The Implications of Detrital Zircon Maximum Depositional Age (MDA) from Large Sample Datasets (<em>n</em>>500)

The youngest sub-population of a detrital zircon geochronological dataset is routinely used to approximate the age of deposition for a sample. The ages represent a maximum depositional age (MDA) because the detrital zircons analyzed crystallized at depth, in a magma chamber prior to being exposed at the surface through erosion or volcanic eruption. Dickinson and Gehrels (2009) demonstrate four methods of calculating the MDA of a zircon population using U-Pb ages, which are assessed in this study. Previous MDA studies used relatively small datasets (n<100), reducing the likelihood of finding the youngest population in a sample. We consider large-n datasets (n>500), which have a greater likelihood of capturing a significant proportion of the youngest population and therefore have the potential to improve the accuracy of a calculated MDA. We assess the effects of sample size and MDA calculation methods using a numerical model consisting of a simulated population of detrital zircon grains with known ages. Using our population of 25048 simulated grains, we ran repeated trials of varying sample sizes (n=50, 100, 300, 500, 700, 1000, 1500) to compare the output of MDA calculation techniques. As sample size increases the youngest sub-population of zircons is better defined, and the MDA decreases and becomes more precise. As a further test, model results are compared to U-Pb data (n=695) from a sample of the Maasrichtian-Paleocene Gabriola Formation (Nanaimo Group, B.C., Canada). Similar trials of varying sample sizes show the same decrease in MDA. Biostratigraphic analysis assigned the formation to the Maastrichtian (72.1 - 66.0 Ma), however, our results indicate that deposition took place in the Danian (66.0 - 61.6 Ma). This result has implications for the timing of forearc basin fill, and more broadly, the evolution of the Western North American Cordillera. MDA methods on large-n datasets can be used to hone stratigraphic correlations and calculate sediment accumulation rates.