After nearly two decades of diverse adaptive comanagement (ACM) studies, this field of research is at an important juncture, where taking the next step in understanding requires coordinated efforts. While different paths forward are possible, a diagnostic approach has been identified as a particularly promising avenue to allow for this coordination (Plummer et al. 2012, 2014). A diagnostic approach provides a way to build a systematic understanding in complex situations (Meinzen-Dick 2007, Ostrom 2007, 2009, McGinnis and Ostrom 2014). It provides a common set of “tools” to construct causal explanations (McGinnis and Ostrom 2014). We make operational the idea of a diagnostic approach as a new direction for ACM research. Specifically, our aim is to present the core features and dimensions of a diagnostic framework so that future ACM research can be approached in a systematic and comparative manner. This is necessary to build the theory of adaptive comanagement in ways that enhance practice.
Our insights come from a multiyear research project that sought to develop such a framework and to trial the framework in four UNESCO biosphere reserves: two in Canada and two in Sweden. After briefly explaining the rationale for a diagnostic approach, a framework is set out which organizes knowledge about ACM into main variables and presents foundational nomenclature. We subsequently outline a suite of tested methods and established measures for adaptive comanagement, and offer a way to make the diagnostic approach operational for data collection in empirical settings.
How did ACM arrive at a cross-road? Adaptive comanagement emerged as one promising approach to the governance of social-ecological systems (Olsson et al. 2004, Folke et al. 2005, Armitage et al. 2007). It combines and builds upon adaptive ecosystem-based management and collaborative (co-) management. Adaptive comanagement is understood as “…flexible, community-based systems of resource management tailored to specific places and situations, and supported by and working with various organizations at different scales” (Olsson et al. 2004:75). In theory, it enables faster and more appropriate responses to system change, as it draws on the capacities and competencies of a diverse set of actors, while continuously improving practices in a learning-by-doing process. In addition to offering a suite of ideas or prescriptions about how desirable environmental governance may be accomplished (Huitema et al. 2009), ACM is a real-world phenomenon. In the almost 20 years since its genesis, numerous initiatives have emerged that resemble ACM, in part or fully. Particularly striking is the diversity of situations (e.g., forestry, fisheries, wildlife, waterscapes, parks and protected areas, climate change, settlement) and geographical distribution in which ACM has been documented.
Scholarship on ACM has grown rapidly and generated a host of valuable insights. However, a systematic review (see Plummer et al. 2012) to comprehensively synthesize these insights uncovered a challenging, but not uncharacteristic, issue for a young and developing field. That is, conceptual imprecision and methodological inconsistency has limited the ability to make rigorous comparisons across settings and has precluded drawing robust evidence-based findings about the interrelationships between/among ACM variables, actual outcomes, and successes/failures. Several important questions therefore remain open. What works where and under what conditions? Which variables are essential to ACM and what is their relationship to outcomes and success (Huitema et al. 2009, Plummer 2009, Plummer et al. 2012, Fabricius and Currie 2015)? Are the promises of improved outcomes (social and ecological) under ACM being realized? Answering such questions requires a common conceptual framework and employment of rigorous methods across cases/studies to draw stronger causal inferences, limit errors in generalization, and eventually start developing theory (Plummer et al. 2012).
The complexity of social-ecological systems limits the utility of blueprint approaches and management panaceas (Holling and Meffe 1996, Cox 2011). In contrast, the use of diagnostic approaches has emerged as a strategy to consider complexity in a more systematic manner (Meinzen-Dick 2007, Ostrom 2007, 2009). In the context of social-ecological systems, diagnosis is analogous to health professionals querying symptoms and thereby gaining an understanding of a complex situation: “a [diagnostic] framework provides the basic vocabulary of concepts and terms that may be used to construct the kinds of causal explanations expected of a theory. Frameworks organize diagnostic, descriptive, and prescriptive inquiry” (McGinnis and Ostrom 2014: Why a framework? section, para. 1).
Connections between the notion of a diagnostic approach as advanced by Elinor Ostrom (2007) and its potential to inform understanding of ACM processes have been identified (e.g., Plummer et al. 2012, Whaley and Weatherhead 2014, Fabricius and Currie 2015). In particular, a diagnostic approach was advocated as a way forward for synthesizing research on ACM in specific and coordinated ways that ultimately provide a basis to develop theory (Plummer et al. 2012). Plummer et al. (2014) provided an initial step down this path by proposing a diagnostic framework, conceptually establishing main variables for consideration from antecedent scholarship, and discussing how it may be used in the context of biosphere reserves. In the spirit of continual refinement, we build upon that discussion paper in this section by offering a working framework (Fig. 1) to serve as an organizational device, and present a corresponding common nomenclature for ACM. The spirit of the diagnostic approach and social-ecological systems framework by Ostrom (2007, 2009) informs our thinking in important ways (e.g., decomposability of systems, development of nested conceptual maps), but our focus is exclusively on understanding ACM, and we thus emphasize specific antecedents, process hallmarks (i.e., collaboration and learning), and outcomes.
