Current effects of human activities on the fundamental processes that regulate the functions of Earth’s systems, namely, global change (Steffen et al. 2004), have defined a new geological era called the Anthropocene (Crutzen and Stoermer 2000, Ellis 2011). The recognition of this era has fostered the development of holistic and interdisciplinary approaches to understanding nature conservation and sustainability. Coupled human-nature systems are frequently treated as social-ecological systems (SESs), i.e., complex adaptive systems with key characteristics such as: (1) integrated biogeophysical and socio-cultural processes, (2) self-organization, (3) nonlinear and unpredictable dynamics, (4) feedback between social and ecological processes, (5) changing behavior in space (spatial thresholds) and time (time thresholds), (6) legacy behavioral effects with outcomes at very different time scales, (7) emergent properties, and (8) the impossibility to extrapolate the information from one SES to another (Holland 1995, Berkes and Folke 1998, Liu et al. 2007, Du Plessis 2008). Several theoretical and conceptual frameworks have been proposed to guide the understanding of linked social and ecological systems (e.g., Berkes and Folke 1998, Walker et al. 2002, Anderies et al. 2004, Ostrom 2009, Díaz et al. 2011, Becker 2012).
Among the aims for a better understanding of SESs is the adoption of natural resource management systems that allow their sustainable functioning and the integration of different levels of governance. Community-based natural resources management (CBNRM), as a conceptual approach, has evolved since the mid-1980s as an alternative to top-down strategies in natural resource management. There is no single definition of CBNRM, but the core of the concept is the coexistence of people and nature, as distinct from protectionism and the segregation of people and nature (Western and Wright 1994). CBNRM is characterized by local stakeholder involvement, public participation, and interorganizational collaboration (Tang and Zhao 2011). It assumes that communities and community-based organizations closely connected to natural resources are most likely to foster sustainable resource use and possess the knowledge required to do so (Blaikie 2006). Accordingly, CBNRM has been often considered as a suitable approach to govern the commons (Ostrom 2007), given that it aims to ensure community participation in decision-making and to integrate community ideas, local institutions, customary practices, and knowledge systems. The full incorporation of the community in management, regulatory, and enforcement processes is likely to prevent local resistance to conservation measures (Pomeroy 1995, Borrini-Feyerabend 1996, Barrett et al. 2001, Armitage 2005). However, even if the CBRNM approach has attracted considerable interest in the last few decades (Shackleton et al. 2010) because of its role in conservation strategies worldwide (Dressler et al. 2010), it is not a panacea (Berkes 2007). For instance, the institutions, endowments, and rights, as well as the social actors involved, highly influence CBNRM performance (Leach et al. 1999).
To explore the CBNRM management within a SES, we addressed the later as “a structure composed of a common-pool resource, its users and an associated governance system” (Janssen and Anderies 2007:44). Given that the understanding of SES functioning is still limited, fragmented, and uncertain (Kates et al. 2001, Ives and Carpenter 2007, Bettencourt and Kaur 2011, Cumming et al. 2013), we address this knowledge gap by eliciting and analyzing the social perceptions of three SESs managed under CBNRM and the social and environmental challenges they face. Understanding the perceptions of natural resources dynamics, challenges, and crises in SESs are often decisive factors that influence the involvement of local communities in management and the emergence of local rules for sustainable resource use (Siar et al. 1992, Pomeroy et al. 2001, Ferse et al. 2010).
To examine the complexity of the elements and interactions present in a system and identify which key variables could determine the system’s current and future conditions, we applied a participatory prospective technique that helps embracing complexity and analyzing current and future factors in a systematic way (Godet 1994, European Commission 2014). Prospective structural analysis (PSA) is a methodological tool included in the group of scenario-building methods, “la prospective” (Godet 1986), that contributes to strategic management, helps coping with uncertainty, and provides alternative perspectives in the face of challenges. Strategic foresight methods were initially designed to support public institutions in regional development, i.e., regional foresight (Kelly et al. 2004, Stratigea and Papadopoulou 2013), but have also been extensively used by the private sector, i.e., corporate foresight (Lafourcade and Chapuy 2000, Benassouli and Monti 2005, Chapuy and Gros 2010).
Given the complexity of SESs and of the human and natural discrete and heterogeneous elements that are connected by different types of interactions, links (e.g., causality or dependence), and flows (e.g., information, energy, or materials), SESs have been conceptualized as social-ecological networks (Janssen et al. 2006, Bodin and Tengö 2012). The universality of the network topography has allowed researchers from different disciplines to embrace network theory as a common paradigm (Barabási 2009), including social-ecological networks, of which properties can be analyzed quantitatively through network theory (Gonzalès and Parrott 2012). Increasing numbers of scientists are devoting efforts toward assessing the sustainability of SESs using network analysis (NA), i.e., metrics from network theory (Gonzalès and Parrot 2012). Networks were first studied by social scientists to understand the structure of communities (Borgatti et al. 2009), but have also been used by natural scientists interested, for example, in food webs (Tylianakis et al. 2007). NA also has been applied to the study of CBNRM. For instance, Bodin and Crona (2009) and Marín and Gelcich (2012) applied NA in SESs to study the role played by social capital in the management of fisheries at a community level in Kenya and Chile, respectively. Cumming et al. (2010) argued for the potential and challenges of NA in conservation biogeography. However, to our knowledge, no study has yet applied NA to a social-ecological network, within a CBRNM context, with different types of nodes (e.g., social, environmental, institutional), which has been pointed to as a challenge: “Can node attributes be used to link social and ecological aspects of the same system?” (Cumming et al. 2010:417).
