Wolff et al. [ 2016] argue that (1) prior studies show no evidence for measurable SPE influence on nitrate deposition, (2) without a Carrington signature there is no longer any smoking gun in the data, and (3) large events such as the Carrington “cannot be logged through nitrate.

” We disagree.

First, prior studies of possible SPE impacts have been very incomplete, and second, there are other major SPE/nitrate spike candidates in the ice core sequence that have not been properly studied.

We address both issues here.


Most past numerical simulations involving analytic range-energy calculations [e.g., Duderstadt et al. [ 2014], and references therein] used an incomplete model that is more appropriate for soft spectrum SPEs such as the August 1972 and November 2000 events.

These calculations use atmospheric inputs covering a limited energy range and ignore elementary particle interaction cascades in the atmosphere.

These studies contradict experimental data—balloon-measured ionization—which show substantial SPE-induced ionization in the lower stratosphere and troposphere, revealing an SPE ionization peak at about 17 km, the bottom of the stratosphere at midlatitudes [Nicoll and Harrison, 2014].

Most studies that have included a more complete air shower simulation [e.g., Usoskin et al., 2011, 2013] have not looked specifically at nitrate deposition.

The models and simulations have ignored high-fluence, hard spectrum SPEs that can produce enough ionization in the troposphere and lower stratosphere to be deposited as nitrate spikes in the snow in just weeks to months, particularly if they occur at the right time of year (i.e., winter).


Calisto et al. [ 2013] modeled a scaled-up, Carrington-like, February 1956 SPE at the end of August and predicted up to a 50% increase in total nitric acid deposition for the next month over the South Pole.

In asserting that once the Carrington Event is removed from consideration, “…the idea that any nitrate spikes above background are due to (SPEs) reverts to speculation.

” Wolff et al. [ 2016] ignore the 23 Feb 1956 SPE, which also has a well-resolved, associated nitrate peak in a Greenland ice core [Smart et al. 2014, Figure 1].


The 1956 SPE is the largest amplitude (a 46-fold increase in 15 min data) and the largest fluence (above 200 MeV) ground level event (GLE) in the cosmic ray monitoring history [Kovaltsov et al., 2014], and it had a hard spectrum [Pfotzer, 1958; Smart and Shea, 1990; Belov et al., 2005].

This event was worldwide in character, observed at the geomagnetic equator, and had estimated maximum proton energies >50 GeV [Sarabhai et al., 1956].

In contrast, even though low-latitude aurorae coincident with an extreme geomagnetic storm were associated with it, there is no spectral information on the Carrington event.


Using greatly improved simulations that follow the development of the proton-induced air shower and its ionizing effect on the atmosphere, we found consistency with the recent experiments showing substantial excess ionization down to 10 km and demonstrate sufficient nitrate deposition from the 1956 SPE.

We discuss our results in section 4.


SPE candidates deserving consideration in addition to the February 1956 event include the impulsive nitrate spikes observed in the fine resolution Boston University (BU) core that correspond to the very hard spectrum solar cosmic ray ground level events observed by muon detectors in 1942, 1946, and 1949.

Other candidates include the probable medieval SPEs of 775 A.D. and 994 A.D. [Miyake et al., 2012, 2013; Melott and Thomas, 2012; Thomas et al., 2013; Ding et al., 2015].

Wolff et al. [ 2016] assert that the 1937–1951 nitrate spikes in the BU core are not reliably reproduced in the GISP2-H core.


In section 3.3 we show why their claim cannot be substantiated without fine-resolution, replicate core studies.


The 775 A.D. event may have been 25–50 times stronger than the February 1956 SPE [Usoskin et al., 2013; Thomas et al., 2013] and should be detectable at Summit if it occurred in winter.


There is also evidence suggesting that the 775 A.D. probable SPE may have been several somewhat smaller events occurring in Northern Hemisphere summer 776 A.D. [Ding et al., 2015], which would not be detectable at Summit or in Antarctic ice cores with resolutions corresponding to fewer than 10 samples per year.


The problem is that almost no replicate ice core nitrate profiles exist with the fine temporal/depth resolution required to detect such events.


This is why the inadequate resolution of the ice cores presented by Wolff et al. [ 2012] is a major issue in the SPE/nitrate deposition debate and why we are calling for a dedicated campaign of replicate, fine resolution, ice core analyses.


We do not dispute Wolff et al. ’s [2016] general method of applying a rapid concentration change at the melter for characterizing smoothing in their system or that this method can be used to derive a resolution that is attainable, in principle, by an ideal Continuous Flow Analysis (CFA) system.

However, this method is valid “only for systems with negligibly small dispersion” [Breton et al., 2012], and it is indisputable that there was a huge loss of resolution, whatever the cause, somewhere between the value Wolff et al. [ 2012] derived by this method (and reported in their Table 1) and the final statistical resolution that they actually obtained from the Zoe and D4 cores.


The resolution of any time series is generally characterized as the ability to distinguish between two adjacent peaks and, in the case of discrete sampling of an ice core, is only limited by the sample size, sampling rate, and postdepositional processes, whereas what Wolff et al. [ 2012, 2016] are calling resolution corresponds much more closely with their sampling rate, which is already twice the best attainable (Nyquist frequency) resolution in discretely sampled data.

With CFA, resolution is additionally limited by mixing issues on the melthead and significant dispersion effects in the meltwater stream, due to nonuniform velocity profiles of a nonideal (real) fluid, causing smoothing of the resulting signal [Breton et al., 2012].


In the case of the ice cores listed in Table 1 of Wolff et al. [ 2012], it is clear that significant dispersion effects combined with multispecies analysis and other issues inherent with all CFA logging systems [Rasmussen et al., 2005] considerably limited their final, effective resolution.


Using the data provided by Wolff and McConnell (personal communication, 2012), we determined their time resolution [Smart et al., 2014] and demonstrated that there is no significant signal in Zoe and D4 for periods smaller than about 4 months.


Our determination of the time resolution, using both power spectral analysis and examination of the full width at half maximum of peaks in each of the four data sets, showed the Zoe and D4 resolutions are far more coarse (by factors of at least ~7–13) than those reported in Table 1 of Wolff et al. [ 2012].


Summer peaks, which are sharp in other data (i.e., GISP2-H and BU), are spread out over months in Zoe and D4 [Smart et al., 2014, Figures 4 and 5], and nitrate spikes are completely indiscernible.


Asserting in Table 1 of Wolff et al. [ 2012] resolutions of ~0.05 years (0.6 months) for Zoe and ~0.025 years (0.3 months) for D4, both sampled by CFA, versus 0.067 years (0.8 months) for the discretely sampled GISP2-H core without any additional qualification, thereby implying that their ice core resolutions were finer, is misleading, regardless of whether “all the sources of dispersion” have been accounted for or how these numbers were derived.


These results show that either the “empirical and direct way” Wolff et al. [ 2012] used to characterize their resolutions in Table 1 and elsewhere needs substantial revision or the term “sampling rate” and the corresponding numbers should be stated instead.


Wolff et al. [ 2016] claim that we ignore the inherent limits on resolution, but the fact is that this issue has not been investigated with fine-resolution, multispecies ice core studies, as our analysis demonstrated.

We agree that it is difficult to attach precise calendar dates at subannual resolution because of snowfall variability.

Nevertheless, precise dating is not required for deriving useful SPE statistics, and subannual dating estimates can be improved by applying what we know about the distribution of other species and snowfall within a year (e.g., more snow accumulation in summer than winter at Summit, Greenland [Dibb and Fahnestock, 2004]).

Wolff et al. [ 2016] acknowledge that a resolution of order 1 month (significantly finer than the ~4 months in their data) is probably attainable after controlling for postdepositional processing, and we assert that this should be sufficient for deriving the statistics of large high-energy, hard spectrum SPEs.


Wolff et al. [ 2016] attempt to compare the BU and GISP2-H cores by rebinning the BU data to mimic the coarser resolution of GISP2-H—and note that they do not exactly correspond.

Their choice of 1.5 cm samples or 0.03 year sections is not appropriate.

The correct rebinning should be closer to 0.04 years.

We do agree that natural variability occurring at the time of deposition and afterward makes matching of fine-resolution nitrate spikes deposited over ~0.5–3.5 months challenging but, we assert, not impossible as discussed below.


The issue of reproducibility of ice cores has long been a legitimate concern.

This was a major reason for the duplicate drilling programs of GISP2 (American) and Greenland Ice Core Project-GRIP (European) at Summit, which showed similarities and differences in two parallel ice cores 28 km apart over long time intervals.

Significant variability between pits and cores only a few meters apart at annual, as well as sub-annual, time scales (e.g. [Laird, 1987] and others) that is primarily related to post-depositional processes is well known.

It is possible that some of the misdating scenarios detailed by Wolff et al. [ 2016], occurred with the GISP2-H core during this interval and simple seasonal adjustments would improve peak matching between cores.

The comparison is also complicated by the fact that the BU core had a number of breaks and missing data in this time interval.


We do not accept Wolff et al. ’s [2016] assertion that such an effort “cannot be successful,” and we take strong issue with the comparison of GISP2-H data and binned BU data to show a lack of correlation and to support their asserted futility of finding repeatable SPE nitrate spikes.

Just as two deep ice cores 28 km apart could be synchronized over periods in excess of 30,000 years, with careful analysis, it should be possible to extract information at the subannual scale from 4 to 5, relatively shallow ice cores a few meters apart covering a few thousand years.

While a casual observer with little or no glaciological experience (with nitrate in particular) might be inclined to agree that there appears to be no matching nitrate spikes between the two cores presented in the figure of Wolff et al. [ 2016], careful, informed analysis suggests otherwise.


We note that the two cores were dated using different methodologies.

In the case of GISP2-H, after selecting the yearly fiducial marks based on volcanic tie points and winter nitrate minima (supplemented with annual accumulation information), samples were linearly interpolated to assign subannual dates.

The BU dating included an additional step where an annual accumulation cycle (based on surface measurements) was assumed (more snow in summer and less in winter) and dating was interpolated nonlinearly between yearly fiducial marks.

This different approach introduced more apparent sample offset between the two cores than would exist had they been analyzed by the same method and is partially responsible for the misalignment of nitrate spikes.


Working backward through the Wolff et al. [ 2016] figure and analyzing their alleged lack of matches, we find approximately six major BU and/or GISP2-H spikes:


First, the BU spike near the end of summer 1949 could correspond to the smaller spike on the right side of the GISP2H 1949 summer nitrate maximum with their different magnitudes resulting from post depositional processes.

Natural variability complicates the business of matching peaks, but these issues can be potentially resolved.


