152
and single sectors (Curtin and Prellezo 2010 ; Grumbine 1994 , 1997 ). EAM aims to
provide a system of management that views the ecosystem as a whole, embracing
ecological boundaries and integrity in such a way that all relevant ecosystem drivers
and their impacts are considered in relation to their effects on ecosystem functioning and societal parameters. In broad terms EAM calls for adaptive, precautionary
and knowledge-based measures across national and administrative borders to protect and restore key ecological functions of the environment (Backer et al. 2010 ). It
is accordingly argued in EAM that management cannot be limited by administrative
or political boundaries but needs to be delineated by appropriate biophysical boundaries. In this respect, institutional change and innovation is vital in dealing with and
responding to institutional barriers (cf. Backer et al. 2010 ; Österblom et al. 2010 ).
Emphasis is also given to interagency cooperation and promoting cooperation
between various affi liated international, national and subnational management
agencies. Furthermore, calls have been made for cross-sector integration, crosssector resource management and integration of information across disciplines and
harmonising institutions (Barnes and McFadden 2007 ; Berghöfer et al. 2008 ;
Murawski 2007 ). While acknowledging interaction between ecology and society,
and the important role of stakeholder inclusion, the ‘social dimension’ (including
environmental justice, quality of life, social cohesion, work etc.) of sustainable
development has so far garnered less attention than the environmental dimension in
EAM thinking (see Dreyer et al. 2011 ).
EAM acknowledges the complexity of ecosystems and the uncertainty related to
management, realising that all factors affecting ecosystems are not well understood
and may never be. In EAM, scientifi c knowledge is perceived to be provisional,
because management is viewed as a learning process, incorporating the results of
previous actions and allowing management to adapt to uncertainty. Learning about
ecosystem processes and the interpretation and responses to ecosystem feedback at
multiple scales requires different types of knowledge, not only scientifi c biological
knowledge but also other types of knowledge gained from experience with concrete
ecosystems (farming, fi shing , recreation etc.) (Barnes and McFadden 2007 ; Galaz
et al. 2008 ). Broadening of stakeholder engagement is a key component in EAM,
not only to formalise existing knowledge but also to complement poor governance
and data so as to fi nd consensual solutions and legitimise the management process
(Arkema et al. 2006 ; Curtin and Prellezo 2010 ; Murawski 2007 ; Tallis et al. 2010 ).
The concept of refl exive governance, which will be introduced later, shares several features of this perspective. Indeed, the very notion of refl exivity, which has to
do with learning based on self-reference and self-critique, has affi nities with the
kind of adaptive management that EAM calls for. However, we are not just adding
the theory of refl exive governance because it appears to fi t well with EAM. Rather,
to understand the conditions (barriers and opportunities) for a governance system to
adopt features of EAM, we argue that it is important to learn from social scientifi c
understanding of governance structures and processes. We also have to take into
account a theory that helps us understand institutional change and inertia in a
realistic way. We consider the theory on refl exive governance relevant and useful
for that purpose (see also Hassler et al. 2013 ).
M. Boström et al.
and single sectors (Curtin and Prellezo 2010 ; Grumbine 1994 , 1997 ). EAM aims to
provide a system of management that views the ecosystem as a whole, embracing
ecological boundaries and integrity in such a way that all relevant ecosystem drivers
and their impacts are considered in relation to their effects on ecosystem functioning and societal parameters. In broad terms EAM calls for adaptive, precautionary
and knowledge-based measures across national and administrative borders to protect and restore key ecological functions of the environment (Backer et al. 2010 ). It
is accordingly argued in EAM that management cannot be limited by administrative
or political boundaries but needs to be delineated by appropriate biophysical boundaries. In this respect, institutional change and innovation is vital in dealing with and
responding to institutional barriers (cf. Backer et al. 2010 ; Österblom et al. 2010 ).
Emphasis is also given to interagency cooperation and promoting cooperation
between various affi liated international, national and subnational management
agencies. Furthermore, calls have been made for cross-sector integration, crosssector resource management and integration of information across disciplines and
harmonising institutions (Barnes and McFadden 2007 ; Berghöfer et al. 2008 ;
Murawski 2007 ). While acknowledging interaction between ecology and society,
and the important role of stakeholder inclusion, the ‘social dimension’ (including
environmental justice, quality of life, social cohesion, work etc.) of sustainable
development has so far garnered less attention than the environmental dimension in
EAM thinking (see Dreyer et al. 2011 ).
EAM acknowledges the complexity of ecosystems and the uncertainty related to
management, realising that all factors affecting ecosystems are not well understood
and may never be. In EAM, scientifi c knowledge is perceived to be provisional,
because management is viewed as a learning process, incorporating the results of
previous actions and allowing management to adapt to uncertainty. Learning about
ecosystem processes and the interpretation and responses to ecosystem feedback at
multiple scales requires different types of knowledge, not only scientifi c biological
knowledge but also other types of knowledge gained from experience with concrete
ecosystems (farming, fi shing , recreation etc.) (Barnes and McFadden 2007 ; Galaz
et al. 2008 ). Broadening of stakeholder engagement is a key component in EAM,
not only to formalise existing knowledge but also to complement poor governance
and data so as to fi nd consensual solutions and legitimise the management process
(Arkema et al. 2006 ; Curtin and Prellezo 2010 ; Murawski 2007 ; Tallis et al. 2010 ).
The concept of refl exive governance, which will be introduced later, shares several features of this perspective. Indeed, the very notion of refl exivity, which has to
do with learning based on self-reference and self-critique, has affi nities with the
kind of adaptive management that EAM calls for. However, we are not just adding
the theory of refl exive governance because it appears to fi t well with EAM. Rather,
to understand the conditions (barriers and opportunities) for a governance system to
adopt features of EAM, we argue that it is important to learn from social scientifi c
understanding of governance structures and processes. We also have to take into
account a theory that helps us understand institutional change and inertia in a
realistic way. We consider the theory on refl exive governance relevant and useful
for that purpose (see also Hassler et al. 2013 ).
M. Boström et al.