# Local agricultural transition, crisis and migration in the Southern Andes

Ramiro Barberena, Pierre Luisi, Paula Novellino, Augusto Tessone, Daniela Guevara, Angelina García, Elizabeth Nelson, Petrus Le Roux, Claudia Herrera, Graciela Coz, et al.

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## To cite this version:

Ramiro Barberena, Pierre Luisi, Paula Novellino, Augusto Tessone, Daniela Guevara, et al. Local agricultural transition, crisis and migration in the Southern Andes. 2025. pasteur-05263947

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# Local agricultural transition, crisis and migration in the Southern Andes

## Nicolas Rascovan

nicolas.rascovan@pasteur.fr

Institut Pasteur https://orcid.org/0000-0002-2129-5878

## Ramiro Barberena

Universidad Catolica de Temuco https://orcid.org/0000-0002-4323-1197

## Pierre Luisi

Instituto de Antropología de Córdoba (IDACOR), CONICET, Universidad Nacional de Córdoba,

## Paula Novellino

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

## Augusto Tessone

Instituto de Geocronología y Geología Isotópica (INGEIS), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)

## Daniela Guevara

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

## Angelina García

Instituto de Antropología de Córdoba (IDACOR), CONICET, Universidad Nacional de Córdoba

## Elizabeth Nelson

Institute Pasteur

## Petrus le Roux

University of Cape Town https://orcid.org/0000-0002-5930-4995

## Claudia Herrera

Comunidad Huarpe Guaytamari

## Graciela Coz

Comunidad Huarpe Llahué Xumec

## Matías Candito

Comunidad Huarpe Guaytamari

## Maria Lopopolo

Institut Pasteur, Université de Paris Cité, CNRS UMR 2000, Microbial Paleogenomics Unit

## Maël Le Corre
BioArchéologie, Interactions Sociétés environnements (BioArch - UMR 7209), Muséum National d'Histoire Naturelle (MNHN), Centre National de la Recherche Scientifique

**Lorena Becerra-Valdivia**

Linacre College, University of Oxford

**Miren Iraeta-Orbegozo**

University of Copenhagen https://orcid.org/0000-0002-6965-6353

**Gaetan Tressieres**

Laboratoire d'Anthropologie Moléculaire et d'Imagerie de Synthèse, CNRS UMR 5288, Université de Toulouse, Université Paul Sabatier https://orcid.org/0000-0001-5827-5398

**Gustavo Lucero**

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

**Marcelo Cardillo**

Instituto Multidisciplinario de Historia y Ciencias Humanas, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)

**Julia Merler Carbajo**

Instituto de Geocronología y Geología Isotópica (INGEIS), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)

**Gabriela Da Peña**

Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Museo de Ciencias Naturales y Antropológicas Juan C. Moyano https://orcid.org/0000-0003-3410-3027

**Jorge Suby**

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

**Maguelonne Roux**

Institut Pasteur

**Maria Eugenia de Porras**

Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales

**Candela Acosta**

Museo de Ciencias Naturales y Antropológicas Juan C. Moyano

**Claudia Mallea**

Instituto de Investigaciones Arqueológicas y Museo "Prof. Mariano Gambier", Facultad de Filosofía, Humanidades y Artes, Universidad Nacional de San Juan

**Lumila Menéndez**

Theoretical Biology Unit, Department of Evolutionary Biology, University of Vienna

**María Quintana**

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

**María López**
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), UNLP

**Andrés Troncoso**

Departamento de Antropología, Universidad de Chile

**Julie Luyt**

Department of Archaeology, University of Cape Town https://orcid.org/0000-0001-8331-6147

**Kerryn Gray**

Department of Geological Sciences, University of Cape Town

**Francisca Santana Sagredo**

Pontificia Universidad Católica de Chile https://orcid.org/0000-0003-2593-5737

**Ludovic Orlando**

Centre d'Anthropobiologie et de Génomique de Toulouse https://orcid.org/0000-0003-3936-1850

**Victor Duran**

Universidad Nacional de Cuyo

**Judith Sealy**

University of Cape Town https://orcid.org/0000-0001-5071-8211

**Etienne Patin**

Université Paris Cité https://orcid.org/0000-0002-9911-4459

**Lluís Quintana-Murci**

Institut Pasteur, Université Paris Cité, CNRS UMR2000, Human Evolutionary Genetics Unit

**Hannes Schroeder**

Copenhagen https://orcid.org/0000-0002-6743-0270

**Social Sciences - Article**

**Keywords:** Isotopes, Archaeology, Paleogenomics, Human Genetics, South America, Mobility, Diet, Tuberculosis, Social structure

**Posted Date:** May 5th, 2025

**DOI:** https://doi.org/10.21203/rs.3.rs-6450570/v1

**License:** © Ⓟ This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License

**Additional Declarations:** There is **NO** Competing Interest.
# Local agricultural transition, crisis and migration in the Southern Andes

## Authors

Ramiro Barberena<sup>1,2,3,4,40-</sup>; Pierre Luisi<sup>5,6,7,8,40-</sup>; Paula Novellino<sup>9</sup>; Augusto Tessone<sup>10,11</sup>; Daniela Guevara<sup>9</sup>; Angelina García<sup>6,7</sup>; Elizabeth A. Nelson<sup>5,12</sup>; Petrus le Roux<sup>13</sup>; Claudia Herrera<sup>14</sup>; Graciela Coz<sup>15</sup>; Matías Candito<sup>14</sup>; Maria Lopopolo<sup>5</sup>; Maël Le Corre<sup>16</sup>; Lorena Becerra-Valdivia<sup>17,18</sup>; Miren Iraeta Orbegozo<sup>19</sup>; Gaétan Tressières<sup>5,20</sup>; Gustavo Lucero<sup>21</sup>; Marcelo Cardillo<sup>22,23</sup>; Julia Merler Carbajo<sup>10,11</sup>; Gabriela Da Peña<sup>9</sup>; Jorge Suby<sup>24,25</sup>; Maguelonne Roux<sup>26,27,28</sup>; María Eugenia de Porras<sup>29</sup>; Candela Acosta Morano<sup>30</sup>; Claudia Mallea<sup>31</sup>; Lumila Menéndez<sup>32,33</sup>; María Fernanda Quintana<sup>2</sup>; María Laura López<sup>34,35</sup>; Andrés Troncoso<sup>36</sup>; Julie Luyt<sup>37</sup>; Kerryn Gray<sup>13</sup>; Francisca Santana-Sagredo<sup>38</sup>; Ludovic Orlando<sup>20</sup>; Víctor Durán<sup>2,3</sup>; Judith Sealy<sup>37</sup>; Etienne Patin<sup>26</sup>; Lluis Quintana-Murci<sup>26,39</sup>; Hannes Schroeder<sup>19</sup>; Nicolás Rascovan<sup>5,4</sup>

<footnote n="1"><sup>1</sup>Centro de Investigación, Innovación y Creación (CIIC-UCT), Facultad de Ciencias Sociales y Humanidades, Universidad Católica de Temuco, Chile</footnote>

<footnote n="2"><sup>2</sup>Instituto Interdisciplinario de Ciencias Básicas (ICB), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina</footnote>

<footnote n="3"><sup>3</sup>Facultad de Filosofía y Letras, Universidad Nacional de Cuyo (UNCUYO), Mendoza, Argentina</footnote>

<footnote n="4"><sup>4</sup>National Institute for the Humanities (NIHU), Kyoto, Japan</footnote>

<footnote n="5"><sup>5</sup>Institut Pasteur, Université de Paris Cité, CNRS UMR 2000, Microbial Paleogenomics Unit, F-75015 Paris, France</footnote>

<footnote n="6"><sup>6</sup>Instituto de Antropología de Córdoba (IDACOR), CONICET, Universidad Nacional de Córdoba, Córdoba 5000, Argentina</footnote>

