  The sample frequency spectrum (SFS) is a widely-used summary statistic of
genomic variation in a sample of homologous DNA sequences. It provides a highly
efficient dimensional reduction of large-scale population genomic data and its
mathematical dependence on the underlying population demography is well
understood, thus enabling the development of efficient inference algorithms.
However, it has been recently shown that very different population demographies
can actually generate the same SFS for arbitrarily large sample sizes. Although
in principle this nonidentifiability issue poses a thorny challenge to
statistical inference, the population size functions involved in the
counterexamples are arguably not so biologically realistic. Here, we revisit
this problem and examine the identifiability of demographic models under the
restriction that the population sizes are piecewise-defined where each piece
belongs to some family of biologically-motivated functions. Under this
assumption, we prove that the expected SFS of a sample uniquely determines the
underlying demographic model, provided that the sample is sufficiently large.
We obtain a general bound on the sample size sufficient for identifiability;
the bound depends on the number of pieces in the demographic model and also on
the type of population size function in each piece. In the cases of
piecewise-constant, piecewise-exponential and piecewise-generalized-exponential
models, which are often assumed in population genomic inferences, we provide
explicit formulas for the bounds as simple functions of the number of pieces.
Lastly, we obtain analogous results for the "folded" SFS, which is often used
when there is ambiguity as to which allelic type is ancestral. Our results are
proved using a generalization of Descartes' rule of signs for polynomials to
the Laplace transform of piecewise continuous functions.
