JOURNAL ARTICLE
Predictable shifts from nutrient to energy limitation determine the responses of planktonic autotrophs, bacteria and mixoplankton to browning.
Published In: Journal of Plankton Research, 2025, v. 47, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Cagle, Sierra; Diehl, Sebastian 3 of 3
Abstract
The article focuses on a conceptual, process-based numerical model developed to investigate how "browning"—the increase of colored dissolved organic matter (CDOM) from terrestrial sources—affects interactions within aquatic microbial food webs comprising autotrophic phytoplankton, heterotrophic bacterioplankton, and bacterivorous phago-mixoplankton. The model integrates resource dynamics of light, inorganic phosphorus, and dissolved organic matter to simulate plankton biomass responses across gradients of CDOM and phosphorus supply, highlighting how mixoplankton’s relative investment in autotrophic versus phagotrophic resource acquisition influences their performance. Key findings include increased bacterial biomass and inorganic phosphorus, decreased light penetration, a unimodal phytoplankton biomass response to browning, and a complex U-shaped response of mixoplankton biomass, with mixoplankton performing best when relying more on photosynthesis for energy and on prey for phosphorus. The study underscores the importance of nutrient versus energy limitation in shaping plankton community structure under changing CDOM inputs and provides insights into the ecological role of mixoplankton in linking bacterial production and primary production in aquatic ecosystems.
Additional Information
- Source:Journal of Plankton Research. 2025/01, Vol. 47, Issue 1, p1
- Document Type:Article
- Subject Area:Environmental Sciences
- Publication Date:2025
- ISSN:0142-7873
- DOI:10.1093/plankt/fbae066
- Accession Number:182906096
- Copyright Statement:Copyright of Journal of Plankton Research is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.