Ecological Nowcasting in Chesapeake Bay

1. Ecological Nowcasting in Chesapeake Bay Christopher Brown NOAA Satellite Climate Studies Branch CICS - ESSIC University of Maryland, College Park

2. Importance of Coastal Ocean Monitoring & Prediction • National Goal • Congress initiated efforts to establish a coastal monitoring system and develop coastal hydrodynamic models • NOAA Goal • VADM Lautenbacher stated that an ecosystem assessment and prediction capability was a critical NOAA to provide information on coastal and marine ecosystems • He also wrote that by 2011 NOAA “should be able to forecast routinely the extent and impact of critical ecosystem events, such as harmful algal blooms” • Biological Oceanography Goal • Develop the understanding and the means to detect and predict distribution pattern of organisms

3. Motivation for Study • Detect and predict distribution pattern of organisms that affect society, both beneficial and harmful • Few existing methods work well and in near-real time Bloom of the coccolithophorid Emiliania huxleyi in the Barents Sea in July 2003 in SeaWiFS imagery. Image courtesy of NASA SeaWiFS Project and OrbImage.

4. Approaches for Predicting Organisms • Process-Oriented or Mechanistic Modeling • Empirical or Statistical Modeling

5. Mechanistic Modeling

6. Statistical Modeling • Develop multi-variate empirical habitat models • Quantitatively define the preferred environmental conditions of the organism • Based on Concept of Ecological Niche • Identify the geographic locations where ambient conditions coincide with the preferred habitat of target organism

7. Habitat Model Hybrid Statistical – Mechanistic Approach • Develop multi-variate empirical habitat models • Drive habitat models using real-time data acquired from a variety of sources

8. Hybrid Statistical – Mechanistic Ecological Approach • Old technique employed in new way • GAP Analysis: retrospective analysis • Ecological Nowcasting: near-real time

9. Ecological Nowcasting In Chesapeake Bay • Currently generate nowcasts of two species in Chesapeake Bay • Sea Nettles, Chrysaora quinquecirrha • Dinoflagellate Karlodinium micrum Chance of encountering sea nettle, C. quinquecirrha, on August 15, 2004 Relative abundance of the harmful algal bloom K. micrum on May 27, 2004

10. Nowcasting Sea Nettle Distributionsin Chesapeake Bay: An Overview C. W. Brown1, R. R. Hood2, T. Gross3, Z. Li3, M.-B. Decker2, J. Purcell2 and H. Wang4 1NOAA/NESDIS Office of Research & Applications 2Horn Point Laboratory, UMCES 3NOAA/NOS Coast Survey Development Laboratory 4VIMS, College of William and Mary Funded by NORS Grant, Maryland SeaGrant, NCCOS EcoFore 04 Chrysaora quinquecirrha (Photo by Rob Condon)

11. ephyra juvenile medusa (adult) egg strobila scyphistoma larva polyp Life Cycle of Chrysaora From: T.L. Bryant and J.R. Pennock (eds). 1988. The Delaware Estuary: Rediscovering a Forgotten Resource. University of Delaware Sea Grant College Program. Newark, DE. Introduction: Sea Nettles • Chrysaora ephyra and medusa seasonally populate Chesapeake Bay • Chrysaora is biologically important and impacts recreational activities • Knowing the distribution of Chrysaora would provide valuable information

12. SST Likelihood of Chrysaora Habitat Model Salinity Sea Nettle Nowcasting Procedure • Estimate current surface salinity and temperature fields • Georeference salinity and SST fields • Apply habitat model • Generate image illustrating the likelihood of encounter of Chrysaora

13. 35 30 25 20 15 10 5 0 Surface Salinity • Generated using hydrodynamic model developed for the Chesapeake Bay • Model forced using near-real time input • Model attributes: • Horizontal Resolution: 1-5 kilometers • Vertical Resolution: 1.52 meters • Error: 2 - 3 ppt Model generated surface salinity in Chesapeake Bay for April 20, 2005

14. 35 30 25 20 15 10 5 0 Sea-Surface Temperature Two Sources: • Generated by hydrodynamic model • Error: 2 - 3 °C • Derived from NOAA AVHRR satellite imagery • Resolution: 1 km • Weekly composite • Bias: 0.5 °C; STD: 1.0°C Sea-surface Temperature (ºC) Model generated sea-surface temperature in Chesapeake Bay for April 20, 2005

15. Sea Nettle Habitat Model • Models developed to predict: • Probability of encountering Chrysaora • Density of Chrysaora • Analyzed relationship between Chrysaora, salinity and sea-surface temperature • Samples collected in surface waters (0 –10 m) of Chesapeake Bay (n = 1064) • 2/3 model training • 1/3 model testing

16. Sea Nettle Habitat Nettle medusa occupy narrow temperature (26-31 °C) and salinity (10-16 PSU) range. Salinity optimum = 13.5 PSU.

