For Peer Review - University of California, Irvinedust.ess.uci.edu/ppr/ppr_RBB07.pdf · Research interests: Quaternary paleoecology, paleoenvironments, vegetation dynamics, climate

For Peer ReviewPhytolith Concentration and Morphotypes in Modern Soils of the Columbia

Basin, USA as Indicators of Vegetation Composition and Cover

Journal: Journal of Biogeography Manuscript ID: draft

Manuscript Type: Original Article Date Submitted by the

Author: n/a

Complete List of Authors: Reyerson, Paul; University of Wisconsin-Madison, Geography Blinnikov, Mikhail; St. Cloud State University, Geography Busacca, Alan; Washington State University, Crop and Soil SciencesGaylord, David; Washington State University, Earth and Environmental Sciences Rupp, Richard; Washington State University, Crop and Soil SciencesSweeney, Mark; University of South Dakota, Earth Sciences Zender, Charlie; University of California Irvine, Earth System Science

Key Words: Grasslands, modern analogs, phytoliths, morphotypes, Columbia Basin, Palouse, soils, North America

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Article Type: Original Article Title: Phytolith Concentration and Morphotypes in Modern Soils of the Columbia Basin, USA as Indicators of Vegetation Composition and Cover Authors: Reyerson, P.E., St. Cloud State University, first author Current Address:

University of Wisconsin-Madison Department of Geography 160 Science Hall 550 North Park Street

Madison, WI 53706 Phone: 320-260-0624

Email: [email protected] Research interests: Quaternary paleoecology, paleoenvironments, vegetation dynamics, climate change

Blinnikov, M.S., St. Cloud State University Current Address: St. Cloud State University Department of Geography Stewart Hall 344 720 4th Ave South St. Cloud, MN 56301 Phone: 320-308-2263 Email: [email protected] Research interests: Quaternary paleoecology, phytolith analysis Busacca, A.J., Washington State University Current Address: Washington State University Department of Crop and Soil Sciences 245 Johnson Hall P.O. Box 646420 Pullman, WA 99164 Phone: 208-885-7505 Email: [email protected] Research interests: Pedology, geomorphology, paleoclimatology Gaylord, D.R., Washington State University Current Address: Washington State University Department of Earth and Environmental Sciences 1146 Webster Physical Sciences Building Pullman, WA 99164 Phone: 509-315-8127 Email: [email protected] Research interests: Quaternary eolian-climatic interactions; clastic sedimentary environments of deposition Rupp, R., Washington State University Current Address:

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Washington State University Department of Crop and Soil Sciences 405 Johnson Hall Pullman, WA 99164 Phone: 509-335-2381 Email: [email protected] Research interests: remote sensing, GIS Sweeney, M.R., University of South Dakota Current Address: University of South Dakota Earth Sciences Department 414 East Clark Street Vermillion, SD 57069 Phone: 605-677-6142 Email: [email protected] Research interests: Geomorphology, sedimentology, paleoclimatology Zender, C.S., University of California-Irvine Current Address: University of California-Irvine Department of Earth System Science 3200 Croul Hall Irvine, CA 92697 Phone: 949-824-2987 Email: [email protected] Research interests: Climate, surface-atmosphere interaction

ABSTRACT Aim: To assess feasibility of using 1) total opal concentration (TOC) of phytoliths in modern soils and 2) morphotypes in soil assemblages to infer paleovegetation cover and composition. Location: Columbia Basin, southeastern Washington State, USA. Methods: Phytolith concentration in modern soils was measured and all morphotypes counted from 37 plots in undisturbed grassland sites with different values of shrub and grass cover. Estimates of vegetation cover were obtained using two methods: in-field visual inspection and NDVI values derived from georeferenced multispectral digital imagery. Vegetation composition by species was determined in-field. Linear regressions were used to predict vegetation cover based on TOC in soils. Canonical Correspondence Analysis (CCA) was used to assess the degree of similarity between field plots; plant species and morphotypes in soils comprised the main matrix. Plant species and phytolith morphotypes comprised the environmental matrix. Results: All plots yielded abundant phytoliths. TOC in modern soils ranged from 0.99% to 4.28% of dry weight. In-field inspection and NDVI values revealed direct positive relations (0.21 R2 for in-field estimation and 0.49 R2 for NDVI) between vegetation cover

