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Ecological Modelling 222 (2011) 3559– 3567

Contents lists available at SciVerse ScienceDirect

Ecological Modelling

jo ur n al homep ag e: www.elsev ier .com/ locate /eco lmodel

odelling the effects of ‘coastal’ acidification on copper speciation

ussell Richardsa,∗, Milani Chaloupkab, Marcello Sanòa, Rodger Tomlinsona

Griffith Centre for Coastal Management, Griffith University, Gold Coast, Queensland 4222, AustraliaEcological Modelling Services Pty Ltd, University of Queensland, PO Box 6150, St Lucia, Queensland 4067, Australia

r t i c l e i n f o

rticle history:eceived 7 April 2011eceived in revised form 15 August 2011ccepted 18 August 2011vailable online 28 September 2011

eywords:

a b s t r a c t

We present here a copper speciation model that accounts for the long-term (‘coastal-acidification’) andshort-term (daily and seasonal variation) variability in water pH and water temperature. The developedmodel is applied to a sub-tropical estuary (Moreton Bay, Australia) at a one hundred year time scale sothat outputs are consistent with climate change projections. The model predicts that the mean cupricion concentration (Cu2+) in the estuary will increase by 115% over the next 100 years as a result of theprojected decrease in pH and increase in water temperature. Through calibration, the estimated con-

cean acidificationopper speciationtochastic modeloastal watersoreton Bay

centration of copper-complexing dissolved organic matter (DOM) in the estuary is found to be 22.5 nM.An increase in the concentration of Cu2+, which is the most toxic and bioavailable form of copper, hasimplications for ecosystem health and may have a negative effect on the detoxifying capacity of DOM.Models that provide a framework for coupling biological, chemical and physical processes are impor-tant for providing a holistic perspective of coastal systems, especially for better understanding a systemwithin the context of climatic and non-climatic drivers.

. Introduction

Almost half of the global population lives within the coastalone (Mee, in press) resulting in increasing pressure on coastalcosystems. This has resulted in the degradation of coastal watershrough receiving a wide range of pollutants from point and diffusesources within coastal catchments. Copper is one such pollutanthat has been the focus of widespread research (e.g. Brand et al.,986; Richards et al., 2010) because it is one of the most ubiqui-ous and pervasive aquatic contaminants found in coastal watersnd sediments throughout the world (Eisler, 1998). While copperccurs naturally in coastal and marine waters, arising from geolog-cal weathering (Phillips, 1980) and is an essential micronutrientor normal growth of marine organisms (Hebel et al., 1997), humanctivities such as effluent discharge have caused copper to be dis-harged into coastal waterways at a rate that is significantly greaterhan the natural geological rate (Phillips, 1980). The resulting ele-ated copper concentrations have been shown to have deleteriousffects on a range of biota including phytoplankton, echinoids, gas-ropods and fish (Brand et al., 1986; Moffett et al., 1997; Kobayashind Okamura, 2002; de Oliveira-Filho et al., 2004). Furthermore,

he environmental fate of the copper is sensitive to changes in thembient aquatic biogeochemistry resulting from climatic and non-

∗ Corresponding author. Tel.: +61 7 373 55018; fax: +61 7 555 28067.E-mail address: r.richards@griffith.edu.au (R. Richards).

304-3800/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.ecolmodel.2011.08.017

© 2011 Elsevier B.V. All rights reserved.

climatic perturbations (Vignati et al., 2005; Strandesena et al., 2007;Millero et al., 2009).

Recently, the spectre of unavoidable impacts of human inducedclimate change including increased sea surface temperatures anddecreased pH has arisen (Orr et al., 2005; IPCC, 2007). Globaloceanic pH has decreased by 0.1 pH units (ca. 25% increase in acid-ity) since the onset of the industrial revolution and is projectedto decrease by up to another 0.4 pH units by 2100 (Orr et al.,2005; IPCC, 2007). This ‘ocean acidification’ (OA) effect (Caldeiraand Wickett, 2003; Orr et al., 2005; IPCC, 2007; Doney et al., 2009)is due to increased carbon dioxide in the global atmosphere lead-ing to an overwhelming of the natural buffering system of seawater.The area of OA research is only in its infancy (Doney et al., 2009),however the implications of OA are expected to have widespreadimpact on oceanic biogeochemistry (Caldeira and Wickett, 2003;Millero et al., 2009). There is particular concern about the impactson coastal waters where “the ecosystem responses to ocean acid-ification could be more serious for humankind” (Doney et al.,2009) especially in concert with projected increases in sea sur-face temperature (IPCC, 2007) that will exacerbate the onset ofOA (Doney et al., 2009). The risks associated with this increasing‘coastal acidification’ has therefore provided even more motivationfor understanding the environmental fate of pollutants especiallyunder conditions of higher water temperature, which together are

