Marine Pollution Bulletin 87 (2014) 11–21

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Can we predict temperature-dependent chemical toxicity to marine organisms and set appropriate water quality guidelines for protecting marine ecosystems under different thermal scenarios? Guang-Jie Zhou a,b,⇑, Zhen Wang a, Edward Tak Chuen Lau a, Xiang-Rong Xu b, Kenneth Mei Yee Leung a,c,⇑ a b c

The Swire Institute of Marine Science and School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China State Key Laboratory in Marine Pollution, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China

a r t i c l e

i n f o

Article history: Available online 28 August 2014 Keywords: Environmental risk assessment Species sensitivity distribution Assessment factor Temperature Toxicity

a b s t r a c t Temperature changes due to climate change and seasonal fluctuation can have profound implications on chemical toxicity to marine organisms. Through a comprehensive meta-analysis by comparing median lethal or effect concentration data of six chemicals for various saltwater species obtained at different temperatures, we reveal that the chemical toxicity generally follows two different models: (1) it increases with increasing temperature and (2) it is the lowest at an optimal temperature and increases with increasing or decreasing temperature from the optimal temperature. Such observations are further supported by temperature-dependent hazardous concentration 10% (HC10) values derived from species sensitivity distributions which are constructed using the acute toxicity data generated at different temperatures. Considering these two models and natural variations of seawater temperature, we can scientifically assess whether applying an assessment factor (e.g. 10) to modify water quality guidelines of the chemicals can adequately protect marine ecosystems in tropics, subtropics and temperate regions, respectively. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction The parallel analysis of exposure and effect of chemicals remains the central part of screening level ecological risk assessment (ERA) for marine ecosystems, because it essentially characterizes the risk of a chemical based on a hazard quotient (HQ) which is equal to a ratio between measured environmental concentration (MEC) or predicted environmental concentration (PEC) and predicted no effect concentration (PNEC) of the chemical (i.e., HQ = MEC or PEC/PNEC). If HQ is greater than one, then there is potential risk of the chemical and further risk management action is required. Thus, an accurate determination of the PNEC is crucial to effective ERA. Currently, most PNECs are derived from toxicity data generated from laboratory based on standard acute and/or chronic toxicity tests and often referred as water quality guidelines (WQG), water quality criteria or environmental quality standards etc. (Leung et al., 2014; Merrington et al., 2014). However, such ⇑ Corresponding authors at: The Swire Institute of Marine Science and School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China. Tel.: +852 22990607; fax: +852 25176082. E-mail addresses: [email protected] (G.-J. Zhou), [email protected] (K.M.Y. Leung). http://dx.doi.org/10.1016/j.marpolbul.2014.08.003 0025-326X/Ó 2014 Elsevier Ltd. All rights reserved.

standard acute toxicity tests are usually conducted at a single, fixed temperature without considering daily and seasonal fluctuations of seawater temperature in the natural environment. The selected test temperature can be an average water temperature in the region or an optimal temperature for the test organism (Lau et al., 2014). Conceivably, if the toxicity of a chemical increases at temperatures higher or lower than the selected test temperature, the toxicity endpoint generated from the standard toxicity test would underestimate the actual chemical toxicity in the field where experiences a range of temperature fluctuations in different temporal scales. When such toxicity endpoints are employed for deriving WQG or for ERA, they cannot encompass the variation of chemical toxicity under different temperature regimes. It is, therefore, rational to question how chemical toxicity to marine organisms can vary across the natural range of seawater temperatures, and what assessment factors should be applied to encompass such variations when deriving WQG of chemical contaminants for protecting marine ecosystems. Temperature is one of the most important environmental factors influencing the toxicity of chemicals to aquatic organisms (Heugens et al., 2001). It is common to report that chemical toxicity increases with increasing temperature. For example, both the

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G.-J. Zhou et al. / Marine Pollution Bulletin 87 (2014) 11–21

(A)

Temperature (oC)

ECx Temperature (oC)

Temperature (oC)

(F)

(E) ECx

Performance

(D)

(C)

(B)

Temperature (oC)

Cumulative mortality

that they have to enter anaerobic respiration. At the high temperatures, a temperature-mediated increase of metabolic rate creates an additional demand of oxygen supply that is greater than the supply, resulting in the energetic mismatch and entering anaerobic respiration (Pörtner, 2002). Based on the thermal performance curve concept, a TDCT model has been proposed to describe the relationship between temperature and chemical toxicity to a number of freshwater ectothermic animals (Lau et al., 2014) and some marine organisms such as the copepod T. japonicus (Bao et al., 2008) and the marine medaka fish Oryzias melastigma (Li et al., 2014). This model (i.e., Model-II, Fig. 1F) describes that chemical toxicity to an aquatic ectothermic organism is generally the lowest at its optimum temperature and increases with increasing or decreasing temperature from the optimum temperature (Bao et al., 2008; Lau et al., 2014). Yet, more marine species must be investigated to further confirm if this proposed TDCT model or the aforementioned linear model can be generally applied to most of marine organisms. To reduce the probability of underestimating risk, assessment factors (also referred to as safety factors or uncertainty factors) are often applied to ERA when extrapolating available laboratory results to field situations (Chapman et al., 1998; Duke and Taggart, 2000). For example, an assessment factor of 10 (AF10) was recommended when extrapolating temperate freshwater data to derive WQG for tropical regions, or adopting temperate WQG to protect tropical freshwater ecosystems (Kwok et al., 2007). According to a recent meta-analysis on the application of AF10 to freshwater ecosystems, it was revealed that AF10 would be sufficient to protect 90% of freshwater ectothermic animal species over a range of temperatures for six tested chemicals in the tropics, but insufficient for most of these chemicals in temperate regions where experience larger temperature variations than tropical regions (Lau et al., 2014). Nevertheless, little information is available about whether the application of AF10 is sufficiently adequate to account for the variation in chemical toxicity to marine organisms resulted from environmental temperature fluctuation, and to protect marine ecosystems over a range of environmental temperatures from chemical toxicity. Therefore, this meta-analysis study was specially designed with three main objectives. First, we investigated the relationship between acute chemical toxicity and temperature for a number of chemicals on an array of saltwater ectothermic organisms under

