Old dog, new tricks?

It's the end of another perfectly good summer, and what do you have to show for it? Hopefully some good research data and a pounding hangover. For me, the summer research has been quite a learning experience.

You might recall that my original objective in the lab was to extract all of the phytoliths (more generically called biogenic silica or BSi) from my Nebraska soil samples. This hasn't changed, but the methods have. The old BSi extraction method follows that used by Piperno (2006). It is called the density extraction method, and it goes something like this:

1. Dry and weigh your sample.


2. Add hydrogen peroxide (H202) and hydrochloric acid (HCl) to remove organics and carbonates, respectively.


3. Add sodium metaphosphate to deflocculate the sample.


4. Wet seive the sample through a 53 micron seive to remove larger particles. [There are phytoliths which are larger than 53 microns, but most researchers tend to ignore these, as they are rare.]


5. Perform gravity sedimentation to remove particles smaller than 4 microns. [Again, there are phytoliths and phytolith fragments smaller than 4 microns, but researchers ignore these because it is very difficult to extract them in the density extraction method. Only later did I discover that a large percentage of BSi is actually smaller than 4 microns. This is one of the reasons I ultimately decided to abandon this technique.]


6. Float BSi in heavy liquid, such as sodium polytungstate, set at 2.3 g per cubic cm. Extract with pipette or peristaltic pump.


7. Dry BSi extract and weigh.

There are many reasons why this method should not be used for quantifying total BSi. First, seiving and gravity sedimentation exclude BSi which is >53 or <4>

Based on these drawbacks, I decided to turn elsewhere in my BSi quest. There is another method out there, termed alkali dissolution, which is promising (see my reviews of Jones 1969, Herbauts et al 1994, Saccone 2005). The basic method is as follows:

1. Add H2O2 and HCl to remove organics and carbonates. [This step is recommended by Saccone et al 2005, since it allows for easier dissolution of the silica.]


2. Add sodium carbonate or sodium hydroxide (both alkali solutions) to the sample, and heat for about 5 hours. [Heating and digestion times vary depending upon the researcher.]


3. Once an hour, remove a subsample of the supernatant and run it through the spectrophotometer to determine the dissolved silica concentration.

As time progresses in the digestion process, the concentration of dissolved silica will increase. If this were a perfect world, all of the dissolved silica would be from BSi. But unfortunately silica is a very common mineral, and can be found in many different inorganic forms, such as quartz, allophane, feldspars, aluminosilicates, and many others. These inorganic silicates usually have a lower dissolution rate than BSi. Moreover, the inorganics tend to dissolve at a linear rate through time. BSi, on the other hand, tends to dissolve quickly within the first two hours. This is shown graphically above (this graph is taken from DeMaster 1981). The x-axis is time, while the y-axis is the concentration of dissolved silica. Thus, the graph shows the increase of dissolved silica through time. As you can see, there is a large increase in silica in the first two hours, meaning that something is dissolving quickly (duh, right?). After two hours, the dissolution slows down and progresses at a linear rate. With this graph, DeMaster was able to estimate accurately the amount of BSi. Up to that point, nobody had been able to do this, since dissolved silica is all the same, regardless of the source. But DeMaster worked off of a few assumptions. First, that the BSi would dissolve quickly. Second, the inorganic silicates are going to be dissolving throughout the whole process, even in the beginning. But since the silicates dissolve at a linear and predictable rate, it was easy to determine how much dissoved silica was added from them. All one needs to do is follow the flat part of the curve back to time zero. All of the silica added below this extrapolation would presumably be from the BSi, while all that above the line would be from silicates.

This is a novel approach to determining total BSi, and quite a few researchers have used it, in both soil and aquatic sciences. However, it is not without its drawbacks. The DeMaster graph works best when there are very few inorganic silicate mineral types present. The graph above illustrates the dissolution of only one silicate. But what happens when there are multiple silicates, as is the case in soils?

This is where accuracy can take a back seat to averaging. In the case of soils, one must look at the dissolution curve and try to find the average silicate dissolution curve (hopefully there will be one dominant silicate which controls the curve). There are other options worth looking into as well. First, I wonder if it would be possible to document all of the silicates in a sample, and their relative proportions. If the dissolution rates are known for each silicate, then maybe it would be possible to get more accurate results. I guess all you would really need to know is the most reactive silicate, right after the BSi.

