Influence of BSi from terrestrial vegetation on riverine systems and diatom evolution - Masters Thesis - University of Toronto

Influence of Biogenic Silica from Terrestrial Vegetation on
Riverine Systems and Diatom Evolution
by
Beata Opalinska
A thesis submitted in conformity with the requirements
for the degree of Masters of Applied Science
Department of Earth Science
University of Toronto
© Copyright by Beata Opalinska 2014

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ii
Influence of Biogenic Silica from Terrestrial Vegetation on
Riverine Systems and Diatom Evolution
Beata Opalinska
Masters of Applied Science
Department of Earth Sciences
University of Toronto
2014
Abstract
Presently within the scientific literature no terrestrial biogenic silica models exist that compare
by magnitude, processes transporting silica. Change in vegetation type has the potential to alter
dissolved concentrations of Si in rivers and ultimately the oceans. Diatoms greatly depend on Si
concentrations for growth, and as a result land cover change may have influenced onset diatom
radiation during the Cenozoic. To expand our understanding of this cycle, a terrestrial biogenic
silica model is proposed. This model accounts for biogenic silica production, dissolution and
leaching through soils, as well as providing estimates for annual silica soil storage. A case study
performed using the constructed biogenic silica model, showed an increase in oceanic DSi
concentration during the Miocene (period of diatom diversification). However, this increase does
not appear to have been sufficient to trigger global diatom radiation, suggesting multiple
geographically isolated locations for this diversification.

iii
Acknowledgements
Thank you to Professor S. A. Cowling for your assistance and guidance, as well as your ever
constant insight into the Walking Dead series plot. Thank you to my committee, S. Finkelstein,
U.G. Wortmann and C. Mitchell for your constructive input for this thesis. Thanks to Kimsa
Dinh, Katie Schmidt, Anna Phillips, Veronica DiCecco and Sara Rhodes for all your
motivational support and ice-cream/pie breaks. Vasa Lukich for your ability to keep me
entertained when writing was not exciting enough, particularly with your familiarity of tumblr
and Supernatural. Thanks to Gary Vinegrad for inspiring last minute panic and Jessica Arteaga
for skating it out ;). Thanks to the physics gang (Josh Guerrero, Bob Tian, Bruno Opsenica, Sean
Langemeyer and Eric Goldsmith) for overwhelming me with learning new boredgames (ha!) and
fluid mechanics. Thanks to all of the 2013 and 2014 graduate students for your constant
amusement and friendship. Finally, thanks to my family who let me crash with them all of my
life, for those amazing lunches packed by my mom and clothes stolen from my sister.

iv
Table of Contents
List of Tables……………………………………………………………………………………... vii
List of Figures…………………………………………………………………………………….. ix
List of Abbreviations……………………………………………………………………………... xi
List of Appendices……………………………………………………………………………...... xii
Chapter 1 Introduction……………………………………………………………………………. 1
1.1 Terrestrial Sphere……………………………………………………………………... 2
1.1.1 Sources of Terrestrial Biogenic Silica…………………………………..... 3
1.1.2 Benefits of using Biogenic Silica…………………………………………. 4
1.1.3 Soil Silica Storage………………………………………………………… 5
1.1.4 Marine and Terrestrial Silica Dissolution Kinetics……………………….. 7
1.1.5 Ecosystem Mass Balance…………………………………………………. 8
1.2 Aquatic Sphere……………………………………………………………………...... 9
1.2.1 Marine Silica Cycle……………………………………………………….. 9
1.2.2 Diatoms, Frustule Formation and Dissolution……………………………. 10
Chapter 2 Model and Materials…………………………………………………………………… 13
2.1 Terrestrial Biogenic Silica Model……………..……………………………………… 13
2.1.1 Production Reservoir………….…………………………………………. 14
2.1.2 Dissolution Flux………………………….………………………………. 14
2.1.3 Leaching Coefficient...………………………………………..…………. 15
2.2 Model Materials…………………………………………………………………........ 16
2.2.1 Gauge Selection…………………………………………………………… 16
2.2.2 Drainage Area Extraction…………………………………………………. 16
2.2.3 Land Cover………………………………………………………………… 17
2.2.4 Soils……………………………………………………………………….. 17
2.2.5 Precipitation and NPP Data……………………………………………….. 18
Chapter 3 Result and Model Validation…………………………………………………………... 22
3.1 Watershed Characteristics…………………………………………………..………… 22

v
3.1.1 Watershed Productivity…………………………………………………… 22
3.1.2 Precipitation and Discharge…………………………………………......... 23
3.2 Watershed Silica Fluxes…………………………………………………………........ 24
3.2.1 Biogenic Silica Fixation Flux……………………………………...……… 24
3.2.2 Biogenic Silica Dissolution……………………………………………….. 24
3.2.3 Biogenic Silica Storage Reservoir………………...……………...……… 25
3.3 Biogenic Silica Riverine Flux……………………………………….……………….. 26
3.3.1 Leaching………………………………………………………………….. 26
3.3.2 Riverine DBSi Estimation……………………………………………...... 26
3.3.3 Riverine DBSi Seasonal Variation……………………………………….. 27
3.3.4 Soil Influence…………………………………………………………….. 27
3.4 General Trends in Riverine Biogenic Fluxes…………..……………………………. 28
Chapter 4 Discussion……………………………………………………………………………. 37
4.1 Biogenic Si Contributions………………………………………………………....... 37
4.2 Conifer Anomaly……………………………………………………………………. 37
4.3 Phytolith Dissolution……………………………………………………………....... 38
4.3.1 Surface Area Size and Dissolution……………………………………….. 39
4.3.2 Aluminum Induced Reduction in Dissolution…………………………..... 39
4.3.3 Influence of Soil Acidity (pH)….………….……………………..…….... 40
4.4 Soil Silica Transportation to Streams……………………………………………...... 41
4.5 Wetland Silica Retention…………………………………………………………….. 42
4.6 Regional Implications……………………………………………………………….. 43
4.7 Vegetation Influence on River BSi………………………………………………….. 44
4.8 Sources of Errors……………………………………………………………………. 45
Chapter 5 Case Study………………………………………………………………………….... 48
5.1 Global Oceanic Biogenic Silica Input………………………………………………. 48
5.2 Methods……………………………………………………………………………… 49

vi
5.2.1 Oceanic Si Estimation……………………………………………………. 49
5.2.2 Paleo-Land Cover Distribution…………………………………………… 50
5.3 Results and Discussion………………………………………………………………. 50
5.3.1 Eocene to Pliocene Land Cover Change…………………………………. 50
5.3.2 Eocene to Pliocene Oceanic Si Change…………………………………... 51
5.3.3 What this mean for Diatom Radiation……………………………………. 52
5.3.4 What this means for Grasslands as an Instigator…………..……………… 53
5.3.5 Silica Retention…………..………………………………………………. 53
5.4 Global Impact………………………………………………………………………... 54
Chapter 6 Conclusions and Future Direction of TBSi Cycles………..…………………………. 59
References……………………………………………………………………………………….. 61
Appendix I……………………………………………………………………………………….. 76
Appendix II...……………………………………………………………………………………. 86

vii
List of Tables
Table 1: Soil silica concentrations for various soil types, pH values and land covers. SiO2 is
dissolved silica.……………………..…………………………………………………....12
Table 2: Modeled Si fluxes, pools and rate constants for terrestrial systems with respective
calculations and variables. LF is the leaching factor, DF is the dissolution factor, NPP is
net primary productivity, %BSi is percent biogenic silica as net dry weight, SiD is the
average annual DSi, F is the soil water flow, mp is phytolith mass, SSA is the phytolith
specific surface area.………………………………………………………………..……19
Table 3: Phytolith specific surface area (SAA) and mass used for grasslands, wetlands,
coniferous and deciduous forests. …………………….…………………………………19
Table 4: Net primary productivity (NPP) and percentage biogenic silica net dry weight (%BSi)
of total plant weight for four analyzed land cover types………………………………..29
Table 5: Average catchment parametric values used for the calculation of dissolved silica (DSi)
fluxes for the four land cover types analyzed.........……………………………………...29
Table 6: Annual biogenic silica fixation rates from literature for wetland, grassland, coniferous
and deciduous ecosystems.…………………………………………………………..…..30
Table 7: Annual biogenic silica dissolution rates from literature for grassland, coniferous and
deciduous ecosystems.……………………………………………………………......….31
Table 8: Annual biogenic silica soil storage for wetland, grassland, coniferous and deciduous
ecosystems.……………………………………………………………………..………..32
Table 9: Annual dissolved silica flux from wetland, grassland, coniferous and deciduous
ecosystems.…………………………….………………………………………………...33
Table 10: Annual dissolved biogenic silica (DBSi) fluxes estimated using seasonality and
calculated leaching rates, in relation to annual dissolved silica (DSi) flux for four
ecosystems.……………………………………………………………………...……….34

viii
Table 11: Modeled average catchment fluxes for each land cover type analyzed and corresponding
coefficients………………………………………………………………………………………..35
Table 12: Global area coverage (in ha) by various land cover types from the Eocene to
Pliocene…………………………………………………………………………………..56
Table 13: Total global flux of dissolved biogenic silica ( ) to oceans from the Eocene to
Pliocene and resulting oceanic biogenic silica concentrations (Tsi). Using ocean a
volume of 1.3 billion km3
………………………………………………………………..56

ix
List of Figures
Figure 1: Dissolution rates of phytoliths and quartz as a function of pH. Taken from Fraysse et
al., 2006.……………………..…………………………………………………………...12
Figure 2: Schematic of terrestrial biogenic silica model. Boxes in blue reflect dissolved silica,
boxes in white reflect silica in solid state. Dotted boxes refer to dominating processes
which influence the fluxes in the direction of the arrows. SAA refers to specific surface
area………………………………………………………………………………..……...20
Figure 3: Map of the United Sates of American showing point locations of the twenty-six gauges
studied and corresponding land cover. …………………………………………………..21
Figure 4: Depiction of relationship between precipitation, discharge and drainage for the
twenty-six gauges analyzed. Showing decrease in discharge with decrease in drainage
area and precipitation…………………………..……………………...…………………35
Figure 5: Dissolved silica fluxes and annual precipitation relationship between four studied
land cover types depicting leaching coefficients (r2
values). A. Grasslands B.
Wetlands C. Coniferous forests D. Deciduous forests…………………………………...36
Figure 6: The near 1:1 ratio of predicted dissolved biogenic silica flux using leaching coefficients
and seasonal segregation………………………………………………………………....36
Figure 7: A. Depiction of the relationship between Ge/Si ratios during the growing season,
and winter. B. Relationships of 30
Si/28
Si isotopes during the growing and winder
seasons. After White et al., 2012……………..………………………………………….47
Figure 8: Relationship between annual silica production and export. Coniferous regions show
low fixation but high export, while grasslands show the reverse………………………..47
Figure 9: Reconstruction of Eocene (55 Mya) land cover type and distribution. From After and
Ree, 2006……………………………….………………………………………………..57

x
Figure 10: Reconstruction of Oligocene (27 Mya) land cover type and distribution. After Fine
and Ree, 2006 and Lunt et al., 2007…………………………………………………..…57
Figure 11. Reconstruction of Miocene (11 Mya) land cover type and distribution. After Pound et
al., 2011………………………………………………………………………….……….58
Figure 12. Reconstruction of Pliocene (3 Mya) land cover type and distribution. After
Haywood et al., 2004.…………………………………………………..……….……….58

xi
List of Abbreviations
Al: Aluminum
ATP: Adenosine Triphosphate
ASi: Amorphous Silica
TBSC: Biogenic Terrestrial Silica Cycle
DBSi: Dissolved Biogenic Silica
DSi: Dissolved Silica
GCM: Global Climate Model
Ge: Germanium
TBSi: Terrestrial Biogenic Silica
TSC: Terrestrial Silica Cycle
Na: Sodium
NPP: Net Primary Productivity
Si: Silica

xii
List of Appendices
Appendix I Biogenic silica content of vegetation (%DW) belonging to grasslands, wetlands,
coniferous forests and deciduous forests………………………………………………...73
Appendix II Calculated terrestrial BSi model parameters for each catchment…………………..83