In the following sections, we move from the broadest conceptual level to unpack the variables in nested tiers. Each first- and second-tier variable is briefly described, and then third-tier variables are detailed (see Table 1). The organization of the nested conceptual map for ACM draws upon the utility and logic set forth by Ostrom (2007, 2009), where the higher level variables offer an organizing framework from which more indepth investigations may occur, as well as offering a mechanism for testing relationships between and among variables at a broader level. In our specific framework, the higher level variables offer a common nomenclature to facilitate the systematic accumulation of knowledge about ACM, the lower level variables may be operationally tailored to specific ACM inquiries, and in concert, causal inferences may be developed to start building general theory about ACM.
For simplicity, we consistently refer to the study object as the ACM process. However, we wish to clarify that the diagnostic approach is not limited to studying the characteristics of a process being predefined/determined to resemble ACM. Rather, a main merit (as we argue here) is its ability to facilitate cross-case empirical inquiries in relation to if, how, and to what extent the different key features (i.e., variables) of ACM relate to each other and to social and ecological outcomes. Thus, our diagnostic approach will hopefully be of value to scholars who do not necessarily define themselves as specifically interested in ACM but in other similar frameworks (e.g., adaptive governance, biosphere stewardship).
Steps for diagnosis are keyed to main variables for analysis, and occur with acknowledgment of the setting in which they are embedded. Adaptive comanagement is tailored to particular places and situations (Olsson et al. 2004, Armitage et al. 2009) and is always related to the setting in which it occurs. The setting informs diagnosis of the condition in a specific circumstance and provides a basis upon which cross-site comparisons are predicated. Attention to the institutional context, biophysical conditions, and social-ecological connections is required.
Diagnosis begins with the search for antecedents that signal an ACM process—actors, activities, and practices. Antecedents direct attention to circumstances that signal ACM may be present. Interactions among multiple types of actors across decision-making levels with some degree of power sharing are essential properties of ACM (Folke et al. 2005, Berkes et al. 2007, Schultz et al. 2011). Activities include what is being done on-the-ground as individuals work together to manage, govern, and/or solve environment and resource challenges in a particular place. Practices draw attention to the manner in which the activities come about and/or the customary performance of the formal or nonformal organization.
Antecedents are emblematic of an iterative and ongoing process. As actors engage in ACM, a unique process is engendered that brings together and builds upon the linking function of collaboration with the learning aspect of adaptive management (Berkes et al. 2007, Armitage et al. 2009, Plummer et al. 2014). Considering process attributes is the second step in the approach to diagnosing ACM, specifically features of collaboration and learning (Fig. 1).
Collaboration is a major narrative in resource management and environmental governance, with a litany of cases providing valuable insights about opportunities arising when multiple actors come together to pursue a shared interest. In relation to the ACM process, we draw attention to the qualities as well as the structure of collaboration. The quality of the process by which a decision is reached has repeatedly been found to mediate claims about participation in empirical studies of resource and environmental management (Reed 2008). Several works have synthesized principles and attributes that constitute a quality process in this context (e.g., Webler et al. 2001, Lockwood et al. 2010), with more recent works (Sandström et al. 2014, Birnbaum et al. 2015) emphasizing qualities associated with legitimacy (e.g., openness, deliberation, mutual respect, transparency). Many of these qualities transcend collaboration of different types, but specificity is required if empirical appraisals are to be meaningfully considered in relation to the aims and outcomes of the process (Conley and Moote 2003). In this regard, our diagnostic approach is informed by characteristics of ACM and process parameters (Plummer and Armitage 2007), including attributes of pluralism and linkages, communication and negotiation, and transactive decision-making. The structure of collaboration may be understood using a social network approach, where actors engaged in ACM are connected to each other via collaborative ties. The potential of network measures to provide insights into ACM has been highlighted (Folke et al. 2005, Plummer et al. 2012), including network change over time (Baird et al. 2016). Network attributes associated with the ACM process include social cohesion, heterogeneity of actors, and centralization.
