1. Satellite Observations in Support of LME Governance:
A Case Study for Data Exchange in the Wider Caribbean LME
Frank Muller-Karger, Gerardo Toro-Farmer, Digna Rueda
Institute for Marine Remote Sensing
University of South Florida
Exchange of Experiences on LME- related data and information issues
Buenos Aires, Argentina, June, 2013

2. Requirement for Dynamic LME Governance
Governance requires ‘knowledge’ (understanding of what is happening). Knowledge has to be:
Co-derived (joint natural + social science effort)
Inexpensive to local governments
Timely
LMEs are ‘Large’: They require a synoptic framework of observations
LME’s change continuously: They require time series of observations

3. Synoptic ocean time series
Regional-global context to understand processes, stocks, and diversity within different parts of a dynamic LME
Means to quantitative measure change in LME’s
Place point observations in regional context
Initialize and validate simulations / ecological forecasting
An important element of the knowledge base for LME governance are synoptic time series. For the ocean, satellite datasets provide relevant and timely information. They also provide

4. Today’s Tools
Atlas – today we can show dynamic aspects of an ecosystem
Climatologies (monthly, annual) as ‘baseline’ to measure
short-term change
Long-term trends
Occurrence and impacts of extreme events
Time series (observations and anomalies)
Other dynamic information: individual historic and current observations, forecasts

5. Prototype datasets for the Caribbean
Regional-scale and local satellite data products
Printed Atlas: Wider Caribbean LME
Digital Atlas examples:
Caribbean Marine Atlas (IODE)
NOAA Gulf of Mexico Data Atlas

6. Satellite derived synoptic data
Sea surface temperature (SST) 1 km
Ocean color (turbidity, CHL, CDOM) 250 m – 1 km
Wind km
Sea Surface Height/currents ~ km
Sea Surface Salinity ~300 km
Geomorphological and Habitat: ~2 m > 30 m
Coastal
Coral Reef Millennium Map

7. Examples of satellite derived data for the Wider Caribbean LME

8. SST long term bimonthly means (1985-2009)
Mar-Apr
May-Jun
Latitude
Sep-Oct
-85
-80
-75
-70
-65
-60
-55
-50
-45
Nov-Dec
Longitude
Jul-Aug 5 10 15 20 25
Jan-Feb
SST long term bimonthly means ( )
(°C)
Application 1: Satellite Climatologies
Example: Sea Surface Temperature (SST) from AVHRR
The SST climatology makes possible to characterize…
Higher SSTs during Sep-Oct

9. Application 1: Satellite Climatologies
Longitude (°W)
Time (month)
SST (°C)
Latitude (°N)
Application 1: Satellite Climatologies
Example: Southern Caribbean upwelling system (coastal SSTs)
The SST climatology makes possible to characterize the upwelling cycle and its variability along the coast

10. Climatology Anomaly Time Series Application 2: Satellite Time Series
Southern Caribbean upwelling system
Climatology
Time Series
Anomaly
Weekly time series (March 12-18, 2005) °C
Anomaly = Time series - Climatology
Climatologies are baseline’ to measure short-term change, occurrence and impacts of extreme events
Anomalies (time series - climatology) allow a clear visualization of the real SST changes compared with the mean conditions.
In this example the warmest areas are the coastal upwelling, and second, the eastern Caribbean.

11. Application 2: Satellite Time Series
Southern Caribbean upwelling system (coastal SST anomalies time series)
Longitude (°W)
Latitude (°N) °C
Time (year)
Clear visualization of the extension and duration of coastal SST anomalies
Sardine crash!

12. Application 2: Satellite Time Series
Southern Caribbean upwelling system (coastal SST anomalies time series) °C
Time (year)
Spanish sardine capture crashed after two consecutive years of weak upwelling
Clear visualization of the extension and duration of coastal SST anomalies
Sardine crash!

13. Application 4: Satellite Tendencies
Bimonthly SST linear trends ( )
(°C/decade) ns
Jan-Feb
Mar-Apr
May-Jun
Latitude
Jul-Aug
Sep-Oct
Nov-Dec
OJO: in grey No Significant values (p>0.05)
Warming most pronounced during the period with higher SSTs (Sep-Oct), which might imply more stress to certain organisms/ecosystems.
Upwelling getting warmer in the beginning of the upwelling season (Dec-Feb).
Tendency is calculated with anomalies
Relevant for fisheries … life cycles, spawning or reproduction months
Longitude

14. What is the relation between Climate Change and Coral (benthic) health?
Eakin et al. (2010)
Application 5: Thermal Stress and Coral Bleaching
A) Maximum NOAA Coral Reef Watch Degree Heating Week (DHW) during 2005.
(B) means of coral bleached as either percent live coral colonies (circles) or cover (diamonds).
High temperatures cause stress and mass bleaching events. Low temperatures also affect coral. Need to monitor temperature changes to understand ecological shifts.
Eakin CM, et al. (2010) Caribbean Corals in Crisis: Record Thermal Stress, Bleaching, and Mortality in PLoS ONE 5(11): e13969.

15. Can we identify benthic composition?
Application 6: Mapping Benthic Coverage
Can we identify benthic composition?
Is Coral (benthic) coverage changing over time?
Benthic coverage is affected by natural / anthropogenic events
Need to monitor / understand interannual variations and ecological shifts
Palandro et al. (2008). Quantification of two decades of shallow-water coral reef habitat decline in the Florida Keys National Marine
Sanctuary using Landsat data ( ). Remote Sensing of Environment, 112,
Classified dataset based on Landsat for Looe Key Reef (red: coral, brown: covered hardbottom, yellow: bare hardbottom, green: sand. Palandro et al. (2008)

16. Decision Support Tools for an Ecosystem Based Management
Developing a flexible framework for integrated, distributed,
and interlinked regional coastal and marine data atlases based on the NOAA Gulf of Mexico data atlas
Goals
Integrate scientific and socio-economic information
through an online data atlas
to help visualize and analyze historical datasets,
understand connectivity, trends, and variability
in order to help assess the socio-economic implications

17. Decision Support Tools for Ecosystem-Based Management
Objectives:
Identify and integrate additional specific data sets
Implement a framework for embedding regional data atlases
Enhance the user interface of existing web-based data atlas(es) for displaying, querying and analyzing information, providing meaningful statistics for decision-making
Develop a prototype for a mobile platform

18. Decision Support Tools for Ecosystem-Based Management
Gulf of Mexico Data Atlas (NOAA)

19. Decision Support Tools for Ecosystem-Based Management
Currents
(m s-1)
Chlorophyll
(mg m-3)
Wind (m s-1)
Gulf of Mexico Data Atlas (NOAA)

20. Recommendations
Use existing datasets developed for the Caribbean LME atlas as initial layers for the IODE Caribbean Marine Atlas: (
Link the Gulf of Mexico and Caribbean Atlases
Develop a framework for an integrated global atlas that:
Uses existing (easily available) ocean and land satellite data
Provides the framework and technology tools to incorporate new regions around the world
Develop an inter-operable data platform