measures. Within
each time point, we also tested whether single climate vari-
ables were correlated with genetic diversity or phenotypic
traits, using appropriate Holm–Bonferroni correction
(Holm, 1979) to avoid type I error inherent in multiple com-
parisons. All correlations were tested using least squares re-
gression in JMP
®
12.0.1.
Results
Genetic diversity
Population and collecting year-specific values for genetic
diversity measures are summarized in Tables 1 and 2.
Overall, genetic diversity was slightly but not significantly
higher in 2012 than in 1981. The total number of alleles
was 137 (t
1
) and 149 (t
2
). Sixteen alleles were unique to
1981 (with frequencies between 0.002 and 0.01) and 28 al-
leles were unique to 2012 (frequencies between 0.002 and
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0.031). In both collecting years these unique alleles were
randomly distributed across the study area. Mean allelic
richness per site increased from 1.66 (t
1
) to 1.80 (t
2
).
Overall, the mean number of alleles per site increased sig-
nificantly, from 63.4 (t
1
) to 69.05 (t
2
). Depending on site,
the change in number of alleles ranged however from a re-
duction of 10 alleles to an increase of 25 alleles. In each site
there were, on average, 52.2 alleles that were found in both
collecting years, 11.2 alleles found only in 1981, and 16.85
found only in 2012. The average number of collecting sites
in which aspecific allelewas found increased from 7.6 to 8.3.
The total number of ML genotypes increased from 164
(t
1
) to 181 (t
2
). The mean number of ML genotypes per
population increased from 10.8 (t
1
) to 12.1 (t
2
). Of the
164 ML genotypes present in 1981, 118 (71.96%) were un-
ique (i.e. found in one individual only), 25 were present in
more than one individual within the same site, and 21 were
shared across 2–14 sites (average 3.52). In 2012, 150
(82.87%) ML genotypes were present in only one individ-
ual, nine were present in more than one individual within
the same site, and 22 were shared among 2–15 sites (aver-
age 3.82). Among both collecting years, a total of 317 ML
genotypes were recorded. In total, 136 were found only
in 1981, 153 only in 2012 and 28 were common to both col-
lecting years. Of the 28 common genotypes, 10 were re-
corded in the same sites in both years (seven in only one
site each, and one genotype in 5, 6 and 13 sites). Overall,
all but four sites (7578, 7594, 7596 and 7635) had 1–3ML
genotypes found in both collecting years.
Population differentiation and structure
Population subdivision decreased as evidenced by a
reduction in Dand F
ST
.Ddecreased from 0.157 (t
1
)to
0.021 (t
2
), F
ST
from 0.486 (t
1
)to0.14(t
2
)(Table 1). D
and F
ST
values calculated at each site between t
1
and t
2
samples to estimate differentiation within a location var-
ied from 0.0 to 0.54 (D)and0.0to0.85(F
ST
)(Table 2). The
F
ST
values for sites 7594 and 7596 were particularly high
(0.85 and 0.78), indicating much change, while the aver-
age F
ST
of the remaining sites was as low as 0.07, indicat-
ing very little change. Pairwise genetic distances
measured as F
ST
within collecting years were significantly
higher in 1981 than in 2012.
The high collecting year-specific F
ST
and Dvalues in
1981 were mainly caused by samples collected in sites
7594 and 7596. When these two sites were excluded from
the estimates, the F
ST
and Dof the remaining 18 samples
were reduced to 0.217 and 0.036 for 1981, while values
for 2012 remained mostly unvaried with 0.147 and 0.021,
still showing stronger subdivision in 1981.
The ΔKmethod (Evanno et al., 2005) applied to
InStruct results suggested subdivision into two clusters
in 1981. Sites 7594 and 7596 constituted one small cluster,
while all remaining 18 populations were assigned to the
second cluster (Fig. 2). The average population assign-
ment coefficient was >0.97 for both clusters. Fifteen indi-
viduals were genetically admixed, i.e. had a cluster
assignment coefficient q< 0.8 and 14 of these were lo-
cated in site 7578.
