This report not t o be a-uoted without prior reference to the Councilx

International Council for the Exploration of the Sea

C.M. 1991lPOLL: 2 Ref. MEQC + Statistics Committee

Report of the Working Group on Statistical Aspects of Trend Monitoring

Copenhagen, 11-15 March 1991

This document is a report of a working group of the International Council for the Exploration of the Sea and does not necessarily represent the views of the Council. Therefore it should not be quoted without consultation with the General Secretary.

'General Secretary ICES Palaegade 2 - 4 D K - 1 2 6 1 Copenhagen K Denmark

T A B L E O F C O N T E N T S

OPENING OF THE MEETING AND ORGANIZATION OF WORK . . . . . . . . . . . . 1

ADOPTION OF THE AGENDA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

WGSATMTERMSOFREFERENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

SPECIFIC TASKS FOR THE 1991 MEETING OF WGSATM . . . . . . . . . . . . . . . . 1

REVIEW OF THE 1990 ACMP REPORT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

(ACMP) COMPLETION OF THE REPORT ON THE STUDY OF COOPERATIVE ICES MONITORING STUDIES PROGRAMME DATA FOR TRENDS IN CONTAMINANT LEVELS IN FISH LIVER AND BLUE MUSSELS . . . . . . . . . . . . . . . . . . . . . . . . 2

(ACMP) POOLING AND ECONOMIZING OF A SAMPLING PROGRAMME . . . . . 2

(ACMP) OPTIMAL HANDLING OF TEMPORAL TREND DATA CHARACTERIZED BY SIGNIFICANT INCONSISTENCY IN REGRESSION COEFFICIENTS AND VARIANCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

8.1 Modeling temporal trends in marine populations: pollutant concentrations as a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . function of length 3

8.2 OtherMethods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

(ACMP) STATISTICAL DESIGN OF SAMPLING TECHNIQUES FOR INVESTIGATING THE SPATIAL DISTRIBUTION OF CONTAMINANTS AND REVIEW THE APPLICATION OF SPATIAL-STATISTICAL TECHNIQUES (E.G. KRIGING) TO CMP DATA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

(ACMP) CONSIDERATION OF REVISIONS TO THE SAMPLING AND ANALYTICAL GUIDELINES FOR THE MEASUREMENT OF SPATIAL DISTRIBUTIONS AND TEMPORAL TRENDS OF CONTAMINANT LEVELS . . . . . . . . . . . . . . . . . . . . 5

(ACMP) REPORT ON METHODS FOR THE STATISTICAL ANALYSIS OF POOLED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DATA 5

12 . OTHER STATISTICAL METHODS FOR THE ANALYSIS OF TREND DATA . . . . . 6

12.1 Summarizing trends with locally-weighted running line smoothers . . . . . . . . . . . 6 12.2 The power of the ICES Cooperative Monitoring Programme to detect linear trends

and incidents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 12.3 Modeling trends with Arima models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1. OPENING OF THE MEETING AND ORGANIZATION OF WORK

The Chairman, Dr. J. Uthe, opened the meeting at 9:30 a.m. on 11 March. He welcomed all the members and apologized for the necessity of closing the meeting on Thursday, rather than Friday. This was due to airline schedules between Europe and Canada.

2. ADOPTION OF THE AGENDA

The Agenda is attached as Annex 1. There were a number of items added under "other business" and the agenda reflects these. A list of participants is at Annex 2.

3. WGSATM TERMS OF REFERENCE

The general terms of reference for the Working Group on Statistical Aspects of Trend Monitoring (WGSATM) are:

(a) to develop statistical protocols for the determination of temporal and spatial trends in the concentrations and distributions of contaminants in marine biota, sediments and sea water;

(b) to analyse data for the elucidation of temporal and spatial trends of contaminants in marine biota, sediments and sea water;

(c) to provide statistical advice with respect to other monitoring issues, as required;

(d) to liaise with the Statistics Committee as appropriate.

4. SPECIFIC TASKS FOR THE 1991 MEETING OF WGSATM

The specific tasks for the 1991 meeting of the Working Group on Statistical Aspects of Trend Monitoring were:

(C. Res 1990/2:27:9)

(a) to review and report on the results of further studies of the optimization of analytical costs through pooling;

(b) to consider and report on the optimal handling of temporal trend data characterized by significant inconsistency in regression coefficients and variances;

(c) to consider the statistical design of sampling techniques for investigating the spatial distribution of contaminants, and review the application of spatial statistical techniques (e.g. Kriging) to the assessment of data submitted under geographical distribution monitoring;

(d) to review and report on the results of an intersessional Study Group that will consider revisions to the sampling and analytical guidelines for the measurement of spatial distribution and temporal trends of contaminant levels;

(e) to review and report on methods to aid the statistical interpretation of temporal trend monitoring data;

(0 to review and report on methods for the statistical analysis of pooled data.

5 . REVIEW OF THE 1990 ACMP REPORT

The WG reviewed the relevant sections of the 1990 ACMP Report and agreed that it accurately reflected the deliberations and decisions taken by the WG at its 1990 meeting. Concern was expressed, however, regarding the status of the report on the use of seaweeds in contaminant monitoring. This report was considered at the 1990 meeting of the Working Group on Environmental Assessments and Monitoring Strategies who recommended that it, along with ancillary information, be passed along to the Marine Chemistry Working Group. WGSATM feels that there is inordinate delay in the progress of this and other documents relating to marine monitoring techniques. The WG feels that this is partially due to the lack of rather firm guidelines on the preparation, progress and final publication medium. ACMP is, therefore, requested to prepare guidelines for its Working Groups on the preparation, review and publication of these documents to drastically shorten the time from first reception of a manuscript by a Working Group to dissemination to outside agencies.

6. (ACMP) COMPLETION OF THE REPORT ON THE STUDY OF COOPERATIVE ICES MONITORING STUDIES PROGRAMME DATA FOR TRENDS IN CONTAMINANT LEVELS IN FISH LIVER AND BLUE MUSSELS

Simon Wilson informed that the final draft of the Report on the Study of Cooperative ICES Monitoring Studies Programme Data for Trends in Contaminant Levels in Fish Liver and Blue Mussels was essentially complete and had incorporated comments from the selected ACMP reviewers. The report was finalized during the meeting by Simon Wilson and a small group from WGSATM and would be published as soon as possible.

7. (ACMP) POOLING AND ECONOMIZING OF A SAMPLING PROGRAMME

Jaap van der Meer stated that he felt that the work he had included in the 1990 report was complete and that its application to a variety of monitoring programmes was relatively straight-forward. This led to a discussion regarding the adequacy of the present sampling guidelines and the frequency with which guidelines should be revised. The WG agreed that revision of guidelines should be undertaken only when there is a substantive reason for doing so, i.e., the previous guidelines have been shown to be in error. Casual revision can only lead to the monitoring agencies losing faith in guidelines and developing their own. This would lead to an increase in incompatibility of data sets from different agencies.

However, it may not be sufficiently clear to monitoring agencies that their individual sampling programmes may be fine tuned according to their own particular set of circumstances, yet still comply with the guidelines. For example, the mussel guidelines require "at least 3 pools of at least 20 animals", but allow for the numbers of pools or numbers of animals to be increased on the basis of knowledge about the relative values of environmental and analytical variability.

8. (ACMP) OPTIMAL HANDLING OF TEMPORAL TREND DATA CHARACTERIZED BY SIGNIFICANT INCONSISTENCY IN REGRESSION COEFFICIENTS AND VARIANCES

8.1 Modeling temporal trends in marine populations: pollutant concentrations as a function of length

Bill Warren described the utility of Kalman-filtering for assessing time trends in fish data, as exemplified by his preliminary study on trends in mercury levels in Atlantic cod. The Kalman filter provides a general mechanism for modeling situations where the parameters of a relationship are not constant, but evolve over time. A Kalman-filter model was therefore constructed for the mercury content of muscle tissue of cod (sampled in ICES rectangle 31F2) as a function of fish length for the years 1978-1989. Since plots of concentration vs. fish length appeared to be essentially linear, logarithmic transformation was not incorporated. For the Kalman-filter model, the slope of the relationship was assumed constant, apart from random variations, whereas the intercept was assumed to be made up of a drift plus random variation. Three estimates were presented:

1. The ordinary least-squares regression.

2. Regressions as given by the Kalman-filter model in which the regression in year t is based on the data for all years up to and including year t. Note that this involves two steps, (a) the prediction for year t based on the data prior to year t, and (b) the updating of this estimate with the data from year t.

3. The "smoothed" regressions that are based on the data for all years simultaneously. Confidence intervals on the estimated parameters are also calculated.

In years where the length data covered a relatively wide range, there is, in general, little difference between the three estimates. On the other hand, in years where the length data are poorly dispersed or in which there is seemingly an outlier in the concentrations, the Kalman filter estimates are such as to agree better with the overall trend. In particular, in one year the slope of the least-squares regression is negative, in contrast to positive for all other years. The data for that year, however, form a rather amorphous cluster so that, by the usual criteria, the Kalman-filter regression is but slightly inferior to the least-squares.

