Gramer, L.

Hendee, J.

Jankulak, M.

Manzello, D.

Aquatic Commons

 Integrating Near Real-Time Data for Coral Reef Ecological Forecasting 
 
 JIM HENDEE1, LEW. GRAMER2, DEREK. MANZELLO3 and MICHAEL. JANKULAK2 
1Atlantic Oceanographic and Meteorological Laboratory 
National Oceanic and Atmospheric Administration 
4301 Rickenbacker Causeway, Miami, FL 33149-1026 
2Cooperative Institute for Marine and Atmospheric Sciences, 3School of Marine Biology and Fisheries 
Rosenstiel School for Marine and Atmospheric Sciences 
University of Miami 
4600 Rickenbacker Causeway, Miami, FL 33149 
 
ABSTRACT 
The National Oceanic and Atmospheric Administration (NOAA) has committed to integrating ocean data from a vari-
ety of sources into an Integrated Ocean Observing System, and to work towards operational ecological forecasting as part of 
its Ecosystem Approach to Management.  Consistent with this, NOAA’s Coral Reef Conservation Program has committed 
to integrating coral data from a variety of sources for the specific benefit of coral reef researchers and Marine Protected 
Area (MPA) managers; and NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML), together with its 
NOAA and academic research partners, is contributing to this goal through their Integrated Coral Observing Network 
(ICON) project.  ICON provides Web-based software to integrate satellite, monitoring station (in situ), and radar data 
sources in near real-time; and utilizes an inference engine (artificial intelligence software) to provide ecological forecasts 
using some or all of these data. The capabilities of ICON software were initially focused upon one area in particular, Molas-
ses Reef in the Florida Keys National Marine Sanctuary, to provide proof-of-concept, and to provide a “discovery proto-
type” for consideration by the MPA managers assembled at the GCFI conference.  Since GCFI 59, this focus has expanded 
to stations throughout the Caribbean, as well as those on the Great Barrier Reef and Ningaloo Reef in Australia. Feedback to 
ICON developers from MPA managers – based upon their own specific management requirements and priorities, and 
knowledge of the prototype capabilities – is essential to set priorities and enable additional ICON software engineering spe-
cifically tailored to MPA managers’ needs.  Featured in the prototype are several levels of user access:  layperson, re-
searcher, site maintainer, MPA manager, and software developer colleague.  Depending upon user access, information prod-
ucts can include recent and historical single-source and integrated data output, custom graphics output, and ecological fore-
casts for coral bleaching, coral spawning, upwelling, pollution impacts, meteorological extremes, and larval drift. 
 
KEY WORDS:  coral, MPA, integration, data, in situ, NOAA, ICON, software, artificial intelligence, Florida Keys National 
Marine Sanctuary, Molasses Reef 
 
La Integración de Datos en Tiempo Real por Pronósticos Ecologicos de Arrecifes Corales 
La administración para el océano y la atmósfera de los E.U. (NOAA) esta haciendo esfuerzos para integrar datos 
oceanográficos  provenientes de diferentes fuentes en un sistema unico integrado para la observación de los océanos y 
desarrollar un sistema de pronostico ecológico operacional como parte de su enfoque para el manejo de los ecosistemas. De 
cuerdo a esto, el Programa de Conservación de  Arrecifes de Coral de NOAA (CRCP) se ha comprometido a integrar datos 
sobre corales provenientes diversas fuentes para el beneficio directo de investigadores y administradores de Áreas Marinas 
Protegidas. El Laboratorio del Atlántico para las investigaciones del océano y la atmósfera de NOAA (AOML) en 
colaboración con investigadores de las universidades y de otras dependencias de NOAA se ha unido a este esfuerzo con la 
creación del proyecto “Red Integrada para la Observación de Corales” (ICON). El mismo proporciona programas accesibles 
en la Internet para integrar datos satelitales, datos tomados en el campo (in situ) y datos de radar en tiempo real; que usando 
un mecanismo de inferencia (inteligencias artificiales) proporciona pronósticos ecológicos utilizando alguna o toda la 
información disponible. Las funcionalidades del programa ICON se enfocaron inicialmente en un área en particular, el 
arrecife Molasses en el Santuario Marino Nacional de los Cayos de la Florida, para ofrecer prueba-de-concepto y un 
"prototipo de descubrimiento", sometido a consideración de los administradores de AMP participantes en la reunión de 
GCFI. Desde GCFI 59, este enfoque se ha expandido a estaciones en todo el Caribe, así como a estaciones en la Gran 
Barrera de Coral y en el Arrecife Ningaloo en Australia. Los comentarios que proporcionen los administradores de áreas 
marinas protegidas a los diseñadores del ICON de acuerdo a sus prioridades y requerimientos específicos del manejo, 
servirán para implementar cambios al diseño de los programas del ICON que respondan a las necesidades del manejo por 
parte de los administradores de las AMP. En el prototipo se ofrecen varios niveles de acceso a usuarios: empleado, 
investigador, encargado, administrador del AMP y colaborador para la programación. En dependencia del tipo de usuario, 
se tendrá acceso a información integrada  reciente o histórica de una sola fuente o de varias, gráficos, pronósticos 
ecológicos para blanqueamiento de corales, desoves, afloramientos, contaminación, transporte de larvas y otros. 
 
