What is the robustness of early warning signals to temporal aggregation? - Frossard, Victor; Saussereau, Bruno; Perasso, Antoine; Gillet, François (Frontiers in Ecology and Evolution, 2015)
A number of methods have recently been developed to identify early warning signals (EWSs) within time-series structure typically characteristic of the rise of critical transitions. Inherent technical constraints often limit the possibility to obtain from sediment both regular and high-resolution time series rather most palaeoecological time series obtained from sediment records represent time-aggregated ecological signals. In this study, the robustness of EWS detection to temporal aggregation was addressed using simulated time series mimicking ecological dynamics. Using a stochastic differential equation based on a deterministic model exhibiting a critical transition between two stable equilibria, two different scenarios were simulated using different combinations of forcing and noise intensities (critical slowing-down and driver-mediated flickering scenarios). The temporal resolution of each simulated time series was progressively decreased by averaging the data from Δt = 1 up to Δt = 10 time-unit intervals. EWSs (standard deviation, autocorrelation at lag-1 (AR(1)), skewness and kurtosis) were applied to all time series. Robustness of EWSs to data aggregation was assessed through a block-based approach using Kendall rank correlation Tau. Standard deviation appeared to be robust to data aggregation up to Δt = 10 for the slowing-down scenario and up to Δt = 5 for the driver-mediated flickering scenario while autocorrelation remained robust up to Δt = 2 for the slowing-down scenario and did not support data aggregation for the driver-mediated scenario. Skewness and kurtosis performed poorly for the two scenarios and were not considered as robust EWSs even for the original simulated time series using the block-based approach. Our results suggest that high-resolution palaeoecological time series could be in a large extent suitable to support EWS analyses.