Overall, the slopes showed a decreasing tendency over time with a commensurate, albeit less well defined, increasing trend in the intercepts. This suggests that, in the Kalman-filter model, it may have been more appropriate to place the drift on the slope parameter.

The following points were noted from the discussion:

1. Inter-year differences in the regression parameters are to be expected. In the weighted procedure (Section 8.2, below) estimates of statistical parameters for individual years and their sampling errors are utilized, without modification, in the analysis of temporal variations and time trends. Under the Kalman filter, which is an empirical Bayes procedure, data from all years are used to adjust individual year estimates, under the assumption that the relationships in successive years are not completely independent. Whilst there is no means of determining whether either of these two approaches is "correct", each provides insight into the nature of the difference.

2. The interpretation would be superficially altered if logarithmic transformation of the concentrations were employed; the parameters take on a somewhat different meaning. The

method, per se, is in no way negated by this.

3. The approach down-weights outliers and provides a means of extracting order from seemingly diffuse data sets.

The WG recognized the utility of the technique for incorporating new years of data in the analysis, compared with the techniques currently used by the WG when analyzing CMP data which results in revisions of existing trend estimates when updating results. The technique should be widely applicable for updating the assessments of trends in both the CMP and other ongoing monitoring programmes on an annual or less frequent basis.

8.2 Other Methods

Dr. Misra presented a paper (Annex 3) reviewing the problem of comparing multiple means, in particular those associated with significant inconsistencies of regression slopes and variances. He noted that there are fundamentally sound reasons for these inequalities since biological variables are subject to polygenetic and environmental influences, in particular those which are associated with growth and are employed in trend analysis. He noted that, in the past, two statistical procedures for trend analysis had been offered, a univariate method of weighting and a multivariate method which employed principal components of biological variables in regression analysis. The WG noted that these deliberations throw much doubt on the usefulness of fish contaminant data as indicators of environmental quality. However, the WG also noted that it feels strongly that no single compartment measurement by itself can adequately reflect environmental quality and that combination studies, e.g. fish population data, input data, etc. when coupled with expert interpretation is the best indication of changes in environmental quality within a region. The WG further noted that there are always difficulties associated with any measurement, but that one should not get too hung up on the problems, particularly to the point where the intentions of the monitoring programmes are ignored. It was noted that trends in space and time have been demonstrated in a variety of compartments and species in those contaminant situations in which changes of a rather large magnitude have occurred, e.g. either through spills or regulation. It is not surprising that, as the magnitude of environmental change decreases, the importance of other factors in controlling concentrations within a compartment should become increasingly important.

9. (ACMP) STATISTICAL DESIGN OF SAMPLING TECHNIQUES FOR INVESTIGATING THE SPATIAL DISTRIBUTION OF CONTAMINANTS AND THE APPLICATION OF SPATIALSTATISTICAL TECHNIQUES (E.G. KRIGING) TO CMP DATA

In past years, WGSATM has concentrated on the analysis of time trends. However, samples have been collected for investigation of spatial distributions as well. Methods of analyses for assessing spatial distributions were considered in a paper by a sub-group (Annex 4) in which three methods of analysing spatially distributed data were considered: (1) comparison to pre-selected value(s), e.g. a maximum "normal" value to identify contaminated areas and assess their degree of contamination; (2) analysis of variance of the data for the spatial component; and (3) the use of various interpolation techniques for spatial information. Within the last group, the technique known as Kriging was employed by Bill Warren to investigate an ICES data set.

Bill Warren stated (and discussions in plenary concurred) that Kriging provides estimates of standard errors for the interpolated means and is potentially useful for other matrices (sediments and biota), although this has not been adequately tested. Bill Warren intends to continue testing Kriging

intersessionally, particularly in regard to its usefulness with other matrices.

The subgroup noted that, no matter which interpolation method is chosen, the results (isolines) must be carefully examined for discrepancies and adjusted accordingly if need be by experts familiar with the techniques employed and the behaviour of the determinand in question.

The further refinement of Kriging and its applicability to the analysis of CMP data sets is to be presented at the 1992 meeting.

The WG is cognizant of the NSTFIJMG 1990 baseline studies on contaminants in sediments and in fish and shellfish and the recommendation that spatial studies focus on the analysis of sediments and that spatial studies employing fish and shellfish are now of secondary priority (ICES, CRR No. 167, 1989). WGSATM, in 1990, pointed out that there is a firm theoretical basis for believing that the use of fish and shellfish for spatial monitoring is more difficult than their use in temporal trend monitoring. WGSATM does not have sufficient information to contrast and compare spatial distributions in contaminant levels in sediments and biota. The WG awaits the final reception of the results of the 1990 sediment and biota baseline studies. Simon Wilson will distribute the data (or selected parts thereof) to a number of members for intersessional study and, hopefully, some assessment related to spatial distributions by the 1992 meeting.

10. (ACMP) CONSIDERATION OF REVISIONS TO THE SAMPLING AND ANALYTICAL GUIDELINES FOR THE MEASUREMENT OF SPATIAL DISTRIBUTIONS AND TEMPORAL TRENDS OF CONTAMINANT LEVELS

As noted above (Section 7), the WG does not recommend modifications to the sampling guidelines until there is firm evidence demonstrating the need for modification. The WG recognizes that there is also a need to ensure that compatibility between the data obtained under previous sampling schemes and any new scheme is maintained. However, the question of utilization of data obtained under one objective for assessment under another objective is still to be investigated as time permits. No such investigations were completed in time for the present meeting. In addition, it is expected that assessment of the 1990 baseline survey results may lead to recommendations for changes in the sampling protocols related to spatial studies.

11. (ACMP) REPORT ON METHODS FOR THE STATISTICAL ANALYSIS OF POOLED DATA

There were no reports of new studies on the statistical analysis of pooled sample data. However, the WG notes the wide interest which has been expressed in the use of pooled samples for reasons of economy and to provide adequate tissue material for analysis. The WG also notes that, while it has considered aspects of pooling in each of its previous WG reports, there is no compilation of these investigations nor information on the completeness of these investigations. The WG recognizes that it is presently difficult for an investigator to assess this information on pooling and develop an appropriate sampling and analytical scheme based upon it. Therefore, to rectify this situation Jaap van der Meer and Mike Nicholson will coordinate intersessional work on preparing a manuscript for the Techniques in Marine Environmental Sciences (TIMES) Series on the application of pooling for use by investigators in planning such investigations. This will include information on the economies associated with the use of pooling.

12. OTHER STATISTICAL METHODS FOR THE ANALYSIS O F TREND DATA

12.1 Summarizing trends with locally-weighted running line smoothers

Rob Fryer summarized the results of his and Mike Nicholson's application of smoothers to CMP data (Annex 5). They commented that the statistical methods used in the analysis of contaminant levels in the CMP data had focussed on the problem of detecting significant variation between years; further analysis was then directed towards identifying linear trends. However, between-year variation may consist of a simple pattern of change which is non-linear, or which consists of a sequence of simple patterns, e.g. a period of decrease followed by a stable, lower level. They suggested that fitting running-line smoothers may offer a method of summarising these more complex changes, producing a non-parametric line which captures the general movement in the time series and is unaffected by isolated, outlying points. Results were presented where the method had been applied to some of the longer time series of data on contaminant levels in fish muscle tissue reported in the CMP. The Working Group agreed that the method showed considerable potential for summarizing non-linear trends. The authors commented that this application was intended to be part of a management package that would make the results from a large number of CMP data sets more accessible.

12.2 The power of the ICES Cooperative Monitoring Programme to detect linear trends and incidents

Mike Nicholson described his and Rob Fryer's assessment of the power of the CMP to detect changes in contaminant levels with time (Annex 6). Their results examined the probability that a pre-specified pattern of year-to-year change would be found to be significant using the current sampling programme and method of analysis. From the CMP data on contaminants in fish muscle tissue, they estimated the level of random between-year variation against which a particular pattern would need to be measured. They found, for example, that, for the species considered (cod, flounder and herring), to achieve a 90% chance of detecting a linear trend of 10% per m u m , a time series of approximately 10 years would be necessary for Cu, Hg, Zn, PCB, and Ni, whereas a series greater than 20 years would be necessary for Pb and Cr. They presented equivalent results for detecting an 'incident', i.e. where results are elevated in a single year. Work would continue intersessionally using additional 'long time-series' data sets, including data on contaminants in fish liver tissue and extended series of contaminants in fish muscle tissue. Simon Wilson agreed to supply relevant data sets to Mike Nicholson and Rob Fryer.

12.3 Modeling trends with ARIMA models

Jaap van der Meer described his preliminary assessment of some 20-year benthic animal studies being carried out at his institute. Using ARIMA models, it appeared that the series of data on two small worms and a larger one which preys on the first two could be well described by a simple predator- prey relationship and a winter temperature effect on the predator. Chlorophyll-a data did not add any significant explanation to the relationships. This was in contrast with previous ideas that eutrophication, i.e. chlorophyll-a, mainly caused the time trends in the small worm populations.

