Integrating inductive and deductive analysis to identify and characterize archetypical social-ecological systems and their changes

Pacheco-Romero, Manuel; Kuemmerle, Tobias; Levers, Christian; Alcaraz‐Segura, Domingo; Cabello, Javier

doi:10.1016/j.landurbplan.2021.104199

Cited by 19 publications

(13 citation statements)

References 55 publications

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“…In our case, this enabled us to identify distinct frontier types, characterized by similar land-use and woodland loss dynamics in space and time. Such archetypical, high-level patterns and outcomes of human-environment interactions can help to structure complexity in land-use change (Vaclavik et al 2013, Levers et al 2018, Pacheco-Romero et al 2021, foster a more mechanistic understanding of landuse change (Magliocca et al 2018), and contribute to developing theories of the middle range (Meyfroidt et al 2018). Importantly though, identifying archetypes, such as recurring frontier types, allows for the more context-specific, regionally-targeted land governance increasingly asked for (Kuemmerle et 7) are regions where restoration efforts in degraded lands are most suitable.…”

Section: Discussionmentioning

confidence: 99%

“…Although methods to quantitatively characterize such complexity across large agricultural frontiers are still underdeveloped, there is a need to capture and describe complex frontier dynamics to support context-specific land governance and address sustainability challenges in frontier regions . Archetype analyses aimed at identifying major patterns of human-environment interactions (Lambin et al 2003, Geist and Lambin 2004, Eisenack et al 2006, Rocha et al 2020, such as typical land systems (Vaclavik et al 2013, Levers et al 2018, landuse change trajectories (Levers et al 2018, or land-use outcomes (Cumming et al 2014, Pacheco-Romero et al 2021, are a potentially powerful way to structure diversity and complexity of land-use dynamics for that purpose.…”

Section: Introductionmentioning

confidence: 99%

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Frontier metrics for a process-based understanding of deforestation dynamics

Baumann

Gasparri

Buchadas

et al. 2022

Environ. Res. Lett.

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Agricultural expansion into tropical and subtropical forests often leads to major social-ecological trade-offs. Yet, despite ever-more detailed information on where deforestation occurs, how agriculture expands into forests remains unclear, which is hampered by a lackof spatially and temporally detailed reconstruction of agricultural expansion. Here, we developed and mapped a novel set of metrics that quantify agricultural frontier processes at unprecedented spatial and temporal detail. Specifically, we first derived consistent annual time series of land-use/cover to, second, describe archetypical patterns of frontier expansion, pertaining to speed, diffusion and activity of deforestation, as well as post-deforestation land use. We exemplify this approach for understanding agricultural frontier expansion across the entire South American Chaco, a global deforestation hotspot. Our study provides three major insights. First, agricultural expansion has been rampant in the Chaco, with more than 19.3 million ha of woodlands converted between 1985 and 2020, including a surge in deforestation after 2019. Second, land-use trajectories connected to frontier processes have changed in major ways over the 35-year study period we studied, including substantial regional variations. For instance, while ranching expansion drove most of the deforestation in the 1980s and 1990s, cropland expansion dominated during the mid-2000s in Argentina, but not in Paraguay. Similarly, 40% of all areas deforested were initially used for ranching, but later on converted to cropping. Accounting for post-deforestation land-use change is thus needed to properly attribute deforestation and associated environmental impacts, such as carbon emissions or biodiversity loss, to commodities. Finally, we identified major, recurrent frontier types that may be a useful spatial template for land governance to match policies to specific frontier situations. Collectively, our study reveals the diversity of frontier processes and how frontier metrics can capture and structure this diversity to uncover major patterns of human-nature interactions, which can be used to guide spatially-targeted policies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frontier metrics for a process-based understanding of deforestation dynamics

Baumann

Gasparri

Buchadas

et al. 2022

Environ. Res. Lett.

