Needles in a haystack: Detrital zircon UPb ages and the maximum depositional age of modern global sediment

Journal Pre-proof Needles in a haystack: Detrital zircon U-Pb ages and the maximum depositional age of modern global sediment

Glenn R. Sharman, Matthew A. Malkowski PII:

S0012-8252(19)30650-6

DOI:

https://doi.org/10.1016/j.earscirev.2020.103109

Reference:

EARTH 103109

To appear in:

Earth-Science Reviews

Received date:

1 October 2019

Revised date:

7 January 2020

Accepted date:

29 January 2020

Please cite this article as: G.R. Sharman and M.A. Malkowski, Needles in a haystack: Detrital zircon U-Pb ages and the maximum depositional age of modern global sediment, Earth-Science Reviews(2019), https://doi.org/10.1016/j.earscirev.2020.103109

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

Journal Pre-proof

Needles in a Haystack: Detrital Zircon U-Pb Ages and the Maximum Depositional Age of Modern Global Sediment Glenn R. Sharman1 and Matthew A. Malkowski2 Department of Geosciences, University of Arkansas, Fayetteville, AR

2

Department of Geological Sciences, Stanford University, Stanford, CA

of

1

Keywords: Detrital zircon, U-Pb, maximum depositional age, true depositional age,

ro

contemporaneous zircon

-p

ABSTRACT

re

The practice of using the youngest detrital grains from a sedimentary deposit to constrain its depositional age has grown rapidly over the past two decades. Researchers that use the maximum

lP

depositional age (MDA) as a proxy for a deposit’s true depositional age (TDA) assume that processes of mineral crystallization, exhumation, and transport are rapid, such that minimal time

na

elapses between the time recorded by a mineral’s age and time of deposition. However, this assumption is in many cases untestable, as it requires independent age constraints that would

ur

preclude the need to interpret MDAs as TDAs. This study uses a global compilation of >70,000

Jo

detrital zircon U-Pb ages from 792 modern and Holocene sediment samples to evaluate the frequency with which various MDA methods approximate the TDA (~0 Ma) and to determine the geologic factors that control the global distribution of Earth’s youngest detrital zircon. We show that young (< 2 Ma) zircon are rare (~0.4% of the total dataset) and are largely restricted to areas associated with active volcanism. Dilution of the youngest, volcanically sourced grains may preferentially occur in large sediment routing systems (i.e., river catchments > 10 6 km2 ), causing the youngest grains to be missed during routine provenance analysis. Methods of calculating the MDA that rely on just one or two grains yield results that are closest to the TDA in the modern-Holocene dataset. However, use of more conservative MDA methods that rely on multiple, overlapping age measurements are likely necessary for avoiding calculation of an MDA that is younger than the TDA in ancient samples. A number of strategies can be used to

Journal Pre-proof increase the likelihood of finding young grains, if present, including conservative mineral separation, ‘high-n’ sampling strategies, and depth-profiling of whole zircon grains. Future efforts to maximize the benefit of MDA analysis could include increased sampling of modern sedimentary systems in underrepresented tectonic settings and depositional environments, development of improved methods for recognizing Pb loss in detrital zircon, and improvements to the precision and accuracy of high-throughput detrital geochronology.

of

1. Introduction

ro

Because detrital sedimentary rocks cannot be deposited before their constituent particles are formed, the youngest particles in a given deposit constrain the maximum age of deposition.

-p

This simple concept, “the law of detrital zircons” (Gehrels, 2014; Herriott et al., 2019), underlies a powerful approach in constraining the oldest possible age of a sedimentary deposit: the

re

maximum depositional age (MDA). Any measure of grain age, such as the timing of mineral

lP

crystallization or cooling, can be used to constrain the MDA, provided that the grain age does not reflect post-depositional processes (e.g., metamorphism). However, technological advances in

na

the ability to date individual particles of sediment, most notably detrital zircon U-Pb dating via laser ablation mass spectrometry, has led to a rapid increase in use of the MDA in constraining

ur

the age of sediment and (meta)sedimentary rock (Fedo et al., 2003; Gehrels, 2014; Coutts et al., 2019; Johnstone et al., 2019a; Fig. 1).

Jo

MDA constraints have had a widespread impact on the geosciences community over the past two decades (Fig. 1), particularly in circumstances where other, more established methods of constraining depositional age (e.g., biostratigraphy, volcanic ash geochronology) are limited (e.g., Robb et al., 1991; Thomson and Herve, 2002; Karlstrom et al., 2018). For instance, tectonicists use the MDA of metamorphosed units to constrain the age of the protolith (Barbeau et al., 2005; Gerdes and Zeh, 2006; Jones et al., 2009; Jacobson et al., 2011). The MDA has been increasingly used in paleontological studies to constrain the age of fossiliferous units where other radiometrically datable materials (e.g., volcanic ash beds) are not present (Tucker et al., 2013; Sickmann et al., 2017; Suarez et al., 2017; George et al., 2019; Marsh et al., 2019). Petroleum geologists may use MDA analysis in well core or cuttings where biostratigraphic age constraints are limited, as is typical in frontier petroleum-prospective basins (Lease et al., 2014; Masalimova

Journal Pre-proof et al., 2017; Bidgoli et al., 2018; Wahbi and Blum, 2019). MDA interpretations are also used to refine the timing and rates of deposition in sedimentary successions (Schwartz et al., 2016; Englert et al., 2018; Daniels et al., 2018, 2019; Johnstone et al., 2019a; Gehrels et al., 2019) and the timing of major tectonic events (Fildani et al., 2003; Surpless et al., 2006; Orme and Surpless, 2019). Despite the increasingly widespread use of the MDA to constrain the age of sediment deposition (Fig. 1), there are two primary ambiguities that can hamper straightforward application of this approach (Gehrels, 2014). First, the MDA may be older, even much older,

of

than the true depositional age (TDA). The degree to which the MDA provides useful chronologic

ro

constraints on the TDA reflects the degree to which the timing of the crystallization (or cooling) of the youngest sedimentary particles approximates the timing of deposition. In an absolute

-p

sense, no MDA can be a TDA because the dated minerals are detrital and some time has elapsed

re

between mineral formation and deposition. However, the time it takes for a grain to transit from where its age was recorded to where it was deposited may be small relative to the dating

lP

uncertainty of the grain. Herein, we use the term contemporaneous to describe detrital minerals whose crystallization or cooling age is within uncertainty of its TDA. For detrital zircon U-Pb

na

dates, uncertainty is usually reported as measurement precision (Puetz et al., 2018). The decision of whether to label a detrital mineral age as indistinguishable from the TDA will depend on the

ur

degree of confidence that is desired (e.g., 1σ or 2σ confidence bounds). However here, for simplicity, we consider a detrital zircon U-Pb date to be contemporaneous if its measurement

Jo

precision is within 2σ of its TDA. Such young detrital grains that are close in age to the timing of deposition have also been described as syn-depositional (e.g., Pullen et al., 2014; Gehrels et al., 2019) or near-depositional (e.g., Englert et al., 2018; Coutts et al., 2019). Identification of contemporaneous grains in a given sedimentary deposit depends on geologic context (i.e., a source of such grains) and sufficient analytical characterization such that contemporaneous grains, if present, are dated. In practice it can be difficult to evaluate whether both of these conditions are fulfilled for ancient samples because it is generally not known whether a source of contemporaneous grains was present and what the abundance of such grains may be, if present at all. Although this manuscript focuses on zircon U-Pb crystallization ages, many of the concepts presented herein apply to other detrital mineral chronometers (e.g., apatite monazite, sanidine; Carrapa 2010; Hereford et al., 2016).

Journal Pre-proof Second, the MDA may be younger than the TDA as a result of geological and/or analytical complexities that include measurement uncertainty, systematic uncertainties, Pb loss, and spurious common Pb corrections (Gehrels, 2014; Andersen et al., 2019); unrecognized postdepositional mineral growth during diagenesis or metamorphism (Rubatto, 2017); and sample contamination. The degree to which an MDA is younger than the TDA thus depends on a number of factors, including geologic context (i.e., the abundance of contemporaneous grains and their history) and analytical conditions (i.e., the precision and accuracy of the measurements themselves; Schaltegger et al., 2015).

of

Uncertainty in whether a given MDA is older than, within error of, or even younger than

ro

the TDA is a fundamental challenge to interpreting MDAs in ancient (meta)sedimentary successions. This manuscript aims to evaluate the extent to which MDA calculations

-p

approximate the TDA through analysis of a global database of detrital zircon U-Pb ages from

re

modern and Holocene sediment (70,737 analyses from 792 samples) where the depositional age is known with certainty (i.e., <0.0117 Ma; Cohen et al., 2013; updated). We evaluate the

lP

geological conditions that favor MDA calculations similar to the TDA and conduct a numerical thought experiment to assess the relationship between MDA calculations and the TDA as a

na

function of sample age. We show that although young zircon is rare (e.g., <2 Ma zircon constitutes just 0.4% of the total dataset), it is commonly found in areas with an active

ur

(Holocene) volcanic source. We also show that the probability of calculating an MDA younger than or within error of the TDA increases in older samples as a result of greater absolute dating

Jo

uncertainty in older grains. However, the likelihood of calculating an MDA that is too young is reduced by using conservative methods that rely on multiple, overlapping grain dates. We discuss several strategies for optimizing the likelihood of finding the youngest detrital grains that represent the proverbial ‘needles in a haystack’. Finally, we propose a number of future research directions that may improve the reliability and utility of MDA analysis in the geologic community.

2. Challenges to calculating and interpreting maximum depositional ages There are several pertinent questions related to conducting an MDA analysis on a given set of samples: (1) What is the likelihood that contemporaneous, or near-contemporaneous,

Journal Pre-proof grains are present? (2) How many grains should be analyzed?, (3) What is the most appropriate way to calculate an MDA?, and (4) How might MDA calculations relate to the TDA? The following sections describe a number of considerations and challenges related to each of these questions. 2.1. Are contemporaneous grains present? Samples that lack contemporaneous, or near-contemporaneous, grains will produce MDAs that are older than the TDA (Fig. 2A). In this case, the MDA may not provide new insight

of

or increased age resolution. For a deposit to yield an MDA that is close to the TDA, two fundamental requirements have to be met: (1) there must be a source of contemporaneous grains,

ro

and (2) there must be a sufficient supply of that source relative to older sediment sources.

-p

2.1.1. Requirement 1: A source

re

For the crystallization or cooling age of a detrital mineral to be similar to its depositional age, the mineral must crystallize or cool below a characteristic closure temperature and be

lP

rapidly conveyed to the Earth’s surface where it can be transported within a sediment routing system and deposited. Because zircon that crystallizes within plutons typically forms at depth of

na

5 km or more (Paterson et al., 1996), the lag time required to exhume the zircon crystal from depth is typically several myr or longer. Rare occurrences of Pleistocene plutonic rocks and

ur

associated detrital zircon have been found in regions undergoing exceptionally rapid exhumation

Jo

(Ito et al., 2013; 2017; Spencer et al., 2019). Barring exceptionally high rates of exhumation (e.g., Baldwin et al., 2004), volcanic eruptions represent a more plausible mechanism for producing contemporaneous mineral grains at the Earth’s surface due to the rapid rate at which minerals are transported from depth to the Earth’s surface. High-precision lag times associated with crystallization and eruption of volcanic zircon are commonly less than the million- year uncertainties calculated from MDAs (e.g., Crowley et al., 2007; Woztlaw et al, 2013; Keller et al, 2017). Correspondingly, the association of active volcanic sources and contemporaneous mineral ages has been made by many studies (Cawood et al., 2012; Perez and Horton, 2014; Schwartz et al., 2016; McKenzie et al., 2016; Jacobson et al., 2011; Kochelek et al., 2011), including several cases where detrital zircon from

Journal Pre-proof sandstone are analyzed within the context of non-detrital volcanic ash beds (e.g., Orme and Laskowski, 2016; Malkowski et al., 2017a; 2017b; Daniels et al., 2018). However, not all phenocrysts in the products of active volcanic systems crystallize during the latest episode of magmatism. Zircon, and other refractory minerals, can have different origins that reflect the long-lived nature of magmatic systems and the assimilation of surrounding host rocks (Miller et al., 2007; Grunder et al., 2008; Rossignol et al., 2019; Table 1). Inherited minerals entrained from the magma source and xenocrysts incorporated from surrounding wallrock can pre-date the most recent magmatic system by millions to hundreds of millions of years

of

(e.g., Compston et al., 1986; Harrison et al., 1987; Miller et al., 2007; Rojas-Agramonte et al.,

ro

2011; and many others). Antecrysts crystallize during earlier episodes of the same magmatic system and may be tens to hundreds of thousands of years older than the youngest magmatic

-p

event (Vasquez, 2011; Barboni and Schoene, 2014; Heinonen et al., 2016; Table 1). Autocrysts

re

are phenocrysts that crystallized in the magma driving an eruptive event and thus represent the most coeval eruptive products of magmatic systems. Autocrystic zircon may only be up to

lP

hundreds, thousands, or tens of thousands of years old until they erupt or become part of the antecrystic fraction. Because magmatic systems can be long-lived (e.g., up to 11 Myr for the

na

Aucanquilcha Volcanic Cluster in the northern Chilean Andes), minerals that crystallize from the same magmatic system can have ages that span up to several Myr (Miller et al., 2007; Grunder et

ur

al., 2008). Consequently, xenocrysts, antecrysts, and autocrysts may be difficult to differentiate from each other in systems with protracted magmatic histories. Furthermore, individual minerals

Jo

may preserve multiple episodes of growth within different domains. For example, an autocrystic rim may overlie an ante- or xenocrystic core (e.g., Siégel et al., 2018). Of these various volcaniclastic mineral types (Table 1), autocrystic and antecrystic minerals are most favorable for acquiring contemporaneous ages and xenocrystic and inherited minerals are the most problematic. Thus the extent to which the youngest detrital minerals from an active volcanic source will reflect the TDA depends on the extent to which autocrystic and antecrystic minerals are present. Although antecrystic zircon crystals in silicic magmas may begin to crystallize 100s of kyr prior to eruption (Reid et al., 1997; Simon et al., 2008), the time lag between crystallization of minerals and eruption age may not be discernable for ancient samples using SIMS and laser ablation ICP-MS (typically 1-4% 2σ relative measurement

Journal Pre-proof precision; Schaltegger et al., 2015; Puetz et al., 2018). However, this lag may be discernable for very young samples or ancient samples that are analyzed using high-precision ID-TIMS (<0.1% 2σ relative precision; Schaltegger et al., 2015). Finally, the assumption that detrital minerals sourced from an active volcanic region are autocrystic (or even antecrystic) may be spurious even in volcaniclastic sediment (e.g., McKay et al., 2015; Rossignol et al., 2019). Some of the approaches that are used to identify autocrystic minerals in igneous samples are also applicable to detrital studies, including consideration of morphology (size, color, grain shape) and examination of rim and core relationships (e.g., Siégel et al., 2018; and references within).

of

2.1.2: Requirement 2: Sufficient supply

ro

In order to be detected during measurement, the supply of zircon from a source of

-p

contemporaneous detrital minerals must be sufficient relative to zircon supplied from other sources. Andersen (2005) discussed the applicability of the binomial probability formula in

re

determining the probability of analyzing at least one grain from a grain age fraction (sensu Andersen et al., 2018) of a given relative abundance. The limiting relative age fraction size (XL),

lP

or detection limit, is dependent upon both the number of analyses drawn (n) and the chosen

na

confidence level (pL) (Andersen, 2005):

ur

𝑋L = 1 − (1 − 𝑝L )1/𝑛 (Equation 1)

Jo

For example, a sample comprised of 100 random analyses yields a 50% and 95% probability of detecting at least one grain from a grain age fraction that comprises ~0.7% and ~3% of the total, respectively (Fig. 3A).

