Aquatic Ecology33: 3–16, 1999.© 1999Kluwer Academic Publishers. Printed in the Netherlands.

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The relative importance of light and nutrient limitation of phytoplanktongrowth: a simple index of coastal ecosystem sensitivity to nutrientenrichment

James E. CloernUnited States Geological Survey, MS496, 345 Middlefield Rd., Menlo Park, CA 94025, USA (Fax: (1) 650-329-4327; E-mail: jecloern@usgs.gov)

Accepted 27 January 1999

Key words:estuaries, eutrophication, resource management

Abstract

Anthropogenic nutrient enrichment of the coastal zone is now a well-established fact. However, there is stilluncertainty about the mechanisms through which nutrient enrichment can disrupt biological communities andecosystem processes in the coastal zone. For example, while some estuaries exhibit classic symptoms of acuteeutrophication, including enhanced production of algal biomass, other nutrient-rich estuaries maintain low algalbiomass and primary production. This implies that large differences exist among coastal ecosystems in the ratesand patterns of nutrient assimilation and cycling. Part of this variability comes from differences among ecosystemsin the other resource that can limit algal growth and production – the light energy required for photosynthesis.Complete understanding of the eutrophication process requires consideration of the interacting effects of light andnutrients, including the role of light availability as a regulator of the expression of eutrophication. A simple indexof the relative strength of light and nutrient limitation of algal growth can be derived from models that describegrowth rate as a function of these resources. This index can then be used as one diagnostic to classify the sensitivityof coastal ecosystems to the harmful effects of eutrophication. Here I illustrate the application of this diagnosticwith light and nutrient measurements made in three California estuaries and two Dutch estuaries.

Introduction

Nutrient enrichment of the coastal zone is now a well-established fact. We know, for example, that nutrientloadings to the Baltic Sea, North Sea, Adriatic Sea,Gulf of Mexico, Chesapeake Bay, and San FranciscoBay have increased this century, especially during therapid growth of population, agriculture and fertilizerproduction beginning in the 1950’s (Nixon, 1995).Coastal eutrophication is a societal concern becauseanthropogenic nutrient enrichment can stimulate al-gal production and biomass accumulation, leading toevents of anoxia and large-scale mortalities of fish andshellfish (Rosenberg & Loo, 1988).

The study of coastal eutrophication is young andlags by about two decades the effort of limnologists(Nixon, 1995), who produced a set of simple but pow-

erful empirical models of lake eutrophication. Thesemodels describe algal biomass as a function of nu-trient loading normalized to lake basin morphometryand hydraulics (Dillon & Rigler, 1975; Vollenweider& Kerekes, 1980). They are based on the preceptsthat phytoplankton biomass (as chlorophyll concentra-tion) is a meaningful indicator of lake trophic status,and that phytoplankton biomass is regulated by thenutrient (P) resource. Our collective study of coastaleutrophication has not yet produced a model analog todescribe the functional relation between phytoplank-ton biomass and nutrient loading, although we havefollowed the lead of our limnologist colleagues. Forexample, Gowen et al. (1992) proposed an equationto calculate the potential yield of phytoplankton bio-mass in coastal ecosystems as a function of nutrient(N) loading. Most reviews of coastal eutrophication

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include attempts to find a general empirical relationbetween algal biomass or production and nutrient con-centration or loading (e.g. Boynton et al., 1982; Nixon,1992; Borum, 1996).

The lake eutrophication models are appealing be-cause of their simplicity, but we are now beginningto suspect that one general loading-plot model of eu-trophication might not succeed in the coastal zone.Whereas a clear stimulation of phytoplankton pri-mary production occurred in the Kattegat during theera of nutrient enrichment from the 1950’s to the1980’s (Richardson & Heilmann, 1995), other coastalecosystems such as North San Francisco Bay have ex-perienced declines in phytoplankton production as nu-trient enrichment proceeded (Alpine & Cloern, 1992).Chlorophyll concentrations in Chesapeake Bay in-creased significantly from the 1950s to 1970s whenDIN concentrations doubled (Harding, 1994); butchlorophyll concentrations remained unchanged in theYthan River estuary (Scotland) from the early 1960’sto 1990’s, when N loading increased fourfold (Ballset al., 1995). Although Delaware Bay and Mobile Bay(USA) have comparable high rates of N and P loading,these estuaries have distinctly different phytoplanktonbiomass, primary production, and oxygen dynamics(Pennock et al., 1994). Similarly, Vilaine Bay (France)experiences acute symptoms of eutrophication with al-gal blooms, oxygen depletion, and fish kills (Chapelleet al., 1994), while the nearby Bay of Brest has beenresistant to these symptoms even during the past twodecades of increased nutrient loading (Le Pape et al.,1996).