<footnote n="7"><sup>7</sup>Departamento de Antropología, Facultad de Filosofía y Humanidades, Universidad Nacional de Córdoba, Córdoba 5000, Argentina</footnote>

<footnote n="8"><sup>8</sup>Programa de Referencia y Biobanco Genómico de la Población Argentina (PoblAr), Buenos Aires C1425FQD, Argentina</footnote>

<footnote n="9"><sup>9</sup>Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Museo de Ciencias Naturales y Antropológicas Juan C. Moyano. Mendoza, Argentina</footnote>

<footnote n="10"><sup>10</sup>Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina</footnote>

<footnote n="11"><sup>11</sup>Instituto de Geocronología y Geología Isotópica (INGEIS), Universidad de Buenos Aires, Argentina</footnote>

<footnote n="12"><sup>12</sup>Department of Anthropology, Southern Methodist University, Dallas, TX, USA</footnote>

<footnote n="13"><sup>13</sup>Department of Geological Sciences, University of Cape Town, Cape Town, South Africa</footnote>

<footnote n="14"><sup>14</sup>Comunidad Huarpe Guaytamari</footnote>

<footnote n="15"><sup>15</sup>Comunidad Huarpe Llahué Xumec</footnote>

<footnote n="16"><sup>16</sup>BioArchéologie, Interactions Sociétés environnements (BioArch - UMR 7209), Muséum National d'Histoire Naturelle (MNHN), Centre National de la Recherche Scientifique (CNRS), 75005 Paris, France.</footnote>

<footnote n="17"><sup>17</sup>Department of Anthropology and Archaeology, University of Bristol, Bristol, UK</footnote>

<footnote n="18"><sup>18</sup>Linacre College, University of Oxford, Oxford, UK</footnote>
¹⁹The Globe Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

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

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

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

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

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

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

²⁶Institut Pasteur, Université de Paris Cité, CNRS UMR 2000, Human Evolutionary Genetics Unit, F-75015 Paris, France

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

²⁸Deceased

²⁹Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Mendoza, Argentina

³⁰Museo de Ciencias Naturales y Antropológicas Juan C. Moyano. Mendoza, Argentina

³¹Instituto de Investigaciones Arqueológicas y Museo "Prof. Mariano Gambier", Facultad de Filosofía, Humanidades y Artes, Universidad Nacional de San Juan, San Juan, Argentina

³²Theoretical Biology Unit, Department of Evolutionary Biology, University of Vienna, Vienna, Austria

³³School of Anthropology, University of Costa Rica, Costa Rica

³⁴Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), La Plata, Argentina

³⁵Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, Argentina

³⁶Departamento de Antropología, Universidad de Chile, Santiago, Chile

³⁷Department of Archaeology, University of Cape Town, Cape Town, South Africa

³⁸Facultad de Ciencias Sociales, Escuela de Antropología, Pontificia Universidad Católica de Chile, Santiago, Chile

³⁹Collège de France, Paris, France

⁴⁰These authors contributed equally to this work

✉e-mail: rbarberena@mendoza-conicet.gob.ar; pierre.luisi@unc.edu.ar; nicolas.rascovan@pasteur.fr

## Keywords

Isotopes; Archaeology; Paleogenomics; Human Genetics; South America; Mobility; Diet; Tuberculosis; Social structure;

## Summary

The transition to agriculture was a transformative process in human history with demographic and social consequences that varied widely. However, its dynamics in South America remain understudied. The Uspallata Valley (UV), located within the southern frontier of Andean agriculture, provides a singular setting to explore how farming societies emerged and evolved. Here, we show that agriculture in UV was initially 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 within
the American diversity, suggesting a unique population history. Following agricultural intensification, we identify the arrival of migrants from nearby regions into UV between ~810–700 cal years BP, shortly predating the Inka arrival. Genomic, isotopic, and kinship analyses indicate that these migrants had intensive maize diets and moved in family groups, likely organized around matrilineal ties. They show multiple signs of stress, including nutritional deficiencies and tuberculosis, confirmed by bacterial genome sequencing, and experienced a long-term demographic decline. We suggest that social organization and mobility were forms of resilience in response to intersecting stressors—subsistence dependency and specialization, environmental instability, and disease—within a broader socio-ecological crisis.

## Main text

The transition to agriculture was one of the most transformative processes in human history, resulting in profound changes in mobility, demography, economic systems, health, and social organization¹⁻³. This transition occurred independently in multiple regions, each following a unique trajectory, shaped by local environments and cultural contexts. However, overarching patterns emerge, making comparative studies essential for understanding the shared and distinct mechanisms underlying the adoption and development of agriculture. Archaeogenetic research has greatly advanced our understanding of these processes, particularly in Eurasia, where studies have shown that the Neolithic transition was largely driven by the migration of Anatolian farmers, followed by moderate admixture with local hunter-gatherers⁴. These processes may have differed across continents and in other less-studied regions, such as South America, a major center of plant domestication⁵,⁶.

The Americas (often referred to as Abya Yala by Indigenous communities as part of ongoing efforts to decolonize its name) had four geographic centers of domestication during the Early (13,500-8,500 years Before Present [BP]) and Mid-Holocene (8,500-3,500 BP), from which farming spread in multiple directions. These innovation centers were located in the Eastern North American plains, where multiple plant species were independently domesticated⁷, southern Mesoamerica, where maize and the common bean were first domesticated during the Early Holocene⁸, the Amazonian lowlands, where domestic manioc and squash have been recorded since the Early Holocene⁶, and the Andean highlands, where quinoa, potatoes, and amaranth were domesticated⁵ during the Mid-Holocene, alongside with llama and alpaca camelids⁹, leading to agropastoral intensification and the emergence of complex political systems¹⁰.

The Uspallata Valley (UV; Figs. 1a-b), in Central Western Argentina, is located at the southern frontier of Andean farming expansion. Unlike the Central Andes, where key crops and some animals were first domesticated during the Early Holocene, populations in UV adopted agriculture only during the Late Holocene (after 3,000 cal BP, Fig. 1c), incorporating species domesticated elsewhere⁵. These societies practiced mixed subsistence strategies involving hunting, gathering, horticulture, intensive agriculture, and camelid pastoralism. UV represents an ideal setting to examine the role of cultural transmission versus migration in the spread of farming in South America, the interactions between hunter-gatherers and farmers, the demographic and ecological impacts of agricultural intensification, and the impact on health of a farming-based lifestyle. To explore these questions, we investigated the history of UV's farming populations using an integrative approach combining ancient human and pathogen genomics, bioarchaeology, isotopic and chronological data, paleoclimatic records, and archaeological contexts.

## Community engagement

The present work investigates the history of ancestral populations of the Huarpe. The ethnic denomination Huarpe derives from the combination of Hunuc Huar, meaning god of the mountains, with Pe, meaning people¹¹. For the communities Guaytamari and Llahué Xumec from UV, this is reinterpreted as people of the mountains. These communities were actively involved in the design and implementation of this study. Over the past five years, they have maintained
close collaboration with RB and his team, jointly securing financial support for multivocal research, formalized, for instance, in a temporary museum exhibit (Fig. S1). Since 2022, archaeogeneticists (PL, EAN, NR) have held regular meetings with the communities to support Indigenous claims and integrate their knowledge into the interdisciplinary research presented here. Community representatives (CH, GC, MCan) are co-authors of this article and actively contributed to shape the study's narrative (Supplementary Material Section A.). A translation of the article to Spanish with explanations for a non-specialized audience is available to facilitate the diffusion of the results to local stakeholders (Supplementary Text S1).