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and TOC. The first two CCA axes explain about 32% of the total variance in the data (28% for the 1st axis); the morphotypes-species correlations for the axes are moderately high (Pearson correlation coefficient r=0.863 for the first axis, and r=0.660 for the second axis), suggesting that phytoliths reflect vegetation plot compositions with moderately high certainty. Main conclusions: Phytoliths allow reconstruction of modern vegetation of grasslands and shrublands on the Columbia Basin. Estimates of vegetation cover and composition (from in-field and NDVI data) reflect annual and inter-annual trends, while the phytolith assemblages from modern soils reflect the vegetation averaged over a few decades. While individual species’ presence or absence cannot be detected with certainty from the phytolith assemblages, a few regionally dominant grasses from different genera are detected in the soil samples. Keywords Grasslands, modern analogs, phytoliths, morphotypes, Columbia Basin, Palouse, soils, North America INTRODUCTION Modern assemblages are frequently used in paleoenvironmental reconstructions involving pollen, diatoms, macrofossils and opal phytoliths (e.g., Overpeck et al., 1986; Guiot et al., 1993; Fredlund & Tieszen, 1997b; Whitmore et al., 2005). Since the pioneering work of Fredlund and Tieszen in the Great Plains (1994, 1997a), phytolith assemblages have become a powerful method by which to infer paleovegetation composition in arid environments where pollen is not well preserved. More recently, Dehlon et al. (2003) used phytolith analogs to infer changes in Holocene-aged Mediterranean vegetation of the middle Rhone valley in France. Bremond et al. (2005) demonstrated the utility of using phytoliths to reconstruct vegetation along a forest-savanna transect in southeastern Cameroon in Africa. Lu et al. (2005) provided compelling evidence that phytolith morphotypes can be used to reliably estimate mean annual precipitation, temperature and other climate variables on regional scales in China.

Blinnikov et al. (2002) used the modern analog method to reconstruct paleovegetation of the interior Pacific Northwest (Columbia Basin) using phytoliths from long (ca. 100,000 years old) sections in the Palouse loess. However, the modern dataset used in that study was chosen to distinguish forest, grassland and shrubland vegetation of the large region, not specific grassland types, and did not provide adequate quantitative estimates of the modern vegetation cover. This study attempts to fill the gap in knowledge of the phytolith production rates under a range of grassland and shrubland vegetation types in the modern Columbia Basin. By focusing on a smaller region than most of the above studies, this study attempts to test the limits of applying phytoliths to resolving local-scale differences in vegetation cover and composition.

In this paper, we refer to vegetation composition as the presence and abundance of certain silica-producing plant species and vegetation cover as percent of plant canopy cover, which is estimated by in-field visual inspection and also normalized difference vegetation index (NDVI). Many studies have shown that vegetation composition influences, at the very least, the presence and abundance of specific phytolith


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morphotypes (Verma and Rust, 1969; Blinnikov et al., 2002; Reyerson, P.E.). However, few studies have focused on the relationship between vegetation cover and total opal concentration (TOC) in modern soils (expressed as a percentage of dry weight of extracted opal relative to the dry bulk soil weight). The relationship between vegetation cover and TOC remains unclear. A quantification of this relationship needs to be addressed for more accurate paleoenvironmental reconstructions.

The Columbia Basin is an arid portion of the U.S. Pacific NW that has been subject of many paleoenvironmental reconstructions using pollen, phytoliths, faunal burrows in loess or stable isotopes as proxies (Barnosky, 1985; Minckley & Whitlock, 2000; Whitlock et al., 2000; O’Geen & Busacca, 2001; Blinnikov et al., 2001; Blinnikov et al., 2002; Reyerson, P.E.; Blinnikov, 2005; Stevenson et al., 2005). Few sedimentary repositories have existed on the Columbia Basin during the Late Pleistocene to Holocene which have favored either pollen or macrofossil preservation. Lakes are scarce, and the majority are less than 10,000 years old (Mack et al., 1976). Minckley & Whitlock (2000) presented a regional modern pollen assemblage dataset to assess the spatial variability of the influx of pollen across the entire Pacific Northwest; but. that study focused mostly on sites closer to the Cascades, where lakes are more abundant. In addition to this geographic limitation, their study does not provide a record of potentially revealing phytolith populations that can accompany and complement pollen accumulation. Thus, the semi-arid interior of the Columbia Basin may have been under-represented by their findings.

