development of biotic ligand models for chronic manganese toxicity to fish, invertebrates, and algae

Environmental Chemistry

DEVELOPMENT OF BIOTIC LIGAND MODELS FOR CHRONIC MANGANESE TOXICITYTO FISH, INVERTEBRATES, AND ALGAE

ADAM PETERS,*y STEPHEN LOFTS,z GRAHAM MERRINGTON,y BRUCE BROWN,§y WILLIAM STUBBLEFIELD,kand KEVEN HARLOW#

ywca environment, Brunel House, Faringdon, Oxfordshire, United Kingdom

zCentre for Ecology and Hydrology, Lancaster Environment Centre, Bailrigg, Lancaster, United Kingdom

§Environment Agency, Wallingford, Oxfordshire, United Kingdom

kEnvironmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, USA

#International Manganese Institute, Paris, France

(Submitted 4 April 2011; Returned for Revision 2 May 2011; Accepted 18 July 2011)

Abstract—Ecotoxicity tests were performed with fish, invertebrates, and algae to investigate the effect of water quality parameters onMn toxicity. Models were developed to describe the effects of Mn as a function of water quality. Calcium (Ca) has a protective effect onMn toxicity for both fish and invertebrates, and magnesium (Mg) also provides a protective effect for invertebrates. Protons have aprotective effect on Mn toxicity to algae. The models derived are consistent with models of the toxicity of other metals to aquaticorganisms in that divalent cations can act as competitors to Mn toxicity in fish and invertebrates, and protons act as competitors to Mntoxicity in algae. The selected models are able to predict Mn toxicity to the test organisms to within a factor of 2 in most cases. Underlow-pH conditions invertebrates are the most sensitive taxa, and under high-pH conditions algae are most sensitive. The point at whichalgae become more sensitive than invertebrates depends on the Ca concentration and occurs at higher pH when Ca concentrations arelow, because of the sensitivity of invertebrates under these conditions. Dissolved organic carbon concentrations have very little effect onthe toxicity of Mn to aquatic organisms. Environ. Toxicol. Chem. 2011;30:2407–2415. # 2011 SETAC

Keywords—Metal toxicity Metal bioavailability Biotic ligand model Manganese Water quality guidelines

INTRODUCTION

The United Kingdom (UK) has commissioned a program toderive environmental quality standards (EQS) for substancesfalling under Annex VIII of the Water Framework Directive.These substances, commonly known as specific pollutants, arethose discharged into the environment in significant quantitieswith the potential to cause ecological impacts.

Not surprisingly, many ubiquitous metals, such as Mn, arecandidate-specific pollutants because they are present in meas-urable quantities in many effluent discharges. However, metalspresent some unique implementation challenges to regulatorscompared with synthetic organic chemicals. These challengescan arise from significant background concentrations, the exis-tence of different chemical species, and changes in this speci-ation in response to local physicochemical conditions.

The process of EQS derivation for the Water FrameworkDirective usually addresses uncertainty by the application ofsafety/assessment factors to laboratory toxicological data. Thepredicted-no-effect concentration (PNEC) derived from toxi-cological data is divided by this assessment factor, which canrange from 1 to 100 or more. For naturally occurring substances,such as metals, this degree of precaution can raise real diffi-culties when implementing an EQS, because the value may be ator below ambient background concentrations in freshwaters ormay be smaller than the window of essentiality, effectivelyresulting in deficiency in essential elements.

A draft U.K.-specific pollutant EQS for Mn was developedin 2006 and encountered these implementation difficulties.These difficulties can, of course, result in the derivation ofan EQS for a metal that bears little relationship to the organismsit has been designed to protect. Furthermore, the use of anoverly stringent EQS for a naturally occurring substance, ineither compliance assessment or permitting, would result inwidespread but meaningless failures. The cost of meetingthese spurious failures would be high, and they woulddeliver no tangible environmental benefit. The draft2007U.K. EQS for Mn (http://www.wfduk.org/LibraryPublic-Docs/sr12007-manganese) was not implemented within theWater Framework Directive for these reasons.

However, for many metals, a generic EQS is only part of theanswer when developing a robust regulatory approach. Typi-cally, generic values for environmental quality standards formetals are derived under physicochemical conditions that max-imize bioavailability. Therefore, without a mechanism toinclude the mitigating effects of the local prevailing waterchemistry, any assessment of compliance is likely to be overlyprecautionary. Regulators will often use either total or dissolvedmeasures of metal exposure to assess environmental risk infreshwaters, yet neither is an entirely accurate predictor ofaquatic toxicity because metals exist in the environment indifferent chemical species, and changes in this speciation,according to local physicochemical conditions, can lead todifferent toxicity as a result of varying amounts of bioavailablemetal [1].

Biotic ligand models (BLMs) allow chemical and biologicalinteractions to be taken into account more completely. Bioticligand models consider the local water chemistry and relatemetal toxicity to a dissolved concentration, which can then be

Environmental Toxicology and Chemistry, Vol. 30, No. 11, pp. 2407–2415, 2011# 2011 SETAC

Printed in the USADOI: 10.1002/etc.643

* To whom correspondence may be addressed([email protected]).

