Quantifying Metabolically Driven pH and Oxygen Fluctuationsin US Nearshore Habitats at Diel to Interannual Time Scales

Hannes Baumann1& Erik M. Smith2

Received: 29 May 2017 /Revised: 5 September 2017 /Accepted: 5 September 2017# Coastal and Estuarine Research Federation 2017

Abstract We compiled and examined 15 years (2002–2016)of high-frequency monitoring data from the NationalEstuarine Research Reserve System (NERRS) to characterizediel to interannual variability of pH and dissolved oxygen(DO, % saturation) across 16 diverse, shallow-water habitatsalong the US Atlantic, Gulf ofMexico, Caribbean, and Pacificcoasts. We asked whether these systems exhibit a commonpH/DO relationship, whether there were detectable interannu-al trends in temperature, pH, and DO within and across sys-tems, and how pH/DO dynamics would relate to measuredlevels of nutrients and chlorophyll. Our analyses confirmedthat large, metabolically driven, and thus concurrent fluctua-tions of pH and DO are a unifying feature of nearshore habi-tats. Moreover, we derived well-constrained relationships thatpredict (i) monthly mean pH or (ii) mean diel pH fluctuationsacross systems based on habitat mean salinity and (i) meanDO or (ii) mean diel DO fluctuations. This suggests that com-mon metabolic principles drive diel to seasonal pH/DO vari-ations within as well as across a diversity of estuarine envi-ronments. Yearly pH and DO anomalies did not show monot-onous trends over the study period and differed considerably

between sites and regions. However, weekly anomalies ofmeans, diel minima, and diel ranges of pH and DO changedsignificantly over time and were strongly correlated to tem-perature anomalies. These general patterns lend strong empir-ical support to the notion that coastal acidification—in addi-tion to being driven by eutrophication and atmospheric CO2

increases—is exacerbated simply by warming, likely via in-creasing community respiration. Nutrient and chlorophyll dy-namics were inversely related in these shallow, well-mixedsystems, but higher nutrient levels were still associated withlower pH and lower DO levels in most, but not all, systems.Our analyses emphasize the particular dynamics of nearshorehabitats and the critical importance of NERRS and its system-wide monitoring program.

Keywords Eutrophication . Ecosystemmetabolism .Oxygensaturation . Ocean acidification .Multistressor . Climatechange . National Estuarine Research Reserve System(NERRS)

Introduction

Well-functioning coastal ecosystems are of vital importance tohumans. They provide critical services such as food, protec-tion, and recreation (Costanza et al. 1997) and are utilized by amajority of economically and ecologically important marineorganisms during all or parts of their life cycle (Harley et al.2006). Coastal regions are generally more productive and en-vironmentally dynamic than the open ocean (Cloern et al.2016) while being most heavily affected by human activitiesthat exacerbate a suite of stressors concurrently. For example,excessive carbon dioxide (CO2) emissions not only increaseglobal atmospheric and oceanic pCO2 levels and thus marineacidity; they also lead to ocean warming, increased

Communicated by Richard C. Zimmerman

Electronic supplementary material The online version of this article(https://doi.org/10.1007/s12237-017-0321-3) contains supplementarymaterial, which is available to authorized users.

* Hannes Baumannhannes.baumann@uconn.edu

1 Department of Marine Sciences, University of Connecticut, 1080Shennecossett Road, Groton, CT 06340, USA

2 North Inlet-Winyah Bay National Estuarine Research Reserve,Baruch Marine Field Laboratory, University of South Carolina,PO Box 1630, Georgetown, SC 29442, USA

Estuaries and CoastsDOI 10.1007/s12237-017-0321-3

stratification, and therefore deoxygenation (Keeling et al.2010; Levin and Breitburg 2015; Long et al. 2016).Furthermore, the unprecedented discharge of reactive nitrogenand phosphorus into coastal waters (eutrophication) due in-dustrialized agriculture and growing coastal populations(Cloern et al. 2016; Rockstrom et al. 2009) is causing exces-sive primary production, microbial degradation, and thus met-abolic oxygen depletion and CO2 enrichment (acidification) inmany coastal waters (Breitburg et al. 2015a; Wallace et al.2014). Together, warming, acidification, and deoxygenationcomprise the general syndrome of marine climate change(Pörtner 2012). It is obvious, however, that coastal habitatsare structurally and functionally diverse, because they differin morphology, climatology, freshwater influx, productivity,or human pressure. Hence, they likely have different baselinesof natural variability and respond differently to anthropogenicdisturbance. Understanding both the diversity and unifyingprinciples of environmental variability across coastal marinehabitats is essential for effective management and for recog-nizing the symptoms of unfolding change.

To study concurrent dynamics of key parameters such astemperature, pH, and oxygen in coastal habitats, consistentmeasurements are required across time and space. Such mea-surements are most reliably produced by routine monitoring,i.e., by efforts that prioritize future over immediate scientificinsights and thus comprise a prescient if undervalued societalservice. Nearshore water temperatures at Great Harbor,Woods Hole (MA, USA), for example, have been measuredalmost daily since 1886, thus revealing seasonal and long-term patterns of coastal ocean warming (Nixon et al. 2004).Systematic monitoring of dissolved oxygen (DO) levels beganduring the second half of the twentieth century following therecognition of eutrophication-induced or eutrophication-exacerbated hypoxia in coastal waters such as ChesapeakeBay (Hagy et al. 2004), the northern Gulf of Mexico (Turneret al. 2008), or the Baltic Sea (Conley et al. 2002). Marine pHand pCO2, on the other hand, have only been monitored atvery few locations prior to 1990s (Bates et al. 2012; Doneyet al. 2009; Duarte et al. 2013), but new observing platformshave been added rapidly following the recognition of the “oth-er CO2 problem,” ocean acidification (Doney et al. 2009). Incombination, however, the routine monitoring of temperature,pH, and DO has only been gradually implemented in manycoastal habitats over the past two decades.

As a result, and because of inherent logistical and fundingchallenges, most time series of coastal environmental condi-tions are constrained in one or several dimensions, and mosttherefore offer only a selective glimpse of the full dynamicpicture. Some time series span impressively long periods, butonly cover single parameters (Nixon et al. 2004) or have toolow sampling frequencies to reveal shorter-term patterns.Conversely, many high-frequency, multi-parameter time se-ries are limited to single locations or regions (Baumann et al.

2015). Again, others feature spatially diverse observations butare restricted in parameters or time span (Hofmann et al. 2011;O’Boyle et al. 2013) or when compiling data across studiesneed to compromise on consistent quality control, sensor cal-ibration, and other methodological issues.

For this study, however, we assembled and analyzed envi-ronmental data collected by the National Estuarine ResearchReserve System (NERRS). TheNERRS is a network of 29USestuarine sites that represent the diversity of nearshore coastalenvironments. Established by the Coastal Zone ManagementAct (as amended in 1990), it operates as a partnership betweenthe National Oceanic and Atmospheric Administration(NOAA) and the coastal states. A core component ofNERRS is its System-Wide Monitoring Program (SWMP),which was formally established in 1994 to facilitate quantita-tive measurements of short-term variability and long-termchanges in water quality, biological systems, land use, andland cover characteristics of estuarine ecosystems to betterinform effective coastal zone management (Buskey et al.2015; Wenner and Geist 2001). A hallmark of SWMP is theuse of standardized instrumentation, protocols, and reportingstandards to collect consistent and comparable data across allsites within the NERRS (Porter et al. 2004).

