JOURNAL ARTICLE
Larvae of the American lobster Homarus americanus H. Milne Edwards, 1837 (Decapoda: Astacidea: Nephropidae) in midcoast Maine USA from 2018–2023: density, seasonality, and carapace lengths.
Published In: Journal of Crustacean Biology, 2025, v. 45, n. 1. P. 1 1 of 3
Database: Environment Complete 2 of 3
Authored By: Glon, Heather E; Waller, Jesica D; Annis, Eric R; Carloni, Joshua T; Niemisto, Maura; Wilson, Carl; Reardon, Kathleen; Russell, Robert 3 of 3
Abstract
This article focuses on a six-year study (2018–2023) of the planktonic larval stages of the American lobster (Homarus americanus) in midcoast Maine, conducted by the Maine Department of Marine Resources (MEDMR). The study addresses a longstanding data gap by consistently monitoring larval lobster density, seasonality, carapace lengths, and environmental factors such as temperature, salinity, and zooplankton community composition, including the key prey species Calanus finmarchicus. Results indicate an earlier seasonal peak in Stage I larvae compared to historical data, multiple seasonal peaks in postlarval abundance, and significant correlations between early-stage larval food availability and later postlarval densities. The study also finds that salinity positively correlates with postlarval carapace length, while temperature and prey abundance show no significant direct effect, highlighting the complex environmental influences on larval development in the warming Gulf of Maine.
Additional Information
- Source:Journal of Crustacean Biology. 2025/03, Vol. 45, Issue 1, p1
- Document Type:Article
- Subject Area:Geography and Cartography
- Publication Date:2025
- ISSN:0278-0372
- DOI:10.1093/jcbiol/ruaf017
- Accession Number:184348252
- Copyright Statement:Copyright of Journal of Crustacean 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.