Understanding the variables and links that constitute a social-ecological network is therefore critical to analyze the network’s social-ecological sustainability and resilience, i.e., the degree of disturbance the system can absorb before changing to another stable regime that is controlled by a different set of variables organized in a different structure (Holling 1973). Social-ecological networks change and adapt over time according to their resilience, so a SES’s sustainability depends on its capacity to assume different degrees of uncertainty and to face disturbances without losing its self-organizing capacity and the regulating mechanisms that determine its structure and functioning (e.g., Carpenter and Gunderson 2001, Folke et al. 2002, Gunderson and Holling 2002, Folke 2006). However, given that resilience is difficult to assess and the concept is difficult to translate into clear and measurable system variables, “in cases where a SES can be represented as a network, NA may provide tools to measure certain structural characteristics relevant to the system’s resilience” (Gonzalès and Parrot 2012:79). Ostrom (2009) states that the application of a network approach to ecosystems provides a conceptual framework for assessing the consequences of perturbations at the community level. The study of networks can enable anticipation of change, provide early warning, and enable faster response to change (McCulloh and Carley 2008). In this sense, NA might be a useful heuristic framework, given that it requires few data but allows critical parts of the network to be identified (Bunn et al. 2000, Urban and Keitt 2001, Zetterberg et al. 2010). We integrated PSA with NA of the three social-ecological networks, integrated by social and ecological nodes, to explore the relation of the network metrics with the SESs’ resilience.
Our main objective was to explore the local perceptions of the social-ecological dynamics in three SESs with different types of CBNRM in Latin America using PSA and NA. In particular, we: (1) identify the key variables influencing the dynamics of the SESs, (2) explore the influence and dependence of these key variables, (3) compare the three systems under Ostrom’s (2009) framework by examining different and similar patterns, (4) zoom into the social-ecological networks and describe the roles played by the key variables as well as the feedback loops that occur among them, and (5) reflect on the resilience of the SESs (Appendix 1). The three case studies are based in Colombia, Mexico, and Argentina.
The outcomes presented here are part of broader research aiming to identify sustainable community-based governance models in the management of environmental challenges. The case studies were selected because they present a gradient of interesting strategies of CBNRM in the face of environmental challenges considered representative of the region. The case studies examine water and biodiversity management for a Council of Black Communities in the Colombian Pacific, forest management for a community in the Mexican Sierra of Oaxaca, and marine and coastal area management for the Argentine Bahia Blanca Estuary and its adjacent coast (see http://www.comet-la.eu for more information).
The selected SES corresponds to the collective territory of the Bajo Calima Community Council (Fig. 1, Table 1), located in the biogeographic region of Chocó, a biodiversity hot-spot (Arbeláez-Cortés 2013) characterized by high levels of rainfall and covered by tropical rain forests. The territory is part of the municipality of Buenaventura, where the most important harbor of the Colombian Pacific is located. Therefore, the zone is strategic for its environmental richness and biodiversity and for its geopolitical and economic position.
The Community Council was created in 2001 under Law 70 of 1993; it owns 12,335 ha of the territory, inhabited by 3419 residents. The local population is divided into six settlements scattered across the territory. The index of unsatisfied basic needs reaches 98.1% for inadequate public services and 26.6% for inadequate housing (Departamento del Valle del Cauca 2013). The SES is managed under a complex and polycentric governance system in which public, private, and mixed institutions are integrated, and the Community Council plays a central role. The CBNRM is relatively recent, and some of the institutions and rules are not always recognized. Additionally, the monitoring, exclusion, and enforcement rights are still limited.
The local economy is based on natural resources, mainly logging, subsistence farming, fishing, and mining, and it is strongly influenced by traditional ecological knowledge. Legal and illegal wood extraction is commercialized in regional and national markets, whereas the products of hunting, fishing, and agriculture are mainly used for personal or local consumption. The intimate connection between the communities and their natural environment is the central axis of the social organization, and it reinforces the sense of belonging to the territory (Farah et al. 2012).
Nonetheless, the persistence of an armed conflict in recent years has generated tears in the social fabric because of the competition for natural resources and the displacement and oppression of the population. Powerful external actors linked to illicit crops (Erythroxylum coca) and mining do not recognize the community-based rules and are important drivers of conflict. Public policies have addressed the problem through actions such as aerial spraying of coca plots with glyphosate, which have resulted in indirect negative effects on agriculture and the health of local inhabitants. Other challenges to the sustainability of the SES are related to megaprojects, deforestation, weaknesses in formal education, poor solid residue and water management, and the effects of climate change.
Santiago Comaltepec is a community located in the Sierra Norte of Oaxaca in southwestern Mexico (Fig. 2, Table 1). The area covers 18,366 ha and ranges from 200 to 3000 m above sea level. Temperate rain forests, tropical rain forests, and mountainous cloud forests are the main ecosystems, and each plays a key role in hydrological regulation. The area is characterized by high conservation values and is one of the most diverse ecosystems in Mexico.