In the absence of a pollen record, opal phytoliths have emerged as a powerful proxy for determining vegetation composition in arid regions in North America (Fredlund & Tieszen, 1994; Fearn, 1998; Kelly et al., 1998; Kerns, 2001; Blinnikov et al., 2001, 2002; Blinnikov, 2005). Phytoliths are produced by many taxa (primarily by grasses, but also by forbs, shrubs and trees) in their cell walls and intercellular spaces. Their production is taxonomically specific and is genetically controlled (Hodson et al., 2005). The abundance of phytoliths produced and their persistence in soil are considered by many researchers to be directly related to the plant biomass of silica-producing species and the amount of time the vegetation persisted at the site (Bartoli & Wilding, 1980; Fredlund & Tieszen, 1994; Alexandre et al., 1997b; Blinnikov, 2005; Blecker et al., 2006). Phytoliths are presumed to accumulate mostly in situ (Piperno, 1988, 2006), but surface sedimentary processes may rework some of these particles (Fredlund & Tieszen, 1994). In temperate, semi-arid areas such as the Great Plains or the Columbia Basin, phytoliths can persist for thousands of years in soils under a variety of pH and moisture conditions (Kelly et al, 1998; Blinnikov et al., 2001; Blinnikov et al., 2002; Reyerson, P.E.). Phytoliths have been utilized to identify the spread of grasslands in the Eocene and early Miocene (Strömberg, 2004). Modern phytolith assemblages and distributions for a given region must be recognized before paleoenvironmental reconstructions can be undertaken with any certainty, however (Bowdery, 1998; Carnelli et al., 2001; Lu & Liu, 2003; Blinnikov, 2005).

This study explores the relations between phytolith assemblages and documented modern vegetation compositions and cover for selected grassland and shrubland sites within the Columbia Basin; the goal of this comparative study is to provide a calibration tool for on-going and future paleoclimatic research. Correlations between the volume of plant biomass/cover and soil opal on modern plots and documentation of the relations


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between plant species and soil morphotypes from those same plots will permit researchers to reconstruct both the composition and structure of past vegetation. Such reconstructions can be used as vegetation boundary conditions in paleoclimate models. These paleobotanical inputs are necessary to understand dust flux rates and direction of wind in a sand-loess coupled system in the past, particularly during the last deglaciation, when loess accumulation rates were higher in the region (Sweeney et al., 2005, Mahowald et al., 2006). Two specific research questions are explored in this paper: (i) can assemblages of phytolith morphotypes from modern soils collected on standard vegetation plots (6 x 6 m) within a relatively small geographical area (ca. 30,000 km2) reliably indicate modern grassland and shrubland vegetation composition and (ii) can measures of TOC in modern topsoil be used to predict visually-estimated plant percent cover obtained through remote sensing (e.g., NDVI) on the same plots? By answering these questions, future research can then extol potential insights that such understanding will have for constraining regional climate circulation models and paleoenvironmental reconstructions.

Study Area The Columbia Basin is a subprovince of the Columbia Intermontaine

Physiographic Province (Freeman et al., 1945). It is a basin surrounded by the Blue Mountains on the south, the Cascade Mountains on the west, the Okanogan Highlands on the north, and the Rocky Mountains on the east (Fig. 1). The Columbia Basin lies south of the maximum extent of the Cordilleran Ice Sheet, and mainly east of the Columbia River. Its total area encompasses over 160,000 km2, and it is the structural and topographic low within the Intermontane Province (Baker et al., 1991).

Part of the Columbia Basin in SE Washington State is blanketed by thick layers of loess (windblown silt). Known informally as the Palouse Loess, it is one of many important geologic features found within the Columbia Basin (Baker et al., 1991; Busacca & McDonald, 1994). The source of loess is primarily slackwater sediment from late-glacial Lake Missoula outburst floods (Bretz, 1969; Baker et al, 1991; Sweeney et al., 2005) that accumulated primarily in lowland areas along the Columbia River and upwind, southwest, of the Palouse. Other, secondary source areas that could have contributed eolian silt include the Miocene-Pliocene Ringold Formation, Pleistocene outwash sediment and Quaternary alluvium from the Columbia River Valley (Busacca & McDonald, 1994). Modern soils that developed on the loess are mostly Mollisols or Aridisols (Baker et al, 1991). These surface soils form along a strong climatic (primarily moisture) gradient that correlates with a decrease in precipitation across the Columbia Basin from NNW to SSE (Bolig et al., 1998). A detailed chronology of the loess deposition is well established (Baker et al., 1987; McDonald et al., 1988; Baker et al., 1991; Busacca et al., 1992; McDonald & Busacca, 1992; Busacca & McDonald, 1994; O’Geen & Busacca, 2001; Sweeney et al., 2004; 2005). The use of luminescence techniques and tephrochronology have greatly facilitated reconstruction of the loess depositional history (Berger, 1991; Busacca et al., 1992; McDonald & Busacca, 1992; Berger et al., 1995; Richardson et al., 1997; Richardson et al., 1999; King et al., 2001). METHODS


