Evaluation of monochloroacetic acid (MCA) degradation and toxicity to Lemna gibba, Myriophyllum spicatum, and Myriophyllum sibiricum in aquatic microcosms

Aquatic Toxicology 61 (2002) 251 /273 www.elsevier.com/locate/aquatox

Evaluation of monochloroacetic acid (MCA) degradation and toxicity to Lemna gibba, Myriophyllum spicatum, and Myriophyllum sibiricum in aquatic microcosms Mark L. Hanson a,*, Paul K. Sibley a, David A. Ellis b, Scott A. Mabury b, Derek C.G. Muir c, Keith R. Solomon a a

Centre for Toxicology, Department of Environmental Biology, University of Guelph, Guelph, Ontario, Canada N1G 2W1 b Department of Chemistry, University of Toronto, Toronto, Ontario, Canada M5S 3H6 c National Water Research Institute, Canada Centre for Inland Waters, Burlington, Ontario, Canada L7R 4A6 Received 7 January 2002; received in revised form 7 June 2002; accepted 19 June 2002

Abstract The fate of monochloroacetic acid (MCA), a common phytotoxic aquatic contaminant, and its toxicity to the aquatic macrophytes Lemna gibba (L. gibba ), Myriophyllum spicatum (M. spicatum ), and Myriophyllum sibiricum (M. sibiricum ) under semi-natural field conditions was studied. Replicate 12,000 l enclosures were treated with 0, 3, 10, 30 and 100 mg/l of MCA. Each microcosm was stocked with eight individual apical shoots of M. spicatum and M. sibiricum 1 day prior to initiation of exposure. Plants were sampled after 4, 7, 14 and 28 days of exposure and their response assessed using numerous somatic and biochemical endpoints. L. gibba was introduced into the microcosms the day of MCA treatment and monitored regularly for 21 days. The half-life of MCA in the water column ranged between 86 and 523 h. The most sensitive plant species was M. spicatum , followed by M. sibiricum and L. gibba . All species demonstrated toxicity within a threefold range of each other. Endpoint sensitivity varied depending on the duration of exposure and the level of effect chosen. Most species endpoint ECx values were less than an order of magnitude different. Citrate levels in Myriophyllum spp. were not influenced by exposure to MCA. The toxicity of MCA to M. spicatum and M. sibiricum was very similar and thus highly predictive of toxicity observed for each other. The EC10 was a more conservative estimate of toxicity than the statistically derived no observed effect concentration. Current concentrations of MCA are not likely to pose a risk to these aquatic plants in surface waters. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Myriophyllum spp.; Monochloroacetic acid; Lemna gibba ; Microcosms

1. Introduction

* Corresponding author. Tel.:
/1-519-837-3320; fax:
/1519-837-3861 E-mail address: [email protected] (M.L. Hanson).

Haloacetic acids (HAAs), such as monochloroacetic acid (MCA), are environmental contaminants that have been detected in aquatic ecosystems, rainwater, fog, and snow (Haiber et

0166-445X/02/$ - see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 6 - 4 4 5 X ( 0 2 ) 0 0 0 8 9 - 9

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al., 1996; Mu¨ller et al., 1996; Hashimoto et al., 1998; Wujcik et al., 1998, 1999; Berg et al., 2000; Martin et al., 2000; Scott et al., 2000; Ro¨mpp et al., 2001; Scott et al., 2002). MCA is commonly detected in aquatic environments, with concentrations ranging up to 450 ng/l in rainwater and 250 ng/l in lake water from Canada (Scott et al., 2000). Sources of MCA include water disinfection via chlorination (Uden and Miller, 1983) and as the degradation by-product of herbicides (Wilson and Mabury, 2000). Moreover, MCA is produced at a rate of 300,000 tonnes annually as intermediate in the production of other chemicals, including some herbicides (Reimann et al., 1996). It is a strong acid and not likely to volatilize out of water, much like other HAAs (Bowden et al., 1996, 1998a,b). Therefore, aquatic organisms are potentially at the greatest risk from MCA exposure. MCA was the second most abundant HAA after dichloroacetic acid (DCA) in Canadian lake water. Among other HAAs, MCA was nine and 178 times the concentrations of trifluoroacetic acid (TFA) and trichloroacetic acid (TCA), respectively (Scott et al., 2000). However, both TCA and TFA have attracted more attention in terms of sources, effects and fate (Boutonnet et al., 1999; Berends et al., 1999; Ellis and Mabury, 2000; Frank et al., 1994; Haiber et al., 1996; Hanson et al., 2002a,b; Norokorpi and Frank, 1995; Juuti et al., 1995; Wujcik et al., 1998), but are not very toxic to aquatic macrophytes under field conditions (Hanson et al., 2002a,b). MCA has been demonstrated to be phytotoxic (Frank et al., 1994). Impacts have been documented at concentrations as low as 25 mg/l for the green algae Scenedesmus subspicatus (72 h biomass EC50) (OECD, 1996). The same algae species when exposed to TFA had an EC50 of
/120 mg/l TFA (Boutonnet, et al., 1999). Aquatic macrophytes such as Myriophyllum spp. and Lemna gibba (L. gibba ) can form the bulk of the standing biomass in aquatic communities (Chilton, 1990; Duarte and Roff, 1991; Lewis, 1995). However, potential effects of MCA on these organisms are scarce. The mode of action of MCA in plants is not well understood. Fluoroacetic acids are suspected to act through the inhibition of the enzyme aconitase

in the citric acid cycle (Buffa and Peters, 1950; Berends et al., 1999). When aconitase is inhibited, elevated levels of citrate are normally observed in the tissue (Bosakowski and Levin, 1986; Keller et al., 1996). Studies with TCA, TFA, and CDFA have not found elevated citrate levels in aquatic plants exposed to these compounds under seminatural field conditions (Hanson et al., 2001, 2002a,b), but elevated levels have been noted with DCA exposure (Hanson et al., submitted for publication). The use of non-target plants in the regulatory risk assessment of pesticides in both Canada and the United States has recently come under review with recommendations for increasing the number of species used in the process (Davy et al., 2001). The rooted macrophyte Myriophyllum sibiricum (M. sibiricum ), a dicot, has been suggested as a new possible mandatory test species for the registration of pesticides. Specific issues need to be addressed before this plant can be integrated into the risk assessment process. Firstly, toxicity testing with Myriophyllum spp. can evaluate numerous endpoints (American Society for Testing and Materials, 1999), but the relative sensitivity of these endpoints, especially under field conditions, has not been characterized. Currently, only L. gibba is required for pesticide registration and the relative sensitivity of this plant to Myriophyllum spp under field conditions has not been evaluated. Field studies are expensive and time consuming to conduct (Shaw and Kennedy, 1996), so picking the most sensitive endpoints and plants makes sense economically. Of course, the ecological relevance of an endpoint and plant must be considered when deciding upon those to be evaluated. Secondly, estimates of low toxicity with Myriophyllum spp. should be examined. The most common approach to estimate low, or no toxicity, is through the use of the no observed effect concentration (NOEC), the highest test concentration that is not statistically different (P
/0.05) than control values. The NOEC has been criticized for a variety of reasons. These reasons include: (1) the NOEC must be one of the concentrations tested, (2) the NOEC tends to increase as the precision of the study decreases,

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and (3) the NOEC depends on the chosen significance level (Chapman et al., 1996; Van der Hoeven, 1997; Van der Hoeven et al., 1997; Pires et al., 2002). Studies with aquatic plants that report both an ECx value and a NOEC often show the NOEC is well within the range of the estimated EC50 (Fairchild et al., 1997). A recent suggestion is to replace the NOEC with a low effective concentration (ECx ) estimate, such as the EC10 (Van der Hoeven, 1997; Van der Hoeven et al., 1997). The ability of one species to predict the toxicity of another is important when data for a specific toxicant are lacking for a species. This can be done through regression of one specie’s toxicity measures with that of another (Suter et al., 1987). In fact, when the toxicity between two species is highly correlated, it has been argued that both species need not be tested as the regression derived equations can be used to predict one from the other (Kenaga, 1978, 1979). Since field studies are so time consuming and expensive, it is effective to use only one species of plant when the toxicity relationship to another species is well defined. This study was part of a larger investigation into the sources (Martin et al., 2000; Wilson and Mabury, 2000), fate (Ellis et al., 2001, Hanson et al., 2001) and toxicity (Hanson et al., 2001, 2002a,b) of HAAs in the environment. Most work with MCA has focused on the sources and environmental concentrations of this compound, with little attention to the ecotoxicological effects. The main purpose of this study was to characterize the fate and toxicity of MCA on three common aquatic macrophytes under seminatural field conditions. These data were then to be used in an ecological risk assessment for MCA. The second objective was to examine the utility of field derived Myriophyllum spp. data in the context of an ecotoxicological risk assessment. These data include the NOEC, which are commonly reported and used in risk assessment, the predictive relationship of one Myriophyllum species for another in regards to toxicity, and the relative sensitivity of the three macrophytes and their associated toxicity endpoints.

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2. Materials and methods 2.1. The microcosms The University of Guelph Microcosm Facility is located at the Guelph Turfgrass Institute, Ontario, Canada and consists of 30 outdoor artificial ponds or microcosms. The microcosms are approximately 1.2 m deep with a water depth of 1 m, a diameter of 3.9 m, a surface area of 11.9 m2, and water volume of approximately 12,000 l. The microcosms are below ground with the tops flush with the surface. Galvanized steel panels and support struts provide the basic frame, and food grade polyvinylchloride liner (Fox Pools Canada, Burlington, ON, Canada) are used to create a closed system relative to the other microcosms so that no groundwater flow can occur between microcosms. To establish a semi-natural system, each microcosm bottom was filled with 46 plastic trays (approximately 52/25/7 cm deep) (Plant Product Company, Brampton, ON, Canada) filled with an amended sediment (Evergreen Sod Company, Waterdown, ON, Canada). The sediment consisted of 1:1:1 mixture of sand, loam and organic matter (mainly composted manure) by volume, and was hand sifted through a screen with 12 mm mesh. The total carbon content of the soil was 12.8% with the inorganic and organic carbon content being 3% and 9.8%, respectively, as determined by combustion in a Leco CR12 Carbon Analyser (Leco, St. Joseph, MI). The sediment trays covered approximately 60% of the total surface area of the bottom of the microcosm. The water for the microcosms originated from an on-site irrigation pond (62 /62/4 m deep) that is supplied by a 100 m deep well. Water was circulated between the microcosms and the irrigation pond at a rate of 12,000 l/day for 2 weeks prior to treatment with MCA. This circulation ensured consistent assemblages of zooplankton, algae and water chemistry parameters in each system. The microcosms were stocked with breeding fathead minnows (Pimephales promelas L.) and pumpkinseed sunfish (Lepomis gibbosus ). Both were contained in separate hanging mesh cages in the microcosms as part of a separate