In his comparison of algal-nutrient dynamics in 40estuaries, Monbet (1992) found very different rela-tions between algal biomass and nutrient (N) concen-tration for tidally energetic and tidally weak systems;he concluded that macrotidal estuaries have an in-herent tolerance to nutrient enrichment. Although theevidence is unequivocal that phytoplankton biomassand production are generally related to nutrient load-ing (Nixon, 1992), there are large deviations aroundthe empirical functions used to describe the algal re-sponse to nutrient input (Figure 1a). These deviationsimply that we might have great difficulty in findinga simple and robust tool to describe a general eu-trophication response in coastal waters, or to classifycoastal ecosystems with respect to their susceptibil-ity to eutrophication. Le Pape et al. (1996, p. 1886)suggest that ‘Many coastal and estuarine ecosystemsare collecting high nutrient loading, but they responddifferently to such inputs and it is almost impossible

Figure 1. (a) Annual phytoplankton production versus nitrogenloading to shallow coastal ecosystems; from Borum (1996). Thefitted curve explains 36% of the variance in production. (b) Annualphytoplankton production in estuaries versus mean photic depth (es-timated as the depth of 1% surface irradiance); from Peterson et al.(1987).

to propose a general rule for phytoplankton biomassvariability on a seasonal time scale’.

Our failure to find a satisfying analog to the lakeeutrophication model is probably a reflection of theextreme diversity of the physical-biogeochemical sys-tems represented by estuaries, tidal rivers, inlets, bays,shelf waters, and river plumes. Among these ecosys-tems, Visser & Kamp-Nielsen (1996, p. 240) offerthe perspective that ‘eutrophication is not simply aquestion of nutrient loading, but (that) the pathwaysthrough which nutrients impact on marine productivityare many and varied, being governed as much by phys-ical as biological processes’. As we work to developgeneral tools to measure the susceptibility of coastalecosystems to the harmful effects of eutrophication,we need to consider all the physical processes thatconstrain the phytoplankton response to nutrient en-richment. For example, the transformation of nutrientelements into algal biomass requires solar radiationas the energy source to drive photosynthesis, so the

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expression of eutrophication can be constrained by theset of physical processes that govern the availability ofsunlight energy to the phytoplankton. This constraintis powerful in turbid coastal systems such as SanFrancisco Bay (Cloern, 1987), Ems-Dollard estuary(Colijn, 1984), Schelde estuary (Kromkamp & Peene,1995), and the Mississippi River plume (Lohrenz etal., 1990) where photic zones are shallow, fluctuationsin primary productivity are highly correlated with lightavailability, and where nutrient concentrations are per-sistently high because the light limitation of photosyn-thesis also limits the capacity of the phytoplankton toassimilate and transform dissolved nutrients into newalgal biomass. Among some coastal ecosystems, lightavailability appears to be an equally good predictorof phytoplankton primary production as nutrient load-ing (Figure 1b). Therefore, management strategies toprotect coastal ecosystems from acute responses to eu-trophication should be developed around the conceptthat algal population growth and production can belimited by other resources (and processes) in additionto nutrient loading.

Our slowness at developing a general model ofphytoplankton resource limitation comes, in part, fromthe absence of a simple assay for measuring lightlimitation of phytoplankton in nature. Considerableeffort has been directed to assess nutrient limitationwith mesocosm experiments (Riemann et al., 1988;Oviatt et al., 1995; Escaravage et al., 1996), bioas-says (Granéli, 1987), and measures of algal elementalcomposition (Paasche & Erga, 1988) or nutrient ra-tios (Bauerfeind et al., 1990; Fisher et al., 1992).A much smaller effort has been directed to comparelight and nutrients as limiting resources, although re-cent developments have included use of steady-statemodels of phytoplanktonbiomass (Carignan & Plenas,1994), dynamic models of estuarine eutrophication(DeGroodt & de Jonge, 1990; Madden & Kemp,1996), and simple scaling of the light and P re-source in lakes against the phytoplankton demands(Millard et al., 1996), as approaches to distinguishconditions of light and nutrient limitation. Pennock& Sharp (1994) combined the light-limitation modelof Wofsy (1983) with nutrient bioassays to infer largespatial and seasonal variations in the relative strengthof light and nutrient limitation in Delaware Bay. Arethere other approaches we can follow to assess theresource limitation of algal growth, and can theseapproaches be used to classify the susceptibility ofcoastal ecosystems to the stimulation of algal produc-tion in response to eutrophication? Here I suggest one

index of resource limitation, and then illustrate its ap-plication with measurements made in estuaries of theNetherlands and California.