13. ACMP SUMMARY AND PROGRESS REPORT

The WG wishes ACMP to note the following with respect to the tasks set out by ACMP for the 1991 meeting of the Working Group on Statistical Aspects of Trend Monitoring:

13.1 Completion of the Report on the Study of Cooperative ICES Monitoring Studies Programme Data for Trends in Contaminant Levels in Fish Liver and Blue Mussels

The final draft of the Report on the Study of Cooperative ICES Monitoring Studies Programme Data for Trends in Contaminant Levels in Fish Liver and Blue Mussels has been completed and will be published as soon as possible, as Cooperative Research Report No. 176.

13.2 Pooling and Economizing of a Sampling Programme

There was no further work on the coupling of economics and pooling strategies. None was felt to be needed as the procedure is relatively straightforward and easily applied. However, the past studies will be incorporated into a comprehensive document on the application of pooling techniques in monitoring programmes (see Item 6).

13.3 Optimal handling of temporal trend data characterized by significant inconsistency in regression coefficients and variances

The Working Group was informed of the applicability of the Kalman filter to the handling of this type of data (Section 8.1). The Kalman filter provides a general mechanism for modeling situations where the parameters of a relationship are not constant. The applicability of this approach to environmental monitoring data will continue to be investigated by WGSATM.

The Working Group reiterated their past reporting of two methods for improved handling of this type of data: one a weighted univariate method of handling significant inconsistency in regression coefficients and variances; the other a regression approach using principal components of independent variables in the regression equation to overcome problems of multicollinearity.

13.4 Statistical design of sampling techniques for investigating the spatial distribution of contaminants and the application of spatial-statistical techniques (e.g. Kriging) to CMP data

Three methods of analysing spatially distributed data were considered (Annex 4): (1) comparison to a preselected value, e.g. a maximum "normal" value to identify contaminated areas and assess their degree of contamination; (2) analysis of variance of the data for the spatial component; and (3) the use of various interpolation techniques for spatial information. Within the last group, the technique known as Kriging appears to be a promising technique although investigations on its applicability within CMP will be continuing. It is emphasized that no technique can be used independently of the input of experts familiar with the techniques employed and the behaviour of the determinand in question.

13.5 Consideration of revisions to the sampling and analytical guidelines for the measurement of spatial distributions and temporal trends of contaminant levels

As noted above, the Working ~ r o u p does not recommend modifications to the sampling guidelines until there is firm evidence demonstrating the need for modification. The WG also recognizes that there is a need to ensure that compatibility between old and new data is maintained. However, the question of utilization of data obtained under one objective for assessment under another objective is

under continuing review. In addition, it is expected that assessment of the 1990 baseline survey results may lead to recommendations for changes in the sampling protocols related to spatial and temporal studies.

13.6 Report on methods for the statistical analysis of pooled data

There were no reports of new studies on the statistical analysis of pooled sample data. However, the Working Group plans to prepare a collation and assessment of its past and other studies on pooling for publication in the TIMES series.

13.7 Other items for ACMP consideration

WGSATM requests ACMP to prepare guidelines to expedite the preparation, review and publication of monitoring documents. Currently, referral of manuscripts to various Working Groups, when couplkd with only annual Working Group meetings, results in inordinate publication delays.

WGSATM requests ACMP to emphasize to its Working Groups the requirement that a statistician must be involved as early as possible in all studies where statistical analysis is an ultimate requirement for interpretation. Although it is often possible for a statistician to "rescue" a study after the fact, failure to include one from the beginning may result in a compromised experiment and less indepth analysis.

WGSATM wishes to bring to the attention of ACMP recent information regarding the variability which has been found in laboratory bias, a factor which was believed to be relatively constant over a significant time period (Section 14.1). In order to recommend levels of quality assurance needed to control the effect of this bias, it is essential that other Working Groups supply WGSATM with estimates of expected trends, e.g. percent change per year.

14. OTHER BUSINESS

14.1 Intralaboratory Quality Control

There were no reports of new statistical investigations under this item. However, the WG wishes to reiterate the following:

Mike Nicholson stated that there is evidence that the simple interpretation of intra-laboratory variability, i.e., that the variability within a laboratory is comprised of a random variation component and a bias component which is fixed over a significant period of time, is unsubstantiated. There is evidence emerging from analysis of the results of intercomparison exercises that the bias component changes quite rapidly. However, in a limited amount of data submitted to Mike Nicholson, quality control checks were not sufficiently frequent to quantify this change. Conversely, this implies that laboratories are not making sufficient checks to control varying bias.

2. There does not seem to be an understanding in many laboratories that quality control and assurance procedures must be comprehensive and applied to all steps of an analysis, e.g. sample selection, each measurement made on a sample, and the handling of the resulting data.

3. Assessment groups should supply information on the levels of temporal and spatial changes they wish to detect. This information is required to enable definitions of targets for laboratory performance to be made.

4. These targets for analytical performance should be included in the objectives for intercomparison exercises.

5 . When submitting results for intercomparison exercises, laboratories should report whether or not they believe their method to be in control.

14.2 Statistical Analysis of Intercalibration Exercise Results

Two statistical procedures of analysing data on chlorobiphenyls (CBs) from two different intercomparison studies were discussed. The first of these was presented by Jaap van der Meer, the second by Raj Misra. In the first case, laboratory means on six CBs (Nos. 52, 101, 108, 138, 153, 180) were first transformed to principal components. The first component appeared to co-vary positively and strongly with almost all CBs for the two sample materials. It accounted for a large part of the total variation. Thus, these components could be interpreted as the "overall" bias of the laboratories. These first components of the analyses of the two sample materials were compared. Differences might suggest the sources of the laboratory bias. The second study used samples containing four CBs (Nos. 52, 86, 101, 153) in a spiked and unspiked herring oil. Eighteen laboratories reported results for both oils using two cleanup procedures, a common one and the laboratory's own. Paired-experiment intercomparative studies were done based upon the analysis of four data sets. Treatments compared were: Set 1. Unspiked oil: Common cleanup method s. laboratory cleanup method; Set 2. Spiked oil: Common cleanup method x. laboratory cleanup method; Set 3. Common cleanup method: spiked u. unspiked oil; and Set 4. Laboratory cleanup method: spiked B. unspiked oil. Each study was based on the analysis of paired differences (CB, for treatment 1 - CB, for treatment 2), and paired sums (CB, for treatment 1 + CB, for treatment 2), difference values reflecting only random variations and sum values including laboratory bias as the dominant factor. Multivariate analysis (which employed Hotelling's T2 and multivariate chi-square) of paired differences and paired sum values were carried out to compare laboratories, to rank these laboratories in order of their performance, and to identify possible outliers.

14.3 Request from the Working Group on Biological Effeets of Contaminants

The group considered a document entitled, "Notes of Statistical Analysis", containing the preliminary presentations of the results from the ICES/IOC (Bremerhaven) Workshop on Techniques for Measuring Biological Effects of Contaminants. The document was supplied as "information" to WGSATM on activities being coordinated by the Working Group on Biological Effects of contaminants. Although the WGSATM members who reviewed the document were not familiar with the project in question, they noted a few observations, as follows:

1. The objectives of the project and of the results presented could not be deduced from the material available; it was, however, understood that the manuscript was intended as a basic presentation of preliminary results for workshop participants and did not yet contain a detailed description of what was carried out;

2. The report did not contain any formal statistical analyses, and although transformations had been applied to some data, it was not possible to consider the appropriateness of these;

3. Confidence intervals of the measured parameters have been produced, but no interpretation of the results was included in the material available;

4. Further statistical analyses are encouraged to allow for full assessment of the data;

5. Comparison intervals (Sokal and Rolf, 1981) or significance bands (Nicholson, 1985) might be substituted for confidence intervals. This would assist readers to visually evaluate the significance of the results in relation to differences between stations.

14.4 Request from the Working Group on Marine Sediments

A small sub-group reviewed the documentation on Phase I1 Intercomparison Exercise on Suspended Particulate Matter (SPM), prepared by the Working Group on Marine Sediments in Relation to Pollution. They felt that the documentation was incomplete and, therefore, could not offer detailed statistical advice but offered the following comments:

Filters for each laboratory should be randomly selected;

There is a need to obtain some estimate of between-filter variability. This could be done by the coordinating laboratory which could analyse a large number (220) of blank filters and a similar number of filters carrying SPM;

Are 3 blank filters and 3 filters carrying SPM sufficient to assess the bias and precision of each laboratory? Is there already an estimate of between-filter variability? What level of bias is to be detected? Without an answer to each of the above questions, there is no statistical basis for recommending the appropriate number of filters per laboratory;

There is some evidence that the within-laboratory variability of measurements made on the same day (within batch) is often less than the within-laboratory variability of measurements made some time apart, e.g. a week or a month apart (between batch). If an assessment of the reproducibility of results obtained at different times by the same laboratory (between batch) is required, it is necessary for laboratories to analyse a number of filters at regular time intervals.

