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Accelerated global loss of fish species underscores an urgent need to elucidate constraints on spatial and temporal population patterns and to mitigate human-mediated impacts accordingly. We analyzed 20 yr of trawl data using a spatiotemporally explicit, hierarchically Bayesian model to define density distributions of an imperiled anadromous fish-the delta smelt, Hypomesus transpacificus-in the heavily modified and highly used San Francisco Estuary. To reduce management reliance on an estuary-wide annual population index that minimizes data resolution and ignores uncertainty, our model took a spatiotemporal approach and explicitly accounted for sampling and observation variation through both stochastic and deterministic elements. Our model demonstrated that juvenile smelt density has decreased over annual time and displays a distinct seasonal pattern (the combination of growth into the sampled size class, mortality, and movement into and out of the sampling frame) of increases in March-June followed by decreases in June-August. Smelt density was highly spatiotemporally autocorrelated and strongly tracked prey availability yet was also constrained by local hydrological factors (salinity, turbidity, velocity). Specifically, juvenile smelt preferred slightly saline, turbid, generally slow-moving water with ample cope-pod prey. However, poor swimming capabilities reduced the capacity of juvenile smelt to mix throughout the estuary and find optimal habitat, emphasizing the importance of accounting for spatiotemporal auto-correlation in species distribution models. Further, whole-estuary outflow appeared to influence the spatial distribution of covariates and juvenile smelt, such that smelt densities tended to peak closer to the marine zone at a lower maximal value when outflow was high. The predictions of our model accurately matched observed patterns of juvenile delta smelt catch while embracing uncertainty (although the predictions tended to exaggerate the total smelt catch variation via over-predicted tail values). These results highlight that data-driven improvements can be made to the analytical methods currently used to guide delta smelt management and lay the groundwork for mitigating the impacts of water use, flow regimes, habitat alteration, and climate change on an endangered species in a highly impacted and economically consequential habitat.