Learning, similarly, is an essential ingredient when collectively navigating complexity and uncertainty. We follow Argyris and Schön (1974), and define learning as a social process of iterative reflection that takes place when experiences, ideas, and environments are shared. Individuals as opposed to organizations learn (Fazey et al. 2005), and yet the social situation in which learning occurs is essential. The social unit is thus important to recognize, and we adopt the perspective that learning here is a “…change in understanding that goes beyond the individual to become situated within wider social units or communities of practice through social interactions between actors within social networks” (Reed et al. 2010: Conclusions section, para. 1, Diduck 2010). Effects on cognitive, normative, and relational learning are considered. Attributes of learning at the group or organization social unit of analysis also warrant consideration and draw attention to where errors are corrected from routines (single loop), values and policy adjustments occur (double loop), and governance norms and protocols are revised (triple loop).
Outcomes are anticipated to arise from the ACM process, and making connections to them is an important aspect of the diagnostic framework (Fig. 1). Providing solid evidence that connects decisions to impacts on ecosystems and human well-being is essential to accelerate momentum toward sustainable development (Guerry et al. 2015). The third step in our approach builds upon the resilience-based proposal for evaluating ACM by Plummer and Armitage (2007), and has been discussed in relation to diagnosis and cross-site comparisons (Plummer et al. 2014). Outcomes coming about from ACM are accordingly considered as to results and effects. Results are products (tangible and intangible) arising from the ACM process. They may stem from the initiative immediately (first order) or indirectly (second order) (cf Innes and Booher 1999). Whereas results capture what comes about from ACM, effects entail their consequences. Contributions from ACM in this regard are appraised with consideration to ecological sustainability and human livelihoods.
While there are many ways in which this diagnostic framework may be operationalized, we describe our approach to data collection and data treatment in the following section.
We closely applied the diagnostic framework (Fig. 1, Table 1) to investigate cases of ACM in four biosphere reserves in Canada and Sweden. Biosphere reserves are designated by the United Nations Educational, Scientific and Cultural Organization (UNESCO) as sites where conservation, development, and logistical support are pursued in concert. They are described as “learning sites for sustainable development” (UNESCO 2008:5), and as such offer cases where the features of ACM are exhibited. Biosphere reserves generally have a single manager or coordinator, and engage multiple, often diverse stakeholders in governance of a region, though no formal authority is held by biosphere reserves in Canada and Sweden. We used a mixed methods approach for data collection. Quantitative and qualitative approaches provided opportunities to collect data related to all first-, second-, and third-tier variables, and many of the methods used collected data for multiple first-tier variables (see Appendix 1 for variable map showing the linkages between instruments and variables in the diagnostic framework). The instruments developed for the purposes of collecting data related to the variables identified in the diagnostic framework are provided in the appendices and are referred to in this section.
Documents were requested from the manager in each case to collect data about the setting and antecedents (specifically the activities that formed each case). Additional documents were collected by desktop study, including government documents and scholarly literature related to the cases, to gain a broader understanding of the setting.
A social-ecological inventory (SEI) was conducted with managers and individuals they identified as participants in governance. The SEI is a semistructured interview tool developed by Schultz et al. (2007) that was designed to capture the activities being undertaken in steward groups and who is involved, thereby bridging social and ecological systems, and explicitly considering local knowledge. In this project, the SEI was used to capture qualitative data about the actors engaged in biosphere reserve governance, including motivations for involvement, activities they have engaged in, perceptions of prioritization of biosphere reserve goals, and any concerns about the biosphere reserve generally, thereby providing data related primarily to the second-tier variables corresponding to setting and antecedents in Table 1 (see Appendix 2 for the instrument). Respondents were also asked to identify any other individuals involved that should be included in the study. Thus, a snowball sampling strategy was employed in addition to the lists provided by the managers. All interviews were audio-recorded and transcribed.
Indepth interviews were administered to key individuals in each case. Questions focused on gaining insights about the setting (e.g., the history of the cases) and process (e.g., how learning occurred and how opportunities were created, networks and how they formed, skills and strategies used by the managers to overcome challenges and be successful) (see Appendix 3 for the instrument). All interviews were audio-recorded and transcribed.
