Multivariate Bayesian analysis to predict invasiveness of Phytophthora pathogens

Three excel databases have been compiled to provide the background data for this manuscript. Supporting Data 1. All internal transcribed spacer GenBank records of Phytophthora (over 15,000 records). All records were checked within phylogenetic analyses to confirm the species identity. An explanation for the process used is given in the first datasheet. Supporting Data 2. All disease records for Phytophthora based on both host and country. Publicly available records of plant diseases involving Phytophthora species were collated to establish a global database of Phytophthora diseases. Reports of formally and provisionally described species for which host genus or species information was available were included until March 2022. Reports were scoured from plant disease databases and large collections including: • United States Department of Agriculture (USDA), global fungal database (Farr and Rossman 2022) using the R package (rusda – Krah 2016, n = 8 497 reports), • Centre for Agriculture and Bioscience International (CABI), • The New Zealand Forest Research (Scion) forest health database (collected through correspondence = 1 504 records), • Landcare Research, New Zealand fungi database (collected through correspondence = 1 559 records), • Australia Phytophthora disease records (sourced from Burgess et al. 2021 = 7 629 records), and • NCBI Genbank records (manual download Appendix S1 = 12 957 records). Additional plant disease records not available in the above databases were manually downloaded Supporting Data 3. A trait database for all described Phytophthora species including morphological and physiological traits, the type of disease caused by each species and the environments where the different species have been recovered. The morphological and physiological traits were from species descriptions and the type of disease caused, and the environments where the species has been found were from disease reports. The variables collated are explained in the final datasheet of the file.本研究共构建三个Excel数据库，为本论文提供背景数据支撑。 支持数据集1：疫霉属（Phytophthora）所有内转录间隔区（internal transcribed spacer, ITS）GenBank记录（共计逾15000条）。所有记录均通过系统发育分析核验以确认物种身份，相关流程说明详见首个数据表。 支持数据集2：基于寄主与国家信息的疫霉属病害全记录。本数据集整合公开可获取的疫霉属植物病害记录，以构建全球疫霉病害数据库。截至2022年3月，所有包含寄主属或种信息的正式发表及暂定描述物种的病害报告均被纳入。数据采集自以下植物病害数据库及大型馆藏资源： • 美国农业部（United States Department of Agriculture, USDA）全球真菌数据库（Farr与Rossman, 2022），通过R包rusda（Krah, 2016）检索，共计8497条报告； • 农业与生物科学国际中心（Centre for Agriculture and Bioscience International, CABI）； • 新西兰森林研究所（New Zealand Forest Research, Scion）森林健康数据库（通过信函征集获取，共计1504条记录）； • 新西兰土地保护研究所（Landcare Research）真菌数据库（通过信函征集获取，共计1559条记录）； • 澳大利亚疫霉病害记录（源自Burgess等, 2021，共计7629条记录）； • NCBI GenBank记录（手动下载，详见附录S1，共计12957条记录）。 此外，针对上述数据库未收录的植物病害记录，本研究亦进行了手动下载补充。 支持数据集3：所有已描述疫霉属物种的性状数据库，涵盖形态与生理性状、各物种引发的病害类型，以及各物种的分离环境。其中形态与生理性状源自物种描述，病害类型及物种发现环境均来自病害报告。本数据集整合的变量说明详见文件的最后一个数据表。