ENVIRONMENTAL POLICY ANALYSIS

R I S K

Fallacies in Ecological Risk Assessment Practices M . P O W E R Department of Agricultural Economics University of Manitoba Winnipeg, Manitoba, Canada R3T2N2

L . S. M c C A R T Y L. S. McCarty ycientific Research and Consulting Oakville, Ontario, Canada L6K2J2

Anthropogenic stresses have forced society to use science to understand the impact of these stresses on ecosystems. One of the more popular tools in this effort is ecological risk assessment. The develop­ment of risk assessment has been driven by the need to allocate scarce resources to reduce human-related risks. Although risk assessment is widely used, consensus on an acceptable, comprehensive decision-making framework that clearly estab­lishes the roles of policy and science in formulat­ing environmental management principles has not emerged (2).

Inevitably, risk assessment involves both policy and science. The process of identifying ecological endpoints demands societal involvement in select­ing valued ecosystem components [2, 3). How­ever, selection of appropriate indicators for mea­suring and detecting potential changes in ecosystem components is a science-based issue (4). Lying at the interface of science and policy, risk assess­ment has been subjected to the demands of both. Moreover, attempts to use it as a bridge between science and science-based policy have resulted in a variety of myths and misconceptions, which. 3X6 now part of commonly accepted practice. Signifi­cant among these misconceptions, because of the consequences of their use in managing and/or reducing impacts on the environment are the notions that

• a "most sensitive," or "sentinel," species can be chosen and reliably used for environmental protec­tion;

• chronic toxicity data are "better" than acute data for regulatory purposes; and

• controlled experimental data can be accu­rately extrapolated to predict the nature and mag­nitude of population-, community-, and ecosystem-level responses to known stresses.

Related misconceptions—ecosystems are uni­formly sensitive to different forms of stress; data ob­tained from time-limited tests can be used to accu­rately characterize the long run; reductions in ecosystem complexity imply increasing system in­stability and impending catastrophe; and "good sci­ence" will remedy any and all environmental prob­lems—contribute, individually or in combination, to significant errors in gathering, analyzing, and inter­preting data used to characterize environmental changes {5-7). Analytical and interpretative errors may lead to mistakes in attributing ecological sig­nificance to observed changes and, consequently, mistakes in environmental policy and manage­ment (8) If the aim of risk assessment is to protect conserve and sustain ecological resources it is im-nnrtant to understand the influence of possible mis-conceptions about its practice

Since the advent of ecological risk assessment, there has been insufficient time to develop a consensus that guides its use in science-based policy activities. As a part of risk assessment, science and policy have been combined to cre­ate several myths that hold important conse­quences for environmental decision making. These myths—a "sensitive," or "sentinel," spe­cies can be selected and appropriately used; chronic data are better suited to regulatory needs than are acute data; and controlled ex­perimental data can be accurately extrapolated to the field—continue because of failures to clearly distinguish between the roles and uses of science and policy. Though possibly useful from a policy perspective, these myths are not scientfically valid. Issues of representativeness, lack of ecological knowledge, and variability question their scientific foundation. Science has played an important part in developing assess­ment techniques, but it cannot address all the issues surrounding environmental risk manage­ment. Policy must be used to make manage­ment-related decisions. Therefore, the most im­portant role for science is the provision of information to be used in environmental decision making.

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The illusion of a sentinel species One of the abiding hopes for risk assessment has been to develop an environmental equivalent to the lit­mus test. That data obtained from laboratory-based toxicity tests, or spatially and temporally lim­ited field work, may be used to predict die deleterious effects of exposure for all species inhabiting an ecosystem is appealing: Discover the most sensi­tive species and adjust media quality guidelines to ensure its survival, and it follows (or so the argu­ment goes) that all other ecosystem inhabitants will be sufficiently protected. If such a species could be found, the task of regulation would be greatly sim­plified, and attendant monitoring and enforcement costs reduced.