Questionnaires were administered at two times to all actors involved in biosphere reserve governance: first at the outset of the study, and second, 1.5 years later to capture change over time and feedbacks. The questionnaire included a combination of open and closed Likert-type questions, as well as a social network section where respondents provided information regarding with whom they communicate about the biosphere reserve (Plummer et al. 2014). The instrument collected data related to the first-tier variables of antecedents, process, and outcomes in the diagnostic framework, and probed the second- and third-tier variables therein (Fig. 1, Table 1). Specifically, it queried perceptions of collaborative qualities (Plummer and Armitage 2007, Plummer et al. 2014, Sandström et al 2014, Birnbaum et al. 2015), learning (Baird et al. 2014, Plummer et al. 2014), results (Plummer and Armitage 2007, Plummer et al. 2014), and effects (Plummer and Armitage 2007, Plummer et al. 2014). The first and second questionnaire instruments were very similar, but some questions were omitted where longitudinal data were not required, and some questions were altered slightly to capture data since the first questionnaire. Quantitative data were imported into SPSS 21 (IBM Inc.). The first questionnaire is provided in Appendix 4.
The instruments described were prepared in English and translated to Swedish, using the most similar terms in meaning possible in the translation. Qualitative responses from all instruments were imported into NVivo 11 (QSR International) for coding. An extensive codebook was created using the first-, second-, and third-tier variables in Table 1 (Appendix 5). This codebook was used to code all documents and qualitative responses for all relevant codes. Swedish responses were coded by a Swedish researcher using the English codebook. Swedish responses were translated to English as needed for further analysis.
Qualitative and quantitative data were collected in relation to the variables identified in Table 1, using the instruments described. The breadth of data collected by using this approach provides opportunities to measure and build an understanding of ACM and the context within which it is situated. The depth of data collected (third-tier variables in most cases) allows specific variables of interest to be examined in detail. Databases of this sort are needed for consistent and systematic analysis of ACM (Plummer et al. 2012). The diagnostic framework presents the architecture needed for creating and maintaining a growing database of case study data that responds to this need and provides opportunities for new analytical approaches for ACM.
Measures for each of the variables in Table 1 and those previously described were developed and tested. Each (at the second-tier level) is described briefly.
Each respondent identified their primary job and affiliation in open questions in the questionnaire, and from that information, stakeholder type and level, and diversity of these third-tier variables was determined using a preset list of potential options. Activities were requested in an open question in the questionnaire, and were coded according to type and frequencies used in subsequent analyses. Practices were gleaned from the SEI and deep interviews via qualitative data coding.
Several closed questions probed the process and outcomes variables shown in Table 1. Quantitative data were assembled into validated and tested measures of second- and third-tier variables using Cronbach’s alpha and exploratory and confirmatory factor analysis. Each variable was confirmed as internally valid, and thus may be used as reliable measures in other studies. Of course, the opportunity exists to delve deeper into third-tier variables as well (i.e., identify and test fourth-tier variables) using a similar approach. Social network data were prepared in a matrix format and imported into Ucinet 6 (Analytic Technologies Inc.). Measures of ego network size and diversity were obtained from these data. The questions in the questionnaire that corresponded to the third-tier variables are identified in Appendix 4.
From the robust collection of data, a multifaceted understanding of ACM may start to develop. In particular, the multitier nature of the variables in the framework creates a platform for which to move forward with a range of analyses. These may be indepth investigations of a single variable using qualitative (e.g., process tracing) or quantitative (e.g., exponential random graph modeling) methods. Importantly, the validated measures developed from the collected data also make possible relational analyses. These measures can be used to examine relationships among variables and feedbacks in ACM (e.g., via structural equation modeling approaches). These examples represent approaches taken in our own research, and a summary of insights gained from them is provided in Table 2. Considered together, the insights illuminate important nuances of the tenets of ACM and yield policy-relevant findings (Plummer et al., unpublished manuscript).
The example analyses provided represent only a subset of potential approaches that could be taken to analyze data collected. As the database grows with the addition of more data from case studies, it creates a substantial opportunity for systematic analytical approaches in ACM research.
The state of ACM scholarship is indicative of an emerging and quickly growing area of scholarship. Despite illuminating some very valuable insights, Plummer et al. (2012) were not able to draw robust evidenced-based findings and address several important questions from a systematic review of the ACM literature due to conceptual imprecision and methodological inconsistencies. Such uncertainty often prompts calls for novelty and/or the creation of new frameworks—in this specific situation, a diagnostic approach was identified as one way to systematize analyses of ACM (Plummer et al. 2012, 2014).
