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Local agricultural transition, crisis and migration in the Southern Andes

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Abstract

The transition to agriculture was a transformative process in human history with wide-ranging demographic and social consequences1. Across South America, agriculture was adopted at different times and through diverse pathways, resulting in a mosaic of regionally distinct farming histories2,3. The Uspallata Valley, at the southern frontier of Andean farming, offers a unique opportunity to examine a case of late adoption of agriculture. Here we show that agriculture in the Uspallata Valley was adopted by local hunter-gatherers, as evidenced by genetic continuity between pre-farming and farming populations inferred from 46 newly sequenced ancient human genomes. These groups carried a distinct genetic component in Indigenous American diversity, indicating a unique population history in the region. Palaeodietary isotopes (δ13C/δ15N) reveal fluctuating maize intake consistent with flexible farming. Strontium isotopes (87Sr/86Sr) indicate the arrival of migrants from nearby regions between around 810–700 cal years BP, shortly before the Inka expansion. Genomic and isotopic analyses show that these migrants belonged to the same regional metapopulation as local groups, relied heavily on maize, probably moved in matrilineally organized family groups, exhibited stress markers (including malnutrition and diseases, such as tuberculosis, as confirmed by pathogen genomics) and experienced a long-term demographic decline. Our results suggest that these groups used social organization and migration as resilience strategies in the face of a multidimensional crisis.

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Fig. 1: Study area and temporal framework.
Fig. 2: A distinct genetic ancestry in the Southern Andes.
Fig. 3: Local agricultural transition and intensification of maize farming.
Fig. 4: Situating migration in space and time.
Fig. 5: Demography, social organization and diseases in migrant farmers.

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Data availability

The ancient human genomic data generated in this study are deposited in the Owey repository (https://doi.org/10.48802/owey.bdxssypL) under controlled access. We selected this repository to support long-term, community-responsive data governance: in recent years, Indigenous communities’ perspectives on the implications of genetic data and its re-use have evolved rapidly, and it is realistic to expect that expectations and access conditions may continue to evolve. Our engagement framework therefore aims to ensure that data access policies can be adapted over time, including the introduction of additional conditions or consultation requirements if requested by the communities. Owey allows us to retain direct oversight of access decisions and to update access terms without dataset migration or changes in repository governance. Access requests are managed by the data generators (N.R. and P.L.) in coordination with local partners and Indigenous communities. Requests should be submitted via the repository interface; a response is typically provided within two weeks. Approved uses are limited to non-commercial academic research and must not include redistribution of the data, upload to open-access databases, or uses that could stigmatize or harm Indigenous communities. Previously published genomic data used for comparative analyses are available from the original publications.

Code availability

R scripts generated to plot the genomic (P.L.) and isotopic (A. Tessone, M.L.C. and R.B.) analyses, as well as for Random Forest modelling of bioavailable strontium (M.L.C.) and OxCal (L.B.-V.) modelling are available at https://github.com/pierrespc/BarberenaLuisi_2025. All of the genomic data processing software used are contained in the singularity image provided by nf-core/eager/2.4.2 (https://github.com/nf-core/eager/releases/tag/2.4.2), Contamination software; Nuclear contamination, in the same singularity image, ContamLD (https://github.com/nathan-nakatsuka/ContamLD), ContamMix (https://github.com/DReichLab/ADNA-Tools), Haplocheck (https://github.com/genepi/haplocheck), Schmutzi (https://github.com/grenaud/schmutzi). Genomic analysis software: Haplogrep (https://haplogrep.i-med.ac.at/), Yleaf (https://github.com/genid/Yleaf), ADMIXTURE (https://dalexander.github.io/admixture/), ADMIXTOOLS (https://github.com/DReichLab/AdmixTools), POPSTATS (https://github.com/pontussk/popstats), BEAST and TreeAnnotator (https://beast.community/programs), TreeStat (http://tree.bio.ed.ac.uk/software/treestat/), hapROH (https://github.com/hringbauer/hapROH) and HapNe (https://github.com/PalamaraLab/HapNe).

