INTRODUCTION

Because natural systems (i.e., not affected by human enterprise) are becoming rare across the world (Allan et al. 2017, Riggio et al. 2020), there is an increasing need for analyzing and understanding land through the lens of coupled human-nature systems. Humans are not mere inhabitants of ecosystems but strongly influence ecological processes (Ellis and Ramankutty 2008, Maxwell et al. 2016). Ecoregions and biomes are useful geographic units to represent coherent patterns of biophysical features. However, to characterize the current configuration of land systems, which necessarily involves human activity (Verburg et al. 2009), we need a land classification scheme that integrates both the social and the biophysical dimensions.

With increasing data availability, new opportunities for large-scale and synthesis research arise. Nevertheless, comparing findings from different locations and linking them to global or distant processes is still a challenge (Rocha et al. 2020), partly due to the lack of appropriate spatial frameworks at large scales to place them in context (Kuemmerle et al. 2013). Land system science, as a research field, is growing fast, and new methodological approaches to address this gap are diversifying and consolidating (GLP 2016). An example is the syndrome and archetype analysis (Meyfroidt et al. 2018, Oberlack et al. 2019, Sietz et al. 2019), which analyzes social-ecological systems (SES) by means of identifying recurrent patterns of land use characteristics and processes, and have been used to detect the occurrence of target social-ecological systems across the territory as well as to generate land system classifications.

Several endeavors applied the archetype logic to generate large-scale land classifications of social-ecological systems. Early global initiatives by Ellis and Ramankutty (2008) combined land cover with irrigation and population data to generate the Anthropogenic Biomes of the world. Subsequent efforts included more detailed data on production activities. Letourneau et al. (2012) generated a global land use systems map; van Asselen and Verburg (2012) produced the global scale land systems, and Václavík et al. (2013) developed global land system archetypes. At a continental scale, Levers et al. (2018) analyzed archetypical patterns and trajectories of land systems in Europe.

These studies combined data of different aspects of nature (e.g., land cover, land use intensity, biophysical factors) using computerized classification methods (e.g., multi-stage empirical process, hierarchical clustering, self-organizing maps) to produce maps with medium spatial resolution (~10 km to 20 km). However, they tended to center their classifications on land use, particularly identifying different types of agropastoral production. In most cases, information on the characteristics of the social communities was represented only through population density or accessibility, as indicators of land use intensity. Political, environmental, and socioeconomic factors were used in some cases ex-post to describe the classes, but not to generate them. Culture and governance are important to reflect the complex behavior of agents influencing the landscape (Lambin et al. 2001, Verburg et al. 2009, Rounsevell et al. 2012) and are very difficult to include in global models (Václavík et al. 2013). The most comprehensive was the global land systems archetypes (Václavík et al. 2013). They produced an exhaustive classification that considered several physical variables, photosynthetic activity (NDVI), gross domestic production (GDP), and political stability.

Classifications at the global scale are ideal to present general patterns across the world, but they fall short in understanding land systems at regional or local scales. For example, Václavík et al. (2013) classified roughly half of the South American continent (~12,000 km²) as the same class: “forest systems in the tropics.” Working at finer spatial scales would allow for more detail in the classes’ descriptions, the inclusion of variables of regional relevance with particular values range, and higher likelihood of finding complete and coherent sets of specific variables; such as cultural and political variables.

South America has particular characteristics that justify having a specific continental classification scheme that enhances the understanding within and across the local social-ecological systems. These include, for example, low overall human population density with more than 80% of the population concentrated in urban areas; a history of land use strongly influenced by social groups in high altitude regions, followed by a highly transformative European colonization period, including a massive replacement of wild herbivores by livestock; numerous Indigenous communities with diverse cultural heritage legacies; economy and agriculture production oriented toward exports and linked with some of the highest deforestation rates in the world.

In a first attempt to integrate social-ecological knowledge into the characterization of land systems for Latin America, Boillat et al. (2017) proposed a “simplified biome-level typology of social-ecological land systems (SELS).” They described seven SELS based on biophysical, economic, settlements, institutions, technology, historical legacies, and potential future trends. Nevertheless, this typology was exclusively based on expert knowledge and lacks a map connecting to a specific spatial representation, thus limiting its use and application.

In this study, we made the concept of SELS operational with a precise and systematic spatial classification for South America. Our overarching goal was to contribute to the development of a geographical reference framework to facilitate contextualizing the discussion of social-ecological findings and studies in land system science and territorial planning. More specifically, we (1) created a map of SELS typologies for South America, (2) analyzed the key variables that differentiated the typologies, and (3) described and discussed the resulting SELS map regarding the representation of our territorial knowledge and adequacy to the conceptual SELS descriptions from Boillat et al. (2017). Additionally, we highlight key data gaps that would allow further delving into characterizations of this kind.

METHODS

We generated a classification of South America into general typologies of social-ecological land systems by analyzing spatial patterns of characteristics along a multidimensional continuum and depicting areas with similar profiles (Meyfroidt et al. 2018, Sietz et al. 2019). Our research objective may not have a single one correct solution, thus we prioritized further applicability value by heeding the collective experience of researchers working on the region.

We designed a hybrid methodology combining machine learning techniques for analyzing a set of social and environmental spatial data, with a knowledge-based evaluation by an interdisciplinary group of regional specialists (authors). The computational spatial analysis allowed for replicability and spatial explicitness, whereas the expert-knowledge approach contributed with enhanced collective criteria for making decisions on the analysis design as well as on the interpretation of the outputs. We decided not to rely exclusively on automated data analysis, acknowledging data constraints (i.e., usage of proxies due to data gaps), which were also unbalanced across the variable’s domains impacting more heavily on the social than the biophysical aspects. Under this scenario, mathematical optimal solutions might not always be the thematically most meaningful ones. Therefore, expert knowledge was applied to favor coherent territorial clusters, making subjective decisions on top of the evidence provided by the results. The potential bias of these subjective decisions was minimized through diversifying the profiles of the group of regional specialists.