1
Chapter 1
1.0 Introduction
The importance of silica in the terrestrial environment has been recognized since the late 1980’s,
but has just recently come of interest for biogeochemists. Prior to the 1980’s and even now, the
role of biogenic silica has been largely excluded from global continental cycles of carbon and
silicon. As a result, our current understanding of the biogenic terrestrial silica cycle (TBSC) is
limited. While several studies have attempted to describe and compare terrestrial biogenic silica
by magnitude, processes transporting silica and fluxes are neither well known nor quantified. In
contrast, the role of lithogenically derived silica has been well established within terrestrial and
marine environments. In nearly all marine silica models lithogenic silica is the only noted source,
and the quantity of published work pertaining to this silica attests to its dominance in this
subject. In order to expand our understanding of TBSCs, and minimize the discrepancy in what
is known between the two sources, a terrestrial silica model is proposed that can be applied for
several vegetation types.
Biogenic silica (BSi) reservoirs in terrestrial environments include living vegetation, soils and
rivers. Generally, these pools are mainly influenced by processes of deposition, dissolution and
leaching. Silica initially enters the biogenic terrestrial cycle as lithogenic silica and is converted
into biogenic forms by vegetation. Upon deposition, silica from vegetation is added to the soil
reservoir and undergoes dissolution (Collin et al., 2012). Changes in vegetation greatly influence
soil silica quantities through litterfall. As vegetation belonging to one land cover type shows
comparable biogenic silica values, land cover classes can be associated with specific production
rates. The dissolution of BSi in soils occurs at a much higher rate than inorganic silica, resulting
in a dissolved biogenic silica (DBSi) reservoir. This dissolved silica leaves the system through
leaching primarily by way of precipitation. Once leached from soils, the DBSi component in
addition to lithogenically derived dissolved silica enters rivers and subsequently oceans. Current
estimates of oceanic biogenic silica contributions are 1.1 Tmol Si year-1
(Treguer and De La
Rocha, 2012). This value is made up of two components, one comprised of freshwater diatom
silica and the other of silica reworked by vegetation. Many studies have found the flux of

2
biogenic silica to oceans to be less than that of lithogenic yet significant, and integration of this
component into marine models would be beneficial. Understanding how this value changes with
changing terrestrial ecology could have severe implications for the amount of silica entering,
circulated and deposited within the marine system.
The applications of a terrestrial Si model are vast and its inclusion to current silica models would
greatly enhance our understanding of biogeochemical cycles; not to mention emphasize the
complex influence terrestrial ecology has on earth systems. One application of this model would
be to estimate global silica concentration changes from one geologic period to another using
change in land cover through deep time and global climate models (GCMs). This information
can be used to predict changes in oceanic biogeochemistry through time. In addition, this model
can inform us on oceanic silica conditions during diatom diversification/radiation events.
Diatoms are the focal organisms that use silica for corporeal functions and in photosynthesizing
provide a large portion of our breathable oxygen.
Due to the potential influence of terrestrial biogenic silica for biogeochemical cycles and
productivity in the oceans, it is necessary to quantify and model this poorly understood system.
In order to do so, research was conducted with objectives as follows:
(1) To create a simple terrestrial biogenic silica model
(2) To determine if land cover influences concentration of silica in riverine settings, and if so
(3) How would land cover changes influence global dissolved silica, and could it be used to
(4) Determine if changes in terrestrial ecology during the Oligocene triggered diatom
evolution/radiation
1.1 Terrestrial Sphere
A fair amount of information is available concerning silica in the terrestrial environment. For
instance, silica concentrations of various plants have been of interest since the late 1970’s, and
can now be compiled into a sizable database (Klein and Geis, 1978; Hodson et al., 2005). In

3
addition, soil silica distribution concentrations are relatively abundant as are dissolved riverine
concentrations (Sommer et al., 2006). Recently, several attempts have been made to construct
mass balance reconstructions of silica for numerous environments, but often not all silica pools
are analyzed. This discrepancy makes our understanding of the terrestrial silica cycle (TSC) still
incomplete. To create a comprehensive model of silica movement, compilation of data from
these sources and quantification of processes is necessary. Silica housed in vegetation is the
primary source of biogenic silica within the terrestrial sphere, eventually deposited into soils
upon plant death. Once in soils, this silica undergoes dissolution, leaching into rivers and
movement into oceans.
1.1.1 Sources of Terrestrial Biogenic Silica
Vegetation, both terrestrial and aquatic, can be divided into two categories based on silica
accumulation. Accumulator species which contain >10 mg g-1
of BSi are considered enriched in
Si. Within angiosperms, species belonging to orders Poales, Saxifragales and Arecales
accumulate some of the highest values of BSi. Bamboo species generally have 13 to 23% BSi,
grasses 2 to 4.7% BSi and rice approximately 2% BSi, however these tend to range broadly
between species (Bezeau et al., 1966l; Collin et al., 2012). Different parts of plants can express
large variations in silica accumulation. For bamboo, approximately 58% of silica can be found in
leaves, 14% in branches, 17% in stems and 10% in roots (Ding et al., 2009). Non-accumulator
species contain <5 mg g-1
of BSi and include the majority of dicots (flowering plants), ferns and
conifers. Representatives from these taxa have very low amounts of biogenic silica in their
vegetative structures, 0.48% BSi in oaks and 0.13% BSi in pines (Geis, 1978; Klein and Geis,
1978). The reasons for variation in accumulation between species can be mainly attributed to the
ability of Si uptake by roots. These variable silica concentrations of plant species can be
averaged providing general biome Si accumulation rates (Carey and Fulweiler, 2012).
The differential use of silica in plants has been found to alleviate many stresses ranging from
predation to maintaining stem rigidity. The expenditure of silica for these functions has been
evolutionarily selected for, leading away from the use of carbon materials (Raven, 1983).
Incorporating silica has been found to be energetically cheaper than other leading structural

4
materials, particularly lignin. Through the use of Si stoichiometry and Si presence in cell walls of
plants, it has been found that only one adenosine triphosphate (ATP) is required per Si
conversion. No further energy is required for metabolism and transport of Si from point of
entrance to precipitation. Thus one molecule of SiO2 can be precipitated in cell walls at the cost
of one ATP (Raven, 1983). In contrast, on a weight basis, the energetic cost of incorporating
lignin is 27 times that of incorporating 1 g of SiO2. When converting into volumes, more
pertinent for rigidity, 1 g of lignin is 20 times more costly and polysaccharides 10 times.
Although silica is more efficient to metabolize, it is not as common a structural material in the
plant kingdom and this is believed to be a result of available SiO2 depletion in soils (Cooke and
Leishman, 2011)
There are two leading mechanisms thought to be responsible for soluble silica uptake by plants.
The two processes include active transport of silicic acid by metabolic processes and passive,
nonselective flow of silicic acid from soil water through transpiration. Following up take, silica
is moved from cortical cells to the xylem, mediated by energy dependent transport processes. In
the xylem, silicic acid polymerizes to form silica gel. This polymerization occurs when silicic
acid concentrations exceed a threshold value of 2 mM (Ma, 2006). In the shoots of plants silicic
acid is further concentrated through transpiration. Silica is deposited as a layer in the space
directly below the cuticle layer in leaves, forming a cuticle Si double layer as seen in Si
accumulator species like rice (Ma, 2006). In the leaf blades two types of silica forms can be
found: silica cells and silica bodies. Silica cells include free floating unbound biogenic silica,
while silica bodies consist of phytoliths and opal which are precipitated forms of silica cells.
1.1.2 Benefits of using Silica
Deposition of silica in both phytolith and unbound forms acts to benefit plants from both biotic
and abiotic stresses. High concentrations of silica in rice, strawberry plants, barley and
muskmelon have been found to supress the effect of fungal disease (Datnoff, et al., 2007; Kanto
et al., 2006; Zeyen, 2002; Fauteux et al., 2011). Two hypotheses are available for explaining this
silica-based resistance to disease. One explanation is that the Si deposited beneath the cuticle
player acts as a physical barrier preventing infiltration of fungal pathogens and making the

5
tissues less susceptible to enzymatic degradation. An alternate explanation is that Si enhances the
production of phytoalexin, an antimicrobial chemical (Ma and Miyake, 2001). Furthermore,
silica accumulation acts to increase abrasiveness of foliage deterring herbivory through tooth
enamel reduction and increases energy required for digestion (Gali-Muhtasib et al., 1992;
Massey et al., 2006; Massey et al., 2007; Garbuzov et al., 2011).
Silica has also been seen to alleviate physical stresses caused by radiation, water stress, and high
winds. The incorporation of silica into stems promotes wall thickening by increasing the size of
vascular bundles, thereby increasing rigidity of stalks preventing irreparable damage (Ma and
Miyake, 2001; Casler and Jung, 2006; Hill and Pickering, 2009). The Si-cuticle double layer that
is formed upon deposition of Si bodies is seen to significantly reduce transpiration allowing Si
accumulating plants to better cope with water-stressed conditions (Ma et al., 2001). The decrease
in transpiration also aids plants grown under saline conditions by blocking the pathway through
which sodium (Na) is absorbed (Yeo et al., 1999). In addition to these abiotic stresses, Si
accumulation also assists with preventing heavy metal toxicity particularly involving manganese,
iron and zinc, but also aluminum. In the case of these metals Si accumulation leads to reduced
uptake, encourages homogenous distribution, and modifies cation binding properties (Okuda and
Takahashi, 1962; Horst and Marschner, 1978; Horst et al., 1999).
1.1.3 Silica Soil Storage
Typically, soils are the main and largest medium in which terrestrial processes facilitate both
chemical and biological interactions. Soil silica goes through a process of formation, deposition,
dissolution and leaching. Because silica is not synthesized by biological processes, vegetation
must accumulate Si from a source and subsequently convert it to useable forms. The soil silica
pool includes two forms of silica, one being mineral and the other amorphous (ASi). From these
two groups, relative contributions are not well quantified making an analysis of this system
challenging. The mineral pool is comprised of two silicate forms, primary minerals which are
inherited from parent materials and secondary minerals that are developed through soil
formation. Crystalline silicates include quartz, plagioclase, clay minerals and feldspar while the
amorphous forms are mainly dominated with phytoliths and biogenic silica converted by plants.

