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the Creative Commons Attribution 4.0 License.
| 22 May 2024
Mixed signals: interpreting mixing patterns of different soil bioturbation processes through luminescence and numerical modelling
Abstract. Soil bioturbation plays a key role in soil functions such as carbon and nutrient cycling. Despite its importance, fundamental knowledge on how different organisms and processes impact the rates and patterns of soil mixing during bioturbation is lacking. However, this information is essential for understanding the effects of bioturbation in present-day soil functions and on long-term soil evolution.
Luminescence, a light-sensitive mineral property, serves as a valuable tracer for soil bioturbation. The luminescence signal resets (bleaches) when a soil particle is exposed to daylight at the soil surface and accumulates when the particle is buried in the soil, acting as a proxy for subsurface residence times. In this study, we compiled three luminescence-based datasets of soil mixing by different biota and compared them to numerical simulations of bioturbation using the soil-landscape evolution model ChronoLorica. The goal was to understand how different mixing processes affect depth profiles of luminescence-based metrics, such as the modal age, width of the age distributions and the fraction of bleached particles.
We focus on two main bioturbation processes: mounding (advective transport of soil material to the surface) and subsurface mixing (diffusive subsurface transport). Each process has a distinct effect on the luminescence metrics, which we summarized in a conceptual diagram to help with qualitative interpretation of luminescence-based depth profiles. A first attempt to derive quantitative information from luminescence datasets through model calibration showed promising results, but also highlighted gaps in data that must be addressed before accurate, quantitative estimates of bioturbation rates and processes are possible.
The new numerical formulations of bioturbation, which are provided in an accompanying modelling tool, provide new possibilities for calibration and more accurate simulation of the processes in soil function and soil evolution models.
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RC1: 'Comment on egusphere-2024-1466', Shannon Mahan, 05 Jul 2024
GENERAL COMMENTS:
The authors have done a commendable job is taking a system that is very complex and reducing it understandable and concise sections with their ChronoLorica modeling. I could not find much to quibble about with the equations and modeling. Sometimes I was unclear about a parameter or decision but overall in the paper I found the murkiness resolved itself upon further reading. The following comments are meant to help refine small points I found need either editing, additional clarification, or deleting.
Overall the conclusions are justified given the discussion and often confusing scatter in the measurements and model data. I look forward to the next round that includes ants and one site with both quartz and feldspar.
SPECIFIC COMMENTS:
There is an over-use of the word “fundamental in the first paragraph of the introduction. I think that the word “fundamental” is perhaps not quite the correct word to describe what is lacking about our knowledge of soil processes or the way the organisms bioturbate. We actually know a lot about the fundamental details of soil (see the endless journals devoted to it such as Soils Systems, European Journal of Soil Science, Soil Advances, etc.) we just don’t know the depth per dominate organism or the depth is highly regional. We also don’t whether mounding or subsurface dominates at any given depth (some being obvious in the top 10 cm). I think the words you are looking for are a “simple tracer" to measure depth of organism effect on soil processes. You actually say it in lines 65. Rewrite to indicate the “fundamental" question is not about soil processes but the depth and type of processes per a known biological agent.
Table 1. I find it interesting that you used quartz for the termites and feldspar for the worms and ants. Was there no situation which you could also have tested the quartz and feldspar at one condition (I personally would have chosen the worms that had both mounding and subsurface mix)? I understand that you are limited by the source geology but there must have been one site/condition you could have tested both? I’m sure I don’t need to iterate to the authors that knowing the bleaching rates of both minerals in one condition site would be extremely useful.
Lines 129-133. Please define soil particles or luminescence particles by some weight or dimension. At this point in the paper the reader has no idea if we are talking cm and mg or cm and g. Just give some idea of the size and weight if possible. Are they really sand sized particles?
Figure 2. In caption or elsewhere clarify that bioturbation from termites, worms, and ants are definitely not limited to the first meter and indeed in termites’ case may be several meters. Obviously, you had to limit the depth for your model but make it clear you do understand the bioturbation processes vary in depth. Justify why you picked 1m or 1.5 m or 2 m.
Line 175. I thought the model depth was 1 meter not 2 meters? Figure 4 shows 1.5 m depth. I have some confusion about the depth of model and measurements. Were measurements made for an entire 2 m to represent both mounding and subsurface? I missed where the mounding ends and subsurface begins. Line 184 indicates a 1 m depth.
