An integrated methodology for assessment of estuarine ... · Ecological Modelling 169 (2003) 39–60 An integrated methodology for assessment of estuarine trophic status S.B. Brickera,∗,

Ecological Modelling 169 (2003) 39–60

An integrated methodology for assessment ofestuarine trophic status

S.B. Brickera,∗, J.G. Ferreirab, T. Simasba NOAA—National Ocean Service, National Centers for Coastal Ocean Science, 1305 East West Highway, Silver Spring, MD 20910, USAb IMAR—Institute of Marine Research, Centre for Ecological Modelling, DCEA-FCT, Qta. Torre, 2829-516 Monte de Caparica, Portugal

Received 30 July 2002; received in revised form 16 April 2003; accepted 12 May 2003

Abstract

This paper describes an integrated methodology for the Assessment of Estuarine Trophic Status (ASSETS), which maybe applied comparatively to rank the eutrophication status of estuaries and coastal areas, and to address management op-tions. It includes quantitative and semi-quantitative components, and uses field data, models and expert knowledge to providePressure-State-Response (PSR) indicators.

A substantial part of the concepts underlying the approach were developed as the United States National Estuarine Eutrophi-cation Assessment (NEEA), which was applied to 138 estuaries in the continental United States. The core methodology relies onthree diagnostic tools: a heuristic index of pressure (Overall Human Influence), a symptoms-based evaluation of state (OverallEutrophic Conditions), and an indicator of management response (Definition of Future Outlook).

Recently, the methodology has been extended and refined in its application to European estuaries, and a more quantitativeapproach to some of the metrics has been implemented. In particular, the assessment of pressure is carried out by means of simplemodeling techniques, comparing anthropogenic nutrient loading with natural background concentrations, and the quantitativecriteria for classification of system state based on different symptoms have been refined to improve comparability.

The present approach has been intercalibrated with the original NEEA work, for five widely different U.S. estuaries (LongIsland Sound, Neuse River, Savannah River, Florida Bay and West Mississippi Sound) with good results. ASSETS additionallyaims to contribute to the EU Water Framework Directive classification system, as regards a subset of water quality and ecologicalparameters in transitional and coastal waters.© 2003 Elsevier B.V. All rights reserved.

Keywords:Eutrophication; Estuary; Index; NEEA; EU water framework directive; Model

1. Introduction

During the past four decades it has become clearthat eutrophication is a significant problem in many es-tuaries and coastal zones. Symptoms such as high lev-

∗ Corresponding author. Tel.:+1-301-713-3020x139;fax: +1-301-713-4353.

E-mail address:[email protected] (S.B. Bricker).URL: http://www.noaa.gov.

els of chlorophylla (Boynton et al., 1982; Nixon andPilson, 1983), excessive seaweed and epiphyteblooms, occurrence of anoxia and hypoxia (Whitledge,1985; Gerlach, 1990; CENR, 2000), and harmful andtoxic algal blooms (ORCA, 1992; Rabalais et al.,1996) have occurred in many areas, including someU.S estuaries, (e.g. toxic blooms in the Pamlico andNuese River Estuaries,Burkholder et al., 1992a,1995, 1999; harmful blooms in Lower Laguna Madre,Whitledge and Pulich, 1991) the southern North

0304-3800/$ – see front matter © 2003 Elsevier B.V. All rights reserved.doi:10.1016/S0304-3800(03)00199-6

40 S.B. Bricker et al. / Ecological Modelling 169 (2003) 39–60

Sea (Gillbricht, 1988), Baltic Sea (Bonsdorff et al.,1997), Mediterranean Sea (e.g. Lac de Tunis:Kellyand Naguib, 1984), Northern Adriatic (Chiaudaniet al., 1980), Australia (Hodgkin and Birch, 1982;Hodgkin and Hamilton, 1993) and Japan (Okaichi,1989; Okaichi, 1997).

Nutrient enrichment of coastal areas may havefar-reaching consequences, such as fish-kills (Glasgowand Burkholder, 2000), interdiction of shellfish aqua-culture (Joint et al., 1997), loss or degradation of seagrass beds (McGlathery, 2001; Twilley et al., 1985;Burkholder et al., 1992a) and smothering of bivalvesand other benthic organisms (Rabalais and Harper,1992). These modifications have significant economicand social costs (Turner et al., 1998), some of whichmay be readily identified (e.g. direct costs such asproductivity losses), whilst others (e.g. indirect andnon-use values) are more difficult to determine andtend to be ignored (Turner et al., 1999).

Eutrophication in estuaries has historically beenquantified using the classical freshwater approach(e.g.Carlson, 1977), i.e. through the measurement ofvariables such as transparency, nutrients and chloro-phyll a (chl a) and the establishment of nutrient-basedclassification systems, following whatCloern (2001)terms a “Phase I” approach. However, in the lastdecades it has been recognized that estuarine andcoastal eutrophication is potentially a far more subtleproblem, which may manifest itself, e.g. through theappearance of nuisance and harmful algae, or throughchanges in the composition of intertidal and sub-tidalbenthic communities. Furthermore, it has becomeapparent that nutrient concentrations may not be arobust diagnostic variable: high concentrations arenot an obligatory indicator of eutrophication, and lowconcentrations do not necessarily indicate absenceof eutrophication (Cloern, 2001; Dettmann, 2001).Nutrients are the primary cause, but there are manyother factors that determine the ultimate level andtype of expression of eutrophic symptoms within anestuary including tidal exchange, freshwater inflow,etc (Cloern, 1999; NRC, 2000; Boesch, 2002).

Over the last few decades, the increase in researcheffort and discussion on coastal eutrophication pro-cesses has advanced of our understanding of theproblems, and produced recommendations for re-mediation and proposed research (e.g.NAS, 1969;Neilson and Cronin, 1981; Hinga et al., 1991; USEPA,

1994; Bricker and Stevenson, 1996; NRC, 2000).“Threshold risk levels” have been tentatively definedfor specific compartments such as submerged aquaticvegetation (SAV) (Stevenson et al., 1993; Burkholderet al., 1992b; Boynton et al., 1996; Dunton, 1996),and increasingly effective models have been devel-oped to explore cause/effect relationships (NOAAand EPA, 1988; Madden and Kemp, 1996; Lowery,1996; Weisberg et al., 1993; Dettmann, 2001).