6
Silica concentrations in soils can be seen to vary widely ranging from < 1 to 45% dry weight
(Sommer et al., 2006). Silica inputs into the soil system primary include dust or aeolian materials
and litter fall into topsoils. Soil phytolith distributions vary with depth, often reflecting a
negative asymptotic curve. In several grassland systems, the top 20 cm of soil reflects over 60%
of the total soil phytolith assemblage (Blecker et al., 2006). Translocation also occurs from
surface sources displacing phytoliths further down the soil profile. After rainfall events,
mineralization, increase in acidity and formation of organic compounds in top soils occurs.
Organic compounds react with soil minerals resulting in high concentrations of Si as well as Al
and Fe. This process occurring in the topsoils, where phytolith restitution occurs, results in high
dissolved silica concentrations closer to the surface through the soil profile. However, dissolution
of lithogenic silica leads to an increase of dissolved silica at depth, matching dissolved silica
quantities at the surface (Gerard et al., 2002).
Following deposition and formation, silica in soils undergoes a process of dissolution. Solubility
of quartz and amorphous silica differs greatly, 1.8 to 2 mM Si and 0.10 to 0.25 mM Si
respectively. This is attributed to a higher density of tetrahedral structure in quartz silica and
crystal order (Drees et al., 1989). Within plant species dissolution rates of phytoliths (a part of
ASi) differ based on sorption of Al and other metals (Fe3+
and Zn2+
). For instance, the solubility
of pine phytoliths is several times lower than beech on account of higher Al substitution seen in
pine (Hodson and Evans, 1995). Silica in soils can appear as either silicic acid and/or an ionized
solution [Si(OH)3O-
]. Soil silica concentrations can vary from 0.03 to 0.6 mM (Epstein, 1994). In
the case of acidic podzol soils, clay breakdown can lead to the mobilization of Si increasing
concentrations (Sommer et al., 2006; Frank 1993). Dissolution rates also differ with the presence
or absence of vegetation where rates are lower without plants (Hinsinger et al. 2001).
Vegetation also influences silica concentrations in soils through weathering and absorption.
Terrestrial plants affect silicate mineral weathering through changing soil temperatures,
preventing erosion, altering pH through organic acid production, modifying soil solution
concentrations and water dynamics (Drever, 1993). Although vegetation exerts process both
promoting and hindering weathering, the net influence is to increase weathering. Studies have
found that weathering and nutrient release rates increase by a factor of 2 to 5 with the presence of

7
vegetation (Moulton and Berner 1998, Hinsinger et al. 2001). Silica released during weathering
is recycled and forms a component within soils where DSi is available for plant-uptake. If a
region is characterized by BSi accumulator species then silica in soils is significantly reduced
until deposition of foliage (Meunier et al., 1999). Silica concentrations found in soils are greatly
influenced by overlying plant material, pH and soil type (Table 1). Various soil types are able to
display different Si concentrations even with similar land cover and pH due to the influence of
underlying parent material (Sommer, 2006).
1.1.4 Marine and Terrestrial Silica Dissolution Kinetics
Understanding the dissolution kinetics of biogenic silica is essential as this process will dictate
silica leaving soil systems. Several equations have been derived predicting silica dissolution rates
following the general form given by Lasaga et al. (1984);
∏ (1)
where, is the dissolution rate (mol cm-2
s-1
), k is the rate coefficient of the dissolution reaction,
A is the surface area (cm2
g-1
), Ea is the activation energy, R is the universal gas constant, T is
temperature (K), a is the pH dependent term, and Gr is the Gibbs free energy of reaction.
Following the dissolution reaction, the kinetic energy possessed due to motion varies from 0.09
to 60 mol g-1
h-1
for cool waters and from 0.65 to 450 mol g-1
h-1
for warm waters (Rickert et
al., 2002). General dissolution rates for BSi in both freshwater and marine waters varies from 0.1
to 10.1 mol g-1
h-1
under constant abiotic conditions (Loucaides et al., 2008). Case specific
dissolution models can be viewed in Dove et al. (2007), Loucaides et al. (2008), and Fraysse et
al. (2008, 2009).
Silica dissolution models reveal rates to be greatly influenced by temperature, salinity and pH.
Dissolution of silica appears to occur at a faster rate in waters of higher temperature, and
similarly in environments of higher pH and salinity (Fraysse et al., 2006). A temperature rise
reflects increased energy available to initiate bond breakage from biogenic silica to silicic acid
and water. While an increase in pH leads to increased deprotonation of surface silanol bonds also

8
resulting in bond breakage. The relationship between dissolution and pH can be expressed as a
negative parabolic function with a vertex centered at a pH of 3 to 5 depending on the silica
source (Figure 1). When analyzing phytolith BSi dissolution, the vertex occurs at a pH of 3,
while diatom derived biogenic silica dissolution is at a minimum at a pH of 5 (Greenwood et al.,
2001). As a result, dissolution rates of silica define three regions. For strong acidic solutions (pH
< 3) rates increase with , at 3 ≤ pH ≤ 5 rates are independent of pH, and at pH from 5 to 12
dissolution rates increase. Various studies have shown that dissolution rates of quartz and
amorphous silica increase 50 to 100 times with an increase in alkalinity (Van Cappellen and Qui,
1997; Dove et al., 2007).
1.1.5 Ecosystem Mass Balance
When reviewing literature regarding biogeochemical processes of TSCs, it is evident that there is
a paucity of mass balance reconstructions and no analytical models have been established. Of
ecosystems to be studied grasslands demonstrate the highest Si fixation rate ranging from 166 to
350 kg ha-1
yr-1
(Bartoli, 1983). Comparably, bamboo forests produce large quantities of BSi 97
to 138 kg ha-1
yr-1
(Meunier et al., 1999). Bamboo forests show inflated silica fixation rates a
result of rapid plant growth (averaging 3-10 cm day-1
) (David, 1984). Temperate deciduous and
coniferous forests display some of the lowest fixations rates, 27 kg Si ha-1
yr-1
and 8 kg Si
ha-1
yr-1
, respectively (Carnelli et al., 2001). On an annual basis the amount of Si taken up by
vegetation is equal to or less than Si deposition though litterfall. DSi that is returned to the soil
interface from vegetation can be taken up once again and forms a recoverable component of the
Si mass balance. This recycled component, equivalent to the biomass BSi, does not contribute to
leached DSi in rivers, and in fact delays DSi transport. In one forest site it was estimated that
80% of the DSi export was recycled through a deciduous ecosystem, compared to 20% for a
coniferous forest (Bartoli, 1983). Silica in soils is a function of biomass BSi, where higher
biomass BSi leads to increased soil Si. Ecosystem Si can be seen to range from 50, 000 kg ha-1
in
coniferous forests to 250, 000 kg ha-1
in grasslands (Bartoli, 1983; Blecker et al., 2006).
Terrestrial mass balance calculations of silica reveal that biogeochemical cycling occurring in
forested/grassland ecosystems is considerable. Export from these systems is relatively minute

9
considering the volume of biogenic silica stored in soils, yet the main source of DSi delivered to
oceans. When looking at the balance of silica at a watershed scale, understanding Si pools and
pathways is necessary. In soils, silica goes through recycling by reabsorption via vegetation,
immobilization through plant retention, net deposition and finally leaching. Leaching is the
process by which silica moves through the soil profile, stimulated by precipitation. Several
studies have found that land cover indeed influences DSi concentrations in rivers, but the extent
of this relationship varies between ecosystems. Calculated relative influence factors for land
covers on observed Si fluxes varied between 0.041 for deciduous forests to 0.260 for wetlands
(Carey and Fulweiler, 2012). To further support this claim a study conducted by Song et al.
(2011) revealed that there is a significant difference in SiO2 concentrations for bamboo, mixed
forest and broadleaf watersheds. Concentrations reflected 120 × 10-6
mol L-1
, 40 × 10-6
mol L-1
and 65 × 10-6
mol L-1
for bamboo, mixed forest and broadleaf watersheds respectively.
1.2 Aquatic Sphere
In order to appreciate effects that the TBSC might have on the marine ecosystems, a general
review of silica in the oceans is given. Vegetation can influence marine DSi through mass
production or retention, or have no effect. Ultimately, 7.3 Tmol Si year-1
is exported into ocean
waters, which undergoes intense remineralisation by diatoms and deposition (Treguer and De La
Rocha, 2012).
1.2.1 Marine Silica Cycle
Our current understanding of the marine silica cycle is limited by our lack of knowledge
concerning biogenic silica inputs from rivers. However, our general understanding of other
source fluxes, circulation and deposition into the oceanic sphere is well supported by both
theoretical and physical evidence. Recent riverine estimates of current global biogenic silica
contributions are of 1.1 Tmol Si year-1
, while lithogenic contributions are of 6.2 Tmol Si year-1
(Treguer and De La Rocha, 2012). Silica also enters the marine cycle by means of groundwater,
sea floor weathering, aeolian and hydrothermal processes, adding approximately 3.6 Tmol Si
year-1
(Treguer et al., 1995). Once in the oceans the dissolved amorphous silica is used by
diatoms to synthesize skeletal structures and as a by-product produce biogenic silica. It is

10
estimated that diatoms produce approximately 240 Tmol Si year-1
and resultantly account for
40% of marine primary productivity and 50% of organic carbon burial in marine sediments
(Nelson et al., 1995; Falkowski et al., 2004). Following biogenic silica production,
approximately 105 Tmol Si year-1
leaves surface waters. Of that, 6.3 Tmol Si year-1
is deposited
in costal and abyssal sediments, with the difference in fluxes recycled within the water column.
The overall residence time of silica in the oceans is estimated to be 10,000 years (Treguer and De
La Rocha, 2012), falling between that of nitrogen, < 3,000 years (Sacramento and Gruber, 2006),
and phosphorous, 30,000-50,000 years (Delaney, 1988). This value and the resident time relative
to biological uptake suggest that silica in the oceans is cycled approximately 24 times before
deposition to sea floor sediments (Treguer and De La Rocha, 2012).
1.2.2 Diatoms, Frustule Formation and Dissolution
Ultimately, the silica flux into oceans directly influences primary productivity. Ocean NPP is
highly dependent upon silica concentrations as diatoms which are large oceanic NPP contributors
metabolize silica intended for creating skeletal structures. Numerous studies have shown that the
concentration of silicic acid in aqueous environments acts as a regulating nutrient for diatom
dominance (Jorgensen, 1952). In particular, a study conducted by Egge and Aksnes (1992)
showed that a minimum requirement of 2 of dissolved silicic acid is necessary for diatom
dominance to attain 70% richness. For Cenozoic diatom evolution, this absolute requirement is
thought to have been catalyzed by some event that led to an increase of soluble silica in marine
ecosystems (Rabosky and Sorhannus, 2009). One hypothesized such event is the evolution and
expansion of grasslands that occurred concurrently with diatom radiation. Presently, the use of
silica by diatoms and other siliceous organisms such as sponges and radiolarians, has led oceans
to be ubiquitously undersaturated in silicic acid (Siever, 1991). Diatoms first appear in the fossil
record approximately 185 mya and in abundance 40 mya during the Eocene/Oligocene transition.
Before the evolution of siliceous plankton DSi was relatively abundant in seawaters with
concentrations near saturation. Presently, diatoms have depleted the oceans of Si where
concentrations are generally <10 at the surface and <160 in deep waters (Treguer and De
La Rocha, 2012).