For comparison, we examined InStruct results for 2012
populations at the same K= 2. One cluster contained two
sites, the other 18. The average population assignment co-
efficient to clusters was 0.82, significantly lower than in
1981 (P< 0.0001, Tukey–Kramer HSD (honestly significant
difference)). Genetic admixture was much higher than in
1981, found in 135 individuals. Five populations were
also physically admixed, i.e. where an individual assigned
Table 1. Genetic diversity measures by collecting year
Variable 1981 2012 Significance
a
Total number of alleles 138 149
Number of alleles unique to collecting year 16 28
Mean allelic richness per population 1.658 1.799 ns
Mean number of alleles per population 63.4 69.05 P= 0.0497
D0.157 CI (95%): 0.148–0.165 0.021 CI (95%): 0.017–0.026
F
ST
0.486 CI (95%): 0.456–0.512 0.14 CI (95%): 0.113–0.168
Total number of ML genotypes 164 181
ML genotypes unique to collecting year 136 153
ML genotypes recorded in one individual only 118 150
ML genotypes repeated within a site 25 9
ML genotypes repeated/shared across sites 21 22
Mean number of ML genotypes per population 10.8 12.1 ns
ML, multi-locus.
a
Tukey–Kramer HSD.
Changes in barley (Hordeum vulgare L. subsp. vulgare) genetic diversity and structure in Jordan over a period of 31 years 5
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Table 2. Genetic diversity measures by population
Site
IPK accession
number
1981 samples
a
IPK accession
number 2012
samples
F
ST
between
collecting
years
Dbetween
collecting
years
Allelic
richness
1981
Allelic
richness
2012
ML
genotypes
1981
ML
genotypes
2012
ML
genotypes
present in
both years
Alleles
1981
Alleles
2012
Alleles
present in
both collecting
years
7574 HOR 22359 HOR 22643 0.0916 0.0052 1.70 1.60 11 12 1 65 61 55
7578 HOR 22363 HOR 22650 0.4701 0.1192 2.02 1.75 15 14 0 77 67 54
7582 HOR 22365 HOR 22653 0.0225 0.0012 1.31 1.72 8 8 2 50 66 45
7572 HOR 22358 HOR 22642 0.0511 0.0019 1.65 1.62 13 12 2 63 62 51
7566 HOR 22356 HOR 22633 −0.0097 2.00 × 10
−04
1.47 1.72 8 12 3 56 66 53
7567 HOR 22357 HOR 22634 0.0297 0.0039 1.70 2.09 11 12 3 65 82 55
7594 HOR 22369 HOR 22659 0.8466 0.5384 1.10 1.76 6 11 0 42 67 18
7596 HOR 22370 HOR 22662 0.7771 0.5119 1.43 1.92 3 12 0 55 73 33
7618 HOR 22377 HOR 22677 −0.0013 0.0018 1.88 2.22 5 16 3 72 86 63
7608 HOR 22374 HOR 22669 0.0943 0.0061 1.26 1.63 13 10 1 48 62 45
7611 HOR 22375 HOR 22671 0.0034 9.00 × 10
−04
1.57 1.67 12 11 2 60 64 56
7613 HOR 22376 HOR 22673 −0.0056 6.00 × 10
−04
1.65 1.83 10 13 2 63 70 61
7619 HOR 22378 HOR 22678 0.0047 0.0011 1.47 1.68 11 12 3 56 64 52
7620 HOR 22379 HOR 22679 0.0224 0.0018 1.70 1.60 12 10 2 65 61 55
7626 HOR 22382 HOR 22683 0.0358 0.0054 1.86 1.69 12 12 2 71 65 58
7622 HOR 22380 HOR 22680 0.0074 0.0016 1.96 2.00 14 16 1 76 77 62
7630 HOR 22383 HOR 22685 0.0976 0.0089 1.83 1.73 13 11 1 70 66 59
7633 HOR 22385 HOR 22686 0.0255 0.0027 1.70 1.97 12 15 1 65 76 55
7635 HOR 22386 HOR 22687 0.2261 0.0342 1.96 2.14 13 11 0 75 82 56
7639 HOR 22387 HOR 22688 0.0485 0.0032 1.93 1.67 13 12 2 74 64 58
ML, multi-locus.
a
Seed samples obtained from NordGen were accessioned in IPK.
