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.
