Regime shfits montpellier

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Regime shfits montpellier

  1. 1. Causes & Consequences of Regime Shifts: A Network Analysis of Global Environmental Change Juan-Carlos Rocha, Oonsie Biggs & Garry Peterson
  2. 2. The Anthropocene
  3. 3. The Anthropocene
  4. 4. The Anthropocene Social challenge: Understand patters of causes and consequences of regime shifts ! How common they are? 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
  5. 5. Regime Shifts DataBase Established or proposed feedback mechanisms exist that maintain the different regimes. ! The shift substantially affect the set of ecosystem services provided by a social-ecological system ! The shift persists on time scale that impacts on people and society
  6. 6. Methods •Bipartite network and one- mode projections: 25 Regime shifts + 60 Drivers •10 4 random bipartite graphs to explore significance of couplings: mean degree, co-occurrence & clustering coefficient statistics on one- mode projections. •Multi-dimensional scaling Regime shiftsDrivers Regime Shift Database 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 Ecosystem services Ecosystem processes Ecosystem type Impact on human well being Land use Spatial scale Temporal scale Reversibility Evidence ...
  7. 7. Methods •Bipartite network and one- mode projections: 25 Regime shifts + 60 Drivers •10 4 random bipartite graphs to explore significance of couplings: mean degree, co-occurrence & clustering coefficient statistics on one- mode projections. •Multi-dimensional scaling Regime shiftsDrivers Regime Shift Database 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 Ecosystem services Ecosystem processes Ecosystem type Impact on human well being Land use Spatial scale Temporal scale Reversibility Evidence ...
  8. 8. Agriculture Atmospheric CO2 Deforestation Demand Droughts Erosion Fishing Floods Global warming Human population Landscape fragmentation Nutrients inputs Rainfall variability Sea level rise Sea surface temperature Sediments Sewage Temperature Upwellings Urbanization Arctic sea ice Bivalves collapse Coral transitions Dry land degradation Encroachment Eutrophication Fisheries collapse Floating plants Forest to savannas Greenland Hypoxia Kelps transitions Mangroves collapse Marine Eutrophication Marine foodwebs Monsoon weakening Peatlands River channel change Salt marshes Sea grass Soil salinization Soil structure Thermohaline circulation Tundra to Forest Western Antarctic IceSheet Collapse Simulation results for 25 Regime Shifts across the globe 1 3 5 7 9 11 14 17 21 Degree distribution Degree 051015202530 Clustering Coefficient Clustering coefficient Density 0.25 0.30 0.35 0.40 0.45 010203040 Drivers Network Co−occurrence Index s−squared Density 3.0 3.2 3.4 3.6 3.8 4.0 01234 Regime Shifts Network Co−occurrence Index s−squared Density 16 17 18 19 20 21 22 23 0.00.20.40.6 Average Degree in simulated Drivers Networks Mean Degree Density 27 28 29 30 31 32 33 0.00.20.40.6 Average Degree in simulated Regime Shifts Networks Mean Degree Density 18 19 20 21 22 23 24 0.00.40.81.2
  9. 9. Global drivers of Regime Shifts Agriculture Climate change Deforestation Disease Droughts Erosion Fertilizers use Fishing Floods Green house gases Landscape fragmentation Nutrients inputs Rainfall variability Sea surface temperature Sediments Sewage Temperature Turbidity Urbanization Few frequent drivers: Only 5 out of 60 drivers influence more than 1/2 of the regime shifts analyzed. More shared drivers: 11 drivers interact with >50% of other drivers when causing regime shifts. Food production & climate change drive the most frequent drivers of regime shifts
