Chapter 25 Seagrass Conservation Biology
613
conservation goals for seagrasses. The program responded to a crisis of seagrass loss by organizing
human resources, setting a clear and concise goal
for expected distribution and abundance of seagrass
communities, and developed a model that predicted
environmental criteria necessary for seagrass survival. Performance measures were assessed by monitoring the response of seagrass distribution to water quality conditions. For the very first time in the
U.S. the designated criteria for seagrass growth and
survival were driving the actions necessary to manage nutrient and sediment loadings in the watershed. Also included in these criteria were parameters that affected other community components, such
as fish (i.e. dissolved O 2 ) and plankton (i.e. chlorophyll a), directly linking seagrasses to other important resources within the framework of the Bay-wide
ecosystem criteria. Faunal community components
depending on seagrass habitat (e.g. fish and shellfish)
were identified by the social and economic needs of
the Bay residents as vital to the health and well-being
of the seagrass biome and economy of the Bay. At
the same time, the scientists met with resource managers from several state jurisdictions and encouraged
them to adopt the criteria.
2. Developing Metrics of Seagrass Systems
Evaluation of community level performance measures that address the biodiversity of seagrass
ecosystems requires a great deal of time-consuming
and expensive monitoring. Recently, scientists involved in ecosystem level research in seagrass systems have suggested that we can utilize reasonably
simple metrics of dominant community types to act
as surrogates for habitat quality, community composition, and ecological services (Dennison et al.,
1993; Fonseca et al., 2000; 2002). For example,
something as simple as a visual estimate of seagrass
cover and the maximum depth to which seagrasses
grow may be adequate to predict the seagrass community response to improved water quality. The difficult process of articulating the scope of resource
services into simple metrics is one aspect of seagrass science that must be effectively translated for
managers and the public for support of conservation efforts. If it is possible to collapse many services into one or a few metrics, the cost of monitoring and protection could be greatly reduced. The
appropriate scaling of functions and services is an
important area of future research. Time, personnel,
and budget constraints demand the selection of functional indices in system-wide monitoring programs
designed to identify responses to threats, areas for
protection, and the performance of protected areas
recommended by managers. We need quantifiable
metrics that address community composition and dynamics but are easily measured, repeatable, and can
be transferred between geographic locations, systems and even biomes (e.g. Neckles, 1994; Fonseca
et al., 2000a).
In the Chesapeake Bay program the metric was
defined by seagrass cover to a specified water depth.
Seagrasses and freshwater angiosperms were, as
suggested by Dennison et al. (1993), the ‘canaries
in the mine’; in this instance the mine is the estuary and the seagrasses are signaling the health of
the water body. Future expectations were developed
from an historical data set of seagrass distribution
and predictions of the outcome of various management options were made with an optical water quality model, which identified the environmental conditions permitting the seagrass distribution. The example in the Chesapeake Bay is a new approach to
setting water quality criteria where the target community drives the criteria and not the inverse, which
was the usual case for setting standards (Kenworthy
and Haunert, 1991; Dennison et al., 1993; Gallegos
and Kenworthy, 1996; Gallegos, 2001). At the time
the Chesapeake Bay program started, existing water quality criteria were set without detailed empirical information or synthesis and were based on the
requirements of freshwater SAV and plankton and
were inadequate for seagrass protection (Kenworthy
and Haunert, 1991). The Chesapeake Bay example illustrates the possibilities that can be realized
through collaboration across scientific disciplines
and cooperation between stakeholders across jurisdictional boundaries (Batiuk et al., 2000). The dialog
between scientists and managers continues, the criteria are improving, and overall, the program is a
good example of adaptive management for seagrass
conservation in developed countries. For future application of this approach in developing regions, scientists and managers will have to initiate monitoring
and resource inventory programs located in remote
areas that will have to depend on innovative techniques, remote sensing technologies and indigenous
populations to gather the information and implement the conservation programs necessary to protect
seagrasses: a challenging but necessary scientific
endeavor.
