Marc Linderman

Exploring Complexity in a Human–Environment System: An Agent-Based Spatial Model for Multidisciplinary and Multiscale Integration

Abstract Traditional approaches to studying human–environment interactions often ignore individual-level information, do not account for complexities, or fail to integrate cross-scale or cross-discipline data and methods, thus, in many situations, resulting in a great loss in predictive or explanatory power. This article reports on the development, implementation, validation, and results of an agent-based spatial model that addresses such issues. Using data from Wolong Nature Reserve for giant pandas (China), the model simulates the impact of the growing rural population on the forests and panda habitat. The households in Wolong follow a traditional rural lifestyle, in which fuelwood consumption has been shown to cause panda habitat degradation. By tracking the life history of individual persons and the dynamics of households, this model equips household agents with “knowledge” about themselves, other agents, and the environment and allows individual agents to interact with each other and the environment through their activities in accordance with a set of artificial-intelligence rules. The households and environment coevolve over time and space, resulting in macroscopic human and habitat dynamics. The results from the model may have value for understanding the roles of socioeconomic and demographic factors, for identifying particular areas of special concern, and for conservation policy making. In addition to the specific results of the study, the general approach described here may provide researchers with a useful general framework to capture complex human–environment interactions, to incorporate individual-level information, and to help integrate multidisciplinary research efforts, theories, data, and methods across varying spatial and temporal scales.

Modeling the choice to switch from fuelwood to electricity Implications for giant panda habitat conservation

Despite its status as a nature reserve, Wolong Nature Reserve (China) has experienced continued loss of giant panda habitat due to human activities such as fuelwood collection. Electricity, though available throughout Wolong, has not replaced fuelwood as an energy source. We used stated preference data obtained from in-person interviews to estimate a random utility model of the choice of adopting electricity for cooking and heating. Willingness to switch to electricity was explained by demographic and electricity factors (price, voltage, and outage frequency). In addition to price, nonprice factors such as voltage and outage frequency significantly affect the demand. Thus, lowering electricity prices and increasing electricity quality would encourage local residents to switch from fuelwood to electricity and should be considered in the mix of policies to promote conservation of panda habitat. # 2002 Elsevier Science B.V. All rights reserved.

Land use change: complexity and comparisons

Research on the determinants of land use change and its relationship to vulnerability (broadly defined), biotic diversity and ecosystem services (e.g. Gullison et al. 2007), health (e.g. Patz et al. 2004) and climate change (e.g. van der Werf et al. 2004) has accelerated. Evidence of this increased interest is demonstrated by several examples. Funding agencies in the US (National Institutes of Health, National Science Foundation, National Aeronautics and Space Administration and National Oceanic and Atmospheric Administration) and around the world have increased their support of land use science. In addition to research papers in disciplinary journals, there have been numerous edited volumes and special issues of journals recently (e.g. Gutman et al. 2004; Environment & Planning B 2005; Environment & Planning A 2006; Lambin and Geist 2006; Kok, Verburg and Veldkamp 2007). And in 2006, the Journal of Land Use Science was launched. Land use science is now at a crucial juncture in its maturation process. Much has been learned, but the array of factors influencing land use change, the diversity of sites chosen for case studies, and the variety of modeling approaches used by the various case study teams have all combined to make two of the hallmarks of science, generalization and validation, difficult within land use science. This introduction and the four papers in this themed issue grew out of two workshops which were part of a US National Institutes of Health (NIH) ‘Roadmap’ project. The general idea behind the NIH Roadmap initiative was to stimulate scientific advances by bringing together diverse disciplines to tackle a common, multi-disciplinary scientific problem. The specific idea behind our Roadmap project was to bring together seven multi-disciplinary case study teams, working in areas that could be broadly classified as inland frontiers, incorporating social, spatial and biophysical sciences, having temporal depth on both the social and biophysical sides, and having had long-term funding. Early in our Roadmap project, the crucial importance of modeling, particularly agent-based modeling, for the next phase of land-use science became apparent and additional modelers not affiliated with any of the seven case studies were brought into the project. Since agent-based simulations attempt to explicitly capture human behavior and interaction, they were of special interest. At the risk of oversimplification, it is worth briefly reviewing selected key insights in land use science in the past two decades to set the stage for the papers in this themed issue. One of the earliest realizations, and perhaps most fundamental, was accepting the crucial role that humans play in transforming the landscape, and concomitantly the distinction drawn between land cover (which can be seen remotely) and land use (which, in most circumstances, requires in situ observation; e.g. Turner, Meyer and Skole 1994). The complexity of factors influencing land use change became apparent and led to a variety of ‘box and arrow’ diagrams as conceptual frameworks, frequently put together by committees rarely agreeing with one another on all details, but agreeing among themselves that there were many components (social and biophysical) whose role needed to be measured and understood. A series of case studies emerged, recognizing the wide array of variables that needed to be incorporated, and typically doing so by assembling a multidisciplinary team (Liverman, Moran, Rindfuss and Stern 1998; Entwisle and Stern 2005). The disciplinary make-up of the team strongly influenced what was measured and how it was measured (see Rindfuss, Walsh, Turner, Fox and Mishra 2004; Overmars and Verburg 2005), with limited, if any, coordination across case studies (see Moran and Ostrom 2005 for an exception). In large part, the focus on case studies reflected the infancy of theory in land use science. Teams combined their own theoretical knowledge of social, spatial and ecological change with an inductive approach to understanding land use change – starting from a kitchen sink of variables and an in-depth knowledge of the site to generate theory on the interrelationships between variables and the importance of contextual effects. This lack of coordination in methods, documentation and theory made it very difficult to conduct meta-analyses of the driving factors of land use change across all the case studies to identify common patterns and processes (Geist and Lambin 2002; Keys and McConnell 2005). Recognizing that important causative factors were affecting the entire site of a case study (such as a new road which opens an entire area) and that experimentation was not feasible, computational, statistical and spatially explicit modeling emerged as powerful tools to understand the forces of land use change at a host of space–time scales (Veldkamp and Lambin 2001; Parker, Manson, Janssen, Hoffmann, and Deadman 2003; Verburg, Schot, Dijst and Veldkamp 2004). Increasingly, in recognition of the crucial role of humans in land use change, modeling approaches that represent those actors as agents have emerged as an important, and perhaps the dominant, modeling approach at local levels (Matthews, Gilbert, Roach, Polhil and Gotts 2007). In this introductory paper we briefly discuss some of the major themes that emerged in the workshops that brought together scientists from anthropology, botany, demography, developmental studies, ecology, economics, environmental science, geography, history, hydrology, meteorology, remote sensing, geographic information science, resource management, and sociology. A central theme was the need to measure and model behavior and interactions among actors, as well as between actors and the environment. Many early agent-based models focused on representing individuals and households (e.g. Deadman 1999), but the importance of other types of actors (e.g. governmental units at various levels, businesses, and NGOs) was a persistent theme. ‘Complexity’ was a term that peppered the conversation, and it was used with multiple meanings. But the dominant topic to emerge was comparison and generalization: with multiple case studies and agent-based models blooming, how do we compare across them and move towards generalization? We return to the generalization issue at the end of this introductory paper after a brief discussion of the other themes.

