ABSTRACT. Interactions between distant places are increasingly widespread and influential, often leading to unexpected outcomes with profound implications for sustainability. Numerous sustainability studies have been conducted within a particular place with little attention to the impacts of distant interactions on sustainability in multiple places. Although distant forces have been studied, they are usually treated as exogenous variables and feedbacks have rarely been considered. To understand and integrate various distant interactions better, we propose an integrated framework based on telecoupling, an umbrella concept that refers to socioeconomic and environmental interactions over distances. The concept of telecoupling is a logical extension of research on coupled human and natural systems, in which interactions occur within particular geographic locations. The telecoupling framework contains five major interrelated components, i.e., coupled human and natural systems, flows, agents, causes, and effects. We illustrate the framework using two examples of distant interactions associated with trade of agricultural commodities and invasive species, highlight the implications of the framework, and discuss research needs and approaches to move research on telecouplings forward. The framework can help to analyze system components and their interrelationships, identify research gaps, detect hidden costs and untapped benefits, provide a useful means to incorporate feedbacks as well as trade-offs and synergies across multiple systems (sending, receiving, and spillover systems), and improve the understanding of distant interactions and the effectiveness of policies for socioeconomic and environmental sustainability from local to global levels.

This paper applies methods of multiple resolution map comparison to quantify characteristics for 13 applications of 9 different popular peer-reviewed land change models. Each modeling application simulates change of land categories in raster maps from an initial time to a subsequent time. For each modeling application, the statistical methods compare: (1) a reference map of the initial time, (2) ence map of the subsequent time, and (3) a prediction map of the subsequent time. The three possible two-map comparisons for each application characterize: (1) the dynamics of the landscape, (2) the behavior of the model, and (3) the accuracy of the prediction. The three-map comparison for each application specifies the amount of the prediction's accuracy that is attributable to land persistence versus land change. Results show that the amount of error is larger than the amount of correctly predicted change for 12 of the 13 applications at the resolution of the raw data. The applications are summarized and compared using two statistics: the null resolution and the figure of merit. According to the figure of merit, the more accurate applications are the 123Comparing the input, output, and validation maps for several models of land change 13 ones where the amount of observed net change in the reference maps is larger. This paper facilitates communication among land change modelers, because it illustrates the range of results for a variety of models using scientifically rigorous, generally applicable, and intellectually accessible statistical techniques. JEL Classification

No abstract

ABSTRACT. Conserving wildlife while simultaneously meeting the resource needs of a growing human population is a major sustainability challenge. As such, using combined social and environmental perspectives to understand how people and wildlife are interlinked, together with the mechanisms that may weaken or strengthen those linkages, is of utmost importance. However, such integrated information is lacking. To help fill this information gap, we describe an integrated coupled human and natural systems (CHANS) approach for analyzing the patterns, causes, and consequences of changes in wildlife population and habitat, human population and land use, and their interactions. Using this approach, we synthesize research in two sites, Wolong Nature Reserve in China and Chitwan National Park in Nepal, to explicate key relationships between people and two globally endangered wildlife conservation icons, the giant panda and the Bengal tiger. This synthesis reveals that local resident characteristics such as household socioeconomics and demography, as well as community-level attributes such as resource management organizations, affect wildlife and their habitats in complex and even countervailing ways. Human impacts on wildlife and their habitats are in turn modifying the suite of ecosystem services that they provide to local residents in both sites, including access to forest products and cultural values. These interactions are further complicated by human and natural disturbance (e.g., civil wars, earthquakes), feedbacks (including policies), and telecouplings (socioeconomic and environmental interactions over distances) that increasingly link the focal systems with other distant systems. We highlight several important implications of using a CHANS approach for wildlife research and conservation that is useful not only in China and Nepal but in many other places around the world facing similar challenges.

Global and regional economic and environmental changes are increasingly influencing local land-use, livelihoods, and ecosystems. At the same time, cumulative local land changes are driving global and regional changes in biodiversity and the environment. To understand the causes and consequences of these changes, land change science (LCS) draws on a wide array synthetic and meta-study techniques to generate global and regional knowledge from local case studies of land change. Here, we review the characteristics and applications of synthesis methods in LCS and assess the current state of synthetic research based on a meta-analysis of synthesis studies from 1995 to 2012. Publication of synthesis research is accelerating, with a clear trend toward increasingly sophisticated and quantitative methods, including meta-analysis. Detailed trends in synthesis objectives, methods, and land change phenomena and world regions most commonly studied are presented. Significant challenges to successful synthesis research in LCS are also identified, including issues of interpretability and comparability across case-studies and the limits of and biases in the geographic coverage of case studies. Nevertheless, synthesis methods based on local case studies will remain essential for generating systematic global and regional understanding of local land change for the foreseeable future, and multiple opportunities exist to accelerate and enhance the reliability of synthetic LCS research in the future. Demand for global and regional knowledge generation will continue to grow to support adaptation and mitigation policies consistent with both the local realities and regional and global environmental and economic contexts of land change.Electronic supplementary materialThe online version of this article (doi:10.1007/s10113-014-0626-8) contains supplementary material, which is available to authorized users.

China’s forest cover exhibited a positive trend that was significantly related with the implementation of a national conservation policy.

The challenge confronting those seeking to understand the institutional dimensions of global environmental change and patterns of land-use and land-cover change is to find effective methods for analyzing the dynamics of socio-ecological systems. Such systems exhibit a number of characteristics that pose problems for the most commonly used statistical techniques and may require additional and innovative analytic tools. This article explores options available to researchers working in this field and recommends a strategy for achieving scientific progress. Statistical procedures developed in other fields of study are often helpful in addressing challenges arising in research into global change. Accordingly, we start with an assessment of some of the enhanced statistical techniques that are available for the study of socioecological systems. By themselves, however, even the most advanced statistical models cannot solve all the problems that arise in efforts to explain institutional effectiveness and patterns of land-use and landcover change. We therefore proceed to an exploration of additional analytic techniques, including configurational comparisons and meta-analyses; case studies, counterfactuals, and narratives; and systems analysis and simulations. Our goal is to create a portfolio of complementary methods or, in other words, a tool kit for understanding complex human-environment interactions. When the results obtained through the use of two or more techniques converge, confidence in the robustness of key findings rises. Contradictory results, on the other hand, signal a need for additional analysis.

No abstract