Second, the lack of a match between the winter nitrate peak identified as late 1946 in the BU core alleged by Wolff et al. [ 2016] is predicated on “Assuming that the dating of both cores is correct…” We assert that in fact the dating around this time, interval of at least one of the cores is incorrect, and this is the same peak as that dated to early 1947 in GISP2-H. Both peaks clearly occurred in winter, and the year breaks were chosen, in part, on the basis of the minimum nitrate values in those intervals, which are essentially the same on both sides of the GISP2H peak.

Another set of analyses might determine that the actual year fiducial mark lies on the right side, at which point the two peaks would line up and match.


Third, the GISP2-H spike on the right side of the summer 1944 nitrate maximum (which Wolff et al. [ 2016] claim occurs in a year when BU has no spike) could correspond to the right side of the slightly offset BU 1944 summer high, which has a break just to the left of this peak.

The missing data between the summer maxima and this peak preclude a proper interpretation.


Fourth to sixth, the BU spikes in 1937, 1939, and 1942 all have potential matches with GISP2-H spikes that may be more subdued, due to post depositional processes, or possibly offset (in the case of 1939).

Again, these potential matches may not be substantiated but neither are they dismissible without additional analysis.


Despite assertions that our peak matching exercise is too flexible (possibly allowing spurious correlations of spikes), our point is that two cores analyzed with different methodologies are not enough for disputing or resolving such ambiguities.

Similarly, arguments that subseasonal resolution at depth in Summit ice cores is inherently unachievable, based on surface studies alone, are also speculative until this issue has been addressed with dedicated, follow-on efforts involving statistical analysis of more than two replicate cores.

The actual useful resolution (probably in the 0.5 to 2 month range) in ice cores at Summit, Greenland, has not been determined, thus reinforcing our call for a study involving multiple ice cores.

Fine-resolution, multispecies analysis of 4 to 5 overlapping cores from the same site should be sufficient to characterize the subannual variance and reproducibility of each impurity (such as nitrate) and would establish limits on inherent depth and time resolutions.

Wolff et al. ’s resistance to this is puzzling.


For example, if the winter nitrate spike in the GISP2-H core is from the 23 February 1956 SPE, it should appear in several (though possibly not all) replicate cores, and methods of superposed epoch analysis, used routinely in such studies, would allow for a more complete characterization of such spikes, including their dating.

A feasibility study covering a reasonable initial test time interval could be accomplished before funding a dedicated campaign to analyze the last several kyr.


The Law Dome ice core [Palmer et al., 2001] cited by Wolff et al. [ 2016] as a discretely sampled, very fine resolution core to dispute identification of nitrate spikes from SPEs is a poor example because this core (with at best 2 month resolution for 1840–1880) (like Halley) was drilled at a coastal site and reflects primarily a maritime climate.

Palmer et al. [ 2001] and Roberts et al. [ 2015] both stated this caveat clearly in their papers.

The fact that statistical evidence for SPEs was found in this core with these resolution and location limitations is remarkable.


Wolff et al. ’s [2016] comments concerning the BU core and the lack of acidic nitrate spikes in the 1940s, especially the two GLEs in 1942 may have some validity, if confirmed with additional fine-resolution studies.

However, the 1946 GLE they use as their example occurred in July (Northern Hemisphere summer) and thus may not be a good candidate for observing in the Greenland ice core record for the reasons stated above, though, it may be observable as a winter peak in Antarctica at some suitable site.

There is also ambiguity in the interpretation of the 1949 results, and the possibility that the GISP2-H conductivity is more compatible with a nitric acid source for the nitrate spike associated with the November 1949 GLE.


Wolff et al. [ 2016] still neglect conductivity associated with the GISP2-H 1956 winter nitrate spike in Figure 1 of Smart et al. [ 2014].

Applying their same conductivity methodology to the 1956 nitrate peak suggests opposite results.

The integrated 1956 peak averages a nitrate concentration of 33.9 ppb (μg/l) or 0.547 μequiv/l above the local background.

Assuming no significant change in sulfate (i.e., no significant volcanic activity was reported during this period), and using standard molar conductivity tables [Haynes, 2015], the conductivity rise is expected to be about 0.23 μS/cm if the nitrate peak is acidic and 0.066–0.079 μS/cm if it is due to a nitrate salt.


The conductivity values colocated with nitrates in Figure 1 in Smart et al. [ 2014] were unfortunately mislabeled and are high by a factor of 100.

The increase above background of the GISP2-H 1956 conductivity peak is actually 0.21 μS/cm.

This is only 11% less than predicted for nitric acid, but a very large 159% to 212% more than predicted if the 1956 spike was formed by a nitrate salt intrusion, which Wolff et al. [ 2008] state is rare at Summit.


In principle, the 1956 peak could result from some combination of sea salt, biomass burning, or dust aerosol scavenged nitrates, as invoked by Wolff et al. [ 2016], but this is increasingly improbable as it requires multiple strong winter sources and ignores the most straightforward explanation, i.e., nitric acid.

This is strong circumstantial evidence against Wolff et al. ’s [2016] thesis that nitrate spikes can only be caused by salts or other aerosol.


Wolff et al. ’s [2016] mention that they await more detailed modeling studies, which are in progress.

Such a study of the 23 February 1956 SPE has recently been conducted where the amount of nitrate available from this SPE has been computed using full spectrum modeling involving high-energy showers [Melott et al., 2016].

These results indicate that nitrate production in the stratosphere is roughly 2–10 times greater than estimated by past limited energy range, analytic models.

Summing the February 1956 SPE ionization from the surface to approximately 45 km at Summit produces the nitrate deposition found in the integrated winter 1956 GISP2-H peak.

Therefore, this event can account for the measured nitrate with timely (~2 months) transport downward to the surface via mechanisms involving the polar vortex, denitrification, and polar stratospheric cloud assisted nitric acid deposition.

These results lend strong support to the conclusion that the 1956 winter nitrate spike in GISP2-H data is mostly acidic and likely due to the February 1956 SPE.


Wolff et al. [ 2016] assert that our SPE/nitrate spike evidence is “very weak” by questioning the GISP2-H dating based on a sample number ratio of 2.5 between 1956 and 1955.

However, our conclusion is also based on the likely acidic composition of this spike, and the new results mentioned above show that 1956 class SPEs can quantitatively explain spikes of this size and does not rely on dating alone.

Additionally, snow accumulation can vary significantly from one year to the next at any given point on the ice sheet.

Sample numbers from the nearby Zoe core dated as 1939 and 1941 differ from 1940 by factors of 2.2 and 2.3, respectively.

The GISP2-H core section was dated using the Hekla (1947), and Askja (1961), Iceland volcanic eruptions as tie points, together with counting and interpolating annual nitrate cycles to determine 1956 as the most probable year for the spike in question.

Wolff et al. [ 2016] consider the dating “uncertain within several months,” but it is difficult to justify shifting the well-defined, midwinter, nitrate low (where the spike is found) from February by more than about 1 month.


This approach is still incorrect for comparing with surface nitrate concentrations, which correlate more with nitric acid at Halley, Antarctica, because the atmospheric reservoir consists largely of organic nitrates [Jones et al., 2011], which have a long residence time compared to HNO3 [Ridley et al., 2000].

Using a percentage enhancement dilutes the expected effect.


Instead, calculations of the absolute amount of additional, SPE-produced, inorganic, nitric acid available for prompt deposition are consistent with the observed nitrate increase [Melott et al., 2016].


We agree that the McCracken et al. [ 2001] calibration must be revised before nitrate spikes in ice cores can be used for identifying extremely large SPEs.

This is very different from Wolff et al. ’s assertion that it cannot be done.


As our understanding of the physics and chemistry by which SPEs can deposit nitrate in the snow has evolved, it has become apparent that most events do not have the spectral hardness and/or sufficient fluence to produce an observable spike, and we agree with the opponents on this point.


Nevertheless, the standard atmospheric production model used by Duderstadt et al. [ 2014] and others has been clearly inadequate.

It totally ignored the particle cascade in the atmosphere that results in energy deposition in the lower atmosphere.


Consequently, we emphatically dispute the extrapolations by Wolff et al. [ 2012, 2016], Duderstadt et al. [ 2014], Duderstadt et al. [ 2015], and others that no SPEs can be found in polar ice core nitrates and that these records cannot be used to obtain statistics of past, very large SPEs.


On the contrary, recent work, showing direct experimental and model evidence of substantial lower stratosphere/troposphere ionization from SPEs (e.g., Usoskin et al. [ 2011, 2013], Nicoll and Harrison [2014], and Melott et al. [ 2016]) indicates exactly the opposite may be the case.


Thus, the SPE/polar ice nitrate hypothesis is still very much alive.


Wolff et al. [ 2016] advocate the alternate use of cosmogenic isotopes such as 10Be to study past SPEs.

We agree that these provide useful information.

However, they omit recently published work [McCracken and Beer, 2015] that has identified all of the same events in the 1940–1956 time interval previously identified as impulsive nitrate events in Figures 1 and 7 of Smart et al. [ 2014].

The 10Be and 14C records are annual; the 10Be increases appear 12–15 months after the high-energy SPEs.

Including fine-resolution nitrate signatures together with the 10Be and 14C records would enhance the overall database of historic high-energy SPEs and might refine the dating and enable estimates of the spectral shape and fluence of past events.


In dismissing nitrates, while acknowledging their production by SPEs in the middle atmosphere and troposphere, Wolff et al. ’s [2016] conclusion that “…we cannot see a plausible route to identifying and using their deposition in snow to diagnose past (SPEs)” is unproven, and their call for excluding nitrates from 14C and 10Be studies, based on inadequate analyses and incomplete models, is premature.

We reiterate and stand by our main point: “Low time resolution analysis of polar ice cores cannot detect impulsive nitrate events.”


To date, validation of most EWS tools has been suboptimal.

Validation of the NEWS quoted within the Royal College of Physicians' NEWS2 report [2] is based on a single centre on the south coast of England [4].

The demographic tested by this validation may not be applicable to other hospitals, particularly those in urban areas.

Although much of the perceived benefit of EWS comes from a systematised approach, rigorous validation of these scoring systems prior to widespread use is Commented [A1]: This statement should have some supporting evidence referenced.


essential to optimise the benefit for patients and reduce the workload for staff.

Most validation of NEWS has been in acute admissions and medical in-patients, settings in which it appears to perform well; there is less validity in elective surgical settings and non-medical specialities.

These findings have encouraged researchers to develop specialty-specific scores and, within these subpopulations, the predictive value of a generic EWS suffers in comparison [5, 6].

The potential inference is that one standardised EWS may not apply with accuracy to all types of patients and all relevant critical illness events.


Validation of EWS models have generally focussed on discrimination (the ability to differentiate between patients who will and will not suffer an adverse event) rather than calibration (the degree of agreement between model predictions and the actual outcomes) [3, 7].

Calibration is most important when models are used in different populations and is often not reported for EWS, despite their use in varying population groups.