The regional specialists were involved for the 22-month duration of this study. We had three stages of personal surveys on input variables and partial results, one in person workshop session at the GLP Open Science Meeting in April 2019, a subgroups’ work instance to thoroughly discuss individual SELS, and overall reviews of the final manuscript. The group of specialists consisted of 21 researchers of different backgrounds, affiliations, disciplines, skill profiles, gender, and nationalities, with extensive local and regional experience covering the geographical and territorial diversity of South America (many co-authors of the publication Boillat et al. 2017). The disciplinary profiles represented in our group included ecology, ethnobiology, geography, agronomy, ecological economics, anthropology, and forestry.

We followed an iterative process of: (1) defining the relevant variables and scale of analysis, (2) generating the maps and identifying key explanatory variables, and (3) discussing the outputs and describing the resulting SELS (Appendix 1, Fig. A1.1). Details on every methodological step (e.g., rationales behind the methods, explanation of statistical analysis, parameters, evaluation metrics) are thoroughly documented in Appendix 1 along its four sections.

Conceptual framework and variable selection

Looking at the definition by Boillat et al. (2017), we understand SELS as nested complex and dynamic systems that developed with humans as the major agent of change but dependent on the underpinning ecological characteristics and opportunities. Each SELS is defined by its particular configuration of social and environmental conditions, settlement patterns, land-use dynamics, and contextual factors. To guide the variable selection process, we used the biome-level SELS typologies described in Table 1 of Boillat et al. (2017; hereafter conceptual SELS) as a reference. In the process of operationalizing the theoretical definitions we: (1) deviated the primary focus on patterns of land-use change toward static conditions that may reflect them, (2) structured the descriptions by organizing them within a classification of research components for social-ecological systems studies (Winkler et al. 2018), and (3) discarded and added variables based on availability of appropriate datasets and balance across different aspects of the social-ecological systems (Appendix 1, Table A1.1).

To be included in our analysis, all spatial datasets were required to cover the full extent of the continent (dismissing islands) with a consistent methodology and a spatial resolution not greater than our grid size (exceptions are the national “governance indicators,” and “plant diversity” with pixel size of 110 km), with preference for datasets representative of the year 2010 (or the closest available). The final set of input data for our analyses (Table 1) consisted of 26 variables organized within 7 dimensions (variables per dimension: 3 physical, 2 biological, 6 landscape, 7 economic, 2 demographic, 4 political, and 2 cultural), 11 of which corresponded to the environmental domain and 15 to the socioeconomic domain. Our input data included both quantitative and qualitative data because two of our variables were represented by categorical data, i.e, urbanization type and anthropization century.

Spatial clustering analysis

Our analysis design was largely shaped by two characteristics of our input data: we mixed quantitative and categorical data, and most of our variables do not present a normal distribution (Appendix 1, Fig. A1.3). We used a hierarchical clustering approach to map SELS, which is widely used for spatial identification of social-ecological typologies (FAO 2011, Letourneau et al. 2012, van Asselen and Verburg 2012, Václavík et al. 2013, Sietz et al. 2019, Rocha et al. 2020). For this, we (1) divided South America into a continuous grid of hexagonal units of 40 km side to side (area ~1400 km², n = 13,287), (2) aggregated variables to the hexagon level, which we then used as input to (3) calculate the distances between every 2 pairs of hexagons along the multidimensional space, and finally (4) computed a divisive hierarchical clustering (DIANA; Kaufman and Rousseeuw 1990) to group hexagons into clusters sharing similar characteristics.

Distances or (dis)similarities were computed with the Gower distance method (Gower 1971) because it is the preferred algorithm for clustering mixed data (Gower 1971, Kaufman and Rousseeuw 1990, Kassambara 2017, Boehmke and Greenwell 2019) and it is less sensitive to outliers and non-normal distributions than other popular methods such as Euclidean (Kassambara 2017, Boehmke and Greenwell 2019). Nevertheless, we applied logarithmic transformations to those variables that presented highly exponential distributions (see Table 1) and range-based standardization to all variables (forcing them to range between 0 and 1) to mitigate potential effects of data artifacts.

Divisive hierarchical clustering (DIANA) is an unsupervised method that constructs a hierarchy of clusters starting by the root (all observations in one cluster) and iteratively divides them until all observations constitute their own cluster (Maechler et al. 2019). At each iteration, the most heterogeneous of the clusters (which contains the largest dissimilarity between any two of its observations) is divided into two new clusters, where the “splinter group” is initiated by its most disparate observation (largest average dissimilarity).

Most methods build their clusters starting from their terminal nodes (leaves), randomly selecting the initial point and considering local patterns or proximate neighbors to make decisions. Instead, DIANA starts from the root of the tree, taking into consideration the overall distribution of the data points for the initial splits, gaining in accuracy and favoring the capture of the main structure of the data prioritizing larger groups coherence rather than smaller groups purity (Kaufman and Rousseeuw 1990, Kassambara 2017, Boehmke and Greenwell 2019).

We considered the results at two nested spatial levels of detail (1^st level corresponds to social-ecological regions or SER and 2^nd level to social-ecological land systems or SELS) because findings at different levels can complement each other and improve analysis robustness (Sietz et al. 2017, 2019, Vallejos et al. 2020). The authors analyzed the clustering outputs (spatial layout, cluster’s statistics, and method’s performance metrics) at successive dendrogram cuts in relation to their territorial knowledge to agree on the optimal number of clusters. Further details are in Appendix 1.

To analyze which are the most informative variables for the classification, we ran boosted regression trees (BRT; Elith et al. 2008) on the cluster classification outputs. Boosted regression trees is a regression-classification technique from machine learning in which a model is trained to relate a response to their predictors by iterative binary splits, where variables’ relative contributions can be measured as the mean number of times it is selected for splitting the tree. To examine case-dependent fluctuations in the relevance of variables, this analysis was repeated several times with different classification targets: two multi-nominal analysis targeting differentiation of all clusters simultaneously in SER and in SELS classifications, and n specific binary analyses for each of the SELS targeting to differentiate that particular cluster from the rest as a whole (n = number of clusters in SELS). Further specifications on model parameters in Appendix 1, Box A1.1.

