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Section III
Multimetric Indexes Convey Biological Information
FIVE ACTIVITIES are central to making multimetric biological indexes effective (Karr and Chu 1997):
- Classifying environments to define homogeneous sets within or across ecoregions (e.g., streams, lakes, or wetlands; large or small streams; warm-water or cold-water lakes; high- or low-gradient streams; depressional or flow-through wetlands).
- Selecting measurable attributes that provide reliable and relevant signals about the biological effects of human activities.
- Developing sampling protocols and designs that ensure that those biological attributes are measured accurately and precisely.
- Devising analytical procedures to extract and understand relevant patterns in those data.
- Communicating the results to citizens and policymakers so that all concerned communities can contribute to environmental policy.
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Premise 8
Understanding biological responses requires measuring across degrees of human influence
Our ability to protect biological resources depends on our ability to identify and predict the effects of human actions on biological systems, especially our ability to distinguish between natural and human-induced variation in biological condition. Thus, even though measures taken at places with little or no human influence (e.g., only from āreferenceā sites) may tell us something about natural variability from place to place and through time at undisturbed sites, they cannot tell us anything about which biological attributes merit watching for signs of human-caused degradation. To find these signs, sampling and analysis should focus on multiple sites within similar environments, across the range from minimal to severe human disturbance.
One could choose sampling sites that represent different intensities of only one human activity, such as logging, grazing, or chemical pollution. It would then be possible to evaluate biological responses to a changing ādoseā of a single human influence. Though rare, such a study opportunity could help identify the biological response signature characteristic of that activity (Karr et al. 1986; Yoder and Rankin 1995b). Knowledge of such biological response signatures would give researchers a diagnostic tool for watersheds influenced by unknown or multiple human activities. In reality, however, it is virtually impossible to find regions influenced by only a single human activity.
In most circumstances, diverse human activities interact (e.g., during urbanization) to affect conditions in watersheds, water bodies, or stream reaches. In such cases, sites can be grouped and placed on a gradient according to activities and their effects: industrial effluent is more toxic than domestic effluent, for example, and both pose more serious threats than low dams, weirs, or levees (Figure 5). Removal of natural riparian corridors damages streams, but conversion to a partially herbaceous riparian area is less damaging than conversion to riprap. Streams grouped this way show striking and systematic differences in biological condition across the gradient of human disturbance (Figure 6).
Figure 5. A priori classification system for ranking Japanese streams according to intensity of human influence (Rossano 1995). Sites were assigned to one of 21 possible categories based on amount and type of effluent, proximity of dams and other structural alterations, and type of riparian vegetation. Even without quantitative measures from each site, this approach allowed sites to be ranked across a range of human influence.
Figure 6. Benthic indexes of biological integrity (B-IBIs) for 115 Japanese streams (from Rossano 1995). The top panel shows B-IBIs calculated from half of the 115-stream data set (circles), which was used to initially select and test metrics for use in the B-IBI. The middle panel shows B-IBI values calculated from the second half of the data set (pluses); the metrics and scoring criteria used for these data were the metrics and criteria developed from the first half. In the bottom panel, all 115 B-IBIs are plotted together; the indexes from both sets correspond closely, ranking the streams comparably according to intensity of land use from low to high. The range of human influence against which the B-IBIs are plotted comes from the classification scheme shown in Figure 5.
Sometimes a single variable can capture and integrate multiple sources of influence. Relatively simple descriptorsāhuman population in the watershed, percentage of impervious area, percentage of land area devoted to agriculture or urban uses, or percentage of developed areaāare adequate for regional watershed analyses (Meeuwig and Peters 1996). The percentage of impervious area, for example, summarizes the multiple effects of paving, building, and other consequences of urbanization, as in a recent study of Puget Sound lowland streams (Figure 7; see also Maxted 1997). This measure provides a simple surrogate of human influence that works well at percentages of impervious area from near 0% to 60%. Unfortunately, it is less useful in understanding the often large variation in biological condition at some percentages of imperviousness (e.g., 3% to 8%; see Figure 7). Finding the differences in human activity that can explain these biological differences requires information from the watersheds that is more detailed.
