Many species exhibit spatially varying trends in population size and status, often driven by differences among factors affecting individual subpopulations. Estimation and differentiation of such trends may be important for management, and a driving force for monitoring programs. The ability to estimate spatial differences in population trend may depend on assumptions regarding connectivity among subpopulations (stock structure or spatial overlap in stressors), information that is often poorly known. Linear state-space models using the Kalman filter were developed, tested, and applied for trend estimation of pup production for the western Alaska stock of Steller sea lions (Eumetopias jubatus), given only count data. Models were able to estimate trends and abundance even when data were missing. Models that assumed spatial correlation in trend among rookeries were more robust to stock structure assumptions when the stock structure was potentially mis-specified. High levels of spatial correlation among rookeries estimated from Steller sea lion pup count data are consistent with large-scale covariance of population trend within the Steller sea lion metapopulation.