Emergent patterns in nature and society

Planet Under Pressure – Poster on Dynamic Tipping Points

Next week I’m going to Planet Under Pressure conference in London. On monday I will be presenting recent work on modeling two-mode networks (bipartite) to understand which regime shifts drivers co-occur more often than expected. I want to understand which combination of tipping points are more likely to interact when looking at global trends of regime shifts.

Here is the abstract submitted and the poster (hi-res pdf) for those that won’t make it among the hundreds of posters presented.

Dynamic Tipping Points: Which Ones Are More Likely to Interact?

Over the past 50 years humans have changed ecosystems faster and more extensively than in any other comparable period in the past 1, 2. These changes have contributed to various global syndromes 3 or regime shifts: abrupt, non-linear changes in social-ecological systems that can severely impact the flux of ecosystem services human societies rely upon4, 5.

Inspired by recent work on the application of relational networks to human diseases 6, 7, this paper explores the patterns of relationships among driving causes of regime shifts. Networks analysis offers a perspective for analysing regime shifts interactions in contexts where (i) time series data needed for early warnings and (ii) detailed information on causal mechanisms needed for models is limited or unavailable. Bipartite or two-mode networks are analysed based on information collected in the Regime Shifts Database (www.regimeshifts.org). Relations among drivers of change are studied for 20 regime shifts in marine, terrestrial and polar ecosystems. These regime shifts impact multiple ecosystem services, including provisioning services such as food (freshwater, crops and fish); regulating services such as water purification; as well as cultural services such as aesthetics and recreation. Regime shifts are a multi-causal phenomena; but preliminary results show that global drivers of regime shifts tend to interact in non-random patterns. Agriculture-related activities, global warming, biodiversity loss and economic drivers are the main causes of regime shifts independently of scale or ecosystem type.

A research framework is proposed to approach regime shifts management in high uncertainty settings which uses recent theoretical progress on network controllability8 to explore the analysis to causal mechanisms, feedback loop strength and cascading effects among regime shifts.

key words: regime shifts, network analysis, dynamic tipping points


1. Rockström, J., et al. (2009) A safe operating space for humanity. Nature 461, 472-475

2. Steffen, W., et al. (2007) The Anthropocene: Are humans now overwhelming the great forces of nature. Ambio 36, 614-621

3. Schellnhuber, H., et al. (1999) Syndromes of Global Change. Gaia 6, 19-34

4. Scheffer, M., and Carpenter, S. (2003) Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol. Evol. 18, 648-656

5. Scheffer, M., et al. (2001) Catastrophic shifts in ecosystems. Nature 413, 591-596

6. Barrenas, F., et al. (2009) Network Properties of Complex Human Disease Genes Identified through Genome-Wide Association Studies. PLoS ONE 4, e8090 EP  –

7. Goh, K.-I., et al. (2007) The human disease network. Proceedings of the National Academy of Sciences 104, 8685-8690

8. Liu, Y., et al. (2011) Controllability of Complex Networks. Nature


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