Modeling the Impacts of Multiple Environmental Stress Factors on

Article pubs.acs.org/est

Modeling the Impacts of Multiple Environmental Stress Factors on Estuarine Copepod Populations John C. Korsman,*,† Aafke M. Schipper,† Lisette De Hoop,† Benoit Mialet,§,∥,⊥ Tom Maris,# Micky L. M. Tackx,§,∥ and A. Jan Hendriks† †

Institute for Water and Wetland Research, Department of Environmental Science, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ Nijmegen, The Netherlands § EcoLab (Laboratoire Ecologie Fonctionnelle et Environnement), UPS, INP, Université de Toulouse, 118 Route de Narbonne, 31062 Toulouse, France ∥ EcoLab, CNRS, 31062 Toulouse, France ⊥ LIENSs (Littoral Environnement et Sociétés) UMR 7266, Université de La Rochelle-CNRS, 2 rue Olympe de Gouges, 17000 La Rochelle, France # Ecosystem Management Research Group, Department of Biology, University of Antwerp, Universiteitsplein 1C, B-2160 Wilrijk, Belgium S Supporting Information *

ABSTRACT: Many studies have focused on natural stress factors that shape the spatial and temporal distribution of calanoid copepods, but bioassays have shown that copepods are also sensitive to a broad range of contaminants. Although both anthropogenic and natural stress factors are obviously at play in natural copepod communities, most studies consider only one or the other. In the present investigation, we modeled the combined impact of both anthropogenic and natural stress factors on copepod populations. The model was applied to estimate Eurytemora af f inis densities in the contaminated Scheldt estuary and the relatively uncontaminated Darß-Zingst estuary in relation to temperature, salinity, chlorophyll a, and sediment concentrations of cadmium, copper, and zinc. The results indicated that temperature was largely responsible for seasonal fluctuations of E. af f inis densities. Our model results further suggested that exposure to zinc and copper was largely responsible for the reduced population densities in the contaminated estuary. The model provides a consistent framework for integrating and quantifying the impacts of multiple anthropogenic and natural stress factors on copepod populations. It facilitates the extrapolation of laboratory experiments to ecologically relevant end points pertaining to population viability.

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INTRODUCTION

nation with metals, organochlorines, polycyclic aromatic hydrocarbons, and nonylphenols.12−14 Although both anthropogenic and natural stress factors are at play in copepod communities,15 most field and laboratory studies and all modeling studies take only either anthropogenic or natural stress factors into account. The aims of the present study were to (1) develop a tool for integrating and quantifying the combined impacts of anthropogenic and natural stress factors on the population development of calanoid copepods, and (2) determine the relative importance of these stress factors for the population development of calanoid copepods in a contaminated and a relative uncontaminated estuary.

Despite their ecological importance, estuaries are often polluted due to industrialization and urbanization.1,2 While contaminants often accumulate in estuarine sediments due to the settling of suspended particulates and partitioning from the water column, accumulation of contaminants potentially leads to toxic effects on aquatic biota.3,4 Calanoid copepods are among the most numerous of all taxa in brackish and estuarine waters, and environmental factors that shape the distribution patterns of calanoid copepods in estuaries have been extensively studied.5−7 Field studies and laboratory experiments have shown that temperature, salinity, and food availability are among the most important natural environmental factors determining the spatial and temporal distribution of calanoid copepods.8−11 Other studies revealed that calanoid copepods are also sensitive to anthropogenic factors such as contami© 2014 American Chemical Society

Received: Revised: Accepted: Published: 5709

January 26, 2014 April 16, 2014 April 23, 2014 April 23, 2014 dx.doi.org/10.1021/es5004439 | Environ. Sci. Technol. 2014, 48, 5709−5717

