With an increased likelihood of major shifts in earth systems, greater emphasis is being placed on maintaining their resilience to disruptive change and even building the ability to steer human-environment systems away from unproductive or hazardous regimes toward more sustainable and less hazardous ones (Walker et al. 2004, Olsson et al. 2004, 2008, Adger et al. 2005, Folke 2006, Bohensky 2008, Folke et al. 2009). Thus the concept of resilience has entered the lexicons of various disciplines confronted with extraordinary change, not only in the natural sciences but increasingly in the social sciences (Cote and Nightingale 2012, Brown 2014). In moving from ecological to social contexts, however, resilience has lost some of its precision and become conceptually vague and fuzzy (Brand and Jax 2007) so that it is now characterized by “blurred boundaries of concepts, metaphors and an implicit mix of normative and positive aspects” (Strunz 2012:114). Fuzzy concepts are described as concepts that lack clear definition, are difficult to operationalize, and lack evidence (Markusen 1999), implying that profound losses of resilience are likely easier to recognize than are resilient systems (Boin et al. 2010).
Conversely, some scholars argue that blurred boundaries and conceptual fuzziness may be particularly appropriate to inter- and transdisciplinary contexts where research questions are unclear and creativity is required (Strunz 2012, Deppisch and Hasibovic 2013), while others maintain they may simply be a function of the concept’s immaturity and detachment from policy (Lagendijk 2003, Chelleri 2012). Whatever the reasons, this ambiguity means that the concept is open to many interpretations across multiple theoretical and practice contexts (Stead 2013). Indeed, we argue that these attempts to apply the concept of resilience to so many different fields have impeded the continual improvement of its operationalization within policy making and implementation because of the following:
These issues are particularly relevant to complex “systems” problems that necessarily involve interactions between different domains where diverse resilience interpretations are used to inform theory and practice.
Our overall aim in this paper is to bring some clarity to the concept of resilience so that it can be interpreted and articulated in ways that enhance its utility and explanatory power. Facilitating common understandings and goals and improving ability to measure progress in developing resilience should provide the basis for the practical operationalization of the concept. Additionally, because we think resilience is still a useful concept, one of our objectives in interrogating the multiple resilience interpretations is to prevent resilience becoming little more than a “rhetorical device with little influence on actual decision-making” (Benson and Craig 2014:780), as has happened to some extent to the concept of sustainability.
Our argument is that the difficulties of defining and operationalizing resilience have arisen because of how the term emerged. Indeed, the escalating adoption of resilience in multiple practice and policy domains increases the urgency to interrogate and better delimit the concept. This paper provides the evidence supporting these arguments through dissecting diverse resilience interpretations. It does so by tracing key foundational theoretical traditions, mapping trends in definitional content and attention to scale, and identifying the key conceptual elements encompassed within resilience definitions. Our analysis shows that some maturation and convergence of interpretations is occurring and that, although there are common conceptual elements across most of the domains, there are also significant differences. From this analysis, we develop two analytical frameworks, a classification of resilience definitions, and a typology of resilience interpretations. Through the typology, we illuminate the multiple differences in interpretation and clarify the conceptual elements central to delineating resilience types. An important insight from the typology is that different levels of system disturbance call for different resilience responses.
As a framework for understanding system dynamics, the resilience concept has undergone some evolution since it was first proposed by Holling in 1973. To demonstrate these changes, we first needed to develop an understanding of progress in conceptual development. To this end, we selected five academic and practice areas that we call “domains” that are at the forefront of researching disruptions from global environmental change, and are recognized as developing traditions of resilience theorizing, namely, ecological (ER), social-ecological (SER), urban (UR), disaster (DR), and community (CR). Within these domains, the aim of climate change adaptation has generally been to increase socioeconomic and biophysical resilience. This makes them useful cases for investigating variations in conceptual composition and evolution. To further enhance the study, the author team applied their expertise from a wide range of relevant research areas, including ecology, climatology and oceanography, social-ecological systems, disaster, urban and community development studies, sustainability, and environmental governance. Their expertise was applied to literature reviews of each domain and synthesis of these data sets.
