<strong>Unravelling the role of oceanographic connectivity in intra-specific diversity of marine forests at global scale</strong>

<br>Table S6: Estimates of oceanographic connectivity between marine forest populations. Species, genetic marker (Marker), genetic differentiation index (Diff.Index), dataset speciality (Spec), hexagon ID of “from” sampling site (Site.From), longitude of “from” sampling site centroid (Site.from.Lon), latitude of “from” sampling site centroid (Site.from.Lat), hexagon ID of “to” sampling site (Site.From), longitude of “to” sampling site centroid (Site.from.Lon), latitude of “to” sampling site centroid (Site.from.Lat), longitude of “from” sampling population (Sample.from.Lon), latitude of “from” sampling population (Sample.from.Lat), longitude of “to” sampling population (Sample.to.Lon), latitude of “to” sampling population (Sample.to.Lat), genetic differentiation (Gen.Diff), oceanographic connectivity of probability (Con.Prob), distance-transformed oceanographic connectivity of probability (Con.Dist), oceanographic connectivity of probability using a fixed PD of. 7.43 d (Con.Prob.Fixed), distance-transformed oceanographic connectivity of probability using a fixed PD of. 7.43 d (Con.Dist.Fixed), harmonic centrality of “from” sampling site (HC.From), harmonic centrality of “to” sampling site (HC.to), number of stepping-stones (Step.Stones), number of stepping-stones using a fixed PD of. 7.43 d (Step.Stones.Fixed) are reported.<br>Figure S7: Connectivity networks and fit between predicted and observed genetic differentiation for each dataset. Panel A depicts the network’ relative probability of oceanographic connectivity between sampled populations. Note that multi-generations connections are depicted, and not stepping-stone pathways. Panel B displays the predicted genetic differentiation with CCM against the observed genetic differentiation. Dotted black lines indicate the linear regression trend and blue shading represents the corresponding 95 % confidence interval.Figure S8: Hexagons implementation of the biophysical model. Each hexagons as an edge length of 8.45 km. Within this global coastline representation, we selected for the purpose of our study hexagons that overlaid the global marine forest distribution, totalling 16,489 hexagons. Geo-referenced data is available as a shapefile. GitHub_Data: Data to generate results, figures and supplementary information throught the R code avalaible in the GitHub repository (https://github.com/TerenceLegrand/connectivity_of_marine_forests)

附表S6：海洋森林种群间海洋学连通性估算。本报告涵盖以下参数：物种、遗传标记（Marker）、遗传分化指数（Diff.Index）、数据集特性（Spec）、「来源」采样样地六边形ID（Site.From）、「来源」采样样地质心经度（Site.from.Lon）、「来源」采样样地质心纬度（Site.from.Lat）、「目标」采样样地六边形ID（Site.From）、「目标」采样样地质心经度（Site.from.Lon）、「目标」采样样地质心纬度（Site.from.Lat）、「来源」采样种群经度（Sample.from.Lon）、「来源」采样种群纬度（Sample.from.Lat）、「目标」采样种群经度（Sample.to.Lon）、「目标」采样种群纬度（Sample.to.Lat）、遗传分化（Gen.Diff）、概率型海洋学连通性（Con.Prob）、经距离变换的概率型海洋学连通性（Con.Dist）、使用固定传播延迟（PD）7.43天的概率型海洋学连通性（Con.Prob.Fixed）、使用固定传播延迟7.43天的经距离变换的概率型海洋学连通性（Con.Dist.Fixed）、「来源」采样样地谐波中心性（HC.From）、「目标」采样样地谐波中心性（HC.to）、踏脚石路径数量（Step.Stones）、使用固定传播延迟7.43天的踏脚石路径数量（Step.Stones.Fixed）。附图S7：各数据集的连通性网络及预测与观测遗传分化的拟合情况。图A展示了采样种群间海洋学连通性的相对概率网络，需注意本图呈现的是多世代连通关系，而非踏石扩散通路。图B绘制了基于CCM的预测遗传分化与观测遗传分化的对应关系，黑色虚线代表线性回归趋势线，蓝色阴影区域对应95%置信区间。附图S8：生物物理模型的六边形格网实现方案。每个六边形的边长为8.45 km。在该全球海岸线可视化框架内，本研究选取了与全球海洋森林分布叠加的六边形格网，共计16489个。带地理参考的数据以形状文件（shapefile）形式提供。GitHub数据集：用于生成研究结果、图表及补充信息的代码与数据可通过GitHub仓库（https://github.com/TerenceLegrand/connectivity_of_marine_forests）中提供的R代码获取。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于全球尺度下海洋连通性对海洋森林种内多样性的作用，包含海洋连通性估计、遗传分化数据以及生物物理模型的空间实现。数据集提供了原始表格、图形和代码，支持对基因流、多代连通性等宏观遗传学问题的分析，适用于生物海洋学和生态连通性研究。