Disturbance interactions: characterization, prediction, and the potential for cascading effects

Disturbances are fundamental components of ecosystems and, in many cases, a dominant driver of ecosystem structure and function at multiple spatial and temporal scales. While the effect of any one disturbance may be relatively well understood, multiple interacting disturbances can cause unexpected disturbance behavior (e.g., larger extents), altered return likelihoods, or reduced ecosystem resilience and regime shifts. Given the long‐lasting implications of such events, and the potential for changes in disturbance rates driven by climate change and increasing anthropogenic pressures, developing a broad conceptual understanding and some predictive ability regarding the likelihood of interactions between disturbances is crucial. Through a broad synthesis of the literature, and across multiple biomes, disturbance interactions are placed into a unified framework around the concept of changing ecosystem resistance (“linked interactions,” alterations to likelihood, extent, or severity) or ecosystem resilience (“compound interactions,” alterations to recovery time or trajectory). Understanding and predicting disturbance interactions requires disaggregating disturbances into their constituent legacies, identifying the mechanisms which drive disturbances behavior (or ecosystem recovery), and determining when and where those mechanisms might be altered by the legacies of prior disturbances. The potential for cascading effects is discussed, by which these interactions may extend the reach of anthropogenic or climate change‐induced alterations to disturbances beyond what is currently anticipated. Finally, several avenues for future research are outlined, as suggested from the current literature (and areas in which that literature is lacking). These include the potential for cross‐scale interactions and changing scale‐driven limitations, further work on cascading effects, and the potential for cross‐biome comparisons. Disturbance interactions have the potential to cause large, nonlinear, or unexpected changes in ecosystem structure and functioning; finding generality across these complex events is an important step in predicting their occurrence and understanding their significance.