Abstract
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.