likely to significantly (and adversely) influence metal speciation,mobility and toxicity (Millero et al., 2009; Doney et al., 2009).

A significant challenge in assessing the potential long-termeffects of acidification and other climatic variables on the

3560 R. Richards et al. / Ecological Modelli

Ft

etd(dttisegpe

aewutiswsimwac

2

2

iApb2

ig. 1. Study site location, box model regions and monitoring locations (�) used inhis study.

nvironmental fate of pollutants in coastal waters is that these sys-ems are characterised by large variations in pH in the short-termue to river basin runoff (Mathis et al., 2011), seasonal upwellingFeely et al., 2008), primary production (Mathis et al., 2011) andirect acid deposition (Doney et al., 2007). It is therefore of interesto describe the combined effect of this short-term variation withhe underlying long-term decrease in pH caused by ocean acid-fication. Understanding the short-term changes can reveal howpeciation will be influenced under OA (Millero et al., 2009) whilexploring the combined long-term and short-term variation mightive insight into the vulnerability of a system to ecological ‘tippingoints’ whereby a small change in pH leads to a regime shift in thecosystem characteristics and attributes (Hall-Spencer et al., 2008).

In this study we present an acid–base equilibria modellingpproach to investigating both the short-term and long-termffects of pH on copper speciation in a sub-tropical coastal water-ay over a 100-year timescale. Speciation calculations are oftensed to supplement measurements of aqueous copper because ofhe difficulty in directly measuring the concentration (activity) ofndividual species in natural waters (Vignati et al., 2005). Withoutpecialised monitoring equipment, sampling in the water columnill typically only elicit an aggregated concentration of a range of

pecies for a heavy metal. However, it is often the species-levelnformation that is of most interest (Moffett, 1995). The speciation

odel presented here is developed within a stochastic frame-ork, which accounts for the uncertainty in model parameters. This

pproach also enables the short-term variability of environmentalonditions, specifically pH, to be accounted for.

. Materials and methods

.1. Study area

Moreton Bay (Fig. 1) is a semi-enclosed embayment locatedn southeast Queensland, one of the fastest growing regions of

ustralia. The catchment area has a population of 2.8 million peo-le that is expected to increase to approximately 4 million peopley 2030 (Queensland Department of Infrastructure and Planning,008). The Bay has a strong ecological value and is characterised by

ng 222 (2011) 3559– 3567

a range of coastal ecosystems including mangroves, swamps andsea grass beds (Dennison and Abal, 1999). It hosts endemic andmigratory birds, turtles and endangered marine mammals includ-ing dugongs (Dugong dugong) and was designated as a Marine Parkin 1992 to protect the ecologically sensitive habitats located within(Dennison and Abal, 1999).

A west–east (high–low) copper gradient across the Bay has beenobserved in sediment (Burton et al., 2005) and biota (Richards et al.,2010) samples. Contamination patterns have been linked to landuse patterns around the Bay (Cox and Preda, 2005) particularlynear marinas and moorings that have implicated the use of anti-biofouling paint (Leon and Warnken, 2008). Copper concentrationsmeasured in oysters collected from the eastern shoreline of thestudy area characterise minimally impacted conditions (Richardset al., 2010).

2.2. Model description

A conceptualisation of the stochastic copper (Cu) speciationmodel (SCuSM) used in this study is presented in Fig. 2. In thismodel, Cu is assumed to form ionic bonds with hydrous (OH−)and carbonate (CO3

2−) ions, which dominate dissolved inorganic Cuspeciation in seawater (Rijstenbil and Gerringa, 2002). The modelalso allows for complexation with other dissolved inorganic lig-ands in seawater including chloride (Cl−) and sulfate (SO4

2−) ionsalthough these interactions are expected to play much smaller rolesin Cu complexation (Millero and Schreiber, 1982).

