Hiring! Postdoctoral Research Associate in Global Synthesis of Large-Scale Land Acquisitions

In collaboration with colleagues of mine at the University of Maryland, we are starting an exciting interdisciplinary project with global impact! And we’re hiring!

Postdoctoral Research Associate in Global Synthesis of Large-Scale Land Acquisitions

Large industrial soybean fields in midst of Cerradao forest (Source: http://news.stanford.edu/2016/03/11/amazon-model-fragoso-031116/)

Application deadline: July 1, 2017.

Start date: January 15, 2018.

Applications are invited for a 2-year position as a Postdoctoral Research Associate as part of a newly NASA-funded interdisciplinary project titled “The Global Land Rush: A Socio-Environmental Synthesis”. This project will conduct an integrated global synthesis of large-scale land acquisitions (LSLAs), a growing phenomenon in the global South as governments and transnational investors seek to secure access to land in developing countries to produce food, bio-fuels, and non-agricultural commodities. Consequences of LSLAs vary widely across the globe, ranging from land improvement and creation of new livelihood opportunities to land degradation and dispossession of land from local inhabitants. Distant connections between land systems are not new, but rising evidence indicates that such cross-scaled telecoupled socio-economic and environmental interactions as a result of LSLAs have grown stronger, with more rapid feedbacks.

Primary responsibilities will include: 1) acquisition, management, and analysis of LSLA locations and associated socio-economic, political, and biophysical data; 2) integration of spatio-temporal statistical analyses with remote sensing time series; and 3) synthesis of a large number of local case studies to infer common causes and consequences of LSLAs globally. The postdoc will work directly with Dr. Nicholas Magliocca (co-I) in the Department of Geography at the University of Alabama, Tuscaloosa, but will also regularly work with Dr. Ariane de Bremond (PI) and Dr. Evan Ellicott (co-I) in the Department of Geographical Sciences at the University of Maryland, College Park.

The successful applicant will generate publications, build competencies in advanced geospatial and synthesis methods, gain experience in grant writing, and have the opportunity to mentor undergraduate students. Opportunities will be available to visit and collaborate with partners at the Centre for Development and Environment, University of Bern, Switzerland, as well as to visit one or more field sites associated with “Land Observatories” in Madagascar, Tanzania, Peru, Laos, or Cambodia. Professional development activities, such as attending professional meetings and skill-building workshops, will be encouraged and fully funded.

Qualifications: Candidates must have a PhD in one or more disciplines associated with Land System Science (e.g., geography, natural resource economics, sociology, political ecology, remote sensing). Proficiency with the management and analysis of geospatial data in geographical information systems (e.g., ArcGIS, QGIS, etc.) and experience with acquiring, managing, and harmonizing heterogeneous data types are required. Preferred candidates will also have experience with spatio-temporal statistical analysis (e.g., spatial regression, survival analysis), synthesis methods (e.g., meta-analysis, archetype analysis, qualitative comparative analysis (QCA)), and/or familiarity with remote sensing data and time series analysis.

Application Details: This position is based in the Department of Geography at the University of Alabama located in Tuscaloosa, AL. Salary will be commensurate with experience and includes benefits. This is a full-time, 12-month, fixed-term position with a start date of January 15, 2018, but that is flexible. Review of applications will begin July 1, 2017 and will continue until the position is filled. Apply online through the UA Department of Geography ‘postdoc pool’ using the following link: https://facultyjobs.ua.edu/postings/40867.
Please direct questions about this position to Dr. Nicholas Magliocca (nrmagliocca@ua.edu).

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Let’s talk about … synthesis!

Check out the marvelous work of SESYNC’s communications officer, Melissa Andreychek, in this new post to SESYNC’s blog about some of the synthesis and modeling work my colleagues and I have been doing. Thanks Melissa!

Second Round RFP: Data-Intensive Analysis and/or Modeling for Socio-Environmental Synthesis

After a successful inaugural call for proposals, the National Socio-Environmental Synthesis Center (SESYNC) invites applications for the second round of data-intensive analysis and/or modeling projects for socio-environmental synthesis. Projects employing agent-based modeling approaches are particularly encouraged to apply. Please see the message below for details.

