Influence of Biogenic Silica from Terrestrial Vegetation on Riverine Systems and Diatom Evolution

Home , Biogenic silica, Calamagrostis rubescens, Lithogenic silica

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

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.

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.

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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

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

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

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 vii

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

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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 30Si/28Si 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

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

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

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

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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

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

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

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

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.

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 6

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

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

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

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).

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).

Table 1. Soil silica concentrations for various soil types, pH values and land covers. SiO2 is dissolved silica.

Parent -1 Soil Type pH Land Cover SiO2 (mg g ) Material 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 12

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.

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)

-1 -1 -1 -1 where BSipro is production (kg ha yr ), NPP is net primary productivity (kg ha yr ) 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)

-1 where is the dissolved riverine silica concentration (mol l ), Q is the water percolation -1 through soils (L s ), 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

deciduous forests and grasslands/wetlands, respectively (Table 3). The dissolution factor as seen in eqn. (2) is calculated as;

⁄ , (5)

-1 -1 where R is the dissolution flux (kg ha yr ) 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

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.

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.

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.

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.

SSA Phytoliths (cm2/g) Mass (g) Horsetail 928000 0.5 Larch 1950000 0.25 Elm 1210000 0.3

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.

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

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

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 23

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 24

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 25

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 26

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 30Si (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 27

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.

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 ANPP SE %BSi SE Cover (kg/ha · yr) 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.

Average Average DSi Land Annual Annual Drainage Area Flux Cover Precip Discharge Range (kg/ha (cm) SE (f3/s) SE (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

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 59-67 United States Blecker et al., 2006 127 United States Alexandre et al., 2010 67 United States Alexandre et al., 2010 ~25 Global Carey and Fulweiler, 2012 70 United States Carnelli et al., 2011 65.65 ± 4.09 United States This study

Coniferous 8 United States Bartoli, 1983 15.8 Netherlands Markewitz and Richter, 1998 forest 10.8-32.3 United States Garvin, 2006 29 United States Cornelis et al., 2010 42.2 United States Cornelis et al., 2010 2.1 United States Cornelis et al., 2010 24 United States Carnelli et al., 2011 11.87 ± 4.08 United States This study

Deciduous 26 United States Bartoli, 1983 ~50 Global Carey and Fulweiler, 2012 forest 19.3 United States Cornelis et al., 2010 17.8 United States Cornelis et al., 2010 13.29 ± 0.57 United States This study

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

Grasslands 43-57 United States Blecker et al., 2006 43-51 United States Blecker et al., 2006 16-17 United States Blecker et al., 2006 103 United States Alexandre et al., 2010 74 United States Alexandre et al., 2010 50 United States Alexandre et al., 2010 62 United States Alexandre et al., 2010 4.7 ± 0.59 United States This study

Coniferous 4 United States Bartoli, 1983 10-29.9 United States Garvin, 2006 forest 30.46 ± 11.62 United States This study

Deciduous 22 United States Bartoli, 1983 8.14 ± 2.13 United States This study forest

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

Grasslands 10-16 United States Blecker et al., 2006 4-13 United States Blecker et al., 2006 6-9 United States Blecker et al., 2006 12-24 United States Alexandre et al., 2010 5 United States Alexandre et al., 2010 60.59 ± 3.96 United States This study

Coniferous 1 United States Bartoli, 1983 11.9 Netherlands Markewitz and Richter, 1998 forest 0.8-2.4 United States Garvin, 2006 27.9 United States Cornelis et al., 2010 41.3 United States Cornelis et al., 2010 -7.4 United States Cornelis et al., 2010 -20.51 ± 11. 05 United States This study

Deciduous 0 United States Bartoli, 1983 13.3 United States Cornelis et al., 2010 forest 11.1 United States Cornelis et al., 2010 5.15 ± 1.97 United States This study

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 0.903 ± 0.320 United States This Study

Coniferous 26 United States Bartoli, 1983 17 Netherlands Markewitz and Richter, 1998 forest 15 United States Garvin, 2006 1.1 United States Cornelis et al., 2010 0.7 United States Cornelis et al., 2010 9.4 United States Cornelis et al., 2010 4.70 ± 1.54 United States This Study

Deciduous 0 United States Bartoli, 1983 6.0 United States Cornelis et al., 2010 forest 6.7 United States Cornelis et al., 2010 0.384 ± 0.032 United States This Study

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.

Leaching DSi Silica DBSi Silica Flux Rate Land Cover Gauge Flux 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

Table 11. Modeled average catchment fluxes for each land cover type analyzed and corresponding coefficients.

Land Cover L Grassland Wetland Coniferous forest Deciduous forest (kg/ha · yr) (kg/ha · yr) (kg/ha · yr) (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 0.903 0.258 4.70 0.385 BSi Flux 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. 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.

r ² = 0.985 25

Aug to Apr DBSi Flux (kg ha-1 y-1) ha-1 (kg Flux DBSi Apr to Aug

0 5 10 15 20 25 Predicted DBSi Flux (kg ha-1 y-1)

Figure 6. The near 1:1 ratio of predicted dissolved biogenic silica flux using leaching coefficients and seasonal segregation.

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 30Si. 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

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

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

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-1and 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-1and 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;

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

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

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 43

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.

4.8 Sources of Error

Within in this study several assumptions were made concerning both data collection and calculation. Data collection was severely limited by data available temporally and spatially. This concept of Si influencing the terrestrial biosphere is relatively new, and the collection of data used to define this relationship, has just recently been implemented. The use of only twenty-six catchments begs the question of whether or not the depiction of land cover types is representative. For grasslands, wetlands and deciduous forests low standard errors suggest that land covers are depicted to a fair degree of accuracy. For coniferous catchments, which reflect large standard errors, an explanation needs to be invoked. Large differences in conifer flux calculations are a result of increased variation in dissolved silica concentrations measured in waters. These variations may be accounted for by differing lithology, local soil type, coniferous taxa (which are seen to have great variation in Si content between species), topography, or yet another factor.

Amalgamation of direct measurements for the creation of generalizations, specifically for use in a mathematical relationship, often leads to errors which must be recognized. Inaccuracies of data collection could lead to misinterpretation of results and overall amplification of error. This is particularly significant if a series of equations are nested and rely on previously calculated values, as is the case in this study. In the case of wetland NPP estimations, it is entirely possible that values are exaggerated. Particularly in wetland regions, photosynthesis and carbon production can be greatly influenced by NPP contributions from algae, and other phytoplankton. As these organisms do not produce Si to any great extant, Si production for this land cover type could be overstated. For dissolution calculations, riverine DSi was assumed to be a suitable representative of dissolved silica in soils. Dissolution in soils can be greatly influenced by soil specific process, such as bacterial Al-silicate formation (Konhauser and Urrutia, 1999). Riverine DSi may not be directly related to soil DSi on account of unique processes, but once again data availability dictated the information used within the model.

