Navigating uncertainty in environmental DNA detection of a nuisance marine macroalga
Data files
May 30, 2024 version files 87.05 MB
-
Melt_Curve_Derivative_Results_SYBR.csv
5.62 MB
-
Quantification_Amplification_Results_SYBR.csv
2.74 MB
-
Quantification_Cq_Results_SYBR.csv
67.52 KB
-
README.md
2.33 KB
-
Sequence_Data.zip
78.61 MB
-
Sequence_metadata.csv
1.46 KB
-
Standard_Curve_Data.csv
1 KB
-
VSEARCH_output.txt
2.41 KB
May 30, 2024 version files 87.05 MB
-
Melt_Curve_Derivative_Results_SYBR.csv
5.62 MB
-
Quantification_Amplification_Results_SYBR.csv
2.74 MB
-
Quantification_Cq_Results_SYBR.csv
67.52 KB
-
README.md
2.41 KB
-
Sequence_Data.zip
78.61 MB
-
Sequence_metadata.csv
1.46 KB
-
Standard_Curve_Data.csv
1 KB
-
VSEARCH_output.txt
2.41 KB
Abstract
Early detection of nuisance species is crucial for managing threatened ecosystems and preventing widespread establishment. Environmental DNA (eDNA) data can increase the sensitivity of biomonitoring programs, often at minimal cost and effort. However, eDNA analyses are prone to errors that can complicate their use in management frameworks. To address this, eDNA studies must consider imperfect detections and estimate error rates. Detecting nuisance species at low abundances with minimal uncertainty is vital for successful containment and eradication. We developed a novel eDNA assay to detect a nuisance marine macroalga across its colonization front using surface seawater samples from Papahānaumokuākea Marine National Monument (PMNM), one of the world’s largest marine reserves. Chondria tumulosa is a cryptogenic red alga with invasive traits, forming dense mats that overgrow coral reefs and smother native flora and fauna in PMNM. We verified the eDNA assay using site-occupancy detection modeling from quantitative polymerase chain reaction (qPCR) data, calibrated with visual estimates of benthic cover of C. tumulosa that ranged from < 1% to 95%. Results were subsequently validated with high-throughput sequencing of amplified eDNA and negative control samples. Overall, the probability of detecting C. tumulosa at occupied sites was at least 92% when multiple qPCR replicates were positive. False-positive rates were 3% or less and false-negative errors were 11% or less. The assay proved effective for routine monitoring at shallow sites (less than 10 m), even when C. tumulosa abundance was below 1%. Successful implementation of eDNA tools in conservation decision-making requires balancing uncertainties in both visual and molecular detection methods. Our results and modeling demonstrated the assay’s high sensitivity to C. tumulosa, and we outline steps to infer ecological presence-absence from molecular data. This reliable, cost-effective tool enhances the detection of low-abundance species, and supports timely management interventions.
https://doi.org/10.5061/dryad.kkwh70s8q
https://doi.org/10.1371/journal.pone.0318414
Quantitative PCR (qPCR) data were exported as CSV files directly from the CFX Manager software (Bio-Rad) and includes:
- quantification amplification results (Quantification_Amplification_Results_SYBR.csv) - raw qPCR data used to visualize amplification curves (cycle number, relative fluorescence units "value", detection threshold as calculated by the CFX96 and efficiency "E", as calculated using the CFX Manager software)
- melt curve derivative results (Melt_Curve_Derivative_Results_SYBR.csv) - raw qPCR data used in the melt curve analysis (cycle temperature C, negative first derivative of the melting-curve, -dF/dT "value", detection threshold as calculated by the CFX96 and efficiency "E", as calculated using the CFX Manager software)
- quantification Cq results (Quantification_Cq_Results_SYBR.csv) - raw qPCR detection data used for modeling of site occupancy (quantification cycle Cq, detection threshold as calculated by the CFX96, efficiency "E", as calculated using the CFX Manager software, efficiency-corrected eDNA starting quantities "SQ.E")
- standard curve data (Standard_Curve_Data.csv) - raw qPCR data from standards formatted for plotting standard curve for rbcL marker (quantification cycle Cq and expected starting quantities "SQ") using serial dilutions of template DNA extracted from Chondria tumulosa tissue
High-throughput sequence data run on an Illumina MiSeq includes:
- raw sequence data - Raw sequence data (fastq.gz) from environmental DNA of surface seawater samples
- sequence metadata (Sequence_metadata.csv) - sample IDs, plate IDs, forward reads filename, reverse reads filename
- VSEARCH output - (VSEARCH_output.txt) - command line interface codes and outputs from amplicon metabarcoding workflow
Project metadata is provided in each and includes site IDs, date of sample collection, region, island, locales, site depth (meters), Chondria tumulosa percent benthic cover from visual surveys (%), binary visual presence-absence, and GPS coordinates (Lat./Long.) and X-Y position coordinates.
Entries marked "NA" are not-applicable. Entries marked "NULL" are absent.