Ecological Forecasting:

New Tools for Coastal

and

Marine Ecosystem Management

National Oceanic and Atmospheric Administration

Conrad C. Lautenbacher, Jr.

Vice Admiral US Navy (Ret.)

Under Secretary of Commerce for Oceans and Atmosphere

National Ocean Service

Richard W. Spinrad, PhD

Assistant Administrator for NOAA National Ocean Service

Nathalie Valette-Silver and Donald Scavia, Editors

NOAA Technical Memorandum NOS NCCOS 1

July 2003

US Department of Commerce

Donald L. Evans

Secretary
Citation for this Report

N.J. Valette-Silver and D. Scavia. 2003. Ecological Forecasting: New Tools for Coastal and

Ecosystem Management. NOAA Technical Memorandum NOS NCCOS 1. 116 pp.
1

 National Ocean Service, National Oceanic and Atmospheric Administration, Silver Spring, MD 20910

Introduction

Ecological forecasts predict the impacts of

physical, chemical, biological, and humaninduced change on ecosystems and their

components (CENR 2001).

Sustained ecosystem health and delivery of good and

services are essential to the US economy. For example,

each year U.S. ecosystems provide over $227 billion

in added value to the US economy (USDA 1999, CENR

2001). Ecosystems also provide other, harder to quantify, but critical benefits: air and water purification;

waste detoxification and decomposition; pollination of

crops and natural vegetation; maintenance of biological diversity; and recreational and spiritual renewal

(Daily et al. 1997). Finally, ecosystem viability is important for national security because ecological damage,

environmental degradation, and depletion of natural resources can easily give rise to political unrest and conflict

among nations (NSTC 1995).

Tohelp sustain ecosystem health, decision-makers must take

into account effects of both natural and human-induced

stressors on ecosystem structure, functioning, and productivity. A central role for science in the decisionmaking process is to provide forecasts of the magnitude,

direction, and nature of those effects (Graedel 2001).

Ecological processes operate on a wide array of time

and space scales. During the last decade, using technological and scientific innovations, scientists were able

to develop and test ecological forecasts in ways that

were not feasible only a few years ago (Clark et al.

2001). Within the next few decades, technological advances will bring even greater capabilities that are

needed for ecosystem analysis at unprecedented scales:

local, regional, and global.

Ecological forecasts also require a better understanding of how complex ecological communities form and

evolve, including interactions among species, the physical

environment, evolutionary history, and the ‘assembly

rules’ by which ecosystems are formed. Many important ecological processes depend on the little-studied

‘hidden players’ such as microbes, fungi, and marine

and soil invertebrates. Understanding interactions at

very small scales will be made possible through recent

and future developments of new micro- and nano-sensing

technologies.

Ecological systems are strongly influenced by physical

and biogeochemical processes and by historical events that

introduce time lags and non-linearity, shape complex

changes in community composition and functioning,

and affect the capacity of ecosystems and communities

to be restored once damaged. Unprecedented developments in our ability to sequence genomes and compute

protein structures allow researchers to understand better

ecosystem functioning. At the same time, understanding

and observations of coupled physical-chemical-biological

Introduction to Ecological Forecasting:

New Tools for Coastal and Marine Ecosystem Management

Nathalie J. Valette-Silver and Donald Scavia1

Abstract

Policy makers, natural resource managers, regulators, and the public often call on scientists to estimate the

potential ecological changes caused by both natural and human-induced stresses, and to determine how those

changes will impact people and the environment.

To develop accurate forecasts of ecological changes we need to: 1) increase understanding of ecosystem

composition, structure, and functioning, 2) expand ecosystem monitoring and apply advanced scientific

information to make these complex data widely available, and 3) develop and improve forecast and interpretative

tools that use a scientific basis to assess the results of management and science policy actions.

1
2

Figure 1. Time/Space Scale of Ecosystem Response. The five key ecosystem

stressors - pollution, land and resource use, invasive species, extreme

natural events, and climate change - can challenge the integrity of

ecosystems and impede the delivery of their goods and services. These

stressors can act alone or together, and their cumulative effects are poorly

understood. Ecosystem responses are as varied as the inputs that strain

them, playing out in scales from hours to decades and from local to global.

systems are producing larger scales, new theories

and models of interactions within these complex

systems. The integration of these genomic insights

with ecological theory and larger-scale ecosystem dynamics, along with new computational and

modeling technologies, holds the promise of revolutionizing our knowledge about how ecosystems

respond to natural and human influences (CENR

2001).

The Benefits of Ecological Forecasting

Forecasting future conditions based on a series of

potential scenarios is a new tool that allows for

the evaluation of management options and associated tradeoffs. In addition to the forecasts, the

data and information collected to develop and test

them enrich scientific knowledge and improve the

decision making process.

Ecological forecasts strengthen the link between research and management. When managers understand and can clearly articulate forecast needs, scientists can in turn better understand those needs.

In particular, when scientists and managers work

together to define, develop, interpret, and evaluate ecological forecasts, the researchers stay better informed about manager’s needs and the managers help identify gaps in scientific knowledge.

Developing and evaluating forecasts can also help

set science priorities. Among potential research,

modeling, and monitoring efforts, it is possible to set

priorities based on the need to create and increase the

reliability of desired forecasts. For example, monitoring priorities should support and validate forecasts,

modeling priorities should lead to quantitative forecasts,

and research priorities should increase the information

content or decrease forecast uncertainties.

Forecasting the Effects of Ecosystem Stresses

Extreme natural events, climate change, land and resource use, pollution, and invasive species are five key

drivers of ecosystem change (CENR 2001) that interact across wide time and space scales (hours to decades

and local to global) (Figure 1).

• Extreme natural events can cause significant

ecosystem change. It is important for resource

managers to forecast their effects on ecosystems

as well as their interactions with other stressors.

Forecasting the ecological disturbance from natural

hazards such as wildfire, flood, drought, hurricanes,

windstorms, and other similar events will allow

resource managers and the public to anticipate,

minimize and mitigate their impact.

• Climate change and variability are likely to have

widespread and significant impacts on ecosystems.

Resource managers and policy makers must

account for these potential changes by developing

coping and adaptation strategies to minimize

impacts on ecosystems, species, and valued ecological resources.

• Changes in land and resource use (e.g., changes

in marine resource harvests, conversion of land from

forest to agriculture or from agriculture to urban)

affect ecosystems ability to function properly.

Forecasts of the ecosystem implications of these

shifts, and their consequent impacts on environment and society, will help guide management and

policy actions.

Principal Scales of Stress Principal Scales of Stress
3

• Pollution and in particular harmful chemicals and

excess nutrients remain one of the American

public’s top concerns. Demands for forecasts of

pollution impacts present significant challenges.

While the known and measurable point sources of

pollution have been fairly well handled, the diffuse

non-point sources are more difficult to measure and

control. State and local governments as well as

natural resource management agencies are seeing

increased costs of meeting water and air quality

standards and goals in addition to increased uncertainties in achieving desired ecological outcomes.

• The introduction and spread of non-native species

impacts the abundance and the very existence of

native species as well as the proper functioning of

healthy ecosystems. The prevalence of this problem

in most US ecosystems, and its integral link to domestic and international activities, make forecasts of

alien species introduction and spread a critical and

badly-needed management tool.

Most ecosystems are subjected to mixtures of these

stresses. For example, a hurricane may open the door for

new species invasions, and the success of that invader may

be enhanced by altered climate, the extent to which the

marine resources are used, and the extent of pollution

in the environment being invaded. Building the capacity to forecast the cumulative effects of multiple stresses

is one of ecology’s most significant research challenges.

Some of the most severe and longest-lasting ecosystem effects result from chronic but subtle influences.

Shorter-term, extreme events are also important drivers of both short-term and longer-term ecosystem

change.

Next Steps

To make progress, an integrated science-management

effort is needed to 1) understand the kind of forecasts

desired, 2) adequately communicate the value and limits of forecasts, and 3) understand the iterative nature

of forecasts in adaptive management. Already some scientists and modelers are providing rudimentary forecasts, but the efforts are neither well-organized nor coordinated. Presently, forecasts are still limited by gaps

in understanding, data, and models. Building an ecological forecasting capability will require 1) integration across disciplines, 2) inclusion of the social and

economic sciences; 3) development of ways to minimize forecast uncertainty; and 4) use of technological

innovations.

Understanding complex ecosystems is one of the most

significant forecasting challenges. Improvements in

understanding ecosystem composition, structure, and

functioning have occurred recently. However, there is

still a clear research need in order to provide decision

makers with reliable ecological forecasts at the larger

time and space scales (National Academy by Sciences

2001. President’s Committee of Advisors on Science

and Technology 2000). Reliable ecological forecasts

cannot be made or tested without accurate and timely

information about historical and current ecosystem

conditions and without models integrating ecological,

hydrological, chemical and physical parameters.

Likewise, the efficacy of management actions, based

on those forecasts, cannot be evaluated without monitoring ecosystem change. Rapid advances in remote and

in situ sensing provide new opportunities to gather these

data. Effective forecasts rely on synthesizing, analyzing,

and interpreting complex information, ranging in scale

from molecular to regional and from both the social

and natural sciences.

This need for synthesis of large, widely distributed,

diverse data sets, and the ability to analyze, model, and

interpret them at varying scales, expands the boundaries of current computation and information science.

New applications of geo-spatial and temporal data

management, simulation, and visualization will require

increased collaboration between computer and ecological scientists.

Both deterministic and statistical models are needed,

and to be most effective, scientists and decision-makers must have confidence in them. Efforts are needed

to define and evaluate the information content of models, which is controlled by uncertainty in model structure, parameters, and inputs. Advancements will require

developing new modeling approaches, and formalized

assessment of error propagation in both models and

associated field data.

Finally, it must be understood that a forecast is an iterative process that must be reevaluate and improved

along the way.

Conclusion

At present, rudimentary ecological forecasts are being

made and used. Some examples are explored within

this volume (Figure 2). Developing and improving forecasts provide a focus for the science-policy interface.

They also help set science priorities, in particular what
4

to monitor, characterize and map, and what

models are needed. Which natural processes

need to be understood and what social and

economic science is needed. Advancing ecological forecasting requires collaboration

among scientists and managers at the Federal and state level, academia, non-governmental organizations, and the private sector.

Acknowledgements

We would like to thank all the scientists and

managers who participated in the sessions

organized in 2001 around the theme of Ecological Forecast at the meetings of the Coastal

Zone 2001 (Cleveland, Ohio), Estuarine Research Federation ERF’01 (St. Petersburg,

Fl) and the Ecological Society of America

ESA 2001 (Madison, WI). We also want to

acknowledge the efforts of the members of

the Committee on Environment and Natural

Resources, Subcommittee on Ecological Systems who participated in the formulation of

a 2001 CENR report on ecological forecasting (CENR 2001).

References

Committee on Environment and Natural

Resources. Subcommitte on Ecological

Systems. 2001. Ecological Forecasting:

Agenda for the Future. Washington DC, 12 pp.

Graedel, T. 2001. Resolution for a more livable planet. The

Scientist, March 5, p. 6.

USDA Economic Research Service. 2003. www.econ.ag.

gov/briefing/fbe/

Daily, G.C., S. Alexander, P. R. Ehrlich, L. Goulder, J.

Lubchenco, P. A. Matson, H.A. Mooney, S. Postel, S.H.

Schneider, D. Tilman, G.M. Woodwell. 1997. Ecosystem Services: Benefits Supplied to Human Societies

by Natural Ecosystems. Issues in Ecology No. 2. Ecological Society of America, Washington, DC. pp: 275-293.

National Security Science and Technology Strategy. 1995.

National Science and Technology Council, Committee

for National Security, Washington, DC. http://

clinton3.nara.gov/WH/EOP/OSTP/nssts/html/

nssts.html

Figure 2. Time/Space Scale of Ecosystem Response. The name of the first author

in each paper presented in this document is displayed on the time/space scale

of ecosystem response diagram showing where their contribution lays. Their

position on the diagram also identifies their contribution to a specific stressor.

Principal Scales of Stress Principal Scales of Stress

Clark, J.S., S.R. Carpenter, M. Barber, S. Collins, A. Dobson,

J. Foley, D. Lodge, M. Pascual, R. Peilke, Jr., W. Pizer,

C. Pringle, W.V. Reid, K.A. Rose, O. Sala, W.H.

Schlesinger, D. Wall, and D. Wear. 2001. Ecological

forecasts: An emerging imperative, Science 293:657-

660.

President’s Committee of Advisors on Science and

Technology. 2000. Clean Water Action Plan,

Washington. DC, p. 34.

National Academy of Sciences. 2001. Grand Challenges in

the Environmental Sciences, Global Change

Ecosystems Research, Science and the Endangered

Species Act, Rediscovering Geography, Priorities for

Coastal Ecosystem Science, The National Academies

Press Publish., Washington D.C., pp.106.
5

Introduction

Accurate ecological forecasts made months in advance

can give resource managers time to minimize or

ameliorate stresses that threaten ecosystems (Clark et

al. 2001, National Biological Information Infrastructure

2001). Forecasting stress responses to intervene and

prevent damage requires linking changes at lower levels

of biological organization with the fitness of individuals, and accurately assessing the long-term,

ecosystem-level consequences.

Stress often indirectly affects ecosystems by overwhelming defenses at lower levels of the biological

hierarchy: molecular, cellular, and organism-level

homeostatic processes. Stress then reduces individual

fitness, alters demographic parameters, and diminishes

the structure, function, and resilience of communities

which comprise the biotic component of ecosystems.

Changes in molecular and cellular parameters may

precede ecosystem-level responses to chronic stress by

days, months, or years.

Mid-range Prediction of Coral Bleaching:

A Molecular Diagnostic System Approach

John E. Fauth1, Craig A. Downs2, John C. Halas3, Phillip Dustan4, and Cheryl M. Woodley5

Abstract

One challenge facing ecological forecasters is making accurate, mid-range predictions of changes to ecosystem

health. Accurately forecasting disease and degradation months in advance could allow resource managers time

to minimize or ameliorate stressors that threaten ecosystem structure, function, and integrity.

This research has resulted in a molecular biomarker system to assess the physiological status of corals by

assaying specific parameters of cellular physiology indicative of individual colony health. Field samples of

boulder corals (Montastraea annularis) from the Florida Keys, USA, showed a strong correlation between

molecular biomarker levels and bleaching of individual coral colonies. Logistic regression identified molecular

biomarkers capable of predicting the onset of coral bleaching 3-6 months before symptoms became apparent.

Biomarkers useful in forecasting included glutathione (an antioxidant), lipid peroxides (which indicate functional

lipid levels and membrane integrity) and chloroplast small heat shock proteins (chlpsHsp, which protect

photosystem II activity during oxidative stress). Relationships observed between water depth, biomarker levels,

and bleaching suggest deep-water corals cannot accumulate enough chlpsHsp to prevent Photosystem II from

dissociating and generating the oxidative stress that causes bleaching.

Molecular biomarkers provide useful tools for ecological forecasting because they assay vital cellular and

physiological parameters. Understanding the mechanistic basis of degradative processes should help scientists

and resource managers identify and alleviate the environmental stresses that trigger them.

1 Department of Biology, College of Charleston, 66 George Street, Charleston, SC 29424; e-mail fauthj@cofc.edu. Current

address: Department of Biology, University of Central Florida, Orlando, FL 32816

2 EnVirtue Biotechnologies, Inc., 35 W. Piccadilly St., Winchester, VA 226015

3

 Florida Keys National Marine Sanctuary, U.S. National Oceanic and Atmospheric Administration, P.O. Box 1083, Key

Largo, FL 33037 4

 Department of Biology, College of Charleston, 66 George Street, Charleston, SC 29424

5 US National Oceanic and Atmospheric Administration, Hollings Marine Laboratory, 331 Ft. Johnson Rd., Charleston,

SC 29412
6

A molecular diagnostic system (MDS) has been

developed to monitor cellular stress responses and to

develop forecasting capabilities (Downs et al. 2000,

Downs, Fauth & Woodley 2001, Downs, Dillon &

coathors 2001, Woodley et al. 2001, Downs, Shigenaka

& coauthors 2002, Downs, Fauth & coauthors 2002).

The MDS assays specific cellular and molecular

parameters indicative of cellular physiological status,

including whether 1) cell structural integrity was

compromised, 2) cells experienced an oxidative stress,

and 3) defenses were mounted to a particular stress

(i.e., heat, pesticide, heavy metal).

Using molecular biomarkers to assess organism or

ecosystem health is a popular idea with considerable

evidence of its effectiveness (reviewed by Feder &

Hoffman 1999). However, most studies examine only

a single or several physiological parameters to gauge

overall response to acute and chronic stress. Surprisingly few studies evaluate overall physiological status

with multiple, specific molecular biomarkers (Schaeffer

et al. 1988, Stegeman et al. 1992, Joanisse & Storey

1996a,b, Downs et al. 2000, Downs, Fauth & Woodley

2001, Downs, Dillon & coathors 2001, Woodley et al.

2001, Downs, Shigenaka & coauthors 2002, Downs,

Fauth & coauthors 2002). The goal is to develop a

comprehensive molecular diagnostic system to assess

ecosystem health, providing resource managers timely

information to better understand, monitor, and manage

at-risk ecosystems.

While in theory the MDS can be applied to any living

organism, the first forecast to be developed was for

bleaching of reef-building corals in the Florida Keys

National Marine Sanctuary (FKNMS), USA. The

forecast arose from a research partnership between the

Center for Coastal Environmental Health and Biomolecular Research (US National Oceanic and Atmospheric Administration/National Ocean Service), the

FKNMS, EnVirtue Biotechnologies, Inc., and academic

scientists. There were three objectives: 1) to identify

probable mechanisms of coral bleaching, 2) to identify

factors that influenced susceptibility of individual coral

colonies to bleaching, and 3) to develop a method of

forecasting bleaching of individual coral colonies. Once

tested and verified, forecasts could be used by National

Marine Sanctuary managers to identify vulnerable coral

reefs, to assess the relative importance of different

stresses on coral bleaching, and to set quantitative

recovery endpoints for restoring reefs degraded by

chronic stress.

Case Study

During bleaching, corals lose their symbiotic algae and/

or their photosynthetic pigments. Bleaching robs corals of nutrients, slows growth (Hughes and Tanner

2000) and recovery from injuries (Mascarelli and

Bunkley-Williams 1999), increases susceptibility to

diseases (Kushmaro et al. 1996), delays reproduction

(Hughes and Tanner 2000, Omori et al. 2001), and can

kill entire colonies (Carriquiry et al. 2001).

Bleaching is caused by diverse stresses (reviewed in

Williams and Bunkley-Williams 1990, Brown

1997a,b). However, heat stress is the primary trigger

of mass bleaching events that have degraded coral reefs

worldwide over the past 20 years (Williams and

Bunkley-Williams 1990, Goreau et al. 1992, Goreau

and Hayes 1993, Brown et al. 1996, Brown 1997a,b).

Recently, Stone and co-workers (Huppert and Stone

1998, Stone et al. 1999) showed episodes of mass coral

bleaching were associated with increased severity and

frequency of El Niño events, which are symptomatic

of global warming. Increased ocean temperatures

compounded with other agents of coral degradation

(e.g., pollution, disease, predation) pose an increasing

and significant threat to the health and vitality of coral

reef ecosystems worldwide (Dustan 1999).

The US National Oceanic and Atmospheric Administration currently uses remote sensing of unusually

warm surface waters to identify areas where bleaching

may occur (Carriquiry et al. 2001, NOAA National

Environmental Satellite, Data, and Information Service

2002). However, even during extreme sea surface temperature anomalies, phylogenetic, spatial, temporal,

bathymetric, and size-related variation in coral

bleaching and mortality (Williams and BunkleyWilliams 1990, Hughes and Tanner 2000, Hueerkamp

et al. 2001, Loya et al. 2001) can obscure both the

intensity of bleaching events and their underlying

causes. This is the situation for corals along the Florida

Keys, which are subjected to different stressors from

Key Largo to the Dry Tortugas (Dustan 1999, Porter

et al. 1999).

A series of recent papers (Downs et al. 2000, Woodley

et al. 2001, Downs, Fauth & coauthors 2002) demonstrated how heat stress affects coral MDS parameters

under both laboratory and field conditions. In the

laboratory, corals (Montastraea faveolata) heatstressed under normal photosynthetically active

radiation (PAR) produced more chloroplast small heat
7

shock protein (chlpsHsp) than either the controls or

corals heat-stressed in the dark (Downs et al. 2000,

Woodley et al. 2001). Chloroplast small heat shock

protein protects Photosystem II activity during

oxidative stress (Downs et al. 2000, Downs, Fauth &

coauthors 2002); when this defense is overwhelmed,

oxyradicals are produced. In cnidarians, these reactive

oxygen species cause lipid peroxidation, protein

degradation, and DNA damage, and ultimately tissue

and cell death (Hawkridge et al. 2000).

As expected, corals heat-stressed under PAR had

significantly higher levels of lipid peroxide than those

heat-stressed in the dark, indicating greater damage to

lipid membranes. A similar pattern was seen in ubiquitin

levels, which signals protein degradation (Downs et

al. 2000). Superoxide dismutases (SOD) such as Cu/

Zn and Mn SOD defend against oxidative damage by

scavenging oxyradicals. Levels of Mn SOD and

especially Cu/Zn SOD were significantly higher in

corals heat stressed under PAR than in the dark, further

evidence that elevated water temperatures caused

oxidative stress (Downs et al. 2000, Woodley et al.

2001, Downs, Fauth & coauthors 2002). Independent

researchers also concluded that heat stress causes

oxidative stress, and leads to coral bleaching (Jones et

al. 1998).

In 1999, coral colonies (Montastraea annularisspecies

complex) sampled off Key Largo, FL, were exposed

to elevated ocean temperatures for several months

(Figure 1). There were strong positive correlations

between oxidative damage and subsequent bleaching

during the March-October sampling period (Woodley

et al. 2001, Downs, Fauth & coauthors 2002). Corals

with high levels of chlpsHsp and SODs – typically

colonies in shallower water – had less oxidative damage

and did not bleach.

In contrast, corals with low chlpsHsp and SODs –

mostly those colonies in deeper water – experienced

oxidative stress, had higher chaperonin levels (Hsp 60

and Hsp 70) and protein turnover activity, and bleached

in late summer. Thus, bleaching was tightly coupled to

the anti-oxidant and cellular stress capacity of coral

colonies, suggesting bleaching was a final defense

against oxidative stress (Downs et al. 2000, Woodley

et al. 2001, Downs, Fauth & coauthors 2002).

This forecast exploits the time lag between temperatureinduced cellular responses of corals (including

upregulation of chlpsHsp and antioxidant enzymes) and

accumulation of damage products and subsequent

Figure 1. (A) Seasonal variation in ocean temperatures recorded

beside coral colonies (Montastraea annularis species complex)

sampled near Molasses Reef, off Key Largo, FL, USA. Bars show

depth of colonies: black = 3.0 m, grey = 6.1 m, red = 9.1 m, blue

= 18.3 m. Temperature beside the corals in summer differed significantly from all other months except May. Yellow line is mean sea

surface temperature data recorded by a C-Man Buoy at Molasses

Reef, 1-3 miles from our sites. Green line indicates the 30o

C

temperature threshold at which bleaching often occurs. (B) Colony

condition in September, 1999 varied significantly among depths.

Healthy colonies, black bars; paling colonies, gray bars; bleached

colonies, red bars. (C) Chlorophyll a levels reflect photosynthetic

activity and density of symbiotic zooxanthellae inhabiting coral

tissue, providing a quantitative measure of bleaching. Bars show

mean (+ 1 SE) concentration of chlorophyll a in five coral colonies

repeatedly sampled at each depth. Differences among months and

the month x depth interaction were statistically significant

(Woodley et al. 2001).
8

bleaching. Using logistic regression, the relationship

between MDS parameters in spring and early

summer and coral condition in September was

examined. One biomarker sampled in March

(chlpsHsp) explained significant variation in

bleaching six months later, when bleaching became

visible to SCUBA divers (Figure 2). The variation

in bleaching explained by chlpsHsp levels (35

percent) was greater than that explained in an

identical analysis using March ocean temperature

(27 percent) as the independent variable. In both

analyses, the proportion of variation explained by

logistic regression was determined by the coefficient

of determination (R2

).

Ocean temperatures continued rising through late

spring. Corals in deeper water did not produce

enough chlpsHsp to prevent oxyradicals from

damaging cell components. Therefore, colonies in

deeper water accumulated significantly more lipid

peroxide (LPO) than colonies in shallower water

(Woodley et al. 2001, Downs, Fauth & coauthors

2002).

May LPO levels also were significant predictors of

bleaching (Logistic regression: X2 = 28.9, P<0.0001,

2 df,R2

=0.76). Colonies with <30 micromoles LPO/

mg total protein in May were almost certain to be

healthy in September, while those with greater damage

were likely to pale or bleach.

As water temperatures continued increasing, the

superoxide dismutases assayed – those produced only

by the coral and not by their algal symbionts (Downs

et al. 2000, Woodley et al. 2001, Downs, Fauth &

coauthors 2002) – also could be used to forecast coral

condition. June levels of Cu/Zn SOD, which localizes

to the cytosol, explained variation in coral condition

more than Mn SOD, which was restricted to the

mitochondria (34 percent versus 19 percent, respectively). Colonies with high levels of these antioxidant

enzymes were significantly less likely to pale or bleach

than those with low antioxidant levels (Logistic

regressions: both X2 = 7.33, P<0.03, 2 df). Thus, three

separate biomarkers could be used to forecast coral

bleaching, anywhere from three to six months in

advance of sea surface temperature anomalies.

This forecasting method is in the early stages of

development and testing. Eventually, forecasts could

be used like hurricane forecasts – to alert resource

managers and reef-based industries to the likelihood

of a mass bleaching event in the upcoming year.

Because the MDS allows identification of potential

stressors, it also can be used to differentiate between

global/regional and local forcing functions (i.e., climate

warming and El Niño events versus point source

pollution). Once sufficient data is obtained, it should

be possible to forecast the effectiveness of different

management options on coral bleaching.

Lessons Learned and Next Steps

The National Action Plan to Conserve Coral Reefs calls

for a science-based ecosystem approach to coral reef

conservation, and adaptive management to track and

respond to environmental change and other anthropogenic threats (US Coral Reef Task Force 2000). This

project includes close cooperation between a US

NOAA/NOS biotechnology laboratory, US NOAA

National Marine Sanctuaries managers, a private biotechnology company, and faculty from several colleges

and universities.

The project has developed from an initial test of a

promising new technology to a broader, more management-based initiative. Recently a wider geographic

region was sampled, including two more sites off Key

Figure 2. Cumulative logistic plot of the probability a coral colony

was healthy (green), paling (red), or bleaching (black) in September,

as a function of chloroplast small heat shock protein levels in March.

Symbol size is proportional to the number of colonies with the same

values. Smooth blue curves partition the probability axis among the

three conditions, with healthy at lower right, bleaching at upper left,

and paling in between. The probability of a colony having a specified

condition when sea surface temperatures exceed 30o C for a prolonged

period is given by the vertical distance between its probability curves.

For example, the probability of a coral with 1 pg chlpsHsp in March

being healthy six months later is 30 percent; of paling, 15 percent,

and of bleaching, 55 percent.
9

Largo and within Biscayne National Park. The

expanded project includes additional sentinel species

in the coral reef community: an alga (Halimeda

opuntia), corallivorous snail (Coralliophila

abbreviata), bicolored damselfish (Pomacentrus

partitus), and white grunts (Haemulon plumieri).

Infrastructure is in place to transfer forecasts of coral

bleaching from the research and development stage to

the operational stage. EnVirtue Biotechnologies, Inc.

created monospecific polyclonal antibodies against the

coral cellular parameters discussed above, which

substantially enhanced MDS resolution and precision.

The new antibodies also require less coral tissue,

minimizing damage to stressed colonies. EnVirtue

Biotechnologies, Inc., also developed a robotic, highthroughput system which greatly reduces the time

required for multiple assays on a large number of

samples. Currently, acquiring samples using SCUBA

divers and grinding the samples under liquid nitrogen

in preparation for MDS assays are the most timeconsuming steps.

While the forecasting model used here was simplistic,

it improves on efforts based solely on sea surface temperature, which cannot predict small-scale or bathymetric variation in bleaching. Verifying the accuracy

of MDS-based forecasts requires obtaining more

samples from diverse areas, and from additional species

of corals, an effort already underway in the Florida

Keys. The forecast also can be improved using

multivariate statistical techniques, such as discriminant

function and neural network analyses.

The most difficult steps remaining are 1) linking MDS

parameters to other important life history traits of coral,

such as growth, reproduction, survivorship, and

susceptibility to disease, and 2) identifying the stressors

impacting corals at different sites. Overcoming these

hurdles will require vertically integrated sampling and

experimentation, similar to what was successfully used

to investigate declining amphibian populations (Collins

et al. 2001).

Conclusions

There is an inverse relationship between expression of

protective antioxidant/stress proteins and the incidence

of oxidative damage and bleaching in boulder corals

sampled within the Florida Keys National Marine

Sanctuary, USA. Corals with high levels of anitoxidants

and heat-shock proteins, which protect against the

damaging effects of unusually high ocean temperatures,

did not bleach. Those with low levels of protectant

molecules suffered more oxidative damage, and were

more likely to bleach (lose/expel their symbiotic algae).

This correlation is similar to that observed in several

human diseases (i.e., Alzheimer’s disease, Parkinson’s

disease), where elevated levels of antioxidant and stress

proteins protect cells from the degenerative influences

of oxidative stress.

A simple regression procedure shows bleaching of

individual coral colonies could be forecast months in

advance from knowledge of specific biomarker levels.

More sophisticated forecasts can be made using

multiple markers and multivariate statistical techniques,

but these will require larger data sets to test the accuracy

of predictions than our logistic regression method.

Acknowledgements

Coral samples were collected under Florida Keys

National Marine Sanctuaries and National Marine

Fisheries Service permits. Statistical analyses were

supported by NSF Grant DEB9727039 and EPA Grant

R825795 to J. E. Fauth.

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and mortality in the Mexican Pacific during the 1997-

1998 El Niño and prediction from a remote sensing approach. Bull. Mar. Sci. 69: 237-249.

Clark, J.S., S.R. Carpenter, M. Barber, S. Collins, A. Dobson,

J. Foley, D. Lodge, M. Pascual, R. Pielke, Jr., W. Pizer,

C. Pringle, W.V. Reid, K.A. Rose, O. Sala, W.H.

Schlesinger, D. Wall, and D. Wear. 2001. Ecological

forecasts: an emerging imperative. Science 293: 657-660.

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A. Storfer. 2001. Global amphibian declines: an

interdisciplinary challenge for the 21st century. In: M.

J. Lannoo (ed.) Status and conservation of US amphibians. Volume 1: conservation essays. University of

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Woodley. 2000. A molecular biomarker system for

assessing the health of coral (Montastrea faveolata)

during heat stress. Mar. Biotech. 2: 533-544.

Downs, C.A., J.E. Fauth, and C.M. Woodley. 2001.

Assessing the health of grass shrimp (Palaeomonetes

pugio) exposed to natural and anthropogenic stressors:

Amolecular biomarker system. Mar. Biotech. 3: 380-397.

Downs, C.A., R.T. Dillon, J.E. Fauth, and C.M. Woodley.

2001. A molecular biomarker system for assessing the

health of gastropods (Ilyanassa obsoleta) exposed to

natural and anthropogenic stressors. J. Exp. Mar. Biol.

Ecol. 259: 189-214.

Downs, C.A., G. Shigenaka, J.E. Fauth, C. Robinson, and

A. Huang. 2002. Cellular physiological assessment of

bivalves after 10 years of chronic exposure to spilled

Exxon Valdez crude oil using a novel molecular

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2987-2993.

Downs, C.A., J. E. Fauth, J C. Halas, P. Dustan, J. Bemiss,

and C.M. Woodley. 2002. Oxidative stress and seasonal

coral bleaching. Free Radical Biol. Med. 33: 533-543.

Dustan, P. 1999. Coral reefs under stress: sources of mortality

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12
13

Introduction

Many stock assessments require assumptions that the

biological and physical relationships between a particular stock and their ecosystems have less of an impact

on the population dynamics of the stock than fishing.

Consequently studies to understand how marine ecosystems function frequently are viewed as being less

important than the requirement to determine acceptable catch levels of the commercially-important species within the ecosystems. Recent evidence that climate has profound impacts on fish populations

(Beamish et al. 1998, Finney et al. 2000) is a clear indication that ecosystem dynamics are an essential component of stock assessment. Forecasting the consequences of climate related changes in marine ecosystems on fish populations will become more important

because this is a period of significant environmental

change (IPCC 2001a,b).

Case Study

This study shows the Strait of Georgia ecosystem (Figure 1) is profoundly affected by decadal-scale changes

in climate. This discovery is important because the Strait

of Georgia may be the most important marine ecosystem on Canada’s west coast. It is the early marine rearing area for Pacific salmon that traditionally accounted

for about 40 percent of the total Canadian catch. In the

1970s, an assumption that marine ecosystems were not

limiting Canadian Pacific salmon abundance led to a

belief that the wild salmon production at the time could

be doubled to historic levels by artificially rearing more

juveniles in fresh water (Fisheries and Environment

Canada 1978). Catches did reach historic high levels

in the mid-1980s (Figure 2), although the enhanced

portion of the catch was only about 13 percent (Beamish

and Noakes 2003). However, by the late 1990s, catches

were at the lowest levels in history even though the

The Importance of Incorporating Information

on Climate-Related Marine Ecosystem Changes

into Fisheries Management

R.J. Beamish, C.M. Neville and R.M Sweeting1

Abstract

The Strait of Georgia is a semi-enclosed marine ecosystem on the West Coast of Canada. The majority of the

population of British Columbia is concentrated around the Strait, making it an important area for recreation

and commerce. It is also the early marine rearing area for Pacific salmon (Oncorhynchus sp.) that ultimately

account for up to 40 percent of Canada’s total Pacific salmon catch.

There were climate-related shifts in the dynamics of the Strait of Georgia after 1977, 1989, and 1998. The

change after 1989 resulted in a reduced capacity to produce coho. The change after 1998 doubled the productivity

of the ecosystem. In 2000, following the 1998 regime shift, there was a dramatic increase in primary production

as indicated by the doubling of euphausiids and the modelled increase of phytoplankton. A comparison of the

modelled biomasses of key species before and after the change in 2000 indicated the trophic structure of the

Strait of Georgia will continue to adjust to the new, increased productivity. Future regime shifts will occur and

the natural fluctuations may be affected in frequency and amplitude by greenhouse gas induced climate change.

1

 Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, B.C. V9T 6N7
14

releases of artificially reared salmon juveniles remained

about constant.

We now know that it was incorrect for the enhancement program to assume that most of the natural losses

regulating salmon abundance occurred in fresh water.

Not only is ocean mortality important, but also, mortality trends shift quickly in response to climate. This

paper summarizes the evidence that ecosystem variability in the Strait of Georgia is an essential component of fisheries management.

Regimes and the Strait of Georgia – The oceanography of the Strait of Georgia is strongly influenced by

the flows of the Fraser River (Thomson 1981). As

freshwater flows out from the Fraser River into

the surface of the Strait and eventually into the

open Pacific Ocean, denser, nutrient rich bottom

water flows into the Strait of Georgia. In the spring,

the combination of temperature, winds, sunlight,

and nutrients initiate the production cycle that regulates the relative abundances of plants and animals

in the ecosystem.

Interannual changes in climate can affect ecosystem productivity (Parsons et al. 1981, Gargett

1997). However, recent studies indicate that the

dynamics of the Strait of Georgia are influenced

by persistent states of climate and ocean regimes

that shift abruptly (Beamish and Neville 1999). It

is not known how regime shifts alter the long-term dynamics of the

Strait of Georgia naturally. This is

particularly worry-some because

we also do not know how global

warming will affect the natural occurrence of these regimes. Forecasting the changes to ecosystems,

therefore, requires an understanding of the consequences of regime

shifts, as well as the trophic relationships within a regime.

In recent years, large-scale regime

shifts have occurred in 1977, 1989,

and probably in 1998 (Hare and

Mantua 2000, McFarlane et al.

2000). In the Strait of Georgia, the

impact of these shifts is evident in

the temperatures of the surface, at

10 m, in bottom waters, and in the

annual trend in Fraser River flow

(Figure 3).

Regime changes are not cycles in which parameters

oscillate back and forth. Regimes represent new ecosystem organizations both physically and biologically.

Consequently, the regime after 1977 was characterized

by reduced discharge from the Fraser River and increasing water temperatures. After 1989 there was not a

major change in the annual pattern of Fraser River

flows, but spring freshets were earlier, ocean temperatures continued to increase, and the dominant winter

wind direction changed from winds predominately from

both the SW and SE to winds predominantly from the

SE. The changes in wind direction were associated with

increases in sea levels and lower surface salinities. The

Figure 1. The Strait of Georgia is located between Vancouver Island and the mainland of

British Columbia on the west coast of Canada.

Figure 2. The total Canadian catch of all species of Pacific salmon. The

estimate for 2001 is preliminary.
15

changes in the ecosystem after 1998 remain to be determined, but appear to be characterized by extreme

variation in total annual Fraser River discharge, lower

temperature, and reduced storminess in the winter with

a more even distribution of winds from the SE and SW.

The biological changes associated with regimes and

regime shifts profoundly affected the dynamics of

salmon species in the Strait, as well as the economics

of local communities. After the 1977 change, local

stocks of chinook salmon had reduced marine survivals (Beamish et al. 1998), whereas Pacific herring and

hake stocks increased in abundance (McFarlane et al.