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evaluation. At least Five potted M. spicatum , obtained from a nearby pond, were placed in each microcosm to provide habitat for juvenile fish and zooplankton. The potted plants were not used in the toxicity assessment. The microcosms were open to aerial colonization by insects and the sides provided a substrate for periphyton growth. 2.2. Myriophyllum spp. experimental design M. spicatum L. (Haloragaceae) and M. sibiricum Komarov (Haloragaceae) used in the field study were obtained from laboratory cultures maintained according to standard methods (American Society for Testing and Materials, 1999). Plants were axenically cultured in 50 ml borosilicate test tubes with Andrews media fortified with 15 g/l of sucrose in an environmental growth chamber (Model E7H, Controlled Environments, Winnipeg, MB, Canada). To introduce the lab cultured plants into the microcosms, they were transferred to 150 ml plastic ‘‘cone-tainers’’, or planting tubes, in a planting tray (Steuwe and Sons, Corvallis, OR). The ‘‘cone-tainers’’ were 13 cm long with a 3.7 cm internal diameter. Prior to the plant transfer, each tube was filled with the same sifted soil used in the microcosm sediment trays, placed into the planting trays and allowed to soak overnight in the irrigation pond to allow the soil to settle. Both species were cut to 5 cm apical shoot lengths and soaked (approximately 30 min) in irrigation pond water to remove the media in which they were cultured. Each shoot was planted approximately 1 cm into the soil of the pre-soaked tube and then surrounded by approximately 0.5 /1 cm of Turface (Applied Industrial Materials, Buffalo Grove, IL) to secure the plants in the sediment. Every microcosm was supplied with a total of eight plants of each species evenly spaced in the tray. The surface of the tubes rested approximately 17 cm from the bottom of the microcosm. Plant trays were randomly assigned to a microcosm, and placed in the centre of a microcosm. The centre was chosen to provide maximum sunlight and reduce potential edge effects. The plants were placed in the microcosms on June 9, 1999. The plants were acclimatized for

only 1 day in the microcosms prior to the introduction of MCA. This shorter acclimatization period allows for a more sensitive investigation of root endpoints. Roots grow rapidly in these systems and a longer acclimatization period would result in well-established root systems that may not detect toxicity in the roots. Plants were sampled 1 day prior to dosing with MCA, and 4, 7, 14, and 28 days post treatment. The final day of the Myriophyllum spp. study (28 days) was July 7, 1999. At each sampling point, two plants of each species were removed and evaluated, except for day 1. On day 1, ten plants of each species from the laboratory culture were evaluated as 5 cm apical shoots, for the endpoints described below. On the other sampling days, plants were removed randomly from the microcosms and transported back to the laboratory in their respective microcosm water for immediate processing and analysis. Numerous endpoints, both somatic and biochemical, were monitored over the course of the study. The somatic endpoints were growth (plant length), biomass (wet mass and dry mass), root number (primary roots from the plant stem), primary root lengths (total and longest), and number of nodes. The biochemical endpoints were chlorophyll-a, chlorophyll-b and carotenoid content. The pigment levels were determined on a fresh-weight basis according to standard methods (American Society for Testing and Materials, 1999) on a Beckman DU-65 Spectrophotometer (Beckman Coulter, Fullerton, CA). 2.3. L. gibba experimental design Duckweed, L. gibba L. (G-3), was originally obtained from a laboratory colony cultured at the University of Waterloo, Waterloo, ON, Canada and maintained according to established methods (Greenberg et al., 1992) except that 10 g/l of sucrose was used instead of 30 g/l in half-strength Hutner’s growth media. L. gibba was introduced into the microcosms immediately after MCA treatment for a 21 day exposure. The plants were transferred from the laboratory colonies to the microcosms where they were contained in floating wooden cages (38 /14 cm). The cages were

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subdivided into three sections, each section having a surface area of 100 cm2. The top and bottom of the trays were covered with a black plastic mesh (4 /3 mm) to ensure containment of the L. gibba , but also allow for exposure to sunlight and water movement. Three plants with four fronds each were introduced into the three sections per wooden cage. A second study with L. gibba was undertaken between day 21 and day 28 for a total exposure duration of 7 days. It was conducted in the nominal 10, 30 and 100 mg/l MCA microcosms, and control. The endpoints evaluated were frond number, plant number, wet and dry mass, chlorophyll-a and b and total chlorophyll content, and growth rate for fronds and plants (Greenberg et al., 1992). Frond and plant number were enumerated every 2 /3 days. Chlorophyll concentrations were determined simultaneously by extraction in ACS grade N ,N ?-dimethyl formamide (Fisher Scientific, Fair Lawn, NJ) using the method and calculations outlined by Porra et al. (1989) and measured with a Pharmacia LKB Novaspec II spectrophotometer (Amersham Pharmacia Biotech, Piscataway, NJ) on a fresh-weight basis.

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the system. After approximately 5 min, when it was evident that there was water circulation around the entire microcosm, the high-pressure pump was turned on, drawing microcosm water from a separate pail and introducing it into the stream created by the Robusta bilge pump. The prepared MCA solution was siphoned via the venturi effect created by the high-pressure pump circulation and injected into the water stream. Each microcosm was circulated for 15 min after injection, including controls to ensure thorough distribution of the MCA. Water samples for MCA analysis were taken at /1 day, and at 1 h, 4, 7, 14, 21 and 28 days. A metal depth-integrated water column sampler was used to collect the water residue samples (Solomon et al., 1982). Integrated subsamples from a minimum of four randomly selected locations in the microcosm were collected to a volume of approximately 1 l. A 250 ml aliquot was taken and stored in a thoroughly cleaned amber bottle (Ellis et al., 2001). These samples were stored at 4 8C until analysis. 2.5. Water chemistry and photosynthetically active radiation measurements

2.4. Treatment and sampling regime The treatments applied to the microcosms were 0, 3, 10, 30 and 100 mg/l MCA (99%, Acros Organics, Geel, Belgium), as the sodium salt. Each treatment was randomly assigned to three separate microcosms. The MCA was dissolved in redistilled deionized water. Each solution was then neutralized to pH 7 /8.5 with ACS grade sodium hydroxide (98%, Fisher Scientific). Application of the MCA to the microcosms took place on June 10, 1999. Immediately prior to treatment, waterflow into each microcosm from the main irrigation pond was terminated, creating a closed system relative to the other microcosms and the irrigation pond. Subsurface injections of MCA into the microcosms were made using a high-pressure pump (Model 360, Proven Pony Pumps, Los Angeles, CA). A high-volume Robusta bilge pump (ABS, Gothenburg, Sweden) was first used to draw water from the microcosm and begin circulation within

Maximum and minimum temperatures and dissolved oxygen measurements, determined on a YSI model 57 meter (YSI, Yellow Springs, OH), were taken almost daily during the course of the macrophyte toxicity study. On sampling days for water residue analysis of MCA, the following specific water parameters were measured: hardness, alkalinity, and pH. Water hardness and alkalinity were determined using a Hach Digital Titrator Model 16900-01 with standard methods and kits from Hach (Hach Company, Loveland, CO). Measurements of photosynthetically active radiation were taken at regular time intervals during the course of the study on clear sunny days, between 12 noon and 2 p.m. when sunlight was at maximum intensity. The measurements were taken as close to the actual sampling date as possible, weather permitting. Readings were taken on a Li-Cor Quantum/Radiometer/Photometer Model LI-185A (Li-Cor, Lincoln, NB). A reading

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was taken at a depth of 60 cm in each microcosm in a glass jar, at the surface in the open, and at the surface in a glass jar in order to normalize the reading. 2.6. Analysis of MCA Analysis of MCA residue samples was performed by ion chromatography in a method previously described by Ellis et al. (2001) using a Dionex-DX 500 Chromatography System with a CD20 Conductivity Detector, a GP50 Gradient Pump and an EO 1 Eluent Organiser. An AS-11-2 mm column was used in combination with an AG11-2 mm guard column. A gradient program was used to analyze for MCA. Samples were initially injected at an eluent concentration of 90% deionized (DI) water and 10% 0.005 M NaOH for 5 min. Between 5 and 6.5 min the eluent was changed to 99.9% DI water and 0.1% 0.1 M NaOH. This was immediately followed by an eluent gradient between 6.5 and 18 min to give a final eluent concentration of 82% DI water and 18% 0.1 M NaOH. Between 18 and 22 min, the eluent was changed to 100% 0.1 M NaOH for 14 min. MCA eluted at approximately 5.7 min. Calibration was performed through the use of four external standard solutions, plus a quality control sample, and linear regression analysis. Standards were made up in field water obtained from a control microcosm. Quality control samples were control pond water spiked with MCA. Each sample was run in duplicate. MCA for the standards was obtained from Acros Organics (99%). Although not formally established, LOD and LOQs were observed to be significantly lower than the lowest concentration used to calibrate the instrument. Half-lives were calculated using pseudo-first order reaction kinetics, where ln [C]t kt
ln [C]0

(1)

and [C]0 is the initial concentration of MCA and [C]t is the concentration of MCA at time t. The assumption is that the reaction occurs independent of the initial concentration. This was tested by comparing the estimated slopes of the regression lines, or the rate constants, in an analysis of

covariance (P 5/0.05) (Zar, 1984). Half-lives were calculated for the period after the induction period, or lag phase. This is the time during which there is preferential growth of microorganisms capable of degrading MCA (Chen and Alexander, 1989; Van Ginkel, 1996), prior to their actively breaking it down. For this study, the induction period was the time period in which no change (B/ 10%) was noted from the initial (1 h) concentration. 2.7. Statistics Myriophyllum spp. data were analyzed using General Linear Models of SAS 8.0 (SAS Institute, Cary, NC). The effect of MCA concentration on each endpoint at specific time-points was evaluated in a one-way analysis of variance (ANOVA) design that allowed for subsamples. Any data that did not meet normality or equal variance assumptions were ln or square root transformed. Any data that did not meet normality requirements after transformation were compared with a non-parametric test, Kruskis/Wallis one-way ANOVA on ranks in SIGMASTAT 2.0 (Jandel, San Rafael, CA). If significance (P 5/0.05) was found, the means were compared to the control using Dunnett’s test (a /0.05), from which a no observed effect concentration (NOEC) and lowest observed effect concentration (LOEC) could be determined. Since ANOVA assessments of microcosm data sometime fail to detect certain effects and do not allow for the estimation of effective concentration (ECx ) values (Liber et al., 1992), the data at each time point were also modeled using non-linear regression techniques designed to evaluate plant toxicity. It has been recommended, where possible, that microcosm data be analyzed in an ANOVA fashion with regression analysis (Sanderson, 2001). Regression analysis of the plant toxicity data was performed in SYSTAT VERSION 9 (SPSS, Chicago, IL) according to the procedure outlined in Stephenson et al. (2000). Stephenson et al. (2000) describe the three models, logistic, hormetic and exponential, and the criteria for selecting one model over another when describing the concentration-response relationship. In the present study, we also included linear and Gompertz equations in

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a suite of possible models, to make a total of five possible tested fits (Table 1). The concentrations of MCA used to conduct the statistical evaluations at all time-points were those detected at the initiation (1 h) of the study (Table 2). Prior to use in the regression analyses, /1 day values were subtracted from later time-points for shoot growth, wet mass, dry mass and node number so that only new growth was utilized for assessment of effects. Data were evaluated so that the ANOVA analysis included all the growth data and regression analysis included only new growth of the plants after day 1. By using only new plant growth for regression analysis, a more conservative estimate of toxicity is obtained. It also allows for the comparison of toxicity between plant species and studies when the initial starting conditions of the plants are different, such as their day 1 lengths. The advantage of using the complete growth data for ANOVA analysis is that a more sensitive evaluation of toxicity is obtained as the coefficient of variation (CV) about the means will be less with the inclusion of the existing growth relative to variation for new growth only. The smaller the CV, the greater the ability of an ANOVAto detect significant differences (Sanderson, 2001). In both cases, toxicity estimates are more conservative, especially at the initial stages of the study when new tissue is starting to form. Table 1 The reparamaterized equations used to fit the concentration / responses of MCA-exposed M. spicatum , M. sibiricum and L. gibba in SYSTAT 8.0 Regression

Equationa

Linear Logistic Gompertz Exponential

y/((/b /0.5)/x )/x0
/b y/a /[1
/(x0/x )b ] y/g /exp ((log (0.5))/(x0/x )b ) y/a /exp (log ((a/a /0.5/b /0.5)/a ) / (x0/x ))
/b y/(t /(a
/h /x ))/(1
/((0.5
/h /x )/0.5)/ (x0/x )b )

Hormetic

In SYSTAT 8.0 the log term represents the natural ln. a The variable x is the calculated EC50 for the concentration /response relationship modeled, x0 is the actual concentration (i.e. mg/l), y is the response or change from control of the endpoint modeled, and a , b , g , t , and h are constants.

