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Abstract

Plant litter decomposition in terrestrial ecosystems involves the physical and chemical breakdown of organic matter. Development of databases is a promising tool for achieving a predictive understanding of organic matter degradation at regional and global scales. In this paper, we present aridec, a comprehensive open database containing litter mass loss data from aridlands across the world. We describe in detail the structure of the database and discuss general patterns in the data. Then, we explore what are the most appropriate model structures to integrate with data on litter decomposition from the database by conducting a collinearity analysis. The database includes 184 entries from aridlands across the world, representing a wide range of climates. For the majority of the data gathered in aridec, it is possible to fit models of litter decomposition that consider initial organic matter as a homogenous reservoir (one pool models), as well as models with two distinct types of organic compounds that decompose at different speeds (two pool models). Moreover, these two carbon pools can either decompose without interaction (parallel models) or with matter transfer from a labile pool to a slowly decomposing pool after transformation (series models). Although most entries in the database can be used to fit these models, we suggest that potential users of this database test identifiability for each individual case as well as the number of degrees of freedom. Other model applications that are not discussed in this publication might also be suitable for use with this database. Lastly, we give some recommendations for future decomposition studies to be potentially added to this database. The extent of the information included in aridec in addition to its open-science approach makes it a great platform for future collaborative efforts in the field of aridland biogeochemistry.

Published in

Earth System Science Data
2022, volume: 14, number: 7, pages: 3471-3488
Publisher: COPERNICUS GESELLSCHAFT MBH

SLU Authors

UKÄ Subject classification

Bioinformatics and Computational Biology (Methods development to be 10203)
Geosciences, Multidisciplinary

Publication identifier

  • DOI: https://doi.org/10.5194/essd-14-3471-2022

Permanent link to this page (URI)

https://res.slu.se/id/publ/118585