Without confirmation of acceptable discrimination and calibration, the predictive accuracy of a model cannot be stated firmly [8].

Early warning scores developed using statistical analysis of routinely collected data representative of clinical practice appear to provide both improved discrimination and calibration, with potential for better and more efficient care than is possible [9] using consensus-based scores, such as NEWS-2.


The linked study by Chiu at al. in this issue of Anaesthesia may represent the next step towards future EWS, namely population-specific predictions developed using statistical analyses of large populations derived from electronic health records (EHR) [10].

Cardiac surgery in the UK has many good risk prediction tools, but these all aim to predict mortality pre-operatively (EuroSCORE-2 [11]) or in the first 24 h of ICU admission (ICNARC ARCtIC [12]).

This new study analysed data from 13,631 patients who were discharged to the ward from ICU, extracted from an electronic vital signs charting system from four UK cardiac surgery centres.

The centres varied in case-mix and workload.

Logistic regression was used to model the relationship between bedside observations and subsequent adverse event (death, cardiac arrest or ICU admission) within 24 h. The performance of the resulting prediction score was compared with that of NEWS scores.

Model validation on a subset of the dataset held out from model derivation ('internal validation') suggested that the logistic EWS achieved greater discrimination and better calibration than the NEWS.

However, the improvement was only modest: for a score threshold of 5 during the previous 6 h, sensitivity increased from 48% to 52%, with no change in the specificity of 92%.


Importantly, Chiu et al. did not validate their score using external validation in a dataset separate from that used to develop the score.

External validation of a model is important, because we expect worse performance in a new dataset than in a selected (hence representative) subset of the development dataset.

Given that NEWS was not derived from the dataset analysed by Chiu et al., the performance of their EWS is expected to be somewhat better.

It remains possible that the performance of NEWS and the logistic EWS would be similar in an external dataset.


The event rates in the dataset analysed by Chiu et al. were low and re-admission to ICU was the most frequent event within the composite outcome and therefore dominates the model predictions.

Re-admission to ICU is sensitive to the varying cultures, behaviours and resources between different hospitals using EWS.

For example, one in 25 patients in Papworth are re-admitted to ICU, compared with 1 in 45 in Wolverhampton.

Differences in case-mix will not fully account for this variation.

Further, the logistic EWS predicts only subsequent re-admission to ICU, rather than all clinically meaningful deteriorations requiring intervention.


Using EHRs to collect large volumes of data from multiple sites represents the future in the development of EWS.

As more UK hospitals adopt EHRs and standardise data definitions, the sample size and generalisability of EWS based on very large, combined datasets will increase.

Linking physiological data to robust clinical outcomes stored elsewhere in the EHR is likely to improve the granularity and accuracy of predictions.

To do this on a large scale requires national health and care data repositories.

An early example of this is the National Institute of Health Research Critical Care Health Informatics Collaborative [13], which combines ICU data from seven UK centres (including physiological observations, drugs, interventions and outcomes at hourly intervals) that can be linked to Office for National Statistics and NHS Digital records. Were large numbers of UK hospitals to be included, then the opportunities for healthcare research would be myriad.


The NHS has advocated a 'bottom-up' approach to information technology solutions - that NHS trusts should choose their own EHR systems.

To reap the potential research benefit of EHRs, these systems must communicate at the 'back end', even if the 'front end' between different software suppliers looks different.

Ideally, any repository would use a federated model, where centres classify and store their EHR data in a standardised way, with access to a central 'port' allowing retrieval of the required data from local sites.


Machine-learning techniques may improve predictions when based on very large, detailed clinical databases.

Applications of machine-learning are beginning to appear [14].

A sample size of 13,000 patients, as in the data analysed by Chiu et al., is at the lower end of when machine-learning is useful compared with standard regression modelling.

Machine-learning avoids strong parametric Commented [A2]: Needs reference.


Commented [A3R2]: This is data from the Chiu et al paper from which the editorial is about.


Commented [A4]: NHS Digital is the name of a body that deals with digital records of the NHS and therefore needs a capital letter.


assumptions, though this may be at the expense of being able to succinctly present or explain the patterns behind underlying predictions [15].

It is vital that predictions based on machine-learning are validated before they are implemented in clinical practice.


Chiu et al. should be applauded for developing an EWS driven by data, rather than using an expert- opinion based approach.

However, before logistic EWS models can replace current, consensus-based scores such as NEWS2, clinically important performance improvements, based on robust external validation must be demonstrated.

This will require large datasets, representative of the clinical setting within which the EWS will be used, which in turn requires the infrastructure to store and retrieve relevant data.

This will only be possible with investment and a collaborative approach at a regional and national scale from healthcare providers, academic institutions and industry.

It is critical that with the development of increasingly sophisticated EWS models, we retain an end-user experience that is simple and standardised.

Accurate model predictions are of no use with an inadequate response from the clinical team.

To this end, widespread dissemination, training and behavioural interventions are vital to realise the potential benefits of future specialty-specific EWS.


We thank Oglesby et al. for their interesting editorial [1] accompanying our recent paper [2].

We value their interpretation that a single standardised early warning score (EWS) may not be applicable to all types of patients and are grateful for their qualified support of the concept of future population-specific EWS.

We are also in agreement that evidence-based models are preferable to consensus-based scores and that external validation is imperative for all EWS.


We have a major issue with their criticism that we failed to externally validate our score in a dataset separate from that used to develop the score.

Multiple external validation techniques were undertaken and have been available in electronic format in the online supplement since 3 July 2019 [2].

Each centre independently collected four separate datasets.

Although these datasets were ultimately merged into one combined database, we retained the ability to use separate databases for external validation purposes.

Multiple predictive performance and validation techniques were utilised and these are summarised in the supporting information which accompanies the online version of our paper (Appendix 3: Table A3.4.) [ 3].

Debray et al. [ 4] define external validation as using new participant-level data, external to those used for model development, to examine whether the model’s predictions are reliable in individuals from potential population(s) for clinical use.

Following that definition, we purposely held back from using some centres to fit/develop the model so that we could then use them to externally validate a model fit for other centres.

We even reported all possible combinations of doing this, which were a sensible cut of the dataset to avoid accusations of potential bias.


Given the revised evidence-based physiological weightings, novel sub-division of oxygen therapy and potential clinical utility of our app [5] in discriminating between ‘hard’ and ‘soft’ escalation threshold NEWS scores (≥ 5 or ≥ 7), we believe ‘Improving EWS – more data, better validation, a step in the right direction’ may have been a more accurate choice of editorial title.


We thank Mackay et al. [ 1] for their comments on our editorial [2], which accompanied the article by Chiu et al. [ 3] and agree with the definition of external validation that they provide.

In broad terms, internal validation is concerned with reproducibility of a prediction model, whereas external validation is concerned with the transportability of model predictions to other settings and populations.

Randomly selected patients whose data are not included in the training data used to develop the prediction model and used for validation are similar to, and subject to, the same sampling processes as patients in the development dataset.

Internal validation based on such data will address overfitting due to selection of predictor variables and parametrisation of their associations with the outcome, but does not address transportability to different settings.


We applaud Mackay et al. for conducting a range of validation exercises; given the importance of validation we encourage authors and editors to include results of such exercises in the main text of published papers rather than in supplementary material.

From our reading of the manuscript and supplementary material, it appears that no population from a different source was used to externally validate their model.

The populations used for validation were subsets of data from the same four hospitals used in model development (some excluded from the development data set), either: (1) using three hospitals other than Papworth; (2) using data from all four hospitals in 2017; and (3) using the temporally ‘last 10%’ of data from each patient.

These discussions illustrate that there is a continuum within the dichotomy ‘internal validation’ and ‘external validation’ [4].


Siltstone, argillaceous siltstone, silty carbonate, siliciclastic mudstone, shale, and chert are common lithofacies in the upper Devonian-Middle Mississippian strata in basins of the North American midcontinent that accumulated in epeiric seas of the southern Laurentian craton (Fig. 1, 2; Lowe 1976; Lane and De Keyser 1980; Gutschick and Sandberg 1983; Over 1990; Schwartzapfel 1990; Turner et al. 2015; Schieber 2016; Kondas et al. 2018; Godwin et al. 2019; Mazzullo et al. 2019; Miller et al. 2019b; Milad et al. 2020; Price et al. 2020).

These strata are commonly organic-rich, and have attracted interest primarily as petroleum source rocks and unconventional reservoirs (Barrows and Cluff 1984; Smith and Bustin 1998; Smith and Bustin 2000; Slatt and O'Brien 2011; Angulo and Buatois 2012; Higley 2013; Wang and Philp 2019), but they also provide insights on climate, tectonics, and carbon sequestration of the time.


The range of explanations for deposition of the siliciclastic fraction includes: 1) suspension settling (Kirkland et al. 1992; Smith and Bustin 1998), 2) a combination of suspension settling, density currents, and gravity flows (e.g., Wright and Friedrichs 2006; Loucks and Ruppel 2007; Macquaker et al. 2010; Milad et al. 2020; Price et al. 2020), and 3) wind- or tide-induced subaqueous currents that remobilized muds across ancient epeiric seas (Schieber 2016).

Although all accept that these strata ultimately accumulated in marine environments, the provenance, generation, and transport of the significant volume of uniformly fine-grained siliciclastic material remain enigmatic and unresolved.


To better constrain the origin and transport of the siliciclastic fraction in these units, this work integrates new data from Upper Devonian and Lower-Middle Mississippian siltstone, mudrock, and argillaceous carbon- ate units across Oklahoma with compilations of coeval systems of the greater midcontinent region (Fig. 2).

We use these data to test the hypothesis of eolian delivery to the marine environment for the vast volume of this material.

An eolian-dust origin explains otherwise perplexing attributes of these units and has significant implications for interpretation of organic productivity in these and analogous systems.


The Anadarko Basin was initially part of the Oklahoma Basin (Johnson et al. 1988) which originated in the Early Cambrian in response to thermal subsidence associated with development of the Southern Oklahoma Aulacogen (Wickham 1978; Dalziel 1991; Meert and Torsvik 2003).

Cambro-Ordovician carbonate strata accumulated in the axis of the failed rift (Perry 1989) as a broad epicontinental shelf developed on the flanks (Johnson et al. 1989).

Carbonate and, subordinately, siliciclastic mudstone sedimentation continued through the Devono-Mississippian (e.g., Hunton Limestone, Woodford Shale, Sycamore Limestone, Caney Shale), a time of relative tectonic quiescence in this part of the Anadarko Basin and greater midcontinent region, before the tectonic disruptions of the Ouachita and Ancestral Rocky Mountains orogenies (Feinstein 1981; Johnson et al. 1988, 1989).