2000, Beamish et al. 2001a). The decline in the

marine survival of chinook was one of the early

indications that there was an ocean carrying capacity that limited the productivity of Pacific

salmon, and that the bottleneck to salmon production was not only the number of juvenile

salmon that entered the ocean.

The 1989 regime shift was associated with a

collapse of the recreational fishery in the Strait

of Georgia. Coho marine survival declined from

approximately 15 percent in the 1970s and

1980s to less than 2 percent in the 1990s

(Beamish et al. 2001a). The decline in marine

survival occurred synchronously throughout the

southern coho stocks and was associated with

a change in migration behavior. In 1991, and

from 1994 through to 2000, virtually all coho

left the Strait of Georgia and did not return

during the sports fishing season, collapsing one

of the most important sports fisheries in

Canada.

In 2000, following the 1998 shift, there was a

dramatic increase in production in the Strait of

Georgia as indicated by a large increase in euphausiid biomass (Beamish et al. 2002). Significantly increased early marine survival was

evident for juvenile coho, chum, and pink

salmon. In addition, individual size of juvenile

coho, chum, and pink was larger than previous

years. The individual size of euphausiids was

also significantly larger in 2000 compared to

1999.

Ecopath was used as a trophic accounting

model (Christensen and Pauly 1992). The

model was balanced for 32 functional groups

for the Strait of Georgia ecosystem in 1998

(Figure 4). Euphausiids are a major prey species that in 1998 had an ecotrophic efficiency (the biomass consumed by predators or transported out of the

ecosystem) of 0.76. After the regime change in 2000 a

new model that increased the consumption on the euphausiids by increasing the biomass of the consumers

of euphausiids was required. The biomass increases included shellfish (50 percent), small pelagics (270 percent), miscellaneous demersal fish (250 percent),

salmon (100 percent), hake (40 percent) and herring

(40 percent) (Beamish et al. 2001b). After increasing

the consumption of euphausiids, the ecotrophic efficiency

was 0.58. The low ecotrophic efficiency of euphausiFigure 3. (A) Average annual flow (April to March) of the Fraser River

from 1912 to 2000. The extreme variation in flow from 1996 to 2000 is

unique to the time series. (B) Average daily flow of the Fraser River in April

from 1912 to 2001. Increased average April flows in the 1990s indicate an

earlier spring freshet. April flows decreased in 2001. (C) The average annual

water temperature at surface, 10m and bottom (>200m) at Nanoose Bay in

the Strait of Georgia.
ids indicates the ecosystem in the Strait of Georgia will

continue to adjust to the new increased productivity.

A consistent indicator of changing regimes has been

the intensity of the Aleutian Low, the area in the North

Pacific bounded by 100.5 kPa in the winter. From 1977

to 1989, the area of low pressure increased in the winter in the subarctic Pacific and the strength of the low

intensified. After 1989 there were weak lows. Other

indicators of regime shifts are the temperature-based

Pacific Decadal Oscillation (Hare and Mantua 2000)

and the atmospheric forcing index (McFarlane et al.

2000) that combines an index of the Aleutian Low, the

Pacific Decadal Oscillation and a measure of atmospheric circulation.

For the Strait of Georgia, an aggregate of physical and

biological indices may best define when regimes shift.

However, the research needed to develop this index

remains to be funded. Management of individual species may be more effective using ecosystem species

management charts (Figure 5, Beamish and McFarlane

1999). These charts are still experimental, but highlight key physical and biological relationships that both

alter and stabilize recruitment.

Lessons Learned

Environmental factors have not been a major consideration in fisheries management because there was little

evidence that such factors were non-random. The discovery of persistent climate and ocean states that shift

suddenly from one state to another indicates the need

to re-think how marine fisheries are managed. The rethinking also applies to the science that supports resource management.

Climate is an important consideration in determining

fishing quotas. Fishing impacts remain important, but

they can no longer be considered the sole regulator of

population size. Once climate is recognized as important in the natural regulation of fish abundance, what

humans add to the air becomes as important to fish as

what gets added to the water. Also, recognizing the role

of climate in the regulation of fish abundance and the

discovery of regimes may make it possible to forecast

the average productive state of an ecosystem.

Conclusion

Forecasting marine ecosystem change has not occurred

possibly because stock assessment scientists tend to

view the ocean as rather homogeneous over time and

space. However, the scientists studying the impacts of

global warming may provide the needed focus by developing models with an ability to capture decadal-scale

processes such as the Pacific Decadal Oscillation and

North Atlantic Oscillation.

Figure 4. A schematic representation of the relationship in 1998 between the 32 function groups in the Strait of Georgia Ecopath

model. Prey items representing greater than 25 percent (solid lines) and 20-25 percent (dashed lines) of a functional groups diet are

represented in the diagram. Prey items representing less than 20 percent of a functional groups diet are not included.

1

4

3

2

5

Trophic Level

16
Figure 5. Coho ecosystem management chart. The chart identifies

the relationships that may be most influential in the regulation of

abundance. The three key areas are fishing, climate-ocean

environment, and associated species. Climate, as a regime, affects

Fraser River flows, which may affect primary productivity in the

Strait. Climate also affects the freshwater habitat, but the principal

impact of climate is on the timing of copepod migration into the

surface waters. The timing of this migration affects the survival of

key competitors for food of coho during their summer feeding period.

Growth during the summer affects the survival in the winter and

fall and eventually determines brood year strength. Predation is

important but adult abundance is determined through growth. A

manager needs to know if the regime is productive or less productive

for coho and this would be determined using climate indices and

timing of copepod migration. In favourable regimes, more smolts

would be required to achieve the maximum productivity. In less

favourable regimes, the manager needs to be sensitive to the impacts

of associated species and other coho. In less favourable regimes,

adding more coho smolts or other potential competitors may also

increase competition for food resources, which are less available.

The importance of regimes is evident in the increased

number of publications examining decadal scale oscillations. Since 2000, the Strait of Georgia appears to be

entering a more productive phase. The well-documented, abrupt increase in productivity was related to

a large-scale regime shift in 1998 and should provide

the evidence needed to begin to change fisheries management science. Hopefully the surprising changes in

1977, 1989, and now in 1998 will be recognized as a

signal of how little is known and the learning needed

before advising policy makers of the future impacts of

climate and climate change.

References

Beamish, R.J., J.R. King, D. Noakes, G.A. McFarlane, and

R. Sweeting. 1998. Evidence of a new regime starting

in 1996 and the relation to Pacific salmon catches.

(NPAFC Doc. No. 321). Dept. of Fisheries and Oceans,

Sciences Branch – Pacific Region, Pacific Biological

Station, Nanaimo, BC, Canada, V9R 5K6.

Beamish, R.J. and G.A. McFarlane. 1999. Applying ecosystem management to fisheries in the Strait of Georgia.

Ecosystem approaches for fisheries management.

Alaska Sea Grant College Program, AK-SG 99-01.

Fairbanks, Alaska. pp 637-664.

Beamish, R.J., and C.-E.M. Neville. 1999. Large-scale

climate-related changes in the carrying capacity for

salmon in the Strait of Georgia and northern North

Pacific ecosystems. In K. Sherman and Q. Tang (eds.)

Large marine ecosystems of the Pacific Rim:

assessment, sustainability, and management. Blackwell

Science, Malden, Massachusetts. pp. 27-41.

Beamish, R.J., G.A. McFarlane, and J. Schweigert. 2001. Is

the production of coho salmon in the Strait of Georgia

linked to the production of Pacific herring? Herring:

expectations for a new millennium. Alaska Sea Grant

College Program, AK-SG-01-04. Fairbanks, Alaska. pp

37-50.

Beamish, R.J., G.A. McFarlane, C.M. Neville, and I. Pearsall.

2001. Changes in the Strait of Georgia ECOPATH

model needed to balance the abrupt increases in productivity that occurred in 2000. PICES Scientific Report

No. 17:5-9.

Beamish, R.J., C. Neville, R. Sweeting, K. Poier, and R.

Khan. 2002. Recent increases in coho production in the

Strait of Georgia are related to changes in climate. In T.

Droscher (ed.) Proceedings of the 2001 Puget Sound

Research Conference. Puget Sound Water Quality

Action Team. Olympia, Washington. pp. 10-19.

Beamish, R.J., and D.J. Noakes. 2003. Global warming, aquaculture, and commercial fisheries. Second International

Symposium on Stock Enhancement and Sea Ranching,

Kobe, Japan. (In press).

Christensen, V. and D. Pauly. 1992. ECOPATH II – a software for balancing steady-state ecosystem models and

calculating network characteristics. Ecol. Modelling

61:169-185.

Finney, B.P., I. Gregory-Eaves, J. Sweetman, M.S.V.

Douglas, and J.P. Smol. 2000. Impacts of climate change

and fishing on Pacific salmon abundance over the past

300 years. Science 290:795-799.

Fisheries and Environment Canada. 1978. The Salmonid

Enhancement Program. A public discussion paper.

Information Branch, Fisheries and Marine Science,

Vancouver. 86 p.

Gargett, A.E. 1997. The optimal stability “window”: A

mechanism underlying decadal fluctuations in North

Pacific salmon stocks. Fish. Oceanogr. 6(2): 109-117.

17
18

Hare, S.R. and N.J. Mantua. 2000. Empirical evidence for

North Pacific regime shifts in 1977 and 1989. Progr.

Oceangr. 47:103-145.

IPCC. 2001a.Climate Change 2001: Impacts, Adaptation,

and Vulnerability. Contribution of Working Group II to

the third assessment report of the Intergovernmental

Panel on Climate Change (IPCC). [McCarthy, J.J.,

Canziani, O.F., Leary, N.A., Dokken, D.J., White, K.S.

(eds.)]. Cambridge Univ. Press. UK.

IPCC. 2001b. Climate Change 2001: The Scientific Basis.

Contribution of Working Group I to the third assessment

report of the Intergovernmental Panel on Climate

Change (IPCC). [Houghton, J.T., Ding, Y., Griggs, D.J.,

Noguer, M., van der Linden, P.J., Dai, X., Maskell, K.,

Johnson, C.A. (eds.)]. Cambridge Univ. Press. UK.

McFarlane, G.A., J.R. King and R.J. Beamish. 2000. Have

there been recent changes in climate? Ask the fish. Prog.

Oceanogr. 47: 147-169.

Parsons, T.R., J. Stronach, G.A. Borstad, G. Louttit and R.I.

Perry. 1981. Biological fronts in the Strait of Georgia,

British Columbia, and their relation to recent measurements of primary productivity. Mar. Ecol. Prog. Ser.

6:237-242.

Thomson, R. E. 1981. Oceanography of the British Colombia Coast. Can. Spec. Publ. Fish. Aquat. Sci. 56. 291 p.
19

Introduction

Sea level rise and the resulting coastal habitat loss are

potentially major new threats to ecological resources,

including migratory shorebirds. These birds typically

breed in the arctic or subarctic and migrate south to

spend the winter in Central and South America. These

long-distance travelers require a succession of foodrich stopover sites where they can replace fat reserves.

During their migrations, most of the birds feed mainly

on sandflats or mudflats with dense invertebrate populations.

Sea level rise may result in the loss or changes to such

habitats. Also, at most coastal sites, sea level rise is

not the only factor threatening shorebird habitats. Land

reclamation, urbanization, and industrialization,

together with land subsidence due to aquifer depletion

add to the potential rate of intertidal habitat loss.

As sea levels rise, state and local authorities will be

challenged to find ways to protect currently dry land.

Simply constructing protective structures may meet this

requirement. However, what are the potential implications for coastal natural habitats and how should

Global Climate Change and Sea Level Rise:

Potential Losses of Intertidal Habitat for Shorebirds

H. Galbraith1, R. Jones2, R. Park and J. Clough3, S. Herrod-Julius4, B. Harrington5, G. Page6

Abstract

Global warming is expected to result in acceleration in current rates of sea level rise, inundating many lowlying coastal and intertidal areas. This could have important implications for organisms that depend on these

sites, including shorebirds that rely on them for feeding habitat during their migrations and in winter.

This case study modeled the potential changes in the extent of intertidal foraging habitat for shorebirds at five

sites in the United States that currently support internationally important numbers of migrating and wintering

birds: Willapa Bay (WA), Humboldt Bay (CA), San Francisco Bay (CA), Bolivar Flats (TX) and Delaware Bay

(NJ). Parts of these sites are currently managed as nature reserves because they are crucial stopover sites for

migrating shorebirds.

It is likely many shorebird populations may be limited by availability of feeding habitat at migration and wintering

sites (Goss-Custard 1980) and loss of these habitats could have important conservation implications. Foraging

habitat loss (together with other climate change-induced changes in their tundra breeding habitats) could

result in major reductions in bird populations. Using the SLAMM 4 model, projections were made on the impact

of rising sea levels on intertidal shorebird feeding habitat at the study sites.

The results suggest major changes in the ability of coastal sites to continue providing habitat for important

shorebird populations and communities. In areas where sea walls are constructed to protect human interests,

the ecological impacts may be intensified. Finally, at some sites, lessening existing stressors such as aquifer

depletion might mitigate the greatest impacts.

1 Galbraith Environmental Sciences, 289 Wiswall Hill Rd., Newfane, VT 05345, USA; hg1@sover.net, and to whom correspondence should be sent.

1

 Stratus Consulting Inc.

2

 EcoModeling

3

 US Environmental Protection Agency

4

 Manomet Center for Conservation Sciences

5

 Point Reyes Bird Observatory
20

planners balance the needs of society with those of

nature? This study is an attempt to begin addressing

these questions.

Case Study

This predictive study has four main components.

1) Selecting study sites on the east, west, and Gulf of

Mexico coasts of the United States that have at least

hemispheric importance for their shorebird

populations. Each of the five sites (Willapa Bay

(WA), Humboldt Bay (CA), San Francisco Bay

(CA), Bolivar Flats (TX) and Delaware Bay (NJ);

Figure 1) may support up to a million or more birds

annually.

2) Quantifying the current intertidal

feeding habitats for shorebirds at

each study site using maps from

the U.S. Fish and Wildlife National Wetlands Inventory.

3) Identifying probabilistic sea level

change scenarios at each site. The

scenarios used in the analy-ses

were obtained from US EPA

(1995) supplemented in southern

San Francisco Bay with data from

local tide gauges. The tables in US

EPA (1995) allow the estimation

of probabilistic sea level changes

for specific sites and are partly

based on historical rates of sea level change

superimposed on projected 50 percent and 5 percent

probability global sea level change by 2100 (34 cm

and 77 cm, respectively). These were obtained from

tide gauges at or close to each site. The projections

for 50 percent and 5 percent probability are based

on assumed global tempera-ture increases of 2o

C (50

percent probability) and 4.7o

C (5 percent

probability), respectively. These temperature change

estimates conform closely to the estimates in the

most recent Intergovernmental Panel on Climate

Change (IPCC) ‘best estimate’ for 2100 (IPCC

2001).

4) Forecasting changes in the intertidal feeding habitats

in response to sea level change. Habitat change in

response to sea level rise was modeled using the

fourth version of the Sea Level Affecting Marshes

Model (SLAMM 4). SLAMM uses a decision tree

to convert the habitat type occurring in a 30-meter

cell to another for a given change in the inundation

regime. The variables included in this process are

elevation, habitat type, slope, sedimentation,

accretion and erosion rates, substrate type (rock,

sand, mud, etc.), over wash, the degree of exposure

to the open ocean, salinity, the water table in the

affected area, and the extent to which the affected

area is protected by sea walls. Further details

regarding SLAMM in general have been presented

in Park et al. (1993). SLAMM 4 is the most recent

version of this model, originally developed in the

early 1980s.

The changes in the extent of intertidal habitats projected

by SLAMM 4 for each study site are shown Table 1. These

projections indicate that with the exception of Bolivar

Flats, all of the study sites will lose tidal flat habitat,

but the extents of loss will be site-specific. The habitat

loss will be most marked at southern San Francisco

Bay, Humboldt Bay, and Delaware Bay. Substantial

areas of tidal flats are projected to be lost at these sites

as soon as 2050, even using the 50 percent probability

scenario. Without considering any future acceleration

due to climate change (keeping the current rate of sealevel rise), southern San Francisco Bay may lose more

than half of the current tidal flats by 2100. Using the

historical rate of sea level rise, Bolivar Flats may lose

all the tidal flat habitat by 2100.

This level of habitat loss is likely to result in major

reductions in shorebird populations at these sites.

Indeed, if the 50 percent probability predictions for

south San Francisco Bay and Delaware Bay are

accurate, it is difficult to imagine how these sites could

continue to support anything close to the current

numbers of shorebirds.

Figure 1. Location of study sites.
21

This study also illustrates another important point –

climate change does not happen in a vacuum. Climate

change impacts will interact with other stressors. For

example, the modeling predicts the extent of habitat

loss at any one site will vary depending on local

geomorphologic and anthropogenic factors. These

include local land surface movements (subsidence,

isostatic rebound, etc.), human exploitation patterns in

the area (e.g., of aquifers), and human responses to the

inland movement of the coastline.

At sites where crustal movements exacerbate the rate

of sea level rise, the loss of feeding habitat is likely to

be accelerated. For example, under the 50 percent

probability scenario, southern San Francisco Bay will

lose about 70 percent of its intertidal feeding habitat

by 2100. Comparing that with the corresponding

prediction for northern San

Francisco Bay (a loss of

about 39 percent) shows

much of the habitat loss in

the southern bay is likely

from factors un-related to,

but exacer-bating, the rise

in sea level.

In parts of southern San

Francisco Bay, the land

surface has historically

been subsiding partly

because of aquifer depletion and crustal compaction.

This subsidence, superimposed on global sea level

rise, creates a much larger

affected area. If current

trends continue, by 2100

crustal subsidence alone

is likely to have been

responsible for about 1.5

m of the 1.8 m change in

sea level predicted by the

50 percent probability

scenario. In this case,

human exploitation of local

natural resources greatly

exacerbates the likely

effects of global climate

change. In contrast,

Willapa Bay has only

modest rates of habitat

loss projected because

the global sea level rise is being mitigated by crustal

rise.

All the above model predictions assume that no new

coastal protection structures will be installed. However,

it is likely that the local human populations at these

sites will protect valuable areas from sea level rise. To

evaluate the likely influence of human responses, one

simple protection scenario is considered for Bolivar

Flats – all current dry land areas will be protected with

new sea walls. This model reduced the amount of

upland habitat to be lost, but had a 20percent increase

in intertidal habitat loss by 2100 under the 50 percent

probability scenario. This is because building sea walls

stops the coastal system from simply moving inland

and thereby exacerbates the effects on intertidal areas.

Therefore, the protection measures work because upTable 1. Current area (ha) and projected future percent changes in intertidal and upland habitat at the

study sites under three sea level rise scenarios.

* The historical rate of sea level change projections are based on actual past sea level changes

measured at the site. The 50 percent probability projections represent future sea level change

with an assumed 20

C warming (US EPA’s ‘best estimate’ temperature scenario); 5 percent

probability projections represent future sea level change with an assumed 4.70

C warming (US

EPA’s 5 percent probability temperature scenario)

Habitat Current 2050 2100 2050 2100 2050 2100

Willapa Bay

 Tidal Flats 21,157 -0.7 -0.7 -7.5 -18.1 -25.8 -61.5

 Salt Marsh 3,455 12.8 12.8 9.5 10.5 13.6 12.8

 Upland and Other 62,389 -0.5 -0.5 -0.5 -0.7 -0.8 -1.3

Humboldt Bay

 Tidal Flats 1,078 -0.1 -0.1 -13 -28.6 -42.4 -91.3

 Salt Marsh 40 72.6 72.6 88.9 175.6 229.2 1,886

 Upland and Other 12,750 -0.2 -0.2 -0.3 -0.6 -0.7 -6

San Francisco Bay – 

Northern

 Tidal Flats 4,117 0 -4 -11.9 -39.4 -35.9 -80.7

 Salt Marsh 613 0 0 0 0 0 -18.1

 Upland and Other 1,294 0 0 0 0 0 0

San Francisco Bay – 

Southern

 Tidal Flats 12,039 -12.9 -53.9 -24 -69.9 -42.9 -83.1

 Salt Marsh 3,534 0.8 -50.7 -2.2 -63.2 -11.6 -82.9

 Upland and Other 75,694 0 -0.2 -0.1 -0.5 -0.2 -0.6

Bolivar Flats

 Tidal Flats 398 -14.6 -93.8 -37.6 1.8 -80.6 1073

 Salt Marsh 5,774 4.3 40.9 4.7 48.5 14.2 53.5

 Upland and Other 18,275 -1.3 -12.9 -1.4 -17.6 -4.4 -53.1

Delaware Bay

 Tidal Flats 2,665 -6.1 -23 -19.8 -57.4 -43.1 19.8

 Salt Marsh 13,776 6.4 9.3 9 12.2 11.3 -4.2

 Upland and Other 20,538 -3.5 -5.5 -5.3 -7.5 -6.9 -11.3

5% Probability*

Historical Rate of 

Sea Level Change* 50% Probability*
22

land habitats are protected, however, this protection is

at the expense of the intertidal habitats.

Lessons Learned

Three are major lessons this in study. All have important

conservation policy and management implications.

First, there will be major changes in the ability of

coastal sites to continue providing foraging and

migration habitat for shorebirds. If the projections are

correct, sites like San Francisco Bay may suffer serious

adverse impacts. Second, the greatest impacts might

be mitigated by lessening existing stressors such as

aquifer depletion. Third, constructing sea walls to

protect human interests may intensify the ecological

impacts.

Conclusions/Next Steps

This study did not attempt to incorporate the potential

effects of climate change-induced habitat loss for

shorebirds in their wintering or breeding areas. The

late winter months may comprise energy ‘bottlenecks’

for many shorebirds and less than adequate food

reserves can result in increased mortality rates

(Davidson and Evans 1982). Loss of feeding habitat

due to sea level change could reduce the carrying

capacity of wintering areas in Central and South

America. Also, the high-latitude areas where shorebirds

breed are expected to have the greatest habitat impacts

of climate change.

Recent studies show climate-induced habitat changes

may already be occurring in the arctic and subarctic

areas of North America (Chapin et al. 1995). The

combined effects of habitat change on their breeding

areas (Zockler and Lysenko 2000), and intertidal habitat

loss at their wintering and migratory staging sites could,

potentially, have even more severe effects than could

be brought about by any one factor.

Future research will adopt a ‘life-cycle’ approach by

incorporating climate change-induced effects on the

wintering and breeding grounds. This should provide

a more comprehensive appraisal of the likely effects

of climate change on this group of migratory birds.

References

Chapin, F.S., G.R. Shaver, A.E. Giblin, K.J. Nadelhoffer

and J.A. Laundre. 1995. Responses of arctic tundra to

experimental and observed changes in climate. Ecology

76:694-711.

Davidson, N.C. and P.R. Evans. 1982. Mortality of redshanks

and oystercatchers from starvation during severe

weather. Bird Study 29: 183-188.

Goss-Custard, J.D. 1980. Competition for food and interference among waders. Ardea 68: 31-52.

Intergovernmental Panel on Climate Change (IPCC). 2001.

Summary for Policymakers: Climate Change 2001.

Impacts, Adaptation, and Vulnerability. IPCC, Geneva,

Switzerland.

Park, R.A., J.K. Lee, and D. Canning. 1993. Potential effects

of sea level rise on Puget Sound wetlands. Geocarto

Intern. 8(4):99-110.

US EPA. 1995. Probability of sea level rise. EPA 230-R-95-

008. US Environmental Protection Agency, Office of

Policy, Planning, and Evaluation, Washington, DC.

Zockler, C., and I. Lysenko. 2000. Water birds on the edge:

First circumpolar assessment of climate change impact

on arctic breeding water birds. World Conservation

Monitoring Centre, Cambridge, UK.
23

Introduction

Over half of the United States population lives on the

17 percent of land considered coastal (Culliton 1998),

and these areas become more crowded every year.

Within the next 25 years, the coastal population is likely

to increase by approximately 25 percent (about 18

million people) with most of the growth occurring in

the already crowded states of California, Florida, Texas

and Washington (Boesch et al. 2000). This growth,

along with rising affluence, is increasing the demand

for coastal and marine resources, which increases both

the stress on those resources and their vulnerability to climate change and variability. Conversely, climate change

will interact with these existing and increased stresses,

potentially accentuating their negative impacts.

Forecasts of Climate Change – The climate change

scenarios used in this analysis were based on general

circulation model (GCM) simulations conducted by the

United Kingdom’s Hadley Centre for Climate Prediction (HadCM2) and the Canadian Climate Centre

(CGCM1), referred to here as the Hadley and the

Canadian models, respectively (NAST 2001). Output

from these models – scenarios – were used as two

plausible climate futures rather than accurate

predictions of what will happen.

Over the next 100 years, global warming is expected

to accelerate sea level rise from 9 to 88 cm. While

climate change effects on hurricane frequency are hard

to discern, hurricane wind strength could increase as a

result of elevated sea surface temperatures. Regardless

of potential changes in frequency and intensity, coastal

storms and surges will be riding in on a higher sea level.

The hydrologic cycle is likely to change with a changing

climate. However, regional projections of precipitation

vary considerably among the models. That has a

significant impact on the projected effects of river

runoff. Water-balance models (Wolock and McCabe

1999), based on the precipitation and temperature

projections from the climate models, provide forecasts

of river runoff that vary considerably (Figure 1). For

example, Hadley projections result in a 34 percent

increase in total runoff along US Atlantic and Gulf

coasts, but the Canadian model projections result in a

32 percent decrease. These analyses suggest changes

in the freshwater delivery to coastal environments are

highly uncertain. Also, both models predict an increase

in flashiness of extreme rainfall-runoff events, which

can significantly increase the chemical and sediment

load delivered to the coast.

Mean temperature of the upper 300 meters of the oceans

has increased by 0.31o

C (Levitus et al. 2000), primarily

due to human-caused climate change (Levitus et al.

2001). The decline in aerial extent and thickness of

Arctic ice over the last 20 years (Johannessen et al.

1999, Rothrock et al. 1999) has also been attributed to

Forecasting Climate Impacts on Coastal Ecosystems

Donald Scavia1, John C. Field2, Donald F. Boesch3

Abstract

Increases in greenhouse gases are expected to lead to increased air and ocean temperatures, increases in

oceanic CO2

, and changes in precipitation patterns. An assessment of the potential impacts of these and

other changes on coastal and marine systems (Boesch et al. 2000, Scavia et al. 2002) was done as part of

the National Assessment of Potential Consequences of Climate Variability and Change (NAST 2001).

These analyses used a range of forecasting capabilities, from prediction to scenario analysis. In most

cases, ecological scenarios were built upon predictions and simulations of changes in the physical

1

 National Ocean Service, National Oceanic and Atmospheric Administration, Silver Spring, Maryland 20910

2

 College of Ocean and Fisheries Science, University of Washington

3

 Center for Environmental Studies, University of Maryland
24

human-induced global changes (Vinnikov et al. 1999).

The ocean currents, fronts, and upwelling and downwelling zones that influence the distribution and production of marine ecosystems are likely to change in

response to the above alterations in temperature, precipitation, runoff, salinity, and wind. While these

changes will occur over a wide range, some projections

show significant alterations in the deep-water ‘conveyer

belt’ circulation, with potentially dramatic feedback on

large-scale climate patterns (Driscoll and Haug 1998;

Broecker et al. 1999).

Case Study

The forecasts just described discuss changes in the

climate drivers most relevant to coastal and marine

systems. These were used to assess potential impacts

on shorelines and developed areas, wetlands, estuaries,

and coral reefs. Because the ability to accurately

forecast quantitative impacts is low at this time, these

climate forecasts were extended into coastal and marine

physical and ecological impacts through ecosystem

scenarios. These results are described by Boesch et al.

(2000) and Scavia et al. (2002) and summarized here.

Shorelines and Developed Areas – Rising sea level

can inundate lowlands, erode beaches, cause barrier

islands to migrate inland, increase coastal flooding, and

increase the salinity of rivers, bays, and aquifers.

Changes in the frequency of severe storms and

increased rainfall intensity could further aggravate

flooding and storm damage. Land most vulnerable to

inundation is in Florida, Louisiana, North Carolina,

Texas, and Maryland (Titus and Richman 2001). Even

those states with relatively little low land, however,

have shorelines that are vulnerable to erosion and storm

damage.

Storm-surge floods, waves, and coastal erosion are

natural processes that pose hazards. Increasing

economic damages over time have been shown to be

largely a consequence of increased development and

appreciation in value of coastal property (Pielke and

Landsea 1999).

While there is not yet any clear evidence for climateinduced changes in the frequency or intensity of

hurricanes and coastal storms, there is little doubt that

future waves and storms will be superimposed on a

rising sea. In addition to increased erosion and

flooding, a rising sea may also increase salinity of

freshwater aquifers, alter tidal ranges in rivers and

bays, change sediment and nutrient transport, and alter

patterns of coastal chemical and microbiological

contamination. Secondary impacts, including

inundation of waste disposal sites and landfills, may

introduce toxic materials into the environment, posing

new threats to the health of coastal populations and

ecosystems. Steep, rocky shoreline areas might

experience relatively little change, but low-lying,

gently sloping areas could experience severe

inundation.

Coastal Wetlands – Coastal marshes, mangroves,

forested wetlands, mudflats, and estuarine beaches

provide critical refuge and forage opportunities for

wildlife, fishes, and invertebrates supporting the

commercial and recreational livelihoods of many

communities. Survival of these ecosystems under a

changing climate depends primarily on their ability to

adapt to change, but also on whether human activities

impair their natural adaptive capacity. Natural

Figure 1: Projected changes in average annual runoff for basins draining to coastal regions from the Canadian

and Hadley Centre General Circulation Models. Data from Wolock and McCabe (1999).

Percent Change in Runoff

California

Pacific NW

W Gulf

Mississippi R

S Atlantic-E Gulf

Mid-Atlantic

New England

-100 -75 -50 -25 0 25 50 -75 -50 -25 0 25 50 75 100 125 150

Percent Change in Runoff

2025-2034 2090-2099

Hadley

Percent Change in Runoff

California

Pacific NW

W Gulf

Mississippi R

S Atlantic-E Gulf

Mid-Atlantic

New England

-100 -75 -50 -25 0 25 50 -75 -50 -25 0 25 50 75 100 125 150

Percent Change in Runoff

2025-2034 2090-2099

Canadian

Hadley
25

biological and geological processes allow responses

to gradual changes. However, accelerated sea level rise

threatens these habitats with inundation, erosion, and

saltwater intrusion. If landward margins are armored,

effectively preventing inland migration, then wetlands

could be lost if they are unable to accumulate substrate

at a rate adequate to keep pace with future increased

rates of sea level rise.

Changes in the timing and volume of freshwater

delivery to coastal wetlands will also be critical, yet

perhaps the most difficult to assess. In contrast to

uncertainties associated with regional impacts of

climate change on hydrology, it is clear increased

human population and coastal development will create

higher demands for freshwater resources. Changes in

delivery of fresh water will affect sediment supply,

which is needed to allow wetlands and mangroves to

cope with rising sea level (Reed 1990, 1995). This is

particularly critical for areas where wetlands are not

able to migrate because they are blocked by bluffs,

coastal development, or shoreline protection structures.

In steeply sloped or developed areas where inland

migration is not possible, wetlands and mangroves will

have to rely on vertical growth to adjust.

Estuaries – Estuarine impacts from climate change will

be manifest through exacerbation of current stresses,

including those imposed by a significantly altered

nitrogen cycle (Howarth et al. 1996; Vitousek et al.

1997). Eutrophication can lead to greater phytoplankton

biomass, decreased water clarity, anoxic and hypoxic

bottom waters, more frequent and longer lasting

harmful algal blooms, degraded sea grasses and corals,

altered plankton and the benthic community structures,

and loss of biotic diversity and fisheries (Jørgensen and

Richardson 1996; NRC 2000).

Estuaries vary significantly in their vulnerability to

eutrophication. Climate change will likely influence

that vulnerability in several ways, including changes

in mixing characteristics caused by alterations in

freshwater runoff, and changes in temperature, sea

level, and exchange with the coastal ocean (Kennedy

1990; Peterson et al. 1995, Moore et al. 1997, Najjar

et al. 2000). Changes in freshwater inflow, air

temperatures, and precipitation patterns can also

influence water residence time, nutrient delivery,

dilution, vertical stratification, and control of

phytoplankton growth rates (Malone 1977, Cloern

1991, 1996, Howarth et al. 2000, Buckey et al. 1998).

Increased air temperature may also lead to earlier

snowmelt and the resulting peak in freshwater inflow.

In those cases, summer flows may be reduced as a result

of greater evapotransporation. This would increase

estuarine salinity and modify stratification and mixing,

thus influencing biotic distributions, life histories, and

biogeochemistry.

Decreased freshwater runoff will increase estuarine

water residence time, whereas increased runoff will

decrease residence time (Moore et al. 1997). Impacts

in regions experiencing decreased precipitation may

be exacerbated because water managers will likely store

more freshwater within the watershed in response to

drought. The effects of altered residence times can be

considerable because, even at their fastest growth rates,

phytoplankton populations are only able to double once

or twice per day.

Increased and earlier freshwater inflow can also modify

salinity regimes. For example, shifting freshwater flows

from spring to winter will contribute to an overall rise

in estuarine salinity. If this effect strengthens significantly over the next century, increased winter flows and

decreased spring flows will decrease salinities in the

wet season and dramatically increase salinity levels in

the dry season, further stressing an ecosystem already

heavily impacted by a wide range of other problems.

Changing precipitation patterns can also influence

nutrient delivery. The effects of precipitation variability

on delivery of nitrogen may also be magnified by land

use practices. Nitrate tends to build up in soils during

dry years, largely from reduced uptake of soil nutrients

by crops, and is flushed into streams at much larger

rates during subsequent wet years (Goolsby et al. 1999).

Thus, a wet year that follows dry years tends to produce

the largest fluxes of nitrate from the catchment to the

Gulf of Mexico. If future precipitation regimes are more

variable, a trend suggested by many climate models, this

could increase the net impacts to coastal areas affected

by nutrient over-enrichment and eutrophication.

Coral Reef Ecosystems – Coral reef communities,

damaged by acute events, will recover if other

environmental factors remain generally favorable.

Conversely, coral communities may persist under suboptimal conditions in the absence of acute stresses.

However, once a chronically stressed reef is severely

damaged, recovery is unlikely (Hughes 1994).

This is important because chronic stresses, such as

nutrient loading and overfishing are likely to increase

the vulnerability of coral ecosystems to climate-related
26

acute stresses such as high-temperature episodes. Reef

ecosystems are susceptible to changes in the frequency

and/or magnitude of temperature extremes because

many coral species live near their upper limits of

thermal tolerance. Bleaching can occur when

temperatures exceed the maximum needed for survival.

If the high-frequency SST variation of the past 20 years

continues and is superimposed on a general warming

trend, accelerated bleaching and mass mortality events

could increase dramatically in many tropical reefs.

When dissolved in seawater, increased carbon

dioxide reduces alkalinity and calcium carbonate

concentrations. This, in turn, decreases calcification rates of reef-building corals and coralline

algae. These calcification rates are likely to decline

between 17 and 35 percent by the year 2100

(Figure 2, Gattuso et al. 1999, Kleypas et al.

1999a, 1999b); as much as one-third of that

decrease may already have occurred. The resulting

reduced skeletal density and/or growth rate will

increase reef vulnerability to physical damage,

bioerosion, some forms of predation, and the

corals’ ability to compete for space.

The effect of increased atmospheric CO2 will be

greatest at the northern and southern margins of

coral distributions because CO2 is more soluble in

cooler waters. Therefore, these effects will be most

severe at higher latitudes, reducing the ability of

reefs to expand their ranges poleward as might otherwise be expected in response to ocean warming.

Changes in sediment, fresh water, and nutrient loads,

as discussed in the Estuaries and Coastal Wetlands,

will also impact coral ecosystems adjacent to coastal

watersheds (Jickells 1998, Moffat 1998, Lapointe

1999). Negative impacts can be expected in areas

where changes in land use and precipitation patterns

lead to increased sedimentation, eutrophica-tion, or

algal growth.

Lessons Learned

Adaptation and Coping Strategies – There are

significant uncertainties in forecasts of some important

climate forces on coastal systems, particularly at

regional scales. While forecasts of changes in atmospheric

CO2

, air temperature, and sea level are becoming more

reliable as model and data resolution increase, potential

changes in the timing and strength of regional river

runoff, coastal ocean and estuarine temperatures, and

coastal circulation remain uncertain. However, enough

is known to begin assessing their potential impacts.

The assessment (Boesch et al. 2000, Scavia et al. 2002)

was able to draw on expert opinion and relatively

simple, and in many cases, qualitative analysis to

extend forecasts of climate change into potential

scenarios of impacts on coastal systems. The assessment identified important areas that should be

supported to improve forecasts of these impacts. For

coastal systems, these center on developing quantitative

predictions of:

• How sea level rise, erosion, coastal storms, and

upland development impact the transformation of

coastlines and wetlands;

• How changes in precipitation, freshwater loads, and

land-use will influence the susceptibility of estuaries

to nutrients; and

• How changes in ocean temperature and CO2 will

impact the growth and biogeography of coral ecosystems.

Although these potential impacts of climate change and

variability will vary from system to system, it is important to recognize they will be superimposed upon, and

in many cases intensify, other ecosystem stresses

(pollution, harvesting, habitat destruction, invasive

species, land and resource use, extreme natural events),

which may lead to more significant consequences.

Thus, there is a growing need for models and analysis

tools capable of assessing multiple stresses.

