Population Growth Rate Responses of Ceriodaphnia dubia to Ternary

Subscriber access provided by UniSA Library

Article

Population growth rate responses of Ceriodaphnia dubia to ternary mixtures of specific acting chemicals: Pharmacological versus eco-toxicological modes of action. Carlos Barata, Maria Fernandez-San Juan, Maria Luisa Feo, Ethel Eljarrat, Amadeu Soares, Damia Barcelo, and Donald Baird Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es301312h • Publication Date (Web): 24 Jul 2012 Downloaded from http://pubs.acs.org on July 29, 2012

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Environmental Science & Technology

275x190mm (96 x 96 DPI)

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

Page 2 of 31

TITLE PAGE:

2 3

Population growth rate responses of Ceriodaphnia dubia to ternary

4

mixtures of specific acting chemicals: Pharmacological versus Eco-

5

toxicological modes of action.

6 7

Carlos Barata 1*, María Fernández-San Juan 1, Maria Luisa Feo 1, Ethel Eljarrrat 1,

8

Amadeu M.V.M. Soares 2, Damià Barceló 1, Donald J. Baird 3

9 10 11

1 Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Jordi

12

Girona 18, 08034 Barcelona, Spain.

13 14

2 Departamento de Biologia, Universidade de Aveiro, 14 3810-193 Aveiro, Portugal

15

3 Environment Canada @ Canadian Rivers Institute, Department of Biology, University

16

of New Brunswick, Fredericton, NB. Canada.

17 18 19 20 21

*Address correspondence to Carlos Barata, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Jordi Girona 18, 08034 Barcelona, Spain. Telephone: +34-93-4006100. Fax: +34-93-2045904. E-mail: [email protected] D.J.

22 23 24


1

Page 3 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


25

ABSTRACT

26

When considering joint toxic apical effects at higher levels of biological organization,

27

such as the growth of populations, the so-called pharmacological mode of action that

28

relies on toxicological mechanistic effects on molecular target sites, may not be

29

relevant. Such effects on population growth rate will depend on the extent to which

30

juvenile and adult survival rates and production rates (juvenile developmental rates and

31

reproduction) are affected by toxic exposure, and also by the sensitivity of population

32

growth rates to life-history changes. In such cases, the ecotoxicological mode of action,

33

defined as the crucial life-history trait processes and/or xenobiotic-life-history trait

34

interactions underlying a toxicological effect on population growth rate, should be

35

considered. Life-table response experiments with the crustacean Ceriodaphnia dubia

36

exposed to single and ternary mixtures of nine compounds were conducted to test the

37

hypothesis that joint effects on population growth rates could be predicted from the

38

mixture constituent ecotoxicological mode of action. Joint effects of mixtures

39

containing pharmacologically dissimilar compounds (cadmium, λ-cyhalothrin and

40

chlorpyrifos) that differentially affected life-history traits contributing to population

41

growth rates were accurately predicted by the independent-action concept. Conversely,

42

the concentration-addition concept accurately predicted joint effects of two different

43

mixtures: one containing pharmacologically similar acting pyrethroids that also affected

44

similarly life-history traits, other one that included pharmacologically dissimilar

45

compounds (3, 4 dichloroaniline, sodium bromide and fenoxycarb) acting mainly on

46

reproduction rates. These results indicate that when assessing combined effects on

47

population growth rate responses, selection of mixture toxicity conceptual models based

48

on the ecotoxicological mode of action of mixture constituents provided more accurate

49

predictions than those based on the pharmacological mode of action.


2


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

50

Key Words: mixture toxicity, mode of action, metals, insecticides, pyrethroids,

51

Ceriodaphnia, population growth rate.

Page 4 of 31

52


3

Page 5 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


53

INTRODUCTION

54

Mixture toxicology aims to quantitatively predict the effects of combinations of

55

chemicals from information about the toxicity of the individual components. This aim

56

can be realised by making the assumption that the components of a mixture produce a

57

common effect and act non-interactively, in an additive fashion. Two different

58

conceptual models, termed concentration addition (CA) and independent action (IA),

59

describe general relationships between the effects of single substances and their

60

corresponding mixtures. The concentration addition model is founded on the

61

assumption that mixture components each possess a similar pharmacological mode of

62

action, hereafter defined following Escher et al. 1and Borgert et al. 2 as the crucial

63

biochemical processes and/or xenobiotic-biochemical interactions underlying a

64

toxicological apical effect, and thus is most applicable for toxic substances that have the

65

same molecular target site 3. The alternative model of independent action assumes a

66

dissimilar pharmacological mode of action of mixture constituents that interact with

67

different target sites, leading to a common toxicological endpoint within an organism 4.

68

A basic assumption of this model is that the effects of individual constituents are

69

expected to be independent in a strictly probabilistic sense.

70

The combination of accurate experimental and biometric methods employing a large

71

number of substances with defined mode of actions, have confirmed the adequacy of

72

both, CA and IA models, to predict combined joint effects of complex mixtures even at

73

very low doses

74

experimental framework has mostly been tested using highly standardized bioassay

75

systems focus on short-term integral adverse effects. The accuracy of existing mixture

76

toxicity models predicting combined long term toxicity effects remains unresolved, due

5-11

. Nevertheless, as pointed out by Altenburger et al.


12

, the above

4


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 31

77

to the inability to define the mode of action for most substances and species in the field

78

and the lack of good empirical data on long term population responses.

79

There is a long lasting debate in both human and environmental risk assessment about

80

the question whether the validity of the concept of CA or IA is strictly confined to

81

mixtures of toxicants with identical primary molecular mechanisms of action, or

82

whether a more phenomenological understanding of “similar action” in terms of

83

common apical endpoints or common adverse outcomes provides a sufficient basis for

84

expecting concentration-additive joint actions of toxicants

85

studies have reported that joint effects of pharmacologically dissimilar and specifically

86

acting mixtures are additive and predicted by the CA concept in molluscs, insects,

87

crustaceans, fish and mammals

88

Daphnia, fish and rats that for mixtures having pharmacologically dissimilar modes of

89

action or different chemical structures, joint effects changed across lethal and sublethal

90

endpoints in an unpredicted way according to the IA concept 22-26. Cleuvers

91

that joint toxicity of pharmaceuticals with putative different modes of action but that

92

acted by non polar narcosis to algae was additive and predicted by the IA. Christiansen

93

et al.22 showed that mixtures of anti-androgens having distinct chemical structure and

94

dosed below their no-observed-adverse-effect-level impaired sexual differentiation of

95

male rats in a way expected by the CA. Thus, the above mentioned studies support the

96

view that whole organism responses are the outcome of an array of interactions of

97

compounds in various organs and tissues within individuals, and thus primary and

98

secondary mode of actions may be equally important, hence it may not be useful to

99

classify toxic chemicals by a given mode of action and, indeed, it may result in

100

erroneous conclusions regarding the hazard posed by such mixtures. Several authors,

101

thus, have proposed that in mixture toxicity risk assessment

13-19

. Several experimental

11, 20-22

. There is also reported evidence in plants,


26

reported

induction of similar

5

Page 7 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


102

phenomenological effects should be sufficient for accepting that mixture constituents

103

had a “similar mode of action” 13, 19. Opposite to the previous opinion, several empirical

104

studies conducted in unicellular algae

105

pharmacologically dissimilar acting mixtures are better predicted by the IA concept

106

even al low exposure levels8,

107

Kortenkamp et al.