In addition to this, DSi concentrations measured in rivers by USGS contractors does not discriminate against Si biomineralized by fresh water diatoms. Riverine cycling of Si is similar

to oceanic, especially in how it is influenced by diatoms. Diatoms take up dissolved silica; convert it to biogenic forms for skeletal formation, and upon death biogenic silica is mobilized. This means that inorganic silica is converted to biogenic in river water by diatoms, and not solely from vegetation as is assumed in this study. Diatom influence on biogenic silica can influence the biogenic component extracted from dissolved values through inflation. This study was limited by available data and as more research is performed on these systems, adjustments and corrections should be implemented accordingly.

Figure 7. A. Depiction of the relationship between Ge/Si ratios during the growing season (Green), and die back (Brown). B. Relationships of 30Si/ 28Si isotopes during the growing and winder seasons. After White et al., 2012.

8 Deciduous Broadleaf Coniferous Grassland 6 Wetland

Si Riverine Flux (kg ha-1 y-1) ha-1 (kg Flux Riverine Si

0 0 20 40 60 80 100 120 140 Si Fixation (kg ha-1 y-1)

Figure 8. Relationship between annual silica production and export. Coniferous regions show low fixation but high export, while grasslands show the reverse. 47

Chapter 5

5.0 Case Study

5.1 Global Oceanic Biogenic Silica Input

The effects of the TBSi cycle previously established can be applied globally to assess the impact of vegetation on oceanic inputs. It has been seen that vegetation type can influence the amount of silica entering riverine settings through silica production and dissolution. Recognizing how land cover can influence global dissolved oceanic silica concentrations will lead to a better understanding of the global silica cycle. The connection between oceanic Si and diatoms is well established within literature and of significant importance since diatoms are responsible for nearly half of oceanic productivity (Nelson et al., 1995). Diatoms are first seen to arise during the Jurassic and reflect rapid radiation during the last 40 million years. Diatom influence on oceanic primary productivity has led to great interest in clarifying factors that led to this diversification. One current proposition is that geochemical coupling between terrestrial grasslands and marine ecosystems through global silicon cycles stimulated this radiation (Falkowski et al., 2004; Conley, 2002).

Diatoms have an absolute requirement for silica of 2 µmol (Egge and Aksnes, 1992), and as such, their prominence in modern oceans is thought to have been promoted by processes that increased the flux of silica into marine systems (Egge and Aksnes, 1992). At concentrations of less than 2 µmol diatoms were seen in low abundance, a result of nutrient starvation. The rise of grasslands in the Miocene is debated as being a trigger leading to increased weathering and Si biomineralization. This theory is supported though synchronous diatom diversification (Falkowski et al., 2004). Grasslands take up substantially more silica per unit carbon than many other terrestrial systems. Through doing this, they create a substantial phytolith and silica pool within their soils. Some of this silica is released during small scale events, such as fires (Pisaric, 2002), while the remaining silica is stored. Large scale tectonic pulse events, such as earth quakes, would also substantially increase rates of Si release from grasslands regions. This principle of grasslands influencing diatom radiation is reliant upon the assumption that great

quantities of silica are more readily released from grasslands than other ground types. The constructed biogenic silica cycle of this study suggests that this is not the case as we see conifer dominated catchments having a greater biogenic Si flux. However, this is not to say that grasslands did not influence diatom radiation as on a global scale ground cover area would greatly influence oceanic silica concentrations. If grasslands dominate continental cover it is still likely that biogenic Si exports from these systems was considerable. In addition to biogenic Si, if grassland enhanced weathering increased fluxes of lithogenic silica, oceanic concentrations would similarly increase.

To reiterate the objectives stated in the introduction, the aims of this case study were as follows:

(1) Define land cover change on global oceanic dissolved silica, and

(2) Determine if changes in terrestrial ecology during the Oligocene triggered diatom evolution/radiation

5.2 Methods

5.2.1 Oceanic Si Estimation

To assess the potential impact of biogenic silica fluxes and defining land cover classes on diatom evolution, global terrestrial export values and oceanic silica concentrations were estimated. Global terrestrial silica fluxes were projected for the Eocene, Oligocene, Miocene, and Pliocene, and used to estimate oceanic BSi concentrations. These time periods were selected to represent oceanic conditions prior to and following diatom diversification, which occurred during the Eocene-Oligocene transition. These values were subsequently compared to the dominance threshold of 2 µm as a diatom growth requirement, determined by Egge and Aksnes (1992) for contemporaneous oceanic conditions. The global biogenic silica flux into oceans was calculated using the equation;

∑ , (7)

-1 -1 where is Area (ha) and is the total amount of biogenic silicon lost (kg ha y ) for each land cover class. Tropical and temperate forest silica fluxes were approximated using the average deciduous DBSi flux, while grasslands and coniferous forests were assigned their respective flux values calculated using the TBSi model. Using the calculated oceanic flux, oceanic silica concentrations were estimated with;

Tsi = / , (8) where is the flux of terrestrial silicon to oceans (kg) and is volume of ocean water (l). Concentrations were calculated for total oceanic volume, 1.3 billion km3 of water, and a top layer of 5 m. Total ocean volume was obtained from Garrison (2005). A top layer of 5 m was selected representing ocean diatom and dissolved Si distributions.

5.2.2 Palaeo-Land Cover Distribution

To calculate terrestrial silica fluxes across the Cenozoic, reconstructed global vegetation distributions were collected and input into ArcGIS to obtain areal extents in km2 (Figure 9 to 12). Eocene and Oligocene distributions were obtained from Fine and Ree (2006) and were created using modern day correlates, composite parameters and global carbon models (GCMs). The Miocene data was adapted from Pound et al. (2011), which defined boundaries using TEVIS (Tertiary Environments Vegetation Information System), palaeogeography, orography and sea surface temperature. Finally, the Pliocene extents were obtained from Dowsett et al. (1999) and were estimated using the Pliocene research interpretation and synoptic mapping 2 (PRISM2) dataset.