Far from being homogeneous units, clusters involve some internal heterogeneity. To unravel variations in the clusters’ representativity across their territorial extents, we evaluated the clusters’ internal heterogeneity (as means of average dissimilarity) and generated a map that depicts core and marginal zones of clusters’ representativity. We propose this metric as an indicator of spatial variations in classification uncertainty. The level of uncertainty for each hexagon was calculated by averaging the dissimilarity values between that hexagon and all the others within the same SELS cluster. Greater dissimilarity meant greater deviation of that hexagon to the average characteristics of the SELS cluster it belonged to. All analyses were performed in R version 3.6.1 (R Core Team 2019). For clustering, we used the “daisy” (distance calculation) and DIANA (clustering analysis) functions from the “cluster” package (Maechler et al. 2019). For the boosted regression trees analyses, we used the “gbm” (multinomial models) function from the “gbm” package (Greenwell et al. 2019) and “gbm.step” (binary models) function from the “dismo” package (Hijmans et al. 2017).

Social-ecological land systems (SELS) interpretation

To generate a sound interpretation of the resulting SELS, the authors of this publication were arranged into panels of four to seven regional specialists specific for each SELS. The panels thoroughly discussed the consistency between the SELS and their territorial knowledge, described the characteristics of that SELS, named it, and evaluated its alignment with the conceptual SELS from Boillat et al. (2017).

RESULTS

Our classification divided the continent into five larger-sized typologies of social-ecological regions (SER), which reflected main biomes and dominant land uses (Fig. 1A). Nested within these, 13 smaller-sized typologies of social-ecological land systems (SELS), each with distinctive characteristics representing more specific features of their territories (Fig. 1B). The SELS classification uncertainty was lower on the flat inner portion of the continent than on the coastal areas and nearby regions (including the Andes cordillera; Appendix 2). Some regions with greater uncertainty included: the eastern cordillera of the northern Andes, the eastern coast of Venezuela, the central portion of the Guayanas, and the northernmost and southernmost regions of the Brazilian coast.

Influence of variables on the social-ecological land systems (SELS) classification

The most relevant variables for characterizing the classes varied depending on the scale of analysis. The variables relevant for separating the 5 SER were a subset of those relevant for sorting the 13 SELS (Fig. 2), which indicated that the diversity of variables facilitated the specificity of the SELS classification. This was even more evident when looking at the most relevant variables for differentiating each of the individual SELS from the rest (Appendix 3, Table A3.1). Several variables that showed very little influence over the general SER classification resulted among the most informative variables to define some of the individual SELS.

The five most relevant variables in defining the classification were shared by both SER and SELS levels: forest cover, percent of flat land, plant diversity, travel time to cities, and temperature, adding up to 70.60% (SER) and 65.58% (SELS) of the explained variance of the cluster’s distribution (Fig. 2). Forest cover was dominant, representing approximately one-third of the explained variance, more than double than the second-ranked variable in both classification levels. Differences arose between the sixth and tenth positions in the contribution of relative information: the SER model relied more on population and cattle density, whereas the SELS model on cropland and the century of anthropization (Fig. 2). Except for the political dimension, all other 6 dimensions were represented within the 10 most relevant variables in both cases (i.e., SER and SELS models). However, there was a domain shift in dominance with more environmental variables occupying the highest positions and more socioeconomic variables toward the middle range.

On the other extreme of the relative importance ranking, 5 variables ranked 6^th or lower in all 15 models examined (Appendix 3, Table A3.1): plantation cover, land cover diversity, World Bank indicator rule of law, urbanization type, and language density.

Typologies of social-ecological land systems in South America

We describe the five typologies at the SER level. Due to length concerns, the 13 SELS’ descriptions and associated diagnostic plots are in Appendix 4 and 5, respectively.

SER A. Sparsely populated, southern cold lands

Includes both forested and non-forested ecosystems, which despite this key ecological difference (driven mainly by differences in moisture) share important social-ecological characteristics: (1) cold climate and associated slow biogeochemical cycles (reflected for example in the existence of peatlands (mallines and bofedales), (2) relatively low levels of biological diversity, but high levels of endemism associated with historical biogeography, (3) little potential for cultivation outside localized irrigated valleys, (4) low human population and very extensive unpopulated areas, (5) extensive minor livestock (i.e., sheep and goats) and cattle, often in decline, (6) widespread (although often underdeveloped) mining activities, most commonly associated with the energy industry (e.g., gas, oil, coal, lithium), (7) growing importance of tourism, (8) extensive protected areas, and ongoing processes of spontaneous rewilding of native fauna (e.g., guanacos in Patagonia, vicuñas in Puna and their associated predators). The temperate forests sectors are characterized by a very distinctive biota derived from Gondwanic lineages with high levels of endemism, partly threatened by the expansion of exotic invasive species (e.g., beaver, deer, pines, many ornamental plant species). This SER comprises four SELS, detailed in Appendix 4.

SER B. Arid and semi-arid highlands and adjacent coast, with a long history of agriculture and mining

Corresponds to the Central Andes of Peru, Bolivia, Chile, and Argentina, the Ecuadorian dry inter-Andean valleys, the dry Pacific coast of Peru and Chile, as well as the Mediterranean Andes. It is characterized by a rough geomorphology, wide altitudinal ranges, high-climatic diversity (overall cool and dry), ancient settlement history, and relatively high population density (including some major cities). As a coastal area it is largely influenced by the economics of overseas trade. Due to climatic conditions, agriculture is limited to irrigated areas in valleys and coasts or seasonally rainfed subsistence cultivation in the highlands. With high biological and cultural diversity, this SER ranks highest in crop diversity, but also in mining density. This SER comprises one SELS, detailed in Appendix 4.