Second, aquatic researchers have made use of silicon-aluminum ratios (Si/Al) to estimate the amount of silica added from silicates. Koning et al (2002) dissolved various aquatic samples in sodium carbonate, and simultaneously measured dissolved silica and aluminum concentrations through time. The underlying idea is that BSi has a very high Si/Al (meaning it has very little Al), while silicates tend to have much lower Si/Al (alot more Al). As with the DeMaster graph above, Koning et al. displayed the change in dissolved silica through time. But they also added the change in dissolved Al as well. Armed with this type of graph and some really scary looking equations, they were able to differentiate BSi from up to three different types of silicates in a sample. The researchers were trying to show that they could identify individual silicates in their samples; they really didn't care about the BSi. But Si/Al ratios can still be used to quantify the total BSi: one simply needs to find the shift from high to low Si/Al ratios in the dissolution curve. It doesn't matter how many silicates are in the sample, so long as they all have lower Si/Al ratios than the BSi.

A third possible method is the use of stable isotopes. Derry et al. (2005) and Kurtz et al. (2002) looked at the ratio of germanium to silica in soils (see my earlier reviews of these articles). In a nutshell, higher Ge/Si indicates a more weathered soil. While I'm not concerned about soil weathering, I am interested in the Ge/Si of soils, because it may be vastly different than that found in BSi. There is no study that I am aware of which has documented the Ge/Si in BSi. Combined with Si/Al, Ge/Si could be a valuable tool. It's only drawback would be the expense.

So all in all, that is what I've been working on this summer. Joe and I have ordered the supplies to begin the dissolution extraction method. A few of the supplies are on backorder, so here I sit and wait. And blog.

Review of Derry et al. (2005)

Derry, L.A.; Kurtz, A.C.; Ziegler, K.; Chadwick, O.A. 2005. Biologic control of terrestrial silica cycling and export to watersheds. Nature 433.

Background and problem statement. Germanium/silicon (Ge/Si) ratios in streams are always lower than the primary bedrock from which they drain. Kurtz et al. (2002) found that the excess Ge is stored in secondary minerals. Therefore, higher Ge/Si values should be found in older soils. However, dissolved Ge and Si in rivers can come from both primary and secondary minerals, which can skew the interpretation. Primary minerals will export more Si to streams than secondary minerals. Thus, these primary minerals should have a high dissolved total Si (termed [Si]) and a low Ge/Si. Conversely, secondary minerals (clays) should have a low [Si] and a high Ge/Si. The authors term this the Murnane, Stallard, Froelich (MSF) model, after the authors who proposed the idea. This model can be seen in Fig. 1, or in my recreation above. Since the authors are concerned with two sources of Si in the study streams (primary vs. secondary mineral weathering), they have termed the high Ge/Si and low [Si] (derived from clays; old soils) sources as component 2. Low Ge/Si and high [Si] (derived from primary minerals) sources are termed component 1. Thus, armed with the two measurements and the hyperbolic curve in the figure below, it is possible to ascertain what the percentage of each component is present in the stream.

Despite this novel approach, the MSF model does not consider the role of biologic processes in the Si cycle. It assumes direct control by mineral weathering. [Si] should be controlled by the amount of weathering and Si export to streams. Likewise, Ge/Si should be controlled by the state of the weathering: older soils with more secondary minerals such as clays will be Ge enriched (which means a higher Ge/Si). But the ultimate pathway which Si takes before it reaches the stream may be highly influenced by vegetation. Plant uptake of DSi, precipitation of Si as phytoliths, and subsequent deposition of the phytoliths back into the soil may ultimately control the rate of Si dissolution and export. Further, a sizable amount of Si may be stored in the soil as phytoliths.

Goals. “To test the predictions of the MSF model and to investigate the impact of biogenic silica cycling on stream export.”

Study area. A chronosequence of Hawaiian streams.

