JOURNAL ARTICLE
Effectiveness of an ecological flow regime to assure successful recruitment of anadromous Coregoninae populations in the Rupert River (northern Quebec, Canada).
Published In: Transactions of the American Fisheries Society, 2024, v. 153, n. 4. P. 422 1 of 3
Database: Environment Complete 2 of 3
Authored By: Belzile, Louis; Guay, Jean‐Christophe 3 of 3
Abstract
This article focuses on evaluating the effectiveness of an ecological flow (e-flow) regime implemented in the Rupert River, northern Quebec, Canada, following a 52% reduction in mean annual flow at the river mouth due to partial diversion for hydroelectric development. An eight-year monitoring program (2008–2015), including two years before diversion, assessed the total annual abundance, timing, and spatial distribution of anadromous Coregoninae larvae (Cisco Coregonus artedi and Lake Whitefish C. clupeaformis) as indicators of recruitment success. Results showed that larval abundance remained stable within the natural variability range observed pre-diversion, larval drift timing correlated with spring water temperature rather than flow changes, and larvae predominantly drifted near the water surface with spatial distribution influenced by flow conditions. The study concludes that the e-flow regime, designed to mimic natural seasonal flow variations, was adequate to maintain Coregoninae populations over at least six years post-diversion, though natural fluctuations may mask subtle effects of flow reduction.
Additional Information
- Source:Transactions of the American Fisheries Society. 2024/07, Vol. 153, Issue 4, p422
- Document Type:Article
- Subject Area:Power and Energy
- Publication Date:2024
- ISSN:0002-8487
- DOI:10.1002/tafs.10463
- Accession Number:178715879
- Copyright Statement:Copyright of Transactions of the American Fisheries Society 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.