Tides, light and the distribution of Zostera marina in Long Island Sound, USA

Koch, Evamaría W.; Beer, Sven

doi:10.1016/0304-3770(95)01015-7

Cited by 83 publications

(53 citation statements)

References 19 publications

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“…Due to the complex and important links that macroalgae establish with other components within the system (e.g. seagrasses- Herman et al, 1996;McClelland & Valiela, 1998;Lapointe et al, 1994;Short et al, 1995;Masini et al, 1995;Koch & Beer, 1996;macrofauna-Raffaelli et al, 1991;Lillebø et al, 1999;Pardal et al, 2000;wading birds-Mú rias et al, 1996wading birds-Mú rias et al, , 1997Lopes et al, 2000; sediment characteristics and composition- Neira & Rackemann, 1996;nutrient cycles-Viaroli et al, 1992nutrient cycles-Viaroli et al, , 1993nutrient cycles-Viaroli et al, , 1996Valiela et al, 1997), it becomes essential to know which variables control macroalgal abundance and regulate their biomass. Although, these factors will differ from system to system, their identification at each coastal area will contribute to a deeper knowledge of coastal eutrophication and, possibly deal with this environmental problem by helping to find suitable mitigation measures.…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamics as a Major Factor Controlling the Occurrence of Green Macroalgal Blooms in a Eutrophic Estuary: A Case Study on the Influence of Precipitation and River Management

Martins

Pardal

Lillebø

et al. 2001

Estuarine, Coastal and Shelf Science

167

162

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Data on the variation of physicochemical parameters, biomass and growth of green macroalgae (mostly Enteromorpha) collected between January 1993 and January 1997 in the Mondego Estuary (western coast of Portugal) was analysed with the aim to identify the factors that control opportunistic macroalgal abundance in the system.The annual biomass of Enteromorpha spp. is strongly dependent on the amount of fresh water that enters the system during winter and spring. In turn, the input of fresh water is regulated by precipitation and by river management practices. The optimization of the rice crops from the upstream valley depends on their water level, which determines the number of days and hours per day during which sluice gates remain open in winter and spring. River flow has significant impacts on salinity, N:P ratios, current velocities and light extinction coefficients within the system. The interaction of all these factors controls macroalgal growth and biomass loss processes.In winters and springs during which sluice gates are often closed due to water deficiency of the rice fields (dry winter and spring or dry winter followed by rainy spring), little fresh water enters the system and consequently, salinity remains high, N:P ratios around 20, light penetration increases, and current velocities fall. These conditions facilitate macroalgal fixation, enhance their growth and spring blooms occur. On the contrary, during winters and springs when fresh water is in excess of rice fields' needs (rainy winters and springs), sluice gates remain open for long periods of time. High input of fresh water to the system causes salinity and light penetration to decrease, while N:P ratios and current velocities increase. These conditions contribute both to reduced Enteromorpha growth and higher loss of macroalgal biomass from the system to the ocean.The present work shows that the inter-annual variation of macroalgal biomass in the Mondego Estuary is controlled by hydrodynamics, which in turn depends on precipitation and on river management, according with the water needs of the upstream rice crop. Academic Press

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Section: Introductionmentioning

confidence: 99%

Hydrodynamics as a Major Factor Controlling the Occurrence of Green Macroalgal Blooms in a Eutrophic Estuary: A Case Study on the Influence of Precipitation and River Management

Martins

Pardal

Lillebø

et al. 2001

Estuarine, Coastal and Shelf Science

167

162

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“…Wave attenuation by seagrass canopies has been measured only in shallow systems where canopies occupy a large fraction of the water column (Fonseca & Cahalan 1992, Koch & Beer 1996, Mork 1996, Chen et al 2007, Bradley & Houser 2009. Posidonia oceanica, which is the dominant seagrass species in the Mediterranean Sea, forms extensive meadows in depths up to 45 m (Procaccini et al 2003) and the canopy often occupies less than 20% of water column height.…”

Section: Introductionmentioning

confidence: 99%

Effect of a seagrass (Posidonia oceanica) meadow on wave propagation

Infantes¹,

Orfila²,

Simarro³

et al. 2012

Mar. Ecol. Prog. Ser.