A phytoplankton growth-rate model

The analyses presented here begin with a model ofphytoplankton population growth:

µ = PB(Chl:C)− r, (1)

whereµ is the specific growth rate (d−1), PB is dailycarbon assimilation rate per unit chlorophyll [mg C(mg Chl a-d)−1], Chl:C is the ratio of phytoplank-ton chlorophyll to carbon biomass (g g−1), and r isrespiratory loss (d−1). This equation describes algalpopulation growth as the product of the carbon assim-ilation ratePB (a function of photosynthetic efficiencyand light availability) and the ratio Chl:C (a func-tion of temperature, photo-adaptation, and nutrientavailability). Therefore, the model includes functionalresponses of population growth to both the light andnutrient resources required for the synthesis of newalgal biomass. I assume here that the critical nutri-ent is nitrogen (e.g. Smetacek et al., 1991; de Jongeet al., 1995; Wetsteyn & Kromkamp, 1994; Oviattet al., 1995), but the analysis can be extended toconsideration of limitation by other elements. Otherapproaches have been used to model algal growth, butthe model used here includes an interactive responseto light and nutrient availability such that algal growthefficiency in low-light environments is enhanced bynutrient enrichment (Cloern et al., 1995); this inter-active effect is not included in models that describegrowth rate as the product or minimum of separatelight- and nutrient-limitation functions.

Implementation of equation 1 requires functionaldescriptions of the three components of growth: thephotosynthetic ratePB , the Chl:C ratio, and the res-piration rater. Cloern et al. (1995) fit Equation (1) byleast squares to 145 measurements of growth rate andphotosynthesis from published experiments with algalcultures, giving estimates of the respiration loss:

µ = 0.85PB(Chl:C)− 0.015. (2)

The daily carbon assimilation ratePB can be cal-culated from the diel- and depth-variations of sun-light, and parameters of the photosynthesis-irradiancefunction (e.g. Platt et al., 1990):

pBz,t = pBm[1− exp(−Iz,tα/pBm)]. (3)

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Figure 2. Contours of calculated phytoplankton growth rate (from Equations (2) and (5)) as a function of mean daily light exposureI ′ (= I/KI )and concentration of limiting nutrientN ′ (= N/KN ). The contours were produced by interpolation (kriging) of growth rates calculated at 7750combinations ofI ′ andN ′ between 0 and 6.25 (= 0–15 mol quanta m−2 d−1 and 0–15µM DIN, respectively).

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Figure 3. Phytoplankton resource-limitation map as contours of R, the ratio of growth-rate sensitivity to light and nutrients. Large values ofR (>10) are resource combinations where growth rate is strongly limited by light availability; small values ofR (<0.1) are regions of strongnutrient limitation. The lineR = 1 defines the combinations ofI ′ andN ′ for which growth rate is equally limited by light and nutrientresources. This map was produced from interpolation of calculated values ofR (Equations (6) and (7)) at 7750 combinations ofI ′ andN ′.

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Here,pBz,t is the instantaneous rate of photosynthesisper unit chlorophyll at depth z and timet, Iz,t is theinstantaneous irradiance (PAR,µmol quanta m−2 s−1)at depthz, pBm the light-saturated assimilation rate,andα defines photosynthetic efficiency at low irradi-ance. The diel component of light variability can bedescribed as a sin function of photoperiod and irradi-ance at solar noon (Platt et al., 1990), while the depthvariation of irradiance can be described as an expo-nential decay with the attenuation coefficient k. Thenthe daily, depth-averaged carbon assimilation rate in auniform water column (or mixed layer) of thickness His:

PB = (1/H)24∫

0

H∫0

pBz,t dz dt

= (1/H)24∫

0

H∫0

pBm[1− exp(−Iz,tα/pBm)] dz dt,

(4)

Platt et al. (1991) give a series approximation to thisintegral that can be easily incorporated into a computerprogram for calculatingPB .

The ratio Chl:C can be estimated with an empiri-cal function fit to published measures of chlorophylland carbon content of phytoplankton grown under awide range of light- and nutrient-limiting conditions(Cloern et al., 1995):

Chl:C= 0.003+ 0.0154[exp(0.050T )][exp(−0.059I)][N/(KN +N)], (5)

T is temperature (◦C), I is mean daily irradiance av-eraged over depthH [= (Iφ/kH)(1 − exp{−kH }),where Iφ is daily surface irradiance as mol quantam−2 d−1], N is concentration of the limiting nutrient,andKN is the half-saturation constant for algal growthlimited by that element.

