biogeosciences phytoplankton primary production in the world’s … · 2020. 7. 25. · j. e....

Biogeosciences 11 2477ndash2501 2014wwwbiogeosciencesnet1124772014doi105194bg-11-2477-2014copy Author(s) 2014 CC Attribution 30 License

Biogeosciences

Open A

ccess

Phytoplankton primary production in the worldrsquos estuarine-coastalecosystems

J E Cloern1 S Q Foster1 and A E Kleckner1

1US Geological Survey Menlo Park California USA now at Boston University Boston Massachusetts USA

Correspondence toJ E Cloern (jecloernusgsgov)

Received 17 October 2013 ndash Published in Biogeosciences Discuss 15 November 2013Revised 10 March 2014 ndash Accepted 17 March 2014 ndash Published 7 May 2014

Abstract Estuaries are biogeochemical hot spots becausethey receive large inputs of nutrients and organic carbonfrom land and oceans to support high rates of metabolismand primary production We synthesize published rates ofannual phytoplankton primary production (APPP) in marineecosystems influenced by connectivity to land ndash estuariesbays lagoons fjords and inland seas Review of the sci-entific literature produced a compilation of 1148 values ofAPPP derived from monthly incubation assays to measurecarbon assimilation or oxygen production The median valueof median APPP measurements in 131 ecosystems is 185 andthe mean is 252 g C mminus2 yrminus1 but the range is large fromminus105 (net pelagic production in the Scheldt Estuary) to 1890g C mminus2 yrminus1 (net phytoplankton production in TamagawaEstuary) APPP varies up to 10-fold within ecosystems and5-fold from year to year (but we only found eight APPP se-ries longer than a decade so our knowledge of decadal-scalevariability is limited) We use studies of individual places tobuild a conceptual model that integrates the mechanisms gen-erating this large variability nutrient supply light limitationby turbidity grazing by consumers and physical processes(river inflow ocean exchange and inputs of heat light andwind energy) We consider method as another source of vari-ability because the compilation includes values derived fromwidely differing protocols A simulation model shows thatdifferent methods reported in the literature can yield up to 3-fold variability depending on incubation protocols and meth-ods for integrating measured rates over time and depth

Although attempts have been made to upscale measuresof estuarine-coastal APPP the empirical record is inadequatefor yielding reliable global estimates The record is deficientin three ways First it is highly biased by the large number

of measurements made in northern Europe (particularly theBaltic region) and North America Of the 1148 reported val-ues of APPP 958 come from sites between 30 and 60 N wefound only 36 for sites south of 20 N Second of the 131ecosystems where APPP has been reported 37 are basedon measurements at only one location during 1 year The ac-curacy of these values is unknown but probably low giventhe large interannual and spatial variability within ecosys-tems Finally global assessments are confounded by mea-surements that are not intercomparable because they weremade with different methods

Phytoplankton primary production along the continentalmargins is tightly linked to variability of water quality bio-geochemical processes including oceanndashatmosphere CO2 ex-change and production at higher trophic levels includingspecies we harvest as food The empirical record has defi-ciencies that preclude reliable global assessment of this keyEarth system process We face two grand challenges to re-solve these deficiencies (1) organize and fund an interna-tional effort to use a common method and measure APPPregularly across a network of coastal sites that are globallyrepresentative and sustained over time and (2) integrate datainto a unifying model to explain the wide range of variabil-ity across ecosystems and to project responses of APPP toregional manifestations of global change as it continues tounfold

Published by Copernicus Publications on behalf of the European Geosciences Union

2478 J E Cloern et al Phytoplankton primary production in the worldrsquos estuarine-coastal ecosystems

1 Introduction

Estuaries have large supplies of organic carbon because oftheir connection to land that delivers organic matter fromrunoff and nutrients that support high rates of primary pro-duction (Hopkinson et al 2005) As a result estuaries func-tion as fast biogeochemical reactors that operate on the en-ergy derived from respiration of their external and internalsupplies of fixed carbon Total annual ecosystem respirationgenerally exceeds gross primary production (Caffrey 2004Gattuso et al 1998) so most estuaries are heterotrophicecosystems that transform organic matter into inorganic nu-trients and CO2 are oversaturated in CO2 with respect to theatmosphere and unlike the open ocean are sources of CO2to the atmosphere Although estuaries occupy a small frac-tion of the Earthrsquos surface their CO2 emissions are globallysignificant ndash estimated at 043 Pg C yrminus1 (Borges 2005) Theaddition of this term to CO2 budgets reverses the functionof the coastal ocean from being a net sink to a net source ofCO2 and this term reduces the calculated global ocean CO2uptake by 12 Therefore sound understanding of oceanndashatmosphere CO2 exchange requires globally distributed mea-surements of primary production external supplies of or-ganic carbon and respiration across the diversity of estuarineecosystems

The organic carbon supplied to estuaries is packaged indifferent forms Detritus delivered by land runoff is derivedprimarily from terrestrial vegetation and is an important en-ergy supply that fuels estuarine metabolism This pool of or-ganic matter may be old (Raymond and Bauer 2001) is re-fractory has low nitrogen content is metabolized primarilyby microbial decomposers and has little direct food value forherbivores (Sobczak et al 2005) Much of the organic matterproduced by vascular plants and macroalgae is also routedthrough decomposers or exported only about 20 of sea-grass marsh and macroalgal primary production is consumedby herbivores (Cebrian 1999) A third supply is primary pro-duction by microalgae including phytoplankton and benthicforms The biogeochemical and ecological significance ofmicroalgal production differs from the other forms becauseit is enriched in nitrogen and lipids including essential fattyacids packaged in a form easily accessible to consumer or-ganisms and because this pool turns over rapidly ndash on theorder of days (Furnas 1990) Most (sim 90 ) phytoplank-ton production is consumed or decomposed to support localheterotrophic metabolism whereas a substantial fraction ofmacrophyte production (24ndash44 ) is exported or buried anddoes not contribute to local metabolism (Duarte and Cebrian1996) Therefore the different forms of organic matter aremetabolized through different routes and have different eco-logical and biogeochemical ramifications We focus on an-nual phytoplankton primary production (APPP) as a supplyof labile organic carbon that plays a central role in the eco-logical and biogeochemical dynamics of estuaries and othermarine ecosystems influenced by connectivity to land Al-

though we do not consider primary production by benthicmicroalgae this supply of labile organic matter is compara-ble to that of phytoplankton in some estuaries (Underwoodand Kromkamp 1999) and it plays a similarly important rolein the dynamics of ecosystem metabolism and energetics

With the exception of very turbid estuaries such as theScheldt (Gazeau et al 2005) net planktonic production ispositive in estuaries so it is an autotrophic component oper-ating within heterotrophic ecosystems Phytoplankton pro-duction is the primary source of organic carbon to someestuarine-coastal systems such as the Baltic Sea (Elmgren1984) South San Francisco Bay (Jassby et al 1993) andMoreton Bay (Eyre and McKee 2002) Much of the annualproduction occurs during seasonal or episodic blooms whenphytoplankton photosynthesis exceeds total system respira-tion and estuaries shift temporarily to a state of autotrophy(Caffrey et al 1998) leading to depletion of inorganic nu-trients as they are converted into organic forms incorporatedinto algal biomass (Kemp et al 1997) drawdown of CO2(Cloern 1996) and shifts in pH oversaturation of dissolvedoxygen (Herfort et al 2012) rapid phytoplankton uptake ofcontaminants such as PCBs methyl mercury (Luengen andFlegal 2009) and dissolved trace metals such as cadmiumnickel and zinc (Luoma et al 1998) increased growth andproduction of copepods (Kioslashrboe and Nielsen 1994) andbivalve mollusks (Beukema and Cadeacutee 1991) as the algalfood supply increases and sedimentation of phytoplankton-derived organic carbon that accelerates benthic respirationand nutrient regeneration rates (Grenz et al 2000)

Trophic transfer of the energy and essential biochem-icals contained in phytoplankton biomass is the resourcebase supporting production at higher trophic levels includ-ing those we harvest for food Annual phytoplankton produc-tion is highly correlated with fishery landings (Nixon 1988)biomass of benthic invertebrates (Herman et al 1999) andsustainable yield of cultured shellfish (Bacher et al 1998)Increasing nutrient runoff during the past century has pro-voked increases of phytoplankton production supporting 3ndash8-fold increases of fish biomass in the Baltic Sea JapanrsquosSeto Inland Sea northern Adriatic Sea shelf waters of theBlack Sea and the Nile Delta (Nixon and Buckley 2002Caddy 2000) However the increased phytoplankton pro-duction of organic carbon has exceeded the assimilative ca-pacity of these and other ecosystems leading to the globalexpansion of marine dead zones (Diaz and Rosenberg 2008)loss of habitat for seagrasses demersal fish and shellfish(Carstensen et al 2003) and shifts in fish communities(Kemp et al 2005 Caddy 2000) The link between phyto-plankton production and estuarine biogeochemistry is illus-trated in a compelling way by the systemic changes that oc-curred in Narragansett Bay during a 25-year warming periodwhen the winterndashspring phytoplankton bloom disappearedprimary production declined 40ndash50 benthic metabolismslowed and the bay switched from being a net consumerto a net producer of fixed nitrogen (Nixon et al 2009)

Biogeosciences 11 2477ndash2501 2014 wwwbiogeosciencesnet1124772014

J E Cloern et al Phytoplankton primary production in the worldrsquos estuarine-coastal ecosystems 2479

Therefore understanding variability of phytoplankton pro-duction is a key to understanding variability of ecosystemrespiration and metabolism cycling of nutrients carbon andtrace metals water and habitat quality secondary produc-tion by herbivores fish catch production of cultured shell-fish and the cumulative value of all these ecosystem servicesjudged to be highest in estuaries among all biomes (Costanzaet al 1997)

Frequent and globally distributed satellite observationsof ocean color have provided a robust understanding ofthe rates and patterns of primary production across ter-restrial and marine biomes Annual net primary produc-tion in the world oceans is on the order of 60 Pg C(Behrenfeld et al 2005) and areal rates range from about160 g C mminus2 yrminus1 in oligotrophic regions of the open oceanto about 1300 g C mminus2 yrminus1 in the most productive (Peru-vian) upwelling system (Chavez et al 2010) Howeversatellite-based methods have not yet been developed forroutinely measuring phytoplankton production in shallowcoastal waters where suspended sediments dissolved organicmatter and interference from land confound interpretation ofocean color (Moreno-Madrintildeaacuten and Fischer 2013) There-fore our knowledge of phytoplankton primary production inestuaries and other shallow coastal domains is based almostentirely on direct measurements which are labor-intensiveand therefore distributed much more sparsely in time andspace than can be accomplished through remote sensing

Here we present an inventory of APPP from direct mea-surements reported in the readily accessible scientific lit-erature as an update to the last review published threedecades ago by Walter Boynton and colleagues (Boynton etal 1982) This work follows recent syntheses of the sea-sonal patterns (Cloern and Jassby 2008) scales of variabil-ity (Cloern and Jassby 2010) and phenology of phytoplank-ton biomass (Winder and Cloern 2010) in estuarine-coastalecosystems Our objectives are to summarize the patterns andrates of APPP contained in the available data records andto determine if they contain sufficient spatial and temporalcoverage to establish reliable global assessments of this im-portant Earth system process We first summarize the datacompilation to show where how and when measurements ofAPPP have been made and then explore the data to illustratepatterns of variability over time between and within ecosys-tems We then provide a synthesis of the literature to summa-rize what is known about the underlying causes of this vari-ability We use a simulation model to estimate how much ofthe between-ecosystem variability could result from the sub-stantial differences in methods used across studies We endwith perspectives on the contemporary state of knowledgeof APPP in estuarine-coastal ecosystems reliability of APPPestimates at the global scale and steps required to reduce thelarge uncertainty of those estimates

2 An inventory of annual phytoplankton primaryproduction measurements

We compiled measurements of APPP reported in referencesfound through searches in Scopus Google Scholar andWeb of Science Our target was reported values of depth-integrated APPP across the worldrsquos estuaries bays lagoonstidal rivers inland seas and nearshore coastal marine wa-ters influenced by connectivity to land We only included val-ues derived from direct measurements of oxygen evolution orcarbon assimilation that were made at least once each monthexcept at high latitudes where winter measurements are notmade under ice Reports were excluded if they did not in-clude a description of the methods used sampling frequencyand period or if the sampling locations were not specifiedThe final compilation (summarized in the Supplement Ta-ble S1) includes 1148 values of APPP from 483 samplingsites within 131 ecosystems (places) Primary production hasbeen measured in many other studies (eg Gilmartin 1964Henry et al 1977) but the results were not reported with theinformation required to calculate APPP In order to stream-line our presentation we define our uses of ldquoprimary produc-tionrdquo as the phytoplankton contribution to system produc-tion ldquoproductionrdquo as either the process or the mass of car-bon fixed over a period of time and ldquoproductivityrdquo as a rateof production ndash usually an hourly or daily rate

The compilation includes 389 measurements of APPP re-ported as gross primary production (GPP) 254 measure-ments reported as net primary production (NPP) that in-clude measures of either net phytoplankton production inthe euphotic zone (eg Moll 1977 Rivera-Monroy et al1998) or net pelagic production (eg Gazeau et al 2005)and 505 measurements reported only as ldquoprimary produc-tionrdquo (Fig 1) Phytoplankton production measurements be-came routine components of some research and monitoringprograms after the 1950s and we found around 200 annualmeasurements each decade from the 1960s to 1980s Re-ported measurements peaked at 388 in the 1990s but wefound only 84 from the first decade of the 21st century(Fig 1) This seems to be strong evidence of a reducedeffort at measuring APPP in estuarine-coastal ecosystemsThe overwhelming majority of measurements were reportedfrom European (more than half from the Baltic region) andNorth American estuarine-coastal waters We found a totalof only 58 APPP measurements from studies in Asia (31)SouthCentral America (15) AustraliaNew Zealand (13)and Africa (1) Phytoplankton production was most com-monly (974 of 1148) made as measures of14C assimilation159 were made from rates of oxygen production and 15 from13C assimilation (Fig 1)

wwwbiogeosciencesnet1124772014 Biogeosciences 11 2477ndash2501 2014


GPP

NPP

U

0 200 400

Count

What

1950s

1960s

1970s

1980s

1990s

2000s

0 200 400

Africa

Asia

AustraliaNewZealand

Baltic Region

Europe

North America

SouthCentral America

0 200 400

13C

14C

O2

0 200 400 600 800

When

Where

How

Fig 1 Summary statistics of 1148 measurements of annualphytoplankton primary production (APPP) in estuarine-coastalecosystems Top panel shows what was reported U= unspecifiedNPP= net primary production GPP= gross primary productionPanels below show the distribution of measurements by decade re-gion and method Median APPP and data sources for each of 131ecosystems are given in the Supplement Table S1

3 Patterns of variability

31 Latitudinal patterns

APPP at individual sites ranged fromminus692 g C mminus2 yrminus1

(net pelagic production which includes respiration by het-erotrophs) at site 2 in the Scheldt Estuary during 2002(Gazeau et al 2005) to 1890 g C mminus2 yrminus1 (net phytoplank-ton production) in the Tamagawa Estuary during 1988 (Ya-maguchi et al 1991) We show the distribution of individualmeasurements by latitude and the geographic distribution of

effort as the number of reported measurements at sites binnedwithin 20 latitudinal bands (Fig 2) Of the 1148 reportedvalues 958 come from sites between 30 and 60 N Thishighly skewed distribution of effort reflects the exceptionalnumber of APPP measurements made in the Baltic Sea andnearby coastal waters (420) mostly in Danish or Swedishcoastal waters (368) and notably in the Kattegat where 249measurements have been reportedminus23 of the global totalWe found only 36 reported measurements of APPP for sitessouth of 20 N none between the equator and 25 S and only15 in the Southern Hemisphere Therefore our knowledgeof annual phytoplankton production in the worldrsquos estuarine-coastal ecosystems is strongly biased by the high concentra-tion of sampling effort at northern latitudes and particularlyin the Baltic region In contrast we found little published in-formation about the annual production of estuarine-coastalphytoplankton in tropical-subtropical ecosystems and in theSouthern Hemisphere The highly skewed geographic distri-bution of sampling coupled with the large variability withinlatitudinal bands (Fig 2) constrains our ability to answer afundamental question does APPP vary systematically withlatitude

