Narragansett Bay Hypoxic Event Characteristics Basedon Fixed-Site Monitoring Network Time Series:Intermittency, Geographic Distribution, SpatialSynchronicity, and Interannual Variability

Daniel L. Codiga & Heather E. Stoffel &Christopher F. Deacutis & Susan Kiernan &

Candace A. Oviatt

Received: 11 July 2008 /Revised: 2 April 2009 /Accepted: 12 April 2009 /Published online: 23 May 2009# Coastal and Estuarine Research Federation 2009

Abstract Low dissolved oxygen events were characterizedin Narragansett Bay (NB), a moderate-size (370 km2)temperate estuary with a complex passage/embaymentgeometry, using time series from 2001 to 2006 at ninefixed-site monitoring stations. Metrics for event intensityand severity were the event-mean deficit relative to athreshold (mg O2 l−1) and the deficit-duration (mg O2 l−1

day; product of deficit and duration [day]). Hypoxia(threshold 2.9 mg O2 l−1) typically occurred intermittentlyfrom late June through August at most stations, as multiple(two to five per season) events each 2 to 7 days long withdeficit-duration 2 to 5 mg O2 l

−1 day. Conditions were moresevere to the north and west, a pattern attributed to a north–south nutrient/productivity gradient and east–west structureof residual circulation. Spatial patterns for suboxic and

severely hypoxic events (thresholds 4.8 and 1.4 mg O2 l−1)

were similar. The view that different processes governevent variability in different regions, each influenced bylocal hydrodynamics, is supported by both weak spatialsynchronicity (quantified using overlap of event times atdifferent sites) and multiple linear regressions of biologicaland physical parameters against event severity. Interannualchanges were prominent and season-cumulative hypoxiaseverity correlated with June-mean river runoff and June-mean stratification. Benthic ecological implications forareas experiencing events include: NB hypoxia classifiesas periodic/episodic on a near-annual basis; highest directmortality risk is to sensitive and moderately sensitivesessile species in the northern West Passage and westernGreenwich Bay, with some risk to Upper Bay; direct risk tomobile species may be ameliorated by weak spatialsynchronicity; and indirect impacts, including reducedgrowth rates and shifts in predator–prey balances, are verylikely throughout the sampled area due to observed suboxicand hypoxic conditions.

Keywords Hypoxia . Suboxic . Oxygen deficiency .

Time series . Narragansett Bay .Water quality

Introduction

One of the most widespread and deleterious anthropogenicimpacts to estuarine and coastal waters is eutrophication-driven oxygen depletion or hypoxia (Diaz 2001; Diaz andRosenberg 2008), which has been identified as a significantstressor on benthic communities including fish and inver-

Estuaries and Coasts (2009) 32:621–641DOI 10.1007/s12237-009-9165-9

Electronic supplementary material The online version of this article(doi:10.1007/s12237-009-9165-9) contains supplementary material,which is available to authorized users.

D. L. Codiga (*) :H. E. Stoffel : C. A. OviattGraduate School of Oceanography, University of Rhode Island,Narragansett, RI 02882, USAe-mail: d.codiga@gso.uri.edu

C. F. DeacutisNarragansett Bay National Estuary Program,University of Rhode Island,Box 27, Bay Campus,Narragansett, RI 02882, USA

S. KiernanDepartment of Environmental Management,Office of Water Resources,235 Promenade Street,Providence, RI 02908, USA

tebrates (Pihl et al. 1992; Gray et al. 2002; Diaz et al.2004). Diaz and Rosenberg (2008) classified estuarinesystems depending on whether hypoxia persists on time-scales of weeks to months (“seasonal,” e.g., main stem ofChesapeake Bay; Hagy et al. 2004) or more episodically ontimescales of days to weeks (“periodic,” e.g., NarragansettBay (NB); Bergondo et al. 2005). In response to seasonalhypoxia, benthic communities appear to follow the Pearson–Rosenberg organic loading stress model (Pearson andRosenberg 1978; Dauer et al. 1992; Dauer and Alden1995; Diaz et al. 2004). Community responses to episodic/periodic hypoxia can be more complex and are often moldedby factors such as the spatial extent, duration, frequency, andintensity of hypoxic events (Pihl et al. 1992; Diaz et al. 2004;Jewett et al. 2005). For example, less hypoxia-sensitivespecies may thrive in episodic moderately hypoxicsituations, through decreased predation rates due to areaavoidance by more sensitive predator species (Pihl et al.1991; Sagasti et al. 2001; Jewett et al. 2005; Altieri andWitman 2006; Montagna and Ritter 2006; Altieri 2008). Inmany estuaries, hypoxia appears to have followed a phasedimpact progression, with conditions worsening over anumber of years from episodic/periodic to seasonallypersistent (Diaz and Rosenberg 2008).

Hypoxic events can be detrimental to benthic communi-ties through both direct and indirect effects; the relativeprevalence of direct and/or indirect effects and the nature ofrecovery are associated with the spatial extent, duration,frequency, and severity of hypoxia (Diaz and Rosenberg1995, 2008; Wu 2002; Shimps et al. 2005; Montagna andRitter 2006). Direct effects include mortality from lethaldissolved oxygen (DO) concentrations and changes inabundance and biomass due to combinations of mortalityof sessile organisms and area avoidance by mobile species(Altieri and Witman 2006; Montagna and Ritter 2006).Taxonomic rankings indicate that fishes and crustaceansshow greatest sensitivity, followed by echinoderms, whileannelids and especially cnidarians and molluscs tend toshow greatest tolerance (Diaz and Rosenberg 1995; Gray etal. 2002; Vaquer-Sunyer and Duarte 2008). The highest riskof direct mortality occurs at concentrations of 0.5 to 1.0 mgO2 l

−1 for many moderately sensitive species and at 1.0 to2.0 mg O2 l−1 for many sensitive species, with mortalityoften taking place within the first 4 to 7 h; for highlytolerant benthic infaunal species, risk requires more severeconditions (≤0.5 mg O2 l−1) of greater duration (days toweeks; Rosenberg et al. 1991; Sagasti et al. 2001; Wu2002; Person-LeRuyet et al. 2003; Vaquer-Sunyer andDuarte 2008). Larvae are generally more acutely sensitivethan juveniles and adults (USEPA 2000); Rhode Island (RI)water quality regulations, designed for protection of larvae,treat a 1-day exposure to 2.9 mg O2 l−1 as a violation(RIDEM 2006).

Indirect effects include significant decreases in growthrates and habitat compression that force species into areasof adequate DO but subject them to other stressors: lowerprey density (Eby and Crowder 2002; Powers et al. 2005;Eby et al. 2005), nonpreferred thermal regimes (Craig andCrowder 2005; Niklitschek and Secor 2005), higherpredator concentrations (Eggleston et al. 2005), and/orpotential increased predation risk due to emergence of deepburrowers in sediments (Pihl et al. 1992; Taylor andEggleston 2000; Wu 2002; Montagna and Ritter 2006).For example, significant decreases of >50% in growth rateswere noted by (Eby et al. 2005) for juvenile demersal fishspecies due to decreased prey density in areas experiencinghypoxia. The spatial synchronicity of hypoxic events, orextent to which events in different subregions of an estuaryare simultaneous, is a less-studied factor that may shape thenature of direct and indirect impacts on mobile species.

NB is a medium-sized (370 km2) northeastern USestuary (Fig. 1) that has been experiencing hypoxic eventsfor at least the last several decades (Olsen and Lee 1979;Oviatt et al. 1984; Bergondo et al. 2005; Deacutis et al.2006; Melrose et al. 2007; Deacutis 2008; Saarman et al.2008). We address areas south of the shallow northernmostportions of the Providence River estuary. Hypoxia severitygenerally follows the north–south gradient of nutrients,phytoplankton, and fresh water influence, decreasing inintensity with distance from the estuary head in the north;an exception is that hypoxia is severe in western GreenwichBay, a shallow embayment located south of the region ofpeak nutrient enrichment (Oviatt et al. 2002; Prell et al.2004; Melrose et al. 2007; Deacutis 2008; Oviatt 2008;Saarman et al. 2008).

Monitoring stations to collect continuous measurementsof DO and associated water quality parameters in NB werefirst established by researchers in the mid-1990s. Over thenext decade, stations were added (Fig. 1) by researchers andgovernment entities, and the Narragansett Bay Fixed-SiteMonitoring Network (NBFSMN) developed through inter-agency collaboration. Its aim is improved monitoring ofwater quality, in particular hypoxia, through sustained timeseries observations that include DO, chlorophyll, tempera-ture, and salinity (NBFSMN 2007). Our analysis oftemporal and spatial DO variability using multiple yearsof network observations complements previous descriptionsbased on synoptic spatial conductivity–temperature–depth–oxygen (CTDO) surveys (Deacutis 1999; Prell et al. 2004;Deacutis et al. 2006), an early subset of NBFSMN timeseries (Bergondo et al. 2005), towed-body surveys (Melroseet al. 2007), and combinations of the three datasets(Saarman et al. 2008).

The relative importance of numerous biological andphysical processes that shape NB hypoxia is poorlyunderstood. We hypothesize a strong influence of hydro-

622 Estuaries and Coasts (2009) 32:621–641

dynamics local to each bay subregion. The monitoringnetwork time series facilitate the exploratory analysispresented here. Seven parameters are most relevant, basedon previous studies: river flow, chlorophyll, temperature,density stratification, tidal range, large-scale north–southnontidal sea level difference, and northeastward wind.River flow is both the dominant pathway for delivery ofnutrients (e.g., Nixon et al. 2008) and a major influence onflushing rate (Pilson 1985) and stratification. Chlorophyllindicates abundance of phytoplankton, the main source ofdecaying material that fuels hypoxia, and temperatureregulates the metabolic rate of DO consumption. Stratifica-tion reduces vertical exchange and aeration of deep water,and tidal range captures changes in spring- and neap-tideconditions, which regulate tidally driven flushing andvertical mixing and appear to influence hypoxic event

timing (Bergondo et al. 2005). Large-scale north–southnontidal sea level difference is a proxy for circulationfluctuations driven by north–south winds, which regulateflushing of bottom waters (Bergondo 2004). Finally,detailed hydrodynamic modeling indicates that northeast-ward wind stalls the exchange of water among baysubregions (Rogers 2008).