11
The uptake of silicic acid by diatoms can occur through one of many transporter genes
responsible for regulation to maintain supersaturation. Once within the organism, polymerization
of the silica occurs converting monosilicic acid to hydrated amorphous silica (general reaction
SiO2(s) + 2H2O = H4SiO4). This reaction is an overall thermodynamically favourable process.
Silica polymerization occurs within tracellular compartments, called silica deposition vesicles
(SDVs), which are bound by silicalemma converting aqueous silica into solid deposits. Not only
does the SDV play a role in polymerization, but once the silica has been formed, it also acts as a
mold by the cytoskeleton to form the final silicified profiles of frustules. Under silica limited
conditions most diatom species are unable to complete wall formation, inhibiting cell division
and growth. This explains why growth is more rapidly hindered under Si starvation as opposed to
other nutrients.
In addition to silicic acid limits on diatom metabolism, other nutrients and water-atmospheric
conditions, will determine the distribution of diatoms. Current diatom distributions have been
modeled and reflect diatom dominance in high and low latitudes and in equatorial and coastal
upwelling regions (Kamykowski et al., 2002; Gregg and Casey, 2007). Diatoms are typically
found in regions with plentiful nutrients (nitrogen, ammonium and iron), abundant light and in
cooler waters, this is believed to be a result of high maximum growth rates, related to the
efficiency of metabolizing silica (Gregg and Casey, 2007). Although diatoms can be seen to
dominate over other phytoplankton found in these zones, the persistence of diatoms can also be
greatly limited by alkalinity of the water. In waters of high pH, dissolution rates for BSi are
increased, however, whether this negatively influences diatoms through cell wall dissolution, or
positively influences them through regeneration of bioavailable DSi, is unknown (Lewin, 1961;
Ryves et al., 2006; Loucaides et al., 2008).

12
Table 1. Soil silica concentrations for various soil types, pH values and land covers. SiO2 is
dissolved silica.
Soil Type pH
Parent
Material
Land Cover SiO2 (mg g-1
)
Podzol 3.7 – 3.9 Mica schist Coniferous
55 3
Podzol 3.12 – 4.6 Mica schist Deciduous broadleaf
Podzol 2.7 – 3.8 Sandstone Deciduous broadleaf 9 3
Luvisol 3.7 – 4.4 Loess Deciduous broadleaf 12 3
Regosol 7.0 Loess Deciduous broadleaf -
Vertisol 4.4 – 5.1 Claystone Deciduous broadleaf 18 3
Planosol 3.2 – 3.8 Gneiss Coniferous 6 3
Leptosol 7.2 Limestone Deciduous broadleaf -
Chernozem 5 – 8.4 Sedimentary Grassland 22 – 93 3
Histosols 6.5 – 7.5 1
Organic Peat Wetlands 2.3 2
1. Given and Miller, 1985 2. Struyf and Conley, 2009 3. Saccone et al., 2007
Figure 1. Dissolution rates of phytoliths and quartz as a function of pH. Taken from
Fraysse et al., 2006

13
Chapter 2
2.0 Model and Materials
As of yet, no study has attempted to construct a terrestrial plant Si model predicting
concentrations of silica within reservoirs. Terrestrial mass balances are available for various land
covers, but belong to specific vegetation types and environmental conditions. This specificity
makes case generalizations and comparisons between regions challenging. To model how land
cover influences pools and fluxes of the Si cycle, several relationships are described using
theoretical approaches, and quantified using numerous data resources.
2.1 Terrestrial Biogenic Silica Model
The biogenic silica model in this study was constructed to reflect the movement of biogenic
silica through the terrestrial sphere, from phytolith to dissolved forms. The model constructed
emphasizes the transition of plant BSi to DSi within soils through the process of dissolution, and
subsequent leaching. Biogenic silica can be found in four reservoirs within the terrestrial system
(Figure 2). The first reservoir expresses biogenic silica that is found within vegetation, the
production of siliceous materials, which are deposited through litterfall and buried in soils. The
second reservoir of biogenic silica consists of phytoliths that are found within the soil annually
and do not exit the system through dissolution. The third reservoir consists of dissolved biogenic
silica that can be found in soils annually that does not leach from the system. Finally, the fourth
reservoir is made up of leached dissolved riverine biogenic silica that is eventually deposited into
the oceans. Seawater can be considered a fifth reservoir when including marine environments.
The dissolved biogenic flux of silica into rivers can be estimated using the equation;
= LF · DF (NPP · %BSi), (2)
where, LF is the leaching factor, DF is the dissolution factor, NPP is the net primary productivity
(kg ha-1
y-1
) and %BSi is the percent of biogenic silica found in plant tissues. The relationships
and calculations for all reservoir turnover and flux rates can be viewed in Table 2.

14
2.1.1 Production Reservoir
Biogenic silica content data for vegetation was divided into four categories reflecting land cover
classes. Grassland, wetland, coniferous forest and broadleaf deciduous forest classes were
selected to represent broad regional ecosystems analogous to those of the Cenozoic.
Additionally, each of these classes is believed to influence dissolved riverine biogenic silica
concentrations differently. Biogenic silica content was estimated using;
BSipro =NPP · %BSi, (3)
where BSipro is production (kg ha-1
yr-1
), NPP is net primary productivity (kg ha-1
yr-1
) and %BSi
is biogenic silica content in plant tissues as dry weight. A biogenic silica concentration database
was constructed to determine potential silica inputs into the terrestrial cycle (Appendix I). This
database was limited to foliage silica and included only vegetation found within the United
States. The data was collected from and categorized by plants belonging to the four land cover
classes as mg kg-1
and then converted to percentage. To account for silica allocation in structures
not deposited annually (i.e. stems), forest %BSi was weighted to account for 30% of forest NPP
(Litton et al., 2007).
2.1.2 Dissolution Flux
The dissolution flux was calculated for each catchment (Appendix II) irrespective of initial
phytolith biogenic silica stored in soils assuming that the system is not limited by this silica
reservoir. The rate of phytolith dissolution was adapted from the equation developed by Fraysse
et al. (2009);
, (4)
where is the dissolved riverine silica concentration (mol l-1
), Q is the water percolation
through soils (L s-1
), msi is the mass of phytoliths (g), and S is the specific surface area of
phytoliths (cm2
g-1
). Water percolation was estimated using hydraulic conductivities for specific
soil types and integrated per area. The mass and specific surface area of larch, elm, and horsetail
phytoliths were taken from Fraysse et al. (2009), and used to represent conifer and broadleaf

15
deciduous forests and grasslands/wetlands, respectively (Table 3). The dissolution factor as seen
in eqn. (2) is calculated as;
⁄ , (5)
where R is the dissolution flux (kg ha-1
yr-1
) and BSipro is the biogenic silica production flux (kg
ha-1
yr-1
). This constant is dependent upon vegetation class and its inclusion into the model
allows for correction of the amount of silica leaving terrestrial systems.
2.1.3 Leaching Coefficient and Factor
The leaching coefficient which reflects the movement of dissolved silica through soils and into
rivers was calculated using regression analysis of precipitation and DSi concentrations found in
river waters. Precipitation in this case acts as the moving mechanism and medium by which
silica is transported through soils. Dissolved silica concentrations were obtained from the USGS
Water-Quality data set (See 2.2.1 Gauge Selection) and converted into silica fluxes by
incorporating discharge and standardizing by drainage area. This linear relationship suggests a
constant of proportionality for DSi in waters that can be explained by leaching, and not through
diatom production or direct riverine substrate dissolution. The leaching factor in eqn. (1) is
calculated as;
⁄ , (6)
where DSi is dissolved silica (measured in rivers by the USGS) (kg ha-1
yr-1
), R is the dissolution
flux (kg ha-1
yr-1
), and LC is the leaching coefficient, described above. This constant is also
dependent upon vegetation class and further constrains predicted DBSi leaving systems.
Constants were analyzed and compared among and between soil types to discern geologic
influence. Due to limitation of data availability this relationship includes both amorphous and
mineral forms of silica as opposed to solely desired biogenic forms. To distinguish between the
two components dissolved silica data was separated into two periods, one from October to April
and the other from May to August. The October to April silica values represent mostly biogenic
silica inputs; this time period corresponds to the non-growing season when uptake from soils is

16
minimized and organic soil horizons have added material. During these months weathering
processes also decline to a minimum reducing the contribution of mineral silicates. The May to
August period represents a time during which lithogenic silica dominates the DSi flux.
2.2 Model Materials
2.2.1 Gauge Selection
To study the influence of biogenic silica on riverine systems, dissolved silica data was collected
from twenty-six (26) gauges distributed across the U.S (Figure 3). Gauge data was obtained from
the U.S Geological Survey Water Quality Field/Lab sample database as dissolved silica in mg l-1
.
Each gauge represents a minimum of eight monthly observations per year to accurately estimate
annual average riverine dissolved silica content. For several gauges which expressed an
abundance of observations, monthly averages were calculated as well. Gauges were also
constrained by drainage area (1 to 500 sq. mi), proximity to urban developments such as cities,
and period of record (2005 to 2012). The twenty-six selected gauge locations were imported into
ArcGIS and used to predict drainage areas, subsequently used to extract land cover type, soil,
NPP and precipitation data for use in the terrestrial silica model.
2.2.2 Drainage Area Extraction
Once gauges exhibiting desired parameters were selected, they were viewed using the USGS
National Water Information System Map Viewer and exported as an ESRI shapefile and
imported into ArcGIS. To predict the drainage basin extent of each gauge, the hydrology based
spatial analyst toolset within ArcGIS was used. Digital elevation models (DEMs) for this
analysis were obtained from the USGS National Elevation Dataset and collected at 1 arc second
(30 m resolution) in a raster arcgrid format. Using the hydrologic analysis tools and acquired
DEMs, flow accumulation and direction was calculated to delineate watershed area that would
contribute to and influence dissolved silica concentrations at the gauge point locations.
Following drainage extent determination, areas were geographically overlaid with the physical
data files to determine corresponding dominate cover, soil types and average precipitation.

