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Rocha, J. C., M. Baraibar, L. Deutsch, A. de Bremond, J. Oestreicher, F. Rositano, and C. Gelabert. 2019. Toward understanding the dynamics of land change in Latin America: potential utility of a resilience approach for building archetypes of land-systems change. Ecology and Society 24(1):17.
https://doi.org/10.5751/ES-10349-240117
Research, part of a special feature on Seeking sustainable pathways for land use in Latin America

Toward understanding the dynamics of land change in Latin America: potential utility of a resilience approach for building archetypes of land-systems change

1Stockholm Resilience Centre, Stockholm University, 2Beijer Institute of Ecological Economics, Swedish Royal Academy of Science, 3Department of Economic History and International Relations, Stockholm University, 4Institute of Latin American Studies, Stockholm University, 5Centre for Development and Environment, University of Bern, 6International Programme Office, Global Land Programme, 7University of Maryland, College Park, 8Universidade de Brasília, Centro de Desenvolvimento Sustentável, 9Universidad de Buenos Aires, Facultad de Agronomía, Departamento de Producción Vegetal, Cátedra de Cerealicultura, Buenos Aires, Argentina, 10CONICET, Universidad de Buenos Aires, IFEVA, Buenos Aires, Argentina, 11Universidad de Buenos Aires, Facultad de Agronomía, Departamento de Economía, Desarrollo y Planeamiento Agrícola, Cátedra de Sistemas Agroalimentarios, Buenos Aires, Argentina, 12Universidad Nacional de Misiones, Facultad de Ciencias Forestales, Área de Producción vegetal con énfasis en sistemas agroalimentarios sustentables, Eldorado, Misiones, Argentina

ABSTRACT

Climate change, financial shocks, and fluctuations in international trade are some of the reasons why resilience is increasingly invoked in discussions about land-use policy. However, resilience assessments come with the challenge of operationalization, upscaling their conclusions while considering the context-specific nature of land-use dynamics and the common lack of long-term data. We revisit the approach of system archetypes for identifying resilience surrogates and apply it to land-use systems using seven case studies spread across Latin America. The approach relies on expert knowledge and literature-based characterizations of key processes and patterns of land-use change synthesized in a data template. These narrative accounts are then used to guide development of causal networks, from which potential surrogates for resilience are identified. This initial test of the method shows that deforestation, international trade, technological improvements, and conservation initiatives are key drivers of land-use change, and that rural migration, leasing and land pricing, conflicts in property rights, and international spillovers are common causal pathways that underlie land-use transitions. Our study demonstrates how archetypes can help to differentiate what is generic from context dependant. They help identify common causal pathways and leverage points across cases to further elucidate how policies work and where, as well as what policy lessons might transfer across heterogeneous settings.
Key words: archetypes; land-use change; Latin America; regime shifts; resilience assessment

INTRODUCTION

Latin America has experienced faster agricultural expansion than any other world region in the past two decades, mostly at the expense of tropical forests (Gibbs et al. 2010). At the same time, the region is also identified as holding the largest potential area available for further agricultural expansion globally (Lambin et al. 2013, Graesser et al. 2015, MacDonald et al. 2015). Together these factors point to a future in which the region continues to play a pivotal role in global food production and exports. Although Latin America’s rural areas are some of our planet’s most biodiverse (Kuemmerle et al. 2017), they are also characterized by contested and weak property rights to land. The pace of land-use change has intensified environmental conflict across the region in recent decades, heightening concerns about environmental sustainability and social desirability of such changes (de Castro et al. 2016). Amidst such land use and livelihood transformations, Latin American countries have committed to achieving the 17 sustainable development goals (SDGs) of the UN Agenda 2030 (UN General Assembly 2015). Meeting these goals requires planning for resilient landscapes that can support inclusive economic development and the provisioning of nature’s benefits to people, while coping with changes in climate alongside growing demand for land and its products (Flachsbarth et al. 2015).