Drawing upon our multiyear research project to create, test, and refine the diagnostic approach for ACM, we have (1) set out a common framework and nomenclature with which to systematically compare experiences with ACM (and similar approaches), and (2) provided corresponding methods and measures to facilitate research in a variety of empirical settings over time. The diagnostic approach and framework put forth serve to move ACM research forward in a new direction. The framework overcomes past imprecision and inconsistencies to enable the systematic culmination of knowledge and deriving causal inferences essential without sacrificing flexibility. We demonstrate that both quantitative and qualitative approaches to data collection and analysis may be employed using the framework, and that insights may be drawn from individual studies or identified by synthesizing the findings of diverse research efforts.
Several key insights come from our multiyear efforts to move from appeal and proposal of a diagnostic approach to trial and establishment. First, the steps set out in the diagnostic framework facilitated systematic inquiry while maintaining an awareness of and sensitivity to context. Agreeing upon an established nomenclature in advance and adhering to it throughout all stages of the inquiry was essential. Common nomenclature of the phenomenon (ACM) and tiers of variables (setting, antecedents, process attributes, outcomes) served as conceptual anchors, which were routinely revisited throughout fieldwork and data collection. Identifying tiers of variables as well as their relationship at higher levels, using the framework, facilitated analysis within and across variables. We developed measures of variables that were subsequently tested and confirmed to be internally valid for major first-, second-, and third-tier variables related to the main process variables of ACM (learning, collaboration) and social and ecological outcomes. Establishing methods and measures a priori was necessary because they made the diagnostic framework operational. This does create a tension, given the significant benefits of a more grounded research approach in which participants help determine the validity of measures given their local circumstances. However, a priori identification of methods and measures permitted the multiyear inquiry to be undertaken in a robust and consistent manner across multiple cases in different countries. The measures, in particular, made possible comparative analysis between/among the cases and summative analysis involving all cases in relation to understanding specific variables central to ACM as well as linkages among variables at a range of levels.
Our experience has also exposed some noteworthy challenges, which should be anticipated but which also can be managed. Systematically investigating ACM in a consistent manner in a variety of situations lies at the heart of the diagnostic approach. However, this is a challenging task that requires constant attention and commitment. Working in varied contexts and in different cultures necessitates going well beyond the surface of scholarly constructs to carefully consider how ideas and terms are appropriate or may be interpreted. Even with the utmost commitment to ensure consistency, the attendant realities of conducting primary research with people poses a persistent challenge to the maintenance of a consistent approach. As well, implementation of the diagnostic approach used multiple methods from a myriad of sources and yielded a significant amount of qualitative and quantitative data. Forethought about data organization and ongoing attention to data management are paramount so that treatment of data will be tailored to the specific research question being posed. For example, in modeling causal relationships in ACM using path analysis, it is important to have three to five indicators of a single variable (Plummer et al., unpublished manuscript). In such a case, selecting appropriate indicators (e.g., third-tier variables to represent a second-tier variable) is important and requires consideration prior to engaging in the research.
In moving forward, the diagnostic approach to ACM we presented offers a foundation to make rigorous comparisons across settings, draw robust evidence-based findings that link actions to outcomes, and ultimately, advance theoretical development. The framework offers opportunities to undertake deep inquiries into specific variables, build an understanding of the tiered nature of variables, and as more data are collected using this approach, the power to test more nuanced linkages among variables. We offer the instruments used to collect data related to the framework (in the appendices) as an inspiration for others’ work in ACM to facilitate cross-case comparisons and to continue to build the literature in this field. The diagnostic framework complements other types of approaches to investigate ACM and will not always be desirable or appropriate. Nevertheless, it affords a means by which progress may be made to address ongoing challenges that confront the evolution of adaptive comanagement. And finally, as stated earlier, although our focus has been on ACM, we hope that the value of the diagnostic framework will extend to scholars who are not entirely focused on ACM per se, but in conceptually similar and perhaps even partly overlapping collaborative natural resource management/governance scholarly frameworks.
This research has been made possible by funding received from Vetenskapsrådet, the Swedish Research Council, grant 2012-5498. We also acknowledge funding by MISTRA through a core grant to Stockholm Resilience Centre. We gratefully acknowledge participation in this research by the managers and others from the Frontenac Arch, Georgian Bay, Kristianstads Vattenrike, and Östra Vätterbranterna Biosphere Reserves. Our appreciation is also extended to our colleague Beatrice Crona for her constructive insights with conceptualizing the research project. Finally, we wish to thank Disa Hansson, Malena Heinrup, Katrina Krievins, Flor de Luna Estrada, and Kerrie Pickering for research assistance.
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