Highly pathogenic avian influenza (HPAI) is a threat to poultry production. It is desirable to be able to forecast HPAI outbreaks to allow for the implementation of elevated biosecurity measures. The Bird Flu Radar tool is an early warning system for HPAI based on wild bird movement and abundance. Here we develop the wild bird movement component of the Bird Flu Radar model by exploiting abundance data, which have greater spatio-temporal coverage than movement (ring-recovery or tracking) data. We explore two approaches for estimating bird movement from abundance data, building on recent migratory connectivity studies. In the first, week-to-week movement between areas of high abundance was estimated using a graph-theoretic approach, with abundance in the intervening area also informing connectivity between locations. In the second, movement from breeding areas to wintering areas and back was simulated using an individual-based model, the parameter values of which were calibrated for each species using weekly abundance maps. The output pseudo-movements from the individual-based model were easily integrated into the long-distance movement model in the early warning system for HPAI, to update the long-distance movement estimates for all 25 wild bird study species. Overall, we find that there are fundamental shortcomings of abundance data for inferring bird movement. However, when the accuracy of abundance-derived pseudo-movements can be confirmed, then they can complement ring-recovery or tracking data. Spatio-temporal coverage is still sparser for movement data than for abundance data, and so efforts to develop methods to exploit abundance data are likely to be useful in future endeavours estimating bird movement, and in downstream applications such as forecasting HPAI transmission.