Heredity (2016) 116, 477–484
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ORIGINAL ARTICLE
Maize diversity associated with social origin and
environmental variation in Southern Mexico
Q Orozco-Ramírez1, J Ross-Ibarra2, A Santacruz-Varela3 and S Brush4
While prevailing theories of crop evolution suggest that crop diversity and cultural diversity should be linked, empirical evidence
for such a link remains inconclusive. In particular, few studies have investigated such patterns on a local scale. Here, we address
this issue by examining the determinants of maize diversity in a local region of high cultural and biological richness in Southern
Mexico. We collected maize samples from villages at low and middle elevations in two adjacent municipalities of differing ethnicity:
Mixtec or Chatino. Although morphological traits show few patterns of population structure, we see clear genetic differentiation
among villages, with municipality explaining a larger proportion of the differentiation than altitude. Consistent with an important
role of social origin in patterning seed exchange, metapopulation model-based estimates of differentiation match the genetic data
within village and ethnically distinct municipalities, but underestimate differentiation when all four villages are taken together.
Our research provides insights about the importance of social origin in structuring maize diversity at the local scale.
Heredity (2016) 116, 477–484; doi:10.1038/hdy.2016.10; published online 24 February 2016
INTRODUCTION
The past decade has seen progress in research about crop diversity
linked to cultural diversity and social factors, but there is still much
to understand about this complex relationship. Crop evolution and
diversity depend on selection mediated both by the environment and
by farmers (Harlan, 1975). Previous research has shown that social
factors such as ritual use, identification as indigenous or mestizo, or
even simple aesthetics, can contribute to the maintenance of particular
landraces (Bellon, 1996; Zimmerer, 1996; Brush and Perales, 2007).
We might thus expect that culturally determined preferences and
perceptions have molded crop populations, but demonstrating the role
of cultural diversity in generating crop diversity has been more
difficult. Differentiation of maize populations and other crop populations by cultural variation was suggested long ago (Anderson, 1946;
Hernández, 1972), but only recently has empirical data been reported
(Perales et al., 2005; Labeyrie et al., 2014).
In México, most maize farmers select seed for use in each coming
year, and this selection contributes toward maintaining distinctive
morphological traits in the face of abundant pollen flow and extensive
seed movement (Louette and Smale, 2000; Ortega-Paczka, 2003;
Pressoir and Berthaud, 2004a). The diversity resulting from farmers’
management has conventionally been classified as races, and there are
59 maize races accepted based on research using extensive morphological and isozyme data (Sánchez et al., 2000). Cultural differences
between groups may be expressed as preferences for colors, textures
and uses for particular varieties (Ortega-Paczka, 2003), and also serve
to erect barriers to the movement of seed (Hernández, 1972).
Consistent with this, many workers have documented correlations
between ethnolinguistic and biological diversity (Maffi, 2005), but
the relationship between these factors is complex, in part, because
ethnolinguistic groups often inhabit different environments and
ecological niches (Brush, 2004). In contrast to the popular assumption
that there is a direct relationship between ethnolinguistic diversity
and maize diversity, there is little research that has formally and
systematically addressed that interaction.
Genetic research describes continuous variation among maize,
although regional clusters are apparent (Matsuoka et al., 2002;
Vigouroux et al., 2008). Clustering is more evident in the use of
morphological traits than with genetic markers to assess relationships among ecogeographic regions (Doebley et al., 1985; Sánchez
and Goodman, 1992; Sánchez et al., 2000). Using morphological
characteristics that are under farmer selection, social scientists and
plant biologists have shown that farmers maintain morphologically
distinct maize populations at much smaller regional scales (Pressoir
and Berthaud, 2004a; Perales et al., 2005). Particularly, Perales et al.
(2005) found that ethnolinguistic diversity in the same environment
was linked to maize morphological diversity, but not to genetic
differentiation. In contrast to the findings of Pressoir and Berthaud
(2004b) and Perales et al. (2005); van Etten et al. (2008) found that
maize populations from different villages within a small, culturally
homogeneous region in Guatemala are both genetically and phenotypically separated. They confirmed, however, the central finding
of the Mexican case studies that social origin has a significant role
in determining the patterns of maize in the region. Interestingly, maize
diversity in NW Guatemala was more discernible between communities than between regions, a finding that van Etten et al. (2008)
1
Centro de Investigaciones en Geografía Ambiental, UNAM, Antigua Carretera a Pátzcuaro, Morelia, Michoacán, México; 2Department of Plant Sciences, Center for Population
Biology, and Genome Center, University of California, Davis, CA, USA; 3Colegio de Postgraduados. Montecillo, Texcoco, Estado de México, México and 4Department of Human
Ecology, University of California, Davis, CA, USA
Correspondence: Dr Q Orozco-Ramírez, Centro de Investigaciones en Geografía Ambiental, UNAM, Antigua Carretera a Pátzcuaro, Col. San José de la Huerta 8701, Morelia,
Michoacán 58190, México.
