JOURNAL ARTICLE
Behavioural adjustments enable the colonization of subterranean environments.
Published In: Zoological Journal of the Linnean Society, 2024, v. 201, n. 2. P. 549 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Lunghi, Enrico; Mammola, Stefano; Martínez, Alejandro; Hesselberg, Thomas 3 of 3
Abstract
This article examines the convergent evolution of behavioural adaptations among subterranean species in response to the unique ecological constraints of underground environments, specifically constant darkness, limited food resources, and reduced predator pressure. Through a systematic review and quantitative analysis of 254 studies covering four behavioural categories—exploratory, anti-predator, foraging, and social behaviours—the authors found that species highly adapted to subterranean life (troglobites and troglophiles) generally exhibit reduced anti-predator responses and agonistic interactions, alongside broader trophic niches, compared to less adapted species (trogloxenes). While exploratory behaviour did not show significant differences, trends suggest more adapted species may explore more due to lower predation risk and reliance on non-visual senses. The study highlights behavioural plasticity as an early and stable adaptation facilitating subterranean colonization and calls for expanded research on diverse taxa, especially invertebrates and non-apex species, to deepen understanding of subterranean ethology.
Additional Information
- Source:Zoological Journal of the Linnean Society. 2024/06, Vol. 201, Issue 2, p549
- Document Type:Article
- Subject Area:Zoology
- Publication Date:2024
- ISSN:0024-4082
- DOI:10.1093/zoolinnean/zlad133
- Accession Number:177633113
- Copyright Statement:Copyright of Zoological Journal of the Linnean 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.)
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