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Admixture and Social Status in Chile

Authors:
  • Ulster Institute for Social Research

Abstract

We investigated how genetically measured ancestry relates to social status in Chile. Our study is based on a dataset of 1,805 subjects previously analyzed in another study. Ancestry was measured using genetic analysis based on microarray data. Overall we find that compared to European ancestry (44%), the Amerindian ancestries Mapuche (central Chile, 36%) and Aymara (northern, 17%) both predict lower social status (standardized betas =-1.77 and-0.97, p's < .001). The amount of African ancestry was relatively minor in this sample (3%), but tentatively was associated with lower social status (beta =-2.15, p = .03). These differences held controlling for age, gender, and region of residence. Our analyses of the regional-level data (n=13) did not produce any findings. The sample size is probably too small and coarse-grained for this analysis to be viable.
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Admixture and Social Status in Chile
Emil O. W. Kirkegaard*
Ulster Institute for Social Research, London
* Address for correspondence: the.dfx@gmail.com
We investigated how genetically measured ancestry relates to social
status in Chile. Our study is based on a dataset of 1,805 subjects
previously analyzed in another study. Ancestry was measured using
genetic analysis based on microarray data. Overall we find that compared
to European ancestry (44%), the Amerindian ancestries Mapuche (central
Chile, 36%) and Aymara (northern, 17%) both predict lower social status
(standardized betas = -1.77 and -0.97, p’s < .001). The amount of African
ancestry was relatively minor in this sample (3%), but tentatively was
associated with lower social status (beta = -2.15, p = .03). These
differences held controlling for age, gender, and region of residence. Our
analyses of the regional-level data (n=13) did not produce any findings.
The sample size is probably too small and coarse-grained for this analysis
to be viable.
Key Words: Mapuche, Aymara, SES, Ancestry, Admixture
Previous studies have demonstrated that average social status and
intelligence of ethnic and racial groups in the Americas are largely but not entirely
consistent with origin countries’ levels of the same (Fuerst & Kirkegaard, 2016;
Lynn, 2008, 2015). This consistency suggests genetic or otherwise stable cultural
causation (Easterly & Levine, 2012; Spolaore & Wacziarg, 2013). Admixture
analysis has been proposed by both proponents and critics of genetic causation
to clarify the matter (Jensen, 1969, 1998; Nisbett, 2009; Scarr et al., 1977;
Shockley, 1966; Templeton, 2001), and has only recently become technically
feasible.
Conventionally, one would classify subjects into discrete ethnic or racial
groups based on self-report, parent report, or interviewer judgment. However, this
crude approach misses a sizable amount of within-group genetic ancestry
variation. With genome-wide genetic data, it is possible to precisely calculate
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each subject’s ancestry proportions. For instance, African Americans (blacks)
average about 80% African ancestry, but the standard deviation is 11%, and
some individuals identify as African American despite quite low levels of African
genetic ancestry (e.g. below 20%, famously Rachel Dolezal) (Fuerst et al., 2021).
The possibility of accurate ancestry estimates allows one to use regression
analysis to see if such ancestry proportions predict outcomes of interest net of
the desired controls. The reasoning behind this modeling was set forth by
Kirkegaard et al. (2019). A number of prior studies have used this method to
examine whether genetic causation is likely for differences in intelligence, social
status, a host of diseases, and physical characteristics such as height and eye
color (Aldrich et al., 2012; Becker et al., 2011; Chacón-Duque et al., 2014, 2018;
Cheng et al., 2012; Flores et al., 2012; Fuerst et al., 2021; Kirkegaard et al., 2017,
2019; Lasker et al., 2019; Pemberton et al., 2018; Warne, 2020). Most studies
find that genetic ancestry retains validity controlling for plausible confounders
(and mediators) such as own or parental social status, and the usual age, sex,
and location.
A recent study examined differences in gallbladder cancer rate among a
largely Amerindian sample from Chile (Bermejo et al., 2017). Their dataset was
posted publicly per the data sharing requirements of the journal (PLoS Genetics).
The purpose of the present study was to examine the predictive validity of genetic
ancestry for social status in this sample.
Data
The dataset provided by Bermejo et al. (2017) contains 1,805 subjects from
different regions of Chile.
