Data and code from: Substrate-dependent variation in environmental DNA persistence and degradation from a small mammal
Data files
Sep 24, 2026 version files 26.71 MB
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eDNA_analysis_master_script_.R
40.15 KB
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Raw_FASTQ_Files.zip
26.54 MB
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README.md
3.99 KB
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RTP_qPCR_degradation_data_full.csv
118.79 KB
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RTP_qPCR_vs_metabarcoding_data.csv
7.38 KB
Abstract
Environmental DNA (eDNA) analyses have been applied to terrestrial environments for over a decade, yet knowledge of the dynamics of eDNA deposition, movement, and degradation remains limited. To address this, we conducted a controlled experiment over one year to assess the persistence of eDNA from an arboreal mammal across four substrates (water, sediment, sheltered nest-box surfaces, and exposed nest-box surfaces). Environmental DNA deposited by captive individuals was sampled repeatedly following removal of animals and analysed using both species-specific qPCR and metabarcoding. We modelled degradation using quantitative decay, detection probability, and conditional decay frameworks, and applied a random breakage model to estimate theoretical fragment persistence. Decay followed first-order exponential kinetics in water and on nest box surfaces but not in sediment, which exhibited high stochasticity. We observed a large difference in degradation rates between substrates, with exponential decay models revealing half-lives ranging from 7.3 days in water to 276 days on sheltered internal surfaces. Rapid decay in water restricted detections to approximately 77 days, while persistence models predicted that sheltered surfaces could retain detectable DNA for more than four years (>1,700 days), indicating the potential for prolonged accumulation of legacy DNA in protected environments. While species-specific qPCR offered higher sensitivity than metabarcoding, both methods had parallel decay patterns. Our findings indicate that substrate selection is a critical decision when designing an eDNA survey, as there is a trade-off between maximising detection probability and maintaining temporal resolution.
Author Information
Principal Investigator: Austin M. Guthrie
Institution: School of Molecular and Life Sciences, Curtin University
Contact: austin.guthrie@curtin.edu.au
File Overview
This repository contains the data, raw sequencing files, and R script required to reproduce the statistical analyses and figures presented in the manuscript.
RTP_qPCR_degradation_data_full.csv: Cleaned dataset containing qPCR amplification results for the temporal degradation experiment.RTP_qPCR_vs_metabarcoding_data.csv: Dataset containing matched samples analysed via both species-specific qPCR and the 12sv5 metabarcoding assay.Raw_FASTQ_Files.zip: Compressed directory containing demultiplexed, single-end FASTQ reads from the metabarcoding sequencing run.eDNA_analysis_master_script_.R: The complete R script used to conduct all linear mixed-effects modelling (LMEM), generalized linear mixed-effects modelling (GLMM), exponential decay kinetics, and figure generation.
Data Dictionaries
File 1: RTP_qPCR_degradation_data_full.csv
- id: Unique numerical identifier for the data row.
- sample_id: Unique alphanumeric identifier for each biological sample.
- enclosure_id: Unique identifier for the specific nesting box/enclosure from which the sample was collected (used as a random effect in mixed models).
- substrate: The environmental medium sampled (soil, water, roller_in, roller_out).
- time_point: The day the sample was collected post-exposure (Day 0 through 365).
- qPCR_rep: The technical replicate number for the qPCR assay (1, 2, or 3).
- ct_value: The cycle threshold value returned by the qPCR instrument ("Undetermined" for no amplification).
- conc_copies_rxn: Raw starting copy number calculated from the standard curve.
- conc_lod: Adjusted copy number with values below the limit of detection (LOD) set to zero.
- lod: Binary indicator of detection (1 = amplified above LOD, 0 = no amplification or below LOD).
- control: Binary indicator identifying negative/positive control samples (0 = experimental sample, 1 = control).
- compromised: Binary indicator identifying samples that were compromised during the field study (0 = intact, 1 = compromised/excluded).
- plate_id: Numerical identifier for the qPCR plate run.
File 2: RTP_qPCR_vs_metabarcoding_data.csv
- enclosure_id: Unique identifier for the specific nesting box/enclosure.
- substrate: The environmental medium sampled (roller_in, roller_out, soil, water).
- time_point: The day the sample was collected post-exposure.
- conc_copies_rxn: Raw starting qPCR copy number calculated from the standard curve.
- lod: Binary indicator of qPCR detection (1 = amplified above LOD, 0 = no amplification).
- 12sv5_reads: The raw read count of Phascogale calura DNA returned by the 12sv5 metabarcoding assay.
- plate_id: Numerical identifier for the sequencing/PCR plate run.
File 3: Raw_FASTQ_Files.zip
Demultiplexed, single-end FASTQ files are provided for the metabarcoding samples. The files follow a strict naming convention to identify the sampling parameters: T[Time]_[EnclosureNumber]_[Substrate].relabeled.fastq
Example: T0_01_RI.relabeled.fastq
- T0 = Time point (Day 0)
- 01 = Enclosure ID (E1)
- RI = Substrate (Roller Inside)
Substrate Codes:
- RI = Roller Inside (Internal surfaces)
- RO = Roller Outside (External surfaces)
- W = Water
- S = Sediment (Soil)
Code and Software Requirements
The analyses were conducted using R. The script eDNA_analysis_master_script.R is designed to be run top-to-bottom.
To run the script, ensure both .csv data files are saved in the same working directory as the .R file.
The following R packages are required to execute the script:
- tidyverse
- dplyr
- ggplot2
- lme4
- lmerTest
- ggeffects
- scales
- blme
- car
- emmeans
- minpack.lm