32 Variability between sampling sites

From 1148 individual measurements of APPP we calcu-lated median values at the 483 sites where measurementshave been reported Median APPP ranged across sites fromminus278 g C mminus2 yrminus1 (net pelagic production) at station 2in the Scheldt Estuary to 1890 g C mminus2 yrminus1 (net phyto-plankton production) at one site in Tamagawa EstuaryGiven the large weight of measurements from the Balticregion we separated these from sites in other world re-gions The mean and median of the median APPP mea-sured at sites in the Baltic region are similar 118 and112 g C mminus2 yrminus1 respectively (Fig 3a) However the dis-tribution of median APPP measured at sites in other regionsis skewed by a small number of high values (Fig 3b) sothe overall mean (238 g C mminus2 yrminus1) is larger than the me-dian (174 g C mminus2 yrminus1) Based on the median APPP re-ported at these 409 non-Baltic sites 121 are classified asoligotrophic (lt 100 g C mminus2 yrminus1) 178 as mesotrophic (100ndash300 g C mminus2 yrminus1) 69 as eutrophic (300ndash500 g C mminus2 yrminus1)and 41 as hypertrophic (gt 500 g C mminus2 yrminus1) following ScottNixonrsquos classification (Nixon 1995)

33 Variability between ecosystems

We used the 1148 individual measurements to calculate me-dian APPP for the 131 ecosystems where measurements havebeen reported We use the word ldquoecosystemrdquo in the sense of aplace either an individual bay or estuary or a subregion of theBaltic Sea or connected coastal waters (eg Gulf of FinlandKattegat) Forty-seven of these values are single measure-ments made at one site during 1 year but the others represent



0 250 500 750

Number of Measurements

-40

-20

0

20

40

60

80

La

titu

de

0 500 1000 1500

APPP (g C m-2 yr-1)

A B

Fig 2 (A) Distribution of effort as the number of APPP measurements reported within 20 latitudinal bands and (B) latitudinal distributionof 1148 APPP measurements (one value ofminus692 g C mminus2 yrminus1 from the Scheldt Estuary is not shown)

the medians of measurements made at multiple sites andorduring multiple years (see Fig 5) The ranked distribution ofmedian values is shown in Fig 4 which provides a summaryof APPP measurements reported for the worldrsquos estuarine-coastal ecosystems The overall median is 185 and mean is252 g C mminus2 yrminus1 However the spread around these mea-sures of central tendency is fromminus105 to 1890 g C mminus2 yrminus1considerably larger than variability of APPP between regionsof the world oceans (Chavez et al 2010) As an index ofhow the empirical record has grown we also show in Fig 4the ranked distribution of APPP reported for 45 estuaries byBoynton et al (1982) which averaged 190 g C mminus2 yrminus1 andranged from 19 to 547 g C mminus2 yrminus1

34 Variability within ecosystems

Many of the 131 ecosystems represented in the data com-pilation are estuaries fjords bays or lagoons ndash aquatic habi-tats situated within the landndashocean continuum that have largespatial gradients of phytoplankton biomass and environmen-tal factors that regulate phytoplankton growth rate such asnutrient concentrations bathymetry mixing turbidity andgrazing losses As a result these ecosystem types are char-acterized by large spatial variability of primary production(high-resolution visualizations of spatial patterns are begin-

ning to emerge from remote sensing data and improved bio-optical models for estuaries Son et al 2014) We selected11 examples where APPP was measured at multiple (min-imum 9) sites during 1 year (Fig 5a) Spatial variability islarge within some of these ecosystems eg ranging from 70to 810 g C mminus2 yrminus1 in Tomales Bay during 1985 from 15to 516 g C mminus2 yrminus1 in Howe Sound during 1974 and from78 to 493 g C mminus2 yrminus1 in the Westerschelde (= Scheldt Es-tuary) during 1991 Annual phytoplankton production doesnot follow a normal distribution (eg Figs 3 4 ShapirondashWilk testW = 0780p lt 00001 Shapiro and Wilk 1965)so we used the ratio of median absolute deviation (MAD)to median APPP as a robust index of dispersion (Ruppert2011) that can be compared within and between ecosystemsFor a univariate data set the MAD is defined as the medianof the absolute deviations from the series median The ra-tio MAD median of APPP within ecosystems ranged from020 to 083 (Fig 5a) ndash ie the characteristic deviation fromthe median APPP within an ecosystem ranged from about20 to about 80 of that median For comparison theMAD median of the median APPP between the 11 ecosys-tems was 074 This comparison shows that the spatial vari-ability of APPP within some ecosystems can be comparableto the variability between ecosystems Therefore measure-ments at single sites are unlikely to yield reliable estimates



0

5

10

15

20

Nu

mb

er o

f Me

asu

rem

ent

s

0 200 400 600 800 1000 1200 1400 1600 1800

Annual Phytoplankton Primary Production (g C m-2 yr-1)

0

20

40

60

80

Num

ber

of M

easu

rem

ents

Oligo- Eu- Hyper-trophicMeso-

Baltic Region mean = 118 median = 112

Other Regions mean = 238 median = 174

A

B

Fig 3 Frequency distributions of median APPP measured (A) at 74 sites in the Baltic region and (B) at 409 sites in other regions Shadedregions (B) partition measurements into Nixonrsquos classification (Nixon 1995) of 4 trophic states from oligotrophic (lt 100 g C mminus2 yrminus1) tohypertrophic (gt 500 g C mminus2 yrminus1)

of ecosystem-scale phytoplankton production (Jassby et al2002)

Phytoplankton biomass in estuarine-coastal ecosystemscan fluctuate substantially from year to year (Cloern andJassby 2010) so we might expect comparably high inter-annual variability of APPP We probed the data inventoryto explore interannual variability of APPP using studies at10 sites where phytoplankton production was measured dur-ing at least 7 consecutive years Annual phytoplankton pro-duction at some sites such as Kattegat station 413 (mea-sured from 1989 to 1997) and Gullmar Fjord (from 1985 to2008) was stable over time (Fig 5b) However at other sitessuch as Massachusetts Bay station N18 and Boston Harborstation F23 interannual variability was large ranging from207 to 664 g C mminus2 yrminus1 and 224 to 1087 g C mminus2 yrminus1 re-spectively during the 1995ndash2005 study period The medianindex of interannual variability (MAD median of APPP)at these 10 sites was 023 smaller than the index of vari-

ability between the sites of 057 Although the numberof records is small the available data suggest that vari-ability of APPP between ecosystems gt spatial variabilitywithin ecosystems gt variability between years Therefore thehighly skewed geographic distribution of APPP measure-ments (Fig 2) differs markedly from the global distribu-tion of sampling required to adequately capture the largestcomponent of variability ndash between ecosystems The avail-able data probably underestimate interannual variability be-cause most records are short and variance of APPP increaseswith series duration (Jassby et al 2002) We found onlyeight APPP series longer than a decade but these representnotable advances since the 1980s when none were avail-able (Boynton et al 1982) The longest series were fromTampa Bay (29 years Johansson 2010 J O R Johans-son personal communication August 2013) Gullmar Fjord(23 years Lindahl et al 1998 O Lindahl personal com-munication June 2009) and Rhode River Estuary (20 years



10 100 1000


0

40

80

120

160

Ran

k

GPP

NPP

U

Boynton et al 1982

Kattegat

Westerschelde

Long Island Sound

Tokyo Bay

Terminos Lagoon

Chesapeake

Gulf of Finland

Lower St Lawrence

Tasman Bay

Bedford Basin

Ria de Viga

Neuse

Cienaga Grande

Colne

Limfjord

Aarhus Bay

Dumbell Bay

Gulf of Bothnia

Bay of Brest

South San Francisco Bay

Cochin Backwater

Delaware Bay

Saanich Inlet

Seto Inland Sea

Swan

Tampa Bay

Fig 4Ranked distribution of median APPP reported for 131 estuarine-coastal ecosystems U= unspecified NPP= net primary productionGPP= gross primary production (values and data sources for each ecosystem are given in the Supplement Table S1) Thirty ecosystemsare represented twice because both GPP and NPP were reported One negative value (minus105 g C mminus2 yrminus1) from the Scheldt Estuary is notshown For comparison the gray line and circles show the ranked distribution of the 45 measurements of APPP available when Boynton etal (1982) last reviewed phytoplankton production in estuaries

Gallegos 2014 C L Gallegos personal communicationMay 2013) The scarcity of decadal time series precludes as-sessments of change over time to complement assessments ofclimate-driven changes in oceanic primary production (egBehrenfeld et al 2006)

4 What drives this variability

41 A conceptual model

The values of APPP reported here are the time integral ofdaily depth-integrated primary productivity measured in dis-crete water samples Primary productivity is the product ofplant biomass times its turnover rate so the variability ofAPPP described above is ultimately determined by processesthat drive temporal and spatial variability of phytoplanktonbiomass and growth rate within estuaries (Fig 6) Poten-tial biomass production is set by the nutrient supply rate(Howarth 1988) but the realization of that potential variesgreatly across estuaries (Cloern 2001) and is determined by

the balance between three sets of dynamic processes (1)transport processes including import of ocean-derived phy-toplankton biomass export by washout during events of highriver flow and sinking (2) biomass growth and (3) mortal-ity that includes losses to grazers and cell lysis induced byviral infection (Brussaard 2004) Phytoplankton growth rateis regulated by water temperature nutrient concentrationsand forms and the amount and quality of photosyntheticallyavailable radiation (PAR) The conceptual model depicted inFig 6 grounded in the foundational work of coastal scien-tists such as Bostwick Ketchum (Ketchum 1954) and Boyn-ton and colleagues (Boynton et al 1982) links these pro-cesses and provides a framework for exploring the drivers ofAPPP variability over time within and between estuaries

The construct of primary productivity as biomass timesgrowth rate is the basis for models of varying complex-ity used to estimate phytoplankton primary production andto compare the strength of different controls The simplestmodel describes primary productivity as a linear function ofphytoplankton biomassB (as chlorophylla concentration



0

400

800

AP

PP

(g

C m

-2 y

r-1)

St Law

rence

Core-B

ogue

Chesapeake

How

e

Patuxent

Westerschelde

Str G

eorgia

Narragansett

Burrard

Tomales

Sw

an

A Spatial Variability

034053

022

083 047056 023

057

031

027

020

0

400

800

AP

PP

(g

C m

-2 y

r-1)

Kattegat 413

Oslashresund

Kattegat 1993

Gullm

ar

Oosterschelde O

21

Rhode R

iver 52

Oosterschelde P

3

Massachusetts N

04

Boston F23

Massachusetts N

18

B Interannual Variability

019

022

008

015

031025

044

055

047016

Fig 5Examples of (A) spatial variability of APPP across multiple sites within estuarine-coastal ecosystems and (B) interannual variabilityof APPP at individual sites Boxplots show median interquartile ranges (boxes) and ranges (vertical lines) Numbers above are medianabsolute deviation (MAD) divided by median APPP for individual ecosystems or sites These compare to MAD median of 074 betweenecosystems in (A) and 034 between sites in (B) (A) Lower St Lawrence River Estuary (Therriault and Levasseur 1985) Core-BogueEstuarine system (Williams and Murdoch 1966) Chesapeake Bay in the 1960s (Flemer 1970) Howe Sound (Stockner et al 1977) PatuxentRiver Estuary (Flemer et al 1970) Westerschelde (Kromkamp et al 1995) Strait of Georgia (Stockner et al 1979) Narragansett Bay(Oviatt 2002) Burrard Inlet (Stockner and Cliff 1979) Tomales Bay (Cole 1989) and Swan River Estuary (Thompson 1998) (B) Kattegatsites 413 and 1993 (Carstensen et al 2003) Oslashresund (AEligrtebjerg 1981) Gullmar Fjord (O Lindahl personal communication 25 June 2009)Oosterschelde sites O21 and P3 (Wetsteyn and Kromkamp 1994) Rhode River site 52 (Gallegos 2014) Massachusetts Bay sites N04 andN18 and Boston Harbor site F23 (Oviatt et al 2007)

chla) which varies 500-fold across estuarine-coastal ecosys-tems (Cloern and Jassby 2008) For example 64 of thedaily variability of phytoplankton productivity in Saanich In-let is explained by daily fluctuations of chla (Grundle etal 2009) and 81 of the variability of APPP in BostonHarbor-Massachusetts Bay is explained by variability of an-nual mean chla (Keller et al 2001) A second class ofmodels describes primary productivity as a linear function of9 middot B middot I (Behrenfeld and Falkowski 1997) where the coef-ficient 9 is an index of light-utilization efficiency (a phys-iological component) andI is a proxy for light availabil-ity such as depth-averaged PAR Models of this form ex-plain 60ndash95 of the daily primary-productivity variabilityin estuarine-coastal ecosystems such as San Francisco Bay

Puget Sound Neuse River Estuary Delaware Bay HudsonRiver Estuary plume (Cole and Cloern 1987) Tomales Bay(Cole 1989) the Sacramento-San Joaquin Delta (Jassby etal 2002) and Escambia Bay (Murrell et al 2007) A fur-ther level of complexity is required to explain the full rangeof primary-productivity variability in other estuaries such asthe Chesapeake (Harding et al 2002) Rhode River Estuary(Gallegos 2014) and Tokyo Bay (Bouman et al 2010) Inthese places the photosynthetic efficiency9 varies signifi-cantly because the maximum carbon-assimilation ratepmax(see Sect 51) fluctuates with seasonal temperature variabil-ity or with variability of dissolved inorganic carbon along theestuarine salinity gradient (Gallegos 2012) Models based onthis last approach are the foundation for computing oceanic



BIOMASS13 13 13 GROWTH13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 RATE13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

Sinking13 Temperature13 13 13 13 Nutrients13 supply13 13 13 13 13 concentra5on13

PRIMARY13 PRODUCTIVITY13 =13 Light13

Fig 6 Primary productivity is the product of phytoplanktonbiomass (regulated by import export sinking mortality nutrientsupply and growth rate) times phytoplankton growth rate (regulatedby light temperature and nutrient concentrations)

primary production from remotely sensed chla water tem-perature and optical properties of the upper ocean Accurateestimates of primary production from all these model classesrequires calibrations that capture seasonal and regional vari-ations in photosynthetic efficiency expressed aspmax (SauxPicart et al 2014)

The use of different model classes implies that the phys-ical and biological regulators of primary production shownin Fig 6 take on varying degrees of importance acrossthe diversity of ecosystem types at the landndashsea inter-face However underlying all models is a strong empir-ical relationship between primary production and phyto-plankton biomass This relationship has been formalizedby C Gallegos (httpwwwbiogeosciences-discussnet10C75742013bgd-10-C7574-2013-supplementpdf) as an al-ternative conceptual model for understanding variability ofAPPP over time (Fig 5b) within (Fig 5a) and between(Fig 4) estuarine-coastal ecosystems as a process tightly tiedto processes of phytoplankton biomass variability We usecase studies to illustrate responses to four of these processes

42 Nutrient supply

Estuaries receive larger nutrient inputs than any other ecosys-tem type (Howarth 1988) and the importance of nutrientsupply is reflected in the wide distribution of APPP mea-surements shown in Fig 4 The hypertrophic systems atthe upper end of this distribution sustain exceptionally highphytoplankton biomass with peak chla concentrations of98 microg Lminus1 in Tamagawa Estuary (Yamaguchi et al 1991)gt 100 microg Lminus1 in Swan River Estuary (Thompson 1998) and181 microg Lminus1 in Cieacutenaga Grande de Santa Marta (Hernandezand Gocke 1990) These compare with chla concentra-tions sim 01 microg Lminus1 in oligotrophic ocean domains All hy-pertrophic ecosystems have large nutrient supplies eitherfrom urban sources including sewage (eg Tamagawa Es-tuary Yamaguchi et al 1991 Boston Harbor Oviatt et al2007 and Kaneohe Bay before sewage diversion Smith etal 1981 Swan River Estuary Thompson 1998 western

Long Island Sound Goebel et al 2006) or runoff fromagricultural watersheds (eg Golfo de Nicoya Gocke et al2001 Huizache-Caimanero Lagoon Edwards 1978 Cieacute-naga Grande de Santa Marta Hernandez and Gocke 1990)By contrast the low-production ecosystems at the other endof the distribution include those with small nutrient inputssuch as Biscayne Bay (Roman et al 1983) and PetalionGulf in the oligotrophic Aegean Sea where nitrate concentra-tions are typically lt 01 microM (Becacos-Kontos 1977) There-fore nutrient supply is a key mechanism of variability acrossecosystems

However nutrient loading alone is not a good predictor ofphytoplankton production because individual estuaries haveattributes that regulate their efficiency at converting exoge-nous nutrients into phytoplankton biomass (Cloern 2001)The hypertrophic estuaries bays and lagoons have high pro-duction efficiency because they have features that either pro-mote fast phytoplankton growth such as shallow depth (Cieacute-naga Grande de Santa Marta Hernandez and Gocke 1990) ora long growing season in the tropics (Golfo de Nicoya Gockeet al 2001) or slow transport processes that retain nutrientsand phytoplankton biomass (Chesapeake Bay Harding et al2002 Swan River Estuary Thompson 1998) Other nutrient-rich estuaries such as northern San Francisco Bay and theScheldt estuary are inefficient at converting exogenous nu-trients into phytoplankton biomass because of fast grazingor strong light limitation (see below) The distribution of nu-trients along the landndashocean transition can generate a spa-tial pattern of decreasing phytoplankton production with dis-tance away from the nutrient source ndash eg river inflow to theDouro Estuary (Azevedo et al 2006) or sewage dischargesto Long Island Sound (Goebel et al 2006)