E-mail: qorozco@gmail.com
Received 22 June 2015; revised 19 December 2015; accepted 22 December 2015; published online 24 February 2016
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attribute to patterns of seed exchange at local and regional levels and
to diffusion of innovations at the regional level.
This paper examines maize diversity and population structure at the
local scale and their relation to both ethnolinguistic variation and
environmental variation (elevation) in southern Mexico. We studied
farmers who speak either Chatino or Mixtec, two languages of the
Oto-Manguean family that have been separated for ~ 4700 years
(Kaufman, 1990). We collected maize samples from two environments
—low and middle elevation—in two neighboring, indigenous municipalities with different language affiliations. We hypothesized that
maize collections from the same municipality would be more similar
than those from different municipalities, even though comparable
environmental variation occurs within each. We found the effects of
social origin (municipality) in structuring morphological and genetic
diversity stronger than that of elevation. Application of a metapopulation model suggests that genetic differentiation is because of the lack
of seed flow between municipalities.
MATERIALS AND METHODS
Study site
Fieldwork was carried out in the Sierra Sur of Oaxaca (Figure 1). This
mountainous range extends along the Pacific Coast from southern Jalisco to the
Isthmus of Tehuantepec in the state of Oaxaca. We worked in the Mixtec
municipality of Santiago Amoltepec and the Chatino municipality of Santa
Cruz Zenzontepec. These indigenous communities have an ancient and shared
history: their townships are only 12 km apart and they have been affected by the
same regional and historical dynamics. The topography is abrupt, with
mountains, canyons and hills leading to elevation variation from 105 to 2150
meters above sea level (masl). The climate is hot in the lowlands and temperate
in the higher elevations. The mean annual temperature is 26 °C in the lowlands
and 18 °C in the upper elevations. The rainy season starts in May and ends
in October, with an average from 1500 to 2000 mm precipitation per year
(INEGI, 2013). Humidity differs strongly between the lowlands and highlands
and is affected by the exposure in the hills. Soil diversity is high because of the
complex geology; according to the most detailed available information, Litosol
and Regosol eutrico are the most important soil types (INEGI, 2013).
In general, soils present some level of erosion because of agricultural practices,
runoff and wind. Municipalities are integrated internally through local
governments and markets. Beyond some seed in backyard gardens, no hybrid
or improved seed has been planted in the area. Crop management is similar in
both municipalities, the only difference being the use of fertilizer in the Chatino
municipality, Zenzontepec and not in the Mixtec municipality, Amoltepec.
Maize collections and reciprocal common gardens
To test whether maize diversity and population structure are shaped by social
origin and/or environmental factors, maize collections and common garden
experiments with morphological characterization were performed. Four villages
were selected: one Chatino and one Mixtec at middle elevation (1000–1300 masl)
identified as Ch-M and M-M, respectively; and one Chatino and one Mixtec in
the lowlands (400–600 masl), identified as Ch-L and M-L. A detailed description
of the sampling and common gardens are described elsewhere (Orozco-Ramírez
et al., 2014). We collected a total of 135 maize samples from the four villages
(33 from M-M, 23 from M-L, 44 from Ch-M and 35 from Ch-L). Each maize
sample consisted of 12 seed quality ears of each farmer-identified type that the
household planted in the previous year. Ecological information and management of each sample was recorded by a survey, we considered that each sample
represents one maize population. The samples of each village were grouped by
local name and race, and organized according to variation in ear morphology.