It was agreed that Norman Green would communicate these points to Jens Skei, the coordinator of the exercise.

WGSATM wishes to reiterate their recommendation that a statistician be involved in planning, execution and analysis of intercomparison exercises from the earliest possible moment. This is the same recommendation as given in IS0 document 5725-1986(E), Section 2, Item 8.2.

14.5 Request from the Working Group on Marine Sediments, North Sea Task Force Data Assessment Sub-group

The Working Group took note of a series of questions forwarded to WGSATM from a subgroup of WGMS, which had met at ICES in the week prior to the WGSATM meeting. The WGMS subgroup is responsible for conducting an assessment, in March 1992, of data from the North Sea, in the context of the NSTF, for the purpose of describing the spatial distributions of contaminants in the sediments of the North Sea.

The questions concerned the appropriate methods of averaging raw data consisting of (i) replicate chemical analyses, (ii) replicate subsamples, and (iii) multiple samples from a single monitoring station, taking into account the proposed normalization procedures. Also the problem of (iv) aggregating results from several stations within a given geographical area, for the purpose of producing distribution maps, etc., was raised.

The WGSATM response was passed to Simon Wilson, who will be responsible for distributing data sets and preparing basic data products for the assessment group. Essentially, WGSATM recommended the application of arithmetic means to "collapse" the data on replicate analyses. Arithmetic means should also be used to compute a single station value from data on replicate subsamples and multiple samples at a single station. Normalization procedures should be applied to the data for replicate subsamples or station replicates prior to averaging. In relation to the problems of aggregating data for mapping, it was recommended that the basic mapping product comprise a presentation of the unaggregated station data, coupled with other data products, e.g. maps of interpolated data. Summary statistics of aggregated data, etc. should be additional to this.

Finally, it was agreed that, in addition to distributing the complete (unaggregated) data sets to the WGMS subgroup, Simon Wilson should distribute sediment data sets to some members of WGSATM (Bill Warren, Anders Bignert and Jaap van der Meer), who will attempt some exploratory statistical analyses of the data sets, time permitting. It was anticipated that other members of the Working Group would be consulting with WGMS subgroup members at their home institutes in relation to the sediment assessment exercise.

14.6 Monitoring Studies along the French coast

Two documents (in French) from the R6seau National d'observation de la Qualit6 du Milieu Marin @NO) were presented. These were: "Surveillance du Milieu Marin, Travaux du RNO, Editions 1988 and 1989-90". Each consisted of three parts: 1. A description of the organization and of the annual sampling programmes, 2. Summaries of nitrates and phosphates in sea water, and of PCB, PAH, CDDT, Lindane, Hg, Cd, Pb, and Zn in biota, and 3. An overview, showing in 1988 the increase in nitrates in the Rade de Brest, and in 1989-90 some preliminary results of biological effect studies for acetylcholinesterase and ethoxyresorufin-Odeethylase (EROD).

The analysis of nitrates and phosphates in sea water compared the relationships between their levels and salinity (fitted by least-squares) for 1987, 1988, and 1989 with those from a reference period of 1974-1984.

The analysis of contaminants in biota gave summary statistics after iterative elimination of outliers based on + 3 standard deviations, maps of average levels, a graphical presentation of the distribution of results (F + 2 standard errors) for each station arranged in geographical order, and a plot of the trends in contaminant levels for selected stations. The trend was tested for linearity.

During the discussion, it was explained that the poorer association between phosphates and salinity in 1988 in the Rade de Brest area was because data from the rivers Aulne (agricultural) and Elorn (industrial) had been aggregated for the purpose of the report; quality of data are to be improved. The difficulty of comparing the results for different species (oysters and mussels) was overcome by using different scales in the graphical displays of the distribution of the results.

14.7 Monitoring the effects of mine discharges in Greenland

Martin Munk Hansen summarized a paper, "Marine Organisms as Indicators of Heavy Metal Pollution - Expeiience from 16 Years of Monitoring at a Lead-Zinc Mine in Greenland." The results are given for sixteen years of study at a Greenland mine (which discharged large quantities of lead, zinc, and cadmium to the marine environment) and measurements of spatial and temporal trends of metals in sea water and a variety of biota. The following points were noted:

Fish and prawns did not reflect water concentrations of zinc and cadmium.

Seaweed and mussels did not reflect water concentrations of cadmium.

Zinc concentrations appeared to be regulated in mussels.

The livers of relatively stationary fish species, shorthorn sculpin and spotted wolffish, reflected lead concentrations in sea water. However, in the case of spotted wolffish, correlation was improved by deletion of lead values below reference site values. This is believed to exclude that part of the sample composed of animals which had recently migrated into the sampling location.

Migratory fish species, such as Greenland cod and Greenland halibut, did not have higher concentrations than reference site values in any tissue analysed.

Lead concentrations in mussels and seaweed were highly correlated to the seawater concentrations in the months preceding biota sampling.

He concluded that the use of biota to monitor trends are, with exceptions, of little use in evaluating seawater concentrations.

In the discussion it was noted that, if a good correlation exists between seawater and biota concentrations, there was little point in analyzing biota. However, the high variability in seawater concentrations necessitates analysis of a rather large number of samples over time; a few biota samples may give the same information. In addition, monitoring of biota is required in its own right, from the viewpoint of health considerations for both man and marine biota. It was further pointed out that research into the speciation and bioavailability of cadmium is needed to better understand the behaviour of this element.

14.8 Nomination of the Chairman

A request for nominations for the position of Chairman received no nominations. The Chairman then asked if any member of WGSATM would volunteer his services, but none was forthcoming. At this point a group spokesman stated that, based upon discussions with WG members during the preceding days, the WG wholeheartedly endorsed the continuation of Dr. Uthe in the Chair, in spite of his attempts at soliciting a replacement. He further noted that the Working Group had been a highly productive, highly complimented Working Group, and that there was a strong group feeling against change at this time in light of the group's successes. After consultation with the Chairman of ACMP, Dr. Uthe agreed, pending approval of ACMP, to serve in the chair for one more year.

15. RECOMMENDATIONS

The Working Group on Statistical Aspects of Trend Monitoring recommends:

a. that ACMP bring to the attention of other Working Groups the requirement that a statistician should be involved in all steps of studies that will require statistical evaluation from the earliest possible moment;

b. that the Working Group on Statistical Aspects of Trend Monitoring meet for four days in Halifax, Canada, in late April, 1991, to consider, inter alia:

1. the application of the Kriging technique to spatial distribution studies of contaminants in sediments, fish, and water;

2. the results of the statistical analysis of data on contaminant levels in North Sea sediments;

3. further progress on methods of handling temporal trend data characterized by significant inconsistency in regression coefficients and variances, in particular, the application of the Kalman filter for updating the analysis of CMP and other ongoing monitoring programmes;

4. the results of comparative statistical studies on the 1990 fish and sediment data;

5. revisions to the sampling and analytical guidelines for the measurement of spatial distributions and temporal trends of contaminant levels;

6. statistical aids for interpreting trend data; and,

7. interactions between analytical noise and trend data.

16. CLOSING OF THE MEETING

On Thursday afternoon, 14 March, having thanked the members of the WG for their enthusiasm, dedication and productivity, which allowed the Working Group to complete its tasks early, the Chairman closed the meeting.

17. ACTION LIST

1. Bill Warren and Mike Nicholson to consider the application of the Kalman filter techniques for updating CMP data.

2. Bill Warren to report on his continuing investigation on the usefulness of Kriging in analysing data from fish and other matrices.

3. Simon Wilson to distribute data from the NSTFIJMG 1990 sediment baseline survey to Bill Warren, Anders Bignert and Jaap van der Meer who, hopefully, will have time intersessionally to carry out some exploratory analyses on these data for spatial distributions.

4. A sub-group consisting of Norman Green (coordinator), Bill Warren, Jaap van der Meer, and Martin Munk Hansen should try to prepare a report on the appropriate methods for improving

the application of data collected under one objective to another objective.

5. Jaap van der Meer and Mike Nicholson are to coordinate the preparation of a review on the application of pooling within CMP studies, including the economic aspects of pooling.

6. All WG members are to consider information on an "as available" basis upon which modifications to the sampling guidelines could be recommended.

REFERENCES

Nicolson, M.D. 1985. The treatment of time-effects in the statistical analysis of contaminant monitoring data. Doc. ICES C.M. 1985/E:31.

ANNEX 1

WORKING GROUP ON STATISTICAL ASPECTS OF TREND MONITORING

(Copenhagen, 11-15 March 1991)

AGENDA

Opening of the meeting and organization of work.

Adoption of the agenda.

WGSATM terms of reference.

Specific tasks for the 1991 meeting of WGSATM.

Review of the ACMP report.

(ACMP) Completion of the report on the study of Cooperative ICES Monitoring Studies Programme data for trends in contaminant levels in fish liver and blue mussels.