Equations (2)–(5) can be used to estimate thegrowth rate of a phytoplankton population when thefollowing quantities are known or specified: watercolumn heightH, daily surface irradianceIφ , lightattenuation coefficientk, temperatureT, nutrient con-centrationN, photosynthetic parametersα and pBm,and the half-saturation constantKN . Although eachof these quantities influences algal growth rate, thefocus here is on the relative importance of light andnutrients as resources that can limit growth. I usedEquations (2)–(5) to calculate growth rates across a

matrix of light and nutrient availability, but with fixedvalues for the other quantities:H = 10 m; k =0.4 m−1; T = 15◦C; pBm = 7.8 [g C (g Chl a-h)−1]; α = 0.03 [mg C (mg Chla-h)−1 (µmol quantam−2 s−1)−1]; andKN = 1.5µM dissolved inorganicnitrogen, DIN. Equations (2)–(5) were solved for 7750different combinations of light and nutrient availabil-ity, ranging fromI = 0 to 15 mol quanta m−2 d−1

and N from 0 to 15µM. These results define algalgrowth rate within the light-nutrient space shown inFigure 2, where the two resources have been nondi-mensionalized for comparison. The light resourceI ′is daily light exposureI divided byKI , the daily ir-radiance at which growth rate is half the maximum(KI = 2.4 mol quanta m−2 d−1 whenN = KN ). Thenutrient resourceN ′ is nutrient concentration dividedbyKN (N ′ = DIN/KN). This figure shows that algalgrowth rates are small (or negative) near the axes, andthey progressively increase away from the origin aslight and nutrient resources increase. For this definedset of conditions, the maximum growth rate is about0.7 d−1, corresponding to a population doubling timeof one day.

An index of sensitivity to light and nutrients

Although the specific response of algal growth to lightand nutrients depends on all the quantities that influ-ence growth rate, the pattern of responses shown inFigure 2 is general. This general response can be usedto estimate the relative sensitivity of algal growth toincremental changes in light energy and nutrient con-centration. The shading patterns in Figure 2 depictgradients of the growth rate response to light and nu-trients; regions of sharp gradients represent regions ofhigh sensitivity to changing resources. Shading gradi-ents along the x-axis are derivatives ofµ with respectto I ′ (∂µ/∂I ′), and these measure the sensitivity tolight. Gradients along the y-axis are derivatives ofµ with respect toN ′(∂µ/∂N ′), and these measuresensitivity to nutrients. The ratio of the two deriva-tives,R = (∂µ/∂I ′)/(∂µ/∂N ′), is an index that tellswhether phytoplankton growth rate is more sensitiveto changes in light (R > 1) or nutrients (R < 1).

The growth-rate equation used here cannot bedifferentiated explicitly, but the partial derivatives∂µ/∂I ′ and ∂µ/∂N ′ can be evaluated numericallywith finite difference approximations. I used the five-point formula (Hildebrand, 1974, p. 111) to calculate:

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∂µ0/∂I′ = (µ−2− 8µ−1+ 8µ+1

− µ+2)/(12hI ′), (6)

where∂µ0/∂I′ is the derivative of growth rate with

respect to light for a particular combination ofI ′ andN ′; µ−1 is the growth rate calculated for the samevalue ofN ′ but a small (h = 0.0025) decrement inI ′ (i.e., growth rate at irradianceI ′ − hI ′); µ−2 is thegrowth rate at irradianceI ′ − 2hI ′; µ+1 is the growthrate calculated for a small increment inI ′ (atI ′+hI ′);andµ+2 is the growth rate at irradianceI ′ + 2hI ′.Similarly, we can evaluate:

∂µ0/∂N′ = (µ−2 − 8µ−1+ 8µ+1

−µ+2)/(12hN ′), (7)

whereµ−1 is now the growth rate calculated for asmall (h = 0.0025) decrement in the nutrient resourceN ′, etc.

With this procedure I calculated∂µ0/∂I′, ∂µ0/∂N

′,and their ratiosR at each of the 7750 combinationsof I ′ andN ′ used to calculate the growth rates inFigure 2. Over this range ofI ′ andN ′, the ratioRvaries by eight orders of magnitude, so results aredisplayed in Figure 3 as logarithms ofR. The darkshadings in the upper left represent large values ofR,regions where the sensitivity of growth rate to light isgreater than the sensitivity to nutrients. Lighter shad-ings toward the lower right are regions ofI ′–N ′ spacewhere algal growth is more limited by nutrients thanlight. These two domains are separated by the middlesolid line, where both resources are equally important(R = 1). The upper line (R = 10) shows the do-main where light limitation is ten times stronger thannutrient limitation; the lower line (R = 0.1) showsthe domain where sensitivity to nutrients is ten timesgreater than the sensitivity to light.

