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

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

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

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

Prototype datasets for the Caribbean Regional-scale and local satellite data products Printed Atlas: Wider Caribbean LME Digital Atlas examples: Caribbean Marine Atlas (IODE) http://www.caribbeanmarineatlas.net/ NOAA Gulf of Mexico Data Atlas http://gulfatlas.noaa.gov

Satellite derived synoptic data Sea surface temperature (SST) 1 km Ocean color (turbidity, CHL, CDOM) 250 m – 1 km Wind 25 km Sea Surface Height/currents ~100-300 km Sea Surface Salinity ~300 km Geomorphological and Habitat: ~2 m > 30 m Coastal Coral Reef Millennium Map

Examples of satellite derived data for the Wider Caribbean LME

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 (1985-2009) 30 28 26 24 22 20 (°C) Application 1: Satellite Climatologies Example: Sea Surface Temperature (SST) from AVHRR The SST climatology makes possible to characterize… Higher SSTs during Sep-Oct

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

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.

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!

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!

Application 4: Satellite Tendencies Bimonthly SST linear trends (1985-2009) (°C/decade) 1 0.5 0 -0.5 -1 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

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 2005. PLoS ONE 5(11): e13969.

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 ( 1984-2002 ). Remote Sensing of Environment, 112, 3388-3399 Classified dataset based on Landsat for Looe Key Reef (red: coral, brown: covered hardbottom, yellow: bare hardbottom, green: sand. Palandro et al. (2008)

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

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

Decision Support Tools for Ecosystem-Based Management Gulf of Mexico Data Atlas (NOAA) http://gulfatlas.noaa.gov/

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) http://gulfatlas.noaa.gov/

Recommendations Use existing datasets developed for the Caribbean LME atlas as initial layers for the IODE Caribbean Marine Atlas: (http://www.caribbeanmarineatlas.net/) 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