The model also accounts for Cu forming complexes withdissolved organic matter (DOM). Characterising the interactionbetween DOM and Cu is not straightforward due to the com-plex polymeric properties of DOM itself (Shank et al., 2004). Oneapproach is to use discrete classes of non-specific ligands that canbe described by simple coordination complexation theory (Hirose,1994). Often two-ligand class models are employed where one classrepresents ‘strong’ ligands and one for ‘weak’ ligands (Hirose, 1994;Midorikawa and Tanoue, 1994). In the absence of any site-specificfield data, we use DOM as the calibrating parameter and thereforea single class approach is used to avoid problems of identifiablyduring calibration.

Cu adsorption to suspended particulates (particulate Cu) canaccount for a significant proportion of overall Cu speciation in sea-water (Turner and Millward, 2002). This particulate phase can bebroadly categorised into four functional groups: (i) inorganic, (ii)biogenic, (iii) hydrogenous (metal oxides, carbonates, sulfides andhumic aggregates) and (iv) anthropogenic (Turner and Millward,2002). The biogenic and hydrogenous particulates are the mostreactive to Cu (Turner and Millward, 2002) and are assumed here torepresent the organic and inorganic phases respectively. A two-siteadsorption model was employed to account for surface complex-ation, enabling the specification of strong and weak adsorptionsites respectively. Cu preferentially adsorbs to higher affinity sitesand when these start to become exhausted, Cu begins bindingwith lower affinity sites (González-Dávila et al., 2000). Particu-lates capable of adsorbing Cu are represented in the model byhydrous ferric oxides (HFO) (Dzombak and Morel, 1990), particu-late organic matter (POM) and phytoplankton biomass, calculatedfrom chlorophyll-a within the model and converted to biomassusing a conversion factor of 2% chlorophyll-a per dry weightbiomass (Bowie et al., 1985).

3. Speciation calculation

Speciation calculations are based on the method of princi-pal components (Morel, 1983; van der Lee, 1998; Richards et al.,2010), which requires that all species involved in the reactions are

R. Richards et al. / Ecological Modelling 222 (2011) 3559– 3567 3561

F s are

p

wrcfiGctstatfAe

3

uctt

l

Atf

ig. 2. Conceptualisation of the copper speciation model. Principal chemical specierobability density function.

ritten in terms of a set of principal components from which theemaining secondary components can be calculated. Species con-entrations are estimated using a modified Newton Raphson rootnding method whereby a solution vector (dC) is solved usingaussian elimination and a ‘polishing factor’ that is used to assist inonvergence (van der Lee, 1998). The Davies equation is employedo account for the non-ideal behavior of the dissolved inorganicpecies in the seawater (Coale and Bruland, 1988). For adsorptiono particulate surfaces, electrostatic effects are ignored based on thessumption that the high ionic strength of the water shields elec-rical binding sites (Buffle and De Vitre, 1993). Details about theundamentals of speciation calculations are provided in Appendix

while further explanatory information is provided in Richardst al. (2010).

.1. Temperature effects

The stability constants were adjusted for temperature effectssing the Van’t Hoff equation (1), where �H◦ is the enthalpyhange, R is the gas constant, T1 and T2 are the reference and actualemperature (K) respectively and K1 and K2 are the referenced andemperature adjusted stability constants respectively.

n(

K2

K1

)= −�H◦

R×

(1T2

− 1T1

)(1)

trigonometric function (2) is fitted to water temperature data forhe study area (EHMP, 2008) and is used to provide temporal trendsor this parameter. T̄ is the annual average value for temperature,

highlighted in circles. ‘pdf’ indicates values for principal species are drawn from a

TR is the annual range, t is Julian day, and tlag represents the phasedifference, where tlag is the calibrated parameter.

T = T̄ +(

TR

2× sin

2 × � × (t + tlag)365.24

)(2)

T̄ is modelled as a linear positive function of time to reflect expectedincrease in water temperature under future climate change. Thegradient of this underlying function is drawn from a normal dis-tribution that represents the difference between the current meantemperature for the study area (22.36 ◦C; EHMP, 2008) and the tem-perature changes projected for 2070 (mean = 24.2 ◦C, sd = 0.3 ◦C)based on six model scenarios (IPCC, 2007).

