A key goal in ecology is to develop more effective ways to understand species’ distributions in order to facilitate both their study and conservation. Many species distribution modeling analyses have been performed to date, using either structured survey data or unstructured citizen science data; these two pools of data have tradeoffs in terms of data density, spatiotemporal coverage, and accuracy. Recent studies have shown that combining structured and unstructured survey data can greatly improve the accuracy of species distribution models for birds, but most of this work has focused on north temperate bird species and uses bird atlas data that is much more common in the temperate zone than elsewhere. We sought to adapt a data pooling approach from the literature on north temperate bird biology to create distribution models for a selection of secretive suboscine bird species that occur in a highly diverse region of the southwestern Amazon.