Nutrient supply to estuaries is strongly influenced by hu-man activities and changes in nutrient supply over time havecaused significant changes in phytoplankton biomass andproduction especially since the mid 20th century Chloro-phyll a concentrations increased 5- to 10-fold in the lowerChesapeake Bay after the 1950s in response to increasedloadings of reactive nitrogen (N) and phosphorus (P) (Hard-ing 1997) APPP in the western Wadden Sea ranged be-tween 100 and 200 g C mminus2 yrminus1 in the 1960s and 1970sbut increased to 300ndash400 g C mminus2 yrminus1 in the 1980s afterriverine nutrient inputs to the North Sea increased (Cadeacuteeand Hegeman 1993) APPP in the Kattegat more than dou-bled after the 1950s and is significantly correlated withannual N loading (Carstensen et al 2003) Equally com-pelling case studies show significant reductions of phyto-plankton biomass and production following steps to reduceanthropogenic nutrient input Net APPP in Kaneohe Bay was894 g C mminus2 yrminus1 in 1976 when total N (TN) loading was256 kmol N dminus1 but it fell to 294 g C mminus2 yrminus1 in 1978 af-ter TN loading was reduced to 61 kmol N dminus1 by divertingsewage to the ocean (Smith et al 1981) Similar changeswere measured in the upper Tampa Bay where mean annualGPP declined from 750 g C mminus2 yrminus1 to 410 g C mminus2 yrminus1



after inputs of dissolved inorganic N were reduced fromgt 3000 to lt 1000 kg N dminus1 (Johansson 2010) Thereforelong-term observations confirm linkages between nutrientsupply and phytoplankton biomass and primary productionHowever steps to reduce nutrient inputs have not always ledto expected declines of phytoplankton biomass or production(Duarte et al 2008) and this experience confirms also thatthe efficiency of incorporating nutrients into biomass is reg-ulated by processes that vary across ecosystems and changeover time We highlight three of these processes

43 Light limitation

Phytoplankton growth rate in nutrient-rich estuaries is deter-mined in large part by light availability measured as meanPAR (Alpine and Cloern 1988) which varies with inci-dent solar irradiance turbidity and depth of the mixed layer(Wofsy 1983) All three components play major roles inregulating phytoplankton production Phytoplankton produc-tion at high latitudes is constrained by a short growth sea-son because solar irradiance does not reach the water sur-face when it is covered by ice and snow The open-waterseason in Dumbell Bay (8030prime N) lasts about a month soAPPP is only 9 g C mminus2 yrminus1 (Apollonio 1980) APPP is10 g C mminus2 yrminus1 in Young Sound (74 N) where the ice-freeseason is 2 months (Rysgaard et al 1999) These values ofannual phytoplankton production are smaller than the peakdaily phytoplankton production (16 g C mminus2 dminus1) in the trop-ical Cieacutenaga Grande de Santa Marta (Hernandez and Gocke1990)

Many estuaries have high concentrations of mineral sed-iments delivered by land runoff and kept in suspension bywind waves and tidal currents (May et al 2003) Sediment-associated turbidity constricts the photic zone to a thin layerleading to light limitation of photosynthesis over the wa-ter column and slow incorporation of nutrients into phyto-plankton biomass The Scheldt Estuary is an iconic exam-ple of a high-nutrient estuary where mean PAR in the watercolumn is insufficient to support positive net pelagic pro-duction (Gazeau et al 2005) Wofsy (1983) developed asteady-state model to explore relationships between phyto-plankton growth and turbidity from suspended particulatematter (SPM) Model simulations are consistent with obser-vations that phytoplankton biomass in nutrient-rich estuar-ies is inversely related to SPM concentration blooms cannotdevelop when SPM concentration exceeds about 50 mg Lminus1

(except in shallow waters) and phytoplankton respiration ex-ceeds photosynthesis when the optical depth ndash the productof mixed depthH (m) times the light attenuation coefficientk (mminus1) ndash exceeds 5 The empirical record supports thesegeneralizations Annual phytoplankton production in the in-ner Bristol Channel is only 68 g C mminus2 yrminus1 because of ldquose-vere light limitationrdquorsquo of photosynthesis by suspended sedi-ments where the euphotic zone is less than 50 cm deep (Jointand Pomroy 1981) Other nutrient-rich high-SPM estuar-

ies have ultra-low phytoplankton production such as Ros-keeda Bay with NPP of 4 g C mminus2 yrminus1 (Raine and Patching1980) Colne Estuary with GPP of 5 g C mminus2 yrminus1 (Kocumet al 2002) and turbid regions of the Ems-Dollard (Colijnand Ludden 1983) and Columbia River (Small et al 1990)estuaries with APPP of 26 and 38 g C mminus2 yrminus1 respectivelyThese examples are important reminders that phytoplanktonrequire both light energy and nutrients as essential resourcesSimple models (eg Cloern 1999) provide tools for assess-ing the relative importance of light and nutrient limitation ofphytoplankton growth and making comparisons of resourcelimitation across estuaries and over time

Much of the spatial variability (Fig 5a) of phytoplanktonproduction within some estuaries is a consequence of SPMgradients that generally decrease along the riverndashocean con-tinuum as sediments sink and their concentrations are dilutedby clear ocean water A characteristic pattern of high pro-duction near the estuary mouth and low production near theriver source of SPM or in the estuarine turbidity maximum isseen in many estuaries including Corpus Christi Bay (Flint1970) Howe Sound (Stockner et al 1977) Bristol Channel(Joint and Pomroy 1981) Ems-Dollard Estuary (Colijn andLudden 1983) Delaware Bay (Pennock and Sharp 1986)San Francisco Bay (Cloern 1987) and the Seine Estuary(Garnier 2001) The water column of turbid estuaries cansupport positive net phytoplankton production in shallow re-gions where the optical depth is less than 5 Thus lateralshallows of coastal plain estuaries such as South San Fran-cisco Bay (Cloern et al 1985) Chesapeake Bay (Malone etal 1986) and James River Estuary (Bukaveckas et al 2011)are zones of high phytoplankton biomass and they functionas autotrophic domains that export phytoplankton biomassto fuel metabolism in deeper heterotrophic domains (Caffreyet al 1998) Thus complex spatial patterns of phytoplank-ton production and net ecosystem metabolism are establishedacross estuarine gradients of bathymetry and SPM concentra-tion (Lucas et al 1999)

Net phytoplankton production can be positive even indeep turbid estuaries when the water column stratifies to es-tablish a surface layer where the optical depth lt 5 and phyto-plankton biomass grows rapidly Much of the annual phyto-plankton production in estuaries occurs during blooms andsurface blooms develop under conditions of salinity stratifi-cation in many estuaries such as Chesapeake Bay (Maloneet al 1986) South San Francisco Bay (Cloern 1996) andTokyo Bay (Bouman et al 2010) The primary source ofbuoyancy to stratify estuaries is freshwater inflow so strati-fication and phytoplankton production dynamics are tied tovariability of river discharge In tidal estuaries both strat-ification and turbidity oscillate over the fortnightly neapndashspring cycle with the lowest SPM concentrations strongeststratification and highest phytoplankton biomass during thelow-energy neap tides followed during spring tides by thebreakdown of stratification increases of SPM by suspensionof bottom sediments and rapid declines of phytoplankton



biomass and primary productivity (Cloern 1996) Variabil-ity of primary productivity over the neapndashspring tidal cy-cle is a prominent feature of phytoplankton dynamics inPuget Sound Saanich Inlet Lower Saint Lawrence Estuary(Sinclair et al 1981) subestuaries of the Chesapeake Bay(Haas 1977) and South San Francisco Bay (Cloern 1984)Light availability to phytoplankton also fluctuates with windstress that breaks down stratification and generates wavesthat penetrate to suspend bottom sediments in shallow estu-aries Wind mixing can be a mechanism of annual variabilityof the phytoplankton production that occurs during seasonalblooms For example MarchndashApril phytoplankton produc-tion in South San Francisco Bay was 67 g C mminus2 during ayear (1990) of relatively calm spring winds and low turbid-ity but only 18 g C mminus2 the following year when winds werestronger SPM concentrations higher and the spring bloomwas suppressed by high turbidity (May et al 2003) Hu-man modifications of hydrologic systems have altered sed-iment discharge in many of the worldrsquos rivers with down-stream effects on estuarine primary producers SPM concen-trations and turbidity of the northern San Francisco Bay havedecreased 50 since 1975 following decades of channeliz-ing and damming its tributary rivers This implies a doublingof the euphotic-zone depth illustrating that changes in sedi-ment supply can be a process of long-term change in estuar-ine phytoplankton production (Cloern and Jassby 2012)

44 Top-down regulation

While the growth rate of phytoplankton cells is determinedby temperature nutrient concentrations and light availabil-ity the rate of biomass change is determined by the balancebetween rates of growth and mortality including consump-tion by grazers (Fig 6) Rates of growth and consumption areoften in balance except following events such as lengthen-ing of the photoperiod in spring (Riley 1967) pulsed inputsof nutrients (Ara et al 2011) germination of phytoplanktonresting stages (Shikata et al 2008) or setup of stratification(Pennock 1985) when phytoplankton growth rate temporar-ily exceeds grazing rate and biomass builds The most prob-able fate of phytoplankton cells is to be consumed by grazersthat include fast-growing microzooplankton (flagellates cili-ates mixotrophic phytoplankton) and mesozooplankton suchas copepods On an annual basis the grazing loss to meso-zooplankton is a small fraction (sim 10 ) of APPP in produc-tive estuaries (Calbet 2001) but year-to-year variability inthe seasonal timing of copepod population growth can be animportant regulator of APPP For example anomalous lowAPPP occurred across all sampling sites in MassachusettsBay in 1998 a year with an unusually warm winter and earlygrowth of copepods whose grazing suppressed the winterndashspring bloom that normally contributes over 40 of APPP(Keller et al 2001)

Although micro- and meso-zooplankton consume mostphytoplankton production in the open ocean (Calbet 2001

Calbet and Landry 2004) their role as grazers can be lessimportant in shallow estuaries and bays where benthic sus-pension feeders especially bivalve molluscs are the dom-inant grazers (Murrell and Hollibaugh 1998) Bivalves arethe important grazers in shallow waters because they can fil-ter the overlying water column on timescales of days (Cloern1982) and because they both compete with zooplankton forthe phytoplankton food resource and prey upon microzoo-plankton (Greene et al 2011) and copepod nauplii (Kim-merer et al 1994) Grazing by bivalves can be a strong reg-ulator of phytoplankton biomass and production This reg-ulation is evident from phytoplankton biomass budgets thatcompare seasonal rates of growth with grazing by zooplank-ton and bivalves (Cloern 1982) It is evident from com-parative analyses showing that mean annual phytoplanktonbiomass (chla) is inversely correlated with mussel biomassin 59 Danish estuaries (r = minus071 Kaas et al 1996) and15 estuaries of Prince Edward Island (r = minus092 Meeuwig1999) It is also evident from case studies of changing phy-toplankton biomass after bivalve populations either abruptlyincreased or decreased Phytoplankton primary productionin the low-salinity habitats of northern San Francisco Baydecreased from 106 to 39 g C mminus2 yrminus1 after the nonnativeclam Potamocorbula amurensiswas introduced and rapidlycolonized bottom sediments in 1987 (Alpine and Cloern1992) An equally large and abrupt decline of phytoplanktonbiomass followed colonization of the Ringkoslashbing Fjord bythe clamMya arenariaafter water exchange with the NorthSea was modified (Petersen et al 2008) The inverse patterndeveloped in the South San Francisco Bay after the NE Pa-cific shifted to its cool phase in 1999 when bivalve biomassdeclined and APPP increased from lt 200 g C mminus2 yrminus1 togt 400 g C mminus2 yrminus1 (Cloern and Jassby 2012) Thereforegrazing by pelagic and especially benthic suspension feed-ers is a key process of phytoplankton production variabilityover time and between estuarine-coastal ecosystems

45 Physical processes

Phytoplankton production is tightly regulated by physicalprocesses that deliver nutrients control the efficiency withwhich nutrients are converted into biomass and transport nu-trients and biomass away from and into estuaries and baysThe key physical forcings operate across the interfaces be-tween estuaries and their tributary rivers the coastal oceanand atmosphere (Cloern 1996)

451 River flow

Empirical observations over decades have established astrong association between river discharge and phytoplank-ton primary production in estuaries However the relation-ship between the two is complex system specific multidi-mensional and is best understood through comparisons ofthe timescales of biomass production loss and transport



(Lucas et al 1999) Complexity arises because variability ofriver inflow drives variability of some processes that promoteand other processes that suppress phytoplankton biomass ac-cumulation and production Positive associations derive fromrivers as a source of both nutrients and low-density freshwater that stratifies estuaries and creates a horizontal den-sity gradient that drives gravitational circulation and retainsphytoplankton biomass within estuaries As a result seasonaland annual variability of phytoplankton production are posi-tively correlated with freshwater inflow to many estuaries in-cluding the South San Francisco Bay (Cloern 1991) NeuseEstuary (Mallin et al 1993) Chesapeake Bay (Adolf et al2006) Escambia Bay (Murrell et al 2007) Patos Lagoon(Abreu et al 2009) and Sagami Bay (Ara et al 2011)

However river inflow is also a source of sediments (Wet-steyn and Kromkamp 1994) and colored dissolved organicmatter (Lawrenz et al 2013) that constrain phytoplanktonproduction by attenuating light and altering its spectral qual-ity Freshwater inflow also drives seaward advective trans-port that can be faster than phytoplankton growth rate andprevent biomass accumulation during periods of high dis-charge Thus seasonal phytoplankton biomass and produc-tion are inversely related to river inflow to other estuariessuch as northern San Francisco Bay (Cloern et al 1983) andNorman River Estuary (Burford et al 2012) These contrast-ing examples illustrate the dual functions of river inflow asboth a nutrient source that promotes biomass growth and atransport process that can prevent its accumulation withinestuaries The balance between these functions varies withriver flow so the functional relationships are complex as ob-served in the New and Neuse River estuaries where the trans-port timescale (freshwater flushing time) ranges from lt 2 h togt 7 months Phytoplankton biomass in these estuaries is max-imal when flushing time is about 10 days At longer flushingtimes (low flow) biomass growth rate is limited by nutrientexhaustion at shorter flushing times (high flow) biomass ac-cumulation is limited by washout (Peierls et al 2012)

A generally accepted principle of estuarine ecology is thatphytoplankton production is highest in coastal systems thathave the longest flushing times and retain nutrients and phy-toplankton biomass (Gilmartin and Revelante 1978) For ex-ample the yield of chla per unit nitrogen input is five timeshigher in Chesapeake Bay than the Hudson River Estuary inpart because gravitational circulation retains nutrients withinthe Chesapeake Bay whereas a large fraction of the nutri-ents delivered to the Hudson River Estuary is exported tothe coastal plume (Malone et al 1988) However other ex-amples show that long retention does not necessarily pro-mote accumulation of phytoplankton biomass and high pri-mary production The New and Neuse Estuary examples il-lustrate a nonmonotonic relationship with peak phytoplank-ton biomass at the inflow that optimizes the balance be-tween riverine nutrient supply and downstream transport loss(Peierls et al 2012) A further level of complexity emergeswhen we consider losses of phytoplankton biomass to graz-

ing and respiration The principle of long retention and highproduction does not hold when these losses exceed GPP Inthose circumstances phytoplankton biomass is inversely re-lated to retention time (Lucas et al 2009) Phytoplanktonproduction is thus governed by the relative timescales of netgrowth and transport (Fig 6) both of which are directly re-lated to river inflow

452 Ocean exchange

Physical processes that propagate into estuaries from thecoastal ocean play an equally important role in regulat-ing phytoplankton production Whereas freshwater inflowis a source of buoyancy that stratifies estuaries to pro-mote blooms as episodes of fast phytoplankton productiontidal currents are a source of mechanical energy to breakdown stratification Variability of phytoplankton productionis therefore tied to seasonal inputs of freshwater but also toinputs of tidal energy that vary over hourly semidiurnal andneapndashspring periods (Koseff et al 1993) Tidal stresses onthe bottom also maintain sediments in suspension that atten-uate light and constrain phytoplankton production As a re-sult of these processes low-energy microtidal estuaries (tidalamplitude lt 2 m) have a 10-fold higher yield of chla per unitnitrogen than energetic macrotidal estuaries (Monbet 1992)and many of the eutrophic and hypertrophic systems shownin Fig 4 have no or weak tides

The coastal ocean can be an important source of nutrientsto estuaries and phytoplankton responses are most clearlyobserved in estuaries and bays connected to eastern bound-ary current systems dominated by wind-driven coastal up-welling (Hickey and Banas 2003) Mean phytoplankton pro-ductivity in Spainrsquos Riacuteas Baixas is 24 g C mminus2 dminus1 (withpeaks up 4 g C mminus2 dminus1) during the summer upwelling sea-son but only 1 g C mminus2 dminus1 during the spring and autumnwhen upwelling is weaker (Figueiras et al 2002) Short-termvariability around these seasonal means is large because up-welling events bring cold salty nutrient-rich shelf water tothe surface that is advected into the Riacuteas by density-drivencirculation and promotes phytoplankton biomass growthdownwelling events reverse the circulation pattern and retainthat biomass within the Riacuteas (Figueiras et al 2002) Phyto-plankton production in Saldhana Bay is similarly supportedby nutrients imported from shelf waters during the summerupwelling season (Pitcher and Calder 1998) Upwelling sys-tems can also be a source of phytoplankton biomass that isproduced in shelf waters and transported into estuaries andbays such as those connected to the California Current sys-tem Tomales Bay (Smith and Hollibaugh 1997) San Fran-cisco Bay (Martin et al 2007) and Willapa Bay (Banas etal 2007) The import of ocean-derived phytoplankton is animportant exogenous source of organic carbon to fuel estu-arine metabolism (Smith and Hollibaugh 1997) and supplyfood to herbivores including commercially harvested oysters(Banas et al 2007) and mussels (Figueiras et al 2002) The



ocean supply of both nutrients and phytoplankton biomasssets up the spatial variability of APPP in Tomales Bay (seeFig 5a) which is highest (810 g C mminus2 yrminus1) at the estu-ary mouth and lowest (70 g C mminus2 yrminus1) at the estuary head(Cole 1989) A similar spatial pattern develops in WillapaBay where chla decreases within the estuary because ocean-derived phytoplankton biomass is consumed rapidly as it istransported by tidal currents over dense oyster beds (Banaset al 2007)

The rate and direction of ocean exchange vary withoceanographic conditions basin topography of estuariesand hydrology For example the net flux of phytoplanktonbiomass (chla) is into San Francisco Bay during the sum-mer upwelling season but out of the bay during other sea-sons (Martin et al 2007) Coastal lagoons in Mexico withrestricted openings to the sea and long water retention havean APPP 6 times larger than lagoons with direct and con-tinuously open connections and faster water exchange withthe ocean (Flores-Verdugo et al 1988) Many estuaries inarid climates are closed to ocean exchange after blockage bysand bars during the dry season and phytoplankton biomasscan accumulate when they are closed South Africarsquos Md-loti and Mhlanga estuaries receive large nutrient suppliesfrom treated sewage When they are closed phytoplanktonbiomass accumulates to extremely high levels (chla concen-trations gt 100 and gt 300 microg Lminus1 respectively) and these es-tuaries must enter a hypertrophic state when closed (Thomaset al 2005)

Shelf waters connected to estuaries and bays are stronglyinfluenced by regional climate trends and basin-scale climateoscillations captured in indices such as the North AtlanticOscillation and Pacific Decadal Oscillation Observationalrecords are now becoming long enough to reveal that os-cillations of these large-scale climate patterns induce vari-ability of phytoplankton primary production within estuar-ies Mean winter temperature of coastal waters off the north-eastern US have warmed 17 C since 1970 and this re-gional warming trend is synchronous with a trend of increas-ing winter cloudiness and a 40ndash50 decline of APPP inNarragansett Bay (Nixon et al 2009) South San FranciscoBay was transformed from an oligotrophic-mesotrophic es-tuary to a mesotrophic-eutrophic estuary after the PacificDecadal Oscillation and North Pacific Gyre Oscillation re-versed signs in 1999 signaling a shift of the NE Pacific to itscool phase (Cloern and Jassby 2012) The mechanism of thisregime shift was a climate-induced trophic cascade that be-gan with increased production of marine predators (flatfishcrabs shrimp) that migrated into the bay preyed on bivalvemolluscs and released their grazing pressure on phytoplank-ton

453 Heat light and wind energy

Physical processes impinging on the water surface of estu-aries and bays also regulate the production and accumula-

tion of phytoplankton biomass Gordon Riley and his con-temporaries understood that spring blooms in North Atlanticestuaries are triggered by seasonal increases in photoperiodand daily solar radiation (Riley 1967) Variable heat inputhas two effects First water temperature sets an upper limitto phytoplankton growth rate (Eppley 1972) and photosyn-thetic efficiency (pmax) fluctuates significantly with seasonalvariability of water temperature in Narragansett Bay (Durbinet al 1975) Bristol Channel (Joint and Pomroy 1981)Chesapeake Bay (Harding et al 2002) Tokyo Bay (Boumanet al 2010) Tagus Estuary (Gameiro et al 2011) RhodeRiver Estuary (Gallegos 2012) and Neuse Estuary (Peierlset al 2012) Second warming during heat waves can ther-mally stratify estuaries and trigger intense blooms of motilephytoplankton such as the dinoflagellateAkashiwo sanguin-uea(Cloern et al 2005) and the phototrophic ciliateMeso-dinium (Myrionecta) rubrum(Cloern et al 1994) Bursts ofprimary production during these blooms can be significantcomponents of annual primary production (Herfort et al2012) Wind stress on the water surface mixes estuaries setsup waves that suspend bottom sediments and drives coastalcurrents that can influence residence time of phytoplanktonin coastal bays For example red tide blooms develop in MirsBay and Tolo Harbor during the winter monsoon when NEwinds drive landward surface currents that retain phytoplank-ton biomass by slowing exchange with coastal waters of theSouth China Sea (Yin 2003)

Therefore physical processes operating within estuaries(horizontal and vertical mixing advection sediment suspen-sion light absorption) and across their interfaces with wa-tersheds (freshwater nutrient sediment input) the coastalocean (tidal oscillations exchanges of salt heat nutrientsplankton and predators) and atmosphere (heat exchangewind stress photon flux to the water surface) all play essen-tial roles in driving the variability of phytoplankton produc-tion in ecosystems at the landndashsea interface (Cloern 1996)

5 Methods as a source of variability

No currently used method gives an unambigu-ous measure of photosynthesis (Laws et al2000)

We next consider another source of primary-production vari-ability ndash that associated with methods All measurements re-ported here are based on rates of phytoplankton oxygen pro-duction or CO2 assimilation in water samples contained inbottles incubated at different depths or irradiance It is wellestablished that the two approaches measure different quan-tities (Laws et al 2000) and the ratio of oxygen producedto carbon fixed (photosynthetic quotient PQ) is not constantAmong the studies included here the reported PQ rangedfrom 1 (Flores-Verdugo et al 1988) to 14 (Cermentildeo et al2006) However 85 of the APPP values in our compila-tion were derived from measurements of14C-assimilation



rates and we might be tempted to assume these values areintercomparable Although the14C method has been usedfor 60 years and its interpretation has been the subject ofmany studies uncertainty persists about what14C assimila-tion measures because of the confounding effects of light anddark algal respiration refixation of respired14C excretionof radiolabeled carbon and grazing (Marra 2002) Furtherthere is uncertainty about the comparability of14C-based pri-mary production measurements between studies using differ-ent incubation protocols (Harrison et al 1985)

Standard methods have been developed for using14C as-says to measure phytoplankton primary production in theocean such as the US Joint Global Ocean Flux Study(JGOFS) protocol that prescribes dawnndashdusk in situ incu-bations at 8 depths (Knap 1994) However a surprisinglyvaried suite of protocols for handling and incubating watersamples and integrating rates over depth and time have beenused to measure phytoplankton primary production in estu-aries Some protocols included screening of water samplesto remove mesozooplankton grazers (Thayer 1971) whileothers did not some included measures of14C in dissolvedorganic carbon fixed by phytoplankton and excreted duringthe incubation period (Sellner 1976) but most did not someincluded correction for isotopic discrimination of the heavyisotope14C (Becacos-Kontos 1977) some (Gallegos 2014)included corrections to account for changing spectral qualityof light with depth the euphotic depth was assumed to be ei-ther 01 (Kromkamp and Peene 1995) or 1 (Gazeau etal 2005) of surface irradiance and it was determined frommeasured light attenuation coefficients (eg Moraacuten 2007)or a transformation of Secchi depth (eg Medina-Goacutemezand Herrera-Silveira 2006) or estimated from other quan-tities such as wind and tidal currents (eg Montes-Hugo etal 2004) The frequencies of measurements yielding APPPranged from twice weekly (Gleacute et al 2008) to monthly (moststudies) the number of incubation depths or irradiance ex-posures ranged from 1 (Flores-Verdugo et al 1988) to 20(Bouman et al 2010) and incubation durations ranged from20 min (Thompson 1998) to 72 h (Apollonio 1980) Incu-bations were done in situ (eg Steemann Nielsen 1952) inoutdoor incubators exposed to natural sunlight (eg Umaniet al 2007) or in laboratory incubators exposed to artificiallight (Azevedo et al 2006) In addition a wide variety ofapproaches have been used to integrate results of bottle incu-bations over the euphotic (or water) depth and over time tocompute daily depth integrated phytoplankton productivityDifferent approaches have been used to estimate NPP from14C assays such as assuming that phytoplankton respirationis a fixed proportion ofpmax (eg Cole et al 1992) or a dy-namic quantity modeled to include components of light anddark respiration (Tillman et al 2000 Langdon 1993)

51 A model to simulate incubation assays formeasuring phytoplankton primary productivity

How much variability can be expected between these meth-ods and is that variability large enough to confound compar-isons of APPP between studies We constructed a model tosimulate incubation assays of carbon fixation and used themodel to measure this variability across a range of incuba-tion protocols and computational procedures reported in theliterature The model (distinct from models of14C assimi-lation and recycling eg Marra 2002) describes the timeevolution of phytoplankton biomassB (mg C mminus3) in a bot-tle during an incubation period and it applies the equationset developed by Platt et al (1990) to compute daily integralphotosynthesis

dBdt = B(pb middot Chl Cminus R) (1)

t is time (h) pb is C-assimilation rate (mg C mgminus1

chl a hminus1) Chl C is the ratio of phytoplankton chla tocarbon biomasspbmiddot Chl C is growth rate (hminus1) and R

is phytoplankton respiration rate (hminus1) Phytoplankton C-assimilation rate is a function of irradiance (I )

pb = pmax[1minus exp(minusIzt middot αpmax)] (2)

pmax is maximum C-assimilation rateIzt is photosynthet-ically active radiance PAR (micromol quanta mminus2 sminus1) at time t

and depthz (m)α is photosynthetic efficiency as initial slopeof thepb minus I curve Instantaneous irradiance is given by

I0t = Imax[sin(πtD)] (3)

Izt = I0t [1minus exp(minusk middot z)] (4)

I0t is incident PAR at timet Imax is incident PAR at solarnoonD is photoperiod andk is the vertical light attenuationcoefficient

We used these equations to simulate outcomes of differ-ent experimental protocols using a fixed set of parametersrepresentative of summer conditions at a temperate latitude(eg Cloern et al 1995)Imax = 1250 micromol quanta mminus2 sminus1D = 14 h k = 092 mminus1 (ie euphotic depthzp = 5 m)α = 002 [(mg C mgminus1 chl a hminus1)(micromol quanta mminus2 sminus1)]pmax = 5 (mg C mgminus1 chl a hminus1) Chl C= 0025 mg chlamgminus1 C The respiration rateR was fixed at 0004 hminus1 suchthat respiration loss is 15 of GPP ndash within the range mea-sured in cultures of a marine diatom (Laws and Bannister1980)

Equation 1 was solved using the differential equationsolver ode in R package deSolve version 110-3 (Soetaert etal 2010) using a time step1t = 005 h and initial condi-tion B = 200 mg C mminus3 Gross production (mg C mminus3) wascomputed at each incubation depth as the cumulative sumof B(pbmiddot Chl C)1t and net production was computed as



the cumulative sum of (gross productionminusR middot B) from thestart (ti) to ending time (tf) of a simulated incubation Depth-integrated primary production was computed with trape-zoidal integration of production at each simulated depth fromthe surface tozp This yielded daily depth-integrated netphytoplankton productivity PN and gross productivity PG(mg C mminus2 dminus1) for 24 h incubations For shorter incubationswe used a variety of approaches reported in the literature toconvert production measured over several hours into dailyproduction

52 Comparison of phytoplankton productivity derivedfrom different incubation protocols

Our design of simulation experiments is illustrated in Fig 7The color contours show the time and depth distribution ofhourly primary productivity over a 24 h period beginningat sunrise and using parameters listed above The goal ofprimary production measurements is an accurate estimationof the time and depth integral of this function The upperpanel of Fig 7 shows the prescribed diel cycle of incidentirradianceI0t (blue line) and a set of incubation durations(black horizontal lines) for which simulated PN was com-puted these ranged from 2 h incubations centered aroundnoon to 24 h incubations beginning at sunrise (ti = 0) Theright panel shows the vertical profile of irradiance at solarnoon (blue line) and the depths at which incubations weresimulated shown here ranging from 2 (surface andzp) to 16depths We first used the model to calculate PN for a proto-col of incubating samples at 25 depths for 24 h beginning atsunrise similar to the JGOFS protocol We use PN from thissimulation 879 mg C mminus2 dminus1 as a benchmark for compar-ing outcomes of other protocols

We then used the model to simulate 16 other protocolsrepresenting a subset of the many different approaches usedto measure daily phytoplankton productivity in estuarine-coastal waters The simulations were organized into threeexperiments designed to measure sensitivity to (1) numberof depths (irradiances) at which samples are incubated (2)processes included in the incubation protocols (3) durationand timing of incubations and computations used to convertshort-term C-assimilation rates into daily productivity Com-puted PN ranged from 527 to 1551 mg C mminus2 dminus1 amongthe 17 simulated assays (Table 1) revealing a potential 3-fold range of measured daily productivity by a phytoplank-ton community having fixed initial biomass and photosyn-thetic efficiency in a prescribed light field depending on themethod used Experiment 1 shows that one source of vari-ability is the error from measuring phytoplankton primaryproductivity at a small number of depths in the exponen-tial light gradient in a water column (Fig 7) Incubation ofsamples at only two depths surface andzp yielded PN of1551 mg C mminus2 dminus1 77 above the benchmark ComputedPN then decreased continuously as the number of simulatedincubation depths was increased (Table 1) This variability

expresses the error from approximating a continuous non-linear function with a small number of straight lines (trape-zoidal depth integration) This error is small when the num-ber of sample depths is about eight but many published val-ues of PN are based on sample incubations at only one to sixdepths

Results from experiment 2 illustrate that different pro-tocols include different processes and therefore are ex-pected to yield different outcomes The benchmark PN rep-resents the general approach of computing depth-integratedphytoplankton productivity from C-assimilation measured insamples incubated in situ for 24 h A second general ap-proach is to incubate samples in a light gradient over a shortperiod derivepmax and α from these assays then fromthe resultingpb minus I function compute (gross) productivityfrom measures of phytoplankton biomass incident irradi-ance and the light attenuation coefficient Platt et al (1990)derived an accurate series approximation of daily integralproductivity based on this approach which yields a PG of620 mg C mminus2 dminus1 ndash 40 smaller than the benchmark PG of1034 mg C mminus2 dminus1 (Table 1) This deviation arises becausethe benchmark approach includes the process of phytoplank-ton biomass change ndash in this particular case growth and accu-mulation in bottles during the 24 h incubation However theapproach of Platt et al (1990) and others used to estimateoceanic primary productivity from satellite-derived oceancolor assumes that phytoplankton biomass is static The dif-ference in simulated primary productivity derived from insitu incubation compared to that derived from C-assimilationnumbers is determined by the sign and magnitude of phyto-plankton biomass change over the incubation period ndash iethe balance between biomass production and loss (eg graz-ing) If losses exceed production then the productivity de-rived from time- and depth-integration of thepbminusI functionwill be larger than productivity measured in situ If lossesequal production the two methods will yield similar out-comes so there is no general scaling of productivity valuesderived from these two common approaches This is con-firmed with experimental comparisons of the two approachesthat have yielded varying results (eg Harrison et al 1985Lohrenz et al 1992) We considered another process ndash pro-duction and excretion of dissolved organic carbon duringan incubation period Simulated PN was 1072 mg C mminus2 dminus1

when we accounted for excreted production by assuming itis 22 of particulate C-assimilation (Tillman et al 2000)

