Unifying ecosystem responses to disturbance into a single statistical framework

Natural ecosystems are currently experiencing unprecedented rates of anthropogenic disturbance. Given the potential ramifications of more frequent disturbances, it is imperative that we accurately quantify ecosystem responses to severe disturbance. Specifically, ecologists and managers need estimates of resistance and recovery from disturbance that are free of observation error, not biased by temporal stochasticity and that standardize disturbance magnitude among many disparate ecosystems relative to normal interannual variability. Here, I propose a statistical framework that estimates all four components of ecosystem responses to disturbance (resistance, recovery, elasticity and return time), while resolving all of the issues described above. Coupling autoregressive time series with exogenous predictors (ARX) models with impulse response functions (IRFs) allows researchers to statistically subject all ecosystems to similar levels of disturbance, estimate lag effects and obtain standardized estimates of resistance to and recovery from disturbance that are free from observation error and stochastic processes inherent in raw data.