257

L. gibba toxicity data were evaluated in a similar fashion to that of Myriophyllum spp. The three subsections from the L. gibba holding trays were averaged and the means analyzed in a one-way ANOVA in SIGMASTAT 2.0. Data that did not meet normality requirements were ln transformed. The data were then evaluated using the non-linear regression techniques outlined above to calculate ECx values. 2.8. Citrate analysis Citrate levels were determined for the control and 10 mg/l MCA exposed plants as previously described (Hanson et al., 2001) with an enzymatic kit (TC Citric Acid Cat. No. 139 076) from Boehringer Mannheim (Mannheim, Germany) and a Bio-Rad 3550-UV microplate reader (BioRad Laboratories, Hercules, CA) on days 1, 4, 7, 14 and 28. Citrate levels in exposed and unexposed Myriophyllum spp. were compared using Student’s t-test (P B/0.05) in SAS 8.0 at each date evaluated. 2.9. NOEC and EC10 comparison, relative macrophyte sensitivity, and toxicity prediction The NOEC and EC10 values were compared to determine the sensitivity between these two measures. This was done by calculating the ratio between EC10 and the NOEC where values existed for both measures (Suter et al., 1987). Ratios greater than one indicate a NOEC less than EC10 and those ratios below one have a NOEC greater than EC10. A ratio of one indicates the values are equal. The sensitivity of M. spicatum relative to M. sibiricum to MCA was compared in a similar fashion to that of Fletcher et al. (1990), where the ratio of EC50 for endpoints from the two species on days 4, 7, 14 and 28 were calculated. Response ratios B/1 indicate M. spicatum is more sensitive than M. sibiricum and ratios
/1 indicate M. sibiricum is more sensitive to MCA toxicity than M. spicatum . A response difference was also calculated for each effect measure where the larger EC50 was divided by the smaller. The relative sensitivity of L. gibba to MCA exposure as compared with Myriophyllum spp. was calculated

258

MCA (mg/l)

Maximum temperature (8C) (n/27)

Minimum temperature (8C) (n /27)

pH (n/7)

DO (mg/l) (n/19)

Alkalinitya (mg/l) (n/7)

Hardnessa (mg/l) (n/7)

PARb (mE/m2/s1) (n/5)

0 (control) 1 3 30 100

23.29/2.7 23.49/2.9 23.89/2.8 23.79/3.1 23.29/3.0

18.79/3.1 18.99/3.3 19.19/3.2 18.89/3.1 19.09/3.1

7.79/0.5 7.79/0.5 8.09/0.7 7.79/0.4 7.79/0.7

9.89/2.0 9.99/2.0 11.39/2.2 9.89/1.8 9.29/2.4

1719/22 1669/20 1589/21 1669/17 1669/17

3249/20 3279/18 3269/17 3279/17 3259/20

3959/113 3639/146 4199/154 4029/123 4719/97

Measurements were taken regularly over the 28 day period. At each measurement event the mean for each MCA treatment level was taken. These means were then averaged for all the measurement events taken at that concentration over the 28 day period and reported as the mean9/SD. DO, dissolved oxygen; PAR, photosynthetically active radiation. a Measured as mg/l of CaCO3. b Measurements were taken at a depth of 60 cm.

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Table 2 General chemical/physical parameters of the microcosms when Myriophyllum spp. and L. gibba were exposed to MCA

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var(Y ½X0 )F1
F2 (X0  X¯ )2 ;

by taking the ratio of the dry and wet mass EC50s from the 21 day L. gibba test to that of the 28 day Myriophyllum spp. tests. We also examined the relationship between M. spicatum and M. sibiricum toxicity and the extent to which the field data for one species could be used to predict the response in the field for the other. This was done by taking the calculated EC50 data for these plants, converting them to log values, and regressing the data for M. sibiricum against M. spicatum . The chosen regression model was an errors-in-variables model as opposed to an ordinary least squares regression for reasons discussed by others (Ricker, 1973; Suter et al., 1987). The model is described by Suter et al. (1987) where the slope is estimated from b
P

(7)

and for the inverse regression, the variance of a predicted X value is var(Y ½X0 )G1
F2 (Y0  Y¯ )2 ;

(8)

These variances can be used to calculate prediction intervals (PI) about the estimate by PIt0:05(n2) (F1
F2 (X0  X¯ )2 )1=2 ;

(9)

where t0.05(n2) is the critical t-value at n/2 degrees of freedom for a two-sided distribution.

3. Results 3.1. General parameters

y2  l

P

x2




P

y2  l P 2 xy

P

x2

2


4l(xy)2

1=2  ; (2)

and the y-intercept from, (3)

where x /Xi /X¯ and Y /Yi /Y¯ for i/1. . .n. The parameter l is estimated as 1. The variance of a single predicted Y value for a given X value (X /X0) is estimated as,     2 2 y 1 b var s2e 1

1
x0 n l








(X0  X¯ )  P 2 u

s2e 

b

x2  2b

P

xy


n2

3.3. Macrophyte toxicity

P

y2

;

(5)

and 2

u 

X

 2 X 2b b y2 ; x
P
l xy l 2

which is reduced to

The initial (t/1 h) MCA concentrations and half-lives are presented in Table 3. The half-lives ranged from 86 to 523 h with an induction period, or lag phase, of approximately 48 h (Fig. 1). The slopes of the microcosms with the initial nominal concentrations of 30 mg/l MCA were found to be significantly different (P B/0.05) from the 3, 10 and 100 mg/l MCA microcosms using Tukey’s test (a /0.05), implying that degradation is not necessarily independent of concentration.

(4)

;

where, P 2

Water quality, pH, temperature and photosynthetically active radiation, throughout the test period are presented in Table 2. There were no significant differences between treatments. 3.2. Fate of MCA

a yb ¯ x¯

X

259

(6)

The plants exposed to MCA showed clear concentration /response relationships which were modeled using non-linear regression techniques (Tables 4/6) for most of the effect measures monitored (Fig. 2). Relative sensitivities of the effect measures varied depending on the plant, duration of exposure and the effect level chosen to make the assessment. Pigments were relatively weak indicators of toxicity in the studies with Myriophyllum spp. and L. gibba , as few significant

260

M.L. Hanson et al. / Aquatic Toxicology 61 (2002) 251 /273

Table 3 The induction periods, half-lives, and initial concentrations for three levels of MCA applied to field microcosms Nominal concentration (mg/l MCA)

Initial concentration (mg/l MCA)a

Induction period (h)

Half-life (h)b

Slope or rate constantc

Linear fit (r2)

3 10 30 100

2.79/0.1 11.39/0.3 35.69/0.8 116.69/2.8

/48 /48 /48 /48

163 (127, 226) 254 (163, 460) 86 (9, 166) 523 (197, 1000)

/0.1018* /0.0654* /0.1944# /0.0318*

0.929 0.825 0.929 0.740

Half-lives are listed based on pseudo first-order kinetics, calculated subsequent to the induction period. a The values are 9/SD (standard deviation) of the mean of the three microcosm values for that concentration at 1 h. b The values reported in parentheses are the 95% confidence intervals as calculated about the regression line. c Slopes that share the same symbol are not significantly different (P B/0.05) from each other as compared with Tukey’s test (a / 0.05).

Fig. 1. The fate of MCA monitored over 28 days in outdoor aquatic microcosms. Error bars represent the standard deviation about the mean (n/3).

differences by ANOVA or clear concentration response relationships (low corrected r2) were observed (Tables 4/7). Pigment (chlorophyll a, b and carotenoid) concentration response relationships for M. spicatum were also plotted with outliers removed. These points tended to be samples where the plant tissue was overgrown with periphyton in the highest treatment level. Chlorophyll concentrations did show hormetic responses with exposure to MCA. Stimulation at the lower concentrations was observed in both Myriophyllum species (Tables 4, 5 and 7). Based on the EC50 values, M. spicatum mass measures were usually the most sensitive indicator of

toxicity followed by root length and plant length, then root number, root length and node number and finally pigments. In M. sibiricum , on day 4, root measures were about twofold more sensitive than wet or dry mass or plant length, followed by the number of nodes and concentration of pigments. In M. sibiricum , dry mass was a weak indicator of response during the first 7 days of the study because little new dry mass was evident, even in the controls. However, by days 14 and 28, both wet and dry mass measures were the most sensitive endpoints for M. sibiricum exposed to MCA. Overall, the endpoints were rarely more than an order of magnitude different in terms of their sensitivity to MCA toxicity. A second 7 day toxicity test was conducted on day 21 with L. gibba at the nominal concentrations of 10, 30 and 100 mg/l of MCA. Based on the degradation rates for these microcosms (Table 3) their predicted concentrations at 21 days would be 3.3, 0.9 and 63.7 mg/l MCA, respectively. At the highest dose, L. gibba, were completely dead by day 7, with total necrosis and chlorosis of the plants. Only the three lowest concentrations were used to model the concentration response, excluding the 63.7 mg/l MCA values, as it was felt that this data would drive the regressions and unfairly bias the models. The calculated models resulted in significant extrapolation beyond the concentrations modeled to derive many ECx values.