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Acknowledgements

We deeply thank the Huarpe communities from Uspallata (Guaytamari and Llahué Xumec) for their cooperation in the development of this multivocal project. We acknowledge the former and current authorities of Cultural Heritage of Mendoza Province, Argentina (H. Chiavazza, C. Sonego), and the Director of the Museo de Ciencias Naturales y Antropológicas Juan C. Moyano (G. Campos) for the authorization to study the remains from UV. M. Vázquez and J. Avalos, from the Registro Nacional de Yacimientos Colecciones y Objetos Arqueológicos (RENYCOA) of Argentina, for their assistance in the exportation of the studied samples. We thank the aDNA facility from the Institute of Genomics, University of Tartu, and especially C. L. Scheib, H. Kabral, K. Tambets and L. Saag for their support. We would also like to thank the HPC Core Facility of Institut Pasteur (IP) for their support for computational analyses and M. Monot, L. Motreff and F. Jagorel from the IP Biomics Platform (supported by France Génomique ANR-10-INBS-09-09 and IBISA) for their assistance in sample sequencing. We are thankful to E. Willerslev, O. Caceres Rey, T. O’Connor and V. Borda for facilitating access to sequencing data for some modern individuals included in this study. We acknowledge P. Rossetti and the CIRAM Lab for their advice on radiocarbon dating and the HPC Core Facility of Institut Pasteur for their support with computational work. Research was financed by European Research Council ERC-2020-STG - PaleoMetAmerica – 948800 (N.R.), Institut Pasteur and CNRS UMR funding (N.R.), INCEPTION program (Investissement d’Avenir Grant ANR-16-CONV-0005) (N.R.), National Geographic Society grant no. NGS-92679R-22 (R.B.), Wenner-Gren Foundation (grant no. 2368532037 to R.B. and ERG-60 to P.L.), Fondation pour la Recherche Médicale Postdoctoral fellowship (E.A.N.), EU-MSCA postdoctoral fellowship PATHOGEN (E.A.N.), Consejo Nacional Nacional de Investigaciones Científicas y Técnicas (CONICET) Extraordinary Postdoctoral fellowship (P.L.), ERC-2023-SyG-Horse Power–101071707 (L.O.) and PIP-CONICET (grant no. 11220210100098CO to P.N.).

Author information

Author notes

  1. These authors contributed equally: Ramiro Barberena, Pierre Luisi

  2. Deceased: Maguelonne Roux

Authors and Affiliations

  1. Instituto Interdisciplinario de Ciencias Básicas (ICB), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Mendoza, Argentina

    Ramiro Barberena

  2. Centro de Investigación, Innovación y Creación (CIIC), Universidad Católica de Temuco, Temuco, Chile

    Ramiro Barberena, María Fernanda Quintana & Víctor Durán

  3. Facultad de Filosofía y Letras, Universidad Nacional de Cuyo (UNCUYO), Mendoza, Argentina

    Ramiro Barberena & Víctor Durán

  4. National Institute for the Humanities (NIHU), Kyoto, Japan

    Ramiro Barberena

  5. Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Microbial Paleogenomics Unit, Paris, France

    Pierre Luisi, Elizabeth A. Nelson, Maria Lopopolo, Gaétan Tressières & Nicolás Rascovan

  6. Instituto de Antropología de Córdoba (IDACOR), CONICET, Universidad Nacional de Córdoba, Córdoba, Argentina

    Pierre Luisi & Angelina García

  7. Departamento de Antropología, Facultad de Filosofía y Humanidades, Universidad Nacional de Córdoba, Córdoba, Argentina

    Pierre Luisi & Angelina García

  8. Programa de Referencia y Biobanco Genómico de la Población Argentina (PoblAr), Buenos Aires, Argentina

    Pierre Luisi

  9. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Museo de Ciencias Naturales y Antropológicas Juan C. Moyano, Mendoza, Argentina