The purpose of this study is to expand our understandingof a system subject to episodic/periodic hypoxia, throughuse of multiyear time series observations from a spatiallydistributed array of stations. Temporal intermittency, geo-graphic patterns, spatial synchronicity, and interannualvariability of low-DO events are characterized using threethreshold DO concentrations to describe suboxic, hypoxic,and severely hypoxic conditions. New metrics designedspecifically for time series (deficit-duration and fractionaloverlap, described below) are used, and comparison ismade to a metric used by a state regulatory agency.Potential influences of biological and physical drivingfactors (specifically, the seven listed above) in shapinghypoxic event severity and interannual variability areexplored. Finally, ecological implications of both directand indirect effects of hypoxia on benthic communities aredescribed; the state of NB with respect to phased hypoxiaprogression (Diaz and Rosenberg 2008) is assessed, andcommunity shifts affecting commercially important shell-fish are interpreted.

Methods

Monitoring Network Observations

The 33 time series analyzed are from nine sites (names andabbreviations given in Table 1; Fig. 1) sampled over theyears analyzed, 2001 to 2006 (NBFSMN 2007). Mostcalculations are carried out on an individual-site basis.However, for some calculations and for convenience whenpresenting and summarizing results, it is helpful to use thefollowing descriptive names for four groups: Upper Bay(UB) for BR, CP, and NP; West Passage (WP) for MV andQP; East Passage (EP) for PP and TW; and Embayments(EB) for GB and MH. Each site had (Table 1) a near-bottom (0.5 or 1.0 m above the seafloor) and a near-surface(0.5 m deep) Yellow Springs International sonde, samplingat 15-minute intervals. We used DO from the deep sonde,chlorophyll from the shallow sonde, and salinity andtemperature from both. When referencing the data collec-tion network, or more generally its products, we cite thenetwork website (NBFSMN 2007) where data are distrib-uted; individual years' products are described for 2001 and2002 by Bergondo (2004), for 2003 by Stoffel (2003), andfor remaining years by NBFSMN (2004, 2005, 2006).

BR

CP

NP

PP

TW

MH

GB

MV

QP

UUppppeerr BBaayy

MMoouunntt HHooppee BBaayy

RRhhooddee IIssllaanndd SSoouunndd

GGrreeeennwwiicchh BBaayy

PPrroovviiddeennccee RRiivveerr

EEssttuuaarryy

UUppppeerr WWeesstt

PPaassssaaggee

LLoowweerr WWeesstt

PPaassssaaggee

LLoowweerr EEaasstt

PPaassssaaggee

UUppppeerr EEaasstt PPaassssaaggee

SSaakkoonnnneett RRiivveerr

Blackstone River

Woonasquatucket River and

Moshassuck River

SSeeeekkoonnkk RRiivveerr

Taunton River

Pawtuxet River

Ten Mile River PROVIDENCE

NEWPORT

Fig. 1 Narragansett Bay region and bathymetry, with pertinentgeographic features and primary river inflows labeled. The ninestations are: Bullock Reach (BR), Conimicut Point (CP), NorthPrudence Island (NP), Mount View (MV), Quonset Point (QP),Poppasquash Point (PP), T-Wharf (TW), Greenwich Bay Marina(GB), and Mount Hope Bay (MH). Providence and Newport sea levelstations indicated by stars

Estuaries and Coasts (2009) 32:621–641 623

Quality assurance measures include verification ofcalibrations and consistency among multiple instruments,corrections for sensor drift and biases due to biofouling,removal of outliers, and interpolation across selectedintervals of missing data (RIDEM 2007). Protocols forcalibration, field maintenance, and quality assurance andquality control (QA/QC) procedures are consistent withNational Estuarine Research Reserve System-Wide Moni-

toring Program standard operating procedures (Small2008). Stations are serviced by swapping the deployedinstruments with newly calibrated instruments on a 2-weekinterval. Calibrations and sensor drift corrections areverified through a three-point comparison: data from theretrieved sonde are compared to the newly calibrated sonde,as well as an independent profiling sonde, all at thedeployment depth. Outliers are removed based on exceed-

Table 1 Station and sampling characteristics of fixed-site network

Group Station Latitude Longitude Water deptha [m] Deep sensordepth [m]

Yearsb Cumulativesamplingc [day]

Upper Bay (UB) Bullock Reach (BR) 41 44.434′ 71 22.480′ 6 5.5 2001 121

2002 100

2003 104

2004 102

2005 121

2006 110

Conimicut Point (CP) 41 42.828′ 71 20.628′ 7 6.5 2003 100

2005 101

2006 107

North Prudence (NP) 41 40.224′ 71 21.283′ 11 10.5 2001 81

2002 108

2003 110

2004 113

2005 119

2006 86

West Passage (WP) Mt. View (MV) 41 38.304′ 71 23.021′ 7 6.5 2004 86

2005 86

2006 113

Quonset Point (QP) 41 35.288′ 71 22.839′ 7 6.5 2005 40

2006 82

East Passage (EP) Poppasquash Point (PP) 41 39.807′ 71 19.066′ 8 7.5 2004 84

2005 56

2006 121

T-Wharf (TW)d 41 34.731′ 71 19.287′ 6 5 2003 91

2004 120

2005 105

2006 121

Embayments (EB) Greenwich Bay Marina (GB)d 41 41.090′ 71 26.762′ 3 2.5 2003 96

2004 120

2005 113

2006 118

Mt. Hope Bay (MH) 41 40.808′ 71 12.913′ 5 4.5 2005 87

2006 121

All data available at http://www.dem.ri.gov/bart/stations.htma Depths relative to mean lower low waterb Sampling coverage and gaps are shown in Fig. 4 for each year, from May to Octoberc June 1 through September 30d T-Wharf and Greenwich Bay Marina are dock-based stations; all others are buoy stations

624 Estuaries and Coasts (2009) 32:621–641

ing two standard deviations or the 95th percentile, usingmonthly data for each station, in conjunction with incon-sistencies in other parameters (RIDEM 2007). Gaps incoverage, affecting up to 6% of the record at an individualstation in a given year (station year), are filled by linearinterpolation following protocols detailed in the QualityAssurance Project Plan (RIDEM 2007). All data removedfor QA/QC reasons is documented in the metadatadocumentation accompanying the data products (NBFSMN2007).

Thresholds: Suboxic, Hypoxic, and Severely Hypoxic

The threshold values T4.8=4.8 mg O2 l−1, T2.9=2.9 mg O2 l

−1,and T1.4=1.4 mg O2 l−1 bounding ranges for suboxic,hypoxic, and severely hypoxic conditions (Tables 2 and 3)are used because they are explicitly incorporated in waterquality regulations adopted by the state of Rhode Island(RIDEM 2006, p. 19) as survival-protective under chronic,24-h, and 1-hr exposures, respectively. These regulationswere developed based on Environmental ProtectionAgency (USEPA 2000) criteria, with incorporation oflarval recruitment effects. Though the value 2.9 mg O2 l

−1

does not appear explicitly in the text of USEPA (2000), itresults from the equation in Table 6 on p. 37 of thatdocument for an exposure interval duration of 1 day (asshown at left end point of curve in Fig. 7 of USEPA2000) to determine the allowable minimum concentrationprotective of larvae, juveniles, and adults; the value

2.3 mg O2 l−1 (USEPA 2000) used in other studies isprotective of juveniles and adults only. Our thresholds arein the ranges commonly implemented in the literature asreviewed by Vaquer-Sunyer and Duarte (2008).

Event Characterization Using MWT Algorithm

An algorithm referred to as a “moving window trigger”(MWT; Codiga (2008)—“Electronic supplementary materi-al”) was developed and applied to identify and characterizelow-DO events from each time series in a systematic way.The MWT is designed to treat time series of arbitrarysampling resolution and duration and to handle gaps insampling coverage that typically characterize such records(Table 2). MWT parameters used here (Tables 2 and 3)were based on attempting to match, as closely as possible,the state water quality criteria (RIDEM 2006). The 9-h trigger duration was motivated by our focus on eventslonger than tidal timescales and chosen to be (a) longer thanhalf the period of the dominant M2 (12.42 h) tidalcomponent of variability, in order that when DO variabilitywas primarily tidal, as occurred during short portions ofsome records, alternating halves of a series of tidal cycleswould not be identified as individual events, and (b) asdifferent from a multiple of half the tidal period as possible,to improve the accuracy of the event start and end timesassigned by the MWT algorithm (Codiga 2008). Resultsusing trigger durations of 7 or 11 h differ in minor waysthat do not affect our conclusions.

