Discontinuous world models and ecological resilience

Beginning in the 1970s, attempts to understand and manage natural resource crises have generated new conceptual models to try to understand why we are so often surprised by natural catastrophes (Holling, 1986). Assumptions of continuity in system behavior and in spatial distribution of resources seemed to blind people to the possibilities of sudden change, so new conceptual models were developed that focus on non-linearities in space and time. Catastrophe theory (Casti, 1982) emerged to explore non-linear system dynamics and hierarchy theory (Allen and Starr, 1981; O’Neill et al., 1986).

Adaptive Management for Resilience in Human and Natural Systems 25

Stored capital

C o n n e c t e d n e s s

E x p l o i t a t i o n

R e l e a s e R e o r g a n i z a t i o n

C o n s e r v a t i o n

α Κ

r Ω

Figure 2.2. The Adaptive Cycle – The four phases of ecosystem dynamics that correspond to the functions of exploitation (r), conservation (K), release (Ω), and reorganization (α). Source: After Holling et al. (1995).

Holling (1973, 1992, 1995, 1996) integrated these new models of a hierarchical world to develop “ecological resilience” as an overarching concept of the functional relations that sustain the integrity of systems. He illustrates system dynamics using the Adaptive Cycle (Figure 2.2) to portray sudden change as inevitable, emerging from the endogenous dynamics of the system, not as an inexplicable departure from the norm created by exogenous factors. This cycle divides system dynamics into four phases, commonly viewed from the “birth” of a system as it self-organizes from a relatively undifferentiated state. The phases are: Exploitation (r), Conser-vation (K), Release (Ω), and Reorganization (α). The first two phases (r to K) su-perficially resemble the classical Clementsian view of ecological succession from barren ground to climax forest, which makes any sudden change in the system’s tra-jectory look like a “disturbance” that prevents the system from realizing an “ideal”

end-state. Holling (1996) uses the Adaptive Cycle to extend the Clementsian view to incorporate surprising deviations, catastrophe, and renewal. The transition from r to K shows how self-organization enhances the system’s stocks to the point where it eventually becomes so dense and over-connected that it is “an accident waiting to

26 Jan Sendzimir Table 2.1. Factors that contribute to the resilience of human and natural systems.

Regulation of renewal Control of disturbance or regenerative potential Disturbance frequency and intensity Stored resources

– Chesapeake shellfish fishery – Soil depth, organic content, seed bank – Herbivore grazing/browsing – Water (aquifer, lake, river)

– Fire in forests, grasslands – Nutrients in biomass – Lightning in mangroves

Capacity to absorb disturbance Facility of response – Landscape morphometry – Re-colonization distance

– Habitat availability – Biodiversity

– Ability to migrate – Cross-scale functional reinforcement (connectivity of landscape) – Within-scale functional diversity Processing and cycling of resources Availability of information

– Cross-scale functional reinforcement Viability of cultural information transfer language

– Within-scale functional diversity Customs (education, discourse) Politics

Human memory Population age structure

happen.” At that point, any contagious process (fire or pest outbreaks) can spread a pandemic of destruction (Omega phase) that releases the system’s resources. The future of the system resides in how these resources are recaptured and used to build a new system. This pivotal juncture, when a forest may degrade to a grassland or desert, or a lake may suddenly shift from clear water to an algal broth, is represented by the Reorganization (Alpha) phase.

The Adaptive Cycle illustrates the potential paths of change as a series of dy-namic transitions that can renew systems periodically when their resilience is high or can degrade them when their resilience declines. Resilience has no numeric measure. It is a qualitative indicator of a system’s capacity to maintain its integrity.

It focuses on how much shock or change a system can undergo and still remain the same system. For example, the rich, productive grassland of the Jornada valley supported intense grazing for centuries in New Mexico. Within just a few years in the late 19th century the grassland shifted to a shrub desert unfit for grazing, though no major change in farming practices had occurred. The system’s resilience declined to the point where almost any small factor could cause the entire system to flip to another state.

While resilience theory has not advanced to the point of quantifiable indices, it usefully focuses attention on those factors that sustain and promote resilience.

Adaptive Management for Resilience in Human and Natural Systems 27 Two broad categories of factors that contribute to resilience are control of bance and regulation of renewal (Table 2.1). In the first category, while distur-bances are inevitable, their effect may be less than catastrophic. Communities will probably survive and thrive on those disturbances with frequencies and intensities to which they have evolved for long periods of time. Experimentation continues to improve management practices (controlled fire or grazing) that can effectively achieve proper disturbance rates and intensities. Disturbance intensity can also be adjusted technologically to achieve viable economic/ecological systems. For example, the Chesapeake Bay shellfish fishery was headed for extinction due to overexploitation until the state government set a technical limit on fishing capacity by requiring that all fishing vessels be powered by sail. This lowered the fishing disturbance intensity to a level that allowed for viable shellfish populations.