Methods. The authors use two measurements: Ge/Si and total DSi [Si]. The authors recognized that DSi in the streams must be of two components (see above). The reason for this is simple: the Ge/Si value from fresh basalts is ~0.2 x 10-6 mol/mol. But the Ge/Si values recorded for the Hawaiian streams did not agree with the basalt Ge/Si. In fact, a mixing was recorded: Ge/Si of 0.2 x 10-6 mol/mol and [Si] >600 µM for component 1; and a Ge/Si of 2.6 x 10-6 mol/mol and [Si] ≤25 µM for component 2. As can be seen on the graph above, these components don’t quite fit together. The MSF model predicts that component 1 must be originating from bedrock weathering at the soil-regolith interface or from young soils which aren’t yet depleted of Si. On the other hand, component 2 should be originating from weathered soils, where the dissolution of clays and secondary minerals is dominant.

To test the MSF model, the authors recorded Ge/Si and [Si] in a chronosequence of Hawaiian streams. In other words, stream 1 drained a young watershed, stream 2 drained an older one, and so on. The chronosequence ranged from 0.3 to 4100 kyr (see Kurtz et al. 2002). The soils in the young watersheds have low Ge/Si and high [Si], as expected. Soil water solutions were extracted, and tested for Ge and Si.

Results. At sites older than 20 kyr, something strange was noted. Below 15 cm, DSi concentrations range from 6 to 45 µM, which is expected. The streams had concentrations of 100 µM or more, which is in contradiction of the MSF model. However, the top 15 cm of the soil did have roughly equal DSi concentrations as the streams. Thus, most of the DSi in the old soils is found in the topsoil, even though DSi is easily leached. Other studies had found this oddity as well, but had attributed the high Si values in the topsoil to dust import.

At the young sites (0.3 kyr), DSi is high: 200-600 µM. This is expected from young soils. However, the DSi in the topsoil is extremely high; in some soils it approaches the point of DSi saturation. Ge/Si values are lowest in these young topsoils. As with the old soils, something seems to be pumping Si into the topsoil.

At the old sites, the soils below 15 cm are in agreement with the MSF model as component 2. At the young sites, below 15 cm, Ge/Si is too high and does not agree with that found in streams. To put this another way, the lower soils of the old sites are in agreement with the MSF, but the upper parts have too much [Si] (Si enriched). At the young sites, the upper soils are in agreement, but the lower soils have abnormally high Ge/Si (Si depleted). The young soils are only in agreement with the MSF model in the upper layers.

These high [Si] and low Ge/Si findings in the upper soils at all sites are the result of phytolith entrainment. Next, the authors apply their data to form the mixing model (% component 1, % component 2), which I’ll skip.

As one may guess, the amount of Si exported to streams cannot exceed the supply in a long term fashion. The authors found an export of DSi at 150-5400 mol ha-1 yr-1 in the streams. In the upper zones of the soils, they found an export 400-9400 mol ha-1 yr-1. There seems to be a large surplus, so where is all the excess going? The authors propose that there is a rapid cycling of Si in the upper soils. Namely, any excess which is not exported to streams will probably be dissolved and taken up by plants: internal cycling.

Discussion. These data suggest that most Si in streams has passed through the internal vegetation Si cycle. This means that Si directly from mineral weathering passing into streams is only of a minor constituent. The authors go on to suggest that the phytoliths and other BSi is much more prone to dissolution than primary and secondary minerals.

Tropical humid soils are usually Si depleted, meaning that most of the Si is trapped in the vegetation. Any which is deposited in the soil is rapidly recycled. The high amount of [Si] in the upper soils acts as a buffer to toxic Al levels.

Conclusions. This is one of a series of papers which came out in 2005 detailing the biologic control of plants on Si.

Review of Kurtz et al. (2002)

Kurtz, A.C.; Derry, L.A.; Chadwick, O.A. 2002. Germanium-silicon fractionation in the weathering environment. Geochimica et Cosmochimica Acta 66, 9.

Problem statement. Trace elements can be a useful aid in understanding soil weathering processes. There is a need to better understand Germanium behavior and Ge/Si fractionation. How are the two related? Also, previous studies have identified a low Ge/Si ratio in streams (Mortlock & Froelich 1987; Murnane and Stallard 1990). It was known that Si exports to streams, but it was not known where the Ge was going.

Goals. Attempt to understand Ge behavior in the soil weathering environment and Ge/Si fractionation. Where is the extra Ge going?

