Spatial perspectives in state‐and‐transition models: a missing link to land management?

Summary 1.  State‐and‐transition models (STMs) synthesize and communicate knowledge about the alternative states of an ecosystem and causes of state transitions. Data supported narrative descriptions within STMs are used to select or justify management actions. State transitions are characteristically heterogeneous in space and time, but spatial heterogeneity is seldom described in STMs, thereby limiting their utility. 2.  We conducted a review that indicates how spatially explicit data can be used to improve STMs. We first identified three spatial scales at which spatial patterns and processes are manifest: patches, sites and landscapes. We then identified three classes of spatial processes that govern heterogeneity in state transitions at each scale and that can be considered in empirical studies, STM narratives and management interpretations. 3.  First, spatial variations in land‐use driver history (e.g. grazing use) can explain differences in the occurrence of state transitions within land areas that are otherwise uniform. Secondly, spatial dependence in response to drivers imposed by variations in soils, landforms and climate can explain how the likelihood of state transition varies along relatively static environmental gradients. Thirdly, state transition processes can be contagious, under control of vegetation‐environment feedbacks, such that the spatiotemporal evolution of state transitions is predictable. 4.  We suggest a strategy for considering each of the three spatial processes in the development of STM narratives. We illustrate how spatial data can be employed for describing early warning indicators of state transition, identifying areas that are most susceptible to state transitions, and designing and implementing monitoring schemes. 5.   Synthesis and applications . State‐and‐transition models are increasingly important tools for guiding land‐management activities. However, failure to adequately represent spatial processes in STMs can limit their ability to identify the initiation, risk and causes of state transitions and, therefore, the appropriate management responses. We suggest that multi‐scaled studies targeted to different kinds of ecosystems can be used to uncover evidence of spatial processes. Such evidence should be included in STM narratives and can lead to novel interpretations of land change and improved management.