AI Article Synopsis

  • - A comprehensive dataset was created covering gross nitrogen transformation rates (GNTR) in various terrestrial ecosystems, compiling data from 331 studies published between 1984 and 2022, encompassing 581 sites worldwide.
  • - The dataset includes 1552 observations along with standardized data on soil, vegetation, and climate factors (49 variables) to understand the variations in GNTR better.
  • - By employing machine learning to address missing data, this resource enhances the understanding of nitrogen processes and can guide future research in identifying gaps and validating ecological models.

Article Abstract

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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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413239PMC
http://dx.doi.org/10.1038/s41597-024-03871-3DOI Listing

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