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Field data collection A total of 37 modern soil samples were taken for the study. Sample sites were chosen primarily on the basis of their undisturbed nature. Very few such natural sites exist in this heavily agricultural region, which greatly limited our choices. We eventually chose four nature preserves along a climatic gradient all of which have substantial areas covered by pre-agricultural vegetation. The preserves are roughly aligned NNE-SSW, with relatively cooler and wetter preserves in the north. Two field sites were located in southeastern Washington (Marcellus and Kahlotus Ridge preserves of the Washington Department of Natural Resources), and two in northeastern Oregon (Lindsay Prairie and Boardman Preserves of the Nature Conservancy). Table 2 lists exact geographic coordinates. Despite our best efforts to find pristine sites, all preserves contain some invasive species, most notably annual cheat brome (Bromus tectorum L.). However, due its relatively recent introduction, B. tectorum is expected to have a minimal impact on the modern phytolith record. Each soil sample was obtained as an aggregate of 10 random pinches within a 6x6 m square plot. The size of the plot was larger than 4 m2 plots used in Daubenmire (1970), so that they could be located from the aerial surveys conducted as part of this study. While the plots were clustered geographically into 4 nature preserves, we assumed each plot to be an independent sample. To achieve this, we attempted to maximize the vegetation variability within each preserve to include as many combinations of grassland and shrubland types and cover as possible in our plots. Each square plot was aligned to the north using a compass.

The UTM coordinates of the southwest corner of each study plot were recorded using a professional mapping grade handheld Trimble GPS receiver with differential correction to the nearest permanent station, which allowed us to achieve about 1 m horizontal positional accuracy. Next, a permanent spike with a tag was placed on the southwest corner. The linear boundaries were then entered into a GIS polygon layer.

Overhead color images of each study plot were obtained to create a permanent archive of plant cover and composition using a hand-held camera mounted on a 12-ft. tall pole. Plant cover and composition and percent plant cover for each species were estimated in the field at the height of the flowering season (mid-May 2003). Due to budget constraints, airborne surveys of the same plots were flown in May, 2004 using a digital multispectral camera with 10cm on-the-ground resolution. All evident plant species that had been identified and recorded in May, 2003 were deemed appropriate given the similarity in growing seasons between the two years. Each plant was assigned to one of 4 functional groups (shrub, grass, forb or legume). The region of study has very few C4 grasses and none were encountered on field plots, thus all the grasses in this study are C3.

Ten random pinches were taken from each study plot, placed in a plastic bag and thoroughly mixed. These samples were taken at the surface free of detritus to a depth of less than 2 cm. Any evident plant detritus was manually removed before sampling the soil. The total amount of sample procured from each plot was approximately 50 g. No attempt was made to subsample within each plot.

Phytolith processing


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Phytoliths were extracted using the modified wet oxidation technique of Pearsall (2000). Soils were left in a drying oven at approximately 80 ºC overnight and then weighed and placed in 100 ml beakers. Carbonates and most organics from the soil were removed by digesting with 10% HCl for 15 minutes and then boiling in 70% HNO3 with a pinch of KClO3 for 1.5 hours. After deflocculation in 5% sodium pyrophosphate solution, the samples were subjected to heavy liquid flotation in ZnI2 solution calibrated at 2.3 g/cm3 density. This was repeated three times to ensure that most of the opal in the sample was retrieved. The ratio between the resulting dry weight of retrieved opal and the original dry soil weight yielded the total opal concentration (TOC) and is expressed as a percentage in Table 2.

Visual inspection of the resulting opal under a light microscope verified that the extraction was reasonably pure (less than 5% of observed fragments were non-silica in origin). Steps were taken to remove as much opal from the samples as possible, and to avoid any loss. Microscopy of the decanted liquid in a few samples did not reveal loss of phytolith material.

Individual opal phytolith shapes (morphotypes) were tallied under a Leica optical microscope, model DMLB, at 400x magnification mounted in immersion oil type A, which permitted the rotation and movement of the phytoliths on the microscope slide to augment the estimation of true three-dimensional shapes. Twenty-six distinct morphotypes were identified and recorded for each sample (Fig. 2). Digital images and permanent slides were created for future reference.

Morphotypes were scanned in a row-like fashion, from left to right and from top to bottom of each slide. Any object whose size was between 20 µm and 120 µm was counted, and was assumed to be biogenic silica in origin, unless it was apparent that it was not (e.g., dark organic material or distinctly shaped thin mica plates). Morphotype classes were modified from Blinnikov (2005). At the conclusion of the counting, seven of the originally defined morphotypes were removed from consideration because they occurred only in trace amounts. These morphotype classes were not considered statistically significant because they occurred in such low numbers to have little impact. For example, only 7 saddle (SA) morphotype grains were counted for the entire study, out of a total of over 11,000. Further, these morphotypes were not considered primarily diagnostic; that is, their presence or absence did not indicate the presence or absence of a key species. Refer to Table 1 for final morphotypes, nomenclature and species associations. Image processing Additional estimates of long-term vegetation cover/biomass were obtained by measuring Normalized Difference Vegetation Index (NDVI) on bands 4 and 3 of color-infrared digital aerial imagery, using a standard algorithm (Jensen, 1996, p. 182). A chartered plane was flown in May 2004 to obtain GPS-referenced digital images with 4 channels (blue, green, red and near-infrared) comparable in spectral sensitivity to the first four bands of Landsat ETM+. Image resolution was approximately 0.16 m2 on the ground per pixel. GPS-obtained vector files of the sample plot boundaries were matched to the existing digital orthoquads of the preserve areas to extract 37 grids of pixels