Toxicity

Metabolism or Chemical uptake

uptake rate and sensitivity of cadmium to the water flea Daphnia magna increased with increasing water temperature (Heugens et al., 2003). Similarly, the survival of the marine copepod Tigriopus japonicus exposed to copper or tributyltin (TBT) also decreased with increasing temperature (Kwok and Leung, 2005). Such a linear response (i.e., Model-I, Fig. 1A–C) was partly attributable to a fact that both daphnia and copepod can enter dormancy (i.e., metabolic depression) at low temperatures leading to a reduced uptake of the chemical and hence lowering the chemical toxicity (Bao et al., 2008). However, when closely examining the literature, the pattern of temperature-dependent chemical toxicity (TDCT) is actually far more complex, and it is highly chemical- and species-specific (Kwok et al., 2007). Prof. John Cairns and his colleagues had done some pioneering studies of the TDCT on a wide range of aquatic organisms and chemical substances, and demonstrated that the relationship between chemical toxicity and temperature varied from no relationship to negative or positive relationship (Cairns et al., 1975, 1978). As such, it seemed difficult to make any generalization on the TDCT. After almost 30 years, Bao et al. (2008) revisited the problem and took on a more fundamental approach to first examine how aquatic organisms respond to different temperatures in terms of their physiology based on relevant literature, and then developed a hypothetical model for testing. Empirically, aquatic organisms cannot survive at extremely low and high temperatures. Through an acute exposure to either gradual increase or decrease of temperature from an optimal temperature, aquatic organisms often display an exponential increase in the cumulative mortality from the optimal temperature to the extremely low or high temperature, and thus the relationship between temperature and the cumulative mortality resembles a U-shape curve (Fig. 1D). For the majority of aquatic animal species, the performance of their growth, metabolism, feeding and other activities generally follows the thermal performance curve (Schulte et al., 2011; Fig. 1E). In brief, the performance linearly increases with increasing temperature from the lowest tolerable thermal point to attain a peak at an optimum physiological temperature, and then it rapidly declines from the optimum temperature to the highest tolerable thermal point. At the low temperatures, there are restricted blood circulation due to reduced heart rate, and vasoconstriction (Pörtner, 2002). While having reduced blood circulation, the animals have limited amount of oxygen supply leading to an energetic mismatch

o Temperature ( C)

Temperature (oC)

Fig. 1. Our proposed two conceptual models for predicting temperature-dependent chemical toxicity (TDCT) to marine organisms. Linear relationship: (A) increasing metabolism/chemical uptake with temperature; (B) increasing chemical toxicity with temperature; and (C) decreasing Effect Concentration (ECx) value with temperature. Inverse V-shape relationship: (D) U shape curve of cumulative mortality with temperature; (E) inverse V-shape of performance with temperature (i.e., thermal performance curve); and (F) inverse V-shape of ECx with temperature.

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G.-J. Zhou et al. / Marine Pollution Bulletin 87 (2014) 11–21 Table 1 Summary statistics for the range of seawater temperature variations (°C) at three different latitudes (0°N–15°N, 15°N–30°N, 30°N–60°N).

Mean Median Standard deviation Standard error Maximum Minimum 10th percentile 90th percentile

0°N–15°N

15°N–30°N

30°N–60°N

3.0 3.0 1.0 0.2 4.7 1.3 1.8 4.5

11.1 10.6 2.7 0.6 17.2 5.8 9.0 15.1

17.4 16.9 3.8 0.8 25.5 9.7 13.0 21.3

different temperatures to verify if either of the two TDCT models (i.e., Model-I and Model-II) fitted well to the observed patterns. Second, we constructed and compared temperature-dependent saltwater species sensitivity distributions (SSDs) based on acute toxicity data for three metals (cadmium, chromium and zinc) to further validate the two TDCT models. Following the method used in Lau et al. (2014), we finally evaluated whether the application of AF10 to saltwater WQG would be adequately protective for marine ecosystems in order to account for the variation in chemical toxicity to marine organisms resulted from natural temperature fluctuations in tropical, sub-tropical and temperate regions. The results of this study have imperative implications for the extrapolation of laboratory derived toxicity data for ERA and derivation of WQG, as well as the subsequent decision-making for marine water quality management in different geographical regions.

2. Methods 2.1. Coastal water temperature Considering chemical pollution mainly occurred in coastal regions, coastal water surface temperature data were obtained from the daily satellite reading provided by the United States National Oceanic and Atmospheric Administration (NOAA) (http://www.seatemperature.org), which has a database containing several years of average water surface temperature data collected around the world. We extracted the data from nine countries situated between 90°E–150°E and 0°N–60°N, and the data were grouped into three different parts on the basis of different latitudes (i.e., 0°N–15°N, 15°N–30°N, 30°N–60°N) (Table S1 in Appendix A). The mean, median, 10th percentile and 90th percentile values for the range of coastal water temperature variations were extracted and applied in the present study (Table 1). 2.2. Data mining Acute toxicity data of median lethal or effect concentrations (i.e., LC50 and EC50) and the corresponding experimental temperatures were extracted from the United States Environmental Protection Agency (US EPA) ECOTOX database (http://www.epa.gov/ ecotox/) and peer-reviewed literature for seven groups of marine organisms, including algae, crustaceans, fish, insects, molluscs, worms and other invertebrates. Data were collected for six

Table 2 Studies on effect of multiple temperatures (n P 3) on saltwater organisms in the same study. ‘‘;’’ and ‘‘"’’ indicate that the Effect Unit (Eu) decreased and increased with increasing temperature, respectively. ‘‘^’’ indicates that Eu increased with rising temperature then started decreasing after reaching a certain temperature point. ‘‘v’’ indicates that Eu decreased with rising temperature then started increasing after reaching a certain temperature point. The details of the cited references are present in Supplementary Materials (Appendix A). Chemical

Scientific name

Taxonomic group

Temperature range (°C)