(ACMP) Pooling and economizing of a sampling programme.

(ACMP) Optimal handling of temporal trend data characterized by significant inconsistency in regression coefficients and variances.

8.1 Modeling temporal trends in marine populations: Pollutant concentrations as a function of length.

8.2 Other methods

(ACMP) Statistical design of sampling techniques for investigating the spatial distribution of contaminants and the application of spatial-statistical techniques (e-g. Kriging) to CMP data.

10. (ACMP) Consideration of revisions to the sampling and analytical guidelines for the measurement of spatial distributions and temporal trends of contaminant levels.

11. (ACMP) Report on methods for the statistical analysis of pooled data.

12. Other statistical methods for the analysis of trend data.

12.1 Summarizing trends with locally weighted running line smoothers. 12.2 Modeling temporal trends in marine populations: pollutant concentrations as a function of

length. 12.3 Modeling trends with ARIMA models. 12.4 The power of the ICES Cooperative Monitoring Programme to detect linear trends and

incidents.

13. ACMP summary and progress report.

14. Other business.

14.1 Intralaboratory Quality Control. 14.2 Statistical analysis of intercalibration exercise results. 14.3 Request from the Working Group on Biological Effects of contaminants. 14.4 Request from the Working Group on Marine Sediments. 14.5 Request from the North Sea Task Force. 14.6 Shellfish monitoring studies along the French coast. 14.7 Monitoring the effects of mine discharges in Greenland. 14.8 Nomination of the Chairman.

15 Recommendations.

16 Closing of the Meeting.

17 Action List.

ANNEX 2

WORKING GROUP ON STATISTICAL ASPECTS OF TREND MONITORING

LIST OF PARTICIPANTS

Mr. A. Bignert National Museum of Natural History P.O. Box 50007 S-104 05 Stockholm SWEDEN Telephone: 866641 15 Telefax: 8-66641 12

Dr. R. Fryer Marine Laboratory P.O. Box 101 Victoria Road Aberdeen AB9 8DB UNITED KINGDOM Telephone: 224-876544 Telefax: 224-879 156

Mr. N.W. Green Norwegian Institute for Water Research P.O. Box 69 Komoll N-0808 Oslo 8 NORWAY Telephone: 2-235280 Telefax : 2-394 1 89

Mr. M. Munk Hansen Greenland Envir. Research Institute Tagensvej 135, 4 DK-2200 Copenhagen N DENMARK Telephone: 35-821415 Telefax : 35-82 1420

Mr. C. Thye Hansen Greenland Envir. Research Institute Tagensvej 135,4 DK-2200 Copenhagen N DENMARK Telephone: 35-82 1415 Telefax: 35-821420

Mr. J. van der Meer Netherlands Institute for Sea Research P.O. Box 59 NL-1790 AB Den Burg, Texel THE NETHERLANDS Telephone: 2220-69357 Telefax: 2220-19674

Dr. R.K. Misra Department of Fisheries and Oceans P. 0. Box 550 Halifax, N.S., B3J 2S7 CANADA Telephone: 90242643208 Telefax: 902-426-2256

Mr. M.D. Nicholson Fisheries Laboratory Lowestoft, NR33 OHT Suffolk UNITED KINGDOM Telephone: 0502-562244 Telefax: 0502-5 13 865

Mr. J .Y. Quintin IFREMER, Centre de Brest DELIQMISMB B.P. 70 F-29280 Plouzane FRANCE Telephone: 98-224332 Telefax: 98-224548

Dr. J .F. Uthe (Chairman) Department of Fisheries and Oceans P.O. Box 550 Halifax, N.S., B3J 2S7 CANADA Telephone: 902-426-6277 Telefax: 902-426-2256

Dr. W . Warren Dr. S. Wilson Department of Fisheries and Oceans ICES P.O. Box 5667 Pakgade 2-4 St. John's, Newfoundland A1C 5x1 DK-1261 Copenhagen K CANADA DENMARK Telephone: 709-772-4835 Telphone: 33-154225 Tel efax : 709-772-2156 Telefax: 33-934215

ANNEX 3

Optimal Handling of Temporal Trend Data Characterized by Significant Inconsistency in Regression Coefficients and Variances

R.K. Misra and J.F. Uthe

Marine Chemistry Division Department sf Fisheries and Oceans

P.O. Box 550, Halifax, Nova Scotia Canada B3J 2S7

Introduction

A primary objective in the assessment of the pollution of marine waters is to investigate the magnitude and direction of time trends in contaminant levels in fish stocks. A length-stratified sampling scheme was designed to provide well-structured data for the ICES Cooperative Monitoring Programme (CMP) for fish and shellfish. Guidelines for analysing these data for temporal variations in general, and time trends, in particular, have been established (Anon 1987a, p. 115). Model 2 (Anon 1987a), which is an analysis of covariance (ANCOVA) type of procedure, was recommended. The procedure employs only one concomitant variable (fish length) and thus has the advantages of simplicity of application and interpretation of results (Nicholson and Wilson 1987).

Difficulties in Employing ICES Model 2

The Model 2 procedure requires that, for K years, neither the K coefficients of regression, Bj, j = 1, . . ., K of Yi, the logarithm of concentration of a contaminant on X, which is (log) fish length, nor their K residual error variances, a2j, be unequal. Frequent violations of these requirements were reported in the analysis of CMP data sets (see, e.g. Anon. 1987b). The CMP length-stratified sampling scheme is intended to provide well-structured data and must not be undermined. Poor data structure would promote irregularities of not properly defining the effects of the covariate on contaminant concentration levels. However, considerations presented in Misra et al. (1989) show that these parameters should not be expected to be equal even with close adherence to the CMP sampling guidelines. These considerations are summarized as:

1. Several concomitant variables, and not just fish length, will often affect the contaminant concentration levels. Omission of covariates, which are correlated with the covariate X, of ICES Model 2, will bias the value of Oj.

2. When covariates are biological variables (e.g. length, weight, age, etc.) correlations computed from a data set are "phenotypic" correlations. Further probing into the possible causes of correlations among variables requires subdivision of the phenotypic correlation to the "genetic" and "environmental" correlations and to the examination of possible causes of variations in these.

3. Variances and covariances of biological characteristics are also the properties of populations and of the environmental circumstances to which individuals are subjected.

Inter-year variability of statistical parameters will, therefore, exist and should not be viewed as a statistical artifact.

What Analyses Can Be Done?

1. Multiple comparisons of K means is potentially difficult. Misra et al. (1990) considered a weighted procedure of analysing for temporal variations and time trends in contaminant levels. Homogeneity of regression coefficients and of residual variances are not required in order to use this procedure.

2. The number of regressor variables can be large, especially when one considers the possible inclusion of functions (such as squares, cross products, etc.) of the original concomitant variables also in the suite of regressor variables. Effects of multicollinearity (i.e. correlated regressor variables) have been extensively studied. Although a number of techniques for handling multicollinearity are available, no technique will work well in all circumstances or is without any constraint. Draper and Smith (1981) suggest that the approach of working with one selected technique is important in the long run. Misra et al. (1989) chose to employ the principal components (PCs) of concomitant variables as regressor variables. Reasons for this choice were that PCs have been employed (a) widely, and (b) over a long time in numerous biological studies. Meanings assigned to PCs have acquired conventionality over the years, which makes them useful as working variables. The problem of multicollinearity is circumvented with the use of PCs as covariates. If m PCs are employed as regressor variables, the combined effect of m partial regression coefficients is partitioned orthogonally. Therefore, PCs can be used as covariates singularly or simultaneously.

REFERENCES

Anon. 1986. Report of the meeting of the ad hoc Group of Statisticians Assisting the Working Group on Marine Pollution Baseline and Monitoring Studies in the North Atlantic on Trend Monitoring. ICES C.M. 1986/E:39.

Anon. 1987a. Report of the ICES Advisory Committee on Marine Pollution, 1986. Cooperative Research Report No. 142. 128 pp.

Anon. 1987b. Report of the 1987 meeting of the Working Group on Statistical Aspects of Trend Monitoring. ICES C.M. 1987/E:24.

Anon. 1989. Statistical analysis of the ICES Cooperative Monitoring Programme data on contaminants in fish muscle tissue (1978-1985) for determination of temporal trends. Cooperative Research Report No. 162. 147 pp.

Draper, N.R., and H. Smith. 1981. Applied Regression Analysis. John Wiley & Sons, Inc., New York, NY. 709 pp.

Misra, R.K., J.F. Uthe, and W. Vynke. 1990. A weighted procedure of analysing for time trends in contaminant levels in CMP data: application to cod and flounder data of the Belgian coast. Journal du Conseil international pour 1' Exploration de la Mer. 47: 65-75.

Misra, R.K., J.F. Uthe, D.P. Scott, C.L. Chou and C.J. Musial. 1989. Time trends of chemical contaminant levels in Canadian Atlantic cod (Gadus morhua) with several biological variables. Marine Pollution Bulletin. 20: 227-232.

