Abstract
Humans place strong pressure on land and have modified around 75% of Earth’s terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world.
Original language | English |
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Article number | 27 |
Number of pages | 85 |
Journal | Ecology and Society |
Volume | 27 |
Issue number | 2 |
DOIs | |
Publication status | Published - Jun 2022 |
Bibliographical note
Funding Information:This study is part of Lucía Zarbá's PhD thesis supported by a scholarship from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina. Partially support was provided by the grant PICT 2015-0521 from Fondo para la Investigación Científica y Tecnológica (FONCyT). ESRI Travel Grant and GLP Travel Grant supported MPR and LZ attendance to the GLP OSM 2019. We thank GLP for holding an in-situ meeting of the project as well as to the external attendees that participated in that meeting enriching the discussion.
Publisher Copyright:
© 2022 by the author(s). Published here under license by the Resilience Alliance.
Funding
This study is part of Lucía Zarbá's PhD thesis supported by a scholarship from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina. Partially support was provided by the grant PICT 2015-0521 from Fondo para la Investigación Científica y Tecnológica (FONCyT). ESRI Travel Grant and GLP Travel Grant supported MPR and LZ attendance to the GLP OSM 2019. We thank GLP for holding an in-situ meeting of the project as well as to the external attendees that participated in that meeting enriching the discussion.
Funders | Funder number |
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Consejo Nacional de Investigaciones Científicas y Técnicas | PICT 2015-0521 |
Fondo para la Investigación Científica y Tecnológica |
Keywords
- automatization
- hierarchical clustering
- multidisciplinary data
- participatory mapping
- social-ecological mapping