PALABRAS CLAVES: coral, AMP, integración, datos, in situ, NOAA, ICON, inteligencia artificial, Florida Keys National 
Marine Sanctuary, Molasses Reef 
Page 456  59th Gulf and Caribbean Fisheries Institute  
 
INTRODUCTION AND BACKGROUND 
The Integrated Coral Observing Network (ICON), 
based at NOAA's Atlantic Oceanographic and Meteoro-
logical Laboratory (AOML) in Miami, Florida, is dedicated 
to continuous environmental monitoring of coral reef sites, 
to provide scientists and Marine Protected Area (MPA) 
managers with data critical to understanding the complex 
physical, chemical, and biological processes that influence 
these complex ecosystems. ICON stations are currently 
operational at North Norman’s Reef near the Island of 
Exuma, Bahamas; at Salt River, St. Croix in the U.S. 
Virgin Islands; at La Parguera, Puerto Rico; and at 
Discovery Bay, Jamaica, with plans for additional stations 
in the Caribbean and Indo-Pacific. 
The ICON project focuses its efforts in two primary 
areas: (1) integration of data from many sources, and the 
ability to draw automated, near real-time inferences about 
ecologically important events on the basis of those data; 
and, (2) continued deployment of new stations and 
innovative new in situ environmental sensors, as well as 
maintenance of existing stations and in situ sensors 
(Hendee et al, 2006). Data are also gathered and analyzed 
from monitoring networks of partner organizations, 
including the SEAKEYS Network (Florida Keys National 
Marine Sanctuary), the Australia Institute of Marine 
Science (AIMS) Weather Network, and NOAA's Coral 
Reef Ecosystem Division network in the Pacific islands. As 
an integral part of data integration efforts, ICON gathers 
satellite-derived data products for these monitored reef 
sites, and for other "virtually monitored” reef sites around 
the world where no in situ monitoring is yet done. 
The resulting near real-time, reef site-specific 
integrated data streams are then used as input to an expert 
systems (artificial intelligence) inference engine. Rules 
based on current research are defined in this expert system, 
to recognize in real time, environmental conditions that are 
likely to be conducive to, respectively, coral bleaching 
events, upwelling and other nutrient delivery onto the reef, 
and reproductive activities of corals and other reef 
organisms. The resulting ecological forecasts 
("ecoforecasts") are then distributed via email alerts and 
via the NOAA ICON Ecoforecast Website (see Refer-
ences), to researchers, resource management decision 
makers, and the public. Continuous baseline data collec-
tion, in conjunction with real-time monitoring tools like 
ICON/G2, allows scientists and managers to better 
understand and predict processes that drive coral reef 
ecosystems. Such tools provide information that is 
necessary to properly manage and protect these unique and 
valuable natural resources. Many US governmental, 
academic and international partners are involved in this 
ongoing research effort. 
 