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to one cluster resides in a site dominated by individuals be-
longing to another cluster.
The ΔK method applied to InStruct results for the sam-
ples collected in 2012 suggested a subdivision into six
clusters (Fig. 2). All 20 populations were physically ad-
mixed containing individuals assigned to 2–5 different
clusters. When we applied K= 6 to the 1981 populations,
13 populations were physically admixed with individuals
assigned to two or three populations. The population as-
signment coefficient was not significantly higher in 1981
(0.54) compared with 2012 (0.47).
Phenotypic variation
Growth habit, leaf hairiness, stem pigmentation, awn type,
awn barbs, lemma colour, kernel covering, spike density
and row number showed no or little variation between po-
pulations and collecting years. Quantitative traits varied
within sites and across the study area in both collecting
years (Table 3 and online Supplementary Table S4). Days
from emergence to heading, plant height at harvest,
number of tillers, spike length, total seed harvested per
plant, showed comparatively larger differences between
Fig. 2. Assignment of barley landrace individuals and populations to clusters identified by InStruct for K= 2 and K=6.
Changes in barley (Hordeum vulgare L. subsp. vulgare) genetic diversity and structure in Jordan over a period of 31 years 7
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collecting years than other traits. Plants from 2012 tended to
take more days from emergence to heading, to have more
and longer tillers, and to produce more seed per plant
(Table 3).
The first principal component represented 31.1% of
phenotypic variation. ANOVA showed that multi-trait
phenotype, measured as the first principal coordinate, dif-
fered among sites in 1981 but not in 2012 (Table 4). Site by
collecting year effect was not significant, indicating that dif-
ferences among collecting years were similar in direction
and magnitude within sites. Relative change in phenotype
among collecting years was statistically significant
(Table 4).
Climate changes in Jordan from 1980 to 2013
Values for all five temperature variables (annual mean tem-
perature, mean temperature of coldest and hottest month
and quarter) increased significantly over the 34-year period
in the 20 sites. Precipitation values measured as annual pre-
cipitation, precipitation of wettest and driest month and
quarter showed a negative trend over the 34-year period
in all 20 sites. The decreases over time in precipitation of
driest month and quarter were statistically significant in
most sites. The results of the regression analyses on tem-
poral trends are summarized in online Supplementary
Table S3. These trends confirm that the precipitation and
temperature values of the two collecting years are part of
a broader trend and do not represent anomalous values.
Precipitation of wettest month was significantly lower
and all temperature values were significantly higher in t
2
compared with t
1
. Precipitation and temperature values
for t
1
and t
2
are summarized in online Supplementary
Figs S1 and S2. Changes in precipitation and temperature
values within single sites are visualized in online
Supplementary Figs S3 and S4. These latter show that in-
creases in warmest quarter and warmest month tempera-
tures as well as decrease in annual and wettest quarter
precipitation were more pronounced in the southern sites.
Table 3. Phenotypic trait values by collecting year, providing percentages for categorical variables and means followed by
standard deviation for quantitative variables
Trait 1981 2012
Days to emergence
a
9 days: 70%
10 days: 30%
9 days: 100%
Growth habit
a
Erect Erect
Leaf hairiness Present Present
Stem pigmentation Green: 29.47%
purple (basal only): 70.53%
Green: 35.63%
Purple (basal only): 64.38%
Awn type Awnleted: 5.46%
Awned: 94.53%
Awnleted: 4.76
Awned: 95.24
Awn barbs Smooth: 17.42%
Intermediate: 68.39%
Rough: 14.19%
Smooth: 0.0%
Intermediate: 71.75%
Rough: 0.95%
Lemma colour Amber Amber
Days from emergence to heading* 61.82 (10.17) 65.51 (9.61)
Plant height* 65.87 (14.86) 76.57 (15.31)
Number of tillers* 9.08 (8.41) 12.53 (9.81)
Spike length* 6.4 (1.85) 7.3 (1.59)
Spike density Lax: 70.0%
Intermediate: 30.0%
Lax: 66.56%
Intermediate: 33.44%
Kernel number per ear* 14.36 (5.95) 15.76 (4.44)
Kernel row number 2 2
Kernel covering Semi-covered Semi-covered
TSW* 52.15 (7.64) 52.97 (7.06)
Seed area* 32.9 (2.44) 32.26 (2.13)
Seed width* 3.46 (0.17) 3.46 (0.17)
Seed length* 11.52 (0.76) 11.29 (0.61)
Number of seeds harvested per plant* 71.76 (64.32) 103.00 (80.83)
a
Traits ‘days to emergence’and ‘growth habit’were recorded at population level, all other traits at individual level.