Step 1 involved groups of two and three authors undertaking literature reviews for each domain to locate resilience definitions, conceptual traditions informing them, and evidence of attention to scale. With regard to scale, we focused on cross-scale interactions (CSI) because (i) these are crucial to system resilience (Peterson et al. 1998), and (ii) there are profound implications of ignoring this critical system attribute for resilience’s practical implementation. Publications were selected for the period 2000 to 2014 from Scopus and the top 50 hits on Google Scholar using the search terms “resilience” and “[resilience domain],” for example, “resilience” and “ecology.” Highly cited literature from the prior period and coauthors’ personal libraries were also included in the reviews. The definitional data were used for two purposes: (1) to develop a classification of resilience definitions; and (2) to construct a typology of ideal resilience types, where “ideal” delineates a set of characteristics that are specific or unique to a given type (see Doty and Glick 1994 for further explanation).
For Step 2, different author groupings synthesized these data across the five domains. The data were analyzed to identify changes over time in definitional content, scale treatments, and underpinning theoretical traditions to confirm maturation and perhaps convergence of interpretations and understanding of resilience. This step also involved delineating conceptual elements from the definitions. From the definitions collected for each domain, subcategories were identified based on conceptual content. These formed the classification (Step 3), which was then used as the basis of the typology.
Our task in differentiating between domains was complicated by reliance of some domains on basic ER or SER definitions and by overlaps between some of the domains. Thus, convergence around crises and disasters among CR, DR, and UR domains means that some authors inform more than one domain. However, this limitation is balanced by the fact that these domains are influenced by different drivers. For example, CR is influenced by psychology and disaster management, while its operationalization is being driven on the ground by the community service development sectors. Conversely, DR is strongly influenced by policy and logistics around preventing, preparing for, responding to, and recovering from the increased frequency and intensity of extreme weather events while its practice or implementation is being driven by government and nongovernment agencies responsible for provision of emergency and supporting services to disaster affected communities (Gotham and Campanella 2011). Similarly, the different framings of resilience in the UR domain reflect diverse theoretical and disciplinary traditions, including urban ecosystems, disaster risk management, and global-local environmental and socioeconomic impacts/shocks. Hence, there is sufficient diversity of framings within these domains to counter the problem of an overlapping focus.
Step 4 comprised a process to construct the typology of resilience interpretations, beginning with the classification of resilience definitions. Existing classifications are confined to identification of broad definitional characteristics (Manyena 2006, Cretney 2014), or lists of illustrative definitions (Zhou et al. 2010, Aldunce et al. 2014, Lei et al. 2014), while there are limited examples of more refined resilience classifications (Brand and Jax 2007, Bhamra et al. 2011). Our objectives of demonstrating convergence and evolution among the selected resilience domains and contributing to conceptual clarity necessitated a more conceptually sophisticated classification system. Hence, the conceptual typology was selected as an appropriate methodology.
The main merit of conceptual typologies is in reducing complexity by bringing order to an eclectic mix of cases through sorting them into a few relatively homogeneous types distinguished by a few important dimensions (Bailey 1994). Although typologies have some weaknesses, they are still regarded as providing “a sound foundation for both theorizing and empirical research” (Bailey 1994:33, see also Doty and Glick 1994, Lagendijk 2003, Moore and Koontz 2003, Fiss 2011). These features confirmed the appropriateness of our choice of methodology.
The typology process involved, first, mapping the occurrence of conceptual elements against the classification of resilience definitions and, second, clustering subcategories of definitions based on their constituent elements to construct the typology of ideal resilience types. Each cluster represents an ideal resilience type.
Results from our analysis of the main features of resilience interpretations, associated conceptual traditions, and treatments of CSI are described here to demonstrate their maturation and convergence in understanding resilience. Resilience conceptual elements are outlined and definitional differences within domains are identified as the basis for typology construction.
The earliest definitions of ER describe the capacity of a complex ecological system to persist or to absorb change while preserving its structure and function (Holling 1973). “Ecological resilience,” which Holling (1996) and others (for example, Gunderson 2000) distinguished from “engineering resilience,” addresses concerns related to the unpredictability of change and the uncertainty of the environment, replacing the single equilibrium and single stable state positions of mainstream ecology with the idea of multiple stable states, often far from equilibrium.
