茎流已被越来越多的人认为是植被生态系统中水和营养预算的一个不可或缺的组成部分。在此，我们旨在量化全球范围内入射降水的茎流百分比（St, %）（即茎流产量），并对生物和非生物因素如何影响St进行系统评估。
\n我们从234篇同行评议的论文中汇编了一个全球茎流数据集，其中包括陆地木本植物生态系统中283个地点的488个St及相关生物（林分特征）和非生物因素（气候变量）的观测数据。我们探索了St的全球模式，并采用机器学习方法（提升回归树）来模拟生物和非生物变量对St的影响。
\n在全球范围内，St的中位数（四分位数范围，IQR）为2.7%（1.0-6.3%）。我们发现干旱地区（Köppen-Geiger气候分类中的B型）的St明显高于其他气候类型（P <0.01），我们还发现树木（中位数：2.4%；IQR：1.0-5.3%）和灌木（中位数：7.2%；IQR：5.2-11.9%）之间的St有明显差异（P <0.01）。占最终模型解释偏差很大的预测变量包括植被高度（27.0%）、平均年降水量（16.1%）、平均年温度（14.4%）、林分密度（10.8%）、林分年龄（8.9%）和树皮类型（5.5%）。相比之下，叶面积指数、胸径、基底面积、物候类型、生命形式和叶片类型被列为低重要性。
", "ds_source": "我们在Web of Science上进行了文献搜索，以汇编2020年前发表的合适的研究，以评估生物和非生物变量对全球茎流生产的影响。通过使用关键词 \"茎流 \"进行搜索，产生了来自64个国家的1126篇文章，并认为这些文章可能是合适的。", "ds_process_way": "
我们主要使用提升回归树（BRT）分析来评估 评估各个预测变量对St的影响，其中MAP和 归入非生物因素，10个林分指标归入生物因素。 归入生物因素。BRT是一种机器学习方法、 它结合了两种算法的优势：回归树\n和提升算法。", "ds_quality": "
此外，由于根据Shapiro- Wilk检验，数据不是正态分布（p < 0.05），我们使用Kruskal- Wallis排名和检验比较了气候类型之间、树木和灌木之间以及树皮类型之间的St差异，并使用Dunn's检验和'dunn.test'进行多重比较。我们的综合报告提供了St的跨地点比较，并对气候变量和林分特征如何促进和影响全球茎流生产给出了一个整体的看法。\n
我们的综述对于理解全球范围内气候变量和林分特征在木本植物茎流生产中的作用具有重要意义。茎流在降雨分配研究中的代表性相对较低，近年来得到了越来越多的关注，现在被认为是植被生态系统中水和营养循环的一个不可缺少的组成部分。因此，这里提供的关于陆地木质生态系统中茎流生产的全球综合报告将有助于对水和养分预算进行无偏见的估计。我们鼓励未来的研究包括其他植物的生命形式，如草本植物和作物。", "ds_acq_start_time": "1970-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -75.99166666666666, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 83.63305555555554, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 428344, "ds_files_count": 2, "ds_format": "Excel、Word", "ds_space_res": null, "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "749f7056-2396-432a-ab70-c8ca17032b45.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-06-27 09:59:35", "last_updated": "2023-07-24 10:31:02", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB3958.2023", "i18n": { "en": { "title": "Global quantitative synthesis of effects of biotic and abiotic factors on stemflow production in woody ecosystems（1970-2019）", "ds_format": "", "ds_source": "
We performed a literature search on Web of Science to compile suitable studies published before 2020 to assess the effects of bi-otic and abiotic variables on global stemflow production. Through searching using the keyword ‘stemflow’, 1,126 articles from 64 coun-tries were generated and con-sidered to be potentially suitable.We reviewed each article to determine whether the studies met the following criteria.", "ds_quality": "
Moreover, because the data were not normally distributed ac-cording to the Shapiro– Wilk test (p < .05), we compared the differ-ences in St between climate types,between trees and shrubs, and between bark types using a Kruskal– Wallis rank sum test, and mul-tiple comparisons were done using Dunn’s test with the ‘dunn.test’.\n
Our synthesis has important implications for understanding the roles of climate variables and stand characteristics in stemflow pro-duction of woody plants at a global scale. Stemflow, relatively under- represented in rainfall partitioning studies, has gained increasing attention in recent years, and is now recognized as an indispensable component in water and nutrient cycles within vegetated ecosys-tems. As such, a global synthesis of stemflow production in ter-restrial woody ecosystems as provided here will aid in an unbiased estimation in water and nutrient budgets. Future studies are encour-aged to include other plant life-forms such as herbs and crops", "ds_ref_way": "", "ds_abstract": "
Stemflow has been increasingly recognized as an indispensable component in water and nutrient budgets within vegetated ecosystems. Here we aim to quantify the stemflow percentage (St, %) of incident precipitation (i.e. stemflow production) at a global scale, and to provide a systematic evaluation on how biotic and abiotic factors affect St.
\nWe compiled a global stemflow dataset from 234 peer-reviewed papers, which included 488 observations of St and the related biotic (stand characteristics) and abiotic factors (climate variables) at 283 sites within terrestrial woody plant ecosystems. We explored the global pattern of St and performed a machine learning method (boosted regression trees) to model the effects of biotic and abiotic variables on St.
\nGlobally, the median (interquartile range, IQR) St was 2.7% (1.0–6.3%). We found that St in arid zones (type B in the Köppen-Geiger climate classification) was significantly higher (p <0.01) than in other climate types, and we also detected a significant difference (p <0.01) in St between trees (median: 2.4%; IQR: 1.0–5.3%) and shrubs (median: 7.2%; IQR: 5.2–11.9%). Predictor variables that substantially accounted for the explained deviance of the final model included vegetation height (27.0%), mean annual precipitation (16.1%), mean annual temperature (14.4%), stand density (10.8%), stand age (8.9%) and bark type (5.5%). In contrast, leaf area index, diameter at breast height, basal area, phenology type, life-form and leaf type were classified as low importance.
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "We mainly used boosted regression trees (BRT) analysis to evalu-ate the effects of individual predictor variables on St, with MAP andMAT grouping into the abiotic factors and 10 stand metrics groupinginto the biotic factors. BRT is a machine learning method,which combines the strengths of two algorithms: regression trees and boosting.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "提升回归树", "气候变量", "全球综合", "林分特征", "茎流", "生态系统" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "张亚峰", "email": "zhangyafeng@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王新平", "email": "xpwang@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "潘颜霞", "email": "panyanxia@gmail.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "虎瑞", "email": "hurui22831@163.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "陈宁", "email": "chenning.cn2015@gmail.com", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张亚峰", "email": "zhangyafeng@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "张亚峰", "email": "zhangyafeng@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "生态" }