Cascading effects of critical transitions in
social-ecological systems
Juan Carlos Rocha
@juanrocha
The Anthropocene
The Anthropocene
Social challenge: Understand patterns 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
Cascading Effects CCS2016
Cascading Effects CCS2016
Can the occurrence of 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?
1. A comparative framework: the data
2. Forks: Global drivers & Impacts
3. Domino effects
4. Inconvenient feedbacks
Outline
@juanrocha
1. A comparative framework
1. A comparative framework
Biggs, et al. 2015. bioRxiv:018473.
Regime Shifts DataBase
The shift 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
@juanrocha
@juanrocha
@juanrocha
@juanrocha
Cascading Effects CCS2016
Cascading Effects CCS2016
Cascading Effects CCS2016
Forks: when sharing a 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
2. Forks: Drivers, Risk & Resilience
Rocha, et al. PLoS ONE 10:e0134639
Drivers
Natural or human induced 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
Methods
•Bipartite network and one-
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
Methods
•Bipartite network and one-
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
Agriculture
Aquaculture
Aquifers depletion
Climate change
Coastal erosion
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
Agriculture
Climate change
Deforestation
Disease
Droughts
ENSO like events
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
Agriculture
Climate change
Deforestation
Disease
Droughts
ENSO like events
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
ENSO like events
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
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
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
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.
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
3. Cascading & domino effects
[work in progress]
Causal-loop diagrams
Causal-loop diagrams is a
technique to map out the
feedback structure of a system
(Sterman 2000)
Agriculture
Coral abundance
CPUE
Deforestation
Demand
Disease outbreak
Erosion
Fertilizers use
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.
4. Inconvenient feedbacks
[work in progress]
4. Inconvenient feedbacks
[work in progress]
Cascading Effects CCS2016
Cascading Effects CCS2016
Desertification Soil Salinisation Desertification - Soil Salinisation
0
5
10
15
5 10 15 20
Feedback length
Numberoffeedbacks
Networks
RS1
RS2
RS.mix
Inc.Feed
Desertification - Soil Salinisation
Cascading Effects CCS2016
Regime shifts are tightly 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
Questions??
e-mail: juan.rocha@su.se twitter: @juanrocha
slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog
Questions??
e-mail: juan.rocha@su.se twitter: @juanrocha
slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog
Regime shifts are abrupt 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
Regime shifts are abrupt reorganisation of a system’s structure and
function.

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Cascading Effects CCS2016

  • 1. Cascading effects of critical transitions in social-ecological systems Juan Carlos Rocha @juanrocha
  • 4. Social challenge: Understand patterns 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 occurrence of 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 comparative framework: the data 2. Forks: Global drivers & Impacts 3. Domino effects 4. Inconvenient feedbacks Outline @juanrocha
  • 9. 1. A comparative framework
  • 10. 1. A comparative framework Biggs, et al. 2015. bioRxiv:018473.
  • 11. Regime Shifts DataBase The shift 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
  • 19. Forks: when sharing a 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 human induced 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 and one- 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 and one- 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 Coastal erosion 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 like events 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 like events 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 like events 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 Forest to 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 Forest to 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 diagrams is a technique to map out the feedback structure of a system (Sterman 2000)
  • 33. Agriculture Coral abundance CPUE Deforestation Demand Disease outbreak Erosion Fertilizers use 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.
  • 38. Desertification Soil Salinisation Desertification - 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 are tightly 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 are abrupt 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 are abrupt reorganisation of a system’s structure and function.