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学过程（如种群生存力）与进化过程（如基因流与局部适应）均至关重要。然而，通过直接观测在野外获取准确的扩散估计值往往颇具挑战，甚至无法实现，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，能够为扩散推断提供详尽的依据。因此，我们借助全基因组标记数据，对芬兰西南部奥兰群岛上经典格纹蛱蝶（Melitaea cinxia L.）的集合种群（metapopulation）扩散情况进行了估计。该系统是验证此方法有效性的理想模型：此前已积累大量小尺度空间与时间范围内的扩散研究数据，但大尺度下的相关信息仍较为匮乏。我们在六年时间内，从3732个幼虫家族群中各取样3只幼虫，并对基因组内的272个单核苷酸多态性（Single Nucleotide Polymorphism, SNP）位点进行了基因分型。基于该实证数据集，我们重构了在不同局部种群中检测到全同胞个体的案例，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，该扩散距离为1公里，与此前通过直接观测得到的扩散估计值高度吻合。为评估我们检测全同胞家族的能力，我们利用针对格纹蛱蝶集合种群构建并参数化的基于个体的模型（individual-based model）开展了正向模拟（forward simulations）。结果显示，通过模拟得到的预测全同胞对准确率达100%，且可检测到超过98%的真实全同胞对。因此，我们证明了：即便在一个高度动态的系统中，且仅使用数量相对有限的标记，我们仍可准确重构全同胞家族，并首次实现雌性有效扩散的推断。这一结果凸显了该方法在此前难以获取生态与进化尺度下准确扩散估计值的研究系统中的应用价值。