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Updated: Aug 6, 2026
08:54
Development and Testing of Species-specific Quantitative PCR Assays for Environmental DNA Applications
Published on: November 5, 2020
Accounting for temporal variation and correlation in environmental DNA sampling can improve ecological inferences
Ben C Augustine1, Patrick R Hutchins1, Margaret E Hunter2
1U.S. Geological Survey, Northern Rocky Mountain Science Center, Bozeman, Montana, USA.
Summary
Ignoring temporal correlation in environmental DNA (eDNA) data inflates error rates. Accounting for temporal dependence in eDNA time series is crucial for accurate ecological monitoring and trend detection.
Area of Science:
- Ecology
- Environmental Science
- Statistical Modeling
Background:
- Environmental DNA (eDNA) concentration exhibits spatial and temporal correlations.
- Ignoring temporal dependence in eDNA data can lead to inflated Type I error rates, causing incorrect ecological inferences.
- Temporal correlation in eDNA has been less studied than spatial correlation, yet is vital for understanding time-dependent ecological effects.
Purpose of the Study:
- To develop and apply a hierarchical model to separate temporal ecological variation from sampling and laboratory variability in eDNA time series.
- To evaluate the impact of temporal correlation on eDNA data inference.
- To assess optimal temporal sampling designs for eDNA monitoring.
Main Methods:
- Developed a hierarchical model to distinguish ecological temporal variation from process variability.
- Applied the model to four single-site eDNA time series (17-24 days).
- Conducted a simulation study using empirically estimated parameters to compare sampling designs (sample allocation, spacing).
Main Results:
- Observed substantial sampling variability, temporal variability, and temporal correlation in eDNA time series.
- Models ignoring temporal dependence showed inflated Type I error rates and detected spurious trends when sampling intervals were short.
- Accounting for temporal correlation significantly reduced inflated error rates.
- Clustered sampling was most effective for estimating temporal correlation; evenly spaced sampling maximized trend detection power when temporal dependence was negligible.
Conclusions:
- Temporal dependence significantly impacts inference from quantitative eDNA time series, especially when sampling intervals are near the correlation timescale.
- Sampling designs that ignore temporal dependence risk erroneous ecological change detection.
- The developed framework offers practical guidance for optimizing eDNA sampling effort and interpreting temporal trends.
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