<figure><img src="image_3.png" /><figcaption>**Fig. 1** | Study area and temporal framework. **a.** Study-area with distribution of samples used for the bioavailable strontium isoscape (black points), and location of archaeological sites with published ancient DNA (aDNA) data (red shades), and with isotopes and newly generated aDNA data. The black frame delimits the zoomed area shown in **b.** **b.** Focus on the area encompassing the burial sites for which new data—both isotopic and genomic—is reported. **c.** Temporal framework for the study area. Each rectangle provides the time range reported<sup>12,13</sup> for individuals with published aDNA (left) and for exploitation of archaeological sites analyzed in this study (right). Maize and camelid symbols are used to represent dominant agriculture and foraging subsistence strategies, respectively. The numbers refer to the number of individuals having a given type of data from each site (aDNA: ancient human DNA; Diet: C and N isotopes; Mobility: Strontium isotopes). BR-I: Barrio Ramos I; A.Av.: Agua de las Avispas; US2: Usina Sur 2; PLC: Potrero Las Colonias; T-I, T-II and T-III: Túmulo I, II and III, respectively. The temporal placement of the first cultigens is based on directly dated macrobotanical remains from the study area<sup>14</sup>.</figcaption></figure>

## Archaeological sites and data generation

UV contains several cemetery sites that preserve a unique bioarchaeological record spanning the development of farming societies (Fig. 1; Figs. S2-8; Table S1; Supplementary Material Sections B.1. and B.2.). The archaeological sites from which both genomic and isotopic data was produced are: a) Barrancas B6 (BB6, 2280-2160 BP; Fig. S3), the oldest cemetery in the region (nearby UV) containing the remains of 33 hunter-gatherer individuals (hereafter referred to as Late Holocene Hunter-Gatherers, LH-HG)<sup>15</sup>; b) Túmulo II (T-II, 1300-1230 BP; Figs. S4-5), an early farming site in UV with evidence of significant maize consumption<sup>16</sup>; c) Potrero Las Colonias (PLC, 810-610 BP; Fig. S6), the largest cemetery in the region containing a minimum number of 124 individuals (Fig. S9), including 70 adults (56.5%) and 54 subadults (43.5%), with isotopic values indicating high maize consumption<sup>17</sup>; and d) Barrio Ramos I (BR-I, 620-540 BP; Fig. S8), an Inka-period site with remains of six individuals<sup>18</sup>. Isotopic data was also produced for Usina
Sur 2 (US2), Agua de las Avispas (A.Av.), Túmulo I (T-I), and Túmulo III (T-III). Archaeological contexts and sampling criteria are further described in Supplementary Material Sections B., while details concerning the bioarchaeological analyses are provided in Supplementary Material Section C.1.

Data generation was strategically organized in stages. Firstly, we measured strontium (paleogeography), carbon, and nitrogen (paleodiet) isotopic ratios for 121 human samples (88 individuals) from eight sites (Supplementary Material Section B.4.). To reconstruct life-history changes, we obtained paired results for 32 adults analyzing bones such as crania or mandibles, representing the last years of life, and teeth or petrous bones, revealing childhood diet and mobility¹⁹. By also generating AMS radiocarbon dates for 27 individuals, we were able to track the incidence of farming and migration over time (Table S1; Supplementary Material Sections F.1. and F.2.). We then generated genome-wide ancient DNA (aDNA) data from 46 individuals selected from four of these sites using "Shotgun" sequencing (Supplementary Material Sections E.1. and E.2.) and confirmed that the data was derived from ancient uncontaminated samples (Supplementary Data S3; Figs. S15-16; Supplementary Material Section E.3.). This genomic dataset spans a timeline of 1,700 years, ranging from hunter-gatherers (n=6) to incipient, mixed, and intensive farmers (n=40), from which we could determine the genomic sex for 18 females and 25 males (Fig. S19; Supplementary Material Section E.5.). The final dataset for population genetic analyses included 33 individuals with depth of coverage (DoC) between 0.02x and 5.58x (median 0.87x; Supplementary Data S3; 13 individuals with DoC < 0.01x were excluded), of which 23 were unrelated up to the 2nd-degree (Fig. S22; Supplementary Data S5; Supplementary Material Section E.7.). The latter were analyzed alongside publicly available genotype and whole-genome data from 332 ancient and 909 modern individuals with evolutionary links with South Native Americans²⁰ (hereafter named American individuals; Fig. 2a, Figs. S17-18, Supplementary Data S4; Supplementary Material Section E.4), using a panel containing 1.15M autosomal SNPs (i.e. 1240K panel²¹, Main text) and confirmed using a more extensive SNP panel designed for Shotgun data and containing 5.6M SNPs (SG SNP panel²²; Fig. S23; Supplementary Material Section E.8.).

## A distinct genetic ancestry in the Southern Andes

Previous genetic studies identified maternal lineages in Central and Central Western Argentina that are rare elsewhere, suggesting that these populations carry a distinct genetic signature²³. This finding was further supported by genome-wide analyses in modern populations, which revealed an Indigenous American genetic component termed "Central Western Argentina" due to its predominance in individuals from Calingasta Valley, a region that forms a biogeographical corridor with UV (Fig. 1a)²⁴. This component has not been detected outside the Southern Cone or in any ancient individuals sequenced to date, raising key questions about its origins, its relations with other populations, and how it fits into existing models of continental population history²⁵⁻²⁷.

To address these questions, we first assessed the genetic relationship of ancient individuals in UV to other ancient Americans. A multidimensional scaling analysis (MDS) of genetic distances calculated as [1 - *f*₃(*Individual1*,*Individual2*;*Mbuti*)] showed that individuals from UV form a group clearly differentiated from all other ancient Americans (Fig. 2c, Fig. S24; Supplementary Data S6a-b). Only three published genomes colocalized with UV individuals on the MDS space: two hunter-gatherers from Calingasta dated ~1,500 BP²⁸ chronologically between BB6 and UV sites analyzed in this study, and the Cerro El Toro individual, located further north of Calingasta, who died during Inka times¹³. This pattern was further supported by an ADMIXTURE²⁹ analysis, which identified a distinct genetic component shared by individuals from UV and Calingasta (Fig. S25). Both analyses, together with paternal and maternal lineage diversity (Supplementary Material Section E.6.), show that these populations are genetically distinct from other American groups, likely reflecting the long-term persistence of a deeply rooted ancestral component, combined with limited gene flow and/or high genetic drift.
By incorporating modern individuals and conducting ADMIXTURE (Figs. S26-27) and *f*₄(*Mbuti*, *Modern Group*; UV, *Ancient American Group*) analyses (Fig. S28; Supplementary Data S6c), we found that the genetic component that is maximized in UV individuals is also observed in present-day populations from Argentina. Our results confirm the high prevalence of this component in Central Western Argentina²⁴, and show that its present-day distribution extends more broadly, including regions in Central Argentina (Fig. 2d; Figs. S26-28). Consequently, we have renamed this genetic component as *Central Southern Cone* (CSC). Our analyses suggest that individuals from UV, BB6, and Calingasta formed a genetically distinct metapopulation whose unique ancestry has persisted until today, providing a more comprehensive picture of South American diversity. These findings are important for Indigenous communities, as they confirm genetic continuity between ancient and present-day populations inhabiting Central Argentina, refuting unsubstantiated claims about the regional extinction of Indigenous groups²⁸.