Figure 2: Reductions in coral calcification rates suggested by model

results of increasing atmospheric carbon dioxide (Gattuso et al. 1999)
27

Climate change likely over the next century will have

many consequences for most U.S. coastal ecosystems,

and some of these may substantially alter human

dependencies and interactions with these complex and

linked systems. The climatic effects will be superimposed upon, and interact with, a wide array of current

stresses, including excess nutrient loads, invasive

species, habitat destruction, and toxic chemical

contamination. While the ability of these ecosystems

to cope with or adapt to climate change or variability

is compromised by extant stresses, the inverse is also

likely to be true – ecosystems will be better suited to

deal with climate variability and change if other stresses

are significantly reduced.

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Applications 7:737-750.
29

Introduction

Spatially explicit landscape models can be used to test

hypotheses about landscape functioning and evolution

and how human intervention may alter the landscapes.

These models analyzed how natural forces affect ecosystem function and habitat change in an area of the

Mississippi delta extending west from the Mississippi

River to Freshwater Bayou (Martin et al. 2002). This

allowed management proposals to be based on a more

accurate understanding of coastal and delta systems.

Large-scale management plans are often based on untested hypotheses about landscape functioning and how

management changes alter this functioning. Spatial

models provide an opportunity to test hypotheses by

simulating the dynamics of the natural system and predicting the effects of proposed management plans

through time and across the landscape (Costanza et al.

1990). In the future, linking such models with land-use

change modules has the potential to quantitatively predict impacts of governmental land use policies upon

coastal water quality and habitats.

Case Study

Spatial Modeling of the Mississippi Delta – Presently,

many of the world’s deltas, including the Mississippi

River Delta, are rapidly deteriorating, primarily due to

human activities (Day et al. 1997, Day et al. 1995,

Milliman et al. 1989). Anthropogenic actions isolate

delta wetlands from river inputs and threaten the vitality of these systems and their ecological and economic

resources. Isolated marshes succumb to subsidence and

convert to open water as rapidly as 102 km2

/yr (Reyes

et al. 2000). As a result of losing 787 km2 of land, the

Barataria Basin within the Mississippi Delta suffered a

26 percent decline in net primary productivity from

1956 to 1988 (Day et al. 1997). Over the next 50 years,

the loss of public resources from continued land loss

across the Mississippi Delta is estimated at more than

$37 billion (Bourne 2000). Sea level rise has the potential to exacerbate these processes in deltas across

the globe.

Spatially-explicit landscape models were used to evaluate the effects of accelerated sea level rise and altered

river inputs on the survival and creation of Mississippi

Delta marshes. Scenarios reducing river inputs resulted

in losses of marsh coverage; increased river inputs created new marsh and demonstrated the potential to sustain delta marshes. The models used in this study simulate dynamic and spatial interactions between ecological, edaphic (e.g., soil and sediments), and hydrodynamic processes. They are unique in their ability to predict the growth and regression of deltas for multiple

decades.

1 Ecological Engineering Group, Department of Food, Agricultural, and Biological Engineering, The Ohio State University,

590 Woody Hayes Drive, Columbus, OH 43210-1057.

Simulating Coastal Environments with Landscape Models:

Applications in the Mississippi Delta and Lake Erie

Jay F. Martin1

Abstract

The survival and creation of Mississippi Delta marshes and the impacts of altered river inputs, accelerated sealevel rise, and management proposals were examined with a series of landscape models. The spatially-explicit

models integrated routines and simulated interactions between ecological and hydrodynamic processes. In the

future, models of this type will be used to predict impacts of land use policies on water quality and habitat

distribution along the Lake Erie coastline.
30

The spatial models integrate hydrodynamic routines

with ecological and soil modules across 1 km2 spatial

grids (Figure 1, Martin et al. 2000, Reyes et al. 2000).

Significant advances to the application of spatial models in aquatic-terrestrial interfaces accompanied the development of the Barataria-Terrebonne Ecological

Landscape Spatial Simulation model (BTELSS) and the

Mississippi Deltas Model. The explicit hydrodynamic

module, much enlarged study area, and improved habitat switching algorithm are substantial advances of the BTELSS that

built on a previous modeling study

of coastal Louisiana (Costanza et al.

1990).

The Mississippi Deltas Model is

characterized by a higher resolution

hydrodynamic module, operating at

1 km2 and variable time steps, as well

as the ability to simulate the conversion of open water areas to marsh

habitats. An improved sediment algorithm and marsh colonization routine are pertinent processes to create

new land habitats in delta environments of the Mississippi Deltas

Model.

Forcing functions consist of historical data from 1956

through 1995 (Reyes et al. 2000) that include climatic

variables, river flow and sediment concentration, relative sea level rise, tides, and salinity. For calibration,

the Mississippi Deltas Model was initialized with a 1978

habitat map from the United States Fish & Wildlife

Service (USFWS), and using data series from 1978 to

1988, simulated 10 years of habitat change. The resulting 1988 habitat map was compared to a 1988 USFWS

habitat map with a spatial statistics routine (Costanza

1989) that returned a value of 94.9 out of a possible

100. A validation value of 95.4 was produced when the

Mississippi Deltas Model was initialized in 1988, ran

for 10 years, and compared to the 1998 USFWS habitat map. Further calibration and validation of the models included comparing predicted habitat trends with

historic rates of change, and comparing recorded and

predicted salinity and suspended sediment concentrations at specific locations. The historical data series were

repeated to simulate conditions for scenarios into the

future (Martin 2000, Reyes et al. 2000).

Barataria Basin (NAP) scenarios simulated continuing

current trends and management for each study area

beginning in 1988, and offered a baseline of comparison for other simulations. These scenarios had distinctly

different outcomes for each study area in the BTELSS

and Mississippi Deltas Model (Table 1).

In the Barataria Basin, total marsh coverage declined

by more than 56 percent during the 30 year No Action

Plan (Figure 2, Martin et al. 2000). This marsh loss is

due to the interacton of natural processes such as high

Figure 1. The landscpae models passed information between

modules operating at different time steps. The ecological module

responded to daily salinity and flooding averages from the

hudrodynamic module. The soil module accumulated inputs from

the ecological module to calcuate annual changes in elevation.

Possible habitat change took place each year as salinity and

vegetative biomass corssed thresholds. The hydordynamic routine

reponded to changes in elevation and habitat depandent on

Mannings coefficients. In the Mississippi Delta sModel, unlike the

BTELSS, all modules had a 1-km2 spatial resolution.

Table 1. Changes in marsh coverage due to scenarios simulated with the BTELSS and

Mississippi Deltas Model.

NAP-No Action Plan, RSLR-Relative Sea-Level Rise

Scenario Year (km2

) (% change from 1988)

Initial condidtions 1988 1949

NAP (RSLR=1.2 cm-yr-1) 2018 849 -56.4

RSLR=2.2 cm-yr-1

2018 196 -89.9

Initial condidtions 1988 1696

NAP (RSLR=0.5 cm-yr-1) 2058 1925 13.5

Double river input 2058 2117 24.8

Half river input 2058 1713 1.0

RSLR=1.2 cm-yr-1

2058 1596 -5.9

RSLR=2.2 cm-yr-1

2058 1041 -38.6

Marsh Coverage

Barataria Basin (BTELSS simulations)

Mississippi Deltas Model (MDM) study area

10-1200 sec. timestep

1km2

Water, salt, and

suspepnded sediment

Hydrodynamic Module

Ecological Production

Module

Daily, 1km2

Above ground biomass

Below ground biomass

Soil Building Module

Yearly, 1 km2

Inorganic sediment

Below ground biomass

Habitat

Switching

Module

Yearly, 1 km2
31

rates of relative sea level rise which characterized the Barataria Basin (Penland and Ramsey

1990), and human modification such as levees

and dikes that eliminate the input of river sediments to marches (Martin et al. 2000).

The Mississippi Deltas Model study area experienced a 13.5 percent gain in marsh during

the 70 year No Action Plan, primarily due to

the expansion of fresh marsh coverage associated

with the progradation of the Atchafalaya Delta

(Figure 3). The contrast in habitat change in these

two areas is largely due to regional differences

in river inputs. Because levees and closed river

outlets prevent inputs from the Mississippi River,

the marshes of Barataria Basin cannot keep pace

with Relative Sea Level Rise (comprised of both

subsidence and eustatic sea level rise) and will continue to convert to open water.

The Atchafalaya River deposits river sediments

in its delta, raising the elevation of the substrate

in open water areas until floods decrease, allowing vegetative colonization. Subsequent organic

soil formation leads to additional elevation and

eventual habitat change from open water to

marsh. While the Mississippi Deltas Model study

area as a whole experienced land gain, marshes

more distant from the river mouth, such as those

in Pointe au Fer and Marsh Island, suffered losses

of marsh in the No Action Plan (Figure 3). These

areas received less river input and could not keep

Figure 2. Location of cells that were converted from marsh to open

water during the 30 year no action planin Barataria from 1988 to

Figure 3. The 1988 United States Fish and Wildlife Service habitat map (A)

was aggregated into 6 habitat classifications (swamp, fresh marsh,

intermediate marsh, brackish marsh, salt marsh, and open water) and used

to initialize the 70-year Current Conditions scenario that terminated in 2058

(B). The white line in the Current Conditions scenario map denotes the

shoreline in 1988. Please refer to Table 1 for habitat coverages.

pace with the relative sea level rise of 0.5 cm yr-1, characteristic of the Mississippi Deltas Model study area

(Penland and Ramsey 1990).

Other Mississippi Deltas Model simulations in which

Atchafalaya River inputs were doubled and halved demonstrated the importance of river flow in sustaining marsh

habitats across the model study area (Table 1). Doubling flows increased both the water and sediment delivered to the estuary and increased the growth of the

Atchafalaya Delta by 125 km2 compared to the No Action Plan (Figure 4).

Additionally, marshes farther removed from the deltas

experienced greater accretion, which reversed the trend

in these areas from marsh loss in the No Action Plan to

marsh gain. Pointe au Fer and Marsh Island gained 17

and 31 km2 of marsh, respectively, during this scenario.

Across the study area, there were total gains of 421 km2

of marsh when the flows were doubled. Reducing

(a)

(b)

Fresh Marsh to Open Water

Brackish Marsh to Open Water

Salt Marsh to Open Water

449 km2

467 km2

190 km2

176

1941

462

65

35

5943

Intermediate Marsh

Fresh Marsh

Swamp

Brackish Marsh

Salt marsh

Open Water

Cell Km2 Habitat

Pointe au Fer

Marsh Island
32

Figure 4. Resulting habitat distribution for the year 2058

for the Mississippi Deltas Model simulation in which the

flow and sediment discharge of the Atchafalaya River and

Figure 5. Resulting habitat distribution for the year 2058

for the Mississippi Deltas Model simulation in which the

flow and sediment discharge of the Atchafalaya River and

Wax Lake Outlet were halved.

Atchafalaya River flow by 0.5 decreased the growth of

the Atchafalaya Delta and led to greater land loss across

the study area (Figure 5).

Alterations in river inputs in the Misssissippi Delta and

other deltas throughout the world demonstrate the relevance of this scenario. Natural and anthropogenic

changes have reduced the amount of sediments reaching the Nile, Indus, Ebro, Po, and Mississippi deltas by

over 75 percent(Day et al. 1997, Kesel 1988). Current

projects, such as the Three Gorges Dam along the

Yangtze River, insure continued alterations of river inputs and impacts upon delta evolution.

Because of the ability to simulate the links and feedbacks between hydrodynamics and ecological processes

across large areas over long time periods, models such

as the Mississippi Deltas Model are critical tools to quantitatively assess environmental consequences of historical and future river and marine alterations upon deltas.

Further scenarios demonstrate that subsidence combined

with accelerated sea level rise will amplify relative sea

level rise rates across low-lying deltas over the next

100 years, jeopardizing the ecological and economic

services provided by these systems (Day et al. 1995,

Milliman et al. 1989). The BTELSS predicted marsh

loss to increase by 89.9 percent in the isolated Barataria

Basin as a result of increasing the relative sea level rise

from the No Action Plan rate of 1.2 cm-yr-1 to 2.2 cmyr-1 (Table 1, Reyes et al. 2000). In the Mississippi Deltas

Model study area, the trend of marsh gain was reversed

to marsh loss when relative sea level rise was increased

from the No Action Plan value of 0.5 cm-yr-1 to 1.2 and

2.2 cm-yr-1 (Table 1).

While it is impossible to revert to the natural delta cycle

due to human settlement and use of these environments,

these models provide quantitative predictions of various alternatives to balance human needs and long-term

vitality of delta systems. Results demonstrate maintaining and restoring natural river inputs can create and

preserve delta marshes and mitigate present and future

deterioration of delta ecosystems.

Applications to Lake Erie – A new effort is underway

to develop a spatial watershed model that can quantitatively predict impacts of governmental land use policies upon coastal water quality and habitats. In addition to allowing more complete evaluations of land use

policies, models of this type will illustrate links between

public actions and environmental changes. Because

governmental policies are often evaluated in relative

isolation, unforeseen environmental consequences often affect adjacent ecosystems. Cumulative effects of

non-point source pollution on coastal environments and

Cell Km2 Habitat

Intermediate Marsh

Fresh Marsh

Swamp

Brackish Marsh

Salt marsh

Open Water

176

1941

462

65

35

5943

Cell Km2 Habitat

Intermediate Marsh

Fresh Marsh

Swamp

Brackish Marsh

Salt marsh

Open Water

300

1044

455

238

130

6455
33

Figure 6. Similar to previous landscape models (Martin et al. 2000,

Reyes et al. 2000) the Sandusky watershed model will pass information between modules operating at different time steps. Changes

in land use predicted by the land use change module will alter water

quantity and quality inputs to the hydrodynamic module. The

hydrodynamic module will also respond to coastal habitat changes

to predict water elevations and water quality throughout the watershed. Responding to these variables, the ecological unit module

will predict changes in ecological productivity and substrate elevation. Habitat change may take place each year as water level and

vegetative biomass crossed thresholds. All of the modules will operate

with a 1 km2 spatial resolution.

aquatic life is one example. Unforeseen consequences

are a concern, both nationally and in the Lake Erie watershed (Ohio Lake Erie Commission 1998).

The overall goal is to develop a spatial watershed model

to quantify land use, habitat, and water quality dynamics in coastal areas of Lake Erie that have resulted from

changes in land use policies including zoning and public water provision regulations. The initial application

will focus on how such policies influence the development of agricultural, forest and natural lands within the

Sandusky watershed (Lake Erie, Ohio), and in turn, how

these changes impact coastal water quality and habitat

change.

This is an ideal setting to develop this model because of

the availability of land use change data, a 22 percent gain

in developed land from 1982 to 1992, and past research

of coastal habitats in Sandusky Bay. Results from this

research will aid designing environmental and growth

management strategies aimed at protecting and restoring the water quality and natural habitats of the Great

Lakes.

By integrating ecological and physical processes with

government policies and human behavior, these models will represent a significant advance in disciplines

of landscape ecology and environmental management.

During the past 15 years, landscape models have been

developed to predict and analyze the interaction of

physical and ecological processes in coastal zones (Reyes

et al. 2000, Costanza et al. 1990, Sklar et al. 1985).

Another type of landscape modeling has sought to predict land use change based on governmental policies

(Irwin and Bockstael 2001, Bockstael and Bell 1997,

Landis 1995). The new models will unite these fields

by developing a watershed model to predict environmental patterns arising from government actions and

individual choices regarding land use. By incorporating ecological, physical and economic system processes

and relationships, landscape models can identify both

short- and long-term impacts of land use and other policies across watersheds and coastal zones (Martin et al.

2000). The integration of climatological, hydrological

and ecological processes with human decisions creates

the need for a tool to effectively manage coastal watersheds and to evaluate a variety of government policies

that influence land use change.

The completed watershed model will be composed of

four modules: 1) a land use module, 2) a hydrodynamic

module, 3) an ecological unit module, and 4) a habitat

change module (Figure 6). Integrating these modules

will quantitatively predict the causes and feedbacks between humans and the natural environment.

The land use module will predict watershed land use

patterns resulting from policy decisions such as protecting sensitive environmental areas from development, and regulating public water and sewer provision.

Inputs to rivers within the watershed will be calculated

based on land use types and climatic conditions.

The hydrodynamic module will simulate the transport

of water and materials from the watershed through rivers

to the coastal zone. Biological and physical processes

regulating water quality will be included within the hydrodynamic module.

The ecological unit module will respond to hydrodynamic variables and water quality to simulate biological productivity and habitat change (Fitz et al. 1996)

within the coastal zone.

When completed, the models will be able to analyze

how a variety of changes in the natural and manmade

environment affect coastal zones. Potential scenarios

to be evaluated include the effects of reductions or increases in erosion and fertilizer use and government

Hydrodynamic Module

Ecological Production

Module

Soil Building Module

Habitat

Switching

Module

Yearly, 1 km2

Daily, 1km2

Above ground biomass

Below ground biomass

Organic sediment

1-60 minutes, 1km2

Water, chemicals, and

suspended sediment

Yearly, 1km2

Predict land use change

due to govt. policies and

environmental variables
34

policies regarding land use zoning and the provision of

public services such as roads and public sewers.

Lessons Learned

The general patterns predicted by these models are not

surprising. For instance, the facts that more river flow

and sediment will lead to delta growth and less river

flow and sediment will lead to delta decline have been

part of geological literature for many years (Roberts

1997). The benefit of these models lies in their ability

to quantify the spatial and temporal effects of management plans and changes in forcing functions, which

allows for more precise evaluation system manipulations.

For instance, the coverage of marsh was predicted to

decrease 12.5 percent (compared to the No Action Plan)

if river flow was reduced by one half. Further scenarios

could predict the reduction in river flow needed to allow

for desired decrease in marsh growth to offset another

public works project such as flood management. These

models are best applied to quantify the location and

timing of landscape changes resulting from management plans and environmental changes.

Conclusion

Scenarios of the Mississippi Delta exemplify the

potential of landscape models to quantitatively evaluate

management plans in conjunction with natural

alterations and lead to a more precise understanding of

coastal response to perturbations. During the 15-year

development of these models (Costanza et al. 1990,

Sklar et al. 1985), and in current applications in the

Everglades(Fitz et al. 1996), the goal has been to predict

large-scale temporal and spatial patterns resulting from

regional impacts. Such models can predict effects of

complex interactions, over lengthy time periods and

across entire landscapes.

Spatially-explicit models linking policies to land use

changes and land use changes to environmental changes

can be used to evaluate the impacts of existing and

proposed policies. By establishing and quantifying

these links, these models will elucidate the environmental and economic trade-offs of governmental policies

that impact coastal water quality and habitats.

For example, this approach can account for the

cumulative effects of small-scale land use changes on

eroded soils and the eutrophication of downstream

water bodies. Consequently, the watershed models will

be capable of analyzing the effects of government

policies that may require ten or twenty years to fully

impact the landscape and may have far-removed

consequences.

These models can also evaluate potentially beneficial

effects of restoration initiatives. With limited resources,

it is imperative to identify restoration plans with the

greatest potential to improve coastal water quality and

maintain natural habitats. These models will be capable

of predicting which land use policies will have the

greatest impacts on coastal environments.

Acknowledgements

The United States Army Corps of Engineers, Louisiana

Sea Grant Program, and Ohio Sea Grant Program funded

this work. Thank you to Drs. Enrique Reyes, Paul Kemp,

John Day and Hassan Mashriqui for their help and

support throughout these projects.

References

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quality: The effect of differential land management controls. In: R. Just and S. Netanyahu (eds), International

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and Cooperation on Trans-Boundary Water Resources,

Kluwer Publishing. New York. p. 122-143.

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going... Science 289:1860-1863.

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coastal landscape dynamics. Bioscience. 40:91-107.

Costanza, R. 1989. Model goodness of fit: a multiple

resolution procedure. Ecological Modelling 47:199-215.

Day, J.W.,J.F. Martin, L. Cardoch, and P.H. Templet. 1997.

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25:115-153.

Day, J.W., D. Pont, P. Hensel, and C. Ibanez. 1995. Impacts

of sea level rise on deltas in the Gulf of Mexico and the

Mediterranean: the importance of pulsing events to

sustainability. Estuaries 18:636-647.

Fitz, H.C., E.B. DeBellevue, R. Costanza, R. Boumans, T.

Maxwell, L. Wainger, and F.H. Sklar. 1996. Development of a general ecosystem model for a range of scales

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Irwin, E. G., Bockstael, N.E. 2001. Interacting agents, spatial

externalities, and the endogenous evolution of residential land use pattern. Journal of Economic Geography.

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Kesel, R.H. 1988. The decline in the suspended load of the

lower Mississippi River and its influence on adjacent

wetlands. Environmental Geology and Water Science.

11:271-281.

Landis, J. 1995. Imagining land use futures. Applying the

California urban futures model. Journal of the American

Planning Association 61(4):438-457.

Martin, J.F., E.Reyes, G.P. Kemp, H. Mashriqui, and J.W.

Day. 2002. Landscape modeling of the Mississippi

Delta. Bioscience 54(4):357-365.

Martin, J.F. 2000. Manipulations of natural system functions

within the Mississippi Delta: a simulation-modeling

study. Dissertation. Louisiana State University, Baton

Rouge, Louisiana.

Martin, J.F., M.L. White, E. Reyes, G.P. Kemp, J.W. Day,

and H. Mashriqui. 2000. Evaluation of coastal management

plans with a spatial model: Mississippi Delta, Louisiana,

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36
37

Introduction

Coral populations in the Florida Keys (Figure 1) have

declined precipitously since 1974, when quantitative

monitoring began (Dustan 1977a, Dustan, 1985, Dustan

and Halas 1987, Porter and Meier 1992, Porter et al.

2001a). Between 1974 and 1982, living coral cover and

diversity on Carysfort Reef increased in the shallow

areas of the reef, while there was significant decline

on the deeper fore reef terrace.

Changes in shallow water reefs seemed driven by

physical destruction of dominant stands of Acropora

palmata (elkhorn coral). Cover increased because the

lush, three-dimensional habitat was reduced to planar

rubble by boat groundings and storms. Then smaller

colonizing species settled on the open substrate. On

deeper portions of the reef, colonies were dying from

disease and sediment damage, and were not replaced

by recruitment (Dustan and Halas 1987).

In 1984, observations on the phenotypic condition of

over 9800 corals on 19 different reefs in the Key Largo

region revealed 60 percent exhibited signs of physical

or biological stress, 5-10 percent were infected with

disease (Black Band and White Plague) and about 33

percent appeared healthy. Surprisingly, virtually all the

reefs had approximately the same level of stressed

corals. This argued for widespread stress as causal

agents (such as reduced water quality), rather than

stressors localized to specific reefs (Dustan 1993).

During the same period, high rates of coral tissue loss

and mortality were documented throughout the Keys

(Porter and Meier 1992).

Carysfort Reef continued to decline, and by June 1996

coral cover in the shallows decreased to approximately

10 percent and to 14 percent in the deeper habitat zones.

In July 1998, virtually every colony of Montastrea

annularis species complex was infected with White

Plague; large colonies (>1 m diameter) were rapidly dying.

Since the skeletal growth rate of M. annularis has been

measured at 5 to 10 mm/yr., these colonies were over

100 years old. In 2000, coral cover on Carysfort had

dropped below 5 percent, and corals ceased providing

significant contribution to reef framework construction

(Table 1).

Carysfort Reef has entered ecological collapse. Similar

ecological degradation occurred on many reefs

throughout the Florida Keys. The US Environmental

Protection Agency (USEPA) Coral Reef Monitoring

Project measured a Keys-wide coral cover loss of 38

percent between 1996 and 2000 (Porter et al. 2001a).

Carysfort Reef, however, is the only reef with

quantitative monitoring that began in the mid-1970s

(Dustan and Halas 1987, Porter and Meier 1992).

Ecological Perspective: The Decline of Carysfort Reef,

Key Largo, Florida 1975-2000

Phillip Dustan1

Abstract

The coral reefs of the Florida Keys are in catastrophic decline from nested multiple stressors. This includes

direct and indirect anthropogenic impacts across local and global scales. Between 1974 and 1999, living coral

cover on Carysfort Reef, Key Largo, declined 92 percent. Similar ecological degradation has occurred on many

reefs throughout the Florida Keys, including Molasses Reef, Looe Key, and Sand Key, but there was no other

formal documentation. Starting in 1995, the USEPA Coral Monitoring Project documented a 38 percent loss of

living coral cover over four calendar years, documenting a decline from 10.3 percent to 6.4 percent.

1

 Department of Biology, University of Charleston, SC 29424
38

Quantitative long-term monitoring in the Dry Tortugas

at the ‘other end’ of the Keys revealed coral cover on

Bird Key decreased an estimated 20-25 percent

between 1975 and 2000. It appears corals in the Dry

Tortugas are not stressed to the same degree as in the

‘mainland’ Florida Keys. Dry Tortugas reefs are

buffered from Key West by 65 miles of ocean, which

may explain why these reefs are somewhat healthier

than the Keys reefs.

In 1990, Congress created the Florida Keys National

Marine Sanctuary and mandated the USEPA institute

a water quality assurance and protection plan for the

Florida Keys. From 1996 to 2000, the USEPA Coral

Monitoring Project documented significant increases

in the geographical distribution of coral diseases and the

number of species with diseases (Tables 1 and 2, Porter

et al. 2001b, Richardson 1998, Patterson et. al. 2002).

Coral bleaching also became more common throughout

the Keys. Similar disease trends appear in other marine

organisms, most notably in the Florida Keys where

gorgonians (soft corals), have been infected with

Aspergillus sydowii, an opportunistic terrestrial soil

fungus (Smith et al. 1996).

Case Study

Detection of Ecological Change using Satellite

Imagery – A time series of 20 Landsat Thematic

Mapper images for Key Largo, taken from 1982 to

1996, revealed changes on Carysfort Reef could be

detected from space (Dustan et al. 2001). Changes in

image brightness and spectral band ratios suggested a shift

from a coral to algal-dominated community structure,

although the trends were not

statistically significant. Texture

analysis (spatial heterogeneity)

generated by habitat patchiness or

annual phenology (seasonal

change) decreased over time,

which was consistent with the

observed loss of corals and subsequent algal overgrowth.

Texture analysis was applied to examine change over

time by looking at individual pixels. Each represented

the same geographic point on the earth. High values

indicate variability, while areas with low temporal

texture signify little or no change. In effect, a change

in temporal texture is an analysis that can identify hot

spots without specific target classification.

When applied to Carysfort Reef, the analysis revealed

areas of the reef known to have suffered the greatest

ecological change yielded the highest temporal texture.

Patterns detected by remote sensing were a clear

Figure 1. Location map showing south Florida and the Florida Keys including the Dry Tortugas.

Table 1. Percent cover of living coral on Carysfort Reef between 1974 and 2000. (Data from

Dustan and Halas 1987, Porter et al. 2001a)

Reef Zone 1974-5 1982-3 1996 2000 Change 

Shallow 42% 53% 10.60% 4.10% -90%

Deep 62% 50% 13.50% 4.70% -92%
39

diagnosis of known ecological change without having

to classify the imagery (Figure 1, Dustan et al. 2001).

This raises the possibility that measures of variability

alone may be diagnostic without needing to fully

interpret the spectral information. Furthermore,

comparison of patterns over larger geographic areas

may be employed in a GIS hunt for degradative forcing

functions.

Detecting levels of habitat change without generating

a precise classification opens the possibility of using

currently orbiting technology to examine large areas

of reef tract (or other ecosystems) for change. If hot

spots are detected, more directed field studies and

higher precision remote sensing from aircraft or ‘next

generation’ high resolution orbiting sensors could be

focused on these critical areas to decipher potential

causal factors.

Stress at Nested Scales – Stresses to

Florida Keys coral reefs reside within a

series of nested scales, making it is

extremely difficult to determine the

individual contribution of each stressor.

Growing at the northern limits of reef

development implies high levels of

naturally occurring stress. Many corals

live long, so reef populations are now

exposed to amplifications of naturally

occurring stress, as well as newer stresses.

The impact of stress accumulates and is probably

synergistic. In the Florida Keys, disease is a significant

source of colony mortality. Already stressed corals may

have weakened immune systems that make them even

more susceptible to disease. Coral recruitment and

regeneration rates are low.

Global and Mesoscale Stress – The Keys are downstream from almost every sediment or nutrient source

in the Caribbean basin and Gulf of Mexico. Materials

wash into the sea from both the west

and east coasts of the Florida peninsula

as well as through the Everglades. The

area of sediment influx extends from

the Mississippi River watershed to

throughout the Caribbean Sea. Sediments from rivers as distant as the

Orinoco and Amazon have been

identified on Carysfort Reef (Dustan,

unpublished).

In 1993, floodwaters from the US

midwest combined with the Loop

Current (which flows into the Florida Current) and

reduced both salinity and oxygen concentrations along

Keys reefs. On September 14, 1993, decreases in salinity (from 36 to below 32 ppt) and oxygen (from 7.1 to

5.2 ppm) were recorded on Molasses Reef (Porter,

personal communication). While the magnitude of the

signal is not usually as large, the event clearly showed

the hydrologic connectivity between the interior

watersheds of North America and the Florida Keys.

Coral populations of the Florida Keys, like reefs

everywhere, are also subject to global-scale stresses

such as global warming and increased ultraviolet light

exposure due to ozone thinning. Coral bleaching has

been correlated to increased water temperatures in the

late summer and early fall. Particularly serious mass

bleaching has occurred in the last 20 years. Most of

these events were recorded in many areas of the wider

Caribbean and thus have been linked to warming seas.

However, recent work points to another, previously

unsuspected ecological stressor: transatlantic African

dustfall (Shinn et al. 2000). African dust is composed

of the major crustal elements including iron, phosphorus, sulfate, aluminum, and silica, which may enhance

growth of tropical marine algae, and there is a correlation

between increased amounts of dust and various disease

outbreaks. Isolated African dust collected in the CaribTable 2. Distribution of coral diseases by station 1996-1998. Data from USEPA Coral

Reef Monitoring Project (Porter et al. 2001b)

Table 3. Distribution of coral diseases by species 1996-1998. Data from USEPA Coral

Reef Monitoring Project (Porter et al. 2001b)

Disease Percent

WH BB OD Stations Diseased

1996 7 7 16 26 16%

1997 61 11 66 95 59%

1998 97 28 92 131 82%

 Increase n (96-98) 90 21 76 105

Increase % (96-98) 1285% 300% 475% 404%

Number of Stations with Disease

Disease Types: WH=White Plague, BB=Black Band Disease, OD=Other Diseases

Diseased Percent

WH BB OD Species Diseased

1996 3 2 8 11 27%

1997 22 4 22 28 68%

1998 28 7 28 35 85%

 Increase n (96-98) 25 5 20 24

Increase % (96-98) 833% 250% 188% 218%

Number of Coral Species with Disease

Disease Types: WH=White Plague, BB=Black Band Disease, OD=Other Diseases
40

bean contains spores from

Aspergillus syndowni, a fungus that causes disease in

Caribbean gorgonians and is

also a potential cause of

other synchronous Caribbeanwide coral diseases, including those that killed staghorn

coral (Acropora cervicornis)

and the sea urchin (Diadema

antillarum). Near extinction

of these organisms in 1983 correlates with the period of

highest annual dust transport to the Caribbean since

measurements began in 1965.

Regional Stress – Humans

began manipulating the

surface hydrology of South

Florida shortly after Florida

became a state in 1855.

Large-scale drainage and

flood control programs

drained thousands of acres

of swampland for agriculture and constructed hundreds of miles of canals to

control the surface flow of water from Lake Okeechobee southward to Biscayne Bay. Massive efforts to

channel water flow resulted in excess freshwater,

nutrients, and sediments flowing into coastal waters,

including Florida Bay (cf. Davis and Ogden 1994).

Fertilizers, organic carbon, and urban and commercial

dumping further enriched the watershed’s effluent as

it flowed into the sea. Although concentrations may be

diluted, these agents may still affect reef health, as reef

ecosystems have evolved to be very efficient in trapping

and retaining nutrients even in concentrations that are

technically lower than the limit of detection.

Upstream of the Keys, effluent from cities, towns, and

farms slowly bleeds into the sea through canals, rivers,

and coastal bays. While the distribution of point sources

(i.e., sewage outfalls, wastewater disposal by septic systems or deep well injection, or agricultural irrigation

canals) is controlled through permitting processes,

effluents are not easily traceable once they enter the

tropical shallow-water ecosystems. For example, in

Key Largo, effluent from a Class 5 shallow-well

injection package plants migrates through the Key

Largo limestone at rates of 3 to 30 m/d, depending on

tides, weather condition, etc. Since this is the preferred

method for waste disposal by developments and hotels,

it is a significant pollution threat for the nearshore coastal

environment (Reich et al.1999, Cable et al. 2002).

Urbanization in the Florida Keys has been intense, with

most development centered on Key Largo in the

Northern Keys, Marathon in the Middle Keys, and Key

West in the South. The human population of the Florida

Keys grew tenfold from 1870 to 1990, with about 30

percent of residents living in Key West. Tourism

doubles the population during peak times, with twothirds of the visitors participating in water-related

activities. Over 24,000 boats are registered in Monroe

County and approximately 14 percent of visitors

(roughly 365,000 people) bring boats with them when

they visit the Keys.

Localized Stress – Boat groundings and anchor

damage have been widespread and have caused severe

damage to shallow reefs throughout the Florida Keys

(Dustan 1977b, Davis 1977). In 1999, there were 540

small boat grounding documented in the Florida Keys

National Marine Sanctuary. There have been at least 5

major ship groundings on reefs since 1985, (Hudson

and Diaz, 1988). Installation of hundreds of mooring

buoys throughout the Keys has greatly reduced anchor

Table 4. Florida Keys Coral Reef Ecosystem Timeline outlining significant political protection events

and ecological findings.

Year Protection Event or Scientific Finding

1935 Dry Tortugas National Monument by President Roosevelt 

1960 John Pennekamp Coral Reef State Park established December 10, 1960

1968 Biscayne National Park established

1969 Skin Diver Magazine sounds alarm on reef degradation (Barada 1975)

1972 Marine Protection, Research and Sanctuaries Act

1973 Coral Diseases discovered in Key Largo (Antonius 1974)

1974 Beginning of long term reef monitoring at Carysfort Reef (Dustan 1977b)

1975 Key Largo National Marine Sanctuary established

1981 Looe Key National Marine Sanctuary established

1983 Significant reduction of corals at Carysfort Reef since 1974

1984 Key Largo Coral Vitality Study ~60% corals stressed (Dustan 1993)

1987 Severe coral bleaching throughout Caribbean

1991 Florida Keys National Marine Sanctuary and Protection Act 1990

1992 Reef coral degradation estimated at ~5% loss/yr. (Porter and Meier 1992)

1995 USEPA Water Quality Protection Plan Coral Reef Monitoring project,

1997 Outbreak of White Band Disease 2. pathogen identified (Richardson 1998)

1998 President Clinton issued Executive Order 13089 on Coral Reef Protection. 

First US Coral Reef Task Force Meeting at Biscayne National Park.

1999 Carysfort Reef living coral coverage below 5%

2000 Proposed Tortugas 2000 National Marine Sanctuary

2001 USEPA Coral Monitoring reports 38% loss of living coral 1996-2000

2002 Florida Keys' Particularly Sensitive Sea Area shipping restrictions declared
41

damage on reefs and may serve as a means to regulate

access to reef areas.

Starting in 1982, there was a mass mortality of Diadema

antillarum throughout the Caribbean Sea. Populations

have still not recovered to anywhere near original levels

(Lessios, 1988). Following the mass mortality, macro

algal populations throughout the Caribbean soared as

they were released from herbivore pressure. So while

nutrient levels were apparently rising from increased

urbanization, levels of herbivory on the reef

plummeted. At present, these two scenarios cannot be

uncoupled and there remains a hot debate on which

stressor is ecologically important (see Lapointe 1999,

Hughes and Connell 1999).

The Combination – In summary, increased sedimentation smothers coral tissue; increased nutrients and a

dramatic reduction in herbivory has resulted in algal

overgrowth; and elevated temperatures promote

bleaching and disease. Reefs near population centers have

the increased pressures from sewage, runoff, garbage

dumping, and greatly increased levels of harvesting.

Coral diseases, first discovered in the Keys in the 1970s,

have increased over 400 percent since 1996 (Porter et

al. 2001b).

Protective Measures – The Florida Keys were placed

under state and Federal protection beginning with the

creation of the Dry Tortugas National Monument in

1935 by President Franklin Roosevelt. The rest of the

Keys were protected through the Florida Keys National

Marine Sanctuary Protection Act of 1990 (Table 4).

Presently, the Florida Keys National Sanctuary

(FKNMS) uses the concept of multi-use zoning for

varying levels of resource protection. Mooring buoys

have been installed throughout the Keys to protect reefs

from anchor damage and sanctuary officers patrol the

waters. To reduce large vessel groundings, a series of

radar beacons have been placed on outer reef

lighthouses and the area has been declared a

Particularly Sensitive Area to help warn commercial

shipping traffic of the reefs. Less than one percent of

the FKNMS has been designated as no take zones and

appropriate sewage treatment and issues surrounding

non-point pollution have yet to be adequately addressed.

Forecast – Concerns about reef degradation have been

published in the popular literature since 1969 and

clearly expressed in the scientific literature since 1977

(Dustan, 1977b, 2000, Dustan and Halas 1987,

LaPointe, 1997, 1999, Porter and Meier 1992, Porter

et al. 2001a). Furthermore, these studies suggest that

the rate of loss of living coral tissue may have

accelerated in the last 25 years (Figure 2).