    Paula Novellino, Daniela Guevara & Gabriela Da Peña

  10. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

    Augusto Tessone & Julia Merler Carbajo

  11. Instituto de Geocronología y Geología Isotópica (INGEIS), Universidad de Buenos Aires, Buenos Aires, Argentina

    Augusto Tessone & Julia Merler Carbajo

  12. Department of Anthropology, Southern Methodist University, Dallas, TX, USA

    Elizabeth A. Nelson

  13. Department of Geological Sciences, University of Cape Town, Cape Town, South Africa

    Petrus le Roux & Kerryn Gray

  14. Comunidad Huarpe Guaytamari, Mendoza, Argentina

    Claudia Herrera & Matías Candito

  15. Comunidad Huarpe Llahué Xumec, Mendoza, Argentina

    Graciela Coz

  16. BioArchéologie, Interactions Sociétés environnements (BioArch—UMR 7209), Muséum National d’Histoire Naturelle (MNHN), Centre National de la Recherche Scientifique (CNRS), Paris, France

    Maël Le Corre

  17. Department of Anthropology and Archaeology, University of Bristol, Bristol, UK

    Lorena Becerra-Valdivia

  18. Linacre College, University of Oxford, Oxford, UK

    Lorena Becerra-Valdivia

  19. The Globe Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Miren Iraeta Orbegozo & Hannes Schroeder

  20. Centre for Anthropobiology and Genomics of Toulouse, CNRS UMR5288, Université Paul Sabatier, Toulouse, France

    Gaétan Tressières & Ludovic Orlando

  21. Departamento de Antropología, Facultad de Ciencias Sociales y Humanidades, Universidad Católica de Temuco, Temuco, Chile

    Gustavo Lucero

  22. Instituto Multidisciplinario de Historia y Ciencias Humanas, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

    Marcelo Cardillo

  23. Facultad de Filosofía y Letras, Universidad de Buenos Aires, Buenos Aires, Argentina

    Marcelo Cardillo

  24. Investigaciones Arqueológicas y Paleontológicas del Cuaternario Pampeano (INCUAPA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tandil, Argentina

    Jorge Suby

  25. Universidad Nacional del Centro de la Provincia de Buenos Aires, Buenos Aires, Argentina

    Jorge Suby

  26. Institut Pasteur, Université de Paris Cité, CNRS UMR 2000, Human Evolutionary Genetics Unit, Paris, France

    Maguelonne Roux, Etienne Patin & Lluis Quintana-Murci

  27. Bioinformatics and Biostatistics Hub, Institut Pasteur, Université de Paris Cité, Paris, France

    Maguelonne Roux

  28. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Mendoza, Argentina

    María Eugenia de Porras

  29. Museo de Ciencias Naturales y Antropológicas Juan C. Moyano, Mendoza, Argentina

    Candela Acosta Morano

  30. Instituto de Investigaciones Arqueológicas y Museo “Prof. Mariano Gambier”, Facultad de Filosofía, Humanidades y Artes, Universidad Nacional de San Juan, San Juan, Argentina

