Biologists now use a variety of survey platforms to assess the spatial distribution and abundance of marine birds, yet few attempts have been made to integrate data from multiple survey platforms to improve model accuracy or precision. We used density surface models (DSMs) to incorporate data from two survey platforms to predict the distribution and abundance of a diving marine bird, the Common Loon (Gavia immer). We conducted strip transect surveys from a multiengine, fixed-wing aircraft and line surveys from a 28 m ship during winter 2009–2010 in a 3,800 km2 study area off the coast of Rhode Island, USA. We accounted for imperfect detection and availability bias due to Common Loon diving behavior. We incorporated spatially explicit environmental covariates (water depth and latitude) to provide predictions of the spatial distribution and abundance of wintering Common Loons. The combined-platform DSM estimated the highest Common Loon densities (>20 individuals km−2) in nearshore waters <35 m deep, with an average daily abundance of 5,538 (95% CI = 4,726–6,489) individuals in the study area. The combined-platform model offered substantial improvement in the precision of abundance estimates from the ship-platform model, and modest improvement in the precision of the aerial-platform model. The combined model had relatively low predictive power, which previous research indicates is primarily a consequence of the dynamic marine environment. We show that the DSM approach presents a flexible framework for developing spatially explicit models of a marine bird from different survey protocols.
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