Table 4 The concentration /response relationships for M. spicatum effect measures exposed to MCA as calculated using non-linear regression techniques Endpoint length length length length

Root Root Root Root

number number number number

Total Total Total Total

root root root root

Longest Longest Longest Longest Node Node Node Node

(cm) (cm) (cm) (cm)

length length length length

root root root root

(cm) (cm) (cm) (cm)

length length length length

(cm) (cm) (cm) (cm)

number number number number

EC25

EC50

Model

Parameters

r2

4 7 14 28

5.2 4.4 1.4 4.0

(0, 11.4) (0, 15.9) (0.2, 2.6) (0, 9.8)

7.7 6.7 3.2 5.7

13.3 (0.4, 26.3) 10.3 (2.9, 17.7) 7.3 (3.8, 10.8) 7.8 (3.5, 12.1)

Exponential Logistic Exponential Logistic

a/3.315; b/0.787; x/13.372 t /9.882; x /10.312; b/2.545 a/26.611; b/0.914; x/7.329 t /60.751; x /7.832; b/3.391

0.728 0.716 0.914 0.959

4 7 14 28

8.1 1.9 2.3 2.4

(0, 18.9) (0.4, 3.5) (0.8, 3.8) (0, 6.4)

14.7 (1.9, 27.5) 4.9 (3.2, 6.8) 4.6 (2.7, 6.5) 5.7 (0, 12.0)

26.6 (11.9, 41.4) 11.6 (7.7, 15.5) 9.7 (5.7, 13.7) 13.4 (3.9, 22.9)

Logistic Exponential Exponential Logistic

t /3.33; x /26.617; b/1.85 a/6.847; b/0.091; x/11.587 a/10.132; b/0.724; x/9.671 t /16.446; x /13.407; b/1.277

0.837 0.951 0.934 0.837

4 7 14 28

0.7 1.2 0.9 0.9

(0, 3.5) (0, 2.7) (0.1, 1.6) (0.5, 1.4)

2.5 3.8 2.4 2.0

9.0 9.6 5.7 4.4

Logistic Exponential Exponential Exponential

t /2.903; x /9.04; b/0.854 a/19.773; b//0.487; x /9.614 a/79.966; b//0.112; x/5.713 a/207.868; b/11.087; x/4.366

0.607 0.928 0.934 0.967

4 7 14 28

2.3 8.6 5.3 1.0

(0, 10.6) (4.7, 12.4) (1.3, 9.3) (0, 2.2)

6.5 (0, 14.5) 11.8 (8.1, 15.6) 8.1 (4.6, 11.6) 3.0 (0.5, 5.5)

15.6 (0, 33.5) 16.3 (10.7, 21.9) 12.4 (9.4, 15.4) 9.2 (4.3, 14.0)

Exponential Logistic Logistic Logistic

a/2.644; b//0.005; x/15.635 t /5.921; x /16.33; b/3.404 t /12.85; x /12.405; b/2.582 t /22.913; x /9.158; b/0.99

0.608 0.933 0.945 0.926

4 7 14 28

1.3 1.8 0.3 3.2

(0, 6.0) (0, 11.8) (0, 1.4) (1.3, 5)

3.4 6.1 1.8 4.5

(0, 11.8) (0, 28.6) (0, 6.5) (2.6, 6.5)

9.1 (0, 23.5) 25.9 (0, 78.6) 11.3 (0, 28.4) 6.4 (4.5, 8.4)

Logistic Logistic Logistic Logistic

t /3.180; x /9.079; b/1.124 t /3.363; x /25.901; b/4.276 t /7.666; x /11.321; b/0.594 t /15.996; x /6.425; b/3.134

0.537 0.356 0.678 0.982

(0, 15.5) (0, 17.4) (1.7, 4.8) (0.2, 11.1)

(0, 9.2) (2.1, 5.4) (1.4, 3.4) (1.4, 2.6)

(0, 24.2) (5.6, 13.7) (3.4, 8.1) (3.1, 5.7)

mass mass mass mass

(mg) (mg) (mg) (mg)

4 7 14 28

5.4 2.3 1.3 1.6

(0, 72.0) (0, 8.4) (0, 2.7) (0.1, 3.1)

6.9 4.0 2.7 2.7

(0, 63.6) (0, 11.4) (0.6, 4.8) (0.9, 4.5)

9.0 (0, 44.2) 7.0 (0, 15.5) 5.7 (2.6, 8.8) 4. 6 (2.1, 7.1)

Logistic Logistic Logistic Logistic

t /86.802; x /8.991; b/4.241 t /296.002; x/7.035; b/1.987 t /1081.652; x/5.681; b/1.452 t /4373.719; x/4.599; b/2.073

0.489 0.641 0.914 0.881

Dry Dry Dry Dry

mass mass mass mass

(mg) (mg) (mg) (mg)

4 7 14 28

0.6 0.8 2.2 2.2

(0, 4.3) (0, 2.0) (0.5, 4.0) (0.1, 4.2)

2.0 2.1 3.3 3.5

(0, 10.0) (0.1, 4.0) (1.2, 5.4) (1.0, 5.9)

6.5 5.2 4.9 5.5

Logistic Logistic Logistic Logistic

t /9.221; x /6.497; b/0.923 t /66.790; x /5.206; b/1.193 t /299.690; x/4.874; b/2.832 t /322.07; x /5.475; b/2.385

0.364 0.900 0.901 0.898

4 7 14 14 28 28

39.3 (10.5, 68.0) 42.4 (10.5, 74.2) 124.4 (0, 322.3) 54.5 (30.1, 78.9) 75.6 (42.6, 108.5) 62.7 (42.2, 83.1)

67.6 (18.6, 116.6) 63.8 (11.6, 114.0) 163.5 (0, 455.1) 63.0 (31.0, 95.0) 88.0 (55.1, 120.9) 70.6 (48.3, 92.9)

Hormetic Hormetic Hormetic Hormetic Hormetic Hormetic

t /0.562; t /0.483; t /0.319; t /0.325; t /0.415; t /0.415;

x /205.28; b/0.377 x /143.482; b/0.508 x /295.576; b/0.694 x /85.510; b/1.376 x /116.103; b/1.857 x /87.675; b/2.524

0.713 0.716 0.570 0.910 0.719 0.885

4 7 14

46.2 (11.9, 80.5) 53.2 (0, 107.2) 179.3 (0, 526.6)

73.8 (16.0, 131.5) 196.3 (0, 459.1) 78.6 (0, 167.9) 180.3 (0, 469.9) 232.0 (0, 723.3) 406.7 (0, 1447.3)

Hormetic Hormetic Hormetic

t /0.21; h/0.188; x /196.266; b/0.431 t /0.164; h /0.347; x /180.334; b/0.502 t /0.094; h /0.434; x /406.662; b/0.726

0.662 0.608 0.630

Chlorophyll-a Chlorophyll-a Chlorophyll-a Chlorophyll-a Chlorophyll-a Chlorophyll-a

(mg/mg) (mg/mg) (mg/mg) (mg/mg)a (mg/mg) (mg/mg)a

Chlorophyll-b (mg/mg) Chlorophyll-b (mg/mg) Chlorophyll-b (mg/mg)

(0, 23.2) (2.0, 8.4) (2.1, 7.7) (2.5, 8.5)

205.3 (0, 463.3) 143.5 (0, 310.4) 296 (0, 997.0) 85.5 (28.9, 142.1) 116.1 (71.4, 161.0) 87.7 (59.9, 115.4)

h /0.218; h /0.310; h /0.324; h /0.230; h /0.023; h /0.022;

261

Wet Wet Wet Wet

M.L. Hanson et al. / Aquatic Toxicology 61 (2002) 251 /273

Plant Plant Plant Plant

Time (days) EC10

262

Endpoint

Time (days) EC10

EC25

EC50

Model

Parameters

r2

Chlorophyll-b (mg/mg)a Chlorophyll-b (mg/mg) Chlorophyll-b (mg/mg)a

14 28 28

70.3 (21.9, 118.8) 75.1 (39.9, 110.2) 60.1 (38.3, 81.9)

81.1 (19.1, 143.1) 86.9 (50.2, 123.5) 67.0 (42.1, 91.8)

110.2 (6.0, 214.3) 114.0 (62.8, 165.2) 81.7 (48.9, 114.6)

Hormetic Hormetic Hormetic

t /0.096; h/0.33; x/110.159; b/1.344 t /0.144; h/0.026; x/113.965; b /1.864 t /0.144; h/0.025; x/81.747; b/2.703

0.891 0.675 0.862

Carotenoids Carotenoids Carotenoids Carotenoids

4 7 14 28

NC NC NC 82.3 (52.6, 112.0)

NC NC NC 97.0 (66.5, 127.5)

NC NC NC 130.5 (78.6, 182.4)

NC NC NC Hormetic

NC NC NC t /0.143; h/0.022; x/130.517; b/1.718

NC NC NC 0.759

(mg/mg) (mg/mg) (mg/mg) (mg/mg)

The values reported are in milligrams per liter of MCA. Values in parentheses are the 95% confidence intervals. Any confidence intervals reported as zero were initially calculated as a negative value. The acronym NC refers to not calculated due to a lack of a concentration /response or convergence. The r2 is the corrected r2. a For these regressions, values deemed to be outliers were removed.

M.L. Hanson et al. / Aquatic Toxicology 61 (2002) 251 /273

Table 4 (Continued )

Table 5 The concentration /response relationships for M. sibiricum effect measures exposed to MCA as calculated using non-linear regression techniques EC25

EC50

Model

Parameters

r2

Plant Plant Plant Plant

length length length length

4 7 14 28

14.3 (7.3, 21.4) 4.1 (0, 9.3) 1.6 (0, 4.6) 1.5 (0, 3.0)

17.1 (8.5, 25.6) 7.3 (1.3, 13.2) 4.1 (0, 8.6) 3.1 (0.8, 5.4)

24.3 (8.7, 40.0) 13.0 (6.4, 19.7) 9.2 (3.6, 14.7) 6.4 (2.9, 9.9)

Hormetic Logistic Gompertz Logisticb

t /2.432; h/0.349; x /24.319; b/1.263 t /7.521; x/13.043; b/1.886 g/19.729; x/9.164; b/1.093 t /43.709; x/6.393; b/1.532

0.835 0.858 0.875 0.898

4 7 14 28

4.4 8.1 4.3 2.9

(0, 9.1) (4.4, 11.8) (0, 9.1) (0, 6.1)

7.3 (2.5, 12.1) 11.6 (7.9, 15.3) 8.0 (2.3, 13.8) 7.2 (2.3, 12.0)

12.1 16.5 14.8 15.7

Logistic Logistic Logistic Gompertz

t /7.771; x/12.113; b/2.152 t /7.851; x/16.527; b/3.081 t /20.596; x/14.794; b/1.793 g/11.349; x/15.664; b/1.122

0.904 0.940 0.889 0.940

4 7 14 28

0.9 3.5 0.7 2.2

(0, 3.2) (1.4, 5.6) (0, 1.6) (0, 6.0)

3.1 6.3 2.2 4.4

(0.6, 5.6) (3.5, 9.0) (0.3, 4.1) (0, 9.7)

7.9 (1.8, 14.0) 10.6 (7.3, 13.8) 6.1 (3.2, 9.0) 9.0 (2.3, 15.8)

Exponential Gompertzb Gompertz Logistic

a/20.703; b//0.536; x /7.903 g/39.394; x/10.550; b/1.697 g/101.216; x /6.102; b/0.859 t /187.807; x/9.037; b/1.542

0.796 0.969 0.939 0.828

Root Root Root Root

number number number number

Total Total Total Total

root root root root

Longest Longest Longest Longest

(cm) 4 (cm) 7 (cm) 14 (cm) 28

7.2 4.6 4.5 1.4

(3.9, 10.6) (1.7, 7.6) (1.3, 7.7) (0, 4.5)

9.5 8.5 8.0 5.2

(5.9, 13.1) (5.3, 11.6) (4.3, 11.7) (0, 11.9)

12.5 14.3 13.4 15.8

Logisticb Gompertz Gompertzb Gompertz

t /4.741; x/12.461; b/4.049 g/7.398; x/14.344; b/1.668 g/12.88; x/13.360; b/1.726 g/19.209; x/15.813; b/0.785

0.936 0.965 0.960 0.858

Node Node Node Node

4 7 14 28

8.2 0.1 8.7 2.0

(0, 37.9) (0, 1.1) (3.9, 13.4) (0, 4.5)

20.5 (0, 66.9) 4.9 (0, 49.7) 12.2 (7.3, 17.0) 3.2 (0.2, 6.2)

46.1 (0, 106.9) 288.0 (0, 2491.1) 17.0 (10.4, 23.7) 5.0 (1.1, 9.0)