As a consequence, the NERRS SWMP dataset uniquelycombines all of the four most desirable features in an environ-mental time series. First, it is spatially comprehensive byencompassing the diversity of nearshore and estuarine habitatsalong the three major coastlines of the USA (Atlantic, Gulf ofMexico, Pacific) as well as those of the US Great Lakes.Second, the dataset features high-frequency observations(30 min sampling intervals until 2012, 15 min intervals there-after) of key parameters, including temperature, salinity, pH,and DO. Third, NERRS data are methodologically consistent,because the federal umbrella of the program mandates com-parable monitoring protocols, data QA/QC procedures, andreporting standards. Fourth, this high-quality, high-frequency,multi-parameter, and multi-habitat dataset is readily availablein its entirety since its inception, although time series vary inlength depending on the year a reserve became part ofNERRS. A final distinguishing feature of NERRS sites is thatthey largely represent shallow, well-mixed nearshore habitatsof bays and estuaries, which is particularly relevant to thisstudy. It is these habitats where land and ocean first meetand where human impacts are often most evident (e.g.,Holland et al. 2004; Van Dolah et al. 2008). Nearshore habi-tats provide essential nursery and feeding grounds for manycommercially and recreationally important fish and shellfish(Beck et al. 2003; Kneib 1997), perform other important eco-system services that economically benefit coastal communi-ties (Barbier et al. 2011), and are naturally highly dynamic onshort to seasonal time scales (Wenner et al. 2004).

For this study, we examined a subset of NERRS data withrespect to three issues of contemporary interest, i.e., (1)

Estuaries and Coasts

across-system pH/DO coupling; (2) temporal trends in mean,variance, and extremes of environmental parameters withinand across systems; and (3) the relationship between pH/DOconditions and nutrient and chlorophyll levels.

Question 1: How Are pH and DO Conditions CoupledAcross the Diversity of Nearshore Habitats? Previous stud-ies have amply documented the strong statistical relationshipbetween pH and DO fluctuations at single sites or regions overshort time scales (Baumann et al. 2015; Melzner et al. 2012;O’Boyle et al. 2013; Wallace et al. 2014), thereby suggestingthat metabolic processes (respiration vs. photosynthesis) dom-inate pH/DO dynamics in nearshore marine habitats (Duarteet al. 2013). It has yet to be shown, whether this metabolicdominance holds across the diversity of systems such that acommon relationship between both parameters can be formu-lated.We usedmultivariate regression analysis to model pH asa function of DO and salinity and examined how the pH/DOcoupling persists over increasing time scales from days toyears.

Question 2: Are There Interannual Trends in Temperature,pH, and DO Within and Across Habitats? While the syn-drome of marine climate change predicts a general pattern ofwarming, acidification, and deoxygenation, these processesdiffer regionally and are altered by local factors. We thus ex-pected heterogeneous trends in parameter means, extremes,and variability both within and across nearshore habitats overthe past 15 years.We used time series analyses including basiccorrelation, detrending, and smoothing techniques to addressthese questions.

Question 3: How Are Nutrient and Chlorophyll LevelsRelated to pH and DO Conditions? We examined howpH/DO variability is related to nutrient levels and primaryproductivity in these systems, hence testing the general para-digm of eutrophication, which predicts reduced pH and DO asa result of higher nutrient levels via increased primary produc-tion and microbial respiration (Wallace et al. 2014).While thisis evident for deeper, stratified coastal water bodies, it remainsto be demonstrated whether the same is true across the diver-sity of nearshore, well-mixed NERRS habitats.

Material and Methods

Site Selection and Description

We selected 16 NERRS sites representing different nearshoreestuarine locations spanning all major US coasts (Table 1).Each NERRS site maintains a minimum of four SWMP sta-tions, from which we chose the one monitoring station withthe highest salinity regime and which had essentially T

able1

Characterizationof

the16

USestuariesfrom

theNationalE

stuarine

ResearchReserve

system

(NERRS)

analyzed

inthisstudy

Estuary

Sitecode

StationID

Region

Estuarine

geom

orphictype

Site

habitattype

Relativeanthropogenicim

pact

Meandepth(m

)Tidalrange(m

)

Wells(M

E)

WEL

INNorth

East

Coastalplainsaltmarsh

Openwater

Impacted

4.5

2.8

WaquoitBay

(MA)

WQB

MP

Lagoon/bar-built

Openwater

Impacted

2.2

0.5

NarragansettB

ay(RI)

NAR

TS

Drownedrivermouth

Openwater

Undisturbed

61.1

J.CousteauBay

(NJ)

JAC

B6

Mid-A

tlantic

Lagoon/bar-built

Openwater

Undisturbed

3.9

1Delaw

are(D

E)

DEL

SLDrownedrivermouth

Marsh

creek

Impaired

3.2

1.2

ChesapeakeBay

(VA)

CBV

TC

Drownedrivermouth

Marsh

creek

Undisturbed

1.5

0.9

North

Carolina(N

C)

NOC

RC

SouthEast

Coastalplainsaltmarsh

Marsh

creek

Undisturbed

1.3

2.3

North

Inlet(SC

)NIW

OL

Coastalplainsaltmarsh

Marsh

creek

Undisturbed

21.4

ACEBasin

(SC)

ACE

SPCoastalplainsaltmarsh

Marsh

creek

Undisturbed

22.8

JobosBay

(PR)

JOB

10Caribbean

Lagoon

Mangrove

Undisturbed

1.5

0.5

Apalachicola(FL)

APA

ES

Gulf

Deltaicbay

Openwater

Impacted

10.5

Weeks

Bay

(AL)

WKS

WB

Drownedrivermouth

Openwater

Impaired

1.2

0.4

Tiju

anaRiver

(CA)

TJR

OS

Pacific

Lagoon/bar-built

Marsh

creek

Impacted

1.2

1.8

Elkhorn

Slough

(CA)

ELK

SMCoastalplainsaltmarsh

Openwater

Impacted

2.5

1So

uthSlough

(OR)

SOS

VA

Drownedrivermouth

SAVbed

Undisturbed

1.9

2Padilla

Bay

(WA)

