In the face of unprecedented anthropogenic change, we increasingly turn to emergent technologies and extensive data sets for solutions that complement much needed systemic changes in our societies. These technological solutions, however, must be approached with care. We must recognize and address biases in the way data has been accumulated. In demographic studies, dormant life stages, such as seed banks, and other cryptic factors have often been neglected. The potential consequences of these omissions have been extensively described in the literature. In the first chapter, I analyze patterns of seed bank omissions in demographic models, finding unjustified omissions are widespread across ecoregions, growth forms, and taxonomic groups. More importantly, studies with longer duration and accounting for a greater range of conditions were less likely to neglect the seed bank. Unfortunately, most demographic studies are conducted for relatively short periods and are limited to one or a few sites. In addition, modeling tools often focus on mean behavior and do not properly characterize variability. In the second chapter, I explore the use of Bayesian generalized linear mixed models to characterize vital rates and compare their variation across growing conditions. Using wild and translocated populations of Dicerandra christmanii,this study tests the ability of this approach to evaluate early translocation success and site suitability.In chapter 3, I expand the demographic analysis of Dicerandra christmaniiand provide an example of the use of Bayesian-fitted Integral Projection Models (IPMs) to combine data sources and incorporate seed dynamics into demographic models. By propagating uncertainty from vital rates to derived population metrics, this study illustrates the consequences of accounting for the seed bank stage and site condition to our assessment of population viability. In the final part of this work, I present potential routes to expand field and modeling tools to help address the inclusion of dormant and cryptic life stages into demographic studies. Among these, I recommend exploring more complex sampling schemes, informed priors, and expanded IPMs.