2. When to adapt or when to transform?
Using network controllability to assess
how manageable are regime shifts
!
!
Juan-Carlos Rocha

3. Regime Shifts Transformations
Figures from Arctic Resilience Assessment
Interim Report 2012

4. • Systems can be represented as a
network of interacting elements
• Identifying controlling nodes is
possible using only structural
information of the network:
• # of driving nodes correlates with
degree distribution
• driver nodes tend to avoid high-
degree nodes
• Heterogeneous networks (most real)
are difficult to control. Homogeneous
dense networks are more
controllable = fewer driving nodes
Liu et al, 2012

5. Are regime shifts controllable? To what extent can
we manage them?
• Critics to Liu et al.:
• Topology is not enough
• Internal dynamics
• “We argue that more important
than issues of structural
controllability are the questions
of whether a system is almost
uncrontrollable, whether it is
almost unobservable…”
Cowan et al, 2012

6. • Focus on edge dynamics:
heterogeneous and sparse
networks have more
controllable edge dynamics
than homogeneous dense
networks.
• Contradictory results?
Are regime shifts controllable? To what extent can
we manage them?

7. Driver
… is any natural or human-
induced factor that directly or
indirectly causes a change in
an [eco]system. A direct driver
unequivocally influences
ecosystem processes. An
indirect driver operates more
diffusely by altering one or
more direct drivers.

8. Bivalves collapse
Bivalves
abundance
Dissolved oxigen
Biodiversity
Habitat structural
complexity
Local water
movements
+
+
+
+
+
Fishing
Plankton and
filamentous algae
-
Water turbidity
-
-
B
B
R
R
Nutrients input
Agriculture Urbanization
SewageFertilizer Use
Deforestation
+
+
+
+
+
Demand for
food & fibre
+
mid-predator fish
-
-
+
+
B
Filtration
+
-
Erosion
+
+
Nutrients in water
-+
+
+
+
Logging
+
+
Flooding
+
Disease
-
+
sedimentation
+
-
Shellfish harvest
-
+
+ B
B
Urban Storm
Water Runoff
+
+
Precipitation
Variability
+
+
Aquaculture
+
+
Hurricane
-
+

9. My own critiques
• Unmatched nodes change if the
periphery of the causal networks
change - The limits of the system
blur
• Unmatched nodes change when
joining causal networks to
understand cascading effects.
• I believe there is opportunities to
combine network science and
resilience science to answer the
question: When do we build
resilience and where do we need
transformational change? Causal Loop Diagrams for
19 regime shifts around the world

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Thank you!
Does it make sense??
Ideas, tomatoes or opportunities for collaboration:
e-mail: juan.rocha@su.se twitter: @juanrocha
slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog