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Climate change impacts are pronounced at high latitudes, where warming, reduced sea-ice-cover, and ocean acidification affect marine ecosystems. We review climate change impacts on two major gateways into the Arctic: the Bering and Chukchi seas in the Pacific and the Barents Sea and Fram Strait in the Atlantic. We present scenarios of how changes in the physical environment and prey resources may affect commercial fish populations and fisheries in these high-latitude systems to help managers and stakeholders think about possible futures. Predicted impacts include shifts in the spatial distribution of boreal species, a shift from larger, lipid-rich zooplankton to smaller, less nutritious prey, with detrimental effects on fishes that depend on high-lipid prey for overwinter survival, shifts from benthic- to pelagic-dominated food webs with implications for upper trophic levels, and reduced survival of commercially important shellfish in waters that are increasingly acidic. Predicted changes are expected to result in disruptions to existing fisheries, the emergence of new fisheries, new challenges for managing transboundary stocks, and possible conflicts among resource users. Some impacts may be irreversible, more severe, or occur more frequently under anthropogenic climate change than impacts associated with natural variability, posing additional management challenges. [ABSTRACT FROM AUTHOR]

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]