ENVIRONMENTAL
POLICY ANALYSIS
RISK
Fallacies in Ecological Risk Assessment Practices M . POWER Department ofAgricultural 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
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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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 h u m a n 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 c o m p o n e n t s [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 a n d 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 a n d / o r reducing impacts on t h e environment are t h e 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 ecosystemlevel 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 t h e 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 a n d management (8) If the aim of risk assessment is to protect conserve and sustain ecological resources it is imnnrtant to understand the influence of possible misconceptions about its practice 0013-936X/97/0931-370A$14.00/0 © 1997 American Chemical Society
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 laboratorybased 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 magnitude 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
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.
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-anderror 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 conjuncVOL. 31, NO. 8, 1997 / ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS • 3 7 1 A
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.
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 produc3 7 2 A • VOL. 31, NO. 8, 1997 / ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS
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 endpoints (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
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.
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.
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 phytoplankton 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 laboratoryto-field extrapolations. Overly brief experiments have been misleading because of failures to account for Risk assessors transient dynamics, indirect efare chasing an fects, environmental variability, and site history (33). For example, bio- elusive Holy physical changes along rivers and streams present a complex system of Grail in seeking interdigitating patch types (34), rather than a smooth, continuous a most sensitive gradient for which the rate of change is easily predicted. This has meant species to the fauna in lotic systems are predict invariably associated with local conditions Studies of macroinverte- deleterious brate communities nearly always implicate p H or its close chemical effects for all correlates (e e alkalinitv aluminum concentration) in the separation of species in an sites Other physical characteristics such as distance from sourcp stream ecosystem. link discharge a n d slnnp also nlav m i e s in diffprpntiarine hptwpp hptwppn r n
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remarkably consistent conclusions (35). The inescapable conclusion is that local influences 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. PossibiliVOL. 3 1 , NO. 8, 1997 / ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS • 3 7 3 A
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). Ecotoxicologists have responded by developing multispecies 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 stonyriffle stream sites in Ashdown Although chronic Forest, Sussex, England, showed that community richness and distressors are versity measures were significantly related to variations in more common, it stream pH, the range of stream discharge (m J /s _ 1 ), and sumis not clear that mer temperature (see Figure 2). The pattern of catchment land chronic data are use, in turn, was an important more useful in determinant of stream pH. Few studies have extended commudecision making nity-level analysis to explicitiy address issues of community than acute data. sistence Those that have indicate that heterogeneity affects persistence 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 a m o n g months with distinctive increases in tissue concentrations coincident with the beginning of the growing season (41) The ratios of maximum-tominimum 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' intraannual variation in metabolic activity for rietermin ing the potential expos re conseq ipnces nf a known contaminant 3 7 4 A • VOL. 3 1 , NO. 8, 1997 / ENVIRONMENTAL SCIENCE & TECHNOLOGY / NEWS
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 endpoints. 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.
References (1) (2) (3) (4) (5) (6) (7)
McCarty, L. S.; Power, M. Environ. Toxicol. Chem. 1997, 16, 122-23. Beanlands, G. E.; Duinker, E N. /. Environ. Manage. 1984, 18, 267-77. Calow, P. Environ. Toxicol. Chem. 1994, 13, 1549. Suter, G. W., II. Ecological Risk Assessment, Lewis Publishers: Boca Raton, FL, 1993; p. 538. De Kruijf, H.A.M. Comp. Biochem. Physiol. 1991, 100C, 291-99. Seitz, A.; Ratte, H. T. Comp. Biochem. Physiol. 1991,100C, 301-04. Carpenter, S. R. Ecology 1996, 77, 677-80.
(8) Power, M.; Power, G.; Dixon, D. G. Environ. Manage. 1995, 19, 629-39. (9) Sprague, J. B. In Fundamentals ofAquatic Toxicology, Rand, G. M.; Petrocelli, S. R., Eds.; Hemisphere Publishing: Washington, DC, 1985; pp. 124-63. (10) Calabrese, E. J.; Baldwin, L. A. Performing Ecological Risk Assessments; Lewis Publishers: Boca Raton, FL, 1993; p. 257. (11) Chapman, P. M. Environ. Toxicol. Chem. 1995, 14, 92730. (12) Cairns, J. Bioscience 1986, 36, 670-72. (13) Rand, G. M.; Wells, P. G.; McCarty, L. S. In Fundamentals ofAquatic Toxicology: Effects, Environmental Fate and Risk Assessment, 2nd ed.; Rand, G. M., Ed.; Taylor and Francis: Bristol, PA, 1995; pp. 3-67. (14) Treweek, J. Impact Assess. 1995, 13, 289-315. (15) Walters, C. J.; Collie, J. S.; Webb, T. Can. Spec. Publ. Fish. Aquat. Sci. 1989, 105, 13-20. (16) Ludwig, D.; Hilborn, R.; Walters, C. Science 1993, 260,17, 36. (17) Canadian Water Quality Guidelines; Canadian Council of Ministers of the Environment; Environment Canada: Ottawa, ON; 1991; Appendix IX. (18) Parkhurst, B. R.; Warren-Hicks, W; Noel, L. E. Environ. Toxicol. Chem. 1992, 11, 771-91. (19) Newman, M. C. Quantitaiive Methods in Aquatic Ecotoxicology, 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, 57, 365-71. (25) Goodyear, C. P. In Biological Monitoring of Fish; Hocutt, 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, 4863. (30) Minns, C. K. /. Aquat. Ecosys. Health 1992, 1, 109-18. (31) Carpenter, S. R.; Kitchell, J. F. Bioscience 1988, 38, 76469. (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, 289306. (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. 2 1 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, 52144. (40) Townsend, C. R.; Hildrew, A. G.; Schofield, K. /. Anim. Ecol. 1987, 56, 597-613. (41) Swaileh, K. M.; Adelung, D. Mar. Pollut. Bull. 1195,31,10307. (42) Rigler, F. H. Can. J. Fish. Aquat. Sci. 1982, 39, 1323-31. (43) O'Neill, R. V. et al. A Hierarchical Concept of Ecosystems; Princeton University Press: Princeton, NJ, 1986; p. 253. (44) Power, M. Ecol. Model. 1996, 90, 257-70. You ve also got oils, waxes, gases, plastics, surfactants, catalysts, pharmaceuticals, dyes, organic and inorganic chemicals, and
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