Results summarized in Figure 3 provide a toolfor assessing the relative importance of light energyand nutrients as the two resources that can limit algalgrowth. We can separate thisI ′–N ′ space into five(somewhat arbitrary) domains: strong nutrient limi-tation, whereR < 0.1; nutrient limitation (1 >R > 0.1); exact colimitation (R = 1); light lim-itation (10 > R > 1); and strong light limitation(R > 10). From measurements of lightI ′ and nu-trient N ′ availability in an estuary, we can use thisresource map to make judgements about the relativestrength of light and nutrient limitation at a particulartime and location. By examining the distribution ofmany measurements ofI ′ andN ′ from one ecosystem,

we can make judgements about the sensitivity of thatecosystem to change in nutrient concentration. For ex-ample, if all measuredI ′–N ′ pairs from one estuaryfall in the domain of strong light limitation, then wecan infer that nutrient enrichment will be unlikely tostimulate the growth rate (and biomass) of phytoplank-ton. Perhaps more importantly from a managementperspective, we can also infer from these distributionsthe magnitude of nutrient reduction that would be re-quired to reduce algal growth rates. The resource mapcan be used to help develop a management target.

Before implementing Figure 3 as a tool for explor-ing algal-nutrient dynamics, we should first examinethe sensitivity of this resource-limitation map to allthe quantities that influence algal growth. In particular,we must determine whether the partition line betweenthe domains of light and nutrient limitation (the lineR = 1) is fixed, or whether its location is sensitiveto changes in the biological (pBm, α, KN ) or physicalparameters (T) that influence algal growth rate. I didthis by recalculating the grid ofR values after makingincremental changes in the values ofT, pBm, α, orKNindividually, and then comparing the new locationsof the partition lineR = 1 with the location shownin the reference plot of Figure 3. For example, Fig-ure 4a shows locations of the demarcation lineR = 1when growth rates were calculated forT = 5◦C(bottom line) andT = 25◦C (top line), comparedto the middle reference location forT = 15◦C. Al-though phytoplanktongrowth rate varies exponentiallywith temperature (Equations (2) and (5)), the rela-tive sensitivity of growth rate to incremental changesin light (∂µ/∂I ′) and nutrients (∂µ/∂N ′) does notchange much with temperature. This suggests that theresource-limitation map of Figure 3 is robust withrespect to environmental temperature variability.

Similar calculations were done to compareresource-limitation maps for different values of thehalf-saturation constantKN (Figure 4b). This analysisshows that location of the partition line (R = 1) is sen-sitive to the value ofKN chosen to describe nutrientlimitation of algal growth rate. This sensitivity growsas irradianceI ′ increases; at the highest light availabil-ity used, the deviation (intercept on the righty-axis)was+30% between calculations done forKN = 3µMcompared to calculations for the reference conditionKN = 1.5µM. The partition line deviated−27%from the reference position for calculations done withKN = 0.75µM. The same sensitivity analysis wasdone to determine shifts of the lineR = 1 for growthrates calculated with different values ofpBm (Figure 4c)

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Figure 4. Resource-limitation maps (as in Figure 3) for discrete changes in the model parameters that define phytoplankton growth as a functionof light and nutrients. In each case, the solid middle line (R = 1) shows the demarcation between domains of light and nutrient limitation forthe standard parameter set. Lines above and below show shifts in the location of this demarcation line after incremental increases and decreasesin (a) temperature, (b) the half-saturation constantKN , (c) the maximum assimilation ratepBm, and (d) efficiency of photosynthesis at lowirradianceα.

andα (Figure 4d). Again, a twofold increase or de-crease of these biological parameters caused a shiftin the location of the partition lineR = 1; this shiftwas most pronounced at highI ′. Therefore, the ex-act partitioning of the resource map into light- andnutrient-limited domains does vary withpBm, α, andKN . However, these deviations are all within thebounds between the linesR = 10 andR = 0.1 ofthe resource-limitation map of Figure 3. This sensi-tivity analysis shows that there is some uncertaintyabout the exact position of the boundary line betweenlight and nutrient limitation of phytoplankton growth.This boundary shifts with changes in the physiologicalstate of the phytoplankton (pBm, α, andKN ), so thedomain 0.1 < R < 10 can be considered a region ofpotential colimitation by light and nutrients. However,there is no uncertainty about which resource is limitingwhen measured values ofI ′ andN ′ fall above the lineR = 10 (definitive light limitation) or below the lineR = 0.1 (definitive nutrient limitation).

