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Of the boreal‐ and Arctic‐breeding North American shorebirds that migrate south through the Caribbean, most individuals continue farther south. However, for many species, some individuals remain beyond the southbound migration period (i.e., throughout the temperate winter and/or summer). This variation among individuals adds complexity to observation data, obscures migration patterns, and could prevent the examination of the use of different Caribbean regions by various shorebird species during migration and in the nonmigratory seasons. Here, we present a novel method that leverages a well‐established statistical approach (generalized additive models) to systematically identify migration phenology even for complex passage migrant species with individuals that remain beyond migration. Our method identifies the active migration period using derivatives of a fitted GAM and then calculates phenology metrics based on quantiles of that migration period. We also developed indices to quantify oversummering and overwintering patterns with respect to migration. We analyzed eBird data for 16 North American shorebird species as they traveled South through the insular Caribbean, identifying separate migratory patterns for Cuba, Puerto Rico, Guadeloupe, Aruba, Bonaire, Curaçao, and Trinidad and Tobago. Our results confirm past reports and provide additional detail on shorebird migration in the Caribbean, and identify several previously unpublished regional patterns. Despite Puerto Rico being farther north and closer to continental North America, most species reached Puerto Rico later than other regions, supporting a long‐standing hypothesis that migration strategy (transcontinental vs. transoceanic) leads to geographic differences in migration timing. We also found distinct patterns of migration curves, with some regions and species consistently having either symmetrical or skewed curves; these differences in migration curve shape reflect different migration processes. Our novel method proved reliable and adaptable for most species and serves as a valuable tool for identifying phenological patterns in complex migration data, potentially unlocking previously intractable data. [ABSTRACT FROM AUTHOR]