Table 2 Synopsis of input parameters for the moving window trigger (MWT) algorithm; input parameters are provided to the algorithm togetherwith a DO time series of arbitrary fixed time step and marked missing values

Parameter Meaning Valuea in this paper

Threshold Events are identified as groups of values that are all or mostly(at least 50%) below the threshold

2.9, 4.8, 1.4 mg O2 l−1

Minimum event duration Shorter events are ignored 1 day

Trigger duration Duration of values below/above the threshold that causes (“triggers”)event start/end to be identified

9 h

For complete details, see Codiga (2008)aWe use the terms “suboxic,” “hypoxic,” and “severely hypoxic” when referring to [O2] in the ranges 4.8≥[O2]>2.9, 2.9≥[O2]>1.4, and [O2]≤1.4 mg O2 l

−1 , respectively

Table 3 Synopsis selected output parameters (metrics that characterize each individual event identified) for the moving window trigger (MWT)algorithm

Parameter Meaning Units

Event duration Difference between event end and start times days

Event-mean deficit Average deficit (threshold less DO concentration) over event;higher value more intense hypoxia

mg O2 l−1

Deficit-duration Integrated deficit during event; equivalently, product of event-meandeficit and duration

mg O2 l−1 day

For complete details, see Codiga (2008)

Estuaries and Coasts (2009) 32:621–641 625

The MWT algorithm fills missing-data gaps shorter thanthe trigger duration by linear interpolation. The collectivetotal duration of gaps interpolated was less than 50 h(0.04%) of the 33 time series, and the maximum collectiveinterpolated duration for an individual station year was lessthan 20 h. Gaps longer than the trigger duration are notinterpolated across.

Two key features of the MWT algorithm are demon-strated using T2.9 and the 2006 NP time series (Fig. 2),which exhibits characteristics that typify the data fromother years and other sites. First, events are not terminatedby above-threshold values that persist for less than thetrigger duration. This permits events to be sensiblyidentified without each and every below-/above-thresholdvalue causing an event to start/end, which would result inan unreasonably high number of events with littleecological relevance. Second, the algorithm identifieswhen an event starts/ends adjacent to a missing-dataportion of the time series. Codiga (2008) includes adetailed catalog of plots and tables for all events, relativeto T2.9, from all years and sites.

Metrics used to quantify event characteristics (defined inTable 3) include duration, event-mean deficit, and deficit-duration. Deficit-duration is a mixed measure of severity inthe sense that it increases with both the duration of an eventand its intensity, as measured by its event-mean deficit (the

threshold less the event-mean DO concentration). Twoevents may have quite similar durations yet have verydifferent deficit-durations; for example, in the NP 2006record, events 1 and 2 have durations that differ by lessthan 20% but because event 2 DO values fall only slightlybelow the threshold, its deficit-duration is less than 25%that of event 1 (Fig. 2).

State Regulatory Metric

State of Rhode Island saltwater DO regulations (RIDEM2006) are cast in terms of days exceedance over a chosenseasonal period. Here, time series data were evaluated usingthe RIDEM-adopted software application called DissolvedOxygen Criteria Software for Rhode Island (DOCS-RI;SAIC 2006) to calculate season-cumulative (Jun. 1 to Sep.30) days exceedance. The DOCS-RI algorithm is specific toNB because it is based on a constrained species suite orsubset of those in the EPA criteria (USEPA 2000),appropriate for assessing impairments. DOCS-RI incorpo-rates both a larval recruitment function and a time to deathmodel that estimates larval mortality that occurs underfluctuating conditions within a day (USEPA 2000; SAIC2006). DOCS-RI days exceedance is thus a similar metricto the MWT deficit-duration with respect to the fact that itreflects both duration and intensity of hypoxia. However,

Fig. 2 Representative DO time series record (NP 2006) to demon-strate behavior and results of MWT algorithm applied to a typicalrecord having missing-data gaps and scatter on timescales shorter thanthe trigger duration. Threshold value 2.9 mg O2 l−1, minimum event

duration 1 day, and trigger duration 9 h. Horizontal dashed line is thethreshold value. Asterisk indicates that event 1 begins following amissing-data gap

626 Estuaries and Coasts (2009) 32:621–641

DOCS-RI differs from MWT in that it incorporatesdependence on the nonlinear biological response of specificspecies to low-DO conditions.

Spatial Synchronicity

Spatial synchronicity is the extent to which hypoxic eventsat one location occur simultaneously to hypoxic events at aseparate location. The index used to quantify spatialsynchronicity was the fractional overlap (FO) of timeintervals during which events occur at two stations. FO isthe unitless ratio between (a) the cumulative duration whenevents (MWT relative to T2.9) occupied both stations A andB simultaneously and (b) the cumulative duration of eventsat station A or the cumulative duration of events at stationB, whichever is smaller. The upper limit for FO is 1,representing complete or full spatial synchronicity, whenevents at one station only occur during events at the otherstation; the lower limit for FO is 0, a completelyasynchronous condition for which events at one stationnever occur during events at the other station (Fig. 3). Theextent to which FO is less than 1 is primarily a measure ofreduced overlap in event timing, rather than indicating thetwo stations have events of different durations thatnonetheless overlap. This is demonstrated by the fact that,even for two stations having markedly different eventdurations, for example because at one station events beginlater and end earlier than at the other (B1 in Fig. 3), there isfull spatial synchronicity (FO=1). FO was calculated usingmultiple years’ observations, as necessitated to ensure anadequately high number of events at both stations. All stationpairs were treated that met the data availability criterion: atleast three summers of coincident sampling, during which atotal of at least five events occurred at each station.

Exploratory Analysis of Factors Driving Event Variability

Multiple linear regression (MLR) was used to assess therelative importance of a range of biological and physicalparameters in accounting for variability in hypoxic eventseverity. The dependent (output/response) variable was the

MWT deficit-duration of fully sampled (not begun nor endedby a missing-data gap) hypoxic events relative to T2.9,natural-log transformed to normalize its skewed distribution.The MLR was carried out once using all events from all yearsat the three sites (BR, CP, and NP) in the UB group of stationscollectively, in order to have an adequately high number ofevents (n=39). It was carried out a second time using allevents from all years at the GB site (n=27) becausecharacteristics of events there differ appreciably from thoseat other sites, as described below. Other stations had too fewevents to be treated.

Independent (input/predictor) variables were calculatedfrom seven parameters (see “Introduction”): river flow,chlorophyll concentration, temperature, density stratifica-tion, tidal range, low-passed Providence–Newport sea leveldifference, and northeastward wind. River flow was thesum of daily values (e.g., USGS 2004) from the five mainrivers (Blackstone, Pawtuxet, Ten Mile, Moshassuck, andWoonasquatucket; Fig. 1). Chlorophyll, temperature, andstratification (defined as deep density less shallow density)were from the fixed-site 15-min resolution records(NBFSMN 2007; near surface for chlorophyll, near bottomfor temperature, and both for stratification) at the stationwhere each hypoxic event occurred. Sea level data werehourly observations from the National Atmospheric andOceanic Administration (NOAA) stations (tidesandcurrents.noaa.gov) at Providence and Newport (stars, Fig. 1). Tidalrange was calculated as the daily average of the differencesbetween higher high tide and the succeeding lower low tideusing the Newport station, which for this purpose isrepresentative of bay-wide conditions. The Providence–Newport sea level difference was calculated after applica-tion of the inverse-barometer correction (using hourlysurface air pressure measured at each station) and removalof the tidal component with a low-pass (25-h half-widthtriangle-weight running mean) to each individual record.Winds are 3-h resolution North American RegionalReanalysis data-assimilative operational meteorologicalmodel hindcasts (Mesinger et al. 2006), which comparewell with local wind records (tidesandcurrents.noaa.gov)that were not used due to temporal coverage gaps.

time Station A

Station B1 FO=1

Station B2 FO=0.5

Station B3 FO=0

Fig. 3 Schematic to illustrate definition of fractional overlap index(FO) used to quantify spatial synchronicity. Each rectangle indicates ahypoxic event. Events at stations B1, B2, and B3 are shaded wherethey overlap with events at station A. Between station A and station

B1, the fractional overlap takes its maximum value FO=1.0,indicating complete/full spatial synchronicity for this station pair.For station A and station B2, FO=0.5. There is a complete lack ofspatial synchronicity (FO=0) between station A and station B3

Estuaries and Coasts (2009) 32:621–641 627

Event characteristics can be influenced by the drivingparameters either during the event or prior to it by a leadtime of hours to several days. To incorporate the potentialinfluence of lead times that span this range, threeindependent variables for the MLR were calculated fromeach of the above seven parameter time series by averagingavailable values over different time intervals: during thehypoxic event (“zero lead”) and during the 2-day (“2-daylead”) and 5-day (“5-day lead”) intervals immediatelypreceding the hypoxic event start time.

The forward stepwise approach was used (MatlabR2008b) to maximize adjusted R2 and exclude independentvariables with p>0.05. Relative importance of independentvariables included in the model was based on rank ofstandard partial regression coefficient magnitudes.

Interannual Variability

The relationship between interannual variability in hypoxiaand in the seven parameters treated by the MLR wasinvestigated. The calculation was limited to stations forwhich all years’ (n=6) sampling were available, BR andNP, and hence applies only to hypoxia in the Upper Bayregion. To gauge the annual severity of hypoxia, we usedan index for seasonal hypoxia ISH (mg O2 l−1) defined asthe season-cumulative deficit-duration relative to T2.9,normalized by the days sampled June to September,averaged across the two stations. Higher values of theindex correspond to more severe hypoxia. For each of theseven parameters, interannual variations were first quanti-fied using the mean of all available values during the entirehypoxia season (June to September). Next, the means werecalculated using individual months (May to September), inrecognition that season-cumulative hypoxia severity maydepend primarily on late spring or early summer conditions;this effectively includes leads of one or more months, asappropriate in contrast to the 2- and 5-day leads in the MLRanalysis (described above) for individual-event variability.Kendall’s tau correlations with p<0.05 were used toidentify significant association of the seasonal hypoxiaindex with the entire-season mean of each of the sevenparameters and with the individual-month means.