Published online 12 August 2011 in Wiley Online Library(wileyonlinelibrary.com).

2407

used in compliance assessment. Unlike many other speciation-based approaches, the BLMs have been rigorously tested in thelaboratory and field; they routinely predict toxic effects to manyaquatic taxa across a wide range of water chemistries to within afactor of 2. This variability is acceptable for routine ecotoxicitytesting [2].

The underlying theory of a BLM is not new; through the useof chemical equilibrium modeling, a BLM addresses competi-tion between the free metal ion and other naturally occurringcations, together with complexation by abiotic ligands, forbinding with a biotic ligand, the site of toxic action. Extensivetechnical reviews of the development of BLMs have beenpublished [3]. The use of BLMs in a compliance-based regu-latory framework is an area that has received substantialattention [4–8].

The present study documents the development of BLMs forthree aquatic species, representing three trophic levels. The testsfrom which the models were developed follow standard testingguidelines but were undertaken with a variety of different waterchemistries in order to provide information about the effect ofwater chemistry onMn toxicity to the test species. The develop-ment of models to describe the effects of water chemistry onMntoxicity is detailed, and model validation studies using naturalwaters are summarized.

MATERIALS AND METHODS

Chronic toxicity testing was undertaken on three species,representing three trophic levels. Tests were undertaken byvarying a single water quality parameter while minimizingvariation in other water quality parameters. The effects ofthe following water quality parameters were investigated: cal-cium, magnesium, sodium, and pH. These were selected on thebasis of the results of previous testing and summary reports [9].All of the testing followed standard Organisation for EconomicCo-operation and Development (OECD) or U.S. EnvironmentalProtection Agency (U.S. EPA) test methods.

Manganese chloride (MnCl2) was used as the test materialfor all tests. The test substance, reagent-grade Mn(II) chloride(MnCl2, anhydrous, CAS 7773-01-5, lot J25R029 and lotI04T038), was received from Alfa Aesar. Both lots of testmaterial had a reported purity of 99%. In all studies, exposureconcentrations were confirmed through the analysis of samplesthat had been checked for total and dissolved Mn using aPerkinElmer AAnalystTM 800 high-performance atomicabsorption spectrometer. Samples were analyzed using flameor graphite furnace atomic absorption according to U.S. EPAMethods 243.1 and 243.2. Total and mean dissolved Mn con-centrations were calculated and reported as the measured Mnconcentrations for the test. Baseline water with a low concen-tration of dissolved ions, which was prepared by combiningwell water with deionized water, was used for the toxicity testsand amended by the addition of salts to provide variation in thewater chemistry. This effectively represents a control for thevariation in water chemistries studied.

Dissolved organic carbon tests were conducted through themixing of Ogeechee River (Georgia, USA) water with thebaseline water and also through the addition of Suwannee RiverIsolate (53% carbon) to the baseline water. For the OgeecheeRiver, a 5-to-l sample of natural organic matter isolate wasobtained from Stephen Klaine (Clemson University). Thedetails of isolation and characterization have been describedby Ryan et al. [10]. For the Suwannee River, a 500-mg sampleof natural organic matter isolate was obtained from the Interna-

tional Humic Substances Society (IHSS; catalog 1R101N). Theisolation and characterization were performed in accordancewith IHSS standard procedures [11]. The dissolved organiccarbon control water was prepared 1 d prior to test initiation, toallow sufficient time for equilibration.

Tests on invertebrates were conducted according tostandard U.S. EPA [12] methodologies for short-term chronictoxicity testing. The water flea, Ceriodaphnia dubia, was usedin testing. All tests were initiated with neonates (<24 h old)from the laboratory’s in-house cultures. Ceriodaphnia dubiawere cultured in house in moderately hard reconstituted water(with a hardness of �100mgL�1 as CaCO3) and laboratory-blended water (well water blended with reverse osmosis-treatedwater with a targeted hardness of 100–120mgL�1 as CaCO3).Parental organisms were isolated onto brood boards in order toobtain neonates for testing.

The test chambers were housed in a temperature-controlledenvironmental chamber designed to maintain the test temper-ature at 25� 28C. Lighting was controlled to provide a 16:8-hlight:dark cycle, using cool-white fluorescent lighting. At testinitiation, a single<24-h-old neonate was randomly placed intoeach test chamber for a total of 10 organisms per concentration.Each test chamber was then randomly assigned to a locationwithin the temperature-controlled environmental chamber.Each test chamber was fed 0.3ml of algae (Pseudokirchneriellasubcapitata) and yeast/trout chow/cereal leaf (YTC) suspensionat test initiation and once daily after water renewal, to providebetween 5 and 7mgL�1 food dry weight in the test media.Temperature, dissolved oxygen (DO), pH, and conductivitywere measured in each concentration daily in the newly pre-pared renewal waters. Temperature, DO, and pHwere measureddaily in the old waters (i.e., prepared the previous day). Hard-ness, alkalinity, total residual chlorine, and ammonia (NH3)were measured at test initiation.

