SCOPE 53 - Methods to Assess the Effects of Chemicals On Ecosystems

19.1 INTRODUCTION

Ecological risk assessment remains a jigsaw puzzle for which the picture being created and all the right pieces are present remain uncertain. This chapter addresses three significant issues related to ecological risk assessment: (1) the definition of ecosystem health, (2) the scale at which such assessments should be conducted, and (3) the use of ecological benefits as an analytical paradigm.

The ecosystem health metric proposed is a comprehensive, multiscale, dynamic, hierarchical measure of system resilience, organization, and vigour that closely tracks the concept of sustainability. Assessment scale is an important issue, because it tends to define the scope of the policy options considered for mitigation. Currently, an overemphasis exists on population and process-level analyses at the expense of the ecosystem and ecoregion levels. As with the health metric proposed, assessment of ecosystems at multiple levels is important to insure that the cure is no worse than the disease. Finally, a somewhat different perspective to assess ecological systems is discussed. By considering changes in an ecosystem's delivery of ecological benefits (goods and services), the assessment may be able to answer more directly the question of significance.

19.2 A DEFINITION OF ECOSYSTEM HEALTH

The term "health" is commonly used in reference to ecosystems by both scientific and policy documents, but a satisfactory definition of ecosystem health remains to be developed. While the framework of human health may provide a starting point, severe limitations are imposed on the parallel between human health and ecological health (Norton, 1991b; USEPA, 1992a). In addition to its use in ecological assessments, a definition of ecosystem health also provides a means to aid the integration of the analyses of ecological and economic systems (Haskell et al., 1992).

As a starting point, this analysis begins with five axioms of ecological management (Norton, 1991a):

1. The Axiom of Dynamism. Nature is more profoundly a set of processes than a collection of objects; all is in flux. Ecosystems develop and age over time.
2. The Axiom of Relatedness. All processes are related to all other processes.
3. The Axiom of Hierarchy. Processes are not related equally, but unfold in systems within systems, which mainly differ regarding the temporal and spatial scale on which they are organized.
4. The Axiom of Autopoiesis. The autonomous processes of nature are creative, and represent the basis for all biologically-based productivity. The vehicle of that creativity is energy flowing through systems that in turn find stable contexts in larger systems, that provide sufficient stability to allow self-organization within them through repetition and duplication.
5. The Axiom of Differential Fragility. Ecological systems, which form the context of all human activities, vary in the extent to which they can absorb and equilibrate human-caused disruptions in their autonomous processes.

These axioms regularly recur, even if implicitly, in the following discussion, and they are essential elements of the definition of ecosystem health proposed below.

Ecosystem health is often framed in terms of human health (Rapport, 1989, 1992). While both are complex systems, medical science has a large body of knowledge and expert systems (in the form of doctors) available to advance diagnosis (Schaeffer et al., 1988; Schaeffer and Cox, 1992). Such analytical tools are absent for ecosystems. However, ecosystems have been studied extensively with respect to their stability and resilience (Pimm, 1984; Holling, 1986).

Six major concepts are most often used to describe ecosystem health (Costanza 1992):

1. homeostasis,
2. the absence of disease,
3. diversity or complexity,
4. stability or resilience,
5. vigour or scope for growth, and
6. balance between system components.

Each concept represents a piece of the puzzle, but none is sufficiently comprehensive, especially in terms of being able to deal with many different levels of ecological systems.

Homeostasis is the simplest and most popular definition of system health: any and all changes in the system represent a decrease in health. The greatest difficulty with this approach is in differentiating between naturally occurring stresses and external (including anthropogenic) stresses. This definition is best used for warm-blooded vertebrates, since they are homeostatic and since normal ranges can be more easily determined from large populations. However, for ecological systems, all changes (or even any given change) cannot be assumed to be bad. The best example of this is succession¾for the initial state, succession is an irreversible change, and one that might be necessary for the system to be sustained. Given that ecosystems are constantly changing, this definition does not deal with a fundamental characteristic of ecosystems.

