ENVIRONMETRICS, VOL. 6, 155-163 (1995)

METABOLIC RESPONSES IN THE ASSESSMENT OF POLLUTION EFFECTS

ANA PIRES AND JOAO BRANCO Departmenr of Mathematics, IST, A v . Rovisco Pais. 1096 Lisboa, Portugal

AND

ANA PICADO AND ELSA MENDONCA ITAIINETI, Az. dos Lanieiros (Est. Paco Lumiar) 22, 1699 Lisboa, Poriugal

SUMMARY Adenylate nucleotide concentrations in the polychaete Lanice conchilega were measured and the index ade- nylate energy charge (AEC) was calculated at different times of the year. The organisms were sampled at three sites in the Sad0 River estuary, two of them located near the outfall of a pulp mill effluent and a third one in the south bank, considered as reference. A statistical analysis was conducted on the resulting obser- vations in order to evaluate the site influence. Significant differences were found on the AEC values between more and less polluted sites. Another index was built up using principal components after adequately trans- forming the initial variables. A covariance model with the variable time as covariate was fitted, using GLIM, to see how this index can be used to distinguish between sites. Finally, robust procedures were applied in order to obtain more robust fits.

KEY WORDS ecotoxicology; adenylate energy charge; analysis of variance; principal components; robust procedures

1. INTRODUCTION

To assess the impact of pollutants on living systems is a major objective of ecotoxicology. However, this is a difficult task due to the complexity and multiplicity of living organisms at all levels. The adenylate energy charge (AEC) is a general index which has been applied to biological systems in general to evaluate the physiological state of organisms. It offers the advantage of unicity of the extraction and measurement methods.’-3

The AEC index is computed by the following formula

AEC = (ATP + iADP)/(ATP + ADP + AMP)

where ATP, ADP and AMP are the molar concentrations of adenosine triphosphate, adenosine diphosphate and adenosine monophosphate. The balance between these concentrations is thought to measure the physiological state of individuals. A rather simplified explanation for the particular way used to measure the above mentioned balance in equation (1) is that when an organism needs to support worse conditions, ATP is ‘changed’ into ADP and ADP into AMP, releasing energy. Thus one can say, indirectly, that the greater the concentrations in ADP and

CCC 1 180-4009/95/020155-09 0 1995 by John Wiley & Sons, Ltd.

Received January 1993 Revised December 1993

156 A. PIRES ET AL

Figure 1. Sampling stations of Lanice conchilegu in Sad0 estuary

AMP the worse the physiological conditions which may have been affected by the environmental conditions because man-induced modifications of the environment are likely to stress organisms.

From equation (1) it is obvious that the AEC can take values between 0 and 1 (0 if there is only AMP, 1 if there is only ATP), although the extremes are never attained in practice. According to previous studies' one can consider the following categories of conditions:

optimal conditions: 0.8 < AEC < 0.9 stress conditions: 0.6 < AEC < 0.7 lethal conditions: AEC < 0.5

Thus the smaller the value of the index the worse the conditions. In this paper we will discuss the analysis of the data obtained from an experiment that was

conducted in order to study how the AEC index* can distinguish between sites supposedly charged with different degrees of p o l l u t i ~ n . ~ ' ~ It took place in the Sad0 River estuary in Portugal. Figure 1 shows the location of the sampling stations. Stations 1 and 2 are located in an intertidal zone of the north side of the estuary near the outfall of a paper mill effluent while station 3 is located on the south side. Other industries are also situated on the north bank, namely a shipyard, a power station and a chemical complex manufacturing pesticides and chemical fertilizers. It is therefore assumed that stations 1 and 2 are on a polluted area whereas station 3 is considered as reference. Although the observed effects may result from a compound pollution, due to the proximity to the paper mill, the observations from stations 1 and 2 should reflect mainly the influence of the paper mill effluent.

The organism chosen was the polychaete Lanice conchilega which is a kind of worm common in these waters and easy to catch. Six of them were taken at each of the three sites and at each of

*Other metabolic indexes, including some based on the enzimatic activity, were also studied4 but the results on the AEC turned out to be the most interesting.