17
2.2.3 Land Cover
For this study four land cover regions; grasslands, wetlands, coniferous forests, and broadleaf
deciduous forests, found within the United States of America were defined. To geographically
select gauges belonging to these cover types; the Land Cover database of North America for the
year 2000 was used. This dataset was generated by Natural Resource Canada and the U.S
Geological Survey for the Global Land Cover 2000 (GLC2000) project, implemented by the
Global Vegetation Monitoring Unit, Joint Research Centre of European Commission. For each
drainage the dominant cover type was determined based on at least 70% drainage area coverage.
The Land Cover database of North America was created using SPOT VEGETATION data for
the growing season in 2000 at a spatial resolution of 1 km. This data was subsequently converted
into a regional land cover product map, consisting of thirty-five (35) land cover classes based on
the modified Natural Vegetation Classification Standard (NVCS) used by the U.S Federal
Geographic Data Committee. To reduce the number of land cover classes, to better suit the needs
of this project, a re-classification was performed and land covers were aggregated based on leaf
type (i.e. broadleaved, needleleaved, grassland, wetland), leaf phylogeny (evergreen vs.
deciduous) and climate (temperate vs. tropical).
2.2.4 Soils
To negate the influence of soil mineral silica in dissolved silica concentrations found in rivers,
catchments with similar soils were compared. To geographically distinguish soil type regions,
the United States Department of Agriculture’s Natural Resources Conservation Service Soil
Survey Geographic (SSURGO) map was used. This data is based on a re-classification of the
FAO United Nations Educational, Scientific and Cultural Organization’s (UNESCO) Soil Map
of the World. The SSURGO map combined with a soil climate map, expressing 12 soil orders
according to Soil Taxonomy at three scales.

18
2.2.5. Precipitation and NPP Data
Precipitation data used to reconstruct leaching rates was obtained from the Advanced Hydrologic
Prediction Service (AHPS) database through the National Oceanic and Atmospheric
Administration (NOAA). Files were extracted as monthly observed shapefiles for years dating
2005 to 2012 and imported into ArcGIS. Precipitation was averaged for drainages corresponding
to the selected twenty-six gauges. The data itself was measured as a 24-hour total summed per
month and is displayed as a grid of points with a spatial resolution of 4 x 4 km.
Net Primary Productivity data was obtained from images produced by NASA’s Earth
Observatory Team using TERRA/MODIS satellite imagery. Monthly values in g C m-2
day-1
were obtained for the twenty-six selected catchments using ArcGIS for years 2005 to 2012. For
catchments expressing DSi data as average annual values, NPP was summed to produce annual
averages. Net primary productivity for catchments that were used to display monthly variation
was shown as a daily sum. Net Primary Productivity satellite imagery for the United States was
used at a 1 km resolution.

19
Table 2. Modeled Si fluxes, pools and rate constants for terrestrial systems with respective
calculations and variables. LF is the leaching factor, DF is the dissolution factor, NPP is net
primary productivity, %BSi is percent biogenic silica as net dry weight, SiD is the average
annual Dsi, F is the soil water flow, mp is phytolith mass, SSA is the phytolith specific
surface area.
Flux Calculation
Silica Flux LF · DF (NPP · % BSi)
Burial Production
BSi Storage Production – Dissolution
Dissolution (SiD · Q) / (mp · SSA)
DBSi Storage Dissolution - Leaching
LC Precip Vs. DSi r2
DF Dissolution / Production
LF (Silica Flux / Dissolution) · LC
Table 3. Phytolith specific surface area (SSA) and mass used for grasslands, wetlands,
coniferous and deciduous forests.
Phytoliths
SSA
(cm2
/g) Mass (g)
Horsetail 928000 0.5
Larch 1950000 0.25
Elm 1210000 0.3

20
Figure 2. Schematic of terrestrial biogenic silica model. Boxes in blue reflect dissolved
silica, boxes in white reflect silica in solid state. Dotted boxes refer to dominating processes
which influence the fluxes in the direction of the arrows. SAA refers to specific surface
area.

21
Figure 3. Map of the United Sates of American showing point locations of the twenty six
gauges studied and corresponding land cover.

22
Chapter 3
3.0 Model and Result Validation
Variables used for the construction of the TBSi cycle were approximated using various methods.
Magnitudes of these parameters from modern natural environments coincide with data
established as boundaries defining land cover types. The use of information derived from
existing systems allows for a unique model, nicely rooted by physical data as opposed to
theoretical. Consequently, justification for the selection of average parametric values correctly
describing a land cover is required and given through support from literature. In addition,
although environmental mass balances of Si are scarce, the fluxes calculated in this study
corroborate well with those determined by other researchers.
3.1 Watershed Characteristics
3.1.1 Watershed Productivity
Annual NPP values, used to estimate the quantity of biogenic silica produced, differ between the
four land cover regions (Table 4). By far wetland drainages expressed the highest NPP,
averaging 6978 ± 453 kg ha-1
y-1
. Although large, this value is supported by productivity studies
of marshes and wetlands which have reported among the highest production rates for terrestrial
ecosystems (Wieder and Lang, 1983; Rocha and Goulden, 2008). Such inflated NPP values are
believed to be attributed to wetland high carbon use efficiency (Lorenzen et al., 2001; Van Iersel,
2003). Second greatest NPP was expressed by grasslands, subsequently deciduous forests and
coniferous forests at 2823 ± 131 kg ha-1
y-1
, 2454 ± 105 kg ha-1
y-1
, and 1404 ± 394. kg ha-1
y-1
,
respectively. Within literature, grassland NPP is seen to fluctuate greatly, ranging from 940 to
4200 kg ha-1
y-1
(Hicke et al., 2002; Scurlock et al., 2002; Blecker et al., 2006). This variation is
greatly influenced by precipitation and temperature, as expected considering the effect these
factors have on the success of grasses (Blecker et al., 2006). This study’s used NPP for
deciduous forests tends to fall on the low side when compared to other studies (Norby et al.,
2002; Milesi et al., 2003), but is still comparable. Calculated coniferous forest NPP is also sound

23
as it is in agreement with values ranging 1000 to 3000 kg ha-1
y-1
, determined for similar
vegetation (Gholz, 1982).
Biogenic silica content also differed among vegetation types found within land cover categories.
Grasslands showed the largest BSi content of 2.30 ± 0.13% of net dry weight. This value is
expected as Poaceae grasses have been found to contain the highest relative shoot Si
concentrations among forty-four other angiosperm clades (Hodson et al., 2005). Silica contents
of wetland vegetation, 1.91 ± 0.21% BSi, reflected values similar to that of other studies as well.
The mosses, horsetails, and aquatic grasses, which comprise this group, can have concentrations
of biogenic silica ranging from 2 to 28% BSi, globally (Schoelynck et al., 2009). These high
%BSi values for both grasslands and wetlands can be attributed to the lowered cost of
metabolizing silica. Conversely, biogenic silica of vegetation from both coniferous and
deciduous forests expressed low values at 0.84 ± 0.19, and 0.54 ± 0.11% BSi, respectively.
These values are also comparable with those from literature (Geis, 1978; Hodson and Sangster,
1999; Hodson et al., 2005).
3.1.2 Precipitation and Discharge
Calculated average annual precipitation from 2005 to 2012 was greatest for catchments
dominated by conifers, averaging 146.78 ± 13.74 cm (Table 5). Average precipitation found was
greater than others measured for coniferous forests in the western states. Precipitation data
obtained from field measurements suggests a range of 35.56 to 83.82 cm for this land cover type,
varying greatly on an annual basis and with local geography (Dodson and Root, 2013). Grassland
drainages expressed the lowest annual precipitation, averaging 62.89 ± 7.59 cm. This low value
is in agreement with biome measures made across the American Great Plains (Blecker et al.,
2006).
Average annual precipitation, discharge and catchment area was used to standardize DSi riverine
concentrations between watersheds. The relationship between discharge, precipitation and
drainage area, established for the twenty-six watersheds (Figure 4), shows a decrease in
discharge with a decrease in precipitation and drainage area. This relationship is expected, and is
a result of the positive linear relationship between drainage area and discharge (Menabde and

24
Sivapalan, 2001) and precipitation and discharge (Knighton, 1998). Gathered data conforming to
this general trend allows for its use in predicting fluxes of DSi.
3.2 Watershed Silica Fluxes
3.2.1 Biogenic Silica Fixation Flux
Estimated biogenic silica production rates, based on NPP and %BSi content of foliage, greatly
vary among vegetation types but are also consistent with values estimated in literature (Table 6).
This study found that wetlands produced, on average 154.56 ± 28.83 kg Si ha-1
y-1
, certainly the
largest fixation rate among the four land cover types. Grasslands were found to produce less Si
than wetlands by half, approximately 65.65 ± 4.09 kg ha-1
y-1
. Coniferous and deciduous
catchments showed the lowest rates, 39.57 ± 13.60 kg ha-1
y-1
, and 44.33 ± 1.91 kg ha-1
y-1
,
respectively. However, because total biogenic silica from forests does not enter the soil system
annually, values were weighted for plant retention. This accounts for silica stored in stems and
other structures that do not constitute annual litterfall. True biogenic silica production of both
coniferous and deciduous catchments is 11.87 ± 4.08 kg ha-1
y-1
, and 13.29 ± 0.57 kg ha-1
y-1
,
respectively. Other studies that independently measured production of these vegetation classes,
predicted fixation rates to be greater than what we see with this model but are still within the
same order of magnitude (Table 6). Variation evident between production rates among studies
can be attributed to annual differences in net primary productivity used for rate estimations.
3.2.2 Biogenic Silica Dissolution Flux
Following BSi production, silica within the terrestrial cycle is subject to the process of
dissolution. Biogenic soil silica dissolution for both wetland and conifer dominated drainages
show the highest rates (Table 7). Coniferous silica exhibits a dissolution rate of 30.46 ± 11.62 kg
Si ha-1
y-1
, and wetland silica, 18.07 ± 5.13 kg Si ha-1
y-1
. Silica originating from grasslands has
the lowest dissolution rate, 4.7 ± 0.59 kg ha-1
y-1
. Grassland silica expressing the lowest
dissolution rate is unexpected considering the following: (1) rates calculated by other studies, (2)
high BSi production and (3) relatively large river DSi present in these catchments. Conversely,
conifer dominated regions expressing the highest dissolution was also unexpected. Low Si
production rates dictate dissolution should be low; however this finding is in agreement with