108

constituents are not known for certainty in most studies. Barata et al.

109

when considering whole organism apical endpoints, mixture-toxicity investigations

110

should instead focus on the eco-toxicological mode of action, hereafter defined as the

111

crucial physiological-life-history trait processes and/or xenobiotic- trait interactions

112

underlying a toxicological effect on the studied apical endpoint. In the previous study it

113

was shown that reproduction in the crustacean Daphnia magna responded to

114

pharmacologically dissimilar binary mixtures of the pyrethroid λ-cyhalothrin and the

115

metal cadmium, in a way predicted by the CA concept. Barata et al.

116

demonstrated that regardless of their distinct pharmacological mode of action both

117

chemicals shared a common effect, impaired food acquisition, which is known to be one

118

of the major factors regulating fecundity, in D. magna.

119

In ecological risk assessment of chemicals, population growth rate (hereafter referred as

120

PGR), measured either as the multiplication rate per unit of time λ = e

121

Malthusian parameter r = loge λ, has been proposed as a more relevant measurement of

122

toxicant effects than traditional measures of mortality or reproduction. This is argued

123

from the viewpoint that PGR integrates life-history responses, facilitating the prediction

124

of the impact at higher levels of ecological organisation

125

concerning an individual's contribution to PGR, age at reproduction, offspring

126

production, and juvenile and adult survival rates are measured and combined to

populations

support the view that

27

. Nevertheless, as stated by Borgert et al.2 and

19

, the pharmacological mode of action of the tested mixture 28

argued that

r

28

further

or as the

29, 30


. To make predictions

6


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 31

31

127

calculate a predicted PGR

. Life-table response experiments have made a valuable

128

contribution to the assessment of population consequences of toxicant effects on

129

individual life-history traits 31-33. In life-table response experiments, the extent to which

130

PGR responds to toxicant exposure is determined through decomposition analysis: an

131

analysis of the severity of toxic effects on individual life-history traits as well as the

132

sensitivity of PGR to changes in its constituent individual traits

133

assessment studies that have considered effects on population responses and that have

134

precisely tested CA and IA models have been mostly limited to unicellular organism-

135

systems and short term exposures

136

pharmacological mode of action for predicting mixture toxicity has not been fully tested

137

at the population level or/and using long term responses, and thus has limited

138

application in complex ecological systems.

139

In this study we test the hypothesis that joint effects of mixtures on PGR responses can

140

be predicted from the ecotoxicological mode of action of their individual mixture

141

constituents, which can be considered a special case of the proposed phenomenological

142

mode of action 13, 19. This hypothesis was tested using life table response experiments

143

conducted using the cladoceran Ceriodaphnia dubia Richard, which is commonly used

144

in laboratory ecotoxicity testing 37, exposed to three ternary mixtures. C. dubia's short

145

life-cycle and high fecundity allows the determination of population growth rate

146

parameters from short duration life-history toxicity tests 38. Thus cost–effective complex

147

experimental designs can be performed 33. Mixtures included: (1) chemicals that are

148

known to differentially affect survival and reproduction responses of C. dubia and other

149

related species (cadmium, chlorpyrifos and λ-cyhalothrin) 28, 39; (2) pyrethroid

150

insecticides (λ-cyhalothrin, cypermethrin and deltamethrin) that similarly affect survival

151

and reproduction in C. dubia and other cladocerans 40, 41 and (3) compounds that impair

5-8,

31, 32

. Mixture

34-36

. Thus, so far the usefulness of


7

Page 9 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


152

embryo development and hence offspring survival (3,4 dichloroaniline, sodium bromide

153

and fenoxycarb) 39, 42. Mixtures 1 and 3 included chemicals with putatively dissimilar

154

pharmacological modes of action: λ-cyhalothrin is a sodium-channel blocker and γ-

155

aminobutyric acid (GABA) inhibitor currently used to control arthropod pests 43. The

156

organophosphorus insecticide chlorpyrifos causes cholinesterase inhibition in the

157

cladoceran D. magna, impairing the nervous system 44. Cadmium toxicity in organisms

158

is related to the production of reactive oxygen species and/or interferences with Ca2+

159

transport, disrupting key physiological processes such as osmoregulation 45, 46. In D.

160

magna exposures to cadmium also causes oxidative stress 47. 3, 4 dichloroaniline is the

161

main degradation product of the herbicide propanil 48 and is considered to act in D.

162

magna more specifically than by non polar narcosis 49. Fenoxycarb is a juvenile

163

hormone analog acting in arthropods inhibiting metamorphosis to the adult stage 50. The

164

pharmacological mode of action of sodium bromide in cladoceran species is not known.

165

According to our hypothesis, the combined toxicity of the mixture of the dissimilarly-

166

acting substances cadmium, chlorpyrifos and λ-cyhalothrin on population growth rate

167

response should be predicted by the IA concept and of the pyrethroid mixture and that

168

of 3,4 dichloroaniline, sodium bromide and fenoxycarb by the CA one. PGR responses

169

were estimated from juvenile and adult survival rates, age at first reproduction and

170

reproduction rates using the two stage demographic model of Sibly et al. 51. The

171

ecotoxicological mode of action of single substances and mixtures was determined

172

quantifying their effects on life-history traits using decomposition analysis following

173

Barata et al. 33. Single and joint mixture effects and CA and IA predictions were

174

estimated using a modelling framework proposed by Barata et al. 23.

175

EXPERIMENTAL SECTION

176

Chemicals


8


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 31

177

The substances employed in the toxicity experiments were cadmium (Cd, CAS No

178

7440-43-9), Bromide (Br, CAS No 7647-15-6) prepared from Aldrich (Madrid, Spain)

179

analytical reagent grade salts (99% purity, 3CdSO4.8H2O and NaBr, respectively); λ-

180

cyhalothrin (CAS-No 91465-08-6; 99% purity), deltamethrin (CAS No 52918-63-5

181

99.8%), cypermethrin (CAS-No 52315-07-8, mixture of isomers 95.8 %), fenoxycarb

182

(CAS No 72490-01-8 99.6%), chlorpyrifos (CAS No 2921-88-2 99.9%) from Riedel-

183

de Haën (Seelze, Germany), and 3,4 dichloroaniline (DCA, CAS No 95-76-1, 98%

184

purity) from Aldrich. All other chemicals were analytical grade and were obtained from

185

Merck (Darmstadt, Germany).

