161
oung et al. 2008; Rocha et al. 2015). For instance, regime
shifts in the North Sea and English Channel communities
were only detected 10 years after they occurred (Beaugrand
2004; Auber et al. 2015). This late detection may partly be
explained by the very large scale at which these shifts
occurred and highlights the need of studying different spatial
scales when wanting to understand ecosystems processes
and dynamics. Similarly, temporal scales of changes might
be different depending on the lifespan of the affected organisms and might lead to temporal lags in system responses to
stressors (Holling 1973; deYoung et al. 2008) as it was the
case in the North Sea. These differences in spatio-temporal
patterns need to be addressed and disentangled as they might
hinder or delay regime shift detection and exacerbate social
and economic consequences (Levin 1992; Scheffer and
Carpenter 2003; Kerkhoff and Enquist 2007; Levin and
Möllmann 2015). It might also be necessary to disentangle
regime shifts (sensu Selkoe et al. 2015) from simple logistic
dynamics and highlight hysteresis (which requires additional
observations in time). For these reasons, regime shift detection requires long and extensive observation datasets of the
system which is generally costly in time and money
(Carpenter 2001; Scheffer et al. 2009; Levin and Möllmann
2015). Moreover, the required time to obtain time series of
suitable length might prove too long, particularly when such
shifts strongly impact ecosystems services and human wellbeing. For these reasons, experimental studies are necessary
to enhance the understanding of systems responses to disturbances (Angeler et al. 2016). Particularly, experiments may
help to understand multi-causality and dual relationships
between stressors and systems which generally participate in
hindering detection of regimes shifts (Scheffer and Carpenter
2003; Conversi et al. 2015; Levin and Möllmann 2015).
While regime shifts detection may be delayed, their unexpected and abrupt behavior hinders regime shift prediction,
which is necessary to ensure effective management measures. In addition, a post-regime shift detection may result in
increased management challenges, particularly due to hysteresis, as described in the previous section for coral reefs
(Mumby et al. 2007; Mumby 2009), kelp forests (Steneck
et al. 2002) and various fish stock shifts (Myers et al. 1997;
Hutchings 2000; Myers and Worm 2005; Hutchings and
Rangeley 2011). Challenges in prediction may be partly
related to the common use of linear relationships to statistically describe natural processes which need to be overcome
in favor of more realistic (thus more complex) models
(Holling 1973; Ludwig et al. 1997; Scheffer and Carpenter
2003). Indeed, the non-linear relationships between stressors
and system variables need to be understood to be able to correctly predict system responses. Also, a new branch of science has been currently developing regime shift indicators,
the so-called early-warning signals, to anticipate regimes
shifts. These signals are generally based on the fact that the
recovery of a highly disturbed system to an equilibrium is
slow, i.e., critically slowing down (Scheffer et al. 2001, 2015;
Dakos et al. 2012; Lindegren et al. 2012). Indeed, when systems are close to tipping points, their stability decreases,
generally leading to an increased variability, and autocorrelation of the data describing them. These indicators work
well with simulation models but still they have some limitations in predicting shifts using empirical data (Dakos et al.
2008, 2017; Scheffer et al. 2009; Dai et al. 2013). They may
be constrained by the length of the times series available and/
or the limited amount of data, by methodological assumptions and/or sampling errors (deYoung et al. 2008; Lindegren
et al. 2012). Moreover, they are not suitable to predict stochastically driven shifts. To overcome these limitations Lade
and Gross (2012) developed a new approach to detect early
warning signals with reduced time-series. Lindegren et al.
(2012) recommended a multiple approach based on knowledge of the system and its local characteristics (key ecological thresholds, relationships with drivers), data availability,
sensitivity and bias of the analysis carried out. Such advances
need to be followed by the scientific community to develop
more approaches overcoming these limitations. Alternative
sources of data, e.g., public records and narratives, must be
found and used, particularly when ecological data are not
available, and systems must be monitored at an appropriate
time scale to ensure shift detection as early as possible.
Because prediction of regime shifts is so challenging, and
because the potential consequences for ecosystem services
and human well-being may be abrupt and very difficult (or
even impossible) to reverse, precautionary approaches are
recommended (Holling 1973; Carpenter 2001; Scheffer and
Carpenter 2003; Selkoe et al. 2015). When managing systems
prone to regime shifts, risks and uncertainties must be
assessed before any management action is taken (Levin and
Möllmann 2015; DePiper et al. 2017). Risk assessment
requires a clear definition of the system of interest, its potential tipping points, as well as suitable indicators. However, all
the challenges already mentioned (multiple-causality, dual
relationships to drivers, spatio-temporal different patterns,
limitation of data, etc.) may impede the definition of appropriate indicators (Kelly et al. 2015; Selkoe et al. 2015). For
instance, Vasilakopoulos and Marshall (2015) showed that
the spawning stock biomass (SSB) of Barents Sea cod did not
suffice to detect a regime shift of this population, while SSB
levels are generally the reference points used in current fishery management plans (single- or multi-species advices), and
sometimes the only ones. These results evidence the need to
base scientific advice to fishery managers on the monitoring
of several ecosystem (community/population) parameters,
particularly when suspecting potential impending shifts.