17. Probability of Encountering Sea Nettles • Combination of salinity and SST is a good predictor of Chrysaora presence • If SST < 34°C: • p = elogit / (elogit + 1), • where, • logit = -8.120 + (0.351*SST) - (0.572* |SAL - 13.5|) • Hosmer-Lemeshow Goodness of Fit P = 0.493

20. Nowcasting the Relative Abundance of Karlodinium micrum in Chesapeake Bay Christopher W. Brown1, Douglas L. Ramers2, Thomas F. Gross3, Raleigh R. Hood4, Peter J. Tango5 and Bruce D. Michael5 1NOAA, 2University Of Evansville, 3NOAA & Chesapeake Research Consortium, 4University of Maryland Center for Environmental Science – Horn Point Laboratory, 5Maryland Department of Natural Resources Project Funded by NOS MERHAB Program

21. Karlodinium micrum • A common estuarine dinoflagellate found along the U.S. East Coast • Seasonally abundant in Chesapeake Bay • Contributed to several fish kills in Chesapeake Bay • Significant blooms confined to a relatively narrow range of salinity and temperature Photomicrograph of the dinoflagellate Karlodinium micrum.

22. SST Habitat Model Salinity K. micrum Nowcasting Procedure • Estimate current surface salinity and temperature fields • Georeference salinity and SST fields • Apply habitat model • Generate image illustrating the relative abundance of K. micrum Relative Abundance of K. micrum

23. Habitat Model • Neural Network (NN) employs sea surface temperature, salinity and month to predict the relative abundance of K. micrum at low, medium and high or “bloom” concentrations • NN trained with samples (n = 151) of in-situK. micrum abundance and various environmental variables • A test data set (n = 81) was extracted from the available data to assess the model’s performance

24. X1 X2 X3 Xn PE1 f (W h X + b) wij aPE I N P U T S X Classify a= 1 0 -1 PE out f (W h X + b) aPE PE2 f (W h X + b) h h h aPE = foutput(Woutputhfhidden (Whiddenh X + bhidden) + boutput) h h h PEm f (W h X + b) Xi * wij Schematic Representation ofNeural Network Input Layer Hidden Layer Output Layer

25. Issues and Advantages of Neural Networks • Issues • “Black Box” • Advantages & Uses • Useful for representing and processing inexact and sparse data and for performing approximate reasoning over uncertain knowledge and ill-defined problems • Useful in discerning patterns and relationships • No a-priori distribution assumed

26. K. micrum Neural Network Performance

27. Nowcast vs. In-Situ Comparison May 27, 2004 - Nowcast May 23-26, 2004 - In-situ 0-10 cells/ml 10-2000 cells/ml >2000 cells/ml

28. Nowcast WWW Sites Sea Nettle and K. micrum nowcasts are generated daily and are available on the World Wide Web. http://coastwatch.noaa.gov/seanettles http://coastwatch.noaa.gov/cbay_hab/index.html

29. Future Directions and Work • Continue nowcast validation and refine habitat models of Chrysaora and Karlodinium • Develop habitat models for additional HAB species in Chesapeake Bay • Incorporate additional environmental variables into habitat models and nowcast system to enhance HAB prediction capability • Generate historical distribution patterns of occurrence and relative abundance from retrospective salinity and temperature to document interannual variability

30. Issues With Empirical Approach • Empirical models are specific for each location and population • Development of empirical models require sufficient number of samples • Species acclimate to environment, i.e. habitat model may change

31. Regional Ecosystem Modeling • Objective: Develop a fully integrated, bio-physical model of Chesapeake Bay and its watershed that assimilates in-situ and satellite-derived data. • Purpose: • Near-Real Time Applications: Nowcasting and forecasting of marine organisms, ocean health, and coastal conditions • Climate Research: Estimating effect of climate change on the health of coastal marine ecosystems • Partners: NOAA, CICS-ESSIC, other UMD departments, Meteorology, and programs, e.g. UMCES. SeaWiFS True-Color Image of Mid-Atlantic Region from April 12, 1998. Image provided by the SeaWiFS Project, NASA/Goddard Space Flight Center and ORBIMAGE

35. Regional Ecosystem ModelPlans & Objectives • Develop transportable modeling system that can be modified for other regions • Chesapeake Bay used as “test bed” site due to extensive in-situ data for verification • Employ satellite imagery in system for monitoring, model forcing and data assimilation to permit use in locations where in-situ assets are limited

36. Advanced Study Institute for Environmental Prediction • Institute dedicated to research on environmental prediction and monitoring • Perform research and provide core support to determine what present and future observations need to be sustained beyond numerical weather prediction in support of Earth system predictive models, crops models, and predictive disease models • Staffed by personnel from NOAA, NASA Goddard, and the University of Maryland • $1.5M budgeted for Institute in FY06 2006 Science, State, Justice and Commerce Appropriations conference report

37. Thank You!

39. Interannual Variability Probability of Encountering C. quinquecirrha July 29, 1999 July 25, 1996 Likelihood of Encountering C. quinquecirrha in July 1996 and 1999

40. Vibrio cholerae • Presence predicted as function of water temperature and salinity (Louis et al., 2003) • Association with plankton Electron photomicrograph of Vibrio cholerae: curved rods with polar flagellum. http://microvet.arizona.edu/Courses/MIC420/lecture_notes/vibrio/em.html