Results

Hypoxic Event Characteristics

We first describe events determined using MWT relative toT2.9, which include hypoxic conditions ([O2]<T2.9) or bothseverely hypoxic ([O2]<T1.4) and hypoxic conditions. Eachindividual event from all stations and all years is depictedon a timeline as a rectangle (Fig. 4): the deficit-duration

corresponds to the rectangle area, with the duration andevent-mean deficit represented by the rectangle width andheight respectively. Table 4 lists event statistics for eachstation year: the minimum, mean, and maximum values ofduration, event-mean deficit, and deficit-duration. A groupof bar charts arranged geographically by station (Fig. 5)illuminates spatial patterns by presenting deficit-duration ona season-cumulative basis (total bar lengths), as well ascontributions of individual events (divisions within bars),for all years. Events not fully sampled because they start/end adjacent to missing data are included in Fig. 4 (markedby asterisks) and Fig. 5; as appropriate for statisticalcompilation, they are omitted from Table 4.

A prominent feature of NB hypoxia is temporalintermittency at any given station, within any given year.Events last from a day to a few weeks, conditions denotedperiodic by Diaz and Rosenberg (2008) as discussed above.The number of fully sampled events at an individual stationranged from 0 to 11 in a single season (Table 4). During themost recent 3 years, events at GB were the most numerous,the shortest (typical duration 2 to 3 days) and the mostintense (typical event-mean deficit about 1 to 2 mg O2 l

−1),with typical mean deficit-duration of about 2 to 4 mg O2 l

−1

day.Stations BR, CP, NP (the UB group), and MV (northern

WP) experienced the highest numbers of events followingGB. Based on 6 years of sampling, BR and NP typicallyhad three to four events per summer, each with a durationof about 3 to 5 days, with event-mean deficit in the range of0.3 to 0.9 mg O2 l

−1, and deficit-durations of typically 2 to4 mg O2 l

−1 d. Events at CP (located between BR and NP)based on 3 years’ sampling were comparable; relative to BRand NP, the number of events was in the lower range;durations were similar, and event-mean deficits and deficit-durations were in the lower range. At MV, hypoxia wassevere relative to BR and NP; events there were typicallylonger (mean duration of 10.7 days in 2006) and moreintense (event-mean deficit of typically about 0.5 to 1.5 mgO2 l−1) with correspondingly higher deficit-durations(a 2006 event at MV had the highest deficit-duration,26.5mgO2 l

−1 day, in any year at any station other than GB).Stations farther south and east (QP in WP; PP and TW in

EP; MH), sampled in the later 2 to 3 years only (Table 1),experienced fewer events (typically zero to two persummer). No events were observed at TW in any of the4 years sampled there.

Season-cumulative deficit-durations (Fig. 5) were high-est at GB (reaching 40 to 50 mg O2 l−1 day), followed byMV (up to 39 mg O2 l

−1 day) and BR and NP (up to 20 to30 mg O2 l−1 day). Interannual variability was prominent(as discussed in more detail below) and generally consistentacross all sites, with the exception of GB where it was lesspronounced.

628 Estuaries and Coasts (2009) 32:621–641

Fig. 4 Timeline showing MWTevents (including their durations, event-mean deficits, and deficit-durations) relative to 2.9 mg O2 l−1, andsampling coverage, for all stations and all years. Each hypoxic event isrepresented as a rectangle: deficit-duration is represented as area; eventduration is represented as width (horizontal scale at bottom); event-

mean deficit is represented as height (vertical scale at top). Heavy blacklines indicate sampling coverage and show missing-data gaps; asterisksindicate where an event started or ended during a period of missing data.(Events in Fig. 2 correspond to NP 2006 row here)

Estuaries and Coasts (2009) 32:621–641 629

Suboxic Event Characteristics

Events that include suboxic ([O2]<T4.8) conditions, inaddition to hypoxic and severely hypoxic conditions, weredetermined by MWT relative to T4.8 (Fig. 6, Table 5).Multiple events occurred at every station in everysampled year except for TW in 2004 and 2005, and QPin 2004. Due to the higher threshold, events includingsuboxic conditions were more frequent and longer andhad higher deficit-duration compared to events relative toT2.9 as described in the previous subsection. Higherdurations were particularly pronounced at the UB groupof stations, with peak values (mean 15.3 days; maximum43.2 days) seen at BR in 2001. General patterns ofintermittency and geographic variability paralleled thosedescribed in the previous subsection. For example, eventsat GB were most numerous (12 to 18 per year) and hadtypical durations (about 3 to 4 days) near the low end ofthe observed ranges. However, season-cumulative deficit

durations relative to T4.8 at GB did not exceed those in theUB group to the same degree as was true for eventsrelative to T2.9.

Severely Hypoxic Event Characteristics

Severely hypoxic event ([O2]<T1.4) characteristics werecalculated by MWT as above but with T1.4 (Fig. 7,Table 6). Events were rare except at GB where there werefour, one, three, and five events in years 2003 to 2006,respectively. There were a total of three events betweenBR and NP in all 6 years, and none in the 3 years’sampling at CP. In 2006, the MV site had a relatively largenumber of events and a high season-cumulative deficitduration, each comparable to conditions at GB. At PP,there was one short event in 2006 with low event-meandeficit and low deficit-duration, and at southern andeastern stations (QP, TW, and MH) there were no eventsin any year sampled.

Table 4 Statistics of MWT hypoxic events (threshold T2.9=2.9 mg O2 l−1, minimum event duration 1 day, trigger duration 9 h)

Year Station Numberof events

Duration [day] Event-mean deficit [mg O2 l−1] Deficit-duration [mg O2 l

−1 day]

Min. Mean Max. Min. Mean Max Min. Mean Max.

2001 BR 7 1.6 4.8 17.1 0.2 0.5 1.0 0.3 3.5 16.4

NP 3 1.3 3.4 5.8 0.3 0.6 0.9 0.4 2.7 5.4

2002 BR 3 2.5 3.9 6.0 0.3 0.7 1.5 0.8 3.5 8.9

NP 2 5.6 6.2 6.9 0.7 0.9 1.0 4.0 5.4 6.8

2003 BR 3 1.9 2.9 4.2 0.3 0.3 0.5 0.7 0.9 1.1

CP 1 3.4 3.4 3.4 0.5 0.5 0.5 1.7 1.7 1.7

NP 6 1.3 4.6 9.1 0.4 0.8 1.4 0.6 4.5 12.4

GB 2 1.1 6.9 12.7 2.1 2.2 2.3 2.5 14.8 27.1

TW: no events

2004 GB 5 1.2 2.3 4.2 0.7 1.0 1.4 1.2 2.3 4.8

BR, NP, MV, PP, TW: no events

2005 BR 1 3.3 3.3 3.3 0.3 0.3 0.3 0.9 0.9 0.9

NP 3 1.3 1.6 1.8 0.4 0.5 0.6 0.6 0.8 1.0

MV 2 3.9 4.5 5.1 0.3 0.5 0.7 1.5 2.0 2.6

GB 9 1.6 2.8 5.1 0.5 1.1 2.2 0.8 3.1 4.8

CP, QP, PP, TW, MH: no events

2006 BR 4 1.5 7.2 11.3 0.5 0.7 0.9 0.8 5.3 9.6

CP 4 1.1 3.3 5.4 0.5 0.6 0.9 0.7 2.3 4.7

NP 2 1.4 3.2 5.0 0.3 0.4 0.4 0.5 1.3 2.2

MV 2 1.4 10.7 20.0 0.5 0.9 1.3 0.7 13.6 26.5

QP 3 1.6 2.5 4.1 0.5 0.7 0.8 0.9 1.8 3.4

PP 4 1.1 2.0 2.8 0.7 1.0 1.3 0.8 2.1 3.4

GB 11 1.2 2.2 4.6 0.6 1.4 2.4 1.2 3.4 7.7

MH 2 2.3 2.5 2.7 0.4 0.5 0.6 1.0 1.3 1.7

TW: no events

Events that start/end adjacent to missing data (asterisks, Fig. 4) have been excluded

630 Estuaries and Coasts (2009) 32:621–641

Days Exceedence of State Regulations

In DOCS-RI results (Fig. 8), GB had the highest season-cumulative exceedences (from 43 to 55 days), followed by

MV, BR, and NP (up to 30 to 40 days). Sites in WP hadmore days exceedence than EP sites, with no daysexceedence at TW in any year. Interannual variabilitywas generally similar across sites except for GB where

Fig. 5 Deficit-durations relative to 2.9 mg O2 l−1 from all stations all

years, displayed to demonstrate season-cumulative results andgeographic distributions. Each vertical section of each bar corre-

sponds to an individual event (see Fig. 4). Values shown at tops ofbars indicate number of days sampled

Estuaries and Coasts (2009) 32:621–641 631

it was weaker. DOCS-RI results thus parallel mostclosely those described above for season-cumulativeMWT deficit-duration relative to T2.9 (Fig. 5). Thesesimilarities suggest that the nonlinear biological response

component of DOCS-RI, not included in the MWTalgorithm, is not of primary importance for characteriz-ing events in NB at these stations. DOCS-RI is,however, integral to the approach taken by state regulators

Fig. 6 Same as Fig. 5 except for deficit-duration relative to 4.8 mg O2 l−1

632 Estuaries and Coasts (2009) 32:621–641

to assess whether seasonal ambient water quality con-ditions in NB are in compliance with the saltwater DOcriterion.

