# Explaining and Measuring Social-Ecological Pathways: The Case of Global Changes and Water Security
## 5 Conclusions and Next Steps  
In this paper, we propose a model of change for SES that explains how institutions and natural resources systems co-evolve. In doing so, we offer a more dynamic understanding of SES that stands on three causal mechanisms: institutional complexity trap, rigidity trap, and learning processes. We rely on the adaptive cycle heuristic and focus on the saturation and the reorganization phases of a SES. We discuss how rigidity traps and institutional complexity traps drive the saturation of an SES until a crisis. Rigidity traps reduce adaptive capacity (i.e., the ability to explore alternative patterns) while institutional complexity traps reduce the ability to refine coordination mechanisms (i.e., the marginal effectiveness of new institutions). After a crisis, four ideal-types grasp the SES's diversity of reorganization patterns. We discuss how transformative and resilient synchronous regimes are more likely to frame a rebound. We argue that learning processes are pivotal in defining the ideal-type of reorganization pattern that the SES is likely to follow. Learning mechanisms and capacities contribute to determining the level of innovation and its scope of diffusion in a reorganizing SES. This model of change offers many testable propositions, however there are three main methodological limitations that prevent hypothesis testing and results-comparison. We highlighted these limitations using water security as an illustrative case. These limitations include: heterogeneity of measurement, case specificity and static perspective. These methodological limitations bound the external validity and reproducibility of research designs that address SES dynamics. We suggest three primary recommendations to overcome these limitations: (1) increase number of observations and the geographic scope; (2) provide replicable measurements regarding both data availability and method transparency; and (3) include time variables in the analysis--even in the case of panel dataset. Finally, we put forward Earth Observation Data Cubes (EODC) as a promising avenue to address these methodological limitations and recommendations, and thus to contribute to a better understanding of SES dynamics. Earth observations (EO) enable focusing on the spatial dimension of SESs and water security, but they are difficult to perform. Data cubes provide EO through a handy interface, exclude complex technical knowledge from the users, and combine data over the years. They make easier the combination of social and ecological data over many observations and facilitate broadening the space and time coverage, which makes it a unique tool for testing generalizable propositions of SES change. Moreover, data cubes enable the data structure fits with the theoretical unit of analysis. Indeed, the basic unit of EODC is the pixel, but then pixels could be aggregated according to the analytical needs. It means the dataset is less dependent of administrative boundaries but could stick with the resource footprint for instance. Overall, our contribution to the scholarship and research on SES is threefold. First, we provide a model of change to enable causal hypothesis on the evolution of SES. Second, we identify three methodological limitations preventing reliable and generalizable results on the evolution of SES through an illustration of water security. Finally, we propose new tools through the EODCs to overcome these limitations and enable an easy combination of social and ecological data over space and time. As next steps, we foresee two primary ways to combine social and ecological datasets. The first way consists of building a georeferencing social network and linking this with ecological system [28]. The second way is to give a spatial perimeter to institutions and to link this with the ecological system.