3.2. Model application

The model is applied to Moreton Bay, a large sub-tropicalcoastal embayment located at the southeast corner of Queensland,Australia (Fig. 1). Model parameters are summarised in Table 1.The model inputs consist of concentrations of total Cu (CuTOTAL),DOM, POM, HFO and phytoplankton biomass (as chlorophyll-a),along with water temperature, pH and the stability constants (K).

Concentrations of CuTOTAL, POM, phytoplankton chlorophyll-a (used to calculate phytoplankton biomass) along with pH aredrawn from probability density functions (pdfs) (Table 1). Lognor-mal pdfs for CuTOTAL and POM are derived from measurementsmade in Moreton Bay over a 12-month period (refer Fig. B.1). Spe-

cific details regarding this monitoring are provided in Richardset al. (2010). pH is used here as a determinant variable where val-ues are drawn from a logistic pdf derived from pH measurements(Fig. B.1) collected across Moreton Bay between 2005 and 2008

3562 R. Richards et al. / Ecological Modelling 222 (2011) 3559– 3567

Table 1Parameter probability density functions used in the model.

Parameter Distribution Distribution parameters Data no. Reference

Temperature Trignometric TR = 10.59 2623 EHMP (2008)– Tave = 22.4 EHMP (2008)– lag = 70 days EHMP (2008)Normal (24.2, 0.38)a IPCC (2007)

pH Logistic (8.22, 0.06) 2598 EHMP (2008)Normal (7.845, 0.053)b IPCC (2007)

Chl-a Lognormal (−0.06 to 0.61, 0.05 to 0.51)c 3600 EHMP (2008)

CuTOTAL Lognormal (0.95, 0.49) 67 Richards et al. (2010)POM Lognormal (1.37, 0.84) 94 Richards et al. (2010)HFO sOH – 5.365E−9d 4 Richards et al. (2010)HFO wOH – 2.146E−9e 4 Richards et al. (2010)

a Mean water temperature (year 2070) sampled from normal distribution based on six model scenarios (IPCC, 2007).b Mean pH (year 2100) sampled from normal distribution based on projections of IPCC (2007).

th) ul, 1990l, 1990

(atedpps

aBCntb

oltab

hD(DsFif

cMs

TC

S

c 12 separate lognormal probability density functions (one for each calendar mond Calculated from 1.073E−3 moles Fe/m3 × 5E−6 sites/mole (Dzombak and Moree Calculated from 1.073E−3 moles Fe/m3 × 2E−4 sites/mole (Dzombak and More

EHMP, 2008). The location parameter of this pdf is adjusted using negative linear trend to represent the expected decrease in pH dueo the ‘ocean acidification’ effect (Caldeira and Wickett, 2003; Orrt al., 2005; IPCC, 2007). The gradient of this underlying function israwn from a normal distribution that represents the variation ofH change in the future, based on the 2100 projections of oceanH (mean = 7.845, sd = 0.053) under different IPCC CO2 emissioncenarios (IPCC, 2007).

Input concentrations of phytoplankton are based on aver-ge monthly measurements of chlorophyll-a sampled in Moretonay across 60 sites over the period 2005–2008 (EHMP, 2008).hlorophyll-a concentrations are drawn from month-specific log-ormal distributions (refer Fig. B.2) and multiplied by a constanto convert these into total binding sites per volumetric unit ofiomass.

DOM, which here represents the concentration of dissolvedrganic matter that can complex with Cu, is modelled as a singleigand-type model (González-Dávila et al., 2000). This approach isaken because of the dearth of site-specific data for Moreton Baynd to enable identifiability (and avoid equifinality) during cali-ration (Beven, 2006).

Hydrogenous particulates are represented in the model byydrous ferric oxide (HFO) in the same manner as outlined byzombak and Morel (1990). Surface properties of surface area

m2 g−1) and the site density (sites per mole) are sourced fromzombak and Morel (1990). The total number of potential binding

ites available to the Cu is estimated from the average particulatee concentration measured in Moreton Bay at the two monitor-ng sites in January 2003. Concentrations of major ions are sourcedrom Cox (1998) and are presented in Table 2.

Inorganic stability constants used in the model (Table 3) areompiled from various thermodynamic databases (e.g. Sillen andartell, 1971; Dzombak and Morel, 1990). Organic stability con-

tants (also Table 3) are drawn from lognormal pdfs and are

able 2omposition of the seawater solution.

