Land change science has emerged as a fundamental component of global environmental change and sustainability research. This interdisciplinary field seeks to understand the dynamics of land cover and land use as a coupled human-environment system to address theory, concepts, models, and applications relevant to environmental and societal problems, including the intersection of the two. The major components and advances in land change are addressed: observation and monitoring; understanding the coupled system-causes, impacts, and consequences; modeling; and synthesis issues. The six articles of the special feature are introduced and situated within these components of study.

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Protected areas throughout the world are key for conserving biodiversity, and land use is key for providing food, fiber, and other ecosystem services essential for human sustenance. As land use change isolates protected areas from their surrounding landscapes, the challenge is to identify management opportunities that maintain ecological function while minimizing restrictions on human land use. Building on the case studies in this Invited Feature and on ecological principles, we identify opportunities for regional land management that maintain both ecological function in protected areas and human land use options, including preserving crucial habitats and migration corridors, and reducing dependence of local human populations on protected area resources. Identification of appropriate and effective management opportunities depends on clear definitions of: (1) the biodiversity attributes of concern; (2) landscape connections to delineate particular locations with strong ecological interactions between the protected area and its surrounding landscape; and (3) socioeconomic dynamics that determine current and future use of land resources in and around the protected area.

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Behavioral tasks (e.g., Stroop task) that produce replicable group-level effects (e.g., Stroop effect) often fail to reliably capture individual differences between participants (e.g., low test-retest reliability). This “reliability paradox” has led many researchers to conclude that most behavioral tasks cannot be used to develop and advance theories of individual differences. However, these conclusions are derived from statistical models that provide only superficial summary descriptions of behavioral data, thereby ignoring theoretically-relevant data-generating mechanisms that underly individual-level behavior. More generally, such descriptive methods lack the flexibility to test and develop increasingly complex theories of individual differences. To resolve this theory-description gap, we present generative modeling approaches, which involve using background knowledge to specify how behavior is generated at the individual level, and in turn how the distributions of individual-level mechanisms are characterized at the group level—all in a single joint model. Generative modeling shifts our focus away from estimating descriptive statistical “effects” toward estimating psychologically meaningful parameters, while simultaneously accounting for measurement error that would otherwise attenuate individual difference correlations. Using simulations and empirical data from the Implicit Association Test and Stroop, Flanker, Posner Cueing, and Delay Discounting tasks, we demonstrate how generative models yield (1) higher test-retest reliability estimates, and (2) more theoretically informative parameter estimates relative to traditional statistical approaches. Our results reclaim optimism regarding the utility of behavioral paradigms for testing and advancing theories of individual differences, and emphasize the importance of formally specifying and checking model assumptions to reduce theory-description gaps and facilitate principled theory development.

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