Socioecological Systems

Few ecosystems are free of extensive human influence. Landscapes change constantly from natural and anthropogenic drivers, and land use and land cover changes by humans have been identified as a primary effect of humans on natural systems. These changes underlie fragmentation and habitat loss, which are the greatest threats to biodiversity and ecosystem services. The complex interactions between development decisions and ecosystems, and how the consequences of these decisions may then influence human values and subsequent decisions is an important area of study.

As reciprocal influences among humans and the climate, biota, and ecological goods and services of the world have become both stronger and more widely recognized, there has also been the acknowledgment that in the majority of ecosystems, structure and function are now determined primarily by human interactions, perceptions, and behaviors, so that nowadays it is more appropriate to think of social-ecological systems combining approaches from both environmental and social sciences.

The socioecological system (SES) theory sprang from the recognition of close interaction between society, in terms of social-economic system, and natural system. For this reason, an interdisciplinary approach is needed: in the past the social-economic approach was distinct from that of ecology; the stereotypical economist might say ''get the price right'' without recognizing that price systems require a stable context where social and ecosystem processes behave 'nicely' in a mathematical sense - that is, they are continuous and convex. The stereotypical ecologist might say "get the indicators precise and right" without recognizing the surprises that nature and people inexorably and continuously generate. These simple approaches are often attractive because they seem to replace inherent uncertainty with the fictitious certainty of ideology or precise numbers. But the theories implicit in these approaches ignore multi-stable states that characterize SESs.

SESs show a complex and uncertain nature rooted in the complex systems theory that refers to interrelated theories (catastrophe theory, chaos theory, information theory, hierarchy theory, and self-organization theory) that have originated from different scientific disciplines. Despite their traditional scientific disciplinary origins, they have provocative implications across disciplines and fields and, more generally, for the way we understand various types of phenomena as well as the role of learning in planning and policymaking.

In the past, the usual way to study complex phenomena was based on simplifying them through analytical reductionism (describing them as simple systems, machines) or by aggregating and averaging through statistical analysis (describing them as unorganized complex systems). But complex systems, such as SESs, exist at a threshold between order and chaos, because they are too complex to be treated as machines and too organized to be assumed random and averaged. An example could be the slow erosion of key controlling processes that can abruptly flip an SES into a different state that might be irreversible (the gradual loss of species important for pollination could cause the slump of an economy based on agricultural products).

Human society represents the driving forces of biosphere and ecological systems. So, it is relevant to understand the human sources of ecological change. To do this, we must understand the driving forces motivating human actions. Driving forces are the underlying causes that influence and direct human activities. These forces, either directly or indirectly, result in changes in ecosystems, changes that can degrade ecosystem capability to provide goods and services. The roots of these forces can be economic, political, sociocultural, and/or legal, and rarely occur in isolation, but rather act in conjunction with others. Direct driving forces, such as mining or agricultural practices, are easily recognizable as they often have an immediately discernible effect. Indirect driving forces are less identifiable; however, they have no less of an impact on ecosystems since they influence people's actions. For example, legislation can encourage people to mine rather than farm an area and influence how they will mine. There are several examples in the world: Britain's solution to rising urban pollution levels in the 1800s was to increase the height of factory chimneys. This only postponed the problem in England, while it then introduced problems in Scandinavia. This was only a temporary solution and only at the local scale. The source of the problems, the emissions from industrialization, remained unchanged in quantity or quality. In Europe, the WTO has required the end of European preferential treatment of some banana-producing nations. The opening of trade within the EU could drive land-use changes in other banana-producing countries. The WTO has certainly foreseen this possible outcome. However, it is simply considered a shift of production location based on economic considerations, disregarding both the social and ecological changes that can be driven by such a shift.

Human society is able to choose alternative development scenarios. Initiatives toward development might cause social and ecological changes and bring surprises and uncertainties. It is necessary to plan strategies that enhance system's adaptive capacity to change rather than simply maximize resource consumption. In the case of sweeping surprises, partial solutions, only economic, or social or ecological, bring the loss of benefits coming from the integration among economic, social, and ecological processes. The base of sustainable policies and investments should be turned toward knowledge integration, with the aim to obtain a comprehension based on different viewpoints.

Key Features of SESs

Complex systems theory offers a more sophisticated understanding of the structure and dynamics of both social and ecological systems than the relevant 'normal' scientific disciplines.

The properties of SESs are (Figure 1):

• Nonlinearity. They behave as a system and cannot be understood isolating their components.

• Hierarchy. They are hierarchically nested and the 'effect' exercised by a specific level involves a balance of internal (self-control) and external controls involving other hierarchic levels in a mutual causal way. Such interactions cannot be understood by focusing only on one hierarchical level (multiple scales of interest).

• Internal causality. This is due to self-organization.

• Dynamical stability. There are no equilibrium points for the system.

Figure 1 Characteristics of complex adaptive SESs.

• Multiple steady states. There is not necessarily a unique preferred system state in a given situation, because multiple attractors can be possible in a given situation.

• Catastrophic behaviorIt is typical of SESs, in terms of

(1) bifurcations - moments of unpredictable behavior;

(2) flips: sudden discontinuity; and (3) holling four-box cycle.

• Chaotic behavior. The human ability to predict the future is always limited.

The complexity of a system is the result of the interaction among a great deal of components that cause new, emergent, and unexpected properties. The analysis of these systems suggests that the possibility for a sustainable development depends on changing perception of human society regarding complex systems. Thus, an essential goal is to change the perception and the way of thinking of social actors, moving their attention from increasing productive capacity to increasing of adaptive capacity. This means that it is necessary to turn social actors' attention to a view where society and nature are coevol-ving in the biosphere.

SES theory was pioneered in the 1980s by the Resilience Alliance, a voluntary organization of scientists ofvarious disciplines, to explore the SESs' dynamics and their possible evolutions, but there are several scientific schools interested in their study. These theories are based on concepts as adaptive cycles, resilience, adaptability, transformability, and hierarchy (panarchy), and aim to provide knowledge basis to manage complex adaptive systems and to achieve sustainable development in theory and in practice. The knowledge of these aspects should improve natural systems management and their capacity to support human and natural capital.

The novelty of these theories concerns natural, disturbed, and managed ecosystems, identifying which are the key features of ecosystem structures and functions (Table 1):

• Change is episodic, with periods of slow accumulation of natural capital such as biomass, physical structures, nutrients, punctuated by sudden releases and reorganizations of this biotic capital, as the result of internal or external natural disturbances, or human-imposed catastrophes. Rare events, such as hurricanes or the arrivals of invading species, can unpredictably shape system structure at critical times or location, leading to an increase in fragility. In this way, these rare events can modify the future of the systems for long periods, even if irreversible or slowly reversible states can exist; once the system flip into another state, only an explicit external management intervention could allow the system to come back to its previous self-sustaining state, but its full recovery is not assured.

• Spatial attributes are discontinuous at all scales, from the leaf to the landscape to the whole planet. There are several different ranges of scales, each with different attributes of architectural patchiness and texture and each established and sustained by a specific set of abiotic and biotic processes.

• Ecosystems do not have a single equilibrium and homeostatic controls that keep them near it, but rather multiple equilibria commonly defining different functional states within the same stability domain. Normal movements of state variables maintain structure, diversity, and resilience. Stochastic forces and interactions between fast and slow variables mediate the movements of variables among those equilibria.

• Policies and management that apply fixed rules (e.g., maximum sustainable yield), independently of scale, could lead systems to lose resilience, that is, systems break down in the face of disturbances that previously could be absorbed.

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