Data and code for: Accounting for temporal variation and correlation in environmental DNA sampling can improve ecological inferences

Environmental DNA (eDNA) concentration varies through space and time, and
measurements collected close together are often correlated. Ignoring this
dependence can inflate the rate of incorrect ecological inferences (Type I
error rate). Although spatial correlation in eDNA has received
considerable attention, temporal correlation has been less well studied.
Statistical models and study designs that account for temporal correlation
are increasingly important to understand time-dependent effects in complex
systems. We developed a hierarchical model that separates temporal
ecological variation from variability stemming from sampling and
laboratory processes and applied it to four single-site eDNA time series
collected over 17–24 days, three of which provided sufficient information
for parameter estimation. We then used the empirically estimated parameter
magnitudes in a simulation study to evaluate alternative temporal sampling
designs that considered 1) how a fixed number of samples is allocated
across different numbers of sampling times with different levels of
temporal replication and 2) equally-spaced versus. cluster-spaced sampling
(short bursts separated by longer gaps). Across the three time series
sufficient for analysis, we observed substantial sampling variability,
temporal variability, and temporal correlation, although correlation was
estimated imprecisely (large coefficient of variation). Simulations showed
that when sampling intervals were shorter than the effective temporal
correlation range, models that ignored temporal dependence produced
inflated Type I error rates and frequently detected spurious temporal
trends. Accounting for temporal correlation substantially reduced this
inflated Type I error rate. Optimal sampling strategies depended on study
objectives. Clustered sampling most effectively estimated temporal
correlation. When temporal dependence was negligible, evenly spaced
sampling maximized power to detect trends. Estimating sampling variability
required concentrating effort into fewer sampling times with more
replicates per time, whereas estimating temporal variance was most precise
with intermediate levels of replication. Together, these results indicate
that temporal dependence can strongly affect inference from quantitative
eDNA time series when sampling intervals approach the correlation
timescale. Designs that ignore this dependence risk inferring ecological
change or difference where none exists. Our framework provides practical
guidance for allocating sampling effort in temporally intensive eDNA
monitoring and for interpreting trends from short time series.