In pursuing this line of reasoning, risk assessors are chasing an elusive Holy Grail. Sensitivity is a rel­ative, not an absolute, concept. It is a function of the species, contaminant, and modifying factors oper­ating at the time of the assessment (9). Unfortu­nately, most available toxicity data rarely quantify po­tency consistently or exhaustively over the range of values that modifying factors are likely to exhibit in natural environments (5). Moreover, estimates of modifying factor interactions are scarce, as evi­denced by the extensive use of uncertainty factors in risk assessment to address unknowns {10).

Determining a most sensitive species relies on se­lecting a species from among a limited array of test organisms suited for laboratory experimentation (6). Although recent emphasis has been placed on se­lecting species appropriate for the ecosystem un­der study {11), die result is nevertheless the same. In lieu of a most sensitive species, a most sensitive species tested is obtained {12). The two are not the same, and the former is particularly variable. The re­sults of repeated toxicity tests, even within the same laboratory and on the same species, vary. Variabil­ity among interlaboratory tests is greater, and re­sults may be separated by a half an order of magni-tude or more (9).

Variations in the nature and degree of test inter­pretation and of interspecies and intertest compar­isons are even more problematic {13). For example, toxicological variability among orders, classes, and phyla tends to be large (4). Organisms selected for use in toxicity testing often belong to strains reared under laboratory conditions specifically to reduce in­dividual variability and improve the consistency of experimental results (6). Candidates for sentinels are also routinely selected because of tiieir economic im­portance, protected status, or other human-based bias, even before tiieir sensitivity to stressors has been deteremined. These species may be difficult to sam­ple {14) or are precluded ffom testing because of fheir rare or endangered status Although attempts have

been made to classify species by their use of com­mon resources with guild theory, it is often difficult to correctly assign species to guilds or to select spe­cies representative of a known guild {14). Despite these objections, it is routinely argued that stan­dards developed using sentinels will protect most species that inhabit the ecosystems to which stan­dards are applied.

Experience shows that ecosystem complexity and uniqueness mitigate against the easy transfer of in­formation from one site to the next {15). Indeed, the complexity associated with underlying biological and physical systems virtually precludes the reductionist approach to developing environmental management protocols implied by the most-sensitive-species notion. Accordingly, guidelines for environmental protection must be determined on a trial-and-error basis, as system-specific knowledge increases {16), rather than on the basis of ecologically inap­propriate analogues represented by results of sen­tinel species testing.

The sentinel species approach, however, has value in a retrospective sense. In current environmental as­sessment and monitoring practice, this value re­mains largely unexploited. Sentinel responses are de­scriptive of events that have occurred, not necessarily predictive of things that might happen. In conjunc-

FIGURE 1

Conflicting interpretations of ecological impact Chronic exposure to sublethal amounts oftoxaphene had different effects on the abundance of different brook trout population age classes. Statistically significant changes in age-0 abundance contrasted with statistically insig­nificant changes in adult abundance. The upper and lower dashed lines define the points beyond which positive (in adult abundance) and negative (in age-0 abundance) changes, respectively, were statistically significant at the 0.05 level of significance. Figure based on data from Reference 23.

VOL. 31, NO. 8, 1997 / ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS • 3 7 1 A

tion with comprehensive monitoring data, an ap­propriate stressor classification scheme, and multi­variate statistical analysis, it should be possible to overcome many of the methodological objections to the concept and to determine why certain species have diagnostic properties for a given set of ecolog­ical and stressor conditions.

Limitations of chronic data Reductions in the frequency of concentrated point source contaminant releases have led to an increas­ing emphasis on the significance of sublethal indi­cators as useful early warnings of exposure conse­quences. As a result, chronic data are now seen as more realistic than acute data because they are more reflective of the magnitude and duration of the stresses to which most organisms are exposed. They are also judged to be better suited for regulatory use and the development of science-based policy frame­works (17).

Chronic data are more realistic in the sense that nonlethal stressors, with long-term exposure possi­bilities, are more common. It is not clear, however, that this makes such data more amenable to use in decision making. The action of natural regulating mechanisms makes die interpretation of chronic data equally, if not more, problematic than the interpre­tation of acute data. Furthermore, although it is pos­sible to measure the statistical significance in chronic endpoints under laboratory conditions, it is not cer­tain how ecologically relevant the observed changes are in terms of either natural variability or the en­vironmental management criterion of extinction avoidance.