Reef corals are slow growing, colonial organisms and

clearly, at the present rates of loss, Florida Keys reefs

will not be able to sustain their ecological integrity.

However, because there are multiple stressors and they

are nested, managers have no clear solutions and are

reluctant to apply conservation measures beyond

fisheries regulations, enforcement, and permitting.

The Florida Keys do not have tertiary sewage treatment,

and much of the region is without sewage treatment

beyond septic tanks. These contaminate canals and

coastal waters due to the porosity of the limestone rock

of the Keys. The USEPA Water Quality Assurance

Program is an attempt to collect long-term ecosystem

data on water quality, seagrass beds, and coral reefs,

which will be used to drive ecosystem management.

Figure 2, Carysfort Reef, Key Largo, FL. This composite of remote

sensing and underwater photography reveals changes that occurred

between 1975 and 2000, when the reef lost over 90 percent of its

living coral cover. A) NAPP high altitude aerial photograph showing

general reef features. Shallow areas (bright yellow and brown) are

reef flat, and the seaward edge of the shallow reef. The lighthouse

and its shadow can be seen above and to the left of the arrow tip.

The arrow points to the A. palmata zone which can be seen

underwater in B, a pair of photographs taken from the same vantage

point ten years apart. C) Three-dimensional visualization of reef

change with temporal texture as the vertical axis. Colors are true

colors from TM image. Current living coral cover is below 5 percent

(Dustan et al. 2001).
42

Since the inception of the Program in 1995, the reefs

have lost at least 38 percent of their living coral cover

and little has been done to ameliorate the situation. The

US Coral Reef Task Force, originated by Presidential

Order 13089, has produced an Action Plan for Coral

Reef Conservation, but the plan has not been

implemented. Ecological forecasts of catastrophic

reef decline have been accurate, but policy-makers

and governments have not responded with appropriate actions.

Next Steps

Reefs of the Florida Keys will take hundreds of

years to regrow, once their environment has been

restored. Reefs have interconnected symbioses and

trophic interactions because they evolved in

energy-rich, nutrient-poor waters. These very same

adaptations make reefs vulnerable to degradation.

Ironically, the many values of coral reefs – as a

fisheries resource, for coastal protection and building

materials, and as tourist attractions – now are

contributing to their steady and rapid decline.

As clearly as observational science has demonstrated

this, we still lack knowledge concerning the specific

factors involved and the magnitude of their relative

contributions. This will require detailed long-term

ecological monitoring of marine and terrestrial communities and physical processes. These tasks are greatly

complicated by the geography of the Keys which places

them at the convergence of the South Florida hydroscape and waters exiting the Caribbean Sea and Gulf

of Mexico through the Straits of Florida.

The capability to partition the stress will require a strong

commitment to basic ecological research on understanding underlying processes and ecosystem response

to change, and information modeling. The research and

management communities must increase their levels

of collaboration and cooperation to identify knowledge

gaps and support decision-making processes.

Conclusion

The decline of the Florida Keys coral reefs has been

driven by a wide variety of diffuse stressors applied

over a wide range of multiple scales. There is no one

major cause, and so there is no magic ‘silver bullet’ to

correct the situation. Federal and state efforts have been

aimed at regulating harvesting and physical destruction

while water quality issues encompassing sewage

treatment and watershed runoff have been largely

ignored owing to the magnitude of the problems.

Future research needs to be targeted at nested scale

process studies. At the same time, application of

conservation methods that do work cannot be delayed.

Measures such as marine protected no-take zones, tertiary sewage, and other aspects of watershed conservation can and should be implemented immediately, or it

will be said that the coral reefs of the Florida Keys

disappeared on our watch, while we were watching.

Acknowledgements

Discussions with numerous colleagues helped me

formulate this communication including Gene Shinn,

Rick Schwabacher, Ouida Meier, John Fauth, Jim

Porter, Dennis Slice, John and Judy Halas, and Vladimir

Kosmynin. Portions of this research were funded by

USEPA, NOAA, and the Jeanette and Lafayette

Montgomery Foundation.

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45

Introduction

It has long been recognized that a certain combination

of physical, chemical, and biotic factors may lead to

the development and persistence of biological events.

For example, a specific algal bloom may form if a ‘seed’

population exists in a water column when environmental

conditions, such as light level and nutrient concentrations,

become favorable for that species. Accordingly, if the

abiotic and biotic conditions necessary for the growth

and maintenance of an organism, i.e., its habitat, are

adequately defined, one should be able to predict the

potential occurrence of an organism if one could predict

the relevant conditions. This is the underlying basis

for ‘gap analysis’, a technique employed by the terrestrial

and aquatic research communities to estimate the

potential distribution of a species by locating the geographic

regions where the environmental conditions match the

habitat requirements of the species (e.g., Bushing, 1997;

Jennings, 2000; Zacharias and Howes, 1998).

Recent advances in technology and telecommunications

permit us to monitor numerous environmental factors

in real- or near-real time and some can even be

forecasted; data can be retrieved from geographic

databases, derived from satellites, accessed from insitu deployed instruments, and simulated by numerical

models. Although we still have a considerable way to

go to predict biotic events in the future, the current

capability of retrieving and simulating environmental

information with little or no time delay offers the potential

to locate and predict the presence of organisms in nearreal time, i.e., generate a ‘nowcast’ of the species

abundance in time and space, provided that their habitat

can be defined. This approach is similar to that of gap

analysis, except near-real time data are employed. To

1

 NOAA / NESDIS Office of Research & Applications, Camp Springs, MD

2

 Horn Point Laboratory, University of Maryland Center of Environmental Science, Cambridge, MD

3

 NOAA / NOS Coast Survey Development Laboratory, Silver Spring, MD

4

 Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT

5

 Shannon Point Marine Center, Western Washington University, Anacortes, WA

6 Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, VA

Nowcasting Sea Nettle Distributions

in the Chesapeake Bay: An Overview

C. W. Brown1, R. R. Hood2, T. F. Gross3, Z. Li1,3, M. B. Decker4, J. E. Purcell5, and H. V. Wang6

Abstract

Sea nettles (Chrysaora quinquecirrha), a stinging jellyfish, become abundant every summer in Chesapeake Bay

and interfere with recreational and commercial activities. The adverse impact of their occurrence may be mitigated

if their presence could be monitored and predicted. We have developed a procedure that produces weekly maps

or ‘nowcasts’ of the likelihood of encountering sea nettles in the bay by flagging the geographic locations

where ambient conditions coincide with their preferred salinity and temperature ranges. Estimates of near-real

time temperature and salinity are derived from a hydrodynamic model of the Chesapeake Bay and satellite

imagery, and the empirical habitat model relating these two environmental variables to sea nettle presence was

developed using in-situ data. These maps, as well as general information about sea nettles, are staged on a web

site (http://coastwatch.noaa.gov/seanettles) for dissemination. This project represents an initial step to develop

an approach that combines data derived from disparate sources, such as numerical circulation models,

operational satellites, and moored sensor systems, in order to generate nowcasts and forecasts of the distribution

pattern of marine biota.
46

evaluate the viability of this ecological approach, we

have developed a prototype system to nowcast the

distribution of sea nettles (C. quinquecirrha), a species

of stinging jellyfish, in the Chesapeake Bay.

Case Study

Sea nettles (C. quinquecirrha), a species of stinging

scyphomedusan jellyfish, become abundant every

summer in the Chesapeake Bay. From April through

early summer, when temperature and salinity are

favorable, the bottom-dwelling polyps bud off or

strobilate into immature medusae, called ephyrae,

which measure approximately 0.1 cm in diameter

(Figure 1). The ephyrae grow rapidly into medusae,

the adult form typically called jellyfish. To complete

their life cycle, the adult nettles shed their gametes into

the water and their fertilized eggs form larvae that

attach to hard surfaces such as oyster shells, where they

grow into tiny polyps that develop tentacles. The polyps

over winter in a dormant state attached to the bottom

(Bryant and Pennock 1998; Cargo and Schultz 1966;

Cargo and Schultz 1967).

Sea nettles are biologically important in Chesapeake

Bay and they interfere with recreational activities.Their

presence deters swimming and other water activities

because contact with their stinging tentacles is painful

and may induce allergic reactions. Sea nettles are also

voracious predators, devouring copepods, fish eggs and

larvae, and comb jellies. This predation affects food

web dynamics and energy flow at several trophic levels

(Baird and Ulanowicz 1989) and may impact finfish

production in the Bay (Purcell and Arai 2000). The

adverse impact of sea nettles may be mitigated if their

presence can be monitored and predicted in near real

time.

We have developed a procedure to nowcast the

distribution pattern of C. quiquecirrha in the Chesapeake

Bay by exploiting our knowledge of their temperature

and salinity preferences and our ability to acquire these

two environmental variables in near-real time.

Specifically, a habitat model relating sea nettle presence

to sea-surface temperature and salinity is applied to

estimates of ambient temperature and salinity derived

from a hydrodynamic model of the Chesapeake Bay

and satellite imagery. Weekly maps illustrating the

likelihood of encountering sea nettles are generated by

flagging the geographic locations where ambient

conditions coincide with their preferred salinity and

temperature ranges. The most current nowcast, as well

as information describing sea nettle life history and

the procedure used in creating the maps, are staged on

a web site (NOAA 2003) for dissemination to the public

and interested agencies.

Salinity – The environmental variables

used in the nettle nowcast system are

derived from existing data sources and a

modified version of a well-validated

hydrodynamic model. The distribution of

surface salinity in the Chesapeake Bay is

simulated by using a near-real time version

of the Curvilinear Hydrodynamics in 3

Dimensions (CH3D) model. CH3D was

originally developed by Sheng (1987) and

later extensively modified at the US Army

Corps of Engineers Waterways Experiment Station for the Chesapeake Bay

(Johnson et al. 1991, Wang and Johnson

2002). It is a curvilinear, finite difference

Z-coordinate model and is currently used,

in combination with a water quality model,

by the EPA Chesapeake Bay Program to

study the potential benefits of proposed nutrient loading

reductions. Our version of CH3D was modified to run

using near-real time forcing and is unique in that it

allows us to simulate ambient salinity in an estuary.

The error in the simulated salinity is estimated from

coincident in-situ salinity measurements to be 2-3 in

the Practical Salinity Scale (PSS) and it tends to be

greater in the tributaries than the mainstem of the Bay.

Figure 1. Life cycle of sea nettles, Chrysaora quinquecirrha. From T.L Bryant and

J.R. Pennock 1998.
47

Sea-Surface Temperature – The sea-surface temperature (SST) can be estimated by using either our nearreal time version of the CH3D hydrodynamic model

or derived from data acquired from the NOAA

Advanced Very High Resolution Radiometer (AVHRR)

satellites. We currently generate nowcasts of sea nettle

distributions using model generated SST with an

estimated error of 2-3 o

C (Li et al. 2002).

Although not yet fully implemented, we are developing

the ability to utilize more accurate SST of Chesapeake

Bay derived from AVHRR data processed and distributed by the NOAA CoastWatch Program. AVHRR data

are acquired with a wide-field scanning system that

enables one to two images per day with a ground

resolution of 1.1 km at nadir. A weekly composite of

SST is formed and georeferenced to the CH3D derived

salinities using a Geographic Information System. The

composite is created by selecting for maximum

temperature values in daily images estimated using the

NOAA N(on) L(inear) SST daytime and nighttime

split-window algorithm to eliminate clouds. Coast

Watch NLSST from this region possesses a positive

bias of up to 0.4 o

C and standard deviation of 1.0 oC

(Li et al. 2001).

Habitat Model – Though salinity and temperature were

known to be important physical factors influencing the

presence of Chrysaora in the Chesapeake Bay, a

quantitative relationship between them on a Bay-side

scale did not exist. We developed a bay-wide model to

predict the probability of encountering sea nettles and

their density in surface waters of the Chesapeake Bay

by analyzing coincident data of Chrysaora density,

salinity and sea-surface temperature collected during

the spring, summer and fall of 1987-2000 in surface

waters (0-10 m) of the bay and selected tributaries (n =

1064). In-situ nettle density was measured using a 1-

m2

, 280 mm mesh Tucker trawl with an attached flowthrough meter.

Concentrations of Chrysaora were found within a

relatively narrow, well-defined range of temperature

and salinity (26-30 o

C and 10-16 PSU). The in-situ data

were used to derive an empirical (“logit”) model that

estimates the likelihood of sea nettle encounter (pnettles)

as a function of salinity and temperature (Figure 2):

pnettles = elogit / [elogit + 1],

where logit = -6.995 + (0.30 * temperature) – (0.469 *salinityopt),

temperature is in degrees celsius, and salinityopt = 13.5 PSS.

A Type 1 Long-likelihood Ratio Test with 2 degrees of

freedom comparing the full model (including temperature and salinity) to a reduced model with neither

of the factors indicated temperature and salinity have

a significant effect on the observed frequency of

encountering sea nettles (LR Test stataistic = 475.8,

p < 0.001).

Validation Activities – Validation of nettle nowcasts

has been initiated but remains rudimentary at this stage;

our preliminary nowcasts began in May 2001. Comparison of retrospective Chrysaora ‘nowcasts’ (Figure 3A,

3B) with coincident in-situ measure-ments (Figure 3D,

3E) indicate our model predictions performed well on

a bay wide scale, replicating the relative downstreamupstream position of nettles in the Chesapeake Bay

during the summer of 1996 and 1999. On a finer scale,

however, the nowcasts fail to predict lateral variations

in sea nettle distributions in the mainstem of the Bay,

which suggests that other factors, such as wind and

surface convergences, strongly influence distributions

of nettles in surface waters.

Lessons Learned

Validation is essential in establishing the accuracy of

the generated nowcasts. Paramount in this verification

activity is the availability of accurate in-situ observations for comparison against the model predictions.

Accurate observations from throughout the Chesapeake

Bay, however, have proven difficult to acquire. More

effort should have been devoted to this aspect of the

project from the beginning.

Figure 2. Graphical representation of the logistical

equation describing the probability of encountering

sea nettles, Chrysaora quinquecirrha, in the surface

waters of the Chesapeake Bay as a function of salilnity

and sea-surface temperature.
48

Next Steps

We will continue to evaluate the accuracy of nowcasts

by comparing them against additional in-situ observations of C. quinquecirrha collected at locations

throughout the Bay and obtained from numerous

sources, such as the Alliance for Chesapeake Bay. We

also plan to analytically estimate the total error of the

nowcasting procedure and the relative influence of its

individual components by performing a Monte Carlo

simulation and other appropriate analyses. Future

advances in modeling salinity (and temperature) in the

Bay, particularly in the tributaries, should considerably

improve our nowcasting ability.

Once the sources of error in the model are identified

and quantified, the model can also used to better

understand the spatial and temporal variability of this

currently under-sampled species. For example, the interannual variability of sea nettle distributions throughout

the Chesapeake Bay can be examined by hindcasting

the salinity and temperature fields with CH3D and

applying our sea nettle habitat model as illustrated for

similar dates during July 1996, 1999, and 2001 in

Figure 3 (3A, 3B, 3C). In 1996, an extremely wet year,

our model predicts that nettles should have been located

principally in the lower reaches of the Bay’s mainstem.

While in 1999 and 2001, more typical years for rainfall,

our model predicts that nettles were positioned further

upstream in both the mainstem and several tributaries.

Conclusion

This project represents an initial step towards

developing an approach that combines near-real time

data derived from disparate sources, such as numerical

circulation models, operational satellites, and moored

sensor systems, to generate nowcasts and forecasts of

the distribution patterns of marine biota. Sea nettles

provide an ideal prototype because their distribution is

tightly constrained by two physical factors (temperature

and salinity) that are relatively easy to measure and

predict in near-real time.

Figure 3. The probability of encountering sea nettles, Chrysaora quinquecirrha,

in the surface waters of the Chesapeake

Bay on July 25, 1996 (A), July 1, 1999

(B), and July 26, 2001 (C), and in-situ

observations from the first two nowcast

periods (D, E). The July 1, 1999 nowcast

(3B) is similar in distribution to the

nowcast for July 29, 1999 (not shown).

In-situ observations were collected by

the Trophic Interactions in Estuarine

Systems program.
49

In theory, this approach can be applied to any organism,

but many obstacles and challenges remain. For

example, the habitat of other target species, such as

harmful algae, will likely be much more difficult to

define than that of sea nettles. Moreover, we cannot

routinely measure and estimate key chemical and

biological factors, such as some nutrient concentrations

and food availability, which will be needed to define

the habitat of many species. Nonetheless, we believe

the approach described here, in conjunction with new

technological capabilities, is a powerful tool that holds

great promise for predicting the distribution pattern of

organisms in the ocean.

Acknowledgements

Funding has been provided by a NOAA Ocean Remote

Sensing grant to C.W.B. and a Maryland Sea Grant

Project Development grant to R.R.H.

References

Baird, D. and R.E. Ulanowicz. 1989. The seasonal dynamics

of the Chesapeake Bay ecosystem. Ecological

Monographs 59:329-364.

Bryant, T.L. and J.R. Pennock. 1998. The Delaware Estuary:

Rediscovering a Forgotten Resource. University of

Delaware Sea Grant College Program, Newark, DE. 144

p.

Bushing, W.W. 1997. GIS-based Ggp analysis of an existing

marine reserve network around Santa Catalina Island.

Marine Geodesy 20:205-234.

Cargo, D.G. and L.P. Schultz. 1966. Notes on the biology

of the Sea Nettle, Chrysaora quinquecirrha, in Chesapeake Bay. Chesapeake Sci. 7:95-100.

Cargo, D.G. and L.P. Schultz. 1967. Further Observations

on the biology of the sea nettle and jellyfishes in the

Chesapeake Bay. Chesapeake Sci. 8:209-220.

Jennings, M.D. 2000. Gap analysis: concepts, methods, and

recent results. Landscape Ecology 15:5-20.

Johnson, B.H., K.W. Kim, R.E. Heath and H.L. Butler. 1991.

Development and verification of a three-dimensional

numerical hydrodynamic, salinity, and temperature

model of Chesapeake Bay. US Army Engineering

Waterways Experimental Station. Technical Report HL01-7, Vicksburg, MS. 44 p.

Kidwell, K. 1997. NOAA Polar Orbiter data users guide.

Washington, DC: Department of Commerce, NOAA/

NESDIS, Washington, DC.

Li, X., W. Pichel, P. Clemente-Colôn, V. Krasnopolsky and

J. Sapper. 2001. Validation of coastal sea and lake surface temperature measurements derived from NOAA/

AVHRR Data. International Journal of Remote Sensing

22: 1285-1303.

Li, Z., T.F. Gross, C.W. Brown, H.V. Wang, and R.R. Hood.

2002. A near real time simulation of salinity, temperature

and Sea Nettles (Chrysaora quinquecirrha) in

Chesapeake Bay. In: M.L. Spaulding (ed.) Proceedings

of the 7th International Conference of Estuarine and

Coastal Modeling, St. Petersburg, FL, 5-7 November

2001. American Society of Civil Engineers. pp. 467-

478.

NOAA 2003. http://coastwatch.noaa.gov/seanettles

Purcell, J.E. and M.N. Arai. 2001. Interactions of pelagic

cnidarians and ctenophores with fish: a review.

Hydrobiologica 451 (Dev. Hydrobiol. 155):27-44.

Sheng, Y.P. 1987. On modeling three-dimensional estuarine

and marine hydrodynamics. In: by J.C.J. Nihoul and

B.M. Jamart (eds.). Three-Dimensional Models of

Marine and Estuarine Dynamics. pp. 35-54, Amsterdam:

Elsevier.

Wang, H.V. and B.H. Johnson. 2002. Validation and application of the second generation three dimensional hydrodynamic model of Chesapeake Bay. Journal of Water

Quality and Ecosystem Modeling 1: 59-90.

Zacharias, M.A. andD.E. Howes. 1998. An analysis of

marine protected areas in British Columbia, Canada,

using a marine ecological classification. Natural Areas

Journal 18: 4-13.
50
51

Introduction

Blooms of the toxic dinoflagellate Karenia brevis have

several significant impacts on public health, wildlife,

and tourism in the Gulf of Mexico (Tester and

Steidinger, 1997). The organism is the cause of neurotoxic shellfish poisoning (NSP) in shellfish, leading to

closure of shellfish beds at 5 cells ml-1. At medium to

high concentrations (> 100 cells ml-1), the organism can

cause fish kills and lead to death of marine mammals,

including the endangered manatee. Airborne toxins can

cause human respiratory distress as well. Because the

blooms reappear nearly every year in the Gulf, routine

monitoring and prediction are essential for public health

and for anticipating wildlife response.

Case Study

Management response to harmful algal blooms (HABs)

of Karenia brevis in the Gulf of Mexico requires four

types of forecasts: 1) monitoring movement of

previously identified HABs; 2) distinguishing new

blooms as HAB or non-HAB; 3) forecasting the location

of a HAB since the last known position; and 4) predicting conditions favorable for initiation of a new HAB.

Beginning in 1999, bulletins have provided these types

of forecast information to the states (Stumpf et al. 2003).

The bulletins are based on the integration of several

data sources: ocean color imagery from the SeaWiFSOrbView-II satellite purchased and processed for

coastal waters by NOAA CoastWatch; wind data from

coastal meteorological stations; field observations of

bloom location and intensity provided by the states of

Florida and Texas; and forecasts from the National

Weather Service. The bulletins began with bloom monitoring (type 1) and limited advisories on transport (type 3).

In autumn 2000, the bulletins had improved forecasts

and included the first attempts to predict conditions

favorable for bloom development in Florida (type 4).

In 2002, features considered to be to potential HABs

were flagged.

Figure 1 shows an example bulletin from September

2001. The bulletin includes several parts: 1) an image

of chlorophyll concentration for the Gulf of Mexico

with chlorophyll processed with a Gulf of Mexico algorithm (Stumpf et al. 2000) (in the example bulletin,

the bloom can be found where chlorophyll is >2 μg L-1

in the box), 2) a plot of wind from an appropriate locaForecasting Harmful Algal Blooms in the Gulf of Mexico

R.P. Stumpf1 and M.A. Culver2

Abstract

To respond to harmful algal blooms (HABs) in the Gulf of Mexico, managers require four types of forecasts: 1) monitoring the extent of identified HABs, 2) distinguishing new blooms as HAB or non-HAB, 3) forecasting the

location of a HAB since the last known position; and 4) predicting conditions favorable for initiation of a new

HAB. Beginning in 1999, we have provided the Gulf states with bulletins with the objective of providing these

types of forecasts. The bulletins started with satellite ocean color imagery from SeaWiFS/OrbView-II showing

chlorophyll in response to HAB reports from the states (type 1). In 2000, wind data from coastal meteorology

and forecasts from the National Weather Service were added to indicate direction of bloom movement (type 3).

HAB detection algorithms (type 2) have been incorporated into the analysis in 2001, and added to the bulletins

in 2002. The next addition will use models to predict events (type 4). The bulletins have provided early warning

of some events and provided guidance to sampling of blooms when they have spread. The results help the

understanding of the distribution of these blooms, particularly along the Florida coast.

1

 NOAA National Ocean Service, Center for Coastal Monitoring and Assessment, Silver Spring, MD, USA

2

 NOAA Coastal Services Center, Charleston, SC, USA
52

tion (in the example, Venice Pier is at the coast where

the top of the box intersects the coast), 3) discussion of

relevant wind conditions (below the wind plots), and

4) interpretation of conditions (at the upper left).

Type 1 Forecasts: Monitoring Known Blooms – are

conducted primarily through observations of chlorophyll concentration each 100 cell ml-1 of K. brevis has

approximately 1 og L-1 of chlorophyll (Tester and

Steidinger 1997). With background chlorophyll along

the shelf generally <1 mg, some estimates of concentration and bloom edge can be made through the chlorophyll imagery (see chlorophyll image in Figure 1).

Type 2: Identifying Where High Chlorophyll Concentrations May Be HABs – involves a new method

proposed by Stumpf (2001) from work done by Thomas (2000). The bloom is identified as an anomaly in

the chlorophyll field (Figure 2). This capability also

has potential for monitoring the blooms, and has been

implementing in bulletins starting in late 2002.

Type 3: Forecasting Transport – draws on imagery

and field observations together with observed and predicted winds to forecast along-coast movement. Tester

et al. (1991) showed that wind plays a significant role

in redistribution of K. brevis blooms. Assessing the

along-coast wind transport can provide the forecast of

the current position from the last known position.

Type 4: Predicting Future Blooms – draws on

climatologic information and current winds. Stumpf et

al. (1998) found HABs were correlated with upwellingfavorable winds. The late summer and autumn winds

are monitored for these conditions and are noted in the

bulletins.

The bulletins have provided information on the extent

of blooms, particularly offshore areas where sampling

is problematic. The state of Florida was correctly advised of the potential for a bloom to occur at the end of

September, 2001 (forecasting type 4) and the state was

alerted to the position of previously unknown blooms

Figure 1. Example HAB forecasting report for the Gulf of Mexico. The image shows chlorophyll concentragions in the Gulf, and the wind

speed and the discussion below it indicate whether conditions are favorable for developing a bloom. This is summarized in the discussion

on the left.
53

in January 2000 and October 2000 (forecasting type

2). Monitoring has continued, including estimates of

direction and distance of transport. The combination

of warning and rapid detection is a significant aid to

the states in responding to these blooms.

Lessons Learned

The research aspects have developed in response to the

need for information. Results and observations from monitoring blooms have been interpreted to develop the new

techniques. From the beginning, it was known this program would be successful only with clear and direct

communication with those locally responsible for field

monitoring programs. Direct communication assured the

necessary information was available, and the field data

essential to analysis and interpretation of imagery was

obtained promptly. Analyzing data during blooms produces insights that lead to improved forecasts. For example,

the HAB detection algorithm was developed from interpretations of the imagery made during blooms in 1999.

Forecasting HABs is analogous to hurricane forecasting. For hurricane forecasts, the models and synthesized data give reasonable results for movement or

strength. But ultimately a forecaster must provide guidance to the users on what results should be considered.

Models used in HAB forecasting will be improved and

expanded, reducing the amount of interpretation required. But some expert interpretation will always be

needed because of the complexity of HABs and the

data sets used. Training analysts to interpret HAB data

should not be difficult, and bulletins do not need to be

issued more than 2-3 times per week. This reduces the

number of analysts needed.

Conclusion/Next Steps

The HAB bulletins provide additional information to

the Gulf states, particularly Florida, for monitoring

HABs. Evaluation of events along the Texas coast is

needed, as well as evaluation of existing models and

new analytical capabilities for incorporation into the

bulletins. In addition, a strategy for training analysts

must be developed, so the bulletins can move from experimental (although near-real time) to a demonstration

phase, and ultimately to an operational program.

Acknowledgments

Imagery is processed by the NOAA CoastWatch program, headed by Kent Hughes. Personnel implementing various aspects of the processing are: Michael

Soracco, Ramesh Sinha, Heng Gu, and Varis

Ransibrahmanakul. The HAB-Bulletins are generated

through semi-automated procedures by efforts of Kirk

Waters and Andrew Meredith at NOAA Coastal Services Center. Communication with Earnest Truby of

the Florida Marine Research Institute is essential to the

value of the bulletins in Florida.

References

Stumpf, R.P. 2001. Applications of satellite ocean color

sensors for monitoring and predicting harmful algal blooms.

Journal of Human Ecology and Risk Assessment, Vol.

7, pp. 1363-1368.

Stumpf R.P., V. Ransibrahmanakul, and KA. Steidinger.

1998. Observations of sea surface temperature and

winds in association with Florida, USA red tides

(Gymnodinium breve blooms). In: B. Reguera, J.

Blanco, M. Fernandez, et al. (eds.) Harmful Algae,

Proceedings of the VIII International Conference on

Harmful Algae. Xunta de Galicia and IOC of UNESCO,

Paris, France, pp. 145-148.

Stumpf, R.P. Arnone, R.A. R.W. Gould, P. Martinolich, V.

Ransibrahmanakul, P.A. Tester, R.G. Steward, A.

Subramaniam, M. Culver, J.R. Pennock. 2000. SeaWiFS

ocean color data for US southeast coastal waters.

Proceedings of the Sixth International Conference on

Remote Sensing for Marine and Coastal Environments.

Charleston SC. Veridian ERIM Intl. Ann Arbor MI,

USA, p. 25-27.

Stumpf, R.P., M. Culver, P.A. Tester, S. Varnum, G. J.

Kirkpatrick, B. Peterson, M. Tomlinson, E. Truby, V.

Ransibrahmanakul. 2003. Monitoring Karenia brevis

blooms in the Gulf of Mexico using satellite ocean color

imagery and other data. Harmful Algae. Vol. 2. pp. 147-160.

Figure 2. Identifying a HAB from an anomaly

in the chlorophyll field. The red area is a probable

bloom.
54

Tester PA and K.A. Steidinger. 1997. Gymnodinium breve

red tide blooms: initiation, transport, and consequences

of surface circulation. Limnol. and Oceanog. Vol. 42,

No. 5, pp. 1039-51.

Tester, P.A., R.P. Stumpf, F.M. Vukovich, P.K. Fowler and

J.T. Turner. 1991. An expatriate red tide bloom:

transport, distribution, and persistence. Limnol. and

Oceanog., Vol. 36, pp. 1053-1061.

Thomas, D.G. 2000. The use of SeaWiFS-derived bio-optical

properties to characterize harmful algal blooms. M.S.

Thesis, University of Southern Mississippi. Ocean

Springs, MS. 111 pp.
55

The Challenges of Forecasting and Managing Toxigenic

Pseudo-nitzschia Blooms on the US West Coast

Vera L. Trainer1 and Barbara M. Hickey2

Abstract

The transfer of the toxin, domoic acid, to shellfish, crustaceans, seabirds, finfish and marine mammals has been

recently documented on the US west coast. Data collected during west coast cruises in 1997-2001 indicate that

often the highest toxin levels and greatest numbers of toxic cells are positioned in water masses associated with

offshore eddies or in upwelling zones near coastal promontories. Such cruise data are essential in the characterization of offshore initiation sites that will lead to the effective placement of automated sensors such as moored

arrays.

In addition, beach monitoring is a necessary component of regional species characterization, resulting in the

development of specific molecular and biochemical tools needed to assist managers in each coastal area.

Indeed, beach samples collected in 1998 indicated that a P. pseudodelicatissima bloom was responsible for

razor clam toxicity on the Washington coast, whereas toxin produced by P. australisresulted in sea lion mortalities

in central California. Complete characterization of physical, biological and chemical conditions that favor

harmful Pseudo-nitzschia blooms, only possible through large-scale, synergistic collaboration, is a prerequisite

for forecasting these events. A forecasting capability will substantially improve the management of valuable

coastal resources and the protection of human health, both of which are affected by these toxins.

1 National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northwest Fisheries Science Center, Marine Biotoxin Program, 2725 Montlake Blvd. E., Seattle, Washington 98112

2

 School of Oceanography, University of Washington, Box 357940, Seattle, Washington 98195

Introduction

While domoic acid (DA) poisoning was first recognized

in an outbreak on Prince Edward Island, Canada (Wright

et al. 1989), most of the known toxic events since then

have occurred on the US west coast. Domoic acid, a neurotoxin produced by species of the genus Pseudo-nitzschia

(PN), was first implicated in the illness and death of

brown pelicans and Brandt’s cormorants in Monterey

Bay, California in 1991 (Work et al. 1993). About one

month following the toxic bloom in California, levels

of DA above the regulatory limit of 20 μg/g in shellfish

tissue were found in edible parts of razor clams (Siliqua

patula) and Dungeness crabs (Cancer magister) on the

Washington coast (Wekell et al. 1994). In 1998, impacts of DA to the health of marine life and to the fisheries economy were documented in several regions

along the west coast. In particular, California sea lions

(Zalophus californianus) in central California were

severely affected by DA poisoning (Scholin et al. 2000)

and high levels of toxin in razor clams in Oregon and

Washington resulted in beach closures for more than a

year and a half (Adams et al. 2000, Trainer et al. 2001).

Beach and harvest closures resulting from the toxigenic

PN blooms have a severe impact on both coastal economies

and on tribal communities. In 1991, the closure of Washington State beaches to recreational and commercial shellfish

harvesting resulted in a $15-20 million revenue loss to

local fishing communities (Horner and Postel 1993,

Anderson 1995). The commercial Dungeness crab industry on which Washington’s Quileute tribe depends

for employment lost 50% of their income in 1998 due to

harvest closures (Trainer and Wekell 2000). The entire razor

clam harvest of the Quinault tribe, on which they depend

for both subsistence and commercial revenue, was also

lost in the fall of 1998 (Trainer and Wekell 2000). With

sufficient warning of an incoming bloom, tribal fishers could

seek alternative buyers for eviscerated crab, and shellfish

managers might have longer lead times to schedule closures.
56

Case Study

Physical Oceanography and Transport Processes –

In contrast to most US east coast environments, the US

west coast shelf is relatively narrow, so nutrient-rich

deeper water can be effectively brought to the surface

by wind-driven upwelling that occurs in the growing

season along the entire coastal boundary (Hickey 1989,

Landry et al, 1989). In contrast to most east coast coastal

areas, nutrient input from coastal rivers is negligible

except in the associated estuary and right at the river

mouth. Wind-driven upwelling of nutrients from deeper

water layers fuels coastal productivity, resulting in both

a strong seasonal cycle and several day fluctuations that

mimic changes in the wind direction and, hence, upwelling. During an upwelling, phytoplankton respond to the

infusion of nutrients near the coast, and this ‘bloom’ is

moved offshore, continuing to grow while depleting the

nutrient supply. When winds reverse (usually during

storms), the bloom moves back toward shore where it

can contact the coast or enter coastal estuaries.

In contrast to the east coast, alongshore topography

of the coastline is relatively straight and wind systems

are large scale. Thus currents and water properties

(e.g., temperature, stratification, etc.) are similar

over relatively large distances (>500 km) along the

coast and are reasonably predictable, given information about the alongshore structure of the coastal wind

field (Battisti and Hickey 1984).

Although the west coast topography and forcing are

generally large scale, several coastal promontories and

offshore banks and other topographic features exist. Such features may be particularly important to toxic

PN growth and/or retention (Figure 1). For example, in

regions where large coastal promontories occur, phytoplankton are swept offshore and southward by the

meandering jets and/or eddies that form where the

coastal jets detach from the shelf. These blooms rarely

return to the coast. On the other hand, in regions where

banks and more complex mesoscale topography occur,

retention is more likely. Under certain wind conditions,

phytoplankton in these retention areas can return to the

coast, and if toxic, affect organisms on beaches and

in estuaries.

The Juan de Fuca Eddy, a Retentive Feature off

the Northern Washington Coast – The counterclockwise cold eddy off the Strait of Juan de Fuca

(also called the “Tully” eddy, Tully 1942) is located

southwest of Vancouver Island and offshore of northern Washington. The eddy, with a diameter of about

50 km, forms in spring and declines in fall (Freeland

and Denman, 1982), a result of the interaction between

effluent from the Strait, southward wind-driven currents

along the continental slope, and the underlying topography, a spur of the Juan de Fuca submarine canyon. A

dominant feature of circulation patterns off the northern

Washington coast, it is visible in summertime satellite

imagery as a relative minimum in sea surface temperature

and generally a relative maximum in chlorophyll a.

A connection between the eddy and the Washington

coast was demonstrated in July 1991, when oil spilled

in the eddy was found on the Washington coast 6 days

later (Venkatesh and Crawford, 1993). This suggests that

under certain ocean conditions, phytoplankton residing

in surface waters of the Juan de Fuca eddy can impact

the Washington coast.

Figure 1. Particulate DA in PN species on the US West Coast in 1998.

Maximum concentrations of DA and toxic species are indicated to the

right of each area of high toxin. Each of these areas is associated with

relatively retentive circulation patterns. (adapted from Trainer et al.

2001)
57

Sources of Toxic PN to the Washington Coast – Data

collected on ships of opportunity in recent years show

that the Juan de Fuca eddy usually contains much higher

concentrations of particulate DA than elsewhere on the

Washington coast (e.g., Figure 1). High levels of particulate DA are usually associated with high cell counts

of PN (generally P. pseudodelicatissima). In extensive

surveys in 1997 and 1998

(Horner et al. 2000), the

highest concentrations of

PN cells associated with

the Juan de Fuca eddy were

measured in the upper 5

m (Figure 2).

All measurements made

during cruises and beach

sampling of seawater and

shellfish in 1997 and 1998

are consistent with the possibility that during some

years, DA from this eddy

appears to move southward

in prolonged upwelling

events and then onshore

during the first major storm of the fall season, resulting

in high levels of DA in razor clams on coastal beaches.

Such events result in immediate closure of coastal clamming

beaches, often for the entire season (or longer, due to

the slow depuration of DA from razor clam tissue).

Collaborations and Tools Instrumental in Developing Reliable Harmful Algal Bloom Forecasts – The

impacts of DA on Washington coastal communities are

currently being studied in a 5-year, multi-agency, multidisciplinary program by the Olympic Region Harmful

Algal Bloom (ORHAB) project, supported by NOAA’s

Coastal Ocean Program. The goals of this monitoring

project are to 1) investigate the origins of open-coast

toxic blooms, 2) monitor where and when the toxic

species are present on the coast, 3) assess the environmental

conditions under which blooms occur and are transported

to intertidal shellfish populations, and 4) explore methods

that can be used to forecast harmful algal blooms

(HABs). The monitoring sites (Figure 3) were chosen

because they have harvestable razor clam or mussel

populations.

The ORHAB group is a forum for collaboration and

cooperation among Federal, state, and local government

agencies, academic institutions, coastal Native tribes,

marine resource-based businesses, and public interest

groups. Its mission is to support applied and basic research on HABs and to build a greater local capacity to

monitor and mitigate the effects of such events. The

ultimate goal is to sustain a long-term monitoring project

into the future without reliance on Federal support.

