2. Statistical analyses
All statistical analyses were performed in R version 3.6.1 (R Core Team,
2019). We first performed a generalized linear model (GLM) with Gaussian
distribution to assess the relationship between eDNA persistence, eDNA
state, and environmental conditions. The eDNA decay rate constants (per
hour) were treated as the dependent variable, and the filter pore size
(µm), DNA fragment size (bp), target gene (mitochondrial or nuclear),
water temperature (°C), water source (artificial, freshwater, or
seawater), and their primary interactions were included as the
explanatory variables. We first confirmed that the multi-collinearity
among the variables was negligible (1.028 to 1.096), by calculating the
generalized variance inflation factors (GVIF). We then selected models
based on Akaike’s Information Criterion (AIC), using the dredgefunction in the ‘MuMIn’ package in R (Bartoń, 2019). We adopted the
model with the smallest AIC value, and all models with ⊿AIC (i.e.
difference in the AIC value) less than two were selected as the
supported models (Burnham & Anderson, 2002).
We performed an additional meta-analysis to examine the relationship
between the DNA fragment size and eDNA decay rate constant. Most eDNA
studies conducted to date have targeted short DNA fragments
(<200 bp), and only three papers have reported eDNA decay
rates targeting longer DNA fragments (>200 bp); however,
they yielded inconsistent conclusions (Tables 1 & S1). Taking this into
consideration and targeting eDNA decay rate constants derived from
<200 bp DNA fragments, we performed a linear regression to
assess the effect of DNA fragment size on eDNA degradation.