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dc.contributor.authorAhedo García, Virginia 
dc.contributor.authorZurro, Débora
dc.contributor.authorCaro Saiz, Jorge
dc.contributor.authorGalán Ordax, José Manuel 
dc.date.accessioned2022-01-18T08:03:53Z
dc.date.available2022-01-18T08:03:53Z
dc.date.issued2021-08
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/10259/6334
dc.description.abstractThe transition to agriculture is regarded as a major turning point in human history. In the present contribution we propose to look at it through the lens of ethnographic data by means of a machine learning approach. More specifically, we analyse both the subsistence economies and the socioecological context of 1290 societies documented in the Ethnographic Atlas with a threefold purpose: (i) to better understand the variability and success of human economic choices; (ii) to assess the role of environmental settings in the configuration of the different subsistence economies; and (iii) to examine the relevance of fishing in the development of viable alternatives to cultivation. All data were extracted from the publicly available cross-cultural database D-PLACE. Our results suggest that not all subsistence combinations are viable, existing just a subset of successful economic choices that appear recurrently in specific ecological systems. The subsistence economies identified are classified as either primary or mixed economies in accordance with an information-entropy-based quantitative criterion that determines their degree of diversification. Remarkably, according to our results, mixed economies are not a marginal choice, as they constitute 25% of the cases in our data sample. In addition, fishing seems to be a key element in the configuration of mixed economies, as it is present across all of them.en
dc.description.sponsorshipSpanish Ministry of Science and Innovation: Excellence Networks (HAR2017-90883-REDC) (VA, DZ, JC, JMG) and (RED2018-102518-T) (VA, JMG), as well as the CULM Project (HAR2016-77672-P) (DZ, JC); from the Catalan Government - AGAUR through 2017 SGR 212 (DZ); from the Junta de Castilla y León – Consejería de Educación through BDNS 425389 (VA, JMG); and from the Research Foundation – Flanders (FWO) through the NASA project (VA, JC, JMG). In addition, this work was partially supported by the European Social Fund, as VA is the recipient of a predoctoral grant from the Department of Education of Junta de Castilla y León. Lastly, the publication fee was partially supported by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.format.mimetypeapplication/pdf
dc.language.isoengen
dc.publisherPublic Library Scienceen
dc.relation.ispartofPLoS ONE. 2021, V. 16, n. 8, e0254539en
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherEconomíaes
dc.subject.otherEconomyen
dc.subject.otherSociologíaes
dc.subject.otherSociologyen
dc.titleLet’s go fishing: A quantitative analysis of subsistence choices with a special focus on mixed economies among small-scale societiesen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1371/journal.pone.0254539es
dc.identifier.doi10.1371/journal.pone.0254539
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/HAR2017-90883-REDC/ES/Simular el pasado para entender el comportamiento humanoes
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RED2018-102518-T/ES/SISTEMAS COMPLEJOS SOCIOTECNOLOGICOSes
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/HAR2016-77672-P/ES/MODELADO DEL CULTIVO EN LA PREHISTORIAes
dc.relation.projectIDinfo:eu-repo/grantAgreement/AGAUR//2017 SGR 212/es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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