<figure><img src="image_4.png" /><figcaption>**Fig. 2** | A distinct genetic ancestry in the Southern Andes. **a.** Geographical location of publicly available ancient genomes included in genetic structure analyses (grey shade delimits the study-area). The reference of icons and colors used to identify samples can be consulted in Fig. S17 **b-c.** Multidimensional Scaling analysis (MDS) from pairwise genetic distances among American individuals of the form 1-*f*₃(*Individual1*, *Individual2*; *Mbuti*), showing 2nd vs 1st dimension **b.** and 3rd vs 4th dimension **c.** The percentage of variance explained by each dimension is given on the corresponding axis label. **d.** Geographic distribution of the genetic component identified here as Central Southern Cone (CSC) in modern groups. Pie charts represent the median proportions for CSC (yellow) and all other components (grey) in modern populations. The proportions were obtained from the ADMIXTURE analysis including ancient and modern groups with *K*=15 (Fig. S26). Pie-charts are located at the geographical coordinates reported in the original articles²⁴,³⁰⁻³² (some were slightly shifted for better visualization).</figcaption></figure>

## Adoption of agriculture by local hunter-gatherers

Stable isotopes (δ¹³C*,coll.*, δ¹³C*,ap.*, δ¹⁵N) indicated that individuals from the LH-HG site BB6 (2200-2100 BP) based their subsistence on hunting and gathering C₃-based wild plants and animals, with a low C₄ input (~25%, Table S4; Supplementary Material Section D.2.), which can be accounted by the natural distribution of C₄ vegetation in the lowlands³³. In the ensuing centuries until 500 BP, UV populations experienced a fluctuating input of C₄ foods, which can be largely attributed to maize (*Zea mays*), a C₄ plant with a key role in Andean farming³⁴⁻³⁷. The trends in
$\delta^{13}\text{C}_{\text{ap.}}$ values-best proxy for total human diet-peaked between 800 and 600 BP (Fig. 3a), showing the highest $C_4$ intake (~67%) at the PLC site (Table S4). At the broader spatial scale, these values are the most enriched in the region and near those from other parts of the Andes where maize formed the economic foundation of sociopolitical complexity³⁷ (Fig. 3b, Table S5).

<figure><img src="image_5.png" /><figcaption>**Fig 3** | Local agricultural transition and intensification of maize farming. a. Temporal trends in $\delta^{13}\text{C}_{\text{ap.}}$ values in UV. The continuous lines represent the fitted LOcally Estimated Scatterplot Smoothing curve (loess package in R with span=0, degree=1 parameters), while dashed lines represent the interval corresponding to ± 1.96 Standard Error. b. $\delta^{13}\text{C}_{\text{ap.}}$ and $\delta^{13}\text{C}_{\text{coll.}}$ values for sites along the Andes (Table S5). c. Z-scores derived from $f_4$-statistics showing that the hunter-gatherers (LH-HG) and farmers (LH-F) are genetically closer to each other than to other ancient American populations. Dashed lines indicate the Z-value thresholds of = -3 and Z= 3, used as significance thresholds.</figcaption></figure>

Since our timeline encompasses the emergence of farming in Central Argentina, we next evaluated whether this transition was driven by migrations or by cultural diffusion from preexisting centers of innovation. We did so by comparing the genetic profiles of pre-farming hunter-gatherers from BB6 (LH-HG) with UV farming populations (LH-F). A $f_4$ statistics analysis of the form $f_4(Mbuti, American group; LH-HG, LH-F)$ did not yield any significant result (|Z| <3), indicating that the (LH-HG) and LH-F form a single clade within American genetic diversity (Fig. 3c and Fig. S32; Supplementary Data S6g). Furthermore, $f_4(Mbuti, LH-F; American group, LH-HG)$ statistics showed that the LH-F were significantly closer to LH-HG than to any other American group, with
the exception of Calingasta, which showed a non-significant Z-score, confirming its shared
population history with UV (Fig. 3c and Fig. S32; Supplementary Data S6g). Likewise, f₄(Mbuti,
LH-HG; American group, LH-F) statistics indicated that LH-HG were significantly closer to LH-F
than to any other American group, ruling out potential genetic contributions from other sequenced
groups to the UV populations during the transition to agriculture (Fig. 3c and Fig. S32;
Supplementary Data S6g).

Our analyses thus provide strong evidence of genetic continuity between hunter-gatherers and farmers, indicating that the agricultural transition resulted from the adoption of new economic practices by local populations. Furthermore, we find no genetic evidence for substantial population movements linked to the spread of farming in the Southern Andes. While our results suggest that farming reached the Uspallata Valley through cultural transmission—evidenced by the adoption of crops domesticated in other agricultural centers—they also show that this process did not involve substantial genetic exchange with other genomically characterized populations.

Intensification of maize farming and migration

Strontium (⁸⁷Sr/⁸⁶Sr) isotopes from human remains can be used to track mobility patterns and identify non-local individuals. Previous exploratory work in UV revealed the presence of non-local intensive maize farmers, suggesting that they moved from a single region to UV shortly before death³⁸. To explore these questions further, we generated new ⁸⁷Sr/⁸⁶Sr data for 81 human samples (Supplementary Data S1), and expanded the strontium isoscape³⁸ with the analysis of 20 additional rodent samples from Argentina and Chile (Fig. 1a; Supplementary Data S2), which were used to build an updated random forest model³⁸ (Figs. S13-14; Supplementary Material Section D.1.).

To assess the incidence of immigration through time and analyze if maize intensification was associated with local or non-local groups, we defined a conservative local ⁸⁷Sr/⁸⁶Sr range for this geological region (Fig. 4a, Fig. S2). Our systematic sampling of human burials in UV and nearby regions along the last 1300 years, encompassing the development of agropastoral economies, identified a bimodal distribution of ⁸⁷Sr/⁸⁶Sr values. Indeed, all the analyzed samples from T-II (n=14) and BR-I (n=6), as well as sites in UV with no genomic data such as Túmulo I (T-I, n=14), produced signals within the local bioavailable range (Fig. 4a), whereas Túmulo III (T-III) included 11 local individuals and three non-locals. This supports a continuous local presence in the Uspallata Valley between 1,300 BP and the Inka arrival (~600 BP). In contrast, during the same period, we identified non-locals in A. Av (n=1), Usina Sur 2 (US2, n=2), and PLC. The latter is the most notable case, as it is the largest burial in the region with nearly all individuals (n=32 from 33 tested) falling outside the local isotopic range and only one individual (AR0520) displaying a local signature throughout life. These non-local signatures can reflect migration or post-mortem transport—the latter requiring some degree of body modification³⁹. Our taphonomic, contextual, and anatomical evidence substantially support that these burials were not subject to post-mortem transport and, therefore, the non-local signals reflect migration (Supplementary Materials, Section B.3).

To refine the life-histories in PLC, we classified samples into two sets: (i) an early life set representing childhood signals in adults (n=19), inferred from early-formed petrous bones and teeth, and (ii) a late life set reflecting the last years of life (n=29), informed from most other bones in adults, as well as teeth and petrous bones in subadults. While the early life set exhibits more diverse non-local isotopic values (relative to UV), indicating a broader geographic range during childhood, the late life set is highly homogeneous, suggesting that migrants spent their final years in the same constrained geological area before arriving in UV. In contrast, isotopic comparisons of early and late life tissues of five local adults (T-II and BR-I) showed consistent signals, indicating that they remained in the UV region throughout their lives.

Situating migration in space and time
To determine the most likely geographic origin of the migrants, we applied a Bayesian geographic assignment method relying on our updated isoscape for the southern Andes (Table S3; Fig. S12). Our results suggest that migrants most likely came from the lowlands of Mendoza and San Juan to the east, although the highland Calingasta Valley to the north remains as a possible source area (Fig. 4c and Fig. S14 for individual AR0514). While further regions cannot be rejected, the most likely explanation is that the migrants come from a relatively narrow regional geographic range, approximately 80–300 km from UV (Fig. 4c).

We then investigated the temporal position and duration of the migration process by using a Bayesian modelling approach that incorporates kinship information as a prior⁴⁰ and differences between the dated event and age at death for the individuals (Supplementary Material Section F.2.). The model estimated the start and end of the migration phase to UV to 695-670 and 625-585 BP, respectively, with a duration of 55-100 years (median of 75 years) (Fig. 4b). These start and end estimates are comparable to those produced by the site-specific models for PLC (Fig. S33) and T-III (Fig. S34). The estimated start of the migration phase at UV precedes the beginning of Inka occupation in the region⁴¹ by 90–180 years (Fig. 4b, Supplementary Material Section F.2.), ruling out causal associations. Furthermore, the estimated duration of 75 years and the presence of multi-generational groups—over 120 individuals of all ages repeatedly buried within a century—suggest that this migration period was a deliberate social practice.

<figure><img src="image_6.png" /><figcaption>**Fig. 4** | Situating migration in space and time. **a.** Local range of bioavailable Sr in UV and surrounding areas as defined by rodent samples (purple), and ⁸⁷Sr/⁸⁶Sr values for sites in UV (brown, locals; orange, non-locals). **b.** Bayesian model for the migrant phase at UV, including radiocarbon measurements from PLC (purple), T-III (green), US2 (yellow) and A.Av. (light blue). Bars underneath each distribution denote 95.4% CI. The start of Inka occupation in the region (as calculated in⁴⁰) is colored in red at the bottom and is estimated to have begun 90–180 years after the migrant phase at UV. **c.** Isotopic geographical assignment of strontium signals from PLC migrants (represented by sample AR0510) on a macro-regional scale.</figcaption></figure>

Genetic differentiation of locals and migrants

Although all analyzed individuals from UV and Calingasta belonged to the same distinct
metapopulation (Figs. 2b-c; Figs. S24-25), and migrant and local LH-F farmers are closer to each
other than to other ancient Americans (Fig. S29; Supplementary Data S6d), their distinct
geographic origins raises the question of whether they formed distinguishable subpopulations.
MDS analyses of *f*₃ genetic distances (Figs. 2b-c; Fig. S24) supported genetic differentiation
between migrants and local farmers, with most PLC migrants (n=16/17) clustering separately
from all other American individuals. Conversely, three LH-F local farmers (from T-II, BR-I, and
PLC), three pre-farming hunter-gatherers (from BB6), and the one remaining PLC migrant formed
a gradient between PLC migrants and other South American groups (4th dimension of MDS in
Fig. 2c, Fig. S24). ADMIXTURE analyses confirmed this pattern as the 16 PLC migrants carried
~100% of the CSC genetic component, whereas the other UV individuals had only ~50-60% (Fig.
S25). While strong genetic drift in the PLC migrant group may partly account for this pattern,
these results also suggest that migrants and locals belonged to distinct subpopulations. This is
further supported by the mitochondrial DNA phylogenies, with the C1b32 (newly defined) sub-
haplogroup largely dominating in migrants, whereas C1c and B2ak1b3 (newly defined) prevailing
among locals (Figs. S20-21; Table S6; Supplementary Material Section E.6.). These patterns
may also reflect a matrilocal social structure, in which women remained within their respective
groups, thereby increasing population genetic differentiation in genetic material transmitted
exclusively through the maternal line. However, the migratory process we describe does not
appear to result from reproductive strategies; instead, it involved the movement of entire kin
groups, which were likely organized along matrilineal lines.

Demographic decline in migrant farmers

To further investigate the impact of genetic drift in migrants, we compared their genetic diversity—
approximated by conditional heterozygosity⁴²—and effective population size (Ne)⁴³ to those of
local farmers, pre-farming hunter-gatherers, and other ancient American populations (Figs. 5a-b;
Supplementary Data S7; Supplementary Material Section E.9.). Our results confirmed that PLC
migrants had a smaller Ne than local farmers and likely also than hunter-gatherers from BB6 site
and Calingasta (the latter exhibited significantly higher conditional heterozygosity but lacked
sufficient coverage for Ne estimation). Notably, the Ne of PLC migrants was comparable to that
of hunter-gatherers from the Pampas, California, and Brazil, as well as Early and Mid-Holocene
populations from the Central Andes (Figs. 5a-b; Supplementary Data S7), but substantially
smaller than that of their contemporary Central Andean farming populations, the most extensively
studied case of Andean agricultural societies (Figs. 5a-b; Supplementary Data S7).

To dissect the population dynamics underlying the reduced Ne observed in migrant farmers, we modeled their past demographic history using both autosomal⁴³ and mtDNA data⁴⁴. Our results were consistent across both datasets, indicating that this population experienced a prolonged demographic decline lasting between 20 and 40 generations (~500–1000 years, Fig. 5c). The combination of demographic decline, marked dependency on maize farming, limited mobility through lifetime—evidenced by highly homogeneous ⁸⁷Sr/⁸⁶Sr values—, and recurrent emigration suggests that a long-term socio-economic crisis ultimately shaped the fate of this group.
<figure><img src="image_7.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_10.png" /></figure>

**Fig 5 | Effective population size in migrants.** a, Conditional Heterozygosity (CH) estimated for migrant and local farmers and a set of ancient American groups. Results are ordered by region according to CH means across groups within each region, and then by CH within each group. b, Effective population size (Ne) estimated 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. c, Ne changes across time in migrant farmers (in generations backward) estimated from autosomal (blue) and mitochondrial (green) markers. d, Macro-regional paleoecological archives in the southern Andes for the last 2400 years. Laguna El Calvario (LCA) Titanium/Molybdenum coherent ratio (XRF) and selected pollen taxa percentages (29°S, 3994 masl)⁴⁵; Storm activity in the Andean Laguna Cerritos Blancos (30°S, 3850 masl)⁴⁶; Moisture changes and duration of the ice cover in Laguna Chepical (32°S, 3050 masl)⁴⁷; El Niño activity inferred by lithic influx in a sediment core located offshore from Peru⁴⁸. The orange horizontal bar highlights the period of migrations and the gray bar a broader 400 year period.

Climate, malnutrition, and disease

We then investigated the environmental and social contexts that may have contributed to the demographic decline of the PLC migrant subpopulation. Paleoecological records indicate colder and drier conditions with reduced precipitation and weakened El Niño-Southern Oscillation (ENSO) activity between 1400-850 cal BP, followed by a slight amelioration between 850-500 cal BP (i.e. during the migration period) associated with increased ENSO activity (Fig. S36; Supplementary Material Section G.). These unstable climatic conditions likely posed significant
challenges for sedentary maize farmers in the region, particularly affecting soils reliant on artificial irrigation⁴⁹.

A detailed osteological examination of human remains from UV reveals that the intensive maize farmers (i.e., individuals with high δ¹³C values) have prevalence of systemic nutritional stress, in both locals to UV (T-II) or non-locals (PLC). However, the migrants show an even higher prevalence of most of the proxies of stress across age classes, including porotic hyperostosis, cribra orbitalia, femoralis and humeralis, as well as enamel hypoplasias, ante-mortem tooth loss, and caries, along with nonspecific bone infections (Supplementary Material Section C.2.; Table S2; Fig. S10). All these conditions are also commonly associated with chronic infections⁵⁰, which, combined with the minimal evidence of skeletal trauma indicative of interpersonal violence (Fig. S11, Supplementary Material Section C.2.), suggest that diseases may have contributed to the high mortality observed at PLC. Interestingly, the hunter-gatherers (BB6) and locals with mixed farmer diets (T-I, BRI) display lower prevalence of systemic stress.

We then performed a metagenomic screening of our aDNA data to investigate the presence of pathogens, and we confidently detected *Mycobacterium tuberculosis* complex (MTBC) hits in one sample from PLC (AR0185). Despite multiple attempts to obtain whole genome data through targeted enrichment, only 20% (904,742 positions) of the MTBC reference genome was covered by at least one read in this sample. To authenticate such low-coverage data, we implemented several filters to reduce environmental noise, which confirmed the presence of *M. tuberculosis* (Supplementary Material Section E.10.). A maximum likelihood phylogenetic analysis of the MTBC reads recovered from AR0185 placed this strain within the *M. pinnipedii* lineage, alongside other previously published pre-European contact MTBC strains from South America⁵¹,⁵² (Fig. 6a). Interestingly, the strain was positioned as a sister clade of ancient strains from Colombia and basal to ancient Peruvian strains, suggesting a relatively deep divergence of this southern Andean strain from those in other regions of the continent. Notably, together with AR0192, AR0185 yielded the earliest age for the migrants (modelled date of 690-665 cal BP, Fig. 4b), potentially associating the onset of migration with the rise of infectious diseases, like TB. Although *M. tuberculosis* DNA did not reach significant genome coverage in other individuals to perform phylogenetic analyses, the prevalence of non-specific pathological bone lesions are consistent with a high pathogen load, suggesting that the actual prevalence of the disease may have been higher than detected by ancient aDNA analysis (Supplementary Material Section C.3., Fig. S10).

