Abstract
The research was focused on identification of plant diversity assessment approach which would be suitable for flowing waters. Moreover, the study attempted to estimate the extent of the survey (sampling effort) needed for detection of full plant diversity in river ecosystems. The analysed data were collected in a botanical survey carried out on the lowland sandy bottom Wel river, which is a very common river type in Europe. The Chao method of estimation of total species richness of macrophytes and Hill numbers were used to estimate the diversity based on very common plants (inverse Simpson index) as well as frequent species (Shannon index) and also including rare species (species richness). The analysis showed that using standard macrophyte survey approach for rivers, it is relatively easy to record abundant plants, but it is not possible to detect all species, because many aquatic macrophytes occur with low frequency and abundance requiring enormous sampling effort to detect all of them. It exceeds capabilities of most scientific projects. To improve the accuracy of diversity estimation, the rarefaction and extrapolation method was recommended. This method can reduce the bias in diversity estimates based on limited observational data, and thus limit inaccurate conclusions and subsequent wrong decisions in the conservation frameworks related to freshwater protection.
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Acknowledgements
The study was funded by the Polish-Norwegian Research Fund within the project “Development and validation of methods for integrated assessment of ecological status of rivers and lakes to support river basin management plans” (deWELopment; contract PNRF–220–AI–1/07) and supported by the Ministry of Science and Higher Education (Contracts No. NN305 145839).
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Communicated by Daniel Sanchez Mata.
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Budka, A., Łacka, A. & Szoszkiewicz, K. Estimation of river ecosystem biodiversity based on the Chao estimator. Biodivers Conserv 27, 205–216 (2018). https://doi.org/10.1007/s10531-017-1429-2
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DOI: https://doi.org/10.1007/s10531-017-1429-2