Number of the records: 1  

Everyone makes mistakes: Sampling errors in vegetation analysis - The effect of different sampling methods, abundance estimates, experimental manipulations, and data transformation

  1. 1.
    0533773 - BC 2021 RIV FR eng J - Journal Article
    Lisner, A. - Lepš, Jan
    Everyone makes mistakes: Sampling errors in vegetation analysis - The effect of different sampling methods, abundance estimates, experimental manipulations, and data transformation.
    Acta Oecologica-International Journal of Ecology. Roč. 109, NOV 01 (2020), č. článku 103667. ISSN 1146-609X. E-ISSN 1873-6238
    R&D Projects: GA ČR GA20-02901S
    Institutional support: RVO:60077344
    Keywords : vegetation sampling * sampling error * abundance estimate
    OECD category: Ecology
    Impact factor: 1.674, year: 2020
    Method of publishing: Limited access
    https://www.sciencedirect.com/science/article/pii/S1146609X20301594/pdfft?md5=11e5d33f7b25df51257565254169b3d3&pid=1-s2.0-S1146609X20301594-main.pdf

    Understanding of causes for recent changes in vegetation structure and species richness of natural habitats is crucial for their maintaining for future generations. However, to avoid misinterpretation of vegetation changes in time, we should be aware of limits and errors of methods used for vegetation sampling. In a specific vegetation type, i.e. species rich wet meadow, we quantified sampling error in vegetation sampling at four different sampling levels (visual cover estimation, detailed recording in a grid of small cells, detailed assessment during clipping for biomass, biomass sorting), compared differences among three abundance estimates (frequency, cover and biomass), and assessed the effect of data transformation, and rapid change in vegetation structure caused by experimental species removal. At the 1 m2 scale the captured proportion of species missed by classical relev´e sampling was on average 16%. Subsequent detailed subquadrat sampling captured the majority of previously overlooked species. The chance of a species being overlooked increased both with rarity, and the species richness of the area sampled. Where abundance was measured using metrics of cover and biomass, common species were overvalued, but when abundance was measured using frequency, common species were undervalued. In this study, logarithmic transformation of values provided a more reliable characterization of vegetation, than binarized or untransformed values. With the exception of species abundance, the number of species overlooked, quadrat species richness, and vegetation characterization were all affected by the experimental treatment. Our findings highlight the potential effects of error when conducting vegetation sampling and analyses of community dynamics. Due to these effects, we need to consider the reliability of conclusions drawn when assessing temporal changes in plant dynamics. Data transformation modifies the effect of sampling error in analyses of vegetation data.
    Permanent Link: http://hdl.handle.net/11104/0316026

     
     
Number of the records: 1  

  This site uses cookies to make them easier to browse. Learn more about how we use cookies.