Guided by the practice of explaining and mod-
eling single regime shifts as emergent dynamics
from fast and slow processes ( 2 , 8 , 11 , 20 , 23 – 25 ),
we hypothesized that cascading effects between
regime shift couplings were determined by cross-
scale interactions. For domino effects, we only
found support of cross-scale interactions in
time but not in space. For hidden feedbacks,
we found evidence of matching in space and
time. Together, these results show that the
likelihood of regime shift coupling depends
on cross-scale interactions but differs for each
cascading effect type. Lack of evidence for in-
teractions across spatial scales for domino effects
suggests that stochastic and transient dynamics
might be playing an important role in regime
shifts ( 32 ) and their cascading effects. A major
role of stochastic and transient dynamics in
regime shift–couplings limits the applicability of
early warning signals ( 10 , 21 ) to predict cascading
effects ( 25 ). Developing early warning signals for
coupled regime shifts is therefore a research need.
Synchronization of regime shifts in time or
space is a subject of debate ( 19 , 33 – 35 ). Tem-
poral correlations—typically induced by driver
sharing—canbebrokenbyspatialheterogeneity
( 19 ), indicating that context matters for corre-
lations to emerge. Spatial heterogeneity can
smooth out critical transitions ( 36 , 37 ). Yet,
identifying common drivers is useful for design-
ing management strategies that target bundles
of drivers instead of well-studied variables inde-
pendently, increasing the chances that manag-
ers will avoid several regime shifts under the
influence of the same sets of drivers ( 13 , 38 ). For
example, management options for drivers such
as sedimentation, nutrient leakage, and fishing
can reduce the likelihood of regime shifts such
as eutrophication and hypoxia in coastal brack-
ish lagoons as well as coral transitions in ad-
jacent coral reefs.
Our results complement previous findings
(table S1) by offering a wide spectrum of causal
hypotheses about how regime shifts can be cou-
pled. However, the limitations of our method
need to be acknowledged. Regime shifts were
represented as static networks, and the cascad-
ing effects were identified by matching two
pieces of information: variable names and posi-
tions within the causal diagram. Therefore, the
method identifies structural dependencies but
cannot predict how the dynamics will unfold in
space or time. For example, if a connection be-
tween mangrove collapse and coral transitions
is found through protection against coastal ero-
sion, geographical distance between the two
systems or the direction of oceanic currents
can change or even cancel out the coupling
strength. In fact, coupling strength is expected
to change from one place to another. Hence, our
method identifies plausible connections between
regime shifts, but identifying the conditions that
change plausible to probable requires more
detailed understanding of regime shift mecha-
nisms. Empirical studies and modeling synthe-
ses are required to translate our identification
of possible mechanisms into context-sensitive
forecasts. Dynamic models of these types of
dynamics require careful assumptions about pa-
rameter values and the functional form of the
system equations. Generalized modeling is a
promising technique that does not require
particular assumptions, allowing the researcher
to reach more general conclusions based on
stability properties of the system ( 39 , 40 ). An-
other potential avenue for future research is
looking at how transport mechanisms couple
physically distant ecosystems—for example,
through the moisture-recycling feedback ( 41 )or
internationaltrade ( 18 ). A key lesson from our
study is that regime shifts can be intercon-
nected. Regime shifts should not be studied in
isolation under the assumption that they are
independent systems. Methods and data col-
lection need to be further developed to account
for the possibility of cascading effects.
Our finding that ~45% of regime shift cou-
plings can have structural dependence suggests
that current approaches to environmental man-
agement and governance underestimate the
likelihood of cascading effects. More attention
should be paid to how Earth is social-ecologically
connected ( 18 ), how those connections should be
managed, and how to best prepare for regime
shifts. Our research suggests that regional eco-
systems can be transformed by ecosystem man-
agement far away and, conversely, can themselves
drive the transformations of other distant ecosys-
tems. Decisions made in one place can undermine
the achievement of sustainable development goals
in other places. For example, it has been shown
that many Arctic regime shifts have the potential
to affect non-Arctic ecosystems far away and the
provision of their ecosystem services ( 30 , 42 , 43 ).
It implies that whoever does make decisions on
management is not necessarily the one who has to
deal with the impacts. This issue is evident in
governance of water-transport systems, whether
run-off or atmospheric transport, but it is ap-
plicable to other dynamics that connect faraway
ecosystems through other mechanisms, such as
climate change, fire, nutrient inputs, or trade.
Our results highlight variables that are key for
domino effects and hidden feedbacks, such as
climate, agriculture, transport of nutrients, and
water. They are also good observables for moni-
toring early-warning indicators of the strengthen-
ing of regime shift coupling. How and when
nonlinear change can be transmitted across
space and time in the Earth system should be
considered in assessments and management
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ACKNOWLEDGMENTS
We are grateful to the contributors, reviewers, and developers of
the regime shifts database.Funding:This work was supported
by FORMAS grant 942-2015-731 to J.C.R. and National Science
Foundation grant OCE-1426746 to S.L.Author contributions:
J.C.R. designed the research; J.C.R. and G.P. curated the data;
J.C.R. wrote the code and ran the analysis, with guidance from
G.P., Ö.B., and S.L.; and J.C.R., G.P., Ö.B., and S.L. wrote the
paper.Competing interests:The authors declare no competing
interests.Data and materials availability:Data from the regime
shifts database are publicly available at http://www.regimeshifts.org.
The version of the database used and curated causal networks
are available in both the regime shifts database and at
https://doi.org/10.6084/m9.figshare.7265096.v1. The
development version of the code is available at https://github.
com/juanrocha/Domino.
SUPPLEMENTARY MATERIALS
http://www.sciencemag.org/content/362/6421/1379/suppl/DC1
Materials and Methods
Supplementary Text
Figs. S1 to S5
Tables S1 to S5
References ( 44 – 78 )
10 July 2018; accepted 30 October 2018
10.1126/science.aat7850Rochaet al.,Science 362 , 1379–1383 (2018) 21 December 2018 5of5
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