Department of Biology and Marine Program, Boston University, 5 Cummington Mall, Boston, MA, 02215, USA.
Mol Ecol. 2014 Jun;23(12):2902-13. doi: 10.1111/mec.12782. Epub 2014 Jun 10.
Detecting patterns of spatial genetic structure (SGS) can help identify intrinsic and extrinsic barriers to gene flow within metapopulations. For marine organisms such as coral reef fishes, identifying these barriers is critical to predicting evolutionary dynamics and demarcating evolutionarily significant units for conservation. In this study, we adopted an alternative hypothesis-testing framework to identify the patterns and predictors of SGS in the Caribbean reef fish Elacatinus lori. First, genetic structure was estimated using nuclear microsatellites and mitochondrial cytochrome b sequences. Next, clustering and network analyses were applied to visualize patterns of SGS. Finally, logistic regressions and linear mixed models were used to identify the predictors of SGS. Both sets of markers revealed low global structure: mitochondrial ΦST=0.12, microsatellite FST=0.0056. However, there was high variability among pairwise estimates, ranging from no differentiation between sites on contiguous reef (ΦST=0) to strong differentiation between sites separated by ocean expanses≥20 km (maximum ΦST=0.65). Genetic clustering and statistical analyses provided additional support for the hypothesis that seascape discontinuity, represented by oceanic breaks between patches of reef habitat, is a key predictor of SGS in E. lori. Notably, the estimated patterns and predictors of SGS were consistent between both sets of markers. Combined with previous studies of dispersal in E. lori, these results suggest that the interaction between seascape continuity and the dispersal kernel plays an important role in determining genetic connectivity within metapopulations.
检测空间遗传结构(SGS)模式可以帮助识别基因流在复合种群内的内在和外在障碍。对于珊瑚礁鱼类等海洋生物,确定这些障碍对于预测进化动态和划定具有保护意义的进化单位至关重要。在这项研究中,我们采用了替代的假设检验框架来识别加勒比海礁鱼 Elacatinus lori 的 SGS 模式和预测因子。首先,使用核微卫星和线粒体细胞色素 b 序列估计遗传结构。接下来,应用聚类和网络分析来可视化 SGS 模式。最后,使用逻辑回归和线性混合模型来识别 SGS 的预测因子。这两组标记物都显示出低的全局结构:线粒体 ΦST=0.12,微卫星 FST=0.0056。然而,成对估计值之间存在很大的可变性,范围从相邻珊瑚礁之间的站点没有分化(ΦST=0)到海洋扩张之间的站点强烈分化≥20 公里(最大 ΦST=0.65)。遗传聚类和统计分析为以下假设提供了额外的支持:即海洋景观的不连续性,由珊瑚礁栖息地斑块之间的海洋中断表示,是 E. lori SGS 的关键预测因子。值得注意的是,两组标记物的 SGS 估计模式和预测因子是一致的。结合先前对 E. lori 扩散的研究,这些结果表明,景观连续性和扩散核之间的相互作用在确定复合种群内的遗传连通性方面起着重要作用。