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Journal of Environmental Management 70 (2004) 333–349
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An approach for integrating economic impact analysis into the evaluation
of potential marine protected area sites
Tracey Morin Dalton*
Department of Marine Affairs, University of Rhode Island, Kingston, RI, USA
Received 2 January 2003; revised 30 July 2003; accepted 9 December 2003
Abstract
Marine protected areas (MPAs) are one tool that can be used in the comprehensive management of human activities in areas of the ocean.
Although researchers have supported using MPAs as an ecosystem management tool, scientific research on MPAs in areas other than
fisheries and fisheries management is limited. This paper presents a model for designing marine protected areas that protect important
components of the ecosystem while minimizing economic impacts on local communities. This model combines conservation principles
derived specifically for the marine environment with economic impact assessment. This integrated model allows for consideration of both
fishery and non-fishery resources and activities such as shipping and recreational boating. An illustration of the model is presented that
estimates the total economic impacts on Massachusetts’ coastal counties of restricting fishing and shipping at certain sites in an area in the
southern Gulf of Maine. The results suggest that the economic impacts on the region would differ according to the site in which shipping and
fishing were restricted. Restricting activities in certain sites may have considerable impacts on local communities. The use of the model for
evaluating and comparing potential MPA sites is illustrated through an evaluation of three different policy scenarios. The scenarios
demonstrate how the model could be used to achieve different goals for managing resources in the region: protecting important components
of the ecosystem, minimizing economic impacts on the local region, or a combination of the two.
q 2004 Elsevier Ltd. All rights reserved.
Keywords: Marine protected areas; Spatial analysis; Input–output; Economic impact assessment; Ecosystem management
1. Introduction
Marine ecosystem management has developed over the
past 30 years as an approach for comprehensively managing
marine resources. In reality, marine ecosystem management
does not manage natural resources, but the human
interactions with these resources (Mangel, 2000; Ludwig
et al., 1993; Larkin, 1996). Marine protected areas (MPAs)
are one tool for comprehensively managing human
activities in areas of the ocean (Bohnsack, 1993, 1996,
1998; Mangel, 2000; Ludwig et al., 1993; Agardy, 1994,
2000; Fogarty, 1999; Murray et al., 1999; Dayton et al.,
2000; Hooker et al., 2002). MPAs consist of a portion of the
ocean, including both water column and sediment, where
some legal or regulatory mechanism limits or restricts
human activities to protect the natural resources within
* Tel.: þ401-874-2434; fax: þ401-874-2156.
E-mail address: [email protected] (T. Morin Dalton).
0301-4797/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jenvman.2003.12.012
(IUCN definition). The limitations or restrictions vary
depending on the specific management goals of a MPA.
Examples of marine protected areas can be found
throughout the world. For instance, in the United States,
National Marine Sanctuaries have been established to
manage multiple uses in a site while protecting marine
resources. Fisheries closures in US waters have been
implemented to support the rebuilding of certain fish stocks.
In Canada, Marine Protected Areas are designated by the
Department of Fisheries and Oceans to conserve resources,
habitats, and biodiversity. The Habitats Directive of the
European Union calls for the development of a network of
Sites of Community Importance to protect natural habitats
and rare and endangered species. The Helsinki Convention
has identified 62 protected areas in the Baltic Sea region that
have been designated to protect the most threatened habitats
and species. While Australia’s Great Barrier Reef Marine
Park was established to protect biodiversity while allowing
for reasonable use.
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T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
It is clear that published studies on MPAs have been
increasing in recent years (Conover et al., 2000). Despite
this growing body of research, these academic studies tend
to focus on MPAs as a fisheries management tool, not as a
tool for ecosystem management. Early biological reviews
by Roberts and Polunin (1991), Dugan and Davis (1993),
and Rowley (1994) highlight the efficacy of MPAs as a tool
for fisheries management. Recent studies support these
findings (e.g. Kelly et al., 2000; Chiappone et al., 2000;
Roberts et al., 2001; Rogers-Bennett and Pearse, 2001; Russ
and Alcala, 1999; Cote et al., 2001). Through the use of
bioeconomic models, economists have been exploring the
use of marine protected areas as a fisheries management tool
(e.g. Guenette et al., 1998; Sumaila, 1998; Polacheck, 1990;
Holland and Brazee, 1996; Sanchirico and Wilen, 2001;
Hannesson, 1998; Armstrong and Reithe, 2001). All of these
economic models have focused on the impact on fish stocks
and fishery yield of restricting all fishing activity in specific
areas.
Although researchers have supported using MPAs as one
tool for ecosystem management and many examples from
around the world illustrate their use as an ecosystem
management tool (e.g. Sites of Community Importance in
the European Union, the Great Barrier Reef Marine Park in
Australia, Baltic Sea Protected Areas), the scientific
literature tends to be heavily weighted with studies focused
on MPAs as a fisheries management tool. Exceptions in the
recent biological literature include Gladstone’s (2002)
examination of biodiversity conservation for selecting
reserves off the south east coast of Australia and Hooker
et al. (2002) analysis of trophic interactions in the design of
a marine reserve in a submarine canyon off the east coast of
Canada. Modelers are also extending analysis to include the
impacts of MPAs on non-exploited populations. For
example, Salomon et al. (2002) utilize an ecosystem
model to examine ecological impacts of designating a
reserve in a proposed conservation area in Canada.
This paper presents a model for designing marine
protected areas that protect important components of the
ecosystem while minimizing economic impacts to local
communities.1 Unlike previous models (e.g. Sumaila, 1998;
Holland and Brazee, 1996), this model considers both
fishery and non-fishery resources and activities such as
shipping and recreational boating. This approach to MPA
design combines conservation principles derived specifically for the marine environment with economic impact
assessment. Few published studies have simultaneously
addressed ecosystem and economic considerations in the
selection of potential MPA sites.
