Ecosystem services provided by coral-dinoflagellate mutualisms rival the contributions of other widely studied symbioses. In the coral-dinoflagellate mutualism, each partner benefits as the coral receives photosynthetic sugars from their dinoflagellate symbionts and the algal symbiont receives nutrients and protection in return (Trench, 1979). Though decades of investigations have probed the

Mutualisms where hosts are coupled metabolically to their symbionts often exhibit high partner fidelity. Most reef-building corals form obligate symbioses with 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 hybrid A. prolifera) are geographically sympatric across much of their range in the greater Caribbean, but often occupy different depth and light habitats. Both species and their hybrid associate with Symbiodinium ‘fitti’, a genetically diverse species of symbiont that is specific to these hosts. Since the physiology of the dinoflagellate partner is strongly influenced by light (and therefore depth), we investigated whether S. ‘fitti’ populations from each host source were differentiated genetically. We generated shallow genome sequences of acroporid colonies sampled from across the Caribbean. Single Nucleotide Polymorphisms (SNPs) among S. ‘fitti’ strains were identified by aligning sequences to a ~600 Mb draft assembly of the S. ‘fitti’ genome, assembled from an A. cervicornis metagenome. Phylogenomic and multivariate analyses revealed that allelic variation among S. ‘fitti’ partitioned to each host species, as well as their hybrid, rather than by biogeographic origin. This is particularly noteworthy because the hybrid, A. prolifera, has a sparse fossil record and may be of relatively recent origin. Many of the SNPs putatively under selection were non-synonymous mutations predicted to alter protein efficiency. Differences in allele frequency among S. ‘fitti’ populations from each host taxon may correspond to distinct phenotypes that thrive in the different cellular environments found in each acroporid. The non-random sorting among genetically diverse strains, or genotypes, to different hosts could be the basis for lineage diversification via disruptive selection, leading to ecological specialization and ultimately speciation.

The software program STRUCTURE relies on a Bayesian iterative clustering algorithm to group samples using multi-locus

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.