The concept of SER, which evolved from Hollings’ work on ER, has become accepted as a useful heuristic for understanding, responding to and managing change in linked human-ecological systems (SES). It is widely acknowledged that the concept provides a crucial middle ground between social and environmental sciences, and that it has been important in bringing together scholars from these disciplines with a shared interest in environmental change leading to important research and insights (for example, Davidson 2010 and Kirchhoff et al. 2010). Capacities for adaptability, transformability, self-organization, and learning have become fundamental SER concepts (Folke et al. 2010).
From these advances in ecological theory, understanding has developed that natural and human systems are strongly coupled, behave in complex nonlinear ways, and are continuously changing and evolving. SER theorists found complex adaptive systems (CAS) theory highly compatible with their thinking (Lansing 2003, Levin 2003) and this theory has contributed greatly to understanding the dynamics of change in these linked social-ecological systems, particularly the dynamics of system stability, the operation of structures and functions across spatial and temporal scales, and the ability of drivers on one level to influence change at other levels (Gunderson and Holling 2002, Berkes et al. 2003, Sengupta 2006). Encapsulated as concepts of alternative stable states, the adaptive cycle and the panarchy, respectively (Gunderson and Holling 2002), these understandings have become essential foundations of SER thinking.
In the UR domain, resilience is often discussed in the context of crises and a return to a pre-existing stability domain. However, there is evidence of challenges to this engineering resilience understanding and associated attempts to integrate it into a human-dominated, built environment context (Alberti and Marzluff 2004, Ernstson et al. 2010). The equilibrium view of resilience is apparent in urban planning’s ongoing focus on the structure of cities, including the City Beautiful metaphor that resulted in sterile office blocks and dangerous public housing (Pickett et al. 2004). More recently, nonequilibrium views have emerged in response to arguments that equilibrium-based approaches are ill-equipped to explain the geographical diversity, variety, and unevenness of the resilience of places (Pendall et al. 2010, Pike et al. 2010), while nonequilibrium resilience is said to better account for the ability of urban systems to adapt and adjust to changing influences (Pickett et al. 2004). Thus, an integrated and holistic, dynamic and evolutionary SER interpretation is proving more useful to urban planning (Davoudi 2012, Davoudi et al. 2013).
Within the DR literature, preoccupation with responses to crises often precipitated by natural disasters leads to an emphasis on resistance capability, catastrophe absorption, and timely recovery from loss (Boin et al. 2010, de Bruijne et al. 2010, Zhou et al. 2010). Less reactive definitions focus on disaster prevention, vulnerability reduction, timely adaptation to a changed reality, and regeneration capacity (Paton and Johnston 2006, Maguire and Hagan 2007, Lavell et al. 2012, Heazle et al. 2013), reflecting the view that DR is a more positive and proactive focus for disaster management than disaster vulnerability (Cutter et al. 2008).
Until recently, DR and UR domains tended to an equilibrium view of resilience influenced by a risk and hazard management paradigm and backed by notions of stability and resistance to change (Adger et al. 2005, Berkes 2007). However, recent disaster experiences, such as the catastrophe inflicted on New Orleans by Hurricane Katrina, have exposed the limitations of this approach (Boin and McConnell 2007) so that DR is moving toward a more integrated paradigm (Boin and McConnell 2007, Park et al. 2011, Cox 2012, Djalante et al. 2012, 2013), that has shifted from prevention/control and mitigation/vulnerability to adaptation/transformation (O’Brien 2012, O’Brien et al. 2012, Lei et al. 2014). Hence, emphasis increasingly centers on the ability of complex systems to deal with hazards (Berkes 2007) and community capacities to respond well to crises through building and maintaining social capital and social resilience (Norris et al. 2008, Cutter et al. 2010, Brown and Westaway 2011, Cox 2012, Cohen et al. 2013).
Much of the discussion around CR too occurs in relation to risks and crises so that resilience is often adopted in its engineering sense of ability to bounce back and recover to a pre-existing regime thus emphasizing risk minimization and recovery support (Brown 2014). Broader definitions incorporate social parameters so that CR is interpreted as a capacity of the social system to work toward a common objective (Magis 2010, Berkes and Ross 2013). This interpretation favors community self-reliance and capacity-building to reduce vulnerability to stressful events (Twigg 2009).
