higher than in simpler systems that there will always be some species that will be able to assume a particular functional role. Reliability here derives from the probability that under high diversity a system is better able to provide a consistent level of performance over a given unit of time than less diverse systems. This is because diverse systems offer some functional redundancy in the sense that with multiple species per functional group, there is a pool of other species that can assume the role of those species whose capacity to function is compromised or lost (Naeem and Li 1997; Naeem 1998). Current empirical insights suggest that diversity does indeed enhance both the efficiency and reliability of production and materials cycling.
For example, Hooper and Vitousek (1998) showed experimentally that total resource use, and hence total production, increases with increasing producer diversity owing to the complementary way in which plant species utilize resources, a result of adaptive evolution to minimize between-species competitive interactions. This complementarity in resource use also enhances retention of limiting elements (e.g., nitrogen) within the system. Moreover, higher-level consumers (i.e., different species of predators) can indirectly change the diversity and abundance of producer species simply through predator species-dependent direct interaction with intermediate herbivore consumers (Schmitz 2008). Predator species dependency can speed up or slow down materials cycling in ways that fundamentally alter levels of production (Schmitz 2008). Over the longer term, these indirect effects could alter system structure itself with attendant shifts in production and materials cycling and selection on agents to alter the nature of the direct effects—the basis for a complex adaptive system (Levin 1998).
The insights from these studies extend analogously to technological systems. For example, eco-labeling or sustainability certification can have important direct effects on consumer behavior that can cascade further to influence the diversity of the producer agents and hence production processes. Thus, these measures to enhance sustainability could lead to undesirable outcomes in the absence of formal and careful consideration of what consumer behavior will do to long-term evolution of energy and materials stocks and flows throughout the economic system.
Unlike measures of persistence, there are no hard and fast rules for measuring the reliability of ecosystem services. Ecological science is still evaluating empirically the generality of theory on the link between connectedness, agent diversity, and variability in function. At the very least, indices of reliability should couple a measure of persistence with and attempt to quantify the degree to which levels of production and material flows fluctuate over time with respect to the diversity (both connectedness and kinds of producers and consumers) in a system. Quantitative measures should include the statistical distribution of flow rates (interaction strengths) among agents in a system and the degree to which agents overlap in their demand for particular kinds of materials. Low overlap would indicate a high degree of complementarity in resource use. A reliability index could begin to offer insight into risks that resource supply and/or production will be insufficient to sustain consumption-production processes.
The sustainability measures discussed thus far are appropriate when the expectation of environmental conditions in which the systems operate remain comparatively fixed over time. Many systems, however, exist in environments that must respond to sudden and surprising shocks. Remaining sustainable in uncertain and changing environments requires consideration of a third measure of sustainability: resilience. Resilience is the ability of a system to persist in the face of unexpected stresses and shocks (Holling 1973; Gunderson 2000). A resilient system is one that has the capacity to resist the effects of a shock or recover very quickly to resume normal function once the shock abates. The concept of resilience also embodies the idea that systems can entrain into alternative states or dynamic regimes (Gunderson 2000). Accordingly, a resilient system is one that averts shifts to alternative states by responding flexibly and adaptively to disturbances. Resilience has its pros and cons, depending on whether or not one wants to keep a system of interest in a desirable state or escape an undesirable one in favor of a more desirable alternative (Levin et al. 1998).
Resilience can mean that there is much resistance to change when such change is crucially needed. For example, modern society is locked into production economies that are supported by energy derived from fossil fuels. Moreover, emerging technological societies such as India and China continue to operate within this fossil fuel economic state by investing in more effective ways to extract the ever dwindling supplies of fossil fuels rather than by tran-sitioning toward clean and renewable energy technology. The lack of ability or willingness to innovate and evolve to a new state is what makes this system highly resilient. Alternatively, systems can be locked into undesirable states despite willingness to evolve, simply because agents within the system cannot overcome resilience. For example, the North American automotive industry is almost singularly geared to build large vehicles with high fuel consumption. In the face of sudden and rapidly rising fuel prices, this industry has been very slow to adapt to sudden changes in consumer demands for vehicles that have greater fuel economy and that use alternative energy (hybrid vehicles). Consequently, this industry is now vulnerable to collapse because it does not have the evolutionary capacity to overcome the high sustainability of the undesirable state. By becoming specialized into a particular production mode, the industry has painted itself into a proverbial corner by creating the now undesirable state and losing the adaptive capacity to escape it.
