JOURNAL ARTICLE

In Hot Water: Current Thermal Threshold Methods Unlikely to Predict Invasive Species Shifts in NW Atlantic.

  • Published In: Integrative & Comparative Biology, 2024, v. 64, n. 2. P. 189 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lancaster, Emily R; Brady, Damian C.; Frederich, Markus 3 of 3

Abstract

This article reviews thermal tolerance thresholds of 15 invasive invertebrate species in the Gulf of Maine to assess challenges in predicting their future distributions amid rapid climate warming. It highlights the variability and context-dependence of thermal thresholds—measured by diverse physiological and ecological metrics such as lethal temperatures, reproductive limits, and growth ranges—and emphasizes that no single measurement fully captures species' thermal limits. The study underscores the importance of integrating multiple approaches, including field observations, laboratory experiments, and molecular data, to improve species distribution models. Given the Gulf of Maine's rapid warming and its role as a model system, findings have broader implications for managing invasive species globally, especially as warming facilitates poleward and depth shifts in marine organisms. The authors caution against using isolated thermal thresholds for modeling due to high intra- and interspecific variability and advocate for contextualized, consistent measurement strategies to enhance predictive accuracy.

Additional Information

  • Source:Integrative & Comparative Biology. 2024/08, Vol. 64, Issue 2, p189
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:1540-7063
  • DOI:10.1093/icb/icae102
  • Accession Number:179665276
  • Copyright Statement:Copyright of Integrative & Comparative Biology 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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