I found the descriptions of the model parameters in the text to be actually quite tedious and wished I had a table to refer to that I could quickly see everything described between lines 175-185. In fact such a table would have been more useful than the current Table 2 which I never looked at again. I did look at the model parameters a couple of times to remind myself and flipping back to text was….tedious. Maybe you could put it in this table the depths of the model, subsurface, and mound depths. Just we are all clear and on the same page. Maybe also set up along the lines of what you describe in lines 350-355.TECHNICAL CORRECTIONS:
Wording is awkward here on lines 45-50 “The luminescence signal accumulates over time due to ionizing radiation emitted from radionuclides of elements within the uranium and thorium decay chains, as well as potassium-40, which are present in the soil, and due to cosmic rays”. It makes it sound like cosmic rays control everything. It could be better stated as “The luminescence signal accumulates over time due to ionizing radiation emitted from radionuclides of elements which are present within the soil. The uranium and thorium decay chains, potassium-40, and cosmic rays all contribute varying amounts to luminescence growth in minerals”.
Line 177. Two periods. Delete one.Citation: https://doi.org/10.5194/egusphere-2024-1466-RC1 -
CC1: 'Comment on egusphere-2024-1466', Hao Long, 09 Jul 2024
Publisher’s note: the content of this comment was removed on 11 July 2024 since the comment was posted by mistake.
Citation: https://doi.org/10.5194/egusphere-2024-1466-CC1 -
RC2: 'Comment on egusphere-2024-1466', Hao Long, 11 Jul 2024
General Comments:
This paper aims to differentiate the impacts of various mixing processes, specifically mounding and subsurface mixing, on soil profiles. The authors have compiled three luminescence-based datasets that illustrate soil mixing by different biota and compared them to numerical simulations of bioturbation using ChronoLorica, a soil-landscape evolution model. The research topic is interesting, and the methods and results demonstrate a considerable level of credibility. The paper is well-organized, with a clear logical progression and concise language. It is undoubtedly deserving of publication; however, some modifications are necessary before it can be published.
For lines:
Line 146: Based on the expression of the formula (Eq. 3-5), it seems to me that BTpot represents something akin to the maximum potential disturbance rate, while BT(z) represents the potential disturbance rate at each depth. If that’s the case, it should be clarified in the text.
Line 175: What does "one-dimensional soil profiles" mean? Based on the discussion about “two to three-dimensional settings” in section 4.2, I presume that "one-dimensional" refers to considering only vertical movement of particles in the soil profile. Is that correct? Anyway, adding a sentence or two to clarify this would improve understanding.
Line 177: There is an extra period at the end of the sentence. Please remove one of them.
Line 178: The simulations were conducted using loess-like soil texture. For the simulation itself, such simplification is, of course, reasonable. However, for the calibration as discussed in section 4.3, it might be worth considering simulating it using the soil composition that closely matches the referenced profiles. This modification might also help explain a portion of the deviation observed between the simulated and expected results.
Line 194: How is the "modal age" calculated? Is it similar to the Central Age Model (CAM)? Providing more details on this would be helpful.
Line 205-206: The statement "with a larger contribution ... decrease." compares the overall characteristics among the three profiles. However, the interquartile ranges and the bioturbated fractions vary significantly within each profile, and there is some overlap in the data range among different profiles. This general statement is therefore inaccurate and confusing. It is recommended to modify or completely remove this sentence.
Line 230: It is unclear where the “increase in scatter” is reflected in the data. As the text (L195) states that the interquartile range reflects the width of the distribution, I suppose it’s also a parameter capable of describing the “scatter in the age distribution”. However, as the depth increases, the interquartile range curves for all functions shift to the left (towards younger ages, Figure 4B), and it’s also mentioned in L232-234 that "interquartile ranges… generally decrease down the profile.". Therefore, I am confused what does the “increase in scatter” exactly mean? More explanation is need here.
Line 253: The description of Figure 6A is inaccurate. The statement "termites... show lower modes of ages compared to worms..." is only evident in the lower part of the graph (e.g., the range from 0.6 to 1 on the y-axis). However, in the upper part of the profile, the mode ages of termites are older or comparable to those of worms. It is mentioned in the text that the results of ants were not used for comparison due to calibration issues. Actually, the comparison between termites and worms is also questionable due to significant differences in the age used for calibration.
Line 435: Delete one of the redundant "leading to".