PDB

BY

Deltaicbayin

fjord

SAVbed

Undisturbed

31.5

Estuaries and Coasts

uninterrupted records of pH, DO, temperature, and salinitydata from 2002 through 2016. The excluded NERRS wereeither primarily freshwater habitats, lacked winter sampling,or had shorter than desired data records. Our selection yieldedfour sites within Pacific coast estuaries (Tijuana River, CA;Elkhorn Slough, CA; South Slough, OR; Padilla Bay, WA),two sites within Gulf of Mexico estuaries (Weeks Bay, AL;Apalachicola, FL), one Caribbean site (Jobos Bay, PR), andnine sites within estuaries along the US Atlantic coast(Northeast: Wells, ME; Waquoit Bay, MA; NarragansettBay, RI; Mid-Atlantic: Jacque Cousteau Bay, NJ; Delaware,DE; Chesapeake Bay, VA; Southeast: North Carolina, NC;North Inlet, SC; ACE Basin, SC). As summarized inTable 1, many sites comprise shallow-water locations withina larger embayment, which we reference throughout the studyfor simplicity (e.g., the Chesapeake Bay site, CBV, is locatedin a small, marsh-dominated tidal creak off of the main stem ofthe York River). The 16 sites represent different estuarine geo-morphic types (i.e., coastal plain salt marsh, Lagoon/Bar-built,drowned river mouth, Deltaic bay), habitat types (open water,marsh creek, mangrove, submerged aquatic vegetation), andstates of anthropogenic impact, as qualitatively assessed byreserve staff (i.e., relatively undisturbed, impacted, impaired).

Our final dataset consisted of 12 fully marine (meansalinities of 28–36 psu) and four more brackish sites(mean salinities of 10–11 psu), all of which recordedtemperature, salinity, DO, and pH (National Institute ofStandards and Technology, NIST scale) in intervals of30 min (2002–2012) or 15 min (2013–2016) .Measurements of nutrients (= [PO4

3−] + [NO2−] +

[NO3−] + [NH4

+]) and chlorophyll a (Chl) wereavailable for 13 years (2002–2014) from the NERRSdatabase, but given the lower sampling frequency forthese parameters, they were analyzed as monthlyaverages.

Data Acquisition and Quality Control

All reserves use automated data sondes and sensorsmanufactured by Yellow Springs Instruments (YellowSprings, OH) and standardized calibration, deployment, anddata review protocols. Data were first reviewed for qualityassurance and quality control (QA/QC) at the individual re-serves and then submitted to the NERRS Centralized DataManagement Office (CDMO) where they undergo additionalQA/QC reviews before being made publically available as“authoritative” data via the CDMO data portal (see http://cdmo.baruch.sc.edu for further details and complete programQA/QC documentation).

After downloading data from the NERRS database (www.nerrsdata.org), we used embedded QA/QC flags and codes toexclude any data identified by reserves as suspect or wheresensors had known issues. In addition, data cases that failed to

be within two standard deviations of the mean for a givenmonth were considered potential outliers and excluded on acase by case basis from the analyses. The final datasetcontained a total of 5,288,108 observations across all 15 yearsand 16 NERRS sites.

Data Analysis

Analyses were conducted using either JMP® V10.0 (SAS®)or SPSS® Statistics V20 (IBM®) statistical software. For afirst characterization of the main abiotic variables within eachsite (temperature, salinity, pH, DO), we computed the overallsite-specific mean and seasonality, the latter calculated as therange between the means of yearly 5th and 95th percentiles ofeach variable (Table 2). We further computed site-specificmonthly 5th, 50th, and 95th percentiles of pH and DO tovisualize average seasonal pH and absolute DO (mg L−1) dy-namics for each site (Fig. 1). For all statistical analyses de-scribed below, we exclusively used DO expressed as % satu-ration, because oxygen saturation state normalizes the effectsof temperature and salinity variations on oxygen conditions.Hence, departures from 100% saturation essentially reflect therole of biological processes on ambient oxygen conditions,which were the focus of this study.

Question 1: Within- and Across-System pH/DO CouplingTo quantify the extent to which in situ metabolism (i.e., thedaily balance between primary production and ecosystem res-piration) drives short-term pH variability in the differentNERRS habitats, we first examined the relationship betweendaily DO ranges (ΔDO) and daily pH ranges (ΔpH) at eachsite. Daily DO and pH minima and maxima were used tocalculate daily ΔDO and ΔpH, which were subsequently aver-aged bymonth across all years. Second,we calculatedmonthlyaverages of pH,DO, and salinity.We then constructedmultiplelinear regression models to predict monthly means of (i) dailyΔpH and (ii) pH from site-specific monthly means of salinityand (i) daily ΔDO or (ii) DO. Salinity was included in theregression analysis, because freshwater has a lower pH thanseawater and because salinity influences the measurement ofpH by potentiometric glass electrodes (Provoost et al. 2010).In addition to site-specific relationships (Table 3), we alsocalculated relationships across sites for each month(Table S1) and an overall relationship across sites and months.Leverage plots (also known as partial regression plots) wereused to examine the influence of each predictor variable.

To examine how the strength of the pH/DO couplingchanges with increasing temporal scales, we used site-specific daily means of pH, DO, ΔpH, and ΔDO and thenaveraged and correlated pH vs. DO and ΔpH vs. ΔDO acrossfour different time scales: by day (i.e., 365 × 15 years = 5475data points), by week (i.e., 52 × 15 years = 780 data points), bymonth (12 × 15 years = 180 data points), and by year (15 data

Estuaries and Coasts

points). We then recorded and compared correlation strengthas expressed by Pearson correlation coefficients.

Question 2: Temporal Trends Within and Across SystemsTo characterize interannual trends in temperature, pH, andDO, we first computed monthly anomalies for each variable,site, and year (Am),

Am T; pH;DOð Þ ¼ X T; pH;DOð Þsite;year;month−1

15∑X T; pH;DOð Þsite;month

and then averaged Am to obtain yearly anomalies (AY),

AY T; pH;DOð Þsite ¼1

12∑Am T; pH;DOð Þ

To further visualize general interannual trends, we appliedlocally weighed scatterplot smoothing (LOESS, 50% databandwidth) and overlaid the fit for each parameter and site(Systat SigmaPlot V11.0).

To quantify more subtle interannual trends in parametermeans, variability, and extremes, we computed and then sea-sonally detrended three variables for temperature, pH, andDO: (1) mean (m), (2) daily range (Δ), and (3) daily maxima(temperature) or minima (pH, DO). We first calculated thesevariables for every day per year and site and then averagedthese values for each week of the time series and each site(15 years × 52 weeks = 780 data points per site). We thencalculated weekly anomalies for each variable (AW) bysubtracting the long-term week-specific mean,

AW T m;Δ;max½ �; pH m;Δ; min½ �;DO m;Δ;min½ �ð Þ

¼ X site;year;week−1

15∑X site;week

Weekly anomalies were then correlated with the runningweek of the time series (i.e., 1, 2,…, 780), recording the sign,strength, and p value of the Pearson correlation coefficients(Table 4). Similarly, we examined site-specific and overallcorrelations between weekly temperature anomalies andweekly anomalies of means, diel minima, and diel ΔpH andΔDO to address the question whether increases in temperatureexacerbate pH and DO conditions in nearshore habitats.Lastly, we calculated monthly anomalies of average nutrientand Chl concentrations and correlated them to the runningmonth of the time series, also recording the sign, strength,and p value of the Pearson correlation coefficients (Table 4).

