Dataset: Systematics of the color-polymorphic spider genus Cybaeolus, with comments on the phylogeny of the family Hahniidae (Araneae)

Phylogenetic analysis of the spiders of the genus Cybaeulus, with outgroups in the marronoid clade. Data from six DNA markers, analyzed with maximum likelihood and parsimony. PHYLOGENETIC ANALYSIS We obtained sequences from 26 samples of the three known species of Cybaeolus, and of five additional species of Hahniidae. To these, we added legacy sequences of Cybaeolus and of other genera of Hahniidae, as well as representatives of the remaining families in the marronoid clade. For the new sequences, the extraction and amplification of DNA was made in the Laboratory of Molecular Tools at Museo Argentino de Ciencias Naturales (MACN), from tissues preserved in absolute alcohol at -18ºC. We targeted the markers histone H3 (H3), cytochrome oxidase subunit I (CO1), 28S ribosomal RNA (28S) and 16S ribosomal RNA (16S), previously used to estimate relationships of marronoid spiders (Wheeler et al., 2017). Details of extraction, primers and PCR protocols are the same as in Magalhaes & Ramírez (2022). Sequencing was outsourced to Macrogen Inc., South Korea. The resulting chromatograms were analyzed individually to detect contaminated sequences or ambiguous portions. In addition to these sequences obtained in the laboratory, we combined our data with additional sequences from previous work (Wheeler et al., 2017; Rivera-Quiroz et al., 2020), using the markers mentioned above plus 12S ribosomal RNA (12S) and 18S ribosomal RNA (18S). For the CO1 marker, additional sequences obtained by the Arachnology Division at MACN and deposited in the BOLDSYSTEMS platform (https://www.boldsystems.org/) were also used. Sequences were aligned with MAFFT Online v.7.463 (Katoh & Standley, 2013), using the L-INS-I algorithm. See Table 1 for list of vouchers and sequence identifiers. Maximum likelihoodFor the maximum likelihood analyses we used the program IQ-TREE 2.2.0 (Minh et al., 2020), partitioning the data by marker, and selecting the best combination of partitions and evolution models by Bayesian information criterion (best fitting models were TPM2+I+G4 for H3, GTR+F+I+G4 for 18S, GTR+F+I+G4 for 16S and 12S together, GTR+F+I+G4 for CO1, and GTR+F+I+G4 for 28S). Since the relationships of outgroup taxa in the resulting trees were slightly different to that found in recent phylogenomic studies, we used the study of Gorneau et al. (2023) based on ultraconserved elements as a backbone topology to constrain our tree search, considering only the taxa in common with our analysis (see supplementary Fig. S1); this means that all the rest of the taxa are free to move anywhere during tree search. Support for groups (branches) was estimated by 1000 cycles of ultrafast bootstrapping. Ten independent runs were performed; of those, six converged into nearly identical log likelihood values (-57417.7725 to -57417.9604) and identical topologies; the tree with top-ranking log likelihood is presented in Results, after collapsing branches with bootstrap below 0.5. To estimate the support of an alternative topology with Cybaeolus as sister to the rest of the hahniids, we used TNT 1.6 (Goloboff & Morales, 