Tests on fish were conducted according to standard U.S.EPA [12] methodologies for short-term chronic toxicity testing.The test organisms, fathead minnow (Pimephales promelas),were obtained from the laboratory’s in-house culture, and alltests were initiated with <24-h-old larvae. All exposures wereconducted in 1-L plastic cups (polypropylene) or 250-ml glassbeakers. Each beaker held approximately 200 to 250ml of testsolution. The test chambers were housed in a temperature-controlled environmental chamber designed to maintain thetest temperature at 25� 28C. Lighting was controlled to providea 16:8-h light:dark cycle, using cool-white fluorescent lighting.

At test initiation, 10 larval fathead minnows (<24 h old)were distributed into each test chamber that had been randomlyassigned to a location within the environmental chamber.Organisms were fed 0.15ml per chamber of a concentratedsuspension of brine shrimp (Artemia salina) nauplii twice a day.The amount of food added to the test chambers was docu-mented, and all chambers containing live fish received an equalamount. Temperature, DO, pH, and conductivity were meas-ured in each concentration daily in the newly prepared renewalwaters. Temperature, DO, and pH were measured daily in theold waters. Hardness, alkalinity, total residual chlorine, andammonia (NH3) were measured at test initiation.

Observations of live and dead fish were conducted on a dailybasis, and dead fish were removed immediately. At test termi-nation, the fish in each test chamber were counted, examined forgrossly observable abnormalities, and sacrificed via immersionin ethyl alcohol. The fish from each replicate were then placedinto a preweighed pan and dried at 1058C for over 12 h and thenreweighed to the nearest 0.01mg to obtain a dry weight.

2408 Environ. Toxicol. Chem. 30, 2011 A. Peters et al.

The algal studies were conducted according to standardOECD [13] methodologies for toxicity testing with the fresh-water unicellular green alga, Pseudokirchneriella subcapitata.Algae were obtained from a laboratory in-house culture thatoriginally had been obtained from Aquatic Biosystems. Pseu-dokirchneriella subcapitata was cultured using U.S. EPAmedium specified by the OECD [13]. The cultures were grownat 24� 28C under continuous fluorescent lighting and werecontinuously aerated. At test initiation, cultures were 3 to 4 d oldand in log-phase growth. All tests were conducted without theaddition of ethylenediaminetetraacetic acid (EDTA). The dilu-tion water was amended with U.S. EPA algal nutrient stockswithout EDTA and adjusted to a pH of 7.5� 0.1 with diluteNaOH or HCl (with the exception of the low- and high-pHtests). To control pH, 3-(N-morpholino)propanesulfonic acidwas added at a concentration of 750mgL�1. The mediumwas then sterilized by filtration using a vacuum filtrationapparatus with a 0.2-mm membrane filter. The test chamberswere housed in a temperature-controlled environmental cham-ber designed to maintain the test temperature at 24� 28C.Lighting was controlled to provide continuous illuminationof 4,306� 431 lux. Illumination was measured by a photometer(SPER Scientific).

Algal inoculum was harvested from 3- to 4-d-old culturesgrown in standard U.S. EPA test medium. Approximately100ml of the stock culture was centrifuged for approximately30min. The resulting clear supernatant was decanted, and thealgae were resuspended using test dilution water. This centri-fugation–decantation–resuspension process was repeated twoadditional times. The cell density of the resulting algae sus-pension was determined using a hemocytometer. The algaesuspension was then diluted with the test dilution water toachieve a target inoculum density of approximately 1.0� 106

cells ml�1. The test was initiated by delivering 0.375ml ofalgal inoculum (cell density¼ 1.0� 106 cells ml�1) to each testchamber containing 75ml of test solution, resulting in a nominalcell density of 5� 103 cells ml�1 per chamber. The inoculumwas distributed to the prepared test solutions after randomizingthe test chambers. The test chambers were rerandomized dailyduring test incubation in order to control for variations oflighting or temperature that may have been present in theenvironmental chamber. The test chambers were hand-shakenafter initiation and twice daily for the duration of the test. At testinitiation, three specially prepared surrogate test chambers wereinoculated and sampled for cell density verification purposes.Verification of cell inoculation was performed, but, when usingcell density determination methods, it is difficult to obtainan accurate estimate of the cell density when densities areas low as 10,000 cellsml�1; therefore, the nominal value of5,000 cells ml�1 was used in calculations.

In all of the tests, Ca, Mg, Na, and K were measured viainductively coupled plasma atomic emission spectroscopy.Chloride and sulfate were measured using ion chromatography,and dissolved organic carbon was measured by combustion.Electrical conductivity was not applied as a quality criteria inthese tests because the composition of the media was altered forthe different tests in order to assess the effect of water compo-sition on Mn toxicity.