Similarly, stressors effects may be unclear when studied individually, while their importance may appear only when combined with other stressors (Rocha et al. 2015; Vasilakopoulos
Regime Shifts – A Global Challenge for the Sustainable Use of Our Marine Resources
oung et al. 2008; Rocha et al. 2015). For instance, regime
shifts in the North Sea and English Channel communities
were only detected 10 years after they occurred (Beaugrand
2004; Auber et al. 2015). This late detection may partly be
explained by the very large scale at which these shifts
occurred and highlights the need of studying different spatial
scales when wanting to understand ecosystems processes
and dynamics. Similarly, temporal scales of changes might
be different depending on the lifespan of the affected organisms and might lead to temporal lags in system responses to
stressors (Holling 1973; deYoung et al. 2008) as it was the
case in the North Sea. These differences in spatio-temporal
patterns need to be addressed and disentangled as they might
hinder or delay regime shift detection and exacerbate social
and economic consequences (Levin 1992; Scheffer and
Carpenter 2003; Kerkhoff and Enquist 2007; Levin and
Möllmann 2015). It might also be necessary to disentangle
regime shifts (sensu Selkoe et al. 2015) from simple logistic
dynamics and highlight hysteresis (which requires additional
observations in time). For these reasons, regime shift detection requires long and extensive observation datasets of the
system which is generally costly in time and money
(Carpenter 2001; Scheffer et al. 2009; Levin and Möllmann
2015). Moreover, the required time to obtain time series of
suitable length might prove too long, particularly when such
shifts strongly impact ecosystems services and human wellbeing. For these reasons, experimental studies are necessary
to enhance the understanding of systems responses to disturbances (Angeler et al. 2016). Particularly, experiments may
help to understand multi-causality and dual relationships
between stressors and systems which generally participate in
hindering detection of regimes shifts (Scheffer and Carpenter
2003; Conversi et al. 2015; Levin and Möllmann 2015).
While regime shifts detection may be delayed, their unexpected and abrupt behavior hinders regime shift prediction,
which is necessary to ensure effective management measures. In addition, a post-regime shift detection may result in
increased management challenges, particularly due to hysteresis, as described in the previous section for coral reefs
(Mumby et al. 2007; Mumby 2009), kelp forests (Steneck
et al. 2002) and various fish stock shifts (Myers et al. 1997;
Hutchings 2000; Myers and Worm 2005; Hutchings and
Rangeley 2011). Challenges in prediction may be partly
related to the common use of linear relationships to statistically describe natural processes which need to be overcome
in favor of more realistic (thus more complex) models
(Holling 1973; Ludwig et al. 1997; Scheffer and Carpenter
2003). Indeed, the non-linear relationships between stressors
and system variables need to be understood to be able to correctly predict system responses. Also, a new branch of science has been currently developing regime shift indicators,
the so-called early-warning signals, to anticipate regimes
shifts. These signals are generally based on the fact that the
recovery of a highly disturbed system to an equilibrium is
slow, i.e., critically slowing down (Scheffer et al. 2001, 2015;
Dakos et al. 2012; Lindegren et al. 2012). Indeed, when systems are close to tipping points, their stability decreases,
generally leading to an increased variability, and autocorrelation of the data describing them. These indicators work
well with simulation models but still they have some limitations in predicting shifts using empirical data (Dakos et al.
2008, 2017; Scheffer et al. 2009; Dai et al. 2013). They may
be constrained by the length of the times series available and/
or the limited amount of data, by methodological assumptions and/or sampling errors (deYoung et al. 2008; Lindegren
et al. 2012). Moreover, they are not suitable to predict stochastically driven shifts. To overcome these limitations Lade
and Gross (2012) developed a new approach to detect early
warning signals with reduced time-series. Lindegren et al.
(2012) recommended a multiple approach based on knowledge of the system and its local characteristics (key ecological thresholds, relationships with drivers), data availability,
sensitivity and bias of the analysis carried out. Such advances
need to be followed by the scientific community to develop
more approaches overcoming these limitations. Alternative
sources of data, e.g., public records and narratives, must be
found and used, particularly when ecological data are not
available, and systems must be monitored at an appropriate
time scale to ensure shift detection as early as possible.
Because prediction of regime shifts is so challenging, and
because the potential consequences for ecosystem services
and human well-being may be abrupt and very difficult (or
even impossible) to reverse, precautionary approaches are
recommended (Holling 1973; Carpenter 2001; Scheffer and
Carpenter 2003; Selkoe et al. 2015). When managing systems
prone to regime shifts, risks and uncertainties must be
assessed before any management action is taken (Levin and
Möllmann 2015; DePiper et al. 2017). Risk assessment
requires a clear definition of the system of interest, its potential tipping points, as well as suitable indicators. However, all
the challenges already mentioned (multiple-causality, dual
relationships to drivers, spatio-temporal different patterns,
limitation of data, etc.) may impede the definition of appropriate indicators (Kelly et al. 2015; Selkoe et al. 2015). For
instance, Vasilakopoulos and Marshall (2015) showed that
the spawning stock biomass (SSB) of Barents Sea cod did not
suffice to detect a regime shift of this population, while SSB
levels are generally the reference points used in current fishery management plans (single- or multi-species advices), and
sometimes the only ones. These results evidence the need to
base scientific advice to fishery managers on the monitoring
of several ecosystem (community/population) parameters,
particularly when suspecting potential impending shifts.
Similarly, stressors effects may be unclear when studied individually, while their importance may appear only when combined with other stressors (Rocha et al. 2015; Vasilakopoulos
Regime Shifts – A Global Challenge for the Sustainable Use of Our Marine Resources