The surrounding landscape shapes the abundance of Sphaerophoria scripta and Melanostoma mellinum (Diptera: Syrphidae) in Portuguese vineyards.

  • Published In: Agricultural & Forest Entomology, 2023, v. 25, n. 2. P. 206 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Madureira, Marta; Rodrigues, Isabel; Villa, María; Pereira, José Alberto 3 of 3

Abstract

The intensification of urban and agricultural use in the landscape is the major driver of biodiversity loss and the consequent decrease of ecosystem services provided by insects. Syrphids are important ecosystem service providers, including pest regulation, pollination, and matter decomposition.Understanding how the surrounding landscape to crops affects syrphids is essential to implementing strategies to reverse the negative effects of the agricultural landscape's simplification.This study describes the Syrphidae community in Portuguese vineyards and the response of the most abundant species, Sphaerophoria scripta Linnaeus, 1758, and Melanostoma mellinum Linnaeus, 1758, to the landscape composition and configuration within a gradient of distances (500, 1000, and 2000 m) from the sampled vineyards.The presence of seminatural habitats and other crops in the surrounding landscape increased both species at the largest distance, whereas the presence of artificial territory, olive orchards, and vineyards reduce M. mellinum at some of the buffers.Increasing seminatural habitats in the vineyards surrounding landscape (2000 m) and, potentially, introducing nature‐friendly practices in the principal crops around vineyards may favour syrphid abundance. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Agricultural & Forest Entomology. 2023/05, Vol. 25, Issue 2, p206
  • Document Type:Article
  • Subject Area:Agriculture and Agribusiness
  • Publication Date:2023
  • ISSN:1461-9555
  • DOI:10.1111/afe.12544
  • Accession Number:162878119
  • Copyright Statement:Copyright of Agricultural & Forest Entomology 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.)

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