Species Concentration (�g l−1)

Na 9.608 × 106

K 0.380 × 106

Ca 0.397 × 106

Mg 1.122 × 106

Cl 17.301 × 106

C 0.130 × 106

S 2.500 × 106

ource: Cox (1998).

sed for chlorophyll-a. Only maximum and minimum ranges listed.).).

based on values obtained from studies that explicitly measuredthe interaction between Cu and phytoplankton (Hirose et al.,1982; Midorikawa and Tanoue, 1994) and between Cu and DOM(González-Dávila et al., 2000). pdfs were constructed based on theassumption that minimum and maximum values obtained in lit-erature represent the 2.5th and 97.5th percentiles of a log-normaldistribution as described in Arhonditsis et al. (2008).

A deterministic version of the model was calibrated against 12months of aqueous copper data collected from Moreton Bay (referRichards et al., 2010) by adjusting the concentration of DOM tominimise the least square error between observed and measureddissolved Cu. All stochastic variables (concentrations and stabilityconstants for chlorophyll-a and POM) were set to the mean valueof their respective pdfs.

The calibrated model was then run in its stochastic form fora simulation period of 100 years with a time step of one week.The model was coded within R (Ihaka and Gentleman, 1996; RDevelopment Core Team, 2009), an open-source software environ-ment for statistical computing and graphics.

4. Results

The model was calibrated by fitting a deterministic version to a12-month dataset consisting of dissolved Cu and adjusting the con-centration of DOM (the copper complexing component of dissolvedorganic matter). The model successfully converged for all chemicalspecies modelled. Fig. 3 highlights the resulting fitted data with[DOM] equal to 22.5 nM.

The calibrated model was subsequently run as a stochasticmodel for a 100-year simulation period (Figs. 4 and 5). Variationwas introduced into various model parameters by drawing val-ues from pdfs for CuTOTAL, POM, phytoplankton (as chlorophyll-a)and pH. Additionally, water temperature and pH were both super-imposed on an underlying linear trend that represented expectedchanges to these parameters due to climate change. Uncertaintywas embedded within these underlying trends by drawing valuesfor the gradient from normal distributions calculated from IPCC(2007) projections.

Mean pH decreased from 8.23 to 7.80 and mean water tem-perature increased from 22.4 to 24.3 ◦C over the 100-year period,reflecting the projected change in these drivers over the next cen-tury. The pCu2+ (negative logarithm of the cupric ion) ranged from

7.94 to 15.21 over the simulation period, which is equivalent to acupric concentration range of 1.52 × 10−8–6.2 × 10−16 M. A signif-icant (p < 2 × 10−16) negative trend was observed for the 100-yearperiod, changing the mean value from 10.72 (1.91 × 10−11) to 10.38

R. Richards et al. / Ecological Modelling 222 (2011) 3559– 3567 3563

Table 3Equations and stability constants (mean, standard deviation for log-normal distribution) used in the speciation model. Where a range of stability constants are shown (e.g.ALG s− + Cu2+) this represents the 2.5–97.5th percentile range for a log-normal distribution.

K Equation Reference

1013.998 OH− + H+ ⇔ H2O Sillen and Martell (1971)

107.29 HFO sOH + H+ ⇔ HFO sOH2+ Dzombak and Morel (1990)

107.29 HFO wOH + H+ ⇔ HFO wOH2+

10−8.93 HFO sOH ⇔ HFO sO− + H+

10−8.93 HFO wOH ⇔ HFO wO− + H+

10−5.85 HFO sOH + Ca2+ ⇔ HFO sOCa+ + H+

10−5.85 HFO wOH + Ca2+ ⇔ HFO wOCa+ + H+

102.89 HFO sOH + Cu2+ ⇔ HFO sOCu+ + H+

100.6 HFO wOH + Cu2+ ⇔ HFO wOCu+ + H+

109.12 POM sH + H+ ⇔ POM sH2+ González-Dávila et al. (2000)

109.12 POM wH + H+ ⇔ POM wH2+

10−6.68 POM sH ⇔ POM s− + H+

10−6.68 POM wH ⇔ POM w− + H+

109.12 ALG sH + H+ ⇔ ALG sH2+

109.12 ALG wH + H+ ⇔ ALG wH2+

10−6.68 ALG sH ⇔ ALG s− + H+

10−6.68 ALG wH ⇔ ALG w− + H+

109.52–1012.05 ALG s + Cu2+ ⇔ CuALG s+ González-Dávila et al. (2000);Rijstenbil and Gerringa (2002)108.51–1010.67 ALG w + Cu2+ ⇔ CuALG w+