The definition of ecosystem health as the absence of disease has several failings. First, while various (including anthropogenic) ecosystem stresses can be described, their mere existence does not indicate that they are adverse stresses. A separate, independent definition of ecosystem health would be required. Second, this definition yields only a dichotomous result that is inadequate to characterize complex systems.

The notion of basing a definition of ecosystem health on a system's diversity or complexity rests on the assumption that these characteristics are predictors of stability or resilience and that these are indicators of ecosystem health. Presently, the analytical basis is insufficient to use this concept, but network analysis may yield a more sophisticated framework for incorporating system diversity into a definition of ecosystem health (Wulff et al., 1989; Ulanowicz, 1992).

Stability and resilience are key measures of ecosystem health, since healthy organisms and systems have the ability to recover from stresses or to use the stress in some creative manner to improve their status. A failing is that these measures do not characterize the level of system organization or the level at which the system is functioning.

Odum (1971) has suggested that the level of a system's metabolism (energy flow) is an indicator of its ability to deal with stresses. The concept of ecosystem balance is based in Eastern traditional medicine, and the notion that a healthy system is one that maintains a proper balance between its parts. However, the proper balance can only be determined by some independent measure of ecosystem health.

Based on these framing concepts, a practical definition of ecosystem health must have four essential characteristics. First, it must integrate the definitions described above into one that combines system resilience, balance, organization (diversity), and vigour (metabolism). Second, it must represent a comprehensive description of the ecosystem. Third, it must use weighting factors to compare and aggregate different components of the system. Finally, it must be temporally and spatially hierarchical (Costanza, 1992).

Such a definition would be: "An ecological system is healthy and free from distress syndrome, if it is stable and sustainable¾that is, if it is active, and maintains its organization and autonomy over time, and is resilient to stress" (Haskell et al., 1992). Accordingly, a diseased system is one that is not sustainable, and will eventually cease to exist¾clearly illustrating the importance of the temporal and spatial aspects of the definition. Distress syndrome refers to the irreversible processes of system breakdown leading to death.

Figure 19.1. Ecological benefits profile

Two very important tools for making operational this definition are network analysis and simulation modelling. Network analysis, in this context, refers to all variations of the analysis of ecological and economic networks. It has the potential for yielding an integrated, quantitative, hierarchical treatment of all complex systems, including ecosystems and combined ecological-economic systems. An important area of network analysis is the development of common pricing mechanisms for ecological and economic systems. In complex systems with many interdependencies, a problem with mixed units is often present. Ecological analyses have ignored this problem by choosing a single commodity as an index; yet this ignores interactions between commodities, and is consequently unrealistic and quite limiting.

Evaluating the health of complex systems demands a pluralistic approach (Norgaard, 1989; Rapport, 1989) and an ability to integrate and synthesize the many diverse perspectives that may be present. An integrated, multiscale, transdisciplinary, and pluralistic approach is required to quantitatively model systems (including organisms, ecosystems, and ecological-economic systems). Achieving such a capability requires the ability to predict the dynamics of ecosystems under stress (Costanza et al., 1990) as well as advances in high-performance computing.

Figure 19.2. Old forest growth with forest intact (points are not data)

19.3 REGIONAL SCALE ECOLOGICAL ASSESSMENT

At several points during the previous discussion, the issue of scale has been raised. Assessment of scale is important, because scale tends to define the scope of the policy options considered for mitigation. Currently, an overemphasis exists on population and process-level analyses at the expense of the ecosystem and ecoregion levels. As with the health metric proposed above, assessment of ecosystems will be important at multiple levels to insure that policy decisions do not result in undesired ecological consequences.

Also, guarding against haphazard aggregation of measures across ecological levels of organization will be important. Different scales of ecological systems may be driven by very different dynamics (Norton et al., 1991) so the best indicators or metrics for one level may be inadequate or misleading at another scale.