I. Thormann et al.8
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Temperature andprecipitation data at 16weather stationsin
Jordan showed the same trends as the interpolated data at the
collecting sites. Annual mean temperature measured at the
weather stations trended significantly upwards, while annual
precipitation fell during the period 1978–2008 (data not
shown).
Correlation analyses
Overall climate change, expressed as the first principal
component (64% of variation) was strongly correlated
with latitude (R
2
=0.85, P< 0.0001) and altitude (R
2
=0.66,
P< 0.0001). No other significant correlation between climate,
phenotypic and genetic change was found. Neither was
phenotypic or genetic change correlated with latitude or
elevation. We found no correlations in any time point be-
tween phenotypic traits and genetic or climatic variables.
Discussion
In the present study we used resurrection of old seed samples
and re-collection (Davis et al., 2005;Frankset al., 2008)toas-
sess genotypic and phenotypic changes over time. We com-
pared landrace barley from Jordan, collected in the same
locations 31 years apart. We observed a slight increase in
genetic diversity between 1981 and 2012. Samples collected
in 2012 were more admixed than in 1981 and differentiation
among samples was lower in 2012. There was a relative
change in phenotype among collecting years, and pheno-
types were found to be more homogeneous among sites in
2012. In two sites, we observed complete replacement of the
old material. Climate became hotter and dryer, but we did not
identify any correlation with the observed genetic and
phenotypic variations.
Study system and sampling
Genebanks maintain living germplasm and associated
provenance information from collections of agriculturally
important species. Well-documented and conserved col-
lections can be used to reveal past diversity and serve as
a starting point for temporal studies in genetic diversity
through re-collecting contemporary samples for compari-
son (Maxted and Guarino, 2006;Frankset al., 2008; Deu
et al., 2010; van de Wouw et al., 2010; Thormann et al.,
2015). Several studies have used re-collecting to investigate
temporal changes in landrace diversity, for example in sor-
ghum and pearl millet in Niger (Bezançon et al.,2009) and
rice in Guinea (Barry et al., 2008). Others have compared
barley and wheat samples collected from the same areas
several decades apart (Khlestkina et al., 2004,2006).
Differences in sampling protocols between historical
and contemporary collections have been suggested as pos-
sible reasons for observed temporal genetic changes (del
Rio et al., 1997; Barry et al., 2008). Up to 200 spikes per
sample were collected in 1981, trying to maximize variabil-
ity expressed in phenotypes. Thus, rare types could likely
have been oversampled compared with common types.
Samples of up to 100 spikes were randomly collected
from each site in 2012. These sample sizes are considered
sufficient to adequately represent the diversity of a popula-
tion in the sample (Brown and Marshall, 1995; Hoban and
Schlarbaum, 2014). The difference in sampling method be-
tween 1981, where phenotypic variability was maximized
along a transect, and 2012, where sampling was random,
does not explain our results as higher genetic diversity
might have been expected in the 1981 samples, but this
was not observed, in fact the diversity was slightly higher
in 2012.
Genetic diversity in ex situ collections can be affected by
regeneration and/or other management practices, includ-
ing the sample sizes (Rao et al., 2006; Dulloo et al.,
2008). Gomez et al.(2005) observed lower genetic diver-
sity in ex situ conserved bean samples compared with re-
collected in situ samples due to regeneration processes.
