Assessing the resilience of a real-world social-ecological system: lessons from a multidisciplinary evaluation of a South African pastoral system

In the past decades, social-ecological systems (SESs) worldwide have undergone dramatic transformations with often detrimental consequences for livelihoods. Although resilience thinking offers promising conceptual frameworks to understand SES transformations, empirical resilience assessments of real-world SESs are still rare because SES complexity requires integrating knowledge, theories, and approaches from different disciplines. Taking up this challenge, we empirically assess the resilience of a South African pastoral SES to drought using various methods from natural and social sciences. In the ecological subsystem, we analyze rangelands’ ability to buffer drought effects on forage provision, using soil and vegetation indicators. In the social subsystem, we assess households’ and communities’ capacities to mitigate drought effects, applying agronomic and institutional indicators and benchmarking against practices and institutions in traditional pastoral SESs. Our results indicate that a decoupling of livelihoods from livestock-generated income was initiated by government interventions in the 1930s. In the post-apartheid phase, minimum-input strategies of herd management were adopted, leading to a recovery of rangeland vegetation due to unintentionally reduced stocking densities. Because current livelihood security is mainly based on external monetary resources (pensions, child grants, and disability grants), household resilience to drought is higher than in historical phases. Our study is one of the first to use a truly multidisciplinary resilience assessment. Conflicting results from partial assessments underline that measuring narrow indicator sets may impede a deeper understanding of SES transformations. The results also imply that the resilience of contemporary, open SESs cannot be explained by an inward-looking approach because essential connections and drivers at other scales have become relevant in the globalized world. Our study thus has helped to identify pitfalls in empirical resilience assessment and to improve the conceptualization of SES dynamics.

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.