176
Wood et al. 2008). MPAs, to be successfully
implemented, need to combine conservation
objectives and socioeconomic features, such as
fisheries (Gaines et al. 2010). Within this context, France and the United Kingdom are under
obligations to create a consistent marine protected area (MPA) network that complies with
several conventions, especially the Convention
on Biological Diversity (review in Metcalfe et al.
2013) and the Bird and Habitat European
Directives, while ensuring a viable future for the
wide range of uses within this area.
In the past decade, systematic conservation
planning tools have been increasingly and successfully used to develop spatial conservation
plans – involving MPAs – which meet quantitative targets (e.g., a given protected percentage of
each species distribution or habitat area) while
minimizing enforcement and socioeconomic
costs (Ban et al. 2011; Delavenne et al. 2012;
Leslie 2005; Syakur et al. 2012). This approach
thus provides a framework that is deemed suitable to design consistent MPA networks that are
cost effective and minimize social costs, hence
increasing their likelihood of effective implementation (Smith et al. 2009). However, systematic conservation planning applied to fisheries
accounts neither for (1) changes in fleet dynamics induced by new conservation constraints and
their associated feedbacks on conservation costs
nor (2) their influence on fish population dynamics and distributions, which may in turn alter the
achievement of conservation targets. Such a static
approach may therefore lead to short- or mediumterm misestimates in forecasted costs and target
achievements.
Mixed fisheries simulation models are increasingly used to predict changes in fleet and fish
population dynamics under various fisheries
management scenarios (e.g., Batsleer et al. 2013;
Lehuta et al. 2010), but lack, in most of the
cases, the methodology to translate the results
into advices to support spatial conservation
measures.
In this context, coupling systematic conservation planning tools with mixed fisheries models
(or other types of simulation models, in accordance with the type of issue tackled) seems a
promising approach to test scenarios and build
advice for the management of highly dynamic
and complex systems such as coastal areas under
intense human use.
2
Proposed Approach
2.1
Overview of Selected Tools
2.1.1 Systematic Conservation
Planning
Several tools exist that are dedicated to systematic conservation planning (e.g., Marxan,
Zonation) and can help to design MPAs. Most of
them however rely on a binary and often unrealistic full protection strategy, therefore missing
the complexity of management strategies which
can be deployed through multiple types of
MPAs. A recent tool, Marxan with Zones (MwZ)
(Watts et al. 2009), allows this limitation to be
overcome in an optimal way by extending the
Marxan methodology (based on the minimum
set principle aiming to achieve a given quantitative representation level of species and habitats
at minimal cost) to multiple zone types. It provides the possibility of considering multiple,
possibly concurrent, resource uses which are
managed in different ways, while taking into
account a variety of costs. It has been shown to
be able to provide management scenarios that –
compared to a standard Marxan analysis –
ensure more equitable impacts among different
uses while lowering the overall economic and
social impact and still meeting conservation targets, thus increasing the likelihood of effective
implementation (Klein et al. 2009). However,
unlike Marxan, feedback on MwZ effectiveness
remains scarce.
Marxan with Zones uses a simulated annealing
algorithm to work as an optimization tool which
meets complex constraints such as combinations
of overall percent and/or absolute values of each
feature (often species or habitats) to be protected
in each type of “zone” (with varying protection
levels corresponding to which human use is
maintained). The objective function it minimizes
has the form:
Y. Reecht et al.
Wood et al. 2008). MPAs, to be successfully
implemented, need to combine conservation
objectives and socioeconomic features, such as
fisheries (Gaines et al. 2010). Within this context, France and the United Kingdom are under
obligations to create a consistent marine protected area (MPA) network that complies with
several conventions, especially the Convention
on Biological Diversity (review in Metcalfe et al.
2013) and the Bird and Habitat European
Directives, while ensuring a viable future for the
wide range of uses within this area.
In the past decade, systematic conservation
planning tools have been increasingly and successfully used to develop spatial conservation
plans – involving MPAs – which meet quantitative targets (e.g., a given protected percentage of
each species distribution or habitat area) while
minimizing enforcement and socioeconomic
costs (Ban et al. 2011; Delavenne et al. 2012;
Leslie 2005; Syakur et al. 2012). This approach
thus provides a framework that is deemed suitable to design consistent MPA networks that are
cost effective and minimize social costs, hence
increasing their likelihood of effective implementation (Smith et al. 2009). However, systematic conservation planning applied to fisheries
accounts neither for (1) changes in fleet dynamics induced by new conservation constraints and
their associated feedbacks on conservation costs
nor (2) their influence on fish population dynamics and distributions, which may in turn alter the
achievement of conservation targets. Such a static
approach may therefore lead to short- or mediumterm misestimates in forecasted costs and target
achievements.
Mixed fisheries simulation models are increasingly used to predict changes in fleet and fish
population dynamics under various fisheries
management scenarios (e.g., Batsleer et al. 2013;
Lehuta et al. 2010), but lack, in most of the
cases, the methodology to translate the results
into advices to support spatial conservation
measures.
In this context, coupling systematic conservation planning tools with mixed fisheries models
(or other types of simulation models, in accordance with the type of issue tackled) seems a
promising approach to test scenarios and build
advice for the management of highly dynamic
and complex systems such as coastal areas under
intense human use.
2
Proposed Approach
2.1
Overview of Selected Tools
2.1.1 Systematic Conservation
Planning
Several tools exist that are dedicated to systematic conservation planning (e.g., Marxan,
Zonation) and can help to design MPAs. Most of
them however rely on a binary and often unrealistic full protection strategy, therefore missing
the complexity of management strategies which
can be deployed through multiple types of
MPAs. A recent tool, Marxan with Zones (MwZ)
(Watts et al. 2009), allows this limitation to be
overcome in an optimal way by extending the
Marxan methodology (based on the minimum
set principle aiming to achieve a given quantitative representation level of species and habitats
at minimal cost) to multiple zone types. It provides the possibility of considering multiple,
possibly concurrent, resource uses which are
managed in different ways, while taking into
account a variety of costs. It has been shown to
be able to provide management scenarios that –
compared to a standard Marxan analysis –
ensure more equitable impacts among different
uses while lowering the overall economic and
social impact and still meeting conservation targets, thus increasing the likelihood of effective
implementation (Klein et al. 2009). However,
unlike Marxan, feedback on MwZ effectiveness
remains scarce.
Marxan with Zones uses a simulated annealing
algorithm to work as an optimization tool which
meets complex constraints such as combinations
of overall percent and/or absolute values of each
feature (often species or habitats) to be protected
in each type of “zone” (with varying protection
levels corresponding to which human use is
maintained). The objective function it minimizes
has the form:
Y. Reecht et al.