Furthermore, the need for evaluating the eutrophi-cation status of estuarine and coastal systems, in orderto support policy definition, has led to the develop-ment of different methods which use symptoms-basedmultiparameter assessment. Well-known examplesare the United States National Estuarine Eutroph-ication Assessment (NEEA) (Bricker et al., 1999)and the OSPAR Comprehensive procedure (OSPAR,2001).

The NEEA approach uses a combination of pri-mary and secondary symptoms to derive an OverallEutrophic Condition (OEC) index, which is then as-sociated with a measure of Overall Human Influence(OHI) and the Definition of Future Outlook (DFO).This approach contains the essential components of aPressure (OHI)-State (OEC)-Response (DFO) model,although the OHI also reflects aspects of the state ofthe system, since it includes a susceptibility metric(Fig. 1).

In this paper we outline the NEEA methodologydeveloped byBricker et al. (1999), and extend it to:

(i) apply a modeling approach based on the rela-tive contribution anthropogenic of natural nutri-ent loading to improve the estimation of pressure(OHI);

(ii) combine relational databases, Geographical Infor-mation Systems (GIS) and statistical criteria in amore quantitative procedure for the determinationof parameter values for evaluation of state (OEC).

Some key results of the application of NEEA aregiven for a range of estuarine systems, covering widelydifferent conditions (tidal amplitude, nutrient loading,discharge regime). The application of the ASSETSmethodology is shown for two European and five U.S.estuaries, and an intercalibration of results with theoriginal NEEA approach is illustrated for 82 U.S. es-tuaries.

S.B. Bricker et al. / Ecological Modelling 169 (2003) 39–60 41

Fig. 1. Flow chart of the ASSETS methodology.

2. Methodology

2.1. Indicator selection and characterization

The NEEA methodology has been described in de-tail by Bricker et al. (1999). Sixteen nutrient relatedwater quality parameters were considered (Table 1).These eutrophication indicators were selected in orderto:

• Ensure that accurate characterization of eutrophicconditions could be accomplished and comparedamong highly varied systems;

• Allow a clear separation of estuaries, bearing inmind that eutrophication is a process rather than astate.

Although not all parameters exist or were measuredfor all systems, the suite used is broad enough to as-sess all estuarine types, with emphasis on the magni-tude, timing, and predictability of extreme conditionsof various indicators observed during the annual cycle.

The response ranges (Table 2) were selected to besimple to use and to separate estuaries on a gradientwhenever possible. The value ranges were developedfrom data for the whole U.S. and from discussions


Table 1List of nutrient related water quality parameters considered in the overall U.S. NEI survey

Parameters Existing conditions Trends

Chlorophyll a Surface concentrations Concentrationsa,b

Hypereutrophic (>60 ug l−1) Limiting factorsHigh (>20, ≤60 ug l−1) Contributing factorsc

Medium (>5,≤20 ug l−1)Low (>0 and≤5 ug l−1)

Limiting factors to algal biomass (N, P, Si, light, other)Spatial coveraged, months of occurrence, frequency ofoccurrencee

Turbidity Secchi disk depths Concentrationsa,b

High (<1 m) Contributing factorsc

Medium (≥1, ≤3 m)Low (>3 m)Blackwater area

Spatial coveraged, months of occurrence, frequency ofoccurrencee

Suspended solids Concentrations (No trends information collected)Problem (significant impact upon biological resources)No problem (no significant impact)

Months of occurrence, frequency of occurrenceb

Nuisance algae Occurrence Event durationa,b

Toxic algae Problem (significant impact upon biological resources) Frequency of occurrencea,b

No problem (no significant impact) Contributing factorsc

Dominant speciesEvent duration (hours, days, weeks, seasonal, other)Months of occurrence, frequency of occurrenceb

Macroalgae Abundance Abundancea,b

Epiphytes Problem (significant impact upon biological resources) Contributing factorsc

No problem (no significant impact)Months of occurrence, frequency of occurrencee

Nitrogen Maximum dissolved surface concentration Concentrationsa,b

High (≥1 mg l−1) Contributing factorsc

Medium (≥0.1, <1 mg l−1)Low (≥0 and<0.1 mg l−1)

Spatial coveraged, months of occurrence

Phosphorus Maximum dissolved surface concentration Concentrationsa,b

High (≥0.1 mg l−1) Contributing factorsc

Medium (≥0.01,<0.1 mg l−1)Low (≥0 and<0.01 mg l−1)

Spatial coveraged, months of occurrence

-Anoxia (0 mg l−1) Dissolved oxygen concentration Min. avg. monthly bottomdissolved oxygen conc.a,b

-Hypoxia (>0,≤2 mg l−1) Observed Frequency of occurrencea,b

-Biol. Stress (>2,≤5 mg l−1) No observed Event durationa,b

Stratification (degree of influence) Spatial coveragea,b

High Contributing factorsc

MediumLow


Table 1 (Continued)

Parameters Existing conditions Trends

Not a factorWater column depth

SurfaceBottomThroughout the water column

Spatial coveraged, months of occurrence, frequency ofoccurrence

Primary productivity Dominant primary producer: pelagic, benthic, other Temporal shift

Planktoniccommunity

Dominant taxonomic group (number of cells): diatoms,flagellates, blue-green algae, diverse mixture, other

Contributing factors

Temporal shiftContributing factorsb

Benthic community Dominant taxonomic group (number of organisms):Crustaceans, Molluscs, Annelids, Diverse mixture, other

Temporal shift

Contributing factorsc

Submerged aquaticvegetation (SAV)

Spatial coveragea Spatial coveragea,b

Intertidal wetlands Contributing factorsc

a Direction of change: increase, decrease, no trend.b Magnitude of change: high (>50,≤100%), medium (>25,≤50%), low (>0,≤25%).c Point source(s), nonpoint source(s), other.d Spatial coverage (% of salinity zone): high (>50,≤100%), medium (>25,≤50%), low (>10, ≤25%), very low (>0,≤10%), no

SAV/Wetlands in system.e Frequency of occurrence: episodic (conditions occur randomly), periodic (conditions occur annually or predictably), persistent (conditions

occur continually throughout the year).

with regional experts, and the criteria used to classifyresponses were designed to distinguish the magnitudeof eutrophic symptoms among estuaries. Since estu-aries within a region may respond similarly and/or besubject to similar input sources, these criteria may notdistinguish among estuaries within a region, however,they do distinguish among estuaries on a wider geo-graphic basis.