Resilience is the ability of any system to maintain its function and structure in the face of change, and for this it depends on its ability to learn, adapt, and transform (Folke 2016). Resilience analysis can inform us how difficult it is to shift a system from one configuration (regime) to another; e.g., to adopt a certification program that changes the function and structure of the landscape. Regime shifts are large, abrupt, and persistent critical transitions in the function and structure of social-ecological systems (Scheffer et al. 2001, Folke et al. 2004). They are a useful conceptualization to study land-use and land-cover change because they acknowledge the role of nonlinearities, in other words, the existence of feedbacks (Müller et al. 2014, Ramankutty and Coomes 2016). Although all land-use changes are not regime shifts, land-use change processes exhibit feedback dynamics that can be hard to reverse; making regime shifts a conceptual framework tailored to capture land-use change dynamics. In this context, a regime is a structural configuration of the landscape that reflects both the function and structure of the social-ecological system (e.g., a forest, a silvo-pastoral system, or a soy plantation). Thus, the resilience of a system also indicates how difficult is it to reverse a regime shift once it has occurred. For example, if a subsidy scheme is implemented that promotes the transformation from forest to an agricultural landscape, resilience is related to the probability that the forest will recover if the subsidy is removed. If the removal of the subsidy does not “flip” the system back (i.e., the shift is hard to reverse), then the system is said to exhibit hysteresis, meaning there are new social and ecological feedbacks in place maintaining the agricultural landscape. To avoid regime shifts or improve capacity for transformation toward desirable systems, resilience analysis is a useful approach. Using simple models operationalized in qualitative causal loop diagrams, we aim to map and study systems to identify drivers, pathways, and leverage points: in other words, system archetypes (Bennett et al. 2005).

We operationalize resilience analysis in the context of land-use change through case study comparison and the identification of systems archetypes. Operationalizing resilience has proven a challenge in large scale contexts because existing methods tend to impose high data demands (both spatio-temporal and socioeconomic), although most resilience assessments limit themselves to participatory consultations that do not necessarily scale up (Resilience Alliance 2011, Dakos et al. 2015). Reconciling context dependent features with common regional drivers and trends remains a challenge in sustainability science (Ostrom 2007, 2009), especially in regions with heterogeneous, contentious, and highly dynamic land-use change such as Latin America. To address these challenges, we revisit the approach of resilience surrogates by mapping systems archetypes: causal network representations of the system’s structure and function (Bennett et al. 2005). Archetypes are a formal way of classifying minimal feedback structures responsible for generic dynamical patterns, key drivers, feedbacks, and leverage points (Senge 1991, Wolstenholme 2003, Meadows and Wright 2008). An archetype is a network of interrelations that can lead either to (1) the mutual stabilization of a cluster of trends, therefore generating persistent dynamics (the regimes), or (2) the mutual amplification of a cluster of trends, potentially leading to rapid and drastic changes (the shifts; Eisenack 2014). Recent work has shown that some challenges of operationalization can be overcome through the creation of a database of case studies of land-use change (Ramankutty and Coomes 2016). We aim to set foundations for such a database, adapting the comparative framework developed for the regime shifts database (Biggs et al. 2018) to integrate more socially driven dynamics. The regime shifts database has allowed the creation of a consistent framework to systematically analyze impacts, key drivers, underlying feedbacks, and management options, as well as facilitating comparison between multiple regime shifts. The framework, however, does not target social dynamics such as trade, or the role of public policies on (de)stabilizing regimes. We modify this approach through the development of a new template extending the archetype mapping to social and economic drivers and feedbacks of land-use and land-cover change. In this way, we intend to facilitate future endeavors to upscale resilience analysis and provide a step forward in distinguishing what is generalizable from what is context dependent in social-ecological systems research (Magliocca et al. 2018).

CONCEPTS AND METHODS

Resilience thinking is an analytical approach focusing on stabilizing and destabilizing processes and implies attention to slow variables and feedback processes (Bennett et al. 2005, Biggs et al. 2012). To address these aspects of social-ecological systems we employ systems archetypes. If resilience is the ability to maintain function and structure (Folke 2016), and regime shifts are abrupt, persistent changes in the structure and function of systems (Scheffer et al. 2001), then an archetype is a simplification of that structure.