5.3 Results and Discussion

5.3.1 Eocene to Pliocene Land Cover Change

Land cover distributions from the Eocene onward to the Pliocene were reconstructed using various proxies. Proxy accuracy increases as more geologic and fossil data becomes available, thus maps recreated from the Miocene to Pliocene are likely more accurate. Interpretation of Eocene-Oligocene maps must be done so using caution knowing that reconstruction is based off discontinuous data. During the Eocene, vegetation largely consisted of either temperate or 50

tropical forests (Figure 9) (Ziegler et al., 2003; Fine and Ree, 2013) with some dispersed grasslands (Stromberg, 2011). The Oligocene is characterized by the expansion of grasslands and formation of grassland cover types that thrived near the tropics (Figure 10) (Lunt et al., 2007;

Stromberg, 2011; Fine and Ree, 2013). C4 grassland expansion during the Miocene dominates in the topics displacing tropical forests (Pound et al., 2011; Stromberg, 2011) while in the nothern latitudes coniferous forests begin to take hold as a prominent land cover type (Figure 11). Coniferous fossil evidence suggests evolution during the Permian, however prominence of this functional type did not take hold until after the Cretaceous, when frost tolerance developed (Opalinska and Cowling, 2013). During the Pliocene, grasslands expanded from Africa to much of southwest Asia and coniferous forests expanded further within the high latitudes (Figure 12) (Haywood et al., 2004). We also see development of desert and Arctic tundra cover types during this time. As a general progression from the Eocene to Pliocene we see a change from temperate and tropical forests to grassland and coniferous dominance (Table 11).

5.3.2 Eocene to Pliocene Oceanic Si Change

Changes in land cover distribution and dominance from the Eocene to the Pliocene will affect global BSi fluxes (Table 12). Following the Oligocene, global biogenic silica flux is seen to double, doubtlessly influenced by the presence of vast grassland and coniferous forest expanses. Miocene and Pliocene biogenic silica fluxes are dominated by coniferous forests where 85% of the DBSi flux is attributed to conifers. Prior to the Miocene the dominant Si contributor was that of temperate forests. Tropical rainforests appear to have the lowest impact factor on DBSi input into oceans. The dissolved silica leaving the tropics was estimated using the deciduous DBSi value. This likely under-exaggerated BSi leaving these systems as the tropics are subject to differing precipitation and soil conditions (Cornelis et al., 2011). Modern BSi export values estimated for the Amazonian rainforest are several times larger than that of temperate deciduous forests, and rival that of grasslands (Lucas et al., 1993).

The increased flux of DBSi in the Miocene and Pliocene, attributed to grasslands and conifers forests, reflects an increase in oceanic Si concentrations. Following the expansion of these two functional types, oceanic concentrations of biogenic silica are seen to double. Oceanic

concentrations were predicted for the whole ocean, but it is likely that circulation of silica would lead to increased concentrations nearer the surface. Predicting oceanic concentrations for the top 5 m, where modern diatom abundances are greatest, suggests values of 0.069, 0.054, 0.019, and 0.028 µmol l-1 from the Pliocene, Miocene, Oligocene and Eocene, respectively. Modern oceanic BSi flux was calculated with current distribution of land cover types and revealed a value of 0.42 Tmol Si year-1. This value is less than the biogenic silica estimate of 1.1 Tmol Si year-1 devised by Treguer and De La Rocha (2012). However, it is likely that 0.42 Tmol Si year-1 is understated as it does not take into consideration inputs from groundwater or windblown sources.

5.3.3 What this means for Diatom Radiation

Diatoms have a silica requirement that must be fulfilled in order to allow polymerization of skeletal structures. This requirement dictates that water must contain concentrations of at least 2 µmol of Si. In order for biogenic silica to influence diatom growth, diatoms must either metabolize biogenic silica at a faster/more efficient rate, or the input of biogenic silica must be substantial in magnitude. Capellacci et al. (2012) showed that for two diatom species, growth rates were highest in the presence of dissolved silica derived from crystalline quartz. If vegetation dynamics influenced diatom radiation, then it must have been through the change in sheer magnitude of DBSi during the Miocene. Predicted oceanic concentrations exhibit values that are far less than required, but do show a substantial increase with the presence of grassland and coniferous forest expansion after the Oligocene. This increase in biogenic silica during the Miocene could then be responsible for diversification of diatoms. These concentrations are solely measured for biogenic silica, and the incorporation of lithogenic would undoubtedly increase these values.

Modern flux studies have shown heterogeneity in global riverine silica inputs (Bernard et al., 2011). Coastal regions near the Amazonian and Ganges river basins have the highest dissolved Si outputs attributed to high discharge and drainage areas. It is not too impractical to assume that in these regions biogenic silica fluxes would also be rather high and allow ocean waters to attain values greater than 2 µmol of Si at least near continental shelves. Thus, if the increase in biogenic silica during the Miocene influenced diatom diversification, it would have done so

nearer river outlets on continental margins. While silica presence is the main contributing factor for diatom persistence, other nutrients and their distribution would also influence diatom radiation. For biogenic silica increase to have triggered diatom radiation, lithogenic contributions must have remained close to equal through time. This study’s estimated oceanic concentrations assume a homogenous distribution of silica which is not the case in modern oceans (Durr et al., 2009; Bernard et al., 2011). Nonetheless total rise in oceanic silica may still have had an influence on diatom radiation. To make more concrete inferences using average oceanic concentrations, influence of the lithogenic component must be known.

5.3.4 What this means for Grasslands as an Instigator

The influence of grasslands as primary initiators leading to the diversification of diatoms during the Miocene may not be as momentous as previously presumed. This is especially true when analyzing grassland influence on biogenic silica contributions. Teasing apart grassland and coniferous forest contributions for DBSi is challenging especially when performing analysis at a global scale. A large catchment dominated by grasslands may elevate silica concentrations near the point of export enough to promote diatom diversification regionally. However, this is more likely to be the case with coniferous forests, which have a greater control on the quantity of DBSi exported to oceans. The DBSi oceanic inputs were calculated at steady state on annual bases. Large scale pulse events acting to release large quantities of silica stored in soils, would certainly influence ocean concentrations. Following disturbances such as fires nutrients are released quickly and can reflect losses of up to 85% (Laclau et al., 2002; Pisaric, 2002). Other natural disturbances such as earthquakes and flooding could also potentially increase grassland DSi export. Thus, grasslands may not have contributed greatly to the annual influx of BSi to oceans, however grassland silica storage has potential to increase oceanic DSi concentrations following disturbance.

5.3.5 Silica Retention

The retention of dissolved silica within fluvial systems has proved to decrease DSi exports to coastal waters. Dissolved silica retention can occur due to anthropogenic factors such as damming and eutrophication, and through wetland immobilization (Friedl et al., 2004; Humborg 53

et al., 2008). The construction of dams have been seen to increase diatom growth due to increased residence times, but ultimately leads to intensive sedimentation and net losses of silica to reservoir sediments (Koch et al., 2004). DSi retention has been predominately assessed for individual lakes and reservoirs (Cook et al., 2010, Triplett et al., 2008), and to a smaller degree in rivers and oceans (Humborg et al., 2008; Laruelle et al., 2009; Le et al., 2010; Durr et al., 2011). According to a model of Si mass balances executed by Laruelle et al. (2009), a discrepancy of 96 Mt year-1 of silica was noticed between DSi entering fluvial systems and DSi export. Anthropogenic induced retention can influence rives by decreasing Si export by as much as 91% for specific catchments, and 18% globally (Harrison et al., 2012; Lauerwald et al., 2012). Durr et al. (2011) assumed a natural DSi retention within lakes and wetlands to be 16 ± 8%. Taking retention into consideration more reasonable DBSi fluxes of 6.69, 5.23, 1.79, and 2.75 Mt year-1 can be produced for the Pliocene, Miocene, Oligocene and Eocene, respectively. A higher retention rate due to anthropogenic interference suggests a more precise modern DBSi export of 2.11 Mt year-1.