1
Their dataset includes several different estimates of
genetic ancestry, income, education, social class, age, gender, as well as region
of residence. The raw genetic data were not available for reuse unfortunately,
neither were other covariates such as principal components.
In their study, genetic ancestry proportions were estimated using data from
Illumina’s Human610-Quad beadchip and OmniExpress arrays. Both arrays
include at least 700,000 single nucleotide polymorphisms (SNPs). The further
methods are given in their study.
The control variables were provided only in ordinals: age (<24, 24-26, 27-32,
>32), social class (ABC1, C2, C3, D), education (primary/secondary school,
technical, university/postgrad), income (<350, 350-450, 450+ thousands of
1
https://www.klinikum.uni-heidelberg.de/fileadmin/inst_med_biometrie/Aktuelles/
Statisitische_Genetik/Aggregate-data_Subtypes_of_Native_American_Ancestry_
and_Death.txt
KIRKEGAARD, E.O.W. ADMIXTURE AND SOCIAL STATUS IN CHILE
619
Chilean peso). The statistical impact on our study from this inaccuracy due to
discrete data will be discussed further below.
For regional analysis, we used the 2019 (the newest) human development
index values for 13 regions from the Subnational Human Development Database
(Smits & Permanyer, 2019).
2
We additionally used cognitive data from the
national scholastic test SIMCE (Sistema de Medición de la Calidad de la
Educación, Education Quality Measurement System). The test is administered
nationally to schoolchildren in 2nd, 4th, 6th, and 8th grades (basic education) and
10th and 11th grades (2nd and 3rd years of secondary education) (Wikipedia, 2021).
The study did not have subjects from each region. The data from Arica was
merged with Tarapacá, and the data from Los Rios was merged with Los Lagos,
giving a total of 13 regions for analysis. Because such scholastic tests measure
school learning, they are somewhat contaminated with school factors as an
estimate of general intelligence. For this reason, we label them cognitive ability in
this study.
We computed an overall social status index (S factor) from the three indicator
variables social class, education, and income (Kirkegaard & Fuerst, 2017). We
used factor analysis as implemented in the mirt package for this (Chalmers et al.,
2020). Results showed that social class had a near unity loading, whereas the
other indicators were lower (loadings: social class .99, income .80, education .57).
This suggested that the analysis might be misleading (for a similar situation, see
Kirkegaard & Gerritsen, 2021). For this reason we also computed a unit weighted
factor analysis, which is simply computed from the z-scored means across
variables. This however yielded near identical results (correlation with primary
index .97, loadings: social class 1.00, income .94, education .96). We used the
standardized S factor scores from the first analysis for the remaining analyses.
Analyses
Individual level results
Descriptive statistics are shown in Table 1. As the authors note, the ancestry
sets using the combined Amerindian samples yielded underestimates of
Amerindian ancestry (sum of Aymara and Mapuche from set 3 = 53% vs. set 1-2
46-47%) and overestimated European ancestry equally. African ancestry was
trace-level at 2-3%. In terms of age, the categories were about equally
represented (< 24 years 22%, 24-26 years 25%, 27-32 years 27% and >32 years
26%). As such, the study oversampled young adults compared to children and
middle-aged and older adults. Males were overrepresented at 61%. Sample sizes
2
https://globaldatalab.org/shdi/shdi/CHL/?levels=1%2B4&interpolation=
1&extrapolation=0&nearest_real=0&years=2019
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by region were very unequal and not representative of the Chilean population, as
shown in Table 2.
Table 1. Descriptive statistics for quantitative variables; N = 1805.
Variable
Mean
Median
SD
MAD
Min
Max
Kurtosis
S
0.00
-0.04
1.00
1.21
-1.45
2.77
-0.60
Ancestry set 1
Amerindian ancestry
(HDGP reference)
0.47
0.43
0.16
0.09
0.00
1.00
2.96
European ancestry
0.51
0.55
0.16
0.09
0.00
0.98
2.69
African ancestry
0.02
0.02
0.02
0.02
0.00
0.23
15.28
Ancestry set 2
Mapuche + Aymara
ancestry
0.46
0.42
0.16
0.08
0.00
1.00
3.15
European ancestry
0.51
0.55
0.16
0.09
0.00
0.98
2.77
African ancestry
0.03
0.02
0.02
0.02
0.00
0.24
14.71
Ancestry set 3
Aymara ancestry
0.17
0.08
0.22
0.08
0.00
1.00
5.25
Mapuche ancestry
0.36
0.38
0.14
0.11
0.00
1.00
1.75
European ancestry
0.44
0.47
0.14
0.09
0.00
0.97
2.21
African ancestry
0.03
0.03
0.02
0.01
0.00
0.24
14.45
Table 2. Sample sizes by region.
