Tom P. Evans

A review and assessment of land-use change models: dynamics of space, time, and human choice

A review of different types of land-use change models incorporating human processes. Presents a framework to compare land-use change models in terms of scale (both spatial and temporal) and complexity, and how well they incorporate space, time, and human decisionmaking. Examines a summary set of 250 relevant citations and develops a bibliography of 136 papers. From these papers, 19 land-use models are reviewed in detail as representative of the broader set of models. Summarizes and discusses the 19 models in terms of dynamic (temporal) and spatial interactions, as well as human decisionmaking. Many raster models examined mirror the extent and resolution of remote-sensing data. The broadest-scale models generally are not spatially explicit. Models incorporating higher levels of human decisionmaking are more centrally located with respect to spatial and temporal scales, probably due to the lack of data availability at more extreme scales. Examines the social drivers of land-use change and methodological trends and concludes with some proposals for future directions in land-use modeling.

Agent-based modeling of deforestation in southern Yucatan, Mexico, and reforestation in the Midwest United States.

We combine mixed-methods research with integrated agent-based modeling to understand land change and economic decision making in the United States and Mexico. This work demonstrates how sustainability science benefits from combining integrated agent-based modeling (which blends methods from the social, ecological, and information sciences) and mixed-methods research (which interleaves multiple approaches ranging from qualitative field research to quantitative laboratory experiments and interpretation of remotely sensed imagery). We test assumptions of utility-maximizing behavior in household-level landscape management in south-central Indiana, linking parcel data, land cover derived from aerial photography, and findings from laboratory experiments. We examine the role of uncertainty and limited information, preferences, differential demographic attributes, and past experience and future time horizons. We also use evolutionary programming to represent bounded rationality in agriculturalist households in the southern Yucatán of Mexico. This approach captures realistic rule of thumb strategies while identifying social and environmental factors in a manner similar to econometric models. These case studies highlight the role of computational models of decision making in land-change contexts and advance our understanding of decision making in general.

A dynamic model of household decision-making and parcel level landcover change in the eastern Amazon

The region around Altamira, Brazil, located in the Eastern Amazon, has experienced rapid landcover change since the initiation of government sponsored colonization projects associated with the construction of the Trans-Amazon Highway. The 30 years since colonization (1971) have been marked by a net loss of forest cover and an increase in the amount of cultivated/productive land, particularly for pasture and annual/perennial crop production. This research presents a parcel-level model of landcover change for smallholders in the Altamira study area. The utility of specific land-use activities is calculated to identify those land-uses that are most optimal at each time point, and labor is allocated to these activities based on the availability of household and wage labor. The model reports the proportion of the parcel in the following landcover classes at each time point using a 1-year interval: mature forest, secondary successional forest, perennial crops, annual crops and pasture. A graphical user interface is used for scenario testing, such as the impact of high/low (population) fertility, the increase of out-migration to urban areas, or changes in cattle and crop prices. The model shows a rapid reduction in the amount of mature forest in the 30 years following initial settlement, after which the parcel is composed of a mosaic of secondary succession, pasture and crops. The nature and rapidity of this landcover change is the function of a variety of household and external variables incorporated in the model. In particular, the model produces different landcover compositions as a function of demographic rates (fertility, mortality) and agricultural prices.

Debates—Perspectives on socio‐hydrology: Socio‐hydrologic modeling: Tradeoffs, hypothesis testing, and validation

Socio-hydrology focuses on studying the dynamics and co-evolution of coupled human and water systems. Recently, several new socio-hydrologic models have been published that explore these dynamics, and these models offer unique opportunities to better understand these coupled systems and to understand how water problems evolve similarly in different regions. These models also offer challenges, as decisions need to be made by the modeler on trade-offs between generality, precision, and realism. In addition, traditional hydrologic model validation techniques, such as evaluating simulated streamflow, are insufficient, and new techniques must be developed. As socio-hydrology progresses, these models offer a robust, invaluable tool to test hypotheses about the relationships between aspects of coupled human-water systems. They will allow us to explore multiple working hypotheses to greatly expand insights and understanding of coupled socio-hydrologic systems.

Complex systems models and the management of error and uncertainty

For the complex systems modeller, uncertainty is ever-present. While uncertainty cannot be eliminated, we suggest that formally incorporating an assessment of uncertainty into our models can provide great benefits. Sources of uncertainty arise from the model itself, theoretical flaws, design flaws, and logical errors. Management of uncertainty and error in complex systems models calls for a structure for uncertainty identification and a clarification of terminology. In this paper, we define complex systems and place complex systems models into a common typology leading to the introduction of complex systems specific issues of error and uncertainty. We provide examples of complex system models of land use change with foci on errors and uncertainty and finally discuss the role of data in building complex systems models.

Spatially explicit experiments for the exploration of land‐use decision‐making dynamics

We explore the special outcomes of decision‐making through two laboratory‐based experiments, one with a homogenous land suitability surface and another with a heterogeneous suitability surface. Subjects make resource allocation decisions on an abstract landscape and are given a monetary incentive to maximize their revenue during the experiment. These experimental results are compared with simulation output from an agent‐based model run on the same abstract landscape that uses a utility‐maximizing agent. The main findings are: (1) landscapes produced by subjects result in greater patchiness and more edge than the utility‐maximization agent predicts; (2) there is considerable diversity in the decisions subjects make despite the relatively simple decision‐making context; and (3) there is greater deviation of subject revenue from the maximum potential revenue in early rounds of the experiment compared with later rounds, demonstrating the challenge of making optimal decisions with little historical context. The findings demonstrate the value of using non‐maximizing agents in agent‐based models of land‐cover change and the importance of acknowledging actor heterogeneity in land‐change systems.