Validation testing is required to determine whether the MnBLMs developed from the ecotoxicity tests can accuratelypredict toxicity to test species in natural waters. In line withthe validation testing performed for other BLMs, a series ofnatural waters exhibiting a range of physicochemical character-istics typical of waters throughout the European Union (EU)

was used for the Mn BLM validation. The natural watersselected for testing were chosen to cover a variety of differentwater types, such as both soft–acidic and hard–alkaline watersand waters with different concentrations of dissolved organicmatter. Normally, it is not practical to cover the entire range ofpossible water conditions within such a validation exercise,although it is important to demonstrate that the Mn BLMs arecapable of correctly predicting the different responses of testorganisms to changes in abiotic water conditions. Combinationsof water quality conditions that are particularly important forMn ecotoxicity were considered, such as low hardness andlow pH. The five natural waters used in the validation exercisewere chosen based on the range of bioavailability expected innatural systems. The chronic toxicity of Mn to P. promelas,C. dubia, and P. subcapitata was determined in each of thenatural waters using the standard chronic testing proceduresoutlined above. Results from the empirical tests were thencompared with BLM-predicted values based on the character-istics of the test waters.

MODEL DEVELOPMENT

The toxicity tests for fish covered a range of pH from 5.8 to8.4, Ca from 0.16 to 4.7mM, and K from 7 to 190 mM. Thecorresponding free ion activities were 4� 10�9 to 1.8� 10�6

for Hþ, 0.1 to 2.3mM for Ca2þ, and 6 to 160 mM for Kþ.Invertebrate tests covered a range from 5.7 to 8.7 for pH, 0.17 to5mM for Ca, and 0.08 to 3.7mM for Mg. The correspondingranges of activities were 2.0� 10�9 to 2.0� 10�6 for Hþ, 0.14to 2.9mM for Ca2þ, and 0.05 to 2.5mM for Mg2þ. Algal testscovered a pH range from 5.8 to 8.4 and 0.3 to 3.4mM for Ca.Activities in these tests ranged from 4.0� 10�9 to 1.6� 10�6

for Hþ and 0.2 to 2.1mM for Ca2þ. Dissolved organic carbonwas not added to the majority of tests but was added to a smallnumber of the tests on fish and invertebrates at levels between4 and 13mgL�1.

In previous freshwater BLM development work [14], con-centrations of potentially competing ions (e.g., Hþ, Naþ,Mg2þ, Kþ, Ca2þ) were varied singly, and biotic ligand bindingconstants were derived from the slope of a plot of the toxicendpoint activity of the metal against the activity of eachcompeting ion. Inspection of this data set showed that thevariations in activity of each competing ion in the test solutionwere not sufficiently univariate to allow this method to beapplied. For example, relatively small changes in pH, Mg,Na, and K concentrations in Ca tests meant that variations inthe response could not be attributed to Ca alone. The Mg2þ

and Kþ activities in the suite of Ca tests on fish varied byapproximately an order of magnitude, and Hþ and Naþ activ-ities varied by approximately a factor of 2. Therefore, thealternative method of Thakali and coworkers [15,16] was used.This method involves the use of all the dose–response datagenerated rather than a single endpoint from each water com-position. The method makes the assumption that toxicity isdirectly related to the concentration of toxic metal bound to thebiotic ligand and fits a single dose–response curve to the entireset of data according to Equation 1.

Rx ¼ R0

1þ fMBL

fMBL;LðEÞC50

� �b(1)

where Rx is the calculated response, R0 is the control response,fMBL is the fraction of the biotic ligand sites occupied by thetoxic metal, fMBL, L(E)C50 is the fraction of the biotic ligand

Manganese biotic ligand model Environ. Toxicol. Chem. 30, 2011 2409

occupied by toxic metal when a 50% effect is seen, and b is theslope of the dose–response curve. The fraction of biotic ligandsites occupied can be calculated if the activities and bindingstrengths of the toxicmetal and all significant competing ions areknown, without the need to know the total concentration of thebiotic ligand, according to Equation 2

fMBL ¼ KM � aM

1þ KM � aM þPi1

Ki � ai

(2)

where KM is the constant for binding of the toxic metal to thebiotic ligand, aM is its solution activity, and Ki and ai are thebinding constants and activities of all significant competing ions.Thus the number of parameters to fit is three ( fMBL, L(E)C50, KM,b) plus the number of competing ions. In practice, because theparameters are not independent of each other, it is necessary tofixfMBL, L(E)C50 to a reasonable value prior to fitting the otherparameters.

Data fitting was performed by first calculating the activitiesof Mn2þ and competing ions in toxicity tests using WHAM6[17] and setting a reasonable value of fMBL, L(E)C50. The otherparameters were then fitted, including binding constants forcompeting ions, using a stepwise approach. A baseline fit,considering no competing ions, was initially made. Fits werethen obtained by considering each potentially competing ion inturn, fitting the log10KMBL, log10 KMnBL, and b values. Whenthe improvement in the fit relative to the baseline was found tobe statistically significant, the effect of the ion was subsequentlytested in combination with each of the other ions in turn. Thisprocess was repeated, if necessary, with combinations of threeor more ions, until no further improvements to the fit werepossible. The stepwise improvement in fit resulting from theinclusion of an additional competing ion was assessed bylikelihood ratio testing of the fits in the absence and presenceof the competing ion [18], with p< 0.05 as the criterion forstatistical significance.

