290
Carlos M. Duarte, James W. Fourqurean, Dorte Krause-Jensen, and Birgit Olesen
either because surveys have been lacking or methods have been inefficient. Recording of depth limits
is a relatively simple way of detecting declines in seagrass populations and as turbidity-related reductions
in seagrass cover often affect the deep-water meadows most markedly, the method should be relatively
sensitive (see also predictive models based on underwater light fields, discussed in Zimmerman, Chapter
13). Methods involving measurements of population
change based on rates of shoot recruitment and mortality have also proved sensitive and may allow early
alerts (Duarte et al., 1994; Peterson and Fourqurean,
2001). A large-scale study of the Mediterranean climax species Posidonia oceanica thus showed that
shoot recruitment does not balance shoot mortality,
and the study predicted that shoot density will decline by 50% within 2–24 years if the present disturbance and rate of decline persist (Marb` a and Duarte,
1997). These perspectives are serious, especially because meadows of P. oceanica represent a very old
ecosystem dating back more than 6,000 years, and
slow growth rates imply that recolonization may take
centuries if the process is reversible at all (Duarte,
1995; Marb` a et al., 2002).
VII. Prospect: Forecasting
Seagrass Dynamics
The recent declines in seagrass populations worldwide (Green and Short, 2003; Walker et al., Chapter 23; Kenworthy et al., Chapter 25; Ralph et al.,
Chapter 24) accentuates the need for protecting these
valuable ecosystems. As anthropogenic inputs to
the coastal zone are the primary cause of the declines (Short and Wyllie-Echeverria, 1996), measures should be taken to reduce these inputs. The
many examples of negative cascading effects upon
the loss of seagrass biomass emphasize the need for
taking action at an early stage.
Moreover, the accumulated knowledge on the
mechanism of change and the dynamics in seagrass
meadows should be formalized in models forecasting the dynamics of seagrass meadows, and their
recovery times. Such models should include predictions of the closure of gaps within meadows. These
forecasts are increasingly demanded by managers
and our capacity to deliver them is still meagre.
Much progress has been made in understanding the
dynamics of seagrass meadows since the earlier accounts (den Hartog, 1971). However, although reliable models of clonal growth are now being developed, the prediction of recolonization rates at
the landscape scale is problematical (cf. Bell et al.,
Chapter 26), as the contingencies of patch formation
by sexual propagules or vegetative fragments dispersed into an the area is essentially non-predictable.
Rare events of long-range dispersal of seeds or vegetative fragments, which cannot be predicted, may
play a pivotal role in the recolonization of areas away
from any adjacent seagrass source (cf. Orth et al.,
Chapter 5). Indeed, current knowledge also indicates
that the expectation that knowledge on rhizome extension and patch initiation could suffice to predict
seagrass dynamics, by upscaling these processes to
the landscape scale (e.g. Duarte, 1995), is unsupported, as evidence emerges of increasingly complex
dynamics at greater spatial scales (e.g. Sintes et al.,
2005; Kendrick et al., 2005).
However, the combined knowledge on seagrass
reproduction and dispersal (e.g. Orth et al., Chapter 5), and clonal growth, reviewed above, now allows predictions on the recolonization time scales
inherent for different species, which range from one
or a few years for the fastest growing species, to
several centuries for the slowest-growing ones. As
yet, this knowledge has not been formalized into
in models delivering, predicted seagrass dynamics
under plausible scenarios of growth and new patch
initiation.
Acknowledgement
Dorte Krause-Jensen was supported financially by
the EC project “M & Ms” contract no. EVK3-CT2000-00044 M & Ms
References
Agawin NSR, Duarte CM and Fortes MD (1996) Nutrient limitation of Philippine seagrasses (Cape Bolinao, NW Philippines):
In situ experimental evidence. Mar Ecol Prog Ser 138: 233–
243
Alcoverro T, Duarte CM and Romero J (1995) Annual growth
dynamics of Posidonia oceanica—Contribution of large-scale
versus local factors to seasonality. Mar Ecol Prog Ser 120:
203–210
Alcoverro T and Mariani S (2002) Effects of sea urchin grazing on seagrass (Thalassodendron ciliatum) beds of a Kenyan
Lagoon. Mar Ecol Prog Ser 226: 255–263
Andorfer J and Dawes C (2002) Production of rhizome meristems
by the tropical seagrass Thalassia testudinum: The basis for
slow recovery into propeller scars. J Coastal Res 37: 130–142
Balestri E and Cinelli F (2003) Sexual reproductive success in
Posidonia oceanica. Aquat Bot 75: 21–32
Carlos M. Duarte, James W. Fourqurean, Dorte Krause-Jensen, and Birgit Olesen
either because surveys have been lacking or methods have been inefficient. Recording of depth limits
is a relatively simple way of detecting declines in seagrass populations and as turbidity-related reductions
in seagrass cover often affect the deep-water meadows most markedly, the method should be relatively
sensitive (see also predictive models based on underwater light fields, discussed in Zimmerman, Chapter
13). Methods involving measurements of population
change based on rates of shoot recruitment and mortality have also proved sensitive and may allow early
alerts (Duarte et al., 1994; Peterson and Fourqurean,
2001). A large-scale study of the Mediterranean climax species Posidonia oceanica thus showed that
shoot recruitment does not balance shoot mortality,
and the study predicted that shoot density will decline by 50% within 2–24 years if the present disturbance and rate of decline persist (Marb` a and Duarte,
1997). These perspectives are serious, especially because meadows of P. oceanica represent a very old
ecosystem dating back more than 6,000 years, and
slow growth rates imply that recolonization may take
centuries if the process is reversible at all (Duarte,
1995; Marb` a et al., 2002).