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“…Along with existing plant-trait databases (e.g. [160,161]), other ecological datasets can be immediately applied to CHES models such as an invasive plant dataset with associated bioclimatic variables [162], a database of ecosystem services [163] as well as land use datasets that already contain human environment coupling that can further motivate future models [164][165][166].…”

Section: (B) Incorporating New Datamentioning

confidence: 99%

Modelling coupled human–environment complexity for the future of the biosphere: strengths, gaps and promising directions

Farahbakhsh

Bauch

Anand

2022

Phil. Trans. R. Soc. B

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Humans and the environment form a single complex system where humans not only influence ecosystems but also react to them. Despite this, there are far fewer coupled human–environment system (CHES) mathematical models than models of uncoupled ecosystems. We argue that these coupled models are essential to understand the impacts of social interventions and their potential to avoid catastrophic environmental events and support sustainable trajectories on multi-decadal timescales. A brief history of CHES modelling is presented, followed by a review spanning recent CHES models of systems including forests and land use, coral reefs and fishing and climate change mitigation. The ability of CHES modelling to capture dynamic two-way feedback confers advantages, such as the ability to represent ecosystem dynamics more realistically at longer timescales, and allowing insights that cannot be generated using ecological models. We discuss examples of such key insights from recent research. However, this strength brings with it challenges of model complexity and tractability, and the need for appropriate data to parameterize and validate CHES models. Finally, we suggest opportunities for CHES models to improve human–environment sustainability in future research spanning topics such as natural disturbances, social structure, social media data, model discovery and early warning signals. This article is part of the theme issue ‘Ecological complexity and the biosphere: the next 30 years’.

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“…Václavík et al 2013, Dittrich et al 2017, Martín-López et al 2017, Lazzari et al 2019 and that cross-reference them with SES conceptual frameworks and model lists of variables (e.g. Dressel et al 2018, Rocha et al 2020, Pacheco-Romero et al 2021, which can contribute to develop more comparable research (Cox et al 2020). However, in most of them, indicators are selected ad hoc by the researcher based on literature reviews, the research question, or practical reasons (e.g.…”

Section: Introductionmentioning

confidence: 99%

A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping

Pacheco-Romero

Vallejos

Paruelo

et al. 2022

Environ. Res. Lett.

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The spatial mapping of social-ecological system (SES) archetypes constitutes a fundamental tool to operationalize the SES concept in empirical research. Approaches to detect, map, and characterize SES archetypes have evolved over the last decade towards more integrative and comparable perspectives guided by SES conceptual frameworks and reference lists of variables. However, hardly any studies have investigated how to empirically identify the most relevant set of indicators to map the diversity of SESs. In this study, we propose a data-driven methodological routine based on multivariate statistical analysis to identify the most relevant indicators for mapping and characterizing SES archetypes in a particular region. Taking Andalusia (Spain) as a case study, we applied this methodological routine to 86 indicators representing multiple variables and dimensions of the SES. Additionally, we assessed how the empirical relevance of these indicators contributes to previous expert and empirical knowledge on key variables for characterizing SESs. We identified 29 key indicators that allowed us to map 15 SES archetypes encompassing natural, mosaic, agricultural, and urban systems, which uncovered contrasting land sharing and land sparing patterns throughout the territory. We found synergies but also disagreements between empirical and expert knowledge on the relevance of variables: agreement on their widespread relevance (32.7% of the variables, e.g., crop and livestock production, net primary productivity, population density); relevance conditioned by the context or the scale (16.3%, e.g., land protection, educational level); lack of agreement (20.4%, e.g., economic level, land tenure); need of further assessments due to the lack of expert or empirical knowledge (30.6%). Overall, our data-driven approach can contribute to more objective selection of relevant indicators for SES mapping, which may help to produce comparable and generalizable empirical knowledge on key variables for characterizing SESs, as well as to derive more representative descriptions and causal factor configurations in SES archetype analysis.

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Integrating inductive and deductive analysis to identify and characterize archetypical social-ecological systems and their changes

Cited by 19 publications

References 55 publications

Frontier metrics for a process-based understanding of deforestation dynamics

Frontier metrics for a process-based understanding of deforestation dynamics

Modelling coupled human–environment complexity for the future of the biosphere: strengths, gaps and promising directions

A data-driven methodological routine to identify key indicators for social-ecological system archetype mapping

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