One review of laboratory toxicity test results con­cluded that chronic data (no-observed-effect levels or concentrations) exhibited no less variability than acute data (18). Debates over the methods produc­

ing such chronic data suggest that there is much to be resolved about the validity and utility of chronic data {19). Concerns include variations in the defini­tion of chronic responses; applicability of statisti­cal methods for their determination (as evidenced by the shift to defined response endpoints in die ex­posure concentration [ECx] method); and concerns about interpretational practices, hypothesis test­ing, and extrapolation to higher levels of biological organization {19, 20).

Furthermore, long-term data or information on changes in population-regulating processes such as recruitment, fecundity, and survival are typically re­quired before appropriate ecological significance can be attached to changes in measured chronic end-points (21). The theory of population dynamics ar­gues for more information about population-level ef­fects than is yielded by snapshots of measurable changes (3).

As an example, consider the pre- and postexpo­sure effects of toxaphene on a population of brook trout {Salvelinus fontinalis). Laboratory measure­ments (22) indicated a statistically significant, tiiough nonlethal, effect of chronic exposure on individual growth. Population dynamics data were used in a model (23) to compute probable population-level ef­fects (Figure 1). The 95% confidence intervals for adult abundance showed no statistically significant changes (paired t test, p = 0.56), whereas similar conffdence intervals for young-of-the-year (age 0) showed sta­tistically significant changes (p < 0.01).

In the absence of an understanding of the regu­lating mechanisms acting on a population, pre- and postexposure endpoint measurements yield con­flicting interpretations of the ecological signifi­cance of exposure effects. When information on population-regulating processes is included, the apparent inconsistency in the results can be ex­plained. Changes in growth act through fecundity re­ductions to lower juvenile density. This in turn trig­gers changes in density-dependent survival, resulting in increased juvenile survival, and the elimination of measurable statistical or ecological significance in the adult abundance endpoint.

Although density dependence is an important modifying factor in field population studies, it is rarely addressed in laboratory testing. However, density is a significant factor in the derivation of laboratory test results (24). Differences of a factor of 2 were found in the growth-based chronic response of fish as the density of exposed groups increased. Because pop­ulation-regulating mechanisms are significant in lab­oratory-based measurements, tests that ignore such factors are of unknown reliability.

The time dimension causes problems associ­ated with differentiating potential ecological signif­icance from statistical significance. Chronic expo­sure is defined as a period at least as long as 10% of species life span (13). This is long enough for die ac­tion of natural population-regulating mechanisms to be observed. The effects of stressors acting on pop­ulations are initially mediated by compensatory mechanisms that also dictate population dynamics (25). Accordingly, the nature and timing of regulat­ing processes will have a large influence on deter­mining the ultimate effect of any stress incident and

FIGURE 2

Influence of heterogeneous environments Significant correlations ( p < 0.03 for all) between measures of community richness and diversity and common physicochemical measures, such as stream discharge range, demonstrate the influence of heterogeneous envi­ronments in determining the pattern of species occurrence. The correlations of richness and diversity to discharge range measure 0.415 and 0.515, respectively, and pose obvious problems for attempts to extrapolate experi­mental testing results to the field. Figure based on data from Reference 39.

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may modify initially observed physiological or mor­tality effects. The problem posed for chronic data in regulatory decision making, then, is not the nature or quality of the data but the likelihood that popu­lation-regulating mechanisms will confound easy in­terpretation of the data in the absence of compre­hensive background ecological information.

The myth of laboratory-to-field extrapolation The use of extrapolation assumes that an individu­al's response to a stressor can be precisely mea­sured by controlled tests and used to predict a pop­ulation's response to that stressor in its natural environment. This is the most pervasive of all risk assessment myths. It persists despite ecological ar­guments to the contrary {26). Toxicologists esti­mate environmentally "safe" concentrations based on laboratory-determined exposures, appropriately de­rived safely and/or uncertainty factors accounting for experimental unknowns, and their judgment of over­all risk (27).

