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Premise 12 - Integrating Multiple Factors
Click below to view some of the premises from Karr and Chu (1999).
Tracking complex systems requires a measure integrating multiple factors
FROM "Restoring Life in Running Waters" by James R. Karr and Ellen W. Chu. (Reprinted with permission from Island Press)
Scientists, citizens, and policymakers faced with making decisions about complex systems – economies, personal health, societal well-being, an ecological system –
need multiple levels of information. Consider some of the indexes used to track the health of the national economy: the index of leading economic indicators, the producer price index, the consumer price index, the cost-of-living index, and the Dow Jones industrial average. All these indexes integrate multiple economic factors.
The index of leading economic indicators (Mitchell and Burns 1938) tracks the US economy in terms of 12 measures: length of work week; unemployment claims; new manufacturing orders; vendor performance; net business formation; equipment orders; building permits; change in inventories, sensitive materials, and borrowing; stock prices; and money supply. These measures are combined to form the overall index, which takes as its reference point a standardized year (e.g., 1967); the value of the current year's index is expressed in terms of its value in the reference year. Composite economic indexes like these have survived six decades of discussion and criticism and remain widely used by economists, policymakers, and the media to interpret economic trends (Auerbach 1982).
Similarly, physicians and veterinarians rely on multiple measures and multiple tests to assess the health of individual patients. On a single visit to the doctor, you might be "sampled" for urine chemistry, blood-cell counts, blood chemistry, body temperature, throat culture, weight, or chest X-rays. Clearly, these measurements are not independent of one another, for they come from a single individual whose health is affected by many interacting factors. Further, you would not expect your doctor to rely on only one specialized blood test to diagnose your overall health; rather, you assume that multiple measures will give a more accurate diagnosis. Patterns emerging from these multiple measurements enable the doctor to recognize the signature of a particular ailment and to suggest more targeted measurements if she suspected a certain disease. Only then could she prescribe treatment.
Societal well-being obviously comprises many factors, not just the economic. To foster well-being, policy decisions need to consider as many factors likely to be affected by the outcome as possible. Multiattribute models have been developed to aid this kind of decision making by quantifying the effects of alternative decisions on multiple societal attributes (Gregory 1987).
Multimetric biological indexes calculated from ambient biological monitoring data provide a similar integrative approach for measuring condition and "diagnosing" causes in complex ecological systems. The same logical sequence applies in compiling multimetric economic, health, societal, or biological indexes. First, identify reliable and meaningful response variables through testing; then measure and evaluate the system against expectations; finally, interpret the measured values in terms of an overall assessment of system condition. The resulting index (for economic or biological resources) or diagnosis (for patients) allows people without specialized expertise to understand overall condition and to make informed decisions that will then affect the health of those economies, resources, or patients.
Most multimetric biological indexes for aquatic systems comprise 8 to 12 metrics,1 each selected because it reflects an aspect of the system's biological condition. These metrics are not independent because they are calculated from a single collection of organisms, just as multiple personal health tests are done on a single individual. But even if metrics are statistically correlated, they are not necessarily biologically redundant. Rather, just as a fever plus a high white-blood-cell count reinforces a diagnosis of bacterial infection, multiple metrics all contribute to a diagnosis of ecological degradation, or ecological "disease". Moreover, when more than one metric points to similar reasons for degradation, there is less uncertainty (Smith 1994). Even when some redundancy exists among metrics, multiple lines of evidence are valuable.
The two most common IBIs for streams have been developed, tested, and applied using fish (Karr 1981; Miller et al. 1988; Lyons 1992a; Fore et al. 1994; Lyons et al. 1995, 1996; Simon, in press) and benthic invertebrates (Kerans and Karr 1994; Kleindl 1995; Rossano 1995, 1996; Fore et al. 1996; Patterson 1996). Both incorporate known attributes from multiple levels of biological organization and different temporal and spatial scales. Typically, patterns emerge that are the signatures of biological responses to particular human activities (Karr et al. 1986; Yoder 1991b; Yoder and Rankin 1995b).
Following the success and widespread use of these two indexes, similar indexes are now being developed by a number of state agencies to use with invertebrates and vascular plants in wetlands (Karr 1997); with algae and diatoms in streams (Bahls 1993; Kentucky DEP 1993; Florida DEP 1996; Barbour et al., in press); with diverse taxa in lakes (Harig and Bain 1998; Whittier 1998); and with plants, invertebrates, and vertebrates in terrestrial environments (CRESP 1996; Chu 1997; Bradford et al. 1998; Blair, in press; see also Premise 22). Extending IBI to new taxa, environment types, and geographic areas is like learning to practice medicine in humans, pets, livestock, and others: the expectation of what constitutes "health" depends on the animal, but the same fundamental diagnostic strategy applies in all cases.
1 For species-poor environments such as cold-water streams, the total number of metrics is likely to be smaller (e.g., Lyons et al. 1996).
