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Modeling Seagrass Community Change Using Remote Sensing

Modeling Seagrass Community Change Using Remote Sensing. Marc Slattery & Greg Easson University of Mississippi. Seagrass Communities. Worldwide- one of the most important marine ecosystems:. critical nursery habitat for many coastal & pelagic species

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Modeling Seagrass Community Change Using Remote Sensing

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  1. Modeling Seagrass Community Change Using Remote Sensing Marc Slattery & Greg Easson University of Mississippi

  2. Seagrass Communities Worldwide- one of the most important marine ecosystems: • critical nursery habitat for many coastal & pelagic species • economic resource- fisheries, tourism & biodiversity • feeding grounds for ecologically-important species • baffles for wave energy and coastal erosion • vital refuge for threatened species

  3. Environmental Factors Controlling Seagrass Biomass/Abundance nutrients- H2O column light salinity epiphytes temperature nutrients- sediment August March July December Manatee-grass: Syringodium filiforme seagrass growingseason Seagrass Biology Not all seagrasses are created equal… Turtle-grass: Thalassia testidinum species relevant to Grand Bay NERR… Shoal-grass: Halodule wrightii Widgeon-grass: Ruppia maritima

  4. Modeling Seagrass Communities Problem: management of seagrass communities requires management of seagrass populations [=productivity]… P. Fong & M. Harwell, 1994. Modeling seagrass communities in tropical and subtropical bays and estuaries: a mathematical synthesis of current hypotheses. Bulletin of Marine Science 54:757-781. • Biomassseagrass[t+1] = Biomassseagrass[t] + Productivityseagrass - Lossseagrass  [Loss f (senescence)] Productivityseagrass = Pmaxseagrass (Salinityseagrass x Temperatureseagrass x Lightseagrass x nutrientsseagrass) [productivity  assc w/  nutrients] [productivity  assc w/  light] [productivity  assc w/  temperature] [productivity  assc w/  salinity] Goals of this Project: 1.Assess the capability of remote sensing platforms to provide data relevant to the Fong & Harwell model of seagrass community productivity. Compare data from remote sensing platforms with data collected on the ground to determine which approach provides a better prediction of seagrass community productivity. Considerations: 1. Halodule & Ruppia have similar broad/high tolerances to salinity [McMillan & Moseley 1967; Murphy et al 2003]: exceeds the extremes of GBNERR- disregarded… 2. Halodule & Ruppia have similar high tolerances to nutrient levels [Thursby 1984; Pulich 1989]- since water column nutrient levels are limiting, and epiphytes rely on these, this value impacts seagrasses more…

  5. Resource monitoring data Biomass Ruppia Halodule Fall ‘07 Fall ‘07 Spring ‘08 Spring ‘08 temporal sampling temporal sampling Experimental Design Satellite-based data Light [MODIS- daily] Temperature [MODIS- daily] Nutrients (proxy: Chla) [MODIS- daily] Ground-based data Biomasst+1 = Biomasst + Productivity - Loss [rearrange and solve for loss using satellite-based and ground-based parameters of productivity…] statistics on the two data sets… Light [Onset- continuous; & standardized to IL1700] Temperature [Onset- continuous] Nutrients [Hach- monthly]

  6. Grand Bay NERR Seagrass Ecosystem Middle Bay Grand Bay Jose Bay Pont Aux Chenes

  7. In Situ Data ANOVA: significant time effect, site effect ANOVA: significant time effect, site effect ANOVA: significant site effect ANOVA: significant time effect, site effect

  8. Remote Sensing Data from In situ studies- Nitrate [NO3]: Y=0.255+7.557*X nutrient Phosphate [PO4]: Y=0.135-0.213*X Chla

  9. Comparative Statistics Productivityseagrass = Pmaxseagrass (Temperatureseagrass x Lightseagrass x nutrientsseagrass) + species 2… In situ model 5 Remote model 0 -5 Relative Seagrass Productivity theoretical values -10 -15 09/07 10/07 11/07 12/07 01/08 02/08 03/08 Date Paired t-test: t-value = -1.261 P = 0.2541 Remote-sensing model yields positive seagrass productivity during the growing season!!!

  10. Conclusions Remote sensing platforms can be used, with some considerations, to populate parameters of the Fong & Harwell model of seagrass community productivity. Insitu data provided finer scale resolution of real world conditions; but temporal logistics may offset some of this benefit. Cooperative work between satellite-based and ground-based data acquisition teams appears to offer the greatest opportunities for seagrass resource managers. Future Plans Assess the Fong & Harwell model in St. Joseph’s Bay, FL  system is dominated by Thalassia & Syringodium…

  11. Acknowledgements Anne Boettcher, USA Cole Easson, UM Brenna Ehmen, USA Deb Gochfeld, UM Justin Janaskie, UM Dorota Kutrzeba, UM Chris May, GBNERR Scotty Polston, UM Jim Weston, UM NASA Grant #: NNS06AA65D

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