<figure><img src="image_11.png" /><figcaption>**Fig 6** | Tuberculosis and kinship-based organization in migrant farmers. **a.** Maximum Likelihood phylogeny of representative MTBC genomes, based on an alignment of 3,861,435 genome positions.</figcaption></figure>
Ancient South American strains are highlighted in orange. Blue dots indicate bootstrap support (based on 100 replicates). The AR0185 genome (red) was computed using 399,323 non-N positions retained after applying stringent filters to reduce environmental noise (see Supplementary Material Section E.10). The branch length of AR0185 is zero because private variants were excluded to avoid inflation from low-coverage sequencing errors, which disproportionately affected its terminal branch length. b. Reconstruction of the family networks among individuals represented as circles (females) or squares (males) using color schemes for maternal (fill) and paternal (border) lineages, including age estimates from osteological analyses and estimated median date [those with direct radiocarbon ages in bold, whilst those in italics refer to the median for the site as a proxy (both in BP)]. The related pairs of individuals are indicated by connecting lines, using a color scheme according to the degree, and a line type according to the certainty. We represent only individuals involved in at least one kinship relationship up to the 3rd-degree. For the pedigree at the bottom right, relationships between siblings (1st degree), and uncle/aunt-nephew/niece or grandfather/mother-grandson/daughter (2nd degree) are not shown as they can be derived from the pedigree.

Transgenerational matrilineal migrations

Our kinship analysis (Supplementary Section E.7.) revealed that migrations into UV were structured along family networks spanning at least three generations (Fig. 6b). This is evidenced by an interconnected kinship network involving between 13 and 20 PLC individuals among the 27 for whom kinship data is available. Notably, one of these networks forms a matrilineal pedigree of eight individuals (Fig. 6b), in which the only two men were sub-adult siblings within the third generation. The three generations of women in this pedigree share very similar strontium isotopic signatures from childhood to adulthood, indicating that they originated from the same region outside UV. While maternal lineages among migrants were largely dominated by a single mitochondrial haplogroup (C1b37 in 27 of 31 individuals; Fig. S20; Table S6), paternal lineages were more evenly distributed between two haplogroups (four Q-M3 and eight Q-Z780; Table S7; Supplementary Material Section E.6.). Furthermore, migrants had a higher proportion of women (PLC 57%, Tumulo III 100%) and a considerable number of subadults (44% of PLC), suggesting that women migrated with their offspring. Together, these patterns suggest that women played a central role in structuring these migrating groups, further supporting our hypothesis of a preexisting matrilocal social structure. Additionally, they indicate that women maintained close family bonds throughout migration and up to the mortuary ritual.

These findings, along with the diachronic burial dates of migrants and the consistent ⁸⁷Sr/⁸⁶Sr signatures across generations and sites (PLC, T-III, US2), suggest that migration was a structured, kinship-driven process sustained over time, rather than an uncoordinated demographic movement. Our evidence also indicates social bonds between migrants and locals, as reflected in the absence of violence-related trauma and the shared mortuary contexts at PLC and T-III. In contrast to the South Central Andes, where current evidence suggests limited migration and recurrent conflicts⁵⁴,⁵⁵, UV exhibits regional migration among farming groups without signs of interpersonal violence. These patterns point to a distinct social organization in which migration was likely facilitated by family-based networks and cooperative interactions, underscoring the diversity of social responses to agricultural intensification and social crisis in the pre-Hispanic Andes.

Discussion

The transition to agriculture reshaped human societies globally, yet its demographic and social consequences varied widely. By integrating genomic, isotopic, chronometric, bioarchaeological, and paleoclimatic analyses, along with Indigenous perspectives, our study reconstructs the origins, rise and decline of a farming population in the Southern Andes. We described its unique demographic and social dynamics, broadening our understanding on Andean farming populations.
In contrast with many observations in other regions worldwide, where human migrations fostered agriculture spread, our findings provide solid evidence of genetic continuity from pre-farming hunter-gatherers to late farming populations, supporting the hypothesis that agriculture in Uspallata Valley emerged through cultural transmission and local adoption rather than spread migration. Our quantitative isotopic data indicates that this process was nonlinear through time, with the highest agricultural intensification occurring between 850 and 600 BP, as revealed by unexpectedly high maize consumption reaching isotopic values that are nearly comparable to records in the Central Andes.

At a broader temporal scale, we show that the most maize-dependent groups in this study underwent a sustained population decline coinciding with long-lasting climatic instability. At a finer temporal scale, we further explored the factors contributing to this decline and found direct evidence of nutritional stress, incidence of tuberculosis, and high mortality across all age groups over multiple generations. We thus hypothesize that the decline of these farming populations resulted from a multi-scalar crisis driven by the convergence of factors operating at different temporal scales.

These converging stressors drove a structured pattern of migration, as revealed by ⁸⁷Sr/⁸⁶Sr and genomic analyses. Migrants originated from a nearby but geologically distinct region and followed the same relocation pattern for at least three successive generations. Their movements followed kinship-based strategies, primarily structured through matrilineal descent. The predominance of a single maternal lineage among migrants, the high proportion of women and subadults in most burial sites, and the absence of conflict-related trauma suggest that these migrations were socially coordinated rather than forced. These findings position UV as a model for understanding how maintaining close family and community networks during migration functioned as a cooperative response to social and ecological crises.

At a broader geographic scale, we identified a distinct genetic component among South American ancestries, which we named Central Southern Cone (CSC), and which has persisted in modern populations. This component reveals genetic continuity spanning two millennia—and likely longer—and challenges previous narratives of regional demographic extinction of local populations²⁸. While our attempts to decipher the origin of this genetic component remained inconclusive (Fig. S31; Supplementary Data S6f), they suggest a remote origin, independent from other South American regions (Fig. S30; Supplementary Data S6e). Solving its origins will require expanded ancient DNA sampling across South America, particularly in the Southern Cone.

Beyond its scientific contributions, this study highlights the value of transdisciplinary research and collaboration with Indigenous groups in reconstructing human histories. The participation of Huarpe communities enriched our interpretation of the findings, and reaffirmed their cultural and biological continuity, despite the tragic history of colonization and subjugation they have endured. Our study provides a new perspective on agricultural transitions in peripheral regions of domestication centers illustrating how ecological pressures, economic shifts, and kinship-based mobility intersected to shape both population crisis and social resilience in the ancient Andes.

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# Methods

## Contexts and sample selection

Nearly 200 individuals were buried between 1200 and 600 cal years BP at seven clustered archaeological sites in UV. Most of these sites were excavated in the first half of the twentieth century by the pioneer archaeologist Carlos Rusconi, including the cemetery sites Potrero Las Colonias (PLC [MNI= 124]), Túmulo I (T-I [MNI= 29]), Túmulo II (T-II [MNI= 10]), and Túmulo III (T-III [MNI= 26])⁵⁶,⁵⁷ (Table S1). By including the hunter-gatherer site Barrancas B6 (BB6 [MNI= 34]), emplaced in the eastern lowlands 70 km from UV¹⁵, our total sample sums 233 individuals from hunter-gatherer, mixed and intensive farmers spanning between 2300 and 600 cal years BP. In the Supplementary Materials we describe the archaeological contexts studied (Section B.2.) and the sampling design (Section B.4.).