The challenge in maintaining or transitioning to desirable sustainable states is that uncertainty about future conditions makes it difficult to decide which strategies or processes to maintain (Levin et al. 1998). From an evolutionary perspective, a key strategy is to maintain, at all times, the capacity to innovate and create quickly. This capacity can be built into systems at several levels of a hierarchy. For example, sudden, small shocks can be accommodated by rapid adjustments within the day-to-day operations of a manufacturer. A classic example is the ability to fine-tune efficiency of production streams in the face of small jumps in the price of materials or energy. Intermediate shocks (e.g., shortages in certain materials or energy sources) may require strategic initiatives that alter how products are manufactured. Large shocks (e.g., demand for radically different kinds of products) may cause a particular state to collapse entirely and, in turn, require wholesale change in the way an industry operates (Holling 2001). This collapse provides new creative opportunity and has been termed "creative destruction" (Holling 2001), in that it can lead to an evolutionary change in an entire industry through natural selection of the capacity to provide entirely new ways of manufacturing products and ultimately in the new products that are developed. For example, in the transportation industry, at the turn of the 20th century, manufacturers of horse-drawn buggies went rapidly extinct if they did not have the desire or capacity to build automobiles. Put into a current context, this same industry may need to endure wholesale changes in corporate strategies within the existing economy. Industries may need to overcome strongly individualistic or competitive interactions encouraged by market economics in favor of collaborative and cooperative interactions that are built on fragile trusts among interacting partners (Levin et al. 1998).
The point here is that transitioning to sustainability is customarily looked upon as a socially and environmentally responsible action to achieve goals of human well-being and environmental health. However, the concept of resilience and alternative states teaches us that sustainability can counter-intuitively hinder the attainment of those goals. This is because our tendency as humans is to try to hold on to an existing system out of fear for future, unknown outcomes. Yet, embracing the idea of creative destruction can, from a whole systems perspective, enable the implementation of fresh new ideas and opportunities to transition to more sustainable practices. For example, abruptly collapsing fossil-fuel based economies can select for agents that rapidly compensate by producing alternative, cleaner renewable energy and technology.
The concept of resilience and alternative states is comparatively new to ecological science. A major criticism of the idea is that ecologists have not yet offered sound measurement criteria. The difficulty in identifying alternative states is that it requires experimentation that introduces shocks to the system or retrospective analyses of systems that have responded to shocks. Experimentation with an industrial system is both impractical and unethical. Retrospective analyses show what may happen but do not indicate what will happen. Nevertheless, the specter of alternative states argues for consideration of resilience as part of any sustainability measurement. Indicators of resilience should involve identification of transition points between alternate states and the degree of adaptive flexibility of agents in systems. Ecologists are now deriving leading indicators to anticipate when a system may be shifting to alternative states (e.g., Carpenter et al. 2008). In ecological systems, adaptive flexibility is routinely measured as phenotypic plasticity in species' traits in terms of a species' (agent's) ability to carry out its function across a spectrum of environmental conditions, measured using reaction norms. A reaction norm describes the pattern of phenotypic expression of a single genotype across a range of environments. What determines the "genotype" and important "traits" of a particular agent in an industrial system remains open to consideration and debate. Adaptive flexibility might be measured in terms of the portfolio (diversity) of new product innovations developed by the agent and poised to be implemented given the emergence of a different economic climate. It could also include the capacity of design teams to innovate and manufacturing processes to rapidly implement the new innovations.
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