Five maize samples from each village were selected to plant in the common
gardens; these samples resembled the total variation of maize in that particular
village. In this analysis, we used only the data from two common gardens under
fertilization treatment, one in the Chatino village low elevation (Ch-L) and
another in Mixtec middle elevation (M-M). These were the fields with the
best soil conditions to perform morphological characterization. Each of the
common gardens had a complete random block design with three repetitions,
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60 experimental units per garden (4 villages × 5 samples × 3 blocks). Each
experimental unit had four furrows, each 5 m long and 0.8 m wide. Morphological data recorded from common gardens included days to anthesis, days
to silking, tassel branches, tassel length, stem diameter, leaf length, leaves per
plant, leaf width, ear height on the plant, plant height, ear diameter, ear length,
grain rows, kernels per row and cob diameter. Twenty plants were measured
from the two rows in the center. Flowering time was recorded when 50% of the
plants had reached anthesis or were silking. For ear variables, all the plants of
one row located in the center of the plot were harvested. We averaged each
variable over plots.
Molecular analysis
Molecular analysis was carried out at the Colegio de Postgraduados, Mexico.
We used the same 20 maize samples (but not the same physical individuals)
as used in common gardens for microsatellite genotyping. DNA was extracted
from 10 individuals randomly selected for each sample, using the standard
protocol prescribed by the ChargeSwitch gDNA Plant Kit (Invitrogen, Carlsbad,
CA, USA). We used 100–150 mg of seedling tissue. DNA extraction was made
by a King Fisher Flex (Thermo Scientific, Waltham, MA, USA) automatic
extractor. The DNA samples selected had a DNA concentration above
50 ng μl − 1 and an absorbance ratio from 1.40 to 1.80 at 260/280 nm
wavelength. The DNA was evaluated by a Nanodrop 2000 Spectrophotometer
(Thermo Scientific). Extracted samples were genotyped for 15 microsatellite loci,
listed in Supplementary Appendix Table A1. These markers were chosen from a
larger group of SSRs (simple sequence repeats) optimized for multiplexing, and
proved to be efficient to reveal genetic diversity in maize. For more information
about these SRRs, see http://www.maizegdb.org/data_center/ssr. Fluorescently
labeled primers (ROX, 6-FAM, HEX) were obtained for these loci (Invitrogen).
Multiple PCRs were performed in a 25 μl reaction volume, containing
4 pmol μl − 1 of R and F primer (Invitrogen), 0.16 mM of dNTP mix (Promega,
Madison, WI, USA), 1.2 mM of MgCl2 (Promega), 0.8 × of GoTaq flexi buffer
(Promega), 1 U of GoTaq flexi DNA polymerase (Promega) and 25 ng of DNA.
The amplification program was: 95 °C for 4 min, followed by 25 cycles of 95 °C
for 1 min, 55 °C for 2 min and 72 °C for 2 min, and then followed by extension
at 72 °C for 60 min. PCR was performed in a GeneAmp PCR System 9700
(Applied Biosystems, Foster City, CA, USA). PCR products were analyzed by
capillary electrophoresis in a 3130 Genetic Analyzer (Applied Biosystems).
Fragment sizes were scored using GeneMapper v4.0 (Applied Biosystems).
Statistical analysis
Maize population structure based on morphological traits from the common
gardens was analyzed by principal component analysis. We excluded days to
silking and height of ear in the plant because these variables were highly
correlated to days to tasseling and plant height, respectively. Data were
standardized by subtracting the mean from each observation and dividing by
the standard deviation. The unit of analysis was maize sample at each plot
in the common gardens. To define the effect of elevation and social origin
(different municipality) in structuring populations using morphological traits,
we did a permutational multivariate analysis of variance using distance matrices
(Anderson, 2001). This is a nonparametric method that partitions a distance
matrix among sources of variation. For this analysis, we used the same variables
as for the principal component analysis. The distance matrix was calculated
using the Euclidian method on the standardized data and we allowed for 9999
permutations to calculate F statistics and to have an ample margin to reject the
null hypothesis at an α-level of 0.05. For this analysis, we applied the function
adonis in the package vegan (Oksanen et al., 2015) for R (R Core Team, 2014).
We used model-based clustering to evaluate population structure as implemented in the software STRUCTURE 2.3.4 (Pritchard et al., 2000), using the
admixture model with correlated allele frequencies and allowing the model to use
location information for the samples to assist the clustering. The estimated
proportion of each cluster forming an individual genome (q) was calculated for
K ranging from 1 to 10 populations, with 10 runs for each K-value. We used
a burn-in period of 100 000 and 100 000 iterations for estimating the parameters.
