chapter 3 distinguishing ecotoxic effects

Effects of Pollutants at the Ecosystem LevelEdited by P. J. Sheehan, D. R. Miller, G. C. Butler and Ph. Bourdeau@ 1984 SCOPE. Published by John Wiley & Sons Ltd

CHAPTER 3

Distinguishing Ecotoxic Effects

DONALD R. MILLER

Division of Biological SciencesNational Research Council of CanadaOttawa, Ontario, Canada KiA OR6

3.1 Nature of the Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.2 Identifying Parameters to Monitor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.3 Mathematical Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.4 Estimating Baseline Levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.5 Detecting Changes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3.1 NATURE OF THE PROBLEM

There is a fundamental difficulty involved in the quantitative examination ofecosystems, with which workers in the field are only now beginning to deal, andthat is the feature called environmental fluctuation, biological variability or oneof several other names.

The concept is simple enough: because of differences in physical conditions(temperature, rainfall, etc.), nutrient availability (varying runoff, for example),or other reasons, ecosystems do not behave in constant and repeatable ways. Ifthe same system is observed under what appear to be uniform conditions overseveral seasons, for example, very substantial fluctuations are found in suchvariables as population levels of particular species, or indices of speciesdistribution.

The problem, then, is how to decide whether an observed change in someparameter represents a deviation caused by presence of a pollutant, or whethersuch changes as are seen are part of the 'natural' fluctuations inherent in thesystem.

The question is by no means trivial and, indeed, may be the most importantquestion facing us as we try to refine our techniques for detecting, as a prelude todealing with, ecotoxic effects. In most cases we do not have the kind of baselinemeasurements that allow us to state with confidence just how large inherentfluctuations are, as would be required in order to apply classical statisticalprocedures to decide when the deviation should be regarded as significant in themathematical sense. If enough information existed aboutthevariabilityof the

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16 EFFECTS OF POLLUTANTS AT THE ECOSYSTEM LEVEL

so-called undisturbed system, we could apply mathematical procedures whichare well understood. Unfortunately, since such complete baseline data arelacking in many cases, we are faced with the difficult problem of deciding when achange indicates toxic effects at the ecosystem level without knowing how thesystem behaves in the absence of pollutants.

The problem may profitably be compared to that faced by the physician in theearly stages of examination of a patient. The first thing that must be determined iswhether the patient is sick at all, and physicians would largely agree that aknowledge of the patient is the most important piece of information contributingto that decision. Most of the observations that can be made, such as pulse rate,respiration rate; flushing of skin, fluid balance and even core temperature aresubject to variations as the individual responds to his chemical, physical andpsychological environment. The knowledge of, or ability confidently to predict,the normal ranges of such variations lies at the base of the diagnosis.

Fortunately, physicians have had many centuries of experience in observingwhich variations are natural and which are not, and, furthermore, identifyingwhich parameters are generally indicative of 'poor condition', whatever thatmeans.

Practitioners of ecotoxicology are relative beginners. So far, only limitedinformation is available about normal ranges of variation in those parameters wemight like to use as diagnostic aids; in fact, many scientists do not yet fullyappreciate how many observations are necessary or how long observation mustcontinue, to estimate with confidence a static quantity, let alone monitor a widelyfluctuating variable, the base value of which mayor may not be changing.

3.2 IDENTIFYING PARAMETERS TO MONITOR

Not only is there the problem of not knowing the levelof fluctuation that may beregarded as natural but, more seriously, there is no broad agreement about whatspecific quantities to examine. We do not yet know how to take the 'pulse' of anecosystem. Fortunately, this larger problem seems on its way to a solution, andthis will be explaned later in the book (cf. Chapter 6).

The state of our understanding of how to recognize and interpretenvironmental change seems to be as follows. If we can identify one or a smallnumber of clearly defined variables (as the atmospheric scientists have done withtemperature, carbon dioxide levels and ozone concentration) and record it orthem over a long period, there is little difficulty in deciding whether a shift hasoccurred. (We should rather say that there is little difficulty in deciding how largea shift has to occur in order to be detectable; we do not wish to imply that anyshift at all could be demonstrated.) Why, then, can not the same be done at theecosystem level?

What is needed is the specification of quantities that have certain desirableproperties, including the following:

DISTINGUISHING ECOTOXIC EFFECTS 17

1. indicative of overall condition of the ecosystem;2. comparable for a variety of ecosystems;3. easily and reliably measured;4. related to variables used in quantitative (modelling) studies.