Regarding sections and equations:
Section 4.1: The discussion on the presence of other types of bioturbations is commendable. To strengthen this point, it is suggested to incorporate a bit more information about the geographical and vegetation environment of the referenced luminescence profiles, followed by further discussions on whether or not the three luminescence profiles used in the study are influenced by uprooting or other factors.
More explanations should be made for Eq.6 and 7.
Concerning the soil mixing tracer, I would like to suggest you refer to a recent work on mollisols from China (Zhang et al., 2024, Reconstructing Mollisol Formation Processes Through Quantified Pedoturbation, GRL; https://doi.org/10.1029/2024GL108189)
Regarding graphs and tables:
I noticed a discrepancy in the labeling of figures. The labels in the figures were marked with lowercase letters (a, b, c), while uppercase letters (A, B, C) were used in the captions and references in the text. Please make sure to check the journal requirements and consider unifying the labeling format accordingly.
Figure 6c, please check the x-axis title.
Table 1, please check the spelling of the luminescence method used for ants “IR50IRIRSLe”.
Citation: https://doi.org/10.5194/egusphere-2024-1466-RC2 -
RC3: 'Comment on egusphere-2024-1466', Adrian A Wackett, 02 Aug 2024
GENERAL COMMENTS –
The authors have combined a preexisting soil-landscape evolution model with a new model designed to decipher soil mixing and mounding signals from luminescence depth profiles. They then use their integrated model(s) to estimate total bioturbation rates and partition the relative contributions from mounding vs. subsurface mixing for a set of empirical luminescence datasets where ants, earthworms, and termites are the dominant pedoturbation agents. The scope and depth of the manuscript is reasonable and the text itself is well written, concise, and engaging. I find the research question(s) to be both well motivated and supported by the study design/modeling framework. I believe the manuscript will make a valuable contribution to our emerging understanding of the coupling between soil mixing and soil/landscape development. I have no major issues or concerns with the framing and overall arc of the manuscript – I would mostly like to see the authors more rigorously scrutinize and discuss their model outputs/comparisons and potentially revisit some of the summary statistics/visual outputs that may be obscuring potentially important information about the (experimental or modeled) distributions of interest.
SPECIFIC COMMENTS –
Additional discussion of different species’ functional types: In line 94 the authors state ‘anecic earthworms who both mound and mix the subsurface’, but earlier in the text they introduce anecic earthworms (and earthworms more generally) as a ‘type’ example of subsurface mixing. These statements seem somewhat contradictory. I suggest revising the discussion of earthworms (namely within the introduction, as this is better handled in the discussion) to more clearly delineate how the different earthworm functional types (epigeic, endogeic, anecic, etc., see Marcel Bouche’s original work or Bottinelli et al., 2020 for a more recent (re-)articulation) with different feeding and burrowing behaviors likely contribute variably to advective mounding vs. diffusion. In my experience different earthworm communities/functional types generate very different pedogenic outcomes. For example, purely endogeic species are seemingly the most vigorous subsurface mixers, but their continued casting at the surface may still be viewed as small-scale mounding to some degree? And this type of mounding differs significantly from that of Lumbricus terrestris (nightcrawler) middens. I would also expect similar species-specific interactions and effects for ants, given the divergent behavior between large mound building ants (e.g., Formica rufa) versus subterranean species, for example. I suppose the same goes for termites, too. Which species/functional types were dominant at the sites considered here? Considering that there are many thousands of each of these species, I think a bit more exploration and discussion of these distinct functional niches/behaviors and their documented (or hypothesized) impacts on soil mixing modes and associated luminescence signals would be helpful, both to better interpret the experimental datasets here and to more thoughtfully apply this model elsewhere.