The criterion suggested by Evanno et al. (2005), based on the second-order rate
of change in the log probability of data between successive K-values, was used
to determine the most likely number of clusters (K).
Maize population structure and environmental and social variation
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479
Figure 1 Map of villages and collections.
Owing to low genetic differentiation among maize samples within village
in the software STRUCTURE, we treat each village as a single population to then
perform a locus-by-locus analysis of molecular variance (AMOVA) (Excoffier
et al., 1992) grouping villages according to municipality. We preferred locus-bylocus AMOVA because there were some missing data and we included
individual level in the calculations. Significance was calculated using 16 000
permutations and estimates of the proportion of variation at different levels
were calculated as a weighted average across loci. Because each village within
a municipality is located at different elevation, when testing within municipality,
we are testing for the effect of environment in structuring the population.
We also calculated a matrix of genetic distance (FST) (Wright, 1951) among
villages based on the number of different alleles using Arlequin 3.5 (Excoffier
et al., 2005), with significance (α = 0.05) calculated after 1000 permutations.
Finally, we used the model proposed by van Heerwaarden et al. (2010)
to estimate genetic differentiation because of seed management and seed flow. This
approach models a collection of maize fields as a metapopulation and uses
parameters from maize farmers’ practices in traditional agricultural systems to
estimate FST following Slatkin (1991). We compared model-based estimates of FST
to FST calculated from our SSR genotyping. FST was calculated for each village and
globally using Arlequin 3.5 (Excoffier et al., 2005). FST confidence intervals were
calculated over 2000 bootstraps. The model uses these parameters: number of
demes (n), number of ears planted per deme (Nf), total number of plants per deme
(N), number of migrating ears (Nfm), replacement probability (e), migration
(mixture proportion) proportion (m = Nfm/Nf), proportion of seed mixture (pm)
and proportion pollen migration (mg). Values used for each parameter are in
Table 1, unless otherwise stated data used came from our field surveys (OrozcoRamírez et al., 2014). Values for each parameter were obtained by village (Table 1)
and were averaged or summed accordingly to obtain FST for several hierarchical
levels: by municipality, by elevation and for all villages together as shown in
Table 2. Number of demes (n) were calculated by multiplying number of
households by mean number of samples over households for each village. Number
of ears planted per deme (Nf) was calculated from average planted area, seed sown
per hectare, average kernel weight in the region (Aragón-Cuevas et al., 2012) and
the average number of kernels used as seed from each ear. The total number
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Table 1 Seed management parameters for FST estimationa
Parameter
M-M
M-L
Ch-M
Ch-L
Total/mean
Number of households
Mean number of samples over households
152
1.89
49
1.33
206
1.57
219
1.4
626
1.55
Average planting area (ha)
Seed sow per hectare (kg)
0.52
16
0.61
16
0.56
16
0.78
16
0.62
16
8.32
0.087
9.76
0.087
8.96
0.087
12.48
0.087
9.92
0.087
Mean kernels per earb
Mean of quantity seed exchange (kg)
262
3.3
262
4
262
5
262
4.9
262
4.2
Proportion of exchange seed
0.40
0.41
0.56
0.39
0.42
287
96
65
112
323
103
307
143
970
114
Total number of plants per deme (N)
Number of migrating ears (Nfm)
25 056
38
29 392
46
26 983
57
37 583
56
29 874
48
Landrace replacement probability (e)
Migration proportion (m = Nfm/Nf)
0.097
0.40
0.15
0.41
0.186
0.56
0.061
0.39
0.13
0.42
0.00125
0.00106
0
0
0.00058
Data from surveys
Total seed used per deme (kg)
Ear weight (kg)b
Parameters for the model
Number of demes (n)
Number of ears planted per deme (Nf)
Proportion of seed mixture (pm)
Abbreviations: Ch-L, Chatino at low elevation; Ch-M, Chatino at middle elevation; M-L, Mixtec at low elevation; M-M, Mixtec at middle elevation.
aUsing van Heerwaarden et al. (2010) metapopulation model to estimate genetic structure.
bEstimated according to mean row and grain number per ear used for seed and using the average kernel weight estimated by Aragón-Cuevas et al. (2012) for landraces from the region.