5.4 Global Impact

As land cover changes through time, global biogenic silica inputs have been seen to change and in turn alter oceanic Si concentrations. Positive changes in marine and riverine silica concentrations would have been beneficial for diatom growth, leading to diversification. The doubling of silica flux from the Oligocene to Miocene, on account of coniferous forest and grassland expansion, can be a potential trigger leading up to such a positive change. Although total oceanic concentrations increase minutely with this change in Miocene vegetation, more localized pulses in high Si concentrations, such as those near river deltas, are where we would expect to see diatom radiation occurring.

Global fluxes estimated from the Eocene to Pliocene were completed assuming modern environments, on which the model was constructed, are comparable. Consistency between precipitation, discharge, lithology and soil types must be present through all time periods. The results of this case study suggest that oceanic silica concentrations will increase as coniferous forests and grasslands expand, which will invariably impact diatom evolution. Currently oceans

are ubiquitously undersaturated in silica and changes in land cover (a result of deforestation and/or climate effects) or pulse events could offset this, triggering another diatom radiation event or dieback.

0.385Table 12. Global area coverage (in ha) by various land cover types from the Eocene to Pliocene

Land Cover Pliocene Miocene Oligocene Eocene Area Area Area Area (ha 106) (ha 106) (ha 106) (ha 106) Rainforest 1841 2251 8486 9288 Deciduous 5047 9850 47314 80153 Grassland 6262 5011 1089 - Conifer 15934 11904 - - Desert 11305 8455 - - Total 40389 37471 56889 89441

Table 13. Total global flux of dissolved biogenic silica ( ) to oceans from the Eocene to

Pliocene and resulting oceanic biogenic silica concentrations (Tsi ). Using an ocean volume of 1.3 billion km3.

6 (Kg/yr)(10 ) Land Cover Pliocene Miocene Oligocene Eocene Rainforest 70 86 326 357 Deciduous 194 378 1819 3082 Grassland 565 452 98 - Conifer 7536 5630 - - Total 8367 6548 2244 3439

Tsi (umol/L) 0.00229 0.00179 0.00061 0.00094

Figure 9. Reconstruction of Eocene (55 Mya) land cover type and distribution. After Fine and Ree, 2006.

Figure 10. Reconstruction of Oligocene (27 Mya) land cover type and distribution. After Fine and Ree, 2006 and Lunt et al., 2007.

Figure 11. Reconstruction of Miocene (11 Mya) land cover type and distribution. After Pound et al., 2011.

Figure 12. Reconstruction of Pliocene (3 Mya) land cover type and distribution. After Haywood et al., 2004.

Chapter 6

6.0 Conclusions and Future Directions of TBSi Cycles

Information concerning the terrestrial silica cycle is greatly underrepresented in literature. This is particularly true when discussing the processes through which biogenic silica operates. Through the construction of a terrestrial biogenic silica model, this study attempts to explicate fluxes and storages of biogenic silica within the biosphere. The model created (Equation 2), quantifies the amount of biogenic silica leaving terrestrial systems as a riverine flux. The riverine flux is a function of biogenic silica leaching through soils (LF), the amount of silica dissolved within soils (DF), net primary productivity of vegetation within a catchment (NPP), and finally the biogenic silica content within vegetation of a catchments as percent dry weight (%BSi).

Applying this model to four land cover classes within the United States showed that vegetation composition influences riverine silica concentrations. Catchments dominated by coniferous forests showed to have the largest biogenic silica flux, attributed to high dissolution of coniferous phytoliths. Grasslands showed to have the second largest BSi flux, but several times lower than that of coniferous drainages. Low grassland BSi flux can be attributed to low dissolution rates of grassland phytoliths, a characteristic of land cover class. Deciduous forest riverine flux was the third lowest, with wetlands having the lowest biogenic Si export. Although land cover characteristics influenced the amount of silica leaving these systems, the extent to which land cover directly affects export is dependent upon physical parameters such as retention and soil porosity.

Using the constructed terrestrial BSi model global estimates of biogenic silica export are of 0.42 Tmol Si year-1. This value is comparable to Treguer and De La Rocha’s (2012) estimate of 1.1 Tmol Si year-1. Global BSi flux for four time periods was recreated, demonstrating change in dissolved terrestrial Si export with variation in vegetation cover. During the Eocene and Oligocene DBSi export was approximately 7000 kg yr-1, this values increased substantial during the Miocene to 17900 kg yr-1. This change in DBSi export is concurrent with the expansion of grasslands and dominance of coniferous forests. To address the question of whether this rise of terrestrial DBSi export lead to the triggering of diatom diversification more work is needed. 59

Suggested future work would be to study of DBSi contributions of localized regions such as river outlets to determine if multiple geographically isolated regions of diatom radiation are possible.

Although terrestrial BSi has been established as being an essential component of the carbon cycle and driver of marine productivity, few attempts have been made to model the movement of biogenic silica within the terrestrial sphere. Those studies that address similar processes are set for regional environments and have minimal application for use as global terrestrial BSi cycling models. Even with these localized examples insufficient data exists for tropical savannah, wetlands, shrublands, and tropical dry forest systems. More work is required prying into these less understood BSi systems and the creation of terrestrial BSi models need to be further explored and improved upon. New geochemical and isotopic tracers have allowed for the development of new methodologies in quantifying the influence of BSi in terrestrial environments. These include cosmogenic and stable isotopes of Si and Ge/Si ratios, which will hopefully be used in future studies to further expand upon our knowledge of BSi as both a proponent of diatom diversification and terrestrial cycling. With the use of new technology and methodologies such as Ge/Si ratios, Si isotopes and establishment of simple models, there is upcoming promise in understanding the terrestrial Si cycle.

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Appendix I.

Biogenic silica content of vegetation (%DW) belonging to grasslands, wetlands, coniferous forests and deciduous forests.

Table of biogenic silica content in American grassland species as % dry weight.