First, a description of the combined ecosystem and
economic model and its assumptions are presented. In
1
Several terms have been used to refer to the varying levels of protection
afforded by different MPAs (i.e. fully-protected marine reserve, harvest
refuge, no-take reserve). In this paper, the term MPA is used throughout and
the particular level of protection of the MPA is specified where appropriate.
the next section, the potential use of this approach in an area
in the Gulf of Maine is illustrated. Results from this example
are discussed as are the relevant management implications.
The paper concludes with a discussion of the approach, in
general, and how it can be used as a tool for ecosystem
management.
2. Modeling MPAs
2.1. Difficulties in defining spatial boundaries
Determining which natural features to include in the
analysis of a potential MPA is a challenge. Researchers have
found that a major difficulty in designing MPAs is the
complexity of ocean systems and our limited knowledge of
them (Agardy, 1994; Lauck et al., 1998; Bohnsack, 1993;
Allison et al., 1998; Dayton et al., 2000). Part of this
complexity is due to the fluid nature of the oceans (Carr and
Reid, 1993). In addition, boundaries cannot be defined as
easily as they are on land. For all of these reasons, principles
for terrestrial reserve design cannot be readily applied to the
marine environment (Agardy, 2000; Steele, 1965).
Although we can look to past efforts in terrestrial reserve
design for guidance, MPA design must follow a distinct set
of conservation principles. Despite the challenges to MPA
design, a review of the growing body of literature on MPAs
reveals a set of general conservation criteria for designing
and locating MPAs that protect ecosystems and the
organisms they contain (Table 1).
2.2. Conservation criteria for designing MPAs
One of the most common criteria for MPA design in both
terrestrial and marine environments is the protection of
endangered species (Roberts and Hawkins, 2000; Azimi,
2000). As has been suggested for terrestrial systems (Soule
and Simberloff, 1986), species extinction in the ocean may
disrupt important ecological relationships. Researchers have
also advocated the protection of areas of important
reproductive habitat (Lauck et al., 1998; Fogarty, 1999).
Because many fish species aggregate to spawn, they are
Table 1
Conservation criteria for MPA design
Endangered species
Locations of important reproductive functions
Habitat for keystone, commercially important, or endangered species
Sources and sinks; migration corridors and bottlenecks
Oceanographic occurrences associated with productivity
Replication and representation
High habitat and species diversity
High degree of endemism
Linkages through foodwebs
T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
particularly vulnerable to commercial harvesting when they
aggregate in spawning grounds. Protection of other habitat
areas, in particular habitat for keystone, commercially
important, or endangered species, has also been promoted as
a MPA design criterion (Allison et al., 1998). Examples of
specific habitat areas that have been recommended for
protection include habitat for the most exploited benthic
species (Fernandez and Castilla, 2000) and commercially
exploited groundfish (Lindholm et al., 2000).
The design of MPAs should also consider the
movement of organisms in the ocean (Carr and Reid,
1993; Murray et al., 1999). Like terrestrial corridors that
link important habitat regions (Soule and Simberloff,
1986), migration corridors in the ocean are important
MPA design criteria. Unlike their terrestrial counterparts,
however, the boundaries of these corridors, or routes, are
difficult to define because they exist in three dimensions
and they are constantly changing with changes in
physical properties of the ocean. Habitat regions that
are linked by migration routes, referred to as sources and
sinks, have also been recommended for protection in
MPAs. Particular attention has been given to sources and
sinks in designing marine protected areas that are
effective fisheries management tools (Roberts and
Polunin, 1993; Tuck and Possingham, 2000; Sanchirico
and Wilen, 2001). Not only is it important to consider
where organisms spend different life stages, but also it is
important to consider where organisms move during
those life stages. In addition to considering adult
movements in the ocean, MPA design should also
consider movements of larvae (Valles et al., 2001;
Stobutzki, 2001; Allison et al., 1998).
Researchers have also recommended that MPA design
consider oceanographic occurrences associated with
productivity (Roberts and Hawkins, 2000). In particular,
areas of major upwelling events should be captured
within a MPA boundary (Murray et al., 1999). Replication of biogeographic regions is another criterion for
MPA design. Multiple MPA sites can ensure that a
particular type of habitat or species will not be
permanently lost due to natural or anthropogenic
catastrophes (Murray et al., 1999). Areas of high habitat,
species, and genetic diversity have been recommended
for protection in MPAs (Azimi, 2000). Cardinale et al.
(2002) suggest that biodiversity can enhance ecological
functioning.
For the same reasons that endangered species should be
conserved, it is agreed that endemic species deserve special
protection (Soule and Simberloff, 1986). Endemism has
been largely ignored in the MPA design literature. However,
that is probably due, in part, to the paucity of data on
endemic species in the marine environment. A final criterion
for MPA design includes protection of linkages through
food webs (Hooker et al., 2002; Salomon et al., 2002;
Pinnegar et al., 2000). Like endemism, food web dynamics
335
have been given little attention in the design of MPAs
(Hooker et al., 2002).
2.3. Analysis of MPA design criteria
In the first part of this integrated approach to MPA
design, Geographic Information Systems (GIS) is used to
identify marine regions that meet the recommended
conservation criteria for MPA design. ArcView GIS is the
GIS software used because it is commercially available,
relatively easy to use, and its output is familiar to most
managers and environmental decision makers. ArcView
GIS is used to analyze spatially represented data layers on
the chemical, biological, ecological, and physical properties
of a particular region in the ocean in order to identify
potential MPA sites in that region (Appendix A).