Table 2 Results of van Heerwaarden et al. (2010) metapopulation model to estimate genetic structure based on seed management
Results of the model
M-M
Estimated FST by SSR (95% confidence intervala)
Proportion pollen migration (mg) assumed to fit model
results to FST by SSR
Model estimated FST using reported pollen
M-L
Ch-M
Ch-L
Total
0.114
0.045
0.089
0.077
0.111
(0.077–0.145)
0.0089
(0.018–0.068)
0.0179
(0.043–0.155)
0.0114
(0.049–0.108)
0.0101
(0.085–0.138)
0.0083
0.064
0.045
0.060
0.046
0.056
migration (0.018) by Messeguer et al. (2006)
intervala)
Estimated FST by SSR (95% confidence
Proportion pollen migration (mg) assumed to fit model results to FST by SSR
Model estimated FST using reported pollen migration (0.018) by
Messeguer et al. (2006)
Estimated FST by SSR (95% confidence intervala)
Proportion pollen migration (mg) assumed to fit model results to FST by SSR
Proportion pollen migration (mg) assumed to fit model results to FST by SSR
setting seed migration frequency (m) to ~ 0b and seed mixture (pm) to 0
Model estimated FST using reported pollen migration (0.018) by
Mixtec villages
Chatino villages
0.089 (0.063–0.109)
0.0112
0.082 (0.056–0.115)
0.0112
0.059
0.053
Middle elevation villages
Low elevation villages
0.120 (0.087–0.156)
0.094 (0.071–0.114)
0.111 (0.085–0.138)
0.0085
0.0089
0.0087
0.0089
0.083
0.086
0.064
0.050
Messeguer et al. (2006)
Abbreviations: Ch-L, Chatino at low elevation; Ch-M, Chatino at middle elevation; M-L, Mixtec at low elevation; M-M, Mixtec at middle elevation; SSR, simple sequence repeat.
aCalculated over 2000 bootstraps.
bThe model does not accept zero migrating ears, we used 0.000001.
of plants per deme (N) was estimated by multiplying the average kernels per ear by
Nf. The number of migrating ears (Nfm) was calculated from seed exchange
averaged over farmers in each village (kg). First, we obtained the proportion of seed
exchanged with respect to total seed planted and then multiplied that proportion
by Nf. Replacement probability (e) was calculated as the proportion of new seed
lots with respect to the total reported for the previous season. Initial values of the
proportion of pollen migration (mg) were taken from the literature (Messeguer
et al., 2006), but these were later fitted to the observed FST data.
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RESULTS
Maize diversity in the area of study
On average, the number of landraces cultivated by a single farmer
ranges from 1.33 in M-L to 1.89 in M-M. In M-M, the majority
of farmers (67%) have two landraces. In the other three villages, most
farmers had only one landrace. In M-L, fewer farmers had two
landraces than in the other three villages. Only in Ch-M did farmers
have four landraces, but the percentage of farmers with more than
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Q Orozco-Ramírez et al
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three landraces was very low, and most in that village have only one
landrace. In total, we found seven racial groups in the four villages
(Tuxpeño, Olotillo, Conejo, Tepecintle, Pepitilla, Elotes occidentales and
Zapalote grande). A test for independence of race frequency by village
finds little support for a dependence on elevation (P-value = 0.06793,
1000 Monte Carlo simulations), but strong support for dependence on
municipality (P-value = 0.0001, 1000 Monte Carlo simulations).
Mixtec communities have more of the Conejo and Tepecintle races,
and Chatino villages have more of the Olotillo and Tuxpeño races
(Figure 1 and see Supplementary Appendix Table A2).
According to permutational multivariate analyses of variance,
the main effect of municipality was significant in structuring morphological variation, but neither elevation nor the interaction between
elevation and municipality were significant (Table 3). Comparison of
the mean sum of squares (Anderson, 2001) suggests that municipality
has a stronger effect than elevation in structuring maize populations
(Table 3). Nonetheless, there are some differences between common
gardens. The effect of municipality was weaker in the low elevation
garden, perhaps because of the lower overall morphological variation
observed.
Maize population structure based on morphological traits
Principal component analysis reveals a continuum of maize morphological diversity across the region. The plot of the first two principal
components does not show clearly separate groups in either common
garden (Figure 2). However, in both common gardens it is possible
to see greater clustering of samples when labeled by municipality
(Figures 2a and c) than by elevation (Figures 2b and d). The first
component (PC1) for the M-M garden shows the most important
variables are leaf number, leaf width, plant height, stem diameter
and leaf length, meaning that PC1 accounts overall for plant size,
separating large and small plants (Supplementary Appendix Table A3).