Johnstone et al. (2019a) expanded this concept to determine the number of grains required to detect a given number of grains from the youngest grain age fraction (Fig. 3B). For example, a sample of 1000 random analyses has a 95% chance of detecting at least one grain from an age fraction that comprises ~0.3% of the total, but ~2,100 random analyses are required to detect at least three grains from this same age fraction (Fig. 3B). Most detrital zircon U-Pb studies consist of ~60 to 300 randomly selected analyses per sample, although the number of grain analyses per sample varies widely and depends in part on

Journal Pre-proof the resources available, the number of recovered zircon grains, and the specific objectives of the research. Contemporaneous zircon, if present at all, must be sufficiently abundant in the sample to be consistently detected (i.e., typically 1-10% of the total number of zircon grains) (Fig. 3). Thus a source of contemporary detrital minerals may not be detected, even if present, if this source is diluted by older sediment sources. Dilution may occur due to inadequate sediment supply and/or mineral fertility (Amidon et al., 2005; Samson and Moecher, 2006; Spencer et al., 2018; Malkowski et al., in press). Efforts to identify the youngest zircon may be facilitated by

targeting specific grain morphologies (Siégel et al., 2018).

ro

2.2. How many grains need to be analyzed?

of

dating more grains per sample (>>300; e.g., Pullen et al., 2014; Daniels et al., 2018) and

-p

If the relative abundance of the youngest age fraction of minerals is known, then the number of analyses needed to detect 1-8 grains at 95% confidence can be determined from

re

Figure 3B (Johnstone et al., 2019a). For example, approximately 600 randomly selected grains must be measured to have a 95% chance of identifying three or more grains from a group that

lP

comprises 1% of the total (Fig. 3B). If the group comprises 10% of the total, then only 60 randomly selected grains are needed to find at least three of these young grains (Fig. 3B).

na

However, the abundance of youngest detrital minerals is generally not known, and thus it is

ur

difficult to know a priori how many grains to select for analysis. 2.3. How to calculate the MDA?

Jo

Despite the increasing use of MDA analysis (Fig. 1), there is little consensus on how to calculate an MDA. Although the youngest single grain (YSG) is perhaps the simplest measure of the MDA, researchers have generally argued that the YSG is less robust and less conservative than other MDA metrics that rely on multiple grain dates (Dickinson and Gehrels, 2009; Gehrels, 2014; Gehrels et al., 2019; Herriott et al., 2019) or multiple analyses of the youngest grain (Spencer et al., 2014). A number of factors, including measurement uncertainty and analytical complexities (see Section 2.4), may cause any given grain age to yield a measurement younger than its true crystallization age (Gehrels, 2014). For this reason, the YSG may easily be younger than the TDA in circumstances where a contemporaneous grain fraction is present in the sample (e.g., Herriott et al., 2019; Gehrels et al., 2019).

Journal Pre-proof To avoid reliance on any single grain age, previous studies have used a number of ad-hoc methods of MDA calculation (Table 2). Dickinson and Gehrels (2009) examined 58 Mesozoic sandstone samples from the Colorado Plateau (USA) and applied five MDA methods to assess how well the MDA constrains the depositional age. These included the YSG; the youngest age peak defined by two or more analyses (YPP); the weighted mean average of the youngest cluster of two or more grains that overlap at 1-sigma or 2-sigma uncertainty (YC1σ and YC2σ); and the “youngest detrital zircon” (YDZ), an algorithm in Isoplot (Table 2). Coutts et al. (2019) discussed five additional MDA methods: the weighted average of the youngest three analyses

of

present in the sample (Y3Za) or overlapping within 2-sigma uncertainty (Y3Zo), the weighted

ro

average of the analyses that fall between probability minima of the youngest age peak of a probability density plot (τ method), the TuffZirc algorithm of Isoplot (Ludwig and Mundil;

-p

2002), and the youngest statistical population (YSP) that calculates the weighted average of the youngest group of 2 or more analyses that yield a mean square weighted deviation (MSWD) of

re

~1. Johnstone et al. (2019a) computed the MDA by adapting the approach of Keller et al. (2018)

lP

that uses a Bayesian framework for predicting the timing of magma zircon saturation and subsequent volcanic eruption (t e). The youngest age component as derived from mixture

na

modeling (e.g., the approach of Sambridge and Compston, 1994) has also been used as an indication of the MDA (Vermeesch, 2018; Coutts et al., 2019).

ur

Of the commonly used MDA methods, those based on a single or few youngest analyses are more likely to approximate the depositional age of the sample if few contemporaneous grains

Jo

are measured, these methods are at increased risk of being younger than the depositional age as a result of analytical dispersion (Fig. 2), lead-loss, or contamination (Dickinson and Gehrels, 2009; Coutts et al., 2019). MDA methods based on multiple analyses are more conservative (i.e., less likely to be too young) but are also more likely to be older than the TDA, reducing their utility as a chronologic constraint. Because the depositional age of ancient samples is unknown, it is difficult to determine which MDA method, or combination of methods, is the appropriate choice for a given study (Dickinson and Gehrels, 2009; Coutts et al., 2019). 2.4. How does an MDA relate to the depositional age? Although MDAs always provide a maximum bound on the age of deposition, the extent to which an MDA provides new insights depends on both the resolution of existing chronologic

Journal Pre-proof constraints and the degree to which the MDA approximates the TDA of the sediment. However, the inference that an MDA is a proxy for the TDA can be difficult to assess because any given MDA could be older than, within error of, or even younger than the depositional age (Fig. 2). In many circumstances, the assumption that the MDA approximates the TDA can be difficult to evaluate. Previous research has generally suggested that this assumption may be valid if: (1) the MDA is consistent with other chronologic constraints (e.g., biostratigraphy, ash geochronology, etc.; Schwartz et al., 2016; Orme and Laskowski, 2016; Malkowski et al., 2017b; Johnstone et al., 2019a), (2) MDAs become younger up-section (Rossignol et al., 2019), and (3) the sediment

of

or (meta)sedimentary rock contains first-cycle volcanic detritus (Pujols et al., 2018). Suggestion

ro

that the MDA may be a proxy for the TDA has been made in several ancient convergent margin systems, including the Andes (Perez and Horton, 2014; Schwartz et al., 2016; Malkowski et al.,

-p

2017a; 2017b; Daniels et al., 2018; Pujols et al., 2018), the North American Cordillera (Dickinson and Gehrels, 2009; Jacobson et al., 2011; Kochelek et al., 2011; Orme and Surpless,

re

2019; Gehrels et al., 2019), and in the Himalayan–Tibetan orogen (Wu et al., 2010; Hu et al.,

lP

2016; Orme and Laskowski, 2016).

Circumstances where a calculated MDA is younger than the TDA are problematic and

na

deserve particular consideration. As long as an MDA is treated as a maximum age estimate (i.e., not as a proxy for the TDA), calculating an MDA that is within error of or older than the TDA

ur

will not result in an erroneous interpretation. Calculation of an MDA that is too young, however,

Jo

may lead to an incorrect age assignment for (meta)sedimentary rock. Samples that contain a large number of contemporaneous detrital mineral ages may be at increased risk of yielding MDA calculations that are younger than the TDA as a result of analytical complexities including Pb loss, measurement uncertainty, and systematic uncertainties (Gehrels, 2014). For example, an MDA may be younger than the TDA due to the analytical imprecision that is inherent in geochronologic measurements (Fig. 2B). Coutts et al. (2019) conducted repeated measurement of a zircon standard of known age and demonstrated that analytical dispersion results in the youngest analyses being younger than the known age. In this case, the weighted mean average of all grains in the youngest age fraction provides a more accurate estimate of the known age than just the youngest analyses. But in detrital samples, it is not known the extent to which variations in the age of the youngest grains can be attributed to

Journal Pre-proof analytical scatter about a mean age as opposed to sampling grains derived from sources of different age. The practice of selecting the youngest analyses thus has the potential to result in MDA estimates that are too young, with the greatest risk occurring when a large number of imprecise, contemporaneous grains are measured (Fig. 2B). Radiogenic Pb loss from a zircon grain can also result in an age that is younger than its crystallization age (Spencer et al., 2016; Andersen et al., 2019; and references within). If Pb loss occurs in the youngest grains that have a crystallization age close to the TDA, then MDA calculations are likely to be too young. Lead loss may result from metamorphism (Cherniak and

of

Watson, 2003; Andersen et al., 2013; Orejana et al., 2015; Zeh et al., 2016; Rubatto et al., 2017),

ro

fluid interactions (e.g., Willner et al., 2003), or from damaged (or metamict) grains in which the compromised crystal allows diffusion of the daughter isotopes out of the crystal and provides

-p

additional pathways for fluid alteration (Lee and Tromp, 1995; Nasdala et al., 1998). Crystal

re

damage (and lead loss) may be especially problematic in old grains or grains with high U concentrations where larger amounts of decay (and alpha particle emission) have compromised

lP

the integrity of the crystal lattice. In some circumstances, Pb loss can be difficult to identify on the basis of discordance (Spencer et al., 2016; Andersen et al., 2019).

na

Mineral growth during diagenesis (>100⁰ C) and metamorphism may produce grain ages that are younger than the age of its host sedimentary rock (Hay and Dampster, 2009a, 2009b;

ur

Rubatto, 2017). If metamorphic conditions are sufficient for dissolution and recrystallization of

Jo

the mineral, depending on the analytical technique applied, this effect may go unnoticed and result in mixed age calculations between the initial age of crystallization and timing of metamorphism (Kohn et al., 2015). Grains that are younger than the age of deposition may be introduced through sample contamination, either in the field, during sample transport, or in the laboratory. Field contamination can occur if the sample surface is contaminated (e.g., with alluvium, wind-blown sediment, or drilling mud in the case of subsurface core or cuttings samples).

3. Methods 3.1. Database compilation

Journal Pre-proof A global database of 792 modern and Holocene samples was compiled from published tables of detrital zircon U-Pb ages in the literature (~51% of the total), from published literature compiled in the Global U-Pb Database 2017 (Puetz et al., 2018) (~41% of the total), and from unpublished data collected by the authors (~8% of the total). Primary sources of data are reported in Supplemental Table 1 and in Appendix A. With the exception of data from Puetz et al. (2018), we use the “Best Age” reported by the original publication for plotting and analysis. These ages are typically the younger grains and the

207

206

Pb/238 U age for

Pb/206 Pb age for older grains, with the cut-off specified by the original

of

authors. These grains ages were typically filtered by the original authors to exclude low precision

ro

and discordant analyses following standard data reduction practices. Because Puetz et al. (2018) did not report the “Best Age” from the original publication, we follow these authors’

-p

recommendation in using the age with the highest precision (i.e., their Model 1 age). These data

re

were filtered to exclude analyses with adjusted precision and discordance ratios greater than 2.7 and 4.0, respectively (Puetz et al., 2018). Of the 32,566 grain ages from data sources reported in

lP

Puetz et al. (2018), ~10% are rejected according to these filtering criteria. Approximately 95% of the youngest grains discussed herein (i.e., <10 Ma) compiled by Puetz et al. (2018) use the Pb/238U age and thus are equivalent to the “Best Age” reported by other data sources.

na

206

ur

3.2. Calculation of modern river watersheds Watersheds were computed based on hydrologically conditioned digital elevation models

Jo

for each of the 660 modern river samples using the ArcGIS Spatial Analyst toolset. Fifteen arcsecond resolution flow direction and flow accumulation maps from HydroSHEDS (www.hydrosheds.org) were used for the large majority of samples. A few samples from the arctic regions of Asia, Europe, and North American were outside the HydroSHEDS data coverage. Watersheds for these samples were computed using 30 arc-second resolution GMTED2010 digital elevation models. Sinks in each DEM were filled prior to calculating flow direction and flow accumulation. Sample location coordinates were manually adjusted, if necessary, to ensure that the sample was correctly positioned on the appropriate grid cell of the flow accumulation raster. 3.3. Calculation of maximum deposition ages

Journal Pre-proof We calculate a number of commonly used MDA methods following previous workers (see Dickinson and Gehrels, 2009, and Coutts et al., 2019, for additional information) (Table 2). Python code used to calculate MDAs is available via the detritalPy GitHub repository (www.github.com/grsharman/detritalpy), and more information regarding implementation of these MDA methods is available in the detritalPy manual and within commented lines of the Python code (Sharman et al., 2018). 3.4.1. Youngest detrital zircon (YDZ)

of

The YDZ algorithm of Isoplot (Ludwig, 2008) employs a Monte Carlo simulation that repeatedly subsamples the youngest analyses, defined as being within 5σ of the youngest

ro

analysis. Each age is randomly perturbed by an amount defined by a Gaussian distribution where

-p

the standard deviation is defined by the age’s uncertainty. The youngest age is selected, and the experiment is repeated 10,000 times in total. The reported MDA is the mode of the resulting

re

distribution of youngest ages, and the uncertainty is defined as the upper and lower limits that

3.4.2. Youngest single grain (YSG)

lP

encompass 95% of the results (Ludwig, 2008).

na

The YSG is calculated by sorting each age plus 2σ uncertainty and selecting the first analysis in this list. As defined this way, an older, more precise analysis may be selected instead

ur

of a younger, less precise analysis. For example, if two young grains are present with measured U-Pb dates of 100 ± 4 Ma and 101 ± 2 Ma, the later analysis would be selected as the YSG. This

uncertainty.

Jo

approach follows Sharman et al. (2018) with the exception of using 2σ uncertainty instead of 1σ

3.4.3. Youngest single cluster overlapping at 1σ (YC1σ) or 2σ (YC2σ) uncertainty Calculation of the YC1σ and YC2σ follows the approach outlined in Sharman et al. (2018): 1) a list of ages plus their 1σ or 2σ uncertainties is sorted, 2) the youngest cluster of ages is selected, and 3) the YC1σ or YC2σ is defined as the weighted mean and associated 2σ uncertainty of this cluster. The youngest cluster is defined as the youngest group of analyses that have overlapping uncertainties (i.e., the age minus uncertainty of the oldest grain is less than the sum of the youngest grain’s age and uncertainty). Following Dickinson and Gehrels (2009), by default the YC2σ must have at least three analyses in the youngest cluster (i.e., YC2σ[3+]) and

Journal Pre-proof the YC1σ must have two or more analyses (i.e., YC1σ[2+]). However, the number of required analyses in each cluster can be manually specified in detritalPy. 3.4.4. Youngest probability peak (YPP) The YPP is calculated as the youngest peak of a probability density plot (PDP). By default, at least two analyses must overlap with the YPP on the basis of their age ± 2σ uncertainty. The YPP is reported to the nearest 0.1 Ma, which is the interval at which our age axis is discretized. Note that the YPP does not have an associated uncertainty.

of

3.4.5. Youngest three grains (Y3Za) and youngest three grains overlapping at 2σ uncertainty

ro

(Y3Zo)

The Y3Za is defined as the weighted average and associated uncertainty of the youngest

-p

three analyses in the sample. The Y3Zo is defined similarly, except that the three grains must

re

overlap within 2σ uncertainty. The criteria for defining cluster overlap is the same as described

lP

above for YC1σ and YC2σ. 3.4.6. Youngest statistical population (YSP)

na

The YSP is the weighted mean of the youngest group of two or more analyses that have a MSWD that is closest to one (Coutts et al., 2019). We implement the YSP as follows: 1) all

ur

analyses are sorted by their age, 2) beginning with the first analysis in the list, the mean squared weighted deviation (MSWD) is calculated for the 1 st and 2nd analysis, for the 1st , 2nd, and 3rd,

Jo

analysis, and so on, 3) if none of the MSWD values in the list are less than 1, the youngest analysis is excluded and the process is repeated until an MSWD less than 1 is returned, and 4) if one or more MSWD values less than 1 are present, the YSP is defined as the weighted mean and uncertainty of the cluster of analyses whose MSWD is closest to 1. When using the YSP, one should be aware of the inherent limitations of the MSWD (e.g., Horstwood, 2008; Compston and Gallagher, 2012). Although an MSWD of 1 indicates that scatter in data can be explained by analytical uncertainty alone, an MSWD of 1 can still be accompanied by geologic variation if the scale of that variation is sufficiently small relative to the precision of the analytical method (Horstwood, 2008; Kalsbeek, 1992). Furthermore, an MSWD other than 1 does not necessarily indicate that geologic variation is present. Rather, a

Journal Pre-proof range of MSWD values can be used to define a confidence interval (e.g., 95%) of scatter in data that could have resulted from analytical uncertainty alone (Horstwood, 2008; Wendt and Carl, 1991). Specifically, a larger number of analyses results in a smaller range of acceptable MSWD values at a given confidence interval. 3.4.7. τ method The τ method is defined as the weighted average of all analyses with ages that fall between probability minima (troughs) of the PDP, provided that at least 3 analyses are present

of

(Barbeau et al., 2009). Probability minima are identified using the peakutils library (https://github.com/lucashn/peakutils). Because the first and last PDP peaks will not have an

ro

associated trough on their young and old margins, we select all analyses that fall between these

-p

peaks and 0 and 4,500 Ma, respectively.

re

3.5. Synthetic grain aging

To assess the success of MDA calculations in approximating the TDA of ancient

lP

sediments, we perform a numerical thought experiment where each sample in the modern dataset is synthetically aged. By synthetically aging each grain in increments of 100 Ma (to a maximum

na

of 1000 Ma), the analytical precision of the youngest grains within the global dataset better matches the expected precision in the youngest grains within ancient samples. We conducted this

ur

experiment by modifying each analysis in the dataset as follows.