Temperature-dependent toxicity outcome

Reference

Cadmium Cadmium Cadmium Cadmium Cadmium Cadmium Cadmium Cadmium Chromium Chromium Chromium Chromium Chromium Chromium Chromium Chromium Copper Copper Copper Copper Copper Copper Copper Zinc Zinc Zinc Zinc Zinc Zinc Zinc Zinc Zinc Zinc Nickel Nickel Nickel

Americamysis bahia Leptocheirus plumulosus Penaeus merguiensis Varuna litterata Chasmichthys dolichognathus Pagrus major Capitella capitata Ctenodrilus serratus Skeletonema costatum Acartia clausi Artemia salina Corophium volutator Palaemonetes pugio Macoma balthica Brachionus plicatilis Nereis diversicolor Gymnodinium splendens Isochrysis galbana Thalassiosira pseudonana Penaeus merguiensis Menidia menidia Paralichthys dentatus Hediste diversicolor Gymnodinium splendens Isochrysis galbana Thalassiosira pseudonana Acanthomysis costata Corophium volutator Fenneropenaeus penicillatus Penaeus merguiensis Macoma balthica Capitella capitata Ctenodrilus serratus Corophium volutator Penaeus merguiensis Macoma balthica

Crustaceans Crustaceans Crustaceans Crustaceans Fish Fish Worms Worms Algae Crustaceans Crustaceans Crustaceans Crustaceans Molluscs Other invertebrates Worms Algae Algae Algae Crustaceans Fish Fish Worms Algae Algae Algae Crustaceans Crustaceans Crustaceans Crustaceans Molluscs Worms Worms Crustaceans Crustaceans Molluscs

20–30 15–25 20–35 15–32 24–25 23–25 10–20 10–20 8–15 14–22 10–30 5–15 10–25 5–15 10–31 5–15 16–33 16–28 12–24 20–35 16–20 12–14 12–22 16–28 16–28 12–28 13–15 5–15 25–27 20–35 5–15 10–20 10–20 5–15 20–35 5–15

; ; ; ^ ^ " ^ ^ ^ ; ; ^ ; ; ; ; ^ ^ ^ ; " ; ^ ; ^ ^ ^ ^ v ; ^ ; " ; ; ^

Voyer and Modica (1990) DeWitt et al. (1992) Denton and Burdon-Jones (1982) Kulkarni (1983) Kuroshima and Kimura (1990) Kuroshima et al. (1993) Reish et al. (1977) Reish et al. (1977) Kusk and Nyholm (1991) Moraïtou-Apostolopoulou and Verriopoulos (1982) Persoone et al. (1989) Bryant et al. (1984) Fales (1978) Bryant et al. (1984) Persoone et al. (1989) Bryant et al. (1984) Wilson and Freeburg (1980) Wilson and Freeburg (1980) Wilson and Freeburg (1980) Denton and Burdon-Jones (1982) Cardin (1985) Cardin (1985) Ozoh (1992) Wilson and Freeburg (1980) Wilson and Freeburg (1980) Wilson and Freeburg (1980) Hunt et al. (1989) Bryant et al. (1985) Lin and Tin (1993) Denton and Burdon-Jones (1982) Bryant et al. (1985) Reish and Lemay (1991) Fernandez and Jones (1990) Bryant et al. (1985) Denton and Burdon-Jones (1982) Bryant et al. (1985)

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G.-J. Zhou et al. / Marine Pollution Bulletin 87 (2014) 11–21

A

Model I

B

Model II Other models

42.2%

44.4%

15.6%

11.2%

42.2%

44.4%

D

8

Model I Model II Other models

4

0

No. of marine species

No. of marine species

C 8

4

0 Algae Crustaceans Fish Molluscs

Worms

Algae

Crustaceans Fish

Other Invertebrates

Molluscs

Worms

Other Invertebrates

Fig. 2. Distribution of different models for describing the relationship between the ratio of the effect unit (Eu) and temperature (A and B) and the number of marine species in different models for different taxonomic groups (C and D) (A, C: Effects of chemicals on marine organisms at multiple temperatures (n P 3) based on the combined studies. B, D: Effects of chemicals on marine organisms at multiple temperatures (n P 3) obtained from the same study).

chemicals (cadmium, chromium, copper, zinc, nickel and tributyltin) according to the criteria suggested by Lau et al. (2014); only the data of more than one temperature point with the same exposure period and same salinity were used in the analysis (Table S2 in Appendix A). In addition, we also extracted acute toxicity data obtained at multiple temperatures (i.e., P3 temperature treatments) for a saltwater species from a single study. Since there were a limited number of such studies, we could only include such acute toxicity datasets for five chemicals from saltwater organisms in the meta-analysis (Table 2). Acute toxicity data of three metals (i.e., cadmium, chromium and zinc) obtained at individual temperatures were collected for constructing the temperature-dependent species sensitivity distributions (SSDs) according to the data selection criteria suggested by Wheeler et al. (2002a); only the data of quality of category 2b or above were selected for the analysis (Table S3 in Appendix A). Geometric means were applied when there were multiple data available for the same species and chemical at a particular temperature (Wheeler et al., 2002a). 2.3. Construction of SSDs The SSDs were constructed following the procedure described in Wheeler et al. (2002a, b) and Wang et al. (2014). In brief, toxicity data were ranked in ascending order and assigned percentiles, and then fitted with different parametric regression models including log-normal, log-logistic, Gompertz, Fisher–Tippett, Weibull, logtriangular and Burr Type III. The hazardous concentrations corresponding to 90% protection level (i.e., the 10% hazardous concentration; HC10) and their respective corresponding 95% confidence intervals (95% CIs) were determined based on the best fit model (i.e., one that passed both Shapiro–Francia and Anderson– Darling tests with the minimum corrected Akaike information criterion (AICc) value; see Wang et al. (2014) for more details). In addition, a valid SSD should contain a minimum of eight data