It is stated elsewhere in this book that we ought to be concerned not only with thetoxic effects some chemical might exert on certain individuals or even certainspecies, but also with the effects on overall structure and function (speciesdiversity, material flow and nutrient cycling, etc.). Thus, our measurementsshould relate to a level of complexity or organization above that of organisms,say, at least at the community level (Jacobs, 1975; Whittaker, 1975).This sort ofmeasurement is clearly necessary if an overall assessment of ecosystemperformance is required. We might add that this sort of quantity is needed tocalibrate models of ecosystem dynamics. The whole question of measurementsof ecosystem function will be fully explored in Chapter 6; here it is only necessaryto say that our ability to identify appropriate variables is developing quickly.

3.3 MATHEMATICAL BACKGROUND

In purely mathematical terms, the problem is quite well understood, andcomprehensive treatises on quantitative approaches are available (for example,Box and Jenkins, 1970; Poole, 1978). Nonetheless, it is surprising how rarelyworks appear which address in quantitative terms the questions ofjust how muchdata must be gathered to estimate parameters or to prove that apparent changesare, in fact, real (good examples are Platt et ai., 1970;Platt, 1975; seealso Green,1979; Cairns et ai., 1979).

In summary, the problem may generally be handled in ways familiar to thoseacquainted with standard statistical procedures. If one wishes to estimate anenvironmental parameter which is assumed to be static, or stationary, one firstmust specify the accuracy required in terms, for example, of a 95 per centconfidence interval. Then, after a certain amount of observation devoted toestimating the nature of the underlying distribution, it is possible to specify howmany additional observations are required to achieve the specified accuracy.More generally, guides may be constructed describing the trade-off that appliesbetween sampling economy and estimation accuracy.

If we wish to determine whether a particular quantity is changing in time, weproceed to make a series of observations and fit a model of some degree ofcomplexity. The accuracy with which the various parameters in the model can beestimated, in particular judging whether long-term trends are real (parameterchanges being significantly different from zero), as well as the confidence that theparticular model is appropriate, increases with the number and duration of theobservations in a quantifiable manner. Since good references are available in thestatistical literature (Box and Jenkins, 1970;Poole, 1978), further details are not

givenhere.It ISappropriateto mention, however, that much of this information


is couched in jargon, and notation, that make it quite inaccessible to practisingfield biologists and ecotoxicologists; there is a need for some quite practicalguidebooks in this area. Specifically, there must be more emphasis on the quitefrightening data requirements if the kind of accuracy desired is really to beachieved (see following section). To this writer's knowledge, the best reviews ofthe problem include those by Elliott (1977), Eberhardt (1978) and Green (1979),each of which contains quite practical advice on the estimation of populationlevels. It is to be hoped that the work of the International Statistical EcologyProgram (ISEP) will provide assistance in this area; some publications arecurrently available (for example; Cairns et al., 1979; Patil and Rosenzweig,1979), and several others are in preparation at the time of writing.

In connection with this, we are led to think of theoretical studies, the so-calledecosystem model studies, for in no other approach to such systems have theproblems of state variable identification and numerical prediction been so clearlygiven priority (Pielou, 1969).

Many modelling studies have been carried out. Unfortunately, uponexamining the available literature, the first observation indicates that by far thelarger part of such work has involved only deterministic models, in which themost likelyvalues are predicted, without an analysis of the associated uncertainty(CEC, 1979b). From the present perspective, such modelling approaches aresimply not appropriate.

On the other hand, since the early 1970s there has been a gradual increase ininterest in uncertainty analysis. Omitting various writers who have simplypointed out that uncertainties make analysis difficult, without presenting studiesof how these difficulties might be resolved, we would identify Reichle et at.(1973c), Miller (1974) and Burns (1975) as establishing a technique wherebyuncertainties may be deliberately introduced into the parameters governingmodel behaviour so that output uncertainty may be calculated or experimentallymeasured. The approach may be used to compare predicted behaviour withpermissible errors for validation purposes (Miller et al., 1976), and has theadvantage for the present discussion that fluctuations in the whole system may bepredicted on the basis of fluctuations in individual parameter values. Since theserepresent climatic and related variables, the values of which are often wellknownfrom other sources, the approach is not always subject to the need for full-scaleand expensive baseline studies. Although techniques of modelling of ecosystemsneed to be further developed, the additional approaches of uncertainty analysisare easy to put into place as developments are made (Goodall, 1972; Gowdy etal., 1975; Majkowski et al., 1981).