Family Genus species %Bsi Reference Poaceae Panicum virgatum 1.76 Darmody, et al., 2011 Poaceae Panicum virgatum 0.6955 Banowet, et al., 2009 Poaceae Panicum virgatum 0.8636 Banowet, et al., 2009 Poaceae Panicum virgatum 0.764 Banowet, et al., 2009 Poaceae Panicum virgatum 0.8724 Banowet, et al., 2009 Poaceae Panicum virgatum 0.6958 Banowet, et al., 2009 Poaceae Panicum virgatum 0.7197 Banowet, et al., 2009 Poaceae Panicum virgatum 0.1383 Banowet, et al., 2009 Poaceae Panicum virgatum 1.1219 Banowet, et al., 2009 Poaceae Panicum virgatum 1.3956 Banowet, et al., 2009 Poaceae Panicum virgatum 1.2526 Banowet, et al., 2009 Poaceae Panicum virgatum 1.2543 Banowet, et al., 2009 Poaceae Panicum virgatum 1.3033 Banowet, et al., 2009 Poaceae Panicum virgatum 0.886 Banowet, et al., 2009 Poaceae Panicum virgatum 1.0296 Banowet, et al., 2009 Poaceae Panicum virgatum 1.0501 Banowet, et al., 2009 Poaceae Panicum virgatum 1.131 Banowet, et al., 2009 Poaceae Panicum virgatum 0.9574 Banowet, et al., 2009 Poaceae Panicum virgatum 0.8369 Banowet, et al., 2009 Poaceae Panicum virgatum 0.7497 Banowet, et al., 2009 Poaceae Panicum virgatum 0.4908 Banowet, et al., 2009 Poaceae Panicum virgatum 0.5516 Banowet, et al., 2009 Poaceae Panicum virgatum 0.4535 Banowet, et al., 2009 Poaceae Panicum virgatum 0.3818 Banowet, et al., 2009 Poaceae Panicum virgatum 0.6529 Banowet, et al., 2009 Poaceae Panicum virgatum 0.4533 Banowet, et al., 2009 Poaceae Panicum virgatum 0.9736 Banowet, et al., 2009 Poaceae Panicum virgatum 1.0909 Banowet, et al., 2009 Poaceae Panicum virgatum 1.2969 Banowet, et al., 2009 Poaceae Panicum virgatum 1.2441 Banowet, et al., 2009 Poaceae Panicum virgatum 0.8945 Banowet, et al., 2009 Poaceae Panicum virgatum 0.8146 Banowet, et al., 2009 Poaceae Triticum aestivum 2.455 Guntzer, et al., 2012 76

Poaceae Zea mays 0.827 Guntzer, et al., 2012 Poaceae Triticum aestivum 2.455 Guntzer, et al., 2012 Poaceae Hordeum vulgare 1.824 Guntzer, et al., 2012 Poaceae Triticum turgidum 0.719 Keller, et al., 2012 Poaceae Elymus elymoides 1.6 Blank et al., 1994 Poaceae Avena sativa 2.08 Ma and Takahashi, 2002. Poaceae Triticum aestivum 1.44 Ma and Takahashi, 2002 Poaceae Triticum boeoticum 2.61 Ma and Takahashi, 2002 Poaceae Triticum dicoccoides 1.33 Ma and Takahashi, 2002 Poaceae Triticum percicum 1.7 Ma and Takahashi, 2002 Poaceae - - 5 Pahkala, 2001 Poaceae - - 4.5 Pahkala, 2001 Poaceae - - 4.5 Pahkala, 2001 Poaceae - - 2 Pahkala, 2001 Poaceae Secale cereale 1.4 Robbins et al., 1987 Poaceae Triticum aestivum 2.7 Robbins et al., 1987 Poaceae Achnatherum hymenoides 2 Blank et al. 1994 Poaceae Bromus tectorum 1.5 Blank et al. 1994 Poaceae Agropyron smithii 2.52 Bezeau et al. 1966 Poaceae Agropyron subsecundum 2.23 Bezeau et al. 1966 Poaceae Bromus pumpellianus 1.86 Bezeau et al. 1966 Poaceae Calamagrostis inexpensa 3.8 Bezeau et al. 1966 Poaceae Calamagrostis rubescens 3.29 Bezeau et al. 1966 Poaceae Danthonia intermedia 3.08 Bezeau et al. 1966 Poaceae Danthonia parryi 2.61 Bezeau et al. 1966 Poaceae Deschampsia caespitosa 1.85 Bezeau et al. 1966 Poaceae Elymus cinereus 2.1 Bezeau et al. 1966 Poaceae Elymus innovatus 2.13 Bezeau et al. 1966 Poaceae Festuca idahoensis 3.59 Bezeau et al. 1966 Poaceae Festuca scabrella 3.15 Bezeau et al. 1966 Poaceae Stipa richardsonii 2.64 Bezeau et al. 1966 Poaceae Stipa spartea 2.86 Bezeau et al. 1966 Poaceae Stipa viridula 3.4 Bezeau et al. 1966 Poaceae Bromus inermis 2.47 Bezeau et al. 1966 Poaceae Elymus junceus 2.37 Bezeau et al. 1966 Poaceae Festuca rubra 2.82 Bezeau et al. 1966 Poaceae Phleum pratense 1.59 Bezeau et al. 1966 Poaceae Andropogon gerardi 4.1 Geis, 1978 Poaceae Panicum virgatum 4.39 Geis, 1978 Poaceae Sorgastrum nutans 5.65 Geis, 1978 Lanning and Eleuterius, Poaceae Andropogon gerardi 1.98 1987

Lanning and Eleuterius, Poaceae Andropogon scoparius 6.24 1987 Lanning and Eleuterius, Poaceae Panicum virgatum 4.88 1987 Lanning and Eleuterius, Poaceae Sorgastrum nutans 4.683 1987 Lanning and Eleuterius, Poaceae Stipa spartea 7.15 1987 Poaceae - - 2.5 Pahkala, 2001 Poaceae - - 3.5 Pahkala, 2001 Poaceae Sporobolus cryptandrus 3.5 Smith et al., 1971 Poaceae Lycurus phleoides 4.16 Smith et al., 1971 Poaceae Boutelous gracilis 5.72 Smith et al., 1971 Poaceae Panicum obtusum 4.41 Smith et al., 1971 Poaceae Boutelous curtipendula 5.64 Smith et al., 1971 Poaceae Hilaria jamesii 5.18 Smith et al., 1971 Poaceae Boutelous hirsuta 6.68 Smith et al., 1971 Poaceae Muhlenbergia richardsonis 7.34 Smith et al., 1971 Poaceae Festuca scabrella 2.79 Johnston et al., 1966 Poaceae Festuca scabrella 2.19 Johnston et al., 1966 Poaceae Festuca scabrella 3.41 Johnston et al., 1966 Poaceae Festuca scabrella 3.61 Johnston et al., 1966 Poaceae Festuca scabrella 3.9 Johnston et al., 1966 Poaceae Festuca scabrella 2.4 Johnston et al., 1966 Poaceae Festuca scabrella 2.94 Johnston et al., 1966 Poaceae Festuca scabrella 4.72 Johnston et al., 1966 Poaceae Festuca scabrella 4.33 Johnston et al., 1966 Poaceae Festuca scabrella 3.35 Johnston et al., 1966 Poaceae Festuca scabrella 3.39 Johnston et al., 1966 Poaceae Festuca scabrella 2.39 Johnston et al., 1966 Poaceae Festuca scabrella 3.73 Johnston et al., 1966 Poaceae Deschampsia caespitosa 1.86 Johnston et al., 1966 Poaceae Stipa comata 0.89 Johnston et al., 1966 Poaceae Stipa comata 1.5 Johnston et al., 1966 Poaceae Stipa comata 1.55 Johnston et al., 1966 Poaceae Stipa comata 1.08 Johnston et al., 1966 Poaceae Stipa comata 2.62 Johnston et al., 1966 Poaceae Stipa comata 1.83 Johnston et al., 1966 Poaceae Stipa comata 2.54 Johnston et al., 1966 Poaceae Stipa comata 1.13 Johnston et al., 1966 Poaceae Stipa comata 0.88 Johnston et al., 1966 Poaceae Stipa comata 1.17 Johnston et al., 1966 Poaceae Bouteloua gracilis 2.45 Johnston et al., 1966 Poaceae Bouteloua gracilis 2.6 Johnston et al., 1966 78