186

Experimental animals

187

Individuals of a lab clone of C dubia obtained originally from the National Water

188

Research Institute, Saskatoon, Canada

189

were selected for this study. Ten bulk cultures of 20 animals were maintained in 150

190

mL of ASTM hard water

191

standard organic extract

192

corresponding to 1.8 mg C/L). Photoperiod was set to 12h light: 12h dark and

193

temperature at 24 ± 0.5 oC. Cultures were changed every other day with freshly

194

prepared medium and neonates removed within 24 h of release. Every week, cultures

195

were reset with newly release neonates.

196

Experimental approach

197

Single substance and mixture toxicity tests were measured separately in independent

198

sets of experiments that were repeated at least twice to account for changes in sensitivity

199

54

200

plus a control. Ternary mixture combinations followed a fixed ratio design, in which

201

exposure levels were selected to include constant equitoxic (EC50) mixture ratios and

52

and maintained for over ten years in our lab

53

, in 180 mL screw top glass jars, with the addition of a

33

. Animals were fed with Chlorella vulgaris (5105 cells/ mL,

. Single substance toxicity tests included from seven to twenty exposure treatments


9

Page 11 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


55

202

from 13 to 15 different mixture effect levels

. Chosen EC50s were selected from the

203

single substance toxicity assays. Details of the experimental approach are provided in

204

Methods, Supporting Information .

205

Life- table response assays

206

Population assays were initiated with < 12 h old neonates collected from the bulk

207

cultures and were randomly assigned in groups of five individuals in 40 mL of test

208

medium in 50 mL borosilicate glass jars. Experimental treatments consisted of five

209

replicates. Test solutions were prepared by adding appropriate amounts of a

210

concentrated stock solution to ASTM hard water, and the solution was mixed

211

thoroughly. Acetone (HPLC grade; < 0.5 mL/L) was used as a carrier for all but Cd and

212

Br treatments. A control without acetone was also used to assess the possibility of

213

carrier effects. Culture conditions were similar to those described for bulk cultures, but

214

medium was changed every day. Life-table experiments lasted 10 days. The following

215

demographic parameters were recorded: juvenile and adult survivorship, age of

216

releasing neonates which included age at first reproduction, and daily reproduction rates

217

(number of neonates produced per surviving female per day). Details of life-table

218

response assays are provided in Methods, Supporting Information.

219

Demographic analysis

220

PGR (λ) in the different treatments was determined from the age-specific data on

221

fecundities and survival probabilities over the 5 replicates used. λ was computed

222

iteratively from the simplified 2-stage model of Sibly et al. 51.

t −t

n pjj λ j + pa λ−1 =1 223

(1)


10


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 31

224

Where n is the number of offspring produced per female and day, tj is the average time

225

to first reproduction in days, pj juvenile survival rates (proportion of juveniles surviving

226

per day from birth to first reproduction), pa adult survival rates (proportion of adults

227

surviving per day averaged over the adult period). A time unit of 1 day was employed

228

for all treatments because reproductive output was measured every day. Estimates of the

229

variability of λ levels within treatments were obtained from Jack-Knife pseudovalues56.

230

The ecotoxicological mode of action of a given pollutant was assessed quantifying by

231

how much exposure treatment effects on n, Sj, Sa and tj contribute to the overall impact

232

on PGR (λ). This procedure is termed decomposition analysis, and was performed

233

following Levin et al. (32: Equation 16) using the sensitivity equations given by Sibly et

234

al.

235

Supporting Information.

236

Chemical analyses

237

Duplicated water samples of freshly prepared and old (1 day) test solutions were

238

collected at the beginning and end of the tests to measure toxic substance

239

concentrations, oxygen levels and pH. Analysis of pyrethroids, fenoxycarb, bromide

240

and DCA were restricted to the three concentrations including low, middle and high

241

exposure levels and those of chlorpyrifos to the two highest exposure levels (see Table

242

S1 in Supplementary Information). Details of analytical methods are provided in

243

Methods, Supporting Information.

244

Data analysis

245

Concentration-response relationships of single substances were biometrically modelled

246

considering proportional responses of PGR relative to control treatments (R) and by

247

fitting observed responses to the non-linear allosteric decay regression model depicted

248

in Equation 2 23. Analyses were performed on Jack-Knife pseudovalues of λ 56. Prior to

51

(Equations 4-7). Details of demographic analysis are provided in Methods,


11

Page 13 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


249

analysis, the intrinsic rate of population increase, r, was calculated according to the

250

formula λ = e r. Although both parameters are used in demography as descriptors of

251

PGR, r is considered more appropriate for biometric modelling purposes 51. 1 cik 1+ EC 50 k

252

R (ci ) =

253

where

254

R(ci)

- proportional r- response at concentration ci relative to controls

255

ci

- concentration of the toxic substance (i)

256

EC50 - the half saturation constant (i.e. the concentration that caused an

257 258

( 2)

inhibition of 50% of r). k

-decay index

259

Predicted values for mixture combinations considering the CA and IA models were

260

determined following a previously established procedure of Barata et al.

261

the model of CA and considering a mixture of n chemicals, where each chemical

262

contributes to the overall toxicity proportionally to its EC50i (expressed as TUi),

263

expected proportional responses (R mix) of equation 2 can be re-calculated as: R mix =

1   1 +  ∑ TU i   i =1  n

23

. Following

( 3)

k'

264 where k' =

n

z

∏k i =1

TU i i

, and z =

n

∑ TU

i

i =1

265

Calculating k’ as the geometric mean of the ki obtained for each chemical of the mixture

266

weighted by its relative toxicity (TUi). This approach assumes that the response in a

267

mixture of n chemicals is proportional to the addition of the equi-effective individual

268

concentrations of its constituents. Thus, by solving equation 3, it is possible to obtain

269

expected mixture responses based on the CA model considering non-linear allosteric


12


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 31

270

decay biological responses of the n mixture constituents. Alternatively, expected

271

proportional responses (Rmix) of a n-compound mixture by the IA model can be

272

obtained directly from equation 4 sensu Bliss 4. n

R mix = ∏ R ( c i )

273

( 4)

i =1

274

Where ci is again the concentration of the ith component and R(ci) is the proportional

275

response relative to control treatments of that concentration if the compound is applied

276

alone.