    Claudia Mallea

  31. Theoretical Biology Unit, Department of Evolutionary Biology, University of Vienna, Vienna, Austria

    Lumila Menéndez

  32. School of Anthropology, University of Costa Rica, San José, Costa Rica

    Lumila Menéndez

  33. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), La Plata, Argentina

    María Laura López

  34. Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, La Plata, Argentina

    María Laura López

  35. Departamento de Antropología, Universidad de Chile, Santiago, Chile

    Andrés Troncoso

  36. Department of Archaeology, University of Cape Town, Cape Town, South Africa

    Julie Luyt & Judith Sealy

  37. Facultad de Ciencias Sociales, Escuela de Antropología, Pontificia Universidad Católica de Chile, Santiago, Chile

    Francisca Santana-Sagredo

  38. Collège de France, Paris, France

    Lluis Quintana-Murci

Authors
  1. Ramiro Barberena
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  2. Pierre Luisi
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  3. Paula Novellino
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  4. Augusto Tessone
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  5. Daniela Guevara
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  6. Angelina García
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  7. Elizabeth A. Nelson
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  8. Petrus le Roux
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  9. Claudia Herrera
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  10. Graciela Coz
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  11. Matías Candito
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  12. Maria Lopopolo
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  13. Maël Le Corre
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  14. Lorena Becerra-Valdivia
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  15. Miren Iraeta Orbegozo
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  16. Gaétan Tressières
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  17. Gustavo Lucero
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  18. Marcelo Cardillo
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  19. Julia Merler Carbajo
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  20. Gabriela Da Peña
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  21. Jorge Suby
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  22. Maguelonne Roux
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  23. María Eugenia de Porras
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  24. Candela Acosta Morano
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  25. Claudia Mallea
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  26. Lumila Menéndez
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  27. María Fernanda Quintana
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  28. María Laura López
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  29. Andrés Troncoso
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  30. Julie Luyt
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  31. Kerryn Gray
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  32. Francisca Santana-Sagredo
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  33. Ludovic Orlando
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  34. Víctor Durán
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  35. Judith Sealy
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  36. Etienne Patin
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  37. Lluis Quintana-Murci
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  38. Hannes Schroeder
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  39. Nicolás Rascovan
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Contributions

N.R., R.B. and P.L conceptualized the work. R.B., D.G., G.D.P., P.N., C.A.M., A. Tessone, A. Troncoso and V.D. conducted the sampling. M.L., G.T., M.I.O., E.A.N., L.O. and H.S. performed the aDNA laboratory work. P.L.R., J. Sealy, K.G., J.L. and A. Tessone performed the isotope laboratory work. P.N., D.G., G.D.P., C.A.M., J. Suby and L.M. performed the bioarchaeological analysis, whereas P.L., N.R., R.B., A. Tessone, L.B.-V., M.L.C., M. Cardillo, J.M.C., G.L., A.G., E.A.N., M.R., M.E.d.P. and E.P. performed the data analysis. R.B., P.N., D.G., G.D.P., C.A.M., M.F.Q., M.L.L., V.D., C.M. and F.S.-S. performed the archaeological work. N.R., R.B., L.O. and H.S. acquired funding. N.R., P.L. and R.B. wrote the original draft, whereas L.Q.-M., H.S., E.P., J. Sealy, A. Tessone, L.B.-V., L.M. and A.G reviewed and edited it. R.B., P.L., N.R., P.N., D.G., C.H., G.C., M. Candito and E.A.N. were responsible for community engagement.

Corresponding authors

Correspondence to Ramiro Barberena, Pierre Luisi or Nicolás Rascovan.

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The authors declare no competing interests.

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Nature thanks R. Alexander Bentley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Reference modern groups for human population analyses.

Geographical location of modern individuals with genotype data included in population genetic analyses. Groups included in Unmasked categories include individuals whose sequencing data from the whole-genome was used (data from103,107,108,109 reprocessed by us). Other categories for groups including individuals whose genotyping data was reported in24,53,54,110, and for which we used masked genotype data for genetic ancestry Indigenous to the Americas. Groups whose individuals do not self-identify as Indigenous are represented in grey shades, while groups represented in other colours were reported as Indigenous in the original publications. Dark shade delimits the study area (Fig. 1). Map data from rnaturalearthdata plotted with ggplot2138 and sf139 packages in R55.

Extended Data Fig. 2 Admixture analyses.

a. Admixture analyses performed on ancient American genomes and using the 1240 K SNP panel, filtering out individuals with less than 30000 positions with known genotypes. b. Admixture analyses including modern and ancient individuals with >30000 genotyped SNPs. In both cases, for each value of K (2–15), we ran 10 replicates and showed only the maximum-likelihood run. Cross-Validation scores are provided in parentheses.

Extended Data Fig. 3 Genetic affinity of UV ancient individuals to other Ancient American Groups.

a-b. Heatmaps summarizing Z-scores derived from f4(Mbuti, Uspallata; Ancient American Group 1, Ancient American Group 2), where Ancient American Groups 1 and 2 are represented by point type and colours on the x and y axes, respectively. Ancient groups whose individuals were sequenced through shotgun are marked with black dots. Ancient American groups are ordered by major geographical ranges and periods. Z-scores greater and lower than 0 are coloured in shades of yellow to red and light blue to dark blue, respectively. Results are shown when only including American groups from Early (EH) and Mid-Holocene (MH) in the comparisons (b) or also including Late Holocene (LH) groups (a). f4 were computed considering all autosomal positions on the 1240 K (see Fig. S24 for results on other SNP panels). c-d. Z-scores derived from f4(Mbuti, Ancient Central or North American Group; UV, Ancient South American Group) are shown by grouping the Ancient Central and North American groups along the x-axis by geographical range and period. Points are represented according to the Ancient South American Group. In one column (one violin plot), South American Groups appear N times when the grouping on the x-axis includes N Central or North American groups. Dashed lines for |Z| = 3 are used as thresholds for significance. Results are shown when only including South American groups from Early (EH) and Mid- Holocene (MH) in the comparisons (d) or also including Late Holocene groups (LH) (c). f4 were computed considering all autosomal positions on the 1240 K (see Fig. S25 for results on other SNP panels).

