Species-specific spatiotemporal abundance for 18 waterfowl species across Canada based on annual species counts of 2,227 aerial-survey segments over a period of 25 years (1990–2015). Combining machine-learning and hierarchical regression modelling, we created maps and spatial data objects (TIF files) of recorded total indicated pairs (TIP), predicted TIP, Gaussian Markov random fields (GMRFs) and conditional predictive ordinates (CPO).

We calculated population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, using spatially explicit models on roadside and off-road point-count surveys that incorporate land cover and climate as predictors. We compared our results with population estimates from Partners in Flight (PIF) and developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the 2 estimators.