Ecological Nowcasting in Chesapeake Bay

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Ecological Nowcasting in Chesapeake Bay

• Christopher Brown
• NOAA Satellite Climate Studies Branch
• CICS - ESSIC
• University of Maryland, College Park

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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

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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.
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Approaches for Predicting Organisms

• Process-Oriented or Mechanistic Modeling
• Empirical or Statistical Modeling

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Mechanistic Modeling
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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

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Hybrid Statistical Mechanistic Approach

• Develop multi-variate empirical habitat models
• Drive habitat models using real-time data
  acquired from a variety of sources

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Hybrid Statistical Mechanistic Ecological
Approach

• Old technique employed in new way
• GAP Analysis retrospective analysis
• Ecological Nowcasting near-real time

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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
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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)
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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

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Sea Nettle Nowcasting Procedure

1. Estimate current surface salinity and temperature
   fields
2. Georeference salinity and SST fields
3. Apply habitat model
4. Generate image illustrating the likelihood of
   encounter of Chrysaora

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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
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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 1.0C

Sea-surface Temperature (ºC)
Model generated sea-surface temperature in
Chesapeake Bay for April 20, 2005
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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

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Sea Nettle Habitat
Nettle medusa occupy narrow temperature (26-31
C) and salinity (10-16 PSU) range. Salinity
optimum 13.5 PSU.
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Probability of Encountering Sea Nettles

• Combination of salinity and SST is a good
  predictor of Chrysaora presence
• If SST lt 34C
• p elogit / (elogit 1),
• where,
• logit -8.120 (0.351SST) - (0.572 SAL -
  13.5)
• Hosmer-Lemeshow Goodness of Fit P 0.493

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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
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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.
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K. micrum Nowcasting Procedure

1. Estimate current surface salinity and temperature
   fields
2. Georeference salinity and SST fields
3. Apply habitat model
4. Generate image illustrating the relative
   abundance of K. micrum

Relative Abundance of K. micrum
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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-situ K.
  micrum abundance and various environmental
  variables
• A test data set (n 81) was extracted from the
  available data to assess the models performance

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Schematic Representation ofNeural Network
Hidden Layer
Output Layer
Input Layer
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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

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K. micrum Neural Network Performance
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Nowcast vs. In-Situ Comparison
May 23-26, 2004 - In-situ
May 27, 2004 - Nowcast
0-10 cells/ml 10-2000 cells/ml gt2000 cells/ml
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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//coast
watch.noaa.gov/cbay_hab/index.html
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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

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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

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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
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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

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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

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