Data-Intensive Analysis and/or Modeling for Socio-Environmental Synthesis

The National Socio-Environmental Synthesis Center (SESYNC) invites proposals for data-intensive analysis and/or modeling projects that advance socio-environmental synthesis research. This funding opportunity covers two types of projects:

  • The pursuit of novel, question-driven, and synthetic research into linkages between social and environmental system dynamics that would not be otherwise possible without the use of computationally-intensive data analysis and/or modeling; or
  • The development of advanced data analysis and/or modeling tools that enable cutting-edge socio-environmental synthesis research.

Successful candidates will lead strongly data- and/or modeling-driven research efforts that synthesize understanding at the interface of the social and environmental sciences. Competitive proposals will: 1) bring together social and environmental data in novel ways to address critical socio-environmental research questions that are also actionable, or 2) attempt to advance modeling and/or analytical techniques beyond current applications which may be limited to a single scale of analysis, type of data, and/or disciplinary lenses.

Support Details

SESYNC has significant modeling, data analysis, and database management expertise to guide and support teams that need assistance with the technical aspects of data mining, processing, integration, analysis, visualization, and/or modeling. In addition to providing support for meetings and travel to SESYNC, we may cover the costs of the PI’s salary while in residence at SESYNC and/or salary for a research assistant at the PI’s home institution and/or at SESYNC. A research assistant position could be filled by a graduate research assistant, postdoc, programmer, or database technician depending upon the technical skills required. SESYNC also has standing openings for 2-year Computational Postdoctoral positions that could be associated with a team project if the postdoctoral applicant also has a separate (independent) project they propose through that Computational Postdoc program.

Funded projects will gain access to SESYNC’s advanced cyberinfrastructure, including use of and support for scalable cluster computing and substantial storage capacity (10’s of terabytes per project). Funded projects also receive support for meetings at SESYNC in Annapolis, MD, including travel and group facilitation.

More Information

Visit www.sesync.org/opportunities/data-modeling-ses-2 for complete details.

Applications must be submitted by August 4, 2014.

Cross-site Comparison of Land-Use Decision-Making

The cumulative effects of local land-use and livelihood changes are a global force of environmental and socio-economic change. Land-use changes result from decisions of individual farmers, pastoralists,  and housing consumers and developers (to name a few). Their decisions are influenced by not only local environmental, social, and economic conditions, but also by far-reaching forces such as economic globalization. The choice of a farmer in Brazil to grow soybeans, for example, can be influenced by the consumption of people in China.

Not all land-uses are created equal. Some have minimum impact on the environment, and some offer sustainable livelihoods for local farmers – finding land uses that accomplish both is difficult. Crafting policies to achieve this two-part goal must contend with both local and global considerations.

Location of one of the study sites near Taoyuan, Hunan Province, China.

A study site near Taoyuan, Hunan Province, China.

On January 29th, my colleagues and I published a paper in PLoS ONE, titled “Cross-site comparison of land-use decision-making and its consequences across land systems with a generalized agent-based model” that describes the development and application of an agent-based virtual laboratory for comparing  land-use and livelihood decision-making processes of rural farmers across geographically distant locations and qualitatively different land-use systems. We use this modeling system across multiple study sites to understand the underlying motivations and rationale of land-use and livelihood decisions of our ‘farmer agents’ and the landscape and livelihood changes that result under various environmental, demographic, and economic scenarios.

Since the traditional mode of scientific experimentation is not feasible with real land-use systems – we are talking about people’s land and livelihoods here – we use simulation-based cross-site comparisons to teach us about what drives the choice of particular land uses and livelihood strategies under different conditions. We use the set of study sites as local examples to synthesis more broadly applicable knowledge of which factors are most important in what contexts.

To explore this question, our investigation had to happen at the decision-making level – a task to which agent-based models are well suited. We also needed a modeling framework that was sufficiently general that it could be applied across multiple locations, yet realistic enough that it could be grounded in real-world data. These needs gave rise to an innovative agent-based virtual laboratory approach that provides a powerful tool for model-based experimentation and synthesis.

Such a model synthesis system can generate the kind of high-level knowledge needed to inform regional policies designed to foster sustainable local land uses and livelihood strategies. Cross-site comparisons use each study site as an example of alternative conditions and/or potential future states, which can aid scenario analysis and the exploration of potential adaptive responses to changing conditions. Furthermore, insights gained from the application of the modeling system to one site can improve our understanding of other similar sites, and foster future research and policy efforts that are sensitive to both the global influence on and local realities of land-use and livelihood change.

