Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2021
    In:  Annals of Botany Vol. 128, No. 4 ( 2021-09-03), p. 395-406
    In: Annals of Botany, Oxford University Press (OUP), Vol. 128, No. 4 ( 2021-09-03), p. 395-406
    Abstract: Leaf size has considerable ecological relevance, making it desirable to obtain leaf size estimations for as many species worldwide as possible. Current global databases, such as TRY, contain leaf size data for ~30 000 species, which is only ~8% of known species worldwide. Yet, taxonomic descriptions exist for the large majority of the remainder. Here we propose a simple method to exploit information on leaf length, width and shape from species descriptions to robustly estimate leaf areas, thus closing this considerable knowledge gap for this important plant functional trait. Methods Using a global dataset of all major leaf shapes measured on 3125 leaves from 780 taxa, we quantified scaling functions that estimate leaf size as a product of leaf length, width and a leaf shape-specific correction factor. We validated our method by comparing leaf size estimates with those obtained from image recognition software and compared our approach with the widely used correction factor of 2/3. Key Results Correction factors ranged from 0.39 for highly dissected, lobed leaves to 0.79 for oblate leaves. Leaf size estimation using leaf shape-specific correction factors was more accurate and precise than estimates obtained from the correction factor of 2/3. Conclusion Our method presents a tractable solution to accurately estimate leaf size when only information on leaf length, width and shape is available or when labour and time constraints prevent usage of image recognition software. We see promise in applying our method to data from species descriptions (including from fossils), databases, field work and on herbarium vouchers, especially when non-destructive in situ measurements are needed.
    Type of Medium: Online Resource
    ISSN: 0305-7364 , 1095-8290
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1461328-1
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages