Characterizing dominant patterns of spatiotemporal variation for a transboundary groundfish assemblage

Many mobile marine taxa are changing their distributions in response to climate change. Such movements pose a challenge to fisheries monitoring and management, particularly in systems where climate-adaptive and ecosystem-based management objectives are emphasized. While shifts in species distributions can be discerned from long-term fisheries-independent monitoring data, distilling coherent patterns across space and time from such datasets can be challenging, particularly for transboundary stocks. One approach for identifying dominant patterns of spatiotemporal variation that has been widely used in physical atmospheric and oceanographic studies is empirical orthogonal function (EOF) analysis, wherein spatiotemporal variation is separated into time-series of annual factor loadings and spatial response maps. Here, we apply an extension of EOF analysis that has been modified for compatibility with biological sampling data to a combined US–Russian fisheries-independent survey dataset that spans the eastern (United States) and western (Russia) Bering Sea shelf to estimate dominant patterns of spatiotemporal variation for 10 groundfish species at a shelf-wide scale. EOF identified one axis of variability that was coherent with the extent of cold (≤0°C) near-bottom waters (the cold pool) previously shown to be a key influence on species distributions and ecosystem structure for the Bering Sea. However, the leading axis of variability identified by our EOF analysis was characterized by low frequency changes in the distributions of several species over longer time scales. Our analysis has important implications for predicting variation in species distributions over time and demonstrates a widely applicable method for leveraging combined fisheries-independent survey datasets to characterize community-level responses to ecosystem change at basin-wide scales.