Jo

1) The total dataset was filtered to exclude samples with less than 60 analyses (554 samples and 62,442 analyses were retained). This step was performed to eliminate samples with poor analytical characterization (e.g., Vermeesch, 2004). 2) Each analysis in the filtered dataset is aged in an increment of 100 Ma up to a maximum to 1000 Ma. 3) Because very young detrital zircon have anomalously high absolute precision and anomalously low relative precision relative to their ancient counterparts (Fig. 4), we used a procedure to redistribute analytical uncertainty to the aged grains. An uncertainty (i.e., measurement precision) is selected for each synthetically aged analysis through the following steps. Analyses are selected from the entire, unmodified dataset (i.e., unaged and unfiltered) that

Journal Pre-proof lie within 5% of the synthetic age (e.g., all analyses between 95-105 Ma would be selected for a grain synthetically aged to 100 Ma). Relative analytical uncertainties are selected from this filtered set and further filtered to exclude outliers (i.e., analyses with either very high or very low uncertainty). We excluded the least precise 2.5% analyses and the most precise 2.5% analyses to retain the middle 95% of all uncertainties, as shown in Figure 4. For example, 95% of analyses between 95-105 Ma in the modern-Holocene dataset have relative 2σ uncertainties between 1.4% and 13.5%. A new relative uncertainty is selected randomly from the filtered set of uncertainties according to its probability distribution.

of

4) The aged analysis is adjusted by an amount randomly drawn from the new uncertainty,

ro

and a new absolute uncertainty is computed by multiplying the new relative uncertainty by the

-p

adjusted age.

5) MDAs are calculated for each aged sample. To minimize the effects of stochasticity,

re

we conducted the experiment 10 times and averaged the results.

lP

4. Results 4.1. Dataset summary

na

We present a global compilation of 70,737 detrital zircon U-Pb ages from 792 modern and Holocene samples (Fig. 5; Table 3). Of these, 762 are samples from modern depositional

ur

systems with the remaining 30 samples from dated samples of Holocene age. 79.5% of samples

Jo

are from modern rivers or Holocene fluvial deposits; 9.1% of the dataset are from marine deposits; and the remaining ~10% of the dataset are from aeolian, littoral, deltaic, alluvial, subglacial fluvial, or lacustrine depositional environments (Fig. 5; Table 4). Data is available from all continents except Antarctica (Table 3). The total area of land surface covered by modern river sample watersheds is 49,430,535 km2 , approximately one third of the total land surface area of the Earth (Fig. 5; Table 3). Asia and North America have the highest data density and together account for 64.2% of the total dataset (Table 3). South America and Asia have the highest proportion of their land surfaces covered by sampled watersheds (52.5% and 42.2%, respectively). Europe and Africa together account for 14.9% of the total dataset and have 32% and 39% land surface coverage, respectively (Table 3). Australia only accounts for 4.1% of the total dataset, and although Australia has 40 modern river samples, most of these are from small

Journal Pre-proof watersheds that account for just 2.8% of its surface area. As noted by previous studies (e.g., Condie and Aster, 2010; Puetz et al., 2018), detrital zircon U-Pb age distributions vary widely by continent (Fig. 6). Zircon younger than 500 Ma constitute <20% of the total for Africa and Australia and >40% for Asia, Europe, South America, and North America (Fig. 6). The median sample size is 91 analyses, with 95% of the dataset having between 10 and 259 analyses per sample (Fig. 7). The median age fraction size detection limit (XL) at a 95% confidence level is 3.2% (Eq. 1; Fig. 7), meaning that a sample size of 91 analyses has 95% probability of including at least one analysis from a grain age fraction that comprises 3.2% of the

of

total grain population (Andersen, 2005). Detection limits range from 1.2% to 25.9% for the

ro

middle 95% of the dataset (Fig. 3).

-p

4.2. Young grains

re

4.2.1. Entire dataset

Approximately 0.4% and 1.0% of analyses in the dataset are less than 2 Ma and 10 Ma,

lP

respectively (Fig. 8). The total number of contemporaneous zircon (i.e., overlapping with depositional age within 2σ measurement uncertainty) is ~0.3% of the total dataset. Furthermore,

na

these young grains are disproportionally abundant in relatively few samples; approximately half of all zircon <5 Ma are found in just 4 samples from North and South America.

ur

Approximately 5% and 14% of samples have a single grain that is less than 2 Ma and 10

Jo

Ma, respectively (Fig. 9). These percentages decrease to 2.5% and 7% if at least three grains less than 2 Ma and 10 Ma, respectively, are required. Young zircon are most abundant in South and North America; 14% and 10% of samples from these respective continents have a single grain less than 2 Ma (Fig. 10). Zircon younger than 2 Ma are only found in 3 samples from Asia (~1% of samples) and are missing entirely from Africa, Europe, and Australia. Approximately 40% of samples from Africa, Europe, and Australia have a youngest zircon that is Precambrian in age. The following sections outline the spatial distribution of young zircon in South America, North America, and Asia (Fig. 11). 4.2.2. South America

Journal Pre-proof South America has the most abundant young zircon of any continent; 58% of the total dataset’s detrital zircon younger than 10 Ma is distributed throughout 53 samples in South America (41% of the samples from that continent) (Fig. 11A). Along the western margin of South America, samples with young zircon are associated with regions where the Andean volcanic arc is active (Figs. 11A and 12). This pattern is most pronounced between 15⁰ S and 25⁰ S and south of 34⁰ S latitude where the margin is characterized by steep angles of plate subduction and upper plate magmatism (Cross and Pilger, 1982). The moving average of the youngest single grain age in these regions is less than 10 Ma (Fig. 12). Correspondingly, a-

of

magmatic zones associated with lower angles of plate subduction between 3⁰ S and 15⁰ S and

ro

between 28⁰ S and 34⁰ S (Cross and Pilger, 1982) are characterized by samples with youngest single grains older than 10 Ma (Fig. 12). This pattern is somewhat less well developed in

-p

northern South America where a number of samples have youngest grains older than 10 Ma

re

despite the presence of an active volcanic arc between 6⁰ N and 2⁰ S (Figs. 11A and 12). Young zircon is inconsistently present in samples from the retroarc foreland of South

lP

America (Fig. 11A). For example, four upstream river samples from the Paraná River have young zircon that range from 0.04-13.8 Ma whereas two downstream samples lack zircon

na

younger than 86 Ma (Campbell and Allen, 2008; Rino et al. 2008; Fig. 11B). Similarly, young zircon is inconsistently present in the Amazon River; 9 of 25 samples from the middle and lower

ur

reaches of the Amazon River have a zircon grain younger than 10 Ma (Mapes, 2009). Rivers that lack headwaters in the Andean volcanic arc (e.g., the São Francisco and Orinoco rivers) have

Jo

youngest zircon older than 100 Ma (Goldstein et al., 1997; Campbell and Allen, 2008). 4.2.3. North America

North America has the second highest abundance of young zircon of any continent with 46 of 229 samples containing 35% of the world’s grains younger than 10 Ma (Fig. 11B). As with South America, the spatial distribution of young grains is associated with regions of active volcanism. For example, 20 out of 22 samples from the Bering Sea, located adjacent to the Aleutian Volcanic Arc, have at least one zircon grain younger than 12 Ma (Fig. 11B). Samples from the Sacramento River basin of northern California drain the southern Cascade Volcanic Arc; 5 out of 7 samples from the upper reaches of this system have zircon younger than ~1 Ma

Journal Pre-proof (Malkowski et al., in press; Fig. 11B). In the volcanic Snake River Plain of central Idaho, 10 of 15 samples have a zircon grain younger than 11 Ma. However, the presence of active volcanism in a sample’s catchment area does not guarantee the presence of young zircon. Although young zircon (~1 Ma) are frequent in the upstream Sacramento River, these grains are absent downstream; grains less than 2 Ma are not found in any of the 6 samples from the lower reaches of the Sacramento River and San Francisco Delta (Malkowski et al., in press; Fig. 11B). Similarly, a single sample from the mouth of the Columbia River lacks zircon younger than 37 Ma despite the Cascade Volcanic Arc being

of

present in the lower reaches of this sample’s watershed (Campbell and Allen, 2008; Fig. 11B).

ro

Although intermediate to mafic volcanic rocks that comprise much of the Cascades may not be expected to yield significant zircon (see discussion below), dacites and rhyodacites that erupted

-p

explosively from the arc are also not detected in the dataset.

re

As expected, regions in North America that lack active volcanism lack young zircon (Fig. 11B). Samples from the eastern United States that drain the Appalachian Mountains consistently

lP

lack zircon younger than 140 Ma. Samples with a source in the central and southern Rocky Mountain region and Colorado Plateau generally have youngest zircon that are Late Cretaceous

na

to Miocene in age (Fig. 11B). Only 2 of 48 samples from these regions had a zircon grain younger than 10 Ma: a single sample with a ~3 Ma grain from the lower Mississippi River (Izuka

ur

et al., 2005) and a sample with a ~8 Ma grain from the Yuma River (Kimbrough et al., 2015). In contrast to the Columbia river, the Mississippi River drainage may record the erosion of ash fall

Jo

deposits within its drainage basin. Young ages may be associated with the ‘Pearlette volcanic ash’ of the Yellowstone super-eruptions that blanketed the Mississippi River basin at least 3 times during the Pleistocene (2.09, 1.29, and 0.60 Ma; Wilcox and Naeser, 1992). 4.2.4. Asia Zircon younger than 10 Ma are only found in 6 of 241 samples in Asia, and of these only three samples have a zircon analysis younger than 2 Ma (Fig. 11C). These young grains are found in the Indus River Mouth (~0.5 Ma; Clift et al., 2004), Japan (~1.3Ma and ~9.4 Ma; Clift et al., 2013), in tributaries to the Brahmaputra River (0.7 Ma and 4.8 Ma; Cina et al., 2009 and Zhang et al., 2012), and in the Irrawaddy River (3 Ma; Bodet and Scharer, 2000). The youngest zircon found along the axis of the Himalaya in the upper reaches of the Indus and Brahamaputra

Journal Pre-proof rivers are commonly late Eocene to Miocene age (ca. 40-12 Ma; Fig. 11C); Miocene leucogranites are also present in this region (Harris and Massey, 1994). 4.3. Maximum depositional age calculation MDAs were calculated for each sample using the 9 different MDA methods outlined in Table 2 (Fig. 9). The YDZ and YSG methods yielded the youngest MDA estimates, and the Y3Zo, YC2σ, and τ method yielded the oldest MDA estimates (Fig. 9). For example, the YSG and τ method methods resulted in ~13% and 4% of samples having a MDA less than 10 Ma,

of

respectively. In aggregate, the YC1σ, YPP, and YSP methods yielded intermediate MDA estimates (Fig. 9). Restricting MDA calculations to samples with 60 or more analyses results in a

ro

modest shift towards proportionally younger MDA calculations (Fig. 9B). For example, 7.2% of

-p

the entire sample set has a YC1σ MDA less than 10 Ma. This percentage increases to 8.8% if

re

only samples with at least 60 analyses are considered (Fig. 9B). 4.4. Synthetic aging results

lP

Synthetic aging of modern and Holocene detrital zircon samples shows that the proportion of MDA calculations that overlap with and are younger than the TDA increases with

na

age (Fig. 13). This result is in part a consequence of older grains having higher absolute analytical uncertainties than younger grains (Fig. 4). For example, a 2 Ma grain from a modern

ur

river with a 2σ uncertainty of ±0.2 Ma (10% relative uncertainty) is demonstrably older than its depositional age (0 Ma). However, if this grain was aged 100 Myr to become 102 Ma with a 2σ

Jo

uncertainty of ±4 Ma (4% relative uncertainty), this grain would now overlap within error of its depositional age and be considered “contemporaneous”. The YSG method produced the most MDA calculations that overlap within error of the TDA (35% of samples when aged to 1000 Ma) followed by the YDZ (33%), Y3Za (27%), YC1σ (18%), YC2σ (13%), Y3Zo (11%), YSP (8%), and the τ method (7%) (Supplemental Table 2). Because the synthetic aging algorithm perturbs each grain age according to the probability distribution of its analytical uncertainty, it is possible for a very young grain age to become younger than its depositional age. For example, a group of 0.5 Ma zircon grains when aged 500 Ma would become 500.5 Ma with typical 2σ uncertainties of 15 Ma. Analytical dispersion results in some zircon U-Pb dates being younger and older than their true age (e.g.,

Journal Pre-proof 95% of zircon ages should span between ~485 and 515 Ma). Preferentially selecting the youngest of these analyses may result in a MDA calculation that is too young (Fig. 2B; Coutts et al., 2019). Results of the synthetic experiment show that MDA calculations are infrequently too young (up to 2.4% of cases when aged to 1000 Ma) with the exception of the YDZ method that produces “too young” MDA estimates that range from 0.5% of samples in the unaged, unmodified dataset to 15% of samples in the 1000 Ma aged dataset (Fig. 13; Supplemental Table 2). The YSP, YC2σ, and τ method yielded the most conservative results with the lowest

of

proportions of MDA calculations that are “too young” (0.1-0.3% of samples when aged to 1000

ro

Ma). However, these methods also resulted in the smallest proportion of samples that overlapped

-p

with the true depositional age (6-11% of samples; Fig. 13). 5. Discussion

re

5.1. Limitations of the dataset

lP

The dataset presented herein has a number of limitations that are worth considering. (1) U-Pb age measurements from young (e.g., less than a few Myr) zircon are subject to high

na

relative uncertainty in measurement precision versus their ancient counterparts (Fig. 4). This lack of precision is partly related to relatively little radiogenic lead (e.g.,

206

Pb) accumulating in

ur

zircon over short time spans (the half-life of 238 U is ~4.5 Ga, Jaffey et al., 1971). Measurement uncertainty of 207 Pb is particularly high in very young grains as a consequence of the lower initial 235

U relative to

238

Jo

abundance of

U (~0.7%), despite a shorter half-life (~0.70 Ga, Jaffey et al.,

1971). For this reason, the reliability of very young grain ages reported in this compilation is difficult to assess on the basis of concordance alone. As a corollary, very young zircon grains may be discarded from datasets if discordance filters are applied without regard for analytical uncertainty (e.g., Andersen, 2019). Unfortunately, it is difficult evaluate the extent to which very young grains were discarded in the dataset presented herein, as most studies do not report excluded grain ages within their U-Pb data tables. (2) Inconsistencies between data reduction procedures and uncertainty reporting standards between laboratories complicate strict comparisons of age and measurement precision in large compilations such as the one presented herein (Fisher et al., 2010; Schoene, 2014).