This sensitivity analysis was designed to encom-pass the range of physiological and environmentalvariability expected in temperate estuaries and coastal

waters. For example, in San Francisco Bay water tem-perature ranges from about 8◦C to 24◦C; I rarelyexceeds 10 mol quanta m−2 d−1 (i.e., I ′ < 4.17);and virtually all measurements ofpBm andα fall withinthe ranges (pBm = 3.9 − 15.6; α = 0.015− 0.06)used here. This range encompasses most of the vari-ability of pBm andα measured in other estuaries, suchas the Oosterschelde (Wetsteyn & Kromkamp, 1994)and Westerschelde (Kromkamp & Peene, 1995). Mostdescriptions of nutrient-limited algal growth in thecoastal zone specify values ofKN within the range0.75–3.0µM DIN used here (e.g. Chapelle et al.,1994; Prins et al., 1995). Therefore, across the widerange of environments and algal physiological vari-ability expressed within estuarine-coastal ecosystems,the domains of strong nutrient and light limitationshown in Figure 3 appear to be robust criteria fordefining the most limiting resource for phytoplanktongrowth.

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Application of the index to measure ecosystemsensitivity to nutrient enrichment

The resource map of Figure 3 provides a simple meansof assessing the growth constraints on phytoplanktonfrom measurements of mean irradiance and nutrientconcentration. This index can be used to compare thesensitivity of individual estuaries to changes in nutri-ent concentration. As an example, I apply this indexwith measurements made in five well-studied estuariesfor which the question of resource-limitation has beenexplored in depth: three estuaries on the Pacific coastof the U.S. (latitude 37◦ N) and two estuaries on theDutch North Sea (latitude 52◦ N).

North San Francisco Bay

The northern reach of San Francisco Bay (Califor-nia, USA) is the estuary of the Sacramento and SanJoaquin Rivers. The upper estuary (Suisun Bay) ishighly influenced by river flows that deliver nutri-ents and sediments. Suspended sediment concentra-tions in the surface waters are typically in the range30–100 mg l−1, and the mean light attenuation coeffi-cient in the channel is 4.7 m−1 (Cloern et al., 1985),corresponding to a photic depth of only about one me-ter. Nutrient concentrations are high: DIN usually>30µM, dissolved inorganic phosphorus DIP> 4µM,and dissolved silicate DSi> 100µM (Hager, 1994).However, phytoplankton biomass is persistently low,with chlorophylla < 5 µg l−1. This low-chlorophyllhigh-nutrient condition is the combined result of lightlimitation of algal growth (Alpine & Cloern, 1988) andrapid grazing by bivalve molluscs (Alpine & Cloern,1992).

The index of algal resource limitation in NorthSan Francisco Bay is illustrated with light and nu-trient measurements made monthly at one location(Figure 5). First, daily irradianceI was calculatedas [(Iφ/kH)(1 − exp{−kH })], where Iφ is the 3-day mean surface irradiance (mol quanta m−2 d−1)centered around the sampling date,k is the attenu-ation coefficient, andH is water depth at mean tidelevel. Then, these values were converted to the nondi-mensional formI ′ (= I/KI ). The nutrient resourcewas indexed as total DIN concentration (N = thesum of ammonium, nitrate, and nitrite concentra-tions), and then converted to the nondimensional formN ′ (= N/KN). With the lone exception in June,all the measurements cluster in the upper left cornerof the resource-limitation map (Figure 5), implying

Figure 5. The light and nutrient resources for phytoplankton growthin the upper reach of North San Francisco Bay, from measurementsmade approximately monthly, January 1992 through November1993, at a deep station (USGS station 6, Suisun Bay). Diamondsshow positions of light and nutrient measurements on the resourcelimitation map; numbers above indicate the month of sample col-lection. Daily irradiance (asI ′) was calculated from the extinctioncoefficient k, mean water depth (H = 10 m), and daily surfaceirradiance. Nutrient concentrationN ′ is the total concentration ofdissolved inorganic nitrogen in surface samples, normalized byKN .Values ofN ′ off the map indicate measurements whereN ′ > 6.25(DIN>15µM). Data from Wienke et al. (1993), Hager (1994), andCaffrey et al. (1994).

persistent strong light limitation and small rates ofphytoplankton population growth (see Figure 2). Thisdistribution of measurements exemplifies a coastalecosystem in which high suspended sediment con-centration strongly attenuates the light resource andconstrains the phytoplankton growth rate. Althoughhighly enriched with N, P, and Si, this estuary doesnot exhibit symptoms of eutrophication. In fact, an-nual primary production is only 20 g C m−2 (Alpine& Cloern, 1992), among the lowest rates of productionmeasured in coastal waters.