In Experiment 3 we explored variability arising fromdifferences in incubation duration and timing and proce-dures for computing daily productivity We simulated in-cubations at 25 depths over 2 4 6 and 12 h periods cen-tered around solar noon and used a common approach oftime integration as the product of hourly-mean productionduring the incubation (PNinc) times the ratio of daily inci-dent irradiance (E) to incident irradiance during the incu-bation period (Einc) Results showed a progressive increasein computed PN from 527 mg C mminus2 dminus1 (2 h incubation) to



PG (mg C m-3 h-1)

Fig 7 Schematic showing the 24 h diel cycle of incident photosynthetically active radiance (blue line upper panel) beginning at sunrise(t = 0) and depth distribution of PAR at solar noon (blue line right panel) representative of summer conditions at a temperate latitude Thecontour plot lower left shows the diel and vertical variability of hourly gross phytoplankton primary productivity (PG) based on equationsinitial phytoplankton biomass and photosynthetic parameters described in the text Horizontal arrows (upper panel) and filled circles (rightpanel) show incubation periods and depths prescribed to simulate outcomes of different protocols compared in Table 1

Table 1 Computed depth-integrated daily primary productivity (mg C mminus2 dminus1) of a common phytoplankton sample across a range ofprotocolsD is photoperiod andDinc is incubation duration (h)ti is start time of an incubation (t0 is sunrise)tf is end timeE is dailyPAR andEinc is PAR during the incubation period (mol quanta mminus2 dminus1) PNinc is net production and PGinc is gross production during theincubation period (mg C mminus2)

Primary Number Dinc ti tf Einc PNinc PGinc Method variation Experiment Calculation of Reference forproductivity of depths number productivity the method

879 25 24 0 24 402 879 1034 benchmark method trapezoidal depth integration Harding et al (2002)1551 2 24 0 24 402 1551 1747 benchmark method 1 trapezoidal depth integration

2 incubation depths997 3 24 0 24 402 997 1160 benchmark method 1 trapezoidal depth integration Groslashntved and Steemann

3 incubation depths Nielsen (1957)921 4 24 0 24 402 921 1079 benchmark method 1 trapezoidal depth integration

4 incubation depths886 8 24 0 24 402 886 1042 benchmark method 1 trapezoidal depth integration Cole (1989)

8 incubation depths880 16 24 0 24 402 880 1035 benchmark method 1 trapezoidal depth integration

16 incubation depths620 402 620 assumes static biomass 2 series solution to the time Platt et al (1990)

and depth integration1072 25 24 0 24 402 1072 1261 include excreted production 2 122middot benchmark Tillman et al (2000)527 25 2 6 8 90 118 126 2 h incubation around noon 3 (EEinc)middot PNinc Oviatt (2002)586 25 4 5 9 175 255 273 4 h incubation around noon 3 (EEinc)middot PNinc Mallin et al (1991)657 25 6 4 10 251 410 439 6 h incubation around noon 3 (EEinc)middot PNinc919 25 12 1 13 392 896 962 12 h incubation 3 (EEinc)middot PNinc Kuenzler et al (1979)697 25 6 7 13 196 340 369 6 h incubation PM 3 (EEinc)middotPNinc Anderson (1964)623 25 6 7 13 196 340 369 6 h incubation PM 3 (D-3)middot hourly mean PNinc Parker et al (2012)714 25 7 7 14 201 357 390 7 h incubation PM 3 2middot PNinc Taguchi et al (1977)826 25 2 6 8 90 118 126 2 h incubation around noon 3 Dmiddot hourly mean PNinc Rysgaard et al (1999)893 25 4 5 9 175 255 273 4 h incubation around noon 3 Dmiddot hourly mean PNinc Grundle et al (2009)

919 mg C mminus2 dminus1 (12 h incubation) This variability againreflects the accumulation of phytoplankton biomass as in-cubations proceed in nutrient-rich estuarine waters (mea-sured eg by Alpine and Cloern 1988) As incubation dura-

tion lengthens phytoplankton biomass accumulates (or it de-creases if losses exceed production) continuously in bottlesso measured daily PN yields different results depending uponincubation duration The period of incubation also matters



but to a smaller degree simulated 6 h incubations centeredaround solar noon and beginning at solar noon yielded PNof 657 and 697 mg C mminus2 dminus1 respectively (Table 1) Theunderestimation of PN from short-term incubations relativeto the benchmark also reflects errors from the computationof daily productivity (the time integral of nonlinear functionsof pb vs I and I vs time Fig 7) as a linear function ofPNinc A further source of variability is the variety of lin-ear multipliers used across studies such asEEinc D D-3and 2 (Table 1) This source of error is avoided with numer-ical integrations of short-term C-assimilation rates over time(eg Fee 1973 Platt and Sathyendranath 1995) Lastly weremind readers of processes not included in our model ndash res-piration and subsequent refixation of assimilated14C ndash thatfurther confound interpretation of14C assays for measuringprimary productivity (Marra 2002)

The central points of this analysis are (1) many differentapproaches have been used to measure APPP in estuarine-coastal waters ndash there is nothing approaching a standardmethod (2) method matters because simulations show thatthe measurement of depth-integrated daily productivity of adefined sample in a common light environment can vary by(at least) a factor of two (Table 1) depending on processesincluded or excluded incubation duration and timing andcomputational procedures This variability between methodsis small relative to the wide span of APPP between ecosys-tems (Fig 4) consistent with the principle that phytoplank-ton biomass and light attenuation are the key componentsof primary production variability However differences be-tween methods can be large enough to confound comparisonsacross studies For example the simulations presented heresuggest that 2-fold differences of APPP between sites or overtime (eg Parker et al 2012) cannot be judged significantunless they are derived from common methods

53 Recommendations

On the basis of these simulations we endorse two practicesthat have been recommended by others in the past Firstprotocols for measuring phytoplankton primary productiv-ity should be tailored to study objectives and those objec-tives should be explicitly stated If the objective is grossproductivity analogous to satellite-derived rates in the oceanthen an appropriate method is short-term measurements ofC-assimilation rates across a light gradient and then inte-gration of those rates over time and depth with the seriesapproximation of Platt et al (1990) In our standard casethis yielded a rate of 620 g C mminus2 dminus1 (Table 1) If the ob-jective is net productivity of particulate organic carbon thatincludes biomass change during incubations then an appro-priate method is 24 h in situ (or simulated in situ) incuba-tions distributed over the euphotic zone measurement ofassimilated14C collected on filters and numerical depth-integration of C-assimilation rates In our standard case thisyielded a rate of 879 g C mminus2 dminus1 If the objective is total net

productivity then this protocol can be modified to measureproduction of both dissolved and particulate organic carbonIn our standard case this yielded a rate of 1072 g C mminus2 dminus1

Our second recommendation is to minimize errors in thetime and depth integration of measured C-assimilation ratesOur simulated incubations at a small number of depths re-sulted in large overestimates of productivity relative to thebenchmark Accurate depth integration requires incubationsat a minimum of 8 depths which yielded a 1 error (relativeto the benchmark) in our standard case Time-integration isinherent in 24 h incubations and this is a compelling reasonfor the use of dawnndashdawn incubations If shorter incubationsare used then the nonlinear variability of productivity overthe diel light cycle should be integrated numerically and theimplications of phytoplankton biomass change during the in-cubation period should be considered when interpreting re-sults and comparing them against values derived from othermethods

6 Advances since 1982 and two grand challenges for thefuture

Our goal was to compile and synthesize measurements of an-nual phytoplankton primary production in estuarine-coastalwaters as a fundamental process that drives variability ofwater quality biogeochemical processes and production athigher trophic levels Most primary production measure-ments in estuaries have been made since the 1982 reviewof Boynton et al (1982) when APPP was available for 45estuaries ndash most (32) from North America The record nowincludes APPP measurements from 131 estuaries and its ge-ographic coverage has expanded particularly in Europe In-creased sampling has captured a larger range of variabil-ity mean APPP across 45 estuaries ranged between 19 and547 g C mminus2 yrminus1 (Boynton et al 1982) compared tominus105and 1890 g C mminus2 yrminus1 in the latest compilation (Fig 4)Enhanced sampling has led to discoveries that APPP canvary up to 10-fold within estuaries and 5-fold from yearto year (this is probably an underestimate) some tropical-subtropical estuaries sustain very high rates of primary pro-duction so global upscaling of APPP from measurements intemperate estuaries might have substantial errors synthesisof estuarine APPP is confounded by the use of many meth-ods and daily depth-integrated primary productivity can bemodeled as a function of phytoplankton biomass light avail-ability and photosynthetic efficiency that varies seasonallyand regionally (Son et al 2014) In the past three decadeswe have also developed a deeper understanding that vari-ability of phytoplankton production at the landndashsea inter-face cannot be explained by a single factor such as nutri-ent loading rate (Cloern 2001) Contemporary conceptualmodels now recognize that nutrient loading sets the poten-tial for biomass production in estuaries but the realization ofthat potential changes over time (Duarte et al 2008) and is



shaped by variability of hydrology optical properties trans-port processes inputs of heat light and mixing energy andtop-down control of phytoplankton biomass growth (Fig 6)

As coastal science moves forward in the 21st century agrand challenge is to discover how these multiple drivers in-teract to generate the large spatial and temporal variability ofAPPP inherent to estuaries and other shallow marine ecosys-tems This challenge is important because we have also dis-covered over the past three decades that phytoplankton pro-duction is highly responsive to climate shifts and cycles andhuman disturbances such as nutrient enrichment introduc-tions of nonnative species and water diversions Mecha-nistic models will be required to explain the variability ofestuarine-coastal primary production summarized here andto project and plan for changes in rates of coastal productionas global change proceeds

A second grand challenge is to design and implement aprogram to measure estuarine-coastal phytoplankton produc-tion and its variability at the global scale This challenge isimportant because characteristic values based on sparse datahave been upscaled to calculate sustainable yield of estuar-ine fishery resources (Houde and Rutherford 1993) and tovaluate services provided by estuaries such as food produc-tion (Costanza et al 1997) just as measures of ecosystemmetabolism have been upscaled to assess the role of estu-aries in the global carbon budget (Borges 2005) These as-sessments have inherent errors because of uncertainty in thearea of the worldrsquos estuaries (Borges 2005) However po-tentially larger sources of uncertainty must arise from theuneven spatial distribution of APPP measurements that leavevast knowledge gaps along much of the worldrsquos coastlineand an empirical record built from a suite of nonstandardmethods that measure different quantities

61 Toward an integrative explanatory model

The complex structure of estuaries makes theminteresting study sites but frustrating ones fromthe standpoint of making generalizations andthis observation certainly pertains to developinga predictive understanding of PP [primary pro-duction] (Harding et al 2002)

Much of the regional seasonal and interannual variabilityof phytoplankton production in the world oceans is drivenby variability in the transport of deep nutrient-rich waterto the surface (Behrenfeld et al 2006) However the muchwider span of APPP measurements in estuarine-coastal wa-ters (Fig 2) reflects the many additional processes that op-erate in shallow systems where land and sea meet inputs offresh water sediments and nutrients from land runoff ben-thic grazing and nutrient regeneration the balance betweenthe stabilizing effects of heat and freshwater input with mix-ing by wind and tides ocean exchange as a source or sinkof nutrients and phytoplankton biomass and retention as in-

fluenced by tidal dispersion gravitational circulation wind-and river-driven transport

We have therefore identified the components of the ma-chine that generates high variability of phytoplankton pro-duction in estuarine-coastal ecosystems (Fig 6) Modelshave been developed to describe the isolated responses ofphytoplankton production to variability of some componentstemperature (Durbin et al 1975) light attenuation by sedi-ments (Wofsy 1983) phytoplankton biomass and irradiance(Cole and Cloern 1987) tidal energy (Monbet 1992) lightand nutrient limitation of phytoplankton growth rate (Cloern1999) nutrient inputs (Carstensen et al 2003) and hydraulicresidence time (Peierls et al 2012) However these com-ponents have not been integrated into a unifying statisticalor mechanistic model to explain the wide range of variabil-ity across estuaries (Fig 4) or to project responses of phy-toplankton production to regional manifestations of globalchange The biggest challenge for building and testing a uni-fying model might be the breadth of the data requirement weare not aware of a single ecosystem where all (or even most)of the controlling processes are measured Meeting this grandchallenge will require new studies across a range of ecosys-tem types to measure phytoplankton production as a compo-nent of ecosystem-scale studies that include measurementsof process that generate its variability Until this large holein the empirical record is filled our capacity for explainingthe span of measurements shown in Figs 2ndash5 and for de-veloping scenarios of future production in shallow coastalecosystems will remain limited

62 Toward a globally representative consistent set ofprimary production measurements

Single or even a few estimates of annual primaryproduction from estuaries may not be very char-acteristic of the long-term average One shouldtherefore question attempts to draw generaliza-tions from multiple estuarine data sets whenmany of the examples represent single annualestimates perhaps not even based on compre-hensive spatial and seasonal coverage (Jassbyet al 2002)

Estuaries are considered to be among the most produc-tive ecosystems (eg Kocum et al 2002) but this gener-alization does not apply to phytoplankton production whichranges from trivial to rates as high as primary production ofmangroves tropical forests and salt marshes Therefore thereare bounds on but no characteristic value of phytoplanktonproduction for estuarine-coastal ecosystems Most reportedvalues fall in the ranges classified as either mesotrophicor oligotrophic (lt 300 g C mminus2 yrminus1) but these are heav-ily weighted to measurements from northern Europe andNorth America Much higher phytoplankton production hasbeen measured in some tropical-subtropical systems such



as Cieacutenaga Grande de Santa Marta Golfo de Nicoya andHuizache-Caimanero Lagoon suggesting that our current as-sessments might substantially underestimate primary pro-duction in the worldrsquos estuarine-coastal ecosystems becausewe have greatly undersampled tropical and subtropical sitesTo put the undersampling problem into a broader perspec-tive annual phytoplankton production has been reported foronly 131 places along the worldrsquos 356 000 km coastline

Of the 131 places where annual phytoplankton produc-tion has been reported 37 are based on measurements atonly one location during 1 year Yet we know from a fewwell-sampled places that production varies up to 10-foldwithin estuaries (Fig 5a) and up to 5-fold from year to year(Fig 5b) so there is large uncertainty about how well thesingle measurements represent ecosystem-scale primary pro-duction Although the models used to derive oceanic primaryproduction from ocean color have uncertainties the uncer-tainties are quantified and computations of production acrossthe world oceans are grounded in a robust empirical recordwith global maps of monthly chla at 4 km spatial resolu-tion (Behrenfeld et al 2001) In contrast the direct mea-surements of phytoplankton production in estuarine-coastalecosystems include nonstandard methods are sparsely andunevenly distributed in space and time most have not beensustained over multiple years and therefore the empiricalrecord provides an inadequate basis for global upscalingThus a second grand challenge is to organize and fund aninternational effort to adopt a common method and measureprimary productivity regularly across a network of coastalsites that are representative of the worldrsquos coastline to yieldreliable estimates of global primary production its influenceon biogeochemical processes and food production and itsresponse to global change as it unfolds in the 21st centuryRecent advances in development of bio-optical algorithmsfor turbid coastal waters (eg Son et al 2014) indicate thatremote sensing will play an increasingly important role inmeeting this grand challenge

Supplementary material related to this article isavailable online athttpwwwbiogeosciencesnet1124772014bg-11-2477-2014-supplementpdf

AcknowledgementsWe thank our colleagues E Kress C MartinT Schraga and referees C Gallegos L Harding and M Murrellfor improving the content and clarity of this paper This work wassupported by US Geological Survey Priority Ecosystem Scienceand the National Research Program of the Water ResourcesDiscipline

Edited by J Middelburg

References

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Adolf J E Yeager C L Miller W D Mallonee M Eand Harding Jr L W Environmental forcing of phytoplank-ton floral composition biomass and primary productivity inChesapeake Bay USA Estuar Coast Shelf S 67 108ndash122doi101016jecss200511030 2006

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Anderson G C The Seasonal and Geographic Distribution of Pri-mary Productivity Off the Washington and Oregon Coasts Lim-nol Oceanogr 9 284ndash302 1964

Apollonio S Primary production in Dumbell Bay in the ArcticOcean Mar Biol 61 41ndash51 1980

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Azevedo I Duarte P and Bordalo A Pelagic metabolismof the Douro estuary (Portugal) ndash Factors controlling pri-mary production Estuar Coast Shelf S 69 133ndash146doi101016jecss200604002 2006

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Behrenfeld M J and Falkowski P G A consumerrsquos guide to phy-toplankton primary productivity models Limnol Oceanogr 421479ndash1491 1997

Behrenfeld M J Randerson J T McClain C R Feldman G CLos S O Tucker C J Falkowski P G Field C B FrouinR Esaias W E Kolber D D and Pollack N H Biosphericprimary production during an ENSO transition Science 2912594ndash2597 doi101126science1055071 2001