Study area and background. A sequence of lava flows in Hawaii, ranging from 0.3 ka to 4100 ka. What happens to the Ge/Si as the lavas and soils eventually weather? Ge is a pseudoisotope of Si (Azam and Volcani 1981), meaning it can substitute readily for Si. Ge is a trace element, at ~1 ppm in rocks. As the rocks weather, Ge/Si fractionation occurs. Fractionation can also occur as soils weather. Si is reapidly depleted by weathering, and Al is easily leached (Fig. 2). By 20 kyr, primary minerals have weathered away, replaced by noncrystallines such as allophone. These slowly recrystalize over >1 Ma, forming secondary kaolin (a fine white clay formed from the weathering of aluminous materials) and crystalline sesquioxides (3 oxygens, such as alumina). Kaolin and sesquioxides dominate the older sites (<2 µm fraction). Argillite (clay stone) is present below. Saprolites (soft, disintegrated rock) can also be present.

Methods. Measured Ge with ICP-MS (Mortlock & Froelich 1996). A lot of good detail in the methods section.

Results. According to table 3, the Ge/Si ratio increases with weathering. Fig. 3: scatterplot of SiO2 vs. Ge. This is not showing the ratio; rather it shows that Ge abundance is positively related to SiO2 concentration. Conversely, there seems to be an inverse relation between Ge and Fe2O2.

All the older soils tend to have Ge/Si near 10, but the young soils are around 3. The young soils tend to be Si enriched, while the older soils tend to be Ge enriched (relative to Si, but Ge concentration is much lower than in young soils).

The authors next set out to figure out why Ge seems to concentrate in older soils. They had three plausible explanations: precipitation of secondary aluminosilicate clays, Fe oxides, and the accumulation of organic matter. Three chemical extractions from the soils were performed. First was AOD, which presumably extracted Ge from noncrystalline aluminosilicates and Fe-Al sesquioxides. Second, DC extracted Ge from crystalline Fe- and Al-sesquioxides. Third, NaOH extracted Ge from kaolin and biogenic opal. Ge extractions from these steps would presumably tell the researchers what the proportion of Ge was for each step. I won’t go into too much detail on the numbers. What they did find was that Ge concentration seems to increase with weathering, up to a point. Eventually as deep weathering continues, Ge will decrease as well. Thus, it seems that Ge is enriched for a while, but then progressively drops off. Figure 4 shows this: the Ge/Si increases for a while, but eventually drops off. It was found that organic accumulation has nothing to do with Ge enrichment. Fe seems to have little to do with it either. Rather, it was found that secondary soil silicate fractions tend to have high Ge/Si fractions.

Discussion. I think this is a novel method to determine the amount of weathering in a soil. For my research, this method could prove useful: since it is very difficult to remove clays from small particles of biogenic Si (BSi), it could be possible to measure the Ge/Si for each sample. That way I could have a reliable estimate of the amount of clay Si input into the sample. Assuming of course, that clays and BSi have differing Ge/Si.

This technique could be coupled with Al/Si. Methods similar to those used in radiogenic isotope geochronology could be used. Geochronologists will often compare the ratio of a pair of isotopes against another. For example, it is known that 238U has a shorter radioactive half-life than 235U. With time, one would expect the 235U/238U to increase. By itself, this can be a good chronometer. But as they say, two is always better than one. Geochronologists can add another chronometer: 232Th/230Th. Just as with 235U/238U, 232Th/230Th will increase with time. Thus, the two chronometers offer a robust check against one another. In my research, the Ge/Si could be plotted against the Al/Si. Both are potential proxies for soil weathering, but together they can be more reliable.

Conclusion. This study was performed in humid tropical soils. I wonder how the Ge/Si would behave in a temperate semi-arid environment.

Review of Saccone et al. (2005)

Saccone, L.; Conley, D.J.; Sauer, D. 2005. Methodologies for amorphous silica analysis. Journal of Geochemical Exploration 88.

Problem statement. There are many different silicates in the soil Si pool, including BSi. The authors refer to Si which dissolves in a study – including BSi – as amorphous Si (ASi). In the soil sciences, there is no standard technique to measure ASi. Also, density separation + dissolution (Herbauts et al. 1994) is time consuming.

Goals. To develop a universal extraction technique which can be used in terrestrial and aquatic studies, and to test different methods.