Although regulatory successes have been achieved using safety factors, we question the scientific rigor of the approach. Consider, for example, the list of rea­sons advanced for the use of safely factors. These in­clude the possibility of deleterious effects not con­sidered in the laboratory, inter- and intraspecies variations, possible exposure to mixtures, the ac­tion of ecological compensation and regulation mech­anisms, restricted test-based exposure times, and ex­perimental and statistical error {27, 28)) Though intended to justify the use of safety factors, the rea­sons represent little more th3.n o. comprehensive in­dictment of why laboratory results cannot be reli­ably extrapolated to field situations.

Work continues on refining the estimation of safety factors and determining contaminant concentra­tions that are theoretically hazardous for only a frac­tion of all exposed species. Aldenberg and Slob pro­posed a method that uses sensitivity variability among tested species to compute "safe" concentrations (29). The method presumes that available toxicity re­sults may be treated as random draws taken from a distribution defining sensitivities of all species to a given compound. Unfortunately, only a limited group of species are routinely used for toxicity testing. Their selection for ecise of laboratory handling, economic importance, or other anthropocentric reasons does not make the available sample random in the sta­tistical sense required by the methodology (6 14))

The quintessence of ecotoxicology, as used by risk assessors, is the estimation of dose-response rela­tionships for a limited number of species in artifi­cial test containers and the extrapolation of those results to natural environments. However, the inter­actions of biotic and abiotic materials within an ec­osystem are so complex that they cannot be pre­dicted (30). Furthermore, ecosystems have derivative properties and functions that cannot be routinely in­ferred from detailed knowledge of system compo­nents (5). This observation makes it unlikely that re­sponses at biological organization levels above the population level can be reliably predicted from ex­perimentally based tests.

For example, changes reported in phytoplank-ton abundance during whole-lake grazer manipu-

lations were correctly predicted for only one-third of the taxa tested in controlled experiments (31). In ad­dition, during controversies concerning the causes of eutrophication in the 1960s and 1970s, the signif­icance of inorganic carbon limitation in eutrophi­cation was overstated because of experimentally based conclusions (32).

A distinguishing feature of ecosystems at all scales, heterogeneity poses other problems for laboratory-to-field extrapolations. Overly brief experiments have been misleading because of failures to account for transient dynamics, indirect ef­fects, environmental variability, and site history (33). For example, bio­physical changes along rivers and streams present a complex system of interdigitating patch types (34), rather than a smooth, continuous gradient for which the rate of change is easily predicted. This has meant the fauna in lotic systems are invariably associated with local con­ditions Studies of macroinverte-brate communities nearly always im­plicate pH or its close chemical correlates (e e alkalinitv aluminum concentration) in the separation of sites Other physical characteristics such as distance from sourcp stream link discharge and slnnp also nlav

mies in diffprpntiarine hptwpp 't The ass i ti

hptwppn rn unirips and nh ' l iablp l d

remarkably consistent conclusions (35). The inescapable conclusion is that local influ­

ences affect community composition. The commu­nity conditioning hypothesis corroborates this view. Using experimental microcosms of similarly ex­posed communities, Matthews et al. (36) demon­strated that treatment effects varied throughout an experiment and that no single set of indicator vari­ables or community measures (e.g., species abun­dance or reproduction dynamics) accurately char­acterized observed community responses.

The problem is that ecologists do not yet fully un­derstand which factors are most critical. Possibili-

Risk assessors

are chasing an

elusive Holy

Grail in seeking

a most sensitive

species to

predict

deleterious

effects for all

species in an

ecosystem.

FIGURE 3 The complexity of stress-response relationships The dose-response paradigm, although necessarily simple for experimental practice, does not adequately account forthe multiple, simultaneous stressors to which all species are exposed in natural environments.

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ties include the direct toxicity of acidic waters to some species, indirect effects acting through the food sup­ply, and the level of diversity and predation (37). In the absence of a consensus as to which variables are most critical, it is inappropriate to presume that sim­plified tests can succeed in prediction where ecol­ogy has failed.