2.2. Data acquisition

The data used for determination of OEC were col-lected in a series of surveys carried out by NOAA oneutrophic conditions and trends in 138 U.S. estuariesand the Mississippi/Atchafalaya River Plume (NOAA,1996, 1997, 1997a,b, 1998, 1999). The estuaries in-cluded in the assessment are those characterized in theNational Estuarine Inventory (NEI;NOAA, 1985) andare representative of the U.S. estuarine resources with

regard to size, salinity distribution and other physicaland hydrological characteristics. Together, they rep-resent >90% of the U.S. estuarine surface area and>90% of the freshwater inflow to the coastal region.The NEI salinity characterization provides a consistentspatial framework for information collection. Each pa-rameter was originally characterized for three salinityzones defined in the NEI: Tidal freshwater (<0.5 psu),Mixing (0.5–25 psu) and Seawater (>25 psu), althoughnot all salinity zones are present in all estuaries. Thismodel provides a consistent basis for comparisonsamong these highly variable systems.

Data acquisition was implemented by questionnaireon existing conditions (i.e. observations during a typ-ical flow year) and for available trends from 1970 topresent (Hinga et al., 1991); the responses were sub-sequently complemented by site visits and discussionwith regional experts. Ancillary information on thetiming of events was also requested, including time-


Table 2Indicator parameters and rationale, thresholds and justification for primary and secondary symptoms of estuarine eutrophication

Indicator and rationale Thresholds and ranges Threshold justification

Algal blooms: Chla is used asan indicator of phytoplanktonprimary productivity. Highestconcentrations in an estuaryduring the annual bloomperiod were recorded. Highlevels cause dieoff of SAVand low bottom waterdissolved oxygen.

Hypereutrophic: >60�g Chla l−1

• Estuaries with highest annual Chla less than 5�g l−1 appearunimpacted (Nixon and Pilson, 1983), however, this level isdetrimental to survival of corals (Lapointe and Matzie, 1996).

High: >20 but≤60�g Chla l−1

• At 20�g l−1 SAV shows declines (Stevenson et al., 1993) andcommunity shifts from diverse mixture to monoculture (Twilleyet al., 1985).

Medium: >5 but≤20�g Chla l−1

• At 60�g l−1 high turbidity and low bottom water dissolvedoxygen are observed (Jaworski, 1981).

Low: >0 but ≤5�g Chl a l−1

Macroalgae and epiphytes:excessive macroalgal andepiphyte growth is known tosuffocate bivalves and causedieoff of SAV.

Problem: detrimental impactto biological resources (e.g.dieoff of SAV)

There is no standard measure or threshold above whichmacroalgae and/or epiphytes are considered to be a problem tothe biological resources, and it is rare to find quantitativeinformation. However some studies show that:

No problem: no apparentimpacts on biologicalresources

• Macroalgae (Ulva or Enteromorpha) above 100 g dry wt m−2

causes SAV dieoff (Dennison et al., 1992).• Epiphyte colonizing SAV at a dry weight equal to thedry wt cm−2 of the host plant will cause dieoff of the host plant(Dennison et al., 1992).• In the absence of a standard concentration determinations wereheuristic.

Nuisance and toxic blooms:problem conditions for toxicblooms result from theproduction of toxin by theorganism. For nuisanceblooms, excessive abundanceof small organisms that clogsiphons of filter feeders.

Problem: detrimental impactto biological resources (e.g.dieoff of filter feedingbivalves and fish, respiratoryirritation) No problem: noapparent impacts onbiological resources

Nutrient input increases cause changes in nutrient ratios thatpromote growth of nuisance and toxic algae (Rabalais et al.,1996).• Threshold determination is difficult because toxicity ofchemicals produced by the different species vary, e.g. somedinoflagellates become toxic at cell counts in excess of106 cells l−1, others are a problem at 105 cells l−1; Pfiesteriapiscicida is toxic at levels below 102 cells l−1 (Burkholder et al.,1992a,b).• In the absence of a standard concentration determinations wereheuristic.

Dissolved oxygenconcentrations: bottom waterdissolved oxygenconcentration has become astandard measurement toassess the general conditionof a water body due to itsimportance to the survival ofbenthic organisms.

Anoxia: 0 mg l−1

Hypoxia: >0 but≤2 mg l−1 • Bottom water concentrations of 2 mg l−1 or less, havesignificantly reduced benthic macroinfauna and epifauna, andsuccess of trawling for demersal species (Rabalais and Harper,1992).

Biologically stressful: >2 but≤5 mg l−1

The range of 2–5 mg l−1 is included in this survey since fieldand laboratory observations have also shown oxygen stressresponses in invertebrate and fish fauna at these concentrations(Rabalais and Harper, 1992).

Submerged aquatic vegetation(SAV): the measure of SAV isspatial coverage since this isthe most common dataavailable, though diversityand density of plants isavailable for some estuaries.

High: ≥50 and≤100%estuarine surface water area

Submerged vascular plants, such asZostera marinaandPotamogeton perfoliatus, are though to play a vital role in theecology of nearshore environments to depths of 1–2 m. Theseplants attenuate variable inputs of nutrients and sediment, andare thought to be invaluable nursery areas. In relatively pristinewaterbodies, SAV thrive while die-off and absence of SAV isgenerally believed to be an indication of an eutrophic condition,associated with high turbidity caused by increased nutrient andChl a concentrations (Orth and Moore, 1984; Stevenson et al.,1993; Boynton et al., 1996). Additionally, high nutrientconcentrations may cause an imbalance in nutrient supply ratiosleading to dieoff of SAV (Burkholder et al., 1992a,b).