Journal Pre-proof Notably, systematic uncertainties that incorporate error propagation (e.g., Horstwood et al., 2016) are inconsistently reported, hampering efforts to compare absolute U-Pb dates between different studies. Incorporation of systematic uncertainties may result in a shift (typically 1-2%) in U-Pb ages, and derivative MDA calculations, that depends on a number of factors including the performance of standards (Gehrels, 2014). Furthermore, U-Pb ages from late Pleistocene or Holocene zircon may be influenced by fractionation associated disequilibrium of 230 Th that may produce a date that is up to ~110 kyr too young, unless corrected (Schoene, 2014; Schaltegger et al., 2015). Thus, although young zircon (e.g., <2 Ma) are readily identified by LA-ICP-MS, we

of

do not attempt to interpret minor differences in age or precision between these youngest grains. 230

Th/238 U

ro

We also recognize that for very young zircon, U-series dating that utilizes

disequilibrium provides much higher age resolution than U-Pb dating via LA-ICP-MS

-p

(Schaltegger et al., 2015).

re

(3) Samples presented in this compilation are not equally distributed across the globe; instead, certain regions have higher data coverage than others (Fig. 5; Table 3). Sample coverage

lP

is particularly poor in volcanically active areas in Central America and in western Pacific Ocean, including New Zealand and islands of Southeast Asia and Oceania (Fig. 5). The lack of data

na

points from these regions is unfortunate, as samples from these areas would provide critical information on the abundance of young zircon within oceanic island arcs and other volcanically

ur

active regions that are lacking in the current compilation.

Jo

(4) The majority (~79%) of samples within the dataset are from modern rivers (Table 4), many of which are within the erosional or sediment transfer zones of their respective sediment routing systems (e.g., Romans et al., 2016). The relative scarcity of samples from depositional zones (e.g., marine environments) contrasts with ancient sedimentary systems, which are preferentially preserved in subsiding sedimentary basins. Conversely, samples from non-fluvial environments (e.g., aeolian, marine) cannot be simply related to an upstream sediment source area (i.e., watershed) in the same way that river samples can be (e.g., Figs. 10 and 11). (5) Variability in the intensity of global arc magmatism over time (Condie and Aster 2010; McKenzie et al., 2016) may result in contemporaneous zircon being less or more common in ancient times than in the present. The present Earth may thus not provide a perfect analog for all times and places in the ancient world. For example, periods of intense explosive silicic

Journal Pre-proof volcanism (e.g., mid-Cretaceous Cordilleran high-flux event; Ducea et al., 2015) and Oligocene ignimbrite flare-up in North America (Best et al., 1989; Best and Christiansen, 1991) may have made very young zircon more abundant than they are in the Holocene. (6) Because the data examined herein is largely of the same age (Holocene to modern), we are unable to examine how MDA calculations vary over time in a given locality. For instance, we are not able to test the extent to which up-section younging of detrital mineral ages indicates that the MDA approximates the TDA (e.g., Rossignol et al., 2019). Ultimately, temporal variations in MDA calculations and other chronostratigraphic indicators (e.g., Johnstone et al.,

of

2019a) provides additional, valuable information that cannot be incorporated into our analysis.

ro

5.2 Why is young zircon relatively rare?

-p

Zircon younger than 2 Ma constitutes approximately 0.4% of the global dataset (Fig. 8). At this abundance, approximately 2000 analyses per sample are required to be 95% confident in

re

detecting at least three such grains (Fig. 3). However, the presence of young zircon generally

lP

increases in proximity to an active volcanic source (Fig. 14A). The association with active volcanism and the youngest grains supports the inference that volcanic rocks are the primary

na

source for contemporaneous zircon in sediment. However, even in volcanically active regions, the youngest zircon are much rarer than their older counterparts. The sections below discuss a

sediment.

ur

number of factors that may influence the relative rarity of the youngest zircon in detrital

Jo

5.2.1. Volumetric abundance and zircon fertility of volcanic rocks Volcanic rocks of any age account for just ~6% of all rocks exposed at the Earth’s surface (Hartmann and Moosdorf, 2012). Approximately half of exposed volcanic rocks are comprised of mafic lithologies that are not likely to produce a significant quantity of zircon relative to other rock types (see discussion below; Hartmann and Moosdorf, 2012). Furthermore, only a fraction of the intermediate to felsic volcanic rocks exposed at the Earth’s surface are of appropriate age (i.e., Holocene to late Pleistocene) to produce young (i.e., <2 Ma) zircon. Thus, the relative rarity of young zircon can be viewed in part as a consequence of the volumetric rarity of volcanic units of appropriate age and composition presently exposed on Earth’s surface.

Journal Pre-proof Although zircon is a relatively common and widespread mineral, it is not ubiquitous in all crustal magmatic systems. It is most common in metaluminous and peraluminous intermediate to silicic igneous rocks, and is relatively rare in mafic rocks (Watson, 1979; Watson and Harrison, 1983; Hoskin and Schaltegger, 2003). The solubility (and saturation) of zircon is a function of temperature and melt composition (Harrison and Watson, 1983; Watson and Harrison, 1983; and reviews in Boehnki et al., 2013 and Siégel et al., 2018), with lower temperatures and greater melt polymerization favoring crystallization of zircon. Zirconium behaves as an incompatible element during crystallization of basaltic and intermediate magmas; hence it reaches concentrations

of

sufficient to saturate with respect to zircon in lower-temperature felsic derivatives (Watson and

ro

Harrison, 1983). Most basaltic andesites and andesites that make up arc volcanoes are too low in silica and too hot to have zircon near the liquidus; hence, they are zircon undersaturated resulting

-p

in little or no zircon crystallization (e.g., Siegel et al., 2018). Zircon approaches the liquidus in dacitic–rhyolitic compositions, and these rocks are less abundant in most arcs (Ewart, 1982;

re

Winter, 2001). During cooling, fractional crystallization will cause most magmas to eventually

lP

saturate with respect to zircon. Therefore, it is expected that the plutonic equivalents of volcanic rocks that yield little to no zircon will host more zircon than their volcanic counterparts. Similarly, rhyolitic and dacitic volcanics, which do produce some zircon, will yield even more

na

zircon in their plutonic equivalents.

ur

Additional work is needed to directly compare the zircon fertility of volcanic and intrusive igneous rocks of the same arc. Many studies emphasize the zirconium abundance of

Jo

plutonic rocks and their role as sources for detrital zircon studies and provenance applications (e.g., Moecher and Samson, 2006; Samson et al., 2018). However, for the application of tightly constraining the depositional age of a deposit, the availability and abundance of volcanic zircon is of greater interest than plutonic zircon because syn-depositional eruptions of autocrystic volcanic zircon is the primary mechanism of obtaining near-depositional crystallization ages. The relative zircon fertility of an arc is a function of the proportion of evolved silicic magma, which is largely dependent on the composition of the crust that the arc is built upon. Arcs that erupt through continental crust are likely to produce more andesite, dacite, and, in some retroarc settings, even rhyolite (Hughes and Mahood, 2011). The proportion of evolved magmas is higher on thicker, older continental crust (Leeman, 1983). In contrast, arcs developed on

Journal Pre-proof oceanic crust or young mafic continental crust (including accreted terranes) are dominated by basalt and andesite (Leeman, 1983). For example, the role of crustal composition may be a factor in why some large rivers that drain volcanic arcs (e.g., the lower Columbia and Sacramento rivers of western North America; Campbell and Allen, 2008; Malkowski et al., in press) yield little to no contemporaneous zircon. Here, the crust is relatively young and thin, and consists largely of mafic accreted terranes (e.g., Miller and Paterson, 2001). Conversely, and somewhat surprisingly, Aleutian arc zircon is fairly prevalent in Bering Sea sediment despite the Aleutian arc being an oceanic arc for much of its extent. In this case,

of

the presence of young zircon is likely due the Aleutian arc being one of the only sources for

ro

zircon in the deep Aleutian basin during the present sea-level highstand, which prevents terrestrial material from crossing the vast Bering shelf to the deep Aleutian Basin. Alternatively,

-p

the eastern Aleutian arc, which is more continental, provides a relatively enriched supply of

re

zircon to the Bering Sea. Thus, the detection of young zircon is probably due to reworking ashfall zircon and to the lack of terrestrial sourced detritus available to dilute arc-derived zircon

lP

(Malkowski et al., 2017c).

na

5.2.2. Complexities in the ages of volcanic zircon Another contributing factor to the relative rarity of young zircon (i.e., <2 Ma), even in

ur

some samples that are proximal to active volcanic sources (Fig. 14), may be due to a lack of autocrystic and/or antecrystic zircon (Table 1). That is, the zircon U-Pb ages from volcanic and

Jo

volcaniclastic sources do not necessarily reflect the timing of eruption (e.g., Miller et al., 2007; Rossignol et al., 2019; Table 1). Depending on measurement precision (Schoene, 2014) and the history of crystallization (Matzel et al., 2006; Miller et al., 2007; Wotzlaw et al., 2013; Samperton et al., 2017), sufficient production of autocrystic zircon may be needed for arcderived sediment to yield contemporaneous zircon ages. The crystallization history of a single zircon crystal can be complex. Inherited, xenocrystic, and antecrystic zircon can be abundant in arc systems that develop in rocks that host large zircon grains (Watson 1996), in protracted multi-phase magmatic systems (Miller, et al., 2007), or melts which quickly reach Zr saturation (e.g., Miller et al., 2003). Production of autocrystic zircon primarily occurs in silicic (granitic/rhyolitic), and to a lesser extent intermediate, magmas (i.e., > 64 wt% SiO 2 ); whereas formation of autocrystic zircon in magmas with < 64 wt% SiO 2 may only occur in volumetrically

Journal Pre-proof minor, late-stage melt differentiated pockets where SiO 2 is locally enriched (Siegel et al., 2018). These and other studies highlight the significance, and perhaps relative rarity, of the conditions needed to produce autocrystic, contemporaneous zircon in igneous systems. 5.2.3. Volcanic zircon grain size and potential sampling bias Volcanic zircon are commonly small relative to zircon in plutonic rocks (Corfu et al., 2003; Moecher and Samson, 2006; Sack et al., 2011). Although not well studied, we speculate that grain size may play a role in volcanic zircon being underrepresented in detrital zircon

of

studies. (1) Small, volcanic zircon may be more likely to be a component of sand-sized lithic fragments versus their plutonic or recycled counterparts that are more easily liberated from the

ro

rock matrix during weathering, erosion, and transport (Slama and Kosler, 2012). Volcanic zircon

-p

also occur as inclusions in mafic phases or as part of sand or silt-sized grains composed largely of low-density matrix glass (e.g., Naeser and Naeser, 1984), which may result in that zircon not

re

being recovered by conventional mineral separation procedures that remove components with densities less than 3.3 g/cm3 . Volcanic zircon may exhibit an acicular or skeletal texture (Corfu

lP

et al., 2003) that may promote grain fracturing and disaggregation to smaller particles during sediment transport or rock crushing in the laboratory. (2) Hydraulic washing procedures may

na

preferentially remove smaller grain sizes of a given density (Hietpas et al., 2011; Slama and Kosler, 2012). Thus, volcanic zircon may be preferentially lost relative to larger plutonic or

ur

recycled counterparts. (3) Small zircon grains (e.g., <30 um) may be difficult to measure using

Jo

standard LA-ICP-MS. Although the fine-grained tendencies of volcanic zircon have been documented in the aforementioned instances, to our knowledge this hypothesis of a lithologic and compositional bias associated with the grain size of volcanic zircon remains untested. 5.3. Controls on the likelihood of finding contemporaneous zircon 5.3.1. Presence of an active volcanic source As expected, the presence of one or more active volcanic sources exerts a dominant control on the global distribution of young zircon (i.e., <2 Ma). For instance, approximately 90% of zircon grains <1 Ma are found within 125 km of a Holocene volcano (Fig. 14A). The close association of young zircon with active volcanism can perhaps be best observed in the South American Andes where spatial variations in volcanism are reflected in the age of the youngest

Journal Pre-proof grains (Fig. 12). Although volcanic rocks are present throughout the Andes, only active portions of the volcanic arc produce grains that are young (Fig. 12). This observation suggests that MDAs will lag behind depositional ages during periods of arc inactivity, as has been observed in the California forearc basin during periods of arc shutoff (e.g., Sharman et al., 2015). However, proximity to an active volcanic source does not guarantee identification of young zircon in any given sample. Half of the youngest single grain ages from samples within 100 km from a Holocene volcano Ma are older than ~26 Ma (Fig. 14A). We speculate that, in addition to proximity, drainages downstream or downwind of dacitic to rhyolitic volcanoes increase the

of

likelihood of capturing contemporaneous zircon.

ro

Young zircon are also occasionally found at great distance (e.g., 100’s to 1000’s km) from active volcanism (e.g., Fig. 11). In the majority of cases, such young zircon are associated

-p

with sediment routing systems that have an upstream active volcanic source. For example, zircon

re

<2 Ma are found in a number of South and North American rivers that are 100’s to 1000’s km downstream from an active volcano (Fig. 11). Young zircon is also found in the Bering Sea,

lP

100’s of km from the Aleutian Volcanic Arc (Fig. 11B). These examples demonstrate that young zircon can be distributed far from its source via a number of processes, including eruptive, wind-

na

born, fluvial, and marine sediment transport (e.g., Huff et al., 1992; Dickinson and Gehrels,

5.3.2. Catchment area

ur

2003; Stevens et al., 2010; Boni et al., 2012).

Jo

Modern river samples with young zircon (i.e., less than ~2 Ma) are preferentially found within catchments of intermediate size (generally 10 3 to 105 km2 ; Fig. 14B). The lack of young zircon in the smallest river catchments may partly reflect the coincidence that these samples were not collected in the immediate vicinity of a Holocene volcanic source, which comprise a small portion of the Earth’s land surface area. The lack of young zircon in the largest river catchments, however, may suggest that dilution of Holocene volcanic sources reduces the likelihood that such grains are analyzed. This dilution effect is supported by cases where young zircon are observed in upstream samples but missing in downstream samples, including the Paraná and Amazon rivers of South America (Fig. 11A) and the Columbia and Sacramento rivers of North America (Fig. 11B). Because active volcanism comprises a volumetrically minor fraction of the total

Journal Pre-proof clastic sediment sources in large fluvial systems, it may be necessary to increase the number of analyses per sample to find the youngest zircon grains in these large systems. 5.3.3. Number of analyses As expected based on mathematical principle (Fig. 3), samples with a greater number of grain analyses are generally more likely to identify young zircon (Fig. 14C). For instance, no samples with fewer than 30 analyses has identified a grain younger than 10 Ma (Fig. 14C). Given the proximity of some of these samples to Holocene volcanic sources (Fig. 14A), it is likely that

of

young zircon is present in some of these samples but was missed due to insufficient analytical characterization. However, analyzing a large number of grains does not guarantee identification

ro

of young zircon; half of samples with at least 150 analyses have a youngest grain older than 23

-p

Ma (Fig. 14C). The complete lack of young zircon in certain regions (Europe, northern Asia, Australia; Fig. 10) underscores the inference that increasing the number of grain analyses for the

re

purpose of finding young zircon will only be effective if young zircon are present in the first

5.3.4. Depositional environment

lP

place.

na

Samples with zircon younger than 2 Ma are found in fluvial, littoral, and marine depositional environments (Fig. 5). Although zircon younger than 20 Ma were not found in

ur

aeolian, alluvial, or deltaic deposits, these depositional environments are poorly represented in the dataset (Table 4). Given the lack of representation by non-fluvial deposits, future work is

modern systems.