2023) to modify the optimal tree placing Cybaeolus in such position, and asked for the frequency of the branch of interest (all hahniids except Cybaeolus) in the 1000 bootstrapped trees previously saved by IQTREE.Ancestral character states for the arrangement of spinnerets (grouped; separated in a transversal line) were estimated by maximum likelihood on the optimal tree, using the R packages phytools and ape, under the models ER and ARD, and the best fitting model selected by the Akaike information criterion. ParsimonyFor the parsimony analyses we used TNT 1.6. For the equal weights analysis, a heuristic search was made using a driven search with the default parameters of the “new technologies”, aiming for 10 independent hits to minimum length. The resulting trees were then submitted to an additional round of tree-bisection reconnection (TBR) branch swapping. These results were compared to a simpler search strategy of 300 random addition sequences, each followed by TBR, which produced 20 hits to minimal length. As both strategies reached the same trees with multiple independent hits, it is likely that the optimal trees were found. Finally, the strict consensus of all the optimal trees was obtained, and on this consensus the support values were calculated by means of 1000 bootstrap pseudoreplicates.针对Cybaeulus属蜘蛛的系统发育分析，以marronoid演化支类群作为外类群。数据来源于6个DNA分子标记，采用最大似然法与简约法开展分析。 ## 系统发育分析方法 我们从已知的3个Cybaeulus物种以及5个Hahniidae（暗蛛科）的额外物种的26份样本中获取了序列。此外，我们还加入了Cybaeulus及其他Hahniidae属类群的历史序列，以及marronoid演化支中其余类群的代表物种。针对新获得的序列，DNA提取与扩增工作于阿根廷自然历史博物馆（Museo Argentino de Ciencias Naturales, MACN）的分子工具实验室完成，样本取自-18℃无水乙醇保存的组织。本研究靶向的分子标记包括组蛋白H3（histone H3, H3）、细胞色素氧化酶亚基I（cytochrome oxidase subunit I, CO1）、28S核糖体RNA（28S ribosomal RNA, 28S）及16S核糖体RNA（16S ribosomal RNA, 16S），上述标记此前被用于推断marronoid蜘蛛的系统发育关系（Wheeler等，2017）。提取流程、引物序列及PCR方案与Magalhaes & Ramírez (2022) 中的方法完全一致。测序工作委托韩国Macrogen Inc.公司完成。对所得测序色谱图进行单独分析，以检测污染序列或存在歧义的区段。除上述实验室获得的序列外，我们还将本研究数据与此前发表的研究（Wheeler等，2017；Rivera-Quiroz等，2020）中的额外序列进行整合，使用的标记除上述4个外，还新增12S核糖体RNA（12S ribosomal RNA, 12S）与18S核糖体RNA（18S ribosomal RNA, 18S）。针对CO1标记，我们还使用了MACN蛛形学分部获取并上传至BOLDSYSTEMS平台（https://www.boldsystems.org/）的额外序列。序列比对采用MAFFT Online v.7.463（Katoh & Standley, 2013），使用L-INS-I算法。样本凭证及序列标识符详见表1。 ### 最大似然法分析 我们使用IQ-TREE 2.2.0程序（Minh等，2020）开展最大似然分析，按分子标记对数据进行分区，并通过贝叶斯信息准则筛选最优的分区组合与进化模型（最优模型分别为：H3对应TPM2+I+G4，18S对应GTR+F+I+G4，16S与12S合并对应GTR+F+I+G4，CO1对应GTR+F+I+G4，28S对应GTR+F+I+G4）。鉴于所得系统发育树中外类群类群的拓扑关系与近期系统基因组学研究的结果存在小幅差异，我们以Gorneau等（2023）基于超保守元件的研究作为拓扑框架约束本研究的树搜索过程，仅纳入本分析中共有的类群（详见补充图S1）；这意味着其余所有类群在树搜索阶段可自由调整位置。类群（分支）的支持度通过1000次超快速自展循环进行估算。本研究共开展10次独立运行，其中6次收敛于几乎一致的对数似然值（-57417.7725至-57417.9604）及完全一致的拓扑结构；结果部分展示的是对数似然值最高的树，已将自展支持度低于0.5的分支进行了收缩。为估算以Cybaeulus作为其余暗蛛科类群姐妹群的替代拓扑结构的支持度，我们使用TNT 1.6（Goloboff & Morales, 2023）修改最优树，将Cybaeulus置于该指定位置，并统计IQ-TREE此前保存的1000棵自展树中目标分支（除Cybaeulus外的所有暗蛛科类群）的出现频率。针对纺器排列（分为分组排列、横向分隔排列两类）的祖先性状状态，我们在最优树上采用最大似然法进行估算，使用R包phytools与ape，采用ER与ARD模型，并通过赤池信息准则选择最优拟合模型。 ### 简约法分析 我们使用TNT 1.6开展简约法分析。在等权分析中，采用“新技术”的默认参数进行驱动搜索的启发式搜索，目标是获得10次独立的最短树搜索结果。随后对所得树进行一轮额外的树二分-重连接（tree-bisection reconnection, TBR）分支交换操作。将上述结果与更简单的搜索策略（300次随机添加序列，每次均进行TBR分支交换）进行对比，该策略得到了20次最短树搜索结果。由于两种策略均通过多次独立搜索得到了相同的树，因此可以确认已找到最优树。最终，我们获得了所有最优树的严格一致性树，并基于该一致性树，通过1000次自展伪重复计算各分支的支持值。