3.4 ESTIMATING BASELINE LEVELS

If we are attempting to estimate the average or mean value of some numericalparameter which is assumed to be sensibly constant, at least over the duration of


the measuring period, simple formulae are available for estimating the standarderror of the mean for a simple collection of measurements. In order for thisstandard error to be lessthan some preassigned value, it is only necessary to makethe number of repetitions of the measurement large enough. The same is true forquantities known to vary from place to place, if it is agreed that what we are afteris the overall average value (even if that precise value is true for only a few specificlocations). If the number of observations is large (say, 50 or so) it is legitimate toassume that the value ofthe standard deviation so calculated will span an intervalcontaining about two-thirds of the observations. For any number of samples, theuse of the t-distribution makes it possible to calculate a confidence interval ofwhatever precision we like.

The surprising thing is how often field work is not examined in accordance withthese considerations, and when it is, how poor the actual estimates turn out to be.Hales (1962) illustrated the former point by giving several examples of quitenonmathematical rules of thumb that had been published in various places.

One of the earliest works that points out the magnitude of the problem is thatof Needham and Usinger (1956), in which it is stated that for a 95 per cent levelofsignificance, working with organisms on a rifHein Prosser Creek (California), 194samples would be required to give significant figures for total weights, and 73samples for total numbers. In a similar study in the Logan River, Utah, Hales(1962) showed that the required numbers of samples depended also on genus. Tohave a 90 per cent chance of estimating total numbers with confidence limits of :t25 per cent, for example, would take 18samples for Diptera and 34 samples forEphemeroptera in a typical location.

It is worth pointing out that the situation is not always bad, but depends on theparameter being examined. In the study quoted above. Needham and Usinger(1956) pointed out that only two or three samples would give, with a 95 per centprobability, at least one representative of each of the common genera of insectsbeing examined.

Thoughtful analyses of these problems are increasing. Treshow and Allan(1979) studied the dynamics of a pinyon pine-Utah juniper woodlandcommunity to determine baseline conditions and annual variations, andconcluded that 4 years of study were required to establish a reliable baseline.

Other examples could be quoted; however, the point is not that largequantities of data are needed in some cases while not in others, but rather that agreat deal of biological research is carried out in which the question is not evenaddressed. The difficulty is not in the statistical analysis, but in the practicalISpectSof research design. Cases where sufficient sampling has been carried out0 enable coefficients of variation to be determined were surveyed by Eberhardt1978), who then described how necessary sample size may be calculated. Other:xamples are described by Green (1979), and Resh (1979) has given a detailedaccount of the sources of variability in aquatic insect studies, and sample sizesnecessaryto achievegiven levelsof reliabilityof data.


We may digress for a moment to mention that the problem is being addressedin some important ways through the international programme of BiosphereReserves. Some years ago it was generally recognized that for comparisonpurposes it was necessary to have a certain collection of ecosystems, repre5en-tative of as many classes of ecosystem as possible, preserved in states as closeto the natural systems as possible (MAB, 1974). These will serve, and in manycases are serving, to allow precisely what we identify as missing, namely thecarefully planned study of how systems behave when they are left as much ontheir own as possible. Only with this information will we be equipped to statewith certainty whether a change recognized in an environmental situation reallyrepresents an 'effect' or not. There are, at the time of writing, 177 BiosphereReserves in 46 countries, and more are being identified regularly (Anon, Natureand Resources, 1980).

There is a problem, the resolution of which has not been found. The problem isthat some pollution exists and is measurable on a truly global basis. This means,of course, that even the most isolated Biosphere Reserves exhibit low butdetectable amounts of toxic substances, so the question arises as to whether theircondition really represents baseline or undisturbed behaviour (Brown, 1981). Itis to be hoped, however, that such low amounts would not cause disturbances atthe level we could observe.

3.5 DETECTING CHANGES

In the previous section it was emphasized that, in general, quite inadequateattention is paid to the question of how much data must be gathered in order tosay that an ecosystem has changed at all, much less to blame the change on aparticular pollutant. However, there have been cases where such changes havebeen documented, and it is instructive to mention some of them.

The Continuous Plankton Recorder Programme of the North Sea and theNorth Atlantic surveys some 300 species of plants and animals on commercialshipping routes and has been producing data since 1948.Glover (1979)examinedthese data and detected a long-term trend downward in both copepod abundanceand zooplankton biomass for both the North Sea and the North Atlantic, butwith the North Sea showing a remarkable reversal in the early 1970s.Interestingly, levels of fluctuation, which decreased with biomass, have notcorrespondingly increased. This data bank is a very extensive one, and provides afertile ground for various investigations (Colebrook, 1978).