The resident population of 1115 inhabitants (INEGI 2010) is from the Chinantec ethnic group, speaks its own indigenous language, and is divided into three settlements. The governance regime is based on centuries-old customary practices. The collective property rights for land and resources were recognized by the Agrarian Law of 1953, but the government later gave a 25-yr timber concession to a paper factory, which caused massive deforestation. This is a sensitive subject for the community because, in the late 1980s, they confronted the government to stop the concession and succeeded. As a result, the citizens regained full rights to the forests. This outcome illustrates that the strength of the local formal and informal institutions evolved from a long history of resource management, through which the inhabitants have managed the land under a stable political system. The exclusion, monitoring, and enforcement rights are well established. Traditions inherited from one generation to the next are at the core of this SES, particularly the transmission of forest management knowledge and respect for existing rules for sustainable resource extraction. In fact, this population has become an example of conservation through a traditional and environmentally sustainable CBNRM system based on communal property (Chapela 2007).
The economy of the area relies on logging (managed by local community companies), which provides jobs for 10% of the active population, and subsistence agriculture. However, Comaltepec’s SES is currently facing several challenges such as migration. The lack of individual income (local companies’ benefits are reinvested in the same local companies or in collective goods) is causing important parts of the community, particularly the youth, to move to cities or the USA (remittances play an important role in households). In addition, there is a local perception that the delivery of ecosystem services provided through sustainable environmental management by the community is not sufficiently rewarded in economic terms (e.g., payments for ecosystem services), creating inequities between those that make profit (e.g., water provision for the production of beer in a beer factory downstream) and those that contribute to the delivery of ecosystem services.
Monte Hermoso-Bahia Blanca Estuary is the second largest estuary in Argentina, at 230,000 ha in size. It is located on the southwestern coast of Buenos Aires Province (Fig. 3, Table 1). The estuary hosts salt marshes, a rich fauna, and a unique phytoplankton bloom that supports the trophic food chain for several species and is the basis of the local artisanal fishery. In addition, a section of the beach has been established as a Geological, Paleontological, and Archaeological Provincial Reserve because of the presence of well-preserved fossil footprints (Megatherium).
The SES includes five villages over a stretch of 100 km and hosts the most important Argentine deep-water port. Thus, this territory is strategic for Argentina’s economic development. The estuary communities base their livelihoods in port services and other industries (petrochemical, agri-food, etc.), and the beach communities base their livelihoods on tourism and artisanal fisheries.
Since the 1800s, the appropriation of land was characterized by a war against nomadic natives and the implementation of a private property regime related to agricultural production, with coastal management as a secondary concern. Since the 1900s, population growth has resulted from migration flows. The overall outcome is an absence of unique cultural roots, which hinders the creation of common cultural codes, norms, and rules. Marine and coastal resources are public property in Argentina, whereas the control of resource use (sand mining, fishing, etc.) mainly corresponds to different levels of the State (local, provincial, and national). The main activities (fishing and tourism) are developed by private actors, even though artisanal fishers exploit the common-pool resource. However, artisanal fishers face overlapping and often contradictory norms dictated by other administrations who do not recognize the community-based management.
The socioeconomic indicators of the area show a high level of development in terms of household income, infrastructure, and services. The educational institutions range from primary schools to universities, and there is a central hospital and decentralized healthcare in all of the villages. However, artisanal fishers recognize sustainability as one of the major challenges in the area. Fish catches are declining because of overexploitation (mainly by large fishing trawlers), pollution, and the disturbance of the estuary and the coast (untreated effluents pollute the bay, and changes to the tidal cycle affect phytoplankton production). Fishers also complain about the lack of options for transforming primary products to increase their value. The coast is also facing increasing erosion because construction near the sea blocks the normal circulation of the wind and dunes. Additionally, the sand needed for construction is mined from the dunes; although this sand mining is strictly prohibited, enforcement is not easy.
Recently, environmental organizations have fostered improvements in the performance of social capital. In particular, organizations of fishers have found a place for communication and discussions related to fisheries management. Other current social-ecological challenges confronted by the population are the joint effects of climate variability, coastal erosion, overfishing, overexploitation of beaches (including illegal sand mining from the dunes), and pollution.
We used Ostrom’s (2009) framework to characterize the SESs. The choice of this framework is motivated by various reasons: (1) it covers social and ecological aspects and their interactions, (2) it is applicable to common-pool resources, (3) it includes qualitative and quantitative data, (4) it proposes a broad and flexible spectrum of subvariables and allows them to be adapted to different SESs, (5) it can be used at different scales, (6) it emphasizes the governance rules to manage natural resources and the local stakeholders’ roles, (7) it was designed to analyze the effects of users’ self-organization rules on sustainability, (8) it provides the possibility to compare different case studies, and (9) it helps researchers and policy makers to deliver useful results for knowledge creation and policy planning (Delgado-Serrano et al. 2013, Delgado-Serrano and Ramos 2015). Because Ostrom’s (2009) is a conceptual framework (Binder et al. 2013), some methodological adaptations have been proposed to operationalize it (Cox 2014, Delgado-Serrano and Ramos 2015, Leslie et al. 2015). Here, we adopted the operationalizations proposed by Delgado-Serrano and Ramos (2015). The research was conducted in four steps: data collection, PSA, statistical data analysis, and NA (Appendix 1).
