The inherent qualities of ecological engineering — the combination of science and engineering and the goal of designing and studying ecosystems that have never existed before — lead to a consideration of methods and ways of knowing, which is the subject of a branch of philosophy termed epistemology. Here the orientation used is that given by Gregory Bateson (1979) who defines epistemology as "the study of the necessary limits and other characteristics of the processes of knowing, thinking, and deciding." While science, as the application of the scientific method, is philosophically well understood as a way of knowing, methods of engineering are not well articulated as noted in Chapter 1. For this reason the methods of ecological engineering are considered in the context of ecology, which is a scientific discipline, rather than in the context of engineering. Moreover, from this perspective, ecological engineering can be seen to offer a new way of knowing about ecology, which can be a significant contribution to the science.
Ecologists have not formally examined epistemology very deeply and only a few references have even mentioned the branch of philosophy (Kitchell et al., 1988; Scheiner et al., 1993; Zaret, 1984). Most ecologists seem to consider only the scientific method of hypothesis testing as the way of knowing about nature (Loehle, 1987, 1988). Although standard hypothesis testing is an excellent method, it is not the only approach available for studying ecosystems. For example, Norgaard (1987) discusses how certain indigenous peoples use different thinking processes compared with the traditional Western worldview in dealing with agroecosystems. Also, the complexity found in ecosystems creates challenges to the conventional philosophy of science as discussed by Morowitz (1996) and Weaver (1947). It is proposed here that the new discipline of ecological engineering should utilize a distinct, alternative
FIGURE 9.4 Spectrum of methods for ecology. Note the important new approach of building an ecosystem which is the main activity in ecological engineering.
method of epistemology that arises from the fundamental basis of engineering as a way of knowing.
Figure 9.4 provides a view of the methods used to develop knowledge in ecology along two axes. The horizontal axis represents the degree to which a method involves manipulation of the environment. The vertical axis represents the degree to which a method relies on dissecting a system into parts and mechanisms (i.e., analysis) vs. synthesizing parts into a whole system (i.e., synthesis). The space enclosed by these axes allows for different methods to be contrasted by their relative positions. By moving outward from the ordinate along either axis, a historical track of scientific development in ecology is outlined. Thus, ecology began with simple descriptions of populations and processes and advanced by focusing on experiments (movement along the horizontal axis) or by focusing on modelling (movement along the vertical axis). Each of the four methods shown in Figure 9.4 is a fundamental approach to developing knowledge, and each has a special contribution to make.
Description is the most basic approach in any discipline. It involves observations of systems, which usually lead to classifications of component parts and their behaviors. This approach is highly empirical and is the foundation of any of the other approaches shown in Figure 9.4. It also is the least respected method because the kinds of knowledge that can be generated from pure description are limited. As a science, ecology was in a descriptive phase from its origins around the turn of the century until after World War II when more advanced methods came to dominate the field.
Modelling refers to the mathematical description and prediction of interacting component parts of a system. At minimum, some knowledge of the component parts and how they interact is needed to create a model, and this knowledge comes from description, though other methods can also contribute. Modelling is primarily an act of synthesis as opposed to analysis because the emphasis is on connecting components in such a way as to capture their collective behavior. Although there is continuing interest in the parts, the focus of the modelling method is on the interaction of the parts and the building up of networks of interaction. The construction of the model requires a very systematic and precise description with mathematical relationships. This effort often identifies missing data, which leads to more description or to additional experiments. Once the model is built, it can be analyzed by various techniques. In this sense the model itself becomes an object of description, and the work can be thought to move back down the axis from synthesis to analysis. The models also can be simulated to study their dynamic behavior. This work can lead to a better understanding of the system being modelled and/or to predictions of how the system will behave under some new conditions. A somewhat extreme position on the heuristic value of models was given by H. T. Odum who taught that "you don't really understand a system until you can model it." Model-building itself involves no manipulation of the environment but, once constructed, a model is often "validated" in relation to the systems being modelled through a comparison of predictions with data gathered from the environment.
Experimentation, as shown in Figure 9.4, refers to the traditional scientific method of hypothesis testing. In this sense an experiment is a test of hypotheses. This is of importance in the philosophy of science since, as noted by Frankel and Soule (1981), "human science evolves by the natural selection of hypotheses." Hypotheses are statements about how component parts or whole systems behave, and an experiment is an event in which the validity of a hypothesis is checked. Experiments are carefully designed so that only one variable changes with a treatment, as described by the hypothesis in question. In this way a causal link is established between the treatment and the change in the variable. The method is thus analytical because only one variable at a time is studied while all others are held constant. The critical goal of this method is to disprove a hypothesis rather than to prove it. This is necessary because it is never possible to prove something is always true, but it is possible to demonstrate that something is definitely false. Experiments involve manipulating the environment through various treatments so that the consequences of hypotheses can be examined. Experimentation is the dominant method used in the present state of ecology (Resetarits and Bernardo, 1998; Roush, 1995).