Nicholson, M.D., and S.J. Wilson, 1987. Identification of trends in levels of metals in fish muscle: Appraisal of the statistical analysis and data quality. ICES C.M. 1987/E:24, Annex 3.2., p. 16- 21.

REPORT ON APPROPRIATE TECHNIQUES FOR SAMPLING FOR STUDIES OF

THE GEOGRAPHICAL DISTRIBUTION OF CONTAMINANTS

1 . ABSTRACT

This report gives a general overview of the basic approaches to geographical distribution monitoring of contaminants. Three types are: comparison to sug- gested background/classification levels, use of analysis of variance to deter- mine source of error due to a geographical component and various interpolation techniques. Particular emphasis is given to the Kriging interpolation method -that, in contrast to similar methods, generates an estimate of error. Further testing needs to be done, but Kriging seems potentially useful for mapping con- taminants in sediments and biota. It is noted that the choice of approach to geographical distribution assessment should be relatively simple and robust and be useful on an international level. Furthermore, it is strongly emphasized that the use and interpretation of these approaches must be guided by experts fam- iliar with the techniques and contaminants in question.

2. INTRODUCTION AND PURPOSE

The Working Group on the Statistical Aspects of Trend Monitoring (WGSATM) under the auspices of the International Council for the Exploration of the Sea (ICES) has primarily focused on temporal trend monitoring. Having tackled a number of central issues in this respect, WGSATM has recently addressed the statistical aspects of qeoqraphical distribuion monitoring.

This work is particularly important considering the intensive ICES involvement in on-going international monitoring by the Oslo and Paris Commissions' (OSPARCOM) Joint Monitoring Programme (JMG) and the North Sea Task Force's (NSTF) Monitoring Master Plan. JMG has data from a 1990 supplementary baseline investigation on contaminants in fish and shellfish and a baseline study on con- taminants in sediments to consider in 1991-1992. NSTF has a Quality Status Re- port (QSR) due in 1993 based mostly on 1990-1991 monitoring data. Both of these programmes address strongly geographical distribution aspects of contaminant monitoring.

Item 3 of the 1990 WGSATM Action List states (WGSATM, 1990 ) :

" A Sub-group consisting of Norman Green (coordinator), Bill Warren, Jaap van der Heer and Martin Munk Hansen to prepare a report on appro- priate techniques for sampling for studies of the spatial distribution of contaminants."

Section 10 of the 1990 WGSATM report implies a larger scope of work for the Sub- group (WGSATM, 1990 ) :

".. . t o work intersessionally by correspondence on revisions of these general guidelines, including statements on the need to use the species-specific guidelines and t o maintain the integrity of the sam- pling programmes implemented."

It is noted the action list mandate dealing with a thorough review of existing methods is a necessary prerequisite for reaching the goal stated under Section 10. Hence, the Sub-group gave priority to the former mandate.

Members of the Sub-group were contacted in January-February of 1991. A number of contributions have been received and other contributions will be presented at the 1991 WGSATM meeting. The contributions presented in this report are examples of the main techniques employed by programmes or institutes the members were familiar with: Gr6nlands Milj$unders$gelser, GrBnlands Geologiske Unders$gelse, Norwegian Institute for Water Research, Netherlands Institute for Sea Research, and the Canadian Department of Fisheries and Oceans.

It was found practical to finalize the report during the meeting in order to in- corporate these latter contributions and possible points arising from relevant plenary discussions.

3. INTERNATIONAL GUIDELINES

ICES, JWG and NSTF guidelines for geographical distribution monitoring overlap to a large extent. JMG and NSTF guidelines are largely based on the ICES guide- lines. ICES objective 2 relates to geographical distribution assessment and states: 'Provision over a wide geographical area of an indication of the health of the marine environment in the entire ICES North Atlantic area". The equi- valent objective under OSPARCOM is JMG purpose C: assessment of existing levels of marine pollution".

ICESIJMG guidelines are available for seawater, sediment and biota monitoring. The guidelines for JMG, with revisions to March 1990, have been published (OSPARCOM, 1990). An overview of the biota guidelines for ICES, JMG and NSTF have been presented at the 1990 WGSATM meeting (WGSATM, 1990, Annex 7 ) . There have also been recent revisions for the use of mussels in monitoring in the an- nual report by the ICES Advisory Committee on Marine Pollution (ACMP) (Annex 3 ICES, 1990).

The biota guidelines encompass monitoring for purposes of human health risk and temporal trend assessments, but require slightly different sampling strategies. OSPARCOM requested ICES to investigate the possibility that a single sampling strategy could be used for all purposes. Though ACMP responded that this cannot be done, there are some cases where samples collected under one purpose could be used for another (ICES, 1990, Section 6; also paper 5 of WGSATM 1991). An im- portant conclusion from the ACMP response was that, on the whole, samples col- lected for temporal trends can be used for geographical distribution monitoring.

In the attempt to rationalize the NSTFIJMG geographical distribution monitoring programme of 1990, the NSTF adopted ACMP matrix priority tables (ICES, 1989). As a result, the primary emphasis of NSTF/JMG 1990 monitoring was based primarily on sediment sampling and secondarily on biota (sea water was the primary matrix only for y-HCH and nutrients) (NSTF, 1990). Hence, the Sub-group recommended that WGSATM concentrate on sediment data sets.

4. ICES 1985 BASELINE STUDY

In 1985, a large-scale geographical distribution investigation of contaminants in fish and shellfish was carried out by ICES in conjunction with the JIYG moni- toring programme and the Helsinki Comission. The results describe a variety of problems encountered during the study and list a number of recommendations if a similar baseline study should be carried out in the future (ICES, 1988). One of

the major problems was the lack of comparability amongst the data submitted by the different participating laboratories and countries, primarily due to inade- quate compliance with agreed monitoring guidelines and agreed scale of investi- gation. The authors note: "The potential discrepancies in the data precluded any sophisticated statistical treatment...".

With this in mind, it seems appropriate for WGSATM to concentrate its effort on structuring relatively simple sampling techniques that are practical to execute on an international level, but are potent enough to yield an adequate basis for geographical distribution assessment. In any case, the problems and recommen- dations outlined in the report from the 1985 baseline study (particularly pages 1-15) should be considered in detail.

A; mentioned, the baseline study did not employ any formal statistical testing. Instead, "high", "medium", and "low" levels of contaminant concentrations were determined based on ranges of quartiles of the data submitted. The data were presented in box-and-whisker diagrams.

5. REFERENCE VALUES AND QUALITY CRITERIA PROPOSALS

A typical and basic approach to both temporal trend and geographical distrib- ution assessment of contaminants is to compare concentrations to suggested ref- erence or background values determined by monitoring reference stations and/or by review of relevant literature. This has been practiced to a large extent at the Norwegian Institute for Water Research (NIVA). Extending on this aspect, the Norwegian State Pollution Authority has sought to establish criteria to assess the level of contamination/pollution. In this regard, Knutzen and Skei (1990) have recently published a report on suggested background levels and classi- fication of concentrations exceeding these reference values as a measure of quality criteria for sea water, sediment, and a variety of marine organisms. The report also includes an overview of limits applied by different national health authorities.

It should be stressed that the degree of contamination is classified arbitrarily and, although comparisons of background levels and classes of contamination to concentrations found can be helpful as an initial assessment of the level of pollution, comparisons should be interpreted by qualified personnel. Also, the comparison of reference levels usually does not include any measure of statis- tical significance, even though this could easily be taken into account by sub- mitting a measurement of variation about the mean concentration.

6. METAL CONCENTRATIONS IN MAARMORILIK

The effects of metal load, as a result of mining activities, on the marine en- vironment in Maarmorilik (Greenland) were investigated during the period 1972- 1987 (Gr$nlands Milj@unders$gelser and Gr$nlands Geologisk Unders@gelser, 1988) . Metal concentrations were determined in sea water sediment, and a number of mar- ine organisms including bladder wrack, blue mussel, cod, eider and ringed seal. In addition, the effect of the metal load on the benthic fauna was assessed.

The geographical distribution of the contaminants within this "hot spot" was largely evaluated by comparing metal-perturbed areas with "reference areas". Analysis of variance tested the overall significance of differences among stations for bladder wrack and blue mussel. The concentrations were graphically presented as maps with isolines.

The investigation showed, among other aspects, the importance of sampling more than one species in order to monitor an array of contaminants adequately. For example, it was found that bladder wrack reflected the changes in copper and zinc better than the blue mussels, which can to a certain degree regulate these metals. Furthermore, it was pointed out during the plenary discussion that the principle of time series approach could be applied to geographical distribution, i.e., evolution of spatial distribution in time.

7. VARIOUS INTERPOLATION NETHODS

Inverse distance, Kriging, and minimum curvature are common means of interp- olating between data points in order to determine isolines. These methods are available on a number of statistical packages (e.g., Surfer). The inverse dis- tance method is quicker than the Kriging and minimum curvature methods, but the latter two are considered more accurate. However, the latter two will generate more unpredictable results in areas where data are missing. Kriging was con- sidered in detail because it generates estimates of errors about the interp- olated means.