Gompertz Gompertz Logistic Logistic

g/5.98; x/46.054; b/1.089 g/5.326; x/287.974; b/0.216 t /8.007; x/17.046; b/3.252 t /17.995; x/5.046; b/2.337

0.405 0.200 0.907 0.815

(cm) (cm) (cm) (cm)

length length length length

root root root root

(cm) (cm) (cm) (cm)

length length length length

numbera number number number

(7.5, 16.7) (11.5, 21.6) (7.9, 21.7) (9.7, 21.7)

(8.7, 16.2) (10.8, 17.9) (9.4, 17.3) (4.4, 27.3)

Wet Wet Wet Wet

mass mass mass mass

(mg)a (mg) (mg) (mg)

4 7 14 28

13.8 (4.6, 23.0) 4.1 (0, 17.2) 2.1 (0. 5.0) 1.4 (0, 3.2)

17.1 (6.2, 28.0) 5.9 (0, 18.4) 3.7 (0.2, 7.2) 2.8 (0, 5.7)

26.1 (6.0, 42.3) 8.6 (0, 17.7) 6.5 (2.2, 10.8) 5.6 (1.3, 10.0)

Hormetic Logistic Logistic Logisticb

t /139.396; h/0.316; x/26.125; b/1.063 t /227.119; x/8.6; b/2.949 t /877.806; x/6.502; b/1.960 t /3650.231; x/5.641; b/1.593

0.742 0.768 0.869 0.867

Dry Dry Dry Dry

mass mass mass mass

(mg)a (mg)a (mg) (mg)

4 7 14 28

19.3 (2.7, 35.8) 1.6 (0, 14.4) 3.1 (NC) 0.8 (0, 1.8)

22.5 (4.1, 41.0) 9.9 (0, 55.5) 3.3 (NC) 1.8 (0, 3.9)

30.1 (3.9, 56.2) 60.2 (0, 229.3) 3.5 (0, 15.7) 4.2 (0.3, 8.4)

Hormetic Logistic Logistic Logisticb

t /18.645; h/0.092; x/30.054; b/1.783 t /26.224; x/60.182; b/0.608 t /27.067; x/3.514; b/15.627 t /255.427; x/4.337; b/1.253

0.535 0.255 0.584 0.821

(mg/mg) (mg/mg) (mg/mg) (mg/mg)

4 7 14 28

49.5 (0, 102.3) 58.2 (15.9, 100.6) 73.8 (21.3, 126.3) 106.9 (NC)

123.7 (0, 255.8) 83.8 (15.0, 152.6) 87.3 (19.0, 155.7) 112.9 (109.9, 115.9)

247.4 181.8 125.5 118.4

(0, 511.5) (0, 393.7) (4.9, 246.0) (113.4, 123.3)

Linear Hormetic Hormetic Logistic

b/0.433; x/247.384 t /0.351; h/0.292; x /181.821; b/0.539 t /0.235; h/0.345; x /125.478; b/1.143 t /0.398; x/118.362; b/16.811

0.101 0.731 0.838 0.665

Chlorophyll-b Chlorophyll-b Chlorophyll-b Chlorophyll-b

(mg/mg) (mg/mg) (mg/mg) (mg/mg)

4 7 14 28

70.7 (0, 191.4) 93.1 (0, 189.3) 119.8 (0, 261.4) NC

176.7 (0, 478.5) 135.9 (0, 299.2) 144.2 (0, 327.9) NC

353.4 307.6 216.0 117.7

(0, 957.0) (0, 828.1) (0, 538.0) (116.7, 118.6)

Linear Hormetic Hormetic Hormetic

b/0.156; x/353.442 t /0.110; h/0.307; x /307.640; b/0.505 t /0.062; h/0.513; x /215.950; b/1.014 t /0.121; h/0.009; x /117.667; b/32.789

0.101 0.659 0.807 0.898

NC

NC

NC

NC

NC

NC

Carotenoids (mg/mg)

4

263

Chlorophyll-a Chlorophyll-a Chlorophyll-a Chlorophyll-a

M.L. Hanson et al. / Aquatic Toxicology 61 (2002) 251 /273

Time (days) EC10

Endpoint

264

Endpoint

Time (days) EC10

EC25

EC50

Model

Parameters

r2

Carotenoids (mg/mg) Carotenoids (mg/mg) Carotenoids (mg/mg)

7 14 28

NC NC NC

NC NC NC

NC NC NC

NC NC NC

NC NC NC

NC NC NC

The values reported are in mg/l MCA. Values in parentheses are the 95% confidence intervals. Any confidence intervals reported as zero were initially calculated as a negative value. The acronym NC refers to not calculated due to a lack of a concentration /response or convergence. The r2 is the corrected r2. a In order to model the concentration response curves of these effect measures total growth was evaluated with the lowest value set to zero. b These regressions were weighted with the inverse of their variance.

M.L. Hanson et al. / Aquatic Toxicology 61 (2002) 251 /273

Table 5 (Continued )

M.L. Hanson et al. / Aquatic Toxicology 61 (2002) 251 /273

265

Table 6 The concentration /response relationships for L. gibba effect measures exposed to MCA as calculated using non-linear regression techniques Endpoint

Duration EC10

EC25

EC50

Model

Frond number

21

1.3 (0, 4.3)

3.8 (0.4, 7.1)

9.3 (1.4, 17.3)

Dry mass

21

4.9 (0, 27.6)

7.1 (0, 26.4)

10.3 (0, 20.5)

Wet mass

21

2.4 (0, 6.6)

4.2 (0, 9.2)

7.2 (1.6, 12.7)

Plant number

21

4.2 (0, 14.7)

6.5 (0, 16.3)

9.8 (3.1, 16.5)

Chlorophylls (mg/mg) 21 Frond growth rate 21 (k ) Plant growth rate (k ) 21

NC 12.2 (7.7, 16.7) 11.9 (7.6, 16.3) 2.9 (0, 8.7)

NC 18.5 (12.9, 24.0) 17.4 (11.8, 23.1) 5.1 (0, 22.5)

Exponential a/534.2; b //7.355; x/ 9.315 Logistic t/168.921; x /10.269; b/ 2.973 Logistic t/3243.896; x /7.205; b/ 2.029 Logistic t/119.853; x /9.846; b/ 2.601 NC NC Logistic t/0.249; x /18.451; b /2.663

Parameters

Wet mass (mg)

7

NC 8.1 (3.9, 12.3) 8.2 (4.0, 12.3) 1.7 (0, 12.0)

Dry mass (mg) Frond number Plant number Chlorophylls (mg/mg) Frond growth rate (k ) Plant growth rate (k )

7 7 7 7 7

1.3 (0, 4.2) 1.5 (0, 6.2) 1.6 (0, 3.4) NC 1.4 (0.1, 2.7)

3.2 (0, 10.5) 3.2 (0, 6.8) 3.9 (0, 8.6) NC 3.5 (0.3, 6.8)

6.3 (0, 21.0) 6.8 (0, 24.1) 7.8 (0, 17.1) NC 7.1 (0.5, 13.7)

Linear Logistic Linear NC Linear

t/149.733; x /5.072; b/ 1.975 b/7.431; x /6.347 t/51.000; x /6.820; b /1.431 b/12.694; x/7.8 NC b/0.100; x /7.088

7

1.7 (0, 3.8)

4.3 (0, 9.6)

8.6 (0, 19.2)

Linear

b/0.098; x /8.636

r2 0.761 0.590 0.828 0.795 NC 0.942

Logistic

t/0.266; x /17.438; b /2.889 0.934

Logistic

0.107 0.382 0.319 NC 0.433 0.312

0.184

The values reported are in mg/l MCA. Values in parentheses are the 95% confidence intervals. Any confidence intervals reported as zero were initially calculated as a negative value. NC refers to not calculated due to a lack of a concentration /response or convergence. The r2 is the corrected r2.

3.4. Citrate concentrations Citrate levels in both MCA exposed and unexposed plants were similar over the entire duration of the study. Concentrations declined as the study progressed for both M. spicatum and M. sibiricum . No significant differences (P B/0.05) were found with the exception of 28 day M. spicatum , where the control plants had higher citrate levels than the exposed plants (Fig. 3). 3.5. NOEC and EC10 comparison, relative macrophyte sensitivity, and toxicity prediction NOECs and LOECs for Myriophyllum spp. were calculated when possible for effect measures showing statistically significant differences by ANOVA (Table 7). These values were then compared to EC10s as calculated using non-linear regression techniques (Table 8). Overall, for the

measures where both an NOEC and EC10 were determined for MCA exposure, EC10 yielded a more conservative estimate of toxicity, having a concentration approximately 25% lower than the calculated NOEC. Some endpoints, such as node number and dry mass, were more susceptible to the NOEC overestimating low toxicity than others, such as root number. The sensitivity of M. spicatum endpoints relative to M. sibiricum to MCA was found to be similar (Table 9). After 14 days of exposure to MCA, M. spicatum EC50s were, on average, approximately 0.9 times lower than M. sibiricum ’s. The mean of the EC50 response differences was 1.30 with a standard deviation (9/95%) of 0.21. Therefore, approximately 95% of the time there will be less than 1.5-fold difference between M. spicatum and M. sibiricum toxicity to MCA in the field for these effect measures. The response ratios after 4, 7 and 28 day exposure to MCA were 0.84, 0.74 and 0.85,

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sibiricum toxicity to MCA is shown in Fig. 4. Caution should be used in the extrapolation of the regression beyond the upper and lower bounds of the data (xl /2.1 mg/l and xu /16.3 mg/l; yl /3.5 mg/l and yu /26.1 mg/l MCA) (Fig. 4). The estimate of the slope of the regression (b/1.2) implies that M. spicatum is a slightly more sensitive indicator of plant toxicity than M. sibiricum , at least for MCA exposure.

4. Discussion

Fig. 2. The concentration /response relationships for M. spicatum (A) and M. sibiricum (B) wet mass after 28 days and L. gibba (C) wet mass after 21 days exposure to MCA in outdoor aquatic microcosms. Curves were fitted using a logistic model.

respectively, implying M. spicatum is consistently more sensitive at the EC50 level. The response differences were, on average, 2.729/1.58, 3.169/ 4.73, and 1.619/0.57 after 4, 7 and 28 day exposure to MCA, respectively. Overall, the difference in the calculated EC50s between the two plants was generally less than threefold. L. gibba was found to be moderately less sensitive to MCA exposure than both species of Myriophyllum . Comparing wet and dry mass, EC50s for Myriophyllum spp. were 1.3- to 2.4fold more sensitive to MCA exposure. The predictive relationship, based on EC50 values, between M. spicatum toxicity and M.