Question 3: Nutrient, Chl, and pH/DO Relationships Toexamine how nutrient and Chl levels related to pH/DOconditions within each NERRS site, we first calculatedmonthly means of each parameter and then standardizedthese values to zero overall mean and unit variation inorder to make subsequent correlations comparable acrosssites. Specifically, we correlated standardized monthlynutrients and (i) standardized monthly Chl, (ii) standard-ized monthly pH and (iii) standardized monthly DOlevels for each site (12 months × 13 years = 156 data

Table 2 Overview of long-term average temperature, salinity, dissolved oxygen (expressed as % saturation), and pH conditions in 16 US estuariesfrom the National Estuarine Research Reserve system (NERRS). Seasonality was calculated as the range between the 5th and the 95th percentiles peryear and then averaged over the entire time series (2002–2016)

Estuary Site code Region Temperature °C Salinity Dissolved oxygen (%) pH

Mean Seasonality Mean Seasonality Mean Seasonality Mean Seasonality

Wells (ME) WEL North East 9.6 1.7–18.3 30.9 28.2–32.8 87.8 43.2–113.5 7.9 7.5–8.2

Waquoit Bay (MA) WQB 16.1 3.4–26.2 29.8 28.2–31.1 102.8 60.4–147.5 8.0 7.6–8.4

Narragansett Bay (RI) NAR 12.4 2.3–22.9 29.9 27.0–31.6 96.9 79.1–115.5 8.0 7.7–8.3

J. Cousteau Bay (NJ) JAC Mid-Atlantic 15.2 4.6–24.9 28.9 24.6–31.5 98.8 81.7–113.7 8.0 7.6–8.3

Delaware (DE) DEL 15.1 1.4–27.8 10.9 0.8–22.1 68.7 25.8–99.7 7.2 6.8–7.9

Chesapeake Bay (VA) CBV 17.4 3.5–30 10.7 3.8–16.4 87.1 51.0–121.6 7.6 7.1–8.2

North Carolina (NC) NOC South East 19.9 8.5–29.5 29.9 23.0–34.5 89.0 54.6–115.5 8.0 7.6–8.4

North Inlet (SC) NIW 20.4 8.8–30.3 32.4 25.1–36.5 78.0 38.5–105 7.6 7.2–8.1

ACE Basin (SC) ACE 20.8 9.6–30.4 28.4 21.6–34.3 77.9 38.8–100.7 7.8 7.2–8.4

Jobos Bay (PR) JOB Caribbean 29.0 26.0–31.6 36.2 31.5–39.2 80.9 50.4–108.9 7.8 7.5–8.1

Apalachicola (FL) APA Gulf 23.0 12.4–31.1 9.7 1.0–23.1 77.1 21.4–112.1 7.4 6.2–8.4

Weeks Bay (AL) WKS 22.3 11.3–31.5 9.9 2.7–18.5 94.8 37.0–138.2 7.8 7.0–8.6

Tijuana River (CA) TJR Pacific 18.7 12.8–24.7 32.4 22.9–36 69.4 10.6–125.6 7.8 7.2–8.3

Elkhorn Slough (CA) ELK 16.0 10.8–20.9 32.3 28.7–34.4 85.6 52.3–116.4 8.0 7.7–8.3

South Slough (OR) SOS 12.7 8.5–17.5 27.5 16.6–33.4 94.0 68.7–118.2 7.9 7.6–8.2

Padilla Bay (WA) PDB 10.8 6.6–16.4 29.1 27.3–30.4 95.8 72.6–138.8 7.9 7.7–8.4

Estuaries and Coasts

points per site), followed by plotting the sign, strength,and p value of the Pearson correlation coefficients.

Results

Question 1: Within- and Across-System pH/DO CouplingAverage seasonal patterns in pH and DO conditions exhib-ited a high degree of resemblance within each of the 16NERRS sites (Fig. 1). At all sites, a characteristic summer-to-fall depression in median pH and particularly in medianDO coincided with the annual temperature maximum andwas typically accompanied by increased diel variability(Fig. 2). The magnitude of these pH/DO fluctuations variedwidely across sites, with lower salinity sites exhibiting con-siderably larger pH/DO variations than the more marine sites(Figs. 1 and 2). During summer months (June–August), thelower salinity sites had minimum monthly pH means of~ 7.0, while the higher salinity sites had minimum monthlypH means ~ 7.5. The highest monthly pH means of ~ 8.2occurred during winter months (January–March). DEL, TJR,

and APA showed the lowest monthly DO means of ~ 50%(May–September), while the highest monthly DO means of~ 110% were found for WQB, WKB, and PDB (January–June). The largest daily pH fluctuations of 1 pH unit oc-curred in DEL, WKB, APA, and TJR (March–July), whilethe highest mean daily DO fluctuations of ~ 100% wereobserved in TJR, PDB, and WQB (March–September). Ingeneral, sites on the US Atlantic coast showed greater pHfluctuations than did sites along the US Pacific coast. Inaddition, seasonal pH/DO amplitudes along the USAtlantic coast were of similar or greater magnitude thanshort-term fluctuations, whereas sites along the Gulf ofMexico and the US Pacific exhibited greater short-term thanseasonal pH/DO variability (Fig. 1).

Both monthly mean pH and monthly mean daily ΔpHwerestrongly related to salinity and monthly mean DO or monthlymean daily ΔDO, respectively, across sites (Fig. 3a, b). Themultiple linear regressions took the form (Fig. 3c, d):

(1) pH = 6.124 + 0.014 × Salinity + 0.015 × DO (%)(df = 189, R2 = 0.79, p < 0.001),

Fig. 1 Average seasonal patterns of pH (NIST, red) and absolute dissolved oxygen concentration (mg L−1, blue) of the 16 US NERRS sites examined inthis study. Thick lines depict long-term monthly medians, while shading encompasses long-term monthly 5th and 95th percentiles

Estuaries and Coasts

(2) ΔpH = 0.570–0.016 × Salinity + 0.006 × ΔDO (%)(df = 189, R2 = 0.73, p < 0.001)

Leverage for the two predictors showed that after account-ing for salinity variability, the 16 sites distributed evenly andlinearly with regard to DO. As Fig. 3a, b illustrates, low salin-ity sites experienced considerably lower mean pH and higherdaily ΔpH than high salinity sites at similar mean DO anddaily ΔDO levels, respectively. Monthly regression modelsacross systems explained 50–90% of the variability in meanpH and daily ΔpH (Table S1) and showed a clear seasonal

pattern, with the mean pH model outperforming the meandaily ΔpH model during summer months, but not betweenJanuary and April (Fig. 4). For across-system pH and ΔpHmodels, DO and salinity were highly significant terms(p < 0.01) in the multiple regression, and the two togetheralways explained more variability than either term alone. Forsite-specific pH and ΔpH models, DO was always a highlysignificant predictor (p < 0.01), whereas salinity was a signif-icant term for most, but not all sites (Table 3).