References
Auerbach, A. J. 1982. The index of leading indicators, "Measurement without theory," thirty-five years later. Rev. Econ. Stat. 64: 58-_595.
Bahls, L.L. 1993. periphyton bioassessment methods for Montana streams. Water Quality Bureau, Department of Health and Environmental Sciences, Helena, MT.
Barbour, M.T., J. Gerritsen, B.D. Snyder, J.B. Stribling. In press. Revision to rapid bioassessment protocols for use in streams and rivers: Periphyton, benthic macroinvertebrates, and fish. EPA 841-D-97-002. US Environmental Protection Agency. Washington, DC.
Bradford, D.F., S.E. Franson, A.C. Neale, D.T. Heggman, G.R. Miller, and G.E. Canterbury. In press. Bird species assemblages as indicators of biological integrity in Great Basin rangeland. Environ. Manage. Assess.
CRESP (Consortium for Risk Evaluation with Stakeholder Participation). 1996. CRESP at one year: March 1995-1996. Department of Environmental Health, University of Washington, Seattle.
Chu, E.W. 1997. Why assess ecological risk? Environ. Health News, winter: 3, 9. Department of Environmental Health, University of Washington, Seattle.
Florida DEP (Department of Environmental Protection). 1996. Standard Operation Procedure for Biological Assessment. Florida Department of Environmental Protection, Tallahassee.
Fore, L.S., J.R. Karr, and L.L. Conquest. 1994. Statistical properties of an index of biotic integrity used to evaluate water resources. Can J. Fish. Aquat. Sci. 51: 1077-1087.
Fore, L.S., J.R. Karr, and R.W. Wisseman. 1996. Assessing invertebrate responses to human activities: Evaluating alternative approaches. J.N. Am. Benthol. Soc. 15:212-231.
Karr, J.R. 1981. Assessment of biotic integrity using fish communities. Fisheries 6(6): 21-27.
Karr J.R., K.D. Fausch, P.L. Angermeirer, P.R. Yant, and I.J. Schlosser. 1986. Assessment of biological integrity in running waters: A menthol and it's rationale. Illinois Nat. Hist. Surv. Spec. Publ. 5.
Kentucky DEP (Department of Environmental Protection). 1993. Methods for assessing biological integrity of surface waters. Kentucky Department of Environmental Protection, Division of Water, Frankfort.
Kerans, B.L., and J.R. Karr. 1994. A benthic index of biotic integrity (B-IBI) for rivers of the Tennessee Valley. Ecol. Appl. 4:768-785.
Kleindl, W.J.,1995. A benthic index of biotic integrity for Puget Sound lowland streams, Washington, USA. MS thesis, University of Washington, Seattle.
Lyons, J. 1992a. Using the index of biotic integrity (IBI) to measure environmental quality in warmwater streams of Wisconsin. US For. Serv. Gen. Tech. Rep. NC-149.
Lyons, J., S.Navarro-Perez, P.A. Cochran, E. Santana C., and M. Guzman-Arroyo. 1995. Index of biotic integrity based of fish assemblages for the conservation of streams and rivers in west-cental Mexico. Cons. Biol. 9:569-584.
Lyons, J., L. Wang, and T.D. Simonson. 1996. Development and validation of an idex of biotic integrity for coldwater streams in Wisconsin. N. Am.J. Fish Manage. 16:241-256.
Miller, K.L., and 13 others. 1998. Regional applications of an index of biotic integrity for use in water resources management. Fisheries 13(5): 12-20.
Mitchell, W.C., and A.F. Burns. 1938. Statistical Indicators of Cyclical Revivals. National Bureau Of Economic Research, New York.
Patterson, A. J. 1996. The effect of recreation of biotic integrity of small streams in Grand Teton National Park. MS thesis, University of Washington, Seattle.
Rossano, E.M. 1995. Development of an index of biotic integrity assessed at multiple spatial scales. Landscape Ecol. 11:141-156.
RossAno, E.M. 1996. Diagnosis of Stream Environments with Index of Biological Integrity (in Japanese and English).Museum of Streams and Lakes, Sankaid Publishers, Toyobo.
Simon, T.P. ed. In Press. Assessing the Sustainability and Biological Integrity of Water Resource Quality Using Fish Assemblages. CRC Press, Boca Raton, FL.
Yoder, C.O. 1991b. The integrated biosurvey as a tool for evaluation of aquatic life use attainment and impairment in Ohio surface waters. Pages 110-122 in Biological Criteria: Research and Regulation. EPA-440-5-91-005. Office of Water, US Environmental Protection Agency, Washington, DC.
Yoder, C.O. and E.T. Rankin. 1995b. Biological response signatures and the area of degradation value: new tools for interpreting multimetric data. Pages 236-286 in W.S. Davis and T.P. Simon, eds. Biological Assessment and criteria: Tools for Water Resource Planning and Decision Making, Lewis, Boca Raton, FL.