People and nature are inextricably linked. Overcoming pressing sustainability challenges thus requires an integrated social-ecological science (Liu et al. 2007). This more integrative approach provides a better understanding of systems involving people and natural resources than focusing only on unidirectional relationships, e.g., the human impacts on nature, ecosystem services, and so forth, or on single-component effects, e.g., biological or socioeconomic impacts on fish stocks (Ostrom 2007, Liu et al. 2015). For example, understanding and attending to sustainability challenges in the world’s fisheries requires that biological aspects of fish stocks are considered alongside the livelihoods of coastal communities, the management of other protein sources, and the political and economic interests of large-scale commercial enterprises and governments. Likewise, addressing the recovery of endangered species requires understanding the human activities affecting the viability of the species and also how species dynamics affect human livelihoods. Addressing sustainability challenges requires a shift in focus: from seeing people and nature as separate systems to seeing them as two interacting components of a complex, dynamic, and integrated system (Pollnac et al. 2010, Mace 2014, Fischer et al. 2015).
Social-ecological systems (SES) research sees a delineation between society and the environment as artificial and arbitrary, encouraging a holistic assessment of the dynamics of environmental and social systems (Milner-Gulland 2012, Fischer et al. 2015, Van Noorden 2015). In particular, humans and ecosystems interact creating dynamic feedback loops across multiple interlinked scales (Liu et al. 2007). These interactions and feedbacks can have a positive or negative effect, i.e., benefit or harm, on both social and ecological system components (Daw et al. 2015, Bennett et al. 2016). Left unchecked, these feedbacks can produce regime shifts to undesirable ecosystem states (Biggs et al. 2009, Hicks et al. 2016a) or social-ecological poverty traps (Cinner 2011), or they can lead to unanticipated consequences (Larrosa et al. 2016, Carter et al. 2017). Thus, interdisciplinary research frameworks, methods, and approaches that further understanding of social-ecological dynamics are essential for the design of more effective policies and interventions for sustainability challenges (Levin et al. 2013, Liu et al. 2015).
The role and importance of SES research in advancing sustainability is increasingly acknowledged in the literature (Kajikawa et al. 2014, Fischer et al. 2015). Through SES research, diverse systems, e.g., social, economic, and ecological, and disciplines, e.g., political science, public health, ecology, and sociology, can be integrated to bridge sustainability topics such as biodiversity conservation, agricultural management, sustainable development, and environmental management (Kajikawa et al. 2014). The number of SES research publications rose threefold from 2010 to 2015 (Fig. A1.1 in Appendix 1), and there have been numerous analytical frameworks, methods, and approaches applied to achieve better integration (Schlüter et al. 2012, Binder et al. 2013). However, the potential of a mainstream and integrated SES approach in the environmental sciences has not yet been fully realized (Liu et al. 2015, Bennett et al. 2017). Literature on SES research points to different ways in which integration can occur, i.e., conceptual, disciplinary, methodological, and functional, which we define and for which we provide review questions and references in Table 1. We propose that each dimension, although potentially overlapping, highlights an important aspect of integration, distinguishing the way the problem is conceptualized from the methodological and practical approaches used to address it. Thus, together these four dimensions make up a comprehensive typology, which challenges the generalized narratives about “integrative research.” The extent to which integration has been achieved in past SES research is unclear, despite such knowledge potentially providing critical insights into how to conduct more effective and meaningful SES research.
We systematically review how SES research has been conducted in the environmental, sustainability, and natural resource management literatures with a focus on the extent to which conceptual, disciplinary, methodological, and functional integration is achieved (Table 1). Our review includes different terminologies used within SES research, including social-ecological, social-environmental, and human-environment. Building on discussions of the significant barriers and challenges to integration (Binder et al. 2013, Larrosa et al. 2016), we focus on identifying opportunities to increase integrative SES research. Ultimately, we aim to enhance the degree of integration in SES research, thus advancing both the science and application of cross-disciplinary knowledge contributing to better management and improved environmental and social outcomes.
Focusing on four dimensions of social-ecological integration, i.e., conceptual, disciplinary, methodological, and functional (Table 1), we identified key questions based on available literature and the knowledge and experiences of a team of 13 researchers with expertise in SES research and a background in biology, community development, conservation science, decision science, ecology, economics, engineering, environmental management, fisheries, geography, sustainability, and urban planning. We then conducted an extensive multistep systematic literature review and analysis as described subsequently.