The research process was developed over two years, and the methodology was based on the development of a learning arena where scientific and local knowledge were shared and integrated. Participatory workshops with the local communities were organized every two to three months during the project. To select the participants (15–20 per workshop), we used stakeholder mapping techniques based on criteria such as knowledge of the SES, inclusion of inhabitants from all settlements, leadership roles, and gender and age balance. The first step was a comprehensive characterization of each SES using the applied version of Ostrom’s framework for analyzing sustainability. In total, 132 variables were identified and described by the research team, which included local co-researchers and the local communities. The variables were organized in each of the eight Ostrom subsystems: Settings, Resource Systems, Governance Systems, Resource Units, Actors, Interactions, Outcomes, and Related Ecosystems. In a subsequent workshop, a PSA exercise was developed to select a more manageable number of variables and to identify the key variables in each SES and their roles in the current and future dynamics. The results of both exercises were presented to the communities, and the final outcomes were discussed and approved in a final workshop. Local stakeholders participated not only in the PSA process, but also in the process of adapting the methodologies to the local contexts.
We used PSA to address our first two objectives: identify the key variables in the dynamics of the SES and explore the roles they play in the SES. The influence-dependence relations constructed on the basis of stakeholder perceptions might: (1) be critical for characterizing what is important for the SES functioning according to different groups, (2) help determine how such functions could be affected by management decisions and practices, and (3) help explore the direct and indirect changes in the SES (Chan et al. 2012, Hicks et al. 2013).
The PSA was structured in three phases. In the first phase, participants were asked to select the most relevant of the 132 variables, including at least one variable from each of the eight subsystems. A list of 15–20 variables, identified by consensus, was then compiled. Each variable was clearly defined (Appendix 2), characterized, and understood by the participants. In the second phase, a cross-impact analysis was conducted to assess the variables’ influences on each other, where 0 indicates no influence and 3 indicates the strongest influence, and positive and negative values indicate positive and negative influences, respectively. Thus, an N × N matrix (matrix of direct influence [MDI]) was constructed (Appendix 3). The third phase consisted of the analysis of the resulting data. To capture the indirect influences, the MDI was raised to the second, third, or higher power until the overall ranking of the influence and dependence of the variables remained constant. This stable matrix is the matrix of indirect influences (MII). The direct or indirect influence or dependence of any given variable, k, was calculated as the sum of the values of row k or column k in the MDI or MII, respectively (Appendix 4). This process allowed the variables to be ranked according to their influence and dependence values. Influence and dependence charts were constructed to plot the indirect influences. According to their position on the plot, the different roles played by a variable in the system could be identified as autonomous (low influence and dependence, which means a low effect on the system), determinant (high influence but low likelihood of being influenced), regulatory (medium dependence and influence, which indicates a leverage role), challenge (high capacity to influence and be influenced such that the variable can move the system but is unstable), and result (low capacity for influence but high dependence; these variables are considered to be indicators of system evolution). The MDI shows the actual condition of the system, whereas the MII shows its future evolution (for more details, see Delgado-Serrano et al. 2014). MICMAC software (version 6.1.2, Max Planck Institute for Demographic Research, Rostock, Germany; http://www.nidi.nl/en/research/al/micmac/software) was used for the analysis.
Given the small sample sizes, we used nonparametric Mann-Whitney tests to explore the variability in the influence and dependence of the variables according to their subsystem (Ostrom 2009). According to the characteristics of the case studies, we compared variables within and outside the Settings in the Colombian and Argentine cases. In the Mexican case, because very few of the variables belonged to Settings, we compared the variables within and outside the Governance System. The analyses were conducted with XLSTAT software (2012, Addinsoft, New York, New York, USA).
Following Janssen et al.’s (2006) approach to SESs as networks, we applied network analysis to address the final two objectives: to zoom into the social-ecological networks and describe the roles played by the key variables and the feedback loops that occur among them, and to reflect on the resilience of the SES. Networks are simplified representations composed of two simple elements (Gonzalès and Parrot 2012): nodes (or vertices), which represent discrete entities (in our case, the variables); and edges (ties or links), which represent the interactions between the nodes (in our case, the influence and dependence relationships).
We considered three measures of centrality and one measure of connectivity. The most central nodes are those than can more easily access the rest of the network. The three centrality measures were selected as the most relevant for our objectives according to the available literature on network metrics and resilience (e.g., Bodin et al. 2006, Janssen et al. 2006): distance-weighted directed betweenness (beta = 0.8), closeness, and the eigenvector parameters of the variables within each social-ecological network. Betweenness, calculated as the fraction of the total number of shortest paths between two given nodes divided by the total number of shortest paths between those two nodes passing through a third node, shows all of the shortest paths between two nodes that include a particular node. Closeness is a measure of the average shortest distance from each node to every other node; it is defined as the inverse of farness, which is calculated as the sum of each node’s distances to all other nodes. We used the eigenvector as a measure of network connectivity because it considers not only how many connections a node has, i.e., its degree, but also the degree of the vertices to which it is connected. For our fourth objective, the higher the values for betweenness and eigenvector and the lower the value for closeness that a variable has in relation to the other variables in the network, the more relevant it is within the network. While the identification of the most influencing variables obtained in the PSA partly overlaps with the information given by the network centrality measures, the NA also allows the possibility to analyze the signs of the influences between variables (i.e., including negative edges), hence identifying both positive and negative feedback loops of influence between variables.
We also calculated the density of each network (Appendix 4) and depicted the network graphs. The density of the network indicates how interconnected the vertices are in the network, i.e., what is related with the issues such as knowledge exchange. Within each SES, we selected a smaller group of variables with the highest betweenness, eigenvector, and influence and lowest closeness and dependence, and discussed the group’s relation to social-ecological resilience, following methods of previous work on the adaptive circle model (e.g., Holling et al. 2002a) and on traps outside the three dimensional space (Allison and Hobbs 2004). For the calculations and graphs, we used UCINET 6.0 network analysis software (Borgatti et al. 2002), NetDraw 2.139 (Borgatti 2002), and NodeXL 220.127.116.11 (Smith et al. 2010).