Jo

needed to assess the potential influence of depositional setting on young zircon abundance in

5.4. Strategies for calculating the maximum depositional age of ancient sediment The presence, albeit overall rarity, of contemporaneous zircon in modern and Holocene sediment is a testament to the utility of the MDA in constraining the chronology of volcanically sourced, ancient sedimentary successions, particularly when combined with other chronostratigraphic constraints. However, the importance of treating an MDA as a maximum age constraint, rather than an estimate of the age deposition itself, is evident as 99.7% of zircon analyses considered in this study are recycled (i.e., older than the age of deposition after accounting for analytical uncertainty). That said, our analysis suggests that the likelihood of

Journal Pre-proof finding young detrital minerals that overlap within error of the depositional age increases as a sample becomes older and absolute analytical uncertainty increases (Fig. 13). These same results suggest that samples with a high proportion of very young grains may be susceptible to MDA calculations that are younger than the depositional age, unless a conservative MDA method is used (e.g., the YC2σ, YSP, or τ method; Fig. 13). Other factors not modeled herein (e.g., Pb loss, contamination) may also produce isolated grain ages that are younger than the depositional age in ancient samples.

of

The following sections outline a number of strategies that are intended as general recommendations and guidelines for maximizing the likelihood that MDA calculations from

ro

ancient samples will approximate their depositional age while also reducing the probability that such calculations will be too young. In general, these strategies revolve around appropriate

-p

selection of a study area and sampling program; sample preparation and analysis using LA-ICP-

re

MS; post-analysis data treatment and reanalysis, if warranted; and MDA calculation and integration of results with other chronologic constraints (Fig. 15).

lP

5.4.1. Study planning

na

The global modern and Holocene database of detrital zircon U-Pb dates presented herein supports the inference that an active volcanic source is typically required for young (e.g., <2 Ma)

ur

detrital minerals to be present in sediment (Fig. 14A). It is therefore a reasonable expectation that an active volcanic source is a prerequisite for MDA analysis to provide an age that is close to the

Jo

TDA in ancient sedimentary successions. A number of considerations may help determine whether there was a likely source of contemporaneous volcanism at the time of deposition. For example, active volcanism may be indicated by sediment composition (e.g., volcanic lithic fragments; Graham et al., 1976; Dickinson and Suczek, 1979; Ingersoll et al., 1993) and/or mineralogy (e.g., bentonitic or bauxitic clays derived from alteration of volcanic ash; Boni et al., 2012). The location of ancient volcanic arcs may be inferred from paleotectonic and paleogeographic reconstructions (Schermer and Busby, 1994; Amato et al., 2009; Malkowski et al., 2014). Consideration of dominant paleowind directions relative to volcanic activity may guide expectations on whether wind-born volcanic ash may be present (e.g., Boni et al., 2012). The presence and abundance of contemporaneous zircon may be directly estimated if detrital zircon samples have already been collected from the unit(s) under study. The expected

Journal Pre-proof abundance of contemporaneous zircon can be used to estimate the total number of analyses needed for each sample using Figure 3. 5.4.2. Sample collection and processing Because a given MDA calculation can be younger than, within error of, or older than the TDA (Fig. 2), we recommend collecting samples from known stratigraphic positions relative to each other and in relation to existing chronologic control (e.g., biostratigraphy, paleomagnetism, ash eruption ages, etc.), whenever possible. Integrating MDA calculations with existing age

of

control is critical for determining the extent to which the youngest grains are contemporaneous or recycled and for identifying possible occurrences of the calculated MDA being too young

ro

(e.g., Pb loss). Collecting samples in stratigraphic context has the added benefit of allowing

-p

stratigraphic ordering to be leveraged during MDA analysis (Johnstone et al., 2019a). Vertical sample spacing may be selected based on expectations of the sedimentation rate and relative

re

precision of the analytical approach (e.g., LA-ICP-MS vs ID-TIMS), as discussed by Rossignol et al. (2019). To avoid preferential loss of small, volcanic zircon, we advocate use of a

lP

conservative mineral separation procedure (e.g., Slama and Kosler, 2012; Edwards et al., 2014).

na

5.4.3. Initial sample analysis using LA-ICP-MS

For samples characterized by a small proportion of contemporaneous zircon, increasing

ur

the number of grain analyses may be required to achieve a suitable number (e.g., >3) of analyses for conservative calculation of the MDA (Fig. 3). Because the number of samples and analyses

Jo

per sample that are able to be analyzed is dependent on the goals and resources available, there is no single strategy that is appropriate for all studies. We consider two potential strategies. Strategy 1 involves conducting an initial analytical session, reducing the data, and then collecting more data if warranted. For example, a preliminary dataset of 150 to 300 analyses could be collected, as done in routine provenance analysis. If a sufficient number (e.g., >3) of concordant, young zircon are identified, then more data may not be required. However, if an insufficient number (e.g., < 2) grains are found, then the sample may have additional grains dated to improve the number of young grains identified, particularly if there is a reasonable expectation that young grains may be present in the sample.

Journal Pre-proof Strategy 2 involves conducting a survey of many grains at low resolution using a short ablation time (e.g., Daniels et al., 2018; Chew et al., 2019; Sundell et al., 2019). In this manner, many (hundreds to >1000 grains) may be analyzed to determine whether young grains are present. If young grains are identified, then they are reanalyzed using a longer ablation time to obtain a more precise measurement. Although this strategy may not be appropriate for all studies, this may yield the highest probability of obtaining a cluster of young ages from which to calculate an MDA using a conservative method. Analysts also have the choice of whether to analyze zircon grains at random and/or

of

whether to selectively target grains expected to be young based on parameters such as

ro

appearance (morphology, color; Garver and Kamp, 2002; Markwitz and Kirkland, 2018) or cathodoluminescence (Koschek, 1993; Hanchar and Miller, 1993; Rubatto and Gebauer, 2000).

-p

Selecting grains in an unbiased (random) pattern has the advantage of allowing statistical

re

comparisons with other randomly-analyzed samples (e.g., for provenance analysis; Sharman et al., 2015). A combination approach may be used where subsets of grains are analyzed randomly

publication to avoid ambiguity.

lP

and selectively. We recommend that authors state their methods when reporting data in

na

5.4.4. Depth Profiling during LA-ICPMS

ur

Depth profiling of whole, unpolished grains by LA-ICPMS may also increase the likelihood of capturing the youngest, and therefore contemporaneous, zircon age. Xenocrystic

Jo

zircon incorporated into a melt during crustal melting/anatexis may not dissolve, especially if they are relatively large crystals (Watson, 1996). New growth will occur along the exterior of the grain resulting in a zircon with an older inherited core and a younger rim that more closely reflects the timing and conditions of eruption (Hanchar and Miller, 1993). Likewise, antecrystic grains crystallized in early magmatic pulses but incorporated into later magmatic pulses may also possess age domain heterogeneities (Miller et al., 2007). Mounting and polishing such grains (as is commonly done) will result in a well-exposed larger core and rim growths that may be too small for a laser spot size. In contrast, depth profiling analysis of unpolished grains requires ablation of the outer rim, which is easily detectable in a time series analysis of isotopic measurements of a given grain. If depth-profiling is not an option then the user might consider targeting rims when they are large enough for the laser spot size being used.

Journal Pre-proof 5.4.5. Analysis filtering and reanalysis using LA-ICP-MS and/or CA-ID-TIMS Once an initial LA-ICP-MS dataset is collected, MDA estimates may be improved by close inspection of analyses to identify potentially problematic grain dates related to perturbation of the U-Th-Pb isotopic system (Zimmerman et al., 2018; Rossignol et al., 2019). For example, grains with high U concentration may be preferentially susceptible to Pb loss as a result of radiation damage (Andersen et al., 2019). Such an occurrence may be discernable on a plot of U concentration versus grain age if the youngest analyses have undergone Pb loss as a result of high U (e.g., Gehrels et al., 2019). Similarly, analyses with high analytical uncertainty, 204

Pb concentrations may indicate problematic analyses. One strategy may be to

of

discordance, or

ro

filter such analyses and compare results with the unfiltered dataset.

-p

Provided that the grain is large enough to accommodate an additional laser ablation spot, reanalyzing the youngest grains in a subsequent LA-ICP-MS analytical session provides another

re

check on the repeatability on the youngest analyses (e.g., Herriott et al., 2019). Alternatively, the youngest grains can be plucked from the mount and dated using high-precision ID-TIMS (e.g.,

lP

Suarez et al., 2017). The process of chemical abrasion and thermal annealing has been demonstrated to significantly reduce analysis of metamict zones with Pb loss (Schoene, 2014),

na

and re-dating the youngest analyses using TIMS has the advantage of significantly reducing

5.4.5. MDA calculation

ur

analytical uncertainty, resulting in more precise MDA calculations.

Jo

Although methods that rely on just 1 or 2 analyses (e.g., YSG and YC1σ[2+]) are more likely to yield estimates closer to the depositional age (Fig. 9), these methods are also more prone to yielding an MDA that is too young in ancient samples (Fig. 13), particularly when applied to samples with many young analyses. U-Pb age measurements of individual zircon grains may be perturbed in a number of additional ways not specifically accounted for in our analysis of the modern-Holocene dataset, including Pb loss, spurious common Pb correction, metamorphic or diagenetic zircon grown, and contamination (Dickinson and Gehrels, 2009; Rubatto, 2017; Andersen et al., 2019). Because the consequences of calculating an MDA that is too young are more severe than calculating an MDA that is too old, we follow previous researchers (Dickinson and Gehrels, 2009; Schoene, 2014; Schaltegger et al., 2015; Rossignol et al., 2019; Coutts et al., 2019) in recommending the use of a conservative MDA calculation based

Journal Pre-proof on multiple overlapping measurements (e.g., YSP, YC2σ[3+], Y3Zo, and τ method), whenever possible. We follow Dickinson and Gehrels (2009) in specifically discouraging the use of the YDZ as the MDA, as this approach commonly yields ages that are younger than the YSG (Fig. 9). Although conservative MDA methods are more likely to yield an MDA that is too old (Fig. 9), they are also less likely to yield an MDA that is too young (Fig. 13). The disadvantages of conservative MDA methods may be partly alleviated using the strategies discussed above. 5.5. Suggestions for future research

of

(1) Increased sampling of underrepresented geographic regions and depositional environments. Non-fluvial depositional environments are underrepresented relative to their

ro

fluvial counterparts (Fig. 5; Table 4). Many of the samples collected in this study are not from

-p

depositional basins, but are instead from the erosional and transfer zones of sediment routing systems (Romans et al., 2016). Obtaining samples from a greater diversity of depositional

re

settings would help determine the extent to which depositional environment influences the distribution of very young zircon. In particular, increasing data from marine samples (deltaic,

lP

littoral, and deep-marine) could provide important insights into how young zircon is partitioned

sedimentary successions.

na

from source to sink, with implications for better application to MDA analysis of ancient marine

ur

(2) Investigating grain size bias of young, volcanic detrital zircon. The potential for young, volcanic zircon to be finer-grained than their plutonic counterparts suggests that grain

Jo

size biases may play a role in reducing the likelihood of recovering and dating the youngest volcanic grains. However, more research is needed to investigate the effects grain size biases from both natural and analytical sources and to determine how such biases may be mitigated. (3) Improved methods of recognizing Pb loss in detrital zircon. Because maximum depositional ages are concerned with the youngest grains in a sample, there is a need to more effectively distinguish truly young grains from those that have experienced radiogenic Pb-loss (e.g., Andersen et al., 2019). Future studies investigating multiple ways for identifying metamict zircon (beyond high U-concentration, for example) could prove to be useful in evaluating the youngest detrital zircon ages from a given sample.

Journal Pre-proof (4) Development of robust, standardized methods for calculating the MDA. The choice of which MDA method(s) to use (Table 1) remains a major source of ambiguity in MDA analysis. Although methods that rely on multiple, overlapping ages are more conservative than those that rely on just one or two measurements (Fig. 13), these methods rely upon an arbitrary choice of the minimum number of grains that constitute an age cluster (typically 2 or 3, Table 1). The growing interest to pursue ‘large-n’ datasets (e.g., >>300 analyses per sample; Pullen et al., 2014; Daniels et al., 2018; Sundell et al., 2019) in order to increase the probability of analyzing young grains comes with some caveats. First, increasing the number of analyses increases the

of

probability of measuring grains from the extreme tails of the age of the distribution, resulting in

ro

analysis of multiple grains with measured U-Pb dates that are younger than their true crystallization age (Fig. 2). Second, analyzing more grains also increases the probability of

-p

encountering more problematic grains (e.g., Pb loss) that may coincidentally overlap within error and falsely present a “robust” population, unless revised standards are used (e.g., > 5 grains per

re

age cluster). Development of MDA methods that are flexible and less reliant on arbitrary user

lP

cutoffs would likely improve the robustness of MDA calculations. 6. Conclusions

na

A global compilation of U-Pb detrital zircon ages from modern-Holocene samples reveals a

ur

number of insights into the utility of using MDAs to constrain the age of sedimentary deposits: (1) Young zircon (i.e., <2 Ma) are globally rare, constituting only 0.4% of the total dataset,

Jo

and are largely restricted to regions associated with active volcanism. The global rarity of young zircon is likely due to the combined effects of several contributing factors: the relatively low abundance of recently active volcanic cover rock exposed at the Earth’s surface, low zircon fertility of many of these volcanic rocks, complex age histories in volcanic zircon crystals, and potentially grain size biases. (2) Results do not support the assumption that deposits in proximity to and draining an active volcanic source will necessarily contain sufficient quantities of contemporaneous zircon for indicating the depositional age of deposit. Unless additional age constraints are available, we advocate that MDA estimates always be treated as maximum age constraints, rather than as an approximation of the actual depositional age.

Journal Pre-proof (3) Although no single factor predicts whether a sample will yield young zircon, the likelihood of finding young zircon in a given sample increases with proximity to an active volcanic source, in drainages of moderate catchment size (i.e., 10 3 to 105 km2 ), and with increasing numbers of grain analyses. The occasional absence of young zircon in large river catchments (i.e., >106 km2 ), despite connection with upstream active volcanic sources, can be interpreted as a consequence of down-stream dilution by older, recycled zircon in combination with insufficient analytical characterization. (4) MDA methods based on just one or two grains (e.g., the youngest single grain age) yield

of

results that are closest to the TDA (i.e., 0 Ma) in the modern-Holocene dataset. However,

ro

for ancient samples we strongly encourage the use of one or more conservative MDA methods based on multiple, overlapping age measurements to reduce the risk of

-p

calculating an MDA that is younger than the TDA. Analytical and geologic complexities including measurement uncertainty, systematic uncertainty, Pb loss, and other factors

re

may result in individual zircon U-Pb dates being younger than their true crystallization

lP

age. Calculation of an MDA that is younger than the TDA is more likely in old samples and in circumstances where contemporaneous grains are abundant. (5) Although the World’s youngest zircon are the proverbial ‘needles in a haystack’, a

na

number of approaches can be used to maximize the likelihood of analyzing such grains, if present. These approaches include careful study planning, conservative mineral

ur

separation, pursuit of a ‘high-n’ sampling strategy, and depth-profiling of whole zircon

Jo

grains.

(6) Future efforts to improve the utility of MDA analysis could include development of additional methods of identifying Pb loss in detrital zircon; continued efforts to improve the precision, accuracy, and data acquisition rate of detrital U-Pb geochronology; and development of more flexible methods for calculating the MDA that are less reliant on arbitrary user cutoffs.

7. Acknowledgements We thank Gail Mahood and Sam Johnstone for fruitful discussions and reviewing early versions of the manuscript. Support for this research was provided by the industrial affiliate members of

Journal Pre-proof the Detrital Geochronology Laboratory. Helpful reviews by Camille Rossignol, Christopher Spencer, and an anonymous reviewer improved the accuracy and clarity of the manuscript.

