JOURNAL ARTICLE

Seasonally Changing Interactions of Species Traits of Termites and Trees Promote Complementarity in Coarse Wood Decomposition.

  • Published In: Ecology Letters, 2024, v. 27, n. 10. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Guo, Chao; Tuo, Bin; Seibold, Sebastian; Ci, Hang; Sai, Bi‐Le; Qin, Han‐Tang; Yan, En‐Rong; Cornelissen, Johannes H. C. 3 of 3

Abstract

Complementary resource use by functionally different species may accelerate ecosystem processes. However, how co‐variation in plant traits and animal traits promotes complementarity through temporal plant–animal interactions is poorly understood, even less so in detrital systems, thereby hampering our fundamental understanding of decomposition and carbon turnover. We hypothesised that, in seasonal subtropical forests where termites are major deadwood decomposers, trait complementarity of both termite species and tree species should promote overall deadwood decomposition through different seasons and years. Findings from a four‐year coarse wood decomposition experiment involving 27 tree and 5 termite species support this hypothesis. Phenological and mandibular traits of the two most abundant termite species controlled wood decomposition of tree species differing in wood traits, through the seasons over 4 years, thereby promoting overall deadwood decomposition rates. Our findings indicate that complementarity in functional trait co‐variation in plants and animals plays an important role in carbon cycling. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Ecology Letters. 2024/10, Vol. 27, Issue 10, p1
  • Document Type:Article
  • Subject Area:Zoology
  • Publication Date:2024
  • ISSN:1461-023X
  • DOI:10.1111/ele.70002
  • Accession Number:180608066
  • Copyright Statement:Copyright of Ecology Letters is the property of Wiley-Blackwell 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.