Many new technologies are becoming available to assist with the assessment of fish stocks. These novel datasets have tremendous potential that has not yet been fully explored. One such technology is the drop camera, an underwater camera system lowered into the water column from a vessel to capture video or still images of the seafloor. We develop here straightforward methods for the inclusion of drop camera data within a spatio-temporal stock assessment model, and explore associated benefits via simulations. Results show that comparing drop-camera data and traditional survey data can help estimate survey catchability, while also identifying conditions under which each dataset outperforms the other. A case study on the sea scallop population on Georges Bank highlights this newfound ability to estimate catchability parameters, and demonstrates how to include both datasets within the same statistical framework. This research reveals that incorporating new types of data can substantially improve stock assessment models, but cannot fully replace traditional surveys due to the inability of these new technologies to provide critical data such as weight measurements.
McDonald et al. (2026) studied this question.