SER C. Consolidated large-scale agropastoral plains

Corresponds to plains and low rolling terrains with mostly fertile soils dominated by productive landscapes mostly in Argentina, Uruguay, Brazil, Paraguay, and a separate block in Venezuela and Colombia, but it also includes smaller patches within the Amazon. This SER includes the largest and most productive areas of grain and meat production and exports of the continent, as well as some of the largest cities and the most developed infrastructure for transportation and export of commodities. Biodiversity fluctuates but it is medium in most of the region and there are few protected areas. The area includes natural ecoregions of open vegetation such as the Pampas and Campos grasslands, but also sectors of tropical and subtropical forests such as the Amazon, Chaco, and Espinal. Those forest-embedded sectors are represented by consolidated agricultural clusters commonly developed around middle-sized urban centers or major roads that facilitate their connection to the main cities and to the exporting outlets. A large fraction of the agricultural commodities exported by the continent originated in the area covered by this SER. This SER comprises two SELS, detailed in Appendix 4.

SER D. Historically populated tropical areas with low potential for mechanized agriculture

Includes the south-eastern region of Brazil, mountain regions of Colombia and Ecuador, and a narrow strip along the eastern slopes of the tropical Andes (both humid and dry). In general, the areas have been the basis of pre-Hispanic and early colonial settlements. Human population density continues to be high, however, these areas have become comparatively marginal agricultural lands because they have low capacity for expansion of modern mechanized agriculture due to steep slopes, limited accessibility, comparatively poor or degraded soils, sometimes suboptimal climatic conditions, and land tenure characterized by high fragmentation and small farm size. The region has high biological diversity and endemism. Mainly in association with steep topography, many areas are experiencing forest recovery. The SELS within this SER include a gradient of accessibility to ports, with SELS D2 (South-East Brazil) being the most connected, and in consequence the most developed with largest cities. This SER comprises three SELS, detailed in Appendix 4.

SER E. Tropical forests with low anthropization

Includes the whole Amazon biome, extended to the south over Bolivia, western Paraguay and the north of Argentina. It corresponds to plains and hilly terrains dominated by natural forests with high biodiversity and a huge stock of biomass. It extends over warm and moist climates, with mostly poor and acidic soils, including a gradient of human transformations that encompasses relatively unmodified forests (SELS E3), transition zones with active deforestation frontiers (SELS E1), and areas with a high fraction of protected areas (SELS E2). A dynamic history of agricultural expansion over the plains and low rolling terrains of the continent suggests that in the future the contact between this SER and SER C will experience displacements and zones with characteristics of SER C may expand over areas currently classified as SER E. This SER comprises three SELS, detailed in Appendix 4.

DISCUSSION

Novel ways to use data and synthesis methods that improve our understanding of land systems are among the featured innovations needed to advance key thematic research areas in land system science (GLP 2016); specially by combining social and natural sciences, as well as quantitative and qualitative data (Rounsevell et al. 2012). Our SELS approach improves the understanding of characteristics, extent, and location of human-nature interactions operating at regional scales in South America, carved through centuries of human intervention on the environment. As such, our approach provides new insights into the Anthropocene as well as a transferable geographical framework that facilitates contextualizing and articulating research on land science.

Relevance of variables in defining the social-ecological land systems (SELS)

Both levels of classification (SER and SELS) relied on the same five key variables according to their explanatory power of SELS’ patterns. A handful of variables concentrated most of the relative information for our classification, especially for the coarse SER typologies. However, it is at smaller/detailed scales that we see the real contribution of incorporating larger and more diversified sets of variables that highlight the individual characteristics that differentiate the SELS typologies. For example, the “century of anthropization” was key to differentiate the SELS within the SER D sorting the areas with longer history of use (SELS D1 and D3) from the most recently settled (SELS D2); “density of mine sites” was the second most relevant variable for SER B; “shrub cover” was the most relevant variable for SELS D3 and A4 (Appendix 3, Table A3.1; Appendix 5).

Several of the most relevant variables for the classification (e.g., forest cover, relief, plant diversity, temperature) corresponded to the environmental realm. Hence, our results suggest that biological and physical characteristics, similar to a biome/ecoregion classification scheme, continue to prevail regardless of human impact at that scale. This suggested they have a power to determine or place limits on the development possibilities of certain socioeconomic activities.

The single most relevant variable in defining the SELS distribution was “forest cover,” which accounted for one-third of the explained variance. Such a relevance is reasonable considering that forests occupy a large area of the continent with an uneven distribution (FAO and UNEP 2020), and that “forest cover” is a complex and synthetic variable. It summarizes the combination of physical variables such as altitude, precipitation, and temperature, but it also informs indirectly about anthropic historic and present land use. For example, in cases in which physical conditions are suitable for forests, its absence in certain areas sorts a physically homogeneous region into deforested vs. not converted forest.

The second most relevant variable was topographic relief, represented here as the “percentage of flat terrain,” not only for the general multinomial models SER and SELS, but also ranked within the top 5 for 9 out of the 13 SELS (Appendix 3, Table A3.1). Topography is a main conceptual differentiation for the current and potential of land use in South America because it largely dictates the suitability for mechanized agriculture. In our analysis, the differentiation between mountains vs. rolling and flat plains was critical and probably underpins multiple biophysical and socioeconomic properties. The third explanatory variable was “plant diversity,” and the same as with forest cover, it summarizes major aspects of climatic conditions and resource availability (Kreft and Jetz 2007), which is often assumed as the main organizing variable of biophysical diversity in the continent. The fourth was “travel time to cities,” the only socioeconomic variable within the top five in the relevance ranking. The presence of large cities encompasses two interlinked geographical properties. On one hand, they represent access to infrastructure utilities and economic opportunities, generating a sort of gravitational power over human activities (Lambin et al. 2001, Grimm et al. 2008). On the other hand, most of the cities were strategically settled centuries ago to best serve colonial South America (i.e., warfare against Indigenous people and transporting goods to Europe) and the persistence of their location may have influenced the distribution of human land uses in the present. The fifth was “temperature,” which is not a surprise considering the wide range of temperatures on the continent (mean air temperature from 6° to 24° C; Collins et al. 2009), varying mostly with latitude and altitude.

“Precipitation,” which is often assumed as the main organizing variable of biophysical diversity in the continent, showed up in the 14^th place instead of standing out among the main physical determinants such as relief and temperature. However, it was in the top five for those SELS particularly related to dry climate (A1, A2, A3, and B).