277

Using Equations 3 and 4, then it is possible to plot predicted combine toxicity responses

278

for similarly and dissimilarly acting chemicals according to the CA and IA concepts

279

respectively and, hence, to compare them with observed responses.

280

The adequacy of CA and IA models to predict combined toxicity of the studied

281

mixtures was tested statistically comparing predicted vs. observed joint response

282

regression lines following Barata et al.23. Details are given in Methods, Supporting

283

Information.

284 285

RESULTS

286

Chemical analysis

287

Oxygen levels and pH values varied little in freshly prepared and old test solutions and

288

were within acceptable limits. Only measured concentration levels of chlorpyrifos and

289

those of pyrethroids were not within 10% of nominal ones. Accordingly actual

290

chlorpyrifos, λ-cyhalothrin, cypermethrin and deltamethrin test concentrations were

291

taken as the arithmetic mean between measured initial and final concentrations, which

292

were 87, 72, 76 and 69% of their nominal levels, respectively. Further information is in

293

Results (chemical analyses, Table S1) in Supporting Information.

294


13

Page 15 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


295

Single substance toxicity

296

Proportional PGR responses to the eight studied individual chemicals followed a

297

sigmoid decay curve (Figure 1), which could be modelled as an allosteric decay

298

function ( Equation 2). In all cases, the residuals of the regression models obtained were

299

normally distributed (Kolmogorov–Smirnov tests p>0.05) giving coefficients of

300

determination greater than 0.7 (Table 1). The shape and steepness depicted as the

301

allosteric decay index (k in Table 1) of the curves obtained varied across chemicals with

302

chlorpyrifos and fenoxycarb having the steepest and smoothest curves, respectively.

303

Individual C. dubia toxicity of the eight tested chemicals on PGR differed largely across

304

substances, with EC50 values ranging over six orders of magnitude between

305

deltamethrin (0.08 nmol/L) and Br (129.76 µmol/L) (Table 1). Further details of single

306

exposures are shown in Fig S1 (SI, “Results”).

307

Decomposition analyses depicted in Figure 2 indicate differences among the tested

308

substances in terms of their effects on life-history traits contributing to the observed

309

PGR changes. For Cd, fenoxycarb, DCA and Br effects on reproduction rates had the

310

greatest contribution to observed changes in PGR. For pyrethroid insecticides effects on

311

juvenile survival rates and reproduction rates or age at first reproduction contributed

312

equally to PGR changes, whereas chlorpyrifos affected PGR mainly impairing juvenile

313

and adult survival rates.

314

Mixture toxicity

315

Results of mixture toxicity experiments are given in Figure 3, together with the

316

corresponding fitted models (Table 1). EC50 s of the three assayed mixtures fell into the

317

span of toxicities determined for their individual constituents. Joint effects of the first

318

mixture of Cd, chlorpyrifos and λ-cyhalothrin were accurately predicted by the IA

319

concept (Figure 3A) being largely affected by the contribution of Cd at low effect levels


14


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 31

320

(grey line 1). Observed joint EC50 was closer to the estimated EC50 by the IA (Table

321

1). A closer look of the data plotting predicted vs. observed responses (inner graph in

322

Figure 3A) indicated that fitted linear regression lines of both models (IA and CA) were

323

different (had different slopes and elevations according to Table S2, SI “Results”), and

324

that the IA regression was almost identical to the identity line (y=x), having a slope and

325

elevation of 1 and 0, respectively (Table S2, SI “Results”). These results indicate that

326

combined effects were accurately predicted by the IA. Joint effects of mixtures 2 and 3

327

were accurately predicted by the CA concept (Figures 3 B, C). In both mixtures,

328

observed joint effect levels (EC50s) were closer to the estimated concentration addition

329

EC50s (Table 1). Joint effects predicted by IA and CA concepts versus observed

330

responses (inner graphs) in both mixtures had different slopes and elevations, and CA

331

regressions had slopes and intercepts of 1 and 0, respectively (Table S2, SI “Results”).

332

In mixtures 2 and 3 none of the constituents involved (grey lines 4-9) had a dominant

333

contribution in the observed joint response. Note that in mixture 3 despite that joint

334

toxicity predicted by the IA concept was dominated by fenoxycarb (grey line 9) at low

335

effect levels, observed responses depicted as a doted line did not (Fig 3 C).

336

Decomposition analyses depicted in Figure 2 (bottom panel) of mixture effects on PGR

337

showed high degree of concordance with the reported contribution of mixture

338

constituents. In mixture 1, toxic effects on juvenile survival and reproduction rates had

339

the greatest contribution to observed changes in PGR. For mixture 2, age at first

340

reproduction, juvenile survival and reproduction rates contributed similarly to PGR

341

changes, whereas in mixture 3 effects on reproduction rates had the greatest

342

contribution to observed changes in PGR.

343

DISCUSSION


15

Page 17 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


344

To improve accuracy and repetitiveness, predictive hazard assessment of mixtures at

345

the population level have mostly been performed using unicellular organisms 5-8, 27, 34, 35

346

and, in only a few occasions, attempts have been made to address demographic effects

347

on metazoan species

348

lower to higher levels of biological organisation; pharmacological modes of action have

349

limited utility to predict non interactive joint effects of mixtures according to the

350

concentration addition or independent action concepts

351

population responses are directly driven by cellular responses and hence effects on cells

352

will more easily translate to higher biological levels. Thus it is not surprising to find a

353

close match of joint observed effects and those predicted by the CA and IA concepts for

354

pharmacological similar and dissimilar acting chemicals, respectively

355

metazoan species, this argument is significantly weakened

356

concordance of expected versus observed joint effects in metazoans is, in many cases,

357

likely related to biometric and exposure problems. Increasing biological complexity and

358

exposure periods can decrease repeatability and accuracy

359

such problems were minimized by choosing a small-bodied, clonal species with a short

360

life-cycle and by repeating single and mixture exposures several times. Observed

361

concentration effects on PGR responses (EC50s) were within the same order of

362

magnitude as those reported elsewhere in C. dubia and related Daphnia species 39, 41, 42.

363

Fenoxycarb was an exception in our study since impaired PCR responses (EC 20 = 0.6

364

µg/L, EC 50 = 18 µg/L) at concentrations 20 to 500 fold higher than those reported

365

elsewhere in C. dubia 39, 62. Nevertheless, reported studies conducted in C. dubia and D.

366

magna indicate that fenoxycarb effects on PGR and reproduction responses may vary

367

over three orders of magnitude across assays

368

conditions

39

36, 58-61

. Several authors have noted that moving from effects at

and UV radiation

63

2, 16, 24, 58

. In microbial systems,

5-8, 27

.