Medium: ≥25% but<50%of estuarine surface waterareaLow: ≥1% but<25%estuarine surface water areaVery low: ≥0 but <10%


frame of extreme conditions, whether events are peri-odic or episodic and typical event duration (e.g. days,weeks, seasonal). The trends information collected bythe survey is the most variable, with some systemshaving no trends data and others (e.g. NarragansettBay) having information from as far back as the be-ginning of the 20th century.

A reliability assessment of each status and trend re-sponse was requested to provide a basis for comparinginformation from the same estuary and between estu-aries. The reliability assessment evaluation providesa method of describing how accurately the informa-tion collected represents the conditions within an es-tuary. Since this information varies from statisticallytested scientific data to general observations, the re-liability assessment varies from “highly confident” to“speculative”.

2.3. Index development

2.3.1. Pressure—overall human influenceIn the original NEEA application, a workshop-based

approach was used to assess pressure factors: Par-ticipants used eutrophic condition assessment resultsin combination with other U.S. databases includingSPARROW estimates of N input (Smith et al., 1997),watershed population density (US Bureau of Census,undated), and susceptibility (NOAA and EPA, 1988).The ASSETS methodology has applied a simple modelto combine human pressure and system susceptibility,which is described below.

2.3.1.1. Equations for the determination of OHI.Ifonly conservative (i.e. mixing) processes are consid-ered, an equation for OHI may be derived based ona simple “Vollenweider” mass balance model, mod-ified to include the dispersive exchange between anestuarine black box and the ocean (Ferreira, 2000).Only dissolved inorganic nitrogen (DIN) is consid-ered, and non-conservative terms are neglected sinceonly therelative proportionsof DIN derived from an-thropogenic and ocean sources are of interest in theevaluation of pressure. Although nitrogen sources andsinks, e.g. due to benthic fluxes and primary produc-tion, clearly affect the final DIN concentration, thesewill be evaluated as metrics of system state, in the sec-ond stage of the methodology. Even if some of these

processes were considered as secondary internal ni-trogen sources, they would affect only the magnitudeof the nitrogen load, not the relative importance of an-thopogenic and natural sources.

dMw

dt= Min − Mout (1)

whereMw is the mass of nitrogen in the estuary (kg);t is the time (s);Min is nitrogen loading to the estuary(kg s−1); Mout is nitrogen discharge from the estuary(kg s−1).

Mout is composed of an advective outflow term anda dispersive exchange term (Eq. (2)).

Mout = moutvout + ke,s (mw − msea) (2)

where mw is nitrogen concentration in the estuary(kg m−3); mout is nitrogen concentration in the outflow(=mw for a one box model) (kg m−3); vout is advec-tive outflow (=river inflow) (m3 s−1); msea is nitro-gen concentration in the ocean (kg m−3); ke,s is bulkdispersion coefficient between the estuary and ocean(m3 s−1).

Which allowsEq. (1) to be rewritten as:

dMw

dt= Min − moutvout − ke,s (mw − msea) (3)

For the hypothetical case where there is no nitrogen inseawater (i.e.msea= 0), and consideringMin = Qmin,andvout = Q, whereQ is the river flow (m3 s−1) andmin the nitrogen concentration in the inflow,Eq. (3)may be expressed as:

dMh

dt= Qmin − Qmout − ke,smh (4)

whereMw becomesMh, the human-derived mass, andmw becomesmh, the human-derived concentration. Ifwe consider a steady state for salinity:

ke,s = Qse�s

(5)

wherese is mean estuarine salinity (no units);t is time(s); �s is difference between offshore salinityso andmean estuary salinity (no units) it follows that, if thesystem is well mixed (i.e.mout = mh):

dMh

dt= Qmin − Qmh − Qse

�smh (6)

Considering that for a sufficiently large integration pe-riod (e.g. over a year) dMh/dt = 0, i.e. the system is


in steady state:mhse

�s= min − mh (7)

Which allows mh, the nitrogen concentration in theestuary to be expressed simply as:

mh = min

1 + se/�s(8)

Which rearranged becomes:

mh = min(so − se)

so(9)

Eq. (9)gives the nitrogen concentration in the estuarydue solely to basin loading, but accounts for the dilu-tion effect of tidal exchange, which is reflected in thesalinity terms. Conversely, if only nutrient input fromoffshore seawater is considered,Eq. (1)may be rear-ranged by neglectingMin, since human-derived landinput is zero:

dMb

dt= −moutvout − ke,s(mw − msea) (10)

whereMw becomesMb, the background mass, andmwbecomesmb, the background concentration. Consider-ing vout = Q andmout = mb (see above) andEq. (5),Eq. (10)may be rewritten as:

dMb

dt= −Qmb − Qsemb

�s+ Qsemsea

�s(11)

Considering, as before that the system is in steady-state,and cancelingQ:semsea

�s= mb

(1 + se

�s

)(12)

which may be rearranged to yield:

mb = msease

so(13)

From Eqs. (9) and (13)mc, the expected total con-centration of DIN, considering only conservative pro-cesses may be obtained as:

mc = mh + mb (14)

and the overall human influence is defined asmh/mcexpressed as a percentage, which is classified into oneof five grades (Table 3). This approach considers thatthe background nutrient loads from the watershed arenegligible compared to human pressure.

