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In response to systemic losses of submerged aquatic vegetation (SAV) in the Chesapeake Bay (east coast of North America), the U.S. Environmental Protection Agency's (EPA) Chesapeake Bay Program (CBP) and Maryland Department of Natural Resources (MD DNR) have considered SAV restoration a critical component in Bay restoration programs. In 2003, the CBP created the “Strategy to Accelerate the Protection and Restoration of Submerged Aquatic Vegetation in the Chesapeake Bay” in an effort to increase SAV area. As part of this strategy, large-scale eelgrass (Zostera marina) restoration efforts were initiated in the Patuxent and Potomac Rivers in Maryland. From 2004 to 2007, nearly 4 million Z. marina seeds were dispersed over 10 ha on the Patuxent River and almost 9 million seeds over 16 ha on the Potomac River. Z. marina seedling establishment was consistent throughout the project (

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 though the water column to the depth of SAV growth (PLW min) and as a percentage of light reaching reaching leaves through the epiphyte layer (PLL min). Value were computed by applying, as inputs to this algorithm, statistically dervived values for water quality variables that correspond to thresholds for SAV presence in Chesapeake Bay. These estimates ofPLW min andPLL min compared well with the values established from a literature review. Calcultations 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 chlorophylla (chla), 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 exceededPLL 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 polythaline 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.