The US west coast HAB forecasting capability is in its

early stages. Information to used in developing an effective forecast includes the following.

• Dominant processes are large scale and can be ‘sensed’

over long distances.

• Upwelling is the dominant nutrient source to the US

West Coast.

• Isolated topography-related retention areas may

promote HABs. Possible HAB initiation sites, such

as the Juan de Fuca eddy, have been identified.

• DA and PN are often located in the upper 20 m of the

water column, so sensors (‘Environmental Sensing Platform,’ see the discussion in Next Steps) can be placed

near the surface to detect cells and toxins.

• Blooms move to the coast when winds relax or reverse.

• Strong collaborative West Coast partnership, namely

ORHAB, will be instrumental in providing monitoring

results that will form the cornerstone of future forecasts.

The collaborators in ORHAB will be the primary end

users of the HAB forecasts.

Next Steps

This understanding of west coast physical processes in

combination with remote sensing tools are a key to success

of this program. The ‘Environmental Sensing Platform’

(Scholin et al. 1999) is one instrument, which when

placed in likely initiation sites for HABs, will enhance

Figure 2. Typical vertical profiles of PN densities and DA concentrations in the Juan de Fuca eddy

region.
58

the US west coast forecasting of toxigenic PN blooms.

This instrument has a stand-alone function that uses genetic

techniques to record the presence and abundance of microorganisms. Seawater is collected in discrete samples.

Installed at several locations along the coast, it serves

as a sentinel for toxic algal blooms. A project to make

the instrument more user-friendly has been proposed

as a collaboration between ORHAB and the Monterey

Bay Aquarium Research Institute.

In conjunction with data streams from moored arrays containing sensors such as fluorometers, current meters,

nutrient sensors, thermistors (see the locations of ORHAB

moorings and sensors, Figure 3), as well as ocean satellite images, this autonomous instrument will be a vital

tool in providing reliable early warnings for HABs

(most likely within days). This will allow the early opening

of clamming season or will give commercial fishers time

to seek an alternative buyer (in the case of Dungeness

crab).

In the future, the US west coast will need to focus research and monitoring in the following areas, allowing

refinement of the HAB forecasting capability. Greater

accuracy and longer lead time for prediction of coastal

HABs will be achieved when the following questions

have been answered.

• In the field, what are the conditions (chemical, biological, and physical) that make PN cells toxic? A

number of lab studies have focused on aspects of this

question, but limited field studies have been done.

• What makes one location a more viable initiation site

for HABs than another? What are the environmental

factors that result in bloom initiation and propagation?

• Can a biological/physical model be configured to

effectively link the biological growth (DA production

and cell survival) and physical transport of PN cells

to coastal sites where fisheries are impacted?

Conclusion

The challenges faced on the west coast due to HABrelated mammal mortalities, widespread closures of

beaches to shellfish harvest, and human illness can only

be met by sustained beach monitoring programs such

Figure 3. ORHAB monitoring sites, including areas of beach (seawater) sampling (Neah Bay and Makah Bay by Makah

Tribe, Kalaloch and Copalis by Quinault Tribe, Twin Harbors and Long Beach by Washington Dept. of Fish and Wildlife,

Willapa Bay by Washington Dept. of Ecology and Pacific Shellfish Institute). Razor clam domoic acid analyses are done

by Washington Dept. of Health and Northwest Fisheries Science Center (NWFSC). Moorings are maintained by the

Olympic Coast National Marine Sanctuary and data are analyzed by University of Washington and NWFSC.
59

as ORHAB and dedicated research cruises. These cruises

will provide the means to answer the above questions

through the obser-vation of natural populations of PN

in situ. Complete characterization of physical, biological and chemical conditions that favor harmful PN

blooms, only possible through large-scale synergistic

collaboration, is a prerequisite for forecasting these

events. A forecasting capa-bility will substantially improve the management of valuable coastal resources and

the protection of human health, both of which are affected by toxigenic PN.

Acknowledgments

This project was partially funded by the Olympic Region

Harmful Algal Bloom (ORHAB) program that was supported by the Monitoring and Event Response of HAB

(MERHAB) under the National Centers for Coastal

Ocean Science. The authors extend their sincere thanks

to all ORHAB collaborators.

References

Adams, N.G., M. Lesoing and V.L. Trainer. 2000.

Environmental conditions associated with domoic acid

in razor clams on the Washington Coast. J. Shellfish

Res. 19:1007-1015.

Anderson, D.M. (ed.). 1995. The ecology and oceanography

of harmful algal blooms. A national research agenda.

Woods Hole Oceanographic Institution, Woods Hole,

MA. 66 pp.

Battisti, D. and B.M. Hickey. 1984. Application of remote

wind forced coastal trapped wave theory to the Oregon

and Washington coasts. Journal of Physical Oceanography 14:887-903.

Freeland, H.J. and K.L. Denman. 1982. A topographically

controlled upwelling center off Southern Vancouver

Island. J. Mar. Res. 40:1069-1093.

Hickey, B.M. 1989. Patterns and processes of shelf and slope

circulation. In: M.R. Landry and B.M. Hickey (eds.),

Coastal Oceanography of Washington and Oregon.

Elsevier Science, Amsterdam. pp. 41-115.

Horner, R.A. and J.R. Postel. 1993. Toxic diatoms in western

Washington waters (US West Coast). Hydrobiol. 269/

270:197-205.

Horner, R.A., B.M. Hickey and J.R. Postel. 2000. Pseudonitzschia blooms and physical oceanography off Washington State, USA. S. Afr. J. Mar. Sci. 22: 299-308.

Landry, M.R., J.R. Postel, W.K. Peterson and J. Newman.

1989. Broad-scale patterns in the distribution of hydrographic variables. In: M.R. Landry and B.M. Hickey

(eds.) Coastal Oceanography of Washington and

Oregon. Elsevier Press, Amsterdam. pp. 1-41.

Scholin, C.A., F. Gulland, G.J. Doucette, S. Benson, M.

Busman, F.P. Chavez, J. Cordaro, R. DeLong, A.

DeVogelaere, J. Harvey, M. Haulena, K. Lefebvre, T.

Lipscomb, S. Loscutoff, L.J. Lowenstine, R. Marin III,

P.E. Miller, W.A. McLellan, P.D.R. Moeller,DÉ.L.

Powell, T. Rowles, P. Silvagni, M. Silver, T. Spraker,

V.L. Trainer and F.M. Van Dolah. 2000. Mortality of

sea lions along the central California coast linked to a

toxic diatom bloom. Nature 403:80-84.

Scholin, C.A., G. Massion, E. Mellinger, M. Brown, D.

Wright, and D. Cline. 1999. The development and

application of molecular probes and novel instrumentation for detection of harmful algae. Ocean Community

Conference 98 Proceedings, Marine Technology

Society, Vol. 1 pp. 367-370.

Trainer, V.L. and J.C. Wekell. 2000. The cost of harmful

algal blooms on the West Coast. In: Northwest Fisheries

Science Center and Washington Sea Grant (eds.) Red

Tides Newletter. Autumn 2000. 8pp.

Trainer, V.L., N.G. Adams, and J.C. Wekell. 2001. Domoic

acid-producing Pseudo-nitzschia species off the US

west coast associated with toxification events. In: G.M.

Hallegraeff, S.I. Blackburn, C.J. Bolch, and R.J. Lewis

(eds.) Harmful Algal Blooms 2000. Intergovernmental

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48.

Tully, J.P. 1942. Surface non-tidal currents in the approaches

to Juan de Fuca strait. J. Fish. Res. Bd. Can. 5:398-409.

Venkatesh, S. and W.R. Crawford. 1993. Spread of oil from

the Tenyo Maru, off the southwest coast of Vancouver

Island. Natural Hazards 8:75-91.

Wekell, J.C., E.J. Gauglitz, Jr., H.J. Barnett, C.L. Hatfield,

D. Simons and D. Ayres. 1994. Occurrence of domoic

acid in Washington state razor clams (Siliqua patula)

during 1991-1993. Nat. Toxins 2: 197-205.

Work, T.M., B. Barr, A.M. Beale, L. Fritz, M.A. Quilliam

and J.L.C. Wright. 1993. Epidemiology of DA poisoning

in brown pelicans (Pelecanus occidentalis) and Brandt’s

cormorants (Phalacrocarax penicillatus) in California.

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Wright, J.L.C., Wright, J.L.C., R.D. Boyd, A.S.W. de Freitas,

M. Falk, R.A. Foxall, W.D. Jamieson, M.V. Laycock.

A.W. McCulloch, A.G. McInnes, P. Odense, V.P. Pathak,

M.A. Quilliam, M.A. Ragan, P.G. Sim, P. Thibault, J.A.

Walter, M. Gilgan, D.J.A. Richard and D. Dewar. 1989.

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acid, in toxic mussels from eastern Prince Edward

Island. Can J. Chem. 67: 481-490.
60
61

Introduction

Toxic or harmful algal blooms (HABs) are a serious

economic and public health problem throughout the

US and the world. In New England, the most serious

HAB issue is paralytic shellfish poisoning (PSP), a

potentially fatal illness that occurs when humans eat

shellfish that have accumulated toxins as they feed on

dinoflagellates in the genus Alexandrium (see review

by Anderson 1997). These organisms are responsible

for human illnesses and occasional death due to PSP,

repeated closures of shellfish beds in both nearshore

and offshore waters, the mortality of larval and juvenile

stages of fish and other marine animals (White et al.

1989), and even the death of marine mammals such as

humpback whales (Geraci et al. 1989).

Accurate forecasts of Alexandrium blooms in this

region would therefore have important application,

including the management of shellfish and finfish

resources, aquaculture siting, and effluent discharge.

Furthermore, there are a multitude of coastal zone

management issues that could use the hydrodynamic

forecasts on which ecological predictions of Alexandrium would be based.

Case Study

Within the western Gulf of Maine (GOM) region, which

extends from Penobscot Bay to Massachusetts Bay, a

coupling has been inferred between the abundance and

distribution of Alexandrium and a buoyant coastal current that travels from northeast to southwest. A

conceptual model of this link has been proposed, known

as the River Plume Advection Hypothesis (Franks and

Anderson 1992a,b).

Critical features of this system include: 1) a source

population of cells located to the north of Massachusetts

Bay near the Androscoggin-Kennebec estuary; 2) freshwater outflow from the Androscoggin-Kennebec

estuary resulting in a coastally-trapped, buoyant plume

that supports the growth of Alexandrium cells and

transports them to the south and west; and 3) plume

1 Woods Hole Oceanographic Institution

2

 US Geological Survey

Hindcasting Blooms of the Toxic Dinoflagellate

Alexandrium spp. in the Western Gulf of Maine

D.J. McGillicuddy, Jr.1, C.A. Stock1, D.M. Anderson1, R.P. Signell2

Abstract

Three-dimensional coupled physical-biological models have been developed to facilitate forecasting regional

Alexandrium distributions in the Gulf of Maine. The hydrodynamic basis is a primitive equation model with

advanced turbulence closure; it is forced by tides, winds, heat fluxes, and river discharge. Explicitly modeled

aspects of Alexandrium population dynamics include germination from resting cysts, vegetative growth, and

mortality.

Quantitative evaluation of model skill in a hindcast mode indicates the model is capable of simulating the

observed large-scale patterns in Alexandrium abundance to within a factor of two to three. Sensitivity analysis

suggests uncertainties in loss terms (such as respiration, predation and encystment) may be the most critical

limitation of the coupled model in its present form.

Future developments may yield a predictive model with skill sufficient to produce meaningful ecological forecasts.

However, the need for both physical and biological data streams to drive the models must not be underestimated.

An integrated observational and modeling strategy will be essential in the context of ecological forecasting.
62

behavior, as influenced by the volume of freshwater

outflow, the local wind stress, and underlying GOM

circulation. All of these combine to regulate the

alongshore and cross-shore location of the plume, its

associated cells, and PSP toxicity.

As part of Ecology and Oceanography of Harmful Algal

Blooms-Gulf of Maine (ECOHAB-GOM) and its

predecessor programs, a set of numerical models has

been developed to study these dynamics in a threedimensional context. Thus far, these coupled physical/

biological models have been used only for research

purposes. Quantitative demonstration of predictive skill

in a hindcast mode is a necessary precursor to an

operational model. The following example describes

recent efforts to assess the hindcast skill of one

model.

The hydrodynamic basis of the following coupled

physical/biological simulations is the Princeton

Ocean Model (POM; Blumberg and Mellor 1987)

with variable horizontal resolution of 2-4 km and

12 vertical levels. Vertical mixing is internally

generated from the Mellor-Yamada 2.5 turbulence

closure model. This physical model is forced by

various modeled inputs as well as land-based and

shipboard observations (Table 1). A population dynamics model for Alexandrium has been incorporated

into this physical framework.

Alexandrium has a very complex life cycle (Figure

1). It can lie dormant as a resting cyst in the sediments

for very long time periods, perhaps a decade or more

(Anderson 1984; Keafer et al. 1992). Germination

is regulated by a complex set of processes, including

an endogenous clock (Anderson and Keafer 1987)

and physiological responses to environmental factors

such as temperature (Anderson 1980), light and

oxygen availability (Anderson et al. 1987). Once

emerged from the sediments, the cells swim toward

the surface to begin a phase of vegetative growth. Figure 1: Life cycle of Alexandrium.

Modeled inputs (T, S) Bi-monthly climatological model with tides (Lynch

 et al. 1996)

Cruise data (T, S) Upstream sections and offshore stations

River discharge USGS gauges at 4 major rivers

Wind stress Uniform distribution derived from Portland Buoy

Surface heat flux Derived from temperature at Portland Buoy

Insolation From land-based sensors at Woods Hole

Table 1. Data sets used to force the circulation model.

Photosynthetic production is fundamentally limited by

light and the availability of nutrients, however, maximum growth occurs only within a specific range of

temperature and salinity. When faced with environmental stress such as nutrient limitation, the vegetative cells

form gametes that subsequently fuse into a zygote

(Anderson et al. 1984). The zygote then encysts, and

the cycle is complete.

The model for Alexandrium is

summarized in Figure 2. The

ecosystem in which Alexandrium

resides is not explicitly modeled;

ecosystem effects are parameterized through their influence on the

vital rates of Alexandrium’s

population dynamics processes.

The top panels show the various

factors that contribute to the input

of cells due to germination. On the far left is the

observed distribution of cysts, showing the peak

offshore of Casco Bay. In the middle is the functional

fit to laboratory data on the endogenous clock, shown

as open circles. On the right is the germination rate as

a function of light and temperature, based on laboratory
63

measurements (indicated by black dots). The vertical

axis actually represents light intensity but has been

scaled to depth.

The function describing vegetative growth is based on

data from Watras et al. (1982). It suggests that Alexandrium has a preferred temperature and salinity range

for optimal growth. The range of temperature and salinity conditions that Alexandrium cells encounter in the

simulations described here is indicated by the black

rectangle. The overall growth rate is based on this

function and is modulated by the ambient light field as

per Liebig’s law of the minimum (Liebig

1845). In other words, at each time step, the

lesser of the two growth rates predicted by the

temperature/salinity and light-limitation

functions is specified. Initial experiments with

simple representations of nutrient limitation

have been started, but these effects are not

included here. An upward swimming rate of

10m/day and a net ‘mortality’ of 0.1 per day

are specified. Mortality is in quotes here because it represents the net effect of all loss

processes, which include predation and encystment.

This coupled model has been used to hindcast

a data set collected in the Western Gulf of

Maine in 1993 as part of the Regional Marine

Research Program (RMRP). An example

snapshot from a hindcast simulation is shown

in Figure 3; the entire simulation is available

in animated form on the web site

(McGillicudy et al. 2002). The salinity

field shows the impact of fresh water

plumes origina-ting at the Kennebec/

Androscoggin and Merrimack rivers.

These tend to propagate down the

coast, and also undergo significant crossshore displace-ments due to the effects

of upwelling and downwelling winds.

This partic-ular simulation suggests

offshore initiation of the bloom

because the im-pact of the germination

input from the offshore maximum in

the cyst distri-bution is clearly evident.

Features in the cell distribution are

advected downstream in the coastal

current, and are also clearly impacted

by wind-driven upwelling and

downwelling.

Quantitative evaluation of hindcast accuracy requires

careful consideration of how the model results should

be compared with observations. One might think this

issue is as simple as subsampling the model solutions

at the space/time points at which data are available.

However, such an approach is problematic because the

observations contain small-scale patchiness not

resolved in the model. Plots of autocorrelation functions

for both simulated and observed fields bear this out.

Thus, it is necessary to average both the model results

and the observations on scales resolved by both.

Figure 3: Example snapshot of model output.

Figure 2: Population dynamics model for Alexandrium.
64

Therefore, nine zones in which the mean values of the

simulated and observed concentrations are defined and

compared for each cruise (Figure 4). An error metric J

quantifies the degree to which the simulated means

(sim) match the observed means (obs) for each zone

during each cruise: J = max(sim,obs)/min(sim,obs). A

perfect fit would result in a value of 1.0. This penalty

function grows larger as the simulation diverges from

the observations. A variety of error metrics is possible,

and all have various advantages and disadvantages. One

appealing aspect of this particular metric is that errors at

low concentrations are weighted just as heavily as those

at high concentrations. In summary, this approach yields

a quantitative method of evaluating the large-scale

spatial distribution, timing, and magnitude of the bloom.

There is yet another issue confounding evaluation of

the skill of the model predictions: the simulations are

sensitive to model parameters that are not perfectly known.

Therefore, the adjustable parameters are tuned within

their envelope of reasonable values is a manual procedure for parameter estimation. More sophisticated

estimation procedures have been used in the context

of zero- and one-dimensional models (e.g., Vezina and Platt

1988, Fasham and Evans 1995, Hurtt and Armstrong

1996, Friedrichs and Hofmann 2001), and will be

pursued in the future. At present, the computa-tional

demands of those approaches make them imprac-tical

for the three-dimensional system of interest here.

Quantitative evaluation of hindcast skill facilitates

testing hypotheses concerning bloom controls by

ascertaining whether one model structure fits the data

significantly better than another. As an example, we

compare the results of two different simulations: one

without grazing of Alexandrium, and one that includes

a spatially uniform loss term of 0.1 per day (Figure 5).

Each of the four panels shows the space/time distribution of the error metric. Time runs along the ‘cruise’

axis with cruises 1-5 in April to June. Space runs along

the ‘zone’ axis, with zones 1-9 corresponding to those

indicated in Figure 4. The zones have been separated into

inshore and offshore groups. The height of the vertical

bars indicates the magnitude of the error metric, with

low model values in blue and high model values in red.

In the simulation that does not include mortality, cell

concentration is systematically over-predicted late in

the run in the southern portion of the domain. The

average value of the error metric is 3, so on average

the predicted values are within a factor of three of the

observations. Explicit treatment of loss processes via

a uniform loss rate of 0.1 per day improves the simulations dramatically, reducing the large errors late in the

run in the southern portion of the domain. The error

metric drops to an average of 2.33, so the predictions

are now within a factor of two of the observations.

The conclusion is that a loss term (which may represent

some combination of predation and encystment) plays

a key role in controlling the development and

termination of the bloom. One important caveat is the

circulation fields are assumed to be correct; the possibility that mismatches between the simulated and

observed concentrations of Alexandrium could be a

result of unknown deficiencies in the modeled circulation cannot be excluded. Future research will formulate

measures of misfit for the physical fields as well as the

biological fields.

Lessons Learned

The hindcasts of the 1993 RMRP data do not rely on

data assimilation to keep the circulation fields on track.

The rationale for adopting a purely ‘forward’ modeling

approach hinged on the fact the circulation in this

relatively small region is largely controlled by riverine

buoyancy inputs and wind-forced dynamics. Such is

Figure 4: Typical observational coverage during one

cruise. Colored dots represent observations of Alexandrium

fundyense concentration (cells per liter). The nine zones

chosen for model evaluation are indicated.
65

clearly not the case in larger domains where a wide

variety of other processes influence the circulation.

Results from the ECOHAB-GOM program have shown

the dynamics of Alexandrium blooms are regional in

scope, spanning the waters from the Bay of Fundy down

into Massachusetts and Cape Cod Bays (Anderson et

al. 2000; Townsend et al. 2001). Thus, a gulf-wide

modeling approach will be necessary in an operational

context. Given the complex hydrodynamics characteristic of this large regional domain, data assimilation

will be an essential element of skillful prediction.

An operational system based on a data-assimilative

model implies an observational network to drive it.

Design of such a network would be facilitated by

Observational System Simulation Experiments

(OSSEs; see Robinson et al. 1998; McGillicuddy et

al. 2001). The idea is model simulations can serve as

a space/time continuous representation of reality, which

is then subsampled in a specified fashion to produce a

simulated data set. The simulated data are then fed into

an analysis scheme in which they are synthesized into

a reconstruction of ‘reality.’ Direct comparison of the

reconstructed field with the ‘truth’ as defined by the

original simulation provides a quantitative evaluation

of that particular sampling strategy.

In summary, meaningful ecological forecasts of this

system will require accurate real-time data streams

together with advanced data assimilation techniques.

Conclusion/Next Steps

Coupled physical-biological models of Alexandrium

in the Gulf of Maine have matured to the point that it

is now feasible to assess their operational suitability

and potential value. A strategy for this could consist of

three main elements: 1) thorough evaluation of the predictive skill in a hindcast model using data from

ECOHAB-GOM and its predecessor programs; 2) improvement of the models from what is learned in that

evaluation; and 3) formulation of a plan for transition

of the models to operational use. The second item

implies the dynamics of Alexandrium blooms are not

fully understood, thus additional research is needed on

the underlying physical/biological interactions that

control these blooms.

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Okaichi, D.M., Anderson, and T. Nemoto (eds.) Red

Tides: Biology Environmental Science and Toxicology.

Elsevier, NY pp. 395-398.
67

Introduction

This paper summarizes an oral presentation given at

the 16th Biennial Conference of the Estuarine Research

Federation in November 2001. The paper entitled Forecasting Oyster Mortality in Relation to Variations in

Freshwater Inflow into the Apalachicola-ChattahoocheeFlint (ACF) Basin, is part of an ongoing cooperative

study between NOAA’s National Centers for Coastal

Ocean Science’s (NCCOS) Biogeography Program and

the Florida State University (FSU) Center for Aquatic

Research and Resource Management. The paper was

part of a symposium on ecological forecasting

sponsored by NOAA’s NCCOS (NOAA/NOS 2001).

This summary complements and updates previously

published articles on the topic of forecasting and

assessing changes in the Apalachicola Bay, Florida

ecosystem due to variations in freshwater inflow

(Christensen et al. 1998; Livingston et al. 2000).

The US Army Corps of Engineers requested NOAA/

NCCOS to participate as a cooperating agency in the

preparation of the draft environmental impact statement

(EIS) for the Apalachicola-Chattahoochee-Flint (ACF)

basin. NOAA is participating on the inter-agency team

because of several legislative mandates: develop and

review of ElSs (National Environmental Policy Act);

assess potential impacts on essential fish habitats

(Magnuson-Stevens Fishery Conservation & Management Act); determine if any endangered species will

be affected (Endangered Species Act); and assess

potential impacts to the Apalachicola Bay National

Estuarine Research Reserve (Marine Sanctuaries Act).

This study was conducted jointly by the Apalachicola

Bay EIS Assessment Team that was led by NCCOS

and FSU consulting with the Northwest Florida Water

Management District, University of South Florida, and

the Florida Fish and Game Commission.

1

 NOAA/NOS National Centers for Coastal Ocean Science’s Biogeography Program, Silver Spring, MD

2 The Florida State University, Center for Aquatic Research and Resource Management, Tallahassee, Florida

Forecasting Oyster Mortality in Relation to Variations

in Freshwater Inflow into the Apalachicola-ChattahoocheeFlint (ACF) Basin

Mark E. Monaco 1 and Robert J. Livingston2

Abstract

This paper was part of a symposium on Ecological Forecasting sponsored by NOAA’s National Centers for

Coastal Ocean Science (NCCOS) at the 16th Biennial Conference of the Estuarine Research Federation in

November 2001. The summary below complements and updates previously published articles on the topic of

forecating and assessing changes in the Apalachicola Bay, Florida ecosystem due to variations in freshwater

inflow (Christensen et al. 1998; Livingston et al. 2000).

The eastern oyster, (Crassostrea virginica), was selected to study the impacts of changes in freshwater inflow on

oyster mortality, growth, and ecology. Optimum oyster habitat in Apalachicola Bay is a function of estuarine

circulation, nutrient and salinity concentrations, habitats, basin morphometry, and freshwater inflow. Several

scenarios of the amount of freshwater inflow entering into the Bay were evaluated to provide an ecological

forecast on potential impacts on oyster populations. At the most productive oyster bars in the eastern Bay,

predation, a biological determinant of oyster growth, was predicted to increase due to high salinity concentrations

and stable salinity regimes associated with reduced freshwater inflow. In addition, at the western bars where

greatest young individual oyster growth rates were observed in relatively high salinity waters, potential oyster

mortality increased with decreased freshwater inflow.
68

Because of limited resources and data, the NCCOS/

FSU team focused on the eastern oyster (Crassostrea

virginica), a key ecological species, to assess potential

impacts from reduced freshwater into the ACF basin

on oyster mortality, growth, and ecology. It was selected

for two primary reasons: 1) its importance to the

ecology and economics of the Apalachicola Bay region

and, 2) the availability of a unique database on oyster

biological attributes (e.g., percent mortality at oyster

bars) housed at FSU.

Although oysters can survive in relatively high and

dynamic salinities, high and stable salinity regimes

enable predators and diseases to attack oyster

populations. The dominant marine predator of oysters

in Apalachicola Bay is the oyster drill (Thais

haemastoma, Livingston et al. 2000). Thus the study

focused on the indirect impacts of reduced freshwater

inflow into Apalachicola Bay on the eastern oyster

population due to increased mortality from predation

and disease.

The results of the ecological forecast have been

published in Christensen et al. 1998, Livingston et al.

2000, and the US Army Corp of Engineers Draft EIS.

Plans are to conduct a new assessment if a freshwater

inflow allocation formula can be agreed to between

the States of Georgia, Florida, and Alabama. To date,

no final freshwater allocation formula for the ACF basin

has been approved by the States to enable final

assessment studies. If, and when, a final allocation

formula is approved, the oyster ecological forecast will

contribute information and assessment to the Federal

Inter-agency commissioner and the states on the

potential biological impacts of reduced freshwater

inflow on oyster populations within the Bay. The ecological forecast will assist the Federal commissioner

in evaluating the allocation formula and contribute to

the commissioner’s decision to concur, or not concur,

with the proposed freshwater inflow allocation formula.

This decision will be partially based on the collective

assessment of Federal agencies that comprise the ACF

Inter-agency Team.

Case Study

The ACF ecological forecast case study was developed

to address the problem of competing uses of water

between the three states for water allocation. Relative

to the ACF issues, the primary issues are between

Georgia, who requires additional water to support

domestic and agricultural needs, compared to Florida

who has generally focused on maintaining sufficient

flow for ecological and economic concerns in the

Apalachicola Bay region. NCCOS’s role in this

management issue was to develop a set of forecasts

using various scenarios of the amount of freshwater

inflow into the Bay to predict potential ecological

impacts to the estuary.

Therefore, the objectives of the ecological forecast were to:

1) Model the variation in estuarine salinity and temperature regimes under various freshwater inflow

scenarios.

2) Develop models of potential oyster growth and

mortality under various freshwater inflow scenarios.

3) Integrate the physical and biological models to

provide an assessment of the potential impacts of

changes in freshwater inflow on the Apalachicola

Bay oyster community.

4) Develop digital maps of oyster mortality and growth

estimates under various freshwater inflow scenarios.

Methods – Study results were developed by integrating

several biological and physical models using Geographical Information System (GIS) technology to demonstrate how oyster biological responses (mortality and

growth) could potentially change with reduced freshwater inflow. A series of digital maps were produced

that showed potential oyster mortality due to predation

throughout the Bay under various demands for freshwater in drought, low-moderate, and wetter than normal

summer flow conditions. Based on available data, a

range of potential oyster mortalities under various

alternative flow conditions were modeled and mapped

in Christensen et al. 1998.

The integrated biological (mortality) and physical

(hydrodynamic) models enabled NCCOS to predict

potential oyster mortality using three historical flow

years, eight simulated alternative operation and demand

scenarios, and two existing operations with different

demand scenarios (Christensen et al. 1998). The

scenarios bracketed Bay-wide simulated salinity

regimes (i.e., oyster habitat) under different demands

for freshwater. Three years with different water inflows

were selected for the assessments based on available

oyster mortality and growth data: 1985 with low to

moderate flow conditions, 1986 with drought flow

conditions, and 1991 with wet flow conditions.

Based on this information, the potential of oyster

mortality was forecasted under the various flow

scenarios and varying levels of human demands on
69

those flow scenarios. Potential oyster mortality was

grouped into 10 percent mortality categories (0-1; 1-4;

4-9; 9-16; 16-25; 25-36; 36-49; 49-64; 64-81; 81-100

percent mortality) (Christensen et al. 1998). These

categories were color-coded into map polygons and the

maps represent the spatial distribution of potential

oyster mortality (Christensen et al. 1998).

Results – Low to Moderate Hydrologic Year (1985) –

Digital maps showing potential oyster mortality under

1985 high flow operations with 1995 demands for water

were modeled and mapped (Christensen et al. 1998).

Under the high flow operations, the peak in potential

oyster mortality occurred in the 4-9 percent (not biologically significant) category, indicating that over 50 percent of the Bay would not experience significant

potential oyster mortality. Most important, at the largest

and most productive eastern oyster beds of Cat Point

and East Hole, potential oyster mortality was less than

10 percent (Figure 1). However, in the far western portions of the Bay near Scorpion Bar, the model predicted

this area could experience high potential oyster

mortality (>50 percent) under the same flow conditions.

Under the scenario for 1985 flow conditions with 2050

human consumptive demands, significant oyster

mortality could occur over a greater geographic range

when compared to the 1985 with 1995 high flow operations and demands. This was evidenced by 71 percent

(1985 flows with 2050 low flow operations and

demands) of the potential oyster mortality cells

throughout the Bay fell in mortality categories ranging

from about 10 to 50 percent.

Under the ‘worst case’ scenario for 1985 flow conditions with 2050x demands, a peak in potential oyster

mortality throughout the Bay occurred at 9-16 percent

mortality category (2050x corresponds to a 2.5 times

increase in agricultural consumptive demands).

However, the largest and most productive bars at Cat

Point and East Hole could experience an increase in

potential oyster mortality from <10 percent under 1995

demands to 10-25 percent under 2050x demands. In

addition, the commercially important bars of St.

Vincent Sound could experience potential oyster

mortality ranging from 16-81 percent; with most of

the bars predicted to experience about 25-50 percent

mortality. The mean potential oyster mortality would

increase from 11 percent in 1985 with 1995 consumptive demands to 16 percent under 2050x consumptive

demands.

To better understand and interpret the difference in

predicted mortality across the various alternative

demand scenarios for 1985, a spatial calculation of percent change in mortality was run. This went from the

“Cat Point”

“East Hole”

“Porter’s Bar”

“St. Vincent Bar”

“Platform Reef”

“Scorpion Bar”

Sample Sites

Oyster Reefs 

Case Study

Area

Figure 1. Oyster reef sampling sites in Apalachicola Bay, FL
70

base, or best case flow condition – the 1985 high freshwater inflow operations with 1995 demands, to an extreme condition – the 1985 with low freshwater inflow

with year 2050 demands. The analysis reflects

differential mortality as a percentage of the original

(base year) condition for the alternative flow analysis

of 1985. The analysis indicated that a large portion of

the oyster reefs in the eastern portion of the Bay,

including East Hole and Cat Point, were characterized

by the greatest increases in mortality (>25 percent of

base) from the high flow condition to the low flow

condition. The western reefs of St. Vincent Sound would

also exhibit large increases in potential mortality (>5-

25 percent of base). Thus, the most productive oyster

beds throughout Apalachicola Bay are predicted to

experience the greatest increases in mortality as

freshwater inflow is reduced due to predation and

disease.

No Action Alternative – Under the ‘no action’

alternative (current reservoir operations), few

noticeable changes in the patterns of potential mortality

were detected between the 1985 flow conditions with

1995 consumptive demands and the 1985 flows with

projected 2050 demands. However, comparing the

cumulative distribution of calculated mortality over the

entire Bay, the 1985 flow with 1995 demands

consistently showed a slightly lower mortality rate.

There was an overall greater geographic range of low

potential mortality throughout the Bay (0-9 percent)

under the 1995 demand scenario, while the 2050

scenario indicated a 3 percent greater spatial coverage

for the 9-16 percent mortality range relative to the 1995

demand scenario.

Although the differences appear to be slight, placing

the 2050 demands on existing operations would likely

result in elevated oyster mortalities baywide. This

analysis, when compared to the alternative flow

scenarios, indicates that the greatest effects on

Apalachicola Bay oysters will result from reservoir

operations resulting in low river flows.

Lessons Learned

The development of this ecological forecast has been

critical to providing peer-reviewed science to the draft

EIS and for State and Federal managers to understand

potential biological impacts to the Apalachicola Bay

ecosystem. The forecast only focused on the potential

impacts of increased oyster mortality, decreased

growth, and implications to the overall ecology of

Apalachicola Bay (Livingston et al. 2000). The study

was designed in cooperation with Federal agencies and

the State of Florida. This approach was critical so the

analyses were able to be input into the State of Florida’s

decision process as they developed draft freshwater

inflow allocation formulas. Most important, the

assessment enabled NOAA to meet the request of the

US Army Corp of Engineers to be a member of the

Inter-Agency Team to provide a technical review of a

range of proposed freshwater inflows into the ACF

basin and ultimately into Apalachicola Bay.

Improving the ecological forecast requires additional

data to validate the potential oyster mortality models,

since resource constraints enabled only limited model

validation. Additional field studies to further refine and

validate the model are urgently needed because there

is currently a major drought in freshwater that may have

significant impacts on the capability of the models to

predict ecosystem impacts. This will limit the

information content and quality available to managers

in deciding the potential for oyster mortality. However,

until additional studies are implemented, the joint

NCCOS and FSU study to predict oyster mortality in

Apalachicola Bay due to changes in freshwater inflow

stands as the most robust study to support management

actions relative to flow regimes into the Bay.

Conclusions/Next Steps

Optimum oyster habitat in Apalachicola Bay is a

function of estuarine circulation, nutrient and salinity

concentrations, habitats, basin morphometry, and

freshwater inflow (Livingston e et al. 2000). At the most

productive oyster bars in the eastern Bay, predation, a

biological determinant of oyster growth, was due to

the indirect effects of salinity limitation of marine

predators and disease as well as the actual proximity

of these bars to Gulf entry points into the Bay. In

addition, at the western bars where greatest young

individual oyster growth rates were observed in

relatively high salinity waters, potential oyster mortality

increased with decreased riverine freshwater inflow.

The intricate associations of the physical habitat

(salinity) and the biological responses of oysters is part

of the complex interplay of the various life stages of

the oyster with both physical and biological aspects of

the Bay ecosystem. Many of these attributes are either

directly (increased salinities) or indirectly (predation)

associated with freshwater from the Apalachicola River.
71

The Apalachicola Bay accounts for about 90 percent

of Florida’s and 10 percent of the national commercial

oyster fishery (Livingston et al. 2000). Without the

nutrient loading from the river, coupled with reduced

salinities and variations in salinity concentrations (i.e.,

adequate freshwater inflow) that enable continued

oyster growth and protection from marine predators,

the commercial value of Apalachicola Bay’s oyster

production may be damaged.

At this time, no final allocation formula has been

developed or agreed upon by the States. If, and when,

a final ACF freshwater inflow allocation formula is

published, NOAA will continue to participate in the

technical review process by the Inter-agency Review

Technical Review Team. Plans are to have the NCCOS

and FSU team re-run the potential oyster mortality

models using the final allocation formula and provide

an assessment report to the Federal Commissioner.

Additional model runs to predict potential oyster

mortality under the current reservoir operations will

be conducted to assess the biological impacts from the

current southeast drought conditions that the ACF basin

is experiencing. In addition, in consultation with the

National Marine Fisheries Service, NCCOS will formulate an assessment on the potential impacts of reduced

freshwater inflow into Apalachicola Bay on Federally

managed commercial species (e.g., shrimp).

Acknowledgements

We would like to thank all members of the NOAA/

NCCOS Biogeography Program who contributed to the

development of this assessment with special thanks to

Mr. John Christensen and Mr. Tim Battista. In addition,

we thank Mr. John Klein and Mr. Russell Ives in

supporting the development of the hydrological model

component of the study. We thank members of the FSU

assessment team lead by Mr. Glen Woodsum and

hydrological model runs conducted by Boris Galperin

of the University of South Florida. Finally, we thank

Ms. Jamie Higgins for assisting in the production of

this manuscript.

References

Christensen, J.D., M.E., Monaco, R.J. Livingston, G.C.

Woodsum, T.A. Battista, C.J. Klein, B. Galeprin, and

W. Huang. 1998. Potential impacts of freshwater inflow

on Apalachicola Bay, Florida oyster (Crassostrea

Virginica) populations: coupling hydrologic and biological

models. NOAA/NOS Strategic Environmental

Assessments Division Report, Silver Spring, MD. 58 pp.

Livingston, R.J., F.G. Lewis, G.C. Woodsum, X.-F. Niu, B.