The relationships between (i) mean DO and pH and (ii)mean daily ΔDO and ΔpH both showed strong temporal con-sistency in most systems (Fig. 5). On diel scales, significant

Table 3 Site-specific, multiple linear regressions predicting monthly means (n = 12) of (i) daily ΔpH and (ii) pH frommonthly means of salinity (Sal)and daily ΔDO or DO (% saturation). Where mean salinity was not a significant predictor (p > 0.05), only DO was used

Estuary Region Regression equation Adj R2 df F p

Wells (ME) North East ΔpH = 0.518 − 0.016 × Sal + 0.008 × Δ%DO 0.988 9 471.8 p < 0.001

pH = 7.281 + 0.007 × %DO 0.590 10 16.8 p = 0.002

Waquoit Bay (MA) ΔpH = − 1.374 + 0.049 × Sal + 0.005 × Δ%DO 0.989 9 476.0 p < 0.001

9 0.6 p = 0.553

Narragansett Bay (RI) ΔpH = 1.035 − 0.033 × Sal + 0.006 × Δ%DO 0.952 9 110.1 p < 0.001

pH = 3.369 + 0.060 × Sal + 0.029 × %DO 0.944 9 93.9 p < 0.001

J. Cousteau Bay (NJ) Mid-Atlantic ΔpH = 1.351 − 0.044 × Sal + 0.007 × Δ%DO 0.887 9 44.2 p < 0.001

pH = 4.732 + 0.033 × %DO 0.847 10 62.0 p < 0.001

Delaware (DE) ΔpH = 0.919 − 0.046 × Sal + 0.009 × Δ%DO 0.533 9 7.3 p = 0.013

pH = 6.699 + 0.007 × %DO 0.906 10 106.9 p < 0.001

Chesapeake Bay (VA) ΔpH = 0.424 + 0.004 × Δ%DO 0.587 10 16.6 p = 0.002

pH = 5.164 + 0.017 × Sal + 0.026 × %DO 0.986 9 392.2 p < 0.001

North Carolina (NC) South East ΔpH = − 0.614 + 0.023 × Sal + 0.006 × Δ%DO 0.955 9 118.6 p < 0.001

pH = 5.824 + 0.024 × %DO 0.960 10 262.3 p < 0.001

North Inlet (SC) ΔpH = − 0.422 + 0.021 × Sal + 0.006 × Δ%DO 0.917 9 61.6 p < 0.001

pH = 5.586 + 0.028 × Sal + 0.015 × %DO 0.970 9 175.8 p < 0.001

ACE Basin (SC) ΔpH = 0.221 + 0.008 × Δ%DO 0.949 10 207.1 p < 0.001

pH = 4.708 + 0.032 × Sal + 0.028 × %DO 0.982 9 300.8 p < 0.001

Jobos Bay (PR) Caribbean ΔpH = 0.622 − 0.017 × Sal + 0.004 × Δ%DO 0.949 9 104.3 p < 0.001

pH = 6.993 + 0.010 × %DO 0.873 10 76.4 p < 0.001

Apalachicola (FL) Gulf ΔpH = 0.930 − 0.028 × Sal + 0.001 × Δ%DO 0.974 9 204.6 p < 0.001

pH = 5.707 + 0.048 × Sal + 0.016 × %DO 0.865 9 36.4 p < 0.001

Weeks Bay (AL) ΔpH = 0.983 − 0.038 × Sal + 0.002 × Δ%DO 0.941 9 89.5 p < 0.001

pH = 6.057 + 0.019 × %DO 0.857 10 66.8 p < 0.001

Tijuana River (CA) Pacific ΔpH = − 1.680 + 0.060 × Sal + 0.005 × Δ%DO 0.908 9 55.4 p < 0.001

pH = 7.083 + 0.010 × %DO 0.891 10 91.3 p < 0.001

Elkhorn Slough (CA) ΔpH = 0.090 + 0.004 × Δ%DO 0.889 10 89.1 p < 0.001

pH = 7.579 + 0.005 × %DO 0.368 10 7.4 p = 0.022

South Slough (OR) ΔpH = 0.282 + 0.004 × Δ%DO 0.755 10 34.8 p < 0.001

pH = 6.892 + 0.011 × %DO 0.579 10 16.1 p = 0.002

Padilla Bay (WA) ΔpH = 0.011 + 0.008 × Δ%DO 0.947 10 198.0 p < 0.001

pH = 6.714 + 0.013 × %DO 0.882 10 83.6 p < 0.001

All estuaries ΔpH = 0.570 − 0.016 × Sal + 0.006 × Δ%DO 0.725 189 252.8 p < 0.001

pH = 6.124 + 0.014 × Sal + 0.015 × %DO 0.794 189 345.9 p < 0.001

Estuaries and Coasts

positive correlations were detected for all 16 sites (p < 0.001).Mean pH/DO correlation coefficients ranged from 0.2 to 0.8(Fig. 5a), while ΔDO/ΔpH correlation coefficients ranged 0.06and 0.26 (Gulf of Mexico sites) to 0.40–0.91 (all other sites,mean ± SD = 0.66 ± 0.15, Fig. 5b). Correlation strength gen-erally persisted with temporal scales increasing to weeks andmonths, with the exception of WQB and the two Gulf ofMexico sites (APA, WKB). At the interannual scale, thestrength of the pH/DO correlation decreased substantially inall but two sites (Fig. 5a), whereas the ΔpH/ΔDO relationshipdecreased in eight and persisted in the other eight sites, withparticularly strong interannual correlations in all threeNortheast US sites (0.91–0.94, Fig. 5b).

Question 2: Temporal Trends Within and Across SystemsYearly temperature, pH, and DO anomalies were highly het-erogeneous in magnitude and interannual patterns across sitesand regions, and neither variable showed monotonous trends,except atWQB (ΔT = + 1.1 °C) and PDB (ΔpH = − 0.17 units,Fig. 6). Patterns across the four Pacific coast sites were mostconsistent, albeit of different magnitude, and were character-ized by a warm period that peaked in 2004 (+ 0.1 to + 0.4 °Cabove the mean), a subsequent temperature depression be-tween 2007 and 2012 (mean, − 1.6 to − 0.5 °C), and a suddenshift to large positive anomalies in 2014–2016 that followed alatitudinal gradient (+ 0.9 to + 1.9 °C, north to south). Annual

pH anomalies ranged from − 0.15 to + 0.22 units, with highpH periods occurring in 2005–2008 and subsequent strong pHdeclines coinciding with the strong warming signal in all fourPacific sites at the end of the time series. Annual DO anoma-lies mostly varied from − 5 to + 5% saturation, but in 2016,much larger positive anomalies were observed for TJR(+ 14.0%) and ELK (+ 11.9%, Fig. 6).

The Gulf of Mexico sites exhibited contrasting interannualfluctuations in temperature (− 0.9 to + 0.9 °C) and DO anom-alies, but similar interannual pH patterns that were character-ized by a marked decline towards the end of the time series.Ranges of pH (− 0.6 to + 0.4 units) and DO anomalies (− 17 to+ 9% saturation) were among the largest in the dataset (Fig. 6).The single Caribbean site exhibited generally small, but cor-related pH and DO changes, with a period of cooling (2010 to2014) coinciding with increases in both pH and DO.