CR draws from two disciplinary areas, psychological resilience and disaster management (Berkes and Ross 2013). Psychological resilience is largely concerned with how people cope with adversity, psychological risk factors, and individual sensitivities to trauma (Sonn and Fisher 1998, Juliano and Yunes 2014), although, more recently, psychologists have become interested in how structures and processes within communities and cultures affect how people deal with change (Juliano and Yunes 2014). Contemporary CR scholarship overlaps with disaster management research, and specifically focuses at the community scale (Berkes and Ross 2013), considering the collective resources (capitals) that enable individuals to respond to change, and the collective processes (e.g., governance arrangements) that support them to do so (Cutter et al. 2008). CR considers both system characteristics that support change and the processes through which agency can be created and enacted (Wilson 2012a, Brown 2014) so as to retain a community’s core structures and processes (Magis 2010).
We found limited evidence of CSI interpreted as a key conceptual element of resilience definitions in its own right in any of the five domains. References are restricted to external influences that drive adaptive responses within the focal system (for example, Folke et al. 2010) and limited to the SER domain. And yet, the broader literature suggests there is considerable differentiation between the domains on this aspect and recognition of CSI as a critical resilience factor is increasing. Unsurprisingly, both ER and SER domains have engaged with CSI as an intrinsic property of CAS, along with nonlinearity, self-organization, feedbacks, and emergence (Cumming and Norberg 2008, Levin 1998, Levin et al. 2013). CSI are acknowledged as an essential driver of ecological resilience, dependent on the distributions of functional groups across scales, and functional and response diversity at multiple scales (Drever et al. 2006, Peterson et al. 1998). Similarly, the importance of cross-scale linkages to SES resilience is well-recognized (Berkes 2007) and is most clearly expressed in the concept of the panarchy (Gunderson and Holling 2002). Thus, cross-scale subsidies and resource inputs can either be a source of, or impediment to, resilience (Abel et al. 2006); and solutions to complex problems may require explication of the interconnections of multiple causes, consequences, and cross-scale actors of the problem (FitzGibbon and Mensah 2012), comanagement and multilevel governance approaches (Olsson et al. 2007), or conscious boundary management (Cash et al. 2006); while the establishment of institutional linkages between SES is an effective means of building resilience to nonlocalized hazards (Gotham and Campanella 2011).
Conversely, attention to cross-scale interaction is a recent phenomenon in the remaining domains. Various authors, including Gotham and Campanella (2011) and Pendall et al. (2010), argue for consideration of cross-scale interactions in urban policies, such as policy-setting at the local level, e.g., water supply where there is potential to cause impacts beyond the defined geographical boundary. Cross-scale linkages must also be considered from temporal perspectives in enabling the system to deal with future shocks (Pendall et al. 2010), address temporal trade-offs, and avoid “lock-in” policies that reduce opportunities for learning and changing (Chelleri et al. 2015, Pike et al. 2010). Similarly in the DR domain, consideration of resilience in hazard-prone mountain SES has extended from localized hazards to the need to develop cross-scale institutional linkages between SES facing similar hazards (Gardner and Dekens 2007), while difficulties of building DR at different spatial and temporal scales, e.g., drought resilience in agricultural areas, to reduce disaster impacts are also acknowledged (Zhou et al. 2010).
Moreover, within the CR literature, Brown (2014) argues for consideration of cross-scale issues, given social science critiques of inattention to power and agency in social applications of resilience concepts. Wilson (2012b, 2013) and Singer et al. (2015) maintain that exogenous drivers acting across institutional scales can function to either strengthen or weaken some capitals important for community resilience, and that this is significant for the practical application of resilience concepts, given that these drivers can affect policy corridors, and therefore transformational pathways. Cross scale interactions, evidenced through the likes of climate adaptation planning, also demonstrably affect community economies, cultural practices, and social networks meaning that those with particular capitals become more or less vulnerable than others (Crane 2010, Singer et al. 2015), and therefore that some groups are better able to take advantage of change than others. Many community resilience assessments (e.g., Daze et al. 2009, Bours et al. 2013, Tyler et al. 2014) now explicitly consider cross-scale drivers of community resilience, particularly around governance and planning, in a perceived attempt to ensure the concept is practically relevant.