Archetypes have been treated in the literature as both the structure of variables that produce trends (the equations or causal hypotheses) and the trends themselves, i.e., observables whose change over time can be measured (the dynamic behavior; Wolstenholme 2004). Empirically, such trends can be observed and measured, and their clustering can be identified using multivariate statistical methods (Rocha et. al nonreviewed preprint). For example, Václavík et al. (2013) and Levers et al. (2018) explicitly labeled their data-driven work as land systems archetypes, whereas others have produced similar land classification schemes of land use based on an empirical approach (Ellis and Ramankutty 2008, van Asselen and Verburg 2012, Letourneau et al. 2012, Václavík et al. 2013, Levers et al. 2018). Oberlack et al. (2016) defined archetypes as recurrent patterns that explain how configurations of factors generate an outcome by activating processes of social-ecological interaction. If the recurrent network of interrelations or causal mechanisms cannot be observed, then it becomes necessary to develop models that capture these reappearing patterns of causal interaction. We operationalized analysis of resilience by discerning archetypes that capture both the trends (dynamics) and the structures (causal hypotheses) that produce the trends. We identify archetypes in a qualitative fashion, capturing changes in land use over time in different case studies (Table 1) through literature reviews and expert elicitation. We employed a data template inspired by the regime shifts database that focused our attention on slow variables, feedback processes, and drivers of change. Once the data template is completed, experts evaluate the causal structure of the system enabling the development of causal-loop diagrams (CLDs). Both the trends captured and the causal hypotheses developed are the archetypes operationalized to study resilience.

Data template and case studies

Building on the experience of the regime shifts database framework (Biggs et al. 2018), we developed a template (Appendix 1) for systematically collecting literature-based and expert-knowledge synthetic insights from case studies on land use/cover change. The template substantially reduces complexity from the original source and is designed to facilitate data collection from people without a background in regime shifts, but with extensive expertise on the specific patterns, trends, and drivers involved in particular cases of land-use change. The template includes a text document with semiopen questions and predefined categorical variables, as well as two tables that aim to capture drivers, feedback mechanisms, and the desirability of change by different stakeholders (Appendix 1, 2).

This paper arose from the workshop “Seeking sustainable pathways for land use change” organized by the South American Institute of Resilience and Sustainability Studies (http://saras-institute.org) in Uruguay, March 2016, during which a regionally, and topically, diverse group of scholars came together with practitioners and regional government representatives to conceptualize recent land-use change in Latin America. As an outcome of the conference, seven case studies were identified (Table 1, Fig. 1). The criteria for including case studies was the availability of published evidence of an ongoing shift in land-use change in any country in Latin America in which a field expert with deep case knowledge could provide validation. To assure capture of the appropriate elements, and given that the template was a time-consuming iterative experiment to better capture socially driven regime shifts in land-use systems, experts also provided feedback on the template design. Our small sample size is explained by two factors: first, we endeavored to test this method as a conceptual tool for operationalizing archetypes analysis and not to perform an exhaustive analysis capturing all possible archetypes; and second, the selection of cases required the participation of an identified expert to fill and review the template and complete the causal network interpretation.

Archetypes as causal networks

Causal-loop diagrams (CLDs) are maps of the feedback structure of a system and a tool for communicating causal hypotheses to broader audiences (Sterman 2000). Variables are connected by arrows denoting causal influence. A link is positive if a change in the origin variable produces a change in the same direction on the response variable, whereas a negative link denotes a relationship characterized by the opposite direction. This notation is structural, meaning that it only denotes what one would expect to happen when all else is constant. Feedback loops appear when a chain of causal relationships form a directed cycle, tracing a pathway that starts and ends with the same variable. These structures are important because they determine the amplification or dampening of dynamic processes that underlie nonlinearities of the system (Sterman 2000). Archetypes have been thought of as basic building blocks of minimal feedback structures that can give rise to any observed dynamic (Sterman 2000, Wolstenholme 2003). Figure 1 shows an example of the CLD developed for the certified coffee case study in Colombia.

Causal diagrams are by definition an incomplete depiction of the system (Sterman 2000). Our CLDs are limited by the perceptions of the experts and bounded by the variables reported in the published literature. For example, Figure 1 does not report climate change to be a relevant variable on the case study, because neither the expert nor the literature reviewed report climate as an underlying variable of change. It does not mean climate is not an important factor for the case study, it means that it has not been reported on. The underlying assumption is that the CLD coder only translates what others have described as relevant processes: the expert, the literature, and both are verifiable through the template. The coder should not include his/her own hypothetical thinking if it is not backed up by the literature or expert. This methodological assumption is inherited from the regime shifts database in which only causal hypotheses that can be traced back to scientific publications are reported (Biggs et al. 2018). We have relaxed that assumption to include the knowledge of experts.