For the second component (PC2), tasseling, ear diameter, row number
and cob diameter have a large loading. PC2 differentiates earlier
maturing plants and fatter ears. Principal component analysis for the
Ch-L common garden shows the first component (PC1) accounting
in general for plant size and the second for fatter ears (PC2).
Maize population structure based on molecular markers
Results from software STRUCTURE suggested the existence of two clusters
defined by municipality (Figure 3). The highest value of ΔK was found
at K = 2, but at higher values of K the Mixtec area shows separation
between villages. Chatino villages are not similarly separated. Most
individuals show evidence of admixture, and there is no correspondence between maize race name and STRUCTURE results. The only
exception is a sample from M-M of the Conejo race, which at higher
values of K (⩾4) forms a stable cluster with low admixture. Samples
never cluster by elevation.
Our AMOVA finds relatively strong population structure (Table 4).
Most of the genetic variation (73%) was found within populations, with
less variation assigned to municipality (4%) and elevation (1.75%). The
results of the AMOVA are confirmed by the matrix of pairwise genetic
distance (FST) among villages, although all values are low. FST between
villages of the same municipality (0.019 Mixtec, 0.021 Chatino) are
Figure 2 Principal component analysis plot (PC1 vs PC2) for morphological traits sorted by municipality, and elevation, data from middle and lowlands
common gardens.
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lower than between populations of the same elevation but different
municipalities (0.041 middle, 0.066 low). FST between villages from
different municipalities and different elevations are also large (M-M vs
Ch-L is 0.059, M-L vs Ch-M 0.045), but the largest difference
is between Mixtec Lowlands and Chatino Lowlands (0.066). All FST
values are significant (α = 0.05) calculated after 1000 permutations.
We tested population structure due to race by AMOVA. We
confirmed our STRUCTURE results, finding no evidence of differentiation
between racial groups (FCT = 0.004, P-value = 0.295), but there was
important variation among populations within racial groups (FSC = 0.108,
P-value = 0.000). According to SSR´s racial grouping seems artificial
(See Supplementary Appendix Table A.4).
Table 3 Permutational multivariate analysis of variance on Euclidean
distances matrices for plant morphology traits for each common
garden (M-M, Ch-L)a
Metapopulation model
We used the metapopulation model of van Heerwaarden et al. (2010)
to estimate FST and theoretical pollen migration based on seed
management and exchange from our surveys. Within villages,
to obtain similar modeled values to FST to those calculated by SSR's,
we had to assume similar pollen migration rates to those in the
literature (Messeguer et al., 2006), with the exception of the M-M
village where a very low pollen migration proportion (0.0089) was
needed, the range in the other three villages was from 0.010 to 0.018
(Table 2). We next asked whether the model can be extended
to villages within municipalities, and were able to find a reasonable
fit of the model using pollen flow values within the range used for
within-village comparisons (0.0112), supporting the idea that gene
flow between villages of the same ethnolinguistic group is similar to
that than within villages. In contrast, fitting the model to elevation
groupings or the entire study region, required much lower pollen flow
estimates: 0.0087 for lowlands, 0.0085 for middle elevation and 0.0083
for the region (Table 2).
Factor
M-M common garden
Elevation
D.f.
Sums of sqs
Mean sqs
F. model
Pr(4F)
1
17.8
17.8
1.5
0.200
Municipality
Residuals
1
56
70.1
666.0
70.1
11.9
5.9
0.9
0.000
Total
58
754.0
Elevation
Municipality
1
1
16.8
46.0
16.8
46.0
1.4
3.7
0.235
0.010
Residuals
Total
56
58
691.2
754.0
12.3
0.9
Ch-L commom garden
Abbreviations: Ch-L, Chatino at low elevation; Ch-M, Chatino at middle elevation; M-L, Mixtec at
low elevation; M-M, Mixtec at middle elevation.
aUsing 9999 permutations, two levels for social origin (Mixtec municipality and Chatino
municipality) and two levels for elevation (low and middle elevation).
Figure 3 STRUCTURE graphical results assuming two (a), three (b) and four (c) groups, after a burning period of 30 000 iterations and 1 000 000
replications for estimations. Each individual plant is represented by a vertical line. Each color represents the membership to each cluster (k). Labels in the x
axis show the village of origin.