Closure of the Rheic Ocean and associated contractional tectonics beginning in the latest Mississippian (Serpukhovian)-Early Pennsylvanian (Nance et al. 2010) ultimately produced the thin-skinned Ouachita fold-and-thrust belt and the associated Arkoma basin (Fig. 1).

By Pennsylvanian time, a series of NW-SE-oriented basement-cored uplifts developed, termed the Ancestral Rocky Mountains, including the Wichita- Amarillo uplift and yoked Anadarko Basin of southwestern Oklahoma and northern Texas (Kluth and Coney 1981).

The present configuration of the Anadarko Basin is a product of this compressional inversion of the earlier aulacogen along basement-involved reverse faults, and development of accommodation space along the mountain front (Johnson et al. 1989).


Beyond Oklahoma, Late Devonian orogenic events affected both the eastern and western margins of the craton, in the form of the Acadian- Neoacadian and Antler orogenies, respectively.

The Acadian and Neo- acadian orogenies produced a series of westward-prograding clastic wedges, the Catskill Delta complex during the Middle-Late Devonian and the Price-Pocono-Borden-Grainger delta complex during the Early- Middle Mississippian (Ettensohn and Woodrow 1985; Ettensohn 2004, 2008, 2022), whereas the Antler clastic wedge prograded eastward (Sandberg and Gutschick 1980; Johnson and Pendergast 1981) (Fig. 3).

The Oklahoma Basin remained isolated and far removed from each of these clastic wedge complexes,~1800 km from the Antler and~1500 km from the Appalachian, throughout the Late Devonian to Middle Mississippian (Fig. 3).

Note that we use North American stage names for the Mississippian, for consistency with the literature.


The eastern deltaic complexes are well exposed in outcrops and boreholes throughout the Appalachian Valley and Ridge and Appalachian Plateau provinces, and exhibit both marine and continental facies for nearly 1000 km along orogenic strike (Fig. 1).

The Catskill Delta complex developed during the Middle to Late Devonian, transporting siliciclastics from the Acadian Orogen to marginal marine environments along the Appalachian foreland (Fig. 3).

During the Famennian, this complex comprised an estimated four main delta systems, fed by low-gradient coastal rivers emanating from alluvial systems in the highlands-akin in scale to the Eocene and modern Texas-Louisiana coastal plain, or the northern Sumatran coast (Sevon 1985; Woodrow et al. 1985; Boswell and Donaldson 1988).

Organic-rich black shales characterize the basinal facies of the Catskill Delta complex in the Appalachian foreland basin (Ettensohn and Woodrow 1985; Boswell and Donaldson 1988; Ettensohn 2008; Ettensohn et al. 2019).


Clastic-wedge deposition continued into the Early-Middle Mississippi- an (Kinderhookian-Osagean) in response to Neoacadian tectonism, such that the Appalachian foreland overfilled and deltaic deposition of the Borden and Grainger formations (Figs. 2, 3) prograded into the eastern and southeastern Illinois Basin (Ettensohn et al. 2022).


Active denudation ceased in the Middle to Late Mississippian (Meramecian-middle Chesterian), resulting in extensive carbonate deposition in the Appalachian foreland (Greenbrier and Newman Limestones, Fig. 2; Ettensohn et al. 2022).


Toward the eastern Appalachian foreland basin, carbonate strata intertongue with continental redbeds and eolianites throughout West Virginia, Virginia, and Maryland (Wynn and Read 2008; Ettensohn et al. 2022).


Early development of the Mauch Chunk Delta began in eastern Pennsylvania in the Meramecian (Wynn and Read 2008; Ettensohn et al. 2022) (Fig. 3).


In the extensive cratonic region between the Acadian (east) and Antler (west) orogenic systems, broad epeiric seas, intracratonic basins, and emergent arches prevailed through the Late Devonian-Early Mississippian, FIG.

1.-Major basins (gray shading), domes, and arches (red lines and text) of the U.S. for the Devono-Mississippian.

Abbreviations are as follows: AnB, Anadarko Basin; AntB, Antler Basin, ArB, Arkoma Basin; BWB, Black Warrior Basin; FCB, Forest City Basin; FtWB, Fort Worth Basin; IB, Illinois Basin; MB, Michigan Basin; N. ApB, Northern Appalachian Basin; S. ApB, southern Appalachian Basin; TB, Tobosa Basin; WB, Williston Basin; OD, Ozark Dome.

Numbers within circles correspond to locations of stratigraphic columns in Figure 2.

Yellow polygon outlines the Appalachian Valley and Ridge Province (V&R).

The edge of the brown polygon shows the western limit of the Appalachian Plateau Province (A.P.).

Modified from Adams (1965), Gutschick and Sandberg (1983), Smith and Bustin (2000), Loucks and Ruppel (2007), Soeder et al. ( 2014), Ettensohn et al. ( 2019) and Thomas et al. ( 2020).

Downloaded from http://pubs.geoscienceworld.org/sepm/jsedres/article-pdf/92/12/1186/5763983/i1938-3681-92-12-1186.pdf by McGill University user with organic-rich mudstones, including the Chattanooga Shale, Ohio Shale, New Albany Shale, Antrim Shale, and Woodford Shale accumu- lating east of the Transcontinental Arch and correlative mudstones such as the Bakken Shale and Duvernay Shale accumulating west of the Transcontinental Arch (Figs. 2, 3).


Carbonate-dominant systems with variably silty and cherty components prevailed through the Early-Middle Mississippian (e.g., the North Hill Limestone, Keokuk Limestone, St. Joe Group, Boone Group, Osage Limestone of northern Oklahoma, and Madison Limestone; Figs. 2, 3; Gutschick and Sandberg 1983, and others).


By the Middle to Late Mississippian (Meramecian-late Chesterian), fine siliciclastic contents increased, with deposition of silty carbonates, marine siltstones, and silty organic-rich mudstones (e.g., Blue River Group, Warsaw Shale, Barnett Shale, Meramec and Sycamore formations, Caney Shale and Madison Limestone; Figs. 2, 3).


The midcontinent region ranged from southern subtropical latitudes (~158 S-308 S) in the Late Devonian to near-tropical latitudes (~158 S) by the Early-Middle Mississippian (e.g., Boucot et al. 2013; Domeier and Torsvik 2014; Scotese and Wright 2018) (Fig. 3).

Paleoclimatic reconstructions document arid conditions in lowland regions throughout the midcontinent within these time intervals (Boucot et al. 2013) (Fig. 3).


Additionally, a number of instances of ice-contact deposits document evidence for suspected icesheets and/or alpine and even tidewater glaciation within the central Appalachian orogenic system during the latest Devonian (approximately lower to middle Siphonodella praesulcata conodont zonation; LE, Retispora lepidophyta-Indotriradites explanatus Glaciation in the central Appalachians marks the apex of the late Devonian icehouse.

Glaciation in Gondwanaland began in the late Famennian (approximately Palmatolepis postera conodont zonation; VCo, Diducites versabilis-Grandispora cornuta miospore zonation) and extended to the latest Famennian (Upper Siphonodella praesulcata conodont zonation; LN, Retispora lepidophyta-Verrucosisporites nitidus miospore zonation) (Fig. 2) (Caputo et al. 2008; Isaacson et al. 2008; Lakin et al. 2016).


Additionally, minor evidence for Gondwanan glaciation exists for the Early and Middle Mississippian (Kinderhookian-Osagean and Meramecian-Chesterian) (Caputo et al. 2008; compilation in Soreghan et al. 2019).


In Table 1, we summarize the facies attributes and inferred depositional environments for Upper Devonian and Lower-Middle Mississippian strata of Oklahoma (Figs. 2, 4, 5).

These units are well exposed and have been widely characterized in the Arbuckle Mountains (southern Oklahoma), the Ozark Plateau (northeastern Oklahoma), and the subsurface; correlatives occur throughout the greater midcontinent as demonstrated through hundreds of thousands of well penetrations (Craig et al. 1978; See Supplemental File 1).


To place the study units (Fig. 4) in a broader context (Figs. 2, 3), we used the paleogeographic maps of Blakey (2013), North American Key Time Slices Ó2013 Colorado Plateau Geosystems, Inc, as base maps, and appended these with major climate belts following Boucot et al. ( 2013) and Scotese and Wright (2018).


The Correlation of Stratigraphic Units of North America (COSUNA) project charts (Ballard et al. 1983; Hills and Kottlowski 1983; Patchen et al. 1984a, 1984b; Bergstrom and Morey 1985; Hintze 1985; Shaver 1985; Adler 1986; Mankin 1987; Kent et al. 1988) provided the initial framework for age constraints and correlations, but all unit ages were updated with the most recent available data (Cohen et al. 2013) (See Supplemental File 2 for references).


To assess the nature of and possible transport modes for the siliciclastic material in the target strata, we collected samples for particle-size analysis from seven outcrops and ten cores distributed throughout Oklahoma (Fig. 4; See Supplemental Files 2 and 3).

While carbonate grains and iron oxides, as well as grain coatings, may constitute a minor fraction of the original detrital material, we specifically target the silicate mineral fraction (SMF) as a proxy for detrital grain size.

Therefore, all organic, carbonate, and iron oxide components were removed using a sequential procedure following standard methods (cf.

Sur et al. 2010a, 2010b; Jiang and Liu 2011; Sweet et al. 2013; Foster et al. 2014), as outlined briefly here.


Samples were gently crushed with a ceramic mortar and pestle to pea- size gravel to accelerate chemical reactions, then rinsed with distilled water and sieved at 250 lm to remove any fines generated during crushing.

Samples were subsequently treated with 2N HCl (minimum 24 hours) for carbonate removal.

To remove organic matter and oxidize pyrite, samples were combusted at 500 8C for~12-20 hours.

This does not affect the grain size of silicate minerals, since silicates do not begin to sinter until temperatures exceed 500 8C (Schomburg 1991; Rickard et al. 2016).

Samples were then treated with sodium citrate-bicarbonate-dithionite (CBD) (Sur et al. 2010a; Rea and Janecek 1981) rinses until all iron oxides were removed.


Smear slides of residues were inspected using a polarizing reflected-light microscope under 103, 203, and 403 objectives to assess sample composition and ensure disaggregation, and check for the presence of remnant authigenic phases such as silicified microfossils (Fig. 6).

If present, silicified microfossils were physically removed by sieving following visual inspection to determine appropriate sieve size needed to remove microfossils, microfossil fragments, and/or aggregates while retaining detrital material (See Supplemental Files 2 and 3 for additional information).

Samples exhibiting a predominance of authigenic material were eliminated from analysis.

Furthermore, both visual inspection and previous studies on these strata indicate no to minimal instances of quartz overgrowths (Shelley et al. 2019; Price et al. 2020; Milad et al. 2020; Duarte et al. 2021; Supplemental Files 3, 4), lending further confidence to the fidelity of subsequent particle-size analyses.