109.52–1012.05 POM s + Cu2+ ⇔ CuPOM s+

108.51–1010.67 POM w + Cu2+ ⇔ CuPOM w+

1010.89–1013.8 DOM s + Cu2+ ⇔ CuDOM s+ Hirose et al. (1982)

107.05–1011.8 DOM w + Cu2+ ⇔ CuDOM w+ Midorikawa and Tanoue (1994)

106.0 OH− + Cu2+ ⇔ CuOH+ Sillen and Martell (1971)1013.18 2OH− + Cu2+ ⇔ Cu(OH)2

1014.42 3OH− + Cu2+ ⇔ Cu(OH)3−

1014.56 4OH− + Cu2+ ⇔ Cu(OH)42−

1017.28 2OH− + 2Cu2+ ⇔ Cu2(OH)22+

106.73 CO32− + Cu2+ ⇔ CuCO3

109.83 2CO32− + Cu2+ ⇔ Cu(CO3)2

2−

10−0.18 OH− + Na+ ⇔ NaOH Millero and Schreiber (1982)

101.299 OH− + Ca2+ ⇔ CaOH+ Millero and Hawke (1992)

1010.32 CO32− + H+ ⇔ HCO3

− Sillen and Martell (1971)100.886 CO3

2− + Na+ ⇔ NaCO3− Byrne and Miller (1985)

103.2 CO32− + Ca2+ ⇔ CaCO3 Millero and Hawke (1992)

101.80 Cu2+ + HCO3− ⇔ CuHCO3

+ Byrne and Miller (1985)102.363 Cu2+ + SO4

2− ⇔ CuSO40 Millero and Hawke (1992)

100.43 Cu2+ + Cl− ⇔ CuCl+ Sillen and Martell (1971)100.16 Cu2+ + 2Cl− ⇔ CuCl 0

((

pTootpw

fdaaatt0p

w

2

10−2.29 Cu2+ + 3Cl− ⇔ CuCl3−

10−4.59 Cu2+ + 4Cl− ⇔ CuCl42−

4.12 × 10−11), more than doubling the cupric ion concentration115% increase).

The output plots for CuD and CuP indicate approximately evenartitioning of Cu between the dissolved and particulate phases.his partitioning can be further assessed by examining the ratiof dissolved copper to total copper (CuD:CuT), which showed anverall mean of 0.46 for the simulation period indicating slightendency towards particulate forms of Cu. A significant (p < 0.05)ositive trend was observed over the 100-year simulation periodhereby this ratio increased by ca. 5%.

CuP was dominated by adsorption to POM (Fig. 5a–c), accountingor approximately 86% of the total Cu-particulate budget. A Stu-ent’s t-test indicated that Cu was equally divided between strongnd weak affinity POM sites. Hydrous ferric oxides accounted forpproximately 14% of CuP (Fig. 5d and e) and this was dominated bydsorption to strong affinity sites over weak affinity sites (Student’s-test; p < 2.2 × 10−16). Phytoplankton was a minor contributor tohe CuP budget (Fig. 5f and g), being responsible for approximately

.001%. No significant linear trends were observed in any of thearticulate species over the simulation period.

The dissolved fraction of Cu was dominated by complexationith DOM (Fig. 5h), which accounted for 94% of CuD. The remaining

portion of CuD comprised of copper hydroxides (CuOH+ andCu(OH)2 mainly) and the cupric ion. Significant negative lineartrends were observed for Cu(OH)2, Cu(OH)3 and Cu(OH)4 while asignificant positive trend was observed for Cu(CO3)2.

