Early-warning signals (potentially) reduce uncertainty in forecasted timing of critical shifts

Abstract

Despite the identification of early-warning signals precluding ecosystems regime shifts, limited evidence exists that they can be used to forecast the actual timing of a critical shift. Here, we propose a probabilistic Bayesian approach to forecast the timing of a shift by combining uncertain prior information about the ecosystem dynamics (parameters and drivers) and sampled spatial and temporal correlation and variance of ecosystem states, which are well known early-warning signals. For an ecosystem of logistically growing vegetation under linear increase in grazing pressure, we show that the use of sampled early-warning signals results in lower prediction uncertainty in forecasted timing of shifts compared to forecasts made with sampled mean state variables. In addition, we show that uncertainty in ecosystem parameters decreases well ahead of a shift. An important conclusion of our study is that the use of early-warning signals in forecasting of shifts is promising, provided that a large number of samples are collected (n ≈ 10⁴ in our study). This explains the limited success of finding early warning signals from field studies of real world ecosystems.