Particle sizes of fully disaggregated samples were measured using a Malvern Mastersizer 3000 Laser Particle Size Analyzer (LPSA).

Of 103 samples processed in this way, 82 disaggregated sufficiently to enable analysis by LPSA.


To address compositional characteristics of the silicate material in the Sycamore and Woodford Shale, nineteen~5 g samples from the Arbuckle Uplift transect (Figs. 2, 4) were fragmented to gravel size and submerged in a 1N HCl for 24 hours to eliminate carbonate before whole-rock and trace- element geochemical analysis (acquired by ALS Geochemical).

Remnant CaO values and high loss-on-ignition (LOI) necessitated application of the correction by Taylor and McClennan (1985) to interpret the silicate fraction.

Data were analyzed (and biplots were created) using principal- components analysis (PCA) in R.


To assess provenance of the siliciclastic material, three 10-20 kg samples from each of the Arbuckle Uplift units (Figs. 2, 4; Woodford Shale, Sycamore Limestone, and Caney Shale) were processed for zircon separation and analyzed for detrital-zircon geochronology at the University of Arizona (UA) LaserChron Center.

Analyses were conducted using the Element 2 laser ablation-single collector-inductively coupled plasma- mass spectrometer (LA-SC-ICP-MS) following the analytical and data processing methods described in Pullen et al. ( 2018) and Gehrels and Pecha (2014).

Insufficient yield from the Woodford Shale sample (despite denotes the western limit of the subaqueous delta prograding from the Appalachians (Borden and Grainger Formations) (Ettensohn et al. 2022).


E) Middle Mississippian (~340 Ma, Meramecian) paleogeography of Laurentia.

F

Middle Mississippian, Meramecian, facies and tectonic paleogeographic map.

Both Parts D and F show the midcontinent region within the subtropical arid climate belt, and far removed from shorelines.

Widespread shallow water is present across the craton, including the Appalachian Basin, such as the Newman and Greenbrier limestones (Fig. 2) (Ettensohn et al. 2022).

Stippled clastics in beige in the Appalachian Basin region of West Virginia, Virginia, Pennsylvania, denote late Meramecian coastal sands, silts, and eolian environments (Wynn and Read 2008; Ettensohn et al. 2022).

Stipple and mudstone pattern sketched in Oklahoma (outlined in red), denote the deposition of the Meramec, Sycamore, and lower Caney Shale formations.

Paleogeographic maps A, C, E are from Blakey (2013), North American Key Time Slices Ó2013 Colorado Plateau Geosystems, Inc., and were used as base maps for Parts B, D, F. Paleoclimate boundaries are derived from Boucot et al. ( 2013) and Scotese and Wright (2018).

See Supplemental File 2 for all references pertaining to construction of facies maps and tectonic maps.

Downloaded from http://pubs.geoscienceworld.org/sepm/jsedres/article-pdf/92/12/1186/5763983/i1938-3681-92-12-1186.pdf by McGill University user .

20 kg of processed material) precluded analysis.


Best ages from the Sycamore Limestone and Caney Shale are reported as 206 Pb/ 207 Pb for ages greater than 900 Ma and 206 Pb/ 238 U for ages less than 900 Ma.

These ages are displayed as normalized probability density plots using an Excel macro, provided by the UA LaserChron Center (Gehrels 2010).

Analyses with . 10% discordance and 5% reverse discordance were eliminated before analysis.


To address climatic attributes and potential atmospheric transport pathways, we reanalyzed results of a total of 12 coupled general- circulation-model (GCM) experiments of global climate from intervals spanning the Middle Devonian to the Late Mississippian.

The simulations were performed with the HadCM3BL-M2.1aD atmosphere-ocean-vege- tation model (Valdes et al. 2017) and are described in detail in Valdes et al. ( 2021).

The model uses the same horizontal grid with a resolution of 3.758 3 2.58 in the atmosphere and ocean, with 19 and 20 unequally spaced vertical levels, respectively.

The paleogeographies underlying the model simulations were derived from the PALEOMAP paleogeographic atlas (Scotese and Wright 2018).

Current geographical coordinates of Oklahoma and the Appalachian clastic wedges were rotated using the PALEOMAP rotation model in the pyGplates software (Müller et al. 2018) to place these features within the appropriate paleogeographical positions.

Locations for the Appalachian clastic wedges are from Ettensohn et al. ( 2019).


Besides the model geography, the simulations differ only by a linearly increasing solar constant (0.8% per 100 Myr) and varying concentrations of atmospheric carbon dioxide.

CO 2 levels for the selected simulations were derived from the Foster et al. ( 2017) curve and range from a peak of 1377 ppmv in the Middle Devonian to a minimum of 233 ppmv in the Middle


-The paleoenvironmental conditions of the Oklahoma and greater North American mid-continent region changed from predominantly subtropical and arid conditions during the Famennian to a rather monsoonal system exhibiting seasonal wind reversals and summer precipitation during the Early to Middle Mississippian (Kinderhookian through Meramecian).

These seasonal wind reversals suggest that the greater midcontinent region was influenced by trade winds during Southern Hemisphere winter months (June through August) (Fig. 10).

The simulated surface wind patterns are dominated by an interplay between the prevailing southeasterly trade winds, the land-sea thermal gradient, and local orography.

As such, Acadian and Caledonian orogens tend to, in general, guide the flow of air around the landmasses, and absolute wind speeds are reduced due to the presence of these orogenic belts.


Summer winds in the Late Devonian (Fig. 10A) are shown to have come from the south and diverge across the Laurentian epeiric seas with a westerly jet flowing along the northern front of the Acadian orogen.

During Late Devonian winter months, a more southeasterly trade-wind circulation developed across Laurentia (Fig. 10B).

Westerlies along the southern front of the Acadian orogen show reduced wind values topping over the mountain range with acceleration on the lee side of the orogen.


The Osagean (Fig. 10C, D) and Meramecian (Fig. 10E, F) surface-wind patterns are very similar.

With the progressive northward movement of Laurentia, the mid-continent region came under the influence of cross- equatorial, northwesterly winds bringing moist air and summer precipita- tion toward emergent landmasses (Fig. 10E, F).

Winter precipitation was very low over the adjacent subtropical land masses and limited to tropical regions northwards of 108 S throughout the Devonian to Mississippian (Supplemental File 6).

Surface windflow patterns during the Osagean and Meramecian winter months (JJA) flowed westward along the southern front of the orogen, topped over the range, and swept across the Laurentian epeiric seas in a west to northwestern direction (Fig. 10E, F; Supplemental File 6).


Interpretation.-Model simulations reveal that the Laurentian land- mass and associated orogens strongly influenced surface wind patterns.

In fact, the presence of a larger landmass at subtropical latitudes during the Early Mississippian promoted seasonal wind reversals and a southward migration of the Intertropical Convergence Zone (ITCZ).

According to the model data, a northwestward transport of continental dust toward the midcontinent seems physically plausible, although such a transport would have been possible only during the austral winter months.

These months are characterized by northwestward, offshore winds across the potential source regions of the Acadian-Neoacadian clastic wedges.


These wind patterns may be analogous to present winds along the Andean orogen, where westerlies from the South Pacific anticyclone are deflected northward creating a southerly jet along the Andean Pacific front.

Air masses top over the orogen and descend as warm dry air off the lee side of the mountain range, known as Zonda winds (Puliafito et al. 2015; Zou and Xi 2021) which are similar in process to Foehn winds off New Zealand's Alps, Chinook winds off the North American Rocky Mountains, and Santa Ana winds over the mountains of southern California (McGowan et al. 1996; Hugenholtz et al. 2007;Álvarez et al. 2021).


Even though coastal and continental summer precipitation increased towards the Early Mississippian, winter months around the orogenic highlands were still dry in the simulations and a stronger easterly wind component may have transported material to the Oklahoma region of Laurentia.

A comparable analog for the Early-Middle Mississippian model results, which sits at the same latitude, might be present-day northwestern Australia, where easterlies transport dust westward towards the Indian Ocean during the winter months following the northward migration of the ITCZ (Christensen et al. 2017; Keep et al. 2018).


exhibit much higher values of Al more typical of source material that is highly chemically weathered.

Rather, both units-especially the Sycamore Limestone-exhibit a signature more similar to loess (Fig. 8B).

The inflection in Zr and TiO 2 with respect to Al 2 O 3 (Zr/Ti O 2 , Zr/Al 2 O 3 , TiO 2 / Al 2 O 3 ) in the basal Sycamore Limestone (Fig. 8C) is interpreted to reflect a possible excursion in provenance.

These compositional data are indepen- dent of grain size, as evinced by the weak relationship (r 2 , 0.07) between grain size and these elements (Supplemental File 4), as well as the negligible grain-size variation within each of the Sycamore and Woodford formations in general (Fig. 7).


Delivery of siliciclastic material to epeiric seas occurs by either deltaic or eolian transport.

Various processes can then redistribute this material, e.g., hyperpycnal flows, sediment gravity flows induced by slope failure or wave and/or current agitation, or contour currents (Stow et al. 2001; Walsh and Nittrouer 2009; Macquaker et al. 2010; Schieber 2016).

Assuming an average wave base of 50 m, Schieber (2016) posited a maximum transport distance of 100 km for remobilized sediment, or up to 400 km for an extreme case of a 200 m wave base.

Acknowledging the vast distribution of siliciclastic mud across the Devonian Laurentian sea, Schieber (2016) advocated transport induced by bottom currents, but these arguments demand lateral transport for thousands of kilometers in low-gradient epeiric seas with wave bases ,, 200 m. While we acknowledge these arguments as one hypothesis, we propose that eolian delivery is an additional and simpler explanation that avoids the need to call upon unknown and unusually powerful storms or tides, as well as flocculation, which is inconsistent with the minimal clay-mineral content of the study units.


The genesis and initial mobilization of the large volumes of ubiquitously fine siliciclastic material characterizing the study units remain unresolved, in large part owing to the absence of proximal feeder system(s).

For the Devono-Mississippian Bakken Formation of the Williston Basin, for example, Egenhoff and Fishman (2013) inferred the presence of a deltaic fan that is either not preserved or not exposed.

Similar inferences of deltaic feeder systems are applied to Devono-Mississippian mudstone units of the midcontinent (Price et al. 2020) yet without documentation of any remnants of the subaerial (delta plain) or proximal (subaqueous delta) depositional systems.


It is highly unlikely that researchers have overlooked such delta systems, given the great density of subsurface data points across the midcontinent region (e.g., . 100,000 wells in Kansas and Oklahoma; Newell et al. 1987; Oklahoma Corporation Commission 2021).

