LAUSR

Marine fish populations commonly exhibit low-frequency fluctuations in biomass that can cause catch volatility and thus endanger the food and economic security of dependent coastal societies. Such variability has been linked to fishing intensity, demographic processes and environmental variability, but our understanding of the underlying drivers remains poor for most fish stocks. Our study departs from previous findings showing that sea surface temperature (SST) is a significant driver of fish somatic growth variability and that life-history characteristics mediate population-level responses to environmental variability. We use autoregressive models to simulate how fish populations integrate SST variability over multiple years depending on fish life span and trophic position. We find that simulated SST-driven population dynamics can explain a significant portion of observed low-frequency variability in independent observations of fisheries landings around the globe. Predictive skill, however, decreases with increasing fishing pressure, likely due to demographic truncation. Using our modelling approach, we also show that increases in the mean and variance of SST could amplify biomass volatility and lessen its predictability in the future. Overall, biological integration of high-frequency SST variability represents a null hypothesis with which to explore the drivers of low-frequency population change across upper-trophic marine species. van der Sleen et al. introduce the use of an autoregressive null model to explain low-frequency variability in populations of marine fishes. Using time series of fisheries landings from a global database, their model shows that interannual sea surface temperature variation is integrated through each trophic level of the food web and can underlie observed low-frequency population dynamics.