25
rates determined by other studies. Unfortunately, little data is available concerning dissolution of
biogenic silica in wetland settings, casting uncertainty as to the accuracy of this models
prediction. Deciduous forest dominated catchments express a calculated average dissolution rate
of 8.14 ± 2.13 kg ha-1
y-1
, comparable with the rate determined by Bartoli (1983). The
unexplained differences in dissolution for land cover types can be attributed to several processes
and environmental parameters, ranging from soil aluminum (Al) substitution capacities to
phytolith size.
3.2.3 Biogenic Silica Storage Reservoir
Biogenic silica storage, which is calculated as the difference between BSi in a pool and the BSi
leaving that pool, can be divided into two storage compartments. One portion of this model’s
silica storage reflects Si that does not undergo transformation, a consequence of low dissolution
rates. Wetlands have the greatest storage of solid biogenic silica, 136.49 ± 33.90 kg ha-1
y-1
,
likely attributed to high wetland NPP, plant %BSi, and moderate dissolution. Grasslands and
deciduous forests reflect storage pools of 60.59 ± 3.96 kg ha-1
y-1
and 5.15 ± 1.97 kg ha-1
y-1
,
respectively. Conifer dominated forests displayed the lowest quantity of solid BSi in the soil
pool, -20.51 ± 11. 05 kg ha-1
y-1
, suggesting a system in which BSi is subject to a net loss.
The second recognized soil storage is that of dissolved biogenic silica (DBSi). This reservoir
consists of biogenic silica that has been subject to dissolution, but not leached. Conifer and
wetland dominated regions tend to express the largest DBSi storage, 25.76 ± 10.37 kg ha-1
y-1
and 17.81 ± 5.11 kg ha-1
y-1
respectively, grassland regions have the smallest pool, 3.12 ± 0.57 kg
Si ha-1
y-1
, while deciduous forests show intermediary storage, 7.76 ± 2.15 kg ha-1
y-1
. This
parameter is influenced by both dissolution and leaching rates. Dissolution tends to have a larger
influence than leaching attributed to the difference in magnitudes of both fluxes, as we shall see
shortly. The amount of dissolved silica stored in soils is proportional to the dissolution rate,
explaining the inflated storage of Si found in both conifer and wetland soils.
Both storage components sum to produce total BSi stored in soils. When relating modeled results
of all land cover types to literature (Table 8), coniferous storage shows the largest discrepancy.
While other sources suggest low storage of BSi in soils overlain by coniferous vegetation, they

26
do not reflect values as low as the -20.51 ± 11. 05 kg ha-1
y-1
presented here. This amplified loss
can be likely accredited to the large dissolution flux of conifer phytoliths.
3.3 Biogenic Silica Riverine Flux
3.3.1 Leaching
Acting upon the dissolved biogenic silica pool in soils, are forces that ultimately cause Si
movement through the soil profile into topographic lows, such as rivers. The medium through
which DBSi moves is precipitation. Ideally, this relationship is a function of soil porosity,
percolation and soil type. The leaching factor in this study was calculated to reflect the
relationship between precipitation and dissolved lithogenic + biogenic silica flux, as constants of
proportionality (Figure 5). Three of the land cover types showed a fairly strong positive linear
relationship between precipitation and DSi fluxes. Conifer dominated regions express the largest
leaching factor, 0.78, proposing that 78% of silica found within the dissolved silica flux could be
explained by precipitation. Grasslands express a leaching factor of 0.57 and deciduous forests of
0.33. Wetlands, on the other hand, proved to have a very weak relationship between the two
variables, r2
= 0.25. In wetlands it is evident that precipitation may not be directly involved with
the amount of Si that is leaving those environments.
3.3.2 Riverine DBSi Estimation
Calculated silica fluxes include both biogenic and inorganic sources. To extract the biogenic Si
component, the effect of BSi leaching was considered on the USGS DSi fluxes for each land
cover type. Generally, the dissolved riverine Si fluxes represent a fraction of the dissolved Si
reservoir found in soils. Conifer dominated catchments showed to have the largest biogenic silica
flux, 4.70 ± 1.54 kg ha-1
y-1
, approximately a fifth of the DBSi soil pool. Grassland dominated
regions have the next largest biogenic silica flux of 0.903 ± 0.320 kg ha-1
y-1
. Deciduous and
wetland regions have the lowest biogenic silica fluxes, 0.384 ± 0.032 kg ha-1
y-1
and 0.258 ±
0.064 kg ha-1
y-1
, respectively, suggesting considerable Si retention within soils or other pools.
When comparing DBSi results of this study to other literature some disagreement is apparent
(Table 9). Estimated wetland and deciduous DBSi shows very low values while studies reflect

27
those rivalling that of coniferous catchments. Although other estimates of wetland and deciduous
regions suggest higher DBSi values, these studies are few and may not be representative of
whole ecosystems. A literature review of DBSi found in waters of coniferous catchments
expresses the largest flux. This is in agreement with this study`s findings, and the reasons behind
this are speculated to be rooted in soil processes, described shortly. Also supporting this study`s
results, literature shows that grassland catchments reflect a DBSi flux ranging from 0.2-11 kg
ha-1
y-1
. This range correlates well with this study’s estimated grassland DBSi flux. Both
grassland and coniferous catchments show variation spanning two orders of magnitude. This
variation can be attributed to several factors relating to both biotic and abiotic processes.
3.3.3 Riverine DBSi Seasonal Variation
As an alternative measure to using leaching factors, dissolved biogenic silica was estimated by
separation of USGS DSi data into two monthly categories. One group consisted of DSi data from
May to September, known as the BSi reduction term, during which lithogenic silica is the
dominate component. The other group, known as the BSi accumulation term occurs from
October to April, and reflects a period during which the dominant component is biogenically
derived. This relationship is further supported by Ge/Si ratios and 30
Si (White et al., 2012). For
all land cover types this biogenic component consisted of approximately 65% of DSi (Table 10).
To support the use of leaching rate to estimate DBSi, results were compared with data of DSi
collected from October to April. A residual analysis revealed an r2
value of 0.985 between the
two data sets (Figure 6). Between the DBSi flux calculated using leaching and the DBSi flux
from October to April data, the average difference was much smaller, 0.077 kg ha-1
y-1
. This is in
reaction to the difference between the leaching DBSi flux and DSi flux calculated using annual
USGS data, 1.29 kg ha-1
y-1
. This suggests the use of leaching to be much more comparable in
estimating DBSi than using solely DSi.
3.3.4 Soil Influence
To negate the influence of soils on biogenic silica fluxes, differences between catchment soil
types and lithogenic silica flux were analyzed. Dissolved lithogenic silica was determined as the
difference between calculated DBSi and DSi and soil orders were assumed to retain homogenous

28
Si concentrations. Grassland catchments were dominated either by mollisol or alfisol soil orders.
Mollisol catchments averaged lithogenic silica fluxes of 0.41 ± 0.083 kg ha-1
y-1
while alfisol
catchments averaged 0.27 ± 0.139 kg Si ha-1
y-1
. Overlap in ranges suggests that differences
between the two are not significant. Conifer drainages were seen to be dominated by either
alfisols or inceptisols. Alfisol catchments averaged 1.6 ± 0.278 kg Si ha-1
y-1
and inceptisols
catchments, 2.8 ± 0.9 kg Si ha-1
y-1
, suggesting no statistically significant difference between the
two soils types. Deciduous forests dominated regions also showed no significant difference
between two dominant soils types, inceptisols which reflected 0.644 ± 0.089 kg Si ha-1
y-1
and
spodsols, 0.53 ± 0.06 kg Si ha-1
y-1
. All soils for wetland catchments were spodsols and as a
result riverine dissolved silica was not subject to soil based bias.
3.4 General Trends in Riverine Biogenic Fluxes
Following equation (1) biogenic silica fluxes leaving each distinct ecological region can be
calculated (Table 11). Silica fluxes have been shown to differ between land cover types as silica
contents of rivers are dependent upon vegetation type. This study shows that land cover does in
fact influence riverine silica content through production and dissolution of Si. However the final
process of leaching is dependent upon abiotic conditions of precipitation and soil characteristics.
In general, conifer dominated forests have the largest DBSi flux. This is counterintuitive
considering the low %BSi evident in evergreen vegetation, but appears to be compensated by
increased dissolution and high leaching. Conversely, grasslands which have a high %BSi show a
relative low riverine flux, yet high biogenic silica production. Reduction in the riverine flux can
be attributed to low dissolution rates relative to production, and resultantly high storage. Wetland
catchments express the highest silica fixation of all land cover types, yet some of the lowest
riverine fluxes. For this land cover type, approximately 90% of the silica produced remains in
soils as solid phytoliths, of the 10% that dissolves ~98% remains in soil solution while 2% leaves
catchments. Finally, deciduous forests which have rather low biogenic fixation have comparable
riverine fluxes to grasslands and wetlands. This can be attributed to the evidently low dissolution
rates of deciduous phytoliths, yet moderate leaching coefficient. Each of these environments is
unique in how silica is cycled within, and differences in magnitudes of fluxes and storage can be
attributed to inherent affinities of vegetation to silica, as well as a soil and climate processes.

29
Table 4. Net primary productivity (NPP) and percentage biogenic silica net dry weight
(%BSi) of total plant weight for four analyzed land cover types.
Land
Cover
ANPP
(kg/ha · yr)
SE %BSi SE
Grasslands 2823 ± 131 2.301 ± 0.130
Wetlands 6978 ± 453 1.971 ± 0.211
Coniferous 1404 ± 394 0.844 ± 0.186
Deciduous 2454 ± 105 0.541 ± 0.110
Table 5. Average catchment parametric values used for the calculation of dissolved silica
(DSi) fluxes for the four land cover types analyzed.
Land
Cover
Average
Annual
Precip
(cm) SE
Average
Annual
Discharge
(f3
/s) SE
Drainage Area
Range
(ha)
DSi
Flux
(kg/ha
· yr) SE
Grasslands 60.96 ± 7.59 57.15 ± 18.55 20 719 to 116 286 1.584 ± 0.38
Wetlands 112.69 ± 2.09 40.88 ± 23.52 510 to 72 517 1.033 ± 0.23
Coniferous 146.78 ± 47.26 163.41 ± 68.51 5 638 to 26 590 6.022 ± 1.97
Deciduous 112.97 ± 13.74 83.048 ± 19.49 1 388 to 35 999 1.166 ± 0.07

30
Table 6. Annual biogenic silica fixation rates from literature for wetland, grassland,
coniferous and deciduous ecosystems.
Land Cover Fixation (kg/ha · yr) Location Reference
Wetlands 500 Belgium Struyf and Conley, 2009
430 Poland Opdekamp et al., 2012
700 Africa McCarthy et al., 1989
~200 Global Carey and Fulweiler, 2012
154.56 ± 28.83 United States This study
Grasslands 22-26 United States Blecker et al., 2006
55-58 United States Blecker et al., 2006
127 United States Alexandre et al., 2010
70 United States Carnelli et al., 2011
Coniferous
forest
8 United States Bartoli, 1983
15.8 Netherlands Markewitz and Richter, 1998
10.8-32.3 United States Garvin, 2006
29 United States Cornelis et al., 2010
42.2 United States Cornelis et al., 2010
24 United States Carnelli et al., 2011
Deciduous
forest