Study area. Forest soils from SW Germany, Grassland soils from W USA, and horsetail plants.

Methods. Use of the DeMaster (1981) alkali dissolution technique. Samples are dissolved in 1% Na2CO3 at 85˚C. 30 mg of sample in 40 ml of Na2CO3. Subsamples were taken hourly. There was no mention of excluding certain soil size fractions – check DeMaster (1981). Two analyses were performed: raw vs. pre-cleaned. Pre-cleaning of the samples involved sonicating and digesting with (30%?) H2O2 and 10% HCl (suggested by Mortlock & Froelich 1989). Molybdate blue method. Also test other dissolution methods.

Results. A rapid digestion of phytoliths were observed within the first 3 hours (Fig. 2) – similar to that of diatoms (DeMaster 1981). Pre-cleaned samples show a much larger Si concentration (Fig. 3). Other methods yielded significantly less SiO2.

Benefits and limitations. This study is the first to show that pre-cleaning is important. Does not report if pre-cleaning effects the ASi chemically. Does pre-cleaning make other silicates in the sample more prone to dissolution as well?

Conclusions. Pre-cleaning removes authigenic (a constituent of rock) aluminosilicate phases, allowing ASi to be more readily dissolved.

Review of Herbauts et al. (1994)

Herbauts, J.; Dehalu, F.-A.; Gruber, W. 1994. Quantitative determination of plant opal content in soils, using a combined method of heavy liquid separation and alkali dissolution. European Journal of Soil Science 45.

Problem statement. A previous study (Herbauts et al. 1990) had found that the alkali dissolution technique of Jones (1969) did not account for all forms of silicate dissolution. While Jones (1969) did measure silicate dissolution from quartz, the study failed to recognize Si contributions from other forms, namely feldspars and mica among others.

Goals. To introduce and test a new alkali dissolution technique, which accounts for other forms of silicate dissolution.

Study area. Soil samples from the Belgian Ardennes (low phytolith content) and savannah soils from east, central, and west tropical Africa (high phytolith content).

Methods. Four steps: isolate the 20-50 µm fraction from the samples; density separation of the phytolith content (which presumably includes at least some silicates); dissolution of phytolith extracts in hot alkali solution; and determination of Si content via atomic absorption spectrometry (AAS). The 20-50 µm was isolated by sieving and gravity sedimentation. The authors justify this decision by stating that soil fractions <20 µm pose certain problems when attempting to isolate the phytoliths by density separation. Namely, these small particles tend to “clump” together. Also, fine silt and clay particles tend to readily float in density separation due to their high surface area relative to their volume. Phytoliths were floated in ZnBr2. 0.5-2.0 g of digested, sieved and gravity sedimented sample was used. The standard specific gravity is set at 2.3 g cm-3, but the authors set their heavy liquid at 1.92 g cm-3. The authors claim that, for their samples at least, that the phytoliths still float at this density. They claim that this further reducing the contamination hazard from other particles. Floating phytoliths were removed using a peristaltic pump. Herbauts et al. (1990) found that five floating rounds were required to remove 95% of phytoliths. The authors then filtered the phytolith extract through 5.0 µm pore size filters. The phytolith extract and the filter were then placed in a PTFE-lined pressure vessel with 15 cm3 0.5 M NaOH and placed overnight in an oven at 150˚C. Si concentrations achieved by this method was then compared to the density separation technique via weighing and microscope counting.

Results. In the Jones (1969) paper, that author found that 3.26 mg SiO2 must be taken out of the final SiO2 concentration due to partial quartz dissolution. In the Herbauts et al. (1994) study, those authors point out that this correction factor is quite large when compared to the actual phytolith Si amount (40-400%). Thus, a standard correction factor may not be suitable for all soils. Further, the Jones et al. (1969) paper did not take into account other silicates such as feldspars and micas; both of which can be highly weatherable and occur in large quantities.

To determine whether these factors are significant, the authors tested the dissolution technique on quartz separates. According to table 3, quartz does not dissolve at 105˚C. It dissolves slightly at 120˚C, and more at higher temperatures. Thus, if an NaOH dissolution technique were carried out at temperatures below 105˚C, no quartz should dissolve.