South San Francisco Bay

The southern basin of San Francisco Bay is influ-enced by riverine inputs only during episodes of highdischarge, so the suspended sediment concentration(turbidity) is usually lower than in the North Bay andlight limitation of algal growth is less severe (Alpine& Cloern, 1988). The South Bay is also enriched

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with N and P, but here the nutrient source is dom-inated by municipal wastewater (Hager & Schemel,1996). Algal-nutrient dynamics are characterized byautumn-winter periods of low phytoplankton biomass(chlorophylla ≈ 1–3µg l−1) and high nutrient con-centrations (DIN≈ 50–100µM; DIP ≈ 5–20µM;DSi ≈ 80–160µM), followed by a spring phyto-plankton bloom when chlorophylla reaches peaks of50–70µg l−1 and DIN becomes nearly depleted (Clo-ern, 1996). Light and nutrient measurements from twoannual cycles of sampling at one central site (Fig-ure 6) show a more variable distribution of resourcesthan in the North Bay. Here, some observed combina-tions ofI ′ andN ′ support high phytoplankton growthrates; nutrient limitation does not occur from summerthrough winter, but is stronger than light limitationduring the bloom peak in March or April, when DINbecomes depleted. As the spring bloom ends and DINconcentrations increase again in summer, the estuaryquickly reverts back to the condition of strong lightlimitation. South San Francisco Bay is an example ofa coastal ecosystem in which phytoplankton growth isusually limited by light availability but is episodicallylimited by nutrients. This implies that further nutrientenrichment might not affect algal growth in summer-autumn, but it could stimulate larger spring bloomsthan are currently observed.

Tomales Bay

A different combination of growth resources is seen inTomales Bay, a relatively pristine estuary just north ofSan Francisco Bay. This estuary has a sparsely popu-lated watershed, smaller nutrient loadings, and lowerconcentrations of suspended sediments than San Fran-cisco Bay. Measurements from one annual cycle at acentral site show that light availability to phytoplank-ton is always higher in Tomales Bay (Figure 7) thanin North San Francisco Bay (Figure 5), but that winterstocks of DIN (≈ 10–20µM) are much smaller. Inwinter, light limitation is stronger than nutrient limi-tation. However as algal biomass increases and DINbecomes depleted in spring and summer, algal growthrates are chronically limited by nitrogen availability. Inthis estuary, strong nutrient limitation can occur duringthe months March through August (Figure 7). TomalesBay exemplifies coastal ecosystems in which phyto-plankton growth is frequently limited by nutrients –a situation that implies high sensitivity to changes innutrient loading.

Figure 6. The light and nutrient resources for phytoplankton growthin South San Francisco Bay, from measurements made between Jan-uary 1992 and November 1993, at a deep station (USGS station 27).Data from Wienke et al. (1993), Hager (1994), and Caffrey et al.(1994).

Figure 7. The light and nutrient resources for phytoplankton growthin central Tomales Bay, from monthly measurements betweenMarch 1985 and May 1986 (station 8). Data from Cole et al. (1990).

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Dutch estuaries

The three cases above show how adjacent coastalecosystems can exhibit large differences in theirsensitivity to nutrient enrichment, depending uponthe physical-hydrologic features which regulate lightavailability to the phytoplankton. Seasonal measure-ments from two adjacent estuaries in the southernNetherlands exhibit comparable differences in re-source limitation. The Westerschelde is highly en-riched with N and P, and measurements of light andDIN during 1991 show a pattern of resource limi-tation (Figure 8) very similar to that in North SanFrancisco Bay. Although phytoplankton biomass inthis estuary fluctuates, with chlorophylla between<1and>20 µg l−1, the DIN concentration is alwaysabove 30µM (at Vlissingen, and higher at landwardsites (Kromkamp & Peene, 1995)) and light limita-tion is always stronger than nutrient limitation. Theresource index classifies the Westerschelde as a coastalsystem that is insensitive to fluctuations in nutrientconcentration. On the other hand, measurements inthe eastern Oosterschelde (Figure 9) show resourcedistributions similar to Tomales Bay, with light limi-tation in winter but progressive shift to strong nutrientlimitation from May/June through September. Again,two nearby estuaries show very different patterns ofresource limitation of algal growth.