The coeval deltas nearest to the midcontinent region during Late Devonian time are the Catskill (Appalachian foreland) and Antler (Antler foreland) systems, and, for the early Mississippian, the Price-Pocono-Grainger-Borden (Appala- chians) deltas, that are separated from the midcontinent study area bỹ 1,000-1,500 km (Fig. 3) (Lane and De Keyser 1980; Ausich and Meyer 1990; Gutschick and Sandberg 1991; Richardson and Ausich 2005; Ettensohn 2004, 2008; Ettensohn et al. 2019, 2022).


In contrast to the Upper Devonian, Lower-Middle Mississippian (Kinderhookian-Meramecian) strata in northeastern Oklahoma are cast as a shallow-water carbonate ramp that yielded to the sediment-starved (during Kinderhookian-Osagean) Ouachita trough (southeast) and proto- Anadarko Basin (southwest; Lane and De Keyser 1980; Gutschick and Sandberg 1983; Godwin and Puckette 2019; Miller et al. 2019b).

Yet the occurrence of partial Bouma sequences inferred from the Sycamore Limestone (southern Oklahoma) (Schwartzapfel 1990; Miller and Cullen 2018; Milad et al. 2020), as well as clinoform geometries inferred from A.J. MCGLANNAN ET AL.

University user subsurface data, led Price et al. ( 2020) to similarly infer a nearby fluvio- deltaic source despite the ubiquitous absence of evidence for proximal feeder systems.

The Transcontinental Arch and the Ozark Dome were the most proximal exposed regions during this time but consist of correlative carbonates with limited exposures of Cambro-Ordovician sandstones and localized occurrences of upper Devonian sandstone-insufficient in volume, grain size, and provenance to supply the enormous amounts of exclusively fine-grained material to surrounding regions (Hills and Kottlowski 1983; Bergstrom and Morey 1985; Adler 1986).


If any of these Devono-Mississippian systems were deltaic, they are uniformly ''headless''-missing all but the distal-most ''prodelta'' fines, even where the shelf margins are preserved.

In other words, there were no deltaic systems located nearby to transport the siliciclastic material basinward. And if the siliciclastic units represent prodelta fines, they exhibit remarkably uniform grain sizes that fail to fine with distance from the inferred deltaic feeders, and exhibit an unusual dearth of true clay (minerals or grain sizes; Figs. 7, 8).


In contrast to the prevalence of fluvio-deltaic inferences for the study units, few have mentioned eolian processes (e.g., Miller and Cullen 2018; Shelley et al. 2019), whilst others make a connection to the origin of chert in correlative units such as the Arkansas Novaculite, the Caballos Novaculite, and cherts of the Osage-Meramecian strata (Banks 1970; Lowe 1976; Cecil 2004, 2018b).


Schieber (2016) explicitly dismissed an eolian origin for these mudrocks based on the presumed need for large erg systems to supply the fines via saltation abrasion of sand; yet eolian abrasion produces very little silt (Swet et al. 2019, 2020; Adams and Soreghan 2020).


Dust-emitting regions today include both low-subtropical latitude arid regions such as dry lakes of the Bodélé Depression that supply diatomaceous-rich dust (West Africa; Washington et al. 2006; Chappell et al. 2008) and the Nile Delta plain, which fuels the dunes and loess of the Sinai and Negev (Muhs et al. 2013), as well as high-latitude proglacial and periglacial regions, such as the Copper River delta, which seasonally emits large volumes (60-160 ktons in 2006; Crusius et al. 2011) of glacially ground rock powder over hundreds of kilometers into the Gulf of Alaska (Fig. 11).


In all of these cases, the fines are inherent to, and emanate from, the (subaerially exposed and desiccated) depositional systems, rather than from eolian saltation.


The critical attributes of the Devono-Mississippian mudrocks of Oklahoma and the greater midcontinent include the: 1) fine grain size restricted to very fine silt to very fine sand with little to no clay (mineral or grain size), 2) blanket-like deposition over large regions, including far from paleocoastlines, 3) deposition in both sediment-starved basins as organic- rich and chert-rich mudstone, and in carbonate ramps as dispersed fines, 4) Downloaded from http://pubs.geoscienceworld.org/sepm/jsedres/article-pdf/92/12/1186/5763983/i1938-3681-92-12-1186.pdf by McGill University user ultimate sources in (primarily) the Appalachian orogen, and 5) enigmatic absence of proximal fluvio-deltaic feeder systems.

We propose that delivery of the siliciclastic material to epeiric seas by eolian transport best explains this confluence of attributes.


The predominantly arid and seasonally dry climate in the mostly subtropical latitudes of Devono-Mississippian Laurentia would have favored dust emission.

In this model, the Neoacadian Orogen clastic- wedge system (Catskill delta for the Devonian, and Price-Pocono-Borden delta and Mauch Chunk delta for the Mississippian) then provided the source of the material, which was derived from the orogenic highlands.

Notably, the Catskill delta system drained (at times) regions thought to have been glaciated during the latest Devonian (Brezinski et al. 2008, 2010; Ettensohn et al. 2020a, 2020b), and thus would have delivered a ready and voluminous source of Al-poor rock flour to the alluvial plain, analogous to the Copper River Delta today (Fig. 11).

Although no such glaciation is recorded outside of Gondwanaland for the Mississippian, the subaerial delta plains of the Price-Pocono-Borden-Grainger and Mauch Chunk deltas off the Appalachian Orogen would have supplied abundant silt, analogous to the Nile Delta today.

Mass-balance arguments suggest that only 1% (363 km 3 ) of the total quartz eroded from the Neoacadian orogen and delivered to the Mississippian fluvio-deltaic plains is needed to supply the volume of quartz in the Meramec and Sycamore systems (259 km 3 ) (Supplemental File 5).


The eolian-delivery model holds that austral winter winds along the Appalachian orogen front and Zonda-Foehn-Chinook-like leeward winds translating into easterlies (Figs. 10, 12) deflated these subaerial delta plains and lofted siliciclastic fines for ultimate deposition into the Laurentian epeiric seas and emergent land regions to the (paleo) north and west (Devonian), and (paleo) west (Mississippian) (Figs. 10, 12).

The strength of leeward winds can impose gusty conditions to generate intense dust storms, such as the Zonda winds off the Andes (Norte 2015; Puliafito et al. 2015; Isla et al. 2021), and Chinook winds that rework proglacial strata across the northern Great Plains today (Hugenholtz et al. 2007).

This model also explains the dearth of clay minerals (and thus Al), given the lack of intense chemical weathering expected in this arid climate region and-for the latest Devonian-the source in a proglacial delta system.


This model predicts the occurrence of similarly uniform fines distributed in coeval units across a large region of the Laurentian epeiric sea, as indeed occurs (Figs. 2, 3).

Additionally, it builds on several other examples of eolian-marine units posited for similarly enigmatic, fine-grained (and generally clay-poor) siliciclastic systems that lack fluvio-deltaic compo- nents, such as those of the Permian Delaware Mountain Group (Fischer and Sarnthein 1988), Phosphoria Formation (Carroll et al. 1998), various units of the Permo-Pennsylvanian (Soreghan 1992; Soreghan et al. 2008), and Lower Mississippian units of the Appalachian Basin (Cecil et al. 2018a).

Similarly, it builds on the eolian-dust model for the origin of chert in Paleozoic units of Laurentia proposed by Lowe (1976) and Cecil et al. ( 2018b).


The Woodford Shale consistently exhibits the finest modes of the study units and the highest silica content (Figs. 7, 8).

Its uniquely high silica content is commonly attributed to the influence of biogenic silica from nutrient-rich upwelling (Turner et al. 2016), but this explanation evades the root issue of an ultimate Si source.

Cecil et al. ( 2018b) posited that much Phanerozoic chert was likely sourced by eolian dust; this model seems viable for the Woodford Shale given the paleogeographic context of arid conditions across the midcontinent of Laurentia during the Late Devonian, conducive to the availability and transport of ultrafine siliceous dust.


We propose that the fine detrital modes reflect: 1) the distance to source and 2) the direction of surface wind circulation patterns, given the significant paleogeographic shifts from Late Devonian to Early-Middle Mississippian times (Figs. 10, 12).

During the Late Devonian, Oklahoma and the greater midcontinent seaway were situated west-northwest of the Catskill delta at 308 S. Prevailing winds off the leeward front of the Appalachian orogen would have transported eolian dust in a north and west-northwesterly direction, across the North American midcontinent to Oklahoma and beyond.

The emergent transcontinental arch may have provided temporary storage areas along this path.


Ultimately, the finest fraction accumulated in the Woodford Shale; by the Mississippian, shifts occurred in both the location of the primary source (now the Price-Pocono-Borden-Grainger, the Mauch Chunk deltas, and coastal clastic systems) and the paleolatitude (~158 northward).

By this time, the Price-Pocono-Borden-Grainger and Mauch Chunk deltas were east-southeast of Oklahoma, enabling prevailing easterlies to loft fines from these systems directly into the epeiric seas across the greater midcontinent.

With the northward drift of the Laurentian continent, Southern Hemisphere winter winds off the Appalachian orogen shifted to blow more directly toward the west and west-northwest (less circuitous than those of the Late Devonian).

This shift might explain the elevated grain sizes, higher volume of quartz, and more ''loess-like'' signature in the Meramecian samples of the Meramec and Sycamore (Fig. 8).

Additionally, orographic effects of the greater Appalachian orogen presumably further exacerbated the aridity across Laurentia, leading to enhanced eolian emissions.


Finally, widespread delivery of only minimally (chemically) weathered eolian fines to Devono-Mississippian epeiric seas may have had auxiliary effects on organic carbon in these systems.

The Mississippian (and Upper Devonian) of North America contain anomalously high organic carbon, with the Woodford Shale and correlatives considered world-class source rocks (Ulmishek and Klemme 1990; Kuuskraa 2011; Sonnenberg 2011).

We speculate that this remarkable organic richness might reflect eolian delivery of abundant nutrients (notably iron) to these distal epeiric seas, stimulating productivity and associated preservation of organic carbon, as has been observed in the Bering Sea (Koffman et al. 2021) and suggested in other deep-time, dust-sourced systems (e.g., Carroll et al. 1998; Gabbott et al. 2010; Sur et al. 2015; Abadi et al. 2020).


Siliciclastic silt and mudstone are common throughout Upper Devonian-Lower Mississippian epeiric-sea strata of Laurentia, but no proximal fluvial-deltaic feeder systems exist to explain transport of this material.

Mineralogical data indicate a clear detrital source.

Grain-size modes range from very fine silt to very fine sand, with minimal lateral variation in units across broad swaths of Oklahoma and the greater midcontinent region.

Geochemical signatures are Si-rich and Al-poor, reflective a dearth of clay minerals (particularly Woodford Shale and Sycamore systems).

Detrital-zircon geochronology indicates primary sources in the Appalachian orogen.


Paleogeographic reconstructions show that the midcontinent occupied the subtropical arid belt, in the orographic rain shadow of the growing Appalachian orogen.

