A global dataset of gross nitrogen transformation rates across terrestrial ecosystems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AU4N7F8HE" target="_blank" >RIV/00216208:11320/25:U4N7F8HE - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85204511666&doi=10.1038%2fs41597-024-03871-3&origin=inward&txGid=464ca3768e5ac72d5f352b0e841f95d4" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85204511666&doi=10.1038%2fs41597-024-03871-3&origin=inward&txGid=464ca3768e5ac72d5f352b0e841f95d4</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41597-024-03871-3" target="_blank" >10.1038/s41597-024-03871-3</a>

Result language
angličtina
Original language name
A global dataset of gross nitrogen transformation rates across terrestrial ecosystems
Original language description
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.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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