Under field conditions, MCA dissipated from the water column of microcosms over a 28 day period. Degradation of MCA is most likely due to bacterial utilization of the compound as a carbon source, as this is the most commonly observed means of MCA and other HAA decomposition in aquatic systems (Hirsch and Alexander, 1960; Egli et al., 1989; Ellis et al., 2001; Yu and Welander, 1995). Previous work examining the fate of MCA in aquatic microcosms found the induction period to be approximately 48 h with a half-life of 157 h (Ellis et al., 2001). We calculated an induction period of 48 h with half-lives that were in the range of this value (86 /523 h) with a fair degree of variability within concentration levels. Significant differences between treatment level rate constants were observed, but with no distinct trends between rate of degradation and initial concentration. Ellis et al. (2001) used initial concentrations of 3, 6 and 12 mg/l and when compared to similar initial concentrations in our study (3 and 10 mg/l MCA nominal), the half-lives are in better agreement. Factors that could influence the differences in the observed half-lives included toxicity of MCA towards the bacteria or predation by other organisms upon the bacteria in the perturbed system (Wiggins et al., 1987). Compared to the half-lives of other HAAs studied in these microcosms, MCA degrades slower than DCA, but more rapidly than TCA. TFA and CDFA do not appear to degrade at all under field conditions (Ellis et al., 2001; Hanson et al., 2001, 2002a,b; Hanson et al., submitted for publication). MCA toxicity was clearly manifested in the three plant species tested. Toxicity was generally

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267

Table 7 The no-observed effect concentrations and lowest observed effect concentrations (mg/l) from control as determined by ANOVA (P 5/ 0.05) and multiple comparison with Dunnett’s test (a /0.05) for M. spicatum and M. sibiricum exposed to MCA in aquatic microcosms Endpoint

Time (days)

Plant Plant Plant Plant

length length length length

(cm) (cm) (cm) (cm)

Root Root Root Root

number number number number

M. spicatum

M. sibiricum

Controla

NOECb

LOECb

Control

NOEC

LOEC

4 7 14 28

8.59/0.9 14.29/3.3 31.39/6.7 64.49/4.8

2.7 (9.59/1.0) 11.3 (9.19/1.0) 2.7 (28.49/4.2) 2.7 (65.69/8.3)

11.3 35.6 11.3 11.3

7.49/0.6 12.49/0.9 23.89/5.5 44.69/11.9

11.3 (7.89/0.8) 11.3 (9.29/1.7) 2.7 (20.49/5.3) 2.7 (42.09/11.9)

35.6 35.6 11.3 11.3

(5.79/0.5) (5.99/0.3) (13.09/1.6) (15.49/3.8)

4 7 14 28

39/1 79/1 119/1 159/1

11.3 (29/0) 0 (79/1) 0 (119/1) 2.7 (179/3)

35.6 (19/1) 2.7 (59/1) 2.7 (89/2) 11.3 (69/1)

79/2 89/1 119/2 199/5

2.7 (89/2) 11.3 (69/1) 2.7 (109/2) 2.7 (219/3)

11.3 35.6 11.3 11.3

(49/1) (19/1) (79/0) (129/3)

4.59/2.1 19.09/3.3

11.3 ( 2.79/1.4) 2.7 (16.19/4.3)

35.6 (0.69/0.2) 11.3 (8.89/2.0)

18.49/9 40.39/12.5

2.7 (18.89/4.6) 2.7 (36.69/4.9)

11.3 (6.19/2.6) 11.3 (18.19/4.2)

(6.79/0.6) (6.19/0.2) (13.19/1.8) (18.19/5.3)

Root length (cm) Root length (cm)

4 7

Root length (cm) Root length (cm)

14 28

79.59/10.1 214.69/15.6

2.7 (58.29/19.2) 0 (214.69/15.6)

11.3 (19.89/7.8) 2.7 (151.29/16.8)

101.89/9.4 188.69/74.9

0 (101.89/9.4) 2.7 (161.29/37.9)

2.7 (70.09/23.4) 11.3 (79.09/33.9)

Longest Longest Longest Longest

4 7 14 28

39/1.4 5.79/0.7 139/2.4 22.79/2.4

11.3 (1.89/1.3) 11.3 (4.69/1.1) 2.7 (12.39/2.5) 2.7 (18.39/1.8)

35.6 35.6 11.3 11.3

4.79/1.6 7.79/1.4 14.19/3.6 19.59/5.4

11.3 (2.89/1.1) 2.7 (6.79/0.4) 2.7 (11.59/1.8) 2.7 (15.39/0.9)

35.6 11.3 11.3 11.3

4 7 14 28

229/0 239/0 269/1 359/1

35.6 (209/1) 116.6 (209/2) 35.6 (209/3) 2.7 (349/2)

116.6 (189/1) NC 116.6 (199/5) 11.3 (219/2)

249/1 269/2 289/2 399/3

116.6 (219/2) 116.6 (249/2) 11.3 (279/1) 2.7 (369/10)

NC NC 35.6 (229/1) 11.3 (239/3)

Node Node Node Node

root root root root

(cm) (cm) (cm) (cm)

number number number number

(0.59/0.3) (0.49/0.4) (7.29/1.1) (8.79/2.1)

(0.19/0.1) (4.79/0.5) (7.89/1.0) (11.99/2.4)

Wet mass (mg) Wet mass (mg)

4 7

270.89/45.2 485.89/122.5

116.6 (83.69/15.4) 11.3 (272.29/52.5)

NC 35.6 (204.69/27.5)

334.29/22.0 498.89/40.0

11.3 (346.79/20.0) 11.3 (337.99/92.2)

35.6 (223.49/27.7) 35.6 (273.29/22.4)

Wet mass (mg) Wet mass (mg)

14 28

1285.39/253.8 4556.49/977.9

2.7 (968.39/191.9) 2.7 (3492.09/1402.3)

11.3 (541.8 (80.7) 11.3 (719.39/347.5)

1155.49/165.7 3473.59/1745.8

2.7 (1012.09/341.1) 2.7 (2975.09/834.6)

11.3 (505.39/103.1) 11.3 (941.89/336.9)

Dry Dry Dry Dry

4 7 14 28

36.79/8.7 54.59/14.2 95.39/13.9 327.49/83.8

116.6 (24.39/5.4) 11.3 (33.19/3.8) 2.7 (71.69/12.4) 2.7 (280.59/78.7)

NC 35.6 (30.49/5.3) 11.3 (51.99/10.0) 11.3 (50.69/24.4)

48.59/9.6 53.39/12.1 81.69/4.0 255.59/127.7

116.6 (34.19/4.3) 116.6 (38.79/9.6) 2.7 (81.19/22.3) 2.7 (186.79/69.6)

NC NC 11.3 (51.59/5.6) 11.3 (286.79/16.9)

(mg/mg) (mg/mg) (mg/mg) (mg/mg)

4 7 14 28

0.5749/0.026 0.4909/0.051 0.3649/0.048 0.4179/0.054

2.7 (0.5859/0.087) 2.7 (0.6329/0.155) 2.7 (0.4609/0.106) 116.6 (0.2069/0.173)

11.3 (0.7219/0.037)c 11.3 (0.7259/0.008)c 11.3 (0.8769/0.030)c NC

0.4239/0.047 0.3649/0.030 0.2829/0.080 0.3729/0.016

116.6 (0.3369/0.091) 2.7 (0.4639/0.032) 2.7 (0.3669/0.036) 35.6 (0.4589/0.060)

NC 11.3 (0.6139/0.040)c 11.3 (0.7869/0.050)c 116.6 (0.2419/0.044)

Chlorophyll-b (mg/mg) Chlorophyll-b (mg/mg)

4 7

0.2199/0.014 0.1689/0.025

2.7 (0.2159/0.034) 2.7 (0.2259/0.074)

11.3 (0.2919/0.014)c 11.3 (0.2739/0.011)c

0.1519/0.013 0.1169/0.014

116.6 (0.1299/0.032) 2.7 (0.1489/0.018)

NC 11.3 (0.2179/0.013)c

Chlorophyll-b (mg/mg) Chlorophyll-b (mg/mg)

14 28

0.1149/0.025 0.1469/0.021

2.7 (0.1519/0.042) 116.6 (0.0699/0.069)

11.3 (0.3399/0.009)c NC

0.0819/0.023 0.1229/0.005

2.7 (0.1149/0.023) 11.3 (0.1309/0.016)

11.3 (0.2819/0.028)c 35.6 (0.1609/0.011)c

Carotenoids Carotenoids Carotenoids Carotenoids

4 7 14 28

0.2139/0.012 0.1779/0.016 0.1379/0.022 0.1429/0.020

116.6 (0.2219/0.021) 2.7 (0.2219/0.051) 2.7 (0.1659/0.030) 116.6 (0.1279/0.080)

NC 11.3 (0.2539/0.016)c 11.3 (0.3229/0.027)c NC

0.1529/0.011 0.1339/0.011 0.0969/0.033 0.1269/0.005

116.6 116.6 116.6 116.6

NC NC NC NC

mass mass mass mass

(mg) (mg) (mg) (mg)

Chlorophyll-a Chlorophyll-a Chlorophyll-a Chlorophyll-a

a

(mg/mg) (mg/mg) (mg/mg) (mg/mg)

(0.1879/0.049) (0.1669/0.018) (0.1459/0.025) (0.1289/0.018)

The values in the Control column are the mean9/SD of the control value for that endpoint and exposure duration. The values in parentheses in the NOEC and LOEC columns are the mean9/standard deviation of the NOEC or LOEC value for that endpoint and exposure duration. b

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evident at concentrations greater than 2.7 mg/l MCA with almost complete mortality of the plants at the highest tested concentration (116.6 mg/l MCA). A distinct concentration /response was evident for most effect measures, which enabled the modeling of the toxicity. The EC50 values were generally greater than 10 mg/l MCA for Myriophyllum spp. and L. gibba. MCA was much more toxic than DCA, CDFA, TCA or TFA to these plants under similar field conditions (Hanson et al., 2001, 2002a,b; Hanson et al., submitted for publication). L. gibba did not survive in the highest MCA concentration, despite plant reintroduction and significant MCA degradation. The 7 day toxicity study found EC50 values lower than those calculated in the 21 day study. This may be due to the production of potentially toxic metabolites through the bacterial degradation of MCA. One known by-product of MCA degradation is glycolic acid (Ellis et al., 2001) though its toxicity has not been characterized. When conducting field-level evaluations of toxicity to aquatic organisms it is important to select species and endpoints that will be cost effective and sensitive (Shaw and Kennedy, 1996). If a short-term field exposure is to be conducted examining only a few effect measures, the growth patterns of the plants are important to understand so that efforts are not misdirected. Laboratorybased studies with M. sibiricum shows that root endpoints, such as root mass and root number, tend to be more sensitive than endpoints such as shoot growth (Roshon et al., 1999; Martin et al., 2000). The differences in sensitivity of the various effect measures between plants reflect the different growth strategies between these plants. Based on the control data from similar studies with these two macrophytes (Hanson et al., 2001, 2002a,b; Hanson et al., submitted for publication), M. spicatum tends to experience rapid shoot growth early in its development, meaning endpoints such as mass, and shoot length will likely be impacted first. In contrast, M. sibiricum tends to grow new roots rapidly, so impacts may be observed in these endpoints earlier in studies. Overall, the relative sensitive of an endpoint tended to be highly variable depending on the duration of exposure,

the plant utilized and the level of effect (ECx ) chosen. Endpoints were rarely more than an order of magnitude different from each other, implying that most endpoints monitored were good measures of MCA toxicity for these plants. Chlorophyll, a common endpoint in plant studies, did not appear to be a very sensitive indicator of toxicity. The calculated ECx values were generally higher than for other endpoints. This is partly due to the fact that plants at higher concentrations, especially the highest level, had large amounts of epiphytic algal growth, which may have resulted in an overestimation of the endogenous chlorophyll concentrations as the plants themselves were highly necrotic and chlorotic, with little visible viable plant tissue. At the nominal 30 mg/l concentrations, while there was new growth from the apical shoot, lower tissue tended to become necrotic and chlorotic. This resulted in an overestimation of the chlorophyll concentrations in these plants. ECx estimations for these effect measures should therefore be interpreted with caution. As well, by the 4th week of the study, most of the original shoots in the 10 and 30 mg/l exposures had completely died and were replaced by new secondary growth from the base of the plant. In this regard, the original shoots could be considered to have a chlorophyll concentration of zero at the 10 and 30 mg/l levels and at the 100 mg/l level. Even under natural conditions, M. spicatum can have widely fluctuating levels of chlorophyll, rendering this endpoint more unreliable than others (Marcus, 1980). Inhibition of the citric acid cycle via halocitrates acting on the enzyme aconitase is known for monofluoroacetic acid (MFA). Increased levels of citrate with MFA exposure are observed in monitored tissues (Buffa and Peters, 1950; Bosakowski and Levin, 1986; Keller et al., 1996). Citrate levels in Myriophyllum spp. did not appear to be modified upon exposure to MCA under field conditions. Berends et al. (1999) hypothesized that TFA mediated toxicity through the citric acid cycle. This supposition was based on the observation that the addition of citrate to algae concurrently exposed to TFA experienced an increase in growth as compared with TFA exposed algae without citrate addition. It was surmised that the

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Fig. 3. The tissue citrate concentration in M. spicatum (A) and M. sibiricum (B) that were exposed to 10 mg/l MCA as compared with unexposed plants in outdoor microcosms. Error bars represent the standard deviation about the mean (n/3). An asterisk denotes a statistically significant difference as detected using a Student’s t -test (P B/0.05).