Consequent aridity would have promoted dust emission from vast delta plains draining the Appalachian highlands.

Model simulations of surface wind circulations indicate that Southern Hemisphere winter months were conducive for eolian delivery of fine siliciclastic material toward the west and northwest, derived from Appalachian clastic wedges.

Alpine glaciation in these highlands during at least the latest Devonian produced large volumes of physically weathered fines.


Siliciclastic silt of the Upper Devonian Woodford Shale most likely experienced a circuitous eolian trajectory and greater transport distances, resulting in the predominance of very fine silt modes.

In contrast, Mississippian units were directly downwind of deltaic plains of the northern Appalachian system, resulting in generally coarser silt to very fine sand modes.


Coeval units located most distally contain abundant chert, consistent with sourcing from very fine silica dust.

Eolian transport of material that was minimally chemically weathered to Devono-Mississippian epeiric seas likely delivered essential nutrients that stimulated organic productivity in these commonly organic-rich units-a hypothesis that merits further investigation.


Supplemental material is available from the SEPM Data Archive: https:// www.sepm.org/supplemental-materials.


In a recently published paper, McGlannan et al. ( 2022) posit that the detrital silt component of Upper Devonian and Lower Mississippian shales of the North American midcontinent region is of eolian origin and provided essential nutrients that stimulated the organic productivity that is recorded by these shales.

Our initial concern with their study was the fact that McGlannan et al. ( 2022) studied a stratigraphic interval that spans approximately 20 million years (Late Devonian to Early Mississippian), and applied conclusions reached for the Mississippian part of the succession to the underlying Devonian black shales.

This is surprising because it is well known that a drastic sea-level fall occurred at the end of the Devonian and resulted in an unconformity over much of the North American craton [1][2].

As far as we understand from geologic literature, extrapolating the inferred sedimentary dynamics of one stratigraphic interval (Early Mississippian) across a sequence boundary to rocks that were deposited multiple millions of years earlier (Late Devonian) is neither recommended nor considered good practice.

The aim of this discussion is not to disqualify eolian systems in general as a potential source of fines in shale successions.

It is not implausible that there were times and places in the geologic past when they may have contributed to marine or lacustrine shale–mudstone successions.

We are, however, firmly convinced that in the case of the Late Devonian of North America, no compelling case can be made for a significant eolian clastic component to its widely distributed black-shale successions.

Whereas we disagree with the validity of the McGlannan et al. ( 2022) study at several levels, three principal lines of reasoning stand out to demonstrate that their conclusions are flawed and inapplicable to the Late Devonian black shales of North America.

First, petrographic examination of the rocks in question, collected from the same location as samples used by the authors (Woodford samples from McAlister Quarry), as well as samples from the Woodford of the Central Basin Platform (Permian Basin, Texas, samples courtesy of Kitty Milliken) does not support their assumptions about these rocks.

SEM analysis shows these samples to be strongly dominated by microcrystalline quartz (Fig. 1) and indicates that it is highly unlikely that the authors could have succeeded to extract detrital quartz grains from these rocks with the method described (McGlannan et al. 2022), much less using them for grain-size analysis.

The abundant early diagenetic silica cementation that fed on dissolution of radiolarian tests is so ubiquitous in these shales, generating silt- and even sand-size quartz particles [3][4][5] that upon crushing/processing it would inevitably have generated silt- and sand-size quartz particles (Fig. 2).

Petrographic constraints (Fig. 1), however, should readily disabuse a discerning observer from the notion that these particles are in any kind or form detrital in nature.

In our extensive experience with mudstone petrography, the detrital vs. authigenic nature of silt-size quartz grains cannot be determined (as the authors did) via inspection of “smear slides of residues” with a petrographic microscope.

To make that determination requires the resolution offered by scanning electron microscopes (SEM).

Figure 3 shows our Woodford samples from the perspective of scanned cathodoluminescence, illustrating the type and size range of detrital quartz.

The dominant reddish-orange colors of quartz grains suggest derivation from the same low-grade metamorphic source, the Acadian orogeny, presumed for Late Devonian black shales farther east [6].

The general abundance and size range of detrital quartz from the Woodford is in essence the same as that observed in Upper Devonian black shales of the Illinois Basin ca. 500 km to the east, and in the Williston Basin ca.

900 km to the north (Fig. 3), plausibly suggesting that all three locales received clastics from the Acadian orogen in the east [6].

The lacking differences in size range and source characteristics of detrital quartz (Fig. 3) between samples from Oklahoma and those of time-equivalent Devonian black shales from the Illinois and Williston basins (both far offshore the Acadian source region) supports strong dilution of clastics by early diagenetic silica in the case of the Woodford samples.

No principal difference is observed between these locales.

The randomly oriented phyllosilicate platelets (cardhouse fabric) observed in these rocks (Figs. 1, 4), shielded from compaction by early diagenetic silica cements (Figs. 1, 2), strongly suggests that the detrital component of these shales arrived via bedload transport of flocculated muds, and is quite similar to uncompacted fabrics seen in flume experiments that simulated accumulation of muds from bottom current transported floccules [7][8].

Settling of airborne dust through the water column should produce an entirely different fabric with largely bedding-aligned phyllosilicates [9].

Second, when examined in detail, Devonian organic-rich mudstone successions show rapid shifts of depocenters in response to basin dynamics (e.g., basement fault reactivation [10][11]) as well as accommodation change due to subsidence (tectonic) and sea-level fluctuations.

In the Late Devonian, regional unconformities (sequence boundaries) and their correlative conformities have been recognized and correlated across the United States by various authors [12][13][11].

Yet, these erosional contacts (and implicit hiatuses) are absent from Figure 2 of McGlannan et al. ( 2022), greatly oversimplifying the existing stratigraphic complexities.

Stratigraphic analysis shows that in the Appalachian Basin [14][11], the Illinois Basin [13], and in central Oklahoma [15], Devonian black shale successions invariably onlap onto structural highs (Cincinnati Arch, Nemaha Ridge).

Such distal responses to shoreline progradation and retrogradation imply lateral sediment supply (for example by wind-driven bottom-current systems) from a distal source and require shoreline-attached sediment dispersal systems.

Had eolian sediment dispersal indeed been the key control in the distal realm, stratal units should be expected to drape across positive elements, rather than terminate against them.

The most pervasive chert deposits associated with Late Devonian black shales occur in southern Laurentia, where a proximity to deep ocean waters and a combination of SE Tradewinds with the Ekman spiral provide ideal conditions for upwelling of nutrient-rich waters onto the flooded North American craton [16][17][18][19].

Whereas the influence of this nutrient source likely diminishes away from the cratonic edge [20][21], it is a highly plausible scenario for driving marine productivity in the Anadarko–Arkoma and Permian basins and the Woodford Shale sections sampled by McGlannan et al. ( 2022).

Third, due to the Devonian-age colonization of land masses by trees and other land plants, nutrient supply to shelf and epicontinental seas increased so dramatically [22][23] that eolian sourced nutrients, had they existed, would not have made any difference with regard to the available nutrient supply.

Also, as mentioned above, the very likely existence of upwelling of nutrient-rich deep-sea waters along the southern margin of the Late Devonian inland sea is a much more plausible nutrient source (rather than eolian input) for that part of the Late Devonian black-shale system.

Fundamentally, there is no need, nor any justification, to call on input of eolian clastics as a critical factor for the formation of any of the Late Devonian black-shale successions in North America.

Several studies of geochemical proxies and organic petrography have shown marine–terrestrial co-dependencies linking rapid expansion of land plants, enhanced continental weathering, terrestrial runoff, and nutrient supply from the Acadian borderlands to enhanced primary productivity in the Devonian inland sea [24][22][23][25][26][27].

Collectively, these studies evoke a mental image of coastal plains with thriving forests, unlikely to have facilitated widespread deflation and lofting of siliciclastic fines as postulated by McGlannan et al. ( 2022).

Furthermore, generating copious quantities of dust to fertilize the Devonian inland sea for several millions of years by way of deflation implies a commensurate concentration of sand and formation of eolian dunes [28].

Yet, in spite of a long history of research into Devonian nearshore and onshore deposits of the Appalachian Basin [29][30][31], no eolian deposits have ever been documented.

In summary, the confluence of petrographic constraints, stratigraphic relationships, and global controls thoroughly invalidates the premise of McGlannan et al. ( 2022).

The considered evidence shows a striking mismatch between well documented geologic realities and the “eolian supply” vision proposed by McGlannan et al. ( 2022).


We thank Wilson and Schieber for their discussion, as our paper in Current Ripples presented a new hypothesis, and we welcome tests of that hypothesis.

Current Ripples encourages “provocative papers on sedimentary geology” so we are happy to motivate future research toward advancing knowledge on the Devono-Mississippian of North America.

Through an integration of paleogeography, paleoclimate, grain size, detrital-zircon provenance, geochemistry, and surface-wind models, McGlannan et al. ( 2022) proposed that eolian transport supplied significant siliciclastic material to Devono-Mississippian marine strata of the North American midcontinent [1].

Wilson and Schieber (2024) begin their discussion with the statement that “...extrapolating the inferred sedimentary dynamics of one stratigraphic interval (Early Mississippian) across a sequence boundary to rocks that were deposited multiple millions of years earlier (Late Devonian) is neither recommended nor considered good practice.

” We find this odd, akin to arguing that, e.g., sediment dynamics of glacioeustasy in the Pleistocene cannot apply to glacioeustasy that operated in the Pennsylvanian.

Processes can apply across time, as long as the tenets of uniformity of process (uniformitarianism) are followed.

Wilson and Schieber (2024) then focus on three main arguments to challenge the validity of our hypothesis for the Late Devonian in particular.


Regarding the issue of an authigenic or detrital origin for the silica, we recognize that Schieber and his collaborators have extensive experience with mudstone petrography and petrology, are aware of and respect their work documenting diagenetic silica in mudstones, and indeed acknowledge in our paper the pervasive presence of diagenetic and biogenic silica in the Woodford Shale [1].

For example, Figure 5B in McGlannan et al. ( 2022) illustrates the rhythmic, thin, chert-like beds in the Woodford Shale.

Wilson and Schieber note that they have studied samples from the same sites we studied and found diagenetic silica.

We do not doubt this.

Owing to the common presence of diagenetic silica, we preferentially avoided silica-rich facies and predominantly sampled laminated shale facies.

Wilson and Schieber (2024) suggest that we generated sand- and silt-size particles “upon crushing–processing” but, as we detailed in our paper, “Samples were gently crushed with a ceramic mortar and pestle to pea-size gravel to accelerate chemical reactions, then rinsed with distilled water and sieved at 250 lm to remove any fines generated during crushing” that might be erroneously incorporated in the grain-size analyses.