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corresponding to the 37 plots. NDVI values were estimated for each plot by averaging pixel values (from about 250 pixels per plot) to yield a mean NDVI value for each plot. Statistical Methods Two statistical methods were used to compare phytolith assemblages to recorded vegetation cover and composition. First, linear regression was utilized to determine if there is a relation between total opal concentration (TOC) in soils and vegetation cover. Second, Canonical Correspondence Analysis (CCA) (ter Braak, 1986; McCune et al., 2002; run in PC-ORD) was utilized to evaluate the overall relationship between the plots (rows) and morphotypes (columns) in the main matrix and plots (rows) and percent cover by plant taxa (columns) in the environmental matrix. We chose 10 morphotypes for this analysis including straight rectangular plates, two types of rondels (pyramidal and keeled or horned), short wavy trapezoids, Stipa-type bilobates, grass seed epidermis long cells, regular long cells (rods), silicified hairs and trichomes, epidermal non-grass phytoliths, and blocky forms. This level of phytolith classification was sufficiently broad to allow unambiguous identification of all of these forms. More detailed classifications could be used, but would result in much longer time necessary to count a statistically valid number of grains. The plant taxa used were Agropyron spicatum, Bromus tectorum, Festuca idahoensis, Poa secunda, Stipa spp., Asteraceae forbs, and shrubs. Together, these accounted for about 95% of all plant cover, with the remainder mostly in representatives of Liliaceae and Fabaceae families, which do not produce phytoliths. CCA was chosen because it allows simultaneous ordination of morphotypes (“species”) and plots with plant taxa (“environmental variable”) and plots in a single analysis. This method also allows quick visualization of relations between morphotype and plant taxa data in a single graph. We did not attempt to use CCA to investigate actual environmental (climate or soil) variables in this study given the limited coverage of suitable pristine sample sites in the Columbia Basin. RESULTS Total Opal Concentration in Soils Estimates of Modern Vegetation Cover and NDVI Mean annual precipitation decreases from NNW to SSE, and mean annual temperature increases from north to south across the study area. As a result, Lindsay Prairie and Boardman Reserve are drier and warmer sites, while Marcellus and Kahlotus Ridge are cooler and wetter (Fig. 1). The northernmost Marcellus preserve north of Ritzville, WA is located within the Artemisia tridentata–Festuca idahoensis association of Daubenmire (1970, 1972) and is cooler and wetter than the Kahlotus Ridge site. The Marcellus site also has the highest percentage of shrubs and Festuca cover. The Kahlotus Ridge preserve north of Kahlotus, WA is located in the middle of a grassland immediately north of the Snake River in Agropyron–Festuca association of Daubenmire (1970, 1972) and has very few shrubs. Boardman preserve, OR is a large area of more arid grassland south of the Columbia River. It contains high percentages of Stipa and Agropyron bunchgrasses and some Artemisia shrubs on canyon slopes. The soils more are sand-enriched compared to Marcellus and Kahlotus Ridge. Lindsay Prairie, OR is a small preserve south of


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Boardman, similar to the Boardman preserve and the Kahlotus preserve in that both contain sandy soils and relatively high proportions of Stipa and Agropyron grasses and some shrubby plots. Lindsay Prairie has some unique communities present such as Oryzopsis communities not found in the other preserves examined in this study. As stated above, each plot evaluated in this study was considered separately. In this way, each plot could be compared to other plots individually, regardless of proximity. Each sample plot was selected for its own unique vegetation attributes in order to maximize overall variability of vegetation cover and composition, but it was not possible to have a uniform geographic coverage across the entire region. Modern vegetation cover values on the study plots range from 9% to 98% (Table 2). Grasses are the most dominant functional group, averaging 34% of composition by cover. Of the four preserves, Kahlotus Ridge reported the highest overall vegetation cover (52%), the highest proportion of grasses (44%) and the lowest proportion of shrubs (4%). Marcellus reported a mean vegetation cover of 51%. Its proportion of grasses to shrubs was more balanced than at Kahlotus Ridge, with 29% covered by grasses and 19% by shrubs. Lindsay Prairie averaged 48% vegetation cover, with 42% grasses and 5% shrubs present. One plot at Lindsay Prairie had the overall highest vegetation cover for any plot in the study, at 98% (L4). This is relevant because it shows the high degree of intra-preserve variability, suggesting a low interrelatedness between the plots. Boardman’s average vegetation cover was also overall the lowest, at 32%. Plot B7 in Boardman Reserve held the lowest vegetation cover of all plots, at 9%. The proportion of grasses and shrubs there was 23% and 8%, respectively. As was expected, as MAP increased and MAT decreased, vegetation cover increased.

