Model-based predictors derived from historical data are rarely evaluated before they are used to draw inferences. We performed a temporal validation, (i.e. assessed the performance of a predictive model using data collected from the same population after the model was developed) of a statistical predictor for the number of successful breeding pairs of wolves Canis lupus in the northern Rocky Mountains (NRM). We predicted the number of successful breeding pairs, β, in Idaho, Montana and Wyoming based on the distribution of pack sizes observed through monitoring in 2006 and 2007 (β̂), and compared these estimates to the minimum number of successful breeding pairs, β_MIN, observed through intensive monitoring. β_MIN was consistently included within the 95% confidence intervals of β̂ for all states in both years (except for Idaho in 2007), generally following the pattern β̂_L (lower 95% prediction interval for β̂) < β̂_MIN < β̂. This evaluation of β̂ estimates for 2006 and 2007 suggest it will be a robust model-based method for predicting successful breeding pairs of NRM wolves in the future, provided influences other than those modeled in β̂ (e.g. disease outbreak, severe winter) do not have a strong effect on wolf populations. Managers can use β̂ models with added confidence as part of their post-delisting monitoring of wolves in NRM.