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

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Presentation transcript:

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 economic resource- fisheries, tourism & biodiversity feeding grounds for ecologically-important species baffles for wave energy and coastal erosion vital refuge for threatened species

Seagrass Biology Not all seagrasses are created equal… Turtle-grass: Thalassia testidinum Shoal-grass: Halodule wrightii Widgeon-grass: Ruppia maritima Manatee-grass: Syringodium filiforme species relevant to Grand Bay NERR… July December March August light salinity temperature nutrients- H 2 O column nutrients- sediment Environmental Factors Controlling Seagrass Biomass/Abundance epiphytes seagrass growing season

Modeling Seagrass Communities Problem: management of seagrass communities requires management of seagrass populations [=productivity]… 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… Considerations: 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. 2. Compare data from remote sensing platforms with data collected on the ground to determine which approach provides a better prediction of seagrass community productivity. P. Fong & M. Harwell, Modeling seagrass communities in tropical and subtropical bays and estuaries: a mathematical synthesis of current hypotheses. Bulletin of Marine Science 54: Productivity seagrass = Pmax seagrass (Salinity seagrass x Temperature seagrass x Light seagrass x nutrients seagrass ) [productivity  assc w/  salinity] [productivity  assc w/  temperature] [productivity  assc w/  light] [productivity  assc w/  nutrients] Biomass seagrass[t+1] = Biomass seagrass[t] + Productivity seagrass - Loss seagrass  [Loss f (senescence)]

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

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

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

Remote Sensing Data Nitrate [NO 3 ]: Y= *X nutrient Chla Phosphate [PO 4 ]: Y= *X from In situ studies-

Comparative Statistics Productivity seagrass = Pmax seagrass (Temperature seagrass x Light seagrass x nutrients seagrass ) + species 2… Date Relative Seagrass Productivity Paired t-test: t-value = P = /0710/0711/0712/0701/0802/0803/ theoretical values Remote-sensing model yields positive seagrass productivity during the growing season!!! In situ model Remote model

Conclusion s 1.Remote sensing platforms can be used, with some considerations, to populate parameters of the Fong & Harwell model of seagrass community productivity. 2.In situ data provided finer scale resolution of real world conditions; but temporal logistics may offset some of this benefit. 3.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…

Acknowledgement s 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