The Colombian and Argentine case studies follow a similar pattern in which key variables are concentrated in the external subsystems (Settings and Related Ecosystems; Table 2). This finding reveals the high number of external influences on the SES (public policies and governance for natural resources and external [illegal] actions), as well as the interest of external actors in the SES resources (markets for natural resources, megaprojects). The internal variables are concentrated in the Actors and Governance System subsystems. In Colombia, the relevance of ancestral knowledge and population trends is highlighted, and the need for formal education and locally driven research is revealed. In Argentina, the focus is on the role of fishers’ associations and the conservation measures they implement, the local markets and income, the need for community networking, and the history of the artisanal fishery. The Interactions subsystem comprises five variables in Argentina. Finally, the Outcomes are related to the lack of ecological sustainability in both the Colombian and Argentine SESs.
The Mexican case exhibits a different pattern. Stakeholders selected many variables included in the Actors and Governance System subsystems. They expressed that the governance institutions, property rights systems, collective-choice rules, extraction and exclusion rights, monitoring and sanctioning rights, and unpaid activities could most greatly affect the SES. Furthermore, the two variables selected within the Settings, i.e., political stability and environmental legislation, are those that could most greatly affect the current management system. Finally, as an Outcome, migration is perceived as relevant.
The indirect influence and dependence chart of the Colombian case study (Fig. 4) indicates that the system has almost no autonomous variables and has a predominance of regulatory and key variables. These latter variables can be influenced by and also provoke changes in the system; thus, the system is very dynamic. The variables within the Settings subsystem are less dependent than those in the other subsystems (Mann-Whitney U = 38,201, P < 0.05). An important role is attributed to external variables such as formal education, illicit crops, formal institutions, and mining. However, the community reclaims its control on the SES through ancestral knowledge, locally fostered research, and population trends.
The variables within the Mexican Governance System subsystem are more influential than the other variables (Mann-Whitney U = 1177, P < 0.1); thus, local natural resource management rules are central to the SES. Furthermore, few variables were perceived as key, so the system is dependent on the regulatory variables (Fig. 5). An important role is attributed to internal variables such as collective-choice rules, monitoring and sanctioning processes, and extraction and exclusion rights.
The Settings subsystem has lower dependence than the other subsystems within the Argentine case study (Mann-Whitney U = 10,146, P < 0.05). Given that the upper right quadrant of the influence and dependence chart is empty (Fig. 6), the system as a whole is rather stable and is thus relatively difficult to change. Community networking as well as artisanal fishery and fishers’ associations have a high potential for changing the SES from within. Industry (petrochemical industry pole, pollution, and dredging and liquefied natural gas project) and politics (lack of political interest in environmental sustainability and external governance of the fishery) are important external threats, while tourism and conservation measures are key external opportunities.
Finally, most elements in the Mexican and Argentine cases trended from low dependence and high influence to high dependence and low influence. In contrast, most variables in the Colombian case were perceived as highly influential and dependent (Fig. 7).
In the Colombian case (Figs. 8 and 9A, B; Appendix 4), the variables with the highest betweenness were armed conflict and climate change. Water management and solid waste had greater closeness, while population trends showed the highest eigenvector value. Illicit crops showed high influence and betweenness and large eigenvector values. In contrast, megaprojects presented low values for all network parameters.
When zooming into the group of variables with outstanding network parameter values, we observed that community as a social group was negatively influenced by several factors and was only positively influenced by formal institutions and formal education. A positive feedback loop between armed conflict and illicit crops emerged (i.e., they positively influenced each other), which also negatively affected community as a social group, formal institutions, and formal education. In particular, illicit crops was strongly, negatively, and bidirectionally linked with these latter three variables. Population trends was connected by negative feedback loops with formal institutions, formal education, and climate change (i.e., the population variable positively influenced the other variables, which negatively influenced the population). The density of the entire network was 56%.
In the Mexican case study (Figs. 9C, D and 10; Appendix 4), political stability, which had high dependence, showed the highest betweenness, followed by collective choice rules. The highest value of closeness was achieved by environmental legislation, which showed lower dependence and influence than average and small betweenness and eigenvector values. Several variables had a maximum eigenvector: history of use, with low closeness and high influence; livelihoods (for subsistence), with high dependence and low influence; governance institutions, with low dependence, low betweenness, and the highest influence; and importance of resources, economic activities (forestry and agriculture), and economic value of natural resources.
When focusing on the most relevant variables based on their network parameter values, migration was subject to several negative influences, except from political stability (Fig. 9D). Political stability, in contrast, was positively influenced by all of the other variables except migration. The negative feedback loop between these two variables is significant for the dynamics of the network. The fact that all of the variables that negatively influence migration are also largely and mutually reinforced might be relevant for the resilience of this subnetwork. The density of the entire network was 67%.
In the Argentine case study, fishers associations had the highest betweenness, followed by artisanal fishery and pollution, which were also among the variables with lower closeness and higher eigenvector values (Fig. 11). Lack of political interest in environmental issues and petrochemical industrial pole were highly influential variables that had low betweenness and closeness and high eigenvector values.
