In Spatial Resilience in Social-Ecological Systems, Cumming (2011a: 7) points out that there is not yet a full-blown theory of SES. Instead, we have a number of
identifiable elements of SES-related theory and some promising theory-oriented frameworks (e.g. Holling 2001; Norberg and Cumming 2008; Ostrom 2007; Waltner-Toews et al. 2008).
‘SES theory incorporates ideas from theories relating to the study of resilience, robustness, sustainability, and vulnerability, but it is concerned with a wider range of SES dynamics and attributes than any one of these terms implies; and while SES theory draws on a range of discipline-specific theories, such as island biogeography, optimal foraging theory, and microeconomic theory, it is broader than any one of these individual theories alone’ (Cumming 2011: 8).
Some authors use the expression ‘social–ecological system’ to refer to an intermediate state between fully separated social and ecological systems and fully integrated socioecological or ecosocial systems. Like Cumming, I use ‘social– ecological system’ in the sense of a fully integrated system, as I will now explain. As previously stated, SES are complex integrated systems of people, human society, the economy and the rest of nature (Costanza 1996, 2003, 2011; Costanza et al. 2007a, 2012a). The term ‘social–ecological system’ is used to emphasise the integrative humans-in-nature perspective and to stress that the delineation between social and ecological systems is artificial and arbitrary (Berkes and Folke 1998b: 4). Walker et al. (2006) describe SES as neither humans embedded in an ecological system nor ecosystems embedded in human systems, ‘but rather a different thing altogether’ (p. 1).
The SES perspective recognises the hybrid and reciprocal character of human– environment relations. It acknowledges that social (human actors and institutions) and ecological (bio-geo-physical) entities are, in many cases, intricately interconnected and fundamentally interdependent. This is based on evidence that (1) human actions affect the biophysical environment and ecosystems, (2) biophysical and ecological factors affect human well-being, and (3) humans in turn respond to these factors (Berkes 2011a: 12). In effect, human social systems (including communities, societies, economies and cultures) and ecosystems are in
a continuous dynamic interaction: a two-way feedback relationship. Both the social and ecological domains are integral subsystems of an emergent, complex co-evolving system (Redman et al. 2004: 163; Haberl et al. 2006: 2). Thus, a SES is a system
‘in which the social and biophysical subsystems are so entwined that the system’s condition, function, and responses to a hazard (or any external forcing) is predicated on the synergy of the two subsystems’ (Turner 2010a: 170).
Structurally, a SES is a divisible whole, but functionally it is an indivisible unity with emergent properties (Laszlo and Krippner 1998: 53). Furthermore, a SES’s dynamics are connected via cross-scale linkages to events and changes that occur (or have occurred) at other times and places (Chapin et al. 2009b). Thus, conceptualising and depicting a SES as a model or mind map presents a challenge (Glaser 2006). Interdependent social systems and ecosystems must be clearly expressed as a single, integrated system rather than a social–ecological coupling or nexus. That is, rather than a mere pairing or interface connection between two different entities that belong to epistemologically different worlds.
The question then is what key attributes can be used to map or model a SES? What attributes can be used to (1) define a SES’s boundaries in spatial or functional terms in a specific problem context, and (2) describe SES in general and EASES in particular? In the preceding sections of this chapter I have framed the study of SES in the theoretical context of CAS and Holling’s adaptive cycle. I use this system of concepts to define and describe SES, thus providing the theoretical basis for conceptualising EASES (see Chapter 6). Next, I look at the SES model used in this research.
Social–ecological systems model
With the growing popularity of the SES concept there is an increasing number of both generic and case-specific models of SES in the literature. It is beyond the scope of this thesis to review these. However, as Glaser (2006) points out, high generality mental models (‘mind maps’) of society–nature relations are important
pre-analytical foundations. They help us simplify, visualise and analyse not only the components but also the cross-scale connections and dynamics of complex SES. Therefore, visual representations of the SES concept are important tools for sustainability researchers and decision makers. Here, I present the model that was used for the study of EASES (Figures 2.11 and 2.12). Figure 2.11 depicts key attributes of simplified internal structure and processes, external conditions influencing the system, and significant transboundary interactions.
Figure 2.11 Conceptual model of a macro-regional level social–
ecological system (EASES).
Social and ecological structures and processes tend to self-organise and occupy relatively discrete levels in space and time (Garmestani et al. 2009). Conceptually, a SES may be specified for any particular level of organisation. In practice they are identified at a particular focal level of interest depending on the combination of spatial, temporal, social and ecological scales considered relevant to the aims of governance, management or research. For example, a local level SES model may be constructed for a coastal zone management study concerning small-scale fisheries interactions with community wind power development; whereas for
considering international governance of fisheries impacts on biodiversity, a global level SES model is appropriate.
The boundaries of the focal level system are established around functional groupings of social and ecological entities (e.g. actors, communities, social networks, institutions, cultures, territories, jurisdictions, landscapes/seascapes, natural resources, ecosystem services, ecosystems or biogeographic regions). The boundaries between nested and adjacent systems are open: permeable to flows of energy, mass and information. In a maritime macro-regional SES, transboundary trade, pollution, and movements of humans, fish or other animals are obvious examples of such flows. Identifying boundaries and their conditions is difficult due to intrinsic complexity, ambiguity (‘fuzziness’), interaction of multiple levels and scales, alternative viable system regimes, and spatial–temporal variance (Cumming and Collier 2005).
Figure 2.12 Systems hierarchy showing cross-level interactions (lines)
and cross-scale interactions (arrows) between co-evolving social (yellow) and ecological (green) dimensions.
The world may be imagined as a hierarchical structure of nested, interconnected and interdependent CAS in which the SES at the focal level of interest is embedded in successively higher-level systems. At the same time, the focal level system (in this case EASES at the macro-regional level) encompasses successively lower-level systems (Holling 2001; Warren 2005). Accordingly, the focal level may consist of any number of lower-level nested, adjacent or overlapping SES and/or component social systems and ecosystems. The structural relationship between EASES at the macro-regional focal level and other interconnected system levels is represented in Figure 2.12.
Social–ecological networks
Instead of a hierarchy, SES can be conceptualised as social–ecological networks. From a network perspective, important structural characteristics of SES are represented as nodes and links (Janssen et al. 2006; Cumming et al. 2010; Bodin and Tengö 2012). In this view, nodes are human and social entities (individuals, groups, communities, organisations, economic sectors, etc.) and ecological entities (landscape properties, natural resources, ecosystems, etc.) that are interconnected in a network. The links between nodes, which may be active or inactive, are used to describe the structure of the relationships or interactions between nodes.
Like other CAS, SES are dynamic networks of many agents (actors) continually acting and reacting to other agents’ behaviours as well as external changes. A significant change in the underlying network configuration implies a change in the fundamental function, structure, identity and feedbacks of a SES. The changes in configuration are facilitated and/or constrained by the social–ecological network structure and properties such as connectivity (reachability and density), centrality, modularity/fragmentation, redundancy and control of flow (Janssen et al. 2006; Webb and Bodin 2008). Such network properties are, of course, related to system resilience (see Chapter 3).
In addition to being integrative and useful for capturing dynamic aspects of SES, a network perspective is potentially useful for analysis of cross-level and cross- scale linkages; for example, by taking into account holarchic bottom-up and top- down processes such as cascading effects (Cumming et al. 2010).