Alternatively, sites may be grouped into qualitative disturbance categories. In a study of recreational influence on stream biology in the northern Rocky Mountains (Figure 8), Patterson (1996) classed sites into four categories associated with different levels of human activity: (1) little or no human influence in the watershed; (2) light recreational use (hiking, backpacking); (3) heavy recreational use (major trailheads, camping areas); and (4) urbanization, grazing, agriculture, or wastewater discharge. Patterson demonstrated that light recreational activity did not substantially reduce B-IBIs in comparison with undisturbed watersheds. Heavy recreational use, in contrast, did significantly alter the benthic invertebrates, although not as much as more-intensive uses including urbanization and agriculture.
Figure 7. Benthic index of biological integrity (B-IBI) plotted against the percentage of impervious area for urban, suburban, and rural stream sites in the Puget Sound lowlands, Washington (from Kleindl 1995). Though B-IBI clearly decreases with increasing impervious area, this plot offers no insight into B-IBI differences among sites with similar percentages of impervious area, especially at low percentages (3% to 17%).
Figure 8. Benthic indexes of biological integrity (B-IBIs) for stream sites in or near Grand Teton National Park, Wyoming (from Patterson 1996). Before B-IBIs were determined, these sites had been placed into four categories of human influence: little or no human activity (NHA), light recreational use (LR), heavy recreational use (HR), and other (O). B-IBIs revealed no significant difference between sites with little or no human activity and those having low recreational use. But B-IBIs were significantly lower for sites used heavily for recreation and lower still for sites subjected to other usesāspecifically, urbanization, grazing, agriculture, and wastewater discharge.
A similar approach was taken in a study of biological response to chemical pollution on three continents: South America, Africa, and southeastern Asia (Thorne and Williams 1997). The authors classified sites according to a pollution gradient based on the integration of six measures of chemical pollution. Biological condition, as indicated by metrics such as total taxa richness (families) and mayfly, stonefly, and caddisfly richness, clearly went down as pollution went up. The biological responses in the three tropical regions were similar; the patterns parallel those seen in temperate regions even though the faunas are all very different.
Data collected over a number of years at the same site(s) can also reveal biological responses as human activities change during that period. Regardless of how one represents a range of human influence among study sites, sampling from sites with different intensities and types of human activity is essential to detect and understand biological responses to human influence. The goal is to compare like environments with like environmentsāto isolate and understand patterns caused by human activities at sites within those like environments.
Too many existing studies confound patterns of human influence with natural variation over time at undisturbed sites or across different environment types. In other situations, researchers combine measures of human activity, the physical and chemical manifestations of those activities, and their biological consequences in a heterogeneous analysis with ambiguous results. Those analyses may even include measures of physical environment such as stream gradient. When this range of factors (different human influences on different environment types) is lumped in a single analysis, it becomes almost impossible to understand the causes or consequences of human versus natural events.
Consider the following analogy. Three experiments are designed: one to understand variation in natural biological systems as a function of stream size; another to distinguish the effects of pesticide runoff on streams of first, fourth, and sixth order; and a third to define the effects of pesticides on plants and insects. Analyzing samples from the first series of stream sites would tell you about biological responses to changing stream size. Samples from the second series would illustrate changing human influence as a function of stream size. Samples from the third would distinguish responses of different taxa. It would be silly to mix the data from the three studies in a single statistical analysis, without regard to which study the individual samples came from. Yet by using analytical procedures that mix the effects of natural and human-induced variation (in a single correlation matrix, for example), researchers are essentially doing just that: they are ignoring the context of the different components of their data, making it difficult to distinguish the biological signs relevant to resource management or protection. They then confound the sources of the variance they see, even if their initial sampling setup would have permitted discrimination among those sources. Univariate and multivariate analyses all too often suffer from this flaw.
Sampling only from āreferenceā sites creates a similar problem because it does not provide a way to document which biological attributes vary with human influence (see Premise 31). Careful thought about which variables best summarize human influence and the relationships among those variables should be the foundation of monitoring protocols. Creating opportunities to discover biological patterns in relation to huma...