The authors next tested the mineral separate (i.e. the residue left over after the phytoliths had been extracted) to determine if other silicates were dissolving. According to Fig. 1, both Si and Al concentrations increased with time. Dissolution was undertaken at both 60˚C and 105˚C. This makes clear that mineral dissolution of aluminosilicates does occur, even when steps are taken to avoid it. The authors then conclude that the only way around this is to perform a density separation technique to extract the phytoliths prior to NaOH dissolution.

Dissolution of the phytolith extracts showed that almost all of the phytoliths were dissolved overnight at 150˚C. Fig. 2 shows the dissolution rate curve. Note that the majority of the phytoliths dissolved within one hour: perhaps the other stuff is inorganic??

Following this, the authors reported a highly significant correlation between the first density extraction and the total amount of phytoliths in the sample (r2=0.987). From this, the authors state that successive extraction steps are not necessary; the total phytolith concentration can simply be computed from the first extract (Figs. 2, 4).

The extraction+dissolution technique was found to have a high correlation with extraction+weighing (r2=0.908) and a moderately high correlation with extraction+counting (r2=0.731).

Limitations. As with Jones (1969), only the 20-50 µm soil fraction was used. This effectively makes this technique unacceptable for precise total biogenic Si (BSi) measurements. While the <20 µm fraction is problematic for density separation techniques, I believe the authors should have pointed out that a new technique must be developed to account for this.

The use of a specific gravity of 1.92 g cm-3 is suspect. While it may have worked for their samples, I know from my own experience that it will not work on many soils.

The authors state that there is no way to account for aluminosilicate dissolution in the samples. I would disagree: if the dissolution kinetics were known for each of the aluminosilicates in the sample, correction factors could be introduced on a sample-by-sample basis. The authors do point out that the across-the-board 3.26 mg SiO2 correction factor used by Jones (1969) is too broad, and seem to be suggesting that each sample must be measured for aluminosilicates. I would agree that the extraction+dissolution technique is a novel way around this, but it in truth impractical. A researcher cannot simply ignore BSi found in “inconvenient” soil fractions.

I don’t think I would use only the first phytolith extraction to estimate total phytolith concentration. The authors did use a peristaltic pump, which will aid in precision, but there is simply too much potential error in only using one extraction.

Conclusions. This paper succeeded in highlighting the shortcomings of the Jones (1969) paper. However, instead of producing a new technique which is practical, the authors created a “quick and dirty” method which skirts the true issue: that it is very difficult to completely separate BSi from other silicates (whether by density separation, dissolution, or both). This method could be used in studies where the total phytolith concentration is not the primary focus, such as in reconstructions.

Review of Jones (1969)

Jones, R.L. 1969. Determination of opal in soil by alkali dissolution analysis. Proceedings – Soil Science Society of America 33.


Problem statement. Quantitative estimates of biogenic Si (BSi) have been accomplished by microscope counts or the density separation technique (heavy liquid floatation). Microscope counts can be time consuming and inaccurate. The density separation technique is also time consuming and cannot remove BSi smaller than 5 µm. For these reasons these techniques are better suited for reconstructions in which a quantitative estimate of BSi is not required. Therefore, a new technique is required.

Goals. The authors introduce a new BSi extraction technique which involves boiling the soil sample in hot NaOH. This will actually dissolve the BSi (and some inorganic Si; see explanation below) into solution. The concentration of dissolved Si can then be measured spectroscopically. The goal of this paper is to test this new approach against microscope count and density separation estimates of the same soil samples to which is more accurate.

Study area. Some soils of Illinois (Mollisols and Alfisols).

Methods. 1.000 g of soil per sample (20-50 µm fraction). 100 mL of 0.5 N NaOH. Cook at a rolling boil for 20 minutes. Place an ice-filled beaker on top of the NaOH beaker to act as a condenser. After 20 minutes transfer NaOH beaker to ice bath to stop cooking. Transfer supernatant through #4 filter paper. Rinse sample residue and condenser beaker bottom through filter as well. This will ensure that all of the dissolved Si will be kept. Proceed with steps to prepare the sample for spectroscopic measurement (see Jones & Dreher: Silicon determination by light absorption spectrometry).