31
Table 7. Annual biogenic silica dissolution rates from literature for grassland, coniferous
and deciduous ecosystems.
Land Cover Dissolution (kg/ha · yr) Location Reference
Wetlands 18.07 ± 5.13 United States This study
Coniferous
forest
10-29.9 United States Garvin, 2006
Deciduous
forest

32
Table 8. Annual biogenic silica soil storage for wetland, grassland, coniferous and
deciduous ecosystems.
Land Cover Soil Storage (kg/ha · yr) Location Reference
Wetlands 200 Belgium Struyf and Conley, 2009
154 United States This study
12-24 United States Alexandre et al., 2010
Coniferous
forest
11.9 Netherlands Markewitz and Richter, 1998
0.8-2.4 United States Garvin, 2006
-7.4 United States Cornelis et al., 2010
-20.51 ± 11. 05 United States This study
Deciduous
forest

33
Table 9. Annual dissolved silica flux from wetland, grassland, coniferous and deciduous
ecosystems.
Land Cover Riverine Flux (kg/ha · yr) Location Reference
Wetlands 19 China Nguyet et al., 2012
0.258 ± 0.064 United States This Study
Grasslands 6.3-11 United States Blecker et al., 2006
0.3-1.7 United States Blecker et al., 2006
0.2-0.5 United States Blecker et al., 2006
2.88 United States Alexandre et al., 2010
Coniferous
forest
17 Netherlands Markewitz and Richter, 1998
15 United States Garvin, 2006
Deciduous
forest

34
Table 10. Annual dissolved biogenic silica (DBSi) fluxes estimated using seasonality and
calculated leaching rates, in relation to annual dissolved silica (DSi) flux for four
ecosystems.
Land Cover Gauge
DSi Silica
Flux
DBSi Silica Flux
Leaching
Rate
Oct to Apr R2-value
kg/ha · yr kg/ha · yr kg/ha · yr
Grasslands 05451210 5.55 3.788 3.164
05451080 1.987 1.511 1.133
06306200 1.21 0.924 0.690
Wetlands 01022890 0.985 0.158 0.246
02310947 0.617 0.522 0.154
02299950 1.497 0.813 0.374
Coniferous Forests 11264500 5.11 3.001 3.985
10343500 9.26 7.18 7.222
05014300 6.035 2.5 4.707
09196500 1.592 0.958 1.241
14161500 30.071 24.27 23.455
Deciduous Forests 01349950 1.248 0.965 0.411
01362380 1.446 0.69 0.477
01545600 0.962 0.622 0.317
04063700 0.795 0.49 0.262
01364959 1.206 0.73 0.397
01422747 1.336 0.904 0.440

35
Table 11. Modeled average catchment fluxes for each land cover type analyzed and corresponding
coefficients.
L
Land Cover
Grassland
(kg/ha · yr)
Wetland
(kg/ha · yr)
Coniferous forest
(kg/ha · yr)
Deciduous forest
(kg/ha · yr)
Burial 65.65 154.56 9.94 13.26
BSi Storage 60.95 136.49 -20.51 5.15
Dissolution 4.70 18.07 30.46 8.14
DBSi Storage 3.12 17.81 25.76 7.76
Leaching 0.903 0.258 4.70 0.385
Dissolved Riverine
BSi Flux
0.903 0.258 4.70 0.385
Dissolution Factor 0.071 0.117 3.06 0.612
Leaching Factor 0.19 0.014 0.15 0.047
Leaching constant 0.57 0.25 0.78 0.33
Figure 4. Depiction of relationship between precipitation, discharge and drainage for the
twenty-six gauges analyzed. Showing decrease in discharge with decrease in drainage area
and precipitation.

36
Figure 5. Dissolved silica fluxes and annual precipitation relationship between four studied
land cover types depicting leaching coefficients (r2
values). A. Grasslands B. Wetlands C.
Coniferous forests D. Deciduous forests.
Predicted DBSi Flux (kg ha-1 y-1)
0 5 10 15 20 25
AugtoAprDBSiFlux(kgha-1y-1)
0
5
10
15
20
25
30
r ² = 0.985
Figure 6. The near 1:1 ratio of predicted dissolved biogenic silica flux using leaching
coefficients and seasonal segregation.

37
Chapter 4
4.0 Discussion
4.1 Biogenic Si Contributions
At catchment scales, DSi flux is a function of the following: (1) geology, (2) hydrology, (3) soil
development, and (4) biological processes (phytolith formation). Conley (2002) estimated annual
fixation of phytolith silica at 2 to 6 Gt Si y-1
, most of which is added as litterfall to soil surfaces.
Although there is uncertainty regarding factors controlling the relative solubility of different
types of phytoliths, there is consensus that contributions of phytolith dissolution to DSi export
could be substantial. More recently, soil-plant systems have been detailed using geochemical
tracers, particularly Ge/Si ratios and 30
Si. Fractionation between germanium (Ge) and silica can
be used to trace weathering of silica and dissolved silica from biogenic origins (Kurtz et al.,
2002; Derry et al., 2005). Ge/Si ratios are enriched in Ge for samples dominated by secondary
minerals, while biogenic silica polymerized by plants is depleted (Figure 7) (Delvigne et al.,
2009; Opfergelt et al., 2010; Cornelis et al., 2010). Alexandre et al., (1997) found that Si released
from phytolith dissolution is twice that of Si released from silicate weathering in tropical
systems. In these environments this is expected as depletion of mineral Si and high Si uptake
rates by biomass are evident (Lucas et al., 1993). Ge/Si ratios of stream waters from Hawaiian
basaltic catchments suggest biogenic contributions of up to 90% (Derry et al., 2005). Other
estimated contributions of phytolith dissolution are 30% in a coniferous Siberian forest
(Pokrovsky et al., 2005), 75% in a Congo rainforest (Alexandre et al., 1997) and 47 to 74% in a
California grassland (White et al., 2012). The biogenic silica contributions estimated for each
land cover type from this study ranges from 54% for humid wetlands to 60% for temperate
broadleaf deciduous forests. For a wetland catchment BSi contributions ranging from 15% to
80% emphasize the importance of influencing factors other than biological processes.
4.2 Conifer Anomaly?
Estimated mass balances of Si output in conifer dominated catchments reflect values much larger
than expected considering such low biogenic silica inputs. This is especially true when

38
comparing coniferous catchments to grasslands, whose vegetation is comprised of Si
accumulating plants. This trend of high silica flux leaving coniferous catchments has been
recorded in several independent studies, but not addressed to any great extent (Garvin, 2006;
Cornelis et al., 2010).
The relationship between silica uptake (production) and dissolved outputs has been found to be
negatively correlated (Figure 8). To explain this, it has been suggested that DSi output exceeds
Si production when DSi released by mineral dissolution does not contribute to the BSi pool of
vegetation (Cornelis et al., 2010). In cases where this relationship still holds but fixation is less
than output, vast quantities of Si on an annual basis must be retained in soils or vegetation. The
fact that BSi pools of vegetation act as a sink has been largely recognized in other studies of
forested and grassland ecosystems (Lucas et al., 1993; Aexandre et al., 1997; Giesler et al., 2000;
Gerard et al., 2002). The size of this pool, however, varies between vegetation as seen
previously, where conifer production is the lowest among the most common land cover types. In
addition to Si retention within vegetation, low Si flux rates would suggest large Si storage within
soils. This is indeed found to be the case for grasslands and not for coniferous regions (Sommer
et al., 2000; Conley, 2002, Melzer et al., 2011). If vegetation is rapidly recycling nutrients,
inputting silica into soils, it is likely that primary silicate weathering, and soil DSi, will be
decreased (Kelly et al., 1998). However, Si uptake by vegetation directly impacts soil formation
through dissolution, increasing primary and secondary silicate mineral formation (Lucas, 2001).
These two processes both promote and supress the availability of silica in soils, and may explain
why in grasslands we see high silica uptake with high solid silica soil content. So the question
becomes, why do coniferous catchments export nearly all of their fixed Si, while grasslands
export only a fraction? To answer this question process affecting dissolution and leaching must
be addressed.
4.3 Phytolith Dissolution
Early experiments performed on the dissolution of phytoliths showed that forest BSi is
approximately 10 to 15 times more soluble than grasses (Wilding and Drees, 1974). In addition,
conifer phytoliths have been found to be least stable when compared to those of grassland and

39
broadleaf vegetation (Bartoli and Wilding, 1980). Three main factors have been implicated in
drastically altering BSi dissolution: (1) phytolith surface area, (2) presence and abundance of Al
in soil and plant tissues, and (3) soil pH.
4.3.1 Surface Area Size and Dissolution
Specific surface areas (SSAs) of phytoliths have been proposed to influence physical processes
both in plants and in soils (Bartoli, 1985; Piperno, 2006; Li et al., 2013a
; 2013b
). Phytolith
dissolution rates increase substantially with greater surface areas by increasing solubility
(Fraysse et al., 2006; 2009). This occurs since increasing surface area leads to an increase in non-
proton/hydroxyl reactive surfaces, allowing for increased deprotonation of surface silanol bonds
(Fraysse et al., 2009). In a phytolith dissolution study carried out by Fraysse et al., 2006,
dissolution of bamboo phytoliths revealed an increase of two orders of magnitude for specific
surface areas of 5.18 m2
/g to 159 m2
/g. In addition to phytoliths, the dissolution rates of
diatomaceous frustules also increase with specific surface area (Van Cappellen et al., 2002;
Loucaides, 2010). Species specific variation in surface areas has been found to cause differences
in dissolution efficiency of several orders of magnitude (Martin-Jezequel et al., 2000; Dixit et al.,
2001; Ryves et al., 2001). Conifer phytolith specific surface areas average 195 m2
/g, which are
significantly greater than those of grasses, 92.8 m2
/g, and deciduous trees, 121 m2
/g (Fraysse et
al, 2009). Large specific surface areas found for conifers, and related increase in solubility may
explain why large dissolution rates are evident within coniferous catchments.
4.3.2 Aluminum Induced Reduction in Dissolution
Aluminum ions are toxic to plants and are repressed from plant tissues by the presence of an
endodermis. However, this root layer is not completely effective as Al can often be detected in
shoots and leaves of some plant species (Hodson and Sangster, 1999). Aluminum concentrations
found in grasses are low, while conifer species have been found to accumulate far more Al into
plant tissues (Carnelli et al., 2001). This is the inverse with Si concentrations found in respective
vegetation types. This inverse relationship between Si and Al is expected since silica has been
found to mitigate Al toxicity (Hodson and Evans, 1995; Cocker et al., 1998). Aluminum in
coniferous species has been found in concentrations of up to 28.3 mol g-1
dry weight, while

