Commission Regulation (EC) No 761/2009Show full title

2. DATA AND REPORTING U.K.

2.1. DOUBLING TIME U.K.

To determine the doubling time (T_d) of frond number and adherence to this validity criterion by the study (section 1.6), the following formula is used with data obtained from the control vessels:

T_d = ln 2/μ

where μ is the average specific growth rate determined as described in first and second paragraph in section 2.2.1.

2.2. RESPONSE VARIABLES U.K.

The purpose of the test is to determine the effects of the test substance on the vegetative growth of Lemna. This Testing Method describes two response variables, as member countries have different preferences and regulatory needs. In order for the test results to be acceptable in all member countries, the effects should be evaluated using both response variables (a) and (b) described below. U.K.

(a)

Average specific growth rate: this response variable is calculated on the basis of changes in the logarithms of frond numbers, and in addition, on the basis of changes in the logarithms of another measurement parameter (total frond area, dry weight or fresh weight) over time (expressed per day) in the controls and each treatment group. It is sometimes referred to as relative growth rate (15).

(b)

Yield: this response variable is calculated on the basis of changes in frond number, and in addition, on the basis of changes in another measurement parameter (total frond area, dry weight or fresh weight) in the controls and in each treatment group until the end of the test.

It should be noted that toxicity values calculated by using these two response variables are not comparable and this difference must be recognised when using the results of the test. EC_x values based upon average specific growth rate (E_rC_x) will generally be higher than results based upon yield (E_yC_x) if the test conditions of this Testing Method are adhered to, due to the mathematical basis of the respective approaches. This should not be interpreted as a difference in sensitivity between the two response variables, simply that the values are different mathematically. The concept of average specific growth rate is based on the general exponential growth pattern of duckweed in non-limited cultures, where toxicity is estimated on the basis of the effects on the growth rate, without being dependent on the absolute level of the specific growth rate of the control, slope of the concentration-response curve or on test duration. In contrast, results based upon the yield response variable are dependent upon all these other variables. E_yC_x is dependent on the specific growth rate of the duckweed species used in each test and on the maximum specific growth rate that can vary between species and even different clones. This response variable should not be used for comparing the sensitivity to toxicants among duckweed species or even different clones. While the use of average specific growth rate for estimating toxicity is scientifically preferred, toxicity estimates based on yield are also included in this Testing Method to satisfy current regulatory requirements in some countries.

Toxicity estimates should be based on frond number and on one additional measurement variable (total frond area, dry weight or fresh weight), because some substances may affect other measurement variables much more than the frond number. This effect would not be detected by calculating frond number only.

The number of fronds as well as any other recorded measurement variable, i.e. total frond area, dry weight or fresh weight, are tabulated together with the concentrations of the test substance for each measurement occasion. Subsequent data analysis e.g. to estimate a LOEC, NOEC or EC_x should be based on the values for the individual replicates and not calculated means for each treatment group.

2.2.1. Average specific growth rate U.K.

The average specific growth rate for a specific period is calculated as the logarithmic increase in the growth variables — rond numbers and one other measurement variable (total frond area, dry weight or fresh weight) — using the formula below for each replicate of control and treatments:

where:

— μ_i-j:

average specific growth rate from time i to j

— N_i:

measurement variable in the test or control vessel at time i

— N_j:

measurement variable in the test or control vessel at time j

— t:

time period from i to j

For each treatment group and control group, calculate a mean value for the growth rate along with variance estimates.

The average specific growth rate should be calculated for the entire test period (time ‘i’ in the above formula is the beginning of the test and time ‘j’ is the end of the test). For each test concentration and control, calculate a mean value for average specific growth rate along with the variance estimates. In addition, the section-by-section growth rate should be assessed in order to evaluate effects of the test substance occurring during the exposure period (e.g. by inspecting log-transformed growth curves). Substantial differences between the section-by-section growth rate and the average growth rate indicate deviation from constant exponential growth and that close examination of the growth curves is warranted. In this case, a conservative approach would be to compare specific growth rates from treated cultures during the time period of maximum inhibition to those for controls during the same time period.