2.4. Economic impact analysis
Economic considerations can provide insight into how
protecting certain areas in the ocean might financially
impact local communities (Mangel, 2000; Farrow, 1996;
Sanchirico, 2000; Manson and Die, 2001; Milon, 2000). The
second part of this integrated approach utilizes economic
impact analysis to estimate the regional impacts of
restricting economic activities in areas identified as
potential MPA sites. Economic impact analysis is a useful
tool for understanding and predicting the financial impacts
of a certain policy change. Input – output (I –O) modeling is
a commonly used approach for performing economic
impact analysis (Steinback, 1999). Results of I–O models,
measured in tangible terms like revenues, jobs, and taxes,
provide information on the distributional effects of different
management alternatives across industries in a localized
segment of society.
A few I– O studies show how the models can be used to
estimate the impacts of policy changes on marine resource
activities: recreational fishing in Maine (Steinback, 1999);
fisheries in New England (Marine Policy Center 2000);
economic activity in the Northeast Shelf Large Marine
Ecosystem (Hoagland et al., 2000); and whale watching and
tuna fishing in Stellwagen Bank National Marine Sanctuary
(Perez and Ruth, 2002).
The basic premise of I– O models is that a change in
economic activity in one sector stimulates additional
changes in the regional economy (Miller and Blair, 1985).
I –O models disaggregate the regional economy into sectors
based on the primary good or service of each firm.
Exchanges of goods and services among sectors or
industries are measured in dollar values (Appendix B).
I– O analysis is useful for determining the economic
multipliers for industries in a region. Multipliers are
comprised of direct, indirect, and induced effects. A direct
effect is the change in the sales, income, and employment of
an industry as a direct consequence of a change in demand
somewhere in the economy. Depending on the activities that
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T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
are limited or prohibited by a MPA, directly impacted
industries may include fishing, whale watching, diving, or
shipping.
An indirect effect is due to the change in an industry
resulting from the demands created by the directly affected
industries. For instance, if a MPA prohibits diving in a
particular site and this is the only area where divers go in the
region, indirectly impacted industries may include boat
building or scuba gear manufacturing. This is due to the fact
that the dive boat operator that is directly impacted by the
MPA designation is less likely to buy a new boat and his
dive clients are less likely to buy scuba gear. An induced
effect is the change in spending from households as income
increases or decreases due to direct and indirect effects
(IMPLAN Pro Users Guide, 1997). In this example,
industries affected by induced impacts may include the
automobile industry or the restaurant business. These
industries are negatively impacted because the dive boat
operators, scuba gear manufacturers, boat builders, and
other individuals that have been directly and indirectly
affected by the MPA designation are less likely to buy new
cars or go out to dinner. In other words, there is less
spending in the region overall. The sum of direct, indirect,
and induced impacts on all the industries in the region
provides an estimate of total economic impacts to the
region.2
Using the regional model and IMPLAN’s economic
impact assessment capability, it is possible to estimate the
potential economic effects on the local region of restricting
certain activities in portions of the marine study area. Input
for this impact analysis includes the values of economic
activities spatially distributed across the region. Examples
of these activities may include fishing, shipping, whale
watching, recreational boating, aquaculture, research, diving, or kayaking, etc.
The integrated model combines the two techniques,
spatial analysis and economic impact analysis, in order to
understand how user restrictions in particular areas will
affect the local region. Different options for managing uses
in the study area can be explored through simulations of the
model.
2.5. Assumptions of spatial analysis and economic
impact analysis
Although spatial analysis and I– O analysis can be useful
tools in making policy decisions about marine protected
2
The approach used in this study utilizes the input–output (I–O)
modeling software, IMPLAN. IMPLAN is a popular I –O model because of
its low cost and flexibility (Brucker et al., 1990). IMPLAN is used to create
an input–output model of the economic region associated with the marine
study area using county-level data. Data for constructing this regional
input –output model are provided with the IMPLAN software. The
IMPLAN data consist of national-level matrices that describe dollar
flows among industries in the nation and regional data that is used to adjust
national data for a particular region.
areas, several limitations to their use must be noted. First,
spatial analysis is data intensive and data is limited in the
marine environment. Also, the type of data used in the
analysis may impact the outcome. Although guidelines have
provided for which types of data should be considered in
this analysis, the empirical data that is actually used will
depend on which data has been collected and is available in
electronic format.
In addition, values need to be assigned to the grid cells
that comprise the different data layers. Determining what
these values should be is one of the major challenges in
using spatial analysis. Not only is it difficult to allocate
values among the cells in a data layer (i.e. Should high
diversity get a value of 3 and low diversity get a value of 1
or should high diversity get a value of 10 and low diversity
get a value of 0? and What is meant by high and low
diversity?), but it is even more difficult to allocate values
across the different data layers. For instance, the additive
nature of the grid cells implies that the value of one cell of a
data layer can be compared to the value of one cell of
another data layer.
To illustrate, imagine that one data layer represents
species diversity and another data layer represents abundance of endangered species. If the value of a grid cell of
high species diversity is equivalent to the value of a grid cell
of high abundance of endangered species, it is implied that
one unit of high species diversity is equivalent to one unit of
high abundance of endangered species. With our current
knowledge of marine systems, it is nearly impossible to
make this sort of conclusion. But spatial analysis can be a
powerful tool and if it is going be used to identify regions
that contain important oceanographic features, values must
be assigned to the cells. Whether to assign higher values to
grid cells that contain high species diversity or to grid cells
that contain a high abundance of endangered species is a
management decision and management decisions are
subjective. Although these management decisions can and
should be informed by sound science, they nevertheless
remain a choice for resource managers, users, and the
general public.