Ecologists have recognized for some time that it is difficult to predict community responses to sub­tle changes in environmental conditions (38). Eco-toxicologists have responded by developing multi-species tests that use diversity indices to measure stress, assuming that less impacted systems mani­fest high diversity (38). Work in stream ecology, how­ever, indicates that diversity and persistence mea­sures are as much a function of local conditions as the stresses acting on the community or ecosystem (39, 40))

Data collected from stony-riffle stream sites in Ashdown Forest, Sussex, England, showed that community richness and di­versity measures were signifi­cantly related to variations in stream pH, the range of stream discharge (m J /s_ 1) , and sum­mer temperature (see Figure 2). The pattern of catchment land use, in turn, was an important determinant of stream pH. Few studies have extended commu­nity-level analysis to explicitiy ad­dress issues of community sistence Those that have indicate that heterogeneity affects persis­

tence For example in benthic invertebrate commu­nities sampled at 27 stream sites in southern En­gland persistence was greatest in the shaded headwaters of streams with a restricted range of tem­peratures and discharge (40)

Further attempts to characterize community re­sponses to stress have been confounded because many experiments often exclude, or distort, impor­tant community or ecosystem variables (7). Exam­ples of factors reflective of environmental heteroge­neity routinely excluded from controlled testing include seasonal and diurnal variations, differ­ences in life stage metabolism, and correlations between species occurrence and physicochemical variables. For example, seasonal profiles for concen­trations of Cu, Cd, Pb, and Zn in Diastylis rathkei from Kiel Bay in the western Baltic showed statisti­cally significant (p < 0.001) variations among months with distinctive increases in tissue con­centrations coincident with the beginning of the growing season (41) The ratios of maximum-to-minimum measured concentrations were greatest (2 5x) for Pb least (1 4x) for Zn and 1 6x for Cu and Cd with observed variations attributable to tem­perature- and feeding-induced changes in meta­bolic activitv Changes in metabolic activitv occur between as well as within years Combined these results sueeest the import'ance of inter- and' intra-annual variation in metabolic activity for rietermin ing the potential expos re conseq ipnces nf a known contaminant

The sensitivity of population and community re­sponses to naturally varying environmental factors implies that observed field responses to stress, even in the most carefully selected control and impact comparison cases, cannot be attributed solely to the action of the stressor in question. These difficulties have led some to suggest that generalized risk as­sessment may be impossible, because each popu­lation and community is so tightly integrated into its own particular ecosystem that it is unique (42).

Data from fisheries studies support this view. They show that the sensitivity of manipulative experi­ments at higher levels of biological organization is generally compromised because of local unique­ness and because the variability associated with ex­perimental results does not stabilize (15). A comple­mentary argument suggests that the hierarchical organization inherent in ecosystems does not map directly onto human biological hierarchy notions (43). Consequently, from the outset it is not possible to make accurate inferences about the effects of stres­sors on the environment, because there is no a pri­ori way of knowing the true hierarchical relevance of collected data.

Because nearly all environmental interventions in­clude more than a single impact, or adverse effect, stresses as diverse as physical habitat degradation, exploitation, and multiple chemical exposures will interact to affect the integrity of selected end-points. Studies with population models of brook trout have demonstrated the inadequacy of additivity as­sumptions for predicting cumulative effects (44). Sub­stantial multiplicative interaction means that pre­dictions based on summing individual stressor effects produce larger predictive errors as the intensity of one or both stressors increases (44). Similarly, eco­systems respond in aggregate to the anthropogenic and natural influences acting on them (see Figure 3). This demands an integrative approach to ecosys­tem study that considers multiple the interactions among those stressors

What is wrong with extrapolation from con­trolled experimentation is not experimental integ­rity, but the unintended or inappropriate use of experimental results. In terms of advancing under­standing of the modes of stressor action, experimen­tation has been invaluable. The interpretation of relevance, however, requires insights into the func­tioning of ecological systems as a whole. Therefore, regulatory decision-making successes based on the extrapolation of laboratory results should not be seen as confirmation of the scientific validity of extrapo­lation, but rather as a victory for common sense and good judgment.

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301-04. (7) Carpenter, S. R. Ecology 1996, 77, 677-80.