Initial testing showed that the choice of fMnBL,EC50 influ-enced the fitted value of log10 KMnBL but not the values ofbinding constants for competing ions. An order of magnitudeincrease in the chosen fMnBL,EC50 produced an increase in thefitted log10 KMnBL of unity, and vice versa. The binding con-stants for other competing ions with the biotic ligand are notaffected by changes to the value of fMnBL,EC50. The effect of thechoice of fMnBL,EC50 on the fitting error was negligible, except atrelatively high values (>0.05). Pragmatically, it was decided toset fMnBL,EC50 to 0.01 in all cases (equivalent to 1% occupancyof the biotic ligand by Mn at the 50% effect level in thetoxicity tests).

The ions Hþ, Naþ, Mg2þ, Kþ, and Ca2þ were testedfor competition effects for each species. For fish, both Ca2þ

and Kþ had a significant competitive effect. The results of thefitting are shown in Figure 1. The fit is good (R2¼ 0.82, rootmean squared error in the response¼ 14.0) but with appreciablescatter in the data.

For invertebrates both Ca2þ and Mg2þ had significantcompetitive effects. The model, taking account of competitionfrom both of these competing ions, is shown in Figure 2. It isnotable that, at low effect levels (up to 20%), the horizontalscatter in the data is appreciably larger than that seen at highereffect levels.

The effect of Mn toxicity on algae was influenced only bycompetition from protons (Hþ). Data for the growth endpointonly are shown in Figure 3. Greater effects were observed for

the biomass endpoint in the algal studies, and in all cases themedian effective concentration (EC50) on the growth endpointare extrapolated.

The results of the fitting procedure are shown in Table 1. Thelog10KMBL values represent the binding affinities for the differ-ent ions to the biotic ligand, and the slope term describes theslope of the dose–response curve. Table 1 also includes the rootmean square error (RMSE) in log(dissolved Mn) at the EC50.A relatively large error is seen for the invertebrate modelwith both Ca and Mg as competing ions. This is due to asingle outlier from a low-pH test for which the error islarger for the two-competing-ion model than for either of the

Fractional occupancy of the biotic ligand, fMnBL

10-6 10-5 10-4 10-3 10-2 10-1

Fish

Gro

wth

(% re

lativ

e to

con

trol)

0

20

40

60

80

100

120

140

160

Fig. 1. Manganese toxicity to Pimephales promelas (growth), expressed asthe fractional occupancy of the biotic ligand (BL). The solid line is the fitteddose–response curve.


10-6 10-5 10-4 10-3 10-2 10-1

Inve

rtebr

ate

Rep

rodu

ctio

n (%

rela

tive

to c

ontro

l)

0

20

40

60

80

100

120

140

Fig. 2. Manganese toxicity to Ceriodaphnia dubia (reproduction),expressed as the fractional occupancy of the biotic ligand (BL). The solidline is the fitted dose–response curve.


single-competing-ion models. A relatively large RMSE is alsoseen for the algal growth model when predicting the EC50,although all EC50 values for algal growth are extrapolations.The prediction error is lower at the EC10 (growth) level andcomparable to the biomass model at this effect level.

Further refinement of the models for fish and algae wasundertaken. For algae it was considered to be reasonable that theeffects on the two different endpoints considered were bothresponses to the same common stressor (i.e., the accumulationof Mn at the biotic ligand) but with differing sensitivities to agiven level of Mn accumulation. Thus, the two sets of dose–response data should be described using a common set of bioticligand binding parameters but different fMnBL, EC50 values. Thiswas achieved by fitting a single set of binding constants for Mnand protons at the biotic ligand and simultaneously fittingfMnBL,EC50 for the biomass endpoint while keeping fMnBL,EC50

for the growth endpoint fixed at 0.01. Separate slope values foreach endpoint were also fitted. The resulting model is shownwith the full data set in Figure 4. The revised model parametersare log10KMnBL 2.25, log10KHBL 8.42, fMnBL,EC50 (biomass)0.00101, slope (growth) 0.78, and slope (biomass) 1.03. Therevised model is preferred over the two separate models of Mn

toxicity to algae because the effects observed on two differentendpoints are considered to be different responses to the samecommon stressor (i.e., the accumulation of Mn at a receptorsite).

A more complex model for fish toxicity was consideredbecause of the possibility that of an upper concentration limit tothe protective effect of Ca on metal toxicity. Such an effect hasbeen observed in previous BLM development studies [19,20].The usual approach to handle data of this type in modeldevelopment has been to develop a model that applies onlyto the concentration range over which the effect of the com-peting ion is linear. This may, as a consequence, limit theapplicability of the model to high-water-hardness conditions.