The questions related to the four types of social-ecological integration we explored are presented in Table 1. Four questions relate to conceptual integration, i.e., consideration of social and ecological components, direction of effects, and value orientation (questions 1-4); two relate to methodological integration, i.e., incorporation of frameworks, tools, and theories, and the use of qualitative and quantitative approaches (questions 5-6); two relate to disciplinary integration, i.e., inclusion of multiple disciplines (questions 7-8); and two relate to functional integration, i.e., bridging of science with policy or practice (questions 9-10). Acknowledging the challenge of robustly assessing social-ecological integration, we also included a question to assess overall integration (see wording in Appendix 3). This question reflects the broader way in which the social-ecological concept is understood in the literature, and as such, it is designed to assess the degree to which the social and the ecological aspects of an issue are treated as part of the same system (Berkes et al. 2001, 2003, Gunderson and Holling 2002).
We searched the Web of Science database with a predetermined set of criteria (Appendix 2) to capture papers that apply or consider both ecological and social information in environmental-related topics. At the search date (18 November 2015), this set of criteria resulted in 1760 papers. All abstracts were randomly assigned and read by A. M. Guerrero and A. Nuno, who assessed if each paper fit four predefined criteria: (1) purported description or application of a social-ecological approach, (2) environmental related, (3) inclusion of environmental data, and (4) inclusion of social data. Review papers were excluded, and this resulted in 700 suitable papers. Appendix 2 includes a summary of excluded papers.
One hundred and twenty (17.1%) of these 700 papers were then randomly selected for review. We codesigned the review protocol and reviewed the selected papers based on the questions identified (Table 1). Coauthors and reviewers were selected by A. Nuno and A. M. Guerrero because of their current roles and backgrounds working in SES research. Responses were collected using a standardized online survey tool. The exact wording of the review questions, and the guidelines and definitions used to help reviewers, e.g., of concepts and answer categories, are presented in Appendix 3. Potential reviewer biases were addressed by allocating two reviewers to each paper and, in case of scoring disagreements, assigning a third person (A. M. Guerrero and A. Nuno) to moderate and, when required, facilitate a discussion to find consensus. Out of 120 papers reviewed, 4 (3.3%) papers were classified as “reviews,” and 6 (5%) papers did not include both social and environmental data; thus, these 10 papers (8.3%) were excluded. Results from reviewing the remaining 110 papers were then analyzed descriptively and summarized (Appendix 4). As some questions only applied to empirical studies, we indicate, where relevant, if sample size refers to the subsample of 110 papers or to empirical studies only (101 papers).
Measures of integration for the conceptual, methodological, and functional dimensions were obtained from summarizing responses to review questions (questions 1-4, 5-6, and 9-10 in Table 1, respectively). A measure for disciplinary integration (questions 7 and 8 in Table 1) was obtained by employing graph-theoretic methods. First, disciplines were assigned to papers based on the classification system used by the Web of Science database; in this system, the journal in which a publication has appeared determines the research area to which the publication belongs. From this information, a graph matrix was developed indicating the number of times two disciplines were assigned to the same paper. This was then transformed into a network where each discipline is represented by a node, and they are linked if they are identified in the same paper. The algorithm used in the graph-theoretic analysis assigns locations to nodes, i.e., disciplines, such that nodes with the smallest path lengths are closer together (Krempel 2011), thus indicating that a discipline placed far from the rest of the network has low integration into SES research. We also used the “betweenness centrality” metric (Freeman 1977), which has been applied to a variety of network types including citation data (Hicks et al. 2010), and has been used before as an indicator of journal interdisciplinarity (Leydesdorff 2007). It accounts for indirect links and measures how often a node is on the shortest path between other nodes in the network; the higher the betweenness centrality score, the more that particular node (discipline) connects other nodes (other disciplines) that would otherwise be disconnected. We have used it to reflect the role that each discipline plays in integrating diverse disciplines in SES research. A similar approach was employed to complement descriptive measures for conceptual integration. However, in this network the nodes denote each of the different variables (social or ecological) incorporated in SES studies, and two nodes are linked when the two variables are incorporated within the same study. Again, we used graph-theoretic methods to visually map integration of social and ecological variables and used the betweenness centrality measure to reflect the extent to which each variable is integrated with other variables to SES research.
