@article{fdi:010070855,
  title = {{D}emographic inference through approximate-{B}ayesian-computation skyline plots},
  author = {{N}avascues, {M}. and {L}eblois, {R}. and {B}urgarella, {C}oncetta},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he skyline plot is a graphical representation of historical effective population sizes as a function of time. {P}ast population sizes for these plots are estimated from genetic data, without a priori assumptions on the mathematical function defining the shape of the demographic trajectory. {B}ecause of this flexibility in shape, skyline plots can, in principle, provide realistic descriptions of the complex demographic scenarios that occur in natural populations. {C}urrently, demographic estimates needed for skyline plots are estimated using coalescent samplers or a composite likelihood approach. {H}ere, we provide a way to estimate historical effective population sizes using an {A}pproximate {B}ayesian {C}omputation ({ABC}) framework. {W}e assess its performance using simulated and actual microsatellite datasets. {O}ur method correctly retrieves the signal of contracting, constant and expanding populations, although the graphical shape of the plot is not always an accurate representation of the true demographic trajectory, particularly for recent changes in size and contracting populations. {B}ecause of the flexibility of {ABC}, similar approaches can be extended to other types of data, to multiple populations, or to other parameters that can change through time, such as the migration rate.},
  keywords = {{M}icrosatellites ; {P}opulation genetics ; {P}opulation size change ; {G}eneralized stepwise mutation model ; {A}pproximate {B}ayesian computation},
  booktitle = {},
  journal = {{P}eer{J}},
  volume = {5},
  numero = {},
  pages = {e3530 [17 p.]},
  ISSN = {2167-8359},
  year = {2017},
  DOI = {10.7717/peerj.3530},
  URL = {https://www.documentation.ird.fr/hor/fdi:010070855},
}