points from at least three different taxonomic groups obtained at the same temperature. 2.4. Effect unit (Eu) calculation The proposed temperature-dependent chemical toxicity (TDCT) model (i.e., Model-II) assumes that there is an optimum physiological temperature at which chemical toxicity to aquatic organisms is the lowest (Bao et al., 2008; Lau et al., 2014). Theoretically, this inverse V-shape model has already captured both the positive and negative linear relationships between temperature and toxicity in terms of LC50 or EC50, and both of these lines jointly form the inverse V-shape model. Thus, this integrative model covers the three TDCT models. Using this model, the extracted toxicity data were analyzed in two parts from the optimum temperature. The effect unit (Eu) was calculated by the method described in Lau et al. (2014). In brief, the temperature at which the corresponding LC50 value was the highest was defined as the reference temperature (Tref), and the LC50 value at Tref (i.e., LC50Tref) was defined as 1 Eu. The LC50 value at a specific temperature (LC50T) was then correspondingly expressed in terms of EuT as the quotient between LC50T and LC50Tref. The change in Eu per °C increase or decrease in temperature (DEu °C1) from Tref to T can then be calculated as (1  EuT)/(T  Tref). Multiple data for increasing or decreasing temperature were summarized as geometric means in the analysis. In addition, the extent of temperature increase or decrease that could be encompassed by applying AF10 was calculated using the equation: (1  0.1)/(DEu °C1) (Lau et al., 2014). 2.5. Statistical analysis As suggested by Lau et al. (2014), the 10th percentile and 5th percentile of the extent of temperature increase and decrease that were encompassed by applying AF10 were calculated for each chemical. The two percentile values of the extent of temperature

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G.-J. Zhou et al. / Marine Pollution Bulletin 87 (2014) 11–21

Acartia tonsa Americamysis bahia Corophium insidiosum Eurytemora affinis Leptocheirus plumulosus Neomysis integer Palaemonetes pugio Penaeus merguiensis Rhepoxynius abronius Uca pugilator Varuna litterata Chasmichthys dolichognathus Fundulus heteroclitus Mugil cephalus Pagrus major Mytilus edulis Capitella capitata Ctenodrilus serratus Neanthes arenaceodentata Ophryotrocha diadema

(A) Cadmium

Crustaceans Fish Molluscs Worms

Skeletonema costatum Acartia clausi Americamysis bahia Artemia salina Corophium volutator Palaemonetes pugio Praunus flexuosus Macoma balthica Brachionus plicatilis Nereis diversicolor

∗

∗

(B) Chromium

Algae Crustaceans Molluscs Other invertebrates Worms

∗

∗

1.0

0.5

0.0

0.5

1.0 -1

∆ Eu per °C change in temperature (∆Eu °C )

∗ ∗ 0.9

0.6

0.3

0.0

0.3

0.6

0.9 -1

∆ Eu per °C change in temperature (∆Eu °C )

(C) Copper

Algae Crustaceans Fish Worms

Gymnodinium splendens Isochrysis galbana Thalassiosira pseudonana Acartia tonsa Balanus improvisus Penaeus merguiensis Fundulus heteroclitus Lates calcarifer Menidia menidia Paralichthys dentatus Pleuronectes americanus Priopidichthys sp. Hediste diversicolor

∗ ∗ ∗

∗

∗ ∗ 0.6

0.4

0.2

0.0

0.2

0.4

0.3

∗

0.2

(E) Nickel

0.1

0.0

0.2

Crustaceans Fish

Acartia tonsa

∗ ∗

0.4

0.0

0.2

0.4

0.6 -1

∆ Eu per °C change in temperature (∆Eu °C )

Crustaceans Fish Molluscs

∗ ∆ Eu per °C change in temperature (∆Eu °C )

-1

Acartia clausi Corophium volutator Eurytemora affinis Penaeus merguiensis Pseudodiaptomus coronatus Priopidichthys sp. Macoma balthica

∗

0.6

0.6

(D) Zinc

Algae Crustaceans Molluscs Worms

Gymnodinium splendens Isochrysis galbana Thalassiosira pseudonana Acanthomysis costata Americamysis bahia Corophium volutator Fenneropenaeus penicillatus Penaeus merguiensis Macoma balthica Capitella capitata Ctenodrilus serratus Neanthes arenaceodentata

(F) TBT

∗ ∗

Penaeus duorarum

∗

Penaeus japonicus

∗

∗

Cyprinodon variegatus Fundulus heteroclitus

0.1

0.2

0.3

0.6

-1

0.4

0.2

0.0

0.2

0.4

0.6 -1

∆ Eu per °C change in temperature (∆Eu °C )

∆ Eu per °C change in temperature (∆Eu °C )

Fig. 3. Change in effect unit (Eu) per unit change in temperature for marine species exposed to each of the six chemicals (A): cadmium, (B): chromium, (C): copper, (D): zinc, (E): nickel, and (F): TBT). *Indicates the number of temperature points for the species is 2. Bars on the left- and right-hand side of the graphs represent change in Eu per unit decrease and increase in temperature, respectively. Bars located within the inner dotted lines (at 0.09 Eu per °C) on either side of the graphs represent a protection of at least 10 °C decrease/increase in temperature by an assessment factor of 10, whereas bars located within the outer dotted lines (at 0.18 Eu per °C) on either side of the graphs represent a protection of at least 5 °C decrease/increase in temperature by an assessment factor of 10.

increase and decrease were then summed to provide the overall temperature coverage (TC) by AF10 if 90% (TC90) or 95% (TC95) of all marine species were to be protected. By means of Student’s t-test, the alternate hypothesis Tc < T (i.e., T = a natural range of coastal water temperatures) was tested to determine whether the application of AF10 was adequate to encompass sufficient variations of temperature-dependent chemical toxicities for marine ecosystems. In addition, one-way analysis of variance (ANOVA) test, followed by post hoc Tukey’s multiple comparison test, was used to compare the HC10 values of the same chemical (obtained from the SSDs) among different temperatures.

