Environmental DNA (eDNA), the genetic traces organisms leave in water, is increasingly sampled repeatedly over time to follow seasonal events such as spawning and migration. The timing of these eDNA peaks varies from year to year, which makes the data hard to compare and forecast. Much of this variation is likely driven by water temperature. Temperature controls both how fast organisms develop and release DNA and how fast eDNA breaks down. This project tests whether eDNA seasonal patterns line up better when time is measured on a temperature-adjusted "biological clock" rather than in calendar days. We will build statistical models in which eDNA concentration reflects a balance between DNA release and decay, both depending on temperature. Seasonal pulses will be timed on three alternative clocks: calendar days, degree-days, and metabolic time based on the Arrhenius relation. We will compare how well each clock predicts eDNA patterns in years left out of the model fitting. The models are fitted with Bayesian methods in Stan, using decay rates from experiments to separate DNA release from decay. We will first check on simulated data that the models recover known parameters, and then apply them to multi-year eDNA and water temperature series. The results will show which clock best explains the timing of eDNA signals and help predict when sampling is most informative. All code will be released openly.