Warren presented a synopsis of work he has been doing on Kriging on ICES surface silicate concentrations in the North Sea in 1989. Kriging developed from the mining industry as a means of selecting optimal drilling sites and has recently been employed in mapping marine eutrophic and contaminant parameters. Basic Kriging assumes stationarity . e l no systematic geographical trend in mean concentrations and variance) and isotropy (i.e., spatial correlation is a func- tion of distance but independent of direction). The data were first transformed to approximate Gaussinity.

The application of Kriging enabled estimates of concentrations with their re- spective errors at any point in the North Sea. The resulting maps with isolines were in good agreement with results from other interpolation techniques applied independently at ICES. Warren's study showed, however, that the assumption of stationarity was not met (the istropy assumption was not tested but, super- ficially, there was no suggestion of anisotropy). There are various means of ac- counting for nonstationarity, in particular median polish Kriging which is re- garded as robust but best suited for data obtained on a regular grid. Another means is the fitting of a polynomial (perhaps quadratic) in Cartesian coordi- nates that would likely work well with silicate data which exhibit troughs (minima) in the middle of the North Sea. The application of these means should not change the means significantly but should noticeably reduce the error about these means.

Warren and discussions in plenary concluded that Kriging provides estimates of standard errors for the interpolated means and is potentially useful for other matrices (sediment and biota) though this has not been adequately tested. Warren intends to continue testing Kriging intersessionally, particularly in regard to its usefulness with other matrices.

In general, no matter which interpolation method is chosen the results (isolines) must be carefully examined for discrepancies and adjusted accordingly if need be by experts familiar with the techniques employed and the behavior of the determinand in question.

8. REFERENCES

Grgnlands Milj~unders~gelser/Gr~nlands Geologisk Unders#gelse, 1988. Miljgunder- sqlgelser ved Maarmorilik 1972-1987. (November 1988) 207 pp.

ICES, 1988. Results of 1985 Baseline Study of Contaminants in Fish and Shell- fish. Cooperative Research Report No. 151. 366 pp.

ICES, 1989. Report of the ICES Advisory Committee on Marine Pollution, 1989. Co- operative Research Report No. 167. 173 pp.

ICES, 1990. Report of the ICES Advisory Committee on Marine Pollution, 1990. Co- operative Research Report No. 172. 153 pp.

Knutzen, J. and Skei, J. 1990. Quality criteria for micropollutants in water, . sediments and organisms and preliminary proposals for classification of en-

vironmental quality. Norwegian Institute for Water Research. Project O- 862602. Report number 2540. 139 pp.

NSTF, 1990. North Sea Task Force Monitoring Master Plan. North Sea Environment Report No. 3. North Sea Task Force, Oslo and Paris Commissions, Inter- national Council for the Exploration of the Sea. July 1990. 37 pp.

OSPARCOM, 1990. Principles and Methodology of the Joint Monitoring Programme. Oslo and Paris Commissions (OSPARCOM) Monitoring Manual.

WGSATM, 1990. Report of the Working Group on Statistical Aspects of Trend Moni- toring. Copenhagen, Denmark, 2 0 April - 4 May 1990. ICES C.M. 1990/Poll:6. 76 PP.

A N N E X - 5

THE POWER OF THE ICES COOPERATIVE MONITORING PROGRAMME TO DETECT

LINEAR TRENDS AND INCIDENTS

by M. D. Nicholson and R. J. Fryer

INTRODUCTION

One of the aims of the International Council for the Exploration

of the Sears (ICES) Cooperative Monitoring Programme (CMP) of

contaminants in fish and shellfish is to assess variation in

contaminant levels with time. One question is particularly

important. Do contaminant levels vary with time and if so, can

this variation be summarised in a simple interpretable way?

Power measures the effectiveness of a monitoring programme by

assessing the type and magnitude of changes that are likely to be

detected. Nicholson & Fryer (1990) assess the power of the CMP to

detect changes in contaminant levels with time. This Paper

assesses the power of the CMP to detect two specific types of

variation: a linear trend and an incident (ie an isolated change

in contaminant levels).

SAMPLING ANALYSIS

Consider a monitoring programme in which r samples are taken

annually in each of s successive years. Let y be the i j

log-concentration of sample i in year j.

Summarising between-year variation is complicated because

i) there are many interesting types of change to consider; see,

for example, the idealised scenarios in Nicholson & Fryer (1990).

ii) in practice, interesting systematic changes are likely to be

masked by random between-year variation, which has no easily

quantifiable pattern.

We shall restrict attention to two types of systematic change: a

linear trend and an incident. A plausible model for a linear

change in log-contaminant levels is

y,, = /f, + (t-l)log(l +- + Wt + E t t t 100 i = l...r, t = l...s,

where

a) q measures the rate of change; eg q = 5 correspondes to a 5%

increase in concentration per year

b) {tit) are independent normal random variables with zero mean 2 and constant variance o representing random within-year

variation,

c) {a,} are independent normal random variables with zero mean and

constant variance r2 representing random between-year variation.

The errors {tit} and {at} are assumed to be mutually independent.

Similarly, a plausible model for an incident in year p is

0 + Z i t , i = r I t = l...s, t # p t I

p0 + log(l+- ) + ut + tit, i = 1 . t = p 100

where q measures the magnitude of the incident; eg q = 100

corresponds to an incident with a 100% increase in concentration.

It is difficult to test for trends and incidents using the

observations {yit} because the observations are not independent;

for example, Yl 1 and y2, are correlated since the error ol is

common to both. Fryer & Nicholson (1990) discuss this problem in

more detail. However, appropriate tests can be based on the

yearly means { yt =+I yit), since these are independent.

The models for a linear trend and an incident are written in terms

of 1 as

Q yt = Po + (t-l)log(l +- 100 + Q t f t = l...S,

and

- Y t = { Q t r t = 1.. .s, t # p

Q p + log(1 +- 100 + Q t I t = P 1

the errors 1 { vt = y + f i t ) are independent and normally

i

2 2 distributed with zero mean and constant variance fp2 = s + CT /r.

Evidence of a linear trend is assessed by regressing Yt on t.

Evidence of an incident is assessed by calculating the statistic

* max I Y, - 7 I B = I

- 1 where y = -1 Y,. Fractiles of B* are given in Hawkins (1980) . S

t

POWER STUDIES

The powers of the tests for linear trends and incidents are the

probabilities that a given trend or incident results in a

statistically significant test.

qomer of the test for Iinear tren6

For a given number of years s t the power of the test for linear

trend depends on the magnitude of

As aL increases, the power increases; ie a linear change is more

likely to be detected. The power is positively related to the

magnitude of the trend and inversely related to the residual

variance. Implicitly, the power is also positively related to the

number of samples r taken each year, since as r increases, cp

decreases. The power is also related to the significance level a;

we shall just consider tests at the 5% significance level.

The power of the test is

where F (6) is an F-distribution on vl, v2 degrees of freedom ' v2

with non-centrality parameter 6.

Power curves for s = 5, 10, 20 are shown in Figure 1. Thus, if

s = 10, cp = 0.2, q = 5%, then = 4.9 so the probability of

detecting the trend is 0.50.

pomer of tbe 8* test for incibents

The power of the B* test depends on the magnitude of

1 q - log(1 +- &I - - jT loo ) *

Again, the power is positively related to the magnitude of the

change and inversely related to the residual variance. The power

can not be evaluated analytically so simulation based power curves

are shown in Figure 2. Thus, if s = 10, cp = 0.2, q=100%, then

6 = 3.5 and the probability of detecting the incident is 0.35. I

POWER OF THE CMP FOR CONTAMINANTS FISH MUSCLE -----

Anon (1989) report 62 time series of contaminant levels (mercury,

zinc, copper, PCBs, chromium, nickel or lead) in fish muscle

(cod, flounder, sole, plaice, whiting, herring or dab).

For each data set, the residual between-year variance (p2 can be

estimated by fitting a nonparametric smooth curve through the

annual means (Fryer & ~icholson, 1991). ~stimates of (p2 for data

sets with 6 or more years data are given in Table 1. (The other

data sets are excluded because the corresponding estimates of p2

are horrendously imprecise; this excludes all data for sole,

plaice, whiting & dab.)

Analysis of variance of log((p2) reveals that (p2 varies with

contaminant; however, these is no evidence that (p2 varies with

species. Thus, the power of the CMP can be assessed by

considering each contaminant in turn. Geometric mean estimates of

(p2 by contaminant are given in Table 2.

Table 3 gives the probability of detecting a linear trend given 5,

10, 20 years data for q = 59, lo%, 20%. Table 4 shows the number

of years required to obtain 90% power for a q% trend. Table 5

gives the probability of detecting an incident given 5, 10, 20

years data for q = loo%, 200%, 3002, 400%. Table 6 gives the

number of years required to obtain 90% power for a q% incident.