  10. 10. Global drivers of Regime Shifts Riverchannelchange ArcticSeaIce Thermohaline Greenland WAIS Steppetotundra Tundratoforest Coraltransitions Mangroves Kelpstransitions Fisheries MarineEutrhophication Eutrophication Bivalves SeaGrass Floatingplants Hypoxia Marinefoodwebs Peatlands SaltMarshestotidalflats Encroachment Soilsalinization ForesttoSavana Drylands Moonson Immigration and urbanization Infrastructure development Climate Biogeochemical Cycle Fishing and marine harvest Food production Resource exploitation Ecological processes Land Cover Change Water Nutrients and pollutants Biophysical Frecuency of disturbance Biodiversity Loss 0 4 8 Value 040 Color Key and Histogram Count Few frequent drivers: Only 5 out of 60 drivers influence more than 1/2 of the regime shifts analyzed. More shared drivers: 11 drivers interact with >50% of other drivers when causing regime shifts. Food production & climate change drive the most frequent drivers of regime shifts
  11. 11. How drivers tend to interact? 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 Marine regime shifts share significantly more drivers suggesting high similarity on their feedback mechanisms. Terrestrial regime shifts share fewer drivers. Higher diversity of drivers makes management more context dependent.
  12. 12. Multi-Dimensional Scaling −0.4 −0.2 0.0 0.2 0.4 −0.4−0.20.00.20.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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 Multi−Dimensional Scaling
  13. 13. Multi-Dimensional Scaling −0.4 −0.2 0.0 0.2 0.4 −0.4−0.20.00.20.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Ecosystem type Marine and coastal Freshwater lakes and rivers Moist savannas and woodlandsDrylands and deserts Grasslands Tundra Polar 0.8 0 0.8 0.800.8
  14. 14. Multi-Dimensional Scaling −0.4 −0.2 0.0 0.2 0.4 −0.4−0.20.00.20.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Ecosystem services FreshwaterLivestock Fisheries Climate regulation Water purification Recreation Aesthetic values 0.8 0 0.8 0.800.8
  15. 15. Multi-Dimensional Scaling −0.4 −0.2 0.0 0.2 0.4 −0.4−0.20.00.20.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Land Use Small scale subsistence crop cultivation Large scale commercial crop cultivation Extensive livestock production Fisheries2 Land use impact are primarily off site 0.8 0 0.8 0.800.8
  16. 16. Multi-Dimensional Scaling −0.4 −0.2 0.0 0.2 0.4 −0.4−0.20.00.20.4 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Scale Local Landscape Sub continental Regional Months Years Decades 0.6 0 0.6 0.600.6
  17. 17. Managing regime shift drivers Floating plants Bivalves collapse Eutrophication Fisheries collapse Coral transitions Hypoxia Encroachment Salt marshes Soil salinization Soil structure Forest to savannas Dry land degradation Kelps transitions Monsoon weakening Peatlands Marine foodwebs Greenland Thermohaline circulation River channel change Tundra to Forest Local National International Drivers by Management Type Proportion of RS Drivers 0.0 0.2 0.4 0.6 0.8 1.0 International cooperation to manage most drivers of 75% of regime shifts. Regulating single drivers, such as Climate change, won’t prevent regime shifts. Regulating local drivers can build resilience to global drivers Avoiding regime shifts requires poly-centric institutions.
  18. 18. Regime shifts are tightly connected both when sharing drivers and their underlying feedback dynamics. The management of immediate causes or well studied variables might not be enough to avoid such catastrophes. Food production and climate change are the main causes of regime shifts globally. Marine regime shifts share more drivers, while terrestrial regime shifts are more context dependent. Management of regime shifts requires multi-level governance: coordinating efforts across multiple scales of action. Network analysis is an useful approach to study regime shifts couplings when knowledge about system dynamics or time series of key variables are limited. Conclusions
  19. 19. Questions?? e-mail: juan.rocha@su.se twitter: @juanrocha slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog
  20. 20. Questions?? e-mail: juan.rocha@su.se twitter: @juanrocha slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog
  21. 21. Subscribe  to  our  newsletter   www.stockholmresilience.su.se/subscribe   Thank  you!

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