JOURNAL ARTICLE
Relationships Between Zooplankton and Habitat Conditions in the Upper Mississippi River System.
Published In: River Research & Applications, 2025, v. 41, n. 7. P. 1567 1 of 3
Database: Environment Complete 2 of 3
Authored By: Sobotka, Molly; Fulgoni, Jessica; Johnson, Ashley; Bell, Alex 3 of 3
Abstract
Zooplankton link microbial primary producers to larger consumers (primarily fish) in aquatic food webs. Despite their importance, assessment of zooplankton populations in large rivers is rare, especially across large spatial scales and during the winter. We collected zooplankton alongside summer, fall, and winter water quality sampling events in the Upper Mississippi River Restoration Program's Long Term Resource Monitoring element key reaches during 2019 and 2020. We used generalized additive models to assess the relationships between measures of zooplankton abundance and local and site‐level habitat variables. Zooplankton abundance was strongly tied to measures of productivity (chlorophyll) and water clarity (suspended solids), but the strength and shape of these relationships were not the same between seasons. Abundance of crustacean zooplankton was greatest during the winter at some locations; however, we did not find strong relationships between abundance and chlorophyll during that season. Crustacean density and biomass were negatively associated with the presence of invasive carps and positively associated with abundant aquatic plants. These findings highlight the variability of zooplankton response to physical and temporal habitat conditions. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:River Research & Applications. 2025/09, Vol. 41, Issue 7, p1567
- Document Type:Article
- Subject Area:Biology
- Publication Date:2025
- ISSN:1535-1459
- DOI:10.1002/rra.4445
- Accession Number:188367161
- Copyright Statement:Copyright of River Research & Applications is the property of Wiley-Blackwell 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.