Macroinvertebrate communities are crucial for biodiversity monitoring and assessment of ecological status in stream ecosystems. However, traditional monitoring approaches require intensive sampling and rely on invasive morphological identifications that are time consuming and dependent on taxonomic expertise. Importantly, sampling is often only carried out once in a year, namely during late winter – spring, where most indicator taxa have larval stages in the streams. Hence, species with divergent phenology might not be detected. Here, we use environmental DNA (eDNA) metabarcoding of filtered water samples collected in both spring and autumn from five streams in Denmark to address seasonal turnover in community composition of stream macroinvertebrates. We find that eDNA read data from the same stream sampling site clearly show different communities in spring and autumn, respectively. For three of the five streams, season even appears to be a more important factor than sampling site for explaining the variation in community composition. Finally, we compare eDNA data with a near-decadal dataset of taxon occurrences in the same five streams based on kick sampling conducted through a national monitoring program. This comparison reveals an overlap in species composition, but also that the two approaches provide complementary rather than identical insights into community composition. Our study demonstrates that aquatic eDNA metabarcoding is useful for species detection across highly diverse taxa and for identifying seasonal patterns in community composition of freshwater macroinvertebrates. Thus, our results have important implications for both fundamental research in aquatic ecology and for applied biomonitoring.

This dataset represents environmental DNA sequencing data from spring and autumn sampling at five stream sites in Denmark (see connected publication for details).

DNA has been amplified with primers BF1 (5'-ACWGGWTGRACWGTNTAYCC-3') and BR1 (5'-ARYATDGTRATDGCHCCDGC-3'), targeting a 217 bp fragment of the COI. The libraries have been sequenced using paired end NovaSeq 6000 sequencing (150 BP PE).

The dataset consists of eight zipped libraries (four PCR replicates of the same samples for the two seasons) and eight txt files that can be used for demultiplexing purposes for each library.

Libraries are named K2-K9, and each contain two sequence data files (paired end sequencing) and an MD5 file.

K2* --> Spring samples PCR replicate one

K3* --> Spring samples PCR replicate two

K4* --> Spring samples PCR replicate three

K5* --> Spring samples PCR replicate four

K6* --> Autumn samples PCR replicate one

K7* --> Autumn samples PCR replicate two

K8* --> Autumn samples PCR replicate three

K9* --> Autumn samples PCR replicate four

If you would like to demultiplex this data, we refer to the eight txt files named according to libraries. Each file contains 20 real samples (four samples per site: Borre, Boege, Tjaer, Jeks, and Faebaek). Each site contains one field blank (referred to as location name followed by "K"), and there is a total of four extraction blanks (CNE_P16 - CNE_P19). We also included two PCR blanks (NTCs). In total, after demultiplexing, there should be 31 data files per library (20 samples, five field blanks, four extraction blanks and two PCR blanks).

The txt files include the sample name followed by the PCR replicate number. The two following columns represent the tags used for individual samples. Tags are consistent across PCR replicates.

After demultiplexing, you should be able to do as you please with the data.

If you want to follow the exact filtering and data analysis done in our study, we refer to the manuscript for further details after the demultiplex step. If you have any questions, feel free to send an email to Mads Reinholdt Jensen with any questions you may have.