While the concept of multiscale analyses is logical and desirable, it poses significant difficulties since most ecological research deals with very small geographic areas (usually 1 m²⁾. Recognizing the need for ecological assessments to deal with much larger landscapes, some ecologists began in the 1980s to argue , that regional-level ecology was important to understand the smaller scale (Allen et al., 1984), and that ecological assessments must be capable of assessing at the regional level (Hunsaker et al., 1990). This view was strongly endorsed by EPA's ecological risk assessment peer-review panel, when it noted:

Figure 19.3. Old forest growth with forest clear-cut (points are not data)

"Ecological risk assessment, unlike human health risk assessment, must address a diverse set of ecological systems, from tropical to Arctic environments, deserts to lakes, and estuaries to alpine systems... . Ecological risk assessment may occur over much wider temporal and spatial scales than those for human health risk assessment" (USEPA, 1992).

Meeting this need for multiscale analysis will require the same type of research required to provide the foundation for the definition of ecosystem health, namely, network analysis and simulation modelling. Analyses such as these will be much more feasible because of recent advances in high-powered computing and visualization techniques.

An important element of multi scale analysis of ecosystems, and even single scale analyses, is the proper characterization of uncertainty. Even with the significant advances in modelling, large amounts of uncertainty will exist with respect to anthropocentric effects on ecosystems (Funtowicz and Ravitz, 1991). Developing the means and the methods to characterize this uncertainty in a meaningful manner for policy makers should be a major research area for ecological assessment (Costanza, 1987, 1991; Perrings, 1987, 1989, 1991; Costanza and Perrings, 1990).

Figure 19.4. Old forest growth when recovery begins

19.4 THE ECOLOGICAL BENEFITS PARADIGM

Even when unable to assess ecological health across scales, space, and time, some often-asked issues remain, such as the significance of the findings. Even if public policy advances were at the point where the maintenance of ecological health is considered an important goal, trade-offs and choices will be needed by policy makers. This situation strongly suggests that simply characterizing the risk of potential outcomes will be an inadequate response. By developing the ability to characterize ecological benefits more completely and by characterizing the impact from the loss of ecosystem health on the delivery of those benefits, ecological assessments will make great strides towards resolving the significance matter.

Table 19.1. Ecological benefits 

Current ecological assessment documents and frameworks clearly state that policy makers must be consulted to develop the ecological risk assessment endpoints and that the assessment must ascertain the significance of observed changes.

The selection of assessment endpoints relates in part to policy interests (e.g., to specified regulatory endpoints or to public concerns); thus, changes in assessment endpoints must be related ultimately to changes in parameters of the ecosystem that humans care about (anticipating the significance issue) (EPA, 1992a).

The products that will result from the process clearly will not be couched in terms with which policy makers are most comfortable nor in the metrics that they will understand and be able to communicate to their various constituencies. This tendency to describe scientific findings in terms that are, in the view of the policy- maker, either arcane or in multiple metrics has been referred to as multidimensionality. The result is that the findings are described in a manner so detailed and fragmented that no one can grasp the overall implications (McKelvey and Henderson, 1991).

Benefits are ecological goods and services, and have been compared to ecological structure and function (Westman, 1977). Ecological goods and services can be described as those benefits that humans derive from ecological systems. For example, cut trees provide lumber (an economic and ecological good), while uncut trees take up air pollutants (an economic and ecological service). The uptake benefit will be lost when the trees are cut for lumber, and vice versa.

To economists, the term "benefits" often denotes a monetized valuation of an economic good or service. However, in this context, the term "benefit" is used to refer to all ecological goods and services whether or not their value has been monetized. Since the monetization step is often controversial, leaving it aside permits these efforts to focus on the scientific questions surrounding the identification and quantification of benefits. However, an analytical loss will exist in the absence of monetizing (a common metric with which to measure and express the magnitude of the benefits).

Ecological benefits occur in four groups: (1) market benefits (first wave), such as lumber, for which economic markets exist; (2) non-market use benefits (second wave), such as recreational benefits, for which no direct markets exist; (3) non-market, non-use benefits (third wave), such as the existence value and bequest value; and (4) fourth wave benefits are those that would fit into any of the three previous categories, but which have not routinely included in previous benefits analyses, such as pollution uptake, climate modification, habitat, and biodiversity (Principe, 1992). A more complete listing of benefits that would be included in each of these four categories is shown in Table 19.1.

