Ensemble learning model identifies adaptation classification and turning points of river microbial communities in response to heatwaves

Heatwaves are a global issue that threaten microbial populations and deteriorate ecosystems. However, how river microbial communities respond to heatwaves and whether and how high temperatures exceed microbial adaptation remain unclear. In this study, we proposed four types of pulse temperature-induced microbial responses and predicted the possibility of microbial adaptation to high temperature in global rivers using ensemble machine learning models. Our findings suggest that microbial communities in parts of South American (e.g., Brazil and Chile) and Southeast Asian (e.g., Vietnam) countries are likely to change due to heatwave disturbance from 25 °C to 37 °C for consecutive days. Furthermore, the microbial communities in approximately 48.4% of the global river gauge stations are prone to fast stress inadaptation, with approximately 76.9% of these stations expected to exceed microbial adaptation after heatwave disturbances. If emissions of particulate matter with sizes not more than 2.5 μm (PM2.5, an indicator of human activities) increase by 2-fold, the number of global rivers associated with the fast stress adaptation type will decrease by ~13.7% after heatwave disturbances. Understanding microbial responses is crucially important for effective ecosystem management, especially for fragile and sensitive rivers facing heatwave events. All data and code aim to repeat the above findings.Other public data sourceFor global prediction, the physical and chemical properties of global rivers from the “GEMStat” website (https://gemstat.org/) were analyzed. A total of 6101 stations were extracted, including all physical and chemical river parameters mentioned in Table S3. Due to the scarcity of the data, their high resolution and the large extent of population shifts, human parameters were extracted at the national scale. The extracted human-related information and river parameters are provided in Table S3. Information from the nearest station calculated by the spherical distance was used to replenish the missing data. Net growth rates (number of rivers=5308) were obtained for global rivers.Population of the countries of the world: https://population.un.org/wpp/Download/Standard/Population/(Department of Economic and Social Affairs Population Dynamics)Per capita GDP: http://data.worldbank.org.cn (World Bank Database)Forest cover and education index (HDI): http://hdr.undp.org/en/data (United Nations Development Programme Human Development Reports)Carbon emissions: https://stats.oecd.org/The average annual PM2.5 concentration: healtheffects.org/https://www.stateofglobalair.org/data/#/health/mapGlobal emissions of polluting gases: https://edgar.jrc.ec.europa.eu/dataset_ap50 (Emissions Database for Global Atmospheric Research (EDGAR))Population density: http://data.un.org/Total number of tourists: https://www.unwto.org/(World Tourism Organization (UNWTO))Total in-use vehicles: https://www.oica.net/production-statistics/(World Automobile Organization)Coal consumption: https://www.iea.org/(World Energy Organization)https://unstats.un.org/unsd/mbs/app/DataSearchTable.aspxhttps://data.wto.org/(World Trade Organization)Total amount of goods transported by road: https://d.qianzhan.com/xdata/list/x2HvyF-3.html (Qianzhan website; data from China's National Bureau of Statistics)UN Environment: https://wesr.unep.org/downloaderSocioeconomic Data and Applications Center (SEDAC): https://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density-futureestimates/data-download热浪是全球性问题，可威胁微生物种群并导致生态系统退化。目前，河流微生物群落如何响应热浪，以及高温是否、如何超出微生物的适应阈值，仍未明确。本研究提出四类脉冲温度诱导的微生物响应模式，并借助集成机器学习模型，预测了全球河流中微生物适应高温的可能性。研究结果显示，南美部分国家（如巴西、智利）与东南亚国家（如越南）的河流微生物群落，大概率会因连续多日25℃至37℃的热浪扰动而发生改变。此外，全球约48.4%的河流水文站的微生物群落，易出现快速胁迫适应不良的情况；其中约76.9%的水文站，在热浪扰动后预计会超出微生物的适应阈值。若直径不超过2.5微米的细颗粒物（PM2.5，人类活动指示物）的排放量增加一倍，那么热浪扰动后，全球属于快速胁迫适应类型的河流数量将减少约13.7%。解析微生物的响应机制，对于开展有效的生态系统管理至关重要，尤其是对于面临热浪事件的脆弱敏感河流而言。本研究所有数据与代码均可复现上述结论。 其他公开数据源： 为开展全球预测，本研究分析了来自“GEMStat”网站（https://gemstat.org/）的全球河流水文理化属性数据，共提取6101个监测站点的相关数据，涵盖表S3中提及的全部河流理化参数。鉴于数据稀缺性、高分辨率特征以及种群迁移的大范围性，人类活动相关参数以国家尺度进行提取。提取得到的人类活动相关信息与河流参数详见表S3。本研究采用球面距离计算得到的最近站点信息，对缺失数据进行补全。全球河流的净生长速率（河流样本量=5308）已公开获取。 全球各国人口数据：https://population.un.org/wpp/Download/Standard/Population/（联合国经济和社会事务部人口动态司） 人均GDP：http://data.worldbank.org.cn（世界银行数据库） 森林覆盖率与人类发展指数（HDI）：http://hdr.undp.org/en/data（联合国开发计划署人类发展报告） 碳排放数据：https://stats.oecd.org/ 年均PM2.5浓度：healtheffects.org/；https://www.stateofglobalair.org/data/#/health/map 全球污染气体排放数据：https://edgar.jrc.ec.europa.eu/dataset_ap50（全球大气研究排放数据库（Emissions Database for Global Atmospheric Research, EDGAR）） 人口密度数据：http://data.un.org/ 游客总规模：https://www.unwto.org/（世界旅游组织（World Tourism Organization, UNWTO）） 在用车辆总数：https://www.oica.net/production-statistics/（世界汽车组织（World Automobile Organization）） 煤炭消耗量：https://www.iea.org/（世界能源组织） https://unstats.un.org/unsd/mbs/app/DataSearchTable.aspx https://data.wto.org/（世界贸易组织） 公路货运总量：https://d.qianzhan.com/xdata/list/x2HvyF-3.html（前瞻网；数据来源于中国国家统计局） 联合国环境规划署相关数据：https://wesr.unep.org/downloader 社会经济数据与应用中心（Socioeconomic Data and Applications Center, SEDAC）：https://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density-futureestimates/data-download