The 1981 seeds were from the original collected samples,
which had not been regenerated and been conserved
under standard long-term storage conditions. This excludes
the possibility that genetic diversity has been affected by
Table 4. One way ANOVA on multi-trait phenotypes for each collecting year to test effect of site within year (type of
ANOVA = 1) and two way ANOVA on multi-trait phenotypes of both collecting years to test effect of year, site and their inter-
action (type of ANOVA = 2)
Type of
ANOVA Source of variation
Number of
parameters
Degrees of
freedom
Sum of
squares FRatio Prob > F
1 Site in 1981 19 19 121.63 2.13 0.0048
1 Site in 2012 19 19 50.21 0.8 0.7088
2 Collecting year 1 1 68.16 21.55 <0.0001
2 Site 19 19 97.25 1.62 0.0475
2 Collecting year × site 19 19 74.14 1.23 0.2246
Changes in barley (Hordeum vulgare L. subsp. vulgare) genetic diversity and structure in Jordan over a period of 31 years 9
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inappropriate regeneration and/or other management
practices.
Another concern related to seed storage is the potential
selective loss of genotypes during long-term conservation.
Barley has an orthodox, desiccation tolerant seed storage
behaviour under long-term storage conditions (van
Hintum and Menting, 2003; Nagel et al., 2009) and all sam-
ples used in the present study germinated normally. There
is no indication to suspect that major genotypic changes
have affected seed samples from 1981. In a similar study
on pearl millet landraces, no impact of seed storage over
time was found (Vigouroux et al., 2011). To the best of
our knowledge, no study has yet demonstrated conclusive-
ly that selective mortality occurs during storage.
Our study has furthermore included a relatively large
number of sites covering a range of agro-ecological condi-
tions to provide a good estimate of the overall distribution
of diversity across Jordan. We used 38 SSRs to describe gen-
etic diversity, while 20 are considered sufficient to provide
resolution and representation of diversity for genetic ero-
sion studies (Hoban et al., 2014). We therefore assume
that the seed samples used in this study provide comparable
snapshots of diversity existing at the respective collecting
times, and that observed differences are not significantly
affected by sampling bias or ex situ management practices.
Changes in genetic diversity and population
structure
Landraces are typically genetically heterogeneous (Brown,
1999; Camacho Villa et al., 2005). This has been shown for
barley in Jordan and Syria (Jana and Pietrzak, 1988; Russell
et al., 2003,2011), Ethiopia (Bekele, 1983), Iran (Brown
and Munday, 1982), Nepal (Pandey et al., 2006) and
Spain (Yahiaoui et al., 2008). Landraces of predominantly
self-fertilizing species like barley (Wagner and Allard,
1991; Parzies et al., 2000) are usually composed of mixtures
of many, mostly homozygous, ML genotypes (Pérez de la
Vega and Garcia, 1997). It should also be noted that the bar-
ley landraces collected in Jordan were predominantly used
as animal feed and thus, most likely very limited selection
might have taken place by farmers other than for straw
quality and seed yield.
Genetic diversity was maintained between 1981 and
2012. Most of the alleles were found in both time points.
Only 12 and 18% of alleles in 1981 and 2012 were unique
to the respective time point, and these alleles were found at
very low frequencies, between 0.002 and 0.031. Given that
most alleles were found in both time points and ML geno-
types were maintained in collecting sites, no replacement
by exotic or uniform material seems to have taken place.
Also the very low Dand F
ST
values between time points
within most collecting sites indicate limited genetic change
between time points within sites. Ceccarelli and Grando
(1999,2009) reported that conventional breeding and high-
yielding varieties have had rather limited success in the
Fertile Crescent. However, two sites, 7594 and 7596, pre-
sent an exception. Their very high site-specific Dand F
ST
values indicate that the samples collected there in 2012
are quite different from those collected earlier. Samples
from 1981 collected in these two sites were genetically
and phenotypically distinct from all other samples, as
they formed a separate genetic cluster and were significant-
ly smaller in height and less productive. In 2012, samples
collected in these two sites were no longer distinct. The
barley landraces grown in these two sites in 1981 appear
therefore to have been replaced with material similar to
that re-collected in other sites.
We observed important changes in population structure.
