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
5.
6.
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
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
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
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
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.
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
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.