There are several aspects regarding the use of theabove equations which need careful consideration:

Table 3Thresholds and categories used to classify overall human influence

Class Thresholds Score

Low 0 to <0.2 5Moderate low >0.2 to 0.4 4Moderate >0.4 to 0.6 3Moderate high >0.6 to 0.8 2High >0.8 1

(i) Apart from the natural difficulty in establishingthe mean salinity of an estuarine system, the con-cept of mean salinity only makes sense in sys-tems where there is some regularity in the riverdischarge. In torrential estuaries, such as thoseof the southern and western U.S. and southernEurope, where rainfall is concentrated in a shortperiod of the year and peak discharges may betwo orders of magnitude above the modal flow,it is more appropriate to use the median salinityfor calculating dilution;

(ii) In cases where there is pronounced vertical strat-ification, both the dilution volume and the estu-arine salinity should be that of the upper layer,i.e. surface layerse should be used. Possible im-provements to the model in these cases includethe addition of a vertical dispersion coefficientand the inclusion of a different nutrient concen-tration for each layer;

(iii) Most estuarine systems are subject to human-derived nutrient inputs both from upstream wa-tershed sources and from direct discharges ofeffluents into the estuary itself. The loading fromthe estuarine perimeter may easily be combinedwith the river-borne loading as a summationterm in cases where both are important;

(iv) In coastal lagoons, where river inputs are not im-portant, the nutrient loading may be essentiallydue to urban effluents and diffuse discharges.For such cases, the present approach will notwork, since it considers freshwater discharge asthe main nutrient vector to an estuary.

2.3.2. State—overall eutrophic conditionA subset of six parameters from the set of 16 given

in Table 1was selected to provide an index of state,expressed as overall eutrophic condition. These aredivided into two groups, indicative of primary (early)


Fig. 2. Conceptual model of primary and secondary symptoms ofeutrophication.

and secondary (advanced) symptoms of eutrophica-tion. Chlorophyll a, macroalgae and epiphytes areconsidered to be primary symptoms—excessive con-centration or abundance are considered to diagnoseearly stages of an eutrophication problem. Low dis-solved oxygen (DO), losses of SAV, and occurrenceof nuisance and/or toxic algal blooms are consideredto be secondary symptoms, i.e. indicators of welldeveloped eutrophic conditions (Fig. 2).

In NEEA, a method was developed to combine re-sults for this subset of symptoms (parameters) into anindicator of overall eutrophic condition based on theconcentration, spatial coverage, and frequency of oc-currence of extreme or problem occurrences. No for-mulation was developed, but rather a logic stepwisedecision method was used (Table 4):

1. For each primary symptom an area weighted ex-pression value for each zone was determined, andthe symptom level of expressionSl was then ob-tained by summation (Eq. (15)).

Sl =n∑1

(Az

AeEl

)(15)

whereAz is the surface area of each zone;Ae isthe total estuarine surface area;El is the expressionvalue at each zone;n is the number of estuarinezones.

2. The level of expression of the primary symptomsfor the estuaryPl is determined by calculating theaverage of the three estuary level of expression val-ues (Eq. (16)) and the estuary is then assigned a cat-egory for primary symptoms according toTable 5.

Pl = 1

p

p∑1

[n∑1

(Az

AeEl

)](16)

wherep is number of primary symptoms.3. For each secondary symptom (dissolved oxygen,

submerged aquatic vegetation loss and nuisanceand toxic blooms), an area weighted expressionvalue for each zone is determined as described in(1) above. The level of expression of secondarysymptoms for the estuary is determined by choos-ing the highest of the three estuary level symptomexpression values. Secondary symptoms are con-sidered to be a clear indicator of problems, and theapplication of the precautionary principle meansthat the highest (worst-case) value dictates the clas-sification. The estuary is then assigned a categoryfor secondary symptoms according toTable 5.

4. Finally, the primary and secondary symptoms arecompared in a matrix to determine an overall rank-ing of eutrophic conditions for the estuary (Fig. 3).

In the U.S. NEEA study, the assessments for each ofthe estuaries studied were reviewed and interpreted ata National Assessment Workshop by experts familiarwith local conditions.

ASSETS develops the concepts in several ways,mainly by providing a more robust framework for eval-uating the OEC index. The key improvements, whichwere applied to four North-East Atlantic estuaries inthe European Union, are described below.

2.3.2.1. Data assimilation. A relational databasehas been used to store the raw data required for cal-culation of OEC, and combined with a geographicalinformation system (GIS) to improve zone definitionand to calculate weighted values for each parame-ter. A GIS system based on the bathymetry grid wasimplemented, and salinity zones were determined, us-ing median values extracted from the database—the


Table 4Logical decision process for determination of overall eutrophic condition


Table 5Categories for primary and secondary symptoms

Estuary expression value Level of expression category

≥0 to ≤0.3 Low>0.3 to ≤0.6 Moderate>0.6 to ≤1 High

median avoids excessive weighting of low or highoutliers in the data distribution. An application of themethod is illustrated inFig. 4. The spatial weight ofeach sampling station, calculated through GIS andthe Thiessen polygon method, was also used to ana-lyze the spatial coverage and the frequency of a givenparameter within the salinity zone, in the calculationof data completeness and reliability.

2.3.2.2. Calculation of symptom values.Someprimary symptoms (e.g. epiphytes) and secondarysymptoms (e.g. toxic blooms) may only be assessedheuristically. Others, however, such as chlorophylla

Fig. 3. Determination of overall eutrophic condition based on primary and secondary symptoms.

(primary) and dissolved oxygen (secondary) are eval-uated on the basis of quantitative values. In order toimprove comparability between systems, ASSETS ex-tends the original NEEA approach by setting statisticalcriteria which are used to obtain overall values for eachsalinity zone from the dataset. It is recognized thatthe calculation of chlorophylla concentrations mustbe based on commonly observed peaks, rather than asingle exceptional one, and must reflect a significantevent in space and/or time. Similarly, low values ofdissolved oxygen should be representative of systemconditions, and not a single minimum value. This fol-lows the philosophy applied by the NEEA study, andhas been defined in the present work using a percentilesystem. The criteria used have been the percentile 90value for chlorophylla, and the percentile 10 valuefor dissolved oxygen. The stations for each salinityzone are grouped as metadata (Fig. 5) and the dataextracted are processed in a spreadsheet to determinethe input values for the symptom expression calcula-tions.


Fig. 4. Zonation of an estuary (Tagus, Portugal) for salinity, using a relational database and GIS.

