162
and Marshall 2015). The factors undermining resilience
(eutrophication, global warming, species invasion, etc.)
should be of prior concern as small variations in stressors
might lead to large changes in ecosystem structure and/or
functioning when resilience is eroded (Ricker 1963; Ludwig
et al. 1997; Scheffer et al. 2001; Beisner et al. 2003; Scheffer
and Carpenter 2003). The quantitative assessment of risk and
associated resilience is difficult and challenging. Economic
cost-benefit analysis might be useful when trying to quantify
risks for ecosystem services (Carpenter 2001), however, it
might totally underestimate them when too narrow-focused,
e.g., focusing on yield in fisheries while neglecting age-structure of the stock (deYoung et al. 2008). Quantitative assessment of resilience may prove very useful but requires an
extensive amount of data particularly in complex systems
(Vasilakopoulos and Marshall 2015). Therefore, qualitative
analysis and/or conceptual models may be preferred (DePiper
et al. 2017), particularly when studying data-poor systems or
when dealing with complex adaptive systems such as socialecological ones.
Despite the increasing effort in scientific research, even
when risk (or resilience) may be assessed, ecological uncertainties (about system evolution) and livelihood uncertainties
(about impacts on human communities) related to regime
shifts are high (Pindyck 2000). When managing socialecological systems (SES) prone to regime shifts, policy makers must face these uncertainties and different management
strategies might emerge: reducing or limiting system stressors (mitigation), building up system resilience (adaptation)
and/or reversing a shift (restoration, Kates et al. 2012;
Angeler et al. 2013). These strategies might have different
outcomes, benefits, costs and efficiency depending of goals
and focus of management as well as the status of the system
(Selkoe et al. 2015; Lade et al. 2015; Fenichel and Horan
2016; Mathias et al. 2017). For example, because of hysteresis, building up resilience might be more effective and less
costly than restoration measures (Selkoe et al. 2015). These
measures might also require different levels of governance.
For instance, the reduction of tuna fishing effort in the Pacific
Ocean would require an international consortium for management to be efficient while similar measures applied to a
coral reef fishery would be relevant at the local management
scale. In addition, when mitigation generally requires international and global management (e.g., gas emissions reduction), building up systems resilience (adaptation) may
succeed at local scales, countering global inaction (Rocha
et al. 2015). While decreasing variance of a system may
seem a good idea, Carpenter et al. (2015) highlighted the
adverse effects for system resilience management. Staying
within a safe-operating space (Rockström et al. 2009),
including uncertainties around tipping points and using history as guideline (Fenichel and Horan 2016; Liski and
Salanié 2016) might, however, prove effective and reduce
risks of management failures. Adversely, managers might
need to erode resilience of a system to tip it towards a preferable regime, i.e., more pristine or more valuable (Derissen
et al. 2011). This so-called transformation would require
intentional changes in the institutional framework in which
the utilization of marine systems (e.g., including switch to a
novel management system), as well as a transparent and
equitable redistribution of benefits across stakeholders takes
place (Selkoe et al. 2015). Uncertainties may as well increase
immediate costs, and even if costs of inaction would be high
in the future, they might hinder immediate decisions (Pindyck
2000; Selkoe et al. 2015).
Adaptive co-management might be ideal when cooperation between local and global stakeholders is possible
(Plummer et al. 2017). However, it might slow down management processes opposed to the potential flexibility and
responsiveness of local stakeholders required for a good
management of regime shift effects (deYoung et al. 2008;
Horan et al. 2011; Blenckner et al. 2015a; Rocha et al. 2015;
Valman et al. 2016). Similarly, polycentric governance holds
great potential at the international scale but is vulnerable to
negative interactions between institutions and weak coordination (Galaz et al. 2012; Mathias et al. 2017). In both cases,
the question of responsibility might be raised in case of management failures (Baumgärtner et al. 2006; Fenichel and
Horan 2016). Local and/or global stakeholder cooperation,
as well as responsiveness, may be improved by the knowledge of the stressors involved in regime shifts mechanisms,
their shared interactions with the different components of the
system, and the different scales at which they interact (Rocha
et al. 2015). Such knowledge may also help policy makers to
set suitable management targets otherwise challenged when
uncertainties are high.
Finally, the integration of management and regime shift
theory may prove quite complicated. The complex responses
to stressors, the multiple, cross-disciplinary interactions
between each system components, the high uncertainties and
the different stakeholder perspectives and conflicts need to
be understood and accounted for when considering regime
shifts (and/or resilience) in social-ecological systems (SES)
management decisions. This requires holistic and integrative
approaches such as integrative ecosystem assessment (IEA,
(Levin and Möllmann 2015). In this context, scientists have
recently developed frameworks to conceptualize SES and
assess their sustainability and uncertainties (Ostrom 2009;
Leslie et al. 2015; Levin et al. 2016). Particularly, these
frameworks allow the combination of classic scientific information and local stakeholders’ ecological, cultural and/or
social knowledge of the system. These conceptual models
may be used to promote interdisciplinary research, discussions between stakeholders, and allow a holistic management strategy evaluation after their operationalization (Levin
and Möllmann 2015; Levin et al. 2016; DePiper et al. 2017).