In

2, 16, 23, 58

. The lack of

24, 54, 55

. In this present study,

and are strongly affected by food

64

. Thus the use in this present study of different food


16


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 31

369

conditions and of artificial light produced by low UV-emission neon-lamps may have

370

contributed to these observed discrepancies.

371

Our hypothesis stated that when predicting joint effects of toxic substances on metazoan

372

population dynamics, the ecotoxicological mode of action of mixture constituents

373

should be more relevant. We tested our hypothesis using three ternary mixtures that

374

included: (1) pharmacological and ecotoxicologically dissimilar acting chemicals: Cd,

375

λ-cyhalothrin and chlorpyrifos; (2) pharmacological and ecotoxicologically similarly-

376

acting pyrethroids, and (3) the pharmacological dissimilar but ecotoxicologically

377

similarly-acting compounds Br, DCA and fenoxycarb. In testing our hypothesis, we

378

demonstrated that mixture 1 included chemicals with dissimilar ecotoxicological mode

379

of actions and mixtures 2 and 3 included chemicals with similar ecotoxicological mode

380

of action. Secondly, we demonstrated that the joint toxicity of mixtures 1, 2 and 3 was

381

accurately predicted by the IA, CA and CA concepts, respectively.

382

Results reported in Figure 2 indicated that mixture constituent contributions to PGR in

383

the mixture of Cd, λ cyhalothrin and chlorpyrifos were different. In single exposures,

384

effects of Cd on PGR were mainly associated with lower fecundity rates, while those of

385

chlorpyrifos were linked to reductions on juvenile and adult survival rates, and those of

386

λ-cyhalothrin were related with the increase in developmental rates and the decrease in

387

juvenile survival. In a close relative species, D. magna, Barata et al.

388

sublethal doses, λ- cyhalothrin and Cd inhibited food acquisition resulting in reduced

389

offspring number and/or increased development rates in adult individuals. Barata et al.

390

41, 65, 66

391

demographic responses of D. magna and of the copepod Acartia tonsa impairing

392

survival rates. Thus, the described ecotoxicological modes of action of Figure 2 agree

393

with reported studies. Joint effects of these three substances in mixture 1 reflected their

28

showed that, at

also reported that organophosphorous compounds and pyrethroids affected


17

Page 19 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


394

contributions in single exposure assays, as they were mainly related to detrimental

395

effects on juvenile survival (chlorpyrifos and λ cyhalothrin) and fecundity rates (Cd)

396

(Figure 2). According to our hypothesis, thus, observed joint toxicity responses were

397

additive and predicted by the IA concept (Figure 3).

398

The ternary mixture of pyrethroids was used as positive control provided that included

399

similar acting chemicals that are known to have similar ecotoxicological modes of

400

action. Our results and studies performed in copepods and Daphnia species have shown

401

that these pyrethroids affect juvenile survival and, at sublethal doses, are able to impair

402

juvenile development (here measured as age at first reproduction) and reproduction

403

rates

404

by the CA concept. The remaining mixture of DCA, Br and fenoxycarb included

405

compounds that regardless of their different chemistry and likely distinct

406

pharmacological mode of action, shared a common ecotoxicological effect in C. dubia

407

and other Daphnia species: impaired embryo survival 42, 62, 68. Results depicted in Figure

408

2 indicated that indeed effects on reproduction rates had the greatest contribution to

409

PGR changes in single and mixture exposures. Consequently, joint effects on PGR were

410

accurately predicted by the CA concept.

411

The results presented in this study showed that joint effects of the tested dissimilar

412

mixtures on demographic responses can be predicted from the analysis of their

413

ecotoxicological mode of action. Barata et al. 23, 28 also found that predicted joint effects

414

of binary mixtures of pyrethroids, metals and DCA on D. magna sublethal responses,

415

such as food acquisition and reproduction, agree with their ecotoxicological mode of

416

action but joint effects on survival did not. According to these authors, pyrethroids and

417

metals affected similarly the physiological mechanisms of feeding (i.e. poisoned the gut

418

and disrupted the feeding apparatus) and reproduction (food intake and hence

28, 66, 67

. Consequently, pyrethroid joint toxicity of PGR was accurately predicted


18


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 31

419

reproductive investment or egg number) and, as a result, joint effects of their binary

420

combinations were additive and predicted by the CA. Alternatively, DCA and Cd

421

affected differentially the two responses that determine reproduction rates: egg survival

422

and number, respectively. Consequently, joint effects of binary mixtures of DCA and

423

Cd on reproduction rates were accurately predicted by the IA concept. On the contrary,

424

survival responses to high doses of binary mixtures of metals and pyrethroids were

425

caused by few specific different mechanisms (oxidative stress by Cu, displacement of

426

essential metals in key metabolic paths by Cd, neurotoxicity of pyrethroids) and hence

427

were predicted by the IA concept 23.

428

From a risk assessment perspective, this present study support the phenomenological

429

similarity criterion proposed by Kortenkamp, Backhaus and Faust among other authors

430

13, 18

431

concept of concentration addition could be extended to mixture constituents having

432

common apical endpoints or common adverse outcomes. Furthermore, the results

433

presented here also support the general consensus that CA can be considered a default

434

worse-case conservative model in environmental and human risk assessment of

435

chemical mixtures, provided that CA tends to predict higher joint toxic effects than IA

436

and, in most cases, differences between predictions of both models do not differ more

437

than two fold13, 18.The third mixture tested in our study constituted a clear example

438

supporting the phenomenological similarity criteria, since it included compounds with

439

different structures (fenoxycarb, DCA and BrNa) and probably with different molecular

440

target sites but a common ecotoxicological or phenomenological effect on the level of

441

the studied apical endpoint: all mixture constituents affected PGR mainly impairing

442

embryo survival. Consequently, joint effects of the third mixture were better predicted

443

by CA. Conversely, the first tested mixture included compounds with different

. The phenomenological point of view of similar mode of action states that the


19

Page 21 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


444

molecular target sites and different ecotoxicological or phenomenological effects on

445

PGR. The mixture constituents affected PGR impairing development, reproduction and

446

survival rates, and hence, were better predicted by IA. Moreover, as shown in Table 1,

447

IA and CA predictions of joint effects impairing PGR 50% (EC50) were always within

448

two fold and, in all cases, the effect predictions of CA were greater than those of IA.

449

The proposed ecotoxicological mode of action approach can be considered an

450

operational method to assess common or uncommon phenomenological modes of action

451

among mixture constituents when considering joint effects in populations or whole

452

individuals. When considering population apical endpoints, toxic effects depend on the

453

extent to which juvenile and adult survival rates, juvenile developmental rates and

454

reproduction are affected by a toxicant and the sensitivity of the apical endpoint (i.e.