The benefits shown in this table are very highly aggregated. However, in some instances, a less-aggregated list might be more appropriate for each type of ecosystem or region to be evaluated. These benefits are the sort of metrics with which most policy makers are conversant (except for those benefits they had never really considered). If the assessment can describe the extent to which stresses diminish or increase the delivery of these benefits, a policy- maker will be in a much better position to understand the consequences of choosing among the available options. Thus, the matter of significance is addressed in the most direct possible way.

While a variety of graphical methods can depict the status and change in magnitude of these benefits, a polar-type chart may be best to demonstrate the technique. The polar chart has some appeal, because of its division into four quadrants. By placing first wave benefits in quadrant one, second wave benefits in quadrant two, etc., the status of each wave's benefits can be clearly shown (Figure 19.1).

This graphical analysis might illuminate a policy maker's choices; for instance, consider the choice to harvest the trees in a hypothetical old-growth forest. At the outset, the benefits profiles shown in the following figures are not based on data-they only illustrate the use of the graphical technique.

A virgin old-growth forest might have a benefits profile similar to that shown in Figure 19.2. Most of the benefits are realized as non-market benefits (i.e., in quadrants 2, 3, and 4). The benefits profile shown in Figure 19.3 might reflect the change in benefits resulting from a decision to harvest the old-growth trees. Most of the non-market benefits have been lost, but the market benefits from timber sales have increased the first quadrant benefits. Continuing the analysis for another year (Figure 19.4) provides an important insight: after the trees are harvested, not only are the non-market benefits lost, but so are the market benefits. The resulting benefits profile in Figure 19.4 is much smaller than the benefits profile in Figure 19.1. If the benefits profiles were based on actual data, a policy maker would gain several important insights into the consequences of the impending decision that otherwise might have been obscured or lost by other forms of analysis.

While these graphs are clearly oversimplified for the purposes of illustration, the technique holds considerable promise to making ecological risk-benefit decisions better informed. Further, a significant opportunity exists not only to characterize many of the ecological properties that scientists fear are never considered by policy makers, but also to have scientists provide these policy-makers with a product that will answer the very questions the policy-maker must answer in terms they alone can understand. Yet another likely benefit is the identification of research areas anticipated to have the greatest effect on reducing uncertainty. Finally, this technique readily adapts to a variety of different scales, thereby providing an important degree of flexibility for both scientists and policy makers.

19.5 REFERENCES

Allen, T.F.H., O'Neill, R.V., and Hoekstra, T.W. (1984) Interlevel Relations in Ecological Research and Management: Some Working Principles from Hierarchy Theory. General Technical Report RM-II0. US Department of Agriculture, Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado.

Costanza, R. (1987) Social traps and environmental policy. BioScience 37, 407-412.

Costanza, R. (1991) Ecological economics: A research agenda. Struct. Change Econ. Dynam. 2(2), 335-357.

Costanza, R. (1992) Toward an operational definition of ecosystem health. In: Costanza, R., Norton, B.G., and Haskell, B.D. (Eds.) Ecosystem Health: New Goals for Environmental Management, pp. 236-253. Island Press, Washington, D.C.

Costanza, R., and Perrings, C. (1990) A flexible assurance bonding system for improved environmental management. Ecol. Econ. 2, 57-76.

Costanza, R., Sklar, F.H., and White, M.L. (1990) Modeling coastal landscape dynamics. BioScience, 40, 91-107.

Funtowicz, S.O., and Ravitz, J.R. (1991) A new scientific methodology for global environmental issues. In: Costanza, R. (Ed.) Ecological Economics: The Science and Management of Sustainability, pp. 137-152. Columbia University Press, New York.