269

addition of excess citrate would out-compete the halocitrates for binding with aconitase, reducing inhibition of this enzyme and allowing the citric acid cycle to function normally. Our current observations do not support the theory that MCA acts primarily through this mechanism to induce toxicity in Myriophyllum spp. Studies with TFA, CDFA and TCA with the same plants showed similar results (Hanson et al., 2001, 2002a,b). A comparison of the response ratio for EC50s after various durations of exposure to MCA showed M. spicatum to be slightly, but consistently, more sensitive than M. sibiricum . Both species of Myriophyllum appear to be susceptible to MCA in the field and that the observation of toxicity, or lack thereof, in one species is likely to be seen to a similar degree in the other. Both species were more sensitive to MCA toxicity than L. gibba , but all three species were generally within a threefold range of each other. M. spicatum and M. sibiricum appear to be highly predictive of MCA toxicity observed in the field for each other. The relationship between EC50s of plants within a genus have been observed to be highly correlated, as opposed to those comparisons between genera within a family, between families in an order and orders within a class (Fletcher et al., 1990). The prediction intervals calculated are relatively small compared with

Table 8 The ratio of the NOEC as determined by one-way analysis of variance (ANOVA) (P 5/0.05) and Dunnett’s test (a/0.05) with the EC10 as calculated by non-linear regression techniques for M. spicatum and M. sibiricum Effect measure

Plant length Root number Root length Longest root length Node number Wet mass Dry mass Chlorophyll-a Chlorophyll-b All a

na

NOEC/EC10 Mean

Median

Minimum

Maximum

8 6 7 8 8 8 8 8 7

0.89 1.1 0.55 0.97 0.35 0.53 0.35 1.18 0.76

0.57 0.98 0.39 0.69 0.05 0.5 0.23 0.52 0.61

0.36 0.72 0.06 0.2 0 0.05 0.01 0.36 0.46

1.93 1.63 1.3 1.96 1.19 1.22 1.15 3.48 1.3

68

0.74

0.56

0

3.48

These values are the number of NOECs and EC10s used in the evaluation for that effect measure.

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Table 9 Comparison of day 14 EC50 values of M. spicatum and M. sibiricum exposed to MCA Effect Measure

Plant length Root number Root length Longest root length Node number Wet mass Dry mass

EC50 (mg/l) M. spicatum

M. sibiricum

7.3 9.7 5.7 12.4 11.3 5.7 5.2

9.2 14.8 6.1 13.4 17 6.5 3.5

Mean

Response ratio

Response difference

0.79 0.66 0.93 0.93 0.66 0.88 1.49

1.26 1.53 1.07 1.08 1.5 1.14 1.49

0.919/0.28

1.309/0.21

Response ratios B/1 indicate that M. spicatum is more sensitive than M. sibiricum and ratios
/1 indicate that M. sibiricum is more sensitive to MCA toxicity than M. spicatum .

Fig. 4. The regression of M. spicatum and M. sibiricum MCA EC50 toxicity data from 4, 7, 14, and 28 days of exposure. The mean X and Y and the Fx and Gx are used to calculate the variance and prediction intervals. PI is the prediction interval at the mean X , so mean Y9/PI.

prediction intervals for similar relationships (Suter et al., 1987; Suter, 1995) and provides confidence in using this regression approach in estimating future toxicity within the ranges modeled. In previous work with plants, only the r2 was reported as a means to evaluate the predictive capabilities (Fletcher et al., 1990) and the regressions were not calculated with an errors-in-variables model. Calculating an ordinary least squares regression for the data produced an r2 of 0.52. This is lower than those generally observed by Fletcher et al. (1990), but still within the range of values

they reported. It should be noted that they compared EC50s for plant mortality only, with an average n of 4, while we included all endpoints for which an EC50 was calculated at four time intervals. The relationship described here between the two plant species is highly generalized, as all the endpoints and exposure durations are modeled in one equation, but it is specific to MCA. In order to conduct a more useful analysis of the predictive capabilities of these two plants for specific endpoints under field conditions, more data are needed. Unfortunately, due to the lack of toxicity seen in aquatic macrophytes with other HAAs (Hanson et al., 2001, 2002a,b; Hanson et al., submitted for publication) it is difficult to derive such a relationship at this time. Still, M. spicatum has the potential to be highly predictive of toxicity seen in M. sibiricum under field conditions and vice versa. The use of the NOEC in ecological risk assessment has been debated considerably (Chapman et al., 1996; Bailer and Oriss, 1997; Van der Hoeven, 1997). In this study, the NOEC was found to be a fairly reliable predictor of low toxicity, as defined by EC10. This is partly due to the well-defined concentration /response relationships, which increases our certainty in the estimation of low toxicity of MCA toxicity for these plants. Since the NOEC is highly dependent on the initial concentrations chosen for the toxicity assessment (Chapman et al., 1996), it is quite possible that if other initial concentrations had been used, the

M.L. Hanson et al. / Aquatic Toxicology 61 (2002) 251 /273

results would be significantly different. Still, EC10 was a more conservative estimate of low toxicity than the NOEC and should be considered as a replacement for the NOEC in the risk assessment framework. In conclusion, MCA was demonstrated to induce toxicity to aquatic macrophytes under semi-natural field conditions. It was shown to dissipate from the water column, most likely through the action of bacterial degradation. MCA at current environmental concentrations does not appear to be a risk to aquatic macrophytes. The highest measured concentrations in the environment tend to be in the ng/l range (Berg et al., 2000; Scott et al., 2000), while effects are not observed till the mg/l range. A comprehensive ecological risk assessment for MCA with these plants has been conducted (Hanson and Solomon, in press). Since MCA degrades in aquatic environments, it is unlikely to increase in concentration over time as has been observed or proposed for other HAAs (Boutonnet et al., 1999; Martin et al., 2000). The use of Myriophyllum spp. in ecotoxicological risk assessment, in the context of this study, is promising. These plants can be evaluated for a wide variety of endpoints and develop well in microcosms. All plant species tested exhibited toxicity upon exposure to MCA, with M. spicatum generally the most sensitive. The EC50s for shared endpoints from the three species were within threefold of each other. Mass and root endpoints were generally the most sensitivity indicators of toxicity in Myriophyllum spp. The EC10 was a more appropriate measure of low toxicity than the NOEC in these studies.

Acknowledgements This work was funded by a grant from Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Network of Toxicology Centres (CNTC). We appreciate the reviewers’ comments and those of Dr. Sean Richards in preparing this manuscript and the assistance of the summer field staff at the microcosms in conducting this study.

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References American Society for Testing and Materials, 1999. E 1913 /97 Standard guide for conducting static, axenic, 14-day phytotoxicity tests in test tubes with the submersed aquatic macrophyte, Myriophyllum sibiricum Komarov. In: 1999 Annual Book of ASTM Standards, Section 11, Water and Environmental Technology, Biological Effects and Environmental Fate, Biotechnology, Pesticides. Vol. 11.05, American Society for Testing and Materials, West Conshohocken, PA, pp. 1434 /1448. Bailer, A.J., Oriss, J.T., 1997. Estimating inhibition concentrations for different response scales using generalized linear models. Environ. Toxicol. Chem. 16, 1554 /1559. Berends, A.G., Boutonnet, J.C., de Rooij, C.G., Thompson, R.S., 1999. Toxicity of trifluoroacetate to aquatic organisms. Environ. Toxicol. Chem. 18, 1053 /1059. Berg, M., Mu¨ller, S.R., Muhlemann, J., Weidemar, A., Schwarzenbach, R.P., 2000. Concentrations and mass fluxes of chloroacetic acid and trifluoroacetic acids in rain and natural waters in Switzerland. Environ. Sci. Technol. 34, 2675 /2683. Bosakowski, T., Levin, A.A., 1986. Serum citrate as a peripheral indicator of difluoroacetate and fluorocitrate toxicity in rats and dogs. Toxicol. Appl. Pharmacol. 85, 428 /436. Boutonnet, J.C., Bingham, P., Calamari, D., de Rooij, C., Franklin, J., Kawano, T., Libre, M.-J., McCulloch, A., Malinverno, G., Odom, J.M., Rusch, G.M., Smythe, K., Sobolev, I., Thompson, R., Tiedje, M., 1999. Environmental risk assessment of trifluoroacetic acid. Hum. Ecol. Risk Assess. 5, 59 /124. Bowden, D.J., Clegg, S.L., Brimblecombe, P., 1996. The Henry’s law constant of trifluoroacetic acid and its partitioning into liquid water in the atmosphere. Chemosphere 32 (2), 405 /420. Bowden, D.J., Clegg, S.L., Brimblecombe, P., 1998a. The Henry’s law constant of trichloroacetic acid. Water, Air, Soil Pollut. 101, 197 /215. Bowden, D.J., Clegg, S.L., Brimblecombe, P.J., 1998b. The Henry’s law constant of the haloacetic acids. J. Atmos. Chem. 29, 85 /107. Buffa, P., Peters, R.A., 1950. The in vivo formation of citrate induced by fluoroacetate and its significance. J. Physiol. 110, 488 /500. Chapman, P.M., Caldwell, R.S., Chapman, P.F., 1996. A warning: NOEC’s are inappropriate for regulatory use. Environ. Toxicol. Chem. 1, 77 /79. Chen, S., Alexander, M., 1989. Reasons for the acclimation of 2,4-D biodegradation in lake water. J. Environ. Qual. 18, 153 /156. Chilton, E.W., 1990. Macroinvertebrate communities associated with three aquatic macrophytes (Ceratophyllum demersum , Myriophyllum spicatum , and Vallisneria americana ) in Lake Onalaska, Wisconsin. J. Freshw. Ecol. 5, 455 /466.