2.3.3. Response—determination of future outlookThe response is based on an assessment of the sus-

ceptibility of the system and its foreseeable evolution.The susceptibility component of the approach eval-uates the capacity of a system to dilute and/or flushnutrients (Fig. 6) and is combined with a projectionof future outlook. In NEEA this was initially based ondemographic projections, which were complementedby expert knowledge to grade a system into one ofthree categories:

1. Future nutrient pressures decrease;2. Future nutrient pressures are unchanged;3. Future nutrient pressures increase.

The decision chart for definition of future outlook,based on susceptibility and future nutrient pressures,

is shown inFig. 7. This is an area where develop-ment is clearly needed, in order to provide a morerobust framework for including response into theASSETS methodology. Assessment of nutrient pres-sures must be carried out based on a combination ofdrivers, which include demographic trends, treatmentand remediation plans, and changes in watersheduses, particularly in agricultural practices (see, e.g.Boesch et al., in prep). Since these drivers will affectpressures, but the state of an estuary will be relatednot only to the pressures but also to physical fac-tors such as susceptibility, there is a clear need forscreening models (e.g.Tett et al., in press) whichwill include elements from both natural and socialsciences in order to explore future management sce-narios.


Fig. 5. Metadata for sampling stations, grouped by salinity zone, used for determining OEC symptoms.

2.4. Synthesis—grouping of pressure, state andresponse indicators

The representation of the ASSETS indices, whichare a combination of the three components, is carriedout by combining the various scores to provide anoverall grade. The individual classifications for pres-sure, state and response shown inTable 6are com-bined to provide a grade which may fall into one of fivecategories: High, good, moderate, poor or bad. Thesecategories are colour-coded following the conventionof the EU Water Framework Directive (2000/60/EC),and provide a scale for setting reference conditions fordifferent types of transitional waters, with regard toeutrophication. There are five possible grades for eachcomponent, which theoretically allows 53 possibilities,but 31 combinations were excluded as being highlyimprobable or impossible.Table 6includes 94 differ-ent combinations, which were distributed heuristically.

TheHigh grade will not be assigned if the expectedresponse will worsen system conditions, but a systemmay be rated asGoodbased on high or good conditions

of pressure and state, even if the expectation is that itwill worsen in the future. A grade ofModerateallowsthe greatest combination of pressure and response, aslong as the state generally scores in theModerate lowor ModerateOEC classes. Poor and Bad grades re-flect a range of undesirable pressure and state condi-tions, even if there are management plans for recovery.Since the response metric also includes susceptibility(Fig. 7), if high pressures lead to an undesirable state,it is unlikely that a system will be highly responsiveto remediation in the short-term, because it will mostlikely be moderately or highly susceptible.

3. Results and discussion

The focus of this paper is on the ASSETS method-ology, which means that only a relatively short set ofresults is presented, covering the following two points:

• Illustration of the range of systems studied inNEEA, with examples of how the developments in


Fig. 6. Susceptibility classes based on dilution and flushing po-tential.

OHI and OEC have been incorporated and validatedagainst the original work;

• Review of estuary classifications combining pres-sure, state and response.

Fig. 7. Definition of future outlook based on susceptibility andfuture nutrient pressures.

3.1. NEEA systems and extension of OHI and OEC

A significant part of the U.S. systems evaluatedin NEEA is presented inFig. 8. Eighty-two systemson the U.S. Atlantic seaboard and Gulf of Mexicoare shown, of which 27% have moderately low OECsymptoms, 31% are graded moderate, 22% are mod-erately high and 18% have high OEC. An analysis byregion shows that the Gulf region has a majority ofsystems with moderate OEC, whereas the areas fur-ther north have a higher percentage of systems in themoderate low category. There are a number of rea-sons for this including long growing season and warmwaters due to the subtropical climate, low freshwaterinflow, shallow depth and low tidal energy which allcontribute to making these systems more highly sus-ceptible than estuaries in other regions. The low tidalflushing, in addition to the other factors, increases thecoupling between pressure and state, and the use ofdissolved oxygen (rather than percentage O2 satura-tion) as an OEC secondary symptom probably alsoplays a role. Whilst it is unquestionable that absolutelevels of dissolved oxygen are a key factor for ecosys-tem health, it must also be recognized that estuarieswith higher salinity and temperature are far more frag-ile in terms of oxygen storage capacity, and pressureshould be interpreted accordingly.

The application of the percentile-based approach toprovide a more robust analysis for OEC is illustratedfor chlorophyll a (Fig. 9a) and dissolved oxygen(Fig. 9b) with data from the Tagus estuary. Excep-tionally high (chl a) and low (D.O.) values whichmay occur very occasionally are not considered usingthis approach, which means that there is less risk of asystem being wrongly classified due to outliers. It isimportant to validate this conclusion against a moreextensive set of data from the NEEA survey.

The application of the ASSETS approach for deter-mination of OHI was carried out in Chesapeake Baymainstem, Potomac, Patuxent and James River estuar-ies, Charleston Harbour and Tomales bay in the U.S.and the Sado, Tagus and Elbe in the E.U. (Table 7). Theresults shown for the U.S. estuaries generally appearto match the categories assigned in NEEA, but fur-ther tests are needed so as to include a wider range ofestuary types and regions. The estuaries in the Chesa-peake area are all highly affected by human activity,both in terms of pressure and of the state modifica-


Table 6Aggregation of pressure (OHI), state (OEC) and response (DFO) components to provide an overall classification gradea—percentage oftotal valid combinations shown in brackets

Grade 5 4 3 2 1

Pressure (OHI) Low Moderate low Moderate Moderate high High

State (OEC) Low Moderate low Moderate Moderate high High

Response

(DFO)

Improve

high

Improve low No change Worsen low Worsen high

Metric Combination matrix Class

P

S

R

5 5 5 4 4 4

5 5 5 5 5 5

5 4 3 5 4 3

High

(5%)

P

S

R

5 5 5 5 5 5 5 4 4 4 4 4 3 3 3 3 3 3

5 5 4 4 4 4 4 5 5 4 4 4 5 5 5 4 4 4

2 1 5 4 3 2 1 2 1 5 4 3 5 4 3 5 4 3

Good

(19%)