## Ancient DNA laboratory procedures

Sampling, drilling, DNA extraction and library preparation were conducted in three different ancient DNA facilities (Supplementary Data S3) located at the Globe Institute, University of Copenhagen, Denmark; the Center of Anthropology and Genomics of Toulouse (CAGT), France; and the Institute of Genomics, University of Tartu, Estonia, following widely used protocols for ancient DNA work and established workflows designed to minimize the risk of contamination⁵⁸⁻⁶⁵. We generated single-stranded⁶⁶ and double-stranded⁶⁴,⁶⁷ libraries from standard and USER-treated DNA extracts⁶⁴,⁶⁶ and sequenced genome wide data from 46 ancient individuals using Illumina NovaSeq 6000 150PE and NovaSeq X 150PE from the Novogene, Cambridge, UK and the ICM Institute in Paris, respectively. A detailed description of the procedures is provided in Supplementary Material Section E.1. DNA libraries that were potentially positive
for TB were targeted-enriched using in-solution hybridization capture protocol following the procedures given in<sup>68</sup>.

## Sequencing data processing

To process FASTQ files for human DNA analyses, we used the nf-core/eager workflow<sup>69</sup> v2.4.2 to remove adapters, align the collapsed reads with Burrows-Wheeler Aligner<sup>70</sup> v0.7.17-r1188 to the GRCH37 human reference genome<sup>71</sup> with the aln function (BWA-ALN) for the nuclear DNA, and with circularmapper<sup>72</sup> against the revised Cambridge Reference Sequence<sup>73</sup> for the mitochondrial DNA (mtDNA). Aligned reads were filtered using SAMtools<sup>74</sup> v1.12, and then deduplicated using Dedup<sup>72</sup> v0.12.8. We realigned the Indels with *RealignerTargetCreator* and *IndelRealigner* from GenomeAnalysisTK<sup>75</sup> v3.6, and MD tags were subsequently generated using *calmd* from SAMtools. For each library, we manually assigned a number of base pairs to be trimmed on 5' and 3' ends of the reads (Supplementary Data S3) by visually inspecting the damage plots generated with damageprofiler<sup>76</sup> v0.4.9, and these values were used to trim the reads with *trimBam* from bamUtil<sup>77</sup> v1.0.15. We obtained contamination estimations with 5 methods: Schmutzi<sup>78</sup> v1.5.6, ContamMix<sup>79</sup>, Haplocheck<sup>80</sup> v1.3.3, Nuclear Contamination with ANGSD<sup>81</sup> v0.935, and 8.5. ContamLD<sup>22</sup>. We assessed the authenticity of the sequencing data by inspecting the length distributions (Fig. S15) and misincorporation patterns of the mapped reads with damageprofiler<sup>76</sup> v0.4.9. (Fig. S16). Autosomal genotypes were called with pileupCaller (https://github.com/stschiff/sequenceTools) v.1.4.0.5 after applying *mpileup* from SAMtools for two autosomal SNP panels using all positions or only transversions (TVs): 1240K<sup>21</sup> (1.15M SNPs, 0.22M TVs) and Shotgun<sup>22</sup> (SG; 5.6 M SNPs; 1.82M TVs). More details concerning the sequencing data processing can be found in Supplementary Material Section E.2, and related statistics are included in Supplementary Data S3.

## Kinship and genomic sex

The genomic sex of ancient individuals was assessed from the depth of coverage ratio on the sexual chromosomes and the autosomal (Fig. S19; Supplementary Material Section E.5; Supplementary Data S3). For each pair of newly sequenced individuals with DoC greater than 0.01X, we generated kinship estimations with *READv2*<sup>82</sup> and *KIN*<sup>83</sup> using positions from the SG<sup>22</sup> or 1240K<sup>21</sup> SNP panels. For each pair of individuals, we manually assigned the degree of relatedness after visualizing the indexes generated by each method on each SNP panel, crossed with the number of SNPs used (Fig. S22; Supplementary Data S5; Supplementary Section E.7).

## Uniparental lineages analyses

We generated majority rule mtDNA consensus sequences with ANGSD<sup>81</sup>, that we compared to an in-house database of modern and ancient mitogenomes from the Americas, consisting of >4.000 individuals from most of the regions of the American continent. We reconstructed maximum parsimony trees with mtPhyl v 4.015 (2013; https://sites.google.com/site/mtphyl/home), The sub-haplogroups in the trees were finally annotated with reference to HaploGrep<sup>84</sup> v2 and PhyloTree<sup>85</sup>, and previously published trees<sup>86-88</sup>, which resulted in the definition of three new sub-haplogroups: B2ak1b3, B2b16a and C1b37 (Fig. S20; Table S6. A consensus Bayesian phylogenetic tree was also built with TreeAnnotator<sup>89</sup> using only mitogenomes from the study area (Fig. S21). We also used yLeaf<sup>90</sup> to assess paternal haplogroups to male individuals (Table S7). Details about the procedures can be found in Supplementary Material Section E.6.

## Reference datasets for population structure analyses

We reprocessed whole-genome sequencing data for 332 ancient<sup>13,26-28,30,91-105</sup> and 206 modern<sup>102,106-108</sup> individuals. We also included genotyping data for 713 modern individuals<sup>30,31,109,110</sup>, masked to focus only on positions for which both alleles are assigned to genetic ancestry Indigenous to the Americas. From this data we constructed different reference panels as described in Supplementary Data S4. The individuals from the reference panels were grouped mostly according to the metadata associated with the Allen Ancient DNA Resource<sup>12</sup> v54, but also consulting the groupings used in subsequent references<sup>13,28,91,94,95,98,100,104,105</sup> (Supplementary Data S4; Figs. S17-18). We differentiated between groups whose individuals were sequenced through Shotgun (SG) and in-solution capture (Capt). Groups were subsequently assigned to major geographical ranges, and divided according to period: Early Holocene (EH; before 8,500 BP), Middle Holocene (MH; between 8,500 and 3,500 cal years BP), Late Holocene (LH; after 3,500 cal years BP, and before European invasion ~500 cal years BP), and Modern (present-day).

## Population structure and diversity analyses

We performed multidimensional scaling analyses and neighbour-joining trees<sup>111</sup> from pairwise genetic matrices between all individuals derived from *f*<sub>3</sub>-outgroups (Mbuti population being the outgroup), which was calculated with Admixtools<sup>112</sup> v6 (Fig. S24; Supplementary Data S6a-b). We estimated the proportions
of genetic ancestry components using ADMIXTURE<sup>29</sup> for a number of ancestral populations K from 2 to 15. For every K, we considered 10 independent replicates and present the results for the replicate with the highest likelihood (Figs. S25-26). We computed different D-statistics of the form f<sub>4</sub>(Mbuti, Group1; Group2, Group3), in which the Group1, Group2 and Group3 were chosen according to the hypotheses tested (Figs. S28-32; Supplementary Data S6c-g). Methodological details about these analyses, as well as a comprehensive description of the results obtained are presented in Supplementary Material Section E.8.

## Effective population size reconstructions

We estimated conditional heterozygosity<sup>113</sup> with POPSTATS (https://github.com/pontussk/popstats) and effective population size (Ne) from Runs of Homozygosity with hapROH<sup>114</sup> (Supplementary Data S7). Focusing on the migrant farmers group (LH-MF), we also estimated demographic curves –Ne variation through time– from mtDNA and autosomal markers with BEAST<sup>89</sup> v1.10.4 and hapNe<sup>43</sup>, respectively (Supplementary Material Sections E.6 and E.9).

## Pathogen genomic analyses

We screened all ancient metagenomic datasets for pathogen DNA using the HOPS v0.33 pipeline, which applies MALT to align sequencing reads against a curated RefSeq database of viral, bacterial, and eukaryotic genomes. Among several candidate pathogens, only *Mycobacterium tuberculosis* complex (MTBC) yielded high-confidence results in two individuals. All other hits were excluded due to low read counts or poor alignment quality. To investigate these MTBC-positive samples, we first assembled a reference phylogeny including 59 high-coverage modern and ancient MTBC genomes representative of known lineage diversity. Given the low-coverage of our genomes, we reduced environmental noise by applying an initial filter to keep, from all the MTBC-mapping reads in our samples, only those classified as *M. tuberculosis* by metagenomic taxonomic classifiers. Sample AR0185 had sufficient coverage to be largely genotyped and included in the alignment used for *de novo* phylogenetic reconstruction. The resulting maximum likelihood tree placed AR0185 within the *M. pinnipedii* clade. Methodological details about these analyses can be found in Supplementary Material Section E.10.