Two-site fish model

It is already known from previous BLM modeling that thesignificant protective cations in the fish data set are Kþ

and Ca2þ, so investigation of the possibility of limits to com-petition focused on these two cations. Within the fish toxicitydata set, seven tests could be identified in which Kþ activity wasapproximately constant but Ca2þ activity varied. In thesetests, Kþ activity varied between 6.35 and 7.00 mM,whereas Ca2þ activity varied from 0.14 to 0.90mM. Thesedata indicate a trend similar to trends observed previously inother BLM development studies [19], in which an upper limit tothe protective effect afforded by some competing ions isapparent.

At Ca2þ activities below approximately 10�3M, the increasein Mn2þ activity is linearly related to the increase in Ca2þ

activity, in accordance with BLM theory. However, at higheractivities of Ca2þ, the increase in Mn2þ at the 50% effect leveldrops off, implying a limit to the protective influence ofthe Ca2þ cation. Prediction of the Mn2þ activity at the 50%effect level in these data, using the standard (single-site)BLM, results in aMn2þ, EC50 being overestimated for the points


10-6 10-5 10-4 10-3 10-2

Alg

al R

espo

nse

(% re

lativ

e to

con

trol)

60

70

80

90

100

Fig. 3. Manganese toxicity to Pseudokirchneriella subcapitata (growth),expressed as the fractional occupancy of the biotic ligand (BL). The solid lineis the dose–response curve.

Table 1. Stability constants for the binding of Mn and competing ions tothe biotic ligand (BL) for different models

Organism

Log10 KBL

Slope RMSEaMn2þ Ca2þ Mg2þ Hþ Kþ

Fish Ca 2.63 3.93 — — — 2.51 0.18Fish K 2.1 — — — 3.88 1.47 0.30Fish Ca and K 2.68 3.95 — — 4.05 2.52 0.17Invert Ca 2.05 2.73 — — — 2.63 0.19Invert Mg 2.07 — 2.87 — — 2.85 0.18Invert Ca and Mg 2.23 3.07 3.08 — — 3.14 0.21Algae (growth) 2.09 — — 8.23 — 0.76 0.51Algae (biomass) 3.26 — — 8.43 — 1.04 0.21

a Root mean squared error.


10-6 10-5 10-4 10-3 10-2

Alg

al R

espo

nse

(% re

lativ

e to

con

trol)

0

20

40

60

80

100

Fig. 4. Manganese toxicity to Pseudokirchneriella subcapitata (opencircles, biomass; solid circles, growth), expressed as the fractionaloccupancy of the biotic ligand (BL). The bottom curve indicates thedose–response for biomass, and the top curve indicates the dose–response forgrowth. Both effects are predicted using a single set of BLM constants, withfMnBL allowed to vary.


at which Ca2þ activity is at the upper end of the exposure range,as shown in Figure 5.

It has been suggested that the reasons behind the apparentlimitation to the protective effect of some competing ions maybe the presence of multiple biotic ligands, each having differention binding properties [21]. The binding of a toxic metal to theseadditional ligands may be relatively unimportant except underconditions of high competing ion concentrations, when the mainbiotic ligand site is effectively blocked to the toxic metal by thecompeting ions. The modeling approach is consistent with thistheory.

The trend in aMn2þ, EC50 can be explained by invoking atwo-site BLM, comprising a site to which Mn2þ, Kþ, and Ca2þ

can bind and an additional site to which only Mn2þ can bind.Expressions for the occupancy of these sites by Mn2þ as afunction of Kþ and Ca2þ activities are

fMnBL;1 ¼ KMnBL:1aMn2þ

1þ KMnBL:1aMn2þ þ KKBL:1aKþ þ KCaBL:1aCa2þ

(3)

fMnBL;2 ¼ KMnBL:2aMn2þ

1þKMnBL:2aMn2þ(4)

and the toxic effect of Mn is modeled using the expression

R ¼ 100

1þ fMnBL;1 þ fMnBL;2

fMnBL;EC50

� � (5)

The form of Equation 5 implies that the toxic effect resultingfrom Mn binding to either site is the same for both sites. Asbefore, the parameter fMnBL,EC50 was fixed to 0.01. This leavesfive parameters (KMnBL,1, KMnBL2, KKBL, KCaBL, and b) to befitted. It was found that optimization of KKBL did not signifi-cantly improve fits, leaving only Ca2þ as a protective ion.Optimized values of the parameters were 6.29 for log KMnBL,1

and 1.41 for log KMnBL2. Log KCaBL,1 was 7.90, and the slopeconstant (b) was 2.63. The RMSE for this model was 15.2. The

model fit was a significant improvement over the one-site BLM(p< 0.001).

Predictions of EC50 values as dissolved Mn in the calibra-tion data are shown in Figure 6. The two-site BLM gives aprediction superior to that of the one-site BLM (sum of squareserror in log dissolvedMn¼ 0.49 for the two-site model, 0.81 forthe one-site model). Predictions of EC50s in the validation testsshow similar results (sum of squares error¼ 0.24 for bothmodels).