The final method shown in Figure 9.4 is most important to the present discussion because it relates to ecological engineering. Building ecosystems is the defining activity of ecological engineering, whether it be a treatment wetland for absorbing stormwater runoff, a microcosm for testing toxicity of a pollutant, or a forest planted to restore strip-mined lands. Each constructed ecosystem is a special kind of experiment from which the ecological engineer "learns by building." This action is at once a form of strong manipulation of the environment and a form of synthesis so that the method occupies the extreme upper right-hand portion of Figure 9.4. Moreover, the method of building an ecosystem occupies a critical position in the plot because the science of ecology has no approach for developing knowledge in this region of space in the diagram. Building ecosystems is inherently an engineering method but it represents a whole new epistemology for ecology. In a sense it represents one of the "existential pleasures of engineering" described by Florman (1976). Through the process of designing, building, and operating objects, engineers have always utilized this approach to learning as noted in Chapter 1. It is essentially a kind of trial-and-error method in which each trial (a design) is tested for performance. The test provides a feedback of information to the designer, which represents learning. Engineers search for successful designs or, in other words, things that work. Errors provide a large feedback but, in a sense, they are not really looked upon as problems as much as opportunities to learn, as described by MacCready (1997) in the following quote:
In a new area, where you can't do everything by prediction, it's just so important to get out there and make mistakes: have things break, not work, and learn about it early. Then you're able to improve them. If your first test in some new area is a success, it is rarely the quickest way to get a lasting success, because something will be wrong. It's much better to get quickly to that point where you're doing testing.
You must tailor the technique to the job. Breaking and having something seem like it's going wrong in a development program is not bad. It's just one of the best ways to get information and speed the program along. If you've had nothing but success in a development program, it means that you shot too low, and were too cautious, and that you could've done it in half the time. Pursuing excellence is not often a worthy goal. You should pursue good enough, which in many cases, requires excellence, but in other cases is quick and dirty. The pursuit of excellence has infected our society. Excellence is not a goal; good enough is a goal. Nature just worries about what is good enough. What succeeds enough to pass the genes down and have progeny.
Several other authors have discussed the philosophical view of errors as being an inherent part of the learning process (Baldwin, 1986; Dennett, 1995; Petroski, 1982, 1997b). This kind of trial-and-error is not a blind, random process, but rather it is always informed by past experience. In this way it is self-correcting. Thomas Edison used a variation of this approach, which he called the "hunt-and-try method," as the basis for his inventions. Edison's approach blended theory and systematic investigation of a range of likely solutions. As noted by Millard (1990), "in Edison's lab it was inventing by doing, altering the experimental model over and over again to try out new ideas."
The emphasis of the engineering method is on testing a design to demonstrate that it works. In this way, it differs fundamentally from the scientific method of hypothesis testing described earlier. In hypothesis testing the goal is to disprove a hypothesis, while in the engineering method the goal is to prove that a design works. Philosophically this difference arises because in science there is only one correct answer to a question, and its method works by systematically removing incorrect answers from consideration. However, in engineering many designs are possible solutions to a problem, and its method works by systematically improving designs with continual testing (see Figure 1.4).
Several ecologists have begun to declare the value of building an ecosystem as an epistemological method. In terms of restoration Bradshaw (1987b) called it "an acid test for ecology" and Ewel (1987) added the following quote:
Ecologists have learned much about ecosystem structure and function by dissecting communities and examining their parts and processes. The true test of our understanding of how ecosystems work, however, is our ability to recreate them.
Ecological engineering, then, may increasingly become important as a method for understanding nature, as well as an active, applied field that adds to the conservation value of society as a whole. All of the methods listed in Figure 9.4 should be utilized. A special emphasis on description of the new systems that are emerging both intentionally and unintentionally may be necessary because they may have patterns and behaviors that have not been seen previously. Finally, Aldo Leopold's (1953) famous quote (which, interestingly, implies a machine analogy of nature — see Chapter 7) is particularly relevant to a consideration of the ecological engineering method:
If the biota, in the course of aeons, has built something we like but do not understand, then who but a fool would discard seemingly useless parts? To keep every cog and wheel is the first precaution of intelligent tinkering.
Ecological engineers are doing "intelligent tinkering" when they design, build, and operate new constructed ecosystems.
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