Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Published byMaud Barton Modified over 8 years ago

Similar presentations

Presentation on theme: "Modeling Seagrass Community Change Using Remote Sensing Marc Slattery & Greg Easson University of Mississippi."— Presentation transcript:

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 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

4 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, 1994. Modeling seagrass communities in tropical and subtropical bays and estuaries: a mathematical synthesis of current hypotheses. Bulletin of Marine Science 54:757-781. 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)]

5 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…

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

7 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

8 Remote Sensing Data Nitrate [NO 3 ]: Y=0.255+7.557*X nutrient Chla Phosphate [PO 4 ]: Y=0.135-0.213*X from In situ studies-

9 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 = -1.261 P = 0.2541 09/0710/0711/0712/0701/0802/0803/08 0 5 -15 -5 -10 theoretical values Remote-sensing model yields positive seagrass productivity during the growing season!!! In situ model Remote model

10 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…

11 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

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

Similar presentations

Marine flowering plants.

Marine flowering plants.
201 529 5151 www.hydroqual.com Use of Mechanistic Modeling to Enhance Derivation of Great Bay TN Criteria and Inform Restoration Strategy Thomas W. Gallagher,

Use of Mechanistic Modeling to Enhance Derivation of Great Bay TN Criteria and Inform Restoration Strategy Thomas W. Gallagher,
Great Lakes Observing System GLRI Tributary Monitoring Project

Great Lakes Observing System GLRI Tributary Monitoring Project
IMPACTS OF DISSOLVED ORGANIC NITROGEN LOADING BY SUBMARINE GROUNDWATER DISCHARGE IN LITTLE LAGOON, AL JENNIFER ANDERS 1,2, BEHZAD MORTAZAVI 1,2, JUSTIN.

IMPACTS OF DISSOLVED ORGANIC NITROGEN LOADING BY SUBMARINE GROUNDWATER DISCHARGE IN LITTLE LAGOON, AL JENNIFER ANDERS 1,2, BEHZAD MORTAZAVI 1,2, JUSTIN.
UNEP Coral Reef Unit Division of Environmental Conventions c/o UNEP-World Conservation Monitoring Centre Monitoring of coral reefs.

UNEP Coral Reef Unit Division of Environmental Conventions c/o UNEP-World Conservation Monitoring Centre Monitoring of coral reefs.
Proposed Research: Temporal Dynamics of Coral Reefs using Remotely Sensed Data Alicia L. Simonti

Proposed Research: Temporal Dynamics of Coral Reefs using Remotely Sensed Data Alicia L. Simonti
Algorithm Development for Vegetation Change Detection and Environmental Monitoring Louis A. Scuderi 1, Amy Ellwein 2, Enrique Montano 3 and Richard P.

Algorithm Development for Vegetation Change Detection and Environmental Monitoring Louis A. Scuderi 1, Amy Ellwein 2, Enrique Montano 3 and Richard P.
Seagrasses Support abundant life Provide complex habitat –Trophic support –Refuge –Recruitment –Nursery.

Seagrasses Support abundant life Provide complex habitat –Trophic support –Refuge –Recruitment –Nursery.
Spatial and Temporal Variation of Epiphytic Growth on Zostera marina Tara Seely* and Mike Kennish** *Department of Earth and Planetary Science, Washington.

Spatial and Temporal Variation of Epiphytic Growth on Zostera marina Tara Seely* and Mike Kennish** *Department of Earth and Planetary Science, Washington.
Future Research NeedsWorld Heritage and Climate Change World Heritage and Climate Change - Future Research Needs Bastian Bomhard World Heritage Officer.

Future Research NeedsWorld Heritage and Climate Change World Heritage and Climate Change - Future Research Needs Bastian Bomhard World Heritage Officer.
Foodweb support for the threatened Delta smelt: Phytoplankton production within the low salinity zone Ulrika E. Lidström 1, Anne M. Slaughter 1, Risa A.

Foodweb support for the threatened Delta smelt: Phytoplankton production within the low salinity zone Ulrika E. Lidström 1, Anne M. Slaughter 1, Risa A.
IMOS Coastal Observations A National Perspective John Parslow.

IMOS Coastal Observations A National Perspective John Parslow.
Aquatic Biodiversity Ocean 91% of all water Polar ice caps and glaciers 2.3% Lakes, streams, and rivers 2.8% Rest largely groundwater.

Aquatic Biodiversity Ocean 91% of all water Polar ice caps and glaciers 2.3% Lakes, streams, and rivers 2.8% Rest largely groundwater.
Soil biological indicators: Organic Farming Systems Dr. Rachel Creamer, Prof. Bryan Griffiths Johnstown Castle Environment Research Centre Acknowledgements:

Soil biological indicators: Organic Farming Systems Dr. Rachel Creamer, Prof. Bryan Griffiths Johnstown Castle Environment Research Centre Acknowledgements:
WP3: identifying & quantifying the main driving forces of ecosystem changes influencing the aquaculture sector and developing the appropriate environmental.

WP3: identifying & quantifying the main driving forces of ecosystem changes influencing the aquaculture sector and developing the appropriate environmental.
1 Using Multi-temporal MODIS 250 m Data to Calibrate and Validate a Sediment Transport Model for Environmental Monitoring of Coastal Waters.

1 Using Multi-temporal MODIS 250 m Data to Calibrate and Validate a Sediment Transport Model for Environmental Monitoring of Coastal Waters.
Water quality and river plume monitoring in the Great Barrier Reef: an overview of methods based on ocean colour satellite data Michelle Devlin 1, Caroline.

Water quality and river plume monitoring in the Great Barrier Reef: an overview of methods based on ocean colour satellite data Michelle Devlin 1, Caroline.
Introduction Oithona similis is the most abundant copepod in the Gulf of Alaska, and is a dominant in many ecosystems from the poles to the sub-tropics.

Introduction Oithona similis is the most abundant copepod in the Gulf of Alaska, and is a dominant in many ecosystems from the poles to the sub-tropics.
Florida’s Seagrasses Maia McGuire, PhD FL Sea Grant Extension Agent.

Florida’s Seagrasses Maia McGuire, PhD FL Sea Grant Extension Agent.
Getting Ready for the Future Woody Turner Earth Science Division NASA Headquarters May 7, 2014 Biodiversity and Ecological Forecasting Team Meeting Sheraton.

Getting Ready for the Future Woody Turner Earth Science Division NASA Headquarters May 7, 2014 Biodiversity and Ecological Forecasting Team Meeting Sheraton.

Similar presentations

Ads by Google