I –O models also have several limitations. For example,
conventional I – O models do not capture non-market
transactions. That is, goods and services that are not bought
and sold, like biodiversity or fish habitat, are not included in
the model. Resource depletion and environmental degradation are also not captured in the model. As a result, nonmarket values can only be considered in the policy debate
outside of the discussion of I –O model results.
The I –O model does not provide an estimate of net social
benefits. As a result, I– O analysis is not a tool for benefitcost analysis. Benefit-cost analysis compares the benefits
and costs associated with designating a marine protected
area and supplies the decision maker with the resource
management alternative that will provide the maximum
benefit to members of society as a whole (Farrow, 1996).
T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
In the policy making process, results of an I–O analysis are
complementary to those of benefit-cost analysis.
The I– O model has several simplifying assumptions.
One assumption is that prices of goods and services are fixed
(Partridge and Rickman, 1998). In reality, industries choose
less expensive goods and services as intermediate inputs
when they are available (Liew, 2000). Another assumption
is the linear relationship between input coefficients and
output. The linkages among industries in an economy are
generally non-linear (West, 1995). In addition, the static
nature of the I– O model prevents it from capturing changes
over time, such as those due to changes in technology (Rey,
2000). Finally, the model assumes that supplies to the
production process, such as labor, are unlimited (West,
1995).
This integrated approach is also limited by an assumption
that the economic activity that would be restricted in a MPA
does not shift to another part of the marine region. In reality,
in response to a new policy, resource users may move to
another site in the study area. If this were to happen, the
economic impact would not be felt as strongly by the region.
It is likely, however, that this shift in behavior would not
allow for optimal use of the resource due to overcrowding
and limited resources so that some negative impact would
result. By assuming total loss of an activity that is restricted
by MPA regulations and the loss of its ancillary services and
expenditures to the region, my model will result in
somewhat higher estimates of economic impacts than
might actually be experienced by the region.
337
Despite these limitations, this integrated model allows
one to explore economic impacts of different MPA scenarios.
An illustrative example of the potential use of this ecosystem
approach to designing MPAs is presented in the next section.
3. Integrated approach: example in NMFS Area 514
3.1. Study region
The study region is in the southern portion of the Gulf of
Maine. Designated by the National Marine Fisheries Service
as Statistical Area 514 (NMFS Area 514), this area is
comprised of Massachusetts Bay, Cape Cod Bay, and
surrounding waters. This area was selected because it is a
well-studied site in the Gulf of Maine in terms of natural
features and it supports a number of economic activities.
The economic region associated with the study area is
comprised of the eight coastal counties in Massachusetts
(Fig. 1). IMPLAN was used to develop an I– O model for
this region.
3.2. Conservation priorities
3.2.1. Data
Data used in this example cover a number of the MPA
design criteria discussed in Section 2 including endangered
species, important habitat for commercially important or
endangered species, oceanographic occurrences associated
Fig. 1. NMFS Area 514 and coastal counties of Massachusetts (Data for Massachusetts’ outline and 10 min squares obtained from Butman and Lindsay (2000)).
338
T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
Table 2
Data layers used in spatial analysis of NMFS Area 514 and associated grid
cell values
(a)
Primary production
Spring
Summer
(b)
(c)
3
3
3
3
30
30
Dissolved oxygen
Juvenile Cod habitat
23
3
230
3
230
3
Right Whale habitat
Winter
Spring
3
3
3
3
3
3
Endangered species
Right Whale—spring
Right Whale—summer
Right Whale—fall
Right Whale—winter
Fin Whale—spring
Fin Whale—summer
Fin Whale—fall
Fin Whale—winter
Humpback Whale-spring
Humpback Whale—summer
Humpback Whale—fall
Humpback Whale—winter
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Diversity
Richness and evenness
Evenness
3
3
3
3
3
3
with productivity, and high habitat and species diversity. A
list of data used in this analysis is presented in Table 2. The
data will be described briefly below. For an in-depth
description of the data, the reader is referred to Morin (2002).
In this analysis, areas of highest primary production and
low dissolved oxygen are used as surrogates for oceanographic occurrences associated with productivity. Beardsley
et al. (1996) have found that right whales feed on
aggregations of calanoid copepods greater than 1000
individuals/m3. For this example, areas that contain these
dense patches of copepods are associated with right whale
habitat. Gravel pavement has been associated with habitat
for demersal cod juveniles (Collie et al., 1997). Cod is a
commercially important groundfish in this region. In this
study, potential juvenile cod habitat was identified based on
distribution of gravel pavement in NMFS Area 514. Two
indices were used to estimate the diversity of benthic fauna
at a number of MWRA monitoring stations in Massachusetts Bay. Pielou’s J is an estimate of the evenness of the
species or the distribution of individuals among the species.
Brillouin’s H provides an estimate of species evenness and
richness, where richness describes the number of species in
a population. Data on the sightings per unit effort of
endangered species, including right, humpback, and fin
whales, in NMFS Area 514 were used to represent the
endangered species criterion.
3.2.2. Spatial analysis
An automated program with a user-interface was
developed to perform spatial analyses on the data layers.
This program is not described here due to space limitations.
