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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

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slide1

Modeling Seagrass Community Change Using Remote Sensing

Marc Slattery

& Greg Easson

University of Mississippi

slide2

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
slide3

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

slide4

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…

slide5

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]

slide6

Grand Bay NERR Seagrass Ecosystem

Middle Bay

Grand Bay

Jose Bay

Pont Aux Chenes

slide7

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

slide8

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

slide9

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!!!

slide10

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…

slide11

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