Region
Count
Percent
Population %
Arica
794
43.99
1.33
Metropolitana
312
17.29
40.62
Biobio
158
8.75
11.74
Valparaiso
97
5.37
10.14
Antofagasta
85
4.71
3.46
Araucania
71
3.93
5.50
Tarapaca
69
3.82
1.87
Maule
67
3.71
5.79
OHiggins
35
1.94
5.10
Lagos
30
1.66
4.67
Atacama
25
1.39
1.74
Coquimbo
25
1.39
4.28
Rios
24
1.33
2.25
Aisen
8
0.44
0.60
Magallanes
5
0.28
0.91
KIRKEGAARD, E.O.W. ADMIXTURE AND SOCIAL STATUS IN CHILE
621
The Arica region was heavily oversampled compared to the population that
lives in that area. Figure 1 shows the relationships between the quantitative
variables, using ancestry set 3 as recommended by the originators of the dataset.
Figure 1. Pairwise variable relationships and density distributions. Ancestry data
from set 3. * p < .05, ** p < .01, *** p < .005.
It can be seen that some pairs of ancestry variables were positively
correlated, indicating that naive interpretation of their relations to social status
would be unwise (European x African, r = .18; European x Mapuche, r = .18). The
reason for this is presumably African ancestry is mainly present in the areas
where Mapuche live and Europeans settled, while the Aymara are relatively
isolated. Positive associations between ancestries have been observed in prior
studies of Latin American data, especially regional averages (e.g. Kirkegaard &
Fuerst, 2016). In terms of the correlations with social status, European showed a
slight positive relationship (.13), and Mapuche a slight negative relationship (-
.12). The other variables were near zero (p > .05). In terms of regression slopes
run individually, there were: Mapuche = -0.86 (p <.001), Aymara = -0.06 (p = .599),
European = 0.88 (p <.001), and African = 1.90 (p = .053). Unstandardized
regression slopes are of primary interest because they are an estimate of the
effect of going from 0% to 100% ancestry. Correlations are not informative about
the size of the effect because the standard deviations of the ancestries vary
enormously (refer to Table 1). For more on this approach, see Connor & Fuerst
MANKIND QUARTERLY 2022 62:4
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(2021) and Kirkegaard et al. (2019). As noted before, these are of little interest as
they are confounded by each other as well as by other variables. Table 3 shows
the regression model results. As usual, we leave out European which acts as the
baseline.
Table 3. Regression models predicting social status. * p < .01, ** p < .005, *** p <
.001. Unstandardized betas, standard errors in parentheses.
Predictor/Model
Simple
Basic
Regions
Intercept
0.97 (0.135)***
0.48 (0.131)***
0.23 (0.350)
Aymara ancestry
-1.06 (0.166)***
-0.79 (0.153)***
-0.97 (0.162)***
Mapuche ancestry
-2.11 (0.261)***
-1.87 (0.241)***
-1.77 (0.253)***
African ancestry
-1.09 (1.040)
-1.03 (0.956)
-2.15 (1.003)
age < 24
(ref)
(ref)
age = 24-26
0.03 (0.063)
0.05 (0.063)
age = 27-32
0.68 (0.062)***
0.70 (0.062)***
age > 32
0.93 (0.062)***
0.95 (0.062)***
gender = female
(ref)
(ref)
gender = male
-0.13 (0.046)**
-0.08 (0.047)
Regional controls
no
no
yes
R2 adj.