Extended Data Fig. 4 Genetic differentiation within UV and with major ancient American groups and phylogenetic trees from mitogenomes.

a. Results for Analysis of MOlecular VAriance. Black dots represent the index of differentiation, ϕ, within and among major American groups (categories above the plot) containing at least two population groups (each column of coloured data points) with at least 2 individuals (3 in the case of Central Andes). Standard deviations were obtained from permutations. Error bars show ±3 standard deviations estimated from permutations for each ϕ value. The yellow rectangle shows the range encompassing ϕ among Uspallata and Calingasta Valleys (UV&CV) groups ± 3 standard deviations. Within UV&CV, individuals were grouped as Migrant Farmers (yellow circle; n = 16), Local Farmers (brown circle; n = 2) and Hunter-Gatherers from UV (brown diamond; n = 3) and CV (red circle; n = 2). Population groups’ data points follow the colour code shown in Main Fig. 2a, and the individuals from each group included in this analysis are given in Supplementary Table S4. This figure is associated with Table S8. b. Z-scores derived from f4(Mbuti, Ancient American Group; UV1, UV2) are shown, with pairs of UV1 and UV2 along the x-axis. Points are represented according to the Ancient American Group. Larger points are when the Ancient American Group is one group from UV, following the code shown in legend. Dashed lines for |Z | = 3 are used as thresholds for significance. f4 were computed considering all autosomal positions on the 1240 K (see Fig. S3 for results on other SNP panels). This figure, associated with Table S8 and Supplementary Data S6e, shows that UV individuals can be assigned to one unique group to study the potential origin of the genetic ancestry component specific to UV, and its links with ancient American groups. c. The Maximum parsimony trees for 38 mitogenomes newly sequenced, along with already published mitogenomes from South American individuals. Filled colour corresponds to a geographical region, and border type to a time period. Mutations are shown on the branches; they are transitions unless the base change is explicitly indicated. The prefix @ indicates the reversion of a mutation occurring earlier in the phylogeny. Recurrent mutations in the phylogeny are underlined. Sub-haplogroups defined for the first time in this study (C1b37 and B2b16a) are highlighted with a red fill. Meta-data concerning the mitogenomes analysed are provided in Table S6. d. Consensus bayesian phylogenetic tree from mitogenomes in UV and BB6. Only unrelated up to the 2nd-degree individuals from the present study were considered. Light blue circles size represent branches’ posterior probabilities. The branch lengths are provided in years. Leaves represent mitogenomes from individuals encoded according to the group assigned. LH-HG: Hunter-Gatherer; LH-MF: Migrant Farmer; LH-Mout: Migrant Farmer outlier, LH-LF: Local Farmer; LH-LFplc: Local Farmer from Potrero Las Colonias.

Extended Data Fig. 5 qpWave with duplet left populations being UV and another ancient South American group.

The lower panel summarizes the results, with each line for each ancient South American group included as left population along with UV. Each column represents a qpWave comparison according to the SNP set (shown at the bottom) and the right populations (groups included as right populations are highlighted with a skyblue rectangle in the upper panel). Comparisons that were not performed are shown with grey cells, otherwise the cell colours indicate the one-sided significance for rejection of Rank 0, without correction for multiple testing (white: p-value > 0.05; yellow: 0.01 <p-value < 0.05; dark yellow: 0.005 <p-value < 0.01; orange: 0.005 <p-value < 0.001; and red: p-value < 0.001). This figure is associated with Supplementary Data S7.