5. Discussion

We have presented here a stochastic metal speciation modelfor exploring the combined short-term and long-term effects oftemperature and pH changes for a coastal system. This model hasbeen demonstrated using Cu, a common pervasive contaminant incoastal waterways (Eisler, 1998), and applied at a time period thatis concomitant with climate change projections (100 years). Thisapproach allows short-term perturbations in pH and sea surfacetemperature (SST) that naturally characterise coastal waters to besuperimposed on long-term trends. By using a stochastic model, weexplicitly accounted for the uncertainty that is inherent to many ofthe models parameters, specifically the stability constants, concen-

trations of the dominant copper adsorption/complexation ligandsand the rate of change for SST and pH.

The mean concentration of cupric ion, Cu2+, estimated over theentire simulation period was 3.1 × 10−11 M (pCu2+ = 10.51), which

3564 R. Richards et al. / Ecological Modelli

Fig. 3. Calibrated model output plotted against measured for dissolved copper.

Fig. 4. Selected outputs from the model: (a) pH; (b) p(Cu2+); (c) Cu total; (d) particulate Cand CuD:CuT indicates significant (<0.05) trend. (For interpretation of the references to co

ng 222 (2011) 3559– 3567

is comparable to the cupric concentration reported in several stud-ies where Cu2+ has been measured in seawater (Muller, 1996; Apteet al., 1990; Hirose et al., 1982). The model predicts that the meanpCu2+ in Moreton Bay will increase by approximately 115% dueto the combined effect of sea surface temperature increase andocean acidification (pH decrease). The implications of any increasein Cu2+ will include greater stress on aquatic species especiallyfor those species that have a low tolerance to Cu2+. For example,the cyanobacterium Synechoccus sp., which is a natural regulatorof copper toxicity through the production of chelating exudates(Moffett, 1995; González-Dávila et al., 2000) has been shown tohave a lowered growth rate in the presence of increased Cu2+ con-centrations (Brand et al., 1986). As the biological production ofexudates may be a significant source of copper-complexing DOM inseawater (Moffett, 1995), reduced growth rate may lower exudateproduction, which would lower DOM through a feedback process.

The modelling approach used here relied on thermodynamicstability constants and acid–base chemistry to estimate the distri-bution of chemical species involved. Therefore a major assumptionunderpinning the model was that the system was at equilibrium.While this is a standard assumption in speciation calculations, the

inherent limitation of this assumption when modelling dynamicenvironments is recognised (Turner and Millward, 2002). An alter-native approach would be to use kinetic rate constants, which

u; (e) dissolved Cu; (f) ratio of dissolved to total Cu. Inclusion of red line for p(Cu2+)lour in this figure legend, the reader is referred to the web version of this article.)

R. Richards et al. / Ecological Modelling 222 (2011) 3559– 3567 3565

F uPOMC

wtsMam

ig. 5. Selected outputs from the model: (a) CuPOMs; (b) CuPOMw; (c) ratio of CuDOM; (i) inorganic dissolved copper.

ould allow non-equilibria conditions to be modelled. However,hermodynamic stability constants, for dissolved inorganic con-

tituents at least, are well understood and quantified (Sillen andartell, 1971) whereas kinetic rate constants for rapid processes

re more sensitive to uncertainty (Hofmann et al., 2008). Further-ore, many complexation reactions between dissolved inorganic

s to total CuPOM; (d) HFO sOCu; (e) HFO wOCu; (f) Cu PHY s; (g) Cu PHY w; (h)

species (Zeebe and Wolf-Gladrow, 2001) and even adsorption tobiogenic particulates (González-Dávila et al., 2000) are rapid, last-

ing a matter of seconds to minutes, and may therefore be nearequilibrium, especially at time steps of one week.

The speciation model was calibrated by adjusting the concen-tration of Cu complexing dissolved organic matter (DOM) and

3 odelli

fits2ofwro(3sw

roDfitcaoHtvotcatkco

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566 R. Richards et al. / Ecological M

tting the model to a dataset of dissolved Cu concentrations forhe study area. DOM is known to play a dominant role in Cupeciation in coastal waters (Moffett, 1995; Strandesena et al.,007), however site-specific data for this chelator is often sparser unavailable as was the case for Moreton Bay. Calibration there-ore provides a means of estimating the concentration of DOM,hich was observed to be 27 nM. This concentration is compa-

able to those observed in studies that have explicitly focusedn eliciting this parameter in seawater. For example, Apte et al.1990) found that DOM for the Severn Estuary ranged from 13.3 to7.4 nM (log K = 11.72–12.39) while Vasconcelos et al. (2002) mea-ured DOM to be 72 nM (log K = 12.03) for the northwest coastalaters of Portugal.