Pollution, external governance of fishery, and, most importantly in the Argentine case study, lack of political interest in environmental sustainability negatively and strongly affected other variables, particularly artisanal fishery, resources sustainability, and wildlife (Fig. 9E, F; Appendix 4). In this subnetwork, there were several positive feedback loops with positive influences such as that between fishermen associations and artisanal fishery. Other relevant loops with tourism are as follows: (1) lack of political interest in environmental sustainability negatively influenced tourism, whereas tourism positively influenced the former variable; (2) resource sustainability was negatively affected by tourism, which is fostered by sustainability; and (3) pollution is generated by tourism but also negatively affects tourism (Fig. 9F). Several interesting positive-positive relationships occur such as: external governance of fishery-fishers associations-wildlife, wildlife-artisanal fishery-resources sustainability, and fishers associations-artisanal fishery-tourism. The entire network exhibited a density of 60%.
The analysis of complex systems such as SESs requires methods that recognize this complexity and facilitate understanding. The experiences documented here illustrate that approaching SESs as social-ecological networks and using NA in combination with participatory PSA can: (1) reveal social perceptions of the structure of the studied SESs, (2) help visualize the key variables and drivers of the SES, (3) structure arguments for system management and decision-making by identifying the variable(s) that might be most critical for changing the system, and (4) facilitate participatory analysis among stakeholders to integrate scientific and local knowledge. The PSA approach promoted a socially constructed learning process, as had been previously noted in the literature (e.g., Gertler and Wolfe 2004), in which local perceptions of the SESs were condensed into a semiquantitative, analyzable form for detecting patterns and relationships in the subjective information. This bottom-up model stimulated discussions among the stakeholders (as also reported by Gavigan and Scapolo 2001), leading to better knowledge of the system’s dynamics and of the necessary actions for sustainable management (e.g., to attract the attention of policy makers to how their actions influence the SESs or to identify the internal variables that can act as regulatory and therefore promote changes). Additionally, it created a common language, structured collective thinking, and allowed the participants’ appropriation of results (as also reported by Godet et al. 2004).
As expected, the NA theory and PSA techniques proved to be compatible and complementary. Both have the same basic rationale and data structure, i.e., variables and influences in PSA, and nodes and vertices in NA. In addition, some centrality measures such as degree, betweenness, closeness, and eigenvector values were applicable and shed light on the roles of each variable within the SES, an aspect that PSA only superficially does. However, because the nodes represent the variables and the vertices represent the influences, as opposed to the more frequently used persons or institutions and information or physical flows, interpreting other NA parameters is complicated.
Some other questions and challenges arose during this exercise. Janssen et al. (2006) recognize that the nature of the relationships in social-ecological networks could be either entirely social, entirely ecological, or a mixture of both. However, we could not evaluate whether treating the ecological and social variables on the same level (node) could have consequences for the results or if the use of the more general influences as a vertex definition is equivalent to the typically used information or physical flows. Moreover, the correspondence between the potential influence value of the PSA and the concept of sleeping nodes (Janssen et al. 2006) should be explored in future research. By analyzing the networks created in the subsequent indirect matrixes of the PSA or by repeating the participatory analysis of influences, e.g., under different scenarios, it might be possible to capture the dynamic aspects of a social-ecological network, which is a current challenge (Cumming et al. 2010).
In addition, the process of variable selection influences the metrics of the network, i.e., if the network included different variables from the full set of variables identified in each SES, the network parameters would change (e.g., with more variables, density would increase). Given that we were interested only in comparing variables within a SES (for centrality measures) and between the three cases (for density), i.e., the relative values, this is not a problem in our case.
Finally, two more caveats need to be accounted for in relation to a participatory approach such as the one proposed here. First, given the difficulties of building a large PSA matrix in a participatory manner, a trade-off emerges between the number of variables and participation when integrating PSA and NA. Second, there is an inherent assumption that the social-ecological network is approached as a construct that the participants build, and hence the interpretation is not set in stone but rather is a tool that is meant to help understand the SES functioning as perceived by participants.
Similar patterns could be identified in the case studies, for example, the roles played by megaprojects in Argentina and Colombia. This type of project was observed as an element that exerts a moderate to strong influence in the SESs, but that could not be influenced (low dependence), and was thus determinant, or a controller, of the systems. Megaprojects such as dredging or the liquefied natural gas projects in Argentina and the industrial mining projects in Colombia have a strong influence on the SES and the CBNRM, but are controlled by external forces over which local communities have little power (e.g., Zilio et al. 2013, Göbel and Ulloa 2014). This fact, combined with the large influence of the Settings variables, which are exemplified by the power of external formal institutions in Colombia and the lack of political interest in environmental sustainability in Argentina, is dissimilar to the Mexican case study. In the first two cases, a polarization exists in the way public policies are developed and imposed top-down, with a high degree of political centrality (e.g., Cicalese 1997, SNUCMADS 2014). In contrast, in the Mexican case, the historical CBNRM tradition and the recent rebellion against the timber concession largely justify and sustain the highly empowered local governance system, including the collective-choice rules and the monitoring and sanctioning processes (Tucker 2010). Most likely, this history of strong local governance and struggle explains the perceived high importance of the Governance System variables compared with the other variables. In particular, collective-choice rules play a powerful role in the social-ecological network and hence the current state of the SES.