P

S

R

5 5 5 5 5 4 4 4 4 4 4 4 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 1 1

3 3 3 3 3 4 4 3 3 3 3 3 5 5 4 4 3 3 3 4 4 4 4 4 3 3 3 2 3 3

2 1 5 4 3 2 1 5 4 3 2 1 2 1 2 1 5 4 3 5 4 3 2 1 5 4 3 5 5 4

Moderate

(32%)

P

S

R

4 4 4 4 4 3 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 1

2 2 2 2 2 3 3 2 2 2 2 2 3 3 2 2 2 2 3 3 3 2 2

5 4 3 2 1 2 1 5 4 3 2 1 2 1 4 3 2 1 3 2 1 5 4

Poor

(24%)

P

S

R

3 3 3 3 3 2 2 2 2 2 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1

5 4 3 2 1 5 4 3 2 1 3 2 1 5 4 3 2 1

Bad

(19%)

a Note that the NEEA classification has been changed in ASSETS so that the high score now corresponds to high status, rather than ahigh level of a problem symptom.

tions induced by it, which is reflected in the gradingfor state and in the overall ASSETS grades for the sys-tems, which do not surpass moderate. Charleston Har-bour is classed as moderate in the NEEA OHI and hasan equivalent grade (0.498) in ASSETS. For TomalesBay, the calculated OHI of 0.090 of reflects a lowerlevel of human influence, associated with reduced hu-man pressure (the watershed contains only 11,000 in-habitants) and a lesser influence of freshwater on thesystem. This is despite the fact that the OEC score is

a precautionary moderate high in NEEA (two or poorin ASSETS) because of the occurrence of nuisanceand toxic algal blooms. These are often documentedas starting offshore and moving into the bay, whichhas an interesting parallel with many western Iberianestuaries and rias, where toxic blooms generally startin frontal systems offshore and are advected into theestuaries. In both areas, it is still unclear whether pre-vailing conditions within the estuaries assist in main-taining bloom conditions, for instance through factors


Fig. 8. OEC grading for estuaries on the U.S. eastern seaboard and Gulf of Mexico (converted to ASSETS categories)—adapted fromBricker et al. (1999).

such as cultivation of bivalve filter-feeders (see e.g.Nunes et al., 2003).

The Sado and Tagus are examples of mesotidal estu-aries where tidal exchange and turbidity preclude man-

Table 7Overall human influence calculated with the ASSETS approach and compared to the NEEA score

System ASSETS OHI score ASSETS OHI class NEEA OHI classification

Chesapeake Bay mainstema 0.977 Bad 1Potomac estuarya 0.969 Bad 2Patuxent estuarya 0.933 Bad 2James Rivera 0.921 Bad 2Charleston Harbourb 0.498 Moderate 3Tomales Bay 0.090 High –Sado estuary 0.299 Good –Tagus estuary 0.599 Moderate –Elbe estuary 0.998 Bad –

a Data for OHI calculation provided by theMaryland Department of Natural Resources, 2003and theUS Environmental ProtectionAgency Chesapeake Bay Program Office.

b Offshore salinity unavailable from sampling data, 35 was used. Data for OHI calculation derived fromSouth Carolina Department ofHealth and Environmental Control (1999, 2003).

ifestations of OEC secondary symptoms such as hy-poxia, as is also the case for S. Francisco Bay (Cloern,2001), although, e.g. the annual nitrogen input to theTagus is about 14,000 t per year.


Fig. 9. (a) Percentile 90 for chlorophylla values and (b) percentile 10 for the dissolved oxygen values, in the three salinity zones of theTagus estuary.

For estuaries with a highly irregular freshwater dis-charge regime such as these, the difference betweenthe mean and median estuarine salinity is significant(e.g. for the Sado the mean is 30.3 and the medianis 33.4) and affects the OHI results in terms of sus-ceptibility. The Elbe estuary in Germany is, in sharpcontrast, a heavily impacted system where human in-fluence accounts for virtually 100% of OHI.

3.2. Synthesis of PSR results

The OHI, OEC and DFO results for the 77 sys-tems shown inFig. 8 (five had insufficient data) havebeen combined using the matrix inTable 6, in order to

provide an overall score for each system. The overallASSETS scores are shown inFig. 10. Although theNEEA approach did not explicitly combine the threeindex components, the overall knowledge about thesesystems which was developed based on regional exper-tise was used to make a comparison with the ASSETSindex, both to test for accuracy and for the capacityto distinguish the magnitude of eutrophic symptomsamong estuaries.

Additionally, results are presented for two estuar-ies in the E.U., which are shown as an example ofthe application of this approach to Northeast Atlanticsystems. The comparison made between ASSETS andthe three NEEA components was essentially based on


Fig. 10. Distribution of OEC grades for estuaries on the U.S. eastern seaboard and Gulf of Mexico.

(a) the results of five widely different systems (LongIsland Sound, Neuse River, Savannah River, FloridaBay and West Mississippi Sound—Table 8); and (b)the relative proportion of each class given by ASSETS,and the comparison of this distribution with NEEArankings (Fig. 10).

The five U.S. systems shown inTable 8have AS-SETS scores ranging from bad to good, and the indi-vidual classifications for OHI, OEC and DFO wouldindicate that the index is a good synthesis of the threedifferent NEEA components.

The percentage distribution shown inFig. 10 is asexpected according to the NEEA study, and placesthe majority of systems in the good, moderate andpoor categories. More good and moderate systems arelocated in the South Atlantic zone (54% of the total)whereas estuaries in the other regions appear to bemore degraded.

For both (a) and (b), there is a good match betweenthe two classification systems, so the ASSETS matrix

Table 8Overall score tables, with E.U. Water Framework Directive colours for seven estuaries in the U.S. and E.U.