Spatial Synchronicity

The timelines (Fig. 4) make clear that NB hypoxic eventsdo not occur with geographically widespread synchronousonset and retreat. This deviation from full spatial synchro-nicity (FO=1) is quantified by FO values (Table 7) thatrange from 0.39 to 0.93 and average 0.58. The meaning ofFO=0.58 between a station pair is that 42% of the timewhen conditions are hypoxic at the station with the shorter

cumulative duration of hypoxia, the other station is nothypoxic. The highest fractional overlap values werebetween MV and the three stations in the Upper Baygroup (NP, CP, and BR), with a comparably high valuebetween BR and CP. The three lowest fractional overlapvalues are for station pairs that include GB, and acomparably low FO value applies between stations BRand NP.

Event Timescale Variability: MLR with Driving Factors

The MLR model for the UB group of stations (Table 8)captured a small amount of variance (16.7%; p=0.027) in

Table 5 Same as Table 4 except for threshold T4.8=4.8 mg O2 l−1

Year Station Numberof events

Duration [day] Event-mean deficit [mg O2 l−1] Deficit-duration [mg O2 l

−1 day]

Min. Mean Max. Min. Mean Max Min. Mean Max.

2001 BR 6 1.8 15.3 43.2 0.7 1.3 2.0 1.7 25.8 87.1

NP 8 1.2 3.8 13.2 0.3 0.8 1.9 0.4 4.7 24.9

2002 BR 5 1.6 6.4 15.9 0.4 0.8 1.9 0.6 7.6 29.8

NP 5 2.9 8.2 14.6 0.3 1.0 1.8 1.0 10.5 25.9

2003 BR 4 4.5 10.4 21.5 0.2 0.8 1.6 1.1 11.1 34.4

CP 1 3.0 3.0 3.0 0.2 0.2 0.2 0.7 0.7 0.7

NP 2 1.6 3.0 4.4 0.3 0.4 0.4 0.6 1.0 1.4

TW 1 3.6 3.6 3.6 0.7 0.7 0.7 2.6 2.6 2.6

GB 12 1.1 3.2 13.3 0.8 1.9 4.0 1.4 7.8 51.5

2004 BR 8 1.2 3.8 8.6 0.2 0.6 1.2 0.6 2.6 10.3

NP 10 1.3 2.5 4.1 0.4 0.7 1.3 0.6 2.0 4.6

MV 4 1.5 1.9 2.6 0.4 0.7 0.9 0.9 1.4 2.2

PP 5 1.3 2.1 3.3 0.5 0.7 1.0 0.7 1.7 3.2

GB 17 1.3 3.8 9.7 0.6 1.5 2.8 1.5 6.1 19.4

TW: no events

2005 BR 12 1.3 5.6 20.4 0.2 0.7 1.3 0.4 4.9 26.6

CP 8 1.4 7.2 19.5 0.3 0.8 1.3 0.4 6.9 25.4

NP 7 1.7 7.5 20.5 0.5 1.0 1.5 0.8 9.5 29.9

MV 6 1.6 2.9 5.2 0.1 0.7 1.2 0.2 2.4 6.1

PP 8 1.3 3.0 5.5 0.4 0.7 1.2 0.8 2.3 4.5

GB 16 1.4 4.4 13.6 0.5 1.7 3.6 0.9 8.2 29.6

MH 3 2.1 4.2 7.3 0.4 0.6 1.0 1.0 2.5 3.4

QP, TW: no events

2006 BR 6 1.7 11.7 24.1 0.7 1.4 2.3 1.8 20.2 56.3

CP 5 1.8 12.3 32.5 0.2 0.9 1.5 0.5 17.1 50.3

NP 3 1.2 2.6 4.6 0.8 1.2 1.4 1.5 2.7 3.8

MV 5 2.0 8.4 24.0 0.6 1.4 2.9 1.5 17.7 70.1

QP 4 1.1 4.2 10.3 0.5 0.8 1.8 0.6 5.5 18.7

PP 11 1.2 4.2 12.3 0.6 1.2 2.0 0.8 6.0 19.7

TW 2 2.3 6.3 10.3 0.3 0.7 1.0 0.8 5.5 10.2

GB 18 1.4 4.0 11.6 0.5 1.7 3.4 1.0 7.9 30.0

MH 7 1.7 5.2 11.9 0.3 0.7 1.5 0.5 4.7 18.2

Estuaries and Coasts (2009) 32:621–641 633

hypoxic event deficit-duration. The 5-day lead river flowand zero-lead low-pass sea level difference were the onlytwo independent variables meriting inclusion in the model(p<0.05). River flow was the most important independent

variable and covaried positively such that, for higher riverflow, hypoxic events were more severe. The sea leveldifference covaried negatively such that, for increasedProvidence sea level relative to Newport sea level, hypoxic

Fig. 7 Same as Fig. 5 except for deficit-duration relative to 1.4 mg O2 l−1

634 Estuaries and Coasts (2009) 32:621–641

events were less severe. At the GB site, the MLR model(Table 9) captured more variance than for the UB groupthough still a relatively small amount (34.9%; p=0.003);2-day lead chlorophyll and 5-day lead northeastward windwere the only two variables meriting inclusion, withchlorophyll more important, and each covaried positivelywith deficit-duration.

Interannual Variability: Characteristics and Driving Factors

Interannual variability in hypoxia was prominent, withend points illustrated by comparison of 2006 and 2004(MWT events relative to T2.9: Figs. 3 and 5, Table 4). In2006, all sites experience multiple events, except TWwhere no events were observed in any year; in 2004, noevents occurred except at GB. Interannual variability atGB was the least pronounced. In 2003 and later years,when multiple stations were sampled across severaldifferent regions of the bay, a general pattern of interan-nual variations was shared bay-wide: season-cumulativedeficit-durations were higher in 2001, 2003, and 2006 thanin 2004 and 2005. The index for season-cumulativehypoxia, based on the BR and NP stations, was ISH=0.15, 0.11, 0.15, 0.00, 0.01, and 0.17 mg O2 l−1 in years2001 to 2006, respectively; the corresponding ranking ofyears from most to least hypoxic was 2006, then 2001 and2003 tied, followed by 2002, 2005, and 2004. Twovariables correlated significantly (p<0.05) with ISH:June-mean river flow (p=0.006, τ=0.966) and June-meanstratification (p=0.006, τ=0.966).

Discussion

Geographic Patterns, Intermittency, Spatial Synchronicity

Observed geographic variations in suboxic, hypoxic, andseverely hypoxic events are similar and can be understoodin terms of the spatial distribution of nutrient loads andproductivity together with the long-term average baycirculation pattern. Nutrient loading and productivity bothpeak to the north in the urbanized Providence River estuarywhere delivery of nutrients and freshwater rivers (Fig. 1) isconcentrated (e.g., Oviatt 2008). The typical pattern ofresidual nontidal circulation through the bay (Rogers 2008)consists predominantly of oceanic water moving northwardat depth in the East Passage, entering the Providence Riverestuary, then mixing to shallower depths and reversing itscourse southward to exit the bay largely through the WestPassage; as seen in other estuaries (e.g., Codiga and Aurin2007), the lateral (east–west) asymmetry is due to theCoriolis effect. This combination of patterns in productivityand circulation favors hypoxia at stations in the UB groupand northern WP, as compared to sites farther south and/orin EP, as accounts for the observations. Infrequent eventsobserved at MH in an eastern embayment, based on 2 years’sampling (2005 and 2006), are consistent with the bay-widepattern and with the relatively rapid flushing of Mt. HopeBay (MacDonald 2006). Severe hypoxia at the GB siteappears to be associated with the long flushing time of thewestern embayment, due to its shallow depth, lack of riverinputs, and weak exchange with the WP. The possibility

Table 6 Same as Table 4 except for threshold T1.4=1.4 mg O2 l−1

Year Station Numberof events

Duration [day] Event-mean deficit [mg O2 l−1] Deficit-duration [mg O2 l

−1 day]

Min. Mean Max. Min. Mean Max Min. Mean Max.

2001 BR, NP: no events

2002 BR 1 4.1 4.1 4.1 0.4 0.4 0.4 1.6 1.6 1.6

NP: no events

2003 NP 1 4.0 4.0 4.0 0.7 0.7 0.7 2.8 2.8 2.8

GB 4 1.8 4.1 6.5 1.0 1.1 1.4 1.8 4.3 6.5

BR, CP, TW: no events

2004 GB 1 1.7 1.7 1.7 0.4 0.4 0.4 0.7 0.7 0.7

BR, NP, NV, PP, TW: no events

2005 GB 3 1.1 1.3 1.4 0.6 0.7 0.9 0.6 0.9 1.3

BR, CP, NP, MV, QP, PP, TW, MH: no events

2006 NP 1 4.1 4.1 4.1 0.7 0.7 0.7 2.8 2.8 2.8

MV 1 10.6 10.6 10.6 0.6 0.6 0.6 6.1 6.1 6.1

PP 1 1.1 1.1 1.1 0.5 0.5 0.5 0.6 0.6 0.6

GB 5 1.3 2.1 3.1 0.6 0.9 1.2 1.0 2.0 2.9

BR, CP, QP, TW, MH: no events

Estuaries and Coasts (2009) 32:621–641 635

exists that the reason the northern WP site (MV) is subjectto severe hypoxia is that it is influenced by watersoriginating from both the Upper Bay and Greenwich Bay.

We attribute the temporal intermittency and weak spatialsynchronicity of hypoxic events to the complex bay

geometry and the likelihood that hypoxic events in differentregions of the bay are shaped by different influences. Forexample, characteristics of hypoxic events at GB (inparticular, high frequency, short durations, and less-pronounced interannual variability) suggest that the site is

Fig. 8 Season-cumulative DOCS-RI days exceedance, all stations all years. Values shown at tops of bars indicate number of days sampled

636 Estuaries and Coasts (2009) 32:621–641

influenced by a set of processes distinct from those active atupper bay sites. A potentially important driver of geo-graphic variations in processes influencing hypoxia is localhydrodynamics, which substantially modifies the long-termmean circulation described above on timescales similar tohypoxic events and is shaped by river flow events, windfluctuations, and spring–neap cycles in ways that each varystrongly from site to site.