Results. The authors measured the solubility of the Si in the sample as it dissolved in ten minute increments up to one hour after immersion. The dissolution rate was linear (Fig. 1), which obeys dissolution kinetics. The authors chose a cooking time of 20 minutes for convenience sake, and presumably because the longer the sample cooked, the more inorganic Si would be acquired.

Compared to the density separation method, the dissolution technique matches up well. Fig. 2 shows r=0.97. The intercept of the scatterplot is 3.52 mg SiO2, which the authors say is close to the solubility of quartz. The regression equation is y=2.93x+3.52. For every 1% increase in particulate BSi from the density separation technique, the same sample shows an increase of 2.93 mg of SiO2 by way of the dissolution technique. Thus, a BSi sample of 0% would equal a SiO2 concentration of 3.52 mg: quartz. Indeed, when there is no BSi to be found, then the only SiO2 present must be from other silicates. In this case, the authors state that quartz is the dominant silicate.

Limitations. The authors only process the 20-50 µm fraction of the soil samples. According to Sommer et al. (2006), 18-65% of total BSi concentrations may be smaller than 5 µm. Piperno (2006) states that BSi larger than 50 µm may be significant as well. Jones et al. probably only dissolved the 20-50 µm fraction to be consistent with the other two extraction techniques. For example, density separation typically removes particles smaller than 5 µm because these small particles cannot be removed by differing density. Due to the high surface area and low volume of fine silts and small clays, these particles will typically float along with the BSi. The authors may have also decided to discard the smaller fraction because of concerns of clay dissolution. As stated before, clays have a large surface area and are almost always secondary minerals. For these reasons, clays can be highly reactive. Thus, the authors may have been trying to avoid contamination of their BSi concentration from inorganic clays. The authors also probably removed the larger sand-sized fraction because typically few BSi particles are found in this size range in mid-latitude soils. However, if a researcher is attempting to estimate total BSi, I think every attempt should be made to account for all size ranges, not just the most convenient.

Herbauts et al. (1994) make the point that this study only attempted to minimize Si dissolution from quartz, and ignored Si additions from other silicates such as feldspars and micas. I think this is a valid argument, but the solution offered by Herbauts (density separation followed by dissolution) cannot separate BSi from clays, which are often highly reactive silicates. DeMaster (1981) made the point that BSi will dissolve quickly within the first two hours. Silicates typically dissolve slower and linearly. Thus, it is possible to estimate the amount of BSi present, even if silicates are dissolving (see Fig. 1 in Koning et al. 2002 or Fig. 1 in Saccone et al. 2005). However I am concerned that this technique cannot account for all silicates. This method will only give a “best fit” or average of the silicate dissolution rate. Since there are a myriad of silicates out there, some are bound to have higher dissolution rates than BSi. How does one account for these? What happens if a large proportion of the soil sample is made of highly soluble silicates?

Conclusions. This technique in some ways overcomes some problems associated with density separation. The high solubility of acidic volcanic glass particles will hamper this technique (could be trouble for my research). The dissolution technique is much quicker than density separation and microscope counts.

Review of Blecker et al. (2006)

Blecker, S. W., R. L. McCulley, O. A. Chadwick, and E. F. Kelly. 2006. Biologic cycling of silica across a grassland bioclimosequence. Global Biogeochemical Cycles 20:1.

Problem statement: cycling of Si through biomass in grassland systems remains unknown. This cycling must be measured and be considered in any estimation of Si weathering. Previous studies have examined Si cycling in various forest ecosystems, but never in grasslands. This is somewhat surprising, considering that grasslands are considered to be the highest producers of phytoliths in the world. Also, how does biologic cycling affect mineral Si weathering and export to watersheds?

Goals: To determine if Si stored in biomass varies as a function of climate. To measure how quickly Si is cycled through biomass. To see how much of an impact grasslands have on Si cycling and storage.

Study area: an east-west “bioclimosequence” from W Missouri to NE Colorado. The dominant vegetation transitions from tall-grass prairie in the east to short-grass in the west.

Methods: Soil samples to the base of the C horizon at eight study sites. Soils were described. Water samples were taken to determine dissolved Si. Soil phytoliths were extracted. Plant samples were also taken, and phytoliths were extracted from them.

