Adaptive capacity is the capacity of a socio-ecological system to respond to change of some kind, by generating the appropriate response. This adaptive capacity gives the system a degree of resilience. Resiliency is typically understood as the capacity of a system to maintain functionality given some perturbation.1 Ecosystems and Societies are dynamic entities, invariably they are subject to periodic disturbances and are in the process of recovering from some past disturbance. When a system is subject to some sort of perturbation or disturbance, it responds by moving away from its initial state. The tendency of a system to remain close to its equilibrium state, despite that disturbance, is termed its resistance. On the other hand, the speed with which it returns to its initial state after disturbance may be understood as its resilience.2
Resilience thinking has gone through a number of developments since its introduction as an ecological term by C.S. Holling in the 1970’s. In the following decades, important contributions were made building on this original ideas and they expanded into the vocabulary of socio-ecological systems. The term “resilience” has become popularized, with organizations building resilience concepts and strategies, from governments managing for health care issues to the resilience of cities. The concept of resilience continues to be applied in a greater variety of fields, although a clear approach to modeling and managing resilience is still lacking.
Adaptation & Resistance
A socio-ecological system that is exposed to some alteration causing it stress, can either resist this change or adapt to it. Where resistance involves trying to prevent any alteration to the system at all, it is the system’s ability to withstand a disturbance with little deformation. We can define adaptation as the capacity for a system to change its state in response to some change within its environment. An adaptive system then is a system that can change given some external perturbation, and this is done in order to optimize or maintain its condition within an environment by modifying its state. Both resistance and adaptation are trying to achieve the same end of being able to maintain the system’s functionality, but they are different strategies for achieving it. Resistance as a strategy is part of a command and control approach that tries to achieve stability through controlling and regulating the environment towards the required parameters conducive to the system’s functioning. Adaptation tries to achieve this by instead maintaining a diversity of states in order to be able to respond to changing events. These two approaches are fundamental to the dynamic between socio-ecological systems of all kind. For example, in thinking about climate change we can focus on policies that resist it, such as putting limits on CO2 emissions or we can think about policies for adapting to it, building more agile technology infrastructure and more flexible social institutions.
Resilience & Stability
The conflict between stability and resilience is important for ecosystems and social systems in many ways as there is often a trade-off between them. As we try to increase stability we often reduce resilience and vice versa, in order to increase resilience we often have to reduce the supporting mechanisms that preserve stability. Forest fires are a classical illustration of this, for example around 1900, the United States Forest Service initiated a policy of protecting forests from fire. For the next 80 years, they put out all forest fires as quickly as possible. More and more leaf litter accumulated on the ground because so much time passed without frequent small fires to get rid of the leaf litter. By 1980, leaf litter had accumulated within forests to the extent that they were increasingly susceptible to fire. New forest fires became very difficult to control, particularly in the extensive dry areas of Western United States. The more the forest service tried to protect forests from fires, the worse the problem became because every fire was more difficult to extinguish and could destroy such large areas of natural habitat. Forest protection became increasingly costly because it was necessary to use large numbers of firefighters, fire trucks, and airplanes to drop water. Despite this effort, thousands of square kilometers of forest were sometimes destroyed by a single fire. This example shows how human action trying to resist forest fires, in fact, reduced the forest’s natural resiliency against large fires. Forest managers increased stability by putting out every fire, but they reduced resilience because continuous protection from small fires increased the vulnerability of forests to large-scale destructive fires. Another example would be the use of chemical insecticides to remove unwanted insects from agricultural crops. Unfortunately, the insecticides may well also kill predatory insects as well as pest insects, so the natural control of pest insects by predators is lost. Without natural control, pest insect populations can increase to devastating numbers when insecticides are not in use, making the crops and farmers highly dependent upon insecticides in order to maintain the function of the agricultural system. Here again, we can see the interplay between stability and resiliency.
Adaptive capacity is a product of the system having many different responses to any given perturbation. And this is enabled by the system having experienced and survived some perturbation and stored that state to ensure its capacity to adapt to it in the future, for example, this is how the immune system works. When we use a strategy of resistance and provide stability to the system we may be able to optimize it towards high throughput, as exemplified by modern agriculture, but we also reduce its exposure, we reduce the development of those different states, reduce the diversity and this makes the system more vulnerable to change as it may now lack the components required to deal with that change when it happens. Sufficed to say diversity is an inherent part of adaptive capacity.
