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Cascading Effects CCS2016
- 1. Cascading effects ofcritical transitions in social-ecological systems Juan Carlos Rocha @juanrocha
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- 4. Social challenge: Understandpatterns of causes and consequences of regime shifts How common they are? What are the main drivers? Where are they likely to occur? Who will be most affected? What can we do to avoid them? What possible interactions or cascading effects? Science challenge: understand phenomena where experimentation is rarely an option, data availability is poor, and time for action a constraint The Anthropocene
- 7. Can the occurrenceof one regime shift in an area of the world increase or decrease the likelihood of other regime shifts in apparently disconnected systems? Can the study of regime shifts networks help us elucidate potential hypothesis for these tele-connections?
- 8. 1. A comparativeframework: the data 2. Forks: Global drivers & Impacts 3. Domino effects 4. Inconvenient feedbacks Outline @juanrocha
- 9. 1. A comparativeframework
- 10. 1. A comparativeframework Biggs, et al. 2015. bioRxiv:018473.
- 11. Regime Shifts DataBase Theshift substantially affect the set of ecosystem services provided by a social-ecological system Established or proposed feedback mechanisms exist that maintain the different regimes. The shift persists on time scale that impacts on people and society @juanrocha www.regimeshifts.org
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- 19. Forks: when sharinga driver synchronize two regime shifts Causal chains: the domino effect Inconvenient feedbacks: when two shifts reinforce or dampen each other RS1 RS2 RS3 D1 RS1 RS2D1 ... RS1 RS2 D2D1 Arctic Icesheet collapse Bivalves collapse Coral bleaching Coral transitions Desertification Encroachment Eutrophication Fisheries collapse Floating plants Foodwebs Forest to cropland Forest to savanna Greenland icesheet collapse Hypoxia Kelp transitions Monsoon Peatlands Soil salinization Soil structure Thermohaline Tundra to forest Arctic salt marsh River channel change
- 20. 2. Forks: Drivers,Risk & Resilience Rocha, et al. PLoS ONE 10:e0134639
- 21. Drivers Natural or humaninduced changes that have been identified as directly or indirectly producing a regime shift Causal-loop diagrams is a technique to map out the feedback structure of a system (Sterman 2000) @juanrocha
- 22. Methods •Bipartite network andone- mode projections: 25 Regime shifts + 57 Drivers •10 4 random bipartite graphs to explore significance of couplings: mean degree and co-occurrence statistics on one-mode projections. •ERGM models using Jaccard similarity index on the RSDB as edge covariates & MDS Regime shiftsDrivers A 1 0 1 1 0 0 0 0 1 1 1 1 0 1 0 1 B 1 0 0 0 1 1 0 0 1 1 1 0 0 1 0 1 C Regime Shift Database Ecosystem services Ecosystem processes Ecosystem type Impact on human well being Land use Spatial scale Temporal scale Reversibility Evidence ... @juanrocha
- 23. Methods •Bipartite network andone- mode projections: 25 Regime shifts + 57 Drivers •10 4 random bipartite graphs to explore significance of couplings: mean degree and co-occurrence statistics on one-mode projections. •ERGM models using Jaccard similarity index on the RSDB as edge covariates & MDS Regime shiftsDrivers A 1 0 1 1 0 0 0 0 1 1 1 1 0 1 0 1 B 1 0 0 0 1 1 0 0 1 1 1 0 0 1 0 1 C Regime Shift Database Ecosystem services Ecosystem processes Ecosystem type Impact on human well being Land use Spatial scale Temporal scale Reversibility Evidence ... @juanrocha
- 24. Agriculture Aquaculture Aquifers depletion Climate change Coastalerosion Deforestation Disease Droughts ENSO like events Erosion Estuarine fresh water input Estuarine salinity Fertilizers use Fire frequency Fishing Floods Flushing Green house gases Harvesting (animals) Hunting Ice melt water Impoundments Invasive species Irrigation Landscape fragmentation Logging Low tides Nutrient inputs Ocean acidification Pollutants Precipitation Production intensification Rainfall variability Ranching (livestock) River channelization Roads and railways Salt water intrusion Sea level rise Sea surface temperature Sea water density Sediments Sewage Soil moisture Storms Temperature Thermal anomalies in summer Turbidity Upwellings Urban storm water runoff Urbanization Water depth Water infrastructure Water level fluctuation Water stratification Water vapor Wetland Drainage Wind stress Arctic Sea Ice Bivalves Coral transitions Drylands Encroachment Eutrophication Fisheries Floating plants Forest to Savana Greenland Hypoxia Kelps transitions Mangroves Marine Eutrhophication Marine food webs Moonson Peatlands River channel change Salt Marshes to tidal flats Sea Grass Soil salinization Steppe to tundra Thermohaline Tundra to forest WAIS
- 25. Agriculture Climate change Deforestation Disease Droughts ENSO likeevents Erosion Fertilizers use Fire frequency Fishing Floods Flushing Green house gases Hunting Invasive species Irrigation Landscape fragmentation Nutrient inputs Precipitation Production intensification Rainfall variability Ranching (livestock) River channelization Roads and railways Salt water intrusion Sea surface temperature Sea water density Sediments Sewage Temperature Turbidity Upwellings Urban storm water runoff Urbanization Water infrastructure Water stratification Water vapor Wetland Drainage Wind stress Bivalves Coral transitions Drylands Encroachment Eutrophication Fisheries Floating plants Forest to Savana Hypoxia Kelps transitions Mangroves Marine Eutrhophication Marine food webs River channel change Salt Marshes to tidal flats Sea Grass Soil salinization