Case studies, cross-site comparisons, and the challenge of generalization: Comparing agent-based models of land-use change in frontier regions

Cross-site comparisons of case studies have been identified as an important priority by the land-use science community. From an empirical perspective, such comparisons potentially allow generalizations that may contribute to production of global-scale land-use and land-cover change projections. From a theoretical perspective, such comparisons can inform development of a theory of land-use science by identifying potential hypotheses and supporting or refuting evidence. This paper undertakes a structured comparison of four case studies of land-use change in frontier regions that follow an agent-based modeling approach. Our hypothesis is that each case study represents a particular manifestation of a common process. Given differences in initial conditions among sites and the time at which the process is observed, actual mechanisms and outcomes are anticipated to differ substantially between sites. Our goal is to reveal both commonalities and differences among research sites, model implementations, and ultimately, conclusions derived from the modeling process.

Structuring institutional analysis for urban ecosystems: a key to sustainable urban forest management.

A decline in urban forest structure and function in the United States jeopardizes the current focus on developing sustainable cities. A number of social dilemmas—for example, free-rider problems—restrict the sustainable production of ecosystem services and the stock of urban trees from which they flow. However, institutions, or the rules, norms, and strategies that affect human decision-making, resolve many such social dilemmas, and thus, institutional analysis is imperative for understanding urban forest management outcomes. Unfortunately, we find that the definition of institutions varies greatly across and within disciplines, and conceptual frameworks in urban forest management and urban ecosystems research often embed institutions as minor variables. Given the significance of institutional analysis to understanding sustainable rural resource management, this paper attempts to bring clarity to defining, conceptually framing, and operationally analyzing institutions in urban settings with a specific focus on sustainable urban forest management. We conclude that urban ecologists and urban forest management researchers could benefit from applying a working definition of institutions that uniquely defines rules, norms, and strategies, by recognizing the nested nature of operational, collective choice, and constitutional institutions, and by applying the Institutional Analysis and Development framework for analysis of urban social-ecological systems (SESs). Such work promises to spur the desired policy-based research agenda of urban forestry and urban ecology and provide cross-disciplinary fertilization of institutional analysis between rural SESs and urban ecosystems.

Open Source and Open Content: a Framework for Global Collaboration in Social-Ecological Research

This paper discusses opportunities for alternative collaborative approaches for social- ecological research in general and, in this context, for modeling land-use/land-cover change. In this field, the rate of progress in academic research is steady but perhaps not as rapid or efficient as might be possible with alternative organizational frameworks. The convergence of four phenomena provides new opportunities for cross-organizational collaboration: (1) collaborative principles related to open source (OS) software development, (2) the emerging area of open content (OC) licensing, (3) the World Wide Web as a platform for scientific communication, and (4) the traditional concept of peer review. Although private individuals, government organizations, and even companies have shown interest in the OS paradigm as an alternative model for software development, it is less commonly recognized that this collaborative framework is a potential innovation of much greater proportions. In fact, it can guide the collective development of any intellectual content, not just software. This paper has two purposes. First, we describe OS and OC licensing, dispense with some myths about OS, and relate these structures to traditional scientific process. Second, we outline how these ideas can be applied in an area of collaborative research relevant to the study of social-ecological systems. It is important to recognize that the concept of OS is not new, but the idea of borrowing OS principles and using OC licensing for broader scientific collaboration is new. Over the last year, we have been trying to initiate such an OS/OC collaboration in the context of modeling land use and land cover. In doing so, we have identified some key issues that need to be considered, including project initiation, incentives of project participants, collaborative infrastructure, institutional design and governance, and project finance. OS/OC licensing is not a universal solution suitable for all projects, but the framework presented here does present tangible advantages over traditional methods of academic research.

Carbon stocks in coffee agroforests and mixed dry tropical forests in the western highlands of Guatemala

Tree removal in Latin American coffee agroforestry systems has been widespread due to complex and interacting factors that include fluctuating international markets, government-supported agricultural policies, and climate change. Despite shade tree removal and land conversion risks, there is currently no widespread policy incentive encouraging the maintenance of shade trees for the benefit of carbon sequestration. In facilitation of such incentives, an understanding of the capacity of coffee agroforests to store carbon relative to tropical forests must be developed. Drawing on ecological inventories conducted in 2007 and 2010 in the Lake Atitlán region of Guatemala, this research examines the carbon pools of smallholder coffee agroforests (CAFs) as they compare to a mixed dry forest (MDF) system. Data from 61 plots, covering a total area of 2.24 ha, was used to assess the aboveground, coarse root, and soil carbon reservoirs of the two land-use systems. Results of this research demonstrate the total carbon stocks of CAFs to range from 74.0 to 259.0 Megagrams (Mg)xa0C ha−1 with a mean of 127.6xa0±xa06.6 (SE)xa0Mgxa0C ha−¹. The average carbon stocks of CAFs was significantly lower than estimated for the MDF (198.7xa0±xa032.1xa0Mgxa0Cxa0ha−1); however, individual tree and soil pools were not significantly different suggesting that agroforest shade trees play an important role in facilitating carbon sequestration and soil conservation. This research demonstrates the need for conservation-based initiatives which recognize the carbon sequestration benefits of coffee agroforests alongside natural forest systems.