Quantification of regional or national variation in population trends is an integral component of assessing species conservation status, and ideally uses spatial and temporal replicate surveys across the breeding or wintering ranges. However, populations of boreal-breeding birds are often monitored using site-specific trends in the number of individuals counted migrating through more populated regions en route to or from their breeding grounds. Combining data across sites to quantify regional variation in a population trend has not occurred, and would rely on the success of model selection to choose the model that best describes the underlying pattern of regional population change. Using simulated daily counts of migrating birds with known regional rate(s) of population trend, we tested the utility of Akaike's Information Criterion (AIC) for detecting regional variation in population trend by comparing a series of hierarchical models that estimated nationally or regionally varying trends. When trends varied regionally, AIC successfully ranked the model that best described the regional pattern of population trend as the top model 100% of the time using 40-year time series, and with as few as 3 monitoring sites in each region. When trends did not vary among regions, the more highly parameterized (incorrect) model with regionally varying trends was ranked as the top model ≤20% of the time, regardless of the length of time series or number of sites in a region. In this case, the incorrect regional model resulted in less precise trends but also comparable or higher power and comparable or lower probability of drawing false inference from the data than did the correct model. Compared with sampling 10 sites/region over a 20-year time period, sampling as few as 3 sites/region over a 40-year time period can result in a lower probability of detecting a false trend. Promoting long-term monitoring at fewer sites can also minimize the financial and logistical resources required to maintain migration monitoring programs, and allow more recent population change to be interpreted within the context of long-term population fluctuations.