Application of the sequential t-test algorithm for analysing regime shifts to the southern Benguela ecosystem

Abstract

Long-term ecosystem changes, such as regime shifts, have occurred in several marine ecosystems world-wide. Multivariate statistical methods have been used to detect such changes. A new method known as the sequential t-test algorithm for analysing regime shifts (STARS) is applied to a set of biological state variables as well as environmental and anthropogenic forcing variables in the southern Benguela. The method is able to correct for auto-correlation within time-series by a process known as prewhitening. All variables were tested with and without prewhitening. Shifts that were detected with both methods were termed robust. The STARS method detected shifts in relatively short time-series and identified when these shifts occurred without a priori hypotheses. Shifts were generally well detected at the end of time-series, but further development of the method is needed to enhance its performance for auto-correlated time-series. Since 1950, two major long-term ecosystem changes were identified for the southern Benguela. The first change occurred during the 1960s, caused predominantly by heavy fishing pressure but with some environmental forcing. The second change occurred in the early 2000s, caused mainly by environmental forcing. To strengthen these findings, further analyses should be carried out using different methods.