Behrenfeld M J Boss E Siegel D A and Shea D MCarbon-based ocean productivity and phytoplankton physi-ology from space Global Biogeochem Cy 19 GB1006doi1010292004gb002299 2005



Behrenfeld M J OrsquoMalley R T Siegel D A McClain C RSarmiento J L Feldman G C Milligan A J FalkowskiP G Letelier R M and Boss E S Climate-driven trendsin contemporary ocean productivity Nature 444 752ndash755doi101038nature05317 2006

Beukema J J and Cadeacutee G C Growth-rates of the bivalveMa-coma balthicain the Wadden Sea during a period of eutrophica-tion ndash relationships with concentrations of pelagic diatoms andflagellates Mar Ecol-Prog Ser 68 249ndash256 1991

Borges A V Do We Have Enough Pieces of the Jigsaw to IntegrateCO2 Fluxes in the Coastal Ocean Estuaries 28 3ndash27 2005

Bouman H A Nakane T Oka K Nakata K KuritaK Sathyendranath S and Platt T Environmental controlson phytoplankton production in coastal ecosystems A casestudy from Tokyo Bay Estuar Coast Shelf S 87 63ndash72doi101016jecss200912014 2010

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Brussaard C P D Viral control of phytoplankton populations ndash Areview J Eukaryot Microbiol 51 125ndash138 2004

Bukaveckas P A Barry L E Beckwith M J David V andLederer B Factors Determining the Location of the Chloro-phyll Maximum and the Fate of Algal Production within theTidal Freshwater James River Estuar Coast 34 569ndash582doi101007s12237-010-9372-4 2011

Burford M A Webster I T Revill A T Kenyon R A WhittleM and Curwen G Controls on phytoplankton productivity ina wet-dry tropical estuary Estuar Coast Shelf S 113 141ndash151doi101016jecss201207017 2012

Caddy J F Marine catchment basin effects versus impacts of fish-eries on semi-enclosed seas ICES J Mar Sci 57 628ndash6402000

Cadeacutee G C and Hegeman J Persisting High Levels of PrimaryProduction at Declining Phosphate Concentrations in the DutchCoastal Area (Marsdiep) Neth J Sea Res 31 147ndash152 1993

Caffrey J M Factors controlling net ecosystem metabolism in USestuaries Estuaries 27 90ndash101 2004

Caffrey J M Cloern J E and Grenz C Changes in produc-tion and respiration during a spring phytoplankton bloom in SanFrancisco Bay California USA implications for net ecosystemmetabolism Mar Ecol-Prog Ser 172 1mdash12 1998

Calbet A Mesozooplankton grazing effect on primary productionA global comparative analysis in marine ecosystems LimnolOceanogr 46 1824ndash1830 2001

Calbet A and Landry M R Phytoplankton growth microzoo-plankton grazing and carbon cycling in marine systems LimnolOceanogr 49 51ndash57 2004

Carstensen J Conley D and Muller-Karulis B Spatial and tem-poral resolution of carbon fluxes in a shallow coastal ecosystemthe Kattegat Mar Ecol-Prog Ser 252 35ndash50 2003

Cebrian J Patterns in the fate of production in plant communitiesAm Nat 154 449ndash468 doi101086303244 1999

Cermentildeo P Maranon E Perez V Serret P Fernandez E andCastro C Phytoplankton size structure and primary productionin a highly dynamic coastal ecosystem (Riacutea de Vigo NW-Spain)Seasonal and short-time scale variability Estuar Coast Shelf S67 251ndash266 doi101016jecss200511027 2006

Chavez F P Messieacute M and Pennington J T Ma-rine primary production in relation to climate vari-ability and change Annu Rev Mar Sci 3 227ndash260doi101146annurevmarine010908163917 2010

Cloern J E Does the benthos control phytoplankton biomass inSouth San Francisco Bay Mar Ecol-Prog Ser 9 191ndash2021982

Cloern J E Temporal dynamics and ecological significance ofsalinity stratification in an estuary (South San Francisco BayUSA) Oceanol Acta 7 137ndash141 1984

Cloern J E Turbidity as a control on phytoplankton biomass andproductivity in estuaries Cont Shelf Res 7 1367ndash1381 1987

Cloern J E Annual variations in river flow and primary produc-tion in the South San Francisco Bay Estuary (USA) in Estuar-ies and Coasts Spatial and Temporal Intercomparisons editedby Elliott M and Ducrotoy D Olsen and Olsen Fredensborg91ndash96 1991

Cloern J E Phytoplankton bloom dynamics in coastal ecosys-tems A review with some general lessons from sustained in-vestigation of San Francisco Bay California Rev Geophys 34127ndash168 1996

Cloern J E The relative importance of light and nutrient limitationof phytoplankton growth a simple index of coastal ecosystemsensitivity to nutrient enrichment Aquat Ecol 33 3ndash16 1999

Cloern J E Our evolving conceptual model of the coastal eutroph-ication problem Mar Ecol-Prog Ser 210 223ndash253 2001

Cloern J E and Jassby A D Complex seasonal patterns of pri-mary producers at the land-sea interface Ecol Lett 11 1294ndash1303 doi101111J1461-0248200801244X 2008

Cloern J E and Jassby A D Patterns and Scales of Phytoplank-ton Variability in Estuarine-Coastal Ecosystems Estuar Coast33 230ndash241 doi101007S12237-009-9195-3 2010

Cloern J E and Jassby A D Drivers of change inestuarine-coastal ecosystems discoveries from four decadesof study in San Francisco Bay Rev Geophys 50 RG4001doi1010292012RG000397 2012

Cloern J E Alpine A E Cole B E Wong R L J Arthur JF and Ball M D River discharge controls phytoplankton dy-namics in the northern San Francisco Bay estuary Esuar CoastShelf S 16 415ndash429 1983

Cloern J E Cole B E Wong R L J and Alpine A E Tem-poral dynamics of estuarine phytoplankton ndash a case study of SanFrancisco Bay Hydrobiologia 129 153ndash176 1985

Cloern J E Cole B E and Hager S W Notes on aMeso-dinium rubrumred tide in San Francisco Bay (California USA)J Plankton Res 16 1269ndash1276 1994

Cloern J E Grenz C and Vidergar Lucas L An empirical modelof the phytoplankton chlorophyllcarbon ratio ndash The conversionfactor between productivity and growth rate Limnol Oceanogr40 1313ndash1321 1995

Cloern J E Schraga T S Lopez C B Knowles N Labiosa RG and Dugdale R Climate anomalies generate an exceptionaldinoflagellate bloom in San Francisco Bay Geophys Res Lett32 L14608 doi1010292005gl023321 2005

Cole B E Temporal and Spatial Patterns of Phytoplankton Pro-duction in Tomales Bay California USA Estuar Coast ShelfS 28 103ndash115 1989



Cole B E and Cloern J E An Empirical Model for EstimatingPhytoplankton Productivity in Estuaries Mar Ecol-Prog Ser36 299ndash305 1987

Cole J J Caraco N F and Peierls B L Can phytoplanktonmaintain a positive carbon balance in a turbid freshwater tidalestuary Limnol Oceanogr 37 1608ndash1617 1992

Colijn F and Ludden E Primary production of phytoplanktonin the Ems-Dollard estuary in Primary Production in the Ems-Dollard estuary edited by Colijn F Rijksuniversiteit Gronin-gen 38ndash99 1983

Costanza R drsquoArge R deGroot R Farber S Grasso M Han-non B Limburg K Naeem S Oneill R V Paruelo JRaskin R G Sutton P and vandenBelt M The value of theworldrsquos ecosystem services and natural capital Nature 387 253ndash260 1997

Diaz R J and Rosenberg R Spreading dead zones and conse-quences for marine ecosystems Science 321 926ndash929 2008

Duarte C M and Cebrian J The fate of marine autotrophic pro-duction Limnol Oceanogr 41 1758ndash1766 1996

Duarte C M Conley D J Carstensen J and Saacutenchez-Camacho M Return to Neverland Shifting Baselines AffectEutrophication Restoration Targets Estuar Coast 32 29ndash36doi101007s12237-008-9111-2 2008

Durbin E G Krawiec R W and Smayda T J Seasonal studieson the relative importance of different size fractions of phyto-plankton in Narragansett Bay (USA) Mar Biol 32 271ndash2871975

Edwards R R C Ecology of a coastal lagoon complex in MexicoEstuar Coast Mar Sci 6 75ndash92 1978

Elmgren R Trophic dynamics in the enclosed brackish Baltic SeaRapp P -v Reun Cons Int Explor Mer 183 152ndash169 1984

Eppley R W Temperature and phytoplankton growth in the seaFish B-NOAA 70 1063ndash1085 1972

Eyre B D and McKee L J Carbon nitrogen and phosphorusbudgets for a shallow subtropical coastal embayment (MoretonBay Australia) Limnol Oceanogr 47 1043ndash1055 2002

Fee E J A numerical model for determining integral primary pro-duction and its application to Lake Michigan J Fish Res BoardCan 30 1447ndash1468 1973

Figueiras F G Labarta U and Reiriz M J F Coastalupwelling primary production and mussel growth in theRiacuteas Baixas of Galicia Hydrobiologia 484 121ndash131doi101023a1021309222459 2002

Flemer D A Primary Production in the Chesapeake Bay Chesa-peake Science 11 117ndash129 1970

Flemer D A Hamilton D H Keefe C W and Mihursky JA Final technical report to Office of Water Resources Researchon the effects of thermal loading and water quality on estuarineprimary production Office of Water Resources Research USDepartment of Interior Washington DC NRI Ref No 71-6Chesapeake Biological Laboratory 210 1970

Flint R W Phytoplankton production in the Corpus Christi BayEstuary Contrib Mar Sci 27 65ndash83 1970

Flores-Verdugo F J Day J W Mee L and Brisentildeo-Duentildeas RPhytoplankton production and seasonal biomass variation of sea-grassRuppia maritimaL in a tropical Mexican lagoon with anephemeral inlet Estuaries 11 51ndash56 1988

Furnas M J Insitu growth rates of marine phytoplankton ndash ap-proaches to measurement community and species growth rates

J Plankton Res 12 1117ndash1151 doi101093plankt12611171990

Gallegos C L Phytoplankton photosynthetic capacity in a shallowestuary environmental correlates and interannual variation MarEcol-Prog Ser 463 23ndash37 2012

Gallegos C L Long-term variations in primary production in aeutrophic sub-estuary 1 Seasonal and spatial variability MarEcol-Prog Ser 502 53ndash67 doi103354meps10712 2014

Gameiro C Zwolinski J and Brotas V Light control on phy-toplankton production in a shallow and turbid estuarine systemHydrobiologia 669 249ndash263 doi101007s10750-011-0695-32011

Garnier J Servais P Billen G Akopian M and BrionN Lower Seine river and estuary (France) carbon andoxygen budgets during low flow Estuaries 24 964ndash976doi1023071353010 2001

Gattuso J-P Frankignoulle M and Wollast R Carbon and car-bonate metabolism in coastal aquatic ecosystems Annu RevEcol Syst 29 405ndash433 1998

Gazeau F Gattuso J-P Middelburg J Brion N SchiettecatteL-S Frankignoulle M and Borges A Planktonic and wholesystem metabolism in a nutrient-rich estuary (the Scheldt es-tuary) Estuar Coast 28 868ndash883 doi101007bf026960162005

Gilmartin M The primary production of a British Columbia fjordJ Fish Res Board Can 21 505ndash538 1964

Gilmartin M and Revelante N The phytoplankton characteristicsof the barrier island lagoons of the Gulf of California EstuarCoast Mar Sci 7 29ndash47 1978

Gleacute C Del Amo Y Sautour B Laborde P and ChardyP Variability of nutrients and phytoplankton primary pro-duction in a shallow macrotidal coastal ecosystem (Arca-chon Bay France) Estuar Coast Shelf S 76 642ndash656doi101016jecss200707043 2008

Gocke K Corteacutes J and Murillo M M The annual cycle ofprimary productivity in a tropical estuary The inner regions ofthe Golfo de Nicoya Costa Rica Rev Biol Trop 49 289ndash3062001

Goebel N L Kremer J N and Edwards C A Primary produc-tion in Long Island Sound Estuar Coast 29 232ndash245 2006

Greene V E Sullivan L J Thompson J K and Kim-merer W J Grazing impact of the invasive clamCorbulaamurensison the microplankton assemblage of the northernSan Francisco Estuary Mar Ecol-Prog Ser 431 183ndash193doi103354meps09099 2011

Grenz C Cloern J E Hager S W and Cole B E Dynamicsof nutrient cycling and related benthic nutrient and oxygen fluxesduring a spring phytoplankton bloom in South San Francisco Bay(USA) Mar Ecol-Prog Ser 197 67ndash80 2000

Groslashntved J and Steemann Nielsen E Investigations on the Phy-toplankton in Sheltered Danish Marine Localities Meddelser fraKommissionen for Danmarks Fiskeri- og Havundersoslashgelser Se-rie Plankton bd 5 no 6 1957

Grundle D S Timothy D A and Varela D E Variations ofphytoplankton productivity and biomass over an annual cycle inSaanich Inlet a British Columbia fjord Cont Shelf Res 292257ndash2269 doi101016jcsr200908013 2009



Haas L W The effect of the spring-neap tidal cycle on the verticalsalinity structure of the James York and Rappahannock RiversVirginia USA Estuar Coast Mar Sci 5 485ndash496 1977

Harding Jr L W Long-term increase of phytoplankton biomass inChesapeake Bay 1950ndash1994 Mar Ecol-Prog Ser 157 39ndash521997

Harding Jr L W Mallonee M E and Perry E S Toward a Pre-dictive Understanding of Primary Productivity in a TemperatePartially Stratified Estuary Estuar Coast Shelf S 55 437ndash463doi101006ecss20010917 2002

Harrison W G Platt T and Lewis M R The utility of light-saturation models for estimating marine primary productivity inthe field ndash a comparsion with convential simulated insitu meth-ods Can J Fish Aquat Sci 42 864ndash872 doi101139f85-1101985

Herman P M J Middelburg J J Van de Koppel J Heip C HR Ecology of estuarine macrobenthos in Advances in Ecolog-ical Research vol 29 Estuaries edited by Nedwell D B andRaffaelli D G Elsevier Academic Press Inc San Diego 195ndash240 1999

Henry J L Mostert S A and Christie N D Phytoplankton pro-duction in Langebaan Lagoon and Saldanha Bay T Roy Soc SAfr 42 383ndash398 1977

Herfort L Peterson T D Prahl F G McCue L A NeedobaJ A Crump B C Roegner G C Campbell V and Zu-ber P Red Waters ofMyrionecta rubraare BiogeochemicalHotspots for the Columbia River Estuary with Impacts on Pri-marySecondary Productions and Nutrient Cycles Estuar Coast35 878ndash891 doi101007s12237-012-9485-z 2012

Hernandez C A and Gocke K Productividad primaria en la Cieacute-naga Grande de Santa Marta Colombia Anales del Institutode Investigaciones Marinas de Punta de Betin 19ndash20 101ndash1191990

Hickey B M and Banas N S Oceanography of the US PacificNorthwest coastal ocean and estuaries with application to coastalecology Estuaries 26 1010ndash1031 2003

Hopkinson C J Smith E M del Giorgio P A and Williams PJ le B Estuarine respiration an overview of benthic pelagic andwhole system respiration in Respiration in Aquatic Ecosystemsedited by del Giorgio P A and Williams P J le B OxfordUniversity Press New York 2005

Houde E D and Rutherford E S Recent trends in estuarine fish-eries ndash predictions of fish production and yield Estuaries 16161ndash176 1993

Howarth R W Nutrient limitation of net primary production inmarine ecosystems Annu Rev Ecol Syst 19 89ndash110 1988

Jassby A D Cloern J E and Powell T M Organic-carbonsources and sinks in San-Francisco Bay ndash variability induced byriver flow Mar Ecol-Prog Ser 95 39ndash54 1993

Jassby A D Cloern J E and Cole B E Annual primary pro-duction Patterns and mechanisms of change in a nutrient-richtidal ecosystem Limnol Oceanogr 47 698ndash712 2002

Johansson J O R Long-term and seasonal trends in phyto-plankton production and biomass in Tampa Bay Florida inProceedings Tampa Bay Area Scientific Information Sympo-sium BASIS 5 20ndash23 October 2009 edited by Cooper S Tavailable athttpwwwtbeptechorgBASISBASIS5BASIS5pdf (last access 2 November 2013) 73ndash94 2010

Joint I R and Pomroy A J Primary production in a turbid estu-ary Estuar Coast Shelf S 13 303ndash316 1981