455

PGR) to life-history changes 69. Thus, target sites of intoxication are the life-history

456

traits at the population level. In whole individuals Barata et al. 28 were able to predict

457

joint toxicity of chemical mixtures on D. magna reproduction assessing

458

phenomenological adverse effects on food acquisition and embryo survival.

459

Consequently, we believe that mixture ecotoxicological investigations on whole

460

organism or levels beyond individuals will benefit from assessing ecotoxicological

461

modes of action that can be easily evaluated just studying the concentration at which

462

lethal effects on embryos, juveniles and adults occur and food acquisition is impaired.

463

In Daphnia, other cladoceran, copepod and polychaete species such analysis required

464

only short term acute and sublethal assays 66-68,

465

focus in extending the proposed operational phenomenological mode of action

466

procedure to other taxa and toxicant groups.

467 468

ASSOCIATED CONTENT

469

Supporting Information

70-72

. Thus future research needs should


20


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 31

470

Details of experimental procedures and further explanation of results for chemical

471

analyses, individual and joint responses. This information is available free of charge via

472

the Internet at http://pubs.acs.org.

473

AUTHOR INFORMATION

474

Corresponding Author

475

Phone: +34-93-4006100; fax: +34-93-2045904; e-mail: [email protected]

476

ACKNOWLEDGMENTS

477

This work was supported by the Spanish MICINN grants (CGL2008-01898/BOS and

478

CTM2011-30471-C02-01) and FEDER funds. We thank the constructive suggestions of

479

three anonymous referees.

480

REFERENCES

481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507

1. Escher, B. I.; Hermens, J. L. M., Modes of action in ecotoxicology: Their role in body burdens, species sensitivity, QSARs, and mixture effects. Environ. Sci. Technol. 2002, 36, (20), 4201-4217. 2. Borgert, C. J.; Quill, T. F.; McCarty, L. S.; Mason, A. M., Can mode of action predict mixture toxicity for risk assessment? Toxicol. App. Pharmacol. 2004, 201, 8596. 3. Pöch, G., Combined effects of drugs and toxic agents. Modern evaluation in theory and practice. Springer Verlag: New York, USA, 1993. 4. Bliss, C. I., The toxicity of poisons applied jointly. Ann. Biol. 1939, 26, 585-615. 5. Altenburger, R.; Backhaus, T.; Boedeker, W.; Faust, M.; Scholze, M.; Grimme, L. H., Predictability of the toxicity of multiple chemical mixtures to Vibrio fischeri: Mixtures composed of similarly acting chemicals. Environ. Toxicol. Chem. 2000, 19, (9), 2341-2347. 6. Backhaus, T.; Altenburger, R.; Boedeker, W.; Faust, M.; Scholze, M.; Grimme, L. H., Predictability of the toxicity of a multiple mixture of dissimilarly acting chemicals to Vibrio fischeri. Environ. Toxicol. Chem. 2000, 19, (9), 2348-2356. 7. Faust, M.; Altenburger, R.; Backhaus, T.; Blanck, H.; Boedeker, W.; Gramatica, P.; Hamer, V.; Scholze, M.; Vighi, M.; Grimme, L. H., Predicting the joint toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants. Aquat. Toxicol. 2001, 56, 13-32. 8. Faust, M.; Altenburger, R.; Backhaus, T.; Blanck, H.; Boedeker, W.; Gramatica, P.; Hammer, V.; Scholze, M.; Vighi, M.; Grimme, L. H., Joint algal toxicity of 16 dissimilar acting chemicals is predictable by the concept of independent action. Aquat. Toxicol. 2003, 63, 43-63. 9. Scholze, M.; Boedeker, W.; Faust, M.; Backhaus, T.; Altenburger, R.; Grimme, H., A general best - fit method for concentration - response curves and the estimation of low -effect concentrations. Environ. Toxicol. Chem. 2001, 20, 448-457.


21

Page 23 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555

10. Brian, J. V.; Harris, C. A.; Scholze, M.; Backhaus, T.; Booy, P.; Lamoree, M.; Pojana, G.; Jonkers, N.; Runnalls, T.; Bonfa, A.; Marcomini, A.; Sumpter, J. P., Accurate prediction of the response of freshwater fish to a mixture of estrogenic chemicals. Environ. Health Persp. 2005, 113, 721-728. 11. Ermler, S.; Scholze, M.; Kortenkamp, A., The suitability of concentration addition for predicting the effects of multi-component mixtures of up to 17 antiandrogens with varied structural features in an in vitro AR antagonist assay. Toxicol. Appl. Pharmacol. 2011, 257, (2), 189-197. 12. Altenburger, R.; Scholz, S.; Schmitt-Jansen, M.; Busch, W.; Escher, B. I., Mixture toxicity revisited from a toxicogenomic perspective. Environ. Sci. Technol. 2012, 46, (5), 2508-2522. 13. Backhaus, T.; Faust, M., Predictive environmental risk assessment of chemical mixtures: A conceptual framework. Environ. Sci. Technol. 2012, 46, (5), 2564-2573. 14. Berenbaum, M. C., What is synergy? Pharmacol. Rev. 1989, 41, (2), 93-141. 15. Greco, W. R.; Bravo, G.; Parsons, J. C., The search for synergy: A critical review from a response surface perspective. Pharmacol. Rev. 1995, 47, (2), 331-385. 16. Zwart, D.; Posthuma, L., Complex mixture toxicity for single and multiple species: proposed methodologies. Environ. Toxicol. Chem. 2005, 24, 2665-2676. 17. Kortenkamp, A., Ten years of mixing cocktails: a review of combination effects of endocrine-disrupting chemicals. Environ. Health Persp. 2007, 115 Suppl 1, 98-105. 18. Kortenkamp, A.; Faust, M., Combined exposures to anti-androgenic chemicals: Steps towards cumulative risk assessment. Int. J. Andrology 2010, 33, (2), 463-472. 19. Kortenkamp, A.; Faust, M.; Scholze, M.; Backhaus, T., Low-level exposure to multiple chemicals: reason for human health concerns? Environ. Health Persp. 2007, 115 Suppl 1, 106-114. 20. Deneer, J. W., Toxicity of mixtures of pesticides in aquatic systems. Pest. Man. Sci. 2000, 56, (6), 516-520. 21. van Der Geest, H. G.; Greve, G. D.; Boivin, M. E.; Kraak, M. H. S.; Van Gestel, C. A. M., Mixture toxicity of copper and diazinon to the larvae of the myfly (Ephoron virgo) jufgging additivity at different effect levels. Environ. Toxicol. Chem. 2000, 19, 2900-2905. 22. Christiansen, S.; Kortenkamp, A.; Axelstad, M.; Boberg, J.; Scholze, M.; Jacobsen, P. R.; Faust, M.; Lichtensteiger, W.; Schlumpf, M.; Burdorf, A.; Hass, U., Mixtures of endocrine disrupting contaminants modelled on human high end exposures: An exploratory study in rats. Int. J. Andrology 2012 (in press). 23. Barata, C.; Baird, D. J.; Nogueira, A. J. A.; Soares, A. M. V.; Riva, M. C., Toxicity of binary mixtures of metals and pyrethroid insecticides to Daphnia magna Straus. Implications for multi-substance risks assessment. Aquat. Toxicol. 2006, 78, 114. 24. Cedergreen, N.; Streibig, J. C., Can the choice of endpoint lead to contradictory results of mixture-toxicity experiments? Environ. Toxicol. Chem. 2005, 24, (7), 16761683. 25. Broderius, S. J.; Smith, L. L., Lethal and sublethal effects of binary mixtures of cyanide and hexavalent chromium, zinc, or ammonia to the fathead minnow (Pimephales promelas ) and rainbow trout (Salmo gairdneri ). J. Fish Re.s Board Can. 1979, 36, 164-172. 26. Cleuvers, M., Aquatic ecotoxicity of pharmaceuticals including the assessment of combination effects. Toxicol. Lett. 2003, 142, 185-194.