Percentage inhibition of growth rate (I_r) may then be calculated for each test concentration (treatment group) according to the following formula:

where:

— % I_r:

percentage inhibition in average specific growth rate

— μ_C:

mean value for μ in the control

— μ_T:

mean value for μ in the treatment group

2.2.2. Yield U.K.

Effects on yield are determined on the basis of two measurement variables, frond number and one other measurement variable (total frond area, dry weight or fresh weight) present in each test vessel at the start and end of the test. For dry weight or fresh weight, the starting biomass is determined on the basis of a sample of fronds taken from the same batch used to inoculate the test vessels (see second paragraph in section 1.7.3). For each test concentration and control, calculate a mean value for yield along with variance estimates. The mean percentage inhibition in yield (% I_y) may be calculated for each treatment group as follows:

where:

— % I_y:

percentage reduction in yield

— b_C:

final biomass minus starting biomass for the control group

— b_T:

final biomass minus starting biomass in the treatment group

2.2.3. Plotting concentration-response curves U.K.

Concentration-response curves relating mean percentage inhibition of the response variable (I_r, or I_y calculated as shown in the last paragraph of section 2.2.1 or in section 2.2.2) and the log concentration of the test substance should be plotted.

2.2.4. EC_x estimation U.K.

Estimates of the EC_x (e.g. EC₅₀) should be based upon both average specific growth rate (E_rC_x) and yield (E_yC_x), each of which should in turn be based upon frond number and one additional measurement variable (total frond area, dry weight, or fresh weight). This is because there are test substances that impact frond number and other measurement variables differently. The desired toxicity parameters are therefore four EC_x values for each inhibition level x calculated: E_rC_x (frond number); E_rC_x (total frond area, dry weight, or fresh weight); E_yC_x (frond number); and E_yC_x (total frond area, dry weight, or fresh weight).

2.3. STATISTICAL PROCEDURES U.K.

The aim is to obtain a quantitative concentration-response relationship by regression analysis. It is possible to use a weighted linear regression after having performed a linearising transformation of the response data — for instance into probit or logit or Weibull units (16), but non-linear regression procedures are preferred techniques that better handle unavoidable data irregularities and deviations from smooth distributions. Approaching either zero or total inhibition, such irregularities may be magnified by the transformation, interfering with the analysis (16). It should be noted that standard methods of analysis using probit, logit, or Weibull transforms are intended for use on quantal (e.g. mortality or survival) data, and must be modified to accommodate growth rate or yield data. Specific procedures for determination of EC_x values from continuous data can be found in (17), (18), and (19).

For each response variable to be analysed, use the concentration-response relationship to calculate point estimates of EC_x values. When possible, the 95 % confidence limits for each estimate should be determined. Goodness of fit of the response data to the regression model should be assessed either graphically or statistically. Regression analysis should be performed using individual replicate responses, not treatment group means.

EC₅₀ estimates and confidence limits may also be obtained using linear interpolation with bootstrapping (20), if available regression models/methods are unsuitable for the data.

For estimation of the LOEC and hence the NOEC, it is necessary to compare treatment means using analysis of variance (ANOVA) techniques. The mean for each concentration must then be compared with the control mean using an appropriate multiple comparison or trend test method. Dunnett’s or Williams’ test may be useful (21)(22)(23)(24). It is necessary to assess whether the ANOVA assumption of homogeneity of variance holds. This assessment may be performed graphically or by a formal test (25). Suitable tests are Levene’s or Bartlett’s. Failure to meet the assumption of homogeneity of variances can sometimes be corrected by logarithmic transformation of the data. If heterogeneity of variance is extreme and cannot be corrected by transformation, analysis by methods such as step-down Jonkheere trend tests should be considered. Additional guidance on determining the NOEC can be found in (19).

Recent scientific developments have led to a recommendation of abandoning the concept of NOEC and replacing it with regression based point estimates EC_x. An appropriate value for x has not been established for this Lemna test. However, a range of 10 to 20 % appears to be appropriate (depending on the response variable chosen), and preferably both the EC₁₀ and EC₂₀ should be reported.