The reviewers provided a measure for overall integration (see Appendix 3, question 11) using a Likert scale (1 to 5) where 1 was “minimal integration” and 5 was “a great amount of integration.” We explored potential relationships between the overall integration score and all other measured aspects (Table 1) and assessed the role played by each measure of integration across the four dimensions against the overall integration score. To account for the quantitative nature of Likert scales, but without making assumptions about the distance between ordered categories, ordered logistic regressions were used to assess relationships. To investigate potential effects on binary variables such as occurrence of stakeholder involvement, generalized linear models with quasi-binomial error distribution, to account for overdispersion, and a logit link were fitted. Statistical analyses were conducted in R, version 3.3.2 (R Foundation for Statistical Computing 2016). Because of the wide range of possible answer categories, e.g., types of methods, and subsequent small sample sizes per level, a minimum amount of 10 studies per category was required for inclusion and comparison in the statistical models.
The median overall integration score for all papers reviewed was 3 (mean = 2.8, standard deviation [SD] = 1.2; scale of 1 to 5). Close to half of all papers received the median score for overall integration, whereas a fifth were given the lowest score (Fig. A4.1 in Appendix 4).
Out of the randomly selected 110 papers meeting our predefined criteria, 101 were empirical studies presenting observation or experimental data, and 9 focused on conceptual contributions. Approximately two-thirds (64%) of the empirical papers captured or considered bidirectional interactions. When comparing bidirectionality among studies that used different methodological approaches, i.e., modelling tools, conceptual models, participatory approaches, statistical tools, comparison of social and ecological data, and social-ecological frameworks, we found some evidence, although nonconclusive, that studies using modelling tools (excluding statistical models) or social-ecological frameworks were more likely to consider bidirectional interactions (t = 1.67, p < 0.1 and t = 1.70, p = 0.09, respectively).
The majority of SES research reflects an anthropocentric perspective (81%), with 10% and 7% of papers reflecting ecocentric and relational perspectives, respectively. The most common variables incorporated in empirical SES studies are land use or resource use variables, followed by biophysical and economic variables (n = 75, 72, and 62, respectively; see Table A4.1 in Appendix 4 for detailed results). In addition, network analysis results show that biodiversity and demographic variables are more frequently integrated with other variables in SES research, in addition to land use and biophysical variables (Fig. 1 and Table A4.1 in Appendix 4). Overall integration ratings were not associated with diversity of, i.e., number of, social and ecological variables used (t = −0.26, p = 0.79; Fig. A4.2 in Appendix 4). So although incorporating a greater diversity of social and ecological variables captures more of the social-ecological system, these results suggest current applications are falling short in other aspects of conceptual integration, for example, failing to capture bidirectional interactions.
SES research is characterized by the use of a variety of tools that integrate social with environmental aspects, including social-ecological frameworks, modelling, and spatial, participatory, and statistical tools (Fig. A4.3 in Appendix 4). Their applications and purposes vary considerably. For example, most tools are used or developed to describe a social-ecological system (84%; Table 2), either to understand its components, its relationships, or a problem or gap in understanding. This is especially the case for papers involving social-ecological frameworks, conceptual models, and statistical or descriptive approaches (Fig. 2). Studies designed to test management alternatives, strategies or policies, or hypotheses, are also common (Table 2), especially for those studies applying modelling and scenario assessment tools (Fig. 2). Papers focusing on identifying a desired way forward or on predicting a future change are less common (Table 2). However, prediction is common among the studies using scenario assessment, modelling, and participatory tools (Fig. 2). In addition, stakeholder engagement is common among SES studies applying decision support tools and participatory approaches (Fig. 2).
We found that the number of tools applied by empirical studies increased with higher overall integration scores (t = 2.28, p = 0.02; Table A4.2 in Appendix 4). In addition, higher levels of integration were found in studies in which tools such as modelling approaches, spatial integration tools, and the driver-pressure-state-impact-response framework were used, albeit with small sample sizes (Table A4.3 in Appendix 4). Studies that indicated the use of any theories, e.g., resilience theory, common pool resource theory, and adaptive cycle theory, on average were not found to show higher integration; however, when comparing integration among the theories most commonly used in the reviewed studies, i.e., resilience theory, common pool resource theory, and systems theory, studies using systems theory had significantly higher integration scores (t = 2.09, p < 0.04; Table A4.4 in Appendix 4). Finally, we found that a third of empirical SES studies integrate qualitative and quantitative methods, whereas half of SES research uses quantitative-only methods.