The nine sites along the US Atlantic Coast showed consis-tent interannual temperature anomalies (− 1.3 to + 1.4 °C),which were characterized by multiple periods of warmingand cooling lasting approximately 2–3 years. Almost all sites,but particularly WQB, DEL, NOC, NIW, and ACE, showed astrongwarming signal (> + 0.5 °C) towards the end of the timeseries. While generally correlated to temperature, pH patternsacross Atlantic coast sites were highly variable, with annualanomalies ranging from − 0.32 to + 0.33 units. For example,pH and DO in the northernmost site (WEL) shifted from be-low average between 2004 and 2008 (− 0.18 units and − 19%,respectively) to above average afterwards (2009–2014:+ 0.16 units and + 10%, respectively); however, this patternwas almost reversed in the next site to the south (WQB),where exceptional warming (+ 0.6 °C) and acidification(− 0.09 units) occurred from 2012 to 2016. Similarly, pHand DO trends at CBVand DEL, while in itself highly corre-lated, exhibited divergent interannual patterns (e.g., between2003 and 2006, Chesapeake site pH and DO were depressed,but largely above average for the Delaware site).

Our analysis of temporal trends in weekly anomalies oftemperature, pH, and DO means, diel ranges, and extremesrevealed a suite of subtle, but significant underlying shiftswithin and across regions. Significant correlations (p < 0.05)had coefficients ranging from − 0.56 to + 0.55, meaning thattemporal trends accounted for maximal 31% of the variability(mean ± s.e. = 5.8 ± 0.6%). Mean temperature and diel max-ima significantly increased over the study period in 10 and 8sites, respectively, particularly along the North-Atlantic andPacific coasts (Table 4). However, only three sites showedsignificant increases in the range of diel temperature fluctua-tions over the study period, while five exhibited decreases.

For pH and DO, subtle but significant trends were morefrequent across sites for all three metrics (mean, diel mini-mum, diel range). Increases in mean pH were detected in fourUS Atlantic sites, while decreases in pH occurred at sevensites across all regions over the study period. Diel pH minima

Fig. 2 Examples for a diurnal and b diurnal/tidal pH (red lines) and DOfluctuations (%, blue lines) in two of the studied NERRS sites: a PadillaBay, WA, and b Delaware, DE. Measurements every 15 min are shownover a period of 7 days in May (a) and July 2013 (b)

Estuaries and Coasts

increased in four sites, but decreased in four other sites.Consequently, diel pH ranges mostly decreased (less extremefluctuations, 7 of 16 sites), whereas increases occurred at threesites (WQB, NAR, CBV). Average DO levels decreased sig-nificantly in six sites, while trending upwards at five othersites. Diel DO minima decreased during the study period atseven sites, largely accompanied by increases in diel DOranges. Six sites showed trends towards less extreme dielDO minima, and at five sites, the diel DO range decreased(Table 4). Finally, comparatively strong and positive long-term Chl trends were detected in nine of 16 sites; however,in many of these sites, increasing Chl was associated withdecreasing nutrient levels.

For most sites, weekly anomalies of means, diel min-ima, and diel ΔpH and ΔDO were significantly correlat-ed to weekly temperature anomalies, particularly forsites along the US southeast coast (NIW, ACE, Fig.7). On average, mean and diel minimum pH and DOanomalies were negatively correlated to temperatureanomalies; hence, increasing temperatures lowered boththe mean and minimum pH and DO conditions experi-enced at most sites. In contrast, the positive correlationsfound between temperature anomalies and both dielΔpH and ΔDO anomalies indicate that the range of dai-ly pH and DO fluctuations tended to become more ex-treme with increasing temperature (Fig. 7).

Fig. 3 Predicting pH conditions from DO and salinity across systems. aPredicted mean pH (contours) in relation to mean DO (%) and salinity. bPredicted mean daily ΔpH (contours) in relation to mean daily ΔDO (%)and salinity. Points depict observed site-specific monthly means of DO(a) or ΔDO (b) and salinity. c Predicted vs. observed monthly mean pH

when predicted from mean DO and salinity across systems. d Predictedvs. observed mean daily ΔpH when predicted from mean daily ΔDO andsalinity across systems. Symbols depict monthly means for each of 16NERRS sites. See Table 3 for regression equations

Estuaries and Coasts

Question 3: Nutrient, Chl, and pH/DO RelationshipsStandardized nutrient levels were significantly negatively cor-related to standardized Chl concentrations at 10 of 16 sites(Fig. 8a). Only at one site, NIW, the nutrient-Chl relationshipwas significantly positive. Lagging the Chl data by 1 to3 months did not change this general pattern. Correlation co-efficients for standardized nutrients vs. pH were generallynegative (p < 0.05 for seven sites, Fig. 8b). A single site,NIW, showed a significantly positive nutrient vs. pH relation-ship. The correlations between nutrient and DO levels hadgenerally negative correlation coefficients, which were signif-icant for five sites, mostly located along US Northeast andMid-Atlantic regions (Fig. 8c). Standardized chlorophylllevels were mostly positively related to standardized pH con-ditions, with the exception ofWEL (Fig. 8d), but there was no

overall pattern for standardized chlorophyll vs. DO correla-tions (Fig. 8e).

Discussion

We used long-term monitoring data from the NERRS to studydiel to interannual patterns of pH and DO variability across adiversity of US nearshore, shallow-water, estuarine habitatsalong the Atlantic, Gulf of Mexico, Caribbean, and Pacificcoasts. We asked whether these systems would exhibit a com-mon pH/DO relationship, whether there were detectable inter-annual trends in temperature, pH, and DO within and acrosssystems, and how pH/DO dynamics related tomeasured levelsof nutrients and chlorophyll. Our analyses clearly showed thatlarge, concurrent fluctuations of pH and DO are a characteris-tic feature of nearshore habitats, which is consistent with pre-vious studies (Baumann et al. 2015; O’Boyle et al. 2013;Oczkowski et al. 2016; Provoost et al. 2010; Wallace et al.2014; Wootton et al. 2008) and the paradigm that metabolicprocesses are the dominant driver of coastal pH and DO var-iability (Duarte et al. 2013). This is further corroborated by thepersistence of strong positive pH/DO correlations throughtime at most NERRS sites, with diel correlations primarilyreflecting day/night changes in community metabolism, andcorrelations on larger temporal scales reflecting seasonal tointerannual changes in the systems metabolic states. MostNERRS sites are net heterotrophic, i.e., respiration of alloch-thonous organic material outweighs oxygen production on anannual basis (Caffrey 2003). The corollary of net heterotrophyis reduced mean pH due to the dissolution of metabolic CO2,which is consistent with the notion that productive coastalhabitats largely act as CO2 sources, not sinks, to the atmo-sphere (Borges and Frankignoulle 2002; Wang and Cai 2004).