272

M.L. Hanson et al. / Aquatic Toxicology 61 (2002) 251 /273

Davy, M., Petrie, R., Smrchek, J., Kuchnicki, T., Francois, D., 2001. Proposal to update non-target plant toxicity testing under NAFTA, United States Environmental Protection Agency. Available from: http://www.epa.gov/scipoly/sap/ 2001/june/sap14.pdf (accessed on January 2002). Duarte, C.M., Roff, D.A., 1991. Architectural and life history constraints to submersed macrophyte community structure: a simulation study. Aquat. Bot. 42, 15 /29. Egli, C., Thuer, M., Suter, D., Cook, A.M., Leisinger, T., 1989. Monochloro- and dichloroacetic acids as carbon and energy sources for a stable, methanogenic mixed culture. Arch. Microbiol. 152, 218 /223. Ellis, D., Mabury, S., 2000. The aqueous photolysis of TFM and related trifluoromethylphenols. An alternate source of trifluoroacetic acid in the environment. Environ. Sci. Technol. 34, 632 /637. Ellis, D., Hanson, M., Sibley, P., Shahid, T., Fineberg, N., Muir, D., Solomon, K., Mabury, S., 2001. The aqueous environmental fate of chloroacetic and trifluoroacetic acids. Chemosphere 42, 309 /318. Fairchild, J.F., Ruessler, D.S., Haverland, P.S., Carlson, A.R., 1997. Comparative sensitivity of Selenastrum capricornutum and Lemna minor to sixteen herbicides. Arch. Environ. Contam. Toxicol. 32, 353 /357. Fletcher, J.S., Forrest, J.L., McFarlane, J.C., 1990. Influence of greenhouse versus field testing and taxonomic differences on plant sensitivity to chemical treatment. Environ. Toxicol. Chem. 9, 769 /776. Frank, H., Scholl, H., Renschen, D., Rether, B., Laouedj, A., Norokorpi, Y., 1994. Haloacetic acids, phytotoxic secondary air pollutants. Environ. Sci. Pollut. Res. Int. 1, 4 /14. Greenberg, B.M., Huang, X.-D., Dixon, D.G., 1992. Applications of the higher aquatic plant Lemna gibba for ecotoxicological risk assessment. J. Aquat. Ecosyst. Health 1, 147 / 155. Haiber, G., Jacob, G., Niedan, V., Nkusi, G., Scholer, H.F., 1996. The occurrence of trichloroacetic acid (TCAA)indications of a natural production. Chemosphere 33, 839 /849. Hanson, M.L., Solomon, K.R. A new technique for estimating thresholds of toxicity in ecological risk assessment. Environ. Sci. Technol. (in press). Hanson, M.L., Sibley, P.K., Solomon, K.R., Mabury, S.A., Muir, D.C.G., 2001. Chlorodifluoroacetic acid (CDFA) fate and toxicity to the macrophytes Lemna gibba , Myriophyllum spicatum and Myriophyllum sibiricum in aquatic microcosms. Environ. Toxicol. Chem. 20, 2758 /2767. Hanson, M.L., Sibley, P.K., Ellis, D., Fineberg, N., Solomon, K.R., Mabury, S.A., Muir, D.C.G., 2002. Trichloroacetic acid (TCA) fate and toxicity to the macrophytes Myriophyllum spicatum and Myriophyllum sibiricum under field conditions. Aquat. Toxicol. 56, 241 /255. Hanson, M.L., Sibley, P.K., Solomon, K.R., Mabury, S.A., Muir, D.C.G., 2002. Trichloroacetic acid (TCA) and trifluoroacetic acid (TFA) mixture toxicity to the macrophytes Myriophyllum spicatum and Myriophyllum sibiricum in aquatic microcosms. Sci. Total Environ. 285, 247 /259.

Hanson, M.L., Sibley, P.K., Mabury, S.A., Muir, D.C.G., Solomon, K.R. Field level evaluation and probabilistic risk assessment of the toxicity of dichloroacetic acid (DCA) to the aquatic macrophytes Lemna gibba , Myriophyllum spicatum and Myriophyllum sibiricum . Ecotox. Environ. Saf. (submitted for publication). Hashimoto, S., Azuma, T., Otsuki, A., 1998. Distribution, sources, and stability of haloacetic acids in Tokyo Bay, Japan. Environ. Sci. Technol. 17, 798 /805. Hirsch, P., Alexander, M., 1960. Microbial decomposition of halogenated propionic and acetic acids. Can. J. Microbiol. 6, 241 /249. Keller, D.A., Roe, D.C., Lieder, P.H., 1996. Fluoroacetatemediated toxicity of fluorinated ethanes. Fundam. Appl. Toxicol. 30, 213 /219. Kenaga, E.E., 1978. Test organisms and methods useful for early assessment of acute toxicity of chemicals. Environ. Sci. Technol. 12, 1322 /1329. Kenaga, E.E., 1979. Acute and chronic toxicity of 75 pesticides to various animal species. Down Earth 35, 25 /31. Juuti, S., Norokorpi, J.S., Ruuskanen, J., 1995. Trichloroacetic acid (TCA) in pine needles caused by atmospheric emissions of kraft pulp mills. Chemosphere 30, 439 /448. Lewis, M.A., 1995. Algae and vascular plants. In: Rand, G.M. (Ed.), Fundamentals of Aquatic Toxicology: Effects, Environmental Fate, and Risk Assessment, 2nd ed.. Taylor & Francis, Washington, DC, pp. 135 /169. Liber, K., Kaushik, N.K., Solomon, K.R., Carey, J.H., 1992. Experimental designs for aquatic mesocosm studies: a comparison of the ‘‘ANOVA’’ and ‘‘Regression’’ design for assessing the impact of tetrachlorophenol on zooplankton populations in limnocorals. Environ. Toxicol. Chem. 11, 61 /77. Marcus, B.A., 1980. Relationship between light intensity and chlorophyll content in Myriophyllum spicatum L. in Canadice Lake (New York). Aquat. Bot. 9, 169 / 172. Martin, J.W., Franklin, J., Hanson, M.L., Mabury, S.A., Ellis, D.A., Scott, B.F., Muir, D.C.G., Solomon, K.R., 2000. Detection of chlorodifluoroacetic acid in precipitation: a possible product of fluorocarbon degradation. Environ. Sci. Technol. 34, 274 /281. Mu¨ller, S.R., Zweifel, H.-R., Kinnison, D.J., Jacobsen, J.A., Meier, M.A., Ulrich, M.M., Schwarzenbach, R.P., 1996. Occurrence, sources, and fate of trichloroacetic acid in Swiss waters. Environ. Toxicol. Chem. 15, 1470 /1478. Norokorpi, Y., Frank, H., 1995. Trichloroacetic acid as a phytotoxic air pollutant and the dose /response relationship for defoliation of Scots pine. Sci. Total Environ. 161, 459 / 463. OECD, 1996. Screening information data sheet for high production volume chemicals */OECD initial assessment, monochloroacetic acid and sodium monochloroacetate, Processed by UNEP Organisation for Economic Cooperation and Development OECD Existing Chemicals Programme, Vol. 3(3), 1996.

M.L. Hanson et al. / Aquatic Toxicology 61 (2002) 251 /273 Pires, A.M., Branco, J.A., Mendonca, E., 2002. Models for estimation of a ‘‘no effect concentration’’. Environmetrics 13, 15 /27. Porra, R.J., Thompson, W.A., Kriedemann, P.E., 1989. Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochem. Biophys. Acta 975, 384 /394. Reimann, S., Grob, K., Frank, H., 1996. Chloroacetic acids in rainwater. Environ. Sci. Technol. 30, 2340 /2344. Ricker, W.E., 1973. Linear regressions in fisheries research. J. Fish. Res. Board Can. 30, 409 /434. Ro¨mpp, A., Klemm, O., Fricke, W., Frank, H., 2001. Haloacetates in fog and rain. Environ. Sci. Technol. 35, 1294 /1298. Roshon, R.D., McCann, J.H., Thompson, D.G., Stephenson, G.R., 1999. Effects of seven forestry management herbicides on Myriophyllum sibiricum , as compared with other nontarget aquatic organisms. Can. J. Forest Res. 29, 1158 / 1169. Sanderson, H., 2001. Replication of micro/mesocosm studies. State-of-the-art review. Environ. Sci. Pollut. Res. 8, 43 /50. Scott, B.F., MacTavish, D., Spencer, C., Strachan, W.M.J., Muir, D.C.G., 2000. Haloacetic acids in Canadian lake waters and precipitation. Environ. Sci. Technol. 34, 4266 / 4272. Scott, B.F., Spencer, C., Marvin, C.H., MacTavish, D., Muir, D.C.G., 2002. Distribution of haloacetic acids in the water column of the Laurentian Great Lakes and Lake Malawi. Environ. Sci. Technol. 36, 1893 /1898. Shaw, J.L., Kennedy, J.H., 1996. The use of aquatic mesocosm studies in risk assessment. Environ. Toxicol. Chem. 15, 605 /607. Solomon, K.R., Smith, K., Stephenson, G.L., 1982. Depth integrating samplers for use in limnocorrals. Hydrobiologia 94, 71 /75. Stephenson, G.L., Koper, N., Atkinson, G.F., Solomon, K.R., Scroggins, R.P., 2000. Use of nonlinear regression techniques for describing concentration /response relationships of plant species exposed to contaminated site soils. Environ. Toxicol. Chem. 19, 2968 /2981.

273

Suter, G.W., II, 1995. Introduction to ecological risk assessment for aquatic toxic effects. In: Rand, G.M. (Ed.), Fundamentals of Aquatic Toxicology, Effects, Environmental Fate and Risk Assessment, 2nd ed.. Taylor & Francis, Washington, DC, pp. 803 /816. Suter, G.W., Rosen, A.E., Linder, E., Parkhurst, D.F., 1987. Endpoints for responses of fish to chronic toxic exposures. Environ. Toxicol. Chem. 6, 793 /809. Uden, P.C., Miller, J.W., 1983. Chlorinated acids and chloral in drinking water. J. Am. Water Works Assoc. 75, 524 /527. Van der Hoeven, N., 1997. How to measure no effect. II. Statistical aspects of NOEC and ECx estimates. Environmetrics 8, 255 /261. Van der Hoeven, N., Noppert, F., Leopold, A., 1997. How to measure no effect. I. Towards a new measure of chronic toxicity in ecotoxicology. Introduction and workshop results. Environmetrics 8, 241 /248. Van Ginkel, C.G., 1996. Complete degradation of xenobiotic surfactants by consortia of aerobic microorganisms. Biodegradation 7, 151 /164. Wiggins, B.A., Jones, S.H., Alexander, M., 1987. Explanations for the acclimation period preceding the mineralization of organic chemicals in aquatic environments. Appl. Environ. Microbiol. 53, 791 /796. Wilson, R.I., Mabury, S.A., 2000. The photodegradation of metalochlor: isolation, identification and quantification of MCA. J. Agric. Food Chem. 48, 944 /950. Wujcik, C.E., Zehavi, D., Seiber, J.N., 1998. Trifluoroacetic acid levels in 1994 /1996 fog, rain, snow and surface waters from California and Nevada. Chemosphere 36, 1233 /1245. Wujcik, C.E., Cahill, T.M., Seiber, J.N., 1999. Determination of trifluoroacetic acid in 1996 /1997 precipitation and surface waters California and Nevada. Environ. Sci. Technol. 33, 1747 /1751. Yu, P., Welander, T., 1995. Growth of an aerobic bacterium with trichloroacetic acid as the sole source of energy and carbon. Appl. Microbiol. Biotechnol. 42, 769 /774. Zar, J.H., 1984. Comparing simple linear regression equations. In: Biostatistical Analysis. Prentice Hall, Englewood Cliffs, NJ, pp. 292 /305.