Resilience can also be maintained by factors that increase a system’s capacity to absorb disturbances. For example, river basin landscapes with their original (un-channelized) morphometry have a wider cross-section and can absorb higher flood volumes. Dutch water managers are now starting a program to abandon dikes and channels and reinstate the floodplain morphometries that were originally created and shaped by flooding events (Middelkoop and de Boo, 1999). Another spatial factor that contributes to resilience is the configuration of habitats in the landscape, but the contribution is not always positive. Highly fragmented landscapes are more resistant to invasions and to contagious spread of disease but their lack of connec-tivity may also lead to collapse of animal populations that need mobility to find resources or to reproduce.

System resilience is sustained as well by a diverse and redundant capacity to process energy, nutrients, and resources. Peterson et al. (1998) have integrated wildlife ecology with hierarchy theory by suggesting that terrestrial animals per-ceive a discontinuous landscape and exploit only limited ranges of scale (strata or levels within the landscape hierarchy). Tiny birds search for insects at the finest landscape level, the leaves and needles in the trees, whereas larger birds search for insects or rodents at much coarser levels, such as fields and river edges. Because different animals use a diversity of resources within each scale range Peterson et al.

(1998) propose that ecological function is redundant within each geographic scale range. For example, within a single scale range, such as a tree canopy, different animals use a variety of resources, consuming different groups of insects, fungi, vegetation, mammals or birds.

However, such diversity and redundancy of function exists not only within a single scale range but across all scale ranges as well. Different animals exploit the same resources but at different levels in the hierarchy, so ecological function is redundant across all scale ranges within the landscape. For example, tiny birds may seek and eat individual caterpillars on a single tree branch, but larger birds

28 Jan Sendzimir will come and pursue the same prey when a caterpillar population explosion causes them to saturate an entire patch of trees with high densities. A caterpillar population explosion makes itself evident at the next larger scale, the tree patch. Therefore, at different times, different kinds of birds exploit the same resource (caterpillars) at micro as well as meso-scales. The resilience of such a system is sustained by this capacity to utilize resources and keep them cycling at different times and different scale ranges. A system that loses such capacity will accumulate resources in ways that invite new species to invade and exploit them or new processes to emerge. For example, fire may become more important if biomass begins to accumulate. In this way a system can shift its character, changing the communities of plants and animals that inhabit it.

The other category of factors (Table 2.1) that enhances resilience is “Regula-tion of Renewal.” Once a system’s resources are released in the destructiveΩphase, what factors exist that allow the system to retain those resources and to reorganize and re-establish itself? Stored resources (soils, seed banks, water, and nutrients) certainly retard resource dispersal, and/or contribute stored resources that promote production and pull loose resources into living biomass. Some factors facilitate the response function of resource rescue and renewal. For example, recolonization by plants or animals will be aided if seeds or animals have short distances to travel to disturbed zones. These recolonization distances are shortened by the landscape’s spatial distribution and diversity of habitats. The potential for redevelopment is also enhanced by the same redundancy of function within and across scales previ-ously discussed. Biodiversity contributes to that potential by providing a variety of species which utilize resources at different scales of space and time.

Renewal and regeneration are also promoted when the system can reliably find and use information about the system’s history. Information can be stored in lan-guage, custom, literature, educational tools and traditions, political processes, and human memory. This alludes to the hypothesis that human survival was greatly enhanced once our genes and/or our customs promoted the survival of people old enough to remember long-term events, crises such as floods, fires, droughts, and plagues. A population age structure with sufficient elderly members also has greater reproductive potential among a variety of animals (fish, mammals, birds).

The significance of resilience theory and the indicators it suggests is that it al-lows one to appreciate the complexity of system dynamics and spatial heterogeneity and yet concentrate on the critical factors (turning points, the spatial patterns) that are functionally related to system collapse or renewal. It does not eliminate un-certainty; nothing does. But it provides concepts and vocabulary that help narrow uncertainty to a workable level on which new theory and practice can be tested even as a complex system is managed.

Adaptive Management for Resilience in Human and Natural Systems 29 If our initial successes in eliminating variability and uncertainty have led to more profound catastrophes, how can we responsibly engage or embrace uncer-tainty and effectively respond to change? The challenge for society is that not only must understanding be consistently pursued and deepened to appreciate dynamic and evolving systems, but that one must take action in the midst of this effort. In other words, coping with novelty and surprise requires the sustained capacity to learn and to flexibly manage. For thirty years a decision making process has been evolving to address the twin challenges of learning and management. This process, Adaptive Environmental Assessment (AEA), has been refined in a series of on-the-ground applications in problems of forestry, fisheries, national parks, and river systems. It is currently being applied in two North American river systems, the Mississippi and the Colorado, and offers opportunities to address the development of society on flooding riparian systems. I will describe with examples some of the theory and operation of the AEA process.