The vegetation densities for each plot are reflected in the total opal concentration (TOC). TOC for all 37 modern samples averaged about 2.5% (Fig. 6). Overall, Boardman and Lindsay Prairie had the lowest total opal concentrations (1.4% and 2.2% respectively). Marcellus data yielded the highest average TOC at 3.3%, while Kahlotus Ridge reported 3.0%. The highest TOC was observed at M10 (4.3%; Marcellus preserve) and lowest at B9 and B5 (0.99%; Boardman preserve). All TOC values >3% corresponded to plots from either Kahlotus Ridge or Marcellus preserve (the northern preserves), which have overall higher vegetation (and grass) cover. Plot L4 (Lindsay Prairie) - with highest vegetation cover reported - had only 2.3% TOC. However, this plot is composed mostly of mainly shrubs, not grass. Shrubs, overall, tend to produce a lower proportion of phytoliths when compared to grasses. The five plots which reported the lowest TOC values were all located in the Boardman preserve, with higher average proportions of shrubs and/or weedy annual grass species (Bromus tectorum, Vulfia octoflora), which probably contributed less opal to the soils than grasses normally would

A linear regression analysis was used to determine the relation between vegetation cover on plots (as established visually in the field by an experienced observer) and TOC. The best fitted least squared linear regression equation was VC = 10.7 * TOC + 20.1, where TOC is total opal concentration, and VC is vegetation cover (Fig. 3). As can be seen in Figure 3, there is a direct positive relationship between TOC and vegetation cover, albeit a weak one (R2 = 0.21) Regressions for grasses or shrubs alone had lower R2 values (0.14 for grasses only and 0.07 for shrubs). Despite our best efforts, the ranges of shrub cover values were rather inconsistent. Most plots had shrub cover values of <10% and only 5 had values >25% No plots were found to contain between 12


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and 25% of shrub cover. More continuous ranges were obtained for grass cover with TOC values falling between 8% and 68%, with no major gaps.

In-field estimates of plant cover may be misleading, because it is based on possibly subjective visual observations and, in our case, only a single visit to each plot in May of 2003. A remote sensing technique can provide an objective measure of photosynthetically-active biomass, a factor that is something that human eye cannot see (Fig. 4). Values of vegetation biomass expressed as a normalized difference vegetation index (NDVI) were measured from digital aerial images. Utilizing a separate linear regression, the same TOC values from modern soils were implemented to predict NDVI values. The resulting graph is presented in Fig. 5. The relationship between NDVI values and TOC was more strongly positive than with the in field inspection method. The regression equation is NDVI = -0.432 + 0.0475 x TOC (R2 = 0.49, p<0.001). The robustness of the strong relation between TOC and NDVI should be assessed by applying our methods or equivalent techniques in other regions. This predictive relation between TOC and NDVI can help reconstruct paleo-NDVI from TOC measurements. NDVI can be used to constrain the vegetation boundary conditions in dust entrainment models (e.g., Zender et al., 2003).

Analysis of Modern Morphotypes The majority of the modern samples had similar morphotype assemblages (Fig. 6). Most morphotypes were found on every plot, but in different proportions. Most assemblages were dominated by plates (PR; 10% to 26%), short trapeziforms (WS; 8% to 25%), long cell rods (LR; 5% to 24%), oblong/oval rondels (RO; 4% to 15%), and keeled rondels (RK; 2% to 11%), which are all grass phytoliths. The presence of morphotypes for non-grasses were likewise similar between the plots, with a greater proportion of polygonal forms (EP; 12% to 29%) than blocky forms (BL; 3% to 12%). (Refer to Table 3 for morphotype nomenclature.) The ubiquitous presence of morphotypes on most plots was expected, considering the relatively homogenous species composition on all study plots in the study area. All plots were located in upland non-forested sites with the same seven regionally dominant species of grasses and shrubs (Agropyron spicatum, Festuca idahoensis, Poa sandbergii, Stipa comata, Artemisia tridentata, and two Chrysothamnus spp., Table 2).