For a complete description of the program, the reader is
referred to Morin (2002). Spatial analysis was conducted on
all of the data layers in Table 2. Each of the data layers is
comprised of 540 grid cells. In each of the layers, grid cells
can have either of two values. One of those values is 0, the
other value varies depending on the conservation feature
with which it is associated. To test the sensitivity of the
model results to the values assigned to the grid cells, three
simulations were run with different values assigned to the
non-zero grid cells in the data layers (Fig. 2). Columns a –c
of Table 2 present the values that were assigned to
Fig. 2. Sensitivity analysis: application of different weights to the grid cells
in the data layers: (a) Simulation 1; (b) Simulation 2; (c) Simulation 3.
T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
the non-zero grid cells in each of the appropriate data layers
in the three simulations.
In the first simulation, most of the non-zero grid cells
were assigned a value of 3. Grid cells representing low
dissolved oxygen (below 6 mg/l), however, were assigned a
negative value (2 3) because this approach attempts to
capture features of non-degraded areas. If the management
goals include protecting areas that were degraded, the value
of low dissolved oxygen cells would be positive. Also, high
primary production is assigned a positive value (þ 3). It
should be noted that while high levels of primary production
are used in this particular example to indicate a healthy,
productive system, high levels of primary production do not
necessarily indicate thriving systems. For instance, high
levels of primary production along the coast may be the
result of excessive levels of nutrients in a water body.
High levels of nutrients and primary productivity may lead
to low dissolved oxygen levels or loss of benthic flora,
indicators of degraded systems. Thus, it is necessary to
employ caution when using primary production as an
indicator in MPA design. Finally, values of the non-zero
grid cells in the endangered species data layer were adjusted
to 1 because data on endangered species for each of the four
seasons is used in the analysis.
The second simulation illustrates the effects of assigning a value to the low dissolved oxygen grid cells (i.e.
2 30) that is one order of magnitude higher than that used
in the first simulation. This weighting might be the
optimal choice for the spatial analysis if it is determined
that the level of dissolved oxygen is the most important
feature of the system. The third simulation demonstrates
the effects of assigning values to the water quality
parameters (i.e. low dissolved oxygen and high primary
production) that are one order of magnitude higher than
those used in the first simulation. This weighting might be
the optimal choice for the analysis if it is determined that
water quality is the most important feature of this natural
system or even if water quality is the most important
criterion in MPA design.
The results of the three spatial analyses presented in
Fig. 2 indicate that the model is responsive to different
cell values. However, as evidenced in the results, similar
regions of highest conservation potential emerge in all
three simulations. It is important to note that, although the
general areas of highest conservation potential seem
consistent, the exact boundaries may vary. This variation
in boundaries could be due to a couple of factors. First,
the variation in conservation potential boundaries could be
due to the different values assigned to the data layers in
each of the simulations. This suggests that it may be
helpful to know which grid cells deserve more weighting
in the analysis, or in other words, which parts of the
ecosystem should be valued more highly. However, as
already mentioned, limited knowledge of the controlling
factors in marine systems makes the values for these
weights difficult, if not impossible, to quantify at this
339
time. Currently there is no clear basis for selecting
weights for the grid cells, nevertheless some system for
weighting the grid cells must be applied in order to
conduct the analyses.
Also, the way in which the data is grouped, or
classified, will influence the presentation of the simulation
results. In this example, quantile classification was chosen
as the method for grouping the data because it allows for
comparison among different simulations and it is relatively easy to understand and compute (Slocum, 1999). In
quantile classification, each of the classes (i.e. highest
conservation potential, lowest conservation potential, etc.)
has approximately the same number of features, in this
case, the number of grid cells (Theobald, 2003). The
quantile classification method was used in order to
identify a similar portion of grid cells having the highest
conservation potential in all three simulations. However,
when grid cell values are unevenly distributed, as in this
example, the portion of grid cells in each class may vary
thus affecting the area encompassed by each class in each
simulation.
For the purpose of illustrating the integrated approach,
grid cell values from the first simulation have been used
in the spatial analysis to identify potential MPA sites in
NMFS Area 514. Since there is no clear evidence that any
one particular feature is more important than any other,
values have been assigned to the grid cells that are
relatively equal with some adjustment for degradation and
seasonal abundance of certain species. Until it is possible
to rank features of the marine environment with some
degree of certainty, the decision about weight selection
remains a subjective one. For ease of discussion, grid cell
values in the output layer from the spatial analysis have
been summed for each 10 min square and the 10 min
squares were ranked by this sum. Squares with higher
values are considered to have more conservation potential
than those with lower values. Fig. 3 displays the 10
squares in NMFS Area 514 with the highest conservation
potential.
3.3. Economic activity
3.3.1. Data
This application of the model focuses on two particular
industries in NMFS Area 514: shipping and fishing. For
this analysis, the cost to the shipping industry for having
to reroute around a square in which shipping is prohibited
is a function of the average speed of each vessel, the
average cost per hour of each type of vessel, the number
of vessels passing through each square annually, and the
distance to travel around a 10 min square. The shipping
costs range from $0 to $1 million per 10 min square ($US
1997). Fig. 4 illustrates the relative distribution of these
annual costs to the shipping industry. The cost to the
fishing industry if fishing that had previously gone on in a
square no longer took place and was not substituted
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T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
Fig. 3. Ten minute squares with the highest conservation potential in NMFS Area 514.
elsewhere in the region is represented by the value of
annual landings in Massachusetts from each square. Total
fishery landings in Massachusetts range from $800 to $20
million per square ($US 1997). Fig. 5 illustrates
the relative distribution of total annual fishery landings
in Massachusetts from each square. For a complete
description of the economic data used in this illustration,
the reader is referred to Morin (2002).
Fig. 4. Relative distribution of shipping values in NMFS Area 514; values per square range from $0 to $1 million (data source: Silber et al., 2001;
Kite-Powell, 2001).