Click here to see the web story about this article on SESYNC’s blog.

The Maturation of Socio-Environmental ABMs: Older and Wiser?

After the avalanche of work that was November and December – and the unintentional hiatus from blogging that created – I have now come up for air and am ready to get back to it. Whether it is a particular point in my career or the fact that we are on the eve of a new year, I’m not sure,  but I’m feeling reflective and thought a post about the state of socio-environmental ABMs seemed appropriate. So, here some of my reflections as I look around the landscape of socio-environmental ABMs.

China_FarmerThe first thing that jumps out at me is the explosion of ABM papers in the realm of socio-environmental research. ABMs have achieved widespread use to answer an equally varied assortment of questions ranging from climate change adaptation, agricultural change, urban development, water management, ecosystem services, and on and on …. Fortunately, there are also a number of comprehensive and specialized reviews that have recently been published to help wade through all of this research. Two papers I found particularly useful are reviews of decision-making models in ABMs (An, 2012) and spatial ABMs in socio-ecological systems (Filatova et al., 2013).

As the title of this posts suggests, there also seems to be a maturation in the development and application of ABMs in the socio-environmental realm. I see this maturation in many forms, but two trends in particular are striking. First, the sophistication of these models and the algorithms they employ is incredible. Many ABMs are becoming increasingly computationally intensive. For example, a push for more rigorous sensitivity analyses is leading some to explore the entire parameter space of ABMs with spatially explicit sensitivity analysis (Ligmann-Zielinska, 2013).  I have also seen an increased implementation of sophisticated computer science techniques, such as cognition-based learning algorithms (Magliocca et al., 2011) and encoding of beliefs (Sun and Müller, 2013).

Second, ABMs have become more realistic. A major trend in the ABM field is developing and marketing ABMs for use with policy analysis. Increased availability of highly detailed data sources has led to an explosion of these application-specific (i.e. ‘case-based’) ABMs. Accompanying the development of realistic models has been an advancement of participatory modeling techniques to actively engage in model design and validation (e.g., Zellner et al., 2012).

Yet, this is the point at which I must pause and ask, “The ABM field is clearly older, but is it wiser?”

Despite the popularity of the approach, ABMers still face significant hurdles to having their work and publications accepted. An interesting survey and companion paper was done by Waldherr and Wiejermans (2013). It describes some of the common critiques that ABM researchers still face when trying to get their work published. While some of the critiques demonstrate a lack of understanding on the part of reviewers, they also illustrate areas where much work remains for ABMers to rigorously test and describe their models in order to answer the critics’ questions.

Finally, the abundance of ‘case-based’ ABMs has led some to ask whether additional case-based ABMs are contributing to the ultimate goal of building coherent theory about the structure, dynamics, and sustainability of socio-environmental systems? Is one more place-based model really advancing the community’s knowledge? How do we move forward with models that are both empirically-grounded and general enough to produce theoretical insights? How do we get to these ‘mid-level’ models? These questions, among others, will be the topic of a panel discussion at the upcoming annual meeting of the Association of American Geographers in Tampa, FL, USA from April 8-12, 2014. I have the privilege of accompanying Steve Manson, Tom Evans, David O’Sullivan, Andrew Crooks, Moira Zellner, Li An, and Sarah Metcalf on the panel – looking forward to the discussion!

The promise of the agent-based approach remains high. Applications of ABMs in the socio-environmental context have matured significantly, but we still have a lot of work to do. I am looking forward to the next phase of ABM development in which multiple approaches, algorithms, and techniques are integrated to advance the reliability and usefulness of our models.

New Funding Opportunity: Data-Intensive Analysis and Modeling for Socio-Environmental Synthesis at SESYNC

The National Socio-Environmental Synthesis Center (SESYNC) is inviting proposal submissions for a special funding opportunity designed to support projects pushing the boundaries of computational research in socio-environmental systems. Relevant projects could include (but are not limited to) harmonizing large and/or heterogeneous social and environmental data to answer novel research questions, or developing modeling approaches or applications that are computationally challenging. SESYNC can provide technical support in-house or fund a project team member with sufficient technical skills.

This is an excellent opportunity to push the computational frontiers of your research!

The opportunity listing can be found here: www.sesync.org/opportunities/data-modeling-ses

Submission instructions can be found here: www.sesync.org/opportunities/data-modeling-ses#instructions

NRC Report on Land Change Modeling

Essential reading for all you land change modelers out there!