The analysis of resilience interpretations across the domains identified three definitional levels for each resilience domain. The diversity and maturational paths followed by resilience interpretations are captured in the classification of interpretations and their subcategories derived from the literature reviews and summarized in Table 1. The definitional levels were determined by noting the occurrence of conceptual elements within definitions. These elements are described in Table 2. Although there were distinct differences in definitions that enabled the isolation of the three levels within each domain, generally each succeeding level was observed to build on the previous one by including additional conceptual elements, thus demonstrating maturation or evolution of the concept, as illustrated in Figure 1. For example, early definitions of SER were confined to persistence to or absorption of disturbance, and retaining system identity, while later definitions added self-organization and adaptability, and more recently transformability.
Interestingly, not only is evolution of resilience definitions evident but so is their theoretical convergence particularly around the social-ecological interpretation that is providing the basis for definitions used by other domains, that is, the system can absorb disturbance, reorganize and adapt, yet retain the same function, structure, identity, and feedbacks (Colding 2007, Schewenius et al. 2014). Convergence, particularly around themes of self- or reorganization, adaptability, and transformation, is evident for the most mature interpretations of SER, UR, and CR, but less so for the advanced DR interpretation, where innovation rather than transformation is a key element.
Although increasing adoption of the resilience approach is apparent in those disciplines addressing the impacts of global, mostly environmental, change, we note that this is not universal with uptake among social sciences disciplines being more limited. Indeed, the idea that the ecological concept of resilience can be applied to social systems on the assumption that human communities function and behave similarly to ecological systems is highly contested (Adger 2000, Cote and Nightingale 2012). One proposition that could have significant ramifications for resilience’s practical effectiveness is that it is criticized for being a one-sided paradigm, termed ecological organicism by Kirchhoff et al. (2010), with distinct but unacknowledged cultural presumptions about the relationship between individuality and society, captured by the term individual holism (Kirchhoff et al. 2010). The problem is that other competing notions of individualism that might support consideration of alternative approaches to ecosystem management are excluded (Kirchhoff et al. 2010). Hence, Olsson et al. (2015) contend that the unificatory claims made for resilience theory may be an example of reductive “disciplinary imperialism,” where a single theory outcompetes other superior explanations, in this case, of persistent complex social problems, such as global environmental change.
Concerns have also been expressed about the failure to recognize resilience as socially contingent (“resilience for whom?”), the preoccupation with external drivers (disturbances), and the underplaying of internal social system processes (Brown 2014), while the inability of resilience theory to account for agency as well as structural variables is also disputed (Davidson 2010). Political ecologists criticize the failure to account for politics and power relations, and the effects of power asymmetries, particularly, “who decides the most desirable system state?” (Nadasdy 2007). MacKinnon and Derickson (2013) argue that the inherent conservatism of the concept through the focus on system persistence plays to neoliberal ideas of community responsibility whereby responsibility is devolved to communities to adapt to the logic and effects of global capitalism (Welsh 2014). As well, the resilience framework has been criticized for being too deterministic to account for the multiple incongruities in feedback processes operating in the complex global capitalist system thus challenging the presumption of a direct positive relationship between complexity and resilience (Davidson 2010).
Although the core resilience components of persistence, absorption, recovery, identity retention, self-organization, adaptability, and transformation are common to all definitions to greater or lesser degrees, additional elements of a social nature, such as, preparedness, vulnerability reduction, resilience building, capitalization, and collective capacities and processes, are embedded only in UR, DR, and CR domain definitions. This adds an extra layer of complexity to definitions used within these domains. The difference is that while ecological and engineering systems are managed by people, their resilience is inherent in their own structure and function, whereas the SER, UR, DR, and CR domains make people an integral part of the system.
From the clustering of similar subcategories of definitions and their constituent elements, three ideal resilience types (Fig. 2) were identified:
Analysis of Figure 2 shows that the ideal types of resilience interpretation are defined by their inclusion of particular conceptual elements. Although there is overlap of some elements, each type is differentiated by elements that are specific only to that interpretation type. Type 1, or “basic resilience,” has four core elements comprising persistence or resistance, absorption of disturbance, recovery or bouncing back to a previous stable state, and retention of system identity. Type 2, or “adaptive resilience,” may include some or all Type 1 elements, but self-organization and adaptability typify this ideal. Type 3, or “transformative resilience,” incorporates some elements from Type 1 and both unique Type 2 elements, but transformability is the element that clearly distinguishes this type. The seven conceptual elements included in the three ideal types constitute core elements while the remaining elements are discipline-specific elements restricted to UR, DR, and CR definitions. The elements in this latter group are not essential to the ideal types but may be essential to specific resilience interpretations and applications.
