Phylogeographic ancestral inference is a question frequently arising in population ecology, aiming to understand the geographical roots and structure of species. Here we specifically address relatively small scale mtDNA datasets (typically less than 500 sequences with less than 1000 nucleotides), focusing on ancestral location inference. Our approach uses a coalescent modelling framework projected onto haplotype trees in order to reduce computational complexity, at the same time adhering to complex evolutionary processes. Statistical innovations of the last few years have allowed for computationally feasible yet accurate inferences in phylogenetic frameworks. We implement our methods on a set of synthetic datasets and show how, despite high uncertainty in terms of identifying the root haplotype, estimation of the ancestral location naturally encompasses lower uncertainty, allowing us to pinpoint the Maximum A Posteriori estimates for ancestral locations. We exemplify our methods on a set of synthetic datasets and then combine our inference methods with the phylogeographic clustering approach presented in Manolopoulou et al. (2011) on a real dataset from weevils in the Iberian peninsula in order to infer ancestral locations as well as population substructure.