全球陆地生态系统总氮转化率数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Eunji Byun, Christoph Müller, Barbara Parisse, Rosario Napoli, Jin-Bo Zhang, Fereidoun Rezanezhad, Philippe Van Cappellen, Gerald Moser, Anne B Jansen-Willems, Wendy H Yang, Rieko Urakawa, José Ignacio Arroyo, Ulderico Neri, Ahmed S Elrys, Pierfrancesco Nardi
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引用次数: 0

摘要

氮转化率有助于对复杂的氮循环进行定量描述和预测性理解,但测量这些转化率的成本很高,而且研究人员不易获得。在此,我们汇编了陆地生态系统中矿化、硝化、铵固定化、硝酸盐固定化以及硝酸盐还原成铵的总氮转化率(GNTR)数据集。数据摘自 1984-2022 年间发表的 331 项研究,涵盖 581 个地点。在全球范围内,有 1552 个观测点附加了标准化的土壤、植被和气候数据(共 49 个变量),这些数据可能会导致观测到的 GNTR 变化。我们使用基于机器学习的数据估算来填补部分缺失的 GNTR,从而改善了理论上相关过程之间的统计关系。该数据集是目前对陆地生态系统 GNTR 最全面的概述,也是对各种环境条件下 GNTR 范围和变异性的全球综合。未来的研究可以利用该数据集来确定气候、土壤和生态系统类型方面的测量差距,划分某些生态区域的 GNTR,并帮助验证基于过程的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A global dataset of gross nitrogen transformation rates across terrestrial ecosystems.

Rates of nitrogen transformations support quantitative descriptions and predictive understanding of the complex nitrogen cycle, but measuring these rates is expensive and not readily available to researchers. Here, we compiled a dataset of gross nitrogen transformation rates (GNTR) of mineralization, nitrification, ammonium immobilization, nitrate immobilization, and dissimilatory nitrate reduction to ammonium in terrestrial ecosystems. Data were extracted from 331 studies published from 1984-2022, covering 581 sites. Globally, 1552 observations were appended with standardized soil, vegetation, and climate data (49 variables in total) potentially contributing to the observed variations of GNTR. We used machine learning-based data imputation to fill in partially missing GNTR, which improved statistical relationships between theoretically correlated processes. The dataset is currently the most comprehensive overview of terrestrial ecosystem GNTR and serves as a global synthesis of the extent and variability of GNTR across a wide range of environmental conditions. Future research can utilize the dataset to identify measurement gaps with respect to climate, soil, and ecosystem types, delineate GNTR for certain ecoregions, and help validate process-based models.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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