ASSESSMENT OF POLLUTION EFFECTS 157

Table I. Mean & s.e.m. and range of AEC for each site and date

Site August 88 October 88 January 89 April 89 July 89 October 89

1 0.64 f 0.03 0.71 f 0.02 - - 0.63 i 0.01 0.67 f 0.01 (0.49; 0.71) (0.64; 0.74) ~ - (0.59; 0.66) (0.65; 0.69)

2 0.70 f 0.02 0.74 f 0.02 0.64 f 0.03 0.63 3I 0.03 0.63 i 0.01 0.74 f 0.00 (0.60; 0.75) (0.67; 0.80) (0.56; 0.76) (0.56; 0.76) (0.50; 0.68) (0.72; 0.75)

3 0.75 f 0.01 0.80 3I 0.00 0.77 & 0.02 068 i 0.02 0.71 f 0.01 0.74 i 0.03 (0.71; 0.78) (0.79; 0.81) (0.70; 0.84) (0.60; 0.76) (0.67; 0.77) (0.70; 0.79)

six different times between August 88 and October 89. The variables measured were the ATP, ADP and AMP and from them the AEC was computed. Some summary statistics on AEC for each site and date are shown in Table I. We can see from these values that for a fixed date the mean of AEC is smaller on sites 1 and 2 than on site 3. The differences between the AEC means for sites 1 and 3 are particularly significant (Mann-Whitney test, p < 0.05). According to the classification given before it is possible to say that on sites 1 and 2 the organisms are most of the time under stress conditions while on site 3 they are closer to the optimal.

We shall note that it was impossible to find Lanice in site 1 on two occasions, January and April 89. This is possibly due to the joint effect of higher pollution and of the species biology; it is known’ that the reproduction season of Lanice occurs around this time of the year, causing organisms to be more sensitive.

In the next section we show by means of a very simple analysis of variance that location is a significant factor for the AEC index. A model is built for this index using a function of time as covariate. In Section 3 we propose another index based on principal components and conclude that it can also be used to distinguish between sites.

It is accepted that the kind of experiment performed as well as the process of measurement used are likely to generate some outliers. Moreover, AEC is a ratio variable and therefore expected not to be statistically well (normally) behaved. Because of these reasons the previous steps were repeated using robust procedures. This is described in Section 4. Some concluding remarks are made in Section 5.

2. ANALYSIS OF VARIANCE FOR AEC

We considered site and time as factors with 3 and 6 levels, respectively, and used GLIM to obtain the analysis of variance table shown in Table 11. There is a significant difference between sites and between times but no significant interaction. The analysis of variance table for the model without interaction may be easily obtained from Table 11. In conclusion, the AEC index can be used to distinguish between sites but it is necessary to take time into account.

Table 11. ANOVA table for AEC

Source Seq SS d.f. MS F-ratio p

Site 0.1012 2 0.05060 18.74 0.000 Time 0.1242 5 0.02484 8.94 0.000 Interaction 0.0282 8 0.00351 1.26 0.276 Residual 0.2136 79 0.00270

Total 0.4672 94 0.00497

158 A. PIRES ET AL.

Table 111. ANOVA table for the continuous time model

Source Seq SS Adj SS d.f. Adj MS F-ratio P

Time 0.0857 0.1075 2 0.05375 18.73 0.000 Site 0.1230 0.1230 2 0.06148 2 1.40 0.000 Residual 0.2585 0.2585 90 0.00287

Total 0.4672 94

We noted that the values of the AEC show some periodicity along the year. Thus, instead of considering time as a factor, it was included in the analysis as a continuous variable with a sinusoidal behaviour. This reduces the number of parameters with a small increase in the residual mean square and allows one to predict results for other months. The fitted model is

AEC = 0.6383 - 00382sin(;t) + 0 . 0 3 5 7 ~ 0 ~ - t + 0.0328 site (2) + 0.0921 site (3) + E (2)

where t is time in months, site ( 2 ) and site (3) are indicator variables for sites 2 and 3, respectively, and E is the error term accounting for biological variability among organisms and measurement errors. The analysis of variance table for this model is shown in Table 111. The coefficient of determination R2 is 0.45.

(: 1

Equation (2) deserves a few remarks:

The differences between the AEC at different sites remain constant along the year, this of course due to the lack of interaction. The model is thus composed by three parallel curves, one for each site. The fitted curves together with the observed data are shown in Figure 2. The distance between curves 1 and 2 is 0.0328 and between curves 3 and 1 is 0.0921. The three differences are all significant ( p 60.029). According to the classification of the conditions presented in Section I only the second difference may indicate a distinct category of conditions.

