Emergent patterns in nature and society

The Domino Effect: A Network Analysis of Regime Shifts Drivers and Causal Pathways

My second talk in Resilience 2011 was about my recent work with the regime shifts database. In a nutshell, I’m analyzing regime shift’s causal mechanism in a network context to identify what are their main drivers of change and explore possible cascading effects. Here are the slides and the abstract presented in the conference. My MSc thesis is a simplified version of my work with causal networks of regime shifts. If you are interested it is available here.


The Domino Effect: A Network Analysis of Regime Shifts Drivers and Causal Pathways

Over the past 50 years humans have changed ecosystems faster and more extensively than in any other comparable period in the past. These changes have contributed to various types of regime shifts – abrupt, non-linear changes in social-ecological systems that can severely impact the flux of ecosystem services human societies rely upon.

We present an exploratory analysis of the causal interactions among global change drivers of regime shifts, based on information collated in the Regime Shifts Database*. We reviewed the documented evidence of over 20 policy-relevant regime shifts in ecosystems. Information on the dynamics of each regime shift was synthesized using causal-loop diagrams, a generic structure map of the system. We then identified the main drivers of change, the key impacts on ecosystem services, as well as possible cross-scale interactions among regime shifts drivers using network analysis.

Our results show that agriculture-related activities, global warming, biodiversity loss and economic drivers are the main causes of regime shifts. The ecosystem services most affected by regime shifts include provisioning services such as fisheries, as well as regulating services such as water purification. In addition, our work shows important impacts on cultural services, especially recreation.

Based on an analysis of the shortest pathways among regime shifts, we intuitively suggest five types of cascading effects. First, the exacerbation of feedback loops, which links for example eutrophication with hypoxia or coral bleaching with coral transitions. Second, the neighborhood effect refers to weak links where adjacency is required. Third, on diffuse connections where the linkage does not depend on spatial adjacency but on the connectivity of markets. The last three categories can be regrouped as horizontal coupling between regime shifts because processes affecting them seems to happen at the same scale range.

Our analysis further suggest that vertical coupling between regime shifts has an inherent scale related pattern. Regime shifts that have large spatial and slow temporal processes seems to be more influential when it comes to cascading-down interactions. Regime shifts which whose dynamics are very fast seems to have a role at cascading-up other regime shifts. We believe those scale related patterns are strongly influenced by drivers which change the frequency of the disturbance, such as fire, rain variability, or droughts and floods. Spatially explicit studies and mapping techniques are promising areas for further research exploring such connections.

Questions and critiques at the end of the talk were warmly welcome. They focused on the validity of my methods and some people even suggested different approaches to strength the results, which I appreciated. Right now it is very hard to conclude what are the main drivers of change by basing the analysis only on the network topology. More work is needed to look at the weight of linkages and how signals spread throughout the network. So far my analysis assumes on one hand that everything is or can be connected. On the other hand, it also assumes that all drivers are equally influential from the beginning while in reality they are describing processes at different rates of change and at different scales.  Thanks to these feedback, now I’m more confident of the necessity of weighing links based on dynamic models and adding important attributes about the scale, place and feedback strength.

However, I need some good methods to do so in a highly uncertain environment with poor data, and still grasp some meaning out of it. For this reason I sneak last week in the Network Theory Applications conference held in Stockholm. Some reflections on the lessons learnt will come soon.

Although I don’t have anything written yet of my recent work, comments are more than welcome!!

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One response

  1. Pingback: Networks: my academic muse… and some notes from Nordita conference « Critical Transitions

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