Structural equivalence is a method that allows us to identify nodes that play similar roles in a network (Newman 2009), thus identifying archetypical variables that belong to common structures. We studied the structural equivalence of the variables (called nodes) in these networks by looking at the similarity of their positions (who is connected to whom), as well as their similarity across cases. To find structural equivalence, we joined all causal networks in a unique matrix and measured the Euclidean distance of all variables in the directed graph, as well as the Jaccard distance on the bipartite graph (the network of variables in the CLD and to which case they belong to). With these distances, we approximated structural equivalence with hierarchical clustering to group similar nodes according to their number of connections, i.e., degree centrality, in the combined network of CLDs, or the cases they belong to. Our analysis was performed in the R statistical computing environment (R Core Team 2017) using the Statnet suit of packages (Handcock et al. 2008).

RESULTS

We describe and compare archetypical causal structures, common trends, drivers, and differences across cases and observed leverage points in all cases. Further details on case studies are summarized in Table 2 and respective CLDs in Figure 2, both derived from the completed templates (available in Appendix 1).

Causal structures

Combining all seven case studies into a single composed network shows 96 variables connected through 191 links, of which 10% of links occur in more than one case (Fig. 2a). The most similar cases in terms of common links are Pampas (Argentina) and Litoral (Uruguay), both related to the expansion of soy and a shift from small-scale, relatively low intensity practices to large, high-input operations. The cases in Chaco (Paraguay) and Aveiro/Ruropolis (Brazil) share the link between deforestation and forest cover, whereas the links between grassland area and cattle ranching are common to Chaco and Litoral. The most central variables, as defined by number of connections, are soy cropland, cattle ranching, small-scale producers, and forest (Fig. 3). Similarly, the variables that appear in most cases are rural out-migration and forest (five cases), cattle ranching, deforestation, land prices, and population growth (three cases). Further structural analysis shows that these variables form clusters across cases (with maximum co-occurrence of three out of our seven cases, columns in Fig. 3), as well as clusters of well-connected variables in the causal networks given their numbers of connections (rows in Fig. 3). Drivers and leverage points (policies) tend to have low centrality by both measures and, as expected, tend to lie on the periphery of the causal networks (Liu and Barabasi 2015). Although variables could be grouped under common categories (e.g., policies, commodity demand, commodity prices), the variable names correspond to specific features of cases that are not comparable within the network (e.g., price of gold or soybeans). However, the results point out commonalities that result in differing causal effects (Meyfroidt et al. 2016) from place to place. Such common features highlight the role of international trade, demand for foods, commodity prices, technological improvements, and national policies.

Common trends, drivers, and differences

One of the primary common trends across cases is the importance of trade and international commodity prices as drivers of land-use change. Soybean price is key for the cases in Uruguay, Argentina, and Paraguay; gold for the case in Brazil; and coffee in Colombia. Indeed, the export profiles for Uruguay, Paraguay, and Argentina, in which trade is a strong driver, indicate that soybean and soy products increased their share of exports between 2000 and 2015 (see Appendix 1, Fig. A1.8). Although our cases deal with subnational levels, their land-use patterns reflect changes in national export structure.

The cases in Santander (Colombia), Cabañas (El Salvador), Novo Caminho (Brazil), and Aveiro/Ruropolis (Brazil) involve family farmers rather than actors in the agro-businesses sector. These cases are smaller in spatial extent, with localized effects at the farm or household level; as such, their influence on national economies are less evident. In Santander, niche market trade plays a role by opening access to international certification schemes that have increased forest cover at the farm scale and reduced vulnerability to price volatility for farmers within the certification program (Rueda and Lambin 2013a, b, Rueda et al. 2015). In Aveiro/Ruropolis (Table 1), deforestation and small-scale agricultural encroachment were linked to Chagas disease and mercury intoxication risks, with poor, subsistence farmers being most vulnerable. Here, land-use change was driven by public policies in the form of agrarian credit programs and transportation infrastructure development that allowed farmers to access regional markets and expand and diversify their production. Forest transitions, a regime shift from cropland to secondary forest, were described in the Novo Caminho, Santander, and Cabañas cases. In Colombia and El Salvador, this transition was intentional, with civil society efforts to link conservation to local, small-holder economies. In Brazil, however, new income options from social subsidies and off-farm employment encouraged land abandonment. Forest cover change in Brazil was characterized by rural out-migration, whereas in Santander and Cabañas “bottom-up” initiatives for conservation were underlying land transformation.