0.85

0.8

0.75

0.7

4 0.65

0.6

0 ’55 i O5 1

0

0 0

: - 0.45

0 1 4 5 6 7 8 2 3 9 10

Month

Figure 2. Observed data and fitted curves according to equation (2)

ASSESSMENT OF POLLUTION EFFECTS 159

Table IV. ANOVA table for the statistical index

Source Seq SS d.f. MS F-ratio P

Site 1.547 2 3.774 4.49 0.014 Time 22.730 5 4.546 5.41 0.000 Interaction 4.30 8 0.579 0.69 0.70 1 Residual 66.413 79 0.841

Total 101.320 94 1.078

The annual variation for each curve is of the same order of magnitude as the between site variation. The curves oscillate about the mean with an amplitude of 2 x 0.052. The maximum value of each curve occurs around October-November while the minimum occurs around April-May. This can be a reflection of the life cycle of the species, which, as mentioned in Section 1, has a reproduction season just before the minimum. The model may also produce an explanation for not having found organisms on site 1 in January and April. The expected values of AEC under the model at site 1 on those occasions are 0.650 and 0.587. Although these values do not indicate lethal conditions, the second in particular may explain, if not the impossibility, the great difficulty of finding live organisms.

In conclusion this model has some interesting features and it would be important to derive similar models for analogous situations and compare them.

We shall note that for both models an inspection of the residuals showed that the homoscedascity and normality assumptions are not inadequate, although there are some large values suggesting the use of a robust procedure. This will be done in Section 4.

3. A STATISTICAL INDEX

From the previous section we concluded that the AEC index performs well, but we would like to ask the question, do the data themselves point to an index and will it have some meaning?

When dealing with multivariate data, a common tool employed to derive indexes is principal component analysis.’ In this case it can be seen that the three variables (ATP, ADP, AMP) have very skewed distributions, so a transformation was applied first. After trying several members of the Box-Cox transformation family, the log transformation was choosen based on normal Q-Q

The results of the principal component analysis on the transformed variables are shown below: plots.

log (ATP) log (ADP) log (AMP)

xi: 1.0666 0.2 1424 0.055439 per: 79.8% 16.0% 4.1 Yo

PC 1 PC2 PC3 log (ATP) 0’36859 -0.57551 0.73002

log (AMP) 0.80362 0.59201 0.06097 log (ADP) 0.46727 -0.56419 -0.68070

The first principal component, which explains about 80 per cent of the total variance, will be

160 A. PIRES ET AL.

Table V. ANOVA table for the continuous time model of the statistical index

Source Seq SS Adj SS d.f. Adj MS F-ratio P

Time 13.32 16.28 2 8.14 9.93 0.000 Site 13.82 13.82 2 6.91 8.39 0.001 Residual 74.18 74.18 90 0.82

Total 101.32 94

regarded as our index:

index E 0.37 log (ATP) + 0.47 log (ADP) + 0.80 log (AMP). (3)

We note that this is a weighted mean of the log-concentrations with weights increasing from ATP to AMP. It is thus expected that it can measure the balance between the adenylate nucleotide concentrations and that its value increases as the physiological conditions get worse.

The analysis of variance for this index shows that it can in fact distinguish between sites and times with no significant interaction (Table IV). Annual periodic behaviour was noted and again a model with sinusoidal covariates was fitted:

index = 2.61 + 0.715 sin - t - 0.5804 site (2) - 1.052 site (3) + t. (4) (: 1 The corresponding analysis of variance table is shown in Table V. R2 is 0.26. We note the following:

(i) The cosine function was removed from the model because its coefficient turned out to be

(ii) The model is, like that for the AEC index, formed by three parallel curves. (iii) The higher curve corresponds to the most polluted site (1) and the lower to the least

(iv) The maximum value of each curve occurs in March and the minimum in September. This

In conclusion, it was possible to find a meaningful index that can distinguish between sites. This was accomplished using only the data, without any biochemical considerations other than the choice of the initial variables.

Even though the second and third principal components have much smaller importance than the first, it is worthwhile looking at them. We note that these components represent kinds of contrasts between the adenylate nucleotide log concentrations: the second between AMP and an average of ATP and ADP and the third only between ATP and ADP. Incidentally, we shall mention that the third component is proportional to the logarithm of ATP/ADP, a ratio that is important in the evaluation of the state of organism^.^

Analysis of variance for the second principal component showed that site is a significant factor ( p = 0.002) but time is not ( p = 0.356). However, it can not distinguish between sites 1 and 2 , only between these two and site 3. For the third principal component both factors are significant but time ( p = 0.000) is more significant than site ( p = 0.018).

It seems that the second principal component could be a better index than the first because it can compare sites for different times but is also much less sensitive and has a greater dispersion.

non-significant ( p = 0.283).

polluted site (3), as was anticipated.

agrees better with the reproduction season.

ASSESSMENT OF POLLUTION EFFECTS 161

4. ROBUST ANALYSIS

4.1. Methods

Since the raw data reveal some outlying points it seemed appropriate to apply robust versions of analysis of variance and principal components analysis. This will give more reliable results in the sense that they will tell us what the majority of the data say and will not be influenced by only a small number of observation^.^

The regression approach to the analysis of variance with indicator variables allows the use of robust regression analysis methods. Among all the available methods we have chosen the reweighted least squares based on the least median of squares (RLS based on LMS) which is implemented in the program PROGRESS."