Environmental Science & Technology

Article

Table 1. Ecological Parameters for Eurytemora aff inis under Reference Conditions variable l(a), survival rate (−)a m(a), reproduction rate (d−1)a amax, maximum age (d)a maturation age (d)a ♀/♂, sex ratio (−)a r(0), rate of increase of the population in reference conditions (d−1)b R(0), lifetime fecundity (−)b N(∞) carrying capacity (ind.•m3)c a

value 1 (age 0); 1 (age 15); 0.94 (age 16); 0.88 (age 43); 0.82 (age 51); 0.76 (age 58); 0.71 (age 63); 0.65 (age 65); 0.59 (age 67); 0.53 (age 82); 0.47 (age 83); 0.41 (age 105); 0.35 (age 106); 0.29 (age 112); 0.24 (age 115); 0.18 (age 120); 0.12 (age 122); 0.06 (age 123); 0 (age 125) 0 (age ≤15); 31.7 (age 16 ≤ 101); 0 (age ≥102) 125 16 1 0.26 1.1 × 103 1.3 × 105

From Devreker et al.11 bCalculated from Devreker et al.11 cFrom Heinle and Fleme.r16

N(∞) represents the carrying capacity and the time step Δt is set at 1 day. The rate of increase of the population in reference conditions r(0) is calculated by

Models are an important tool for understanding the temporal and spatial distribution of copepods. We developed a model to predict copepod population densities in the field based on individual-level stressor−response relationships derived from independent laboratory experiments. The model equations were parametrized using survival and reproduction data collected from previously published laboratory experiments on the calanoid copepod Eurytemora af f inis. This species was chosen because E. af f inis is frequently used in laboratory experiments and is often the most abundant calanoid species in both contaminated and uncontaminated estuaries in Europe and North America.16−20 We applied our model to simulate copepod population densities in a contaminated and a relative uncontaminated estuary, using measurement data of environmental conditions pertaining to temperature, salinity, food availability and sediment metal concentrations. Data obtained from the Scheldt estuary (SE), an estuary on the border between Belgium and The Netherlands, were used to assess the relative importance of the various stressors in contaminated conditions. Although water quality in the SE has improved since 1996, sediment concentrations indicate that copepods may be affected by metals.17,21,22 Data from the Darß-Zingst estuary (DZE) in Germany, a relative uncontaminated estuary, were used to obtain results representative of relatively uncontaminated conditions. The model performance was evaluated by comparing the modeled copepod densities with densities observed in both estuaries.

a max

∑ l(a)·m(a)·e−r(0)·a·da = 1 with l(a) representing the survival rate until at least age a and amax representing the maximum age. The age-specific fecundity or reproduction rate m(a) characterizes the number of female offspring produced per interval da (i.e., 1 day) by a female aged a.23 The joint effects of stressors in copepod communities may act additively, synergistically, or antagonistically.26 Laboratory experiments on copepods showed that multiple stress factors, including food availability and an anthropogenic stress factor, act mainly additively.26 Under the assumption that the combined effect of multiple stressors is purely additive, the ratio of the rate of increase of the population under multiple stress conditions r(S)/r(0) can be related to multiple anthropogenic and other environmental stress factors according to r(S(i)) r(0)

MATERIALS AND METHODS General Model Description. The model was based on a framework for assessing the impacts of various stressors on species such as Haliaeetus albicilla, Phalacrocorax carbo, Daphnia magna, and Calandra oryzae. The basic equations are provided below, as details can be found elsewhere.23−25 Population density of adults on a given day N(t) is calculated from the population density on the preceding day N(t − Δt) by N (t ) = N (t − Δt ) +

r(0, t ) ⎛ N (t − Δt ) ⎞ ⎜1 − ⎟ N (∞ ) ⎠ ⎝

⎛ ⎛ i ⎜ −ln⎜∏n = 1 ⎝ ⎝

=

1 ⎞ ⎟ fL , i ⎠

⎛ i − ln⎜∏n = 1 ⎝

ln(R(0))

1 ⎞⎞ ⎟⎟ f E , i ⎠⎠

+1

(3)

where the factors f L,i and f E,i represent age-independent population fractions which are unaffected by stressor i through survival and reproduction, respectively (see Supporting Information (SI) for the derivation of eq 3).25,27 Parameter R(0) represents the lifetime fecundity in reference conditions and is calculated as23,24

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r(S(i) , t )

(2)

0

a max

R(0) =

∑ l(a)·m(a)·da 0

(4)

Ecological Parameters. The rate of increase of the population under reference conditions r(0) and the lifetime fecundity R(0) were calculated using survival and reproduction rates for E. af f inis reported by Devreker et al.11 (Table 1). Daily egg production rates of 26−38 eggs per female measured by Devreker et al.11 are among the highest published for E. af f inis. In comparison, the maximum egg production rate in other studies ranged from 13 to 34 eggs per female per day.28−30 Therefore, an arithmetic mean egg production rate of 32 eggs per female per day, corresponding with a temperature of 14 °C and a salinity of 5‰, was used for calculating the rate of

·r(0, t ) ·N (t − Δt ) ·Δt ·

(1)

where r(S(i),t)/r(0,t) represents the time-varying ratio of the rate of increase of the population as a function of one stress factor r(Si) or more stress factors r(S) and the rate of increase of the population in reference conditions r(0), respectively. 5710

dx.doi.org/10.1021/es5004439 | Environ. Sci. Technol. 2014, 48, 5709−5717


Article

increase of the population under reference conditions.11 Egg production rates were converted to female offspring per female using a 100% hatching success and a 1:1 sex ratio.11 The survival rates measured by Devreker et al.11 under laboratory conditions with a temperature of 14 °C and a salinity of 5‰ are among the highest reported for E. aff inis.29,30 Based on these survival rates (Table 1), we applied linear interpolation to obtain daily survival rates. The survival fractions measured by Devreker et al.11 and the interpolated survival fractions are given in Figure S1 of the SI. Using eq 2, we calculated a rate of increase of the population under reference conditions r(0) of 0.26, which is comparable with values ranging from 0.18 to 0.24, obtained at a temperature of 18 °C and a salinity of 8− 10‰, as reported by others.10,31 For the carrying capacity N(∞), we used a value of 1.3 × 105 adults per m3, based on a total density of nauplii, copepodids, and adults of 3 million per m3 as reported for the Patuxent River estuary,16 which is among the highest densities published for E. af f inis. Stressor−Response Relationships. General Approach. Data were collected from previous studies investigating quantitative relationships between life history data of E. af f inis and temperature, salinity, and food availability (chlorophyll a) (Table 2). In most of the studies, the combined effects of multiple natural factors were analyzed. To eliminate stressor

interactions, we used for each natural stressor only response values measured in otherwise optimal conditions (temperature 5−15 °C, salinity 5−15‰, and sufficient food). Reductions in adult survival in response to a particular stressor were observed after 30−40 days in laboratory studies.11,29,30 Therefore, the arithmetic mean adult survival after 30 and 40 days was used to determine the survival fraction as a function of a particular natural stressor. For reproduction, numbers of female adults produced by a female were used, assuming a 1:1 sex ratio.11 Within each available laboratory study, we used the highest adult survival and the highest number of offspring as a reference value to determine the survival fraction f L and reproduction fraction f E, respectively. If applicable, laboratory results in duplicate were converted to a single observation by calculating the arithmetic mean. Equations relating f L and f E to each anthropogenic and natural stress factor were fitted by nonlinear least-squares curve fitting.32 Subsequently, the equations derived were used for calculating a ratio of increase of the population r(Si)/r(0) as a function of each stress factor (Figure 1). Temperature. As most aquatic organisms are ectotherms, temperature is an important environmental factor controlling physiological processes.33 Following Gasparini et al.,18 a bellshaped function was used to obtain values for the survival fraction f L and reproduction fraction f E as a function of temperature T (°C) by

Table 2. Parameter Values for Stressor−Response Relationships Based on Previously Published E. aff inis Data stress factor

variable

value

temperaturea

aL, slope constant for survival (−) To,L, optimum temperature for survival (°C) aE, slope constant for reproduction (−) To,E, optimum temperature for reproduction (°C) aL, slope constant for survival (−) So,L, optimum salinity for survival (‰) aE, slope constant for reproduction (−) So,E, optimum salinity for reproduction (‰) mmax, maximum reproduction (−) Km, half-saturation coefficient (μg chl a· L−1) LC50, lethal concentration (mg·kg−1 dry weight) EC50, effect concentration (mg·kg−1 dry weight) ß, slope constant (−) LC50, lethal concentration (mg·kg−1 dry weight) EC50, effect concentration (mg·kg−1 dry weight) ß, slope constant (−) LC50, lethal concentration (mg·kg−1 dry weight) EC50, effect concentration (mg·kg−1 dry weight) ß, slope constant (−)

