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NDVI and vegetation volume as predictors of urban bird diversity.

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  • Additional Information
    • Source:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • Subject Terms:
      Biodiversity* ; Birds*/physiology; Animals ; Germany ; Ecosystem ; Cities ; Urbanization ; Conservation of Natural Resources
    • Abstract:
      Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
      Urban expansion and densification pose a challenge to urban biodiversity. Rapid estimation of biodiversity could help urban planners balance development and conservation goals. While the Normalised Difference Vegetation Index (NDVI) has proven useful for predicting urban bird diversity, new products derived from remote sensing, such as vegetation volume, could provide more detailed descriptions of available habitat, potentially improving biodiversity predictions. We evaluated the effectiveness of NDVI and vegetation volume as predictors of urban bird diversity and local community composition for different buffers around 86 sampling points in Munich, Germany. Using linear models, we showed that a 100 m buffer best described bird diversity (highest R 2 ) for both NDVI and vegetation volume compared to the other buffers. Contrary to expectations, NDVI was better than vegetation volume in predicting bird diversity (mean R 2 NDVI = 0.47, mean R 2 vegetation volume 0.37). We found a shift in community composition from species associated with human-modified landscapes to those associated with forests along an urban greenness gradient. In contrast to diversity, we found that vegetation volume was slightly better at predicting community composition. Using NDVI to predict bird diversity across Munich, we demonstrated its potential for predicting city-wide bird diversity. We discuss how such predictive maps can be used for urban planning and conservation. As urbanisation continues to impact global biodiversity, refining ecological models for urban planning will be crucial to developing more biodiverse urban environments.
      (© 2025. The Author(s).)
    • References:
      PLoS One. 2017 Jan 10;12(1):e0169786. (PMID: 28072883)
      J Anim Physiol Anim Nutr (Berl). 2021 Mar;105(2):385-393. (PMID: 32949068)
      Sensors (Basel). 2023 Aug 15;23(16):. (PMID: 37631713)
      Sci Rep. 2024 Jan 9;14(1):894. (PMID: 38195687)
      Ecol Lett. 2022 Mar;25(3):581-597. (PMID: 35199922)
      J Anim Ecol. 2020 Jul;89(7):1546-1548. (PMID: 32627886)
      Conserv Biol. 2014 Aug;28(4):1100-8. (PMID: 24628442)
      Ecol Appl. 2023 Apr;33(3):e2808. (PMID: 36691190)
      Curr Environ Health Rep. 2021 Jun;8(2):146-156. (PMID: 33982150)
      PLoS One. 2022 Apr 8;17(4):e0266557. (PMID: 35395028)
      Proc Biol Sci. 2014 Feb 12;281(1780):20133330. (PMID: 24523278)
    • Contributed Indexing:
      Keywords: Biodiversity; NDVI; Remote sensing; Urban ecology
    • Publication Date:
      Date Created: 20250414 Date Completed: 20250414 Latest Revision: 20250417
    • Publication Date:
      20250417
    • Accession Number:
      PMC11997212
    • Accession Number:
      10.1038/s41598-025-96098-0
    • Accession Number:
      40229343