The collection in 2012 showed less genetic differentiation
among sites and smaller average genetic distances between
samples. The clustering at K= 6, increased genetic admix-
ture, and the presence of physical admixture in all popula-
tions in 2012, indicate that population structure has
decreased. Furthermore, the reduction in Dand F
ST
values
shows that barley samples have become more homoge-
neous across the territory compared with 1981. This hom-
ogenization might be the result of increased seed flow
within the country and increased use of common seed
sources. Accordingly the number of ML genotypes found
in 2012 was higher and the number of ML genotypes un-
ique to the collecting time point increased from 72 to 83%.
In the 1980s, the barley grown in Jordan was primarily
landrace material, from farmers who had been using their
own seeds for generations (Weltzien, 1982,1988; Jana and
Pietrzak, 1988; Brush, 2004; Russell et al., 2011). The three
decades between 1981 and 2012 were characterized by in-
creased urbanization and infrastructure development due
to rapid population growth in Jordan, conversion of range-
lands into cultivated land, and a generalized intensification
of agricultural activities (Khresat et al., 1998; Al-Bakri et al.,
2001,2008;FAO,2006; NCARTT, 2007). Jordan operated a
conventional national barley-breeding programme, which
had released a first variety in 1966. Further varieties were
released in the late 1980s and 2004 (Friedt et al., 2011).
Since 2000, the breeding programme has been decentra-
lized by adopting and implementing the participatory
plant breeding (PPB) approach (ICARDA, 2003; Ceccarelli
and Grando, 2007) involving farmers in different ecogeo-
graphical regions. About 30 PPB varieties were distributed
to a number of regions in Jordan (Al-Yassin, 2012). The
Jordan Cooperative Corporation (JCC) started to coordinate
seed production and distribution throughout Jordan in
1982 (Al-Yassin, 2012). JCC has become the primary seed
source for the majority of barley farmers (N. Al-Hajaj,
pers. commun. NCARE; ICARDA, 2003). The increased
availability of varieties released by the breeding programme
I. Thormann et al.10
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and the operation of the seed cooperative have likely con-
tributed to decreasing differentiation and reduction in
population structure, because seed exchange and genetic
population structure in landraces are found to be inter-
dependent (Parzies et al., 2004; Allinne et al., 2008;
Thomas et al., 2011; Samberg et al., 2013).
Similar observations of weak and unstructured clustering
between barley landrace samples were made in Ethiopia
(Abebe and Leon, 2013; Abebe et al., 2013) and Tunisia
(Ould Med Mahmoud and Hamza, 2009). These observa-
tions were attributed to seed-mediated gene flow among
farmers. Farmer seed management can have a stronger in-
fluence on population structure than any landscape struc-
ture (Samberg et al., 2013), and can explain why
correlations between genetic structure and ecogeographic
variables are often weak or non-existent (Ould Med
Mahmoud and Hamza; 2009; Abebe and Leon, 2013).
Traditional practices and cultural factors are known to influ-
ence population structure and genetic diversity in land-
races of other crops and regions (Guo et al., 2012;
Westengen et al., 2014). To explain population structure
and spatial distribution of diversity in landraces and to de-
sign conservation strategies, it appears therefore necessary
to complement the knowledge about the species biology
and the study of the ecogeographical landscape with the
farmers’social landscape and their seed management
and exchange practices.
Phenotypic variation
High phenotypic variation was found in both collecting
periods in our study in Jordan, consistent with the findings
of other studies. Barley landraces collected from the Fertile
Crescent (Weltzien, 1982,1988; Ceccarelli et al., 1987;
Jaradat 1989a,b) and other countries (Damania et al.,
1985; Demissie and Bjornstad, 1996; Lasa et al.,2001;
Assefa and Labuschagne, 2004; Haseneyer et al., 2010)
are reported to be morphologically very variable. These
observations can also be confirmed for Ethiopia during
the mid-1980s from collecting over 100 barley landraces
across the country (Engels 2016, unpublished data).