Extended Data Fig. 6 Distribution of the main genetic ancestry components observed in Southern Cone modern groups.

Geographic distribution of the six main ancestry genetic components identified in the Southern Cone (shown in Extended Data Fig. 2b) in modern groups across the Americas: a. blue/South Patagonia component; b. green/Brazil component; c. dark brown/Main Central Andes component; d. cardinal red/secondary Central Andes component; e. light brown/Central Chile component, and f. yellow/Uspallata component. For each panel, the right and left plots show the mean proportion ancestry estimates for each South American Indigenous group and Southern Cone non-Indigenous group, respectively, as a pie-chart located at the coordinates reported for that group. Map data from rnaturalearthdata plotted with ggplot2138 and sf139 packages in R55.

Extended Data Fig. 7 Strontium analyses for assessing mobility.

a-b. Bioavailable 87Sr/86Sr isoscape for the southern Andes of Argentina and Chile (a) with its associated spatial error (b). (7a and b were produced in R55, see Methods). c. Variable importance plot depicting the influence of the different covariates on the predicted bioavailable 87Sr/86Sr in the random forest regression. d. Partial dependence plots depicting the relationship between the predictors retained for the final random forest regression model and the predicted bioavailable 87Sr/86Sr. Hash marks on the x-axes correspond to the deciles of the predictor distribution. Description of the predictors is provided in Table S3. e. Bayesian geographical assignment of most likely provenience of individual AR0510 (PLC) based on the random forest strontium isoscape for the southern Andes (updated from125).

Extended Data Fig. 8 Radiocarbon chronology and Bayesian modelling.

Bars underneath each distribution denote 95.4% Confidence Interval (CI). The start of Inka occupation in the region38 is included at the bottom in red. The start and end estimates are 700-670 cal BP (median 685 cal BP) and 625-585 cal BP (median 610 cal BP), respectively, with a likely duration of between 55 to 100 (median 75 years). a. Bayesian model for the migrant phase at Potrero Las Colonias. The analyses were performed including kinship data (DOD values) as prior information. The radiocarbon dates are in purple following colouring in Fig. 4b. b. Bayesian model for the migrant phase at Túmulo III. The radiocarbon dates are in green following colouring in Fig. 4b. c. Consistency of the three Bayesian models. Probability density functions for the difference (‘D’) between the start and end boundaries for the three models created (Fig. 4b). Results show that apart from the start of the migrant phase at UV and the start of the Inka occupation, there is no significant difference between the modelled outputs as the distributions include zero at 95.4% CI.

Extended Data Fig. 9 Effective Population Size.

a. Conditional Heterozygosity (CH) for migrant and local farmers and a set of ancient American groups. Bars indicate ±2 standard errors estimated through block jackknife across the genome. Results are ordered by region according to CH means across groups within each region, and then by CH within each group. Analysis performed on the SG SNP panel and including only ancient individuals with shotgun sequencing data. b. Effective population size (Ne) estimated by maximum likelihood from the number and length distributions of runs of homozygosity under the hapROH model. Bars indicate ±2 likelihood-based standard errors. Ne estimates were obtained for local and migrant farmers, pre-farming hunter-gatherers, and a set of ancient American groups, ordered by region according to Ne mean across groups within region and then by Ne within group. The individuals from each group included in these analyses are given in Supplementary Table S4.

Extended Data Fig. 10 Paleoclimate reconstruction.

Macro-regional palaeocological archives in the southern Andes for the last 2400 years, including the following records: Laguna El Calvario, Laguna Cerritos Blancos, Laguna Chepical, and a proxy of ENSO activity in a sediment core located offshore from Peru134,135,136,137. The orange horizontal bar highlights the period of migrations and the grey bar a broader 400 year period.

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Translation of the paper to Spanish. This article was peer-reviewed in the English language. The translation to Spanish provided as Supplementary Note 1 was not peer-reviewed or checked for correctness by Springer Nature.

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Barberena, R., Luisi, P., Novellino, P. et al. Local agricultural transition, crisis and migration in the Southern Andes. Nature (2026). https://doi.org/10.1038/s41586-026-10233-z

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