0.036
0.197
0.204
N
1805
1805
1805
The regression analysis results differ strongly from the correlations and
singular regressions. Both Amerindian ancestries are now strongly negative
(Aymara = -0.97, Mapuche = -1.77, both p’s < .001). African ancestry is now also
negative -2.15, with a nominal but not impressive confidence (p = .03). This means
that as non-European ancestry increases, social status decreases, no matter
which non-European ancestry. The addition of the basic controls (age, gender)
greatly improved the model fit (adjusted R2 .036 to .197) while the addition of
regions did not much (adj. R2 .197 to .204). The betas here contrast with those
from the singular regression results for Aymara and African, which were both near
zero before, but are both strongly negative now, thus highlighting the dangers of
relying on singular regression (and correlation) when there are more than two
ancestries.
For robustness analysis, we also estimated the models using the other
ancestry sets. As the originators of the data found, these estimates produced
weaker results, both in terms of model fit (both .191 vs. .204 of primary set final
3), and by producing weaker slopes for Amerindian ancestry (slopes -0.68, -0.69,
KIRKEGAARD, E.O.W. ADMIXTURE AND SOCIAL STATUS IN CHILE
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respectively for set 1 and 2, both p’s < .001). This finding is presumably due to
the ancestry estimates incorrectly assigning some Amerindian ancestry to the
European cluster, reducing the slope. Still, it is odd that the slope reduction is so
large. The Amerindian slopes from the primary model average to -1.37, so one
would expect a combined ancestry component would be close to that. However,
it is instead about -0.68. To investigate further, we summed the Aymara and
Mapuche ancestry from ancestry set 3, to produce a joint Amerindian cluster. The
slope in the final model for this was -0.92 (p < .001), illustrating the same apparent
downward bias from adding closely related ancestries. Full regression results can
be found in the R notebook output.
Regional level results
Having concluded the analysis of the individual level data, we move to the
regional data. The results are necessarily quite limited due to the fact there are
only 13 regions in the merged dataset. The ancestries differ markedly by region,
as shown in Figure 2. The correlations among the regional variables are shown
in Table 4.
Figure 2. Regional patterns of ancestry and gallbladder cancer risk. Reproduced
from Bermejo et al. (2017).
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Table 4. Correlations between regional variables. * p < .01, ** p < .005, *** p <
.001. SIMCE = cognitive ability from the scholastic tests, HDI = human
development index. The remaining variables are averages from within each
region in the present dataset.
European
ancestry
Aymara
ancestry
Mapuche
ancestry
African
ancestry
Social
status
SIMCE
Aymara
ancestry
-0.42
1
Mapuche
ancestry
-0.17
-0.82***
1
African
ancestry
-0.03
0.77**
-0.85***
1
Social
status
0.25
0.09
-0.26
0.23
1
SIMCE
0.38
-0.16
-0.07
0.02
0.63
1
HDI
0.16
0.59
-0.75**
0.64
0.59
0.60
Social status, whether measured as human development index or by
averaging social status by individual in our dataset, correlated strongly with
cognitive ability (r about .60). Similarly, the two estimates of social status
correlated strongly with each other (r = .60). Regional ancestry has been
statistically linked to cognitive ability numerous times (Fuerst & Kirkegaard, 2016;
Kirkegaard & Fuerst, 2017; Lynn et al., 2018). The only ancestry that shows a
beyond chance level relationship to any of the social status or cognitive ability
measures was Mapuche ancestry which showed a -.75 correlation with HDI, but
not much with the other two variables. This probably highlights the limited power
of this analysis. Table 5 shows the regression results.
Table 5. Regression results for regional analysis. Outcome = human
development index. B coefficients are shown with standard errors in parentheses.
Predictor/Model
Model
Intercept
0.99 (0.134)***
Aymara ancestry
-0.03 (0.186)
Mapuche ancestry
-0.36 (0.221)
African ancestry
0.09 (1.646)
R2 adj.
0.413
N
13
KIRKEGAARD, E.O.W. ADMIXTURE AND SOCIAL STATUS IN CHILE
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We used the human development index as it is based on much more data
than the social status average from our sample, and thus is expected to be more
reliable. The model fit was beyond chance (chi square test, p = .014). However,
each predictor had a p value very far from significance. The standard errors are
very large, limiting the statistical power of the analysis.