## Bioarchaeological and demographic analysis

The remains from all the sites were analyzed from a bioarchaeological, taphonomic, and paleopathological perspective following standard methods and techniques<sup>39,115-117</sup>. The commingled characteristics of the sites PLC and T-III, comprising the largest part of our sample, combined with outdated recovery techniques applied in the 1930's by the excavator<sup>56,57</sup>, lead to assemblages where the integrity of the remains from each individual were largely lost. For the quantification of these remains, we began by separating specimens from adults and subadults to accurately estimate the Minimal Number of Individuals (MNI)<sup>118</sup>. The analysis was adjusted by considering additional variables such as the morphology, size, robusticity and presence of distinctive morphologic features in the remains. All the bone specimens were carefully inspected for infectious and traumatic lesions. The methods applied for the analysis of demography, nutritional systemic stress, and paleopathology analysis are presented in the Supplementary Materials (Section C).

## Strontium isotope analysis and construction of the isoscape

<sup>87</sup>Sr/<sup>86</sup>Sr analyses were carried out in the MC-ICP-MS facility at the Department of Geological Sciences, University of Cape Town, South Africa. Powdered samples were processed following routine chemical and MC-ICP-MS methods<sup>119</sup>, with strontium isolated in a clean chemistry laboratory utilizing ultra-pure 2-bottle-distilled HNO<sub>3</sub>. Isotope ratios were measured on a Nu Instruments NuPlasma HR. 87Sr/86Sr data are referenced to a value of 0.710255 for the international standard SRM987.

The isoscape of bioavailable <sup>87</sup>Sr/<sup>86</sup>Sr for the study area was generated using a Random Forest (RF) regression model<sup>120,121</sup>. Applied to bioavailable <sup>87</sup>Sr/<sup>86</sup>Sr data alongside environmental covariates affecting <sup>87</sup>Sr/<sup>86</sup>Sr, RF allows for spatial prediction of bioavailable <sup>87</sup>Sr/<sup>86</sup>Sr across large scales even in areas with limited sampling effort<sup>120-122</sup>. RF is a machine learning algorithm that grows multiple regression trees through bagging<sup>123</sup>: for each tree, the dataset is randomly split into a training set and a validation set (or "out-of-bag" samples). Each regression tree is built using the training set and a subset of randomly selected predictors, and the out-of-bag samples are used for internal cross-validation. The final predictions are obtained by aggregating the results of all trees. We built the isoscape using a global dataset of bioavailable <sup>87</sup>Sr/<sup>86</sup>Sr from soil, plants, and animals<sup>120</sup>. This dataset was updated with additional published <sup>87</sup>Sr/<sup>86</sup>Sr and the rodent samples analyzed in this study to improve both regional and local prediction<sup>121</sup>. We generated a prediction profile for each sample, and we selected AR0510 (Fig. 3c), as the representative case of the migrant population (Supplementary Materials, section D.1.), and individual AR0514 (Fig. S14) as a second exemple to show the high similarity of all results.
**Stable isotopes**

Samples were analyzed at the Stable Light Isotope Facility at the University of Cape Town, South Africa. Bone collagen was prepared using the pseudomorph method described in¹²⁴. The samples were combusted in a Flash EA 2000 series elemental analyzer (Thermo Finnigan, Milan, Italy). The gases were passed through to a Delta Plus V IRMS (Thermo Electron, Bremen, Germany), via a Conflo IV gas control unit (Thermo Finnigan, Bremen, Germany). Samples of bone and powdered tooth enamel were analyzed for δ¹³C and δ¹⁵N using the sample preparation methods described in the Supplementary Text (Section D.2).

**Calibration and Bayesian modelling**

All the ages mentioned in the paper are referred to in calibrated years BP, with the present set at 1950 common era. Calibration and Bayesian modelling were undertaken using the SHCal20 calibration curve¹²⁵ in OxCal 4.4¹²⁶. Chronometric data from migrant individuals from each site were included in single-phase, uniform models [individually (Figs. S33-34) and for the greater UV model in Fig. 3b. Kinship data (Fig. 6b) were introduced to the PLC model as prior information using calculated values for the number of years (with uncertainty) that separate the dates of death of two genetically related individuals⁴⁰.

**Data availability**

The genomic data produced in this study is available upon request (DOI : 10.48802/owey.bdxssypL.1.0). To access the genomic data from the literature, please refer to the original articles.

**Code availability**

R Scripts generated to plot the genomic (PL) and isotopic (ATe, MLC, RB) analyses, as well as for Random Forest modeling of bioavailable strontium (MLC) and OxCal (LBV) modeling are available at https://github.com/pierrespc/BarberenaLuisi_2025.

All 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 (http://haplogrep.uibk.ac.at/index.html), Yleaf (https://github.com/genid/Yleaf), ADMIXTURE (https://www.genetics.ucla.edu/software/admixture/download.html), 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), 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 (Horacio Chiavazza, Cristina Sonego), and the Director of the Museo de Ciencias Naturales y Antropológicas Juan C. Moyano (Guillermo Campos) for the authorization to study the remains from UV. Malena Vázquez and Julio 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 Ancient DNA facility from the Institute of Genomics, University of Tartu, and especially Christiana L. Scheib, Helja Kabral, Kristiina Tambets and Lehti 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 Marc Monot, Laurence Motreff and Florence Jagorel from the IP Biotics Platform (supported by France Génomique ANR-10-INBS-09-09 and IBISA) for their assistance in sample sequencing. We are thankful to Eske Willerslev, Omar Caceres Rey, Timothy O'Connor and Victor Borda for facilitating access to sequencing data for some modern individuals included in this study. 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 #NGS-92679R-22 (R.B.), Wenner-Gren Foundation Grant #2368532037 (R.B.), National Institute for the Humanities (NIHU) Transdisciplinary Projects (R.B.), 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.), PIP-CONICET Grant 11220210100098CO (P.N.).

## Author contributions

Conceptualization: N.R., R.B., P.L.

Sampling: R.B., D.G., G.D.P., P.N., C.A., A.Te., A.Tr., V.D.

Ancient DNA laboratory work: M.L., G.T., M.I.O., E.A.N., L.O., H.S.

Isotope laboratory work: P.L.R., J.Se., K.G., J.L., A.Te.

Bioarchaeological analysis: P.N., D.G., G.D.P., C.A.M., J.Su., L.M.

Data analysis: P.L., N.R., R.B., A.Te., L.B.V., M.L.C., M.Car., J.M.C., G.L., A.G., E.A.N., M.R., M.E.D.P., E.P.
Archaeological work: R.B., P.N., D.G., G.D.P., C.A.M., M.F.Q., M.L.L., V.D., C.M., F.S.S.

Funding acquisition: N.R., R.B., L.O., H.S.

Writing – original draft: N.R., P.L., R.B.

Writing – review & editing: L.Q.M., H.S., E.P., J.Se., A.Te., L.B.V., L.M., A.G.

Community engagement: R.B., P.L., N.R., P.N., D.G., C.H., G.C., M.Can., E.A.N.

**Competing interest declaration**

Authors declare that they have no competing interests.

**Additional information**
Supplementary Files

This is a list of supplementary files associated with this preprint. Click to download.

* SupplementaryDataS2Srrodents.xlsx

• SupplementaryDataS7PopSize.xlsx

* SupplementaryDataS1Isotopeshumans.xlsx

* SupplementaryDataS4IndividualsincludedinaDNAanalyses.xlsx

* SupplementaryDataS3Sequencing.xlsx

* SupplementaryTextS1.docx

* BarberenaLuisietal.NatureSupplementaryMaterials20250408785.pdf

* SupplementaryDataS5Kinship.xlsx

* SupplementaryDataS6fstats.xlsx