What are the most relevant indicators for social-ecological archetype mapping? A data-driven methodological routine in Andalusia, Spain

Human-nature interactions and social-ecological systems (SES) research is becoming increasingly significant as biodiversity and climate change result in more attention being paid to these relationships and what characterizes such systems. Different approaches to detecting, mapping and characterizing SESs have been an important tool in empirically researching these, yet few studies have looked at indicators and variables which can help to map and identify diversity in SESs. Thus, Pacheco-Romero et al. (2022) proposed using a data-driven methodological routine to investigate and identify the most relevant indicators for mapping and characterizing SES archetypes in a particular region. Identifying these indicators could allow for the development of a more holistic and standardized way of managing, researching, and monitoring social-ecological systems.

Ohanes, Santuario de Tices, Luajar de Andarax (2014).

The authors investigated the relevance of 86 SES indicators in Andalusia, Spain, as a case study, by applying a methodological routine based on multivariate statistical analysis. Such indicators represented multiple SES variables and dimensions. Then they compared their empirical outcomes with previous expert and empirical knowledge on key variables for characterizing SESs.

Pacheco-Romero et al. (2022) were able to identify 29 key indicators that enabled the mapping of 15 distinct SES archetypes that included natural, mosaic, agricultural and urban systems. These archetypes also represented contrasting land sharing and land sparing patterns throughout Andalusia. By assessing the 49 variables operationalized by the 86 indicators used in the analysis, the authors identified nine groups of variables which reflected a sliding scale from synergy to disagreement between empirical and expert knowledge on their relevance to characterizing SESs. In general, they found agreement on the widespread relevance of some variables, while the importance of other variables was dependent on the context and scales of analysis. The authors also recommended that more research is needed to understand the lack of agreement between empirical and expert knowledge in the remaining variable groups.

Overall, data driven approaches can enable more objectivity and reproducibility in the selection of relevant indicators for mapping and characterizing SESs, along with identifying more representative descriptions of SES archetypes, and finding their causal factors. The authors argued that these key indicators could help to organize social-ecological complexity of the study area and represent different land-use types and characteristics. Generally, the SESs in Andalusia showed a land-sparing pattern, although both agricultural and natural SESs showed both land sparing and land sharing strategies, respectively, with the latter reflecting wildlife-friendly practices. Mosaic systems had more intermediate characteristics, between land sharing and land sparing.

Pacheco-Romero et al. (2022) maintain that their method contributes to improving the treatment of causality in SES archetype analysis, through more representative and holistic descriptions of SES archetypes. They do this by identifying key indicators to explain differences in social-ecological units and exploring the impact of indicators in characterizing SES clusters. Using such data-driven methodological routine can assist the selection of relevant indicators and help to produce more comparable and generalizable knowledge in the spatial mapping of SES archetypes across regions.

Read the full paper by Pacheco-Romero et al. (2022) here.

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