Kaas H Moslashhlenberg F Josefson A Rasmussen B Krause-Jensen D Jensen H S Svendsen L M Windolf JMiddelboe A L Sand-Jensen K and Pedersen M FDanske fjorde ndash status over miljoslashtilstand aringrsagssammenho-enge og udvikling Faglig rapport fra DMU nr 179 Miljoslash- ogEnergiministereriet Danmarks Miljoslashundersoslashgelser Roskildeavailable at httpwww2dmudk1_viden2_Publikationer3_fagrapporterrapporterfr179pdf(last access 2 May 2014)1996

Keller A A Taylor C Oviatt C Dorrington T HolcombeG and Reed L Phytoplankton production patterns in Mas-sachusetts Bay and the absence of the 1998 winter-spring bloomMar Biol 138 1051ndash1062 2001

Kemp W M Smith E M Marvin-DiPasquale M and BoyntonW R Organic carbon balance and net ecosystem metabolismin Chesapeake Bay Mar Ecol-Prog Ser 150 229ndash248doi103354meps150229 1997

Kemp W M Boynton W R Adolf J E Boesch D F BoicourtW C Brush G Cornwell J C Fisher T R Glibert P MHagy J D Harding Jr L W Houde E D Kimmel D GMiller W D Newell R I E Roman M R Smith E M andStevenson J C Eutrophication of Chesapeake Bay historicaltrends and ecological interactions Mar Ecol-Prog Ser 303 1ndash29 doi103354meps303001 2005

Ketchum B H Relation between circulation and planktonic pop-ulations in estuaries Ecology 35 191ndash200 1954

Kimmerer W Gartside E and Orsi J J Predation by an intro-duced clam as the likely cause of substantial declines in zoo-plankton of San Francisco Bay Mar Ecol-Prog Ser 113 81ndash94 1994

Kioslashrboe T and Nielsen T G Regulation of zooplankton biomassand production in a temperate coastal ecosystem 1 CopepodsLimnol Oceanogr 39 493ndash507 1994

Knap A JGOFS Protocols ndash June 1994 Chapter 2 ShipboardSampling Procedures section 40 Primary Production availableat httpusjgofswhoieduJGOFS_19pdf(last access 11 Jan-uary 2014) 1994

Kocum E Underwood G J C and Nedwell D B Simultane-ous measurement of phytoplanktonic primary production nutri-ent and light availability along a turbid eutrophic UK east coastestuary (the Colne Estuary) Mar Ecol-Prog Ser 231 1ndash122002

Koseff J R Holen J K Monismith S G and Cloern J E Cou-pled Effects of Vertical Mixing and Benthic Grazing on Phyto-plankton Populations in Shallow Turbid Estuaries J Mar Res51 843ndash868 1993

Kromkamp J and Peene J Possibility of net phytoplankton pri-mary production in the turbid Schelde Estuary (SW Nether-lands) Mar Ecol-Prog Ser 121 249ndash259 1995

Kromkamp J Peene J Rijswijk P Sandee A and GoosenN Nutrients light and primary production by phytoplank-ton and microphytobenthos in the eutrophic turbid Wester-schelde estuary (The Netherlands) Hydrobiologia 311 9ndash19doi101007bf00008567 1995

Kuenzler E J Stanley D W and Koenings J P Nutrient kineticsof phytoplankton in the Pamlico River North Carolina Report ndash



Water Resources Research Institute of The University of NorthCarolina 1979

Langdon C The significance of respiration in production measure-ments based on oxygen ICES Mar Sc 197 69ndash78 1993

Lawrenz E Smith E M and Richardson T L Spectral Ir-radiance Phytoplankton Community Composition and PrimaryProductivity in a Salt Marsh Estuary North Inlet South Car-olina USA Estuar Coast 36 347ndash364 doi101007s12237-012-9567-y 2013

Laws E A and Bannister T T Nutrient- and light-limited growthof Thalassiosira fluviatilisin continuous culture with implica-tions for phytoplankton growth in the oecan Limnol Oceanogr25 457ndash473 1980

Laws E A Landry M R Barber R T Campbell L DicksonM-L and Marra J Carbon cycling in primary production bot-tle incubations inferences from grazing experiments and photo-synthetic studies using14C and18O in the Arabian Sea Deep-Sea Res Pt II 47 1339ndash1352 2000

Lindahl O Belgrano A Davidsson L and Hernroth B Pri-mary production climatic oscillations and physico-chemicalprocesses The Gullmar Fjord time-series data set (1985ndash1996)ICES J Mar Sci 55 723ndash729 1998

Lohrenz S E Wiesenburg D A Rein C R Arnone R A andTaylor C D A comparison ofin situand simulatedin situmeth-ods for estimating oceanic primary production J Plankton Res14 201ndash221 101093plankt142201 1992

Lucas L V Koseff J R Cloern J E Monismith S G andThompson J K Processes governing phytoplankton blooms inestuaries I The local production-loss balance Mar Ecol-ProgSer 187 1ndash15 1999

Lucas L V Thompson J K and Brown L R Why are di-verse relationships observed between phytoplankton biomassand transport time Limnol Oceanogr 54 381ndash390 2009

Luengen A C and Flegal A R Role of phytoplankton in mercurycycling in the San Francisco Bay estuary Limnol Oceanogr 5423ndash40 doi104319lo20095410023 2009

Luoma S N van Geen A Lee B G and Cloern J E Metaluptake by phytoplankton during a bloom in South San Fran-cisco Bay Implications for metal cycling in estuaries LimnolOceanogr 43 1007ndash1016 1998

Mallin M A Paerl H W and Rudek J Seasonal phytoplanktoncomposition productivity and biomass in the Neuse River estu-ary North Carolina Estuar Coast Shelf S 32 609ndash623 1991

Mallin M A Paerl H W Rudek J and Bates P W Regulationof estuarine primary production by watershed rainfall and riverflow Mar Ecol-Prog Ser 93 199ndash203 1993

Malone T C Kemp W M Ducklow H W Boynton W RTuttle J H and Jonas R B Lateral variation in the productionand fate of phytoplankton in a partially stratified estuary MarEcol-Prog Ser 32 149ndash160 1986

Malone T C Crocker L H Pike S E and Wendler B W Influ-ences of river flow on the dynamics of phytoplankton productionin a partially stratified estuary Mar Ecol-Prog Ser 48 235ndash249 1988

Marra J Approaches to the measurement of plankton productionin Phytoplankton Productivity Carbon Assimilation in Marineand Freshwater edited by Williams P J le B Thomas D Nand Reynolds C S Blackwell Science Ltd Oxford 78ndash1082002

Martin M A Fram J P and Stacey M T Seasonal chlorophyll afluxes between the coastal Pacific Ocean and San Francisco BayMar Ecol-Prog Ser 357 51ndash61 2007

May C L Koseff J R Lucas L V Cloern J E and Schoell-hamer D H Effects of spatial and temporal variability of turbid-ity on phytoplankton blooms Mar Ecol-Prog Ser 254 111ndash128 2003

Medina-Goacutemez I and Herrera-Silveira J A Primary produc-tion dynamics in a pristine groundwater influenced coastal la-goon of the Yucatan Peninsula Cont Shelf Res 26 971ndash986doi101016jcsr200603003 2006

Meeuwig J J Predicting coastal eutrophication from land-use anempirical approach to small non-stratifed estuaries Mar Ecol-Prog Ser 176 231ndash241 1999

Moll R A Phytoplankton in a temperate-zone salt marsh net pro-duction and exchanges with coastal waters Mar Biol 42 109ndash118 1977

Monbet Y Control of phytoplankton biomass in estuaries A com-parative analysis of microtidal and macrotidal estuaries Estuar-ies 15 563ndash571 1992

Montes-Hugo M A Alvarez-Borrego S and Gaxiola-CastroG Annual phytoplankton production in a coastal lagoon of thesouthern California Current System Mar Ecol-Prog Ser 27751ndash60 2004

Moraacuten X A G Annual cycle of picophytoplankton photosynthe-sis and growth rates in a temperate coastal ecosystem a majorcontribution to carbon fluxes Aquat Microb Ecol 49 267ndash279doi103354ame01151 2007

Moreno-Madrintildeaacuten M J and Fischer A M Performance of theMODIS FLH algorithm in estuarine waters a multi-year (2003ndash2010) analysis from Tampa Bay Florida (USA) Int J RemoteSens 34 6467ndash6483 doi1010800143116120138042272013

Murrell M C and Hollibaugh J T Microzooplankton grazing innorthern San Francisco Bay measured by the dilution methodMar Ecol-Prog Ser 15 53ndash63 1998

Murrell M C Hagy III J D Lores E M and Greene R MPhytoplankton production and nutrient distributions in a subtrop-ical estuary Importance of freshwater flow Estuar Coast 30390ndash402 2007

Nixon S W Physical energy inputs and the comparative ecologyof lake and marine ecosystems Limnol Oceanogr 33 1005ndash1025 1988

Nixon S W Coastal marine eutrophication ndash a definition socialcauses and future concerns Ophelia 41 199ndash219 1995

Nixon S W and Buckley B A ldquoA strikingly rich zonerdquo ndash Nutrientenrichment and secondary production in coastal marine ecosys-tems Estuaries 25 782ndash796 2002

Nixon S W Fulweiler R W Buckley B A Granger S L Now-icki B L and Henry K M The impact of changing climate onphenology productivity and benthic-pelagic coupling in Narra-gansett Bay Estuar Coast Shelf S 82 1ndash18 2009

Oviatt C Annual Primary Production in Narragansett Bay with noBay-Wide Winter-Spring Phytoplankton Bloom Estuar CoastShelf S 54 1013ndash1026 doi101006ecss20010872 2002

Oviatt C A Hyde K J W Keller A A and Turner J T Pro-duction patterns in Massachusetts Bay with outfall relocationEstuar Coast 30 35ndash46 2007



Parker A E Kimmerer W J and Lidstroumlm U U Reevaluatingthe generality of an empirical model for light-limited primaryproduction in the San Francisco Estuary Estuar Coast 35 930ndash942 doi101007s12237-012-9507-x 2012

Peierls B L Hall N S and Paerl H W Non-monotonic Re-sponses of Phytoplankton Biomass Accumulation to HydrologicVariability A Comparison of Two Coastal Plain North CarolinaEstuaries Estuar Coast 35 1ndash17 2012

Pennock J R Chlorophyll distributions in the Delaware estuaryRegulation by light-limitation Estuar Coast Shelf S 21 711ndash725 1985

Pennock J R and Sharp J H Phytoplankton production in theDelaware Estuary temporal and spatial variability Mar Ecol-Prog Ser 34 143ndash155 1986

Petersen J K Hansen J W Laursen M B Clausen PCarstensen J and Conley D J Regime shift in a coastal marineecosystem Ecol Appl 18 497ndash510 2008

Pitcher G C and Calder D Shellfish culture in the Benguela sys-tem phytoplankton and the availability of food for commercialmussel farms in Saldhana Bay South Africa J Shellfish Res17 15ndash24 1998

Platt T and Sathyendranath S Software for Use in Calcu-lation of Primary Production in the Oceanic Water Columnavailable athttpwwwioccgorgsoftwareOcean_Productionrptpdf (last access 26 February 2014) Bedford Institute ofOceanography Dartmouth NS 1995

Platt T Sathyendranath S and Ravindran P Primary productionby phytoplankton Analytic solutions for daily rates per unit areaof water surface P Roy Soc B 241 101ndash111 1990

Raine R C T and Patching J W Aspects of carbon and nitro-gen cycling in a shallow marine environment J Exp Mar BiolEcol 47 127ndash139 doi1010160022-0981(80)90107-0 1980

Raymond P A and Bauer J E Riverine export of aged terrestrialorganic matter to the North Atlantic Ocean Nature 409 497ndash500 2001

Riley G The plankton of estuaries in Estuaries edited by LauffG American Association for the Advancement of Science Pub-lication No 83 Washington DC 316ndash328 1967

Rivera-Monroy V Madden C Day J Twilley R Vera-HerreraF and Alvarez-Guilleacuten H Seasonal coupling of a tropicalmangrove forest and an estuarine water column enhancementof aquatic primary productivity Hydrobiologia 379 41ndash53doi101023a1003281311134 1998

Roman M R Reeve M R and Froggatt J L Carbon productionand export from Biscayne Bay Florida I Temporal patterns inprimary production seston and zooplankton Estuar Coast ShelfS 17 45ndash59 doi1010160272-7714(83)90044-6 1983

Ruppert D Modeling Univariate Distributions in Statistics andData Analysis for Financial Engineering edited by Casella GFienberg S and Olkin I Springer Science and Business MediaNew York 118 2011

Rysgaard S Nielsen T G and Hansen B W Seasonal varia-tion in nutrients pelagic primary production and grazing in ahigh-Arctic coastal marine ecosystem Young Sound NortheastGreenland Mar Ecol-Prog Ser 179 13ndash25 1999

Saux Picart S Sathyendranath S Dowell M Moore Tand Platt T Remote sensing of assimilation number formarine phytoplankton Remote Sens Environ 146 87ndash96doi101016jrse201310032 2014

Sellner K G Interpretation of the14C method of measuring thetotal annual production of phytoplankton in a South Carolina es-tuary Bot Mar 19 119ndash125 1976

Shapiro S S and Wilk M B An Analysis of Variance test fornormality (complete samples) Biometrika 52 591ndash611 1965

Shikata T Nagasoe S Matsubara T Yoshikawa S YamasakiY Shimasaki Y Oshima Y Jenkinson I R and HonjoT Factors influencing the initiation of blooms of the raphido-phyteHeterosigma akashiwoand the diatomSkeletonema costa-tum in a port in Japan Limnol Oceanogr 53 2503ndash2518doi104319lo20085362503 2008

Sinclair M Suba Rao D V and Couture R Phytoplankton tem-poral distributions in estuaries Oceanol Acta 4 239ndash246 1981

Small L F McIntire C D MacDonald K B Lara-Lara JR Frey B E Amspoker M C and Winfield T Primaryproduction plant and detrital biomass and particle transportin the Columbia River Estuary Prog Oceanogr 25 175ndash210doi1010160079-6611(90)90007-o 1990

Smith S V and Hollibaugh J T Annual cycle and interan-nual variability of ecosystem metabolism in a temperate climateembayment Ecol Monogr 67 509ndash533 doi1018900012-9615(1997)067[0509acaivo]20co2 1997

Smith S V Kimmerer W J Laws E A Brock R E and WalshT W Kaneohe Bay sewage diversion experiment perspectiveson ecosystem responses to nutritional perturbation Pac Sci 35279ndash395 1981

Sobczak W V Cloern J E Jassby A D Cole B E SchragaT S and Arnsberg A Detritus fuels ecosystem metabolism butnot metazoan food webs in San Francisco estuaryrsquos freshwaterDelta Estuaries 28 124ndash137 2005

Soetaert K Petzoldt T and Setzer R W Solving differentialequations in R Package deSolve J Stat Softw 33 1ndash25 2010

Son S Wang M and Harding Jr L W Satellite-measured netprimary production in the Chesapeake Bay Remote Sens Envi-ron 144 109ndash119 doi101016jrse201401018 2014

Steemann Nielsen E The use of radioactive carbon (C14) for mea-suring organic production in the sea J Cons Int Explor Mer18 117ndash140 1952

Stockner J G and Cliff D D Phytoplankton Ecology of Vancou-ver Harbor J Fish Res Board Can 36 1ndash10 1979

Stockner J G Cliff D D and Shortreed K R S Phytoplanktonof the Strait of Georgia British Columbia J Fish Res BoardCan 36 657ndash666 1979

Stockner J G Cliff D D and Buchanan D B Phytoplanktonproduction and distribution in Howe Sound British Colombia acoastal marine embayment-fjord under stress J Fish Res BoardCan 34 907ndash917 1977

Taguchi S Iseki K and Kawamura T The estimate of an-nual production by phytoplankton in Akkeshi Bay Japan JOceanogr Soc Japan 33 97ndash102 1977

Thayer G W Phytoplankton production and the distribution of nu-trients in a shallow unstratified estuarine system near BeaufortNC Chesapeake Science 12 240ndash253 1971

Therriault J-C and Levasseur M Control of phytoplankton pro-duction in the Lower St Lawrence Estuary light and freshwaterrunoff Nat Can 112 77ndash96 1985

Thomas C M Perissinotto R and Kibirige I Phytoplanktonbiomass and size structure in two South African eutrophic tem-



porarily openclosed estuaries Estuar Coast Shelf S 65 223ndash238 2005

Thompson P A Spatial and Temporal Patterns of Factors Influ-encing Phytoplankton in a Salt Wedge Estuary the Swan RiverWestern Australia Estuaries 21 801ndash817 1998

Tillman U Hesse K-J and Colijn F Planktonic primary pro-duction in the German Wadden Sea J Plankton Res 22 1253ndash1276 2000