C. Sguotti and X. Cormon
and Marshall 2015). The factors undermining resilience
(eutrophication, global warming, species invasion, etc.)
should be of prior concern as small variations in stressors
might lead to large changes in ecosystem structure and/or
functioning when resilience is eroded (Ricker 1963; Ludwig
et al. 1997; Scheffer et al. 2001; Beisner et al. 2003; Scheffer
and Carpenter 2003). The quantitative assessment of risk and
associated resilience is difficult and challenging. Economic
cost-benefit analysis might be useful when trying to quantify
risks for ecosystem services (Carpenter 2001), however, it
might totally underestimate them when too narrow-focused,
e.g., focusing on yield in fisheries while neglecting age-structure of the stock (deYoung et al. 2008). Quantitative assessment of resilience may prove very useful but requires an
extensive amount of data particularly in complex systems
(Vasilakopoulos and Marshall 2015). Therefore, qualitative
analysis and/or conceptual models may be preferred (DePiper
et al. 2017), particularly when studying data-poor systems or
when dealing with complex adaptive systems such as socialecological ones.
Despite the increasing effort in scientific research, even
when risk (or resilience) may be assessed, ecological uncertainties (about system evolution) and livelihood uncertainties
(about impacts on human communities) related to regime
shifts are high (Pindyck 2000). When managing socialecological systems (SES) prone to regime shifts, policy makers must face these uncertainties and different management
strategies might emerge: reducing or limiting system stressors (mitigation), building up system resilience (adaptation)
and/or reversing a shift (restoration, Kates et al. 2012;
Angeler et al. 2013). These strategies might have different
outcomes, benefits, costs and efficiency depending of goals
and focus of management as well as the status of the system
(Selkoe et al. 2015; Lade et al. 2015; Fenichel and Horan
2016; Mathias et al. 2017). For example, because of hysteresis, building up resilience might be more effective and less
costly than restoration measures (Selkoe et al. 2015). These
measures might also require different levels of governance.
For instance, the reduction of tuna fishing effort in the Pacific
Ocean would require an international consortium for management to be efficient while similar measures applied to a
coral reef fishery would be relevant at the local management
scale. In addition, when mitigation generally requires international and global management (e.g., gas emissions reduction), building up systems resilience (adaptation) may
succeed at local scales, countering global inaction (Rocha
et al. 2015). While decreasing variance of a system may
seem a good idea, Carpenter et al. (2015) highlighted the
adverse effects for system resilience management. Staying
within a safe-operating space (Rockström et al. 2009),
including uncertainties around tipping points and using history as guideline (Fenichel and Horan 2016; Liski and
Salanié 2016) might, however, prove effective and reduce
risks of management failures. Adversely, managers might
need to erode resilience of a system to tip it towards a preferable regime, i.e., more pristine or more valuable (Derissen
et al. 2011). This so-called transformation would require
intentional changes in the institutional framework in which
the utilization of marine systems (e.g., including switch to a
novel management system), as well as a transparent and
equitable redistribution of benefits across stakeholders takes
place (Selkoe et al. 2015). Uncertainties may as well increase
immediate costs, and even if costs of inaction would be high
in the future, they might hinder immediate decisions (Pindyck
2000; Selkoe et al. 2015).
Adaptive co-management might be ideal when cooperation between local and global stakeholders is possible
(Plummer et al. 2017). However, it might slow down management processes opposed to the potential flexibility and
responsiveness of local stakeholders required for a good
management of regime shift effects (deYoung et al. 2008;
Horan et al. 2011; Blenckner et al. 2015a; Rocha et al. 2015;
Valman et al. 2016). Similarly, polycentric governance holds
great potential at the international scale but is vulnerable to
negative interactions between institutions and weak coordination (Galaz et al. 2012; Mathias et al. 2017). In both cases,
the question of responsibility might be raised in case of management failures (Baumgärtner et al. 2006; Fenichel and
Horan 2016). Local and/or global stakeholder cooperation,
as well as responsiveness, may be improved by the knowledge of the stressors involved in regime shifts mechanisms,
their shared interactions with the different components of the
system, and the different scales at which they interact (Rocha
et al. 2015). Such knowledge may also help policy makers to
set suitable management targets otherwise challenged when
uncertainties are high.
Finally, the integration of management and regime shift
theory may prove quite complicated. The complex responses
to stressors, the multiple, cross-disciplinary interactions
between each system components, the high uncertainties and
the different stakeholder perspectives and conflicts need to
be understood and accounted for when considering regime
shifts (and/or resilience) in social-ecological systems (SES)
management decisions. This requires holistic and integrative
approaches such as integrative ecosystem assessment (IEA,
(Levin and Möllmann 2015). In this context, scientists have
recently developed frameworks to conceptualize SES and
assess their sustainability and uncertainties (Ostrom 2009;
Leslie et al. 2015; Levin et al. 2016). Particularly, these
frameworks allow the combination of classic scientific information and local stakeholders’ ecological, cultural and/or
social knowledge of the system. These conceptual models
may be used to promote interdisciplinary research, discussions between stakeholders, and allow a holistic management strategy evaluation after their operationalization (Levin
and Möllmann 2015; Levin et al. 2016; DePiper et al. 2017).
C. Sguotti and X. Cormon