Haskell, B.D., Norton, B.G., and Costanza, R. (1992) What is ecosystem health and why should we worry about it? In: Costanza, R., Norton, B.G., and Haskell, B.D. (Eds.) Ecosystem Health: New Goals for Environmental Management, pp. 1-18. Island Press, Washington, D.C.

Holling, C.S. (1986) The resilience of terrestrial ecosystems: local surprise and global change. In: Clark, W.C., and Munn, R.E. (Eds.) Sustainable Development of the Biosphere. Cambridge University Press, Cambridge.

Hunsaker, C.T., Graham, R.L., Suter II, G.W., O'Neill, R.V., Barnthouse, L.W., and Gardner, R.H. (1990) Assessing ecological risk on a regional scale. Environ. Manag. 14, 325-332.

McKelvey, R., and Henderson, S. (1991) The science-policy interface. In: Barker, J.R., and Tingey, D.T. (Eds.) Air Pollution Effects on Biodiversity, pp. 280-292. Van Nostrand Reinhold, New York.

Norton, B.G. (1991a) Toward Unity Among Environmentalists. Oxford University Press, New York.

Norton, B.G. (1991b) Ecological health and sustainable resource management. In: Costanza, R. (Ed.) Ecological Economics: The Science and Management of Sustainability. Columbia University Press, New York.

Norton, B.G., Ulanowicz, R.E., and Haskell, B.D. (1991) Scale and Environmental Policy Goals. Report to the US Environmental Protection Agency, Office of Policy, Planning, and Evaluation, Washington, D.C.

Norgaard, R.B. (1989) The case for methodological pluralism. Ecol. Econ. 1, 37-57. 

Odum, H.T. (1971) Environment, Power, and Society. John Wiley, New York.

Perrings, C. (1987) Economy and Environment: A Theoretical Essay on the Interdependence of Economic and Environmental Systems. Cambridge University Press, Cambridge.

Perrings, C. (1989) Environmental bonds and the incentive to research in activities involving uncertain future effects. Ecol. Econ. 1, 95-110.

Perrings, C. (1991) Reserved rationality and the precautionary principle: technological change, time and uncertainty in environmental decision-making. In: Costanza, R. (Ed.) Ecological Economics: The Science and Management of Sustainability, pp. 176-193. Columbia University Press, New York.

Pimm, S.L. (1984) The complexity and stability of ecosystems. Nature 307, 321-326.

Principe, P.P. (1992) Estimating systemic ecosystem benefits: a new approach. Paper presented to the Second Meeting of the International Society for Ecological Economics, Stockholm, August 1992.

Rapport, D.J. (1989) What constitutes ecosystem health? Perspect. Biol. Med. 33, 120-132.

Rapport, D.J. (1992) What is clinical ecology? In: Costanza, R., Norton, B.G., and Haskell, B.D. (Eds.) Ecosystem Health: New Goals for Environmental Management. Island Press, Washington, D.C.

Schaeffer, D.J., and Cox, D.K. (1992) Establishing ecosystem threshold criteria. In: Costanza, R., Norton, B.G., and Haskell, B.D. (Eds.) Ecosystem Health: New Goals for Environmental Management. Island Press, Washington, D.C.

Schaeffer, D.J., Herricks, E.E., and Kersier, H.W. (1988) Ecosystem health: 1. Measuring ecosystem health. Environ. Manag. 12, 445-455.

Ulanowicz, R.E. (1992) Ecosystem health and trophic flow networks. In: Costanza, R., Norton, B.G., and Haskell, B.D. (Eds.) Ecosystem Health: New Goals for Environmental Management. Island Press, Washington, D.C.

USEPA (1992) Peer Review Workshop Report on a Framework for Ecological Risk Assessment, p. 4. Report No. EPA/625/3-91/022. US Environmental Protection Agency, Risk Assessment Forum, Washington, D.C., February.

Westman, W.E. (1977) How much are Nature's services worth? Science 197, 960-964. 

Wulff, F., Field, J.G., and Mann, K.H. (1989) Network Analysis of Marine Ecosystems:  Methods and Applications. Coastal and Estuarine Studies Series. Springer-Verlag, Heidelberg.