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Quantifying Seagrass Light Requirements Using an Algorithm to Spatially Resolve Depth of Colonization

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Abstract

Depth of colonization (Z c) is a useful seagrass growth metric that describes seagrass response to light availability. Similarly, percent surface irradiance at Z c (% SI) is an indicator of seagrass light requirements with applications in seagrass ecology and management. Methods for estimating Z c and % SI are highly variable making meaningful comparisons difficult. A new algorithm is presented to compute maps of median and maximum Z c, Z c,med, and Z c,max, respectively, for four Florida coastal areas (Big Bend, Tampa Bay, Choctawhatchee Bay, Indian River Lagoon). Maps of light attenuation (K d) based on MODIS satellite imagery, PAR profiles, and Secchi depth measurements were combined with seagrass growth estimates to produce maps of % SI at Z c,med and Z c,max. Among estuary segments, mean Z c,med varied from (±SE) 0.80 ± 0.13 m for Old Tampa Bay to 2.33 ± 0.26 m for Western Choctawhatchee Bay. Standard errors for Z c,med were 1–10% of the segment means. Percent SI at Z c,med averaged 18% for Indian River Lagoon (range = 9–24%), 42% for Tampa Bay (37–48%), and 58% for Choctawhatchee Bay (51–75%). 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 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.

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Acknowledgments

We thank Dr. Peter Tango and two anonymous reviewers for helpful comments on the manuscript. The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency.

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Author notes

  1. Chengfeng Le

    Present address: Ocean College, Zhejiang University, Zhoushan, China

Authors and Affiliations

  1. USEPA National Health and Environmental Effects Research Laboratory, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL, 32561, USA

    Marcus W. Beck & James D. Hagy III

  2. ORISE Research Participation Program, USEPA National Health and Environmental Effects Research Laboratory, Gulf Ecology Division, 1 Sabine Island Drive, Gulf Breeze, FL, 32561, USA

    Chengfeng Le

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  1. Marcus W. Beck
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Correspondence to Marcus W. Beck.

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Communicated by Richard C. Zimmerman

Electronic supplementary material

Fig. S1

Uncorrected satellite estimates of light attenuation in Choctawhatchee Bay (a) and example of correction with in situ data (b). In situ data of light attenuation were estimated as an annual average (2010) for monthly data at the sampling sites labeled as points in (a) (mean depth 7.2 m, minimum 3.5 m). The corresponding satellite data in the same grid cells were compared to the in situ data based on regressions of each dataset with frequency estimates for both. An example correction is shown in (b) where for any uncorrected satellite estimate (point 1), the corresponding frequency estimate on the regression curve from the satellite data was identified (point 2), matched with the corresponding frequency for the in situ data (point 3), and then related to the associated in situ light attenuation value (point 4) to yield the corrected satellite estimate (GIF 63.2 kb)

High Resolution Image (TIFF 339 kb)

Fig. S2

Locations of selected water quality stations monitored by the Hillsborough County Environmental Protection Commission (TBEP 2011). Secchi observations at each station were used to evaluate changes in light requirements of seagrass at approximate biennial intervals from 1988 to 2014. Stations are labeled by their designation and were chosen based on continuity of data for the period of interest. HB Hillsborough Bay, LTB Lower Tampa Bay, MTB Middle Tampa Bay, OTB Old Tampa Bay (GIF 40.1 kb)

High Resolution Image (TIFF 80.1 kb)

Fig. S3

Maximum depth of seagrass colonization (Z c,max, m) and light requirements (% surface irradiance at Z c,max) for multiple locations in Choctawhatchee Bay, Florida. Each location has light attenuation from satellite observations and an estimate of seagrass depth of colonization with a search radius of 0.04 degrees. Box plots show the 25th percentile, median, and 75th percentile. Whiskers extend to the 5th and 95th percentiles with outliers beyond. CCB Central Choctawhatchee Bay, ECB East Choctawhatchee Bay, WCB West Choctawhatchee Bay (GIF 107 kb)

High Resolution Image (TIFF 645 kb)

Fig. S4

Maximum depth of seagrass colonization (Z c,max, m) and light requirements (% surface irradiance at Z c,max) for multiple locations in Tampa Bay, Florida. Each location has light attenuation from satellite observations and an estimate of seagrass depth of colonization with a search radius of 0.1 degrees. Box plots show 25th percentile, median, and 75th percentile. Whiskers extend to the 5th and 95th percentiles with outliers beyond. HB Hillsborough Bay, LTB Lower Tampa Bay, MTB Middle Tampa Bay, OTB Old Tampa Bay (GIF 104 kb)

High Resolution Image (TIFF 556 kb)

Fig. S5

Maximum depth of seagrass colonization (Z c,max, m) and light requirements (% surface irradiance at Z c,max) for multiple locations in Indian River Lagoon, Florida. Each location has an average Secchi depth observation and an estimate of seagrass depth of colonization with a search radius of 0.15 degrees. Map locations are georeferenced observations of Secchi depth. Box plots show 25th percentile, median, and 75th percentile. Whiskers extend to the 5th and 95th percentiles with outliers beyond. BR Banana River, LCIRL Lower Central Indian River Lagoon, LIRL Lower Indian River Lagoon, LML Lower Mosquito Lagoon, LSL Lower St. Lucie, UCIRL Upper Central Indian River Lagoon, UIRL Upper Indian River Lagoon, UML Upper Mosquito Lagoon (GIF 68.9 kb)

High Resolution Image (TIFF 367 kb)

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Beck, M.W., Hagy, J.D. & Le, C. Quantifying Seagrass Light Requirements Using an Algorithm to Spatially Resolve Depth of Colonization. Estuaries and Coasts 41, 592–610 (2018). https://doi.org/10.1007/s12237-017-0287-1

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