−1.6 × 10−3 8.5

salinityb

chlorophyll ac

Cdd

Cu

e

Znf

2

fL = 10aL·(To,L − T )

(5)

and 2

fE = 10aE·(To,E − T )

−3.0 × 10−2 13.9

(6)

where a characterizes the shape of the curve and To represents the optimal temperature for survival or reproduction reported by Devreker et al.11,30,34 Salinity. Beyond optimal salinity levels, copepods experience osmotic stress.20,29 Roddie et al.20 reported a bell-shaped response of survival to salinity. Assuming a bell-shaped response of reproduction as well, we used a bell-shaped function from Gasparini et al.18 to obtain values for f L and f E as a function of salinity S (‰) by

−2.0 × 10−3 8.0 −2.2 × 10−3 8.3 1 0.5 41.4

2

fL = 10aL·(So,L − S)

11.5

(7)

and

0. 9 377.4

2

fE = 10aE·(So,E − S)

43.5

(8)

where a is the shape-fitting parameter and So represents the optimal salinity for survival or reproduction. The functions were fitted to laboratory data.11,29,30,34 Food Availability. Although E. af f inis is known to be omnivorous, chlorophyll a is the most common measure of the food environment of copepods.35,36 Adult copepods can survive for long periods at low food levels and at low chlorophyll a levels.36 At low chlorophyll a levels, E. af f inis adults can also switch to nonchlorophyll food, such as ciliates and detritus.37,38 For this life stage, the survival fraction f L as a function of chlorophyll a concentration was therefore set on 1. At low food concentrations, Ban28 observed a reduction in E. af f inis egg production. Hence, following Bunker and Hirst,39 a Holling functional response was used to determine f E as a function of chlorophyll a concentration C (μg chl a·L−1) by

0.9 290 68.6 0.9

a

Calculated from Devreker et al.11,30,34 bCalculated from Beyrend-Dur et al.29 and Devreker et al.11,30,34 cCalculated from Ban.28 dBased on Amphiascus tenuiremis,62 Rhepoxynius abronius,69 and Schizopera knabeni.55 eBased on Ampelisca abdita,54 Amphiascus tenuiremis,56 Corophium volutator,64 Eohaustorius estuarius,54 Hyale longicornis,67 Hyalella azteca,65 Melita plumulosa,68 and Nitocra spinipes.66 fBased on Amphiascus tenuiremis,56 Corophium volutator,64 Hyalella azteca,65 and Quinquelaophonte sp.63 5711



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Figure 1. Fraction surviving f L, fraction reproduction f E, and the ratio of increase of the population r(Si)/r(0) as a function of (A) temperature, (B) salinity, (C) chlorophyll a, (D) cadmium, (E) copper, and (F) zinc. The response curves for survival f L and reproduction f E were based on measured survival (crosses) and reproduction (circles) data collected from literature. The ratio of increase rate of the population r(Si)/r(0) was calculated according to eq 3

Figure 2. Comparison of simulated and observed population densities N(t) in (A) the Scheldt Estuary (SE)5,17 and (B) the Darß-Zingst Estuary (DZE) from 2003 through 2006.6,44,45 Note the logarithmic scale of the y-axis. The ratio of increase rate of the population as a function of the six stress factors combined r(S)/r(0,t) and each single stress factor r(Si)/r(0,t) in (C) the SE and (D) the DZE.

fE = C·mmax /(C + Km)

fL = 1/(1 + (C /LC 50)1/ β )

(9)

(10)

and

where Km represents the chlorophyll a concentration needed to attain the half-saturation of mmax. Parameter mmax characterizes the maximum reproduction and was set at 1 because the maximum reproduction fraction f E is 1. Food concentrations were converted from algae cells to chlorophyll a.40 The equation was fitted to laboratory data.28 Metal Toxicity. Values for f L and f E as a function of sediment metal concentrations C (mg·kg−1 dry weight) were calculated by the log−logistic functions

fE = 1/(1 + (C /EC 50)1/ β )