Subsistence activities such as agriculture, fishing, hunting, and mining are perceived as more relevant in the Colombian and Mexican cases than in the Argentine case, which is less rural and presents significant institutional fragmentation. In the Argentine case, both internal and external (Settings) governance-related issues are perceived to play more important roles than in the other two cases.
Negative outcomes such as pollution, deforestation, and solid waste only appeared in the Colombian and Argentine cases, whereas no negative outcomes were identified in the Mexican case. This might be because the Mexican community has implemented exemplary, environmentally sustainable forest CBNRM for centuries (García-López 2013). Because of the local awareness and international recognition (by the Forest Stewardship Council, for example), people perceive that the current land uses fully respect the environment.
A group of similar elements perceived as relevant in the three case studies were ancestral knowledge (Colombia), history of land use (Mexico), and history of artisanal fishery (Argentina), all of which are considered to be key to the three SESs. Land use history or resource management history can be observed as part of the social-ecological memory, which is a critical ingredient for social-ecological resilience (Folke et al. 2002). Forms of traditional ecological knowledge are considered fundamental in CBNRM contexts (Berkes 2004). In the case studies here, even if the development of CBNRM could be described as following a gradient from highly developed (Mexico) to an incipient stage (Argentina), the fact that variables such as history of land use and traditional ecological knowledge emerged as relevant can be interpreted as a sign of self-consciousness in terms of community power (e.g., Dadón et al. 2011, Velez and Lopez 2013).
The high number of variables identified within the Actors and Governance System subsystems in the Mexican case reveals the importance of community-based management. In Colombia, the relatively young governance system and the difficulties it faces to be recognized by external actors are evident (Ortiz-Guerrero et al. 2014).
From a resilience perspective, all three social-ecological networks exhibited high densities. This can be interpreted in four ways, as: (1) an opportunity for good information exchange and learning that might improve management (e.g., Pretty and Ward 2001), (2) the substrate for enhanced diffusion of innovations (e.g., Abrahamson and Rosenkopf 1997), (3) the potential for systems to become extremely connected and weak (Redman and Kinzig 2003), or (4) a “dark side” that potentially hinders collaboration with external actors and limits the freedom of actors to pursue ideas outside the norms of the group (Lechner et al. 2010). Even if most, or likely, all four, of these interpretations apply to the three case studies, one interpretation might be dominant in each case. In addition, the overall high density in the three case studies might be related to the adopted methodology, given the need in PSA to score the intensity of the link between all pairs of variables.
Under the adaptive cycle model (Holling et al. 2002a), which has three dimensions of potential or capacity, connectedness, and resilience, all three SESs could be considered (as perceived by participants) to be highly connected and resilient, but with different degrees of potential or capacity. According to the resilience literature, three types of traps outside of the three-dimensional space have been described (Allison and Hobbs 2004): rigidity traps, lock-in traps, and poverty traps. Rigidity traps occur when there is high potential for change, a high degree of connectedness among the structural variables, and high resilience to change; these traps may apply to social systems in which the members of organizations and their institutions become highly connected, rigid, and inflexible (Holling et al. 2002b). A lock-in trap is also characterized by a high degree of connectedness and resilience but a low potential for change (Allison and Hobbs 2004). The third and most well-known trap is the poverty trap, which is defined within the resilience framework as a situation in which connectedness and resilience are low and the potential for change is not realized (Holling et al. 2002b, Allison and Hobbs 2004). Following the aforementioned guidelines, a discussion of each case study follows.
Two particularities of the Colombian case study are worth mentioning. Illicit crops were perceived as one of the most powerful elements in the social-ecological network and, combined with armed conflict, emerged as a key node. Both factors appeared to be highly connected with the rest of the nodes and were thus likely to condition all of the dynamics and resilience of the social-ecological network. Therefore, this area might be considered an example of the disadvantages of a highly resilient SES in terms of the following: (1) the large control exerted by the combination of armed conflict and illicit crops (and aerial spraying), which disturbs the social cohesion and development of the community; (2) the undesirable state from a social-ecological sustainability perspective; (3) the high degree of connectedness; and (4) the low potential for change due to the feedback loop that might push the SES into or near a lock-in trap. However, from the social-ecological network analysis, we observed that the negative feedback loop could be broken by formal institutions, the community, or environmental alterations due to climate change. Similarly, the second particularity of the Colombian case is that it was the only case in which research was mentioned. This outcome could be because of the perception by the local community that collaboration with research institutions is having positive effects. Explicitly defined as locally driven research, this element showed great connectedness, i.e., a great potential to behave as a bridge between otherwise disconnected elements, which might be an interesting insight on how to break the lock-in trap. However, scientific knowledge can only affect decision-making if it is used by the people involved in the decision-making process (Beunen and Opdam 2011).
The communities in the Mexican case seem to be captured in either a rigidity trap or a lock-in trap. After the struggle against the timber concession in the 1980s (a crisis in the SES), the local governance system was reinforced. Since then, the strong CBNRM has ensured environmental sustainability because the local governance structures and agreements have developed a great capacity to focus on this singular approach (Carpenter and Brock  explain how this is related to rigidity traps). However, collective-choice rules that are very connected and that control the social-ecological network are perceived as responsible for SES rigidity because they hinder creativity and innovation. The low capacity of the system to explore alternatives (i.e., the “dark side” of resilience), particularly the lack of creativity and innovation, makes the system more vulnerable and appears as an urgent challenge. According to the literature, policy resistance (Sterman 2001) is a well-known phenomenon in system dynamics and is described as the bite-back paradox in large-scale systems (Gunderson et al. 2002).