Whereas resistance means trying to externalize change, adaptation involves internalizing it, that is to say being able to change with the environment. Resilient systems are ones that can successfully navigate and adapt to the different stages that are an inherent part of the process of development to any complex adaptive system, this process of change is best described with reference to what is called the adaptive cycle. The adaptive cycle is a heuristic model developed by C.S. Holling for understanding the process of change in complex adaptive systems and can be used to identify structural, patterns in both social system and ecosystem as they go through processes of change. The model describes in theoretical terms the change of events through four phases, with these four phases given the letters; r, K, Ω, α.
The r-stage is the regenerative state in the process, it is one of growth, a time of expansion and increasing complexity. A system in the r-stage has successfully reoriented post-crisis and there is now plenty of freely available resources for rapid growth and development. A time dominated by positive feedback and self-organizing processes of assembly. Often marked by abundant resources and entrepreneurial leadership. The system has plentiful untapped and uncommitted potentiality. Reconfiguration from unformed supplies into new configurations is essential to system maturation. Once kick-started along a growth trajectory, many resource flows are available for experimentation. In the r-stage, network connections are established and interdependencies are built. At this stage, positive feedback can work to take hold of some emergent pattern and rapidly scale it up, as might be seen with the exponential growth of a start-up company as it rides the positive feedback loop of economics of scale.3
The K-stage, or equilibrium-stage, is about controlled development and this ‘equilibrium’ is a time of stability. The system has reached a high level of complexity and connection between its parts. In ecological systems, this is equivalent to climax ecosystem state, corresponding to a dynamic equilibrium or steady-state, when the entropy production inside a system is balanced by the entropy flow from the system to its environment. A mature system in the K-stage dynamically performs at a high level of activity and can be seen to be optimal, exhibiting strong stability. At this stage, negative feedback cycles dominate over positive feedback, but as the system settles into a stable configuration there is the possibility of rigidity forming. Characteristics of a rigid system include very few key nodes with a high concentration or influence, and low diversity both in nodes and pathways. Additionally, a rigid system is brittle and vulnerable to disturbance because of reduced diversity and inability to self-organize. This mature act of specialization weakens resilience by permitting systems to become accustomed to and dependent upon their prevailing conditions. In the event of unanticipated shocks; this dependency reduces the ability of the system to adapt to these changes. The system may become rigid and seemingly indestructible, but stagnation and a lack of flexibility may eventually make the system vulnerable to destruction by an external disturbance.4
The Ω-stage is one of crisis and collapse, when the system is destroyed by an external disturbance. Positive feedback generates dramatic change, and the system falls apart as It is pushed out of its stability domain. The test of a system in the Ω-stage is its capacity to survive in the face of extreme disturbance or disordered collapse. A system must maintain vital functions throughout the crises. In social systems, it is often up to the leadership, both formal or informal, to identify and prioritize what this means. One of the ways that the diversity maintained through small-scale disturbances contributes to the resilience of the system is by cultivating a large stock of resources from which it can pull during a crisis, both in terms of organizations and their relationships, which is essential for leadership to emerge during the Ω-stage. Emergent leadership occurs when actors not tasked with leadership roles informally assume key positions during the crisis. Failure to survive this stage can result in a complete breakdown of the system cycle.
Reorganization is a time when the system begins to recover from falling apart. It is a creative time when change can take a variety of possible directions; that is, the system has the possibility of moving into a variety of new stability domains. ‘Chance’ can be important to the way the system reorganizes, determining which new stability domain it enters. The growth stage that follows reorganization depends on the course initiated during reorganization. To reorient after crises, the system must reorganize these pathways and node relations. The release stage provides opportunities for new elements to enter and become more prominent in the system, be they species, nutrients, individual people, citizen groups or institutions. At this stage in the cycle, the probability of several alternative future states is high. The system can reorganize and return to its former regime, shift to a different regime with similar structure but with changes in feedbacks and dominant processes, or transform into a new regime with novel state variables and feedbacks. As novel societal or ecological groups assemble, some succeed and others fail, and the adaptive cycle of r, K,Ω and α stages may then be repeated.5
Although the adaptive cycle is a heuristic model it does tell us something about the different stages to the process of adaptation. And with it, we can understand resilience as the adaptive capacity to successfully navigate all of the different stages within the adaptive cycle. While some stages will involve only small adaptations others will require full-scale transformations to the whole system. But it is in the socio-ecological system’s capacity to effectively adapt to these changes that the system can maintain a dynamic evolutionary state allowing it to develop over time.