(11)

where the lethal concentration LC50 and effect concentration EC50 represent 50% reductions of survival and reproduction, respectively.24 Parameter ß characterizes the slope of the concentration−response curve. Unfortunately, no sediment toxicity data for metals were available for E. af f inis. Therefore, the functions were fitted based on toxicity data of other 5712



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increase in r(Si)/r(0) was observed in relation to increasing temperatures in both the SE and the DZE. Exposure concentrations of Zn ranged from 310 to 598 mg·kg−1 (dry weight) in the SE, yielding ratios r(Si)/r(0) from 0.6 to 0.5, respectively. In the DZE, the Zn concentration ranged from 144 to 207 mg·kg−1 (dry weight), resulting in ratios r(Si)/r(0) from 0.8 to 0.7, respectively. For Cu, the exposure concentrations ranged from 47 to 214 mg·kg−1 (dry weight) in the SE, yielding ratios r(Si)/r(0) from 0.8 to 0.7, respectively. In the DZE, the Cu concentration ranged from 17 to 42 mg· kg−1 (dry weight), resulting in ratios r(Si)/r(0) from 1.0 to 0.9, respectively. For Cd, salinity and chlorophyll a, the ratios were >0.9, suggesting only small impacts on E. af f inis populations in both the SE and the DZE. Environmental stressor data corresponding with the ratio of increase rates of the population r(Si)/r(0) are available in Figure 2 of the SI.

amphipod and harpacticoid copepod species exposed to cadmium (Cd), copper (Cu), and zinc (Zn) (Table 2), assuming that the sensitivity of E. af f inis to metals exposure is comparable to the sensitivity of other amphipod and harpacticoid copepod species. The concentration−response slopes ß for metals usually range from 0.1 to 1.4.41,42 We calculated unusually gentle concentration−response slopes ß of 2, 2.7, and 1.8 for survival in relation to Cd, Cu, and Zn exposure, respectively. In general, unusual slope values can be attributed to unusual experimental conditions or to a lack of data.42 Rather than using unusually gentle slope values for survival, we therefore decided to use the concentration− response slopes ß obtained for reproduction also for survival (Table 2). Model Application. We applied our model to estimate E. af finis densities in the contaminated SE and the relatively uncontaminated DZE estuary and compared modeled with observed population densities. Model estimates were based on temperature, salinity, chlorophyll a, and sediment concentrations of Cd, Cu, and Zn measured from 2003 through 2006. For the contaminated SE, monthly observed population data of E. af f inis and monthly measured values for salinity were obtained from a sampling site near the border between Belgium and The Netherlands, located 57.5 km upstream from the mouth at Vlissingen.5,17 Biweekly values for temperature, chlorophyll a, and sediment concentrations of Cd, Cu, and Zn were taken from the same sampling site.43 For the relatively uncontaminated DZE, two and three weekly observed population data of E. aff inis and daily measured values for temperature, salinity, and chlorophyll a concentration were collected from literature.6,44,45 Following Feike and Heerkloss,44 two and three weekly measured E. af finis densities (ind.·L−1) were converted to monthly densities (ind.·m−3). Monitoring data of contaminant concentrations in sediment measured in the DZE region were taken from ICES.46 Linear interpolation was applied to the environmental stressor data in order to obtain continuous data sets with a time step Δt of 1 day. The arithmetic mean values, the range, and number of measurements for the obtained field data are available in Table S1 of the SI.

