Table 1.
Parameters of the Gamma Density Functions Fit to Results of the Fluorescence Experiment.
Fig 1.
Gamma Distribution Functions Characterizing Probability of Fluorescence.
These gamma distribution functions characterize the probability of the event that fluorescence is observed on an agarose gel given the number of target marker copies in a 1 μl aliquot of extraction elution used in PCR for (a) bighead carp and (b) silver carp.
Fig 2.
Gamma Distribution Functions Characterizing Probability of Successful Sequencing.
These gamma probability distribution functions characterize the probability of the event that PCR amplicons are successfully sequenced given the initial number of target marker copies in a 1 μl aliquot of extraction elution used in PCR for (a) bighead carp and (b) silver carp.
Table 2.
Parameters of Gamma Density Functions Fit to Results of the Sequencing Experiment.
Fig 3.
Probability of detecting bighead carp and silver carp eDNA during a monitoring event.
The probability of detecting the (a) bighead carp and (b) silver carp target marker is shown for monitoring events with 1, 2, 10, 20 and 30 water samples. At low concentrations in the environment, a large number of samples may be needed to detect the target marker with a high level of confidence.
Fig 4.
Simulated frequency of target marker counts in 1 μl aliquots.
The figure shows uncertainty in the number of target markers in aliquots drawn from a 100 μl extraction elution created by processing a 2 L water sample collected from a water body with an environmental concentration equal to (a) 10 copies/L, (b) 100 copies/L, (c) 1,000 copies/L, and (d) 10,000 copies/L.
Fig 5.
Concentration of the target marker that can be detected with probability 0.95.
The figure shows that the minimum concentration that is detectable with probability 0.95 decreases as the number of samples used in the baseline sampling protocol increases.
Fig 6.
Change in sensitivity caused by a one unit increase in selected parameters of the baseline sampling protocol.
The change in sensitivity caused by a change in sampling protocol will depend in part on the environmental concentration of the target marker in the monitored water body and on the target marker.
Fig 7.
Reduction in the minimum target marker concentration that can be detected with probability 0.95.
The figure shows to what extent changes in the baseline sampling protocol can reduce the target marker concentration that can be detected with probability 0.95. Changes in sampling protocol are as follows: 1) increase the number of samples from 10 to 11; 2) increase sample volume from 2 L to 3 L; and 3) increase the number of PCR replicates from eight to nine. The benefits of composite strategies (e.g., 1 & 2) are subadditive.