Figure Captions Figure 1. Cumulative number of publications per year containing the phrase “maximum

of

depositional age” and “detrital zircon”; search results from Google Scholar (after Coutts et al., 2019).

ro

Figure 2. Illustration of two scenarios where calculated MDAs may either underestimate or

-p

overestimate the true depositional age. A) No or few contemporaneous grains are present and/or few grains are measured. MDA calculations are older than the depositional age due to either

re

contemporaneous grains being absent or contemporaneous grains not being sampled. B) Many

lP

contemporaneous grains are present and/or many grains are measured. MDA calculations are younger than the depositional age due the analytical scatter around the mean age of this young

na

age fraction (Coutts et al., 2019).

Figure 3. A) The probability of analyzing at least one grain from a grain age fraction of a given

ur

relative abundance (XL) for a given sample size (n) and confidence level (adapted from Andersen et al., 2005). For example, 100 random analyses (n) yield a 95% probability of detecting at least

Jo

one grain from a grain age fraction that comprises ~3% of the total (XL). B) 95% probability of analyzing 1 to 8 grains (k c) from an age fraction of a given relative abundance (XL) and sample size (n) (adapted from Johnstone et al., 2019b). For example, a sample of 1000 random analyses (n) has a 95% chance of detecting at least one grain (k c) from an age fraction that comprises ~0.3% of the total (XL). Figure 4. Distribution of 2σ relative measurement precision (above) and 2σ absolute measurement precision (below) for all detrital zircon U-Pb analyses in the dataset. Figure 5. Locations of samples used in this study (see Supplemental Table 1 for a list of samples and Appendix A for a list of data sources). Gray polygons indicate watersheds of fluvial samples. Subaerial Holocene volcanoes are from the ESRI Global Holocene Volcanoes layer produced

Journal Pre-proof using data from the Smithsonian Institute. Mafic lithologies include basalt, picro-basalt, foidite, trachybasalt, and tephrite basanite. Intermediate lithologies include andesite, basaltic andesite, phono tephrite, tephri phonolite, trachyandesite, and basaltic trachyandesite. Felsic lithologies include dacite, phonolite, rhyolite, trachyte, and trachydacite. Figure 6. Summary of detrital zircon U-Pb ages by continent. Age distributions are shown as both cumulative (top) and relative kernel density estimations (bottom) (plots generated using detritalPy; Sharman et al., 2018). A) Age distributions from 0-500 Ma. Percentage indicates the proportion of ages relative to the total number from that continent. B) Age distributions from 0-

of

4000 Ma. N = X/Y denotes the number of samples (X) and analyses (Y) for each continent.

ro

Figure 7. A) Distribution of sample size in the filtered dataset. B) Distribution of sample

-p

detection limits (XL) at a 95% confidence level (see Eq. 1 and Fig. 3). The median detection limit is 3.2%, meaning that there is 95% confidence of detecting at least one grain from an age

re

fraction of ~3% relative abundance.

lP

Figure 8. Cumulative proportion versus age of all detrital zircon U-Pb ages in the dataset. Figure 9. Results from maximum depositional age calculations (Table 2). A) All samples in the

na

dataset. B) Samples with > 60 analyses.

Figure 10. Summary of the youngest single grain age found in each sample of the modern and

ur

Holocene dataset. Circles are sample locations. Modern river watersheds are colored according

Jo

to the nearest downstream sample.

Figure 11. Youngest single grain ages found in A) South America, B) North America, and C) southeast Asia. Modern river watersheds are colored according to the nearest downstream sample. Subaerial Holocene volcanoes are from the ESRI Global Holocene Volcanoes layer produced using data from the Smithsonian Institute. See Figure 5 caption for a description of Holocene volcanic lithologies by type. Figure 12. Latitude versus youngest single grain age for samples from western South America. The dashed black line indicates a running average with a window of 3 samples. Open and filled circles indicate samples with a youngest single grain age older and younger than 2 Ma, respectively. Gray triangles indicate the locations of Holocene volcanoes (Fig. 11A). Note the

Journal Pre-proof general decrease in the youngest single grain age in regions where the Andean volcanic arc is active. Figure 13. Results of the synthetic aging experiment for eight different MDA calculation methods (Table 2). The proportion of calculated MDAs that are younger than, within error of, and older than the TDA are shown (results provided in Supplemental Table 2). Figure 14. Plots of the youngest single age versus potential controlling factors: A) sample distance from the nearest Holocene volcano (only including volcanoes on continental

of

landmasses), B) catchment area (river samples only), and C) the number of analyses. Coloration corresponds to the sample distance from the nearest Holocene volcano. Symbol marker

ro

corresponds to depositional environment. Symbol size corresponds to the number of analyses.

-p

Figure 15. Suggested workflow for MDA analysis based on U-Pb age measurements of detrital zircon. The reader is referred to several previous studies for insights gleaned on mineral

re

separations (Slama and Kosler, 2012), analyses (Pullen et al., 2014; Daniels et al., 2018), MDA

lP

calculations (Dickinson and Gehrels, 2009; Coutts et al., 2019), and leveraging stratigraphic

References

na

constraints (Johnstone et al., 2019a). These are also discussed in the text.

Jo

ur

Amato, J. M., Toro, J., Miller, E. L., Gehrels, G. E., Farmer, G. L., Gottlieb, E. S., Till, A. B., 2009. Late Proterozoic–Paleozoic evolution of the Arctic Alaska–Chukotka terrane based on U-Pb igneous and detrital zircon ages: Implications for Neoproterozoic paleogeographic reconstructions. Geological Society of America Bulletin, 121(9-10), 1219-1235. Amidon, W. H., Burbank, D. W., Gehrels, G. E., 2005. Construction of detrital mineral populations: Insights from mixing of U-Pb zircon ages in Himalayan rivers. Basin Research, v. 17(4), p. 463-485. Andersen, T., 2005, Detrital zircons as tracers of sedimentary provenance: limiting conditions from statistics and numerical simulation. Chemical Geology, 216(3-4), 249-270. Andersen, T., 2013. Age, Hf isotope and trace element signatures of detrital zircons in the Mesoproterozoic Eriksfjord sandstone, southern Greenland: are detrital zircons reliable guides to sedimentary provenance and timing of deposition?. Geological Magazine, v. 150(3), p. 426-440. Andersen, T., Kristoffersen, M., Elburg, M. A., 2018. Visualizing, interpreting and comparing detrital zircon age and Hf isotope data in basin analysis–a graphical approach. Basin Research, v. 30(1), p. 132-147. Andersen, T., Elburg, M. A., Magwaza, B. N., 2019. Sources of bias in detrital zircon

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

geochronology: Discordance, concealed lead loss and common lead correction. EarthScience Reviews, 102899. Baldwin, S. L., Monteleone, B. D., Webb, L. E., Fitzgerald, P. G., Grove, M., Hill, E. J., 2004. Pliocene eclogite exhumation at plate tectonic rates in eastern Papua New Guinea. Nature, 431(7006), 263. Barbeau Jr, D. L., Ducea, M. N., Gehrels, G. E., Kidder, S., Wetmore, P. H., Saleeby, J. B., 2005. U-Pb detrital-zircon geochronology of northern Salinian basement and cover rocks. Geological Society of America Bulletin, v. 117(3-4), p. 466-481. Barboni, M., Schoene, B., 2014. Short eruption window revealed by absolute crystal growth rates in a granitic magma. Nature Geoscience, 7(7), 524. Best, M. G., Christiansen, E. H., 1991. Limited extension during peak Tertiary volcanism, Great Basin of Nevada and Utah. Journal of Geophysical Research: Solid Earth, 96(B8), 13509-13528. Best, M. G., Christiansen, E. H., Blank Jr, R. H., 1989. Oligocene caldera complex and calcalkaline tuffs and lavas of the Indian Peak volcanic field, Nevada and Utah. Geological Society of America Bulletin, 101(8), 1076-1090. Bidgoli, T. S., Wang, W., Moeller, A., Stockli, D. F., 2018. Detrital Zircon Geochronology: A Novel Approach for Stratigraphic Correlation of Late Mississippian-Early Pennsylvanian Strata in Southwestern Kansas and Northwestern Arkansas. In ACE 2018 Annual Convention & Exhibition. Bodet, F., Schärer, U., 2000. Evolution of the SE-Asian continent from U-Pb and Hf isotopes in single grains of zircon and baddeleyite from large rivers. Geochimica et Cosmochimica Acta, v. 64(12), p. 2067-2091. Boehnke, P., Watson, E. B., Trail, D., Harrison, T. M., Schmitt, A. K., 2013. Zircon saturation re-revisited. Chemical Geology, v. 351, p. 324-334. Boni, M., Reddy, S. M., Mondillo, N., Balassone, G., Taylor, R., 2012. A distant magmatic source for Cretaceous karst bauxites of southern Apennines (Italy), revealed through SHRIMP zircon age dating. Terra Nova, 24(4), 326-332. Campbell, I. H., Allen, C. M., 2008. Formation of supercontinents linked to increases in atmospheric oxygen. Nature Geoscience, 1(8), 554. Carrapa, B., 2010. Resolving tectonic problems by dating detrital minerals. Geology, 38(2), 191-192. Cherniak, D. J., Watson, E. B., 2003. Diffusion in zircon. Reviews in mineralogy and geochemistry, v. 53(1), p. 113-143. Chew, D., Drost, K., Petrus, J. A., 2019. Ultrafast, >50 Hz LA‐ICP‐MS Spot Analysis Applied to U–Pb Dating of Zircon and other U‐Bearing Minerals. Geostandards and Geoanalytical Research, 43(1), 39-60. Cina, S. E., Yin, A., Grove, M., Dubey, C. S., Shukla, D. P., Lovera, O. M., Kelty, T.K., Gehrels, G.E., Foster, D. A., 2009. Gangdese arc detritus within the eastern Himalayan Neogene foreland basin: implications for the Neogene evolution of the Yalu– Brahmaputra River system. Earth and Planetary Science Letters, v. 285(1-2), p. 150-162. Clift, P. D., Campbell, I. H., Pringle, M. S., Carter, A., Zhang, X., Hodges, K. V., Khan, A.A., Allen, C. M., 2004. Thermochronology of the modern Indus River bedload: New insight into the controls on the marine stratigraphic record. Tectonics, 23(5). Clift, P. D., Carter, A., Nicholson, U., Masago, H., 2013. Zircon and apatite thermochronology

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

of the Nankai Trough accretionary prism and trench, Japan: Sediment transport in an active and collisional margin setting. Tectonics, v. 32(3), p. 377-395. Cohen, K.M., Finney, S.C., Gibbard, P.L., Fan, J.-X., 2013; updated. The ICS International Chronostratigraphic Chart, Episodes 36, 199-204. Compston, W., Gallagher, K., 2012. New SHRIMP zircon ages from tuffs within the British Palaeozoic stratotypes. Gondwana Research, 21(4), 719-727. Compston, W., Williams, I. S., Campbell, I. H., Gresham, J. J., 1986. Zircon xenocrysts from the Kambalda volcanics: age constraints and direct evidence for older continental crust below the Kambalda-Norseman greenstones. Earth and Planetary Science Letters, 76(34), 299-311. Condie, K. C., Aster, R. C., 2010. Episodic zircon age spectra of orogenic granitoids: the supercontinent connection and continental growth. Precambrian Research, v. 180(3-4), p. 227-236. Corfu, F., Hanchar, J. M., Hoskin, P. W., Kinny, P., 2003. Atlas of zircon textures. Reviews in mineralogy and geochemistry, 53(1), 469-500. Coutts, D. S., Matthews, W. A., Hubbard, S. M., 2019. Assessment of widely used methods to derive depositional ages from detrital zircon populations. Geoscience Frontiers, 10(4), 1421-1435. Cross, T. A., Pilger, R. H., Jr., 1982. Controls of subduction geometry, location of magmatic arcs, and tectonics of arc and back-arc regions. Geological Society of America Bulletin, 93(6), 545-562. Crowley, J. L., Schoene, B., Bowring, S. A., 2007. U-Pb dating of zircon in the Bishop Tuff at the millennial scale. Geology, 35(12), 1123-1126. Daniels, B. G., Auchter, N. C., Hubbard, S. M., Romans, B. W., Matthews, W. A., Stright, L., 2018, Timing of deep-water slope evolution constrained by large-n detrital and volcanic ash zircon geochronology, Cretaceous Magallanes Basin, Chile. GSA Bulletin. Dickinson, W. R., Suczek, C. A., 1979. Plate tectonics and sandstone compositions. AAPG Bulletin, 63(12), 2164-2182. Dickinson, W. R., Gehrels, G. E., 2003. U–Pb ages of detrital zircons from Permian and Jurassic eolian sandstones of the Colorado Plateau, USA: paleogeographic implications. Sedimentary Geology, 163(1-2), 29-66. Dickinson, W. R., Gehrels, G. E., 2009. Use of U–Pb ages of detrital zircons to infer maximum depositional ages of strata: a test against a Colorado Plateau Mesozoic database. Earth and Planetary Science Letters, 288(1-2), 115-125. Ducea, M. N., Paterson, S. R., DeCelles, P. G., 2015. High-volume magmatic events in subduction systems. Elements, 11(2), 99-104. Edwards, K. L., Padilla, A. J., Evans, A., Morgan, D. J., Balco, G., Putkonen, J., Bibby, T., 2014. Provenance of glacial tills in Ong Valley, Antarctica, inferred from quartz cathodoluminescence imaging, zircon U/Pb dating, and trace element geochemistry. In AGU Fall Meeting Abstracts. Englert, R. G., Hubbard, S. M., Coutts, D. S., Matthews, W. A., 2018. Tectonically controlled initiation of contemporaneous deep‐water channel systems along a Late Cretaceous continental margin, western British Columbia, Canada. Sedimentology, v. 65(7), p. 24042438. Ewart, A., 1982, The mineralogy and petrology of Tertiary-Recent orogenic volcanic rocks: with

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

special reference to the andesitic-basaltic compositional range; p. 25-95 in, Thorp, R.S., ed., Andesites: Orogenic Andesites and Related Rocks, John Wiley and Sons, New York, 724 p. Fedo, C. M., Sircombe, K. N., Rainbird, R. H., 2003. Detrital zircon analysis of the sedimentary record. Reviews in Mineralogy and Geochemistry, v. 53(1), p. 277-303. Fildani, A., Cope, T. D., Graham, S. A., Wooden, J. L., 2003. Initiation of the Magallanes foreland basin: Timing of the southernmost Patagonian Andes orogeny revised by detrital zircon provenance analysis. Geology, v. 31(12), p. 1081-1084. Fisher, C. M., Longerich, H. P., Jackson, S. E., Hanchar, J. M., 2010. Data acquisition and calculation of U–Pb isotopic analyses using laser ablation (single collector) inductively coupled plasma mass spectrometry. Journal of Analytical Atomic Spectrometry, 25(12), 1905-1920. Fitton, J. G., Upton, B. G. J., 1987. Alkaline igneous rocks. Oxford, Boston: Published for the Geological Society by Blackwell Scientific Publications, Geological Society Special Publication no. 30, 568p. Garver, J. I., Kamp, P. J., 2002. Integration of zircon color and zircon fission-track zonation patterns in orogenic belts: application to the Southern Alps, New Zealand. Tectonophysics, v. 349(1-4), p. 203-219. Gehrels, G., 2014. Detrital zircon U-Pb geochronology applied to tectonics. Annual Review of Earth and Planetary Sciences, v. 42, p. 127-149. Gehrels, G., Giesler, D., Olsen, P., Kent, D., Marsh, A., Parker, W., Rasmussen, C., Mundil, R., Irmis, R., Geissman, J., Lepre, C., in review. LA-ICPMS U-Pb geochronology of detrital zircon grains from the Coconino, Moenkopi, and Chinle Formations in the Petrified Forest National Park (Arizona), Geochronology Discuss. GChron, https://doi.org/10.5194/gchron-2019-12. Gerdes, A., Zeh, A., 2006. Combined U–Pb and Hf isotope LA-(MC-) ICP-MS analyses of detrital zircons: comparison with SHRIMP and new constraints for the provenance and age of an Armorican metasediment in Central Germany. Earth and Planetary Science Letters, 249(1-2), 47-61. George, S. W., Davis, S. N., Fernández, R. A., Manríquez, L. M., Leppe, M. A., Horton, B. K., Clarke, J. A., 2019. Chronology of deposition and unconformity development across the Cretaceous–Paleogene boundary, Magallanes-Austral Basin, Patagonian Andes. Journal of South American Earth Sciences, 102237. Goldstein, S. L., Arndt, N. T., Stallard, R. F., 1997. The history of a continent from U-Pb ages of zircons from Orinoco River sand and Sm-Nd isotopes in Orinoco basin river sediments. Chemical Geology, 139(1-4), 271-286. Graham, S. A., Ingersoll, R. V., Dickinson, W. R., 1976. Common provenance for lithic grains in Carboniferous sandstones from Ouachita Mountains and Black Warrior Basin. Journal of Sedimentary Research, 46(3), 620-632. Hanchar, J. M., Miller, C. F., 1993. Zircon zonation patterns as revealed by cathodoluminescence and backscattered electron images: implications for interpretation of complex crustal histories. Chemical geology, 110(1-3), 1-13. Harris, N., Massey, J., 1994. Decompression and anatexis of Himalayan metapelites. Tectonics, 13(6), 1537-1546. Harrison, T. M., Watson, E. B., 1983. Kinetics of zircon dissolution and zirconium diffusion