“Cattle density” was an important human-related variable, even more than crop cover. Cattle are the main herbivores in the world, and their significance is disproportionally high in South America (Bar-On et al. 2018). Three of the five countries in the world with large ratios between cattle and people occur in the region (Argentina, Brazil, and Uruguay; FAO 2022). Cattle density serves to characterize both intensive production (e.g., intensive systems that compete with croplands in the Pampas or Cerrado) but also to discriminate between non-agricultural regions because extensive cattle production characterizes mesic ecosystems that are not too dry (where sheep and goats dominate herbivory) and not too humid as the Amazon rainforest, where cattle do not occur outside deforested areas (Seo et al. 2010).

The political dimension had in general an intermediate to low influence in characterizing SELS, possibly due to their broad spatial resolution (i.e., country level) of the data. However, some political aspects were shown to be relevant for particular locations (e.g., regulatory quality was the 2^nd variable to sort SELS A1). The low impact of “language density” on the SELS classification was however notable, and contrary to expert expectations and literature findings (Maffi 2005, Gorenflo et al. 2012). It is possible that our measurement unit (i.e., number of languages spoken within a 100 km buffer zone) may have been inadequate, although difficult to contrast given the lack of guiding references from other publications. We encourage future work to further examine this concern and to look for alternative variables to reflect cultural diversity.

In the last decade, there was a clear evolution in land systems classifications to incorporate the complexity of the human-nature interactions. Compared to previous classifications, we delved into a holistic consideration of the social-ecological land systems. We further diversified the input variables achieving the representation of seven complementary dimensions of social-ecological systems: physical, biological, land cover, demographic, economic, political, and cultural. In addition, we prioritized the inclusion of attributes more pertinent to the continent such as mining and distance to ports. Our effort to explicitly incorporate deeper social aspects of human societies represents a clear step toward a qualitative leap in the field from mapping land use systems to mapping social-ecological systems. However, a series of limitations need to be addressed to fully achieve that goal, especially regarding data gaps and quality.

Alignment with the conceptual social-ecological land systems (SELS)

The SELS definitions produced by this study allowed for the refinement of the expert knowledge-based conceptual SELS described in Boillat et al. (2017). Some social-ecological regions had a high correspondence with the conceptual SELS (Fig. 3). These included: (1) the consolidated large-scale agropastoral plains (SER C), which corresponded with the conceptual SELS “South American plateau lowlands/agropastoral historical areas,” and (2) the tropical forests with low anthropization (SER E), which corresponded with the conceptual SELS “South American lowlands/new agropastoral areas.” In this last category, our study added more remote tropical lands, which were not addressed by Boillat et al. (2017) because of their primary focus on land-use change. Such high correspondence showed the importance of the historical occupation in shaping social-ecological characteristics of the South American lowlands.

We found only medium correspondence between spatial and conceptual SELS in the Andean and Patagonian regions. The arid and semi-arid highlands and adjacent coast, with a long history of agriculture and mining (SER B) covered the dry Central Andes and roughly fell within the conceptual SELS “South American highlands and altiplano.” It however differed with the inclusion of Mediterranean Chile and the exclusion of the Northern Andes. Instead, the Northern Andes were included in the “historically populated tropical areas with low potential for mechanized agriculture” (SER D), which corresponded with the conceptual SELS “coastal agricultural lands with long colonization history” covering the Brazilian Atlantic forest and Caribbean and Pacific coastlines. Finally, the highest and coldest areas of the Central Andes fell within the “sparsely populated southern cold lands” (SER A), showing more affinity to the Patagonian Andes due to sparse population and cold climate. Apart from this inclusion, SER A highly corresponded to the “southern temperate forests and drylands” conceptual SELS.

Drylands were the most challenging areas in terms of correspondence in our analyses. The conceptual SELS “dry and mediterranean lands” appeared to be split into three different SER, namely (1) the Mediterranean Andes, which had more affinity with the Central Andes within SER B, (2) the Brazilian Caatinga, which corresponded to SER D representing historically used tropical areas, and (3) Western Argentina, which was assigned to the SER A also covering Patagonia. This showed the ambiguity of the category of drylands that had very different social-ecological configurations depending on the geographic location and settlement histories. This suggests that humans may interact very differently with drylands depending on both biophysical and socioeconomic factors at play.

Nevertheless, given the differences in the methodological approach, the similarity of the two classifications is remarkable. This is underlined by considering the disparity in our input variables. Although we made advancements in quantitative rigor, reproducibility, operability, and spatial explicitness, it is worth noting that the attributes mentioned by Boillat et al. (2017) were included in our analysis through approximate renders and proxies, mainly because of limitations in data availability. The characterization of conceptual SELS by Boillat et al. (2017) were unobstructed by such data constraints and thus were more consistent with the authors’ understanding of the systems. Furthermore, the role of trends in land-use change was central for the conceptual SELS, whereas in this study, we considered the current state only, leaving to future work the mapping of land changes and transitions.

Methodological considerations

Models are inherently simplifications of reality, and as such our maps do not reproduce precisely all the features of the territory to its full extent. Compromises of mapping complex systems are many and we discuss some of them in the following paragraphs. We highlight the hybrid methodology as a strength of this study. Interdisciplinary researchers’ opinions contributed enormously by assessing the performance of the automated process, screening plausible data sources, and discussing the results in light of sound territorial knowledge.

Data constraints

The largest downside of data-driven approaches is that they are limited by the availability of adequate datasets. Often data availability and quality restrict the characterization of important aspects of the systems. In this section, we highlight and discuss a brief summary of the main data gaps we faced in this study that potentially could have enriched it, hoping they can be addressed in the future.