22


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605

Page 24 of 31

27. Walter, H.; Consolaro, F.; Gramatica, P.; Scholze, M.; Altenburger, R., Mixture toxicity of priority pollutants at no observed effect concentrations (NOECs). Ecotoxicology 2002, 11, (5), 299-310. 28. Barata, C.; Baird, D. J.; Nogueira, A. J. A.; Agra, A. R.; Soares, A. M. V. M., Life-history responses of Daphnia magna Straus to binary mixtures of toxic substances: Pharmacological versus ecotoxicological modes of action. Aquat. Toxicol. 2007, 84, (4), 439-449. 29. Forbes, V. E.; Calow, P., Population growth rate as a basis for ecological risk assessment of toxic chemicals. Phil. Trans. R. Soc. B 2002, 357, (1425), 1299-1306. 30. Calow, P.; Forbes, V. E., Does ecotoxicology inform ecological risk assessment? Environ. Sci. Technol. 2003, 37, (7), 146A-151A. 31. Caswell, H., Demography meets ecotoxicology: untangling the population level effects of toxic substances. In Ecotoxicology: A HierarchicalTtreatment., Newman, M. C.; Jagoe, C. H., Eds. CRC Press, Lewis Publishers: London, 1996; pp 255-289. 32. Levin, L.; Caswell, H.; Bridges, T.; DiBacco, C.; Cabrera, D.; Plaia, G., Demographic responses of estuarine polychaetes to pollutants: life-table response experiments. Ecol. Appl. 1996, 6, 1295-1313. 33. Barata, C.; Baird, D. J.; Soares, A. M. V. M., Food supply on density-dependent effects on demographic responses of the cladoceran Moinodaphnia macleayi to heavy metal exposure. Ecol. Appl. 2002, 12, 552-564. 34. Altenburger, R.; Schmitt, H.; Schüürmann, G., Algal toxicity of nitrobenzenes: Combined effect analysis as a pharmacological probe for similar modes of interaction. Environ. Toxicol. Chem. 2005, 24, (2), 324-333. 35. Altenburger, R.; Walter, H.; Grote, M., What contributes to the combined effect of a complex mixture? Environ. Sci. Technol. 2004, 38, (23), 6353-6362. 36. Jonker, M.; Sweijen, R. A. J. C.; Kammenga, J. E., Toxicity of simple mixtures to the nematode Ceanorhabditis elegans in relation to soil sorption. Environ. Toxicol. Chem. 2004, 23, 480-488. 37. EPA Survival and Reproduction Test using Daphnids. Official Name: EPA Test Method 1002.0: Daphnid, Ceriodaphnia dubia, Survival and Reproduction Test.Fourth Edition, October 2002, Unite States Environmental Protection Agency: NY, 2002. 38. Nandini, S.; Sarma, S. S. S., Population growth of some genera of cladocerans (Cladocera) in relation to algal food (Chlorella vulgaris) levels. Hydrobiologia 2003, 491, 211-219. 39. Rose, R. M.; Warne, M. S. J.; Lim, R. P., Food concentration affects the life history response of Ceriodaphnia cf. dubia to chemicals with different mechanisms of action. Ecotox. Environ. Safe. 2002, 51, (2), 106-114. 40. Shen, M. F.; Kumar, A.; Ding, S. Y.; Grocke, S., Comparative study on the toxicity of pyrethroids, α-cypermethrin and deltamethrin to Ceriodaphnia dubia. Ecotox. Environ. Safe. 2012,78, 9-13. 41. Barata, C.; Baird, D. J.; Soares, A. M. V. M., Determining genetic variability in the distribution of sensitivities to toxic stress among and within field populations of Daphnia magna. Environ. Sci. Technol. 2002, 36, 3045-3049. 42. Baird, D. J.; Barber, I.; Soares, A. M. V. M.; Calow, P., An early life-stage test with Daphnia magna Straus: An alternative to the 21-day chronic test? Ecotox. Environ. Safe. 1991, 22, (1), 1-7. 43. Hill, I. R.; Shaw, J. L.; Maund, S. J., Review of aquatic toxicity tests with pyrethroid insecticides. In Freswater field tests for hazard assessments of chemicals., Hill, I. R.; Heimbach, F.; Leeuwangh, P.; Matthiessen, P., Eds. Lewis Publishers, CRC Press: London, 1994; pp 249-271.