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

in granitic melts of variable water content. Contributions to Mineralogy and Petrology, 84(1), 66-72. Harrison, T. M., Aleinikoff, J. N., Compston, W., 1987. Observations and controls on the occurrence of inherited zircon in Concord-type granitoids, New Hampshire. Geochimica et Cosmochimica Acta, 51(9), 2549-2558. Hartmann, J., Moosdorf, N., 2012. The new global lithological map database GLiM: A representation of rock properties at the Earth surface. Geochemistry, Geophysics, Geosystems, 13(12). Hay, D. C., Dempster, T. J., 2009a. Zircon alteration, formation and preservation in sandstones. Sedimentology, 56, 2175-2191. Hay, D. C., Dempster, T. J., 2009b. Zircon behavior during low-temperature metamorphism. Journal of Petrology, 50(4), 571-589. Heinonen, A., Mänttäri, I., Rämö, O. T., Andersen, T., & Larjamo, K., 2016. A priori evidence for zircon antecryst entrainment in megacrystic Proterozoic granites. Geology, 44(3), 227-230. Hereford, R., Beard, S.S., Dickinson, W.R., Karlstrom, K.E., Heizler, M.T., Crossey, L.J., Amoroso, L., House, K.K., Pecha, M., 2016. Reevaluation of the Crooked Ridge RiverEarly Pleistocene (ca. 2 Ma) age and origin of the White Mesa alluvium, northeastern Arizona. Geosphere 12, 768–789. https://doi.org/10.1130/GES01124.1 Herriott, T. M., Crowley, J. L., Schmitz, M. D., Wartes, M. A., Gillis, R. J., 2019. Exploring the law of detrital zircon: LA-ICP-MS and CA-TIMS geochronology of Jurassic forearc strata, Cook Inlet, Alaska, USA. Geology. Hietpas, J., Samson, S., Moecher, D., Chakraborty, S., 2011. Enhancing tectonic and provenance information from detrital zircon studies: Assessing terrane-scale sampling and grain-scale characterization, J. Geol. Soc., 168(2), 309–318, doi:10.1144/001676492009-163. Horstwood, M. S., Košler, J., Gehrels, G., Jackson, S. E., McLean, N. M., Paton, C., Pearson, N. J., Sircombe, K., Sylvester, P., Vermeesch, P., Bowring, J. F., Condon, D. J., Schoene, B., 2016. Community‐derived standards for LA‐ICP‐MS U‐(Th‐)Pb geochronology– Uncertainty propagation, age interpretation and data reporting. Geostandards and Geoanalytical Research, 40(3), 311-332. Hoskin, P. W., Schaltegger, U., 2003. The composition of zircon and igneous and metamorphic petrogenesis. Reviews in mineralogy and geochemistry, 53(1), 27-62. Hu, X., Wang, J., BouDagher-Fadel, M., Garzanti, E., An, W., 2016. New insights into the timing of the India–Asia collision from the Paleogene Quxia and Jialazi formations of the Xigaze forearc basin, South Tibet. Gondwana Research, 32, 76-92. Huff, W. D., Bergström, S. M., Kolata, D. R., 1992. Gigantic Ordovician volcanic ash fall in North America and Europe: Biological, tectonomagmatic, and event-stratigraphic significance. Geology, 20(10), 875-878. Hughes, G. R., Mahood, G. A., 2011. Silicic calderas in arc settings: Characteristics, distribution, and tectonic controls. Bulletin, 123(7-8), 1577-1595. Ingersoll, R. V., Kretchmer, A. G., Valles, P. K., 1993. The effect of sampling scale on actualistic sandstone petrofacies. Sedimentology, 40(5), 937-953. Ito, H., Yamada, R., Tamura, A., Arai, S., Horie, K., Hokada, T., 2013. Earth's youngest exposed granite and its tectonic implications: the 10–0.8 Ma Kurobegawa Granite. Scientific reports, 3, 1306.

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

Ito, H., Spencer, C. J., Danišk, M., Hoiland, C. W., 2017. Magmatic tempo of Earth’s youngest exposed plutons as revealed by detrital zircon U-Pb geochronology. Scientific Reports, 7(1), 12,457. Iizuka, T., Hirata, T., Komiya, T., Rino, S., Katayama, I., Motoki, A., Maruyama, S., 2005. U-Pb and Lu-Hf isotope systematics of zircons from the Mississippi River sand: Implications for reworking and growth of continental crust. Geology, 33(6), 485-488. Jacobson, C. E., Grove, M., Pedrick, J. N., Barth, A. P., Marsaglia, K. M., Gehrels, G. E., Nourse, J. A., 2011. Late Cretaceous–early Cenozoic tectonic evolution of the southern California margin inferred from provenance of trench and forearc sediments. Bulletin, 123(3-4), 485-506. Johnstone, S. A., Schwartz, T. M., & Holm-Denoma, C. S., 2019a. A Stratigraphic Approach to Inferring Depositional Ages From Detrital Geochronology Data. Front. Earth Sci, 7, 57. Johnstone, S. A., Schwartz, T. M., & Holm-Denoma, C. S., 2019b. A Corrigendum on A Stratigraphic Approach to Inferring Depositional Ages From Detrital Geochronology Data. Front. Earth Sci, 7, 161, doi:10.3389/feart.2019.00161. Jones III, J. V., Connelly, J. N., Karlstrom, K. E., Williams, M. L., Doe, M. F., 2009. Age, provenance, and tectonic setting of Paleoproterozoic quartzite successions in the southwestern United States. Geological Society of America Bulletin, 121(1-2), 247-264. Kalsbeek, F., 1992. The statistical distribution of the mean squared weighted deviation--Comment: Isochrons, errorchrons, and the use of MSWD-values. Chemical Geology, 94, 241-242. Karlstrom, K., Hagadorn, J., Gehrels, G., Matthews, W., Schmitz, M., Madronich, L., Mulder, J., Pecha, M., Giesler, D. and Crossey, L., 2018. Cambrian Sauk transgression in the Grand Canyon region redefined by detrital zircons. Nature Geoscience, 11(6), p.438. Keller, C. B., Boehnke, P., Schoene, B., 2017. Temporal variation in relative zircon abundance throughout Earth history. Geochem Perspect Lett, 3, 179-189. Kimbrough, D. L., Grove, M., Morton, D. M., 2015. Timing and significance of gabbro emplacement within two distinct plutonic domains of the Peninsular Ranges batholith, southern and Baja California. Bulletin, 127(1-2), 19-37. Kochelek, E. J., Amato, J. M., Pavlis, T. L., Clift, P. D., 2011. Flysch deposition and preservation of coherent bedding in an accretionary complex: Detrital zircon ages from the Upper Cretaceous Valdez Group, Chugach terrane, Alaska. Lithosphere, 3(4), 265274. Kohn, M. J., Corrie, S. L., Markley, C., 2015. The fall and rise of metamorphic zircon. American Mineralogist, 100(4), 897-908. Koschek, G., 1993. Origin and significance of the SEM cathodoluminescence from zircon. Journal of microscopy, 171(3), 223-232. Lease, R. O., Houseknecht, D. W., Kylander-Clark, A. R. C., 2014. Detrital zircon constraints on Arctic Alaska foreland basin evolution: Brookian chronostratigraphy, clastic progradation, provenance, and orogenic exhumation. In 2014 GSA Annual Meeting in Vancouver, British Columbia. Lee, J. K., Tromp, J., 1995. Self‐induced fracture generation in zircon. Journal of Geophysical Research: Solid Earth, 100(B9), 17753-17770. Leeman, W.P., 1983. The influence of crustal structure on compositions of subduction-related Magmas. Journal of Volcanology and Geothermal Research, 18, 561-588. Ludwig, K. R., 1999. Isoplot/Ex. Version 1.00. A Geochronological Toolkit for Microsoft

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

Excel. Ludwig, K. R., Mundil, R., 2002. Extracting reliable U-Pb ages and errors from complex populations of zircons from Phanerozoic tuffs. Geochimica et Cosmochimica Acta, 66 Supplement 1, 463. Malkowski, M. A., &Hampton, B. A., 2014. Sedimentology, U-Pb detrital geochronology, and Hf isotopic analyses from Mississippian–Permian stratigraphy of the Mystic subterrane, Farewell terrane, Alaska. Lithosphere, 6(5), 383-398. Malkowski, M. A., Sharman, G. R., Graham, S. A., Fildani, A., 2017a. Characterisation and diachronous initiation of coarse clastic deposition in the Magallanes–Austral foreland basin, Patagonian Andes. Basin Research, v. 29, p. 298-326. Malkowski, M. A., Schwartz, T. M., Sharman, G. R., Sickmann, Z. T., Graham, S. A., 2017b. Stratigraphic and provenance variations in the early evolution of the Magallanes-Austral foreland basin: Implications for the role of longitudinal versus transverse sediment dispersal during arc-continent collision. Geological Society of America Bulletin, v. 129(3-4), p. 349-371. Malkowski, M.A., Barth, G.A., Scheirer, D.S., Sliter, R.W., Scholl, D.W., Chaytor, J.D., 2017c. Detrital zircon geochronology of the Bering Sea, shelf to deep basin: climatic and eustatic influence on sediment transport pathways. Geological Society of America Abstracts with Programs, Seattle, Washington, v. 49(6), Paper No. 43-2. Malkowski, M.A., G.R. Sharman, S. Johnstone, M.J. Grove, D.L. Kimbrough, S.A. Graham, in-press. Dilution and propagation of provenance trends in sand and mud: Geochemistry and detrital zircon geochronology of modern sediment from central California (U.S.A.), American Journal of Science. Mapes, R. W., 2009. Past and present provenance of the Amazon River (Doctoral dissertation, The University of North Carolina at Chapel Hill). Markwitz, V., &Kirkland, C. L., 2018. Source to sink zircon grain shape: Constraints on selective preservation and significance for Western Australian Proterozoic basin provenance. Geoscience Frontiers, 9(2), 415-430. Marsh, A. D., Parker, W. G., Stockli, D. F., & Martz, J. W., 2019. Regional correlation of the Sonsela Member (Upper Triassic Chinle Formation) and detrital U-Pb zircon data from the Sonsela Sandstone bed near the Sonsela Buttes, northeastern Arizona, USA, support the presence of a distributive fluvial system. Geosphere, 15(4), 1128-1139. Masalimova, L., Falivene, O., Lawler, T., Ichaso, A., Jaminski, M., Ozkan, A., Jackett, S.-J., McDonald, M., Frascati, A., Bergman, S., Morton, A., Dirk, F., 2017. Sediment dispersal pattern of the Paleogene Wilcox Formation in the deep-water Gulf of Mexico Basin based on detrital zircon analysis and forward modeling: American Association of Petroleum Geologists (AAPG) Datapages/Search and Discovery Article #90291. Matzel, J., Bowring, S.A., Miller, R.B., 2006. Timescales of pluton construction at differing crustal levels: Examples from the Mount Stuart batholith and Tenpeak pluton, North Cascades, WA. Geological Society of America Bulletin 118 (11/12), 1412–1430. McKay, M. P., Weislogel, A. L., Fildani, A., Brunt, R. L., Hodgson, D. M., Flint, S. S., 2015. U-PB zircon tuff geochronology from the Karoo Basin, South Africa: implications of zircon recycling on stratigraphic age controls. International Geology Review, 57(4), 393410.

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

McKenzie, N. R., Horton, B. K., Loomis, S. E., Stockli, D. F., Planavsky, N. J., Lee, C. T. A., 2016. Continental arc volcanism as the principal driver of icehouse-greenhouse variability. Science, 352(6284), 444-447. Miller, C. F., McDowell, S. M., Mapes, R. W., 2003. Hot and cold granites? Implications of zircon saturation temperatures and preservation of inheritance. Geology, 31(6), 529-532. Miller, J. S., Matzel, J. E., Miller, C. F., Burgess, S. D., Miller, R. B., 2007. Zircon growth and recycling during the assembly of large, composite arc plutons. Journal of Volcanology and Geothermal Research, 167(1-4), 282-299. Miller, R. B., Paterson, S. R., 2001. Influence of lithological heterogeneity, mechanical anisotropy, and magmatism on the rheology of an arc, North Cascades, Washington. Tectonophysics, 342(3-4), 351-370. Moecher, D., Samson, S., 2006. Differential zircon fertility of source terranes and natural bias in the detrital zircon record: Implications for sedimentary provenance analysis. Earth and Planetary Science Letters, 247(3–4), 252–266 Molina-Garza, R. S., Geissman, J. W., Wawrzyniec, T. F., Pena Alonso, T. A., Iriondo, A., Weber, B., Aranda-Gómez, J., 2015. Geology of the coastal Chiapas (Mexico) Miocene plutons and the Tonalá shear zone: Syntectonic emplacement and rapid exhumation during sinistral transpression. Lithosphere, 7(3), 257-274. Naeser, N. D., Naeser, C. W., 1984. Fission-track dating. In Developments in Palaeontology and Stratigraphy (Vol. 7, pp. 87-100). Elsevier. Nasdala, L., Pidgeon, R. T., Wolf, D., Irmer, G., 1998. Metamictization and U-Pb isotopic discordance in single zircons: a combined Raman microprobe and SHRIMP ion probe study. Mineralogy and Petrology, 62(1-2), 1-27. Orejana, D., Martínez, E. M., Villaseca, C., Andersen, T., 2015. Ediacaran–Cambrian paleogeography and geodynamic setting of the Central Iberian Zone: constraints from coupled U–Pb–Hf isotopes of detrital zircons. Precambrian Research, 261, 234-251. Orme, D. A., Laskowski, A. K., 2016. Basin analysis of the Albian–Santonian Xigaze forearc, Lazi region, south-central Tibet. Journal of Sedimentary Research, 86(8), 894913. Orme, D. A., Surpless, K. D., 2019. The birth of a forearc: The basal Great Valley Group, California, USA. Geology. Paterson, S. R., Fowler, T. K., Miller, R. B., 1996. Pluton emplacement in arcs: a crustalscale exchange process. Earth and Environmental Science Transactions of The Royal Society of Edinburgh, 87(1-2), 115-123. Perez, N. D., Horton, B. K., 2014. Oligocene‐Miocene deformational and depositional history of the Andean hinterland basin in the northern Altiplano plateau, southern Peru. Tectonics, 33(9), 1819-1847. Puetz, S. J., Condie, K. C., Pisarevsky, S., Davaille, A., Schwarz, C. J., Ganade, C. E., 2018. Quantifying the evolution of the continental and oceanic crust. Earth-Science Reviews, 164, 63-83. Pujols, E. J., López, J. L., Stockli, D. F., Rossi, V. M., &Steel, R. J., 2018. New insights into the stratigraphic and structural evolution of the middle Jurassic S. Neuquén Basin from Detrital Zircon (U‐Th)/(He‐Pb) and Apatite (U‐Th)/He ages. Basin Research, 30(6), 1280-1297. Pullen, A., Ibáñez-Mejía, M., Gehrels, G. E., Ibáñez-Mejía, J. C., Pecha, M, 2014. What

