JOURNAL ARTICLE

Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub.

  • Published In: North American Journal of Fisheries Management, 2023, v. 43, n. 5. P. 1205 1 of 3

  • Database: Environment Complete 2 of 3

  • Authored By: Steffensmeier, Zachary D.; Brewer, Shannon K.; Wedgeworth, Maeghen; Starks, Trevor A.; Rodger, Anthony W.; Nguyen, Erin; Perkin, Joshuah S. 3 of 3

Abstract

The article focuses on delineating the spatial distribution of Prairie Chub (Macrhybopsis australis), a regionally endemic freshwater fish under consideration for Endangered Species Act listing, using the BAM ecological niche framework that integrates abiotic (A), biotic (B), and movement (M) factors. Employing random forest modeling with environmental variables, hybridization data with Shoal Chub (M. hyostoma), and mark–recapture movement data, the study found that the realized niche of Prairie Chub spans approximately 645 km of river in the upper–middle Red River basin, primarily in medium to large rivers with high habitat connectivity. Inclusion of movement data significantly improved model predictions, highlighting the species' capacity for long-distance dispersal, while incorporation of biotic interactions (hybridization zone) did not enhance model accuracy. The findings suggest that maintaining longitudinal habitat connectivity and preventing further fragmentation are critical for conserving Prairie Chub, with potential range expansion achievable through restoration of fluvial connectivity.

Additional Information

  • Source:North American Journal of Fisheries Management. 2023/10, Vol. 43, Issue 5, p1205
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2023
  • ISSN:0275-5947
  • DOI:10.1002/nafm.10860
  • Accession Number:173281720
  • Copyright Statement:Copyright of North American Journal of Fisheries Management 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.)

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