The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/15368442

Gertie Arts; J. van Smeden; M.F. Wolters; J.D.M. Belgers; A.M. Matser et. al. (2021): Dataset underlying the publication Seasonal dynamics of the macrophyte test species Myriophyllum spicatum over two years in experimental ditches for population modelling application in risk assessment. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/15368442.v1

Dataset

• Soils
• Other Environment
• Physical and Chemical Conditions of Water

biomass, ecological data, environmental parameters, field experiment, growth rate, model parameters, Myriophyllum spicatum, nutrients, sediment quality, sediment-rooted, standing crop, water quality, weather data

The dataset covers two calendar years (2017 - 2019).

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Gertie Arts , J. van Smeden , M.F. Wolters , J.D.M. Belgers , A.M. Matser , U. Hommen , E. Bruns , S. Heine , A. Solga , S. Taylor

The dataset covers biotic and abiotic data from the aquatic habitat of a population of the sediment-rooted macrophyte Myriophyllum spicatum in the temperate climate region (The Netherlands). The growth of M. spicatum was monitored in 0.2025 m2 plant baskets installed in an experimental ditch. Parameters monitored included biomass (fresh and dry weight), shoot length, seasonal short-term growth rates of shoots, relevant environmental parameters and weather data. This dataset includes the 2-year experimental biotic (macrophyte biomass and growth data) and environmental data (water quality data, sediment data). A second file includes the statistical data. A third file includes the weather data.

• 2021-11-16 first online, published, posted

4TU.ResearchData

xlsx txt

Seasonal dynamics of the macrophyte test species Myriophyllum spicatum over two years in experimental ditches for population modelling application in risk assessment

Environmental Risk Assessment, Wageningen University and Research
Fraunhofer Institute for Molecular Biology and Applied Ecology, Germany
Bayer AG, Germany;
Adama Agricultural Solutions UK Ltd., United Kingdom