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Quantifying Seagrass Light Requirements Using an Algorithm to Spatially Resolve Depth of Colonization-CERF presentation
Citation:
Beck, M., Jim Hagy, AND C. Le. Quantifying Seagrass Light Requirements Using an Algorithm to Spatially Resolve Depth of Colonization-CERF presentation. CERF 24th Biennial Conference, Providence, RI, November 05 - 09, 2017.
Impact/Purpose:
This abstract is to be submitted for an oral presentation at the 2017 biennial meeting of the Coastal and Estuarine Research Federation. This presentation describes a new algorithm developed to compute maps of seagrass depth of colonization. We present results for four estuaries in Florida.
Description:
Depth of colonization (Zc) is a useful seagrass growth metric that describes seagrass response to light availability. Similarly, percent surface irradiance at Zc (% SI) is an indicator of seagrass light requirements with applications in seagrass ecology and management. Methods for estimating Zc and % SI are highly variable making meaningful comparisons difficult. A new algorithm is presented to compute maps of median and maximum Zc, Zc, med and Zc,max, respectively, for four Florida coastal areas (Big Bend, Tampa Bay, Choctawhatchee Bay, Indian River Lagoon). Maps of light attenuation (Kd) based on MODIS satellite imagery, PAR profiles, and Secchi depth measurements were combined with seagrass growth estimates to produce maps of % SI at Zc,med and Zc,max. Among estuary segments, mean Zc,med varied from (±s.e.) 0.86±0.08 m for Old Tampa Bay to 1.96±0.10 m for Western Choctawhatchee Bay. Coefficients of variation for Zc,med were 1-10%. Percent SI at Zc,med averaged 18% for Indian River Lagoon (range = 9-24%), 42% for Tampa Bay (36-49%) and 55% for Choctawhatchee Bay (46-75%). Estimates of % SI were significantly lower in Indian River Lagoon than in the other estuaries, while estimates for Tampa Bay and Choctawhatchee Bay were higher than the often cited estimate of 20%. Spatial gradients in depth of colonization and % SI were apparent in all estuaries. The analytical approach, implemented in R, could be applied easily to new data from these estuaries or to other estuaries and could be incorporated routinely in assessments of seagrass status and condition