Umani S F Del Negro P Larato C De Vittor C Cabrini MCelio M Falconi C Tamberlich F and Azam F Major inter-annual variations in microbial dynamics in the Gulf of Trieste(northern Adriatic Sea) and their ecosystem implications AquatMicrob Ecol 46 163ndash175 2007

Underwood G J C and Kromkamp J Primary production byphytoplankton and microphytobenthos in estuaries in Advancesin Ecological Research vol 29 Estuaries edited by Nedwell DB and Raffaelli D G Elsevier Academic Press Inc San Diego93ndash153 1999

Wetsteyn L P M J and Kromkamp J C Turbidity nutrientsand phytoplankton primary production in the Oosterschelde (TheNetherlands) before during and after a large-scale coastal engi-neering project (1980ndash1990) Hydrobiologia 282ndash283 61ndash78doi101007bf00024622 1994

Williams R B and Murdoch M B Phytoplankton Production andChlorophyll Concentration in the Beaufort Channel North Car-olina Limnol Oceanogr 11 73ndash82 1966

Winder M and Cloern J E The annual cycles of phyto-plankton biomass Philos T Roy Soc B 365 3215ndash3226doi101098Rstb20100125 2010

Wofsy S C A simple model to predict extinction coefficients andphytoplankton biomass in eutrophic waters Limnol Oceanogr28 1144ndash1155 1983

Yamaguchi Y Satoh H and Aruga Y Seasonal Changes of Or-ganic Carbon and Nitrogen Production by Phytoplankton in theEstuary of River Tamagawa Mar Pollut Bull 23 723ndash7251991

Yin K Influence of monsoons and oceanographic processes on redtides in Hong Kong waters Mar Ecol-Prog Ser 262 27ndash412003



1 Introduction

Estuaries have large supplies of organic carbon because oftheir connection to land that delivers organic matter fromrunoff and nutrients that support high rates of primary pro-duction (Hopkinson et al 2005) As a result estuaries func-tion as fast biogeochemical reactors that operate on the en-ergy derived from respiration of their external and internalsupplies of fixed carbon Total annual ecosystem respirationgenerally exceeds gross primary production (Caffrey 2004Gattuso et al 1998) so most estuaries are heterotrophicecosystems that transform organic matter into inorganic nu-trients and CO2 are oversaturated in CO2 with respect to theatmosphere and unlike the open ocean are sources of CO2to the atmosphere Although estuaries occupy a small frac-tion of the Earthrsquos surface their CO2 emissions are globallysignificant ndash estimated at 043 Pg C yrminus1 (Borges 2005) Theaddition of this term to CO2 budgets reverses the functionof the coastal ocean from being a net sink to a net source ofCO2 and this term reduces the calculated global ocean CO2uptake by 12 Therefore sound understanding of oceanndashatmosphere CO2 exchange requires globally distributed mea-surements of primary production external supplies of or-ganic carbon and respiration across the diversity of estuarineecosystems

The organic carbon supplied to estuaries is packaged indifferent forms Detritus delivered by land runoff is derivedprimarily from terrestrial vegetation and is an important en-ergy supply that fuels estuarine metabolism This pool of or-ganic matter may be old (Raymond and Bauer 2001) is re-fractory has low nitrogen content is metabolized primarilyby microbial decomposers and has little direct food value forherbivores (Sobczak et al 2005) Much of the organic matterproduced by vascular plants and macroalgae is also routedthrough decomposers or exported only about 20 of sea-grass marsh and macroalgal primary production is consumedby herbivores (Cebrian 1999) A third supply is primary pro-duction by microalgae including phytoplankton and benthicforms The biogeochemical and ecological significance ofmicroalgal production differs from the other forms becauseit is enriched in nitrogen and lipids including essential fattyacids packaged in a form easily accessible to consumer or-ganisms and because this pool turns over rapidly ndash on theorder of days (Furnas 1990) Most (sim 90 ) phytoplank-ton production is consumed or decomposed to support localheterotrophic metabolism whereas a substantial fraction ofmacrophyte production (24ndash44 ) is exported or buried anddoes not contribute to local metabolism (Duarte and Cebrian1996) Therefore the different forms of organic matter aremetabolized through different routes and have different eco-logical and biogeochemical ramifications We focus on an-nual phytoplankton primary production (APPP) as a supplyof labile organic carbon that plays a central role in the eco-logical and biogeochemical dynamics of estuaries and othermarine ecosystems influenced by connectivity to land Al-

though we do not consider primary production by benthicmicroalgae this supply of labile organic matter is compara-ble to that of phytoplankton in some estuaries (Underwoodand Kromkamp 1999) and it plays a similarly important rolein the dynamics of ecosystem metabolism and energetics

With the exception of very turbid estuaries such as theScheldt (Gazeau et al 2005) net planktonic production ispositive in estuaries so it is an autotrophic component oper-ating within heterotrophic ecosystems Phytoplankton pro-duction is the primary source of organic carbon to someestuarine-coastal systems such as the Baltic Sea (Elmgren1984) South San Francisco Bay (Jassby et al 1993) andMoreton Bay (Eyre and McKee 2002) Much of the annualproduction occurs during seasonal or episodic blooms whenphytoplankton photosynthesis exceeds total system respira-tion and estuaries shift temporarily to a state of autotrophy(Caffrey et al 1998) leading to depletion of inorganic nu-trients as they are converted into organic forms incorporatedinto algal biomass (Kemp et al 1997) drawdown of CO2(Cloern 1996) and shifts in pH oversaturation of dissolvedoxygen (Herfort et al 2012) rapid phytoplankton uptake ofcontaminants such as PCBs methyl mercury (Luengen andFlegal 2009) and dissolved trace metals such as cadmiumnickel and zinc (Luoma et al 1998) increased growth andproduction of copepods (Kioslashrboe and Nielsen 1994) andbivalve mollusks (Beukema and Cadeacutee 1991) as the algalfood supply increases and sedimentation of phytoplankton-derived organic carbon that accelerates benthic respirationand nutrient regeneration rates (Grenz et al 2000)

Trophic transfer of the energy and essential biochem-icals contained in phytoplankton biomass is the resourcebase supporting production at higher trophic levels includ-ing those we harvest for food Annual phytoplankton produc-tion is highly correlated with fishery landings (Nixon 1988)biomass of benthic invertebrates (Herman et al 1999) andsustainable yield of cultured shellfish (Bacher et al 1998)Increasing nutrient runoff during the past century has pro-voked increases of phytoplankton production supporting 3ndash8-fold increases of fish biomass in the Baltic Sea JapanrsquosSeto Inland Sea northern Adriatic Sea shelf waters of theBlack Sea and the Nile Delta (Nixon and Buckley 2002Caddy 2000) However the increased phytoplankton pro-duction of organic carbon has exceeded the assimilative ca-pacity of these and other ecosystems leading to the globalexpansion of marine dead zones (Diaz and Rosenberg 2008)loss of habitat for seagrasses demersal fish and shellfish(Carstensen et al 2003) and shifts in fish communities(Kemp et al 2005 Caddy 2000) The link between phyto-plankton production and estuarine biogeochemistry is illus-trated in a compelling way by the systemic changes that oc-curred in Narragansett Bay during a 25-year warming periodwhen the winterndashspring phytoplankton bloom disappearedprimary production declined 40ndash50 benthic metabolismslowed and the bay switched from being a net consumerto a net producer of fixed nitrogen (Nixon et al 2009)











GPP

NPP

U

0 200 400

Count

What

1950s

1960s

1970s

1980s

1990s

2000s

0 200 400

Africa

Asia

AustraliaNewZealand

Baltic Region

Europe

North America


0 200 400

13C

14C

O2

0 200 400 600 800

When

Where

How













0 250 500 750


-40

-20

0

20

40

60

80

La

titu

de

0 500 1000 1500

APPP (g C m-2 yr-1)

A B








0

5

10

15

20

Nu

mb

er o

f Me

asu

rem

ent

s

0 200 400 600 800 1000 1200 1400 1600 1800


0

20

40

60

80

Num

ber

of M

easu

rem

ents




A

B







10 100 1000


0

40

80

120

160

Ran

k

GPP

NPP

U

Boynton et al 1982

Kattegat

Westerschelde

Long Island Sound

Tokyo Bay

Terminos Lagoon

Chesapeake

Gulf of Finland

Lower St Lawrence

Tasman Bay

Bedford Basin

Ria de Viga

Neuse

Cienaga Grande

Colne

Limfjord

Aarhus Bay

Dumbell Bay

Gulf of Bothnia

Bay of Brest


Cochin Backwater

Delaware Bay

Saanich Inlet

Seto Inland Sea

Swan

Tampa Bay










0

400

800

AP

PP

(g

C m

-2 y

r-1)

St Law

rence

Core-B

ogue

Chesapeake

How

e

Patuxent

Westerschelde

Str G

eorgia

Narragansett

Burrard

Tomales

Sw

an


034053

022

083 047056 023

057

031

027

020

0

400

800

AP

PP

(g

C m

-2 y

r-1)

Kattegat 413

Oslashresund

Kattegat 1993

Gullm

ar

Oosterschelde O

21

Rhode R

iver 52

Oosterschelde P

3

Massachusetts N

04

Boston F23

Massachusetts N

18


019

022

008

015

031025

044

055

047016












42 Nutrient supply








43 Light limitation
















451 River flow








452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References






















































































































































































































GPP

NPP

U

0 200 400

Count

What

1950s

1960s

1970s

1980s

1990s

2000s

0 200 400

Africa

Asia

AustraliaNewZealand

Baltic Region

Europe

North America


0 200 400

13C

14C

O2

0 200 400 600 800

When

Where

How













0 250 500 750


-40

-20

0

20

40

60

80

La

titu

de

0 500 1000 1500

APPP (g C m-2 yr-1)

A B








0

5

10

15

20

Nu

mb

er o

f Me

asu

rem

ent

s

0 200 400 600 800 1000 1200 1400 1600 1800


0

20

40

60

80

Num

ber

of M

easu

rem

ents




A

B







10 100 1000


0

40

80

120

160

Ran

k

GPP

NPP

U

Boynton et al 1982

Kattegat

Westerschelde

Long Island Sound

Tokyo Bay

Terminos Lagoon

Chesapeake

Gulf of Finland

Lower St Lawrence

Tasman Bay

Bedford Basin

Ria de Viga

Neuse

Cienaga Grande

Colne

Limfjord

Aarhus Bay

Dumbell Bay

Gulf of Bothnia

Bay of Brest


Cochin Backwater

Delaware Bay

Saanich Inlet

Seto Inland Sea

Swan

Tampa Bay










0

400

800

AP

PP

(g

C m

-2 y

r-1)

St Law

rence

Core-B

ogue

Chesapeake

How

e

Patuxent

Westerschelde

Str G

eorgia

Narragansett

Burrard

Tomales

Sw

an


034053

022

083 047056 023

057

031

027

020

0

400

800

AP

PP

(g

C m

-2 y

r-1)

Kattegat 413

Oslashresund

Kattegat 1993

Gullm

ar

Oosterschelde O

21

Rhode R

iver 52

Oosterschelde P

3

Massachusetts N

04

Boston F23

Massachusetts N

18


019

022

008

015

031025

044

055

047016












42 Nutrient supply








43 Light limitation
















451 River flow








452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References














































































































































































































0 250 500 750


-40

-20

0

20

40

60

80

La

titu

de

0 500 1000 1500

APPP (g C m-2 yr-1)

A B








0

5

10

15

20

Nu

mb

er o

f Me

asu

rem

ent

s

0 200 400 600 800 1000 1200 1400 1600 1800


0

20

40

60

80

Num

ber

of M

easu

rem

ents




A

B







10 100 1000


0

40

80

120

160

Ran

k

GPP

NPP

U

Boynton et al 1982

Kattegat

Westerschelde

Long Island Sound

Tokyo Bay

Terminos Lagoon

Chesapeake

Gulf of Finland

Lower St Lawrence

Tasman Bay

Bedford Basin

Ria de Viga

Neuse

Cienaga Grande

Colne

Limfjord

Aarhus Bay

Dumbell Bay

Gulf of Bothnia

Bay of Brest


Cochin Backwater

Delaware Bay

Saanich Inlet

Seto Inland Sea

Swan

Tampa Bay










0

400

800

AP

PP

(g

C m

-2 y

r-1)

St Law

rence

Core-B

ogue

Chesapeake

How

e

Patuxent

Westerschelde

Str G

eorgia

Narragansett

Burrard

Tomales

Sw

an


034053

022

083 047056 023

057

031

027

020

0

400

800

AP

PP

(g

C m

-2 y

r-1)

Kattegat 413

Oslashresund

Kattegat 1993

Gullm

ar

Oosterschelde O

21

Rhode R

iver 52

Oosterschelde P

3

Massachusetts N

04

Boston F23

Massachusetts N

18


019

022

008

015

031025

044

055

047016












42 Nutrient supply








43 Light limitation
















451 River flow








452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References














































































































































































































0

5

10

15

20

Nu

mb

er o

f Me

asu

rem

ent

s

0 200 400 600 800 1000 1200 1400 1600 1800


0

20

40

60

80

Num

ber

of M

easu

rem

ents




A

B







10 100 1000


0

40

80

120

160

Ran

k

GPP

NPP

U

Boynton et al 1982

Kattegat

Westerschelde

Long Island Sound

Tokyo Bay

Terminos Lagoon

Chesapeake

Gulf of Finland

Lower St Lawrence

Tasman Bay

Bedford Basin

Ria de Viga

Neuse

Cienaga Grande

Colne

Limfjord

Aarhus Bay

Dumbell Bay

Gulf of Bothnia

Bay of Brest


Cochin Backwater

Delaware Bay

Saanich Inlet

Seto Inland Sea

Swan

Tampa Bay










0

400

800

AP

PP

(g

C m

-2 y

r-1)

St Law

rence

Core-B

ogue

Chesapeake

How

e

Patuxent

Westerschelde

Str G

eorgia

Narragansett

Burrard

Tomales

Sw

an


034053

022

083 047056 023

057

031

027

020

0

400

800

AP

PP

(g

C m

-2 y

r-1)

Kattegat 413

Oslashresund

Kattegat 1993

Gullm

ar

Oosterschelde O

21

Rhode R

iver 52

Oosterschelde P

3

Massachusetts N

04

Boston F23

Massachusetts N

18


019

022

008

015

031025

044

055

047016












42 Nutrient supply








43 Light limitation
















451 River flow








452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References














































































































































































































10 100 1000


0

40

80

120

160

Ran

k

GPP

NPP

U

Boynton et al 1982

Kattegat

Westerschelde

Long Island Sound

Tokyo Bay

Terminos Lagoon

Chesapeake

Gulf of Finland

Lower St Lawrence

Tasman Bay

Bedford Basin

Ria de Viga

Neuse

Cienaga Grande

Colne

Limfjord

Aarhus Bay

Dumbell Bay

Gulf of Bothnia

Bay of Brest


Cochin Backwater

Delaware Bay

Saanich Inlet

Seto Inland Sea

Swan

Tampa Bay










0

400

800

AP

PP

(g

C m

-2 y

r-1)

St Law

rence

Core-B

ogue

Chesapeake

How

e

Patuxent

Westerschelde

Str G

eorgia

Narragansett

Burrard

Tomales

Sw

an


034053

022

083 047056 023

057

031

027

020

0

400

800

AP

PP

(g

C m

-2 y

r-1)

Kattegat 413

Oslashresund

Kattegat 1993

Gullm

ar

Oosterschelde O

21

Rhode R

iver 52

Oosterschelde P

3

Massachusetts N

04

Boston F23

Massachusetts N

18


019

022

008

015

031025

044

055

047016












42 Nutrient supply








43 Light limitation
















451 River flow








452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References














































































































































































































0

400

800

AP

PP

(g

C m

-2 y

r-1)

St Law

rence

Core-B

ogue

Chesapeake

How

e

Patuxent

Westerschelde

Str G

eorgia

Narragansett

Burrard

Tomales

Sw

an


034053

022

083 047056 023

057

031

027

020

0

400

800

AP

PP

(g

C m

-2 y

r-1)

Kattegat 413

Oslashresund

Kattegat 1993

Gullm

ar

Oosterschelde O

21

Rhode R

iver 52

Oosterschelde P

3

Massachusetts N

04

Boston F23

Massachusetts N

18


019

022

008

015

031025

044

055

047016












42 Nutrient supply








43 Light limitation
















451 River flow








452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References




















































































































































































































42 Nutrient supply








43 Light limitation
















451 River flow








452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References















































































































































































































43 Light limitation
















451 River flow








452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References





















































































































































































































451 River flow








452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References


















































































































































































































452 Ocean exchange













































PG (mg C m-3 h-1)

















53 Recommendations


























References






















































































































































































































































PG (mg C m-3 h-1)

















53 Recommendations


























References
















































































































































































































53 Recommendations


























References
































































































































































































































References













































































































































































































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