The impact of estimator choice: Disagreement in clustering solutions across K estimators for Bayesian analysis of population genetic structure across a wide range of empirical data sets.

The software program STRUCTURE is one of the most cited tools for determining population structure. To infer the optimal number of clusters from STRUCTURE output, the ΔK method is often applied. However, a recent study relying on simulated microsatellite data suggested that this method has a downward bias in its estimation of K and is sensitive to uneven sampling. If this finding holds for empirical data sets, conclusions about the scale of gene flow may have to be revised for a large number of studies. To determine the impact of method choice, we applied recently described estimators of K to re-estimate genetic structure in 41 empirical microsatellite data sets; 15 from a broad range of taxa and 26 from one phylogenetic group, coral. We compared alternative estimates of K (Puechmaille statistics) with traditional (ΔK and posterior probability) estimates and found widespread disagreement of estimators across data sets. Thus, one estimator alone is insufficient for determining the optimal number of clusters; this was regardless of study organism or evenness of sampling scheme. Subsequent analysis of molecular variance (AMOVA) did not necessarily clarify which clustering solution was best. To better infer population structure, we suggest a combination of visual inspection of STRUCTURE plots and calculation of the alternative estimators at various thresholds in addition to ΔK. Disagreement between traditional and recent estimators may have important biological implications, such as previously unrecognized population structure, as was the case for many studies reanalysed here.

Genomic variation of an endosymbiotic dinoflagellate (Symbiodinium 'fitti') among closely related coral hosts.

Mutualisms where hosts are coupled metabolically to their symbionts often exhibit high partner fidelity. Most reef-building coral species form obligate symbioses with a specific species of photosymbionts, dinoflagellates in the family Symbiodiniaceae, despite needing to acquire symbionts early in their development from environmental sources. Three Caribbean acroporids (Acropora palmata, A. cervicornis and their F1 hybrid) are sympatric across much of their range, but often occupy different depth and light habitats. Throughout this range, both species and their hybrid associate with the endosymbiotic dinoflagellate Symbiodinium 'fitti'. Because light (and therefore depth) influences the physiology of dinoflagellates, we investigated whether S. 'fitti' populations from each host taxon were differentiated genetically. Single nucleotide polymorphisms (SNPs) among S. 'fitti' strains were identified by aligning shallow metagenomic sequences of acroporid colonies sampled from across the Caribbean to a ~600-Mb draft assembly of the S. 'fitti' genome (from the CFL14120 A. cervicornis metagenome). Phylogenomic and multivariate analyses revealed that genomic variation among S. 'fitti' strains partitioned to each host taxon rather than by biogeographical origin. This is particularly noteworthy because the hybrid has a sparse fossil record and may be of relatively recent origin. A subset (37.6%) of the SNPs putatively under selection were nonsynonymous mutations predicted to alter protein efficiency. Differences in genomic variation of S. 'fitti' strains from each host taxon may reflect the unique selection pressures created by the microenvironments associated with each host. The nonrandom sorting among S. 'fitti' strains to different hosts could be the basis for lineage diversification via disruptive selection, leading to ecological specialization and ultimately speciation.