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Table 4 Genetic structure as revealed by AMOVA and FST
Source of variation
Sum of squares
Variance components
Percentage variation
Between municipalities
48.40
0.21
Among villages within municipality (elevation effect)
Among individuals within villages
27.85
997.69
0.09
1.13
648.00
1721.94
3.85
5.29
Within individuals
Total
Fixation indices
P-value
4.04
FCT = 0.040
0.000
1.75
21.28
FSC = 0.018
FIS = 0.226
0.000
0.000
72.92
FIT = 0.271
0.000
Abbreviation: AMOVA, analysis of molecular variance.
Notes: P-value calculated after 16 000 permutations.
DISCUSSION
Results from our comparison of genetic and morphological variation
among maize landraces cultivated by Mixtec and Chatino farmers
support the hypothesis that ethnolinguistic differences could shape
diversity as much or more than the environment in this particular
region. Plant morphological characteristics measured in the common
gardens display considerable variation without strong clustering.
Nevertheless, it is possible to see the effect of municipality but not
elevation in structuring the population (Table 3). All statistical
methods used to analyze molecular markers show greater support
for structure because of social origin than environment (elevation).
In a previous paper, we have shown that adaptation of local landraces
is not a primary reason for landrace distribution in the region. We found
that landraces from Ch-L yield better in all four villages (Orozco-Ramírez
et al., 2014). Other factors, besides environment and ethnicity, help
determine landrace distribution, such as infrastructure (roads) and
markets. From our ethnographic work, we know roads are relatively
recent to the region. Previously, the region’s villages were connected by
foot traffic (Orozco-Ramírez, 2014). From our surveys, we know of seed
exchange among villages with no road access, and the average distance
between those villages is 8.5 km. Seed exchange involving moving by both
foot and vehicles occurred over an average distance of 70 km. Most
external seed exchange happened among villages of the same ethnicity
(65%, averaged over all villages) or with Spanish-speaking towns (31%).
The distance by foot between villages of different ethnicities is actually
smaller in some cases than the distance between villages of the same
ethnicity having seed exchange, consequently we cannot argue that maize
population differentiation is a consequence of ease of transport or access.
In relatively rare instances (4%), seed exchange occurred among villages
of different ethnicities, suggesting that ethnicity matters to defining seed
acquisition (Orozco-Ramírez, 2014).
Our results contrast with previous findings in the Oaxaca Valley
(Pressoir and Berthaud, 2004a) and Chiapas (Perales et al., 2005)
that suggest social origin only impacts morphological variation directly
selected by farmers. Pressoir and Berthaud (2004a) argued that
cultivation in different villages and farmer's selection contribute to
morphological differentiation, but that pollen migration among
populations reduces genetic separation. Similarly, Perales et al.
(2005) found morphological but no genetic differentiation between
neighboring ethnolinguistic groups. Their surveys found that a large
majority (470%) of farmers were interested in receiving seed from
villages of a different ethnic group, suggesting that seed movement
may explain the extremely low FST values they observed. Comparing
highland and lowland maize samples from four states in east-central
Mexico, van Heerwaarden (2007) reports genetic differentiation
according to altitude but not according to social origin within
altitudes. Most research, therefore, suggests gene flow is important
among maize populations from different villages and that farmers’
selection is important in maintaining morphological differentiation. In
contrast, we find modest morphological and genetic differentiation
between ethnolinguistic areas (FCT = 0.040) (Table 4) that are geographically quite close, which is larger than among village differentiation (0.003)
reported by Pressoir and Berthaud (2004b) and by van Heerwaarden
(2007) (0.026 for highlands and 0.027 for lowlands). Among these
studies, our study focused on the smallest region followed by Pressoir and
Berthaud (2004b) and van Heerwaarden (2007). Also, we found greater
global differentiation, over all markers, (FST = 0.111 (0.085–0.138, 95%
confidence interval by bootstrapping)) (Table 2) than values reported by
Pressoir and Berthaud (2004b) (FST = 0.011 ± 0.002, 95% confidence
intervals by jackknifing). Our study was carried out in a region with no
roads crossing it and with no common local or regional markets. This
contrasts to the situation in the Central Valleys of Oaxaca, where roads
and markets unify the region and exchange commonly occurs between
villages. In contrast, our results suggest that ethnolinguistic differences
could effectively isolate maize populations in this region that are otherwise
under similar natural and artificial selection pressures. We conclude that,
at least in some cases, ethnolinguistic affiliation can reduce gene flow
more than the environmental obstacles posed by altitude differences. We
posit that ethnically based seed networks foster both morphological and
genetic separation, an idea similar to that of Hernández (1972), who
suggested that indigenous groups isolate maize populations in a way
similar to geographic barriers. An important next step is to expand the
research to a contiguous Zapotec municipality to improve the test of the
effect of ethnicity in structuring maize populations.
