Technical Abstract: Accurate inference of genetic discontinuities between populations is an essential component of intraspecific biodiversity and evolution studies, as well as associative genetics. The most widely used methods to infer population structure are model based, Bayesian MCMC procedures that minimize Hardy Weinberg and linkage disequilibrium within subpopulations. These methods have proven useful, but suffer from large computational requirements and a dependence on modeling assumptions that may not be met in real data sets. Here we describe the development of a new approach, PCO MC, which couples an ordination method, principal coordinate analysis, to a non parametric clustering procedure, modal clustering, for the inference of population structure from multilocus genotype data. PCO MC uses data from all principal coordinate axes simultaneously to calculate a hyperdimensional probability density function (or “density landscape”), from which the number of subpopulations, and the membership within those subpopulations, is determined using a valley-seeking algorithm. Using extensive simulations, we show that this approach outperforms a Bayesian MCMC procedure when many loci (e.g. 100) are sampled, but that the Bayesian procedure is superior with few loci (e.g. 10). When presented with sufficient data, PCO MC accurately delineated subpopulations with population Fst values as low as 0.03 (G’st > 0.2), whereas the limit of resolution of the Bayesian approach was approximately Fst = 0.05 (G’st > 0.35). We draw a distinction between population structure inference for describing biodiversity as opposed to Type I error control in associative genetics. We suggest that discrete assignments, such as those produced by PCO MC, are appropriate for circumscribing units of biodiversity whereas expression of population structure as a continuous variable is more useful for case control correction in structured association studies.