VII. Prospect: Forecasting
Seagrass Dynamics
The recent declines in seagrass populations worldwide (Green and Short, 2003; Walker et al., Chapter 23; Kenworthy et al., Chapter 25; Ralph et al.,
Chapter 24) accentuates the need for protecting these
valuable ecosystems. As anthropogenic inputs to
the coastal zone are the primary cause of the declines (Short and Wyllie-Echeverria, 1996), measures should be taken to reduce these inputs. The
many examples of negative cascading effects upon
the loss of seagrass biomass emphasize the need for
taking action at an early stage.
Moreover, the accumulated knowledge on the
mechanism of change and the dynamics in seagrass
meadows should be formalized in models forecasting the dynamics of seagrass meadows, and their
recovery times. Such models should include predictions of the closure of gaps within meadows. These
forecasts are increasingly demanded by managers
and our capacity to deliver them is still meagre.
Much progress has been made in understanding the
dynamics of seagrass meadows since the earlier accounts (den Hartog, 1971). However, although reliable models of clonal growth are now being developed, the prediction of recolonization rates at
the landscape scale is problematical (cf. Bell et al.,
Chapter 26), as the contingencies of patch formation
by sexual propagules or vegetative fragments dispersed into an the area is essentially non-predictable.
Rare events of long-range dispersal of seeds or vegetative fragments, which cannot be predicted, may
play a pivotal role in the recolonization of areas away
from any adjacent seagrass source (cf. Orth et al.,
Chapter 5). Indeed, current knowledge also indicates
that the expectation that knowledge on rhizome extension and patch initiation could suffice to predict
seagrass dynamics, by upscaling these processes to
the landscape scale (e.g. Duarte, 1995), is unsupported, as evidence emerges of increasingly complex
dynamics at greater spatial scales (e.g. Sintes et al.,
2005; Kendrick et al., 2005).
However, the combined knowledge on seagrass
reproduction and dispersal (e.g. Orth et al., Chapter 5), and clonal growth, reviewed above, now allows predictions on the recolonization time scales
inherent for different species, which range from one
or a few years for the fastest growing species, to
several centuries for the slowest-growing ones. As
yet, this knowledge has not been formalized into
in models delivering, predicted seagrass dynamics
under plausible scenarios of growth and new patch
initiation.
Acknowledgement
Dorte Krause-Jensen was supported financially by
the EC project “M & Ms” contract no. EVK3-CT2000-00044 M & Ms
References
Agawin NSR, Duarte CM and Fortes MD (1996) Nutrient limitation of Philippine seagrasses (Cape Bolinao, NW Philippines):
In situ experimental evidence. Mar Ecol Prog Ser 138: 233–
243
Alcoverro T, Duarte CM and Romero J (1995) Annual growth
dynamics of Posidonia oceanica—Contribution of large-scale
versus local factors to seasonality. Mar Ecol Prog Ser 120:
203–210
Alcoverro T and Mariani S (2002) Effects of sea urchin grazing on seagrass (Thalassodendron ciliatum) beds of a Kenyan
Lagoon. Mar Ecol Prog Ser 226: 255–263
Andorfer J and Dawes C (2002) Production of rhizome meristems
by the tropical seagrass Thalassia testudinum: The basis for
slow recovery into propeller scars. J Coastal Res 37: 130–142
Balestri E and Cinelli F (2003) Sexual reproductive success in
Posidonia oceanica. Aquat Bot 75: 21–32