Galperin, W. Huang, J.D. Christensen, M.E. Monaco,

T.A. Battista, C.J. Klein, R.L. Howell IV, and G.L. Ray.

2000. Modeling oyster population response to variation

in freshwater input. Estuarine, Coastal, and Shelf

Science 50:655-672.

NOAA/NOS. 2001. Ecological forecasting: expanding

NOAA’s assessment & prediction capabilities to support

proactive ecosystem management. National Ocean

Service, Silver Spring, MD. 8 pp.
72
1 NOAA National Ocean Service, Cooperative Oxford Laboratory Oxford, MD 21654

2

 Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL 32561

A Potential Ecoforecast for Protozoal Infections

of the Eastern Oyster (Crassostrea virginica)

 in the Upper Chesapeake Bay

Shawn M. McLaughlin1 and Stephen J. Jordan2

Abstract

Perkinsus marinus and Haplosporidian nelsoni cause devastating infections in populations of the eastern oyster,

Crassostrea virginica, along the US Atlantic coast and Gulf of Mexico. Salinity and temperature are considered major

controlling factors in the prevalence and infection intensity of these two parasites. Fishery managers and oyster

growers use this relationship to make short-term predictions of potential outbreaks of disease in oyster populations

and to determine the best timing, or sites, for planting and harvesting of oysters. Predicting medium or long term

fluctuations in oyster disease in the Chesapeake Bay is limited; however, by the inability to accurately predict

medium- and long-term fluctuations in weather patterns such as occurrence and duration of drought conditions.

Several mathematic models have been developed to assess the health of oyster populations in the Gulf of Mexico

and Chesapeake and Delaware bays. An impediment to the accuracy of mathematical models is the current

severe gap in knowledge regarding the parasites, the host, and their interactions. Continuous advances in shellfish

pathology research can be expected to improve the precision of predictive models. A population dynamics

model recently developed by Jordan and Vanisko (submitted) is discussed as a potential tool for forecasting

oyster disease in the upper Chesapeake Bay.

73

Introduction

Over the last 50 years, two pathogenic parasites, Perkinsus

marinus and Haplosporidian nelsoni, have played a

major role in the decline of the oyster fisheries in the

United States. The parasites cause serious disease in

oysters and lead to severe epizootics (epidemics among

animals). Perkinsus marinus was first discovered in the

Gulf of Mexico during losses of oysters in the 1940s and

causes a disease commonly referred to as Dermo (Mackin

et al. 1950). The range of the parasite now extends from

Mexico (Burreson et al. 1994) to Maine (Barber et al.

1997). Oyster epizootics occur in several regions including the Chesapeake Bay (Andrews and Hewatt 1957,

Andrews 1988, Burreson and Ragone-Calvo 1996).

Haplosporidium nelsoni is known by the acronym MSX

(Multinucleated Sphere Unknown). MSX was first associated with epizootic mortalities in eastern oysters in

the Chesapeake and Delaware Bays in the late 1950s

and early 1960s (Andrews 1966, Haskin et al. 1966).

The range of MSX now extends along the Atlantic coast

from Florida to Maine with major epizootics continuing to occur in the mid-Atlantic and northeastern states

(Ford and Tripp 1996, Barber et al.1997).

Temperature and salinity have been identified as important

environmental factors affecting interactions between

eastern oysters and the parasites P. marinus (Andrews

and Hewatt 1957, Andrews 1988, Burreson and Ragone

Calvo 1996, Ford 1996) and H. nelsoni (Farley 1975,

Ford and Haskin 1982, Ford 1985, Matthiessen et al.

1990). In general, elevated temperatures (20-25o C) and

salinities (>12 ppt) favor proliferation of the parasites

and significant increases in parasite activity.

Prevalences may be observed during extended periods

of low rainfall (Andrews 1988). In regions where oyster

populations harbor significant numbers of parasites, a

mild winter and dry spring may result in increased water

salinities and high parasite prevalences (Burreson and

Ragone-Calvo 1996). Significant rainfall in the early

summer; however, may create less favorable conditions

for parasite proliferation, thus prevalences and mortalities
74

may not be as high as expected. Prolonged drought

conditions may lead to elevated parasite prevalences in areas

where the parasites are endemic in oyster populations.

Indeed, the mild winters and drought conditions of the

late 1980s and early 1990s have been associated with

increased parasite prevalences and geographic range extensions (Burreson and Ragone-Calvo 1996, Ford 1996).

In the last decade, a number of mathematical models

have been developed to assess the relative outcomes of

disease virulence and pressure under varying conditions

for eastern oyster populations (Table 1). There is a consensus among the authors that these models can help

decision-makers determine how best to manipulate oyster

stocks around P. marinus and H. nelsoni in a particular

geographic region. Moreover, a dual disease model presently under development will improve the ability to

examine the relationship between P.marinus andH. nelsoni

and better predict how each produces mortality in oysters under a suite of environmental conditions.

Case Study

State agencies are the regulators of commercial oyster

fisheries and those with significant oyster resources conduct routine surveys to monitor the health of oyster populations. The Maryland Department of Natural Resources

(MDNR) Cooperative Oxford Laboratory (COL) conducts

annual disease surveys of oysters in the upper Chesapeake Bay.

Sixteen years of Chesapeake Bay oyster disease and

population data collected by MDNR provide the basis

of a prototype population dynamics model developed

by Jordan and Vanisko (submitted). The primary purpose

for its development was to predict outcomes of management options for resource restoration and fisheries enhancement. The potential for the model to predict the

effects of oyster disease on the population is examined

in this study.

Jordan and Vanisko’s population dynamics model simuPublication Location Type Topic Variables

Hoffman et al. 1992 Gulf of Mexico, 

Chesapeake Bay

Population Reproduction of eastern oyster Temperature; food supply

Powell et al. 1992 Gulf of Mexico Population Growth and reproduction of 

eastern oyster

Filtration rate

Hofmann et al. 1994 Gulf of Mexico Population Adult size and reproductive effort 

in eastern oyster 

Temperature; salinity, food supply; 

turbidity

Powell et al. 1994 Galveston Bay, 

Chesapeake Bay

Population Mortality rate of eastern oyster Food supply; recruitment; fishing; 

disease; predators

Hoffman et al. 1995 Galveston Bay Coupled oyster 

population-parasite

model

Prevalence and infection intensity 

of P. marinus in the eastern 

oyster

Temperature; salinity, food supply; 

turbidity; parasite density; oyster 

mortality

Powell et al. 1995 Galveston Bay Population Population abundance of eastern 

oyster

Reduced phytoplankton stock

Powell et al. 1996 Gulf of Mexico Coupled oyster 

population-parasite

model

Triggering mechanisms for P.

marinus epizootics

Temperature; salinity; food supply; 

turbidity; recruitment and juvenile 

mortality; parasite virulence and 

disease resistance

Ford et al. 1999 Delaware Bay Coupled oyster 

population-parasite

model

H. nelsoni infection process in

oysters

Parasite proliferation; sporulation; 

transmission; spread in tissues; 

salinity; temperature; oyster and 

parasite mortality

Powell et al. 1999 Delaware Bay, 

Chesapeake Bay

Coupled oyster 

population-parasite

model

Transmission of H. nelsoni in

oysters

Salinity; temperature; season

Paraso et al. 1999 Delaware Bay Coupled oyster 

population-parasite

Model

H. nelsoni infection intensity 

and prevalence in oysters

Salinity

Brousseau and 

Baglivo 2000

Long Island Sound Linear Progression of P. marinus 

disease in eastern oysters

Temperature

Individual-based

oyster model

 Klinck et al. 2001 Delaware Bay Fisheries Time-dependent mortality rates in 

the eastern oyster

Disease; recruitment into market 

stock

Ragone-Calvo et al. 

2000

Lower Chesapeake 

Bay

Transmission, growth, and death 

of P. marinus within the eastern 

oyster

Temperature; salinity

Table 1. Examples of mathematic models available for oyster health assessment.
75

Figure 1. Forecasts of Maryland Chesapeake Bay market oyster stock (top plot) and harvest (bottom plot) based on

four scenarios with different natural mortality rates. M = instantaneous rate of natural mortality; finite annual rates of

natural mortality are shown in parentheses. M = 0.39 is the mean observed rate from 1985-2000, M = 0.47 is a 20

percent increase, M = 0.32 is a 20 percent decrease, and M = 0.28 is a 30 percent decrease in the instantaneous natural

mortality rate.

lates harvestable stock and harvest based on 16 years

of monitoring data from which variable parameters for

natural mortality, fishing mortality and recruitment to

the harvestable stock have been calculated. Fishing mortality and recruitment are random variates with the baseline means and standard deviations from the time series.

The natural mortality parameter in the model is based

on 16 years of box counts (empty articulated oyster

shells) at 43 oysters bars in Maryland. Predation, disease,

senility and other causes of natural mortality are implicit

in the mean and standard deviation of the mortality term.

In the following simulations, the means of natural mortality were varied to illustrate long-term reductions or

increases in salinity (Figure 1). The graphs show the

means of 1000 simulations for each scenario for each

year, smoothed by a trend line. The parameters fishing

mortality, natural mortality, and recruitment (gross recruitment to the market oyster stock) varied randomly

about the observed or simulated mean, constrained by

the observed standard deviations, which were not varied.

A long-term reduction in salinity would be expected to

result in fewer oyster mortalities due to P. marinus or

H. nelsoni infections. Higher mortalities would be expected with long-term increases in salinity.

The mean rates of market oyster mortality modeled are

24 percent, 27 percent, 33 percent and 38 percent. The
76

baseline mean from 16 years of monitoring data was

approximately 33 percent (M = 0.39 + 0.25 standard

deviation). That is, 33 percent of the market oysters died

in an average year. A simulated 20 percent reduction in

instantaneous natural mortality rate equated to 27 percent,

a 30 percent reduction to 24 percent of oysters dying

per year; 38 percent annual mortality was a simulated

20 percent increase in natural mortality rate.

Note the vertical scales are different on the graphs. The

simulated salinity increase results in the stock crashing

over a 20-year period, more rapidly than the predicted

decline at the observed natural mortality rate. Only a

modest increase in stock, or no increase, occurs with

simulated decreases in salinity over the same time period.

Based on the time series of data used to parameterize

the model, the mortality rates simulated can be used to

predict the corresponding changes in salinity. A linear

regression of arcsine-transformed annual oyster mortality

against salinity (measured in the fall at each of 43-64

oyster monitoring sites in Maryland) was employed to

give indications of mean salinities associated with the

simulated mortality rates. The relationship was significant

(p < 0.0001) but imprecise (r2

=0.08, N=444). We note

that the salinity measurements were made only once per

Figure 2. Hindcasts of Maryland Chesapeake Bay market oyster stock (top plot) and harvest (bottom plot) based on

four scenarios with different natural mortality rates. Bars represent actual reported harvest. M = instantaneous rate

of natural mortality; finite annual rates of natural mortality are shown in parentheses. M = 0.39 is the mean observed

rate from 1985-2000, M = 0.47 is a 20 percent increase, M = 0.32 is a 20 percent decrease, and M = 0.28 is a 30

percent decrease in the instantaneous natural mortality rate.
77

year at each site, and that factors other than salinity

influence diseases and mortality. Nevertheless, salinity

was significantly correlated (Spearman’s r; p < 0.0001)

with P. marinus prevalence and infection intensity, and

with H. nelsoni prevalence (H. nelsoni intensity was

not measured quantitatively). Predicted salinity at the

observed mean annual mortality rate (33 percent) was

16.5 ppt, at 24 percent mortality 11.9 ppt, at 27 percent

mortality 13.6 ppt, and at 38 percent mortality18.3 ppt.

This analysis suggests that long-term mean salinity

would have to decrease by about 3 ppt to prevent further

decline of the market oyster stock, and by nearly 5 ppt

to achieve a substantial increase in stock in the absence

of other interventions.

Jordan and Vanisko’s population dynamics model, when

parameterized separately for 3 salinity zones in northern

Chesapeake Bay, indicated that an oyster fishery could

not be sustained when long-term mean salinity exceeded

14 ppt because of the high mortality rates associated

with Dermo and MSX diseases. The model furthershows

that oyster fisheries could be sustained or restored at lower

salinities with moderate reductions in fishing mortality

to compensate for disease-related mortality.

Hindcasts simulate the past and are used to verify models

by comparing results with actual data. Figure 2 shows

the same variations in natural mortality depicted in Figure 1,

simulated for the time span of data used to parameterize

the model. The baseline simulation (M = 0.39) reproduces the trend and end members of harvest over this

period reasonably well, but does not capture the intervening variations in harvest. These variations might be

explained by interannual environmental effects on recruitment and natural mortality, in combination with

non-random variations in fishing mortality. No modelindependent estimates of absolute stock size exist for

comparison with the simulated stock.

Lessons Learned

Development of population dynamic models to predict

oyster disease in the Chesapeake Bay requires a baseline

of information acquired through regular, and costly,

monitoring of oyster populations.

Jordan and Vanisko’s population dynamics model may

support forecasting oyster disease in the upper Chesapeake Bay. The primary purpose for its development;

however, is to predict outcomes of management options

for oyster fisheries enhancement and restoration. Additional

verification of the model as a forecasting tool is needed.

There are gaps in knowledge related to the biology and

life cycles of the oyster parasites, reservoirs of infection, oyster defense mechanisms, susceptibility and

resistance to disease, and host-parasite interactions that

are not adequately addressed in stock assessment models.

Further, the identification of two Perkinsus species, P.

marinus and P. chesapeaki, in softshell clams Mya

arenaria of the upper the Chesapeake Bay (McLaughlin

and Faisal 2000) may compound the interpretation of

prevalence data used in present oyster-disease model

development. Should these gaps be filled, prediction of

oyster disease outbreaks using computer-based simulation

and mathematical models programs will become more precise.

Forecasts of pending oyster disease may be useful to state

agencies and shellfish farmers; however, announcing

such forecasts to the general public may negatively affect

consumer confidence and expectations.

Next Steps

Research to better understand shellfish biology and

pathology, the biology and life cycles of parasitic pathogens,

and host-pathogen interactions needs to be supported. For

example, the invertebrate immune system and defensive

mechanisms have yet to be completely understood. In addition, the impacts of other stressors on the health of

estuarine and marine organisms such as disturbances

in food availability, reproductive failures, increased sedimentation, presence of contaminants or toxins in the

water, and the effects of multiple stressors and increased

susceptibility to disease remain to be understood. All

of these variables are critical to the development of

predictive models.

The relationship between elevated salinity and increased

prevalence of oyster disease has been well documented.

Thus, an alternative to the stock assessment or population

dynamics modeling approach to predicting oyster disease may be to directly predict changes in salinity. For

example, stream flow data recorded by United States

Geological Survey or climate models could be used to

predict variations in salinity in the Chesapeake Bay. A

predicted change in salinity could then provide the basis

for predicating increases or decreases in oyster disease.

The ability to predict salinity changes could be further

applied to forecasts of ecological events of importance

to other users of the Chesapeake Bay. Reduced salinity

due to increases in stream flow may provide an opportunity to predict a higher potential for fish kills resulting

from higher nutrient levels, stimulation of algal blooms,

and decreased oxygen in the bay. Conversely, predicted
78

increases in salinity may be used to forecast a wider

distribution and higher number of sea nettles in Chesapeake Bay waters.

Emerging diseases in marine species have been associated

with ongoing degradation of coastal habitats and global

climate change (Harvell et al. 1999). The limited amount

of data available on the epidemiology of marine diseases

is a serious obstacle to linking climate changes with apparent increases in outbreaks of disease (National Research

Council 2001). Collection of long term spatially resolved

disease surveillance data and centralization of data into

electronic databases would provide baselines against which

changes could be detected and models developed and

validated (National Research Council 2001). Interdisciplinary collaboration among epidemiologists, ecologists,

meteorologists, climate modelers, information technologists, and other scientists should be fostered at national

and international levels. Expansion of recent efforts to

apply remote sensing and other satellite systems to epidemiological investigations should be supported.

Conclusions

Scientists and fishery managers are able to make shortterm predictions regarding potential outbreaks of disease

in oyster populations due to well established associations

between salinity fluctuations and disease. Competency

in predicting medium- or long-term fluctuations in oyster

disease and other ecological events is severely limited

by the inability to accurately predict medium- and longterm fluctuations in weather patterns such as occurrence

and duration of drought conditions.

A multi-disciplinary approach to understanding disease

pathogenesis and mechanisms in estuarine and marine

organisms and the ecological processes influencing disease dynamics is critical for effectively anticipating and

predicting disease outbreaks.

Acknowledgements

The authors gratefully acknowledge Fred Kern (NOAA

Cooperative Oxford Laboratory, Oxford, Maryland) and

Chris Judy (Maryland Department of Natural Resources,

Annapolis, MD) for insights into oyster disease forecast

development. We thank Don Scavia, Nathalie ValetteSilver, Fred Kern (NOAA) for their constructive reviews.

Jane Keller (NOAA) provided editorial assistance.

Maryland Department of Natural Resources staff conducted annual Chesapeake Bay oyster disease surveys.

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80
81

Introduction

Several Ponto-Caspian endemics have recently invaded

the Great Lakes: the zebra mussel (Dreissena polymorpha),

the quagga mussel (D. bugensis); the predatory

cladoceran (Cercopagis pengoi), the benthic amphipod

(Echinogammarus ischnus); the round goby (Neogobius

melanostomus), and the tubenose goby (Proterorhinus

marmoratus). All these euryhaline species arrived in

ballast water.

The Great Lakes have undergone and continue to

undergo profound ecological changes in response to

these invaders. There are also changes from

oligotrophication, piscivorous fish stocking, and other

exotic species, some of which are in concert with effects

of the Ponto-Caspian species. So it is difficult to separate impacts from these invaders from other anthropogenic

changes and makes it challenging to forecast the

impacts of these species on water quality and fisheries

of the Great Lakes.

It is important to document changes in the system since

invasion and attribute them to the invaders. But it is

also necessary to relate – ideally quantitatively – the

changes through a set of plausible underlying mechanisms to these recent invaders to define relative

contributions of all factors. This is an overview of

results from a large synthesis relevant to ecological

forecasting of spread and ecosystem impacts of PontoCaspian species in the Great Lakes and other water

bodies (Vanderploeg et al. 2002).

Case Study

The study began with a synthesis of the ecology of

each Ponto-Caspian invader and the mechanisms they

use to spread and affect ecosystems. This was derived

from studies in the laboratory, in the Great Lakes, and

in other systems.

To generate as holistic as possible case studies of

invader-induced change, the underlying causes, and

predictions for the future, information from the

ecological synthesis was applied to areas in the Great

Lakes where the ecosystem has been monitored. This

exercise was extremely important not only for impact

description but also for revealing mechanisms and

interactions not suspected from prior knowledge of each

1

 GLERL/NOAA, 2205 Commonwealth Blvd., Ann Arbor, Michigan 48105

Ecological Forecasting of Impacts of Ponto-Caspian Species

in the Great Lakes: Describing, Understanding, and

Predicting a System in Transition

Henry A. Vanderploeg1

Abstract

This paper summarizes results relevant to ecological forecasting from a study that described, explained, and

predicted Great Lakes ecosystem impacts of six Ponto-Caspian (the region including the Caspian, Black, and

Azov Seas) endemic species of mussels, crustaceans, and fishes that recently invaded the Great Lakes via

ballast water (Vanderploeg et al. 2002). These are the zebra mussel (Dreissena polymorpha), the quagga mussel

(D. bugensis), the predatory cladoceran (Cercopagis pengoi), the benthic amphipod (Echinogammarus ischnus),

the round goby (Neogobius melanostomus), and the tubenose goby (Proterorhinus marmoratus).

The ecology and possible mechanisms of ecosystem impact were reviewed for each species using information

from a variety of studies and sites, including the Great Lakes. This information was combined with experiments

and monitoring on the Great Lakes to describe ecosystem change, the underlying invader species mechanisms

of impact, and predict future changes.
82

invader alone or from current understanding of aquatic ecosystem processes.

The areas of study included shallow

bays and basins – Saginaw bay (inner

bay mean depth = 5.1 m), western Lake

Erie (mean depth = 5.7 m), Lake St.

Clair (mean depth = 3.0 m) Long Point

Bay on Lake Erie (inner bay mean

depth = 2 m) – as well as deep basins

of Lakes Erie, Ontario and Michigan

(Figure 1). Long Point Bay was of

particular interest in that it is one of

the most important staging areas for

waterfowl in North America.

Lessons Learned

The zebra and quagga mussels are

changing ecosystem function through

ecosystem engineering (increased

water clarity and reef building), fouling

native mussels, high particle filtration

rate with selective rejection of colonial

cyanobacteria in pseudofeces, and

alteration of nutrient ratios. They also

facilitate the rapid spread of their Ponto-Caspian

associates, the benthic amphipod and the round goby,

which feed on zebra mussels.

High concentrations of zebra mussels and quagga

mussels in shallow regions of the Great Lakes (<30 m)

continue to dominate energy flow and nutrient cycling

by their high filtration rate and nutrient excretion.

Dreissenids are able to clear the water in spring in bays

and shallow basins of the Great Lakes, but not in

summer because of reduced filtering and selective

rejection of unpalatable algae in pseudofeces. This

same selective rejection mechanism may be increasing

the probability of toxic blooms of the colonial

cyanobacterium Microcystis on Saginaw Bay and

western Lake Erie.

Dreissena, round gobies, and Echinogammarus form

an ‘invasional meltdown’ assemblage in the Great Lakes

that is becoming an important part of the food web in

many areas of the Great Lakes. That is, rather than

restricting invasion by other species, zebra mussels

facilitated invasion of the Great Lakes by their PontoCaspian associates. Dreissena provides substrate (shell)

and food (pseudofeces and feces) to Echinogammarus.

Round gobies and other fishes feed on Echinogammarus. Round gobies are efficient predators of

Dreissena, and are eaten by a variety of game fishes.

Tubenose gobies, which are too small to feed on

Dreissena, are not expected to cause significant

impacts.

In some shallow systems, certain sport fishes and

waterfowl have benefited from habitat and food web

changes caused by the zebra mussel (Figure 2). Zebra

mussels are a food source for diving ducks. After the

zebra mussel invasion of Long Point Bay, waterfowl

use days increased from 0.04 to 3.5 million. Game

fishes such as smallmouth bass and muskellunge have

additional feeding and spawning habitat created by the

macrophytes that grew because of the increased light

resulting from suspended particle removal by zebra

mussel filtration. Improved habitat and predation on

round gobies have resulted in increased populations of

these game fishes. In Lake St. Clair, a particularly

important area for sport fishing, abundance of smallmouth bass and muskellunge increased 3-fold.

On the other hand, there are negative consequences to

the new food web. Populations of walleyes, which

prefer low light, have decreased by 50 to 75 percent in

Lake St. Clair and the reproductive success of diving

ducks may be affected by contaminants associated with

zebra mussels. Thus, there is concern that Long Point

Figure 1. Case study sites, where monitoring and experimental work permitted

description and mechanisms of invader impact.
83

Bay may be a magnet for contamination

of continental populations of certain diving

ducks. And the zebra mussel round

goby game fish food chain may increase contaminant loads in game fishes.

Zebra mussels, through their fouling native unionid mussel shells, have removed

them from most areas except for refuge

areas in marshes and wave-exposed

shallow areas. Surprisingly, despite dominance of shallow benthic communities by

dreissenids, non-dreissenid benthic

invertebrates other than mussels remained

at pre-invasion levels. The provision of

substrate by mussel shells and increased

primary production of benthic plants

enhanced by increased water clarity

(Figure 2) may have compensated for the

expected loss of the phytoplankton food

base removed by mussel filtering.

Microzooplankton have strongly declined

in shallow areas as a result of dreissenid

filtering, but crustacean zooplankton have

not been greatly affected in areas

examined, and growth of fish dependent

on them has not decreased.

The quagga mussel, a close relative of the

zebra mussel that can colonize soft

substrates, has invaded many deep areas

of the Great Lakes (>30 m) and is

extending the impacts of Dreissena to this

region since it too is a voracious filter

feeder. Diporeia, an amphipod that has

historically dominated the benthos in deep

water (>30 m), is now in decline in many

of the Great Lakes (Michigan, Huron,

Ontario, Erie); this decline may harm

many fishes that prey on it. It is hypothesized that dreissenids are intercepting algae before they

can reach Diporeia at the sediment surface.

Cercopagis pengoi, a predatory cercopagid cladoceran

with large tail spine, is protected from predation by

small fishes because of its tail spine, and will compete

with small fishes for cladocerans and other zooplankton

(Figure 3). It is now found in abundance in summer in

both nearshore and offshore waters of Lake Ontario

and inshore waters of Lake Michigan. Distribution and

impact of Cercopagis in Lake Michigan may be

controlled somewhat by predation by Bythotrephes,

another larger cercopagid that invaded the Great Lakes

from northern Eurasia in 1985.

Next Steps

The Great Lakes ecosystems have been radically

modified, are in a state of transition, and impacts will

continue to evolve. For example, over 90 percent of

the secondary production, grazing, and nutrient

excretion in many areas of the Great Lakes is associated

with zebra and quagga mussels. Thus, old rules of ecological forecasting and management no longer apply.

Figure 2. Food-web and ecosystem-engineering mechanisms of impact of the PontoCaspian benthic assemblage (Dreissena, Neogobius, and Echinogammarus) in bays

(e.g. Saginaw Bay, Long Point Bay), interconnecting waterways (Lake St. Clair)

and nearshore or shallow (Lake Erie) areas of the Great Lakes. Solid arrows indicate

food-web connections. The dashed double-headed arrow indicates potential competitive interactions between the native amphipod, Gammarus, and the non-indigenous

amphipod Echinogammarus, and the dotted arrows indicate the influence of ecosystem engineering through the mechanisms of habitat provision of mussel shells (autogenic

engineering) and water clarity (allogenic ecosystem engineering). WC (for water

clarity) is placed next to solid arrows for predation by vertebrate (visual) predators

(fishes and waterfowl) to indicate possible increases in predation rate associated

with increased water clarity that results from zebra mussel filtering. Because their

filtering activities increase the water clarity, zebra mussels promote growth of benthic

plants, providing habitat for fishes. The shells of the mussels provide habitat for a

variety of invertebrates. The zebra mussel round goby piscivore food chain

and increased macrophytes (cover and spawning habitat of fishes) have resulted in

increased production of certain fish species in shallow areas.
84

Therefore, careful monitoring of the Great Lakes will be

necessary to continue to observe new impacts as they

emerge, and experimental work must be done to develop

new rules for making ecological forecasts and managing

these systems

Figure 3. The role of Cercopagis and Bythotrephes in the food web of

larval fish. Cercopagis and Bythotrephes prey on many of the same

zooplankton that larval fish feed on. Therefore they are in competition

with them. Bythotrephes feeds on and competes with (dotted double

headed arrow indicates competition) Cercopagis, and adult alewives

feed on Bythotrephes. By preying on Bythotrephes, adult alewives may

be allowing Cercopagis to find a niche.

Conclusion

Predictions of impact require intimate knowledge of

food web structure and function in the area of

concern. Extrapolations of impacts from other sites,

such as those in Eastern Europe, often have limited

value for the Great Lakes because of differences in

lake morphometry, nutrient loading, and species

composition, particularly fishes. The ability to

understand present impacts and predict future impacts

is limited by insufficient monitoring and understanding

of Great Lakes food webs. The Great Lakes need to

be carefully monitored to observe changes and to

evaluate impacts of these changes. Experimental work

in conjunction monitoring will be necessary to

develop ecological forecasting tools and models.

Acknowledgements

Thanks to the Canadian Journal of Fisheries and

Aquatic Sciences for permission to reprint Figure 2.

References

Vanderploeg, H.A., T.F. Nalepa, D.J. Jude, E.L. Mills, K.T.

Holeck, J.R. Liebig, I.A. Grigorovich, and H. Ojaveer.

2002. Dispersal and ecological impacts of PontoCaspian species in the Great Lakes. Can. J. Fish.

Aquat. Sci. 59:1209-1228.
85

Introduction

Short-term natural events (days to weeks) causing significant stress to estuarine environments are primarily

weather related: hurricanes, tropical storms or northeasters. These powerful weather systems damage habitats through direct wind or wave action and alter habitats by loading freshwater, nutrients, and pollutants into

the estuary. On a global time scale, periodic disturbances

by these weather systems are highly predictable along

the Gulf of Mexico and the eastern coast of the United

States. Precisely predicting when and where they will

occur on shorter time scales (year-to-year), however,

is highly unpredictable.

This high degree of variation makes detailed studies of

the short and long-term effects of storms on estuarine

environments and populations difficult. Most of the

objective knowledge concerning the effects of these

perturbations comes from ongoing long-term studies

that coincided with a hurricane passing through the

study area. The notable studies are the effect of Hurricane Agnes on the Chesapeake Bay in 1972, and Hurricanes Fran (1996) and Floyd (1999) on North Carolina estuaries (Figure 1).

As a result of the limited number of long-term studies,

separating the effects of these storms from normal environmental variation can only be estimated from current studies of less extreme runoff and wind events.

These smaller events generally occur much more frequently and hence are easier to study because the effects of a larger than normal spring runoff or a major

wind event often mimic, to a lesser degree, the stress

and excess nutrient loading that accompanies a tropical storm or hurricane.

The remainder of this paper briefly reviews what can

Extreme Events and Ecological Forecasting

R. Wayne Litaker and Patricia A. Tester1

Abstract

Almost all extreme events lasting less than several weeks that significantly impact ecosystems are weather

related. This review examines the response of estuarine systems to intense short-term perturbations caused by

major weather events such as hurricanes. Current knowledge concerning these effects is limited to relatively

few studies where hurricanes and storms impacted estuaries with established environmental monitoring programs.

Freshwater inputs associated with these storms were found to initially result in increased primary productivity.

When hydrographic conditions are favorable, bacterial consumption of organic matter produced by the phytoplankton blooms and deposited during the initial runoff event can contribute to significant oxygen deficits

during subsequent warmer periods. Salinity stress and habitat destruction associated with freshwater inputs, as

well as anoxia, adversely affect benthic populations and fish. In contrast, mobile invertebrate species such as

shrimp, which have a short life cycle and the ability to migrate during the runoff event, initially benefit from the

increased primary productivity and decreased abundance of fish predators.

Events studied so far indicate that estuaries rebound in one to three years following major short-term perturbations.

However, repeated storm events without sufficient recovery time may cause a fundamental shift in ecosystem

structure (Scavia et al. 2002). This is a scenario consistent with the predicted increase in hurricanes for the east

coast of the United States.

More work on the response of individual species to these stresses is needed so management of commercial

resources can be adjusted to allow sufficient recovery time for affected populations.

1 Center for Coastal Fisheries and Habitat Research, National Ocean Service, NOAA, 101 Pivers Island Road, Beaufort,

NC 28516
86

accurately be predicted about the effects of major storm

events on estuarine systems, what information managers will need to adequately respond to these storms,

and what research is needed before there can be accurate forecasting of these effects.

To understand the effects of short-term, intense perturbations such as hurricanes on estuarine systems, it is

important to consider three specific aspects of each

storm: the amount of storm runoff, the time of year the

storm occurs, and the amount of direct physical damage caused by the storm. The amount of rainfall and

subsequent runoff varies tremendously between storms

and directly determines the salinity stress. In addition,

it directly affects the overall amounts of nutrients, sediments, and toxicants introduced into the estuary. Secondly, the time of year the storm occurs is critical because

the total oxygen consumption by an estuary is directly

related to ambient temperatures. The warmer the water,

the more rapidly nutrients are taken up by phytoplankton

and converted to carbon sources that promote bacterial

growth and respiration. Given that warm water holds

relatively little oxygen, this increased bacterial respiration often leads to hypoxia or anoxia. Lastly, storms

vary greatly in the amount of direct physical damage

they inflict through wind and wave action. This damage is of particular concern when it destroys environmentally sensitive areas such as breeding habitats of

endangered species.

Case Study

There are no current models that adequately address

how these intense short-term events will impact estuaries. Most of the available data on the response of estuaries to short-term, large scale perturbations comes

from Hurricane Agnes’s strike on Chesapeake Bay in

1972, and Hurricanes Fran and Floyd, which made landfall in North Carolina in 1996 and 1999 (Tester et al.

2003), respectively. Therefore, the following case study

represents a conceptual model based on current knowledge of how various components of estuarine ecosystems are thought to respond to short-term, large-scale

weather-related events.

Salinity Stress – The freshwater input during and after

major storms, particularly hurricanes, determines the

mortality among sessile organisms as well as displacement and mortality of mobile species (Jury et al. 1995).

The mesohaline region of the estuary is generally the

most impacted by runoff during these events. When

runoff reduces the salinity in this region to less than 10

psu for more than a week, extensive mortality occurs

among benthic organisms such as sponges, tunicates,

bryozoans, coelenterates and mollusks (Andrews 1973,

Smock et al. 1994). Some species may be eliminated.

Although species with pelagic larvae and fast growth

rates generally repopulate the region within three

months, repopulation may take years for species with

limited dispersal capacity. Furthermore, if the salinity

stress coincides with the breeding season of a particular organism, fecundity and survival will be affected.

This must be taken into account when anticipating the

effects of hurricanes and tropical storms on an estuary

(Livingston et al. 1999). The closer the storm occurs to

the peak breeding season, the greater the chances of

severe population impacts.

The consequences of acute or prolonged salinity stress

on fish stocks are less understood. Preliminary studies

indicate that runoff causes extensive mortalities of estuarine fish populations (Dorf and Powell 1997, Paerl

et al. 1998). The long-term effects on each population

are now beginning to be rigorously quantified (Crowder,

personal communication). Considerable work remains

to adequately quantify losses and determine how mortalities affect future reproduction and recruitment. These

studies should also include a rigorous examination of

how food web disruption caused by runoff affects the

recovery of various fish species, particularly mesohaline

bottom feeders.

Little is known about how changes in the lower trophic

levels caused by increased runoff impact fish populations. This information is critical to accurately understand how fish stocks will be affected by runoff events.

Stressed fish are more susceptible to disease and parasites (Noga 2000, Paerl et al. 2001). Therefore, fish

mortality due to these causes should also be investigated

immediately following the initial stress event and in

subsequent years when lowered food abundance and

anoxic events may again stress these populations. Ideally,

once the general effects of runoff-induced mortality and

stress are known, local fisheries managers could adjust

catch limits to enable the recovery of fish populations.

Since prolonged salinity stress will severely affect an

estuarine ecosystem, one critical need is to accurately

predict runoff in a watershed under different conditions.

While it provides information for implementing more

accurate evacuation plans, having a precise estimate of

the potential runoff also will help sewage treatment

facilities and farm operations reduce unintended discharges. Large releases of waste material adversely af-
87

fect estuarine water quality for weeks. They also pose

a public health threat (Mallin et al. 1999). The NOAA

weather service, academic scientists and the USGS have

made great strides in developing runoff prediction

models (Pietrafesa et al. 1997). However, much work

remains to improve these models, as was shown from

the unexpectedly severe flooding in North Carolina

during Hurricane Floyd.

Nutrient Loading – Besides reducing the overall salinity regime, runoff adds inorganic nutrients, dissolved

organic nutrients, and sediments to the estuary (Paerl

et al. 1998). More than 50 percent of the annual nutrient loading can occur during a single, large runoff event

(Hahl 1981).

Of all nutrients in the runoff, nitrogen and to a lesser

extent, phosphorous, play a direct role in stimulating

phytoplankton growth (Howarth 1988). In addition,

most hurricanes and tropical storms occur in the warmer

months when the phytoplankton are actively growing.

Under these conditions, phytoplankton take up the inorganic N and P and grow rapidly. These same results

occur during smaller storm events that also contribute

nutrients into the estuary.

The biomass increase generally occurs within a week

following nutrient input and lasts from a few weeks to

three months, depending on the extent of loading

(Loftus et al. 1972, Flint 1985, Bennett et al. 1986). In

estuaries where flushing rates are high, excess nutrients and cells may be transported out of the system,

resulting in a smaller bloom than found in the lagoonal

systems that efficiently trap nutrients.

Figure 1. A) Record rainfall and extensive flooding in the PamlicoAlbemarle Sound system (PAS) accompanied hurricanes Dennis,

Floyd, and Irene, which made landfall in coastal North Carolina in

September-October 1999. The physical and chemical changes

following this massive freshwater influx served as a large-scale

natural experiment. In a matter of weeks, half of the annual supply

of nutrients was delivered to the PAS as freshwater runoff. The

relative abundance and distribution of colored dissolved organic

matter (CDOM) in the PAS system are shown during the flooding

events (B, 15 October 1999) and under a normal flow regime 1

year after the hurricanes (C, 11 Oct 2000). Light blue indicates

relatively low CDOM, whereas dark purple indicates very high

levels of CDOM in the system. The CDOM inputs into the PAS

originate from the surrounding pine forests and swamp. Hence,

CDOM serves as a visual proxy for both the amount of runoff from

the surrounding coastal regions and overall nutrient inputs. The

CDOM data were gathered during a series of over flights of the

PAS using a NOAA observation plane equipped with NASA’s airborne

oceanographic light detection and ranging instrument (AOL3).

Laser induced fluorescence (LIF) at 355 nm served as a measure of

CDOM (Tester et al. 2003).
88

Phytoplankton blooms contain a large quantity of carbon, the base of the food chain. Zooplankton grazing

communities would generally be major consumers of

this increased productivity. However, zooplankton biomass is often significantly decreased following major

runoff events (Shaheen and Steimle 1995). This further favors the development of phytoplankton blooms

and subsequent bacterial use of newly-produced carbon that would otherwise have been consumed by the

zooplankton community.