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DISCUSSION Stressor−Response Relationships. The ratio of increase rates of the population r(S)/r(0) as a function of temperature and salinity can be compared with the population growth rate in an E. af f inis modeling study by Strasser et al.10 Within the temperature and salinity ranges of 5−15 °C and 4−12‰, Strasser et al.10 calculated that E. af f inis populations from the Nanaimo estuary increased above the temperature−salinity plane from 10 °C−4‰ to 10 °C−8‰, whereas the population from the Seine estuary yielded a positive growth rate above the temperature−salinity plane from 8 °C−4‰ to 11 °C−14‰.10 Within the same temperature and salinity range used by Strasser et al.,10 we calculated a population increase above the temperature−salinity plane from 6 °C−4‰ to 6 °C−14‰. Hence, our results regarding population growth correspond with those reported for the Seine Estuary. Due to the limited temperature and salinity range in Strasser et al.,10 we were not able to compare our ratios of increase rates at temperatures and salinities outside the range used by Strasser et al.10 Instead, we compared our survival and reproduction fractions, which we used to calculate the ratio of increase rates, with previously published survival and reproduction data. The calculated bellshaped responses of reproduction and survival to temperature (Figure 1A) correspond well with independent observations. For instance, Ban28 observed higher survival rates of E. af f inis at 15 °C than at 20 °C. According to Roddie et al.,20 survival rates decline rapidly below 5 °C and very poor survival was observed at 25 °C. Our curve of reproduction in relation to temperature is also in line with observations by others, where nauplii did not reach maturity at temperatures below 5 °C and egg production was reduced at temperatures higher than 15 °C.9,18 For salinity, the reproduction and survival curves (Figure 1B) are also in line with independent observations. Lee et al.19 showed a reduction in survival at salinities below 5‰ for saline populations. According to Roddie et al.,20 survival rates decline rapidly below a salinity of 5‰ and survival was poor at salinity 20‰. Devreker et al.47 observed that adult survival was higher at salinity 0‰ compared to salinity 30‰ and that survival at salinity 35‰ was very low. The curve of reproduction in relation to salinity is in line with observations by Devreker et al.,47 where an increased mortality of subadults was observed at salinity levels from 15 to 25‰. For food availability, we used a Holling functional response between reproduction and chlorophyll a (Figure 1C). We calculated a half-saturation coefficient Km of 0.5 μg chl a·L−1, which is comparable with the

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RESULTS Model Testing. The maximum simulated densities in the SE of 6.5 × 102 adults per m3 in 2004 to 1.1 × 104 adults per m3 in 2003 are comparable with the maximum observed densities of 2.3 × 103 and 5.3 × 103 adults per m3 in 2004 and 2003, respectively (Figure 2A). In the DZE, the simulated E. af f inis densities generally started increasing in April, leveled off close to the carrying capacity, and decreased when the temperature dropped in January (Figure 2B). In the DZE in particular, simulated densities were underestimated in spring and overestimated in summer and autumn. In winter, however, the simulated densities followed the lower observed densities. Impact of Anthropogenic and Natural Stress Factors. The ratio r(S)/r(0) as a function of multiple stressors in the SE (Figure 2C) and the DZE (Figure 2D) closely followed the oscillation pattern of temperature. In general, the ratio was lower for the SE compared to the DZE, resulting in lower simulated E. af f inis densities in the SE (Figure 2A and B). The ratio r(Si)/r(0) as a function of temperature was close to 1 in spring and autumn for the SE and the DZE. In winter, this ratio was usually above zero for the SE, but in the DZE, the ratio dropped to −1 due to low temperatures. In summer, a clear 5713