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

happens when n= 1000? Creating large-n geochronological datasets with LA-ICP-MS for geologic investigations. Journal of Analytical Atomic Spectrometry, 29(6), 971-980. Reid, M.R., Coath, C.D., Harrison, T.M., McKeegan, K.D., 1997. Prolonged residence times for the youngest rhyolites associated with Long Valley Caldera: 230Th–238U ion microprobe dating of young zircons. Earth Planet. Sci. Lett. 150, 27–39. Reiners, P. W., Ehlers, T. A., Garver, J. I., Mitchell, S. G., Montgomery, D. R., Vance, J. A., Nicolescu, S., 2002. Late Miocene exhumation and uplift of the Washington Cascade Range. Geology, 30(9), 767-770. Rino, S., Kon, Y., Sato, W., Maruyama, S., Santosh, M., Zhao, D., 2008. The Grenvillian and Pan-African orogens: world's largest orogenies through geologic time, and their implications on the origin of superplume. Gondwana Research, 14(1-2), 51-72. Rojas-Agramonte, Y., Kröner, A., Demoux, A., Xia, X., Wang, W., Donskaya, T., Liu, D., Sun, M., 2011. Detrital and xenocrystic zircon ages from Neoproterozoic to Palaeozoic arc terranes of Mongolia: significance for the origin of crustal fragments in the Central Asian Orogenic Belt. Gondwana Research, 19(3), 751-763. Robb, L. J., Davis, D. W., Kamo, S. L., 1991. U-Pb Ages on Single Detrital Zircon Grains from the Witwatersrand Basin, South Africa: Constraints on the Age of Sedimentation and on the Evolution of Granites Adjacent to the Basin: The Journal of Geology, 98(3), 311-328. Romans, B. W., Castelltort, S., Covault, J. A., Fildani, A., Walsh, J. P., 2016. Environmental signal propagation in sedimentary systems across timescales. Earth-Science Reviews, 153, 7-29. Rossignol, C., Hallot, E., Bourquin, S., Poujol, M., Jolivet, M., Pellenard, P., Ducassou, C., Nalpas, T., Heilbronn, G., Yu, J., Dabard, M. P., 2019. Using volcaniclastic rocks to constrain sedimentation ages: To what extent are volcanism and sedimentation synchronous?. Sedimentary Geology, 381, 46-64. Rubatto, D., 2017. Zircon: the metamorphic mineral. Reviews in Mineralogy and Geochemistry, 83(1), 261-295. Rubatto, D., Gebauer, D., 2000. Use of cathodoluminescence for U-Pb zircon dating by ion microprobe: some examples from the Western Alps. In Cathodoluminescence in Geosciences (pp. 373-400). Springer, Berlin, Heidelberg. Sack, P. J., Berry, R. F., Meffre, S., Falloon, T. J., Gemmell, J. B., Friedman, R. M., 2011. In situ location and U‐Pb dating of small zircon grains in igneous rocks using laser ablation– inductively coupled plasma–quadrupole mass spectrometry. Geochemistry, Geophysics, Geosystems, 12(5). Sambridge, M. S., Compston, W., 1994. Mixture modeling of multi-component data sets with application to ion-probe zircon ages. Earth and Planetary Science Letters, 128(3-4), 373390. Samperton, K. M., Bell, E. A., Barboni, M., Keller, C. B., Schoene, B., 2017. Zircon agetemperature-compositional spectra in plutonic rocks. Geology, 45(11), 983-986. Samson, S. D., Moecher, D. P., Satkoski, A. M., 2018. Inherited, enriched, heated, or recycled? Examining potential causes of Earth's most zircon fertile magmatic episode. Lithos, 314, 350-359. Sundell, K. E., Gehrels, G., Pecha, M., 2019. Collecting large-n U-Pb detrital geochronology data via rapid acquisition (300-1,200 analyses/h) laser ablation multicollector ICP-MS. Geological Society of America annual meeting, Phoenix, AZ. Schaltegger, U., Schmitt, A.K., Horstwood, M.S.A., 2015. U-Th-Pb zircon geochronology

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

by ID-TIMS, SIMS, and laser ablation ICP-MS: Recipes, interpretations, and opportunities: Chemical Geology, v. 402, p. 89-110. Schermer, E. R., Busby, C., 1994. Jurassic magmatism in the central Mojave Desert: Implications for arc paleogeography and preservation of continental volcanic sequences. Geological Society of America Bulletin, 106(6), 767-790. Schoene, B., 2014. 4.10-U–Th–Pb Geochronology. Treatise on Geochemistry, 4, 341-378. Schwartz, T. M., Fosdick, J. C., Graham, S. A., 2017. Using detrital zircon U‐Pb ages to calculate Late Cretaceous sedimentation rates in the Magallanes‐Austral basin, Patagonia. Basin Research, 29(6), 725-746. Sharman, G. R., Graham, S. A., Grove, M., Kimbrough, D. L., Wright, J. E., 2015. Detrital zircon provenance of the Late Cretaceous–Eocene California forearc: Influence of Laramide low-angle subduction on sediment dispersal and paleogeography. Bulletin, 127(1-2), 38-60. Sharman, G. R., Sharman, J. P., Sylvester, Z., 2018. detritalPy: A Python‐based toolset for visualizing and analysing detrital geo‐thermochronologic data. The Depositional Record, 4(2), 202-215. Sickmann, Z. T., Schwartz, T. M., Graham, S. A., 2017. Refining stratigraphy and tectonic history using detrital zircon maximum depositional age: an example from the Cerro Fortaleza Formation, Austral Basin, southern Patagonia. Basin Research, 30(4), 708-729. Siégel, C., Bryan, S. E., Allen, C. M., Gust, D. A., 2018. Use and abuse of zircon-based thermometers: a critical review and a recommended approach to identify antecrystic zircons. Earth-Science Reviews, 176, 87-116. Simon, J. I., Renne, P. R., Mundil, R., 2008. Implications of pre-eruptive magmatic histories of zircons for U–Pb geochronology of silicic extrusions. Earth and Planetary Science Letters, 266(1-2), 182-194. Sláma, J., Košler, J., 2012. Effects of sampling and mineral separation on accuracy of detrital zircon studies. Geochemistry, Geophysics, Geosystems, 13(5). Spencer, C. J., Prave, A. R., Cawood, P. A., Roberts, N. M., 2014. Detrital zircon geochronology of the Grenville/Llano foreland and basal Sauk Sequence in west Texas, USA. Geological Society of America Bulletin, 126(7-8), 1117-1128. Spencer, C. J., Kirkland, C. L., Taylor, R. J., 2016. Strategies towards statistically robust interpretations of in situ U–Pb zircon geochronology. Geoscience Frontiers, 7(4), 581589. Spencer, C. J., Kirkland, C. L., Roberts, N. M., 2018. Implications of erosion and bedrock composition on zircon fertility: Examples from South America and Western Australia. Terra Nova, 30(4), 289-295. Spencer, C.J., Danišík, M., Ito, H., Hoiland, C., Tapster, S., Jeon, H., McDonald, B. Evans, N.J., 2019. Rapid Exhumation of Earth's Youngest Exposed Granites Driven by Subduction of an Oceanic Arc. Geophysical Research Letters, 46(3), pp.1259-1267. Sørensen, H., 1974. The Alkaline Rocks. 622 pp. John Wiley and Sons, London Stevens, T., Palk, C., Carter, A., Lu, H., Clift, P. D., 2010. Assessing the provenance of loess and desert sediments in northern China using U-Pb dating and morphology of detrital zircons. Geological Society of America Bulletin, 122(7-8), 1331-1344. Suarez, S. E., Brookfield, M. E., Catlos, E. J., Stöckli, D. F., 2017. A U-Pb zircon age constraint on the oldest-recorded air-breathing land animal. PloS one, 12(6), e0179262. Sundell, K., Geherels, G., Pecha, M., 2019. Collecting large-n U-Pb detrital geochronology data

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

via rapid acquisition (300–1,200 analyses/h) laser ablation multicollector ICP-MS, Geological Society of America abstracts with programs, 23-11. Surpless, K. D., Graham, S. A., Covault, J. A., Wooden, J. L., 2006. Does the Great Valley Group contain Jurassic strata? Reevaluation of the age and early evolution of a classic forearc basin. Geology, 34(1), 21-24. Thomson, S. N., Herve, F., 2002. New time constraints for the age of metamorphism at the ancestral Pacific Gondwana margin of southern Chile (42-52 S). Revista Geológica de Chile, 29(2), 255-271. Tucker, R. T., Roberts, E. M., Hu, Y., Kemp, A. I., Salisbury, S. W., 2013. Detrital zircon age constraints for the Winton Formation, Queensland: contextualizing Australia's Late Cretaceous dinosaur faunas. Gondwana Research, 24(2), 767-779. Vazquez, J. A., 2011. Next steps in using accessory minerals to date the evolution of silicic magmas. In AGU Fall Meeting Abstracts. Vermeesch, P., 2004. How many grains are needed for a provenance study?. Earth and Planetary Science Letters, 224, 441-451. Vermeesch, P., 2018. IsoplotR: a free and open toolbox for geochronology. Geoscience Frontiers, 9(5), 1479-1493. Wahbi, A., & Blum, M., 2019. Sediment Routing Analysis of the Early Cretaceous McMurray Formation in East Alberta, Canada. In 2019 AAPG Annual Convention and Exhibition. Watson, E. B., 1979. Zircon saturation in felsic liquids: experimental results and applications to trace element geochemistry. Contributions to Mineralogy and Petrology, 70(4), 407-419. Watson, E. B., 1996. Dissolution, growth and survival of zircons during crustal fusion: kinetic principles, geological models and implications for isotopic inheritance. Spec Pap Geol Soc Am 315: 43–56. doi: 10.1130. Watson, E. B., Harrison, T. M., 1983. Zircon saturation revisited: temperature and composition effects in a variety of crustal magma types. Earth and Planetary Science Letters, 64(2), 295-304. Wendt, I., Carl, C., 1991, The statistical distribution of the mean squared weighted deviation: Chemical Geology, 86, 275-285. Willner, A. P., Sindern, S., Metzger, R., Ermolaeva, T., Kramm, U., Puchkov, V., Kronz, A., 2003. Typology and single grain U/Pb ages of detrital zircons from Proterozoic sandstones in the SW Urals (Russia): early time marks at the eastern margin of Baltica. Precambrian Research, 124(1), 1-20. Winter, J. D., 2001. An introduction to igneous and metamorphic petrology (Vol. 697). New Jersey: Prentice hall. Wotzlaw, J. F., Schaltegger, U., Frick, D. A., Dungan, M. A., Gerdes, A., Günther, D., 2013. Tracking the evolution of large-volume silicic magma reservoirs from assembly to supereruption. Geology, 41(8), 867-870. Wu, F. Y., Ji, W. Q., Liu, C. Z., Chung, S. L., 2010. Detrital zircon U–Pb and Hf isotopic data from the Xigaze fore-arc basin: constraints on Transhimalayan magmatic evolution in southern Tibet. Chemical Geology, 271(1-2), 13-25. Zeh, A., Wilson, A. H., Ovtcharova, M., 2016. Source and age of upper Transvaal Supergroup, South Africa: age-Hf isotope record of zircons in Magaliesberg quartzite and Dullstroom lava, and implications for Paleoproterozoic (2.5–2.0 Ga) continent reconstruction. Precambrian Research, 278, 1-21. Zimmermann, S., Mark, C., Chew, D., Voice, P. J., 2018. Maximising data and precision from

Journal Pre-proof

Jo

ur

na

lP

re

-p

ro

of

detrital zircon U-Pb analysis by LA-ICPMS: the use of core-rim ages and the singleanalysis Concordia age. Sedimentary geology, 375, 5-13.

Journal Pre-proof

Table 1. Volcaniclastic Mineral Terminology Terminology

Age relative to the youngest magmatic event or eruption

Mode of formation Crystallized from the youngest magmatic episode

Antecrysts

Tens to hundreds of kyr older

Crystallized from an earlier episode of the same magmatic system and incorporated into a later magmatic episode

Xenocrysts

At least several myr older

Crystals that were assimilated into the magma from surrounding host rocks that are unrelated to the most recent magmatic system

Inherited

At least several myr older

Crystals that have undergone at least one anataxis episode of the magma source rock

of

Autocrysts

~Coeval: hundreds to thousands of years_

ro

Notes: Based on Miller et al. (2007), Rossignol et al. (2019), and references within

Table 2. Summary of Maximum Depositional Age Methods

YC2s τ method

>3* >3*

Method Monte Carlo Single analysis Weighted mean Peak age Weighted mean Weighted mean Weighted mean

Reference 1 2 2 2 3 4 5

Weighted mean Weighted mean

2 6

-p

N n.a. 1 >2* >2* >2* 3 3

lP

re

Abbreviation YDZ YSG YC1s YPP YSP Y3Za Y3Zo

na

Metric Youngest detrital zircon** Youngest single grain Youngest cluster (1σ overlap) Youngest graphical peak Youngest statistical population Youngest three grains Youngest three grains that overlap within error Youngest cluster (2σ overlap) τ method

Jo

ur

*Minimum number of analyses can be specified **See Appendix B for comparison of Isoplot v4.15 and detritalPy implementations of the YDZ algorithm N-Number of grain analyses included in calculation References: 1-Ludwig and Mundil (2002); 2-Dickinson and Gehrels (2009), 3-Coutts et al. (2019); 4-Zhang et al. (2016); 5-Ross et al. (2017); 6-Barbeau et al. (2009)

Table 3. Sample Summary

Continent

# of samples/ % of Sampled catchment Total land area Sampled % analyses total area* (km2 ) (km2 ) of land area North America 238/22,109 31.3 7,745,868 24,617,800 31.5% South America 126/11,856 16.8 9,312,219 17,751,500 52.5% Europe 49/4,309 6.1 3,850,185 3,380,185 38.6% Asia 241/23,280 32.9 18,722,427 44,371,360 42.2% Africa 70/6,218 8.8 9,548,242 30,075,240 31.7% Australia 67/2,924 4.1 251,595 9,031,470 2.8% Other** 1/41 0.06 ---World 791/70,737 100 49,430,535 148,300,000 33.3% World land surface area from Coble et al. (1987). Continental land area from Puetz et al. (2018) *Reflects modern, fluvial samples only **Single sample from Macqurie Island

Journal Pre-proof Table 4. Sample Depositional Environments % of Total 78.9% 9.5% 5.5% 4.0% 1.0% 0.56% 0.43% 0.058%

ur

na

lP

re

-p

ro

of

# of Samples/Analyses 665/55,826 40/6,744 35/3,864 25/2,831 6/732 16/396 4/303 1/41

Jo

Classification Fluvial Marine (offshore) Aeolian Littoral Deltaic Alluvial Sub-glacial river Lacustrine

Journal Pre-proof Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Jo

ur

na

lP

re

-p

ro

of

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11A

Figure 11B

Figure 11C

Figure 12

Figure 13

Figure 14

Figure 15