Previous work has found that both genetic and morphological
variation are strongly structured by elevation (Doebley et al., 1985;
Benz, 1986; Bretting and Goodman, 1989; Vigouroux et al., 2008; van
Heerwaarden et al., 2011). Research on the distribution of maize races in
central Mexico (Perales et al., 2003) and Chiapas (Brush and Perales,
2007) found that maize races are distributed according to elevation, and
common garden experiments suggest local adaptation to elevation
(Mercer et al., 2008). van Heerwaarden (2007) showed close association
between maize genetic structure and elevation at a regional scale in eastcentral Mexico, and genetic analyses find a significant impact of elevation
on genome-wide diversity in both maize and its wild relative teosinte
(Bradburd et al., 2013; Pyhäjärvi et al., 2013; Takuno et al., 2015).
Contrary to these findings, we found no differentiation of maize
populations by elevation (races, morphological traits and molecular
markers), likely because of the much smaller geographic scale of our
population sampling.
Models of metapopulation structure based on our survey data
support a possible role for ethnicity in patterning genetic diversity in
our study area. The metapopulation model of van Heerwaarden et al.
(2010) is able to fit FST values within most villages using pollen
migration values similar to direct estimates reported in the literature
(Messeguer et al., 2006) (Table 2). The strikingly high FST and
correspondingly low pollen migration required to fit the model in
M-M was because of the presence of a highly distinct early maturing
landrace in part of the village (Figure 3); because of differences in
flowering time, we hypothesize there is likely very little pollen
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Maize population structure and environmental and social variation
Q Orozco-Ramírez et al
484
migration between this landrace and others. The model also fits well
observed FST between ethnolinguistic groups using pollen migration
parameters within the range seen in individual villages (Table 2).
Within elevation regimens (between ethnolinguistic groups) or among
all villages, however, the model was only able to fit observed FST values
with a substantial reduction in the pollen migration parameter or with
a smaller reduction in pollen migration and setting seed migration
frequency to zero. Because of the good fit of the model within villages
and between villages within an ethnic group, the decrease in migration
(pollen or seed) required to fit the model to elevation groups or the
entire data is consistent with the idea that ethnolinguistic group is a
limitation to maize gene flow in this region.
Previous studies that have found morphological differences among
maize from different villages have not found much differentiation at
the genetic level, suggesting that selection for a particular maize
ideotype cannot explain the genetic differentiation observed in our
villages. Instead, we suggest that a reduction of gene flow by limited
seed and pollen migration among villages of different ethnolinguistic
groups has effected genetic structure both in morphological traits
and in genome-wide markers. We propose that detailed investigation
of seed networks is an important next step to understanding the
processes that pattern genetic diversity in maize.
DATA ARCHIVING
Data available from the Dryad Digital Repository: http://dx.doi.org/
10.5061/dryad.c25f0.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
We thank CONACYT and UC MEXUS for funding this research through a doctoral
scholarship and a dissertation grant. We thank Msc Flavio Aragon-Cuevas (INAFAP,
Mexico) for maize racial classification; Cinthia Guzman, Laura Carrillo and Juan
Sánchez (Colegio de Postgraduados, Mexico) for genotyping work; Dr Mark Grote
(UC Davis) and Jonathan Fresnedo (UC Davis) for statistical advice; Joost van
Heerwaarden for sharing an R script to run the metapopulation model; and farmers
and authorities from Santiago Amoltepec and Santa Cruz Zenzontepec for their
support and for allowing to carry out this research. We also thank CIGA-UNAM for
a postdoctoral scholarship to improve the writing of this paper. JRI would like to
acknowledge support from USDA Hatch project CA-D-PLS-2066-H and NSF Plant
Genome award 1238014. We thank to four anonymous reviewers for theirs
comments, which improved the quality of this paper.
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