DISCUSSION

The CMP data sets have at most 8 years observations. Thus, at the

moment, trends in copper concentration of 10% per year are likely

to be detected (ie with probability greater than 0.9) . Trends in

mercury, zinc and PCBs of 20% per year are also likely to be

detected. Extending the CMP to 20 years increases the power of

the CMP. Trends of 5% are likely to be detected in all the

contaminants except lead and chromium. Trends of 20% are likely

to be detected in all the contaminants; this is just as well,

since an increase of 20% per year over 20 years corresponds to an

overall increase of 3100% between the start and the end of the

monitoring period.

With 8 years data, an incident of 200% is likely to be detected in

copper; incidents of 400% are likely to be detected in copper,

mercury, zinc and PCBs. After 20 years, incidents of 200% are

likely to be detected in copper, mercury, zinc and PCBs and an

incident of 400% is likely to be detected in nickel.

In the analysis of the CMP data, the tests for linear trends and

incidents are conditional tests; ie they are only used if an

analysis of variance reveals significant between-year variation.

Thus, in practice, their powers will be lower than those given

above. Also, a number of tests are used on the same data set.

Adjusting the significance levels of each test to give the correct

overall significance level will also reduce the powers of the

tests.

REFERENCES

Fryer, R . J . & Nicholson, M.D. (1990). The ICES Cooperative

Monitoring Programme. Part 2. Testing for Trends in Annual Mean

Contaminant Levels. In Report of the Working Group on Statistical

Aspects of Trend Monitoring 1990. ICES C.M.l990/Poll:6.

Fryer, R . J . & Nicholson, M.D. (1991). Summarising trends with

locally-weighted running-line smoothers. Paper for the

Working Group on Statistical Aspects on Trend Monitoring 1991.

Hawkins, D.M. (1980) . Identification of Outliers. Chapman 6

Hall, London. 188pp.

Nicholson, M.D. & Fryer, R.J . (1990). The ICES Cooperative

Monitoring Programme. Part 1. Assessing the Power of Contaminant

Monitoring Studies. In Report of the Working Group on Statistical

Aspects of Trend Monitoring 1990. ICES C.M.l99O/Poll:6.

Table 1. Estimates of (p2

mercury

lead

zinc

chromium

copper

nickel

PCB

cod flounder

Table 2. Estimates of residual between-year variation by

contaminant

copper

mercury

zinc

PCB

nickel

lead

chromium

0.019

0.030

0.039

0.046

0.101

0.46

1.08

Table 3 Power of test for trend

copper

mercury

zinc

PCB

nickel

lead

chromium

Table 4 Number of years required for 90% power for trend

detection

copper

mercury

zinc

PCB I ' nickel

lead

1 chromium

Table 5 Power of test for incidents

copper

mercury

zinc

100 PCB 200

300 400

100 nickel 200

Table 6 Number of years required for 90% power for incident

detection

copper

mercury

z inc

PCB

n i c k e l

lead

chromium

A N N E X 6

SUMMARISING TRENDS WITH LOCALLY-WEIGHTED RUNNING-LINE SMOOTHERS

by R.J. Fryer and M.D. Nicholson

INTRODUCTION

Data collected in the International Council for the Exploration of

the Seats (ICES) Cooperative Monitoring Programme (CMP) of

contaminants in fish and shellfish is analysed in two stages.

First, evidence of between-year variation in log-contaminant

levels is assessed by an analysis of variance. If contaminant

levels vary with time, then the next stage is to summarise the

between-year variation in a simple, interpretable way. However,

there are many plausible, interesting types of change to consider;

for example, a linear trend in log-contaminant levels, an

exponential trend in log-contaminant levels, an incident, etc.

Smoothers summarise the trend of a response variable in terms of

one or more predictor variables, without presupposing any

particular form for the response; ie they let the data speak for

themselves. A good introduction is found in Hastie & Tibshirani

(1990). This Paper uses the locallly-weighted running-line

smoother loess of Cleveland (1979) to summarise trends in the CMP

data on contaminant levels (mercury, zinc, copper, PCBs, chromium,

nickel or lead) in fish muscle (cod, flounder, sole, plaice,

whiting, herring or dab).

A BRIEF SUMMARY OF LOESS

Suppose we have response measurements { yi , i = 1. . . n ) which are

related to predictor measurements x i , i = 1.. n ) . We wish to

estimate the mean response f (x) at xo, say. The loess estimate

using k nearest neighbours is obtained as follows.

1) Find the k nearest neighbours to xo; ie the k values of

{ x i i = 1 . that are closest (in Euclidean distance) to xo.

Call the nearest neighbours N(xo)

2) Calculate A(xo) , the distance of the furthest near-neighbour

from xo; ie

w0) = max Ix, - xoI X i € N(x0)

3 ) Assign weights wi to each point xi using the tri-cube weight

function; thus

if jx, - x,I 5 A(xo)

otherwise

4 ) f (xo) is the fitted value at xo of the weighted regression of

{yi) on {xi) using weights {wi).

The weighting ensures that only the k nearest neighbours have

positive weights in the regression analysis.

The value of k can be adjusted to give different smooths; the

larger the value of k, the smoother the fitted curve. Often, the

number of nearest neighbours is parameterised in terms of the

proportion A of the observations that are included in each

neighbourhood; ie A = 0.5 corresponds to a smooth with 50% of the

observations in each neighbourhood. There are a number of

objective ways of choosing k or A for a particular smooth;

however their performance is not always very satisfactory and

often the simplest approach is to smooth with a variety of values

of A and choose the most appropriate smooth by eye. .

Typically, a loess smooth is computed by calculating {f (xi)) and

then interpolating between these points.

Cleveland (1979) describes how loess can be made robust against

outliers by an interative procedure in which observations with

large residuals are downweighted. He also gives formulae for

robust estimates of the residual variance and pointwise standard

errors of {f (xi) ) .

APPLICATION THE CMP DATA

Smoothed curves for the CMP data sets on contaminants in fish

muscle with six or more observations are shown in Figure 1. Here,

yi is the mean log-concentration level in year i and xi = ti, the

year of the ith sample. Robust smoothing is used. Also, A = 1;

ie all the points are included in the nearest neighbourhood. Also

shown are approximate pointwise 95% confidence limits for the

fitted curve.

There is little evidence of trends in most of the data sets.

However, this is partly because there are so few data points so

the confidence limits are correspondingly wide.

GLOBAL CONFIDENCE SETS FOR THE FITTED SMOOTH USING THE BOOTSTRAP

To assess trends in log-contaminant levels, it is necessary to

have some global confidence sets for the smooth. Pointwise

confidence intervals can be very misleading for this purpose

because they are only a one-dimensional projection of an

n-dimensional global confidence set (see Hastie & Tibshirani

(1990) for details). An alternative approach is the bootstrap

(Efron & Tibshirani, 1986) .

Let ri = yi - f(xi) be the ith residual. A bootstrap sample is

obtained by randomly selecting with replacement n values from

{ri, i = 1.. .n}. *

Let these values be ri , i = 1.. .n. New *

observations y; = f (xi) + ri, i = 1 . l are then smoothed.

Repeating this process a number of times yields a bootstrap

distribution of smooths.

Twenty bootstrap smooths for five CMP data sets are shown in

Figure 2. If nearly all the smooths reveal the same trend as the

original data, then this gives credence to the trend. For

example, there is clearly a negative trend in the levels of

mercury in herring in area 5 0 G 8 . However, it is possible that the

apparent positive trend in mercury levels in herring in area 4667

has occurred through chance.

An advantage of the bootstrap is that it relaxes the assuption

that the observations have a normal distribution about the true

underlying function with zero mean and constant variance. It also

shows the effect of 'large outlyingf observations on a smooth.

This is particularly important when there are so few points in

each data set. For example, in the case of mercury levels in

herring in area 4667, quite a different picture emerges depending

on whether an outlying observation occurs at the start, in the

middle or at the end of the time series.

REFERENCES

Anon, 1989. Statistical analysis of the ICES cooperative

monitoring programme data on contaminants in fish muscle tissue

(1975-1985) for determination of temporal trends. International

Council for the Exploration of the Sea. Palaegade 2-4, DK-1261

Copenhagen K. Cooperative Research Report No. 162.

Cleveland, W.S. (1979). Robust locally-weighted regression and

smoothing scatterplots. J. Am. Statist. Assoc. 74, 829-36.

Efron, B. & Tibshirani, R.J. (1986). Bootstrap methods for

standard errors, confidence intervals and other methods of

statistical accuracy. Statist. Sci. 1, 54-77.

Hastie, T.J. & Tibshirani, R.J. (1990). Generalized Additive

Models. Chapman and Hall, London. 335pp.

data s e t 1 (COD 31F2 HQ)

d a t a se t 37 (COD 31F2 Cu)

d a t a set 47 ( H E R 4667 Hg)

d a t a s e t 48 ( H E R 5068 Hg)

d a t a s e t 62 (HER 60H2 PCB)