It is well known that the least squares method which estimates the parameters of a regression model by minimizing the r!, where ri are the residuals, is not robust.' On the other hand, the LMS which minimizes med r f leads to very robust estimators but with low efficiency. Therefore, the RLS based on LMS was proposed. The estimates are obtained by the minimiza- tion of Cr=l wir: , where the weights w; are determined by the results of the LMS." For the robust principal component analysis a method proposed by Branco and Pires" was used.

4.2. Results

The robust analysis of variance for the AEC led to the same conclusions as the classical analysis: both factors are significant without interaction. However, the fitting of the continuous time model gave a somewhat different result:

AEC = 0.6103 - 0.0823sin(it) + 0.0183cos(il) + 0.0396site(2) +0.0821 site(3) + E (5)

and R2 is now 0.73. This model better fits the majority of the data than the model obtained previously. The mean level of the AEC for each site is smaller and the annual variation is different: the minimum occurs between March and April, the maximum occurs between September and October and the amplitude is 2 x 0.084, approximately twice the between-site variation. Also the expected values of AEC at site 1 on January and April are now 0.585 and 0.530, which give a more convincing explanation for not having found live organisms than that given by the first model.

Comparing the classical and the robust model we see that the second looks better. Of course this may not always be the case, as we will soon see. Robust principal component analysis gave the following results:

log (ATP) log (ADP) log (AMP)

A;: 0.6261 1 0.1 1746 0.049992 per: 78.9% 14.8% 6.3%

PC 1 PC2 PC3 log (ATP) 0.42659 -0.76218 0.48694

log (AMP) 0.70745 0.6 1662 0.34539 log (ADP) 0.56351 -0.19714 -0'80224

This leads to another index with the same structure but weights slightly different from those of

162 A. PIRES ET A L

the previous index and more similar to each other:

index* N 0.43 log(ATP) + 0.56 log(ADP) + 0.71 log(AMP). (6)

The robust analysis of variance showed again significance of both factors and no interaction. The fitted model is:

index* = 2.37 + 0.31 sin - t - 0.51 site (2) - 0.59 site (3) + 6 (7) G 1 with R2 = 0.15. Comparison of this model and the one obtained in Section 3 must be made carefully because the response variables are different. We can say, nevertheless, that we would be happier with the classical results, not only because R2 is better but also because the differences between the coefficients are more marked.

Two conclusions can be drawn from this application of robust methods.

1. There were far more outliers in the data than were expected; from the analysis of the residuals of the least squares method only two observations could be considered as outliers but the LMS-based diagnostics identified 15 outliers for the AEC index and 7 for the statistical index* (which were given 0 weight in the RLS). One explanation for this fact is the so-called masking effect.

2. The outlying observations may distort an analysis in two directions; in the case of the AEC index they were obscuring a better fit but in the case of the statistical index they were forcing the fit and gave misleading results that reflected mainly the strong effect of a small number of observations.

To conclude this section we will briefly refer to the second and third robust principal components. Like the classical components these can also be interpreted as contrasts between the log-concentrations. The third robust principal component is approximately proportional to the logarithm of (ATP) x (AMP)/(ADP)2 which is the equilibrium constant of one of the chemical reactions that regulate the AEC and the ATP/ADP ratio.4 The robust analysis of variance for the two robust components showed that time and site factors are both significant. This is another reason why the second principal component is not a better index than the first (see the end of Section 3). The conclusion that time is not significant was invalid.

5. CONCLUSIONS

We fitted a model to the AEC data, which, allowing for variation with time, shows that this index can reveal the effects of different degrees of pollution on living organisms. Our results on Lanice conchilega reinforce the opinion that the AEC index is an interesting in situ diagnostic tool.

Using a statistical method we derived another index that can also distinguish between places with different degrees of pollution. The formula obtained applies only to Lanice conchilega but the method is a general one and may be used to derive other biomarkers.

The comparison of the two indexes is not straightforward and we think that more data and different experiments are needed before a conclusion can be reached. However, if we look at the R2 and p-values we see that they indicate a superior precision, for the AEC index.

Finally, with the use of robust procedures, we have seen how sometimes our results can be influenced by a small number of observations and how this can distort our conclusions. We therefore strongly advise the routine use of those procedures.

ASSESSMENT OF POLLUTION EFFECTS 163

ACKNOWLEDGEMENTS

The data used in this work are part of the project ‘Ecologic disturbance through waste disposal’ jointly supported by JNICT and British Council. The authors wish t o acknowledge the referees for their helpful comments and suggestions.

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