Event Variability and Driving Factors

In the exploratory MLR analyses, variance in event severitycaptured by the model was low (16.7% for UB group) tomoderate (34.9% for GB site). It is possible that processesgoverning event variability are not represented well by theindependent variables treated, despite use of numerousbiological and physical parameters known to influence NBhypoxia, each with an appropriate range of temporal leads.In addition, relationships of hypoxic events to parametersmay be more complex than can be identified well by theMLR method. While further analysis beyond these intro-ductory calculations is clearly warranted, the MLR-selectedindependent variables support self-consistent interpretationsin terms of expected processes, as follows.

For the UB group of sites, MLR model inclusion of5-day lead river flow with positive coefficient is consistentwith the dual role of rivers in delivering both the nutrientsthat fuel algal growth and freshwater that sustains densitystratification. Higher chlorophyll levels and stronger strat-

ification are closely associated with hypoxic events (e.g.,Bergondo et al. 2005), but the fact that neither meritedinclusion in the MLR model indicates that neither covariesas closely with event deficit-duration as do river flow andsea level difference. MLR model inclusion of zero-lagnorth–south sea level difference with negative coefficient isconsistent with wind-driven variations in circulation influ-encing hypoxic events: northward wind stalls the estuarineexchange flow, increases the north–south sea level differ-ence on timescales of hours (Rogers 2008), and enhancesflushing of deep water thus reducing hypoxia severity(Bergondo 2004).

For the GB site, MLR model inclusion of 2-day leadchlorophyll with positive coefficient is consistent withevent deficit-duration increasing with availability of algalmaterial to decay. The GB site is in a western embaymentdistant from the influence of the major rivers. MLR modelinclusion of 5-day lead northeastward wind with positivecoefficient is consistent with the pattern of decreasedembayment flushing in response to these wind conditions,due to gyre-like flows in Greenwich Bay that apparentlylessen its exchange with the rest of the bay (Rogers 2008).

Though not completely conclusive, taken as a whole, theMLR results lend support to the interpretations that (a)circulation-related conditions (river flow, sea level differ-ence, northeastward wind) play prominent roles in regulat-ing hypoxic event characteristics and (b) characteristics ofhypoxia in different regions of the bay are shaped bydifferent processes, as is consistent with the reduced spatialsynchronicity. Finally, the fact that neither MLR modelincluded tidal range suggests that spring–neap cycles arenot prominent in variability of event severity. The relationof spring–neap cycles to event timing described previously(Bergondo et al. 2005) was either not detected by the MLRor possibly only held during the limited number of yearsexamined in their early analysis.

Interannual Variability

Correlations of June-mean river flow and June-meanstratification with the index for season-cumulative hypoxiaseverity were significant, though based on a small numberof years. The similar results for river flow and stratification

Table 9 Same as Table 8 except for GB site

Independent variable Standard partialregression coefficient

t statistic p valuea

Chlorophyll, 2-day lead 0.347 2.72 0.012

Northeastward wind,5-day lead

0.286 2.24 0.035

a Overall model: n=27; adjusted R2 =0.349; F=7.70; p=0.003

Table 7 Fractional overlap (FO) results

BR CP NP MV

GB 0.45 0.60 0.39 0.43

MV 0.78 –a 0.93

NP 0.47 0.51

CP 0.69

a Data availability criteria listed in “Methods” section not met

Table 8 Results of multiple linear regression for variability inhypoxic event deficit-duration at Upper Bay group of sites (BR/CP/NP)

Independent variable Standard partialregression coefficient

t statistic p valuea

River flow, 5-day lead 0.703 2.87 0.008

Sea level difference,zero lead

−0.504 −2.06 0.049

Independent variables included in model are listed in order ofdecreasing magnitude of standard partial regression coefficient. Allother independent variables were excluded (p>0.05) from the modela Overall model: n=39; adjusted R2 =0.167; F=4.12; p=0.027

Estuaries and Coasts (2009) 32:621–641 637

are an indication that on monthly timescales stratificationvariations at BR and NP are closely regulated by river flowvariations. Given that hypoxia peaks in July and August,likely in association with peak annual temperatures thatenhance respiration rates, the correlation with June-meanconditions implies a 1- to 2-month response timescale forhypoxia. This is loosely consistent with 10- to 40-dayestimates of water residence time bay-wide (Pilson 1985).A similar relation between late spring runoff and summerhypoxia severity has been identified in Chesapeake Bay(Hagy et al. 2004). Although our hypoxia index wascalculated using the BR and NP stations so it does notreflect variability in bay-wide spatial extent of hypoxia,independent datasets with broader geographic coverage(CTDO surveys, e.g., Deacutis et al. (2006); towed-bodysurveys, e.g., Melrose et al. (2007)) indicate that variabilityin spatial extent parallels that of the index (e.g., high spatialextent in 2006 and low spatial extent in 2004). On thisbasis, June-mean river flow appears to be a potentiallyuseful index for the severity of subsequent July–Augusthypoxia as well as its spatial extent.

Implications for Benthic Ecology

Prior studies of the estuarine Providence and SeekonkRivers at the head of NB document heavily degraded waterquality, including common severe hypoxia, occurrence ofanoxic conditions in the Seekonk River, and vulnerabilityto seasonally persistent hypoxia (Turner 1997). Our find-ings apply outside this area to the south and indicate that, inlarge areas of NB, where hypoxic events are commonlyobserved (Table 4), episodic/periodic hypoxia lasts days toweeks during summer months on a near-annual basis. Suchconditions represent the third phase in the four-phaseprogression towards seasonally persistent hypoxia observedin many other systems as described by Diaz and Rosenberg(2008).

Locations at highest risk for direct mortality in benthiccommunities include western Greenwich Bay, deeperportions of Upper West Passage, and the southern part ofthe Upper Bay. This conclusion is based on resultspresented above using T2.9 (Fig. 5) and T1.4 (Fig. 7), aswell as results of the MWT algorithm applied to all sitesand all years using lower threshold and shorter minimumevent duration values (0.5 mg O2 l−1 and 4 h; triggerduration 2 h) as justified in the “Introduction” (Rosenberget al. 1991; Sagasti et al. 2001; Wu 2002; Person-LeRuyetet al. 2003; Vaquer-Sunyer and Duarte 2008). Such moreintense and shorter events occurred only at GB (18, 3, and11 events in 2003, 2005, and 2006, respectively), MV(eight events in 2006), and NP (two and one event, in 2003and 2006, respectively). Our interpretations are conserva-tive in the sense that they are based on measurements at

least 0.5 m above the sediment–water interface, where theDO gradient can be steep, so they probably overestimateDO concentrations experienced by benthic species.

In these regions (containing the GB, MV, and NP sites),lethal levels for most benthic organisms are almost certainlyreached for at least 4-h durations, and for much of theseason concentrations likely cycle between lethal and justabove lethal concentrations. Moderately sensitive sessileorganisms have the greatest risk of mortality (Vaquer-Sunyer and Duarte 2008); highly tolerant species wouldlikely require DO to fall below 0.5 mg O2 l−1 for longerdurations (Sagasti et al. 2001). For many fish, macro-crustaceans, and echinoderms, such conditions are suffi-cient to cause mortality; however, due to their mobility andthe reduced spatial synchronicity we have documented(Table 7), the likely impact is a combination of mortalityand behavioral exclusion (Wannamaker and Rice 2000; Ebyand Crowder 2002; Bell and Eggleston 2005; Hazen et al.2006; Diaz and Rosenberg 2008). While it is recognizedthat, for many reasons, fish kills are not particularly goodindicators of hypoxia impacts, it is relevant to note that theGB site is within 100 m of the central “kill zone” of thelargest fish kill in over 50 years in August 2003 (RIDEM2003).

The Greenwich Bay and Mount View areas havesignificant densities (e.g., RIDEM 2008) of the highlytolerant hard-shelled clam (Mercenaria mercenaria), acommercially important resource. The success of thesepopulations may be partially due to predation refuge sinceoxygen conditions reach levels that exclude typical bivalvepredators for significant periods of the summer (Altieri2008 and Table 4). However, other important filter feederswithin this functional group, such as blue mussels (Mytilusedulis), have experienced significant population losses inthis area (Altieri and Witman 2006; Altieri 2008), suggest-ing that shifts in communities are sensitive to relativetolerance of species.

At all remaining stations, while there is some risk ofdirect mortality from rarer severe events, based on ourresults for hypoxic (Fig. 5) and suboxic (Fig. 6) conditions,we expect the dominant influence of hypoxia to be indirecteffects. These include suboptimal growth or functionality,habitat compression, and shifts in predator–prey balances.Hypoxic conditions in the Upper Bay impair filter feedingby blue mussels (Altieri and Witman 2006), and juvenileflounder species common to NB (Pseudopleuronectesamericanus and Paralichthys dentatus) exhibit significantdecreases in growth at oxygen levels common to NB (Menget al. 2002, 2008; Eby et al. 2005; Stierhoff et al. 2006). Anexample of habitat compression is movement to shallowareas during events by mobile species (Deacutis et al. 2006;Deacutis 2008; Diaz and Rosenberg 2008). Shifts inpredator–prey balances associated with such habitat

638 Estuaries and Coasts (2009) 32:621–641

compression are likely, as the shallow peripheries of theUpper Bay and southern Providence River areas are knownto be important habitat for juvenile fish (Meng and Powell1999; Meng et al. 2005). Suboxic events, with DO levelsthat while not hypoxic nonetheless commonly causeindirect effects (Vaquer-Sunyer and Duarte 2008), wereobserved at every site in every sampled year with fewexceptions. We conclude that indirect effects are very likelycausing significant impacts across broad areas of the bay.