133

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We demonstrate the utility of using the equivalent bottom roughness for calculating the friction factor and the drag coefficient of a seagrass meadow for conditions in which the meadow height is small compared to the water depth. Wave attenuation induced by the seagrass Posidonia oceanica is evaluated using field data from bottom-mounted acoustic doppler velocimeters (ADVs). Using the data from one storm event, the equivalent bottom roughness is calculated for the meadow as k s~ 0.40 m. This equivalent roughness is used to predict the wave friction factor ƒ w , the drag coefficient on the plant, C D , and ultimately the wave attenuation for other storms. Root mean squared wave height (H rms ) is reduced by around 50% for incident waves of 1.1 m propagating over ~ 1000 m of a meadow of P. oceanica with shoot density of ~600 shoots m −2 .

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“…Tidal ranges on the Bay's eastern shoals tend to be higher by 10-15 cm than those on the western side, and ranges are generally 40-50 cm higher in tidal fresh regions of tributaries than at their mouths (Hicks 1964). SAV are generally excluded from intertidal areas because of physical stress (waves, desiccation, freezing), and the shallow depth-limit for SAV distribution therefore tends to be deeper in areas with higher tidal range (Koch and Beer 1996). The deeper depth limit is generally reduced at sites with larger tidal range because of increased light attenuation through the longer average water column (Koch 2001), resulting in an inverse relationship between tidal range and the range of SAV depth distribution (Fig.…”

Section: Water Quality and Sav Habitatmentioning

confidence: 99%

“…Although these water quality variables relate to many aspects of SAV physiology and ecology, their influence on the plant's light climate was recognized to be of primary importance (Dennison et al 1993). Although in recent years these SAV habitat requirements have been widely employed as a diagnostic tool to assess suitability of sites for plant growth or restoration, their use has met with mixed success in Chesapeake Bay Landwehr et al 1999) and many other coastal environments (Dunton 1996;Koch and Beer 1996;Tomasko et al 1996;Valdes-Murtha 1997;Glazer 1999).…”

Section: Introductionmentioning

confidence: 99%

Habitat requirements for submerged aquatic vegetation in Chesapeake Bay: Water quality, light regime, and physical-chemical factors

et al. 2004

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We developed an algorithm for calculating habitat suitability for seagrasses and related submerged aquatic vegetation (SAV) at coastal sites where monitoring data are available for five water quality variables that govern light availability at the leaf surface. We developed independent estimates of the minimum light required for SAV survival both as a percentage of surface light passing through the water column to the depth of SAV growth (PLW min ) and as a percentage of light reaching leaves through the epiphyte layer (PLL min ). Values were computed by applying, as inputs to this algorithm, statistically derived values for water quality variables that correspond to thresholds for SAV presence in Chesapeake Bay. These estimates of PLW min and PLL min compared well with the values established from a literature review. Calculations account for tidal range, and total light attenuation is partitioned into water column and epiphyte contributions. Water column attenuation is further partitioned into effects of chlorophyll a (chl a), total suspended solids (TSS) and other substances. We used this algorithm to predict potential SAV presence throughout the Bay where calculated light available at plant leaves exceeded PLL min . Predictions closely matched results of aerial photographic monitoring surveys of SAV distribution. Correspondence between predictions and observations was particularly strong in the mesohaline and polyhaline regions, which contain 75-80% of all potential SAV sites in this estuary. The method also allows for independent assessment of effects of physical and chemical factors other than light in limiting SAV growth and survival. Although this algorithm was developed with data from Chesapeake Bay, its general structure allows it to be calibrated and used as a quantitative tool for applying water quality data to define suitability of specific sites as habitats for SAV survival in diverse coastal environments worldwide.

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Tides, light and the distribution of Zostera marina in Long Island Sound, USA

Cited by 83 publications

References 19 publications

Hydrodynamics as a Major Factor Controlling the Occurrence of Green Macroalgal Blooms in a Eutrophic Estuary: A Case Study on the Influence of Precipitation and River Management

Hydrodynamics as a Major Factor Controlling the Occurrence of Green Macroalgal Blooms in a Eutrophic Estuary: A Case Study on the Influence of Precipitation and River Management

Effect of a seagrass (Posidonia oceanica) meadow on wave propagation

Habitat requirements for submerged aquatic vegetation in Chesapeake Bay: Water quality, light regime, and physical-chemical factors

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