A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping
The spatial mapping of social-ecological system (SES) archetypes constitutes a fundamental tool to operationalize the SES concept in empirical research. Approaches to detect, map, and characterize SES archetypes have evolved over the last decade towards more integrative and comparable perspectives g...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2022-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ac5ded |
| _version_ | 1797746989899710464 |
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| author | Manuel Pacheco-Romero María Vallejos José M Paruelo Domingo Alcaraz-Segura M Trinidad Torres-García María J. Salinas-Bonillo Javier Cabello |
| author_facet | Manuel Pacheco-Romero María Vallejos José M Paruelo Domingo Alcaraz-Segura M Trinidad Torres-García María J. Salinas-Bonillo Javier Cabello |
| author_sort | Manuel Pacheco-Romero |
| collection | DOAJ |
| description | The spatial mapping of social-ecological system (SES) archetypes constitutes a fundamental tool to operationalize the SES concept in empirical research. Approaches to detect, map, and characterize SES archetypes have evolved over the last decade towards more integrative and comparable perspectives guided by SES conceptual frameworks and reference lists of variables. However, hardly any studies have investigated how to empirically identify the most relevant set of indicators to map the diversity of SESs. In this study, we propose a data-driven methodological routine based on multivariate statistical analysis to identify the most relevant indicators for mapping and characterizing SES archetypes in a particular region. Taking Andalusia (Spain) as a case study, we applied this methodological routine to 86 indicators representing multiple variables and dimensions of the SES. Additionally, we assessed how the empirical relevance of these indicators contributes to previous expert and empirical knowledge on key variables for characterizing SESs. We identified 29 key indicators that allowed us to map 15 SES archetypes encompassing natural, mosaic, agricultural, and urban systems, which uncovered contrasting land sharing and land sparing patterns throughout the territory. We found synergies but also disagreements between empirical and expert knowledge on the relevance of variables: agreement on their widespread relevance (32.7% of the variables, e.g. crop and livestock production, net primary productivity, population density); relevance conditioned by the context or the scale (16.3%, e.g. land protection, educational level); lack of agreement (20.4%, e.g. economic level, land tenure); need of further assessments due to the lack of expert or empirical knowledge (30.6%). Overall, our data-driven approach can contribute to more objective selection of relevant indicators for SES mapping, which may help to produce comparable and generalizable empirical knowledge on key variables for characterizing SESs, as well as to derive more representative descriptions and causal factor configurations in SES archetype analysis. |
| first_indexed | 2024-03-12T15:45:42Z |
| format | Article |
| id | doaj.art-139433df269d48ca9197108ebb5a9869 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:45:42Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-139433df269d48ca9197108ebb5a98692023-08-09T15:26:43ZengIOP PublishingEnvironmental Research Letters1748-93262022-01-0117404501910.1088/1748-9326/ac5dedA data-driven methodological routine to identify key indicators for social-ecological system archetype mappingManuel Pacheco-Romero0https://orcid.org/0000-0002-9914-4009María Vallejos1https://orcid.org/0000-0002-1749-6458José M Paruelo2https://orcid.org/0000-0002-8784-9431Domingo Alcaraz-Segura3https://orcid.org/0000-0001-8988-4540M Trinidad Torres-García4https://orcid.org/0000-0003-2244-1758María J. Salinas-Bonillo5https://orcid.org/0000-0001-6931-6677Javier Cabello6https://orcid.org/0000-0002-5123-964XAndalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería , Almería, Spain; Department of Biology and Geology, University of Almería , Almería, Spain; Faculty of Sustainability, Leuphana University Lüneburg , Lüneburg, GermanyInstituto Nacional de Investigación Agropecuaria (INIA), La Estanzuela , Departamento de Colonia, Uruguay; Cátedra de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, Universidad de Buenos Aires , Buenos Aires, ArgentinaInstituto Nacional de Investigación Agropecuaria (INIA), La Estanzuela , Departamento de Colonia, Uruguay; Cátedra de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, Universidad de Buenos Aires , Buenos Aires, Argentina; Facultad de Ciencias, IECA, Universidad de la República , Montevideo, Uruguay; Laboratorio de Análisis Regional y Teledetección, IFEVA, Facultad de Agronomía, CONICET , Buenos Aires, ArgentinaAndalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería , Almería, Spain; Department of Botany, University of Granada , Granada, Spain; iecolab, Interuniversity Institute for Earth System Research (IISTA), University of Granada , Granada, SpainAndalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería , Almería, Spain; Department of Biology and Geology, University of Almería , Almería, SpainAndalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería , Almería, Spain; Department of Biology and Geology, University of Almería , Almería, SpainAndalusian Center for the Assessment and Monitoring of Global Change (CAESCG), University of Almería , Almería, Spain; Department of Biology and Geology, University of Almería , Almería, SpainThe spatial mapping of social-ecological system (SES) archetypes constitutes a fundamental tool to operationalize the SES concept in empirical research. Approaches to detect, map, and characterize SES archetypes have evolved over the last decade towards more integrative and comparable perspectives guided by SES conceptual frameworks and reference lists of variables. However, hardly any studies have investigated how to empirically identify the most relevant set of indicators to map the diversity of SESs. In this study, we propose a data-driven methodological routine based on multivariate statistical analysis to identify the most relevant indicators for mapping and characterizing SES archetypes in a particular region. Taking Andalusia (Spain) as a case study, we applied this methodological routine to 86 indicators representing multiple variables and dimensions of the SES. Additionally, we assessed how the empirical relevance of these indicators contributes to previous expert and empirical knowledge on key variables for characterizing SESs. We identified 29 key indicators that allowed us to map 15 SES archetypes encompassing natural, mosaic, agricultural, and urban systems, which uncovered contrasting land sharing and land sparing patterns throughout the territory. We found synergies but also disagreements between empirical and expert knowledge on the relevance of variables: agreement on their widespread relevance (32.7% of the variables, e.g. crop and livestock production, net primary productivity, population density); relevance conditioned by the context or the scale (16.3%, e.g. land protection, educational level); lack of agreement (20.4%, e.g. economic level, land tenure); need of further assessments due to the lack of expert or empirical knowledge (30.6%). Overall, our data-driven approach can contribute to more objective selection of relevant indicators for SES mapping, which may help to produce comparable and generalizable empirical knowledge on key variables for characterizing SESs, as well as to derive more representative descriptions and causal factor configurations in SES archetype analysis.https://doi.org/10.1088/1748-9326/ac5dedcoupled human and natural systemsessential social-ecological system variableshuman-environment interactionslong-term social-ecological researchLTSERrandom forest |
| spellingShingle | Manuel Pacheco-Romero María Vallejos José M Paruelo Domingo Alcaraz-Segura M Trinidad Torres-García María J. Salinas-Bonillo Javier Cabello A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping Environmental Research Letters coupled human and natural systems essential social-ecological system variables human-environment interactions long-term social-ecological research LTSER random forest |
| title | A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping |
| title_full | A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping |
| title_fullStr | A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping |
| title_full_unstemmed | A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping |
| title_short | A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping |
| title_sort | data driven methodological routine to identify key indicators for social ecological system archetype mapping |
| topic | coupled human and natural systems essential social-ecological system variables human-environment interactions long-term social-ecological research LTSER random forest |
| url | https://doi.org/10.1088/1748-9326/ac5ded |
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