Acknowledgements Funding from the NOAA Coastal HypoxiaResearch Program (CHRP; Grant NA05NOS4781201) and fromRIDEM-OWR is gratefully acknowledged. This is CHRP Contribu-tion #106. The monitoring network is funded in part by the NOAABay Window Program, EPA Clean Water Act (sections 319 and 106),Narragansett Bay Commission (NBC), NOAA National EstuaryProgram, and State of Rhode Island; all that contribute areappreciated. We thank Sherry Poucher for guidance on DOCS-RI;NBC for Bullock Reach data; and Edwin Requintina and ElizabethCrockford for their help collecting and processing data. Finally, wepay special tribute to the late Dana Kester, for his leadership andvision in fostering development of the fixed-site monitoring network.

References

Altieri, A.H. 2008. Dead zones enhance key fisheries species byproviding predation refuge. Ecology 89(10): 2808–2818.doi:10.1890/07-0994.1.

Altieri, A., and J. Witman. 2006. Local extinction of a foundationspecies in a hypoxic estuary: Integrating individuals to ecosystem.Ecology 87: 717–730. doi:10.1890/05-0226.

Bell, G.W., and D.B. Eggleston. 2005. Species-specific avoidanceresponses by blue crabs and fish to chronic and episodic hypoxia.Marine Biology 146(4): 761–770. doi:10.1007/s00227-004-1483-7.

Bergondo, D.L. 2004. Examining the processes controlling watercolumn variability in Narragansett Bay: Time series data andnumerical modeling. Doctoral dissertation, Graduate School ofOceanography, University of Rhode Island, 136 pp.

Bergondo, D.L., D.R. Kester, H.E. Stoffel, and W.L. Woods. 2005.Time-series observations during the low sub-surface oxygenevents in Narragansett Bay during summer 2001. MarineChemistry 97: 90–103. doi:10.1016/j.marchem.2005. 01.006.

Codiga, D.L. 2008. A moving window trigger algorithm to identifyand characterize hypoxic events using time series observations,with application to Narragansett Bay. GSO Technical Report2008-01, Graduate School of Oceanography, University ofRhode Island, Narragansett, RI, 110 pp. ftp://po.gso.uri.edu/pub/downloads/codiga/chrp/mwt/TechRptMWTCodigaMar08.pdf.

Codiga, D.L., and D.A. Aurin. 2007. Residual circulation in easternLong Island Sound: Observed transverse-vertical structure andexchange transport. Continental Shelf Research 27: 103–116.doi:10.1016/j.csr.2006.09.001.

Craig, J.K., and L.B. Crowder. 2005. Hypoxia-induced habitat shiftsand energetic consequences in Atlantic croaker and brownshrimp on the Gulf of Mexico shelf. Marine Ecology ProgressSeries 294: 79–94. doi:10.3354/meps294079.

Dauer, D.M., and R.W. Alden. 1995. Long-term trends in themacrobenthos and water quality of the lower Chesapeake Bay(1985–1991). Marine Pollution Bulletin 30: 840–850.doi:10.1016/0025-326X(95)00091-Z.

Dauer, D.M., J.A. Ranasinghe, and A.J. Rodi. 1992. Effects of lowdissolved oxygen levels on the macrobenthos of the lowerChesapeake Bay. Estuaries 15: 384–391. doi:10.2307/1352785.

Deacutis, C.F. 2008. Nutrient impacts in upper Narragansett Bay. InScience for Ecosystem-Based Management: Narragansett Bay inthe 21st Century, ed. A. Desbonnet, and B.A. Costa-Pierce, 349–381. New York: Springer.

Deacutis, C.F. 1999. Nutrient impacts and signs of problems inNarragansett Bay. In: Kerr, M. (Ed). Proceedings of a Workshopon Nutrient Removal from Wastewater Treatment Facilities:Nutrients and Narragansett Bay, Rhode Island Sea Grant,Narragansett, RI, pp. 7–23.

Deacutis, C., D. Murray, W. Prell, E. Saarman, and L. Korhun. 2006.Hypoxia in the upper half of Narragansett Bay, RI, during August2001 and 2002. Northeastern Naturalist 13: 173–198.doi:10.1656/1092-6194(2006)13[173:HITUHO]2.0.CO;2.

Diaz, R.J. 2001. Overview of hypoxia around the world. Journal ofEnvironmental Quality 30: 275–281.

Diaz, R.J., and R. Rosenberg. 1995. Marine benthic hypoxia: Areview of its ecological effects and the behavioral responses ofbenthic macrofauna. Oceanography and Marine Biology: AnAnnual review 33: 245–303.

Diaz, R.J., and R. Rosenberg. 2008. Spreading dead zones andconsequences for marine ecosystems. Science 321(5891): 926–929. doi:10.1126/science.1156401.

Diaz, R.J., M. Solan, and R.M. Valente. 2004. A review of approachesfor classifying benthic habitats and evaluating habitat quality.Journal of Environmental Management 73: 165–181.doi:10.1016/j.jenvman.2004.06.004.

Eby, L.A., and L.B. Crowder. 2002. Hypoxia-based habitat compres-sion in the Neuse River Estuary: Context-dependent shifts inbehavioral avoidance thresholds. Canadian Journal of Fisheriesand Aquatic Sciences 59(6): 952–965. doi:10.1139/f02-067.

Eby, L.A., L.B. Crowder, C.M. McMclellan, C.H. Peterson, and M.J.Powers. 2005. Habitat degradation from intermittent hypoxia:Impacts on demersal fishes. Marine Ecology Progress Series291: 249–261. doi:10.3354/meps291249.

Eggleston, D.B., G.W. Bell, and A.D. Amavisca. 2005. Interactiveeffects of episodic hypoxia and cannibalism on juvenile blue crabmortality. Journal of Experimental Marine Biology and Ecology325(1): 18–26. doi:10.1016/j.jembe.2005.04.023.

Gray, J.S., R. Shiu-sun Wu, and Y.Y. Or. 2002. Effects of hypoxia andorganic enrichment on the coastal marine environment. MarineEcology Progress Series 238: 249–279. doi:10.3354/meps238249.

Hagy, J.D., W.R. Boynton, C.W. Keefe, and K.V. Wood. 2004.Hypoxia in Chesapeake Bay, 1950–2001: Long-term change inrelation to nutrient loading and river flow. Estuaries 27: 634–658. doi:10.1007/BF02907650.

Hazen, E.L., K. Craig, C. Good, and L. Crowder. 2006. Fine-scale verticaldistribution of acoustic biomass in hypoxic waters on the Gulf ofMexico shelf. Eos Transactions, American Geophysical Union:Ocean Science Meeting Supplement 87(36), Abstract OS46J-09.

Jewett, E.B., A.H. Hines, and G.M. Ruiz. 2005. Epifaunal disturbanceby periodic low levels of dissolved oxygen: Native vs. invasivespecies response. Marine Ecology Progress Series 304: 31–44.doi:10.3354/meps304031.

MacDonald, D.G. 2006. Estimating an estuarine mixing and exchangeratio from boundary data with application to Mt. Hope Bay(Massachusetts/Rhode Island). Estuarine, Coastal and ShelfScience 70: 326–332. doi:10.1016/j.ecss.2006.06.025.

Melrose, D.C., C.A. Oviatt, and M.S. Berman. 2007. Hypoxic eventsin Narragansett Bay, Rhode Island, during the summer of 2001.Estuaries and Coasts 30: 47–53.

Meng, L., and J.C. Powell. 1999. Linking juvenile fish and theirhabitats: An example from Narragansett Bay, Rhode Island.Estuaries 22: 905–916. doi:10.2307/1353070.

Estuaries and Coasts (2009) 32:621–641 639

Meng, L., C.D. Orphanides, and J.C. Powell. 2002. Use of a fishindex to assess habitat quality in Narragansett Bay, Rhode Island.Transactions of the American Fisheries Society 131: 731–742.doi:10.1577/1548-8659(2002)131&lt;0731:UOAFIT&gt;2.0.CO;2.

Meng, L., G. Cicchetti, and S. Raciti. 2005. Relationships betweenjuvenile winter flounder and multiple-scale habitat variation inNarragansett Bay, Rhode Island. Transactions of the AmericanFisheries Society 134: 1509–1519. doi:10.1577/T04-167.1.

Meng, L., D.L. Taylor, J. Serbst, and J.C. Powell. 2008. Assessing habitatquality of Mount Hope Bay and Narragansett Bay using growth,RNA:DNA, and feeding habits of caged juvenile winter flounder(Pseudopleuronectes americanus Walbaum). NortheasternNaturalist 15(1): 35–56. doi:10.1656/1092-6194(2008)15[35:AHQOMH]2.0.CO;2.

Mesinger, F., G. DiMego, E. Kalnay, K. Mitchell, P.C. Shafran, W.Ebisuzaki, D. Jovic, J. Woollen, E. Rogers, E.H. Berbery, M.B.Ek, Y. Fan, R. Grumbine, W. Higgins, H. Li, Y. Lin, G. Manikin,D. Parrish, and W. Shi. 2006. North American RegionalReanalysis. Bulletin of the American Meteorological Society 87(3): 343–360. doi:10.1175/BAMS-87-3-343.

Montagna, P., and C. Ritter. 2006. Direct and indirect effects ofhypoxia on benthos in Corpus Christi Bay, Texas, USA. Journalof Experimental Marine Biology and Ecology 330: 119–131.doi:10.1016/j.jembe.2005.12.021.