613
conservation goals for seagrasses. The program responded to a crisis of seagrass loss by organizing
human resources, setting a clear and concise goal
for expected distribution and abundance of seagrass
communities, and developed a model that predicted
environmental criteria necessary for seagrass survival. Performance measures were assessed by monitoring the response of seagrass distribution to water quality conditions. For the very first time in the
U.S. the designated criteria for seagrass growth and
survival were driving the actions necessary to manage nutrient and sediment loadings in the watershed. Also included in these criteria were parameters that affected other community components, such
as fish (i.e. dissolved O 2 ) and plankton (i.e. chlorophyll a), directly linking seagrasses to other important resources within the framework of the Bay-wide
ecosystem criteria. Faunal community components
depending on seagrass habitat (e.g. fish and shellfish)
were identified by the social and economic needs of
the Bay residents as vital to the health and well-being
of the seagrass biome and economy of the Bay. At
the same time, the scientists met with resource managers from several state jurisdictions and encouraged
them to adopt the criteria.
2. Developing Metrics of Seagrass Systems
Evaluation of community level performance measures that address the biodiversity of seagrass
ecosystems requires a great deal of time-consuming
and expensive monitoring. Recently, scientists involved in ecosystem level research in seagrass systems have suggested that we can utilize reasonably
simple metrics of dominant community types to act
as surrogates for habitat quality, community composition, and ecological services (Dennison et al.,
1993; Fonseca et al., 2000; 2002). For example,
something as simple as a visual estimate of seagrass
cover and the maximum depth to which seagrasses
grow may be adequate to predict the seagrass community response to improved water quality. The difficult process of articulating the scope of resource
services into simple metrics is one aspect of seagrass science that must be effectively translated for
managers and the public for support of conservation efforts. If it is possible to collapse many services into one or a few metrics, the cost of monitoring and protection could be greatly reduced. The
appropriate scaling of functions and services is an
important area of future research. Time, personnel,
and budget constraints demand the selection of functional indices in system-wide monitoring programs
designed to identify responses to threats, areas for
protection, and the performance of protected areas
recommended by managers. We need quantifiable
metrics that address community composition and dynamics but are easily measured, repeatable, and can
be transferred between geographic locations, systems and even biomes (e.g. Neckles, 1994; Fonseca
et al., 2000a).
In the Chesapeake Bay program the metric was
defined by seagrass cover to a specified water depth.
Seagrasses and freshwater angiosperms were, as
suggested by Dennison et al. (1993), the ‘canaries
in the mine’; in this instance the mine is the estuary and the seagrasses are signaling the health of
the water body. Future expectations were developed
from an historical data set of seagrass distribution
and predictions of the outcome of various management options were made with an optical water quality model, which identified the environmental conditions permitting the seagrass distribution. The example in the Chesapeake Bay is a new approach to
setting water quality criteria where the target community drives the criteria and not the inverse, which
was the usual case for setting standards (Kenworthy
and Haunert, 1991; Dennison et al., 1993; Gallegos
and Kenworthy, 1996; Gallegos, 2001). At the time
the Chesapeake Bay program started, existing water quality criteria were set without detailed empirical information or synthesis and were based on the
requirements of freshwater SAV and plankton and
were inadequate for seagrass protection (Kenworthy
and Haunert, 1991). The Chesapeake Bay example illustrates the possibilities that can be realized
through collaboration across scientific disciplines
and cooperation between stakeholders across jurisdictional boundaries (Batiuk et al., 2000). The dialog
between scientists and managers continues, the criteria are improving, and overall, the program is a
good example of adaptive management for seagrass
conservation in developed countries. For future application of this approach in developing regions, scientists and managers will have to initiate monitoring
and resource inventory programs located in remote
areas that will have to depend on innovative techniques, remote sensing technologies and indigenous
populations to gather the information and implement the conservation programs necessary to protect
seagrasses: a challenging but necessary scientific
endeavor.