• (1) Socio-environmental conflicts: the only dataset we found was by Scheidel et al. (2020), who are developing a comprehensive spatial database, although currently based on self-reporting cases instead of a systematic registry. A potential source worth exploring is data mining through Google searches.
• (2) Natural ecosystems degradation: it modifies environmental processes and ecosystem services with varying impacts on sustainability (Sasaki and Putz 2009, Garrett et al. 2019). Ecosystem degradation is a complex concept, partly value-driven and with extremely variable situations, moving in a continuum from pristine to fully transformed. The lack of consensus on its definition (Schoene et al. 2007) makes its assessment difficult.
• (3) Governance: it influences land systems in a multi-level, partly hierarchical scheme. National level variables are often accessible, but they underestimate the importance of local formal and informal governance rules, which sometimes can be highly influential on land use (Tucker 2020, Rajão et al. 2020).
• (4) Exports: much of South America’s land use is aimed at net food exports (UN 2003). Having export data at a subnational resolution would represent a great improvement. Initiatives such as TRASE (SEI and Global Canopy 2022) can help fill this gap but they do not yet provide wall-to-wall datasets for all of South America.
• (5) Cultural variables: This is probably the least represented dimension within SELS inputs. Some countries such as Bolivia, Brazil, and Colombia have good spatial records of Indigenous and/or traditional communities, but no unified dataset was found at the continental level. Other aspects of cultural diversity, reflecting community cohesion or preferred land use practices would be valuable too. This would be a priority to better synthesize societies’ land-related, decision-making processes into land system science in connection with local governance.
• (6) Land tenure (or farm size): it informs about the most likely farm management types, as well as the degree in which smallholders have access to land. The datasets we could find to represent this variable were either partial (not covering the whole continent; Graesser and Ramankutty 2017), had a country-level resolution, or were heterogeneous in their methodology (compendium of national statistics).

Fuzzy borders, spatial detail, and isolated pixels

We emphasize the importance of considering the classification uncertainty map (Appendix 2) to assist in the interpretation and application of the SELS map.

In our SELS map, observations are hexagons of 1385 km², which include a fair amount of heterogeneity summarized to a single value. A map may appear fuzzy due to classification artifacts or properties of the landscape that may blur the general appearance, but at the same time may present important information. Some spatially succinct events, such as the presence of a city or a humid valley, may differentiate the classification of one hexagon from its surroundings, generating scattered patterns. Mountain regions or heterogeneous landscapes may also show a fuzzy classification. We decided to display our classification output without filtering out the isolated pixels due to the relevant information they can often contain. On the other extreme, some regions appearing homogeneous in the map (e.g., Chile, Western Amazon) do not necessarily have uniform landscapes. Apparent homogeneity should rather be interpreted as having unique characteristics that make those hexagons more similar to each other than to the rest of the hexagons in the continent.

Temporal dynamics and social ecological land systems (SELS)

For this study we only considered static variables, prioritizing consistency of the model structure, however trends and directions of change are very important characteristics of social-ecological land systems and can also be used to differentiate them. We encourage future studies to generate a SELS classification that incorporates land change regimes. In addition, changes could potentially modify the characteristics of regions enough to merit future revision of the typologies assigned in this study, as described for the SELS within the SER A and the SELS within the SER E (Appendix 4).

CONCLUSION

This study presents three major contributions: (1) it provides a comprehensive and reasonable characterization of the social-ecological land systems of South America (SELS), (2) it offers a spatial representation of the SELS in an easily operable and freely available format, and (3) its methodological approach bridges hurdles of social-ecological land classifications such as the combination of qualitative and quantitative data, and the blending of data-driven and expert knowledge-based perspectives.

The hybrid methodology represents a major strength of this study. The inclusion of a group of interdisciplinary experts was crucial to guide the data search and contrast the automated classifications with the territorial knowledge. In addition, it improved the utility of the resulting maps because of the increased coherence and relevance for the researchers’ community and territorial planners.

The SELS classification is a reproducible, sound, and operative characterization of social-ecological land systems of South America that facilitates the incorporation of regional contexts for analyzing local realities in the Anthropocene. We envision the SELS map will provide an orientative geographical framework for analyzing observed patterns within a larger context and for designing system-specific solutions for sustainability.

LITERATURE CITED

Allan, J. R., O. Venter, and J. E. M. Watson. 2017. Temporally inter-comparable maps of terrestrial wilderness and the Last of the Wild. Scientific Data 4(1):170187. https://doi.org/10.1038/sdata.2017.187

Bar-On, Y. M., R. Phillips, and R. Milo. 2018. The biomass distribution on Earth. Proceedings of the National Academy of Sciences 115(25):6506-6511. https://doi.org/10.1073/pnas.1711842115

Boehmke, B., and B. M. Greenwell. 2019. Hands-on machine learning with R. CRC, New York, New York, USA. https://doi.org/10.1201/9780367816377

Boillat, S., F. M. Scarpa, J. P. Robson, I. Gasparri, T. M. Aide, A. P. D. Aguiar, L. O. Anderson, M. Batistella, M. G. Fonseca, C. Futemma, H. R. Grau, S.-L. Mathez-Stiefel, J. P. Metzger, J. P. H. B. Ometto, M. A. Pedlowski, S. G. Perz, V. Robiglio, L. Soler, I. Vieira, and E. S. Brondizio. 2017. Land system science in Latin America: challenges and perspectives. Current Opinion in Environmental Sustainability 26-27:37-46. https://doi.org/10.1016/j.cosust.2017.01.015

Collins, J. M., R. R. Chaves, and V. da S. Marques. 2009. Temperature variability over South America. Journal of Climate 22(22):5854-5869. https://doi.org/10.1175/2009JCLI2551.1

Elith, J., J. R. Leathwick, and T. Hastie. 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77(4):802-813. https://doi.org/10.1111/j.1365-2656.2008.01390.x

Ellis, E. C., and N. Ramankutty. 2008. Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment 6(8):439-447. https://doi.org/10.1890/070062

Food and Agriculture Organization (FAO). 2011. Land degradation assessment in drylands: mapping land use systems at global and regional scales for land degradation assessment analysis. V1.1. United Nations Food and Agriculture Organization, Rome, Italy. [online] URL: https://www.fao.org/publications/card/en/c/5bf2e010-56ce-5c0d-b108-557504dfee50/

Food and Agriculture Organization (FAO). 2022. FAOSTATS. United Nations Food and Agriculture Organization, Rome, Italy. [online] URL: https://www.fao.org/faostat/en/#data

Food and Agriculture Organization (FAO) and United Nations Environment Programme (UNEP). 2020. The state of the world’s forests 2020. Forests, biodiversity and people. United Nations Food and Agriculture Organization, Rome, Italy and UNEP, Nairobi, Kenya. [online] URL: https://doi.org/10.4060/ca8642en