3. Results 3.1. Effect of temperature on Eu Across all six studied chemicals, the valid dataset contained 67 sets of acute toxicity data for the meta-analysis. Among them, 22 cases had toxicity data for two temperature points, while 45 cases had toxicity data for at least three temperature points. To examine if the relationship between chemical toxicity (in terms of Eu) and temperature follows the Model-I or Model-II, we plotted and visually examined the relationship for each of the 45 cases, and some

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G.-J. Zhou et al. / Marine Pollution Bulletin 87 (2014) 11–21

Table 3 p Data availability for effect unit (Eu) calculation. "T and;T denote increase and decrease of temperature from reference temperature, respectively; ‘‘ ’’ indicates data available and ‘‘–’’ indicates absence of data. Taxonomic group Algae Cadmium

"T ;T

Chromium

"T ;T

Crustaceans p p

– – p p

p p

p p

p p

p p

p p

Copper

"T ;T

Zinc

"T ;T

Nickel

"T ;T

– –

TBT

"T ;T

– –

Fish p p – – p p

Insects – – – – – –

– –

– –

p p

– p

– –

p p

p p

representative examples are shown in Fig. S1 (Appendix A). There were 42.2% of the cases following the Model-I in which the Eu decreased with increasing temperature (Fig. 2A). Similarly, there were 42.2% of the cases following the Model-II in which the Eu was the highest at an optimum temperature and then decreased with increasing or decreasing temperature from the optimum temperature (i.e., an inverse V-shape; Fig. 2A). In the remaining 15.6% of the cases, the Eu interestingly increased with rising temperature (13.3%), or decreased firstly and then increased with temperature (2.2%) (Fig. 2A). Since the above meta-analysis also included toxicity data of the same species collected from different studies using different temperatures, differences in experimental conditions between studies could lead to increased variations and potential error in the results. To rectify such problems, we also examined acute toxicity data at multiple temperatures for individual species (n P 3) which were obtained concurrently from individual single experiments. A total of 36 relevant studies available from saltwater organisms for five chemicals was collected (Table 2). Among these studies, 44.4% of cases followed the Model-II and also 44.4% of cases conformed to the Model-I (Fig. 2B). In only 11.1% of cases, the Eu increased with increasing temperature (8.3%), or decreased firstly and then increased with temperature (2.8%) (Fig. 2B). The results were in good agreement with those obtained based on toxicity data for the six chemicals collected from different studies (Fig. 2A), indicating that most aquatic species follow either the Model-I or the Model-II. Among the 45 cases with toxicity data for at least three temperature points, Model-I (Eu decreasing shape) was dominated by crustaceans which accounted for 57.9% of the data; Model-II (inverse V-shape) was dominated by both algae and crustaceans which jointly covered 63.2% of the data (Fig. 2C). Other four taxonomic groups jointly represented less than 40% of data for these two models. Additionally, 85.7% of algae followed Model-II. Across all crustacean species, 55.0% and 30.0% of them followed Model-I and Model-II, respectively. The 36 cases, which were obtained concurrently from individual single experiments, showed similar results as those obtained from different studies (Fig. 2C and D). Across the six studied chemicals, the change in Eu per °C increase or decrease (DEu °C1) varied substantially between saltwater species for the same chemical, and also between chemicals for the same species (Fig. 3). For example, the DEu °C1 decrease and increase for the alga Thalassiosira pseudonana were 0.02 and 0.07, respectively when exposed to copper, but were 0.21 and 0.09, respectively when exposed to zinc (Fig. 3). Crustacean data were available for all of the six tested chemicals (Fig. 3, Table 3), and crustaceans comprised the largest taxonomic

– –

Molluscs p – p – – – p p p p – –

Worms p p p – p p

Other invertebrates – – p – – –

– p

– –

– –

– –

– –

– –

group, representing 49.3% of the dataset, followed by fish (19.4%). The remaining data were represented by worms (13.4%), algae (10.4%), molluscs (6.0%) and other invertebrates (1.5%) (Fig. 3). 3.2. Effect of temperature on the SSDs and HC10 For each of the three studied metals, the saltwater SSDs showed a clear separation among different temperatures (Fig. 4A, C and E). Of various parametric regression models, Burr Type III provided the best fitting for 45.5% of datasets, followed by log-normal (18.2%), Gompertz (18.2%), log-logistic (9.1%) and Fisher-Tippett (9.1%), as indicated by the goodness of fit tests (Shapiro–Francia and Anderson–Darling tests) as well as AICc values (Table 4). However, both Weibull and log-triangular models did not fit well to the SSDs (Table 4). The species composition of each SSD is also given in Fig. 4. The datasets for the three chemicals contained data from a wide spectrum of taxonomic groups, with crustaceans being the most abundant taxonomic group (49.2%, 47.6% and 49.0% for cadmium, chromium and zinc, respectively). In general, the saltwater species tended to be more sensitive to chemicals at relatively high or low temperatures when compared with the optimal temperature (Fig. 4). The HC10 values for the temperature-dependent SSDs with their corresponding 95% CI values, determined by the best regression model, are shown in Fig. 4 and Table 4. It is interesting to note that the HC10 values varied considerably along the temperature gradient. For cadmium, the relationship between HC10 values and temperature clearly followed an inverse V-shape, resembling the Model-II as described above (Fig. 4B). The HC10 values generally decreased with increasing temperature for both chromium and zinc (Fig. 4D and F; i.e., following the Model-I), although uncertainty existed due to a lack of the data at a lower temperature in particular for zinc. These results further support the adoption of the Model-I and Model-II to predict TDCT of chemicals in saltwater organisms. 3.3. Is AF10 appropriate to account for TDCT variation? Mean variation ranges of natural temperature fluctuations in marine coasts situated in 0°N–15°N, 15°N–30°N, 30°N–60°N were 3.0 °C, 11.1 °C and 17.4 °C respectively, and significant differences were found among them (ANOVA: p < 0.001; Table 1). The application of AF10 on water quality guideline (WQG) values, which are derived from toxicity data obtained at a standardized optimum temperature, can encompass 5.1–31.1 °C and 4.3–30.5 °C of temperature variation at 90% and 95% protection level, respectively (Table 5). At 90% and 95% protection levels, WQGs with an

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G.-J. Zhou et al. / Marine Pollution Bulletin 87 (2014) 11–21

100

b

400 300

HC (μg/L)

60 40

a a

100

20 0

0

1

2

3

4

0

5

c 0

10

5

Concentration (Log, μg/L) 8000

80

25

30

a

a

(D) Chromium

6000

HC (μg/L)

