This dataset is from the manuscript 'Environment regimes play an important role in structuring trait-and taxonomy-based temporal beta diversity of riverine diatoms' whereby daily riverine diatom data, flow, nutrients and metal ions data were used to examine i) the relative roles of turnover and nestedness components to trait- and taxonomy-based temporal beta diversity of riverine diatoms; ii) whether trait-based temporal beta diversity provides complementary information to taxonomy-based temporal beta diversity; iii) the relative roles of hydrology (e.g., discharge, antecedent precipitation index), metal ions (e.g., Mg2+, Si2+), and nutrients (e.g., nitrogen, orthophosphate) to the both facets of temporal beta diversity and their components (i.e., total beta diversity, turnover, and nestedness); and iv) whether inclusion of environment regimes increase their explained variations. Therefore, this dataset contains three different data: 1) daily species composition (date*species); 2) species trait (species * traits); 3) environmental variables (date*environmental variables). 

We collected daily mixed water samples at the outlet of the catchment (i.e. Kielstau catchment, Germany) from April 25, 2013 to April 30, 2014. Using two automatic samplers, daily mixed water samples were collected and kept in the refrigerators at 4 °C. They were picked once a week (normally on Monday) and taken back to the lab of the Department of Hydrology and Water Resources Management of Kiel University.

In the lab, Hydrogen Peroxide Method (30% H2O2 solution) and Naphrax (Northern Biological supplies Ltd., UK, R1=1.74) were used to prepare permanent diatom slides. We counted a minimum of 300 valves for each sample using a Zeiss Axioskop microscope at 1000× under oil immersion. Diatoms were identified to the lowest taxonomic level possible (mainly species level).

Water chemical parameters, including metal ions (Cl-, K+, Ca+, Na+, Mg2+ and Si2+), ammonium-nitrogen (NH4-N), nitrate-nitrogen (NO3-N), orthophosphate (PO4-P), and sulphate (SO42-), were measured in the lab (for methods, see Appendix S1). We also collected hydrological variables included daily discharge (Q), discharge skewness (Sk), precipitation (Prec), and antecedent precipitation index (API).

We computed both flow (using Q) and nutrient (using NH4-N, NO3-N, and PO4-P) regimes with R package SER (Guo et al., 2019).