Research Publications: UM-SG-RS-2004-08



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




Kemp, WM; Batiuk, R; Bartleson, R; Bergstrom, P; Carter, V; Gallegos, CL; Hunley, W; Karrh, L; Koch, EW; Landwehr, JM; Moore, KA; Murray, L; Naylor, M; Rybicki, NB; Stevenson, JC; Wilcox, DJ


27 ( 3 ) : 363 - 377




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 alpha (chl alpha), 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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