Poaceae Bouteloua gracilis 3.24 Johnston et al., 1966 Poaceae Bouteloua gracilis 1.3 Johnston et al., 1966 Poaceae Bouteloua gracilis 2.26 Johnston et al., 1966 Poaceae Bouteloua gracilis 2.51 Johnston et al., 1966 Poaceae Bouteloua gracilis 2.51 Johnston et al., 1966 Poaceae Bouteloua gracilis 2.12 Johnston et al., 1966 Poaceae Bouteloua gracilis 1.77 Johnston et al., 1966 Poaceae Bouteloua gracilis 2.73 Johnston et al., 1966 Poaceae Bromus inermis 1.3 Robbins et al., 1987 Poaceae Bromus inermis 0.8 Robbins et al., 1987 Poaceae Bromus tectorum 2.4 Robbins et al., 1987 Lanning and Eleuterius Poaceae Cortaderia selloana 0.04 1989 Lanning and Eleuterius, Poaceae Imperata cylindrica 1.34 1989 Poaceae Phalaris arundinaceae 1.67 Pahkala and Pihala, 2000 Poaceae Phalaris arundinaceae 4.04 Pahkala and Pihala, 2000 Poaceae Festuca arundinaceae 0.67 Pahkala and Pihala, 2000 Poaceae Festuca arundinaceae 1.94 Pahkala and Pihala, 2000 Poaceae Festuca pratensis 0.53 Pahkala and Pihala, 2000 Poaceae Festuca pratensis 2.54 Pahkala and Pihala, 2000 - - - 0.8 Alexandre et al., 2010 - - - 1.13 Carey and Fulweiler, 2012 - - - 1.53 Carey and Fulweiler, 2012 - - - 1.37 Carey and Fulweiler, 2012 AVERAGE 2.301

Table of biogenic silica content in American wetland species as % dry weight.

Family Genus Species %Bsi Reference Poaceae Elymus virginicus 1.56 Lanning and Eleuterius, 1985 Poaceae Phragmites communis 0.6 Lanning and Eleuterius, 1985 Poaceae Ctenium aromaticum 1.41 Lanning and Eleuterius, 1985 Poaceae Ctenium aromaticum 1 Lanning and Eleuterius, 1985 - Elytrigia atherica 1.6 de Bakker, et al., 1999 Poaceae Spartina anglica 1.22 de Bakker, et al., 1999 Poaceae Festuca rubra 0.76 de Bakker, et al., 1999 Asteraceae Seriphidium maritimum 0.31 de Bakker, et al., 1999 Poaceae Puccinellia maritima 0.25 de Bakker, et al., 1999 Amaranthaceae Suaeda maritima 0.12 de Bakker, et al., 1999 Plantaginaceae Plantago maritima 0.12 de Bakker, et al., 1999 Asteraceae Aster tripolium 0.11 de Bakker, et al., 1999 Ceratophyllaceae Certophyllum demersum 2.8 Schoelynck et al., 2009 drocharitaceae Hydrocharis morsus-ranae 1 Schoelynck et al., 2009 Haloragaceae Myriophyllum spicatum 2.1 Schoelynck et al., 2009 Nymphaeaceae Nuphar lutea 0.8 Schoelynck et al., 2009 Potamogetonaceae Potamogeton cripus 0.9 Schoelynck et al., 2009 Potamogetonaceae Potamogeton lucens 1.4 Schoelynck et al., 2009 Potamogetonaceae Potamogeton natans 1 Schoelynck et al., 2009 Potamogetonaceae Potamogeton perfoliatus 1.1 Schoelynck et al., 2009 Alismataceae Sagittaria saittifolia 2.3 Schoelynck et al., 2009 Poaceae Sparganium emersum 1.35 Schoelynck et al., 2009 Apiaceae Berula erecta 0.4 Schoelynck et al., 2009 Poaceae Glyceria maxima 1.3 Schoelynck et al., 2009 Lamiacae Mentha aquatica 0.3 Schoelynck et al., 2009 Poaceae Phalaris arundinacea 1.3 Schoelynck et al., 2009 Poaceae Phargmites australis 1.5 Schoelynck et al., 2009 Brassicaceae Rorippa amphibia 0.4 Schoelynck et al., 2009 Caprifoliceae Symphoricapos occidentalis 0.105 Bezeau et al. 1966 Fabaceae Vicia americana 0.041 Bezeau et al. 1966 Ericaceae Lyonia ferruginea 0.029 Kalisz et al., 1984 Poaceae Achnatherum hymenoides 2 Blank et al. 1994 Poaceae Elymus elymoides 1.6 Blank et al. 1994 Poaceae Cortaderia selloana 0.65 Ma and Takahashi., 2002 Poaceae Cymbopogon citratus 0.85 Ma and Takahashi., 2002 Poaceae Saccharum officinarum 0.77 Ma and Takahashi., 2002 Poaceae Danthonia intermedia 3.08 Bezeau et al. 1966 Poaceae Elymus cinereus 2.1 Bezeau et al. 1966 Poaceae Elymus innovatus 2.13 Bezeau et al. 1966 Poaceae Festuca idahoensis 3.59 Bezeau et al. 1966 80

