Indices of abundance are commonly used in fisheries stock assessment models to represent trends in population size over time; however, an index can misrepresent such trends when catchability varies, sampling gears change or spatial sampling frames shift. Here we develop a state-space model in a Bayesian framework that combines both chevron trap catches and video counts into a single integrated index. The modeling approach accounts for variation in sampling efficiency (catchability) of both sampling gears and adjusts for aspects of changes in the spatial sampling frame (sampling intensity and spatial coverage) through time due to monitoring program development. We validate the model using a simulation study and then demonstrate its utility using data on vermilion snapper Rhomboplites aurorubens from the period 1990–2016. The index suggests high variation in the abundance of vermilion snapper, particularly for years previous to 2000 and a systematic decline in abundance between the early 1990s and 2016. This pattern culminates (2016) with vermilion snapper at about 16% of their average early 1990s abundance which is a stronger decline than is indicated by the current index used for stock assessment of the species.