Grass phytoliths, which include thirteen morphotypes identified in this study, made up between 62% and 84% of all phytoliths in soil samples (Fig. 6). The PR type accounts for 10% to 26% of all morphotypes, and is the dominant short cell. However, the PR type is common in many grasses and is not diagnostic of any specific plant species or genus. The WS (short crenate) type ranges from 8% to 25% of all morphotypes. This opal morphotype is most common in Poa and Koeleria, but can also be found in Bromus. The third most common morphotype in the assemblages was the LR (smooth rod) type, which ranged from 5% to 23% of the total morphotype population. The phytoliths of microhairs (HH) and trichomes (HT) ranged from 0% to 4%. Non-grass types, made up of EP (polygonal) and BL (blocky) forms, range from 24% to 27% of all phytoliths. These two morphotypes represent a great variety of forms, and were not distinguished in this study, but that could be differentiated further with careful screening.


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The eigenvalues for the first CCA axes were λ1=0.027 and λ2=0.004. The species-environment correlations for the axes are moderately high (Pearson correlation coefficient r=0.863 for the first, and r=0.660 for the second). In the DCA environmental matrix, the first axis appears to be most positively correlated with the Bromus tectorum and Stipa cover presence (the former is an annual weed, the latter is a native grass associated with dry soils). The same axis is negatively correlated with the cover of native mesic grass species Poa, Agropyron and Festuca. Therefore, the axis represents a moisture gradient. The second axis was most positively correlated with forbs; Agropyron and Festuca cover and most negatively correlated with Poa, Stipa and shrub cover. Therefore it represents a grassland to shrub steppe gradient. Essentially, both axes capture one complex environmental gradient of shrubland-grassland transition across the region based on moisture. A tight clustering is shown for the samples on the CCA biplot showing two first axes of the main matrix (Fig. 7). The samples from the warmer and drier preserves (Lindsay Prairie and Boardman) cluster in the lower right of the biplot, while the samples from cooler and wetter preserves (Kahlotus Ridge and Marcellus) tend to be located more to the upper left. The northern preserves reported a higher abundance of cooler/wetter climate species, such as Festuca, while the southern preserves reported higher proportions of warm/dry climate adapted Agropyron, Artemisia, and Stipa occurring at overall lower densities.

Additionally, CCA helped us assess the relative position of each plot with respect to different morphotypes from the main CCA matrix. Wavy trapezoids, bilobate Stipa-type and silicified grass hairs, hair bases and trichomes tend to occur on plots from more southern preserves with a higher incidence of Stipa, Oryzopsis (a related genus to Stipa) and Bromus. The oval and keeled rondels and plates, on the other hand, are better represented in the northern preserves on plots with a high incidence of either Festuca, Agropyron or both. Epidermal non-grass phytoliths occur on plots with higher percentages of shrubs and forbs. Blocky forms occur on plots where Festuca and shrubs are common, including a few from Marcellus preserve. Overall, the CCA results agree well with the expected pattern of production of phytoliths in plants based on modern plant studies (Table 1, Blinnikov, 2005).

DISCUSSION Total Opal Concentration in Soils Estimates of Modern Vegetation Cover and NDVI Total opal concentration (TOC), expressed as percent of extracted plant opal relative to the total dry weight of soil, is positively correlated to the observed vegetation cover on the plots (percent ground cover by species and plant functional group) and Normalized Difference Vegetation Index (NDVI) as measured by color-infrared aerial imagery. Although many studies focus on interpreting phytolith assemblages to reconstruct the composition of vegetation found at a particular site (Bartolome et al., 1986; Fisher et al., 1995; Blecker et al., 1997; Fredlund & Tieszen, 1997a; Dehlon et al., 2003; Blinnikov, 2005), few have focused on the TOC in the soil (Prebble, 2003b; Blecker et al., 2006). With accurate TOC values, it is possible to reconstruct paleovegetation cover, which can lead to a clearer understanding of vegetation ecotone dynamics and climate. Indeed, there is precedence for determining TOC values in paleoreconstruction studies. As


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demonstrated by earlier studies, TOC was found to be a good indicator of paleovegetation, especially whether it was a grassland or a forest (Verma & Rust, 1969; Wilding & Drees, 1971). For example, these two papers showed that in Minnesota and Ohio, respectively, prairie sites had 3 to 10 times the TOC values in the top soil compared to forest sites. In the Columbia Basin, paleovegetation cover is an important factor to gauge because of its influence on the eolian activity and soil formation in the past. Vegetation cover also may directly reflect soil moisture (Morrow & Friedl, 1998). Several studies have linked sand dune reactivation and loess deposition with drought (Muhs et al., 2000; Mason et al., 2004; Sweeney et al., 2005; Miao et al., 2007, Sweeney et al., in press). Others have linked increased eolian deflation to a reduction of vegetation cover (Snelder & Bryan, 1995; Arens et al., 2001; Mason et al., 2001; Engelstaedter, 2003; Sweeney et al., in press) and decreased root mass (Dong et al., 2001; Wang et al., 2003). Surface roughness from greater vegetation cover can increase drag on the wind, and protect soils from deflation (Mason et al., 1999; Dong et al., 2003; Crawly and Nickling, 2003). Studies have shown that increased soil moisture and vegetation cover leads to increased rates of pedogenesis (Huang et al., 2001; Wolfe et al., 2001; Gustavson & Holliday, 1995).