Garrett, R. D., S. Levy, K. M. Carlson, T. A. Gardner, J. Godar, J. Clapp, P. Dauvergne, R. Heilmayr, Y. le Polain de Waroux, B. Ayre, R. Barr, B. Døvre, H. K. Gibbs, S. Hall, S. Lake, J. C. Milder, L. L. Rausch, R. Rivero, X. Rueda, R. Sarsfield, B. Soares-Filho, and N. Villoria. 2019. Criteria for effective zero-deforestation commitments. Global Environmental Change 54:135-147. https://doi.org/10.1016/j.gloenvcha.2018.11.003

Global Land Programme (GLP). 2016. Science plan and implementation strategy 2016-2021. Global Land Programme, Bern, Switzerland. [online] URL: https://glp.earth/sites/default/files/glpscienceplan_june_2020.pdf

Gorenflo, L. J., S. Romaine, R. A. Mittermeier, and K. Walker-Painemilla. 2012. Co-occurrence of linguistic and biological diversity in biodiversity hotspots and high biodiversity wilderness areas. Proceedings of the National Academy of Sciences 109(21):8032-8037. https://doi.org/10.1073/pnas.1117511109

Gower, J. C. 1971. A general coefficient of similarity and some of its properties. Biometrics 27(4):857-871. https://doi.org/10.2307/2528823

Graesser, J., and N. Ramankutty. 2017. Detection of cropland field parcels from Landsat imagery. Remote Sensing of Environment 201:165-180. https://doi.org/10.1016/j.rse.2017.08.027

Greenwell, B., B. Boehmke, J. Cunningham, and GBM Developers. 2019. gbm: generalized boosted regression models. R package version 2.1.5. R Foundation for Statistical Computing, Vienna, Austria. [online] URL: https://CRAN.R-project.org/package=gbm

Grimm, N. B., S. H. Faeth, N. E. Golubiewski, C. L. Redman, J. Wu, X. Bai, and J. M. Briggs. 2008. Global change and the ecology of cities. Science 319(5864):756-760. https://doi.org/10.1126/science.1150195

Hijmans, R. J., S. Phillips, J. Leathwick, and J. Elith. 2017. dismo: species distribution modeling. R package version 1.1-4. R Foundation for Statistical Computing, Vienna, Austria. [online] URL: https://CRAN.R-project.org/package=dismo

Kassambara, A. 2017. Practical guide to cluster analysis in R: unsupervised machine learning. STHDA, Marseille, France.

Kaufman, L., and P. J. Rousseeuw. 1990. Partitioning around medoids (Program PAM). Pages 68-125 in L. Kaufman and P. J. Rousseeuw, authors. Finding groups in data: an introduction to cluster analysis. Wiley and Sons, New York, New York, USA. https://doi.org/10.1002/9780470316801.ch2

Kreft, H., and W. Jetz. 2007. Global patterns and determinants of vascular plant diversity. Proceedings of the National Academy of Sciences 104(14):5925-5930. https://doi.org/10.1073/pnas.0608361104

Kuemmerle, T., K. Erb, P. Meyfroidt, D. Müller, P. H. Verburg, S. Estel, H. Haberl, P. Hostert, M. R. Jepsen, T. Kastner, C. Levers, M. Lindner, C. Plutzar, P. J. Verkerk, E. H. van der Zanden, and A. Reenberg. 2013. Challenges and opportunities in mapping land use intensity globally. Current Opinion in Environmental Sustainability 5(5):484-493. https://doi.org/10.1016/j.cosust.2013.06.002

Lambin, E. F., B. L. Turner, H. J. Geist, S. B. Agbola, A. Angelsen, J. W. Bruce, O. T. Coomes, R. Dirzo, G. Fischer, C. Folke, P. S. George, K. Homewood, J. Imbernon, R. Leemans, X. Li, E. F. Moran, M. Mortimore, P. S. Ramakrishnan, J. F. Richards, H. Skånes, W. Steffen, G. D. Stone, U. Svedin, T. A. Veldkamp, C. Vogel, and J. Xu. 2001. The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change 11(4):261-269. https://doi.org/10.1016/S0959-3780(01)00007-3

Letourneau, A., P. H. Verburg, and E. Stehfest. 2012. A land-use systems approach to represent land-use dynamics at continental and global scales. Environmental Modelling and Software 33:61-79. https://doi.org/10.1016/j.envsoft.2012.01.007

Levers, C., D. Müller, K. Erb, H. Haberl, M. R. Jepsen, M. J. Metzger, P. Meyfroidt, T. Plieninger, C. Plutzar, J. Stürck, P. H. Verburg, P. J. Verkerk, and T. Kuemmerle. 2018. Archetypical patterns and trajectories of land systems in Europe. Regional Environmental Change 18(3):715-732. https://doi.org/10.1007/s10113-015-0907-x

Maechler, M., P. Rousseeuw, A. Struyf, M. Hubert, and K. Hornik. 2019. cluster: cluster analysis basics and extensions. R package version 2.1.0. R Foundation for Statistical Computing, Vienna, Austria.

Maffi, L. 2005. Linguistic, cultural, and biological diversity. Annual Review of Anthropology 34(1):599-617. https://doi.org/10.1146/annurev.anthro.34.081804.120437

Maxwell, S. L., R. A. Fuller, T. M. Brooks, and J. E. M. Watson. 2016. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536:143-145. https://doi.org/10.1038/536143a

Meyfroidt, P., R. Roy Chowdhury, A. de Bremond, E. C. Ellis, K.-H. Erb, T. Filatova, R. D. Garrett, J. M. Grove, A. Heinimann, T. Kuemmerle, C. A. Kull, E. F. Lambin, Y. Landon, Y. le Polain de Waroux, P. Messerli, D. Müller, J. Ø. Nielsen, G. D. Peterson, V. Rodriguez García, M. Schlüter, B. L. Turner, II, and P. H. Verburg. 2018. Middle-range theories of land system change. Global Environmental Change 53:52-67. https://doi.org/10.1016/j.gloenvcha.2018.08.006