Changing layer/profile depths – constraining the potential importance of volumetric strain & erosion/deposition, aeolian inputs, etc: Perhaps this comment pertains to the (Chrono)Lorica modeling frameowrk more generally, but I am slightly confused as to how (or whether) this model accounts for time-variant volumetric strain/collapse catalyzed by pedoturbation, or any other additions/losses of sediment (i.e., erosion, deposition, aeolian inputs or losses, etc) over the extended soil residence times? I can appreciate that fixing depths and holding BD constant is helpful and likely necessary to reduce model complexity in this context, but volumetric changes at both the individual layer and entire pedon scale are inevitable consequences of both subsurface mixing and mound construction. This seems crucial when attempting to export the coupled ChronoLorica and Mixed Signals models into real landscapes. Would such changes not be expected to significantly alter luminescence signals? What if the entire surface is aggrading or eroding? Some additional discussion of the emergent volume/depth changes resulting from bioturbation and their potential impacts on model outputs/calibration would help tie the modeling framework to real-world landscapes. Similarly, it would be nice to at least mention somewhere the hypothesized role of erosion/deposition, given its omnipresence and critical role in sculpting earth’s surface. It also seems inevitable that grains are continually being introduced into and exported out of these profiles over the timescales of interest, particuarly in the case of mounding (see Wilkinson et al., 2009 and Wackett et al., 2018 for links between ant mounding and sediment export), but this is largely glossed over besides a brief mention of aeolian inputs in the discussion section. I am certainly not suggesting a comprehensive modeling assessment of these different scenarios: some cursory discussion or perhaps a brief sensitivity analysis should suffice. At the very least there should be some more explicit discussion of how these factors are handled herein without forcing readers track down and read the original ChronoLorica publications and/or wade into the supplementary code.
Distribution statistics and accompanying visualizations: These comprise the bulk of my comments, as I have a few questions/concerns about the selection of distribution statistics and would like to see some additional information included in the figures and/or text to more thoroughly explore some of the model comparisons (this same theme applies to several ensuing comments). First, why not display the median instead of the mode in the various depth figures? It appears that at least some of the soil layers display zero-inflated distributions (based on figure 3, which is the only figure that actually shows the sample point distribution – more to this point below), which could heavily bias estimation of the mode. The median should be far less sensitive to such biases. I know that the median is to some degree captured within the IQR, but it still seems like a more robust statistic. Even if there’s some luminescence-specific or other reason to opt for the mode rather than the median, there should be some discussion of this as it would help readers better understand how and why you made the choices you did.
Implicit/unexplored assumption of normal distributions? There is quite a bit of weight placed on the IQR as a robust metric for measuring the width of the distributions. How sensitive is this metric to the type of distribution (i.e. normal, log-normal, Pareto, gamma, etc.)? The experimental datasets shown in Fig 3 seem to span not only a range of distribution widths, but also different distribution types (although this is a bit hard to discern based on the current visualizations, see comment below). Have you done any chi-squared, Kolmogorov-Smirnov (K-S) or other tests to check the data against theoretical distributions or each other? Later in section 4.3 we are left to compare the modeled vs experimental distributions based only on the summary statistics (i.e., IQR and mode). Although QQ-plots would be the most direct and informative way to compare them I can appreciate that trying to show QQ-plots between different depths would be cumbersome… but why not at the very least show the modeled distributions as faded points as was done in Fig 3? Or perhaps opt for some other graphical means that more clearly conveys information about the distribution?
Visualizing the distributions as a function of depth: I am not a luminescence expert so perhaps the current depth plots (i.e., Figs. 3-5, 8) are standard practice and are strongly preferred by the OSL community. I personally found it somewhat difficult to keep track of the IQR vs. mode lines and interpret their respective meanings. Why not show depth-discrete box-and-whisker plots or violin plots? Either of these (particularly violin plots) would simultaneously display the IQR and median while also permitting direct visual assessment of the actual distribution (including outliers, using whatever criteria made sense to set for outlier detection). The data itself could even be plotted as faded circles behind the violin/box-and-whiskers. By just plotting the IQR as the metric of choice to exemplify the distribution width there is quite a lot of additional information being cast aside…
In summary (for the series of comments above) -- The manuscript would generally benefit from more robust statistical comparisons (between different layers, modeled vs. experiment observations, etc.) rather than relying primarily on qualitative statements of difference (higher, lower, larger, etc). As mentioned above, it would be optimal to assess the complete distributions against each other rather than rely on side-by-side comparisons of summary statistics like IQR, mode, etc. Regardless of whether a summary metric or the entire distributions are being compared, such comparisons between depths, experimental vs. modeled grains, etc. should involve some sort of statistical comparison, or at the minimum more direct statements about the magnitude of difference. I’ll use several sentences from the beginning of the ‘Results’ section (first paragraph in section 3.1, lines 204-210) as an example. Here the authors state ‘…the interquartile ranges increase and the bioturbated fractions decrease’, or ‘… clear differences in the bioturbated fraction’. How much do the IQR ranges increase (numerically)? Are these differences statistically significant between adjacent layers? Or just between the top and bottom layers in the profile? Similarly, are there statistics that support the stated ‘clear difference’ between bioturbated fractions? What vector length between the distributions qualifies as a ‘clear difference’? Something simple like an ANOVA F-test could suffice or perhaps (given the seemingly non-normal and discrete distributions) a non-parametric test like K-S or the aptly named Earth Mover’s Distance could be preferable? Some Bayesian approach may work too (I am just less familiar with these myself). Any combination of these (or others not mentioned here) would help offer some numbers to more clearly articulate where the most meaningful differences and comparisons lie. I do not hope for the text to become bogged down by reporting statistical outputs like P-values, etc. and I imagine many or most of these outputs could easily be embedded within a table (residing in either the main text or SI). Also note that I referenced clips from this one paragraph to offer an example, but similar statements throughout the results and discussions sections deserve some additional statistical scrutiny.