In the Argentine case, the high network density might be interpreted as a sign of good information exchange and learning that might help to improve management. This research has triggered increased connection and awareness among the SES stakeholders, and it seems to be somewhat improving management. For example, fisher associations are realizing the utility and power of their union to communicate with formal institutions at higher levels of governance. The political interest in environmental issues is also increasing, and small steps such as the banning of plastic bags and the regulation of beach use are currently being implemented. In this case, the SES does not seem to be immersed in any trap, but it might be in a dynamic phase of increasing self-organization. Nevertheless, Argentina’s history shows that political and macroeconomic instability act as the main obstacles to self-organization. The sequence of dictatorial and democratic governments has affected the development of social networks, producing a high degree of uncertainty in the local dynamics (Vezzetti 2002).
Although climate change was a common issue in two of the three case studies and was considered in both cases to have a moderate degree of influence, it was perceived as extremely dependent in Colombia and almost independent in Argentina. Several studies highlight how individuals’ perceptions of climate change are linked to equity, development, perceived economic power, socio-political context, and the connection between management and science. Other studies state that more rural or urban contexts play an important role in risk perceptions (Wolf and Moser 2011), which could explain this difference.
The role that CBNRM can play in the sustainable management of environmental challenges is receiving increased attention, but an important number of the key drivers and variables identified are external to the SESs or linked to external stakeholders, e.g., policy makers or armed actors. Our results suggests that the CBNRM approach needs external support and recognition to work effectively. Interesting insights and information emerged from this study that could be useful in policy-making. Internally, the participatory and locally adapted approach helped to integrate local knowledge and perceptions in the exploration of SES dynamics and thus helped to identify better strategies and decisions that might be adopted by all stakeholders. At a higher level, a place-based approach that avoids one-size-fits-all results, which frequently do not recognize the singularities of the resources and the interactions within each SES, might also support research and decision-making in other contexts that are currently facing social-ecological challenges and increasing uncertainty, such as those identified here.
In the three case studies, participants selected as key variables public policies and governance systems, legal frameworks for the management of natural resources, markets, and external megaprojects. All of these variables profoundly affect both system performance and community-based management, but they cannot be influenced locally. Policy-making should increase flexibility and create options for local actors to express their understanding and willingness to change dynamics. Opportunities to collaborate could help neutralize the main obstacles, foster the levers, and enable sustainable management.
The different functioning that community governance currently has or could have in the different SESs might be of interest for decision-making at all levels. In fact, this strength (either already in action, incipient, or potential) could not only be used in the most common, defensive way, but also with the strong potential for interfering with external or larger institutions, particularly when dealing with social-ecological challenges.
Local perceptions and understanding are essential for fostering changes, particularly those linked with internal variables that can be moved by local actors. For this reason, we found the PSA and NA approaches to be complementary and useful for revealing the complexity and understanding of SESs.
In the different case studies, the PSA process allowed active community participation and acquisition of new skills that had four subsequent effects. First, it improved the decision-making process at the community level. For example, in Comaltepec, more informed decisions are now taken at the Assembly, and the Community Council is participating in other research projects analyzing the sustainable use of natural resources. In the Argentine case, some old disputes between use and conservation of beaches are now being settled. Second, it increased local residents’ capacity for planning their own development and managing and resolving conflicts. For example, the three areas are now undertaking integrated development plans. Third, it enlarged the visibility of local governance institutions outside of their territories, beyond the conventional vision as objects of development and biodiversity conservation programs. For example, the artisanal fishers association now has a voice in the discussion of an Argentina law on artisanal fisheries. Finally, it revealed and challenged existing patterns of power and authority.
The level of community-based control of natural resources appeared to determine the concentration of key variables in the internal or external subsystems in the SES. The role that CBNRM plays in each SES, and in relation to current social-ecological challenges, is largely dependent on key factors that are external to the SESs in the Argentine and Colombian cases, whereas CBNRM is basically linked to the local governance system in the Mexican case. This finding reveals the importance of adequately identifying and redistributing responsibilities, and generating a mosaic of institutions with different and partially overlapping geographic and temporal scales that can effectively address the complexity of social-ecological issues (Meadowcroft 2002). Policies designed at the community level may introduce configurations that are better in terms of public and aggregated (e.g., community) private benefits (Carmona Torres et al. 2011).
Planning and decision-making with limited knowledge and high uncertainty are challenging tasks; however, acknowledging the difficult balance between local knowledge and power and globalized or globalizing forces and external powers is even more difficult. We believe that this research and its participatory focus provides a thorough understanding of three SESs. It also highlights the key SES drivers and variables, the roles they play or can play in the future, and how they interact to create blocking or triggering effects in the path toward social-ecological sustainability and equity.
The first two authors (MD and EO) contributed equally to this work. This research was funded by the Seventh Framework Programme of the European Commission in the frame of the project "Community-based management of environmental challenges in Latin America" (FP7-ENV2011-282845 COMET-LA). We acknowledge the collaboration of the people in the Community Council of Black Communities of Bajo Calima (Colombia), Santiago Comaltepec (Mexico), and Bahia Blanca Estuary and adjacent coasts (Argentina) in the research. We also thank Irene Iniesta-Arandia for assistance with network analysis and insightful comments. Finally, we are grateful to two anonymous reviewers for their insightful comments.
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