Two quite different examples oftimewise variation were discussed by Gilboy etal. (1979). One consisted of individual tree rings analysed for metals, the other amoving filter method for particulates in air giving a resolution time of 2 hours.Their analysis confirmed the need for a resolution in the latter case at least thisfine or even better (perhaps I hour) if atmospheric fluctuations were to befollowed. It seems clear that many ecosystems would not require such precision,


although this would ultimately depend on the magnitude of the fluctuations.An example of research in which the problems of monitoring spatial and

temporal variation have been considered is the study oflong-term exposure of aforest to air pollutants described by Legge and coauthors (Legge, 1980; Legge etaI., 1981; Legge, 1982). This 8-year programme combined remote sensing,controlled laboratory fumigation experiments, and detailed field studies todetermine the effects of sulphur gas on foliar accumulation of sulphur andessential nutrients, soil changes, pine tree physiology, and forest productivity. S-gas emissions were continuously monitored at both the incinerator and the flarestacks at the West Whitecourt Gas Plant in Alberta. This monitoringdemonstrated that the incinerator contributed most of the sulphur emissions,but, on individual days, during gas plant operating upset, the flare stackscontributed substantial levels of sluphur pollutants. Intensive on-site air qualitymonitoring was undertaken at 2, 16 and 28 m above ground to measure thevarying concentrations of SOl reaching the lodgepole x jack pine forest atchosen experimental sites. Analogous sampling locations were chosen based onecological variables such as slope, aspect, soil type, species density and diversity,and environmental variables other than pollutants, including temperature, wind,solar radiation and precipitation. Pollutant variables such as the effluentcomposition and the concentration (including the factor of distance from source)were used to locate sites along a pollutant gradient. Concentrated biologicalsurveillance of foliar ATP and photosynthetic rates established a positiverelationship with both parameters, inversely correlated with sulphate-sulphuraccumulation. Statistical analysis of basal area increment data revealed thatdistance from the S-gas source, time in years, and their interaction had significanteffects on the woody production of pine trees.

Examples of studies in aquatic systems are those of Cushing (1979) and Myerset al. (1980). Cushing discussed the case of an exploited economic fishpopulation, for which data are abundant. Myers et al. studied the fish of BantryBay, Ireland, and concluded that observed declines offish stocks were not in factrelated to the explosion of the tanker Betelgeuse, as had been assumed (Cross etaI., 1979). Tont and Platt (1979) used spectral analysis to study phytoplanktondiversity of the California coast, and found considerable cyclic activityassociated with time periods ranging from a small number of weeks to severalyears. These they attributed to upwelling events caused by wind changes.

Longhurst et al. (1972) pointed out that populations of zooplankton andanchovy eggs fluctuate no more in the polluted Los Angeles Bight than elsewhereoff the California coast, and cited other examples on the basis of which theyconcluded that there is a danger of incorrectly ascribing natural fluctuations inanimal populations to the effects of pollutants.

A study specifically directed to the question of adequacy of monitoring is thatof Naiman and Sibert (1977). By taking intensive (3-hour) samples of variousquantities, they determined that the data collected at 2-week intervals, the


current monitoring practice, were adequate for temperature, salinity, nutrients

and chlorophyll a, but not for DOC, ATP and heterotrophic activity.A quantitative analysis of sampling strategies for trace element levels and

benthic invertebrate populations in the New York Bight, involving an explicitoptimization procedure applied to stratified sampling, was described by Saila etal. (1976). It was found that a fully optimized sampling plan required only threereplicates within a station and a small number of stations (seven) for significantresults.

In his discussion of the design of monitoring systems, Holdgate (1979)pointedout that surveys designed to describe average or integrated exposure of a targetmay not be relevant, for more damage may be done by short-lived peaks, that is,periods when exposure exceeds certain limits, and that a sampling system shouldbe able to measure the variation in contamination levels so as to allow for theestimation of possible 'worst case' exposures. Examples of advance baselinestudies include such cases as that of the Surrey, Virginia power plant (and manyothers) where water quality parameters were monitored for some time before theplant went into operation, and also afterwards (Bolus et aI., 1973).Such studiesgenerally have been limited to physical parameter measurements.

Some attempts to review the problem have appeared; the reader is referred topapers by Cushing (1979) and Cairns and van der Schalie (1980) and to laterchapters of this book, especially Chapter 6.

chapter 3 distinguishing ecotoxic effects

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