Poaceae Bromus inermis 2.47 Bezeau et al. 1966 Poaceae Festuca rubra 2.82 Bezeau et al. 1966 Poaceae Phleum pratense 1.59 Bezeau et al. 1966 Poaceae Andropogon gerardi 5.66 Geis, 1978. Poaceae Sorgastrum nutans 9.41 Geis, 1978 Poaceae Saccharum officinarum 1.91 Lanning and Eleuterius, 1985 Poaceae Sporobolus cryptandrus 3.5 Smith et al., 1971 Poaceae Boutelous curtipendula 5.64 Smith et al., 1971 Poaceae Festuca scabrella 2.79 Johnston et al., 1966 Poaceae Festuca scabrella 2.19 Johnston et al., 1966 Poaceae Festuca scabrella 3.41 Johnston et al., 1966 Poaceae Festuca scabrella 3.61 Johnston et al., 1966 Poaceae Festuca scabrella 3.9 Johnston et al., 1966 Poaceae Festuca scabrella 2.4 Johnston et al., 1966 Poaceae Festuca scabrella 2.94 Johnston et al., 1966 Poaceae Festuca scabrella 4.72 Johnston et al., 1966 Poaceae Festuca scabrella 4.33 Johnston et al., 1966 Poaceae Festuca scabrella 3.35 Johnston et al., 1966 Poaceae Festuca scabrella 3.39 Johnston et al., 1966 Poaceae Festuca scabrella 2.39 Johnston et al., 1966 Poaceae Festuca scabrella 3.73 Johnston et al., 1966 Poaceae Bromus inermis 1.3 Robbins et al., 1987 Poaceae Bromus inermis 0.8 Robbins et al., 1987 AVERAGE 1.97

Table of biogenic silica content in American coniferous tree species as % dry weight.

Family Genus Species %Bsi Reference Pinaceae Abies alba 0.006 Carnelli et al., 2001 Pinaceae Juniperus nana 0.007 Carnelli et al., 2001 Pinaceae Larix decidua 0.109 Carnelli et al., 2001 Pinaceae Picea abies 0.085 Carnelli et al., 2001 Pinaceae Pinus cembra 0.011 Carnelli et al., 2001 Pinaceae Pinus mugo 0.009 Carnelli et al., 2001 Pinaceae Pinus strobus 0.085 Klein and Geis, 1978 Pinaceae Pinus resinosa 0.083 Klein and Geis, 1978 Pinaceae Pinus banksiana 0.184 Klein and Geis, 1978 Pinaceae Pinus sylvestris 0.183 Klein and Geis, 1978 Pinaceae Larix laricina 0.237 Klein and Geis, 1978 Pinaceae Larix decidua 1.372 Klein and Geis, 1978 Pinaceae Picea rubens 0.434 Klein and Geis, 1978 Pinaceae Picea mariana 0.168 Klein and Geis, 1978 Pinaceae Picea glauca 1.048 Klein and Geis, 1978 Pinaceae Pseudotsuga menziesii 0.289 Klein and Geis, 1978 Pinaceae Pseudotsuga menziesii 0.453 Klein and Geis, 1978 Pinaceae Tsuga canadensis 0.162 Klein and Geis, 1978 Pinaceae Tsuga caroliniana 0.138 Klein and Geis, 1978 Pinaceae Abies balsamea 0.182 Klein and Geis, 1978 Pinaceae Abies fraseri 0.129 Klein and Geis, 1978 Pinaceae Larix leptolepis 4.08 Ovington, 1956 Pinaceae Pinus nigra 2.82 Ovington, 1956 Pinaceae Pseudotsuga taxifolia 5.1 Ovington, 1956 Pinaceae Larix eurolepis 4.33 Ovington, 1956 Pinaceae Thuja eurolepis 3.97 Ovington, 1956 Pinaceae Pseudotsuga menziesii 1.13 Cornelis et al., 2010. Pinaceae Picea abies 0.97 Cornelis et al., 2010. Pinaceae Pinus negra 0.05 Cornelis et al., 2010. Pinaceae Picea spp. 2.43 Hodson and Sangster, 1999 Pinaceae Larix spp. 2.21 Hodson and Sangster, 1999 Pinaceae Cupressocyparis spp. 1.73 Hodson and Sangster, 1999 Pinaceae Chamaecyparis spp. 1.24 Hodson and Sangster, 1999 Pinaceae Pseudotsuga spp. 0.99 Hodson and Sangster, 1999 Pinaceae Araucaria spp. 0.85 Hodson and Sangster, 1999 Pinaceae Abies spp. 0.57 Hodson and Sangster, 1999 Pinaceae Tsuga spp. 0.43 Hodson and Sangster, 1999 Pinaceae Sequoiadendron spp. 0.39 Hodson and Sangster, 1999 Pinaceae Taxus spp. 0.28 Hodson and Sangster, 1999 Pinaceae Pinus spp. 0.2 Hodson and Sangster, 1999 82

Pinaceae Cryptomeria spp. 0.18 Hodson and Sangster, 1999 Pinaceae Cedrus spp. 0.09 Hodson and Sangster, 1999 Pinaceae Taxodium spp. 0.08 Hodson and Sangster, 1999 Pinaceae Cunninghamia spp. 0.06 Hodson and Sangster, 1999 Pinaceae Pseudolarix spp. 0.06 Hodson and Sangster, 1999 Pinaceae Thuja spp. 0.06 Hodson and Sangster, 1999 Pinaceae Juniperus spp. 0.04 Hodson and Sangster, 1999 AVERAGE 0.844

Table of biogenic silica content in American deciduous tree species as % dry weight.

Family Genus Species %Bsi Reference Salicaceae Populus tremuloides 0.011 Bezeau et al. 1966 Rosaceae Rosa woodsii 0.051 Bezeau et al. 1966 Fagaceae Quercus geminata 0.133 Kalisz et al., 1984 Betulaceae Alnus incana 0.797 Ovington, 1956 Betulaceae Betula alba 0.611 Ovington, 1956 Fagaceae Quercus spp 0.424 Ovington, 1956 Fagaceae Quercus peiraea 0.399 Ovington, 1956 Nothofagaceae Nosbofagus obliqua 0.467 Ovington, 1956 Fagaceae Quercus rubra 0.414 Ovington, 1956 Fagaceae Fagus sylvatica 0.159 Cornelis et al., 2010 Fagaceae Quercus spp. 0.117 Cornelis et al., 2010 Salicaceae Salix spp. 0.05 Bezeau et al. 1966 Fagaceae Quercus myrtifolia 0.044 Kalisz et al., 1984 Fagaceae Quercus chapmanii 0.038 Kalisz et al., 1984 Fagaceae Quercus geminata 0.133 Kalisz et al., 1984 Betulaceae Alnus viridis 0.13 Carnelli et al., 2001 Saliceae Salix helvetica 0.06 Carnelli et al., 2001 Salicaceae Populus deltides 0.94 Geis, 1978 Juglandaceae Julgans nigra 0.28 Geis, 1978 Juglandaceae Julgans cinerea 0.48 Geis, 1978 Juglandaceae Carya cordiformis 0.26 Geis, 1978 Juglandaceae Carya ovata 0.46 Geis, 1978 Juglandaceae Carya laciniosa 0.32 Geis, 1978 Juglandaceae Carya tomentosa 0.36 Geis, 1978 Betulaceae Carpinus caroliniana 0.62 Geis, 1978 Betulaceae Ostrya virginiana 0.31 Geis, 1978 Fagaceae Quercus rubra 0.44 Geis, 1978 Fagaceae Quercus alba 0.9 Geis, 1978 Fagaceae Quercus velutina 0.13 Geis, 1978 Fagaceae Quercus imbricaria 0.38 Geis, 1978 Fagaceae Quercus marcrocarpa 0.44 Geis, 1978 Fagaceae Quercus muehlenbergii 0.58 Geis, 1978 Ulmaceae Ulmus americana 3.3 Geis, 1978 Ulmaceae Ulmus rubra 2.14 Geis, 1978 Moraceae Maclura pomifera 1.2 Geis, 1978 Moraceae Morus rubra 3.79 Geis, 1978 Lauraceae lindera benzoin 0.08 Geis, 1978 Lauraceae Sassafras albidum 0.07 Geis, 1978 Platanaceae Plantanus occidentalis 0.42 Geis, 1978 Rosaceae Prunus serolina 0.04 Geis, 1978

