Effects of Water Quality on Seagrass Productivity and Distribution

The worldwide decline in seagrass distribution and abundance has focused much research on the development of predictive relationships between water quality and the status of submerged vegetation (e.g. Batiuket al., 1992; Morris andTomasko, 1993; Berry et al., 2003). It has been argued that the sensitivity of seagrasses to light availability makes them good indicators of changes in environmental water quality (Dennison et al., 1993; Duarte et al., Chapter 11, section VI.C). Unfortunately, seagrass losses are very difficult to reverse once they are allowed to occur.

The radiative transfer approach outlined here incorporates water quality effects on the irradiance throughout the water column, as well as density-dependent effects on irradiance distribution and light utilization within the canopy. Thus, it can be used to explore water quality issues relevant to light availability that controls seagrass production and distribution before significant impacts occur on sea-grass populations. This approach was tested as part of a pilot study designed to develop a plan for monitoring submerged vegetation resources throughout Puget Sound, Washington, USA (Berry et al., 2003). The model was used to explore the maximum sustainable eelgrass density at Dumas Bay located in the highly turbid southern region of Puget Sound. Estimates of the submarine light environment in the water column were obtained from calculations performed by the radiative transfer model Hydrolight (Ver 4.2 Sequoia Scientific, Inc.) for local solar noon on the spring equinox. Modeled water column Chl concentrations ranged from 20 to 50 mg m-3. Total suspended solids (TSS) concentrations ranged from 0 to 25 mg L-1. Model calculations of daily carbon balance produced well-behaved second-order relationships between maximum sustainable eelgrass density and depth. These simple relationships were then used to populate distribution maps of potential eelgrass density for different water quality conditions. The resulting distributions were qualitatively consistent with a field survey conducted at the site in 1995 by Norman et al. (1995) (Fig. 9). Water column turbidity was identified as a major factor determining eelgrass distributions in Dumas Bay, and model predictions were more sensitive to variations in TSS than Chl. This finding is similar to the situation in other eastern Pacific estuaries such as San Francisco Bay, where light availability is more affected by sediment load than Chl (Alpine and Cloern, 1988; Zimmerman et al., 1991, 1995). The reliability of any numerical model is always limited by the data used to parameterize the important driving variables. In this case, however, uncertainty in shoot morphology, and shoot:root ratios in particular, represented a second-order problem with regard to accurately modeling the eelgrass distribution.

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