Results: Biogenic Si was usually highest in the topsoil, and decreased downward. This is similar to organic carbon content. Plants absorb dissolved Si (monosilicic acid) through their roots in the lower topsoil and B horizon. Upon death, the Si is deposited in the upper topsoil as phytoliths. Through time the phytoliths begin to dissolve in the topsoil and leach into the B horizon, where they are re-absorbed by roots. The authors claim that more phytoliths are entrained in short-grass sites, due to lower precipitation, and therefore lower dissolution. This is somewhat counterintuitive, since tall-grass prairie sites have higher overall phytolith production. However, these results agree with other studies, namely Alexandre et al. (1997), which found high dissolution rates in tropical rainforest systems. It seems plausible then, that lower moisture ecosystems could have a higher phytolith residence time in the soil Si pool.

Limitations: While the authors did use a bioclimosequence, in which sites became progressively drier as one heads west, they did not account for many factors which could have dramatically influenced their results. Some of these factors include:
Differing soil types. Some soils may have high Si amounts, while others may have lower. This will certainly affect the biogenic Si turnover rate. For example, a soil which has a large pool of readily dissolvable mineral Si would presumably have higher soil phytolith residence times. On the other hand, an Si depleted soil would be expected to have higher amounts of biogenic Si dissolution. Therefore, differing soil types and Si pools must be accounted for, or at the very least, every attempt must be made to minimize these differences.
Non-compatible temporal resolutions. Some of the sample sites may be transitory; that is, the type of vegetation found at a given site may vary from year to year. For example, any ecologist knows that marginal species habitat zones are the most susceptible to climate change. Ecosystems which may be mixed-grass at the present may have been tall-grass only a few years ago. Since the temporal resolution of phytolith assemblages can be up to 200 years, the phytolith record would not reflect this change. Thus conflicting results would emerge: mixed-grass vegetation coupled with a low soil phytolith pool (which is most likely present due to the past tall-grass vegetation). Therefore, attempts must be made to determine the medium- to long-term vegetation. This can be accomplished from a number of techniques, the least of which would be to analyze the very phytoliths used in this study (although some may see that as circular reasoning).
An arbitrary classification technique (short-, mixed-, and tall-grass prairie). Look at figure 3c, which shows phytolith abundances throughout the soil profiles of the mixed-grass prairie sites. The three sites vary substantially, even though they are supposedly in the same ecosystem. The authors conclude that the mixed-grass sites have the highest soil phytolith entrainment rate based on this graph. While they certainly could be right, I believe more research is necessary before a sound conclusion can be made.
A small data set. Eight study sites were used in this study, which is probably enough. However, a greater number of samples could have been taken from some of the soil profiles. For example, the Wah-Kon-Tah site had only three soil samples taken. I don’t think one should measure the overall phytolith concentration throughout the entire soil profile based on only three data points.
Improper biogenic Si extraction technique. My own research has shown that a large percentage of central Great Plains soil is made up of Oligocene aged volcanic glass. For phytolith analysis, this can be a problem. The standard procedure for the removal of phytoliths from a soil is to place the entire sample in a “heavy” liquid, with a density of 2.3 g cm-1. Most minerals have a density around 2.65 g cm-1, while phytoliths are usually less than 2.3. When the sample and the heavy liquid are centrifuged, the phytoliths will float to the top of the liquid, since they are lighter. The minerals will sink to the bottom. In this way, the phytoliths are effectively separated from the minerals. Unfortunately, volcanic glass tends to be the same density as phytoliths, making the two very difficult to separate. For any study which is attempting to measure the total amount of biogenic Si (phytoliths plus any other Si which has been used by organisms), traditional floating techniques really aren’t appropriate. First, floatation techniques are very time consuming and expensive. Second, it is almost impossible to completely separate minerals from biogenic Si, even if no volcanic glass is present. Third, traditional methods cannot separate small biogenic Si particles (< 5 µm) from clay and fine silt particles. For these reasons, any quantitative study should look to other methods, such as biogenic Si dissolution using NaOH or Na2CO3 (see Saccone 2005).

Conclusions: I think this study is a good first step for understanding the Soil Si cycle in grasslands. However, I believe that a larger data set should be used. Also, more robust attempts should be made to minimize those variables which could influence the results. In other words, sample sites should be as similar as possible, with respect to soil type, elevation, etc.