Although definitions of resilience have been described as multiple, sometimes contradictory, and somewhat fuzzy, identification of underlying theoretical traditions and the mapping of consistently occurring conceptual elements within definitions has enabled us to demonstrate their evolution and convergence across the five selected domains. Moreover, the nascent trend toward engagement with the significance of interactions with scales beyond the focal scale in UR, DR, and CR domains confirms this observation.
The analysis also suggests, at least in the literature if not in practice, a convergence toward the most mature SER interpretation with additional social elements that reflect the particular emphases of UR, DR, and CR domains. Furthermore, although the selected resilience domains are informed by diverse theoretical traditions, because of the growing convergence around the SER interpretation, the dominant conceptual influence is currently coming from CAS and complexity theory. This trend has implications for resilience application in practical and policy contexts.
Following Kuhn (1970), we could argue from our observations that the current diversity of, and competition among, resilience interpretations constitutes a preparadigmatic movement while the perceived convergence of interpretations and underlying theoretical bases is a possible indicator of an emerging resilience paradigm. Such a paradigm would entail appearance of a consensus around the fundamentals of resilience—its constituent concepts, framing theories, language, and metaphysical assumptions—thus permitting agreement on the problems to be solved and the development of an “exemplary” resilience science. Only then will “intelligent management” of the problems associated with uncertainty and complexity be possible (Ravetz 2006:279).
In this analysis, we utilized the potential of typologies to clarify the conceptual elements that are core to resilience thinking. Conceptual clarity was achieved by (i) systematically ordering diverse resilience definitions according to their constituent conceptual elements and (ii) distinguishing the fundamental conceptual elements essential to understanding resilience as a conceptual framework from those that are associated with particular resilience interpretations but are not essential to the base concept.
By using the typology to clarify the core conceptual elements, we were able to distinguish which elements are closely associated. We also found that closeness of association among elements is controlled by the amount and nature of change influencing systems. Thus, the level of system disturbance is the key dependent variable differentiating the ideal types of resilience.
Moreover, identification of this variable enabled us to make sense of the relationships among elements comprising particular resilience types. For example, the four elements comprising Type 1 basic resilience are related in reducing disturbance and maintaining system status quo, while the elements of Type 2 adaptive resilience are related to adapting to change but maintaining structure and function. Conversely, the defining Type 3 transformative resilience element, transformability, involves a transition, either purposeful or unintended, from the status quo and replacement of adaptation as the lead change response.
Each ideal type is composed of a unique combination of conceptual elements that will characterize a specific resilience response to a given degree of system disturbance. The amount and nature of change varies for each resilience type and will influence the approach taken in operationalizing resilience (Serrao-Neumann et al. 2016). Thus, for Type 1 resilience contexts, where vulnerability to change impacts is relatively low, policy and practice for change can be limited to absorbing or resisting disturbance, and maintaining the current system state and identity. For example, in a system where the climate is stable with a manageable level of impacts including from extreme events, a Type 1 resilience interpretation would be appropriate. To illustrate by way of a concrete example, a Type 1 response to a disaster event such as a wildfire or flood might be the promises made by politicians, or demands made by residents, that the damaged properties be replaced by similar structures in the same location. In more vulnerable and dynamic Type 2 contexts, such as in a system experiencing a changing climate with an elevated level of climate extremes, Figure 2 suggests that policy and practice considerations focused on capacities for renewal through self- or reorganization and adaptability would be sufficient. At this level of disturbance, disaster responses could encompass improved building structures that are adapted to withstand increased flood or wildfire risk, undertaking preventative measures, and establishing collaborative community organizations to build community resilience. In highly vulnerable and dynamic Type 3 contexts, however, such as for a system facing a regime shift as a result of heightened impacts from extreme climate events and possibly other change drivers, operationalizing resilience must follow a transformative path. In this case, disaster responses could involve relocation of whole communities away from vulnerable flood or wildfire prone sites altogether.
