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More than presence-absence; modelling (e)DNA concentration across time and space from qPCR survey data
Milly Jones1, Eleni Matechou2, Diana Cole1
1School of Mathematics, Statistics, and Actuarial Science, University of Kent, Cornwallis South, Canterbury, CT2 7NF Kent England.
Journal of Statistical Theory and Practice
|August 8, 2025
Summary
This study introduces a new model for analyzing environmental DNA (eDNA) data from quantitative PCR (qPCR) to accurately estimate species DNA concentrations in the environment. The framework improves ecological monitoring and conservation by accounting for lab biases and data variability.
Area of Science:
- Ecology
- Molecular Biology
- Bioinformatics
Background:
- Environmental DNA (eDNA) surveys are a powerful, non-invasive tool for species monitoring.
- Quantitative PCR (qPCR) is widely used, but data analysis often focuses on presence/absence, not concentration.
- Existing qPCR data models may not fully account for laboratory biases or data variability.
Purpose of the Study:
- To develop a novel modelling framework for inferring species DNA concentration from qPCR data.
- To address biases like contamination and inhibition in laboratory analyses.
- To incorporate data heteroscedasticity and stochasticity for more accurate estimations.
Main Methods:
- Developed a statistical modelling framework for qPCR data.
- The model accounts for contamination, inhibition, and heteroscedasticity.
- Validated the model using simulation studies and three real-world case studies.
Main Results:
- The new model accurately estimates DNA concentrations across space and time.
- It demonstrates improved accuracy and robustness compared to models ignoring biases.
- Successfully applied to aquatic and semi-aquatic species surveys in the UK.
Conclusions:
- The proposed modelling framework offers a refined tool for ecological monitoring.
- Improved DNA concentration estimates enhance conservation efforts.
- This approach advances the utility of eDNA and qPCR in biodiversity assessment.
