Statistical analyses
All statistical analyses were performed using R version 4.0.4 (R Core
Team, 2021). The decay rates of Japanese jack mackerel eDNA were
estimated using the time-series changes in their eDNA concentrations
from each sampling tank. Previous studies estimated eDNA decay rates by
fitting a monophasic exponential decay model (Strickler et al., 2015; Jo
et al., 2020b) as follows:
\begin{equation}
C_{t}=C_{0}e^{-kt}\nonumber \\
\end{equation}where \(C_{t}\) is the eDNA concentration (copies) at time \(t\)[hour], \(C_{0}\) is the eDNA concentration at time 0, and \(k\) is
the decay rate constant (/hour). We used a linear model to compare the
decay rates of each type of eDNA between BAC treatments, where
log-transformed eDNA concentration was included as the dependent
variable and sampling time point (hour), BAC treatment, and their
interaction were included as explanatory variables.
Alternatively, following Eichmiller et al. (2016), we used a biphasic
exponential decay model if the fitness of a monophasic decay model was
poor and there was an obvious breakpoint between two distinct phases of
eDNA degradation as follows:
\begin{equation}
C_{t}=C_{0}e^{-k_{1}t^{{}^{\prime}}}e^{-k_{2}(t-t^{{}^{\prime}})}\nonumber \\
\end{equation}where \(k_{1}\) and \(k_{2}\) are the eDNA decay rate constants at the
initial rapid and following slower phases, respectively, and \(t^{{}^{\prime}}\)is the time of breakpoint between different degradation phases (hour).
We estimated eDNA decay rates with 95% confidence intervals (CIs) and
breakpoints using the package ‘segmented’ (Muggeo, 2017). We compared
the fitness of monophasic and biphasic decay models between BAC
treatments by calculating Akaike’s information criterion (AIC). All eDNA
samples with concentrations below one copy per reaction were excluded.
Furthermore, we compared time-series changes in fish species composition
inferred by eDNA metabarcoding between BAC treatments. For each time
point, the number of fish species detected by eDNA metabarcoding was
compared between BAC treatments using the exact McNemer test in the
package ‘exact2×2’ (Fay, 2010). We then visualized the community
compositions based on Jaccard dissimilarities using a two-dimensional
non-metric multidimensional scaling (nMDS) with 10000 permutations byvegdist and metaMDS functions in the package ‘vegan’
(Oksanen et al., 2019). In addition, we performed a permutational
multivariate analysis of variance (PERMANOVA) with 10000 permutations
using adonis function to examine whether the community
compositions were different among BAC treatments and/or time points.