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Marine Protected Areas (MPAs) are a cornerstone for the conservation of marine ecosystems. To be effective, these areas must be strategically located. To support the planning and establishment of MPAs and inform marine spatial planning, we identified areas that may support aggregations of foraging seabirds (\u201chotspots\u201d) in the California Current System, a highly productive, large marine ecosystem on the west coast of North America. We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data. The surveys were conducted by several agencies and monitoring programs and extended from north of Vancouver Island to the US/Mexico border and seaward 600 km from the coast. We developed single-species predictive models using a machine-learning algorithm, bagged decision trees. Bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Model predictions were applied to the entire California Current for 4 months (February, May, July, October) as a proxy for seasons in each of 11 years. Single-species predictions were then combined to identify potential \u201chotspots\u201d of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas (\u201ccore areas\u201d) to individual species, and (3) predicted persistence of hotspots across years. Potential hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank) and Southern California (adjacent to the Channel Islands) that are not currently included in protected areas. Prioritization and identification of multi-species hotspots will depend on which group of species is of highest management priority. Modeling hotspots at a broad spatial scale can contribute to MPA planning, particularly if complemented by fine-scale information for focal areas. 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