DOI: 10.5061/dryad.tx95x6b88
title: Data from: Seasonal changes in the structure of river fish communities in temperate Japan depicted using quantitative eDNA metabarcoding

description: Understanding fish communities contributes to various fields ranging from
 theoretical ecology to fisheries management. In rivers flowing from
 mountainous to urban areas and showing seasonal variations in water
 temperatures, fish community structure may be spatiotemporally affected by
 natural and anthropogenic factors. We investigated the spatial and
 seasonal dynamics of fish communities in the Isazu River (17.9 km) and its
 tributary, Ikeuchi River, in temperate Japan, using the eDNA metabarcoding
 method. We detected 78 fish operational taxonomic units across four
 seasons at 12 sites along the river length. The fish community differed
 significantly among different seasons and sites, aligning with the
 distance from the river mouth. In particular, the eDNA concentrations
 (copies/L) of major fishery resources and endangered fish species varied
 among different seasons and sites, which may reflect species migration
 patterns. The river hosts a spatiotemporally dynamic fish community that
 provides substantial ecosystem services. Our study provides valuable
 insights into the complex relationships between fish communities and
 natural, anthropogenic, temporal, and spatial factors affecting these
 communities based on changes in eDNA concentrations.
description: # Seasonal changes in the structure of river fish communities in temperate
 Japan depicted using quantitative eDNA metabarcoding Dataset DOI:
 [10.5061/dryad.tx95x6b88](10.5061/dryad.tx95x6b88) ## Description of the
 data and file structure This supplementary dataset provides the data and R
 code used to generate figures and conduct statistical analyses for the
 study on the spatio-temporal variation of river fish communities in the
 Isazu and Ikeuchi Rivers. The file
 "Isazu2023_All_final_withNA_NCed.csv" contains eDNA copy numbers
 (copies/μL) of detected fish species at each site (st.1–st.12) and in each
 season (spring, summer, fall, and winter). The file
 "Environmental_information_Isazu_eDNA_St_All_240810_ed_withNA2.csv" includes environmental variables such as water temperature and a water quality index. The R script "eDNA_Isazu_dryad.R" describes the procedures for generating the figures and conducting the statistical analyses presented in the study. ### Files and variables #### File: Isazu2023\_All\_final\_withNA\_NCed.csv **Description:**  ##### Variables The variable prefixes sp, su, fa, and wi indicate spring, summer, fall, and winter, respectively. st.1-12 show the sites in the Isazu and Ikeuchi rivers. The unit is copies/μL. * sp.st.1: * sp.st.2: * sp.st.3: * sp.st.4: * sp.st.5: * sp.st.6: * sp.st.7: * sp.st.8: * sp.st.9: * sp.st.10: * sp.st.11: * sp.st.12: * su.st.1: * su.st.2: * su.st.3: * su.st.4: * su.st.5: * su.st.6: * su.st.7: * su.st.8: * su.st.9: * su.st.10: * su.st.11: * su.st.12: * fa.st.1: * fa.st.2: * fa.st.3: * fa.st.4: * fa.st.5: * fa.st.6: * fa.st.7: * fa.st.8: * fa.st.9: * fa.st.10: * fa.st.11: * fa.st.12: * wi.st.1: * wi.st.2: * wi.st.3: * wi.st.4: * wi.st.5: * wi.st.6: * wi.st.7: * wi.st.8: * wi.st.9: * wi.st.10: * wi.st.11: * wi.st.12: * Hit1_spname: The column Hit1_spname represents operational taxonomic units (OTUs) detected through eDNA metabarcoding #### File: Environmental\_information\_Isazu\_eDNA\_St\_All\_240810\_ed\_withNA2.csv **Description:**  ##### Variables * Station: Sites investigated in this study. * Season: The season in which samples were collected (spring, summer, fall, or winter). Represented as “1spring”, “2summer”, “3fall”, and “4winter” in the dataset to preserve chronological order. * station.season: A combined variable of season and station (e.g., "sp.st.1", "wi.st.12") that corresponds to the column names in *Isazu2023_All_final_withNA_NCed.csv*. * Weather: General weather conditions on the sampling day. * Tide: Tidal stage at the time of sampling (lowtide, hightide, ebb, and flood). * Watercurrent: Approximate water flow speed at the sampling site (slow and medium). * Watertemp.: Water temperature at the sampling site, in degree Celsius (°C). * Salinity: Salinity at the sampling site, in parts per thousand (ppt, ‰). * Dist.from.estuary(km): Distance from the river mouth (estuary) to the sampling site, in kilometers. * elevation(m): Elevation of the sampling site above sea level, in meters. * NO2+NO3-N(mg/l): Concentration of nitrite and nitrate nitrogen. * NO2-N(mg/l): Concentration of nitrite nitrogen. * PO4-P(mg/l): Concentration of phosphate phosphorus. * RiverWidth(m): Width of the river at the sampling site, in meters. * NO3-N(mg/l): Concentration of nitrate nitrogen. #### File: eDNA\_Isazu\_dryad.R **Description:** This R script provides all analyses and figure-generation procedures used in the study of spatio-temporal variation in river fish communities based on eDNA data from the Isazu and Ikeuchi Rivers. The script includes the following: Data preparation: Cleaning, transforming, and structuring eDNA detection data and environmental variables. Seasonal and site-level summaries: Calculation of species richness and total DNA copy number for each season and site. Venn diagram analysis: Visualization of species overlap among seasons using both VennDiagram and gplots. Bar plots: Summarized DNA copy numbers by family, genus, and species. Diversity analysis: Shannon diversity index calculation and visualization. Generalized linear modeling: Relationships between environmental variables (e.g., nutrient concentrations) and species diversity, including model predictions with confidence intervals. Community analysis: NMDS (non-metric multidimensional scaling) to visualize Bray–Curtis dissimilarity in fish community composition. Fitting of environmental vectors to ordination space. Cluster analysis using Ward’s method and silhouette analysis to determine the optimal number of clusters. PERMANOVA and pairwise comparisons by site and season. Heatmaps: Visualization of eDNA detection across sites for selected species. Targeted species tracking: Visualization of temporal patterns in total DNA copy number for ecologically important species (e.g., Anguilla japonica, Plecoglossus altivelis). Environmental gradient analysis: Fitting environmental variables (e.g., NO₂, NO₃, PO₄) to geographic gradients using GLMs and visualizing model fits. ## Code/software The data and analyses in this submission can be fully accessed and reproduced using free and open-source software, specifically the R statistical environment. R (version 4.2.2 or higher recommended). Free software for statistical computing and graphics. RStudio (optional but recommended). A free integrated development environment (IDE) for R. Microsoft Excel- For handling .csv files.