Influence of the seagrass, Zostera marina L., on current flow

Fonseca, Mark S.; Fisher, John S.; Zieman, Joseph C.; Thayer, Gordon W.

doi:10.1016/0272-7714(82)90046-4

Cited by 385 publications

(242 citation statements)

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“…However, although the use of pivot feeding to explain the adaptive significance of head morphology in sygnathid fish 19 is important, one cannot ignore the fact that without the ability to approach evasive prey undetected, short range pivot feeding becomes ineffective. The dwarf seahorse, H. zosterae, is found within seagrass beds 34 that dampen water motion and reduces turbulence 25,35 .…”

Section: Discussionmentioning

confidence: 99%

Morphology of seahorse head hydrodynamically aids in capture of evasive prey

Gemmell¹,

Sheng

Buskey³

2013

Nat Commun

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Syngnathid fish (seahorses, pipefish and sea dragons) are slow swimmers yet capture evasive prey (copepods) using a technique known as the 'pivot' feeding, which involves rapid movement to overcome prey escape capabilities. However, this feeding mode functions only at short range and requires approaching very closely to hydrodynamically sensitive prey without triggering an escape. Here we investigate the role of head morphology on prey capture using holographic and particle image velocimetry (PIV). We show that head morphology functions to create a reduced fluid deformation zone, minimizing hydrodynamic disturbance where feeding strikes occur (above the end of the snout), and permits syngnathid fish to approach highly sensitive copepod prey (Acartia tonsa) undetected. The results explain how these animals can successfully employ short range 'pivot' feeding effectively on evasive prey. The need to approach prey with stealth may have selected for a head shape that produces lower deformation rates than other fish.

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Section: Discussionmentioning

confidence: 99%

Morphology of seahorse head hydrodynamically aids in capture of evasive prey

Gemmell¹,

Sheng

Buskey³

2013

Nat Commun

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“…This algorithm complements use of water quality models (Madden and Kemp 1996;Cerco and Moore 2001) for managing SAV resources. Simulation models can be used to predict how SAV habitat conditions respond to scenarios for changing nutrient and Conover 1964;Westlake 1967;Sculthorpe 1967;Scoffin 1970;Phillips 1974;Fonseca et al 1982;Madsen and Sondergaard 1983;Werner and Wise 1982;Fonseca and Kenworthy 1987;Koch 1994;Merrell 1996. b Joanen and Glasgow 1965;Hannan 1967;Rawls 1975;Stevenson and Confer 1978;Stewart et al 1997;Dan et al 1998. c Burrell and Schubel 1977;Carter et al 1985;Posey et al 1993;Short et al 1993;Edgar and Shaw 1995;Dan et al 1998.…”

Section: Concluding Commentsmentioning

confidence: 99%

“…Losses of SAV beds are of particular concern because these plants create rich habitat and food for animals, supporting growth of diverse waterfowl, fish, and invertebrate populations (Lubbers et al 1990;Heck et al 1995). Seagrass and SAV communities also significantly modulate key biogeochemical (Caffrey and Kemp 1990), physical (Rybicki et al 1997;Koch and Gust 1999), and sedimentological (Fonseca et al 1982;Ward et al 1984) processes. Although many factors such as climatic events (Pulich and White 1991), physical disturbance (Quammen and Onuf 1993), and herbicide toxicity may have contributed to these SAV declines, the most prevalent causes appear related to reductions in light availability associated with increased inputs of nutrients and suspended sediments (Kemp et al 1983;Cambridge and McComb 1984;Borum 1985;McGlathery 1995;Tomasko et al 1996).…”

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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“…Vegetation has been shown to effectively attenuate flow (Fonseca et al 1982;Peterson et al 2004) and wave energy (Dean 1978;Fonseca and Cahalan 1992;Kobayashi et al 1993;Mendez et al 1999;Möller et al 1999;Dean and Bender 2006;Augustin et al 2009). Several models for predicting wave dissipation through vegetation have been proposed based on the conservation of energy (Dalrymple et al 1984;Mendez and Losada 2004) and conservation of momentum (Kobayashi et al 1993) for linear waves, and these have subsequently been expanded and new models proposed (Dubi and Torum 1995;Mendez et al 1999;Chen and Zhao 2012).…”

Section: Introductionmentioning

confidence: 99%

Flume instrumentation for measurement of drag on flexible elements under waves

2014

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Understanding energy dissipation by vegetation is critical for the effective management of shoreline erosion. Current methods for estimating energy dissipation require plant-specific parameters that are difficult to estimate for the large variety of plant morphologies used in shoreline protection, requiring testing on each species of interest. A simple and fast method to characterize drag in terms of wave interaction and obstruction natural frequency is needed to fully explore drag forces on vegetation. Our method directly measures hydrodynamic forces on individual plant shoots using a torque sensor mounted beneath the bed of a flume. This sensor allows data to be collected simply and inexpensively with high temporal accuracy that provides insight into drag forces and torque frequency from a variety of flexible elements when coupled with wave monitoring. The technique can evaluate several types of obstructions quickly without the need to set up an entire obstruction field. The data collected also suggest that more flexible objects result in less drag force on each element and suggest that frequency response is related to the frequencies existing in the driving wave and the natural frequency of the obstruction element, although harmonic synchronization appears to occur in some cases doubling the expected drag force magnitude.

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Influence of the seagrass, Zostera marina L., on current flow

Cited by 385 publications

References 10 publications

Morphology of seahorse head hydrodynamically aids in capture of evasive prey

Morphology of seahorse head hydrodynamically aids in capture of evasive prey

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

Flume instrumentation for measurement of drag on flexible elements under waves

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