Lakeshore Living: Designing Lake Places and Communities in the Footprints of Environmental Writers

Notes and Recommended Reading

147 Notes and Recommended Reading Topics are generally presented in the section or chapter in which they first occur. PREFACE Systems Thinking: Senge et al. (1994) provided a good guide to using this approach within an organization, and Meadows (2008) provided a summary of system analysis and how to influence complex systems to obtain desired conditions. One of the first steps in systems thinking requires us to think deeply about a system by writing down our mental models of how we believe the system functions. Important variables influencing water quality or enduring lakeshore communities are often overlooked or can be too difficult to measure. Developing simple conceptual models by diagramming complex interrelationships between variables is helpful when trying to understand lakeshore systems. Selecting variables to include in models is difficult, and the appropriate level of model complexity is dependent on purpose and ease of testing. Human behavior and dynamics are inherently difficult to incorporate, so developing a model framework must first occur. One approach to begin formulating models for complex systems is the creation of cognitive maps. A cognitive map is a simplistic model of causal relationships among variables. Cognitive maps reduce analysis to a matter of identifying variables, the links among them, and the strength of the links. A cognitive map draws a causal picture, and this simple qualitative approach can synthesize expert knowledge and research findings to predict how complex events interact. Cognitive maps have been used in political science to model political situations , and they are used to model ecosystems. Model complexity is under our control, and models are always simpler than reality. Given this simplification, reality may surprise us, and our predictions may prove incorrect. In addition, a cognitive map may not predict lag effects or how delays in the system change outcomes, which are important in modeling economic systems. However, when one is struggling to identify how to make good things happen, it often helps as a first step to draw out the system by identifying important factors and linkages with a cognitive map. Linkages are either simple relationships between factors (positive or negative) or complex feedback loops within factors (positive or negative). In both the real world and the virtual modeling world, changing the linkages changes the behavior of the system . The act of writing down our view of a system forces us to think holistically about factors and linkages that likely exist or should exist. With a working model in hand, the next step in understanding the system’s behavior is to look at long-term data series, if available. Are there observable patterns in the data, and does 148| Notes and Recommended Reading the model produce similar patterns? Focusing on the long-term patterns versus the short-term variability often helps us understand the system. For example, it is common for those collecting Secchi disk water-transparency data to focus on the short-term variability in the data. However, these short-term patterns are often the result of differences in annual precipitation. Generally, there is less runoff and nutrient loading to a lake in dry years compared to wet years, and a series of dry years often has greater water transparency than a series of wet years. While this variability is interesting, the greater need is to understand the long-term pattern in water transparency. Are there any consequences of decades of nearshore sewage treatment, agricultural practices within the watershed, or lakeshore urban runoff? For many lakes, longterm data may be hard find or not present, and caution should be taken about inferring the presence of significant trends without these data. Meadows (2007) spoke of leverage points of change—defined as critical points to intervene in a system. She identified twelve such leverage points and ranked them on their ability to transform a system. Meadows (2008) wrote: “I have come up with no quick or easy formulas for finding leverage points in complex and dynamic systems. Give me a few months or years and I’ll figure it out. And I know from bitter experience that, because they are so counterintuitive, when I do discover a system’s leverage points, hardly anybody will believe me. Very frustrating—especially for those of us who yearn not just to understand complex systems, but to make the world work better.” Managing Lakes as Part of the Landscape: When managing many lakes, Soranno et al. (2008, 2010) advocated that governments rely on predictive models that use landscape variables (e.g., land use, soils, geology, runoff) and...