Modeling Seagrass Community Change Using Remote Sensing
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Modeling Seagrass Community Change Using Remote Sensing Marc Slattery & Greg Easson University of Mississippi
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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 - PowerPoint PPT Presentation
Transcript of Modeling Seagrass Community Change Using Remote Sensing
Modeling Seagrass Community Change Using Remote Sensing
Marc Slattery & Greg EassonUniversity 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- H2O column
nutrients- sediment
Environmental Factors Controlling Seagrass Biomass/Abundance
epiphytes
seagrass growing season
Modeling Seagrass CommunitiesProblem: 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, 1994. Modeling seagrass communities in tropical and subtropical bays and estuaries: a mathematical synthesis of current hypotheses. Bulletin of Marine Science 54:757-781.
Productivityseagrass = Pmaxseagrass (Salinityseagrass x Temperatureseagrass x Lightseagrass x nutrientsseagrass) [productivity
assc w/ salinity] [productivity assc w/ temperature]
[productivity assc w/ light]
[productivity assc w/ nutrients]
• Biomassseagrass[t+1] = Biomassseagrass[t] + Productivityseagrass - Lossseagrass
[Loss f (senescence)]
Experimental DesignSatellite-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 ‘07 Spring ‘08
temporal sampling
Fall ‘07 Spring ‘08
Ruppia
Halodule
Bio
mass
Resource monitoring 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…
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 [NO3]: Y=0.255+7.557*X
nutr
ient
Chla
Phosphate [PO4]: Y=0.135-0.213*X
from In situ studies-
Comparative Statistics Productivityseagrass = Pmaxseagrass (Temperatureseagrass x Lightseagrass x nutrientsseagrass)
+ species 2…
Date
Rela
tive S
eagra
ss
Pro
duct
ivit
y
Paired t-test: t-value = -1.261 P = 0.2541
09/07 10/07 11/07 12/07 01/08 02/08 03/08
0
5
-15
-5
-10
theore
tica
l valu
es
Remote-sensing model yields positive seagrass productivity during the growing season!!!
In situ model
Remote model
Conclusions 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 PlansAssess the Fong & Harwell model in St. Joseph’s
Bay, FL system is dominated by Thalassia & Syringodium…
Acknowledgements
Anne Boettcher, USACole Easson, UMBrenna Ehmen, USADeb Gochfeld, UMJustin Janaskie, UMDorota Kutrzeba, UMChris May, GBNERRScotty Polston, UMJim Weston, UM
NASA Grant #: NNS06AA65D