Despite the unique habitat and geomorphic featuresof each NERRS site, we found well-constrained empir-ical relationships between monthly means of pH and

Fig. 5 Temporal dissipation ofpH:DO correlations acrossregions and sites: graphs showsite-specific Pearson correlationcoefficients of bivariate a meanpH vs. mean DO or b mean dailyΔpH vs. mean daily ΔDO (%)correlations using valuesaveraged by day, week, month,and year. The strong reversal forNIW in b resulted from aparticularly reduced range ofyearly means such that minordeviations of 3 years produced theartifact of a negative correlation

Fig. 4 Adjusted R2 values of multiple linear models predicting mean pHfrom mean DO (%) and salinity (white diamonds) or mean daily ΔpHfrom mean daily ΔDO (%) and salinity (gray circles) across 16 NERRSsites for each month of the year

Estuaries and Coasts

DO and between monthly means of diel pH and DO fluctu-ations across systems, which explained 79 and 73% of theoverall variability, respectively. This observed strong linkagebetween pH and DO fluctuations is consistent with theknown stoichiometry between O2 and CO2 in both photo-synthesis and respiration. More specifically, it indicates thesemetabolic principles hold across the diversity of nearshorehabitats and govern the rates of oxygen demand and CO2

production during periods of net heterotrophy, as well asrates of oxygen production and CO2 demand during periodsof net autotrophy. Our study therefore provides the strongestempirical support to date for the notion that variability inDO conditions can be uniformly used to predict pH condi-tions across most coastal ecosystems as well as within asingle coastal ecosystem (Odum 1961; Odum et al. 1995;Riley 1972). Given the increasing recognition that co-occurring low pH and DO conditions can have additive orsynergistic negative effects on marine organisms (Gobler andBaumann 2016; Gobler et al. 2014), these common relation-ships could be valuable for parameterizing regional modelsof nearshore pH conditions or for predicting pH levels in

systems, where only oxygen has been monitored so far.O’Boyle et al. (2013), for example, used high-frequencypH and DO data collected over 3 years at 90 sites alongthe Irish coast to derive indices of estuarine trophic status,which showed good agreement with other independent mea-sures of eutrophication, such as total chlorophyll concentra-tions and biochemical oxygen demand.

Apart from DO, monthly mean salinity was the secondimportant variable that explained substantial parts of variationin both mean pH and mean daily pH fluctuations across sys-tems. This is because low salinity water has a lower alkalinityand therefore lower buffering capacity than full strength sea-water; hence, equal amounts of metabolic CO2 dissolutionresult in lower pH in low compared to high salinity habitats(Salisbury et al. 2009). However, on the level of individualsites, salinity was only a significant predictor for sites withhigh salinity variability, whereas in less variable habitats suchas those along the US Pacific coast, DO alone remained thebest predictor. Similarly, for some sites, monthly variations inaverage pH were small relative to the diel pH variability(WEL, WQB, ELK); consequently, the ΔpH model

Fig. 6 Interannual trends in temperature (red bars), dissolved oxygen (%,blue bars), and pH (green bars) at 16 US NERRS sites, shown as yearlyanomalies derived from averaging monthly anomalies for each variable,

site, and year. Black trend lines were derived by LOESS smoothing (50%bandwidth)

Estuaries and Coasts

considerably outperformed the average pH model (Table 3).Hence, low frequency sampling programs for pH and otherparameters could easily be biased by unresolved diurnal var-iability at these sites (Zimmerman and Kremer 1984). TheChesapeake and Delaware sites (CBV, DEL), on the otherhand, exhibited the opposite pattern, i.e., a higher predictabil-ity of average monthly pH from DO and salinity compared tomean diel ΔpH. This may indicate that processes other thancommunity metabolism contribute substantially to diel pHvariability at these sites, including sediment processes orephemeral riverine input of acidity (e.g., sulfates) (Salisburyet al. 2008; Soetaert et al. 2007). Finally, the ΔpH modelnotably underpredicted diel pH fluctuations at the two south-ernmost Atlantic coast sites, ACE Basin and North Inlet, like-ly because these two sites are relatively unique in that they areconnected to far more extensive intertidal salt marshes thanany of the other sampling locations in this study. As such, thecreeks represented by these two stations receive large amountsof inorganic carbon (DIC) and low pHwater depending on thevariable alignment of ebb tides and diurnal cycles (Cai et al.2003a, b).

Just as metabolic processes elicit large pH and DO fluctu-ations on short time scales, long-term changes in metabolicstates could also cause large interannual pH and DO variationsin nearshore habitats. For example, while open ocean pH con-ditions have decreased monotonously over the past 15 yearsby approximately 0.027 units (0.0018 ± 0.0004 year−1;Lauvset et al. 2015), the NERRS dataset revealed both posi-tive and negative interannual pH changes within sites thatwere about one magnitude greater (± 0.2 units, Fig. 6). Thisis consistent with previous studies of long-term pH variabilityin other coastal environments (Duarte et al. 2013; Gattusoet al. 1998; Wootton et al. 2008). In addition, we showed thatthese interannual pH fluctuations occurred largely on the scaleof the individual site, with little to no spatial consistency evenwithin regions along the US Atlantic, Gulf of Mexico, andPacific coasts. We conclude that nearshore pH conditionslargely respond to local drivers influencing community me-tabolism, which implies that local management strategies willbe most effective for maintaining healthy pH and DO levels inthese habitats. In addition, small estuaries along the Pacificcoast experience ephemeral, seasonal, and interannual

Table 4 Interannual trends in mean temperature (T), DO, pH, nutrient(Nut), and chlorophyll (Chl) conditions in 16 NERRS sites. Weeklyanomalies in mean, diel maxima (T), diel minima (pH, DO), and dielrange were correlated against week of the time series (i.e.,

15 years × 52 weeks = 780 data points; numbers represent Pearsoncorrelation coefficients) with blue, red, and white cells depictingsignificant negative, positive, or no trends, respectively (p < 0.05)

Estuary / Region

Meandiel

maxdiel min diel range

T DO pH Nut* Chl* T DO pH T DO pH

Wells (ME)N

orth

Ea

st

0.27 0.37 0.25 -0.44 -0.45 0.31 0.37 0.25 0.20 -0.32 -0.18

Waquoit Bay (MA) 0.19 -0.15 -0.16 0.55 0.16 -0.31 -0.23 -0.15 0.32 0.28

Narragansett Bay (RI) 0.23 -0.39 0.16 -0.26 0.20 0.23 -0.46 0.31 0.25

J.Cousteau Bay (NJ)

Mid

-

Atla

ntic

0.17 -0.13 0.25 0.17 -0.26

Delaware (DE) -0.30 0.09 0.20 -0.32 0.13 0.14

Chesapeake Bay (VA) 0.09 0.22 0.45 -0.14 0.17 0.24 0.22

North Carolina (NC)

So

uth

Ea

st

0.08 -0.12 -0.26 -0.56 0.41 -0.18 -0.26

North Inlet (SC) 0.09 0.30 -0.11

ACE Basin (SC) -0.33 0.14 0.13 -0.12

Jobos Bay (PR)

Ca

rib

.

