The development of cancer from healthy somatic cells is fundamentally an evolutionary process. Understanding this stochastic process is vital to improve its clinical management and develop new treatment strategies effectively. Tracing individual human cells’ fates would simultaneously advance somatic evolutionary theory and our understanding of normal tissue function and its change through development and aging. While we cannot use human cell-labeling techniques in vivo, population genetic and phylogenetic models can leverage the information recorded in one of multiple kinds of heritable alterations to reconstruct the unobserved history of somatic cells. Most such methods are limited by the few parameters they can estimate and are usually restricted to neoplastic samples due to the lack of adequate evolutionary signal in healthy somatic cells. Gabbutt et al. recently ameliorated this limitation by introducing fluctuating methylation clocks (FMCs)—tissue-specific CpG sites that randomly change their methylation state—and a model that uses them to reconstruct the evolution of a clonal stem-cell population. In this application, all FMCs in a bulk sample are interrogated using a single standard methylation array to yield estimates of rates of methylation, demethylation and cell replacement, and the size of the stem-cell niche. This technique is especially well suited to studying stem-cell niche dynamics in healthy or pre-malignant tissues organized in clonal cell populations where the whole population can be sampled together, like crypts (e.g., intestine, Barrett’s esophagus) and glands (e.g., endometrium). Here, we introduce a multi-sample extension of this model that simultaneously reconstructs the evolutionary dynamics of stem cells within a niche and the niches within the tissue. This phylodynamic model produces additional estimates like ancestral relationships between sampled clonal populations, their calendar divergence times, the number of effective evolutionary niches in the tissue, and how some of these parameters change over time. In this communication, we will present the method, demonstrate its validity, characterize its accuracy under a broad biologically-reasonable simulation parameter space, and showcase its application in analyzing human data from the following tissues: colon, small intestine, endometrium, and Barrett’s esophagus. We will also discuss the assumptions and limitations of the current model and future improvements that will allow discerning between different mechanisms of stem-cell-niche reproduction and incorporating geographical information. The stochastic nature of neoplastic progression makes it unlikely for any particular somatic alteration to be highly predictive of its outcome. This can explain why developing reliable traditional biomarkers has been so difficult. Alternatively, evolutionary biomarkers measure the characteristics of the process itself and thus should apply to all neoplasms. Parametric phylogenetic reconstruction models like the one introduced here will enable us to develop such universal biomarkers. Our ultimate goal is for evolutionary biomarkers to join evolutionary therapies in the revolution of the clinical management of cancer.