The implications of these three interpretation types are significant for practice. First, this analysis implies that policy makers and managers should have a comprehensive understanding of the change environment with which they are dealing. Second, system dynamics and unpredictability mean that the appropriate interpretation type for resilience practice may be required to change, as the dynamics and stability of systems change. Understanding the shifts in systems and the responses is challenging, given that each type includes different conceptual elements. For example, CR Type 1 could be assessed through persistence of collective identity, e.g., culture; for Type 2, this identity would persist despite adaptability, e.g., changes in agriculture cropping patterns because of climate impacts. For a CR Type 3 interpretation, we would also need to consider indicators of collective capacities and processes that enable a community to adapt in self-determined ways, such as the strength of local governance processes or education levels that would enable more transformative pathways to be identified, e.g., shifts in livelihood strategies from agrarian smallholding to tourism and market-based agriculture. Thus, there are limitations to the application and practice of resilience building when narrow and singular interpretation types are applied.
In developing the resilience typology, the set of interconnected problems we were seeking to address was primarily the following:
Through our analysis we showed that although there are some conceptual elements that appear in most interpretations, other elements occur sporadically and are domain specific (Fig. 1). We argued that it is this variation that makes it difficult to operationalize the concept of resilience in terms of an agreed definition, setting goals, and measuring progress.
The typology contributes to conceptual clarity by: (i) reducing the confusion resulting from multiple resilience interpretations and therefore helping to order systematic research into better understanding resilience across diverse domains; and (ii) integrating diverse resilience interpretations to identify ideal resilience types and clarify core and domain-specific conceptual elements. Albeit that our analysis of these elements is a snap-shot in time, showing which elements are converging and which are not is helpful to conceptual clarity.
Given that resilience is the objective of many management actions, our classification and typology afford guidance on the conceptual elements that could inform resilience applications in different domains, while providing broad conceptual categories as the basis for developing consistent foundational indicators to measure changes in resilience. From a practical perspective, better consistency and clarity in applying resilience, better targeted resilience-building measures/activities, and cross-domain lessons (e.g., SER could learn to incorporate agency by reference to CR scholarship on community capacities and processes) could be expected. Although it is known that domains do not acknowledge the use of resilience in other domains (Alexander 2013), increased recognition of these varied interpretations should be encouraged because individual domains have much to learn from each other. Indeed, this paper represents an example of inter- and transdisciplinary efforts to address these shortcomings and a timely intervention to strengthen the practical and explanatory authority of resilience.
In considering the future of resilience as a useful problem-solving paradigm under conditions of complexity and uncertainty, one option might be to use the common conceptual elements as a starting point to build the consensus of a mature resilience paradigm that allows agreement on fundamentals and the key resilience problems requiring solutions, while providing consistency and clarity in its application. Our demonstration of an emergent resilience paradigm may indicate that competition among disciplines could possibly be replaced by consensus on resilience fundamentals in the near future.
Finally, if SER emerges as the dominant resilience interpretation, conceptual clarity and effective practice require that care be taken to avoid resilience becoming a one-sided paradigm by further interrogating its cultural presumptions and leavening with insights from alternative resilience interpretations and social science traditions. This could involve investigating the links between cultural and ecological diversity and the resulting implications for resilience-building, transformation, and innovation and renewal. Further investigation may also be needed into the issue of power and agency and SER, including, for example, whether SER and CR are more beneficially held in tension or whether they can build from one another. Another area that deserves attention involves the tools used in DR, UR, and CR domains for assessing and/or building resilience, vulnerability, or adaptation, and whether these are fundamentally different; whether these domains could learn from each other, and, if so, how?; and why collective processes, capacities, and resilience building are not being better considered in DR and UR domains. Last, further development of emergent understanding around cross-scale effects within the nonecological domains is also warranted, particularly focusing on CSI as they apply to the core components of the emerging resilience paradigm: adaptation, transformation, innovation, and renewal; how does scale affect these elements?; and what does that mean for the way we assess and build resilience?. Additionally, if we are to advance a more critical understanding of resilience’s multiscale dimensions, CSI could be better recognized as a necessary condition for system resilience and become a core element of resilience definitions across all domains.
The authors wish to express their gratitude to the reviewers for their insightful comments and suggestions and to gratefully acknowledge the support of the University of the Sunshine Coast Research Futures Project funded by the Australian Government’s Collaborative Research Network Program for the collaborative writing program under which this paper was researched and authored.
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