60

10

% of affected species

20 o

(C) Chromium

40

4000

b

2000

20 0

15

Temperature ( C)

100

2

3

4

5

0

6

b 0

10

5

Concentration (Log, μg/L)

15

20

25

30

o

Temperature ( C)

100

(E) Zinc

a

400

a

(F) Zinc

80

HC (μg/L)

60

10

% of affected species

(B) Cadmium

200

10

% of affected species

(A) Cadmium 80

40

b

200 100

20 0

300

1

2

3

4

5

6

0

0

10

5

Concentration (Log, μg/L)

Algae Crustaceans Fish Insects Molluscs Other invertebrates Worms

15

20

25

30

o

Temperature ( C)

o

Light gray=10 C o Gray=15 C o Black=20 C o Open symbol=25 C

Fig. 4. Species sensitivity distributions (SSDs) for three chemicals (cadmium, chromium and zinc) at different temperatures, and their temperature-dependent hazardous concentration 10% (HC10s), as well as the 95% confidence intervals (95% CIs) of the HC10 values (values expressed as lg/L). Symbols for taxa compositions are given in the inner key of the figure. Mean values with different letters (i.e., a, b or c) are significantly different as indicated by the results of the one-way ANOVA and Tukey test (p < 0.05).

application of AF10 would be sufficient to protect marine species in 0°N–15°N region against the six chemicals (i.e., cadmium, chromium, copper, zinc, nickel and TBT), whist they are only adequately protective for chromium and nickel in 30°N–60°N region. In 15°N–30°N region, WQGs with the adoption of AF10 could sufficiently protect 90% marine species over a range of temperatures for chromium, zinc, nickel and TBT, whist they are only protective to marine species against chromium and nickel at 95% protection level (Table 5).

4. Discussion 4.1. Temperature-dependent chemical toxicity (TDCT) to marine organisms As demonstrated by the present and previous studies (Cairns et al., 1975, 1978; Heugens et al., 2001; Kwok and Leung, 2005; Bao et al., 2008; Lau et al., 2014), the toxicity of chemicals on aquatic organisms can be greatly affected by environmental

18

G.-J. Zhou et al. / Marine Pollution Bulletin 87 (2014) 11–21

Table 4 Temperature-dependent hazardous concentrations 10% (HC10s), with their 95% confidence intervals (95% CIs) from the best fit model (values expressed as lg/L), determined by both Shapiro–Francia test (statistic W’, critical value Z = 1.645 under 5% significance level) and the Anderson–Darling (AD) test (statistic AD, critical value AD = 0.752 for lognormal, log-logistic and log-triangular; and critical value AD = 0.757 for Gompertz, Weibull, Fisher–Tippett and Burr Type III, under 5% significance level) passed; and corresponding minimum Akaike information criterion with correction (Min-AICc). Chemical

Temperature

HC10 (95% CI)

Best fit model

W’

AD

Min-AICc

Cadmium

10 15 20 25

197 (155, 244) 326 (272, 401) 154 (139, 169) 36 (25, 52)

Log-normal Burr Type III Log-normal Gompertz

0.791 0.787 0.757 0.257

0.185 0.279 0.234 0.559

85 195 402 57

Chromium

10 15 20 25

5022 (3522, 7161) 4650 (2544, 7055) 1073 (745, 1933) 919 (759, 1084)

Gompertz Fisher–Tippett Burr Type III Log-logistic

0.181 0.085 0.916 0.119

0.202 0.463 0.268 0.204

62 51 200 100

Zinc

15 20 25

297 (220, 410) 304 (244, 386) 109 (61, 184)

Burr Type III Burr Type III Burr Type III

0.950 0.753 1.554

0.267 0.534 0.483

140 307 137

temperature. The results of the present meta-analysis demonstrated that the relationship between chemical toxicity (in terms of Eu) and temperature for saltwater organisms can be readily described by an integrative model for over 80% of the cases, for which a half can be described by the Model-I (i.e., the negative linear model) and the other half by the Model-II (i.e., the inverse V-shape model). The remaining cases included the positive relationship between the two parameters or the V-shape model. The inverse V-shape relationship between temperature and effect unit (Eu) of chemicals (i.e., Model-II) was also commonly observed in freshwater species (Lau et al., 2014). Following the Model-II, chemical toxicity to aquatic organisms at a low temperature generally decreased with increasing temperature until it reached the optimal temperature, from which onwards it started to increase with rising temperature. For example, cadmium was more toxic to the polychaete worm Ctenodrilus serratus at 10 °C, followed by 20 °C and then 15 °C (Fig. S1B in Appendix A). In general, aquatic organisms living in optimal conditions are more tolerable to chemical toxicity than those living in the conditions near to their thermal tolerance limits (Heugens et al., 2001; Kwok et al., 2007; Bao et al., 2008). However, we also found that chemical toxicity, in about 40% of the cases, linearly increased with the increasing temperature (i.e., Model-I). For example, when temperature increased from 10 °C to 25 °C, the toxicity of chromium to the grass shrimp Palaemonetes pugio increased as represented by Eu decreasing from 1 to 0.52 (Fig. S1M in Appendix A). Similarly, elevated temperatures could increase the sensitivity of the snail Physa pomilia to cadmium (Kimberly and Salice, 2013). It was common to observe that many ectothermic organisms entered a state of dormancy when exposed to extreme environmental conditions such as cold temperatures and toxic stresses (McAllen et al., 1999; Leung et al., 2000; Kwok and Leung, 2005; Bao et al., 2008). For instance, at least five species of marine harpacticoid copepods are able to enter a state of dormancy at low temperature conditions (McAllen et al., 1999). While having a low metabolic rate at low temperatures, the uptake and bioaccumulation of chemicals decreased to extremely low levels, thus further decreasing the toxic effect of chemicals to the organisms (McAllen et al., 1999). As shown in the current study, the Model-I was dominated by crustaceans (57.9% of all cases). The Model-I is, indeed, consistent with the results reported in previous studies on temperature-dependent toxicity of chemicals on crustacean organisms (McAllen et al., 1999; Bao et al., 2008; Li et al., 2014). Environmental temperature can markedly influence the toxicity of chemicals on aquatic organisms by affecting their physiological processes, bioavailability and toxicokinetics of chemicals (Cairns et al., 1975; Heugens et al., 2001). As most aquatic organisms are