Our analysis shows that SES research is integrating some disciplines better than others (Fig. 3). Given our focus on environmental-related literature, a large proportion of the SES studies in our review were assigned to environmental sciences and environmental studies (n = 345 and n = 339, respectively). Of the distinct disciplines found in SES research publications, ecology has a high representation (n = 379), followed by biodiversity conservation and geography (n = 75 and 56, respectively; Table A4.5 in Appendix 4). Graph-theoretic analysis results indicate that disciplines such as ecology, urban studies, geography, geosciences, and economics are more integrated into SES research than disciplines such as biology, public administration, sociology, anthropology, and marine and freshwater biology (Fig. 3). The betweenness centrality scores were higher for environmental science and environmental studies, followed by ecology, biodiversity conservation, and urban studies (n = 342, n = 177, n = 137, n = 34, and n = 33, respectively; mean = 21.4, SD = 65.2; Table A4.5 in Appendix 4), suggesting they play a “brokerage” role in the disciplinary integration in SES research.
When compared with quantitative studies, stakeholders were more likely to be involved in qualitative studies (t = 2.40, p = 0.02) or those that combine qualitative with quantitative methods (t = 3.25, p = 0.002; Table 3). However, results indicate that involving stakeholders may not necessarily result in better integration overall (t = −1.34, p = 0.18; Table A4.6 in Appendix 4). However, when comparing potential effects of involving stakeholders at different phases, i.e., problem definition, study design, data collection, analysis/assessment, and/or delivery of outputs, on overall integration scores, studies involving stakeholders in the design phase of a study were rated significantly higher for overall integration (t = 2.46, p < 0.02; Table 4). We also found some evidence that studies that provide recommendations for practice or policy are more likely to be better integrated compared to studies that do not provide recommendations (Table 5), although significant only at the 0.1 level (t = 1.72, p = 0.09).
It is commonly claimed that sustainable environmental management solutions are more likely when both social and ecological components are considered (Ostrom 2007). However, our results indicate that, although moderate levels of integration are common as suggested by the clustering of publications around the median integration levels (3 on the Likert scale), integration of SES research is still lacking across the combination of conceptual, methodological, disciplinary, and functional dimensions. Studies that are able to better integrate human and environmental aspects apply a diversity of tools, involve a variety of stakeholders during the design phase, and often result in practical recommendations. Studies that use modelling tools or social-ecological systems frameworks are better able to capture the bidirectional interactions between social and environmental components. These results highlight how decisions about problem framing, methods, interdisciplinarity, and stakeholder participation are important for integration. Subsequently, we briefly situate our results within the literatures on conceptual, methodological, disciplinary, and functional integration.
At a fundamental level, integration in SES research requires studies to consider both social and ecological aspects, but we found conceptual integration in SES research is still lacking. This is reflected in the underrepresentation of some social variables deemed important for addressing sustainability challenges, e.g., culture, politics, and power (Fig. 1 and Table A4.1 in Appendix 4), and by the results of our initial abstract review, in which a quarter of the initially identified SES studies were excluded because they focused exclusively on social variables (Appendix 2). These results are consistent with Rissman and Gillon (2017) who found that one-third of SES research studies included only social aspects.
The need for the integration of both social and environmental aspects has been well expressed in the academic literature (Ostrom 2007, Pollnac et al. 2010, Bennett et al. 2017). Depending on the system of interest, some aspects, e.g., cultural connections, participation in management, strong institutions, and environmental refuges, can be critical for system dynamics, and thus excluding them from planning could lead to management failure (Ban et al. 2013, Cinner et al. 2016). Understanding the suite of social conditions, e.g., wealth or poverty, demographics, culture, politics, and power, and actions, e.g., harvesting and management, that might be either supporting or undermining management effectiveness is critical for improving sustainability (Reyers et al. 2013, Hicks et al. 2016b, Finkbeiner et al. 2017).
