Agent-based modeling of complex social–ecological feedback loops to assess multi-dimensional trade-offs in dryland ecosystem services

Miyasaka, Takafumi; Le, Quang Bao; Okuro, Toshiya; Zhao, Xueyong; Takeuchi, Kazuhiko

doi:10.1007/s10980-017-0495-x

Cited by 73 publications

(31 citation statements)

References 65 publications

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“…In contrast, lags reduce perception of trade-offs, particularly at the individual cognitive map level, which highlights the need for greater nuance in thinking about how this particular indication of complexity relates to trade-offs. In particular, a central premise of research on social-ecological systems is that lags, whether spatial or temporal, increase the likelihood of unintended (negative) outcomes resulting from actions designed to achieve a particular goal (Miyasaka et al 2017). That we show perception of trade-offs decreases with lags does not contradict this expectation.…”

Section: Individual/collective Dynamics Complexity and Trade-offsmentioning

confidence: 53%

Cognition of complexity and trade-offs in a wildfire-prone social-ecological system

Hamilton

Salerno

Fischer

2019

Environ. Res. Lett.

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Wildfire risk is a defining environmental challenge throughout much of the American West, as well as in other regions where complex social and ecological dynamics defy simple policy or management solutions. In such settings, diverse forms of land use, livelihoods, and accompanying values provide the conditions for trade-offs (e.g. between protecting homes from uncontrollable fires and restoring low-severity fire to ecosystems as a natural disturbance process). Addressing wildfire risk requires grappling with these trade-offs at multiple levels-given the need for action by individuals as well as by large and diverse stakeholder groups-and under conditions of considerable complexity. We evaluated how individual and collective perception of trade-offs varies as a function of complexity through analysis of the cognitive maps-representations of perceived causal relationships among factors that structure an individual's understanding of a system-of 111 stakeholders in the Eastern Cascades Ecoregion of central Oregon. Bayesian statistical analysis revealed a strong tendency against perception of trade-offs in individual maps, but not in a collective map that resulted from the aggregation of all individual cognitive maps. Furthermore, we found that lags (the number of factors that mediated the effect of an action on multiple valued outcomes) limited perception of trade-offs. Each additional intervening factor decreased the likelihood of a trade-off by approximately 52% in individual cognitive maps and by 10% in the collective cognitive map. However, the heterogeneity of these factors increased the likelihood of perception of trade-offs, particularly among individual cognitive maps, for which each unit increase of the Shannon diversity index translated into a 20-fold increase in the likelihood of perception of trade-offs. Taken together, these results suggest that features of complexity have distinct effects on individual-and collective-level perception of trade-offs. We discuss implications for wildfire risk decision-making in central Oregon and in other complex wildfire-prone social-ecological systems.

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Section: Individual/collective Dynamics Complexity and Trade-offsmentioning

confidence: 53%

Cognition of complexity and trade-offs in a wildfire-prone social-ecological system

Hamilton

Salerno

Fischer

2019

Environ. Res. Lett.

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“…Some have used agent‐based modeling to represent the complexity of social and ecological interrelationships (Miyasaka et al. ), and one study by Nielsen et al. () coupled cadastral and forest‐cover data to a national registry of conservation‐program participation, the latter being a data set unique to Denmark and unavailable elsewhere.…”

Section: Introductionmentioning

confidence: 99%

“…A few studies have coupled social and biophysical variables to prioritize landscapes, either by summarizing social receptivity based on qualitative data (Game et al 2011) or by using coarse-resolution data over wide extents (Guerrero & Wilson 2017;Karimi et al 2017). Some have used agentbased modeling to represent the complexity of social and ecological interrelationships (Miyasaka et al 2017), and one study by Nielsen et al (2017) coupled cadastral and forest-cover data to a national registry of conservation-program participation, the latter being a data set unique to Denmark and unavailable elsewhere.…”

Section: Introductionmentioning

confidence: 99%

Microtargeting for conservation

Metcalf

Phelan

Pallai

et al. 2019

Conservation Biology

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Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting—predictive analytics of big data—to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high‐resolution land‐cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social–ecological systems.

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“…On the other hand, social influence also has the potential to decrease the shift, making the land use system more resilient. In their research Miyasaka et al (2017) also indicate the complex character of human environment (i.e. land use) systems caused by feedback loops between agents and their environment and interaction between the agents.…”

Section: Mathematical Programming Modelsmentioning

confidence: 99%

“…For instance, Shi, Yin & Lv (2017) show the impact of interactions and feedbacks on forest cover change. Besides explaining the importance of feedbacks and interactions, Miyasaka et al (2017) show how a threshold can lead to an abrupt land use change. Morris & Rowe (2014) point at the importance of cumulative effects due to human influence, while Nogueira Terra & Ferreira dos Santos (2012) develop a method to measure the cumulative effects on a landscape.…”

Section: Theorymentioning

confidence: 99%

Complexity in land use change; the case of the Dutch dairy sector

Groeneveld

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2.1. Introduction 2.2. Background 2.3. Method 2.4. Results 2.5. Sensitivity Analysis 2.6. Discussion 2.7. Conclusions Appendix 2A. Model description Appendix 2B. Data used Appendix 2C. Additional model results Appendix 2D. Sensitivity Analysis Chapter 3 Complex dynamics in the uptake of new farming practices: a case study for organic waste application Abstract 3.1. Introduction 3.2. Methods 3.3. Results 3.4. Discussion 3.5. Conclusions Appendix 3A. Farm model Appendix 3B. Exogenous values for determining utility functions Appendix 3C. Sensitivity analysis Appendix 3D. Weights for profit, sustainability and labour Chapter 4 Effects on participation and biodiversity of reforming the implementation of agri-environmental schemes in the Netherlands Abstract 4.1. Introduction 4.2. Methods 4.3. Data 4.4. Experimental design 4.5. Results 4.6. Conclusions and Discussion Appendix 4A. GAMS model Appendix 4B. Sensitivity analysis Chapter 5 Effects of changing social influence on participation and biodiversity in agri-environmental schemes in the Netherlands Abstract 5.1. Introduction 5.2. Modelling framework 5.3. Study area 5.4. Method and Data 5.5. Model 5.6. Scenarios 5.7. Results 5.8. Conclusions and Discussion Appendix 5A. Determining the weights of the farmer Appendix 5B. Indicator species and biodiversity scores Appendix 5C. Mathematical programming models Appendix 5D. Variation in model runs Appendix 5E. Initial weights Chapter 6 Conclusions and Discussion References Summary Acknowledgements

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Agent-based modeling of complex social–ecological feedback loops to assess multi-dimensional trade-offs in dryland ecosystem services

Cited by 73 publications

References 65 publications

Cognition of complexity and trade-offs in a wildfire-prone social-ecological system

Cognition of complexity and trade-offs in a wildfire-prone social-ecological system

Microtargeting for conservation

Complexity in land use change; the case of the Dutch dairy sector

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