Journal article
Human activities shape global patterns of decomposition rates in rivers
Science, Vol.384(6701), pp.1191-1195
2024
Abstract
Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf-litter-decomposition rates with impressive accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth, and reveals rapid decomposition across continental-scale areas dominated by human activities.
Details
- Title
- Human activities shape global patterns of decomposition rates in rivers
- Authors
- Scott D Tiegs (Corresponding Author) - Oakland UniversityKrista A Capps (Corresponding Author) - University of GeorgiaDavid M Costello (Corresponding Author) - Kent State UniversityJ P Schmidt (Corresponding Author) - University of GeorgiaC J Patrick (Corresponding Author) - Virginia Institute of Marine ScienceJennifer J Follstad Shah - University of UtahC J LeRoy - The Evergreen State CollegeCELLDEX Consortium (Research Group)Catherine Yule (Consortium Member) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Science, Vol.384(6701), pp.1191-1195
- Publisher
- American Association for the Advancement of Science (AAAS)
- Date published
- 2024
- DOI
- 10.1126/science.adn1262
- ISSN
- 1095-9203
- Data Availability
- All data and code for analyses and figures are available on GitHub.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991033498702621
- Output Type
- Journal article
Metrics
7 Record Views