Using artificial neural networks to map the spatial distribution of understorey bamboo from remote sensing data

Understorey vegetation is a critical component of biodiversity and an essential habitat component for many wildlife species. However, compared to overstorey, information about understorey vegetation distribution is scant, available mainly over small areas or through imprecise large area maps from tedious and time-consuming field surveys. A practical approach to classifying understorey vegetation from remote sensing data is needed for more accurate habitat analyses and biodiversity estimates. As a case study, we mapped the spatial distribution of understorey bamboo in Wolong Nature Reserve (south-western China) using remote sensing data from a leaf-on or growing season. Training on a limited set of ground data and using widely available Landsat TM data as input, a nonlinear artificial neural network achieved a classification accuracy of 80% despite the presence of co-occurring mid-storey and understorey vegetation. These results suggest that the influences of understorey vegetation on remote sensing data are available to practical approaches to classifying understorey vegetation. The success here to map bamboo distribution has important implications for giant panda conservation and provides a good foundation for developing methods to map the spatial distributions of other understorey plant species. Representative schematic of an artificial neural network. The arrows represent a feed-forward process of transforming input data, such as remote sensing imagery, to an output space (e.g. bamboo existence/absence). Networks are trained through a priori knowledge of output and input relations (ground data and corresponding remote sensing pixel values) and a reiterative back-propagation of training errors to update the hidden layer weights.

TEMPORAL CHANGES IN GIANT PANDA HABITAT CONNECTIVITY ACROSS BOUNDARIES OF WOLONG NATURE RESERVE, CHINA

Global biodiversity loss is largely driven by human activities such as the conversion of natural to human-dominated landscapes. A popular approach to mitigating land cover change is the designation of protected areas (e.g., nature reserves). Nature reserves are traditionally perceived as strongholds of biodiversity conservation. However, many reserves are affected by land cover changes not only within their boundaries, but also in their surrounding areas. This study analyzed the changes in habitat for the giant panda (Ailuropoda melanoleuca) inside Wolong Nature Reserve, Sichuan, China, and in a 3-km buffer area outside its boundaries, through a time series of classified satellite imagery and field observations. Habitat connectivity between the inside and the outside of the reserve diminished between 1965 and 2001 because panda habitat was steadily lost both inside and outside the reserve. However, habitat connectivity slightly increased between 1997 and 2001 due to the stabilization of some panda habitat inside and outside the reserve. This stabilization most likely occurred as a response to changes in socioeconomic activities (e.g., shifts from agricultural to nonagricultural economies). Recently implemented government policies could further mitigate the impacts of land cover change on panda habitat. The results suggest that Wolong Nature Reserve, and perhaps other nature reserves in other parts of the world, cannot be managed as an isolated entity because habitat connectivity declines with land cover changes outside the reserve even if the area inside the reserve is well protected. The findings and approaches presented in this paper may also have important implications for the management of other nature reserves across the world.

