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ABSTRACT Using the youngest detrital-zircon date(s) of a sedimentary deposit to constrain its maximum depositional age (MDA) is a widespread and growing application of geochronology. Most MDA studies analyze zircon grains at random, but this strategy can be costly and inefficient in cases where the youngest age group is only a small fraction of the population. We propose that handpicking sharply faceted zircon grains will increase the likelihood of encountering first-cycle zircon that have not undergone significant sedimentary transport, thus producing MDA estimates that are closer to the depositional age. We evaluate this procedure by conducting intra-sample comparisons of randomly selected versus handpicked zircon separates from 30 samples analyzed via laser-ablation–inductively coupled plasma–mass spectrometry (LA–ICP–MS). Our results show that handpicking zircon produces an overall shift towards younger ages in comparison to their randomly analyzed counterparts by an average of ∼ 406 Myr. In randomly analyzed separates, only 1.6% of grains were within 5 Myr of an independent estimate of the MDA, while handpicked separates contained 14.2%, an approximately nine-fold increase. However, handpicking can also lead to selection of older grains if they have been minimally transported, as with one handpicked Mesozoic sample that yielded 81% of ∼ 1.1 Ga zircon interpreted to be derived from a local granitic source. Handpicking is most effective in samples where young, sharply faceted grains are diluted by older, rounded grains, as with one sample that exhibited an ∼ 18-fold increase in the proportion of near-depositional-age zircons relative to its counterpart where grain selection was random. Because handpicking zircon imparts a severe bias on the resulting U–Pb age distribution, we recommend that two separate aliquots be used for quantitative provenance characterization through random analysis and MDA analysis through handpicking. 

Abstract. The loss of radiogenic Pb from zircon is known to be a major factor that can cause inaccuracy in the U–Pb geochronological system; hence, there is a need to better characterize the distribution of Pb loss in natural samples. Treatment of zircon by chemical abrasion (CA) has become standard practice in isotope dilution–thermal ionization mass spectrometry (ID-TIMS), but CA is much less commonly employed prior to in situ analysis via laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) or secondary ionization mass spectrometry (SIMS). Differentiating the effects of low levels of Pb loss in Phanerozoic zircon with relatively low-precision in situ U–Pb dates, where the degree of Pb loss is insufficient to cause discernible discordance, is challenging. We show that U–Pb isotopic ratios that have been perturbed by Pb loss may be modeled by convolving a Gaussian distribution that represents random variations from the true isotopic value stemming from analytical uncertainty with a distribution that characterizes Pb loss. We apply this mathematical framework to model the distribution of apparent Pb loss in 10 igneous samples that have both non-CA LA-ICP-MS or SIMS U–Pb dates and an estimate of the crystallization age, either through CA U–Pb or 40Ar/39Ar geochronology. All but one sample showed negative age offsets that were unlikely to have been drawn from an unperturbed U–Pb date distribution. Modeling apparent Pb loss using the logit–normal distribution produced good fits with all 10 samples and showed two contrasting patterns in apparent Pb loss; samples where most zircon U–Pb dates undergo a bulk shift and samples where most zircon U–Pb dates exhibited a low age offset but fewer dates had more significant offset. Our modeling framework allows comparison of relative degrees of apparent Pb loss between samples of different age, with the first and second Wasserstein distances providing useful estimates of the total magnitude of apparent Pb loss. Given that the large majority of in situ U–Pb dates are acquired without the CA treatment, this study highlights a pressing need for improved characterization of apparent Pb-loss distributions in natural samples to aid in interpreting non-CA in situ U–Pb data and to guide future data collection strategies.