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half-saturation coefficient of 0.6 μg chl a·L−1 calculated by Bunker and Hirst39 based on several adult copepod species. Environmental Impacts on Copepod Populations. Natural Stress Factors. On the basis of laboratory data, we calculated negative ratios of increase rates of the population r(Si)/r(0) at temperatures lower than 5 °C and higher than 22 °C (Figure 1A). These temperatures were regularly recorded in both the SE and the DZE, yielding large seasonal fluctuations in simulated E. af f inis densities. Field studies confirm that temperature is an important factor shaping the distribution of E. af f inis in the SE.5,17 For the DZE, however, field studies showed that E. aff inis was more closely associated with other environmental factors, that are probably affected by temperature, such as pH, oxygen, and food quality.6,45 These latter environmental factors might explain why the model overestimated the population densities in summer and autumn for the DZE in particular. According to Ring et al.,48 the feeding rate of E. af f inis is inhibited at pH > 9. In the DZE, the pH increased often to more than 9.5 in early summer, indicating that pH might be an important factor responsible for the lower E. af f inis densities observed in the summer and autumn.6,44,45 In SE, the pH ranged from 6.7 to 8, suggesting that pH played no role at the sampling site in the SE.43 Field studies showed that oxygen is also an important environmental stress factor in the SE and the DZE.5−7 However, the oxygen concentrations at the sampling sites in the SE and in the DZE that we used in our study were well above the threshold oxygen concentrations for E. af finis to appear in large numbers, suggesting that oxygen played no major role in our study.5,7,43 Indicated by high C/Nratios, the food quality in the DZE might be low in May and June,45 which may have caused the lower observed E. af f inis densities. In contrast, E. af f inis in the SE was not associated with food quality, as the ratio between chlorophyll a and particulate organic carbon was always above the limiting level for E. af f inis’ optimal feeding on phytoplankton.18,49 The pH, oxygen concentration, and food quality were not included in our model due to the lack of quantitative stressor−response relationships for these stressors. Stressor−response relationships can be difficult to obtain when the effect of one particular stressor might depend on other stressors. Moreover, we assumed the combined effect of multiple stressors to be purely additive,26 which is a simplification. In reality, the effect of one particular stressor might depend on other stressors. For instance, the tolerance to low oxygen concentrations decreases at lower salinities and higher temperatures.50 However, quantitative information about potential synergistic or antagonistic interactions among the various stressors is, to our knowledge, very limited, which prevented us from incorporating these interactions in our current model. Overestimated population densities in summer and autumn, as well as underestimated population densities in spring, might also be explained by resting eggs, which are known to be produced by E. af f inis to ensure population recruitment.51 According to Ban and Minoda,52 the production of resting eggs by E. af f inis is induced at a density of 5 × 105 ind·m−3, a low temperature (10 °C), or a short-day photoperiod. In the DZE, densities of nauplii, copepodids and adults combined, regularly exceeded 5 × 105 ind·m−3 in late spring and/or in early summer,6 indicating that E. af f inis was producing resting eggs. Resting eggs hatch after a certain refractory phase if the environmental conditions are more favorable in spring53 and will therefore probably result in higher densities in spring and lower densities in summer and autumn, thus explaining the

differences between the simulated and observed population densities for the DZE in particular. Because the model simulations showed that temperature was the largest stress factor compared to the other natural stress factors, particularly in the DZE (Figure 2), we performed a sensitivity analysis on the parameters controlling the stressor− response relationship between reproduction or survival and temperature. The value of the slope of the curve aL or aE was set to an arbitrary −15%, +15%, and 0%, representing higher, lower, and unchanged tolerance to temperature, respectively. The optimal temperature T0,L or T0,E was changed by an arbitrary −2, 2, and 0 °C. This analysis revealed that the population density N(t) changed only slightly in response to changes in the temperature-related reproduction parameters and that it was insensitive to changes in the survival parameters (Figure S3 in the SI). We also examined the effect of a 0.5 °C change in the water temperature measurements in the DZE and found this to result in only minor changes in the population density N(t) (SI Figure S4). These findings indicate that the model fit can be improved, for example, by including additional stressors, stressor interactions, or resting eggs, rather than via changes in the temperature-related parameters. Anthropogenic Stress Factors. Next to natural environmental factors, we included the impact of Cd, Cu, and Zn in sediment on copepod populations. Both the observed and the simulated copepod densities in the SE were lower compared to the densities in the DZE, which may result from the higher sediment concentrations of Zn and Cu in the SE (SI Figure S2). Few comparisons have been made of copepod populations between contaminated and uncontaminated estuaries. One notable exception is Van Damme et al.21 who observed remarkably lower densities of harpacticoids in the contaminated Western Scheldt estuary compared to densities in the uncontaminated Eems-Dollard estuary and attributed this to metal pollution. Sediment concentrations of other substances, in particular atrazine, fluoranthene, lead, mercury, nickel, and phenanthrene measured in the SE and the DZE from 2003 through 2006 were well below the lethal concentrations for copepods.46,54−56 This suggests that the impacts of these substances on E. af finis populations were limited in both the SE and the DZE. Nevertheless, the potential impacts of other substances that were not measured in the field or that were untested in bioassays, remain unknown. The estimated EC50s and LC50s for Cd, Cu, and Zn are considered to be indicative because the values are based on toxicity tests with other amphipod and harpacticoid copepods. Moreover, the toxicity of sediment metal contamination to benthic copepods depends on the bioavailability of metals in the sediment, which is controlled by the metal binding with particulate sulfide, total organic carbon (TOC), and other metal-binding phases in the sediments.57−59 Binding of metals to sulfide occurs mainly in anaerobic sediments and might therefore be less important in this study as copepods are mainly found in aerobic sediments.60 In aerobic environments, metal sulfides are expected to be oxidized and other factors such as TOC, manganese (Mn), and iron (Fe) oxides can regulate the bioavailability of metals.56,61 The TOC fraction in the SE ranged from 2.9 to 4.9% and in the DZE from 5.5 to 6.6%.43,46 These TOC fractions are comparable with the TOC fraction measured in the laboratory experiments for Cd (3.7 to 3.8%), Cu (< 0.1 to 10.7%), and Zn (1 to 2.8%).54−56,62−69 Unfortunately, Mn, Fe, and other minerals are rarely measured in bioassay studies. Other factors that can control the 5714