ectotherms, their metabolism, physiology and behavior are highly dependent on surrounding temperature, and thus temperature can influence their susceptibility to chemical exposure. Every aquatic organism has a thermal tolerance range (TTR), which is determined by interplay of developmental, genetic and environmental factors (Cairns et al., 1975). When temperature is higher or lower than the TTR of an organism, lethality may occur, and such a thermal stress at temperature extremes may further increase the toxicity of a chemical. For example, the toxicity of copper pyrithione on the marine copepod T. japonicus increased with increasing temperature between assumed TTR between 15 and 31 °C, whereas the toxicity of this chemical to the copepod at 4 °C and 35 °C (in terms of mortality) were significantly higher than those observed at 10 °C and 25 °C (Bao et al., 2008). Temperature may also influence bioavailability and toxicokinetics of chemicals by affecting metabolic rate and feeding activity of an organism, and thus affecting the uptake, elimination and detoxification rates of the chemical (Heugens et al., 2001). As mentioned above, marine harpacticoid copepods enter a state of dormancy with a very low metabolic rate at low temperature conditions, and at this circumstance, the uptake and bioaccumulation of chemicals could decrease to extremely low levels (McAllen et al., 1999). In addition, the chemical toxicity on aquatic organisms sometimes can be complex among different temperatures. For example, the temperature-dependent toxicity of cadmium on the water flea D. magna also followed the Model-I which was thought to be driven by a low chemical accumulation at the lower temperature range and by an increase chemical sensitivity at the higher temperature range (Heugens et al., 2003). 4.2. Temperature-dependent SSDs and HC10 Previous studies have focused on the SSDs comparison between saltwater and freshwater organisms (Wheeler et al., 2002b), and between tropical and temperate organisms (Kwok et al., 2007; Wang et al., 2014) exposed to the same chemicals. Time-dependent SSDs were also reported in a recent study which showed that critical metrics such as HC5 values slightly varied with the duration of the toxicity tests (Fox and Billoir, 2013). Yet, not much work has been done to compare the SSDs and their HCx values obtained among different temperatures. Logically, if most species follow the Model-II (i.e., an inverse V-shape model), then an overall response of the biological assemblage should display a similar temperature dependency and the relationship between temperature and HC10 values should follow the Model-II as well. Our results showed that temperature can affect the SSDs of marine organisms exposed to the same chemical as reflected by well separated SSD curves amongst different temperatures. As shown in this study,

11.7*** 4.1*** 23.7 8.6 3.1 5.5 0.9 32.0 6.0 22.0

4.5 22.0

12.1 40.4

3.1 3.7

3.2 7.4

10.5

4.3. The appropriateness of assessment factor

**

***

p < 0.01. p < 0.001.

4 2 "T ;T TBT

19

most of marine organisms were generally less sensitive to cadmium at the optimal temperature than other temperatures, and the relationship between the HC10 values of cadmium and temperature followed the Model-II. Nonetheless, those for the HC10 values of chromium and zinc generally conformed to the Model-I. These results further indicated that both the Model-I and Model-II are supported not only by individual species’ response to the chemical at different temperatures but also by the overall response of an assemblage of marine organisms as reflected by the temperaturedependent SSDs and HC10 values. Given the above, more attention should be paid to the long-time effect of global climate change on the coastal seawater temperature and its seasonal fluctuation in various geographical regions, and the associated TDCT to individual marine organisms and their communities. To our knowledge, this is the first attempt to document the effect of temperature on the SSDs of marine organisms exposed to chemicals. The shape of an SSD and its HCx values are highly dependent on the data quality and data quantity (Wheeler et al., 2002a; Dowse et al., 2013). Due to the unavailability of toxicity data at different temperatures for a large quantity of marine organisms for most chemicals, we were only able to test three chemicals in the present study. Also, it is important to note that the species composition in the datasets of the three tested chemicals also did not properly represent the natural biological communities in the marine ecosystem. For example, nearly 50% of datasets were from crustaceans, while other six taxonomic groups made up the remaining 50% of datasets. Nevertheless, the current temperature-dependent SSD approach is useful to serve as an independent way of validating if the temperature-dependent chemical toxicity does follow the Model-I or the Model-II. In future, more chemicals should be tested using this temperature-dependent SSD approach, when more toxicity data obtained at different temperatures are available for marine organisms. It is also important to conduct such toxicity tests with a wider range of saltwater species from more diverse taxonomic groups so as to increase the ecological realism of the SSDs.

9.1***

17.5 32.6 30.5 33.6 121.5 5 3 "T ;T Nickel

25.7 37.6

16.2 22.4

57.9 71.0

10.4 19.5

11.0 20.1

31.1

1.1 9 8 "T ;T Zinc

12.7 27.9

10.6 17.7

37.3 112.8

4.1 4.4

5.6 4.8

10.4

31.5

18.3

10.7 19.8

118.8

10.6*** 2.8** 9.4 4.8 4.6 9.3***

27.2

17.4*** 11.3*** 5.0 4.3 1.5 2.8 16.2*** 9.8*** 8.9 5.2 1.5 3.6 1.5 2.0 166.2 391.4 9.9 25.5 7 10 "T ;T Copper

27.0 78.6

1.5 12.5 18.5 10 2 "T ;T Chromium

28.8 19.8

21.1 19.8

92.6 30.0

6.4 9.6

9.3 11.6

20.9

77.1

16.5

4.7

7.9 10.6

66.7

17.4*** 11.3*** 5.0 4.3 2.3 2.0 16.3*** 10.0*** 8.5 5.1 3.0 2.1 1.3 1.9 32.6 225.0 9.0 10.9 16 10 "T ;T Cadmium

13.6 36.6

t-Value (30°N– 60°N) t-Value (15°N– 30°N) t-Value (0°N– 15°N) TC95 5th Percentile t-Value (30°N– 60°N) t-Value (15°N– 30°N) t-Value (0°N– 15°N) TC90 10th Percentile Minimum Maximum Median Mean N Chemical

Table 5 Summary statistics for the extent of temperature increase ("T) and decrease (;T) (°C) that can be adequately encompassed by an assessment factor of 10 for the six chemicals. Tc90 and Tc95 represent the overall temperature coverage by an assessment factor of 10 if 90% and 95%, respectively, of all marine species are protected. The corresponding t-values when compared to the mean range of natural coastal seawater temperature variations in three different latitudes (0°N–15°N, 15°N–30°N, 30°N–60°N) are shown. Significant differences indicate an inadequacy of an assessment factor of 10 to sufficiently account for the natural seawater temperature variation.