Another common trend across case studies is land-use change linked to the technification of agriculture, which includes the use of heavy machinery, genetically modified varieties, supplemental fertilizers and pesticides, as well as standards for certification. In South America, the development of genetically modified crops, in particular soybeans, enabled intensification of export-oriented agricultural practices, raising economic returns, and allowing expansion even into less suitable lands (Gasparri and de Waroux 2014, Garrett and Rausch 2016). The effects of the new technologies in agriculture are complex and differentiated but some evidence suggests that having less capital-intensive and labor-sparing technologies make it increasingly hard for smallholders to compete with large-scale intensive agriculture (Flachsbarth et al. 2015). In Aveiro/Ruropolis, agrarian development programs introduced modified crop and cattle varieties, while income increases allowed family farmers to purchase chemical inputs and expand production. Similarly, new financial tools, such as opportunities to sell harvests on the futures market, have facilitated access to capital for producers, furthering the effect of general increases in foreign direct investments in the region (Bárcena and Prado 2015). Ecological feedbacks were also a common denominator across cases that further reinforced land-cover change. Erosion and soil degradation were particularly important aspects in Uruguay, Brazil, and Paraguay.

Land-rights issues and out-migration are also common across cases, with a tendency toward land ownership concentration. In Argentina, soybean expansion displaced subsistence farmers and their cropping systems (corn, sunflower) from the Pampas plains. In Uruguay, pastures were transformed into soybean plantations displacing cattle ranchers and mixed systems to Paraguay, further engendering land conflict with large investors, local smallholders, and cultural minorities such as indigenous groups, and making explicit that the case studies also serve to illustrate spillover or leakage effects. Such dynamics are absent from regions in which local topography makes large-scale expansion impractical, such as the coffee case in Colombia. Conflict plays a role in some cases, with war driving forest regeneration in El Salvador and property rights disputes between agribusiness, small-scale farmers, and indigenous communities resulting from ranching expansion in Chaco.

The case studies also make explicit how land systems are telecoupled (Liu et al. 2013, 2015) with spillover and leakage effects arising from different legal frameworks and conditioning how international commodity trade influence land-use decision making across Latin America. Argentinean agro-firms expanded into Uruguay beginning in the early 2000s to diversify risk and to take advantage of export and investment friendly political frameworks and regulations (e.g., no export taxes on soybeans in contrast with Argentina). Soybean expansion in Uruguay then led to a rapid rise of land prices, driving Uruguayan ranchers to sell or lease their arable land to new crop firms and move their own operations to less suitable areas in other countries, such as Paraguay. In fact, Argentinian, Uruguayan, and Brazilian ranchers expanded the agricultural frontier through deforestation in the Chaco. These trends are also visible on the trade profile of these countries (Appendix 1, Fig. A1.8).

Leverage points

Policies are often portrayed as drivers of land-use and land-cover change (Duit 2014), yet our case study comparisons indicate that policy outcomes are often nonlinear and have unforeseen consequences. Although the CLD highlights the importance of policies as drivers and as points of intervention, the strength and effectiveness of each policy is context dependent. For example, the increase of export taxes on soybeans in Argentina made it more attractive for Argentinean crop firms to expand cultivations in Uruguay. Policies in one region can have spillover effects and displace land use to other regions (Lambin and Meyfroidt 2011) as was the case in Paraguay in which the “zero deforestation” law, designed to protect the Atlantic rainforest, increased pressures on the forests of the Chaco (le Polain de Waroux et al. 2016). The Paraguayan case also evidences how conflicting policies, such as those that alternatively support communal and private land rights, can be used to exacerbate land conflicts. Current legal frameworks governing land tenure tend to support private, formalized, and individual forms of land ownership over small scale, informal, and common ownership typical of smallholders and indigenous groups, thus favoring elite capture by powerful actors while further marginalizing others. In both Paraguay and Brazil, there have been contradictions between environmentalist and developmentalist policies, although asymmetrical state capacities to implement market-oriented projects over conservation programs means that corporate expansion is often favored over community-based, participatory actions (Abers et al. 2017). This highlights how national policies, the geopolitics of development, and the influential lobbying of powerful actors raise important questions about social equity that are embedded within land-change processes.