Discussion
Using a large sample of mixed ancestry Chileans, we found strong
associations between genetically measured ancestry and social status. The effect
sizes were substantial. Statistically, going from 0% to 100% ancestry, the two
Amerindian ancestries (Aymara and Mapuche) predicted a lower social status by
1-2 standard deviations. These effects remained when controlling for sex, age,
and region of residence. The latter is important as it rules out effects of good
fortune by living in a particular high social status region (e.g. the capital) and
enjoying the benefits provided by others. African ancestry has been found to
predict lower social status numerous times (Kirkegaard et al., 2017). The present
sample had a very low level of African admixture (mean 3%, cf. Table 1). Only in
the final model did we observe some tentative evidence in this direction (cf. Table
3, model 3), in which African ancestry predicted a lower social status level of
about 2 standard deviations (p = .03, SE = 1.00).
These ancestry effects are larger than what would be expected based on an
association with intelligence. Richard Lynn compiled studies of intelligence in
Latin America, including Chile. Overall, the median White IQ was 96 (99 in Chile)
while the median Native American (Amerindian) was 87, and Mestizo (mixed
White and Amerindian) was 94 (no data for Chile unfortunately) (Lynn 2008, p.
180). Thus, we can assume the IQ gap in Chile is about 12 IQ points (99-87)
between Whites and relatively pure Amerindians. If the social status gap is a
cause of this intelligence gap, and the social status-intelligence correlation is
about .60, then the social status gap should be about .50 (12/15 * 0.6 = .48), yet
we find the genetic model estimates this to be 1-2 standard deviations. Hence,
based on this simple meritocratic model, the social status gaps are about 2-4
times as large as they should be. This suggests a deviation from meritocracy in
this country, or extreme subnational variation that for some reason escaped our
control for region of residence.
Alternatively, one might posit that discrimination based on racial
characteristics explains the ancestry associations (Telles, 2014). The existing
studies based on data from the United States are in conflict with this model
(Fuerst et al., 2021; Lasker et al., 2019). They found that visual traits do not
predict social status or intelligence net of control for genetic ancestry. This is the
opposite of what the discrimination hypothesis predicts. Furthermore, sibling
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control studies have not in general found support for discrimination either
(Francis-Tan, 2016; Hu et al., 2019; Mill & Stein, 2016).
Limitations
The main limitations of this study are as follows. First, we did not have a
measure of intelligence, so we could only examine social status. This is
unfortunate but since we relied on archival data, this was not something we could
change. There exist some high quality datasets from Brazil with intelligence data
from the South Brazilian city of Pelotas (Hallal et al., 2018; Santos et al., 2014,
2014; Victora & Barros, 2006). These have so far not been used to study
race/ancestry and intelligence. It is desirable that these datasets are examined
as soon as possible.
Second, the control variables suffered from being discrete rather than
continuous. This reduces their predictive validity and thus our models have some
bias from residual effects of age and sex. Comparing the results in Table 3 across
models that include or do not include age, sex, and regional controls, it appears
the confounding is relatively minor and within sampling error.
Third, because the genomic data was not public, we were unable to calculate
polygenic scores for intelligence, skin color and other relevant traits to see if these
mediated or confounded the observations. Explanations based on skin color
discrimination are popular for the various ethnic gaps seen in Latin America, as
they are for those in North America (Telles, 2014) but could not be assessed here.
Fourth, the regional analysis was severely underpowered due to the low
number of units (n = 13, out of 15). However, it tends to support earlier results
obtained by Verdugo et al. (2020) from a set of 40 communes, showing a negative
correlation between percent Mapuche ancestry and the Human Development
Index. It is desirable to conduct further studies based on data from provinces (n
= 56) or communes (n = 346). It may be impossible at the present time to obtain
genetic ancestry data for these, but self-rated or interviewer accessed ethnicity
may be a sufficient proxy to begin with. This could also throw light on the
meritocracy issue discussed earlier.
Acknowledgements and supplementary materials
We wish to thank Bermejo et al. (2017) for their public dataset which enabled
this study. Supplementary materials including data, R code and output can be
found at https://osf.io/dn4rw/. The R notebook can also be found more
conveniently at https://rpubs.com/EmilOWK/chile_admixture.