T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
341
Fig. 5. Relative distribution of fishing values in NMFS Area 514; values per square range from $800 to $20 million (data source: National Marine
Fisheries Service).
3.3.2. Economic impact analysis
As discussed previously, economic impact analysis is
useful for estimating the effects of a change in economic
activity on the entire region. In IMPLAN, economic impact
analyses were conducted on each of the 42 squares in NMFS
Area 514. Costs to the shipping industry of having to go
around a square and the lost revenue to the fishing industry
of not being allowed to fish in that particular square are used
as input in the economic analysis of each square. Fig. 6
illustrates the total economic impacts of the ten squares
having the highest conservation potential. Total economic
impacts in these squares range from $11,000 to over $18
million ($US 1997). It is clear from Fig. 6 that the economic
impacts felt by the region would differ according to the
square in which shipping and fishing were restricted.
Squares 41 and 27 would have considerably larger impacts
on the region than the other eight squares described in Fig. 6.
As discussed previously, this estimation of economic
impacts assumes that fishing that is prohibited in a 10 min
square does not shift to another part of NMFS Area 514. In
reality, some fishermen may move to other sites in the study
area resulting in less economic impacts on the region. This
simple illustration of the integrated approach does not
account for such shifts in fishing behavior.
Fig. 6 breaks down total economic impact into direct,
indirect, and induced impacts. The direct impacts describe
the impacts on the fishing and shipping industries.
The indirect and induced impacts illustrate the economic
impacts on other industries in the region. Because the
IMPLAN model of the coastal counties in Massachusetts
captures relationships among all of the industries in this
region, it is possible to distinguish which other industries
would be most affected by restrictions on fishing and
shipping, in terms of indirect and induced impacts (Table 3).
Results of the economic impact analyses of the ten
squares with the highest conservation potential indicate that
industries in this particular region that would be most
affected by indirect impacts would be maintenance and
repair, water transportation, wholesale trade, lubricating oils
and greases, development and testing services, and management and consulting. Possible reasons that these industries
would be affected by restrictions on shipping and fishing
might include the reduced probability that fishermen would
get their boats repaired if their business is declining, the
reduction in the use of oils and greases by both fishermen
and shippers, or the reduction in the amount of consultants
hired by shipping companies. Industries in the region that
would be most affected by induced impacts resulting from
restrictions on shipping and fishing include owner-occupied
dwellings, hospitals, dentists and doctors, wholesale trade,
eating and drinking, and real estate. Because people in the
region are directly and indirectly impacted by restrictions on
shipping and fishing, they have less money to spend on other
goods and services in the region. Therefore, they are less
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T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
Fig. 6. Total output impacts for ten squares with the highest conservation potential.
likely to go see a dentist or go out to dinner or buy a new
home.
Section 3.4 describes the economic impacts of three
potential scenarios of MPA placement in NMFS Area 514
(Figs. 7(a), 8(a) and 9(a)). In the first policy scenario, the
two squares with the lowest economic impacts will be
examined. The second policy scenario examines the two
Table 3
Industries in Massachusetts’ coastal counties most impacted by restrictions
on shipping and fishing
Direct
Fishing
Shipping
Indirect
Maintenance and repair
Water transportation
Wholesale trade
Lubricating oils and greases
Development and testing services
Management and consultant services
Induced
Owner-occupied dwellings
Hospitals
Dentists and doctors
Wholesale trade
Eating and drinking
Real estate
squares with the highest conservation potential. The final
policy scenario presents the two squares that have both high
conservation potential and low economic impacts.
3.4. Economic impacts of potential MPAs
in NMFS Area 514
3.4.1. Scenario 1: low economic impacts
Fig. 7(b) shows the direct economic impacts to the
fishing and shipping industries of restricting fishing and
shipping in squares 34 and 42. The total economic impacts
on the region of restricting these activities are shown in Fig.
7(c). As Fig. 7(b) indicates, the direct impacts in square 34
are more evenly distributed between the two industries than
those in square 42. Although square 34 would have less
direct impacts than square 42, its impacts on other industries
in the region would exceed those of square 42.
3.4.2. Scenario 2: high conservation potential
Fig. 8(b) shows the direct economic impacts to the
fishing and shipping industries of restricting fishing and
shipping in squares 36 and 32. Direct, indirect, and induced
impacts are presented (Fig. 8(c)). Of the two squares, square
36 has lower total economic impacts on the region. Direct
impacts are greater in square 32, primarily due to impacts to
the fishing industry.
T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
343
Fig. 7. Scenario 1: low economic impacts (a) squares with the lowest
economic impacts (b) direct economic impacts to the shipping and fishing
industries (c) total economic impacts to the region.
Fig. 8. Scenario 2: high conservation potential (a) squares with the highest
conservation potential (b) direct economic impacts to the shipping and
fishing industries (c) total economic impacts to the region.
3.4.3. Scenario 3: low economic impacts and high
conservation potential
Fig. 9(b) shows the direct economic impacts to the
fishing and shipping industries of restricting fishing and
shipping in squares 37 and 30. Direct, indirect, and induced
impacts are presented in Fig. 9(c). Square 30 has higher
impacts on the shipping and fishing industries and on the
region in general.
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T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
goals for managing resources in the region. For instance, if
economic impacts to the fishing and shipping industries
drive the designation process, Scenario 1 would most
likely be followed and square 34, square 42, or both,
would be selected. As noted above, the distribution of
impacts differ in these two squares. If equity is an
important consideration and only one of the squares could
be selected, it would probably be square 34 because
impacts to the shipping and fishing industries would be
relatively equal in this square. However, if impacts to the
shipping industry are the only concern, then square 42
would make the better candidate.