DATA INTEGRATION  
In 2005, ICON began development on a robust 
commercial expert systems platform called G2 (Gensym, 
Inc.). G2 allows a scientific programmer to implement 
artificial intelligence applications, using object oriented 
design and a combination of natural- and visual-language 
programming tools. The newly developed ICON/G2 
software system automatically combines observations from 
in situ instruments such as pCO2 and pH sensors, visible- 
Name Description Model variables Assumptions 
Coral bleach-
ing 
Recognizes potential physi-
cal stressors capable of 
causing coral bleaching 
Sea temperature; light [sea sur-
face and sub-surface light – Pho-
tosynthetically Active Radiation 
(400-700nm), UV-A and UV-B]; 
wind speed; tidal height. 
Thermal stress leading to coral bleaching 
can be modified by other factors (i.e., 
light, wind, wave action, water clarity) 
Coral spawning Recognizes potential physi-
cal cues for coral spawning 
Sea temperature, light accumula-
tion (PAR), lunar phase. 
Local environmental conditions (in addi-
tion to lunar phase) may be important in 
the timing of planulae release by brood-
ing or broadcast spawning coral spp. 
Nutrient / larval 
delivery 
Recognizes hydrodynamic 
events that may be ecologi-
cally important 
Sea temperature, wind speed and 
direction, chlorophyll a (from sat-
ellite or in situ measurements); 
model is also refined by current 
data (e.g., ADCP, or WERA radar 
ocean surface returns) where 
available 
Simple environmental indicators (i.e., 
wind variability, satellite chlorophyll a, 
sea temperature variability, etc.) are 
sufficient to distinguish anomalous water 
properties as being caused by wind forc-
ing, frontal / topographic interaction, or 
local nutrient load 
Fish spawning 
aggregation 
Recognizes potential physi-
cal cues for spawning aggre-
gations 
Sea temperature, light (PAR), tidal 
height, onshore currents and 
internal wave breaking, lunar 
phase. 
Local environmental conditions (in addi-
tion to lunar phase) may be important in 
the timing of spawning aggregations 
Data quality Recognizes “out-of-range” or 
mutually inconsistent data 
that may indicate sensor 
disturbance 
Any Sensor drift and malfunction can be iden-
tified by values outside of predefined 
range, combined with inter-comparison 
of values from similar sensors 
Table 1. A Summary of ICON Operational and Developmental Ecological Forecasts 
   Hendee, J. et al.  GCFI:59  (2007)             Page 457 
 
and ultraviolet-light instruments, meteorological and 
hydrographic instruments, together with data from satellite 
sensors including MODIS, AVHRR, AMSR-E, TRMM 
and QuickSCAT, as well as data from other remote sensing 
systems, such as ocean surface currents derived from a 
Wellen Radar (WERA) High-Frequency radar operated on 
the East Florida Shelf by the University of Miami's 
Rosenstiel School for Marine and Atmospheric Sciences 
(see References).  All monitoring sites, as well as instru-
ment packages and individual environmental sensors at 
each site, are all represented in ICON/G2 by appropriate 
“object icons”. 
Collaborators around the world partner with ICON to 
provide near real-time data from other networks of in situ 
monitoring stations (e.g., the SEAKEYS Network, the 
AIMS Weather Network, etc.), and from these satellite- 
and radar-derived data products. Data are acquired through 
automated uploads and downloads, or through the use of 
scripting techniques to acquire values posted on partner 
Web sites. In situ data, when it is available, is automati-
cally matched with pixel data from satellites, and in some 
cases with spatially averaged data from WERA radar, 
based on geographic coordinates and dates and times. At 
reef locations where no in situ monitoring occurs, “virtual 
monitoring sites” are established to gather and monitor 
satellite data for wind speed and direction, sea surface 
temperature (SST), color-derived chlorophyll a concentra-
tion, precipitation, and other conditions of ecological 
interest. 
All integrated data streams are then available via query 
and last-report summary, at the ecoforecasting Web site 
previously mentioned. Simple tabular output of integrated 
data is available both hourly and as daily averages, for 
those who wish to conduct their own research. The 
integration of data for various purposes is a stated goal of 
the U.S. Commission on Ocean Policy as part of its 
Integrated Ocean Observing System (U.S. Commission on 
Ocean Policy, 2004, Ch. 26), of which NOAA has taken 
the lead in management authority. The ICON Program is 
one of the leaders in data integration specifically for 
marine ecological forecasting. 
 