Figure 7 shows example fits of both the one-site and the two-site fish models to dose–response curves, for individual testsfrom the model development data set, for a low Ca concen-tration and a high Ca concentration. When the Ca concentrationis approximately 0.1mM, both models provide a similar fit tothe data, but, at 2mM Ca, the two-site model provides animproved fit to the data. At low Ca concentrations almost allof the biotic ligand bound Mn is bound to the first biotic ligand,but at high Ca concentrations more Mn is bound to the secondbiotic ligand, because as Ca concentrations increase Mn2þ

binding to site 1 is suppressed by competition from Ca.The biotic ligand stability constants derived for the three

chronic BLMs for Mn are shown in Table 2 (only the single-sitefish BLM is included here) along with comparable constantsderived for chronic BLMs for other potentially toxic metals.

αCa2+ (mM)0.0 0.5 1.0 1.5 2.0

αM

n2+, E

C50

(mM

)

0.0

0.1

0.2

0.3

0.4

0.5

Fig. 5. Activity of Mn2þ at the 50% effect level, for seven data points atwhichKþactivity is approximately constant, plotted against theCa2þ activityfor fish tests.

1 10 100

Cal

cula

ted

EC

50 (m

g M

n L-1

)

1

10

100

Observed EC50 (mg Mn L-1)1 10 100

Cal

cula

ted

EC

50 (m

g M

n L-1

)

1

10

100

Fig. 6. Observed and predicted median effective concentration (EC50s; asdissolvedMn) in the calibration tests on fish, modeled using a one-site bioticligandmodel (BLM)with competition of Kþ andCa2þ (top) and using a two-site BLM with competition of Ca2þ at site 1 and no competition at site 2(bottom). Open symbols refer to tests for which the observed EC50 is anextrapolation from the individual effect measurements.


The constants for Ca and Mg are comparable across all of themodels and show relatively little variability between differentmodels. Biotic ligand models for the toxicity of other metals toalgae are not directly comparable to that derived here for Mn,

because they are based on empirical relationships with pH,although the principle of the effects is the same. Algae showincreasing sensitivity to the toxic metal with increasing pH.

The binding constants for the two-site fish BLM for Mn arenot shown in Table 2 because the constants for this model arenot directly comparable to those for the other BLMs that havebeen developed. The binding constants for the single-site Mnfish BLM are slightly higher than those for the invertebrateBLM, although the magnitude of the Mn binding constant,relative to the calcium binding constant, is almost identical,indicating a very similar competitive effect for Ca in these twomodels. Biotic ligand binding constants for Mn are somewhatlower than those for Ni, Zn, and Cu, and the stability constantsfor these metals increase in the same order as may be expectedfrom binding to natural organic matter [17].

Validation

Validation tests were performed on five field-collectednatural waters with a range of water quality conditions forcomponents expected to have the greatest influence on Mntoxicity. The tested waters include a soft, acidic, high-dis-solved-organic-carbon water; a high-pH, high-hardness water;and a low-hardness, low-dissolved-organic-carbon water. Thekey water quality parameters of the waters are shown in Table 3.

A comparison of the observed and predicted EC50 values forP. promelas and C. dubia and EC10 values for P. subcapitatagrowth are shown in Figure 8. Predictions of effects of Mn onaquatic organisms from three trophic levels are generally withina factor of 2 of the observed result. A notable exception is themodel for Mn toxicity to algae, which provides relatively poorpredictions at high effect levels but performs very well at loweffect levels. This is due to the relatively low level of responseto Mn toxicity in the algal growth data. A summary of how welleach of the individual models performed in predicting theeffects of Mn in the validation test waters is shown in

MnBL1

MnBL2

MnBLTot

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

Res

pons

e (%

of c

ontro

l)

0

20

40

60

80

100

120

Mean αCa2+ = 0.11mM

αMn2+ (mM)0.00 0.02 0.04 0.06

f MnB

L

0.000

0.004

0.008

0.012

0.016

MnBL1

MnBL2

MnBLTot

αMn2+ (mM)0.0 0.2 0.4 0.6 0.8 1.0 1.2

Res

pons

e (%

of c

ontro

l)

0

20

40

60

80

100

120

αMn2+ (mM)0.0 0.2 0.4 0.6 0.8 1.0 1.2

f MnB

L

0.00

0.01

0.02

0.03

0.04

Mean αCa2+ = 2.12mM

Fig. 7. Example fits of the one-site (dashed line) and two-site (solid line) fishtoxicity models to individual test data having relatively low (upper) and high(lower) dissolved Ca concentrations. Insets show the distribution of Mn2þ

between the two biotic ligands (BL) and the total amount of Mn2þ bound tothe ligands. MnBL1 is the Mn bound to the ligand at which Ca2þ competeswith Mn2þ; MnBL2 is the Mn bound to the ligand at which there is nocompetition for Mn2þ; and MnBLtot is the total Mn bound to both ligands(binding to biotic ligands is expressed as the fractional occupancy).

