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The evolution of phylogeographic data sets
- Garrick, Ryan C., Bonatelli, Isabel A. S., Hyseni, Chaz, Morales, Ariadna, Pelletier, Tara A., Perez, Manolo F., Rice, Edwin, Satler, Jordan D., Symula, Rebecca E., Thomé, Maria Tereza C., Carstens, Bryan C.
- Molecular ecology 2015 v.24 no.6 pp. 1164-1171
- alleles, analytical methods, birds, chloroplast DNA, data collection, ecology, evolution, fish, high-throughput nucleotide sequencing, loci, models, single nucleotide polymorphism, surveys
- Empirical phylogeographic studies have progressively sampled greater numbers of loci over time, in part motivated by theoretical papers showing that estimates of key demographic parameters improve as the number of loci increases. Recently, next‐generation sequencing has been applied to questions about organismal history, with the promise of revolutionizing the field. However, no systematic assessment of how phylogeographic data sets have changed over time with respect to overall size and information content has been performed. Here, we quantify the changing nature of these genetic data sets over the past 20 years, focusing on papers published in Molecular Ecology. We found that the number of independent loci, the total number of alleles sampled and the total number of single nucleotide polymorphisms (SNPs) per data set has improved over time, with particularly dramatic increases within the past 5 years. Interestingly, uniparentally inherited organellar markers (e.g. animal mitochondrial and plant chloroplast DNA) continue to represent an important component of phylogeographic data. Single‐species studies (cf. comparative studies) that focus on vertebrates (particularly fish and to some extent, birds) represent the gold standard of phylogeographic data collection. Based on the current trajectory seen in our survey data, forecast modelling indicates that the median number of SNPs per data set for studies published by the end of the year 2016 may approach ~20 000. This survey provides baseline information for understanding the evolution of phylogeographic data sets and underscores the fact that development of analytical methods for handling very large genetic data sets will be critical for facilitating growth of the field.