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Multivariate analyses constitute an integral part of today's marine integrated ecosystem assessments (IEAs). Principal component analysis (PCA) is one of the most common of these techniques, and the method has been used repeatedly to summarize the dynamics of marine ecosystems. There seems to be little recognition of the potential pitfalls associated with performing PCA on time-series that are autocorrelated and/or non-stationary. We investigate how the descriptive performance of PCAs may be affected by the structure of the underlying timeseries and question whether such analyses can provide useful summaries of ecosystem trajectories. For this purpose, we reanalyse four datasets from the Barents, Norwegian, Baltic, and North Seas. We compare the results with those obtained from simulated datasets that share similar trend and autocorrelation properties, but in which the variables are unrelated. We show that most of the patterns revealed by the PCA can emerge from random time-series and that the fraction of the variance that cannot be accounted for by random processes is minimal. The Norwegian Sea dataset is a pathological case in which the variance explained by the first two components only exceeds what would be expected from randomly simulated time-series by 2%. We conclude that outputs from explorative multivariate analyses provide very little insight into ecosystem status, trajectories and functioning. IEA groups need to be equipped with methods that can provide better insight into how marine ecosystems function, the drivers of their changes and their possible future trajectories. [ABSTRACT FROM AUTHOR]