As phytoplankton cells in the bloom die and sink out

of the water column, bacteria remineralize the phytoplankton. During warmer months, the complete remineralization of N and P is often complete within a few hours

to several days (Nixon 1981). This efficient recycling

mechanism amplifies the productivity stimulated by the

initial nutrient input. Frequently, macrophytic algae also

respond to the increased nutrient load and become so

abundant they also contribute to severe anoxic events

(Sfriso et al. 1987, Barranguet and Alliot 1995).

If runoff-induced phytoplankton blooms occur in stratified salt wedge estuaries, the cells will settle into a highsalinity wedge. This wedge does not mix effectively

with the upper layer. Studies of salt wedge estuaries

that have experienced a runoff event and bloom during

warmer months indicate that if both the carbon supply

from sinking phytoplankton and temperatures are sufficiently high, extensive bacterial respiration will cause

the lower salt wedge to become hypoxic or anoxic (Taft

et al. 1980, Bennett et al. 1986).

Transient hypoxic or anoxic events similarly occur in

shallow estuaries when the primary production (phytoplankton or macrophytes) is sufficiently high so the

biological oxygen demand (BOD) consumes all or most

of the oxygen in the water column (Barranguet and

Alliot 1995, Conley et al. 2000). Sometimes these hypoxic or anoxic zones can be quite large and lead to

additional fish kills or the decimation of the benthic

microfauna (Bianchi et al. 2000). Though anoxia tends

to slow down remineralization of organic compounds

in the benthos, significant amounts of ammonium and

phosphate can be released (De Casabianca et al. 1997).

If nutrients released below the pycnocline are transferred to the upper layers of the estuary via wind, tidal,

or shear mixing forces, additional phytoplankton/macrophyte growth will occur, which will subsequently

increase the BOD (Malone et al. 1986). This cycle accounts for the increasing number of anoxic events with

increased nutrient loading in estuaries.

Accurate models are needed to address the link between

hydrography, nutrients, phytoplankton biomass, and the

potential for anoxia events. The latest generation of

Doppler radar current meters, buoyed meter arrays, and

real-time satellite uplinks make gathering these data

much more practical and should lead to ever improving circulation models. Improved models and real-time

monitoring will give environmental managers better

information relating to potential anoxic events and their

potential to affect environmental and public health. In

addition to determining the effects of hurricanes, these

models would also help predict the potential effects of

increased nutrient loading from aquaculture, waste treatment plants, and agricultural runoff.

Sediment Loading – Runoff from tropical storms and

hurricanes can load more than a year’s worth of sediment into an estuary within several weeks (Gross et al.

1978). Sedimentation affects the benthic community

directly by burying many benthic organisms so they

cannot feed effectively. Indirectly, the sediments represent a large source of organic carbon, exacerbating

the BOD problem during the warmer months (Paerl et

al. 1998). The initial anoxic or hypoxic conditions produced by storm runoff are often not the result of bacterial action on increased algal production, but rather the

immediate metabolism of organic material in the sediment. Over the next three to five years, additional nutrients will be released as this organic matter continues

to be remineralized by benthic bacteria. The particulate organic matter therefore functions as a ‘time-release fertilizer,’ increasing productivity and resulting

in more frequent and severe anoxia events in subsequent years (Paerl et al. 2001).

The increased productivity following runoff events can

have both positive and negative effects. On the positive side, the increased productivity can result in a rapid

increase in the abundance of certain invertebrates. For

example, a general phenomenon is that shrimp harvests

increase substantially the year after hurricanes or tropical storms cause large runoff events (Flint 1985). Presumably the shrimp, either directly or indirectly, take

advantage of the increased primary productivity and

the disruption of predatory fish populations. On the

negative side, certain parts of an estuary, with fairly

low flushing rates and high recycling rates, may experience nuisance algal blooms when there is little wind.

These blooms greatly reduce the recreational value of

the estuary and can supply enough carbon for the bacterial community to create anoxia and related fish kills.
89

Toxic Chemicals – The extent to which hurricanes load

toxicants into estuaries depends largely on upstream

sources. Large amounts of water coming into the system often dilute dissolved toxic compounds below levels of detection. Given sufficient outflow from the estuary during the event, they can be largely eliminated

from the estuary. In that respect, the runoff can cleanse

the estuary.

When the toxic substances are bound to sediment particles, the impact is less clear. If the upstream sediment

is contaminated with heavy metals, pesticides, or other

harmful chemicals, these substances can be retained in

the system. After reaching the benthos, they either remain inertly attached to the particle or are mobilized to

enter the food chain. If the sedimentation rate is sufficiently high, some of the labile material will be buried

and effectively sequestered unless released by

bioturbation. The amount of bioturbation following the

sedimentation event is, therefore, an important component determining how rapidly toxic compounds enter the

food chain.

In cases where bioturbation is important, the remineralization process may be delayed for months after the

runoff event until the benthic community is reestablished. Similarly, physical resuspension of already contaminated sediments by wind and wave action can contribute to the renewed cycling of once sequestered toxic

compounds (Tisue et al. 1992). To adequately understand how runoff will affect the distribution and cycling of toxic compounds in estuarine systems, more

research is needed to estimate potential toxic loads in

upstream watersheds and understand how certain toxins are transformed and transferred at different salinity

regimes within the estuary.

Much is known regarding the migration and transformation of heavy metals, pesticides, and other organic

compounds; however, accurate loading estimates are

often lacking. To obtain a realistic picture of the potential threat to an estuary, toxic compounds will require

frequent monitoring, particularly during runoff events.

Estuaries receiving significant inputs of industrial waste

or pesticides should be given priority monitoring to

identify any significant deterioration in environmental

quality. Given the limited resources available to environmental managers, the toxicants measured should also

be carefully selected so the focus is on those most likely

to cause adverse effects on human or animal health.

Reviews should be made periodically to determine if

other critical toxins should be measured or if more appropriate and economical detection methods have been

developed.

Wind and Wave Damage – Hurricanes, tropical

storms, and particularly northeasters are also notable

for the physical damage they cause to estuarine environments. Much of this damage may persist for years.

In the broadest ecological terms, these physical disturbances can all be viewed as a means to open niche space.

No matter how severe the disruption, the same or different species will eventually exploit the newly-created

habitat.

Vascular plant communities surrounding estuaries are

most visibly affected by major storms. Mortality and

damage occurs from uprooting, stripping leaves and

limbs, and salt spray damage. The periodic damage inflicted by hurricanes, therefore, plays a crucial role in

the structure of these plant communities.

A good example is the mangrove community (Wanless

et al. 1996). Hurricanes open space in mangroves by

destroying trees, removing peat deposits, and by bringing poisonous anoxic sulfide rich sediments to the surface. Once the peat is gone, subsidence occurs; the sulfide released from the mud kills weakened trees and

saplings. Despite this damage, the mangrove and other

plant communities gradually respond with increased

productivity after the storm and generally recover until

the next storm produces similar damage.

The immediate effect of these storms, however, is to

degrade the habitat for many vertebrate and invertebrate

species. Storm surges also frequently disrupt littoral

sand communities and deposit large amounts of sand

onto existing marshes further inland (Courtemanche et

al. 1999), causing significant habitat destruction. Many

species are forced to disperse to find food and shelter,

accounting for the increase in reported hornet and wasp

stings and snakebites following hurricanes (Valiela et

al. 1998).

The physical disturbances caused by major storms cannot be altered and is primarily of concern when there

are endangered species within the affected habitat. In

this case, strategies should be sought to encourage establishing as many spatially-dispersed habitats as possible to ensure the best chances for survival.

The timing of the physical disturbance must also be

considered when determining its specific impact on

various estuarine species. Juvenile oysters, for example,

sometimes benefit from the reduction in predators and

open surfaces created by the physical disturbance dur-
90

ing storms. However, if the storm event occurs during

maximal recruitment or significantly affects the ability

of the adult population to produce larvae, the effects

on the population will be negative (Livingston et al.

1999). Similar effects on different life cycle stages

would apply to hundreds of different organisms. Given

the limited knowedge about many estuarine organisms,

it will be difficult to incorporate timing issues into

models that will accurately predict the effect of hurricanes, tropical storms and northeasters on estuarine

environments and populations.

Conclusions/Next Steps

There is a broad understanding of how major storms

affect estuarine environments, both positively and negatively. Better models of rainfall, runoff and estuarine

circulation are needed to improve predictive capabilities. Without robust basic hydrographic models, it will

not be possible to understand and predict the effects of

a storm on an estuary. Once these models are available, then realistic model inputs for nutrients, toxicants

and other factors can be incorporated and first order

predictions made. Once these predictions are available,

monitoring real time storm events is needed to test the

models’ predictions.

A particularly important model parameter to evaluate

will be nutrient loading. Having accurate loading information is crucial in determining whether nutrient

loading is increasing through time, either through increased hurricane activity or anthropogenic inputs. If

eutrophication is allowed to proceed, there will be adverse long-term biogeochemical and trophic changes

in estuarine and coastal habitats (Paerl et al. 2001). Only

by carefully monitoring nutrient inputs and using sophisticated modeling will it be possible to predict those

changes and justify the political support for modifying

land use, agricultural, and waste treatment policies sufficiently to avoid an unalterable degradation in estuarine environments.

The next steps will be to integrate long-term monitoring projects with intensive acute response monitoring

to determine how large, short-term perturbations such

as hurricanes affect estuarine ecosystems. This is particularly important given the greater eutrophication

stress being placed on estuaries and predictions for increased hurricane activity (Goldenberg et al. 2001).

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92
Introduction

One of the key roadblocks to integration across scales

in population biology has been the failure to include

all factors that might influence the status of a population in a common framework. Generally, researchers

study what is known (e.g., behavior, physiology, toxicology, fisheries interactions) and hope the insights

gained can be brought to bear on understanding the

dynamics of these populations.

Understanding these dynamics is particularly critical

for research on threatened and endangered species like

sea turtles. If factor A reduces fecundity by 10 percent

- is that important? Or if factor B reduces juvenile survival by 5 percent – is that important? Is factor A more

important than factor B? It is necessary to put these

factors into a common ‘currency’ to address the ‘so what?’

questions at the population level. Namely, age- or stagespecific effects on population vital rates must be estimated (e.g. fecundity, growth, and survival). This type

of integration is long overdue. And it is critical to forecasting the effects of environmental change and human activity on the future of sea turtles and other protected species.

All five species of sea turtles that nest in the US are

protected under the Endangered Species Act (1973).

Their population demographics reflect the effects of

multiple stressors. These include a history of terrestrial

habitat loss, terrestrial and aquatic habitat degradation, as

well as direct hunting and non-target bycatch in several

Reversing the Decline of Sea Turtles:

Insights from Life-History Population Models

Larry B. Crowder1

Abstract

All seven sea turtle species on earth are threatened or in danger of extinction. The life history of turtles, including

late age at first reproduction, was successful for over 100 million years. But in the last century, sea turtles have

flirted with extinction.

It is partly related to reproductive success. Some sea turtles begin reproducing in their teens - others wait three

decades or more. Nesting females lay several clutches of eggs every few years over a reproductive span of

decades. In some areas nearly all the eggs were lost to human and other predators.

Fishing gear has added to the problem – it isn’t restricted to small prey. So the protection afforded to sea turtles

by their size and hard carapace is inadequate.

Beginning nearly 20 years ago, more and more precise life-history population models have been developed for

sea turtles. But even simple population forecasting techniques provide useful guidance to researchers and

managers who deal with long-lived species and limited data. Analyses of these models focus researchers and

managers on the life stages and processes that would have the greatest impact on population recovery.

A loggerhead model showed protection of large juveniles and adults was critical to recovery and paved the way

for requiring Turtle Excluder Devices (TEDs) in the US in 1990. TEDs, along with habitat protection, has

brought Kemp’s ridley back from as few as 800 nests per year in the mid-1980s to nearly 6,000 nests in 2002.

Recent analyses suggest that TEDs may need to be enlarged to protect adult loggerheads and leatherbacks. And

of course, TED regulations need to be enforced for sea turtles to benefit.

1 Duke University Marine Laboratory, Nicholas School of the Environment and Earth Sciences, Beaufort, NC 28516-9721,

lcrowder@duke.edu

93
fisheries spreading over ocean basins(NRC 1990, Figure 1). Specifically, beach development, photopollution, beach renourishment,

and beach armoring have reduced

nesting habitat, compromised incubation environments, and reduced hatchling production.

These threats continue, but new

threats include increased egg incubation temperatures due to global

warming (further skewing sex ratios

which are defined by incubation

temperature), and loss of nesting

habitat associated to rising sea

levels on developed and armored

beaches. In marine systems, accumulation of pollutants such as plastics, heavy metals,

environmental estrogens, and oil products in pelagic

nursery and demersal coastal habitats as well as rapid

general degradation of marine and estuarine environments by nutrient runoff have the potential to further

reduce turtle populations. In addition, sea turtles swim

into a gauntlet of fishing gear including trawls, gill nets,

pound nets and longlines as they migrate across ocean

basins. The National Academy of Sciences panel (NRC

1990) concluded that interactions with fisheries were

the most serious threat to sea turtle populations.

Case Studies

Sea turtle populations are monitored by counting nests

(and less often nesting females) on nesting beaches.

For many sea turtle populations, numbers have declined

dramatically from historical peak values (Bjorndal and

Jackson 2002), but others are stable or showing some

signs of recovery (Figure 2). This data for population

trends can be misleading, however, because nesting

females often constitute a very small portion of the

overall population. Loggerheads first nest in their early

30s, so adult females not only account for much less

than one percent of the population, but they reflect nesting success and oceanographic conditions decades ago.

The following is a brief summary of the work in analysis and modeling for loggerhead and Kemp’s ridley sea

turtles.

Loggerhead Sea Turtles – Early deterministic matrix

models of North Atlantic loggerhead sea turtles (Crouse

et al. 1987, Crowder et al. 1994) outlined an approach

to compare multiple management scenarios and determined which of several proposed scenarios was most

(or least) likely to enhance the probability of population recovery.

Using a technique called elasticity analysis, we can

examine the effects on population growth rates of small

proportional changes in survival, growth, and fecundity for a particular sea turtle population. Elasticity

analysis of early determinsitic models pointed to critical and sensitive aspects of the life history and emphasized where uncertainty in parameter values limited

their predictive ability (Crouse et al. 1987, Crowder et

al. 1994, Heppell et al. 1996, Heppell et al. 2000a,

Heppell et al. 2002b). The analysis provided direction

for researchers who wanted to reduce this uncertainty

and so enhance predictions. It also pointed to sensitive

stages (e.g., benthic juveniles) and processes (e.g., survival) which gave direction to managers.

In Australia, more detailed data allowed development

of more elaborate stochastic difference equation models for green and loggerhead sea turtles (Chaloupka and

Limpus 1996, Chaloupka 2001). Of course, a variety

of model structures, from simple deterministic models

to stochastic, individual-based, spatially-explicit models

(Letcher et al. 1998, Walters et al. 2002) could be used.

This approach uses the most appropriate modeling form

as determined by the questions and the available data.

Although data on population vital rates of North Atlantic loggerhead turtles were too limited in the early

1980s to develop more complex models (e.g., stochastic or time-varying), these early analyses proved extremely useful. Elasticity analysis of the loggerhead

Figure 1. Life history of sea turtles, threats and management responsibilities in the US.

94

Beach lighting

Vehicles

Long lines

Plastics/tar ingestion

Sargassum harvest?

Beach lighting

Introduced predators

Beach renourishment

Shrimp trawls

Other fishing gear

Boat strikes

USFWS

USFWS

NMFS

International

Benthic

Juveniles/Adults

Nesting Females

Pelagic Juveniles

Hatchlings
model showed the greatest impact on the population

could be made by increasing survival of large juveniles

and sub-adults – precisely the sizes taken most often in

trawl fisheries.

This evidence was used to support the implementation

of Turtle Excluder Devices (TEDs) in trawl fisheries

in the US just three years after the initial paper was

published (Crouse et al. 1987). Subsequent analyses

showed that TEDs were reducing the number of dead

loggerheads washed up on Atlantic beaches (Crowder

et al. 1995) to levels that might allow population recovery (Crowder et al. 1994). These forecasts were followed by population increases in the South Florida subpopulation (Heppell et al. 2002a), but these increases

appear to have recently leveled off (Blair Witherington,

pers. comm.). The northern population continues to

decline at about 2 percent per year despite all management efforts to date.

Figure 2. Examples of long time series of sea turtle abundance. Data generously provided by B. Witherington, Florida Fish and Wildlife

Conservation Commission, Florida Marine Research Institute, Index Nesting Beach Survey Program (greens and loggerheads), the

Gladys Porter Zoo and SEMARNAT/INE, Mexico (Kemp’s ridleys), Z. Hillis-Starr and B. Phillips, Buck Island Reef NM Sea Turtle

Research Program (hawksbills), D. Dutton and P. Dutton (leatherbacks, St. Croix) and R. Reina and J. Spotila (leatherbacks, Costa

Rica).

95
The TED technology is now spreading globally (Epperly

2002). Recent analyses (NOAA 2001) support increases

in the openings of TEDS to protect adult loggerehads

and letherbacks. It also suggests minimizing takes in

longline fisheries are potentially important to recovery.

Kemp’s Ridley – Kemp’s ridley sea turtles nest primarily on one beach, Rancho Nuevo, Mexico. They were

once considered the most endangered sea turtle, having

only about 300 nesting females per year the mid 1980s.

Modeling Kemp’s ridley population dynamics (Heppell

et al. 1996, 2002c) drew attention to the need for protecting the in-water stages and lent support to efforts to

require TEDs. It also showed a program of captive rearing of hatchlings, referred to as ‘headstarting,’ was unlikely to increase population numbers. Still, the headstarting

program contributed to an important goal of establishing

a second nesting population at Padre Island National

Seashore.

Based on both protection of nests on the beach and TED

regulations, Kemp’s ridleys have been increasing

steadily since the late 1980s at a rate of 11-13 percent per

year (Heppell et al. 2002c.).

New analyses on the spatial distribution of turtles and

shrimpers in the Gulf of Mexico pointed to important

problems. One of these is the effect of the hypoxic ‘dead

zone’ on demersal habitat for fishes, turtles, and marine mammals (Craig et al. 2001). Also, several areas

within the Gulf that seemed to retain relatively high

densities of turtles and so might be appropriate for additional protection as marine reserves (McDaniel et al.

2000). Two of these areas have since been included in

protected areas as part of the expansion of the Dry

Tortugas Marine Sanctuary off the southwestern tip of

Florida and as a seasonal, nearshore shrimp closure

(which also protects turtles) near Padre Island National

seashore off the coast of Texas.

In a recent analysis of strandings data from Texas (the

number of dead turtles washed up on the beach, Lewison

et al. 2003), both sea turtle population growth and

shrimping activity have contributed to the observed

increase in strandings. TED compliance was a significant factor in accounting for annual stranding variability – low compliance was correlated with high

strandings. Forecasts suggest that improved compliance

with TED regulations will reduce strandings to levels

that with other protective measures, should promote

population recovery for both loggerhead and Kemp’s

ridley turtles. Local, seasonal fisheries closures, concurrent with TED enforcement, could reduce strandings to

even lower levels.

The model forecasts the Padre Island shrimp closed area

is also likely to reduce mortality of adult Kemp’s ridley,

potentially as much as 39 percent. In 2001 and 2002, this

reduction was 46 percent and 38 percent, respectively.

This seasonal closure, adjacent to a recently established

Kemp’s ridley nesting beach at Padre Island National

Seashore, may promote long-term population persistence by establishing a robust, secondary nesting site.

Evolving Model Approaches – Subsequent model

analyses have generalized the approach to understanding population dynamics of long-lived species (Heppell

et al. 2000b) and to species with very limited demographic data (Heppell 1998, Heppell et al. 2000c).

Chaloupka (2001) outlines many advantages of developing fully stochastic models, if the data are available.

One basis for his analyses is that variation is probably

greater in processes that influence fecundity and survival

in the egg and early juvenile stages than for survival

later. The qualitative effect on previous conclusions

(based on the deterministic model) is reproduction and

early survival are likely to become more important. This

is similar to results from comparing deterministic and

stochastic models for fish populations (Quinlan and

Crowder 1999).

Recently, there is new information on the subpopulation structures of North Atlantic loggerheads (NOAA

2001). There are no fewer than five genetically distinct

rookery populations. Most are increasing, but the northern nesting population, which produces most of the

males for the regional population is not.

Historical evidence suggests that lost rookeries are seldom recolonized; losing the northern population presents two problems – loss of a distinct segment of the

northwest Atlantic loggerhead population and loss of

the major source of males for the remaining regional

population.

And finally, managers are increasingly considering areabased management alternatives like marine protected

areas. For these reasons, a spatially-explicit approach

to loggerhead population dynamics is being formulated

(Wyneken and Crowder 2001).

Lessons Learned

The key lesson from attempts to forecast populationlevel implications of management alternatives for longlived species like sea turtles is significant progress can

96
be made with even limited data. Conducting even simple

analyses requires critical aggregation and examination

of data. These tools quickly and efficiently focus research and allow comparison of management alternatives,

at least qualitatively. As data quality improves, new

analytical tools can be applied.

For sea turtles and other long-lived species, management alternatives need to be considered even before all

the data are in. In addition to forecasting, TEDs can

work to reduce sea turtle mortality and alter population

trends. The analysis also highlighted the importance of

mortality at sea, requiring entirely new research efforts

on the in-water phase of sea turtle life histories.

Conclusions

Population models provide a useful framework to integrate what is known and to clearly identify what is not.

Analyses of these models also allows comparison of

management alternatives based on the mode of action

and impact to determine which are likely to contribute

most to population recovery. Although the early models of sea turtle population dynamics were simplistic,

the qualitative forecasts are robust for turtles (Heppell

1998) and for most long-lived species (Heppell et al.

2000b).

Model analyses not only identify poorly-known parameters, but point to factors expected to have the greatest

impact on population growth. In this way, research priorities can be more efficiently focused.

Finally, these models are constrained by the available

data and now can only make reliable qualitative predictions. Precise quantitative prediction requires additional data collection. Still, for ecological forecasting,

qualitative predictions have provided useful guidance

to managers responsible for the recovery of these endangered species.

Acknowledgements

I thank Debby Crouse for introducing me to the world

of sea turtles. Selina Heppell and Melissa Snover have

borne the brunt of model development for the last few

years. I also thank Nancy Thompson, Sherry Epperly

and other members of the Turtle Expert Working Group

for all their assistance and encouragement. NMFS has

supported much of this research, but recently EPA, Environmental Defense and Pew Charitable Trusts also

have provided support. To all I am most grateful.

References

Bjorndal, K.A., and J.B.C. Jackson. 2002. Roles of sea turtles

in marine ecosystems: Reconstructing the past., In: P.L.

Lutz, J.A. Musick and J. Wyneken (eds.). The Biology

of Sea Turtles, Volume II. CRC Press, Boca Raton. pp.

259-274.

Chaloupka, M.Y. and C.J. Limpus. 1996. Heuristic modeling of

Chelonia mydas population dynamics – southern Great

Barrier Reef. In: Proceedings of the 15th Annual

Symposium on Sea Turtle Biology and Conservation.

NOAA Tech. Memo. NMFS-SEFSC-387.

Chaloupka, M.Y. 2001. Stochastic simulation modeling of

southern Great Barrier Reef green turtle population

dynamics. Ecological modeling (in press).

Craig, J.K., L.B. Crowder, C.D. Gray, C.J. McDaniel, T.A.

Henwood and J.G. Hanifen. 2001. Ecological effects

of hypoxia on fish, sea turtles and marine mammals in

the Northwestern Gulf of Mexico. In: Nancy N. Rabalais

and R. Eugene Turner (eds.). Coastal Hypoxia: Consequences for Living Resources and Ecosystems. Coastal

and Estuarine Studies 58, American Geophysical Union,

Washington, D.C. pp.269-291.

Crouse, D. T., Crowder, L. B., and Caswell, H. 1987. A stagebased population model for loggerhead sea turtles and

implications for conservation. Ecology 68 (5): 1412-

1423.

Crowder, L.B., D.T. Crouse, S.S. Heppell and T.H. Martin.

1994. Predicting the impact of turtle excluder devices

on loggerhead sea turtle populations. Ecol. Appl. 4:437-

445.

Crowder, L.B., S.R. Hopkins-Murphy, and A. Royle. 1995.

Estimated effect of Turtle-Excluder Devices (TEDs)

on loggerhead sea turtle strandings with implications

for conservation. Copeia (4):773-779.

Epperly, S.P. 2002. Fisheries-related mortailty and turtle

excluder devices. In: P.L. Lutz, J.A. Musick and J.

Wyneken (eds.). The Biology of Sea Turtles, Volume

II, CRC Press, Boca Raton. pp. 339-354

Heppell, S.S., L.B. Crowder and D.T. Crouse. 1996. Models

to evaluate headstarting as a management tool for longlived turtles. Ecol. Appl. 6(2):556-565.

Heppell, S.S. 1998. Application of life-history and

population model analysis to turtle conservation. Copeia

1998:367-375.

Heppell, S.S., L.B. Crowder and T.R. Menzel. 1999. Life

table analysis of long-lived marine species with

implications for conservation and management. In: J.A.

Musick, (ed.). Life in the slow lane: Ecology and

conservation of long-lived marine animals. American

97
Fisheries Society Symposium 23, Bethesda, MD. pp.

137-148.

Heppell, S.S., D.T. Crouse and L.B. Crowder. 2000a. Using

matrix models to focus research and management efforts

in conservation. In: S. Ferson and M. Burgman (eds.).

Quantitative Methods for Conservation Biology.

Springer-Verlag, Berlin. pp. 148-168.

Heppell, S.S., H. Caswell and L.B. Crowder. 2000b. Life

histories and elasticity patterns: Perturbation analysis

for species with minimal demographic data. Ecology

81(3):654-665.

Heppell, S.S., M.L. Snover and L.B. Crowder. 2002. Sea

turtle population ecology. In: P.L. Lutz, J.A. Musick

and J. Wyneken (eds.) The Biology of Sea Turtles,

Volume II, CRC Press, Boca Raton. pp. 275-306.

Heppell, S., D. Crouse, L. Crowder, S. Epperly, W. Gabriel,

T. Henwood, R. Marquez, and N. Thompson. 2002c. A

population model to estimate recovery time, population

size, and management impacts on Kemp’s ridleys.

Chelonian Conservation and Biology (in press).

Heppell, S.S., L.B. Crowder, D.T. Crouse, S.P. Epperly and

N.B. Frazer. 2003. Population models of Atlantic

loggerheads: Past, present, and future. In: A. Bolton and

B. Witherington (eds.) Biology and Conservation of

Loggerhead Sea Turtles. Smithsonian Institution Press.

(in press).

Letcher, B.H., J.A. Priddy, J.R. Walters and L.B. Crowder.

1998. An individual-based, spatially- explicit simulation

model of the population dynamics of the endangered

red-cockaded woodpecker, Picoides borealis. Biol.

Conserv. 86:1-14

Lewison, R., L.B. Crowder, and D.J. Shaver. 2003. The

impact of turtle excluder devices and fisheries closures

on loggerhead and Kemp’s ridley strandings in the

western Gulf of Mexico. Conservation Biology (in

press).

McDaniel, C. J., L. B. Crowder, and J. A. Priddy. 2000.

Spatial dynamics of sea turtle abundance and shrimping

intensity in the U.S. Gulf of Mexico. Conservation

Ecology 4(1):15. http://www.consecol.org/vol4/iss1/

art15.

NOAA. 2001. Stock assessments of loggerhead and

leatherback sea turtles and an assessment of the impact

of the pelagic longline fishery on the loggerhead and

leatherback sea turtles of the western North Atlantic.

NMFS Technical Memorandum NMFS-SEFSC 455,

343pp.

NRC. 1990. The decline of sea turtles. National Academy

Press, Washington, D.C., 280pp.

Quinlan, J.A. and L.B. Crowder. 1999. Searching for

sensitivity in the life history of Atlantic menhaden:

Inferences from a matrix model. Fish. Oceanogr. 8

(Suppl.2):124-133.

Walters, J.R., L.B. Crowder, and J.A. Priddy. 2002.

Population viability analysis for red-cockaded woodpeckers using an individual-based model. Ecol. Applic.

12:249-260.

Wyneken, J and L.B. Crowder, 2001. Evaluating multiple

stressors in loggerhead sea turtles: Developing a twosex spatially explicit model. Research proposal to EPA

STAR Wildlife Risk Assessment Program (GAD R82-

9094)

98
99

Introduction

As a natural resource trustee, under United States and

California laws and regulations, the remediation and

restoration of hazardous waste contaminated sediments

in estuarine and ocean waters is a responsibility of the

Federal and State fish and wildlife agencies. This is

under authorities of the Comprehensive Environmental Response, Compensation and Liability Act

(CERCLA) of 1980, as amended, 42 U.S.C. 9601 et

seq., and the Clean Water Act (CWA), 33 U.S.C. 1251-

1376, and the California Fish and Game Code (§ 2014).

CERCLA, Section 107. They require the governor of

each state to designate state officials who may act on

behalf of the public as trustees for natural resources,

which are defined to include “land, fish, wildlife, biota,

air, water, groundwater, [and] drinking water supplies.”

This Section 107 trustee designation is important because it validates the status of an agency as a relevant

state authority in the CERCLA process (i.e., hazardous

waste cleanup, which may affect natural resources) and

clarifies its jurisdiction. The California Department of

Fish and Game has been delegated as one of the

CERCLA Section 107 natural resource trustees.

Remedial actions (i.e., contaminant cleanup, removal,

or response), at hazardous waste sites, are managed by

Federal and State water quality regulatory agencies (i.e.,

US EPA and CalEPA). There are several regulatory

actions that may be used by the agencies. However, the

principal approach is to perform a Remedial Investigation/Feasibility Study (RI/FS) to determine the relative

threat(s) or risk(s) of the contaminants to human heath

and the environment, and to develop cleanup alternatives. The selected alternative for remediation is included in the Record of Decision (ROD) for the site,

which provides the analyses and justification for the

selection of the remedy.

If remedial actions are judged insufficient to protect

and restore natural resources injured by releases from a

Superfund site, or if the use of the natural resource is lost

or curtailed because of the releases, the natural resource

trustees may seek to collect damages from responsible

parties. Natural resource damages are monetary payments

Contaminant Forecasting at Hazardous Waste Sites

in California Marine Waters

Deborah J. Johnston and Michael Martin1

Abstract

Predicting or forecasting pollutant responses, after their removal, has been accomplished with DDTs for

piscivorous birds and raptors at several marine sites in North America. Conversely, there are few examples of

predictions of contaminant effects on other marine receptors or communities. In California, three major sites

have been contaminated with organochlorine compounds (DDTs and PCBs): Moss Landing Harbor, Richmond

Harbor, and the Palos Verdes Shelf.

The discharge of bioaccumulative contaminants has caused injuries and toxic effects to marine biota. Sediment,

water column, and biota contaminant concentrations, which will protect marine receptors at these sites, were

developed, based upon forecasting estimates. This data and information was evaluated to determine which

cleanup and disposal (= remediation) options best protect State natural resources. At one site (Richmond Harbor),

field measurements (chemistry and biology) were used to evaluate the effectiveness of the predicted environmental

conditions after cleanup. The other two sites are in earlier stages of this process, however natural recovery

predictions (i.e., without remediation) are presented.

1

 California Department of Fish and Game, 20 Lower Ragsdale Drive, Suite 100, Monterey, CA 93940
100

“for injury to, destruction of, or loss of natural resources,

including the reasonable cost of assessing such injury,

destruction, or loss resulting from such a release.”

These payments are considered compensation, and are

intended to cover the past injury and residual costs, or

losses, beyond whatever restoration can be achieved

through remediation. Any payments to natural resource

trustee agencies from responsible parties under these

statues are limited to: 1) reimbursement to the agency

for its past costs of determining injuries and damages

to natural resources and 2) expenditures for planning,

monitoring, and implementation of restoration activities.

Case Study

The issues to be examined in this case study of Californian marine and estuarine sediment cleanups are threefold: 1) prediction of toxic chemical concentrations with

remediation, 2) prediction of ecosystem response (i.e.,

selected biomarker response) following remediation,

and 3) estimates of uncertainty in forecasting ecological responses for remedial actions.

This forecasting is at three sites, which are contaminated

with organochlorine pesticides.The focus is to evaluate

the feasibility of using ecological forecasting in restoration

planning, to establish a framework for ecological prediction and assessment (confirmation), and to identify

problems and recommendations for future forecasting

needs. The forecasts are intended to serve as information

and planning evaluations to portray estimates of ecological

conditions resulting from the remedial actions (and designs),

based upon experiences at the subject sites (Figure 1).

At these three sites (Figure 1), hazardous waste remediation

has occurred or is in stages of planning. The sites are

described in relation to the chemical and ecological

response forecasts, a description of the forecasting

methods, and the likely outcomes, based upon planned

remedial action(s). Forecasts are generally qualitative

predictors of chemical and ecological responses, however,

portions of the predictions are based upon application

of a quantitative equilibrium model of contaminant biomagnification in coastal food webs.

One of the forecast efforts (Montrose NRDA, Palos

Verdes Peninsula) was usedto evaluate the fate of contaminants in coastal sediments, as well as predicting

the bioaccumulation potential for various food web

compart-ments (Hydroqual, 1994). The second site

(United Heckathorn, Richmond Harbor) involved a

completed removal and remediation project (which

anticipated risk reduction to no significant future effects), and subsequent field verification. The third site

(Moss Landing Harbor) included a description of a

planned project, using environmental monitoring data,

with estimates of responses under several cleanup options.

Palos Verdes Peninsula DDT Contamination – The

Palos Verdes Peninsula DDT contamination is the result of over four decades of industrial-produced DDT

waste releases from a major municipal wastewater outfall. Over 1,800 metric tons were discharged. Approximately 100 metric tons of DDT remains sequestered in

surficial sediments on the ocean bottom in the general

vicinity of the outfall. An ongoing cleanup investigation, along with pilot scale remedial activities, is being

conducted by the response agency to determine a recommended alternative for cleanup action.

The food chain mathematical model, based on

organochlorine biomagnification, was used to

estimate concentrations in several receptor species

[fish (Dover sole, white croaker, and kelp bass),

sea lions, peregrine falcons, eagles, and doublecrested cormorants] that would result from exposure to sediments and water column contaminants

at the site, located in approximately 60 meters

of water, off of the Palos Verdes Peninsula, Los

Angeles, California. The model is based upon

two principal variables: 1) the bioaccumulation

potential of the organochlorine contaminant

[DDT (proportional to its octanol water partition

coefficient or Kow)] and 2) the bioenergetic status of the receptor species (= trophic level).

Figure 1. Case Study Sites
101

The model also was used

to project (estimate) future concentrations of

organochlorine contaminants in sediments and

water columns, as well

as future contaminant levelsin target species, under

conditions of natural attrition. Several response

actions were evaluated,

using risk to human health

and the environment as

the receptors of concern.

The initial engineering

evaluation determined

capping with a one meter

cap, constructed of clean

dredged materials was a

potential feasible alternative, and is being evaluated further with pilot project investigations (EPA 2001). The

modeling predictions for recovery responses in bird species are based upon ‘natural recovery,’ that is, with no

remedial actions at the outfall site. Current pilot scale

testing is being evaluated to determine the effects (benefits)

of remedial actions for 1) risk reduction to human health

and the environment, and 2) accelerated recovery of

affected receptor species.

Results of the modeling indicated levels of DDT in the

sediments would increase from the current levels of 1

μg/g to 1.9 μg/g in 2030 due to erosional factors releasing DDT. The levels would then decrease to a final

level of 0.15 μg/g in 2100. Based on the model results,

it is expected that the peregrine falcon population counts

will decrease, corresponding to the increase release of

DDT predicted to occur by 2030. Then as DDT levels

decrease, falcon populations should recover (in 2100)

to what was predicted for the Channel Islands based on

no exposure to DDT.

United Heckathorn DDT and Dieldrin Contamination – Lauritzen Channel is an industrial waterway adjacent to the former United Heckathorn facility in the inner Richmond Harbor area of San Francisco Bay, California (Anderson et al. 2000, Figure 2).

Contaminated marine sediments at this Superfund site

were dredged and disposed in a land facility from late

1996 through early 1997 to remove DDT, and dieldrin,

both of which pose high risks to wildlife. A study assessed

the Lauritzen Channel marine environment immediately

before and approximately one year after the dredging.

The study included chemical analysis of sediments, tissue

concentrations of transplanted mussels, toxicity testing of sediment samples, and characterization of benthic

community structure. The receptors evaluated at this

site were associated with benthic invertebrate’s chronic

toxicity and benthic invertebrate community responses.

The pre-remediation forecast was that removal and

upland disposal of DDT-contaminated sediments would

completely eliminate bioaccumulation and deleterious

effects to aquatic species from organochlorine compound

exposure (Table 1). Results indicated that sediment toxicity

to bivalve larvae (Mytilus galloprovincialis) decreased

in post-remediation samples, but that toxicity to the amphipod (Eohaustorius estuarius)increased significantly,

when exposed to post-remediation samples.

After remediation, assessment of benthos at this site

suggested a transitional benthic community structure.

In addition, post-remediation sediments remained contaminated by a variety of organic chemical compounds,

including DDT, dieldrin, chlordane, polycyclic aromatic

hydrocarbons, and polychlorinated biphenyls. Tissue

concentrations of DDT and dieldrin in mussels (M.

galloprovincialis) were lower than those in preremediation samples, indicating that although sediment concentrations of organochlorine pesticides remained high,

concentrations of these chemicals in the water column

were reduced after dredging. This suggests that their

Figure 2. United Heckathorn Site.