Simulating demographic and socioeconomic processes on household level and implications for giant panda habitats

Human activities have significantly affected wildlife habitats. Although the ecological effects of human impacts have been demonstrated in many studies, the socioeconomic drivers underlying these human impacts have seldom been studied. We developed a household-based, stochastic, and dynamic model that simulates the impacts of household demographic and socioeconomic interactions on fuelwood use, a key factor affecting the quantity and quality of habitats for the giant pandas (Ailuropoda melanoleuca). Using Wolong Nature Reserve (China) as a case study, this model mimics household production and consumption processes and integrates various demographic and socioeconomic factors. Household interviews conducted in 1998 within the Reserve provided the data for parameterization. The simulation results fit well with both the data used in constructing the model and with a set of independent data. Age structure and cropland area were found to be the most sensitive factors in terms of fuelwood consumption, and thus deserve more attention in panda habitat conservation. This model could help reserve managers to understand the interrelationships among local economy, local cultural traditions, and habitat degradation, facilitating more scientific and economically efficient policymaking.

Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world

This paper compares one global parametric land use change model, the artificial neural network - based Land Transformation Model, with two local non-parametric models: a classification and regression tree and multivariate adaptive regression spline model. We parameterized these three models with identical data from different regions of the world; one region undergoing extensive agricultural expansion (East Africa), another region where forests are re-growing (Muskegon River Watershed in the United States), and a third region where urbanization is prominent (South-Eastern Wisconsin in the United States). Independent training data and testing data were used to train and calibrate each model, respectively. Comparisons of simulated maps from the three kinds of land use change patterns were made using conventional goodness-of-fit metrics in land use change science. The results of temporal and spatial comparison of the data mining models show that the artificial neural network outperformed all other models in a short-time interval (East Africa; 5 years) and for coarse resolution data (East Africa; 1 km); however, the three data mining models obtained similar accuracies in a long-time interval (Muskegon River Watershed; 20 years) and for fine resolution data with large numbers of cells (Muskegon River Watershed; 30 m). Furthermore, the results showed that the probability of agriculture gain was more likely in locations closer to towns and large cities in East Africa, urbanization was more likely in locations closer to roads and urban areas in South-Eastern Wisconsin and the probability of forest gain was more likely in locations closer to the forest and shrub land cover and farther away from roads in Muskegon River Watershed.

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.

Time series of remote sensing data for land change science

We discuss the application of wide-field-of-view satellite data to land change science from a user viewpoint. We first identify the key scientific questions and highlight the complex nature of land change. We then examine the basic data requirements and discuss criteria for data selection and the tradeoffs involved. Methods to exploit optimally existing datasets involve multisensor analyses either across spatial resolutions or to produce consistent time series from successive satellite sensors with diverse attributes

Interactive Effects Of Natural And Human Disturbances On Vegetation Dynamics Across Landscapes

Accurate measures of human effects on landscape processes require consideration of both the direct impacts from human activities and the indirect consequences of the interactions between humans and the landscape. This is particularly evident in systems experiencing regular natural disturbances such as in the mountainous areas of southwestern China, where the remaining population of giant pandas (Ailuropoda melanoleuca) is supported. Here the spatiotemporal patterns of human impacts, forests, and bamboo episodic die-offs combine to determine the distribution of panda habitat. To study the complex interactions of humans and landscapes, we developed an integrated spatiotemporally explicit model of household activities, natural vegetation dynamics, and their impacts on panda habitat. Using this model we examined the direct consequences of local fuelwood collection and household creation on areas of critical giant panda habitat and the indirect impacts when coupled with vegetation dynamics. Through simulations, we found that over the next 30 years household impacts would result in the loss of up to 30% of the habitat relied on by pandas during past bamboo die-offs. The accumulation and spatial distribution of household impacts would also have a considerable indirect influence on the spatial distribution of understory bamboo. While human impacts influence both bamboo die-off and regeneration, over 19% of pre-existing low-elevation bamboo habitat may be lost following an episodic die-off depending on the severity of the impacts and timing of the die-offs. Our study showed not only the importance of the spatial distribution of direct household impacts on habitat, but also the far-reaching effects of the indirect interactions between humans and the landscapes they are modifying.