Data deficiencies are a fact of life for ecologists, but forecasting can highlight when these matter and when they do not. Managers need to make decisions about the future based on data from the past, which is inherently an out-of-sample data problem for current models, highlighting the role of validation for models, especially those with data deficiencies. We present examples from northern mountain caribou and boreal caribou (Rangifer tarandus caribou), in the far north and boreal Canada respectively, habitat selection analyses that highlight when some data deficiencies matter and when they do not. Our results highlight how model complexity relates to data deficiencies and when they matter in models when forecasting both spatially and/or temporally. Based on these findings, we developed a method that provides a scalable framework for estimating population status in data-deficient contexts and may be broadly applicable to other species of conservation concern. In the case of managed species that cross jurisdictional boundaries, this study also provides evidence for improved predictions when data outside jurisdictions are used together.

Keywords: validation, spatio-temporal forecasting, habitat selection, demography