Time step sizes and annual mixing rates: It would be nice to add some (even a brief) discussion of the different climates for the 3 case studies and their potential impact(s) on the modeled vs. measured bioturbation rates. It is certainly reasonable to calculate/discuss both the modeled and measured rates per annum, but earthworms in Germany presumably have significantly fewer days to mix soils each year than termites (or any other bioturbating agents) in Ghana, where biota are active year-round. While this is true in nature, my understanding is that the model currently treats ‘time’ identically between the sites, given that all the sites use the same step size (please forgive me if I’m misunderstanding something here), even if they integrate (cumulatively) over different time periods. Over thousands of years the length of this ‘mixing season’ may also shift dramatically at each site, but in a way that should be at least somewhat readily constrained through paleoclimate proxies. I’m thus wondering if some fractional ‘mixing time’ parameter reflecting the proportion of active mixing days/total elapsed time could be readily incorporated into the model? This could then flexibly carry a climate dependency signal that downstream users could easily adapt to their respective needs. My thinking is that a mixing rate of 10 kg m-2 yr-1 is much more impressive in the tundra than the tropics if the mixing agent only has two or three ice-free months to work with… I also wondered whether this could be a potential reason for the offset between the modeled and measured bioturbation rates, although this offset between the modeled vs. measured rates appears consistent irrespective of climate.
Is there a hidden discrepancy between the number of modeled vs the visualized layers? In the ‘Model set-up’ section (line 175) the authors mention that the model simulates 200 soil layers of uniform 1 cm thickness. However, it appears (at least to me) that the ensuing figures don’t depict 200 distinct layers? Are these 1cm layers somehow being aggregated into variably thick horizons across different model runs? Or are they aggregated afterwards for visualization purposes? Some explanation here would be helpful. If they are indeed aggregated in the model (to reflect the layer thicknesses in the empirical dataset, for example), then do you notice any differences in model outputs when holding everything else constant but binning the layers into different thicknesses? I ask because I have used advection-diffusion models for fallout radionuclides where there is a hidden depth dependency baked into the equation… in other words it matters whether you model fluxes in and out of layers that are 1 cm vs. 5 cm vs. 20 cm thick. Such a thickness dependence doesn’t break the model per se, but it would certainly be important to explore and comment on if such a depth dependency is present.
TECHNICAL CORRECTIONS:
Line 64: awkward phrasing here with ‘provide a stronger role of biota…’ Consider changing to ‘better constrain the role of biota’ or similar
Line 288: missing ‘a’ – text should read ‘with only a few luminescent…’
Line 293: Note that the sites in Heimsath et al., 2002 also have significant ant and earthworm-mediated soil mixing and mounding (see Wackett et al., 2018 for discussion about the key role ants play at these sites). I would suggest modifying the wording here to be more inclusive of other mixing agents, as my impression is that root activity/tree throw is a relatively minor contributor relative to other biotic agents like earthworms, ants, wombats, etc.
Lines 304-306: Agreed! Well stated. I’m looking forward to the future work that incorporates additional mixing modes like tree throw/upheaval!
Line 309: another missing ‘a’ and consider swapping ‘measured’ for ‘used’? Text should read ‘where they can be used as a tracer for soil mixing’.
Line 435: delete the extra ‘leading to’
Line 440: either say ‘…due to the overestimation of…’, or alternatively delete ‘the’ so it instead reads ‘…due to overestimating the…’
Citation: https://doi.org/10.5194/egusphere-2024-1466-RC3
Model code and software
Mixed Signals W. Marijn van der Meij https://github.com/MarijnvanderMeij/Mixed-signals_Bioturbation
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