0.08 -0.35 0.52 -0.08 0.10 -0.11

Apalachicola (FL)

Gu

lf 0.23 0.19 -0.11 -0.26 -0.11

Weeks Bay (AL) -0.09 0.16 -0.27 -0.26 -0.11 0.17 -0.52

Tijuana River (CA)

Pa

cif

ic

0.29 -0.23 0.16 -0.08 -0.17 -0.13 0.15

Elkhorn Slough (CA) 0.19 0.19 0.18 0.16 0.20 0.12 -0.15 -0.13 -0.33

South Slough (OR) 0.15 -0.11 -0.11 -0.20 0.18 0.12

Padilla Bay (WA) 0.12 -0.11 -0.44 0.18 0.09 -0.12 -0.42 -0.10 -0.10

Estuaries and Coasts

changes in upwelling intensity that are known to substantiallyinfluence pH conditions (Barton et al. 2012; Branch et al.2013).

Interannual temperature anomalies, on the other hand, var-ied on regional scales, as evident, for example, from thestrong, consistent warming signal at all sites along the USPacific coast towards the end of the time series (likely due toEl Nino), or the related interannual temperature patterns be-tween US southeast Atlantic sites (Fig. 6). Given that temper-ature comprises an important determinant of community me-tabolism (e.g., Kristensen 1993), it may indirectly affect pHand DO conditions in nearshore habitats. Our analyses indeedrevealed significant inverse relationships between weeklyanomalies of temperature and both the means and the dielminima of pH and DO across the 16 NERRS sites.

Temperature anomalies were also positively related to themagnitude of diel pH and DO fluctuations (Fig. 7). ReducedpH and DO and increased diel fluctuations are consistent withthe hypothesis that warming enhances respiration over pro-duction, due to the inherent differences in the temperaturedependencies of these two processes at the ecosystem scale,as demonstrated theoretically and experimentally (Vaquer-Sunyer and Duarte 2011; Yvon-Durocher et al. 2010). Whilemost studies to date have focused on the abiotic consequencesof warming for aquatic habitats (e.g., reduced oxygen solubil-ity and increased stratification), the biotic consequences viaincreases in community metabolism might be just as impor-tant. Similarly, coastal acidification has been attributed pri-marily to enhanced microbial respiration of excessive primaryproduction due to eutrophication (Breitburg et al. 2015b;

Fig. 8 Site-specific correlations between nutrient and a chlorophyll, b pH,CDO, as well as chlorophyll and d pH and eDO across the time series, usingmonthly standardized values. Bars represent site-specific Pearson correlation coefficients (*p < 0.05, **p < 0.01, ***p < 0.001)

Fig. 7 Role of temperaturemediating nearshore DO and pHconditions. a Pearson correlationsof weekly temperature anomaliesand weekly anomalies of (i) meanDO, (ii) mean pH, (iii, iv) meandiel DO and pH minima, and (v,vi) mean diel DO and pH ranges.White circles depict non-significant (p > 0.05) correlations.b Mean ± s.e. of all site-specificcorrelation coefficients. Negative/positive coefficients mean thatDO and pH conditions or rangesdecrease/increase with increasingtemperature

Estuaries and Coasts

Levin and Breitburg 2015; Wallace et al. 2014). Our analysessuggest an additional, thermal pathway of metabolic coastalacidification, which is driven by increases in total communityrespiration in direct response to warming, combined with lowlight availability and/or loss of submerged aquatic vegetationthat used to contribute to productivity in these systems(particularly at CBV, Moore and Jarvis 2008; Zimmermanet al. 2015). Nearshore estuarine environments receive sub-stantial allochthonous terrestrial inputs, such that they are gen-erally both net heterotrophic and net exporters of organic mat-ter to the coastal ocean (Herrmann et al. 2015). Hence, amplesupplies of organic matter are likely to fuel warming-inducedrespiration even in the absence of any concurrent increases inautochthonous production. We conclude that nearshore pH/DO dynamics are metabolically controlled at the local scale,but regional temperature dynamics may act to exacerbate(warming) or ameliorate (cooling) these conditions. As indi-cated by our data and consistent with previous analyses (Hauriet al. 2013; Waldbusser and Salisbury 2014), these changesare likely to manifest primarily as shifts in extreme conditionsand the magnitude of short-term fluctuations.

The final question of this study concerned the relationshipbetween measured levels of nutrients, chlorophyll, and pH/DO dynamics in the NERRS habitats. Given that nutrientsstimulate primary production, our finding of mostly inverserelationships between standardized anomalies of nutrients andchlorophyll may appear counterintuitive. However, dissolvedinorganic nutrient concentrations are a poor proxy for actualrates of nutrient input into a system. Rather, it is likely that inthe predominantly shallow, well-mixed and highly productiveNERRS habitats, fast turnover rates result in the quick deple-tion of water column nutrients by primary producers, hencecausing the observed inverse relationships (Oczkowski et al.2016). A similar explanation may also apply to inconsistenttemporal trends in nutrients and chlorophyll (Table 4). Whileinterannual trends in nutrient concentrations were mostly non-significant or negative, we observed generally positive trendsin nearshore chlorophyll levels across most NERRS sites(2002–2014; Table 4; data not shown); only the latter is con-sistent with the notion of ongoing coastal eutrophication. Site-specific correlations between standardized Chl concentrationsand standardized pH and DO conditions were mostly non-significant, however (Fig. 8). Interestingly, standardized nutri-ent concentrations were negatively correlated to standardizedpH and DO conditions in many, but not all sites. Thus, a clearrelationship between eutrophication and metabolism-mediated coastal acidification and hypoxia does not appearevident at the shallow-water locations represented in thisstudy, as compared to previous observations in deeper-waternearshore sites (Levin et al. 2009; Turner et al. 2008; Zhanget al. 2010). We conclude that pH/DO dynamics in the shal-low, well-mixed estuarine habitats represented by the NERRSare likely more variable and less severe than those in deeper,

stratified water bodies, where production and respiration arespatially (i.e., vertically) decoupled. However, even in near-shore habitats, the principles of eutrophication-mediated pHand DO variability still apply, and future pH shifts in theseenvironments will depend on how future anthropogenic im-pacts and climate change will alter net ecosystem production-respiration balances, which is challenging to predict (Kempand Testa 2011). Given the ecological and economic impor-tance of shallow-water estuarine environments, further effortsat quantifying how future interactions between increasing at-mospheric CO2 conditions and anthropogenic alterations ofproduction-respiration balances will impact pH conditionsare essential to the effective management of these environ-ments. To that end, the NERRS SWMP represents an idealplatform for continued, targeted ocean acidification researchand monitoring at the land-sea interface, which would benefitfrom the inclusion of additional parameters such as alkalinityor pCO2.

Acknowledgements This study would not have been possible if theNERRS System-Wide Monitoring Program did not exist. Our gratitudeto the entirety of the SWMP technical staff for their dedicated and metic-ulous efforts in maintaining this valuable program for more than twodecades cannot be overstated.

Funding This study was partially supported by NSF #1536336 to H.B.and NOAA #NA15NOS4200120 to E. S.

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