Although chronic

stressors are

more common, it

is not clear that

chronic data are

more useful in

decision making

than acute data.

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(8) Power, M.; Power, G.; Dixon, D. G. Environ. Manage. 1995, 19, 629-39.

(9) Sprague, J. B. In Fundamentals of Aquatic Toxicology, Rand, G. M.; Petrocelli, S. R., Eds.; Hemisphere Publishing: Wash­ington, DC, 1985; pp. 124-63.

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(19) Newman, M. C. Quantitaiive Methods in Aquatic Eco-toxicology, Lewis Publishers: Boca Raton, FL, 1995; p. 426.

(20) Hoekstra, J. A.; van Ewijk, P. H. Environ. Toxicol. Chem. 1993, 12, 187-94.

(21) Shuter, B. J. In Biological Indicators of Stress in Fish; Ad­ams, S. M., Ed.; American Fisheries Society Symposium 8. American Fisheries Society: Bethesda, MD, 1990; pp. 145-66.

(22) Mehrle, P. M.; Mayer, F. L.J. Fish. Res. Bd. Can. 1975, 32, 609-13.

(23) Power, M.; Power, G. Ecol. Model. 1995, 80, 171-85. (24) Arthur, A. D.; Dixon, D. G. Can. J. Fish. Aquat. Sci. 1994,

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C. H.; Stauffer, J. R., Eds.; Lexington Books: Lexington, MA, 1980; pp. 253-80.

(26) Munkittrick, K. R.; McCarty, L. S. /. Aquat. Ecosys. Health 1995, 4, 77-90.

(27) Holdway, D. A. In Pollution in Tropical Aquatic Systems; Connell, D. W; Hawker, D. W., Eds.; CRC Press: Boca Ra­ton, FL, 1992; pp. 231-46.

(28) van Straalen, N. M.; Denneman, C.AJ. Ecotox. Environ. Saf. 1989, 18, 241-51.

(29) Aldenberg, X; Slob, W Ecotox. Environ. Saf. 1993, 25, 48-63.

(30) Minns, C. K. /. Aquat. Ecosys. Health 1992, 1, 109-18. (31) Carpenter, S. R.; Kitchell, J. F. Bioscience 1988, 38, 764-

69. (32) Schindler, D. W. et al. Science 1972, 177, 1192-94. (33) Tilman, D. In Longterm Studies in Ecology: Approaches

and Alternatives; Likens, G. E., Ed.; Springer-Verlag: New York, 1989; pp. 136-57.

(34) Naiman, R. J. et al. /. N. Am. Benthol. Soc. 1988, 7, 289-306.

(35) Sutcliffe, D. W; Hildrew, A. G. In Acid Toxicity and Aquatic Animals; Morris, R. et al., Eds.; Cambridge University Press: Cambridge, England, 1989; pp. 13-29.

(36) Matthews, R. A.; Landis, W G.; Matthews, G. B. Envi­ron. Toxicol. Chem. 1996, 15, 597-603.

(37) Hildrew, A. G.; Giller, P S. In Aquatic Ecology: Scale, Pat­tern and Process; Giller, E S.; Hildrew, A. G; Raffaelli, D. G., Eds.; Blackwell Scientific: Oxford, England, 1994; pp. 21-62.

(38) Krebs, C. J. Ecology: The Experimental Analysis of Distri­bution and Abundance, 3rd ed.; Harper and Row: New York, 1985; p. 800.

(39) Townsend, C. R., et al. Freshwater Biol. 1983, 13, 521-44.

(40) Townsend, C. R.; Hildrew, A. G.; Schofield, K. /. Anim. Ecol. 1987, 56, 597-613.

(41) Swaileh, K. M.; Adelung, D. Mar. Pollut. Bull. 1195,31,103-07.

(42) Rigler, F. H. Can. J. Fish. Aquat. Sci. 1982, 39, 1323-31. (43) O'Neill, R. V. et al. A Hierarchical Concept of Ecosys­

tems; Princeton University Press: Princeton, NJ, 1986; p. 253.

(44) Power, M. Ecol. Model. 1996, 90, 257-70.

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