If conservation potential drives the designation process,
squares in Scenario 2 would most likely be considered. A
comparison of the conservation priorities contained in each
square indicates that both squares have high dissolved
oxygen and high levels of endangered species sightings,
especially in the winter. Neither of these squares, and in fact
no squares in the example, contains all of the conservation
priorities. In Scenario 2, right whale habitat and high
primary production is captured in square 36; whereas,
benthic fauna evenness is captured in square 32. If only one
square in this scenario were to be designated, the selection
would be based on the goals for managing the region. If
these goals emphasize right whale habitat, perhaps square
36 would be selected. If they emphasize benthic diversity,
square 32 would be the better choice. If the goals emphasize
minimizing economic impacts on the region, then the
economic impact assessment results suggest that square 36,
with over $65,000 of total impact on the region, would be
selected over square 32, with about $290,000 of total
impact.
Finally, if attempts to locate sites with both high
conservation priorities and low economic impacts drive
the designation process, then Scenario 3 would be followed
and squares 37, 30, or both would be designated. Both of
these sites contain a number of conservation priorities and
have relatively low economic impacts on the region. Again,
if a choice between the two had to be made, it would be
based on goals for managing resources in the region. Square
37 would be a better choice for protecting endangered
species, while square 30 would better protect diversity and
juvenile cod habitat. If the final decision between the two is
based on economic impacts to the region, square 37, with
$18,000 in total impact, would have much less financial
impact on the region than square 30, with a total economic
impact of almost $63,000.
Fig. 9. Scenario 3: low economic impacts and high conservation potential
(a) squares with low economic impacts and high conservation potential (b)
direct economic impacts to the shipping and fishing industries (c) total
economic impacts to the region.
3.5. Discussion of example results
The squares that will ultimately be selected as restricted
shipping and fishing areas will depend on the particular
4. Discussion
Results of the analysis in NMFS Area 514 suggest that
designating certain sites as MPAs may have considerable
impacts on local communities. If MPAs are to be used as a
tool for ecosystem-based management, these impacts
must be considered in their establishment. This model
T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
contributes to ecosystem-based management by considering both ecosystem and economic factors. With this
approach, conservation priorities (i.e. water quality, habitat
for endangered species) and economic impacts of regulating certain activities (i.e. all fishing gears, trawling gear
only, shipping) in potential MPA sites can be evaluated.
The example of the model highlights the potential use of
this approach in designating MPAs. It is important here to
reemphasize that the example is limited by the data that
were used in the analysis. It is likely that there are other
important features in the study region that have been
overlooked because data on these features were not
available. In addition, the economic impact analysis
provides information on the economic effects of restricting
only two activities: fishing and shipping. Because of data
limitations and other constraints, these are the only two
industries that have been included in the example. A more
comprehensive approach to management would consider
more than two industries in the region. Other industries that
could be added to the analysis include boating, whale
watching, and recreational fishing.
Also, the example only examines industries that would
be negatively impacted by designating a MPA. Establishment of MPAs does not always have to result in negative
economic impacts. For instance, if the health of a particular
area improves due to restrictions on certain harmful
activities, it may attract other industries whose use of the
area is permitted, such as diving or kayaking. Increases in
the output of these industries would have positive indirect
and induced impacts on the region. In addition, areas
designated as MPAs often become the subject of much
research. If research activity increases in the region, this
would also have a positive economic impact on the region.
By including industries in the analysis that could be
positively impacted by designating a MPA, such as diving
or research, the model would capture economic gains in the
region.
This approach will enhance how marine protected
areas are planned and designed. However, it is important
to note that the approach leaves a few questions in MPA
design unanswered. Although the model captures important ecological features, it does not address how much
area should be contained within the MPA or how the
MPA should be configured. Should the MPA contain 10,
20, or 30% of the marine resources in NMFS Area 514
or even in the Gulf of Maine? Should the MPA consist
of one large area or should it have several smaller
components that comprise a network of MPAs? The issue
of protected area size and arrangement has been
discussed in the biological literature on MPAs (Murray
et al., 1999; Agardy, 2000; Roberts, 2000). There is no
clear conclusion on what the optimal size should be.
Although this model is not designed to analyze the
optimal size of MPAs, it could be used to examine the
different effects of designating areas of varying sizes and
configurations. That is left for future studies.
345
Uses of this combined ecosystem and economic model
could be extended if additional data were provided. For
instance, the model could be used to examine how
economic impacts might be distributed across the
communities in the local region if data were available
that connected landings values in the marine region to
the ports at which the fish were landed. Because each
fishing community is dominated by different types of
fishing activity (e.g. Gloucester, Massachusetts is dominated by bottom trawlers), the impacts of restricting
fishing in different areas would vary from port to port
within the local region. Results from this type of analysis
would be useful in estimating the potential impacts on
individual communities.
As described above, conventional I– O models do not
capture non-market ecological goods and services, such as
biodiversity, fish habitat, resource depletion, or environmental degradation. I – O models have been modified,
however, to directly incorporate ecological considerations
(Daly, 1968; Leontief, 1970; Isard, 1968; Kim et al., 2001;
Machado et al., 2001; Hubacek and Sun, 2001; Lange,
1998a,b; Weale, 1991). Unlike these models, the model
presented here uses two distinct models to capture
ecological and economic factors. This decision to maintain
separate models follows Bockstael et al. (1995) argument
that a completely integrated model, or what they call a
‘supermodel,’ would reduce the value of information that
each model could provide on its own. As in their model of
agricultural land use in a region in Maryland, the different
parts of this model run separately but share information at
different stages.