Policy clashes are less straightforward in the Uruguayan, Salvadoran, and Colombian cases. Uruguay has favored deregulation and liberalization reforms including absence of export taxes to attract foreign direct investments, but now promotes mandatory crop rotation and regulates the use of pesticides and fertilizers. In El Salvador, resettlement policies for excombatants and refugees were originally in conflict with the goals those same combattants to maintain forest recovery. Bottom-up conservation efforts by these same groups in the postwar years resulted in eventual legal protection of the recovered forests (de Bremond 2007, 2008, 2013). In the Colombian case, without a national level policy to differentiate in the international coffee market, the certification scheme would not have been functional at the municipality level. Understanding how international telecouplings, linking consumers in high income countries with producers in middle and low income countries, is crucial for informing policies that foster transitions toward more sustainable social-ecological interactions. Some policy interventions intended to counteract the negative impacts of agricultural expansion, for example, the soy moratorium in Brazil and the Argentine zonation Law, might be partially responsible for the post-2007 slowdown in agricultural expansion, or merely a reaction to the global economic recession and hence, just a temporary feature (Graesser et al. 2015).

DISCUSSION

We employ archetypes as analytical abstractions intended to capture the processes that underlie recurrent patterns or trends observed in land systems. Our cases describe land-use and land-cover transitions as potential regime shifts (we do not know if they will be stable in the future) either from forest to agriculture, from small-scale cropping to industrialized agriculture, or from cropland to secondary regrowth. The most important links, according to the structural equivalence analysis, were between forest cover, deforestation, and rural migration, indicating that they are key processes across case studies. Land change was also characterized by international trade, food demand, commodity prices, and technological improvements.

Although recent land-use change in Latin America is mainly driven by distal demand (increased demand on soybeans, timber, and meat; Meyfroidt et al. 2014), public policies are also found to be important. However, their effects are not straightforward. When examining policies as leverage points, the presence of policies does not guarantee achievement of intended effects. Even so, policies remain a primary leverage point across cases, making it critical to account for potential synergies and conflicts between policies or institutions in considering potential effectiveness. Our results show how national policy schemes can create spillover effects and impact land-use change in other areas of Latin America. Accordingly, policy design to achieve sustainable development goals in the region will need to take into account telecouplings between different social-ecological systems and move beyond national policy regulation into regional policy coordination, such as through current regional governance structures, e.g., Consejo Agropecuario del Sur (CAS, Agriculture Council of the South).

Toward a database of land-use archetypes that integrates social and ecological dynamics

Databases are useful for cross comparing and synthesizing knowledge; they can support theory building and identify points of intervention (Biggs et al. 2018). Online collaborative platforms that seek to understand local change at the global scale already exist. Earth Systems Science’s GLOBE (http://globe.umbc.edu/) offers a number of global datasets of biophysical and socioeconomic aggregates commonly used in land-use classifications (Ellis 2012). Although process-based modeling for land change does exist (e.g., Magliocca et al. 2013, 2014, 2015), the step toward theory building demands much more attention. Data driven classifications of land use abound (Ellis and Ramankutty 2008, Letourneau et al. 2012, van Asselen and Verburg 2012, Václavík et al. 2013, Levers et al. 2018), but process-based models for theoretical development are lacking despite a relatively rich case study-based literature on land-use and land-cover change (Meyfroidt 2016). We contribute to this body of work by qualitatively developing a series of causal hypotheses in the form of CLDs, and by leveraging comparison, which helps us distinguish more general aspects from context dependence between case studies. Although Václavík et al. (2013) used a data-driven approach to generate 12 global land-use archetypes, our approach complements theirs by including social dynamics, such as the role of policies, the driving force of trade, land conflicts with minorities, or patterns of land ownership, which are not possible to grasp from remote sensing data. This can improve understanding of the linkages between patterns and processes of land-system change.