Some of the least integrated variables are in the “pure” biological sciences, e.g., evolution, genetics, and animal behavior (Fig. 1). Applied science in these fields may not be as well developed as with other variables that are inherently more integrated, e.g., ecosystem services. However, these variables might become more integrated with time as their applicability to management is further developed (e.g., Anthony and Blumstein 2000, Hoban et al. 2013). For example, decision makers generally have a weak understanding of genetics, which is needed for successful monitoring and management interventions for conserving genetic diversity (Hoban et al. 2013). Conservation genetics can be better integrated into management through partnerships between geneticists and policy makers (Hoban et al. 2013). Likewise, the potential for integrating animal behavior with other social-ecological variables is demonstrated in the developing work on human-wildlife conflict (Carter et al. 2012, 2017).
Our finding that two-thirds of SES studies consider bidirectional interactions between social and ecological components is encouraging. This is essential if we want to understand systems dynamics or identify potential unintended feedback effects of conservation interventions, which may lead to inefficient or perverse outcomes (Larrosa et al. 2016). Although our results suggest the benefit of applying social-ecological frameworks, other studies have suggested great variation in the extent to which specific frameworks consider bidirectional interactions (Binder et al. 2013). For example, frameworks such as the human-environment systems framework, the management strategy evaluation framework, and the SES framework consider bidirectional interactions and thus are well positioned to contribute to SES research (Ostrom 2007, Svarstad et al. 2008, Bunnefeld et al. 2011). However, the challenge in the application of these frameworks is to empirically capture and test some of these bidirectional relationships, e.g., impacts and benefits, over time. This will require more sophisticated study designs, methods, resources, and analyses.
Our results suggest that studies that apply a greater number of tools achieve a higher level of integration. This might be because understanding different social and ecological aspects, as well as their interactions, is likely to require the application of multiple tools. There are multiple benefits that can be gained from employing diverse or mixed-methods approaches. A diverse methodological tool kit supports triangulation to better understand the problem. For example, comprehensive models of ecological system dynamics can be achieved using available data of empirical observations in combination with stakeholder knowledge gathered via interviews or via fuzzy cognitive mapping or Bayesian belief network exercises in participatory workshops (e.g., Özesmi and Özesmi 2004, Langmead et al. 2009, Daw et al. 2015). Likewise, focus groups, interviews, or facilitated workshops can help clarify the problem context and identify key factors (social or ecological) and interactions to focus the investigation (e.g., Game et al. 2017, Guerrero and Wilson 2017). Increasing the awareness of the methodological tool kit available (Fig. A4.3 in Appendix 4) and how it can be used (Fig. 2) within a research team or project can help address this challenge.
Our results suggest that the application of theories is not associated with how well a study is integrated, with the exception of systems theory. This might be because many theories have particular disciplinary, e.g., social or ecological, roots, rather than emerging from a holistic perspective. However, new concepts, e.g., telecoupling (Liu et al. 2013), and methods, e.g., social-ecological network analysis (Janssen et al. 2006, Bodin et al. 2016), continue to emerge that may offer new possibilities for integrating social and environmental aspects and the development and integration of theories in social-ecological research. Although promising, the insights that emerging interdisciplinary methods might bring to integrated SES research can only be revealed as empirical applications advance.
Our results indicate that, in addition to environmental science and environmental studies, the ecology, biodiversity donservation, and urban studies disciplines play a key brokerage role in integrating other disciplines into social-ecological research. Perhaps this is because of the types of problems or questions being addressed, the more interdisciplinary nature, or the culture of researchers working within these disciplines. We caution that there are limitations in our approach to measuring disciplinary integration, which is based on the disciplinary classification used by the Web of Science, which assigns disciplines based on journals. Web of Science subject categories are among the most popular classification systems and are easily accessible (Waltman and van Eck 2012), enhancing our ability to undertake comparisons. However, journal-level classification systems have important limitations: multidisciplinary journals might include a wide range of fields, and thus, our approach might miss disciplines in which researchers are working but are not reflected by the journals in which they publish. More complex approaches, such as publication-level systems that classify individual publications into disciplinary clusters based on citations (Perianes-Rodriguez and Ruiz-Castillo 2017), have been proposed and could be adapted for further research. Nonetheless, the promise of integrated SES research will not be achievable unless disciplinary integration is pursued, and our results suggest several areas in which better integration is needed (Fig. 3).