ECOLOGICAL FORECASTING 
Improving on previous efforts to apply artificial 
intelligence to ecological forecasting (Hendee, 1998), data 
quality control, analysis and ecosystem modeling for the 
ICON Program are automatically performed by the ICON/
G2 system. ICON/G2 thus serves as a single platform both 
for integration of environmental data from many sources, 
and for rule-based data quality control on these integrated 
data streams, using value range checks and sensor cross-
comparisons. Email and online alerts are then provided in 
near real-time by the system, whenever sensors report 
apparently anomalous values. 
The ICON/G2 system also serves as the platform for 
creating ecological forecast models based on these near 
real-time integrated, quality-controlled data streams. 
Ecological forecasts predict the impacts of physical, 
chemical, biological, and human-induced stimuli on 
ecosystems and their components (CENR, 2001). Within 
coral reef ecosystems, the best examples of ecological 
forecasts in the published literature are those for coral 
bleaching (based on high sea temperatures; Hendee, 1998; 
Liu et al, 2003), and coral spawning (based on moon phase 
and sea temperature; Harrison et al, 1984). 
However, recent research shows that light is involved 
in chronic photo-inhibition within the coral/endosymbiont 
relationship, related to bleaching. Light is also indicated as 
the underlying mechanism of thermally induced bleaching 
(Warner et al, 1999). Similarly, recent research by van 
Woesik et al (2006) shows that accumulating hours of 
daylight appear to play a role in the inducement of 
spawning in at least some coral species. Finally, research 
has shown that local and regional circulation and mixing 
processes in the water column play a critical role in the 
primary productivity and ecology of coral reefs (Leichter et 
al, 2003; Sponaugle et al, 2005). 
To further explore the complex interactions of physical 
parameters that govern coral ecosystem response, the 
ICON Program is utilizing the wider sources of data now 
available through data integration. The table 1 below 
summarizes ecoforecasting models that the ICON Program 
is currently advancing. 
 
LITERATURE CITED 
Harrison, P. L., R. C. Babcock, G. D. Bull, J. K. Oliver, C. 
C. Wallace, B. L. Willis, 1984: Mass spawning in 
tropical reef corals. Science, 223, 1186–1189. 
Hendee, J., 1998: An expert system for marine environ-
mental monitoring in the Florida Keys National 
Marine Sanctuary and Florida Bay. Proceedings, 2nd 
International Conference on the Coastal Environment, 
Cancun, Mexico, September 8-10, 1998. Computa-
tional Mechanics Publications/WIT Press, Southamp-
ton, 57-66. 
Hendee, J., E. Mueller, C. Humphrey, T. Moore, 2001: A 
data-driven expert system for producing coral 
bleaching alerts at Sombrero Reef in the Florida Keys. 
Bulletin of Marine Science, 69(2), 673-684. (Presented 
at the National Coral Reef Institute's International 
Conference on Scientific Aspects of Coral Reef 
Assessment, Monitoring, and Restoration. Fort 
Lauderdale, FL, USA, April 14-16, 1999. 
Hendee, J., E. Stabenau, L. Florit, D. Manzello, C. Jeffris, 
2006: Infrastructure and capabilities of a near real-time 
meteorological and oceanographic in situ instrumented 
array, and its role in marine environmental decision 
support. In: Richardson, L.L., and E.F. LeDrew, eds., 
Remote sensing of aquatic coastal ecosystem proc-
esses. Kluwer Academic Press, 135-156. 
Leichter, J. J., H. Stewart, S. L. Miller, 2003: Episodic 
nutrient transport to Florida coral reefs. Limnol. 
Oceanogr., 48, 1394–1407. 
Page 458  59th Gulf and Caribbean Fisheries Institute  
 
 Liu, G., W. Skirving, A. E. Strong, 2003: Remote sensing 
of sea surface temperatures during 2002 Barrier Reef 
coral bleaching. Eos, 84(15), 137-144. 
Sponaugle, S., T. N. Lee, V. Kourafalou, D. Pinkard, 2005: 
Florida Current frontal eddies and the settlement of 
coral reef fishes. Limnol. Oceanogr., 50(4), 1033–
1048. 
van Woesik, R., F. Lacharmoise, S. Koksal, 2006: Annual 
cycles of solar insolation predict spawning times of 
Caribbean corals. Ecol. Lett., 9, 390-398. 
Warner, M. E., W. K. Fitt, G. W. Schmidt, 1999: Damage 
to photosystem II in symbiotic dinoflagellates: A 
determinant of coral bleaching. Proc. Nat. Acad. Sci., 
USA, 96: 8007-8012. 
 
Reports and online data: 
Committee on Environmental and Natural Resources 
[CENR], 2001: Ecological Forecasting, Washington, 
D.C., 12 pp. 
NOAA ICON Ecological Forecasting Website. Available 
online as of September 15, 2007 at http://
ecoforecast.coral.noaa.gov 
University of Miami / Rosenstiel School of Marine and 
Atmospheric Sciences, Eastern Florida Shelf High 
Frequency Radar Operations. Available online as of 
September 15, 2007 at http://iwave.rsmas.miami.edu/
wera/ 
An Ocean Blueprint for the 21st Century: Final Report of the 
U.S. Commission on Ocean Policy, 2004: Available 
online as of September 15, 2007 at http://
www.oceancommission.gov/documents 
 


Integrating near real-time data for coral reef ecological forecasting

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Integrating near real-time data for coral reef ecological forecasting

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