As previously stated, this approach identifies potential
MPA sites, but it does not address how to establish them.
Even with the results of the analysis, implementation of
these sites as MPAs would face a number of institutional
and political challenges. A full discussion of these
challenges is beyond the scope of this article. However, a
suggestion for reducing these challenges might be to
compare these results with boundaries of existing MPAs.
Although these MPAs exist in name, many of them do not
have regulations in place that restrict harmful activities.
New regulations or restrictions could be added to whatever
legal or regulatory framework exists for the MPA. This
approach might be more feasible than creating a completely
new MPA.
Finally, as this approach demonstrates, it is useful to have
information on both ecosystem and social factors when
siting MPAs. This approach considers economic factors, but
does not take into account other social factors. Many social
factors are important to consider when establishing MPAs
(Agardy, 2000; Wells and White, 1995). These considerations might include the involvement of local citizens in
management, the perceptions that individuals have about
the resources, and the cultural ways in which user groups
identify with the area.
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T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
5. Conclusion
The model presented here allows for the evaluation of the
conservation priorities and economic impacts of potential
marine protected area sites. Although MPAs have been
described as one tool for ecosystem management, the design
of MPAs has typically involved individual conservation
priorities, such as protecting a commercially exploited
species or an endangered species. The results from this
example illustrate the potential usefulness of a more
comprehensive approach to MPA design. The example
suggests that it is possible to select MPA sites that have high
conservation potential and minimal economic impacts on a
particular region.
This approach, by coupling ecosystem and economic
models, could serve as a foundation for a management
framework that includes additional factors such as nonmarket values and stakeholder attitudes and perceptions. For
example, results from the model presented here could be fed
into an integrated assessment that incorporates not only
ecological and economic models but also methods from
other scientific disciplines, such as stakeholder analysis
and multi-criteria evaluation (Turner, 2000). Either viewed
alone or within the framework of integrated assessment,
results from this model provide information for making
better-informed, more comprehensive decisions for managing human activities in the marine environment. Through
the use of this type of approach, objectives of both a healthy
ecosystem and a functioning human system would be more
readily achieved.
Acknowledgements
I would like to thank Dr David Terkla and Dr Di Jin for
their worthwhile comments on previous versions of this
paper. I also thank two anonymous reviewers for their
valuable insights.
Appendix A
In this study, spatially represented data on the chemical,
biological, ecological, and physical properties of a particular
Fig. A1. Spatial analysis (a) continuous layer of grid cells (b) example of data layers (c) addition of two data layers.
T. Morin Dalton / Journal of Environmental Management 70 (2004) 333–349
region in the ocean are analyzed using a component of the
ArcView GIS software package, Spatial Analyst. Spatial
Analyst is a tool that can be used to perform specified
mathematical functions on the oceanographic data (i.e.
addition, statistics, interpolation). Spatial Analyst is a useful
tool because it reveals spatial relationships in the data.
Data are represented as a continuous layer of individual
grid cells (Fig. A1(a)). There is one data layer that
corresponds with each of the chemical, biological, ecological, or physical features in the analysis. All of the layers are
divided into the same number of grid cells. Individual grid
cells in each layer are assigned a value. Because all data
layers in the analysis have the same number of equally-sized
grid cells, the values assigned to each of the grid cells in the
data layers can be added (Fig. A1(b)). An automated
program employing Spatial Analyst would be used to sum
the grid cells in the different layers. The output of the
summation would be a new data layer with the same number
of equally-sized grid cells as the original data (Fig. A1(c)).
This type of analysis provides useful information to
resource managers and decision makers. For instance, if
resource managers or other decision makers want to protect
areas containing certain features, they would use this
analysis to identify grid cells that correspond with such
areas. In this type of analysis, those grid cells would have
the highest summed values.
Appendix B
I– O models disaggregate the regional economy into n
sectors based on the primary good or service of each firm.
The dollar flow from one sector, i; to another sector, j; is
denoted as zij (Fig. B1). The sum of all flows from sector i to
each of the j sectors is the total interindustry sales of
industry i: Including the final demand for sector i’s product,
yi ; the following equation provides the output from sector i :
xi ¼ zi1 þ zi2 þ · · · þ zij þ yi
Fig. B1. Input– output analysis: dollar flows from industry i to industries
j ¼ 1 and j ¼ 2 in a region.
347
In an I –O analysis, the intersectoral flows, zij ; are captured
in a table, with i industries as row headings and j industries
as column headings. Once this table is established, the
following fixed relationship is computed between a sector’s
inputs and corresponding outputs:
zij =xj ¼ aij ;
where xj is the output of sector j and aij is defined as the
technical coefficient. The result is a n £ n matrix of technical
coefficients, denoted A: In matrix notation, the following
production equation results: x ¼ Ax þ y; where x ¼
ðx1 …xi …xn ÞT is the output vector, Ax represents intermediate demand, and y ¼ ðy1 …yi …yn ÞT represents final demand.
If A and y have nonnegative entries and if each column sum
of A is less than one, then ðI 2 AÞ21 exists (I is the identity
matrix) and the vector x ¼ ðI 2 AÞ21 y is a unique solution
of x ¼ Ax þ y (Miller and Blair, 1985).
The ðI 2 AÞ21 matrix, or the Leontief inverse, is the
matrix of multipliers that describes the response of the
economy to a change in demand: Dx ¼ ðI 2 AÞ21 Dy:
Multipliers describe the change in an industry’s production
or output ðDxÞ given a one-dollar change in demand ðDyÞ:
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