1. Causes & Consequences of Regime
Shifts: A Network Analysis of Global
Environmental Change
Juan-Carlos Rocha, Oonsie Biggs & Garry Peterson

2. The Anthropocene

3. The Anthropocene

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. 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. 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. 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. 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. 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. 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. 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. Multi-Dimensional Scaling
−0.4 −0.2 0.0 0.2 0.4
−0.4−0.20.00.20.4
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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. Multi-Dimensional Scaling
−0.4 −0.2 0.0 0.2 0.4
−0.4−0.20.00.20.4
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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. Multi-Dimensional Scaling
−0.4 −0.2 0.0 0.2 0.4
−0.4−0.20.00.20.4
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Ecosystem services
FreshwaterLivestock
Fisheries
Climate regulation
Water purification
Recreation
Aesthetic values
0.8 0 0.8
0.800.8

15. Multi-Dimensional Scaling
−0.4 −0.2 0.0 0.2 0.4
−0.4−0.20.00.20.4
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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. Multi-Dimensional Scaling
−0.4 −0.2 0.0 0.2 0.4
−0.4−0.20.00.20.4
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Scale
Local Landscape
Sub continental Regional
Months
Years
Decades
0.6 0 0.6
0.600.6

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. 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. Questions??
e-mail: juan.rocha@su.se twitter: @juanrocha
slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog

20. Questions??
e-mail: juan.rocha@su.se twitter: @juanrocha
slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog

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