Oberlack, C., D. Sietz, E. B. Bonanomi, A. de Bremond, J. Dell’Angelo, K. Eisenack, E. C. Ellis, G. Epstein, M. Giger, A. Heinimann, C. Kimmich, M. T. Kok, D. Manuel-Navarrete, P. Messerli, P. Meyfroidt, T. Václavík, and S. Villamayor-Tomas. 2019. Archetype analysis in sustainability research: meanings, motivations, and evidence-based policy making. Ecology and Society 24(2):26. https://doi.org/10.5751/ES-10747-240226

R Core Team. 2019. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [online] URL: http://www.R-project.org/

Rajão, R., B. Soares-Filho, F. Nunes, J. Börner, L. Machado, D. Assis, A. Oliveira, L. Pinto, V. Ribeiro, L. Rausch, H. Gibbs, and D. Figueira. 2020. The rotten apples of Brazil’s agribusiness. Science 369(6501):246-248. https://doi.org/10.1126/science.aba6646

Riggio, J., J. E. M. Baillie, S. Brumby, E. Ellis, C. M. Kennedy, J. R. Oakleaf, A. Tait, T. Tepe, D. M. Theobald, O. Venter, J. E. M. Watson, and A. P. Jacobson. 2020. Global human influence maps reveal clear opportunities in conserving Earth’s remaining intact terrestrial ecosystems. Global Change Biology 26(8):4344-4356. https://doi.org/10.1111/gcb.15109

Rocha, J., K. Malmborg, L. Gordon, K. Brauman, and F. DeClerck. 2020. Mapping social-ecological systems archetypes. Environmental Research Letters 15(3):034017. https://doi.org/10.1088/1748-9326/ab666e

Rounsevell, M. D. A., B. Pedroli, K.-H. Erb, M. Gramberger, A. G. Busck, H. Haberl, S. Kristensen, T. Kuemmerle, S. Lavorel, M. Lindner, H. Lotze-Campen, M. J. Metzger, D. Murray-Rust, A. Popp, M. Pérez-Soba, A. Reenberg, A. Vadineanu, P. H. Verburg, and B. Wolfslehner. 2012. Challenges for land system science. Land Use Policy 29(4):899-910. https://doi.org/10.1016/j.landusepol.2012.01.007

Sasaki, N., and F. E. Putz. 2009. Critical need for new definitions of “forest” and “forest degradation” in global climate change agreements. Conservation Letters 2(5):226-232. https://doi.org/10.1111/j.1755-263X.2009.00067.x

Scheidel, A., D. Del Bene, J. Liu, G. Navas, S. Mingorría, F. Demaria, S. Avila, B. Roy, I. Ertör, L. Temper, and J. Martínez-Alier. 2020. Environmental conflicts and defenders: a global overview. Global Environmental Change 63:102104. https://doi.org/10.1016/j.gloenvcha.2020.102104

Schoene, D., W. Killmann, H. von Lüpke, and M. L. Wilkie. 2007. Definitional issues related to reducing emissions from deforestation in developing countries. Forests and climate change working paper 5. Food and Agriculture Organization of the United Nations, Rome, Italy. [online] URL: https://www.uncclearn.org/wp-content/uploads/library/fao44.pdf

Seo, S. N., B. A. McCarl, and R. Mendelsohn. 2010. From beef cattle to sheep under global warming? An analysis of adaptation by livestock species choice in South America. Ecological Economics 69(12):2486-2494. https://doi.org/10.1016/j.ecolecon.2010.07.025

Sietz, D., U. Frey, M. Roggero, Y. Gong, N. Magliocca, R. Tan, P. Janssen, and T. Václavík. 2019. Archetype analysis in sustainability research: methodological portfolio and analytical frontiers. Ecology and Society 24(3):34. https://doi.org/10.5751/ES-11103-240334

Sietz, D., J. C. Ordoñez, M. T. J. Kok, P. Janssen, H. B. M. Hilderink, P. Tittonell, and H. van Dijk. 2017. Nested archetypes of vulnerability in African drylands: where lies potential for sustainable agricultural intensification? Environmental Research Letters 12(9):095006. https://doi.org/10.1088/1748-9326/aa768b

Stockholm Environment Institute (SEI) and Global Canopy. 2022. TRASE. Stockholm Environment Institute and Global Canopy, Stockholm, Sweden and Oxford, UK. [online] URL: https://supplychains.trase.earth/.

Tucker, J. 2020. Outlaw capital: accumulation by transgression on the Paraguay-Brazil border. Antipode 52(5):1455-1474. https://doi.org/10.1111/anti.12656

United Nations (UN). 2003. UN Comtrade: international trade statistics database. United Nations, New York, New York, USA. [online] URL: https://comtrade.un.org/.

Václavík, T., S. Lautenbach, T. Kuemmerle, and R. Seppelt. 2013. Mapping global land system archetypes. Global Environmental Change 23(6):1637-1647. https://doi.org/10.1016/j.gloenvcha.2013.09.004

Vallejos, M., S. Aguiar, G. Baldi, M. E. Mastrángelo, F. Gallego, M. Pacheco-Romero, D. Alcaraz-Segura, and J. M. Paruelo. 2020. Social-ecological functional types: connecting people and ecosystems in the Argentine Chaco. Ecosystems 23(3):471-484. https://doi.org/10.1007/s10021-019-00415-4

van Asselen, S., and P. H. Verburg. 2012. A Land System representation for global assessments and land-use modeling. Global Change Biology 18(10):3125-3148. https://doi.org/10.1111/j.1365-2486.2012.02759.x

Verburg, P. H., J. van de Steeg, A. Veldkamp, and L. Willemen. 2009. From land cover change to land function dynamics: a major challenge to improve land characterization. Journal of Environmental Management 90(3):1327-1335. https://doi.org/10.1016/j.jenvman.2008.08.005

Winkler, K. J., M. W. Scown, and K. A. Nicholas. 2018. A classification to align social-ecological land systems research with policy in Europe. Land Use Policy 79:137-145. https://doi.org/10.1016/j.landusepol.2018.06.034