G.-J. Zhou et al. / Marine Pollution Bulletin 87 (2014) 11–21

In theory, the TDTC Model-II in fact has already included both the positive and negative linear relationships between temperature and toxicity response and thus also covers the Model-I per se. By using the conceptual Model-II, it is possible to predict the change of chemical toxicity (in terms of Eu) over a range of temperatures and determine appropriate assessment factors for modifying WQGs with a view to protecting marine life from toxic effects of chemicals with consideration of the natural temperature variation in the region of concern. To demonstrate the usefulness of this model, we tested the appropriateness of applying AF10 in derivation of WQGs in this study. This method can also be applied to determine an appropriate AF value to encompass the uncertainty in chemical toxicity responses of marine organisms under any given range of temperature fluctuation in the region of concern. Lau et al. (2014) have recently demonstrated that AF10 can sufficiently protect 90% of the freshwater animal species from the majority of studied chemicals in the tropics, but fails to do so in temperate regions which usually experience larger temperature variations than the tropics. Similar results have also been observed for marine species in the present study. This indicates that the extent of temperature variations in an aquatic environment can significantly affect the degree of protection for marine organisms by WQGs with or without modification using a fixed assessment factor. Based on the data from NOAA (http://www.seatemperature.org), the mean variations of seawater temperature decreased gradually from the equator to north latitude 60°, and significant differences in coastal seawater temperature variation were found

20

G.-J. Zhou et al. / Marine Pollution Bulletin 87 (2014) 11–21

among the three latitudes (i.e., 0°N–15°N, 15°N–30°N, and 30°N– 60°N). At 90% and 95% protection levels, the application of AF10 on the WQGs would be sufficient to protect marine species in the tropics (0°N–15°N) from all of the six test chemicals, whist it is only adequately protective for fewer chemicals in the higher latitude regions. In addition, the degree of protection was highly dependent on the selection of AF values. For example, adoption of AF2 in derivation of WQGs (as suggested by Wang et al., 2014) could protect less marine species than AF10. In the tropics (0°N–15°N), the application of AF2 would be adequate to protect species from all six chemicals (cadmium, chromium, copper, zinc, nickel and TBT) at the 90% protection level and from four out of the six chemicals (chromium, zinc, nickel and TBT) at the 95% protection level. AF2 is only sufficient to protect marine species from chromium and nickel in 15°N–30°N region, and from nickel merely in 30°N–60°N region at the 90% and 95% protection levels (Table S4 in Appendix A). In order to protect the marine ecosystem from the toxicity of chemicals exacerbated by temperature changes, it is very important to select appropriate AFs for modifying the WQG as a means to safeguard marine life in different geographical regions where experience different degrees of temperature variations.

Acknowledgements This research is substantially funded by the Research Grants Council of the Hong Kong SAR Government via a General Research Fund (Project No.: HKU 703511P) to KMYL, and partially supported by the National Natural Science Foundation of China (Project No.: NSFC 41103057 and 51378488). The first author, GJZ, is supported by the Hong Kong Scholars Program (Project No.: XJ2012050). The authors are grateful to Kevin Ho, Andy Yi, Mana Yung and Adela Li for their valuable comments on early drafts of this manuscript. Professor G. Allen Burton, University of Michigan is specially thanked for introducing us to the pioneering work of Professor John Cairns, Jr on studying the effects of temperature upon chemical toxicity to aquatic organisms. The authors also thank the anonymous reviewers for their constructive and useful comments. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.marpolbul.2014. 08.003. References

5. Conclusions Here, we have discovered that most laboratory test marine species follow either of the two TDCT models, i.e., the inverted V-shape relationship (i.e., Model-II) and the linear relationship (i.e., Model-I) between temperature and chemical toxicity (in terms of Eu). The Model-I is a classic model and generally fits well to species like crustaceans which can undergo metabolic depression and minimize chemical uptake and toxic effect at low temperatures. The Model-II, which is founded on both the thermal performance curve (Schulte et al., 2011) and the oxygen-limited thermal tolerance theory (Pörtner, 2002), is applicable to a range of species including crustaceans and fishes as well as microalgae. Furthermore, we also revealed that SSDs of three metals and their HC10 values were temperature-dependent, and the relationship between temperature and HC10 followed either the Model-I or the Model-II. These results suggested that these two TDTC models could also be applied to both individual species and biological assemblages. With these two conceptual TDCT models, it is possible to predict the change of chemical toxicity in any given temperature regime and derive better WQGs for protecting marine ecosystems across different latitudes. Our results also showed that adjusting WQGs with AF10 would be sufficient to protect marine species in the tropics (latitude 0°N–15°N) from all of the six studied chemicals at both 90% and 95% protection levels. The application of AF10 to derive WQGs could only offer protection to marine species for fewer chemicals at sub-tropical and temperate regions, where often faces to a wider range of daily and seasonal temperature variations. Finally, we would like to advocate that if there are resources available, conventional toxicity tests should be conducted at multiple environmentally realistic temperatures (at least having the minimum, optimum and maximum temperature, instead of only having a single, fixed temperature) as a way to better understand and comprehend the TDTC responses of various marine species to chemicals with different modes of toxic action. To gain more insights on the underlying molecular and physiological mechanisms of TDCT responses in different marine organisms, more well designed laboratory or field-based manipulative studies are needed with an integration of advanced molecular tools such as the next generation sequencing (i.e., transcriptomics), microarrays and proteomics.

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