NBFSMN 2004. 2004 datasets. Rhode Island Department of Envi-ronmental Management, Office of Water Resources. www.dem.ri.gov/bart.

NBFSMN 2005. 2005 datasets. Rhode Island Department of Envi-ronmental Management, Office of Water Resources. www.dem.ri.gov/bart.

NBFSMN 2006. 2006 datasets. Rhode Island Department of Envi-ronmental Management, Office of Water Resources. www.dem.ri.gov/bart.

NBFSMN 2007. Narragansett Bay fixed site monitoring network.http://www.dem.ri.gov/bart/stations.htm.

Niklitschek, E.J., and D.H. Secor. 2005. Modeling spatial andtemporal variation of suitable nursery habitats for Atlanticsturgeon in the Chesapeake Bay. Estuarine, Coastal and ShelfScience 64(1): 135–148. doi:10.1016/j.ecss.2005.02.012.

Nixon, S.W., B.A. Buckley, S.L. Granger, L.A. Harris, A.J. Oczkowski,R.W. Fulweiler, and L.W. Cole. 2008. Nitrogen and phosphorousinputs to Narragansett Bay: Past, present, and future. In Science forEcosystem-Based Management: Narragansett Bay in the 21stCentury, ed. A. Desbonnet, and B.A. Costa-Pierce, 101–175. NewYork: Springer.

Olsen, S., and V. Lee. 1979. A summary and preliminary evaluation ofdata pertaining to the water quality of upper Narragansett Bay.URI Coastal Resources Center Report to EPA Region 1.Narragansett, RI In.

Oviatt, C. 2008. Impacts of nutrients on Narragansett Bay productivity: Agradient approach. In Science for Ecosystem-Based Management:Narragansett Bay in the 21st Century, ed. A. Desbonnet, and B.A.Costa-Pierce, 523–543. New York: Springer.

Oviatt, C.A., M.E.Q. Pilson, S.W. Nixon, J.B. Frithsen, D.T. Rudnick,J.R. Kelly, J.F. Grassle, and J.P. Grassle. 1984. Recovery of apolluted estuarine system: A mesocosm experiment. MarineEcology Progress Series 16: 203–217. doi:10.3354/meps016203.

Oviatt, C., A. Keller, and L. Reed. 2002. Annual primary productionin Narragansett Bay with no bay-wide winter–spring phytoplank-ton bloom. Estuarine, Coastal and Shelf Science 54: 1013–1026.doi:10.1006/ecss.2001.0872.

Pearson, T.H., and R. Rosenberg. 1978. Macrobenthic succession inrelation to organic enrichment and pollution of the marineenvironment. Oceanography and Marine Biology: An AnnualReview 16: 229–311.

Person-LeRuyet, J., A. Lacut, N. Le Bayon, A. Le Roux, K.Pichavant, and L. Quemener. 2003. Effects of repeated hypoxicshocks on growth and metabolism of turbot juveniles. AquaticLiving Resources 16(1): 25–34. doi:10.1016/S0990-7440(02)00002-5.

Pihl, L., S.P. Baden, and R.J. Diaz. 1991. Effects of periodic hypoxiaon distribution of demersal fish and crustaceans. Marine Biology108(3): 349–360. doi:10.1007/BF01313644.

Pihl, L., S.P. Baden, R.J. Diaz, and L.C. Schaffner. 1992. Hypoxia-inducedstructural changes in the diet of bottom-feeding fish and Crustacea.Marine Biology 112: 349–361. doi:10.1007/BF00356279.

Pilson, M.E.Q. 1985. On the residence time of water in NarragansettBay. Estuaries 8: 2–14. doi:10.2307/1352116.

Powers, S.P., C.H. Peterson, R.R. Christian, E. Sullivan, M.J. Powers,M.J. Bishop, and C.P. Buzzelli. 2005. Effects of eutrophicationon bottom habitat and prey resources of demersal fishes. MarineEcology Progress Series 302: 233–243. doi:10.3354/meps302233.

Prell, W., E. Saarman, D. Murray, and C.F. Deacutis. 2004. Summer-season, nighttime surveys of dissolved oxygen in upper NarragansettBay (1999–2003). http://www.geo.brown.edu/georesearch/insomniacs.

RIDEM. 2003. The Greenwich Bay fish kill, August 2003: Causes,impacts and responses. Providence, RI. 32 pp. http://www.dem.ri.gov/pubs/fishkill.pdf.

RIDEM. 2006. Water quality regulations. Office of Water Resources,Department of Environmental Management, State of RhodeIsland and Providence Plantations. http://www.dem.ri.gov/programs/benviron/water/quality/surfwq/index.htm.

RIDEM. 2007. Quality assurance project plan: Narragansett Bayfixed-site water quality monitoring network seasonal monitoring.Office of Water Resources, Department of EnvironmentalManagement, State of Rhode Island and Providence Plantations.37 pp. http://www.dem.ri.gov/pubs/data.htm#quapps.

RIDEM. 2008. Management plan for the shellfish fishery sector. Stateof Rhode Island and Providence Plantations, Department ofEnvironmental Management. Providence, RI. 21 pp. http://www.dem.ri.gov/pubs/regs/regs/fishwild/mpshell.pdf.

Rogers, J.M. 2008. Circulation and transport in the heart ofNarragansett Bay. M.S. Thesis, University of Rhode Island,Graduate School of Oceanography, 95 pp.

Rosenberg, R., B. Hellman, and B. Johansson. 1991. Hypoxictolerance of marine benthic fauna. Marine Ecology ProgressSeries 70: 127–131. doi:10.3354/meps079127.

Saarman, E., W. Prell, D. Murray, and C. Deacutis. 2008. Summerbottom water dissolved oxygen in upper Narragansett Bay. InScience of Ecosystem-Based Management: Narragansett Bay inthe 21st Century, ed. A. Desbonnet, and B.A. Costa-Pierce, 325–347. New York: Springer.

Sagasti, A., L.C. Schaffner, and J.E. Duffy. 2001. Effects of periodichypoxia onmortality, feeding and predation in an estuarine epifaunalcommunity. Journal of Experimental Marine Biology and Ecology258: 257–283. doi:10.1016/S0022-0981(01)00220-9.

SAIC. 2006. Development and implementation of saltwater aquatic lifestandards for dissolved oxygen for rhode island waters. ScienceApplications International Corporation, GSA Contract GS-10F-0076J, Data Report. 33 pp. http://www.ci.uri.edu/Projects/RI-Monitoring/Docs/SaltwaterDOStandards_March52009.pdf.

Shimps, E.L., J.A. Rice, and J.A. Osborne. 2005. Hypoxia tolerance intwo juvenile estuary-dependent fishes. Journal of ExperimentalMarine Biology and Ecology 325(2): 146–162. doi:10.1016/j.jembe.2005.04.026.

Small, T.D. 2008. Data management manual, System-Wide MonitoringProgram (SWMP), National Estuarine Research Reserve (NERR),version 6.1. Central Data Management Office (CDMO), NERR,NOAA. Baruch Marine Laboratory, University of South Carolina,

640 Estuaries and Coasts (2009) 32:621–641

Georgetown, SC. 255 pp. http://cdmo.baruch.sc.edu/Docs/SWMP_Data_management_Manual/CDMOManualv6.1_final.pdf.

Stierhoff, K.L., T.E. Targett, and K. Miller. 2006. Ecophysiologicalresponses of juvenile summer and winter flounder to hypoxia:experimental and modeling analyses of effects on estuarinenursery quality. Marine Ecology Progress Series 325: 255–266.doi:10.3354/meps325255.

Stoffel, H.E. 2003. Corrected data sets, NBFSMN 2003. Unpublished.University of Rhode Island, Graduate School of Oceanography.www.narrbay.org.

Taylor, D.L., and D.B. Eggleston. 2000. Effects of hypoxia on anestuarine predator-prey interaction: Foraging behavior andmutual interference in blue crab, Callinectes sapidus, and theinfaunal clam prey, Mya arenaria. Marine Ecology ProgressSeries 196: 221–237. doi:10.3354/meps196221.

Turner, C. 1997. Development of a total maximum daily loading fornutrients in the Providence and Seekonk Rivers: Preliminaryresults and development of a modeling approach. RI Departmentof Environmental Management. Providence. RI. 50 pp.

USEPA. 2000. Ambient aquatic life water quality criteria for dissolvedoxygen (saltwater): Cape Cod to Cape Hatteras. United StatesEnvironmental Protection Agency, Office of Water. Report EPA-822-R-00-012. 49 pp.

USGS. 2004. Water resources data for Massachusetts and Rhode Island,water year 2004. United States Geological Survey, Department ofthe Interior. Water-Data Report MA-RI-04-1. http://pubs.usgs.gov/wdr/2004/wdr-ma-ri-04-1/pdf/ADR200 4WY.pdf.

Vaquer-Sunyer, R., and C.M. Duarte. 2008. Thresholds of hypoxia formarine biodiversity.Proceedings of the National Academy of Sciencesof the USA 105: 15452–15457. doi:10.1073/pnas.0803833105.

Wannamaker, C.M., and J.A. Rice. 2000. Effects of hypoxia onmovements and behavior of selected estuarine organisms fromthe southeastern United States. Journal of Experimental MarineBiology and Ecology 249: 145–163. doi:10.1016/S0022-0981(00)00160-X.

Wu, R.S.S. 2002. Hypoxia: From molecular responses to ecosystemresponses. Marine Pollution Bulletin 45: 35–45. doi:10.1016/S0025-326X(02)00061-9.

Estuaries and Coasts (2009) 32:621–641 641