It is noted that key assumptions and requirements were stillequired for calibrating the model, specifically that a single ‘class’f DOM was sufficient in the model and that a stability constant forOM–Cu (KDOM–Cu) still needed to be specified. Regarding therst point, we acknowledge that the complex polymeric proper-ies of DOM are unlikely to be well described by a single ligandlass model. Studies have shown that a two-ligand class provides

more satisfactory representation of the complexing capabilitiesf DOM than a single class approach (Hirose, 1994; Moffett, 1995).owever, this is tempered in that demarcation between classes is

ypically arbitrary and therefore the derived stability constants canary significantly between studies (Hirose, 1994). In the absencef site-specific data (and therefore the need for reliance on litera-ure values from other study areas), we see the selection of a singlelass over a two-ligand class approach as a compromise betweenccuracy and pragmatism. Regarding the second point, fitting DOMhrough calibration still requires that either DOM or KDOM–Cu isnown. We feel that there is greater utility in estimating the con-entration of DOM for the study area even if this estimate is basedn an estimate of KDOM–Cu sourced from another study area.

The full stochastic model was constrained to using a constantOM concentration throughout the 100-year simulation periodhile other parameters (pH, CuTOTAL, Chl-a and POM) were drawn

rom pdfs. The results of this is seen in the concentration of Cu com-lexed to DOM (Fig. 5h), where there is a clear upper limit at ca..75 �g l−1, indicating that all the available binding sites have beenccupied by Cu. DOM in coastal waters is unlikely to be constant;ather it will be a function of various biogeochemical and phys-cal processes such as the production of algal exudates (Moffett,995; González-Dávila et al., 2000) and will vary both spatiallynd temporally at a range of scales. As highlighted already, theain motivating factor for using a constant value for DOM was the

bsence of site-specific data for DOM and therefore the concentra-ion obtained here through calibration provides at least a baselineor further research into this parameter.

We also imposed a pH on the system in the model rather thaneriving it from the proton activity even though pH is dependentn the biogeochemical processes that are occurring (Stumm andorgan, 1996). Our approach allowed pH to be directly manipu-

ated inline with long-term projections of ocean acidification (e.g.PCC, 2007; Caldeira and Wickett, 2003). There is strong utility inncluding pH as a dependent variable (rather than as a determinant)hat is coupled to CO2 concentrations via the carbonate cycle. How-ver, other factors such as biogenic production, riverine inputs andeasonal upwelling need also to be considered when determiningH, which is not a trivial process (Oschlies et al., 2010).

We applied a number of dissolved inorganic acid-equilibriaeactions in our model even though it was known a priori thatany of these reactions would play minor (almost zero) role in

he overall dynamics of Cu. Such an approach increases the com-utational demand of the model (Hofmann et al., 2008) althoughests indicated that in our case, the removal of ‘minor’ contributorsid not dramatically lessen run time. However, the composition of

ng 222 (2011) 3559– 3567

the dissolved inorganic fraction of copper, including minor species,is often of interest, especially the cupric ion (Brand et al., 1986).

Overall, this model provides a platform for developing morecomprehensive models for assessing the effects of OA and increasedSST on biogeochemical cycles in coastal areas. However, we pro-vide the caveat that even though we have generated projections forwater quality in our study area for the next century, we considerthis type of model as an investigative tool rather than a predic-tive tool as outlined by Blackford (2010). We also agree with thecomments of Blackford (2010), in that to progress a model of thistype, it needs to be embedded within a modelling framework thatexplicitly accounts for ecological and biogeochemical responses,feedback pathways, acclimation and adaptation responses, prefer-ably in the context of spatial heterogeneity. Models that providea framework for coupling biological, chemical and physical pro-cesses are important for providing a holistic perspective of coastalsystems, especially for better understanding a system within thecontext of climatic and non-climatic drivers.

Acknowledgements

We thank the Griffith Climate Change Response Program forproviding funding to undertake this research. We are also grate-ful to the Ecosystem Health Monitoring Program project team fortheir help in providing access to the water quality data. Finally theauthors would like to thank the two anonymous reviewers for theircontribution to enhancing this manuscript.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.ecolmodel.2011.08.017.

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