Richmond Harbor

Table 1. Sediment SDDT and Dieldrin Concentrations Before and After Site Remediation of the Lauritzen Channel.

 Chemical Before After Before After Before After Before After

 SDDT1 77,700.00 21,361.80 47,800.00 27,833.00 26,000.00 15,555.00 2,740.00 840.2

 SDDT2 3,500.00 2,637.26 2,710.00 1,366.81 1,520.00 691.33 189 53.18

 Dieldrin3 748 371 528 619 442 196 35.7 25.8

 Dieldrin4 35.2 45.8 28.7 30.34 25.8 8.71 2.46 1.63

 % Fines 92.3 23.5 85.2 91.59 85.9 94.45 89.5 92.58

 % Organic Carbon 2.38 0.81 1.78 2.09 1.73 2.25 1.46 1.58

1. ug/kg dry weight 2. ug/g OC 3. ug/kg dry weight 4. ug/g OC

Station 1 Station 2 Station 3 Station 4
102

bioavailability may have diminished after the remedial

actions (Table 1).

Moss Landing Harbor Sediments DDT Contamination – Moss Landing Harbor lies at the head of the

Monterey Bay Canyon. It receives non-point runoff

from land based activities primarily agricultural nutrients and pesticides. Regular dredging of the harbor sediments has not occurred due to the high DDT, TBT,

PCBs, and copper levels that exceed EPA standards for

unconfined aquatic disposal of dredged sediment. The

study proposes to collect data and design a model to

project contaminated sediment movement (both geologically and biologically) in the Monterey Bay Canyon. This will enable resource managers to determine

appropriate disposal locations for DDT-contaminated

dredged sediments. This project is in the initial stages

of data collection.

The model proposes to use Cs/K ratios with field and

laboratory validation. It would provide a more site-specific analysis of DDT movement, both within the food

chain (bioaccumulation) and within the canyon (locations dependent on how available the DDT is to the

biota for uptake). Several of the important model components are unknown including: sediment transport

within the Harbor and Canyon, benthic structure in the

Canyon (one potential disposal site), and pollutant

source contribution loads. However, based on model

predictions controlling 10 percent of the agricultural

runoff, DDT levels in sediment should decrease from

current levels of approximately 3,000 ppb to less than

1,000 ppb. Based on uncontrolled agricultural runoff,

modeling results predict DDT levels will decrease due

to the initial dredging removal of the source load, but

then return to current levels.

Lessons Learned

Ecological risk assessment and management guidance

includes scientific/policy/management interactions with

the development and implementation of a hazardous

waste cleanup. This consisted generally of consultations

and scientific reviews of risk assessment and engineering feasibility documents, as the projects progressed.

Technical/scientific personnel interact with policy and

decision-makers, to formulate plans and alternatives that

minimize risk(s) to human health and the environment.

Natural resource trust agencies are now being consulted

and involved with the development of cleanup plans,

with efforts being coordinated and incorporated into

the assessment process. The research and development,

along with verification and monitoring, are key elements

in the on-going assessment process. Most large California cleanup investigations to date include a monitoring

and surveillance component.

Conclusion/Next Steps

The major impediments to improving the forecasts are

the limited data on trophic transfers of toxic chemicals, as well as the abilities of current biomarkers to

accurately predict or identify chronic toxic effects.

Additional data on site-specific characteristics of contaminant exposure to sensitive receptors and understanding and interpretating the biomarker or community response to chemical insult(s) will increase the

accuracy to predict the pathways and improve model

prediction.

Few databases are widely available as comparative or

interpretive tools. There are really only two major predictive models that have been developed (i.e., Equilibrium Partitioning and Food Web Models) to identify

the pathways and exposure characteristics of bioaccumulative toxic pollutants. Other approaches might provide

additional assistance in forecasts of chemical and ecological conditions in the marine and estuarine environment.

Acknowledgements

Partial travel expenses were funded by a grant from

the California Association of Professional Scientists.

References

Anderson, B.S., J.W. Hunt, B.M. Phillips, M. Stoelting, J.

Becker, R. Fairey, H.M. Puckett, M. Stephenson, R.S.

Tjeerdema, and M. Martin. 2000. Ecotoxicologic

change at a remediated superfund site in San Francisco,

California, USA. Environ. Tox. Chem. Vol 19(4): 879-

887.

EPA. 2001. http://www.epa.gov/region09/features/pvshelf/

Hydroqual. 1994. Southern California Bight damage

assessment food web/pathway study. Expert Rept.,

Montrose Natural Resource Trustee Council, Long

Beach, CA. Misc pg.
103

Introduction

During the past few decades, agricultural activities and

increased population has led to increased loading of

pollutants (total suspended solids, phosphorus, nitrogen, dissolved organic material, etc.) into numerous

estuarine and coastal ecosystems. Excessive pollutant

loads have resulted in deteriorated water quality and

high concentrations of chlorophyll a, total suspended

solids, and color (dissolved organic matter). Consequently, light (Photosynthetically Active Radiance or PAR),

essential for seagrass growth, has been significantly

attenuated, leading to loss of seagrass beds and fishery

habitat. For example, there has been a significant loss

of seagrass beds in many Florida estuaries, including

Indian River Lagoon (Figure 1), Tampa Bay (Figure 2),

and Florida Bay.

Resource management agencies for these Florida

estuaries have been developing strategies to reverse

the trend of seagrass loss by establishing pollutant load

reduction goals (PLRGs), i.e., external loading rates

which will lead to the desired water quality, light, and

seagrass biomass targets. Sheng (1998) reviewed the

various types of pollutant load reduction models – a

regression model, a box model, and a process-based

model.

For example, the Tampa Bay Estuary Program

established nitrogen load reduction goals for Tampa Bay

by using simple regression models to relate nitrogen

loads to chlorophyll a, light loss, and seagrass biomass

in Tampa Bay. Sheng et al. (1996a) developed a

process-based integrated modeling system to study the

effect of reduced nitrogen loads in Roberts Bay and

upper Little Sarasota Bay in Florida. (These bays are

connected to the south part of Sarasota Bay.) Sheng et

al. (1996b) and Yassuda and Sheng (1998) developed

a process-based integrated modeling system for Tampa

Bay. Sheng (2000) and Sheng et al. (2002) developed

a process-based integrated modeling system for

determining the pollutant load reduction goals for the

Indian River Lagoon. After model calibration and

validation, these process-based integrated modeling

systems can be used to determine the response of the

estuaries to different loading conditions and to

determine the loading conditions corresponding to

desired targets of water quality, PAR, and seagrass

biomass. Resource management agencies can use these

models to develop pollutant the load reduction goal

(PLRG), total maximum daily load (TMDL), and

minimum flow and levels (MFL) for various water

bodies.

An Integrated Modeling System for Forecasting

the Response of Estuarine and Coastal Ecosystems

to Anthropogenic and Climatic Changes

Y. Peter Sheng1

Abstract

This paper describes the application of an integrated modeling system (CH3D-IMS) which consists of a

hydrodynamic model, a wave model, a sediment transport model, a water quality model, a light attenuation

model, and a seagrass model. The integrated system has been used to forecast the response of two estuaries –

the Indian River Lagoon and Tampa Bay in Florida – to anthropogenic and climate changes.

Model simulations of the Indian River Lagoon in 1998 and Tampa Bay in the summer of 1991 are considered the

baseline conditions. Simulations with reduced nutrient loads and climate change are then conducted to forecast

the response of these estuaries. The forecast results are presented in terms of predicted changes in salinity, water

quality, light, and seagrass biomass.

1

 Civil & Coastal Engineering Department, University of Florida
104

Case Studies

There are two case studies. Both studies use the CH3Dbased integrated modeling system, CH3D-IMS (Sheng

2000, Sheng et al. 2002, University of Florida). CH3D

is a 3-D curvilinear-grid hydrodynamic model originally

developed by Sheng (1986, 1987) and has been significantly enhanced since then (e.g., Sheng 1989, Sheng

et al.1996a, 1996b). The CH3D model uses orthogonal

as well as non-orthogonal curvilinear grids, hence it

can be used to fit the irregular estuarine boundaries rather

accurately. As shown in Figure 3, the CH3D-IMS consists of the 3-D hydrodynamic model CH3D, a wind

wave model (SMB), a 3-D sediment transport model

(SED3D), a 3-D water quality model (WQ3D), a light

attenuation model (LA3D), and a seagrass model (SAV).

These component models are dynamically coupled. For

example, light penetration in the water column is

affected by water, chlorophyll a, total suspended solids,

and dissolved organic matter. Light penetration in turn

affects the growth of chlorophyll a. Seagrass growth

is limited by the water temperature and salinity, as well

as by light and nutrients.

The first case study uses the CH3D-IMS to forecast

the response of the Indian River Lagoon to reduced

nutrient input and total suspended solid loads from

tributaries. The second case study applies the CH3DIMS to forecast the response of Tampa Bay to reduced

nitrogen and freshwater loading from a major tributary –

the Hillsborough River.

Indian River Lagoon, Florida – The Indian River

Lagoon (IRL) extends about 255 kilometers from Ponce

Inlet to the North to St. Lucie Inlet to the South, with a

width of 2-10 km and an average depth of 2 m. This lagoon

is one of the most biologically diverse ecosystems in

the world, with fishery resources valued at almost one

billion dollars per year. In recent decades, however, both

seagrass and fishery resources have declined.

To develop scientifically sound strategy to restore the

ecosystem, efforts began at the University of Florida in

1996 to develop an Indian River Lagoon Pollutant Load

Reduction model (IRLPLR). It is an integrated modeling system which can predict the response of the

ecosystem to changes in pollutant loads. This model is

based on the CH3D-IMS and validated with extensive

physical, chemical, and biological data collected from

the IRL. Critical model coefficients have been determined, based on laboratory and/or field experiments.

Validation of the CH3D-IMS with IRL data was

presented in Sheng et al. (2002). Two types of model

simulation have been conducted and validated. The first

simulates hydrodynamics (water levels, currents and waves),

sediment dynamics, and water quality dynamics during

short-term events (storms). The second is the one-year

continuous simulation during 1998. Model results have

been compared with measured time and space varying

data of water level, currents, salinity, suspended sediment concentration, nutrient concentrations (nitrogen

and phosphorus), chlorophyll a concentration, dissolved oxygen concentration, and PAR.

Figure 1B. Indian River Lagoon and four tidal

inlets. Figure 1A. The Indian River Lagoon study area.
105

Model and data showed more phosphorus loading in the

South and more nitrogen loading in the North. Light

attenuation is mainly influenced by total suspended

solids, while chlorophyll a and color have secondary

importance, particularly at high attenuation (low penetration) situation with the diffuse attenuation coefficient

exceeding one.

The validated IRLPLR model has been applied to

forecast the response of the IRL to reduced loads.

Specifically, a quantitative forecast the response of the

IRL to 1) 100 percent reduction of nutrient loads, 2) 100

percent reduction of nutrient and total suspended solids

loads, 3) increased wind speed, and 4) less evaporation.

For each of the forecasts, the validated IRLPLR model

simulates the 1998 IRL condition, with somewhat

different boundary conditions.

For example, the first

forecast is identical to

the 1998 validation

run except the nutrient

loads (both dissolved

and particulate) from

the tributaries are set

to zero. For the second

forecast, all nutrient

and total suspended

solids loads from tributaries are set to zero.

To examine the effects

of increased wind and

reduced evaporation,

the third and fourth

forecasts are conducted, respectively. These

forecasts are preliminary applications of

the IRLPLR model to

determine the allowable pollutant load

rate and the pollutant

load reduction goal

(PLRG). Results of the

first and second forecasts are discussed in

this paper.

Tampa Bay, Florida –

Tampa Bay resource

managers have developed a seagrass restoration plan based on the regression

models. The restoration plan, which only considers the

light attenuation due to chlorophyll a, appears to have

worked in much of Tampa Bay. Seagrasses have grown

in response to nutrient load reduction. However, seagrass in Old Tampa Bay has failed to respond to lower

nutrient loads and lower chlorophyll a concentrations.

Hence, it is useful to use an integrated modeling system

to examine the response of Tampa Bay to nutrient load

reduction.

An earlier version of CH3D-IMS was validated with

field data obtained in the summer of 1991 (Sheng et

al. 1996b, Yassuda and Sheng 1998). The validated

model forecasts the response of Tampa Bay to 100

percent reduction of freshwater and nitrogen loads, both

dissolved and particulate (Sheng et al. 2001) from the

tributaries.

Figure 2. Tampa Bay, Florida.
These preliminary forecasts for Indian River Lagoon

and Tampa Bay have not been validated with field data,

since 100 percent reduction in loads of nutrients,

freshwater, and total suspended solids cannot be easily

achieved without complicated regulations. However,

since both the IRL model and the Tampa Bay model

have been validated with field data, the model forecasts

are assumed to be fairly reliable.

Forecast uncertainty has not been addressed, but will be

by performing ensemble simulations/forecasts. The IRL

forecasts are expected to be used by the St. Johns River

Water Management District, while the Tampa Bay forecasts may be used by the Tampa Bay Estuary Program

and the Southwest Florida Water Management District.

Lessons Learned

The IRLPLR model has been validated with 1998 field

data. It is in the process of being used to determine

pollutant load reduction goals (PLRGs)

for various segments of the IRL

(Figure 4). As an example, the 1998

IRL simulation is repeated with the

nutrient and total suspended solid

loads completely removed from the

tributaries, and the results are compared with the ‘with loading’ results.

Figure 5 shows the effect of complete nutrient and total suspended

solid load reduction on the increased

amount of PAR (Photosynthetically

Available Radiance) at the bottom of

Segments 5, 6, 7, and 8.

The increase in PAR is not very

dramatic since total suspended solids

are the dominant light attenuator,

accounting for 78 percent of light attenuation, vs. the 5 percent and 16 percent loss to color (dissolved organic

carbon and other matters) and

suspended chlorophyll a, respectively

(Christian and Sheng 2003). Although

total suspended solids are not supplied

through the tributaries, significant

sediment re-suspension in the shallow

IRL causes significant total suspended

solids concentration to maintain

significant light attenuation except

during the summer months. During

the summer months, the forecasted

Figure 4. Segments of the Indian River Lagoon.

Figure 3. Block Diagram of an integrated modeling system: CH3D-IMS (From Sheng

2000).

Northing (m)

106
PAR show more noticeable increases, due to

removal of significant

color and total suspended solid loading and

reduced sediment resuspension during these

months.

The model forecasted

the biomass of Halodule,

the dominant seagrass

species in some parts of

the Indian River Lagoon, show noticeable

increase at the end of

June in 1998, due primarily to the increase in available

light. Reduced nutrient loads did not limit the growth

of seagrass, since sufficient nutrients already exist in

the IRL. The SAV model, which was modified from

Figure 5. Bottom light in Segment 5,6,7, and 8 during 1998 with and without nutrient loading.

Figure 6. Near-surface Cholorphyll a in Tampa Bay during July and September 1991.

the model of Fong et al. (1997), is being further

developed and validated with historical seagrass data.

The Tampa Bay simulations show the hypoxia and high

Chlorophyll a concentrations in the Hillsborough Bay

107
108

during the summer of 1991 (Figure 6) are significantly

reduced if all the nutrient loads into Hillsborough Bay

from the rivers are removed (Figure 7). The reduced

Chlorophyll a concentration leads to increased light

and some slight increase in Thalassia, which is the

dominant seagrass species in Tampa Bay.

Both the Indian River Lagoon model and the Tampa

Bay model are undergoing additional validation using

long-term historical data and current data. The forecasts

show the expected response of the estuarine systems

under extreme conditions (i.e., complete reduction of

nutrient loads). Since these are not realistic, there is no

data to validate the forecasts. They are presented in

this paper to represent preliminary applications of the

CH3D-IMS to determine pollutant load reduction goals

(PLRGs) and total maximum daily loads (TMDLs) for

estuarine and coastal ecosystems. Additional forecasts

should be obtained by considering a wide range of

management scenarios (with 10-100 percent reduction

of individual and combined parameters) and natural

condi-tions (e.g., a typical year vs. an El Niño year, a

relatively calm year vs. a year with many storms or

hurricanes), and by focusing on each of the numerous

watersheds.

To transfer the forecast from research and development

to operational mode, it will be necessary to couple the

integrated modeling system with additional component

models (e.g., meteorological model, ocean model, etc.)

and/or data network to allow dynamically-coupled realtime model forecasting. With forecasted natural

conditions (wind, air temperature, river inflows, and

ocean boundary conditions), it is possible to obtain realtime forecasts of the water quality and ecological

conditions in these estuaries. For example, one can

forecast the location, time, and duration of algal

blooms, hypoxia and

seagrass dieoff. To

verify the accuracy of

the forecast and to

incorporate modifications to improve

the precision or accuracy, more model validation runs need to be

done. In addition, inverse modeling techniques and data assimilation can be incorporated into the model

simulations.

Significant uncertainties are associated with ecological

forecasting using the integrated modeling system. Each

component model in the basic integrated system

contains inherent uncertainties associated with the

model formulation, model coefficients, and model input

(boundary and initial conditions). Monte Carlo

simulations can be conducted for some of the component models, e.g., the light attenuation model and the

seagrass model, which are basically ‘box models’. For

the three-dimensional hydrodynamic model, sediment

transport model, and water quality model, ensemble

simulations in combination with model sensitivity tests

maybe used in place of the Monte Carlo simulations.

Additional ‘forecasting uncertainties’ are associated

with the forecasting of the forcing functions (i.e.,

forecasted model boundary conditions of wind, air

temperature, river inflows, evaporation, and precipitation, etc.).

Next Steps

These two examples show ecological forecasting for

two Florida estuarine ecosystems – Indian River

Lagoon and Tampa Bay. However, much remains to be

done to improve the forecasting. The well-being of ecological systems are threatened by both man-made and

natural changes, hence improvement in ecological

forecasting should lead to better management of ecological systems.

Improvement in ecological forecasting requires

additional data, improvement in data gathering techniques, model advancements, as well as research into

the fundamental ecological processes. The integrated

modeling system presented here can be further

expanded to include additional component models,

Figure 7.Near-surface cholorphyll a in ‘Tampa Bay during July and September 1991 withouto river loading.
109

including atmospheric model, ground water model, and

fishery model, etc.

Conclusions

Examples of ecological forecasting have been presented

here using an integrated modeling system, CH3D-IMS.

Specifically, the validated integrated modeling systems

for these estuaries are used to forecast the response of

these estuaries to reduced loads of nutrients, total

suspended solids, and freshwater from tributaries.

These forecasts of load reduction scenarios correspond

to extreme conditions and hence have not been

validated with data. However, since both the IRL model

and the Tampa Bay model have been validated with

field data, the forecasts are expected to be fairly reliable.

With forecasted natural conditions (wind, atmospheric

temperature, river inflows, and ocean boundary conditions), it is possible to obtain real-time forecasts of the

water quality and ecological conditions in these

estuaries under more realistic load reduction conditions.

For example, by coupling the CH3D-IMS with realtime weather forecast and assimilation of real-time

ocean data, one can forecast the location, time, and

duration of algal blooms, hypoxia, and seagrass dieoff in these estuaries. Forecast uncertainty has not been

quantified, but may be evaluated by a combination of

model sensitivity study, Monte Carlo simulations or

ensemble simulations.

Acknowledgement

The Indian River Lagoon model has been developed

with support from the St. Johns River Water management District, while development of the Tampa Bay

model has been supported by the Tampa Bay Estuary

Program and the University of Florida.

References

Christian, D., and Y.P. Sheng. 2001. Light attenuation by

color, chl_a and tripton in Indian River Lagoon. In:

Proceedings: Tampa Bay Seagrass Conference, St.

Petersburg, September 2000, Tampa Bay Estuary

Program. pp. 91-105.

Christian, D. and Y.P. Sheng. 2003. Relative influence of

various water quality parameters on light attenuation

in Indian River Lagoon. To appear in Estuarine, Coastal

and Shelf Science.

Fong, P., M.E. Jacobson, M.C. Mescher, D. Lirman, and

M.C. Harwell. 1997. Investigating the management

potential of a seagrass model through sensitivity analysis

and experiments. Ecological Application 7(1): 301-315.

Sheng, Y.P. 1986. A three-dimensional mathematical model

of coastal, estuarine, and lake currents using boundaryfitted grids. Technical Report No. 585. Aeronautical

Research Associates of Princeton, Princeton, NJ. 255

pp.

Sheng, Y.P. 1987. On modeling three-dimensional estuarine

and marine hydrodynamics. In: Nihoul, J.C.J. and B.M.

Jamart (eds.) Three-Dimensional Models of Marine and

Estuarine Dynamics. Elsevier Oceanography Series,

Elsevier, pp. 35-54.

Sheng, Y.P. 1989. Evolution of a 3-D curvilinear-grid

hydrodynamics model for estuaries lakes, and coastal

waters: CH3D. In:American Society of Civil Engineers.

Estuarine and Coastal Modeling I. pp. 40-49.

Sheng, Y.P. 1998. Pollutant load reduction models for estuaries. In:American Society of Civil Engineers. Estuarine

and Coastal Modeling V. pp. 1-15.

Sheng, Y.P. 2000. A framework for integrated modeling of

coupled hydrodynamics-sedimentary-ecological

processes. In: American Society of Civil Engineers.

Estuarine and Coastal Modeling VI. pp. 35-362.

Sheng, Y.P., E.A. Yassuda, and C. Yang. 1996a. Modeling

the effect of nutrient load reduction on water quality. In:

American Society of Civil Engineers. Estuarine and

Coastal Modeling IV. pp. 644-658.

Sheng, Y.P., E.A. Yassuda, and X. Chen. 1996b. On hydrodynamics and water quality dynamics in Tampa Bay.

In: Treat, S (ed.). TAMPA BASIS-3. pp. 295-314.

Sheng, Y.P., T. Kim, D. Christian, and J. N. King. 2001.

Using an integrating modeling system to simulate the

effect of reduced freshwater and nutrient loading on

salinity, hypoxia, and seagrass in Tampa Bay. In: Proceedings of a Water Budget for Tampa Bay: Sources,

Trends, and Effects (CD-ROM), Tampa Bay Estuary

Program.

Sheng, Y.P., J.R. Davis, D. Sun, C. Qiu, K. Park, T. Kim, Y.

Zhang. 2002. Application of an integrated modeling

system for estuarine and coastal ecosystems to Indian River

Lagoon, Florida. In:American Society of Civil Engineers.

Estuarine and Coastal Modeling VII. pp. 329-343.

University of Florida. 2003. Frequently asked questions

about CH3D and CH3D-IMS. http://www.coastal.ufl.

edu/~pete/CH3D.

Yassuda, E.A. and Y.P. Sheng. 1998. Modeling dissolved

oxygen dynamics in Tampa Bay during the summer of

1991. In: American Society of Civil Engineers. Estuarine and Coastal Modeling V. pp. 35-50.
110
111

Introduction

The largest zone of oxygen-depleted coastal waters in

the United States and the western Atlantic Ocean exists in the bottom waters of the Gulf of Mexico along

the Louisiana-Texas continental shelf. As required by

the Harmful Algal Bloom and Hypoxia Research and

Control Act of 1998, the Mississippi River/Gulf of

Mexico Watershed Nutrient Task Force (the Task Force)

developed the Action Plan for Reducing, Mitigating and

Controlling Hypoxia in the Northern Gulf of Mexico

(US EPA 2001). The Action Plan was formed by findings of the Committee on Environment and Natural

Resources (CENR) Integrated Assessment of Hypoxia

in the Northern Gulf of Mexico (CENR 2000) along

with comments received and the six hypoxia assessment reports on which it was based.

The Action Plan describes a national strategy to reduce

the frequency, duration, size and amount of oxygen

depletion of the hypoxic zone of the northern Gulf of

Mexico. In view of the complex nature of nutrient cyForecasting Hypoxia in the Gulf of Mexico: Responses

to Nutrient Loadings from the Mississippi River Basin

Victor J. Bierman, Jr.1, Scott C. Hinz1, William J. Wiseman, Jr.2, Nancy N. Rabalais3, R. Eugene Turner2

Abstract

A zone of hypoxia (<2 mg O2/l) forms each spring and summer on the Louisiana-Texas continental shelf and

stresses aquatic life. The principal causes are increased nitrogen loads from the Mississippi River Basin (MRB)

combining with the physical stratification of Gulf waters.

A mass balance model was developed to address broad, macro-scale questions related to the impacts of nutrient

loadings from the MRB on water quality in the northern Gulf of Mexico. Simulations were conducted for a

range of nutrient load reductions to determine how much the bottom water dissolved oxygen concentrations

improved. Reducing the nutrient load 20-30 percent led to forecasts of a 15-50 percent increase of bottom water

dissolved oxygen concentrations. These results were used to develop a 30 percent nitrogen loading reduction

target to meet the long-term Coastal Goal for reducing the areal extent of hypoxia in the northern Gulf of

Mexico.

A major accomplishment in this study established watershed-scale links between nutrient loads from the MRB

and water quality responses in the northern Gulf of Mexico. The study also called attention to some challenging

questions facing policy-makers when implementing forecast results developed from research. In view of the

complexities of the system, uncertainties in the level of knowledge, and the potentially long time period required

to observe changes resulting from management action, an adaptive management approach was recommended.

This approach includes a comprehensive program of monitoring, modeling, and research to facilitate continual

improvement in scientific knowledge and gradual adaptation of management approaches. Specific

recommendations are made for reducing uncertainties and improving forecasting ability for water quality

responses.

Modeling needs cannot be met independently of monitoring and research needs because models are only tools

for synthesizing environmental data, not substitutes for these data. In the end, there must be compatibility

among management questions, model capabilities and available data.

1

 Limno-Tech, Inc.

2

 Louisiana State University

3

 Louisiana Universities Marine Consortium
112

cling and transport within the Basin and Gulf of Mexico,

and the potentially long period of time required to observe changes resulting from management action, the

Task Force recommended an adaptive management

framework. Such an approach includes a comprehensive program of monitoring, modeling, and research to

facilitate continual improvement in scientific knowledge and gradual adaptation of management approaches.

Case Study

The Gulf of Mexico is an ecosystem in distress because

of hypoxia. Hypoxia occurs when dissolved oxygen

concentrations are below the level necessary to sustain

most animal life (<2 mg O2

/l). A zone of hypoxia forms

each spring and summer on the Louisiana-Texas shelf

and stresses aquatic life (Figure 1). Since 1993, midsummer bottom water hypoxia

on the Louisiana-Texas Shelf has

been larger than 10,000 km2

. The

hypoxia zone has been expanding

over time. The zone has more

than doubled since 1985 (Rabalais

et al. 1999) and extended to

20,000 km2 in 1999, about the

size of the state of New Jersey

(Rabalais 1999).

The principal causes of hypoxia

are increased nitrogen loads from

the Mississippi River Basin

(MRB) combining with the physical stratification of Gulf waters.

Nutrient enrichment (eutrophication) resulting in dissolved oxygen depletion, has been widely

observed elsewhere, including the Chesapeake Bay,

Long Island Sound, and other coastal waters, as well

as fresh water lakes and reservoirs. Nutrients stimulate

algae growth which produces organic carbon. When

this organic carbon sinks to the bottom and decays, dissolved oxygen is consumed. This happens mostly in

spring and summer when the water column stratifies

from fresh water runoff and the warming of surface

waters, thus isolating bottom waters from oxygen replenishment.

Sediment cores from the hypoxic zone show algal production and deposition, as well as oxygen stress were

much lower earlier in the 1900s, but significant increases occurred in the latter half of the twentieth century. During this century, there also have been significant increases in nitrogen and phosphorus loads and

decreases in silica loads in the Mississippi River. All of

these have accelerated since 1950 (Turner and Rabalais

1991), and appear to have caused shifts in the offshore

phytoplankton composition and an increase in primary

production (Rabalais et al. 1996).

As part of the NOAA Nutrient Enhanced Coastal Ocean

Productivity (NECOP) program, a mass balance model

was developed for the Louisiana Inner Shelf portion of

the northern Gulf of Mexico (Bierman et al. 1994).

Available historical data and new data generated within

the NECOP program permitted developing a preliminary, screening-level modeling analysis. The model addresses broad, macro-scale questions related to the

impacts of potential reductions in nutrient loads from

the Mississippi-Atchafalaya River system (Limno-Tech,

Inc. 1995). This was to determine whether dissolved

oxygen concentrations in the Gulf are sensitive to

changes in nutrient loads and to forecast the nutrient

loads necessary to reduce seasonal hypoxia.

The forecast consisted of model simulations with reduced nutrient loads to determine the degree of improvement in bottom water dissolved oxygen concentrations on the Louisiana-Texas shelf. The rationale was

that reductions in nutrient loadings from the MRB

would reduce production and settling of organic carbon and hence, the consumption of dissolved oxygen

in bottom waters. These simulations involved reductions from 10 to 70 percent in nitrogen and phosphorus

loads from the MRB. This range was not intended to

Figure 1. Frequency of occurrence of mid-summer hypoxia in the Gulf of Mexico – based on

data from Rabalais, Turner and Wiseman (CENR 2000).
113

represent reductions feasible in terms of technology,

economics or what is socially acceptable; rather, it was

to investigate whether reducing loads 20 to 30 percent

were sufficient to produce a response, or whether reductions of up to 70 percent would be required.

Results indicate the dissolved oxygen concentrations

were sensitive to reductions in nutrient loads; however,

there were large uncertainties in the magnitude of the

responses (Figure 2). The uncertainty is due to insufficient information on controlling physical, chemical and

biological processes, and to the natural variability in

hydrometeorological conditions in the northern Gulf

of Mexico.

Reducing nutrient loads 20-30 percent showed bottomwater dissolved oxygen concentrations might increase

15-50 percent. Any increase in oxygen above the 2 mg/

l threshold has a significant effect on marine life survival, so even small reductions in nitrogen loads are

desirable. The forecast indicated somewhat greater responses when nitrogen loads were reduced as compared

to phosphorus loads. However, the differences are not

large, and considering the uncertainties in the modeling analysis, further investigation is needed to verify

this.

These forecast results were used to develop a 30 percent nitrogen load reduction target to meet the longterm Coastal Goal for reducing hypoxia in the Gulf (US

EPA 2001). Verification of forecast results will require

reductions in nutrient loads from the MRB and continued efforts to monitor the zone of oxygen depletion on

the Louisiana-Texas shelf. For such a large system, it

may require years to a decade before significant changes

can be observed in frequency, duration, size or amount

of oxygen depletion.

Lessons Learned

This case study started in a research program (NECOP);

however, a national science policy dialogue began with

the initiation of the CENR process, with both successes

and frustrations. The science in this overall case study

helped drive the issues, alternatives and potential consequences. A major accomplishment was establishing

watershed-scale links between nutrient loads from the

MRB and water quality responses in the northern Gulf

of Mexico. Successes were achieved by maintaining

visibility with government agencies, legislators, and

stakeholders through communication and public outreach, and by an independent peer review process for

the scientific results.

Frustrations with this science policy dialogue included

preconceptions and misconceptions, and polarization

among the scientists involved. Earlier collaboration

among government agencies, academics and stakeholders could have made the science more useful. Earlier

dissemination of research results might have mitigated

the sharp political battle lines that were drawn.

This case study called attention to some challenging

questions policy-makers and researchers face in transferring forecast results from research and development

to operational decisions. These are just some of the issues.

• When are research results ‘good enough’ to be used

in decision-making?

• How should the need for scientific understnding be

balanced with the management needs to forecast

future behavior?

• How should the policy developed handle uncertainties in research results?

• How should costs and benefits in ecological systems

be evaluated?

It became clear that research should be viewed as part

of an overall management framework. A comprehensive, carefully targeted program of monitoring, modeling, and research is needed to facilitate continual improvement in scientific knowledge. Adjustments in

management practices should be coupled with whatever initial nutrient management strategies are chosen.

This adaptive management scheme should involve continual communication among all stakeholders for interpretation of new information and improved management actions.

10 20 30 50 70

Percent Nitrogen Load Reduction

0

20

40

60

80

100

P

e

r

c

e

n

t

I

n

c

r

e Northern Gulf of Mexico

Forecasted Changes in Dissolved Oxygen

Figure 2. Forecasts from the NECOP water quality model for

changes in bottom water dissolved oxygen concentration in response

to nitrogen load reductions from the Mississippi River Basin. Percent Increase in Oxygen
114

Next Steps

The scientific impediments to improving forecast ability for water quality responses in the northern Gulf of

Mexico can be categorized in terms of monitoring, research and quantitative modeling. The principal impediment is lack of a comprehensive database for the physical, chemical and biological processes that influence

hypoxia. Although the existing database is comprehensive in many respects, the data were acquired primarily to characterize water quality responses, not to provide data for quantitative models or forecasting.

For these purposes, a quantitative conceptual model of

ecosystem structure and function in the northern Gulf

of Mexico should be conceived and used as a foundation for developing future monitoring plans. More funding by government agencies for long-term monitoring

is essential to reduce the uncertainties and to verify the

accuracy of existing forecast results.

Field data generated by a comprehensive monitoring

program are necessary but not sufficient for developing, calibrating and validating quantitative water quality models. There is not yet a complete understanding

of the physical, chemical and biological processes that

influence water quality responses in the northern Gulf

of Mexico. Research is needed to better understand

these processes and to provide information for representing them in quantitative models. The most important unresolved research questions include:

• The influence of large-scale Gulf circulation on the

Louisiana-Texas shelf;

• The influence of sediment diagenesis;

• The relationship between underwater light

attenuation and primary productivity;

• The importance of silica limitation and shifts in

phytoplankton species in influencing fate pathways

for organic carbon; and,

• The processes controlling carbon, nutrient and

oxygen dynamics and cycling.

Modeling advancements are also needed to improve forecasting ability, in particular, the ability to link nutrient

loads from the MRB to frequency, duration and areal

extent of hypoxia in bottom waters. As part of the CENR

Gulf of Mexico Hypoxia Assessment, Brezonik et al.

(1999) recommended the following refinements and

enhancements to the existing NECOP water quality models.

• Advective flows and dispersive mixing coefficients

should be determined using the output of a hydrodynamic model of Gulf of Mexico circulation.

• The temporal domain should be extended to include

a continuous, time-variable representation of water

quality conditions over the complete annual cycle.

• The spatial domain should be extended so that its

seaward boundaries are beyond the influence of

freshwater and nutrient inputs from the MississippiAtchafalaya River system.

• The vertical resolution should be refined to better

represent near-bottom hypoxia on the LouisianaTexas Shelf.

• The horizontal spatial resolution should be

sufficiently refined to assess changes in the area and

volume of hypoxia under different management

strategies.

• Finer spatial-temporal resolution should be employed

to represent the dynamics of nearshore waters (shore

to 60 meter depth) and linkages with estuaries and

offshore waters.

• Model calibration and validation should be conducted

over several years (three or more) with varying

Mississippi-Atchafalaya River hydrology (average,

wet and dry years).

• The conceptual framework should be expanded to

include a sediment diagenesis submodel and explicit

representation of nutrient and dissolved oxygen mass

balances between water column and sediment

segments.

• The conceptual framework should be expanded to

include diatom and non-diatom phytoplankton

functional groups, and silica as a potential limiting

nutrient.

• The water quality model should include a separate

submodel for underwater light attenuation as a

function of background color, biotic solids

(phytoplankton) and abiotic solids.

These modeling needs cannot be met independently of

the above monitoring and research needs. Models are

only tools for synthesizing environmental data and can

not be used as substitutes for these data. For a model

to be useful, it must have the capabilities for addressing

the principal management questions. For a model to

be scientifically credible, there must be adequate field

data for development, calibration and validation. In the

end, there must be compatibility among the

management questions, model capabilities, and the

available field data.

More information on hypoxia, the Mississippi River

Basin, and the Gulf of Mexico can be found on the

following websites.
115

US Environmental Protection Agency

http://www.epa.gov/msbasin/

http://www.epa.gov/gmpo/

National Oceanic and Atmospheric Administration

http://www.nos.noaa.gov/products/pubs_hypox.html

http://www.csc.noaa.gov/products/gulfmex/startup.htm

http://www.aoml.noaa.gov/ocd/necop/

US Geological Survey

http://wwwrcolka.cr.usgs.gov/midconherb/hypoxia.html

Conclusions

Model forecast results indicate dissolved oxygen concentrations in the northern Gulf of Mexico were sensitive to reductions in nutrient loads. However, there were

large uncertainties in the magnitudes of the responses

due to lack of sufficient information on controlling

physical, chemical and biological processes, and to

natural variability in hydrometeorological conditions

in the northern Gulf of Mexico. A major accomplishment was establishment of watershed-scale links between nutrient loads from the MRB and water quality

responses in the northern Gulf of Mexico.

The study called attention to some challenging questions facing policy-makers and researchers in transferring forecast results from research and development to

operational decisions. In view of the complexities of

the system and uncertainties in the level of knowledge,

research should be viewed as part of an overall management framework. A comprehensive, carefully targeted program of monitoring, modeling, and research

to facilitate continual improvement in scientific knowledge and adjustments in management practices should

be coupled to whatever initial nutrient management

strategies are chosen. This adaptive management

scheme should involve continual communication

among all stakeholders for interpretations of new information and improved management actions. In particular, modeling needs cannot be met independently

of monitoring and research needs.

Models are only tools for synthesizing environmental

data and cannot be used as substitutes for data. In the

end, there must be compatibility among the management

questions, model capabilities, and the available data.

Acknowledgements

The NOAA Coastal Ocean Program Office and the U.S.

EPA Gulf of Mexico Program Office provided support

to Limno-Tech, Inc., for this work through Louisiana

State University and the University of Minnesota.

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