eDNA Survey Sampling Parameter Optimization
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Solution Overview
Problem
Current eDNA and eRNA detection methods face challenges such as degradation, inhibition by humic substances, and lack of statistical reliability in survey designs, limiting their adoption for species detection, especially for invasive and endangered species.
Innovation Solution
A computer-implemented method for determining survey sampling parameters for eDNA, eRNA, eDNA metabarcoding, and methylation of eDNA, which iteratively adjusts sampling volumes based on environmental specifications and species selection to ensure detectability, using different algorithms for prediction and design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional PCR or quantitative PCR assays are used to detect eDNA, then species detection can be performed, but sensitivity and specificity are reduced due to inhibition by humic substances
Solution Approach 1:
The patent removes the harmful humic substances from the environmental sample through purification steps before performing PCR or quantitative PCR assays. This extraction of the harmful component eliminates the inhibition effect while preserving the target eDNA for detection, thereby improving sensitivity and specificity without changing the core detection methodology.
2Reliability
If eDNA survey designs are constructed based on guestimates, then surveys can be implemented quickly, but statistical reliability is lacking
Solution Approach 1:
The patent performs preliminary statistical power analysis and detectability assessment before conducting the actual eDNA survey. By calculating required sample sizes, sampling intensities, and detection probabilities in advance based on expected eDNA concentrations and degradation rates, the survey design achieves statistical reliability without requiring complex iterative adjustments during fieldwork.
3Productivity
If eDNA is allowed to persist in the environment for extended periods, then more detection opportunities are available, but false positives increase from extinct or extirpated organisms
Solution Approach 1:
The patent implements repeated sampling at multiple time points to detect eDNA periodically. By analyzing temporal patterns of eDNA detection and comparing results across different sampling events, the methodology distinguishes between persistent eDNA from extinct organisms and recurring eDNA from currently present species, thereby reducing false positives while maintaining detection opportunities.
4Measurement precision
If larger sampling volumes are used to detect rare eDNA, then detection sensitivity improves, but sampling costs and processing time increase
Solution Approach 1:
The patent dynamically adjusts sampling volume and intensity based on real-time detectability predictions and preliminary survey results. For locations or species with high predicted detectability, smaller sampling volumes are used, while areas with low predicted detectability receive intensified sampling. This dynamic allocation optimizes detection sensitivity for rare eDNA while minimizing overall sampling and processing time.
Data Source
AI summary
A computer-implemented method for determining survey sampling parameters for species marker detection comprises receiving a species selection identifying selected species and receiving environmental specifications for an environment to be tested for the species. A sampling plan is generated using the environmental specifications and the species selection, and detectability prediction(s) are generated using the environmental specifications, the species selection, and the current sampling plan to predict whether species marker(s) for the selected species are detectable according to the current sampling plan. Where at least one species marker is undetectable according to the current sampling plan, the process iterates, with each subsequent iteration incorporating an increase in the total volume to be sampled, until either every species marker is detectable according to the then-current sampling plan or an iteration stop limit is reached. The sampling plan(s) and detection prediction(s) are generated using different algorithms.