Transcript Document

Modeling Seagrass Community
Change Using Remote Sensing
Marc Slattery
& Greg Easson
University of
Mississippi
Seagrass
Worldwide- one
of the most important marine ecosystems:
Communities
• 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
NotBiology
all seagrasses are created equal…
Environmental Factors Controlling Seagrass Biomass/Abundance
Manatee-grass:
Syringodium filiforme
nutrientsH2O
column
light
salinity
epiphytes
temperature
nutrientssediment
Turtle-grass: Thalassia
testidinum
species relevant to Grand Bay NERR…
Shoal-grass:
Halodule wrightii
July
March
Widgeon-grass:
Ruppia maritima
December
August
seagrass growing season
Modeling Seagrass
Problem:
management of seagrass communities
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/  salinity]
[productivity  assc w/
 temperature]
[productivity 
assc w/  light]
[productivity  assc
w/  nutrients]
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.
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…
Satellite-based data
Experimental
Design
Fall ‘07
Resource monitoring data
Spring ‘08
Ruppia
Biomass
temporal sampling
Light [MODIS- daily]
Temperature [MODIS- daily]
Nutrients (proxy: Chla)
[MODIS- daily]
Halodule
Ground-based data
Biomasst+1 =
Biomasst +
Productivity
- Loss
[rearrange
and solve for loss using
satellite-based and ground-based
parameters of productivity…]
Light [Onset- continuous;
& standardized to IL1700]
Temperature [Onset- continuous]
Nutrients [Hach- monthly]
Fall ‘07
Spring ‘08
temporal sampling
statistics on the two
data sets…
Grand Bay NERR Seagrass
Ecosystem
Middle Bay
Grand Bay
Jose
Bay
Pont Aux
Chenes
In Situ Data
30
0.04
ANOVA: significant time
effect, site effect
ANOVA: significant time
effect, site effect
Temperature (C)
0.02
0.01
0.00
Nitrate (mg/L)
1.25
09/07
10/07
11/07
12/07
01/08
02/08
20
15
0.8
0.75
0.6
0.50
0.4
instrumentation
error
0.2
0.00
0.0
10/07
11/07
12/07
01/08
02/08
09/07
03/08
10/07
11/07
12/07
01/08
02/08
03/08
500
1.0
1.00
0.25
10
03/08
ANOVA: significant site effect
09/07
25
nd
Phosphate (mg/L)
nd
Corrected Light (uE/m 2/sec)
Chla (ug/ml)
0.03
ANOVA: significant time
effect, site effect
100
50
10
5
09/07
10/07
11/07
12/07
01/08
02/08
03/08
Remote Sensing
Data
30
0.04
Temperature (C)
Chla (ug/ml)
0.03
0.02
0.01
nd
0.00
09/07
20
15
nd
10/07
11/07
12/07
01/08
02/08
10
03/08
09/07
from In situ studies-
10/07
11/07
12/07
01/08
02/08
03/08
Phosphate [PO4]:
Y=0.135-0.213*X
Corrected Light (uE/m 2/sec)
500
Nitrate [NO3]:
Y=0.255+7.557*X
nutrient
25
100
50
10
5
Chla
09/07
10/07
11/07
12/07
01/08
02/08
03/08
Comparative Statistics
In situ model
5
Remote model
0
theoretical values
Relative Seagrass Productivity
Productivityseagrass = Pmaxseagrass (Temperatureseagrass x Lightseagrass x nutrientsseagrass) + species
2…
-5
-10
-15
09/07
10/07
11/07
12/07
01/08
02/08
03/08
Date
Remote-sensing model yields
positive seagrass productivity during
the growing season!!!
Paired t-test:
t-value = -1.261
P = 0.2541
1.
Conclusion
Remote sensingsplatforms 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