Data from: Reduced sampling intensity through key sampling site selection for optimal characterization of riverine fish communities by eDNA metabarcoding
Data files
Nov 20, 2024 version files 248.95 KB
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Data_VanDriesscheEtAl_key_sampling.xlsx
245.43 KB
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README.md
3.52 KB
Abstract
Effective conservation management of river systems requires a comprehensive understanding of local and regional biodiversity, necessitating accurate characterization of species communities. Environmental DNA (eDNA) metabarcoding has emerged as a pivotal tool for assessing aquatic organisms, especially fish communities. However, optimal sampling resolution and site positioning to obtain robust fish diversity indices across heterogeneous river systems remain inadequately understood. This study empirically evaluates the optimal number of eDNA samples needed to accurately capture diversity both locally and stream-wide across three distinct river systems, comparing eDNA metabarcoding results to traditional electrofishing data. Habitat and landscape factors were characterized to interpret the localisation of key sampling sites contributing most to the overall species richness. We detected 30 fish species via eDNA metabarcoding, compared to 28 species by electrofishing, with eDNA requiring fewer sampling sites per river system. To reach ≥95 % of the estimated species richness, eDNA analyses required between one and nine sites across three river systems spanning ten kilometres each. In the most diverse river, a single eDNA sampling site even achieved a higher species richness (n = 20 species) compared to the nine required sites to reach ≥95 % of the estimated species richness via electrofishing (n = 9 species). To account for eDNA particle dilution and degradation over larger distances (>1 km), sampling at both upstream and downstream sites may be crucial, with strategic site selection further refined by factors like adjacent stream inflows, substrate type, and river discharge rate, all of which influence species-specific habitat occupancy. On a smaller scale, the location of key sampling sites only moderately differs within 100-meter transects therewith informing on the precise placement of those sampling sites. Our work highlights the robustness and cost-effectiveness of eDNA analyses for riverine biodiversity assessment, demonstrating strong potential for enhancing various conservation practices.
https://doi.org/10.5061/dryad.0cfxpnwb5
Description of the data and file structure
Dataset title: “Data from: Reduced sampling intensity through key sampling site selection for optimal characterization of riverine fish communities by eDNA metabarcoding”
Principle investigator: Charlotte Van Driessche (charlotte.vandriessche@inbo.be)
Co-investigators: Teun Everts, Io Deflem, Sabrina Neyrinck, Dries Bonte, Rein Brys
Data collection data: May, July 2023
Geographic location of data collection: Belgium, Flanders (Northern Region)
Files and variables
File: Data_VanDriesscheEtAl_key_sampling.xlsx
Creation data file: the 7th of October 2024
The dataset contains one tab page.
1. Full: This is the full dataset already including electrofishing data, as well as eDNA generated fish detections, in both quantified (absolute, relative)as well as binary format.
Column explanations are listed below:
ID: an identification number for easy sorting of the data
Method: detections arrise from either morphological identification (Fish) of via eDNA sampling (eDNA). The data was further given in an absolute number of reads or fish (absolute), a binary format (1 for presence, 0 for absence) or a relative contribution to the overall community (rel). If datapoint are given separate, a number 1 or 2 is added to the adequate ‘Method’, if both replicates are summarized into an average, ‘Method’ is followed by the ‘av’_extension.
FilterNo: If applicable, the filter code was given for the eDNA samples
VolumeFilteredMl: The volume of water filtered to obtain the eDNA sample, given in ml
Date: collection/sampling date
Location: name of the studied river, followed by the distance of the river stransect compared to the river mouth
RiverName: name of the studies river
DistanceM: the distance (in m) from the river mouth where the transect was located
RiverDischargeRate: rate of the river discharge in m3/s
Slope: level of slope of the bottom of the river, subdivided into ‘low-medium-high’ levels of steepness
Meander: number of clear meandering sections within the studied river section
Agriculture: Y/N if agriculture took place at one or both of the riverbeds
Industry: Y/N if Industrial activities took place at one or both of the riverbeds
Substrate: bottom substrate, which was subdived into ‘Mud-Rocks-Sand’
ElectricConductivity: in µS/cm
pH: level of pH if measured
WaterTemperature: in °C
Turbidity: in NTU
DissolvedOxygen: in mg/l
TOTALReads: total number of reads for that filter, which is then used to calculate relative abundances for eDNA-based fish detections
Remaining columns include the latin species names (scientific names) of the species identified morphologically and/or via eDNA sampling.
Missing values are listed as ‘NA’, equal to ‘not available’.
Access information
This data was used generated in the Research Article (manuscript):
Van Driessche, C., Everts, T., Deflem, I., Neyrinck, S., Bonte, D., & Brys, R. (2024) Reduced sampling intensity through key sampling site selection for optimal characterization of riverine fish communities by eDNA metabarcoding. *Ecological Indicators, 169, 112807. *https://doi.org/10.1016/j.ecolind.2024.112807
A comprehensive sampling campaign was conducted across 60 sites in the northern region of Belgium, spanning three rivers: Barbierbeek, Herk, and Berwinne. Each river was sampled on two spatial scales: ten 100-meter transects for small-scale sampling and ten 100-meter sections at 1-kilometer intervals for large-scale sampling. Per sampling site, water samples were taken, followed by electrofishing. The water samples were filtered and analysed using eDNA metabarcoding. This dataset includes the data of the eDNA metabarcoding as well as the raw fish count from the electrofishing activities.
Raw metabarcoding data was deposited on the NCBI's Sequence Read Archive (SRA) under BioProject number PRJNA1165199. The bioinformatical pipeline as used on these raw read counts is available on Zenodo (https://zenodo.org/record/3731310#.Y8pdbXbMI2w). The OBITools software was used for further processing of the generated sequence data. The resulting count table as available here was used for further quality screening and cleaning, as well as for statistical analyses.