- 26. Agriculture Climate change Deforestation Disease Droughts ENSO likeevents Erosion Fertilizers use Fire frequency Fishing Floods Flushing Green house gases Hunting Invasive species Irrigation Landscape fragmentation Nutrient inputs Precipitation Production intensification Rainfall variability Ranching (livestock) River channelization Roads and railways Salt water intrusion Sea surface temperature Sea water density Sediments Sewage Temperature Turbidity Upwellings Urban storm water runoff Urbanization Water infrastructure Water stratification Water vapor Wetland Drainage Wind stress
- 27. Agriculture Climate change Deforestation Disease Droughts ENSO likeevents Erosion Fertilizers use Fire frequency Fishing Floods Flushing Green house gases Hunting Invasive species Irrigation Landscape fragmentation Nutrient inputs Precipitation Production intensification Rainfall variability Ranching (livestock) River channelization Roads and railways Salt water intrusion Sea surface temperature Sea water density Sediments Sewage Temperature Turbidity Upwellings Urban storm water runoff Urbanization Water infrastructure Water stratification Water vapor Wetland Drainage Wind stress Agriculture Climate change Deforestation Disease Droughts Erosion Fertilizers use Fishing Floods Green house gases Landscape fragmentation Nutrient inputs Rainfall variability Sea surface temperature Sediments Sewage Temperature Urbanization > likelihood of drivers co-occurrence if drivers that can be managed at local - regional scales and if they are indirect & generalist
- 28. Bivalves Coral transitions Drylands Encroachment Eutrophication Fisheries Floating plants Forestto Savana Hypoxia Kelps transitions Mangroves Marine Eutrhophication Marine food webs River channel change Salt Marshes to tidal flats Sea Grass Soil salinization
- 29. Bivalves Coral transitions Drylands Encroachment Eutrophication Fisheries Floating plants Forestto Savana Hypoxia Kelps transitions Mangroves Marine Eutrhophication Marine food webs River channel change Salt Marshes to tidal flats Sea Grass Soil salinization Arctic Sea Ice Bivalves Coral transitions Drylands Encroachment Eutrophication Fisheries Floating plants Forest to Savana Greenland Hypoxia Kelps transitions Mangroves Marine Eutrhophication Marine food webs Moonson Peatlands River channel change Salt marshes to tidal flats Sea Grass Soil salinization Steppe to tundra Thermohaline Tundra to forest WAIS Aquatic share more and more or less the same set of drives while terrestrial and sub- continental are more drivers diverse. Higher driver co-occurrence if regime shifts share: ecosystem type, ecosystem processes, impacts on ES and scales.
- 30. The governance &management challenge • Managerial actions need to be coordinated across scales. • 62% of drivers can be managed locally or regionally • Addressing local & regional drivers can build resilience and delay the effect of global ones; but there is not blue print solutions. @juanrocha
- 31. 3. Cascading &domino effects [work in progress]
- 32. Causal-loop diagrams Causal-loop diagramsis a technique to map out the feedback structure of a system (Sterman 2000)
- 33. Agriculture Coral abundance CPUE Deforestation Demand Disease outbreak Erosion Fertilizersuse Fishing Food supply Global warming Green house gases Herbivores Human population Hurricanes Logging Low tides frequency Macroalgae abundance Nutrients input Ocean acidification Other competitors Pollutants Sediments Sewage Space SST Thermal annomalies Top predators Turbidity Unpalatability Urbanization Zooxanthellae A B C Agriculture Fertilizers use Deforestation Coral abundance Zooxanthellae Space Disease outbreak CPUE Food supply Erosion Demand Fishing Logging Herbivores Sediments Nutrients input Top predators Global warming SST Green house gases Ocean acidification Macroalgae abundance Human population Hurricanes Low tides frequency Unpalatability Turbidity Other competitors Pollutants Sewage Thermal annomalies Urbanization D A worked example. A) shows a CLD for coral transitions as reported on RSDB. B) is a network representation of the same CLD where positive links are blue and negative red. C) identifies communities of drivers and processes based on a community detection algorithm. D) shows a network of 19 regime shifts CLD’s where drivers are identified in orange and other variables in yellow. The giant component of the network suggest a large potential pathways of connections between regime shifts drivers and processes, thus plausible cascading effects.
- 34. 4. Inconvenient feedbacks [workin progress]
- 35. 4. Inconvenient feedbacks [workin progress]
- 38. Desertification Soil SalinisationDesertification - Soil Salinisation 0 5 10 15 5 10 15 20 Feedback length Numberoffeedbacks Networks RS1 RS2 RS.mix Inc.Feed Desertification - Soil Salinisation
- 40. Regime shifts aretightly connected both when sharing drivers and their underlying feedback dynamics. Great potential for cascading effects. Food production and climate change are the main causes of regime shifts globally. The management of immediate causes or well studied variables might not be enough to avoid such catastrophes. A graphical framework to explore potential regime shifts interconnections has been developed. An empirical frameworks to test the plausibility of such interconnections is still needed. Conclusions
- 41. Questions?? e-mail: juan.rocha@su.se twitter:@juanrocha slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog
- 42. Questions?? e-mail: juan.rocha@su.se twitter:@juanrocha slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog
- 43. Regime shifts areabrupt reorganisation of a system’s structure and function. collapse collapse recovery Precipitation Vegetation Precipitation Vegetation Precipitation Vegetation Precipitation Vegetation Precipitation Precipitation Precipitation Precipitation low high low high low high low high Vegetation low high Vegetation low high Vegetation low high Vegetation low high Stability Landscape Equilibria
- 44. Regime shifts areabrupt reorganisation of a system’s structure and function.