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copepod densities measured in the Scheldt estuary. Further, we thank Dr. M. Feike and Dr. R. Schumann for sharing biotic and abiotic data collected in the Darß−Zingst Estuary, as well as the anonymous reviewers for their comments and suggestions which have improved the quality of this manuscript.

bioavailability of metals are the organism physiology and behavior.57 The amphipod and harpacticoid copepods used in the laboratory experiments have approximately the same size (between microfauna and macrofauna), burrow irrigation (predominantly benthic-dwelling), and feeding selectivity (such as phytoplankton, ciliates, and detritus) as E. af finis.35,36,56,60,70 Considering the similarities in the TOC fractions used in the laboratory studies and measured in the field, as well as the similarities in physiology and feeding behavior between the test organisms and E. af f inis, we expect that the bioavailability of metals in the SE and the DZE is comparable with the bioavailability in the laboratory studies that we used to quantify metal impacts on reproduction and survival. Yet, according to Ward et al.,71 copepods may show avoidance behavior toward contaminated sediments, indicating that actual exposure might be lower than expected based on spatially averaged sediment contaminant concentrations. Avoidance behavior could not be included in the model, because fine-grained information regarding the heterogeneity of the sediment contaminant concentrations was unavailable for the SE and the DZE. This implies that the model might overestimate the impact of sediment contamination.

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IMPLICATIONS In the present study, we simulated the combined impact of anthropogenic (Cd, Cu, and Zn) and natural (temperature, salinity, and food availability) stress factors on estuarine population densities of the calanoid copepod E. af f inis. The model provides a consistent framework for integrating and quantifying the impacts of multiple anthropogenic and natural stress factors on copepod populations. It facilitates the extrapolation of individual-level stressor−response relationships from laboratory experiments to ecologically relevant end points pertaining to population viability. Further improvements of the model could be made by integrating resting eggs in the model and by including other relevant stressors, in particular pH and food quality, provided that adequate stressor−response relationships are available. Similarly, depending on the availability of quantitative data regarding stressor interactions, our model framework has the potential to incorporate synergistic or antagonistic responses to multiple stress factors.

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ASSOCIATED CONTENT

S Supporting Information *

Further information on the derivation of eq 3 (Text section S1), the measured environmental stressor data (Table S1), the interpolated survival rates (Figure S1), the environmental stressor data corresponding with the ratio of increase rates of the population (Figure S2), and the results of the sensitivity analyses (Figure S3 and S4). This material is available free of charge via the Internet at http://pubs.acs.org/.

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REFERENCES

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AUTHOR INFORMATION

Corresponding Author

*Phone: 0031-62-152-8190; fax: 0031-24-355-3450; e-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We gratefully acknowledge the framework of OMES (Onderzoek Milieu-Effecten Sigmaplan) for the data of 5715



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Modeling the Impacts of Multiple Environmental Stress Factors on

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