Our work highlights the potential importance of combining geospatial data with variables that are not currently included in such global efforts. Such variables include relational data and flows such as national or international trade, rural migration, or remittances; as well as institutional fine-grain data such as land ownership or rules and policies. Thus, our work complements previous efforts for studying regime shifts in social-ecological systems (Biggs et al. 2018) by operationalizing existing frameworks to incorporate socially driven dynamics. Although our analysis is done on a case-by-case basis, we argue for the development of a database (building on the experience of the regime shifts database) to identify a minimal set of causal mechanisms that account for observed trends. Our work contributes to assessments of resilience by identifying key feedback processes, drivers, and leverage points in a comparative fashion (Meyfroidt et al. 2018). Our method explores a middle range assessment that goes beyond individual cases, retains some of their context, but looks for generalization, e.g., common regime shifts in land-use dynamics. Case study comparisons are useful because they help to tease out similarities across systems and can illustrate observable properties related to resilience (Carpenter et al. 2005).

Critical to this immediate research challenge is increasing public data accessibility. The causal networks presented highlight potential destabilizing or stabilizing processes in the system, which are a good place to look for resilience surrogates (Bennett et al. 2005), but they lack temporal information about unfolding dynamics. Hypotheses about the presence, strength and relevant scale of causal mechanisms should be tested with empirical data. Our exercise sheds light on some of the observables or surrogates that could be sampled (land tenure, rural migration, remittances, land conflict records, yields, national and international trade, commodity prices, disease rates) to better characterize regimes on a finer-grained scale than the current global or regional classifications. However, public data are scattered when available and making them open access is necessary to advance science. Although modeling has traditionally been an approach to explore hypothetical scenarios, further testing of mechanisms requires extensive empirical support.

CONCLUSION

Unprecedented land-use and land-cover change has occurred in Latin America over the last two decades. Whereas there has been a growing consensus concerning the necessity of planning for resilient landscapes, not least to fulfill the committed goals of the UN Agenda 2030, attempts to operationalize and upscale resilience analysis have so far been limited, or imposed high data demands. We explored a candidate methodology for filling this gap, revisiting the approach of system archetypes to identify resilience surrogates, with a focus on land-use regime shifts in social-ecological systems in Latin America. By creating a data template to synthesize insights on key processes and patterns of land-use change, and by collecting literature based and expert knowledge on seven case studies across Latin America, we constructed causal loop diagrams and studied their structural similarity in the form of causal networks. This allowed us to facilitate comparison and to identify similarities across systems, such as common drivers and trends, without losing sight of the context specificities. Searching for what is archetypical, we found deforestation, international trade, food demand, commodity prices, and technological change to stand out across cases. Although more cases are needed to characterize land-use change in Latin America, our preliminary results show that policy outcomes are often nonlinear with unforeseen consequences, including leakage effects between cases. Our approach complements existing data-driven approaches to generate archetypes by including social dynamics that are hard to grasp from remote sensing data. Finally, we make a call to the broader scientific community for the development of a database to identify minimal sets of causal mechanisms, i.e., archetypes, which account for observed trends, moving beyond the case-by-case basis analysis. We have shown how comparative analysis can be useful to distinguish generic patterns from context dependent attributes in social-ecological research.

RESPONSES TO THIS ARTICLE

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ACKNOWLEDGMENTS

This work would have not been possible without the creative ideas and initial systematic thinking and coordination of Daniel Ospina. We also appreciate the feedback on early stages of this idea by Francisco Alpizar, Bryan Finnegan, Rafael Bernardi, Nestor Mazzeo, Marten Scheffer, and Matias Piaggio, as well as the general support of the South American Institute for Resilience and Sustainability Studies (SARAS).

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Address of Correspondent:
Juan C. Rocha
Stockholm Resilience Centre
Stockholm University
Kräftriket 2B
10691, Stockholm
Sweden
juan.rocha@su.se
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