Table 2. Comparison of stability constants used for different biotic ligand(BL) models

Metal Organism

Log10KBL

Toxica Ca Mg H

Mn Fish 2.63 3.93 — —Mn Invertebrate 2.23 3.07 3.08 —Mn Alga 2.25 — — 8.42Cu Invertebrateb 8.02 — — 6.67Zn Fishc 5.5 3.6 3.1 6.3Zn Invertebrated 5.3 3.2 2.7 5.8Ni Invertebratee 4.0 3.53 3.57

a Log10KBL for the toxic metal in each model.b De Schamphelaere and Janssen [25].c De Schamphelaere and Janssen [26].d De Schamphelaere et al. [23].e Deleebeeck et al. [22] (alternative approach taken for Hþ in this model).

Table 3. Key water quality conditions of the five validation waters

Water pHDOCa

(mgL�1)Ca

(mgL�1)Hardness

(mgL�1 CaCO3)Alkalinity

(mgL�1 CaCO3)

Peninsula (MI, USA) 7.7 7.0 1 60 56Santiam (OR, USA) 7.6 0.7 7 24 36Texoma (TX, USA) 8.1 4.8 77 304 124Soap Creek (OR, USA) 8.0 1.3 21 84 96Pinelands (NJ, USA) 6.7 12.0 4 12 8

a Dissolved organic carbon.


Table 4 for three different effect levels (EC50, EC25, andEC10). It should be noted that the two-site fish BLM doesnot provide an improved fit to the validation data over thesingle-site fish BLM; the sum of squares error is 0.24 for bothfish models. This is considered to be due to the range of Caconcentrations in the validation test waters. The Ca concen-trations are not high enough in any of the validation waters forthe two different models to behave very differently.

The selected models for the prediction of the effects of Mnon freshwater organisms are the two-site fish model (no. 4 inTable 4), which includes Ca2þ as a competing ion; the inver-tebrate model with both Ca2þ and Mg2þ as competing ions (no.7 in Table 4); and the combined algal model (no. 10 in Table 4),

which includes Hþ as a competing ion. Although the two-sitefish model does not result in a noticeable improvement inprediction accuracy over the single-site fish models for thevalidation data set, the improvement in fit for the modeldevelopment data set (which included tests at higher Ca con-centrations) means that this model is preferred for makingpredictions of Mn toxicity to fish.

CONCLUSIONS

Models have been developed that describe the effects of Mnon three types of organisms, algae, Daphnia, and fish, as afunction of water quality. There is a protective effect of Ca onMn toxicity for both fish and invertebrates, and Mg alsoprovides a protective effect for invertebrates. There is a pro-tective effect of protons onMn toxicity to algae. The models areconsistent with models of the toxicity of other metals to aquaticorganisms [22,23] in that divalent cations can act as competitorsto Mn toxicity in fish and invertebrates and protons act ascompetitors to Mn toxicity in algae.

Fish and invertebrates are more sensitive to Mn toxicity atlow pH, especially if water hardness is low. Under high-pHconditions algae are the most sensitive, regardless of waterhardness conditions. Because of the similarities of the responsesof both fish and invertebrates, there are no conditions underwhich fish would be expected to be the most sensitive trophiclevel. The models are able to predict Mn toxicity to the testorganisms to within a factor of 2 in most cases.

The availability of models that are able to predict the effectsof Mn on different species under variable water chemistryconditions opens the possibility of taking water chemistryconditions into account when setting environmental qualitystandards for Mn. The possibility of normalizing an EQS thatis established for Mn under conditions of high bioavailabilitymay now be a possibility, and identifying specific environmentswhere Mn toxicity is most likely is now possible.

The present study considers the toxicity of Mn2þ, and,although oxidation of Mn2þ to MnO2 may be expected fromthermodynamic considerations, it has been found to be thepredominant form of dissolved (<0.45 mm) Mn in freshwaters[24], although colloidal Mn precipitates do represent a signifi-cant fraction of the dissolved Mn in some waters.

Acknowledgement—This work received financial support from the Interna-tional Manganese Institute and the Environment Agency of England andWales. The authors are grateful to Eric Van Genderen for advice during thiswork and to three anonymous peer reviewers for constructive comments.

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1 2 5 10 20 50

Cal

cula

ted

ECx,

mg

Mn

L-1

1

2

5

10

20

50

Fig. 8. Validation of Mn biotic ligand models (BLMs) for three trophiclevels. Algal growth EC10 (circles), invertebrate reproduction EC50(lozenges), and fish growth EC50 (squares). The solid line indicates a 1:1relationshipbetweenobservations andpredictions; thedashed lines indicate afactor of 2 from the observed result. All concentrations are expressedas mg L�1 dissolved Mn. EC¼ effective concentration.

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Log[diss Mn]EC10

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a The root mean square error (RMSE) is not that derived directly from fittingbut from the prediction of effective concentration values (e.g., EC50values) based on the fitting results.


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development of biotic ligand models for chronic manganese toxicity to fish, invertebrates, and algae

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