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 effectiveness for species detection, especially for rare or invasive 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 detectability predictions using environmental and species-specific data, ensuring all species markers are detectable or reaching an iteration stop limit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If eDNA sampling volume is increased to improve detection sensitivity, then detection reliability improves, but survey cost and time increase
Solution Approach 1:
The patent implements dynamic sampling volume adjustment based on detectability predictions. The system iteratively adjusts the total sampling volume based on predicted detectability of species markers, allowing the survey to adaptively allocate time and resources. When detectability is low, the system increases sampling volume; when detectability is high, it reduces sampling volume, optimizing the time-reliability tradeoff.
Solution Approach 2:
The patent changes the sampling volume parameter dynamically based on predicted detectability. By calculating detectability predictions from environmental and species-specific data, the system adjusts the total sampling volume parameter in each iteration to achieve reliable detection while minimizing time loss. This parameter optimization allows the survey to respond to varying detection conditions.
2Reliability
If conventional survey designs are used for eDNA detection, then survey simplicity is maintained, but statistical reliability is insufficient
Solution Approach 1:
The patent implements a feedback-driven survey design process. Detectability predictions are calculated based on environmental and species-specific data, and these predictions feed back into the sampling plan generation. The system iteratively refines the sampling plan based on predicted detectability, ensuring statistical reliability while managing complexity through automated feedback loops.
Solution Approach 2:
The patent performs preliminary detectability predictions before finalizing the sampling plan. By calculating detectability predictions in advance using environmental and species-specific data, the system can pre-optimize sampling parameters. This preliminary action allows the survey design to be statistically reliable from the outset, reducing the need for complex iterative adjustments during execution.
3Measurement precision
If sampling volume is increased to detect rare species, then detection sensitivity improves, but sampling cost increases
Solution Approach 1:
The patent dynamically adjusts sampling volume based on predicted detectability of rare species. The system calculates detectability predictions considering species rarity, distribution, and environmental factors. When rare species are detected as likely present, the system increases sampling volume to maintain sensitivity; when rare species are unlikely, it reduces sampling volume to control costs. This dynamic adjustment optimizes the sensitivity-cost tradeoff.
Solution Approach 2:
The patent optimizes the sampling volume parameter based on detectability predictions for rare species. By changing the sampling volume parameter dynamically rather than using a fixed high volume, the system achieves high detection sensitivity when needed while controlling overall sampling costs. The parameter adjustment is driven by species-specific detectability calculations that account for rarity and distribution.
Data Source
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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.