Of 19 Woodford Shale samples, 12 were selected for particle-size measurement after smear-slide analysis to verify disaggregation and presence of a preponderance of detrital material (Supplemental File 3 and Fig. 6A in McGlannan et al. 2022), which included not only quartz, but minor feldspar and even (rare) accessory minerals such as zircon.

We acknowledge that, despite our efforts to minimize the inclusion of diagenetic silica, some microcrystallized silica may have remained, which would have skewed particle-size results to finer modes.

Additionally, as discussed in our paper, eolian delivery of silica-rich dust likely provided a significant source of Si for biogenic and diagenetic silica in these Devono-Mississippian units—an interpretation that builds on previous work by others, such as Banks (1970), Cecil (2015), and Cecil et al. ( 2018) [2][3][4].

Wilson and Schieber (2024) then address stratigraphic relationships, implying that we believe the Woodford (and correlative black shales) should exhibit a draping geometry owing to hemipelagic settling, but we do not, in any way, dispute a component of bottom-current transport for the redistribution of detrital material in the Woodford Shale or any of its correlative formations.

We absolutely acknowledge the action of submarine redeposition and redistribution.


We mention several times throughout the paper that these sediments were ultimately deposited in the marine environment, but we hypothesize eolian delivery of the material to the marine system.

Once eolian dust hits the surface waters, it is subject to marine processes, especially if accumulation occurs on submarine slopes.

Analogous systems wherein submarine mass flows redistributed eolian-transported fines include the sandstones and siltstones of the Permian Delaware Mountain Group, for example (Fischer and Sarnthein 1988) [5].

We do not doubt the existence of stratigraphic complexities related to basin dynamics and sea-level fluctuations.

Stratigraphic complexities in the Woodford Shale related to relative changes in base level are certainly present in Oklahoma, but the comparison of these complexities to those in the Appalachian foreland basin and the intracratonic Illinois and Michigan basins requires much further stratigraphic study.

During the Late Devonian the Anadarko basin was essentially a wide passive margin facing southward into the Rheic Sea. Although there appears to be an intra-Woodford unconformity at the Devonian–Carboniferous boundary, the continuity in conodont and pollen biozones at the I-35 South outcrop in Oklahoma (Over 1990, 1992; Kondas et al. 2018) and conodont zones at the Wapanucka (Over 1990), and Hass G (Boardman and Puckette 2006; Haywa-Branch and Barrick 1990; Over and Barrick 1990) locations demonstrate that the Upper Woodford unconformity is of a lower magnitude than certain regions in the up-dip interior Illinois basin (Over et al. 2019; Over 2021; Fig. 6 in Lazar and Schieber 2022) [6][7][8][9][10][11][12][13][14].

Yet, the sediments are similar; therefore eolian delivery provides a viable means for transporting sediments beyond the Appalachian system.


In their third major point, Wilson and Schieber (2024) take issue with the hypothesis of eolian nutrient fertilization.

Regarding nutrients supplied to Devonian epeiric seas, we accept the possibility of eutrophication from the expansion of terrestrial land plants, and deep basin upwelling, in addition to eolian dust fertilization.

We did not exclude any mechanism by which nutrients might have reached the marine system.

Rather, we merely suggested dust as a possible carrier of iron, in light of the eolian hypothesis.

We are raising questions and proposing hypotheses to consider.

We do not know how significant dust may (or may not) have been in delivering nutrients (bioavailable iron) across Laurentian epeiric seas during the Late Devonian.

Mechanisms of nutrient delivery are not mutually exclusive.

In other words, sometimes the answer isn’t either/or, but both, or all the above.


Finally, we disagree with Wilson and Schieber’s (2024) argument on the requirement for “a commensurate concentration of sand and formation of eolian dunes” to generate “copious quantities of dust.” As addressed in our paper, whilst much controversy surrounds the claim that sand dunes and eolian processes produce significant dust deposits, both empirical and experimental studies raise questions about the efficacy of sand saltation in producing silt, and this remains a contentious claim.

For example, although sand saltation has been suggested for small loess accumulations such as the Negev in Israel (Crouvi et al. 2008), relatively minimal eolian silt (loess) occurs in the peri-Saharan region, for example (Smalley 1995)—where one might expect large volumes given the vast sand seas [15][16].

Furthermore, recent experimental studies demonstrate the inefficiency of eolian saltation of sand for silt production (Swet et al. 2019, 2020; Adams and Soreghan 2020) [17][18][19].

Consider that the dustiest source on the planet today, the Bodélé Depression, is not sourced by dunes, but by the desiccated-lake deposits of paleolake Megachad (Warren et al. 2007) [20].

Similarly, dust off the Copper River Delta today is sourced by the deflation of glacially generated fines across (dried) floodplains, not sand dunes (Crusius et al. 2011) [21].

We again thank Wilson and Schieber for the opportunity for additional discussion, and for highlighting the need for further petrographic and SEM studies of the Woodford Shale focused specifically on the detrital component.

It is likely we will continue to agree to disagree on several points; even so, we look forward to further testing and revision of our hypothesis that eolian-transported detrital silt and dust contributed to the Woodford Shale, and many of its correlatives.


Since the 1960s, considerable effort has been made to develop a valid, reproducible and standardised orthodontic index.

Occlusal indices such as the Index of Orthodontic Treatment Need (IOTN) [1], and the Dental Aesthetics Index (DAI) [2] have been developed to rank malocclusion according to the level of treatment need.

Occlusal indices can be defined as methods for determining the level of treatment need or the amount of deviation from normal occlusion and can be used for the evaluation of individual patients and populations [3].

Occlusal indices such as the DAI [2] and the IOTN [1] are used to determine the need or priority for orthodontic treatment in epidemiological surveys.


The Dental Aesthetic Index (DAI), adopted by the World Health Organization, evaluates 10 occlusal characteristics: overjet, negative overjet, tooth loss, diastema, anterior open bite, anterior crowding, anterior diastema, width of the anterior irregularities (mandible and maxilla) and antero-posterior spring relationship [2].

The DAI has four stages of malocclusion severity: a score lower than or equal to 25 (no or slight treatment need), a score between 26 and 30 (elective treatment), a score between 31 and 35 (treatment highly desirable) and a score greater than 36 (treatment mandatory) [4].

The IOTN records the need for treatment based on two components: the Dental Health Component (DHC) and the aesthetic component (AC).

The DHC-IOTN consists of a hierarchical scale with five levels: level 1 represents little or no need for treatment and level 5 represents a great need for treatment.

It evaluates the malocclusion by means of five characteristics: tooth loss, overjet, crossbite, displacement of the contact point, and overbite [1].


Thus, despite having a similar goal, the DAI and the DHC-IOTN exhibit differences that determine their ability to predict malocclusion and the need for orthodontic treatment.

Moreover, the IOTN has been described as an index for easy use [1].

In Brazil, the DAI has been used in national epidemiological surveys organised by the Ministry of Health.

Other epidemiological surveys, using DAI or IOTN, were carried out in Brazil [5], Spain [6], India [7] and United States [8].

This study aimed to assess the validity and reproducibility of the DAI and the DHC-IOTN in the identification of orthodontic treatment needs.


This study was carried out between July and October 2009.

The study involved the assessment of a sample of 131 pairs of dental casts selected randomly from the archive of the Specialization Course in Orthodontics at the Faculty of Dentistry, Universidade Federal de Minas Gerais, Brazil.

This archive 3279 contains 198 models of oral cavities of all orthodontic patients from Universidade Federal de Minas Gerais.

Models in inadequate conditions (with fractures in casts) and models of patients who had received previous orthodontic treatment were not included.

The age of participants whose models were included in this study ranged from 12 to 15 years, an age group recommended in studies of occlusal indices by several authors [2,4].

The patients, whose models were evaluated, were at early permanent dentition.


The sample size calculation was performed by considering the 54.3% prevalence of orthodontic treatment need, as measured by the DAI [9], with a confidence interval of 95% and 5% of level of precision.

Approval was obtained from the Ethics Committee in Research of Universidade Federal de Minas Gerais (No 0369.0.203.000-09).


First, to assess reproducibility, 13 models (10% of the total sample set) were examined on two occasions, with an interval of 30 days between examinations, using the two indices proposed in the study.

The reproducibility analysis was carried out before the validation analysis.


After the reproducibility assessment, the 131 study models were examined by the researcher, an expert in orthodontics, to assess the ability of both indices to identify orthodontic treatment need.

The DAI [2,4] and the DHC-IOTN [1] values were classified according to the specific criteria of each index studied.

The recommendations were used to measure the DHC-IOTN in the models [10].

The aesthetic component of the IOTN was not assessed, as it presents poor association with the clinical condition when used in models [11].

The instruments used were those recommended for each index, namely the periodontal probe for the DAI and the proper ruler for the DHC-IOTN.


The gold standard of orthodontic treatment need was determined by three professors who are experts in the area of orthodontics with at least 10 years of clinical experience [12].

They examined the 131 study models separately.

Each model was coded as "no need for orthodontic treatment", "elective orthodontic treatment" or "orthodontic treatment required" based on the clinical evaluation of each one.

Where there was disagreement in the assessment of the models, there was a discussion among the researchers to reach a consensus [12-14].


The DAI scores and degrees of treatment need as determined by the DHC-IOTN were regrouped in a dichotomous manner as follows: "without treatment needs" and "in need of treatment".

The DAI scores were dichotomised as "no need for treatment" (DAI ≤ 25) and "in need of treatment" (DAI > 25).

The DHC-IOTN is subdivided into three stages of severity according to the need for treatment.

Grade 1 (none) and 2 (little) were considered as not requiring treatment, while Grades 3 (moderate need), 4 (great need) and 5 (very great need) were considered as in need of treatment [1] In the same way, the gold standard evaluation was dichotomised as follows: "without treatment needs" and "in need of treatment".

The latter category included "elective orthodontic treatment" and "orthodontic treatment required".

The time needed to evaluate the indices was measured by a digital stopwatch by the same researcher who evaluated the models.


To assess the reproducibility of the original DAI and DHC-IOTN values, the average estimate of the intraclass correlation coefficient for agreement (95% IC) was calculated.

The Cohen Kappa coefficient (95% IC) and the Prevalence-adjusted bias-adjusted Kappa (PABAK) [15] were calculated to measure agreement between the dichotomised DAI and DHC-IOTN.

An evaluation of the normality of the variable "time" was conducted using the Kolmogorov-Smirnov test.

The comparison between time needed to evaluate the indices was done by the Wilcoxon test.

The significance level was set at 5% for all analyses.

The validation of the indices was done by calculating sensitivity, specificity, positive predictive value, negative predictive value and accuracy (area under the receiving-operating characteristic curve [ROC curve]).

An optimum cutoff point for each of the indexes was determined by plotting ROC curves.