We observed a significant change in multi-variate pheno-
type, where plants from 2012 were on average taller, had
longer spikes and more tillers. While there was significant
heterogeneity between sites in 1981, this was not found in
2012 anymore. Because our data do not include multi-year
and multi-location data, the observed differences in pheno-
type could be attributable to maternal effects and environ-
mental plasticity (Kirkpatrick and Lande, 1989; Mousseau
and Fox, 1998). The year of harvest and the age of seeds –
the 1981 seeds being 31 years older –could have led to
differences in the fitness of the seeds and played a role in
development of the plants. On the other hand, farmers in
Jordan are known to prefer taller plants, which are easier
to harvest from stony fields, and plant height has been an
important selection criterion in PPB activities (ICARDA,
2003). To determine whether observed phenotypic differ-
ences between collecting years might be influenced by
farmer selection, seed fitness or G × E effects, would require
a complex set of additional common garden experiments in
different environments.
Responses to environmental variation
The analysis of the climate data demonstrated that climate
has been changing, in particular the already dry months
have become drier and temperatures have been increasing.
This presents an increasing challenge to agriculture. These
changes were not found to have affected genotypic diver-
sity, however. Two studies have made similar findings.
Significant changes in phenotypic traits were observed by
Cui et al.(2016) in a temporal study (1980–2007) on rice
landraces in China, as a decrease in plant height and an in-
crease in number of grains per panicle. This was attributed
to on-farm selection. Vigouroux et al.(2011) observed earl-
ier flowering and a reduction in plant and spike size as an
adaptation to drought in pearl millet in a similar diachronic
study (1976–2003) conducted in Niger. Analogous to our
results, in both cases, genetic diversity was maintained in
landraces on-farm despite environmental change.
Genetic erosion in landraces and on-farm
conservation
Climate change, replacement with improved varieties, en-
vironmental degradation, changing agricultural systems
and needs, legislation and policy, civil strife and war are re-
ported as main causes for past genetic erosion and threats
to the survival of existing diversity (FAO, 2010,2012). The
picture that so far has emerged from research about the ex-
tent of genetic erosion in landraces, however, appears very
complex and no coherent body of data are available be-
cause methods of assessment are not uniform (Thormann
and Engels, 2015).
While many studies and reports document genetic ero-
sion (FAO, 2010; Thormann and Engels, 2015), other stud-
ies show that in some cases introduction of improved
varieties has not caused loss in landrace diversity or land-
race diversity has been maintained on-farm. Steele et al.
(2009) monitored the adoption of modern rice varieties in
a high-altitude region in Nepal. They found that partial
adoption of a modern variety can increase the overall gen-
etic diversity within the agricultural system, if at least 35% of
the cultivated area continues to be planted to traditional
varieties. Landrace diversity of pearl millet and sorghum
in Niger (Bezançon et al., 2009), sorghum in eastern
Changes in barley (Hordeum vulgare L. subsp. vulgare) genetic diversity and structure in Jordan over a period of 31 years 11
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Ethiopia (Mekbib, 2008) or rice in Yunnan, China (Cui
et al., 2016) and Guinea (Barry et al., 2008) was maintained
over time by farmers. No loss of genetic diversity was found
in wheat collected over an interval of 40–50 years in
Albania, Austria, India and Nepal (Khlestkina et al., 2004).
The presence and extent of erosion varies for the same
crop, by geography, national policy environment and agri-
cultural system. While Khlestkina et al.(2006) observed
maintenance in diversity in barley collected from Austria
and India over an interval of 40–50 years, loss of barley
landrace diversity has been reported from Serbia
(Petrovic and Dimitrijevic, 2012) and Ethiopia (Megersa,
2014).
Our study has for the first time analysed temporal vari-
ation in barley landrace diversity in Jordan. We revealed
a localized loss of genetically and phenotypically distinct
populations in two nearby sites. While genetic diversity
in terms of allelic richness and ML genotypes has been
maintained –and even slightly increased, we observed a
decrease in differentiation between populations, which
have become less locally distinctive. Our results underline
the importance of seed management practices on shaping
diversity in landraces maintained by farmers, which have
likely led to reduced distinctiveness and potential for
local adaptation of landrace populations. The potential
for evolution and adaptation to changing environmental
conditions is one of the major advantages of active on
farm conservation of landrace diversity. The assessment
of changes in landrace diversity over time, combined
with analysis of seed management practices, can provide
useful input to devise concrete interventions for conserva-
tion of landrace diversity.
Supplementary Material
The supplementary material for this article can be found at
https://doi.org/10.1017/S1479262117000028
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