A classification of resource limitation

Seasonal distributions of light and nutrient limitation(e.g. Figures 5–9) can be used to compare the poten-tial sensitivity of individual ecosystems to variabilityof nutrient loading from anthropogenic enrichmentor natural processes. One simple index is the fre-quency distribution of the measurements that showstrong light limitation, strong nutrient limitation, orpotential co-limitation. Figure 10 is an example, whichsummarizes these distributions for the five estuariesconsidered above. North San Francisco Bay and theWesterschelde never exhibit nutrient limitation; theseestuaries could be classified as the least sensitive to nu-trient variability, and the least susceptible to harmfulalgal blooms because phytoplankton growth is alwaysconstrained by the light resource. South San FranciscoBay is nutrient-limited only about 15% of the time;this estuary does not exhibit acute symptoms of eu-trophication now, but the resource index suggests thatit might have larger spring blooms in response to fur-ther enrichment. The Oosterschelde, and especiallyTomales Bay, are often nutrient limited (Figure 10);

Figure 8. The light and nutrient resources for phytoplankton growthin the Westerschelde, from monthly or semi-monthly measurementsat Vlissingen, January through December 1991. Data provided by J.Kromkamp.

Figure 9. The light and nutrient resources for phytoplankton growthin the eastern Oosterschelde, from monthly or semi-monthly mea-surements at station LG-PK, January through December 1991. Dataprovided by J. Kromkamp.

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Figure 10. Frequency distributions of resource combinations thatshow definitive nutrient limitation (top bars), potential co-limitationby light or nutrients (middle bars), and definitive light limitation(bottom bars) for five estuaries. These distributions summarize theresults of the resource-limitation index shown in Figures 5-9. Num-bers above each panel give the percentage of observations showingdefinitive or potential nutrient limitation; these indicate the sensitiv-ity of each estuary to change in nutrient concentration. Estuaries atthe top are least sensitive to nutrient change; estuaries at the bottomare most sensitive.

these estuaries could be classified as highly sensitive tovariability in nutrient loading and most susceptible tothe effects of further nutrient enrichment, because theyprovide a light climate that does not severely constrainalgal growth.

The frequency distributions of resource limitation(Figure 10) provide an index for measuring the suscep-tibility of individual ecosystems to one of the harmfuleffects of eutrophication – overproduction of phyto-plankton biomass. These assessments can be donewith the routine measurements of turbidity, light,and nutrient concentrations included in many mon-

itoring/research programs. As shown above, theseresource assessments can be used as a basis for estu-arine comparisons. They could also be used to helpguide management plans for individual estuaries andtheir watersheds. For example, management strategiesto protect water quality should give high priority toactions that would control nutrient loadings to the es-tuaries classified as nutrient-sensitive, such as TomalesBay. On the other hand, management strategies mightgive stronger consideration to other stressors (e.g.toxic contaminants, habitat loss, exotic species) inthose estuaries classified as nutrient-insensitive, suchas North San Francisco Bay. The index of nutrientsensitivity described here can be used as one step inthe process of prioritizing management concerns aboutnutrient enrichment relative to these other stressors.

Limitations to the approach

The index developed here is appealing for its sim-plicity, but simple models should be applied withcaution and explicit recognition of their limitations.For example:− This index measures only one response to nu-

trient enrichment – the stimulation of phytoplanktonpopulationgrowth rate. Stimulation of growth ratedoes not necessarily imply stimulation of biomassaccumulation, especially in estuaries with short resi-dence times (e.g. Balls et al., 1995; Le Pape et al.,1996) or strong grazing control of algal population de-velopment (e.g. Cloern, 1982; Herman & Scholten,1990). The next level of complexity in the devel-opment of this index should include the effects oftransports and top-down controls on phytoplanktonbiomass.− This index gives no information about estuarine

susceptibility to other manifestations of eutrophica-tion, such as shifts in nutrient ratios that favor devel-opment of harmful species (Cadée & Hegeman, 1986;Radach & Hagmeier, 1990; Smetacek et al., 1991;Escaravage et al., 1996), or stimulation of macroal-gal growth (Balls et al., 1995) or epiphyte growth onvascular plants (Madden & Kemp, 1996).− Application of the index requires careful con-

sideration of the large spatial gradients and temporalvariability of light and nutrient resources that are char-acteristic of estuaries. The index provides meaningfulinformation only if it is applied to data sets which rep-resent the full spectrum of variability in the light andnutrient resources within individual estuaries. Carefulapplication also requires consideration of the spatial

15

domain of concern, because the harmful effects of en-richment can be spatially disconnected from the pointof nutrient delivery to the coastal zone (Rabalais et al.,1996).

With these caveats in mind, the index describedhere can be used as one diagnostic among the bat-tery of tools being developed to assess and control theeutrophication threat to coastal ecosystems.

Acknowledgements

I thank Jacco Kromkamp for providing light and nu-trient data for the Oosterschelde and Westerscheldeestuaries, Brian Cole and Tara Schraga for their care-ful reviews of this manuscript, and Jens Borum forsharing his figure reproduced in Figure 1a.

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