The report Advancing Land Change Modeling: Opportunities and Research Requirements was released recently in pre-publication format via the National Academies Press web site: http://www.nap.edu/catalog.php?record_id=18385 Additional report info can be found here as well: http://dels.nas.edu/Report/report/18385. The study committee included several geographers, assessed the current state of land-change modeling, and identified opportunities for future developments in these models.

Urban development, agriculture, and energy production are just a few of the ways that human activities are continually changing and reshaping the Earth’s surface. Land-change models (LCMs) are important tools for understanding and managing present and future landscape conditions, from an individual parcel of land in a city to the vast expanses of forests around the world. A recent explosion in the number and types of land observations, model approaches, and computational infrastructure has ushered in a new generation of land change models capable of informing decision making at a greater level of detail. This National Research Council report, produced at the request of the U.S. Geological Survey and NASA, evaluates the various land-change modeling approaches and their applications, and how they might be improved to better assist science, policy, and decision makers.

Global Agro-Climatic Zones

If you are interesting in global patterns of agricultural change, as I am, you might find this recently published paper helpful for selecting the most appropriate global framework.

van Wart, J., van Bussel, L. G., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaarde, H., Gerberf, J.,  Muellerf, N. D., Claessensg, L.,
van Ittersum, M. K. & Cassman, K. G. (2013). Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research.

Available here.

Exploring Land-Livelihood Transitions

Figure5_rev (2)Rural livelihoods are changing rapidly with economic globalization and global environmental change, which have direct impacts to environmental and socio-economic suitability. All too often the most vulnerable communities – those with the least resources – face the greatest transitions triggered by changing local and global conditions. Those communities also have livelihoods tied to the land, which may lead to environmental degradation and/or fail to support livelihoods in the future. We must advance our understanding of the causes and consequences of land-livelihood transitions in order to avoid maladapted responses that can lead to a loss of land-livelihood sustainability.

My colleagues and I recently published an article in PLoS ONE that explores these issues with an innovative, generalized agent-based model. Because human decision-making drives land-livelihood transitions, a process-level explanation of adaptive responses is needed to explore the conditions under which land-livelihood transitions emerge. In the short-term, this approach advances the use of agent-based virtual laboratories in sustainability research. In coming generations of this modeling approach, we hope to use model insights to devise effective policy interventions aimed at the decision-making level for supporting sustainability .

Decision-Making in Earth System Models

Recognizing that humans are major drivers of global environmental change, a workshop was convened to explore the challenges and possibilities of integrating human decision-making into regional and global Earth system models. An article describing our findings has just been published and open for discussion in Earth System Dynamics.

Join the interactive discussion! Post comments and receive replies from the authors.

Towards decision-based global land use models for improved understanding of the Earth system

M. D. A. Rounsevell, A. Arneth, P. Alexander, D. G. Brown, N. de Noblet-Ducoudré, E. Ellis, J. Finnigan, K. Galvin, N. Grigg, I. Harman, J. Lennox, N. Magliocca, D. Parker, B. C. O’Neill, P. H. Verburg, and O. Young

A primary goal of Earth system modelling is to improve understanding of the interactions and feedbacks between human decision making and biophysical processes. The nexus of land use and land cover change (LULCC) and the climate system is an important example. LULCC contributes to global and regional climate change, while climate affects the functioning of terrestrial ecosystems and LULCC. However, at present, LULCC is poorly represented in Global Circulation Models (GCMs). LULCC models that are explicit about human behaviour and decision making processes have been developed at local to regional scales, but the principles of these approaches have not yet been applied to the global scale level in ways that deal adequately with both direct and indirect feedbacks from the climate system. In this article, we explore current knowledge about LULCC modelling and the interactions between LULCC, GCMs and Dynamic Global Vegetation Models (DGVMs). In doing so, we propose new ways forward for improving LULCC representations in Earth System Models. We conclude that LULCC models need to better conceptualise the alternatives for up-scaling from the local to global. This involves better representation of human agency, including processes such as learning, adaptation and agent evolution, formalising the role and emergence of governance structures, institutional arrangements and policy as endogenous processes and better theorising about the role of tele-connections and connectivity across global networks. Our analysis underlines the importance of observational data in global scale assessments and the need for coordination in synthesising and assimilating available data.