KIRKEGAARD, E.O.W. ADMIXTURE AND SOCIAL STATUS IN CHILE
627
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Appendix
Data table of regions
Region
N
Euro
Aymara
Mapuche
African
Social
status
SIMCE
HDI
Note
Aisén
8
0.41
0.05
0.52
0.02
-0.36
16.41
0.81
Antofagasta
85
0.43
0.22
0.31
0.04
-0.17
20.93
0.88
Araucanía
71
0.41
0.05
0.53
0.02
-0.18
11.24
0.79
Atacama
25
0.45
0.15
0.35
0.04
-0.16
12.02
0.86
Biobío
158
0.48
0.04
0.46
0.02
-0.26
24.67
0.83
Coquimbo
25
0.43
0.13
0.40
0.04
0.05
19.48
0.83
Los Lagos
54
0.41
0.04
0.53
0.02
-0.32
13.58
0.80
includes
Rios
Magallanes
5
0.46
0.05
0.47
0.02
0.58
34.44
0.86
Maule
67
0.51
0.04
0.43
0.02
-0.26
14.74
0.79
O Higgins
35
0.52
0.05
0.40
0.03
0.02
12.25
0.82
Tarapacá
863
0.40
0.27
0.29
0.04
0.08
16.21
0.88
includes
Arica
Valparaíso
97
0.51
0.07
0.38
0.03
0.11
23.49
0.87
Metropolitana
312
0.52
0.05
0.40
0.03
0.09
35.32
0.88
... Generally, they belong to the lowest socio-economic stratum and perform non-qualified jobs such as construction, basic maintenance, or domestic work (Auyero, 1999;Weeda, 2010). Cognitive and socio-economic indicators have exposed this situation showing a positive correlation between socio-economic status and European ancestry in Argentina and recently also in Chile (Fuerst & Kirkegaard, 2016;Kirkegaard, 2022). ...
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Design Type(s) longitudinal study design • data integration objective Measurement Type(s) Socioeconomic Factors Technology Type(s) digital curation Factor Type(s) geographic location • temporal_interval Sample Characteristic(s) Afghanistan • Angola • Albania • Argentina • Armenia • Australia • Austria • Azerbaijan • Burundi • Belgium • Benin • Burkina Faso • Bangladesh • Bulgaria • Bosnia and Herzegovina • Belarus • Belize • Bolivia • Brazil • Barbados • Bhutan • Botswana • Central African Republic • Canada • Switzerland • Chile • China • Cote d'Ivoire • Cameroon • Democratic Republic of the Congo • Republic of Congo • Colombia • Comoros • Cape Verde • Costa Rica • Cuba • Czech Republic • Germany • Djibouti • Kingdom of Denmark • Dominican Republic • Algeria • Ecuador • Egypt • Eritrea • Kingdom of Spain • Estonia • Ethiopia • Finland • Fiji • French Republic • Gabon • United Kingdom • Georgia • Ghana • Guinea • Gambia • Guinea-Bissau • Equatorial Guinea • Greece • Guatemala • Guyana • Honduras • Croatia • Haiti • Hungary • Indonesia • India • Republic of Ireland • Iran • Iraq • Italy • Jamaica • Jordan • Japan • Kazakhstan • Kenya • Kyrgyzstan • Cambodia • South Korea • Kuwait • Laos • Lebanon • Liberia • Libya • Lesotho • Lithuania • Latvia • Morocco • Moldova • Madagascar • Maldives Archipelago • Mexico • Macedonia • Mali • Malta • Myanmar • Montenegro • Mongolia • Mozambique • Mauritania • Mauritius • Malawi • Malaysia • Namibia • Niger • Nigeria • Nicaragua • The Netherlands • Kingdom of Norway • Nepal • New Zealand • Pakistan • Panama • Peru • The Philippines • Poland • Portuguese Republic • Paraguay • Palestinian Territories • Romania • Russia • Rwanda • Saudi Arabia • Sudan • Senegal • Sierra Leone • El Salvador • Somalia • Serbia • South Sudan • Sao Tome and Principe • Suriname • Slovak Republic • Slovenia • Sweden • Swaziland • Syria • Chad • Togo • Thailand • Tajikistan • Turkmenistan • Timor-Leste • Trinidad and Tobago • Tunisia • Turkey • Tanzania • Uganda • Ukraine • Uruguay • United States of America • Uzbekistan • Venezuela • Viet Nam • Vanuatu • Kosovo • Yemen • Republic of South Africa • Zambia • Zimbabwe • anthropogenic environment Machine-accessible metadata file describing the reported data (ISA-Tab format)
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