40
cereals have been found to contain concentrations of no more than 5 mol g dwt-1
(Hodson and
Sangster, 1999). The presence of aluminum and its adsorption onto BSi in plant tissues and soils
has been found to deter phytolith dissolution (Dixit et al., 2001; Rickert et al., 2002). Adsorption
of Al by siliceous particles results in co-deposition of Si-Al insoluble aluminosilicates, reducing
dissolved silica mobilization (Cappellen and Qiu, 1997).
The effect aluminum has on silica dissolution from both organic and inorganic origins and the
knowledge that conifers tend to show higher concentrations of Al, would suggest conifers have
low dissolution rates. However, this is contradictory to what is seen in both this study and other
literature. A study performed by Cornelis et al., 2010 shows that black pine takes up and deposits
considerable amounts of Al, 3.3 kg ha-1
y-1
and 12.2 kg ha-1
y-1
, respectively. Yet, this land cover
still reflects a Si soil water output of 9.4 kg ha-1
y-1
. A Deciduous tree, European beech, which
takes up and deposits only 0.8 kg Al ha-1
y-1
and 5.9 kg Al ha-1
y-1
, respectively, still only releases
6 kg Si ha-1
y-1
(Cornelis et al., 2010). To assess this relationship more fully, the magnitude of Al
influence on each respective environment is needed. In this case, it is entirely likely that although
aluminum induced stabilization of Si reduces DBSi export, it is muffled by the surge of
dissolution caused by higher SSA of coniferous phytoliths. Conversely, it can be argued that
dissolution of phytolith BSi is severely halted by Al adsorption in the surface soil layers where
phytoliths and Al (Nikodem et al., 2007) are in abundance. The progressively high DSi export
could then be mostly attributed to mobilized lithogenic Si, provided that BSi contributions for
coniferous catchments have been over projected.
4.3.3 Influence of Soil Acidity (pH)
Dissolution kinetics of BSi, in both terrestrial and marine environments increases with extremes
of pH. Highly alkaline and acidic soils experience increased deprotonation of silanol groups,
which form biogenic silica molecules. This process facilitates the breakage of siloxane bonds,
believed to be the rate-limiting step in the dissolution process of silica (Dove and Elston, 1992).
The effects of pH on the kinetics of silica dissolution have been established for quartz (Dove and
Elston, 1992) and BSi (Fraysse et al., 2006; 2009; Loucaides et al., 2008). In a recent study,
Loucaides et al. (2008) demonstrated that dissolution between pH of 6.3 and 8.1 double in rate;

41
in a study by Fraysse et al. (2008) dissolution was seen to increase by a factor of 15 for every
order of pH from 4 to 12, as well as for pH solutions from 3 to 1 (Figure 1). This would suggest
that catchments containing very acidic soils, like coniferous podzols, and those containing very
alkaline soils, like grassland chernozems, should express the highest dissolution rates.
The relationship between soil pH and calculated dissolution rates holds weakly for this study’s
reviewed land covers. Coniferous podzols show pH values ranging from 3 to 4 (Sommer et al.,
2006; Neubauer et al., 2013), while grassland soils express pH values ranging from 5.5 to 8.4
(Saccone et al., 2007). Using the dissolution and pH relationship developed by Frassye et al.
(2009), dissolution in grasslands should be nearly a factor of 10 greater than in coniferous
forests. This does not appear to be the case; in fact the dissolution rate of grassland silica is 6
time less than that of coniferous catchments. The calculated conifer silica dissolution rate has
large standard errors, reflective of great variation in DSi concentrations of river waters. This
variation may arise due to differing lithology, topography, or a yet unexplored confounding
factor. The dissolution calculation requires the concentration of dissolved Si in soils that is
leaving the system. This value is scarce in literature and often not measured, and was thus
approximated using DSi concentrations found in riverine waters, suggesting an alternative source
of error.
4.4 Soil Silica Transport to Streams
In tandem with silica dissolution, the rate of leaching also influences silica export. The
availability of silica along a soil profile influences riverine Si deposition, contingent upon
whether dominant water movement process are occurring at the surface as runoff, or as
horizontal flow at depth. If deposition and dissolution of Si is greatest in organic and A horizons
with water translocation occurring in soils closer to the surface, then biogenic silica contribution
to rivers should be great. Conversely, if dissolution is occurring lower down the soil profile, and
water translocation is occurring at the surface, Si export will be low (White et al., 2012).
However, if in this case the dominant form of water movement is subsurface lateral flow, then
lithogenic contributions to silica should be greater than biogenic, as phytolith deposition is

42
limited to the surface. Transportation of silica laterally to rivers is thus a function of dissolution
location, and water movement processes defined by precipitation.
For nearly all systems phytolith deposition and dissolution will appear greatest near the surface
in the soil A horizons (Blecker et al., 2008; Fishkis et al., 2010; White et al., 2012). Following
episodes of rainfall, water percolation and gravity will transport this dissolved silica to regions of
low water potential, such as rivers or other bodies of water. In the case of grasslands, clay to silty
clay loam soil textural classes have low hydraulic conductivities of < 0.13 to 2 cm hr-1
(O’Green,
2012). Low hydraulic conductivity decreases infiltration rate and percolation lowering the rate of
water outputs, and dissolved silica within. This is especially true under conditions of low
precipitation. In cases where precipitation surpasses infiltration, runoff may not have opportunity
to absorb dissolved silica and direct it towards rivers. Cases such as these are evident in
grassland environments, dominated by silty clay textured soils which have low infiltration rates
on account of lowered pore connectivity and colloid formation (Kurz et al., 2005). Coniferous
podzol silty loams have hydraulic conductivities of 2 to 12.7 cm hr-1
(O’Green, 2012). Increased
connectivity and pore space in forest soils, as well as higher annual precipitation suggests the
potential for greater nutrient leaching rates than in grasslands. The stratigraphic location of silica
dissolution occurs near the surface of both grasslands and coniferous forests; however, export of
this silica is limited by the rate of water percolation/infiltration through soils, substantially lower
for grassland ecosystems.
4.5 Wetland Silica Retention
Wetland systems are the link between terrestrial and marine environments, and as a result
interact strongly with river biogeochemistry. Modeled wetland systems show high biogenic silica
production moderate dissolution, but low leaching rates resulting in a great DBSi storage
component. Wetland soils contain between 0.6 and 0.9% solid biogenic silica by weight,
considerably lower than other soils (Norris and Hackney, 1999; Struyf et al., 2005). Low solid Si
concentration is consistent with high dissolution rates, and further supported by large quantities
of dissolved silica (Struyf et al., 2007). Although large amounts of silica are being fixed and
dissolved in these environments, we do not see increased export, or at least a proportional

43
relationship between rates of land cover classes analyzed. In a study on the effectiveness of
wetlands at retaining nitrogen and phosphorous, 80% of wetlands held onto 70% of nitrogen and
60% of phosphorous inputs (Fisher and Acreman, 2004). A DSi budget constructed by Struyf
and Conley (2009), shows that wetlands are capable of preserving up to 21% of Si dissolved in
soils.
Nutrient cycling within wetlands is a function of catchment features (drainage shape, water
discharge, vegetation, and hydrology) which manipulate the length of time it requires for water
to pass. Low discharge of wetland waters leads to increased residence of nutrients (van der Valk
and Arnold, 2009). Consequently, low discharge seen in the three wetland catchments may
explain why silica export and recycling proved to be low. In addition to water flow,
sedimentation can also act to sequester biogenic silica within soils. The process of sedimentation
contributes to wetland functioning, attributed to sediment sorption of nutrients and other
minerals (Craft and Casey, 2000). Sedimentation rates in wetlands range on the order of 271 to
712 g m-2
d-1
which greatly acts to withdraw nutrients from the water column (Nahlik and
Mitsch, 2008).
The difference in DBSi and the silica dissolution flux used to estimate the storage DBSi in soils
can also be greatly influenced by diatoms. Fresh water diatom metabolises of silica depletes
dissolved riverine concentrations (Martin-Jezequel et al., 2000; Struyf and Conley 2009;
Carbonnel et al., 2009; Luu et al., 2012; Viaroli et al., 2013). As a result DSi concentrations may
be biased through the amount of Si recycled by these diamateous organisms. This is especially
true if diatom abundances differ between wetland catchments, and separation of DSi into
seasonal components to extract DBSi did not yield reasonable estimates of the biogenic portion.
4.6 Regional Implications
The TBSi simulations from this study have many implications for the functioning of Si within
the terrestrial biosphere as we have seen, and globally. At a regional scale, biogenic silica input
within conifer dominated environments is low. However, quick dissolution and increased
mobility of silica through soils in these environments allows for a large silica flux. In these
coniferous systems, the solid biogenic silica pool in soils is very low or negative suggesting that

44
the soil storage-export balance is not in steady state. Either disequilibrium is occurring or nearly
all biogenic silica produced is being dissolved. In grassland systems the moderate annual BSi
production constituent undergoes weak dissolution. This dissolution rate is a consequence of low
phytolith surface area and soil hydraulic conductivity. As a result of such low dissolution these
environments accumulate large quantities of solid biogenic silica in soils. Low leaching rates in
grasslands, attributed to a weak correlation between precipitation/riverine Si flux and low
hydraulic conductivity of soils, dictates that these environments have low DBSi outputs.
Wetlands which reflect a high Si production flux and dissolution rate show a surprisingly low
riverine flux. This inconsistency is likely a result of nutrient retention within soils. In this case
retention is a function of low discharge and sedimentation. Deciduous catchments appear to be a
sensible case in both flux and storage of silica. Moderately low annual production, storage and
dissolution show a proportional DBSi flux in-between that of grassland and wetland
environments. When interpreting the results of this study at a broader scale, it becomes apparent
that coniferous catchment Si export dominates, followed by grasslands, deciduous forests and
finally, wetlands.
4.7 Vegetation Influence
The influence of land cover class on dissolved biogenic silica concentrations is dominated by
process of Si production and dissolution. Coniferous catchments which show a large riverine
flux, attributed to BSi dissolution, is a function of vegetation type owing to phytolith specific
surface area. For land covers such as grasslands and wetlands, high biogenic Si production and
low riverine BSi flux, can be explained by biologically influenced processes of dissolution as
well. However in these systems having low soil porosity or high retention, are abiotic processes
influencing leaching, which also lead to reduced DBSi export. In the case of coniferous and
deciduous forests, riverine BSi flux appears to be more greatly influenced by vegetation type
than grassland and wetland environments. Forested regions appear to have a higher biological
control on BSi leaving these environments. This is not to say that there is no relationship
between vegetation cover and riverine flux for grasslands and wetlands. This relationship is
simply reflected through abiotic characteristics associated with their respective vegetation types.

Influence of BSi from terrestrial vegetation on riverine systems and diatom evolution - Masters Thesis - University of Toronto

Influence of BSi from terrestrial vegetation on riverine systems and diatom evolution - Masters Thesis - University of Toronto

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