Rosaceae Prunus viginiana 0.52 Geis, 1978 Aceraceae Acer saccharum 0.98 Geis, 1978 Aceraceae Acer negundo 0.34 Geis, 1978 Tiliaceae Tilica americana 0.49 Geis, 1978 Cornaceae Cornus stolonifera 0.2 Geis, 1978 Oleaceae Fraxinus americana 0.42 Geis, 1978 AVERAGE 0.54

Appendix II.

Calculated terrestrial BSi model parameters for each catchment

Average Average Average BSi Annual Drainage DBSi Flux BSi Dissolution Annual Annual Annual DSi Flux NPP % Production Land Cover Gauges Area DSi Precip Discharge Oct to Apr BSi NPP · % BSi (SW·Q)/(M · SAA) mg/l in ha f3/s kg/ha · yr kg/ha · yr kg/ha · yr kg/ha · yr kg/ha · yr 8064700 9.65 36.06 50762 117 2.14 - 3887.21 2.30 89.45 3.29 5451210 19.13 41.69 58013 179 5.55 3.79 2984.91 2.30 68.69 6.52 5451080 24.67 25.10 20719 18 1.99 1.51 3418.87 2.30 78.67 8.41 6306200 10.79 15.45 22868 29 1.21 0.92 2173.58 2.30 50.02 3.68 Grasslands 6306200 11.25 15.45 22868 18 0.79 - 2533.77 2.30 58.31 3.84 6295113 14.20 18.45 31855 14 0.64 - 2334.95 2.30 53.73 4.84 6340000 8.64 23.25 116281 56 0.37 - 2731.76 2.30 62.86 2.95 6342260 10.59 26.47 37812 35 0.77 - 2720.28 2.30 62.60 3.61 5056340 15.31 20.94 82876 44 0.80 - 2892.45 2.30 66.56 5.22 1022890 2.38 44.91 510 2 0.99 0.16 10754.87 1.97 212.03 8.11 Wetlands 2310947 6.13 45.46 72517 83 0.62 0.72 6581.81 1.97 129.76 20.91 2299950 7.39 42.75 16912 36 1.50 0.81 6182.81 1.97 121.89 25.20 11264500 6.23 46.95 46877 403 5.11 3.00 902.17 0.84 7.62 20.22 10343500 26.78 37.92 2719 10 9.26 7.18 1534.89 0.84 12.97 86.92 5014300 2.31 102.67 3755 109 6.04 2.50 730.00 0.84 6.17 7.50 Coniferous 9196500 2.05 21.77 19631 168 1.59 0.96 626.70 0.84 5.30 6.65 Forest 5124480 1.50 20.00 65783 166 0.30 - 1404.91 0.84 9.87 4.87 6102500 4.60 19.00 28488 176 2.55 - 1004.91 0.84 11.87 14.93 6279795 24.00 23.00 4920 41 17.98 - 1326.91 0.84 118.88 77.90 6332515 7.60 29.00 19165 3 5.35 - 936.91 0.84 7.24 24.67 1349950 1.50 45.13 17766 154 1.25 0.97 2382.83 0.54 12.91 6.53 1362380 1.10 56.17 8158 122 1.45 0.69 2245.09 0.54 12.17 4.79 Deciduous 1545600 1.78 43.49 11965 72 0.96 0.62 2751.77 0.54 14.91 7.75 Forest 4063700 4.22 20.22 35999 72 0.80 0.49 2666.91 0.54 14.45 18.41 1364959 0.96 56.78 1388 21 1.21 0.73 2589.43 0.54 14.03 4.18 1422747 1.66 45.13 6397 56 1.34 0.90 2088.42 0.54 11.32 7.22

Calculated terrestrial BSi model parameters for each catchment, continued from above

Leaching BSi Storage DBSi Storage BSi Storage DBSi Storage Rate Gauges BSi - BSi - 2 BSi - BSi - pro dis R -value pro dis BSidis BSistream BSidis BSistream kg/ha · yr kg/ha · yr kg/ha · yr kg/ha · yr kg/ha · yr 8064700 86.16 1.15 1.22 86.16 1.15 5451210 62.17 0.97 3.16 62.17 0.97 5451080 70.26 6.43 1.13 70.26 6.43 6306200 46.34 2.47 0.69 46.34 2.47 Grasslands 6306200 54.47 3.04 0.45 54.47 3.04 6295113 48.89 4.2 0.36 48.89 4.2 6340000 59.92 2.58 0.21 59.92 2.58 6342260 58.99 2.84 0.44 58.99 2.84 5056340 61.34 4.42 0.46 61.34 4.42 1022890 203.93 7.86 0.25 203.93 7.86 Wetlands 2310947 108.85 20.76 0.15 108.85 20.76 2299950 96.7 24.82 0.37 96.7 24.82 11264500 -12.6 16.23 3.99 -12.6 16.23 10343500 -73.95 79.7 7.22 -73.95 79.7 5014300 -1.33 2.79 4.71 -1.33 2.79 9196500 -1.36 5.41 1.24 -1.36 5.41 Coniferous Forest 5124480 7 4.63 0.23 7 4.63 6102500 -3.06 12.94 1.99 -3.06 12.94 6279795 -66.03 63.88 14.02 -66.03 63.88 6332515 -12.8 20.5 4.17 -12.8 20.5 1349950 6.39 6.11 0.41 6.39 6.11 1362380 7.38 4.31 0.48 7.38 4.31 1545600 7.16 7.44 0.32 7.16 7.44 Deciduous Forest 4063700 -3.96 18.15 0.26 -3.96 18.15 1364959 9.85 3.79 0.4 9.85 3.79 1422747 4.1 6.78 0.44 4.1 6.78