System Pressure (OHI) State (OEC) Response (DFO) ASSETS grade

Long Island Sound Moderate high—2 Moderate high—2 No change—3Neuse River High—1 High—1 No change—3Savannah River Low—5 Moderate—3 Worsen low—2Florida Bay Moderate high—2 High—1 Improve low—4West Mississippi Sound Moderate—3 Moderate low—4 No change—3Tagus Low—5 Moderate low—4 Improve low—4Sado Low—5 Low—5 Improve high—5

(Table 6) is considered to be an adequate first approachfor synthesis of pressure, state and response descrip-tors. It must be considered that this classification sys-tem is based on expert knowledge, and is subject torefinements. Some adjustments for PSR combinationswere made based on NEEA results for the EasternU.S. and Gulf of Mexico, and validation of the presentscale may be carried out on other estuarine datasets, inparticular on U.S. west coast estuaries, where NEEAhas been applied. There are a number of estuaries andcoastal areas in the E.U. where this approach can betested, e.g. parts of the Baltic Sea and major estuariessuch as the Scheldt and the Po.

Many eutrophication models are reported in theliterature, ranging from simple statistical approaches(e.g. Vollenweider, 1975) to complex 2D and 3Ddynamic simulations (e.g.Radach and Moll, 1989;Baretta et al., 1995). These models tend to relate nu-trient concentrations to phytoplankton blooms, and insome cases link phytoplankton and detrital dynamics


to dissolved oxygen. Such models have been success-ful in freshwater systems such as lakes and reservoirs(e.g. Jørgensen, 1976) and in the last decades havebeen applied with relative success to coastal ecosys-tems (e.g.Lancelot et al., 1997; Le Gall et al., 2000).

In estuaries and coastal lagoons, a general eutroph-ication model may have to account for factors suchas tidal range effects, toxic algal species, benthicsymptoms of eutrophication, or top-down control ofphytoplankton by filter-feeders. A number of dynamicmodels have successfully focussed on specific as-pects of eutrophication, such as the growth of oppor-tunistic seaweeds (e.g.Alvera-Azcárate et al., 2003;Ménesguen and Salomon, 1988), but the relationshipbetween nutrient pressure and estuarine changes ofstate is simultaneously so complex and so variable thata general dynamic model is still an ambitious goal.

The approach described in this work may be clas-sified as a screening model (for other examples, seeCSTT, 1997; Stigebrandt, 2001), where a more simpli-fied approach based on a combination of data, dynamicsimulations, statistical modelling and other techniquessuch as GIS may profitably be combined into a man-agement tool.

ASSETS is intended as a model for broad assess-ment of organic enrichment, both within and betweensystems, and it contains some of the elements of stateand biological structure identified byBoesch and Paul(2001) as potential indicators of ecosystem health.These indicators contribute to the practical implemen-tation of frameworks such as the Vigor-Organization-Resilience model (Costanza and Mageau, 2001), butas pointed out byBoesch and Paul (2001), substantialadvances are required to the state-of-the-art before ro-bust application is possible by decision-makers of theconcept of ecosystem health.

4. Conclusions

The methodology presented in this paper strives tobuild on the work of the U.S. NEEA, by providinga more consistent analysis based on a Pressure-State-Response framework. OHI is quantified by means of amore formal approach, and the application of GIS andstatistical thresholds to OEC determination is aimedat improving comparability. As stated previously, nu-trient concentrations are not necessarily a robust de-

scriptor of eutrophication in estuarine systems (see,e.g.Cloern, 2001; Boesch, 2002), in contrast to tech-niques developed historically for freshwater. This isan important point to bear in mind, considering thelimited cost-benefit of the sampling effort necessaryto compensate for the natural variability of dissolvedsubstances in estuaries. Likewise, turbidity is of onlyrelative interest since, in many mesotidal or macroti-dal systems, suspended matter in the water column isdictated more by the difference in current velocity andbed shear stress over the fortnightly Spring–Neap cy-cle than by phytoplankton blooms.

DFO is an area where more effort is clearly needed,in order to provide a robust assessment of potentialmanagement response. The involvement of socialscientists and economists is essential for developinginterdisciplinary metrics flexible enough to accom-modate different watershed development componentssuch as agricultural change, effluent treatment anddemographic changes, and also estuarine uses suchas aquaculture. These metrics must incorporate costfunctions, in order to provide decision-makers withthe tools necessary for valued judgement regardingecosystem conservation and rehabilitation.

ASSETS combines the three different NEEA com-ponents to provide a single grade for classifying estu-arine systems into one of five categories. Since OECwas the most developed component of the NEEAapproach, quantitative comparisons between systemstended to be based on state. Whilst this is appropriate,it seems nevertheless desirable to attempt to developthe U.S. classification into a more unified system,where the relationship between pressure, state andresponse may be clear to management, and thereforeencourage more proactive approaches to maintenanceof estuarine health. ASSETS additionally aims tocontribute to the classification systems which are a re-quirement for the E.U. Water Framework Directive, asregards some quality elements for transitional waters.

Both the U.S. and the E.U. share many commonfeatures in their estuarine systems and coastal zone:diverse tidal range and anthropogenic inputs, a widerange of uses and conflicts, and intense demographicpressure on the coastal zone. There are also obvi-ous differences: Enclosed “estuarine” seas such as theBaltic, and subtropical areas such as the Gulf of Mex-ico. It is apparent that there is much to gain in trying tosimultaneously leverage commonality and differences


into a unified system or systems which may accom-modate the great diversity of pressure, state and re-sponses. ASSETS aims to be one more stepping stonein that direction.

Acknowledgements

The authors would like to thank NOAA’s SpecialProjects Office and the National Centers for CoastalOcean Science, INAG and the EU OAERRE (EVK3-CT1999-0002) project for supporting this work, andS.E. Jørgensen for encouraging us to write this paper.We are grateful to J. Lencart-Silva and A. Nobre for theGIS work, to K. Schiffereger for applying the originalNEEA methodology to the Mira and Sado estuariesand to A. Mason for clerical support. Finally, we owea debt of gratitude to all those who produced and madeavailable estuarine datasets, commented and improvedon ideas, and provided encouragement throughout, in-cluding S.E. Jørgensen and an anonymous reviewer.

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