Data from: Inferring dispersal across a fragmented landscape using reconstructed families in the Glanville fritillary butterfly

Dispersal is important for determining both a species ecological processes, such as population viability, and its evolutionary processes, like gene flow and local adaptation. Yet obtaining accurate estimates in the wild through direct observation can be challenging or even impossible, particularly over large spatial and temporal scales. Genotyping many individuals from wild populations can provide detailed inferences about dispersal. We therefore utilized genomewide marker data to estimate dispersal in the classic metapopulation of the Glanville fritillary butterfly (Melitaea cinxia L.), in the Åland Islands in SW Finland. This is an ideal system to test the effectiveness of this approach due to the wealth of information already available covering dispersal across small spatial and temporal scales, but lack of information at larger spatial and temporal scales. We sampled three larvae per larval family group from 3,732 groups over a six-year period and genotyped for 272 SNPs across the genome. We used this empirical dataset to reconstruct cases where full-sibs were detected in different local populations to infer female effective dispersal distance, i.e. dispersal events directly contributing to gene flow. On average this was one kilometer, closely matching previous dispersal estimates made using direct observation. To evaluate our power to detect full-sib families we performed forward simulations using an individual-based model constructed and parameterized for the Glanville fritillary metapopulation. Using these simulations 100% of predicted full-sibs were correct and over 98% of all true full-sib pairs were detected. We therefore demonstrate that even in a highly dynamic system with a relatively small number of markers, we can accurately reconstruct full-sib families and for the first time make inferences on female effective dispersal. This highlights the utility of this approach in systems where it has previously been impossible to obtain accurate estimates of dispersal over both ecological and evolutionary scales.扩散（dispersal）对于决定物种的生态过程（如种群生存力）与进化过程（如基因流与局域适应）均至关重要。然而，在野外通过直接观测获取准确的扩散估算值往往极具挑战，甚至不可能实现，尤其是在较大的空间与时间尺度下。对野生种群中的大量个体进行基因分型，可为扩散研究提供详尽的推论。因此，我们利用全基因组标记（genomewide marker）数据，对芬兰西南部奥兰群岛上的经典格兰维尔蛱蝶（Glanville fritillary butterfly, Melitaea cinxia L.）复合种群（metapopulation）的扩散情况进行估算。该系统是验证此方法有效性的理想模型，因为此前已有大量关于小尺度空间与时间范围内扩散的研究数据，但大尺度相关信息仍较为匮乏。我们在六年时间内，从3732个幼虫家族组中各采集3头幼虫，对基因组上的272个单核苷酸多态性（Single Nucleotide Polymorphism, SNP）位点进行基因分型。我们利用该实证数据集，重构出全同胞个体（full-sibs）分布于不同局域种群的案例，以此推断雌性有效扩散距离——即直接贡献于基因流的扩散事件。平均而言，该距离为1公里，与此前通过直接观测得到的扩散估算值高度吻合。为评估我们检测全同胞家系的能力，我们针对格兰维尔蛱蝶复合种群构建并参数化了个体基模型（individual-based model），并利用该模型进行正向模拟。通过这些模拟，100%的预测全同胞对均为正确结果，且超过98%的真实全同胞对被成功检测到。因此，我们证明了，即便在一个高度动态且标记数量相对较少的系统中，我们仍可准确重构全同胞家系，并首次针对雌性有效扩散做出推论。这凸显了该方法在此前无法获取生态与进化尺度上准确扩散估算的系统中的应用价值。