Systems Analysis

If the environment is organized and can be viewed as networks of ordered and functioning systems, then it is necessary that we have analysis tools and investigative methodologies that capture this wholeness. Just as one cannot see statistical relationships by visually observing an ecosystem or a mesocosm experiment, one must collect data on the local interactions that can be estimated or measured, then analyze the connectivity and properties that arise from this. In that sense, systems analysis is a tool, similar to statistical analysis, but one that allows the identification of holistic, global properties of organization.

Historically, there are several approaches employed to do just that. One of the earliest was Forrester's (1971) box-and-arrow diagrams. Building on this approach, Meadows et al. (1972) showed the system influence primarily of human population on environmental resource use and degradation. The Forrester approach also later formed the basis for Barry Richmond's STELLA® modeling software first developed in 1985, a widely used simulation modeling package. This type of modeling is based on a simple, yet powerful, principle of modeling that includes Compartments, Connections, and Controls. One of Richmond's main aims with this software was to provide a tool to promote systems thinking. The first chapter of the user manual is an appeal for increased systems thinking (Richmond, 2001). In order to reach an even wider audience, he developed a "Story of the Month" feature which applied systems thinking to everyday situations such as terrorism, climate change, and gun violence. In such scenarios, the key linkage is often not the direct one. System behavior frequently arises out of indirect interactions that are difficult to incorporate into connected mental models. Many societal problems, which may be environmental, economic, or political, stem from the lack of a systems perspective that goes to remote, primary causes rather than stopping at proximate, derivative ones.

Many systems analysis approaches are based on state-space theory Zadeh et al. (1963), which provides a mathematical foundational to understand input-response-output models. Linking multiple states together creates networks of causation Patten et al. (1976), such that input and output orientation and embeddedness of objects influence the overall behavior. Box 5.1 from course material of Patten describes a progression from a simple causal sequence in which one object, through simple connectance, exerts influence over another. Causal chains and networks exhibit indirect causation, followed by a degree of self-control in which feedback ensures that an object's output environ wraps back around to its input environ downstream. Lastly, with holistic causation, systems influence systems. Using network analysis several holistic control parameters have been developed (Patten and Auble, 1981; Fath, 2004; Schramski et al., 2006). Further testing is necessary but these approaches are promising for understanding the overall influence each species has in the system.

Another approach to institutionalize system analysis is Odum's use of energy flow diagrams, which has since spawned the entire industry of emergy (embodied energy) flow analysis for ecosystems, industrial systems, and urban systems (e.g., Odum, 1996; Bastianoni and Marchettini, 1997; Huang and Chen, 2005; Wang et al., 2005; Tilley and Brown, 2006).

Box 5.1 Distributed causation in networks

1. The causal connective: B —■ C There is only a direct effect of B on C.

B affects C directly, but A influences C indirectly through B, and C has no knowledge of A.

{A} is a system, with a full interaction network giving potential for holistic determination.

4. Self influence: {A(C)} — B({A(C)}) — C C is in network {A} and exerts indirect causality on itself.

5. Holistic influence: {A(B,C)} — B({A(B,C)}) — C

B is also in {A} so that B, C and all else in {A} influence C indirectly.

The systems analysis approach is also an organizing principle for much of the work at the International Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria. The institute was established during the Cold War as a meeting ground for East and West scientists and found common ground in the systems approach (www.iiasa.ac.at). Although its focus was not ecology, it has produced several large-scale, interdisciplinary environmental models such as the Regional Air pollution INformation and Simulation (RAINS), population development environment (PDE) models, and lake water quality models.

Another systems approach, food web analysis, is the main ecological approach, but as stated earlier has limited perspective by including only the feeding relations of organisms easily observed and measured, largely ignoring abiotic resources, and operating with a limited analysis toolbox. For example, without the basis of first principles of thermodynamics or graph theory (which are more recently being incorporated) the discipline has been trapped in several "debates" such as "top-down" vs. "bottom-up" control, and interaction strength determination, which have ready alternatives in ENA. Specifically regarding top-down versus bottom-up, Patten and Auble (1981), Fath (2004), and Schramski et al. (2006) all use network analysis to demonstrate and try to quantify the cybernetic and distributed nature of ecosystems.

The latter methodology, ENA, arose specifically to address issues of wholeness and connectivity. It has two major directions, Ascendency Theory concerned with ecosystem growth and development, and a system theory of the environment termed Environ Analysis. Ascendency theory is summarized elsewhere in this volume (see Box 4.1). After some general remarks on ENA, the remainder of this chapter will sketch connectivity perspectives from the "13 Cardinal Hypotheses" of environ theory.

5.5 ECOSYSTEM CONNECTIVITY AND ECOLOGICAL NETWORK ANALYSIS

The exploration of network connectivity has led to the identification of many interesting, important, and non-intuitive properties. ENA starts with the assumption that a system can be represented as a network of nodes (vertices, compartments, components, etc.) and the connections between them. When there is a flow of matter or energy between any two objects in that system we say there is a direct transaction between them. These direct transactions give rise to both direct and indirect relations between all the objects in the system.

Nobel prize winning economist Wassily Leontief first developed a form of network analysis called input-output analysis (Leontief, 1936, 1951, 1966). Based on system connectivity, it has been applied to many fields. For example, there is a large body of research in the area of social network analysis, which uses the input-output methodology to investigate how individual lives are affected by their web of social connections (Wellman, 1983; Wasserman and Faust, 1994; Trotter, 2000). Input-output analysis has also successfully been applied to study the flow of energy or nutrients in ecosystem models (e.g., Wulff et al., 1989; Higashi and Burns, 1991).

Bruce Hannon (1973) is credited with first applying economic input-output analysis techniques to ecosystems. He pursued this line of research primarily to determine interdependence of organisms in an ecosystem based on their direct and indirect energy flows. Others quickly picked up on this powerful new application and further refined and extended the methodology. Some of the earlier researches in this field include Finn (1976, 1980), Patten et al. (1976), Levine (1977, 1980, 1988); Barber (1978a,b), Patten (1978, 1981, 1982, 1985, 1992), Matis and Patten (1981), Higashi and Patten (1986, 1989), Ulanowicz (1980, 1983, 1986), Ulanowicz and Kemp (1979), Szyrmer and Ulanowicz (1987), and Herendeen (1981, 1989). Both environ analysis and ascendancy theory rely on the input-output analysis basis of ENA.

The analysis itself is computationally not that daunting, but does require some familiarity with matrix algebra and graph theory concepts. The notation and methodology of the two main approaches, ascendency and network environ analysis (NEA) differ slightly and have been developed in detail elsewhere (see references above), and therefore, we will not repeat here (see Box 5.1 for a very brief introduction to Ascendency). Furthermore, the development of user-friendly software such as ECOPATH (Christensen and Pauly, 1992), EcoNetwork (Ulanowicz, 1999), and more recently WAND by Allesina and Bondavalli (2004) and NEA by Fath and Borrett (2006) are available to perform the necessary computation on network data and will ease the dissemination of these techniques. Following a short NEA primer we sketch the 13 Cardinal Hypotheses (CH) (Patten, in prep) associated with NEA that arise from ecosystem connectivity.

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