There are, of course, numerous well-recognized challenges to interdisciplinary research, including funding challenges, distinct disciplinary methods and cultures, longer time requirements, and institutional constraints (Campbell 2005, Bromham et al. 2016). Greater disciplinary integration may be achieved by creating interdisciplinary teams, taking time to build trust and learn about different disciplines, ensuring that there is adequate funding and time, and designing projects with representatives from different disciplines (Christie 2011, Bennett et al. 2017). Inclusion of researchers from different disciplines will increase knowledge of different social and ecological considerations, thus increasing conceptual integration, and provide a more diverse methodological toolbox from which to draw, thus increasing methodological integration.
Our research showed that most SES research seeks to inform management actions in some way, whether it is through describing a system or through the testing of management alternatives; 70% of studies reviewed provided, even if general, recommendations. However, only 43% of the studies examined contained practical recommendations for management or policy.
Studies containing practical recommendations generally scored higher for the overall integration of social and ecological components than those that did not. This might be because more studies with an applied goal, e.g., research for a specific policy intervention, require integrated approaches, but it also suggests that the better integrated a study is, the more likely it is that researchers will be able to provide practical recommendations that are then more likely to create on-the-ground impacts. Nonetheless, this is an important finding for researchers interested in developing knowledge to support policy and practice and warrants further consideration of the most appropriate methodologies to support integration of different types of knowledge in SES research.
The importance of involving stakeholders in sustainability research that is intended to influence policy and practice is increasingly recognized (Lang et al. 2012, Wiek et al. 2012, Mauser et al. 2013). Literature on science-policy interfaces, knowledge coproduction, and boundary organizations highlights the importance of codesigned approaches to project planning that include shared problem definition among academics (natural and social scientists) and practitioners (from policy, community, business, and industry), integration of different types of knowledge, and engagement activities that enable the research to address challenges of practical relevance and promote stakeholder ownership (Young et al. 2014, Brondizio et al. 2016, Chapin et al. 2016, Turner et al. 2016). Our findings suggest that stakeholder involvement should be strategic and occur at appropriate stages of the research process, particularly in the design phase. This might be because, with appropriate design and facilitation, dialogue between scientists, policy makers, practitioners, and impacted stakeholders can improve knowledge exchange and learning, decision making, and research influence (Reyers et al. 2015, Nel et al. 2016), all critical components of improving the quality and impact of integrated research. Utilizing a broader knowledge base for study design can also facilitate a better description of the study system with its diverse social and natural components. Strategic involvement at key stages can also reduce the costs, e.g., time and resources, of involving stakeholders at all stages (Reid et al. 2016), support the design of relevant project objectives and research questions, enable the identification of practical recommendations that will be useful, and help capture the values and preferences that can reveal potential conflicting objectives and trade-offs between possible management solutions (Gregory and Keeney 1994). Early stakeholder analyses can help identify stakeholder groups that should be considered to facilitate engagement (e.g., Prell et al. 2009). Although participation of stakeholders within research processes is commonly proposed to improve the use of knowledge in policy or practice, to be effective these engagements require dedicated time and resources to support facilitation, mediation, and communication between different perspectives (Cash et al. 2006). Moreover, such engagements must be supported by a core ethic of trust, respect, and empathy and should not engage stakeholders in a tokenistic fashion that is purely about meeting research objectives without acknowledging the real-world implications for those engaged in the process (Reed et al. 2014).
This systematic review highlights significant work needed to achieve the promise and potential of integrated SES research. This is critical if we are to solve complex real-world sustainability problems. The results of our review, and their contextualization using literatures on conceptual, methodological, disciplinary, and functional integration, point to six clear recommendations for improving integration in future SES research projects:
Although many of the suggestions are not particularly new, our research provides evidence that these actions are warranted and necessary to improve the process and outcomes of SES research. The urgency and complexity of contemporary environmental challenges requires that our SES research efforts are more strategically integrative to effectively address sustainability problems.
We acknowledge the Australian Research Council Centre of Excellence for Environmental Decisions for funding and support. N. Carter was supported by Boise State University (NSF award IIA-1301792 from the NSF Idaho EPSCoR Program and the National Science Foundation). A. Nuno was supported by Defra Darwin Initiative.
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