23

Page 25 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654

44. Barata, C.; Solayan, A.; Porte, C., Role of B - esterases in assessing toxicity of organophosphorous (chlorpyrifos, malathion) and carbamate (carbofuran) pesticides to Daphnia magna. Aquat. Toxicol. 2004, 66, 125-139. 45. Stohs, S. J.; Bagghi, D., Oxidative mechanisms in the toxicity of metal ions. Free Rad. Biol. Med. 1995, 18, 321-336. 46. Pyle, G. G.; Kamunde, C. N.; Mc Donald, D. G.; Wood, C. M., Dietary sodium inhibits aqueous copper uptake in rainbow trout (Oncorhynchus mykiss). J. Exp. Biol. 2003, 206, 609-618. 47. Barata, C.; Varo, I.; Navarro, J. C.; Arun, S.; Porte, C., Antioxidant enzyme activities and lipid peroxidation in the freshwater cladoceran Daphnia magna exposed to redox cycling compounds. Comp. Biochem. Physiol. Part C. 2005, 140, 175-186. 48. Santos, T. C. R.; Rocha, J. C.; Alonso, R. M.; Martínez, E.; Ibañez, C.; Barceló, D., Rapid degradation of propanil in rice crop fields. Environ. Sci. Technol. 1998, 32, (22), 3479-3484. 49. Neuwoehner, J.; Zilberman, T.; Fenner, K.; Escher, B. I., QSAR-analysis and mixture toxicity as diagnostic tools: Influence of degradation on the toxicity and mode of action of diuron in algae and daphnids. Aquat. Toxicol. 2010, 97, (1), 58-67. 50. Dhadialla, T. S.; Carlson, G. R.; Le, D. P., New insecticides with ecdysteroidal and juvenile hormone activity. In 1998; Vol. 43, pp 545-569. 51. Sibly, R. M.; Hansen, F. T.; Forbes, V. E., Confidence intervals for population growth rate of organisms with two-stage life histories. Oikos 2000, 88, 335-340. 52. Robinson, K. A.; Baird, D. J.; Wrona, F. J., Surface metal adsorption on zooplankton carapaces: Implications for exposure and effects in consumer organisms. Environ. Pollu. 2003, 122, (2), 159-167. 53. ASTM, Standard methods for measuring the toxicity of sediment-associated contaminants with freshwater invertebrates. E 1706 -95b. In Annual book of ASTM standards, ASTM: Philadelphia, 1999; Vol. 11.05, pp 65-68. 54. Cedergreen, N.; Kudsk, P.; Mathiassen, S. K.; Sørensen, H.; Streibig, J. C., Reproducibility of binary-mixture toxicity studies. Environ. Toxicol. Chem. 2007, 26, (1), 149-156. 55. Altenburger, R.; Nendza, M.; Schüürmann, G., Mixture toxicity and its modeling by quantitative structure-activity relationships. Environ. Toxicol. Chem. 2003, 22, (8), 1900-1915. 56. Meyer, J. S.; Ingersoll, C. G.; McDonald, L. L.; Boyce, M. S., Estimating uncertainty in population growth rates: jackknife vs. bootstrap techniques. Ecology 1986, 67, 1156-1166. 57. Zar, J. H., Bioestatistical Analysis. Third edition ed.; Bioestatistical Analysis, Prentice-Hall International, Inc: New Jersey, 1996; p 662. 58. Cedergreen, N.; Abbaspoor, M.; Sørensen, H.; Streibig, J. C., Is mixture toxicity measured on a biomarker indicative of what happens on a population level? A study with Lemna minor. Ecotox. Environ. Safe. 2007, 67, (3), 323-332. 59. Chen, X. D.; Stark, J. D., Individual-and population-level toxicity of the insecticide, spirotetramat and the agricultural adjuvant, Destiny to the Cladoceran, Ceriodaphnia dubia. Ecotoxicology 2010, 19, (6), 1124-1129. 60. Enserink, E. L.; Maas-Diepeveen, J. L.; Van Leeuwen, C. J., Combined effects of metals; An ecotoxicological evaluation. Water Res. 1991, 25, (6), 679-687. 61. Bjergager, M. B. A.; Hanson, M. L.; Lissemore, L.; Henriquez, N.; Solomon, K. R.; Cedergreen, N., Synergy in microcosms with environmentally realistic concentrations of prochloraz and esfenvalerate. Aquat. Toxicol. 2011, 101, (2), 412-422.


24


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690

Page 26 of 31

62. Oda, S.; Tatarazako, N.; Watanabe, H.; Morita, M.; Iguchi, T., Production of male neonates in four cladoceran species exposed to a juvenile hormone analog, fenoxycarb. Chemosphere 2005, 60, (1), 74-78. 63. Hosmer, A. J.; Warren, L. W.; Ward, T. J., Chronic toxicity of pulse-dosed fenoxycarb to Daphnia magna exposed to environmentally realistic concentrations. Environ. Toxicol. Chem. 1998, 17, (9), 1860-1866. 64. Beketov, M. A.; Speranza, A.; Liess, M., Ultraviolet radiation increases sensitivity to pesticides: Synergistic effects on population growth rate of Daphnia magna at low concentrations. Bull. Environ.l Cont. Toxicol. 2011, 87, (3), 231-237. 65. Barata, C.; Baird, D. J.; Amat, F.; Soares, A. M. V. M., Population responses to contaminants, between laboratory and field: an approach using Daphnia magna ephippial egg banks. Funct. Ecol. 2000, 14, 513-523. 66. Barata, C.; Medina, M.; Telfer, T.; Baird, D. J., Determining demographic effects of cypermethrin in the marine copepod Acartia tonsa: Stage-specific short tests versus life- table tests. Arch. Environ. Contam. Toxicol. 2002, 43, 373- 378. 67. Barata, C.; Baird, D. J.; Medina, M.; Albalat, A.; Soares, A. M. V. M., Determining the ecotoxicological mode of action of toxic chemicals in meiobenthic marine organisms: stage-specific short tests with Tisbe battagliai. Mar. Ecol. Prog. Ser. 2002, 230, 183-194. 68. Barata, C.; Baird, D. J., Determining the ecotoxicological mode of action of chemicals from measurements made on individuals: Results from instar-based tests with Daphnia magna Straus. Aquat. Toxicol. 2000, 48, (2-3), 195-209. 69. Forbes, V. E.; Olsen, M.; Palmqvist, A.; Calow, P., Environmentally sensitive life-cycle traits have low elasticity: Implications for theory and practice. Ecol. Appl. 2010, 20, (5), 1449-1455. 70. Allen, Y.; Calow, P.; Baird, D. J., A mechanistic model of contaminant-induced feeding inhibition in Daphnia magna. Environ. Toxicol. Chem. 1995, 14, (9), 16251630. 71. Méndez, N.; Linke-Gamenick, I.; Forbes, V. E.; Baird, D. J., Sediment processing in Capitella spp. (Polychaeta: Capitellidae): Strain-specific differences and effects of the organic toxicant fluoranthene. Mar. Biol. 2001, 138, (2), 311-319. 72. Orchard, S. J.; Holdway, D. A.; Barata, C.; Van Dam, R. A., A rapid response toxicity test based on the feeding rate of the tropical cladoceran Moinodaphnia macleayi. Ecotox. Environ. Safe. 2002, 53, (1), 12-19.


25

Page 27 of 31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


691 692

Table 1. Allosteric decay regression models fitted to the single and ternary mixtures. All regressions were significant at P

Population Growth Rate Responses of Ceriodaphnia dubia to Ternary

Recommend Documents