Regression analysis revealed a significant positive trend. As TOC values increased, vegetation cover also rose (Fig. 3). Thus, our TOC values are able to help us predict the modern vegetation cover on our sample plots. Although we treated all plots as independent samples, the plots in southern preserves under warmer and drier conditions at present (Lindsay Prairie and Boardman) had lower overall vegetation densities and also lower TOC values than their counterparts to the north,. Thus, we do observe the regional signal in the TOC values. When a given plot is over-predicted (such as B2) or under-predicted (such as L4), it may be the result of short-term fluctuation in biomass at the specific site. For example, plot L4 had vegetation cover of 98%, yet its TOC value was 2.3%. TOC values reflect an averaging of vegetation densities temporally. Therefore, the regression equation predicted the vegetation cover on L4 at 45%. However, 40% of the vegetation cover on L4 is explained by exotic annual Bromus tectorum. While B. tectorum produces phytoliths, its contribution to the assemblage per unit of covered area is likely to be much lower than for native bunchgrasses (e.g., A. spicatum or F. idahoensis), given its much smaller biomass and tendency towards interannual fluctuations in growth. If B. tectorum cover is ignored, the predicted value (45%) is closer to that observed (58%). However, the full ramifications of ignoring exotic species in a plot has not been thoroughly researched, and should be approached with caution. Plot B2, with 13% observed cover, had a TOC value of 2.1%, which is roughly the median value for the entire dataset. This overabundance of TOC may suggest that the observed plant cover is lower than suggested by phytoliths. Therefore there may be some inheritance of phytoliths on the plots from earlier growing episodes.

NDVI regression was stronger than that based on visual observation of plants in the field. While in both cases measurements were performed within a single day (no repetitive estimates of cover or NDVI), NDVI captures the difference in photosynthetic rates of plants as opposed to merely recording plants covering a certain percentage of the ground surface. Higher photosynthetic activity results in more active growth of plant tissues and should translate into overall higher silica deposition in plants as well. Two sources of uncertainty play a role in estimating NDVI in our study: imprecise


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georeferencing of the aerial photos and seasonality. The first is easier to estimate, because we were able to match the aerial imagery with our rectified in-field square plot boundaries in a GIS. Since the georeferencing positional accuracy is around 1 m, an offset by 1 m linearly in both X and Y directions would allow 11 m2 out of the total 36 m2 of the plot be excluded from the pixel count, and an additional 11 m2 to be brought in from the surrounding plot vegetation. All of our plots, however, were chosen inside a larger patch of similar, homogenous vegetation, so this effect was probably not very pronounced. The second source of error is the fact that only one digital fly-over image was available per plot taken on a specific day at the height of vegetative season for all preserves. We do not have multidate or multiyear samples. However, the reasonably high linear relationship between TOC and NDVI suggests that this again was not a significant problem. Modern Morphotypes Distinguish Shrublands from Grasslands and Dry Grasslands from Mesic Grasslands The overall diversity of phytolith morphotypes in our samples was relatively low. We deliberately chose to use only a few broad categories of phytoliths in this study, avoiding very detailed classification. While more detailed morphotype classifications can be tested, our goal was to assess the broad applicability of the method to resolving significant differences in plant cover and composition. Most samples were dominated by the same seven morphotypes: plates (RP), short trapeziforms (WS), keeled and oval rondels (RK and RO, respectively) and long cell rods (LR) from grasses and blocky (BL) and epidermal polygonal (EP) forms from shrubs and forbs (Fig. 6). This is not surprising, given the relatively few dominant species that are phytolith producers in the region (Blinnikov, 2005). No panicoid or chloridoid species of Poaceae are dominant in the region. Thus, essentially all phytoliths in our records come from Fectucoid native (Agropyron, Festuca, Poa) and non-native (Bromus tectorum) grasses. The bilobate forms of Stipa are not “true bilobates”, but rather trapezoids of transitional type (Mulholland, 1989). A handful of true bilobates and saddles were identified in the record and most likely were also contributed by Stipa and/or Oryzopsis.

This study focused on a narrow environmental gradient ranging from dry sagebrush communities to moderately mesic grasslands within a relatively small geographic area and all clustered in only 4 preserves. Much higher variability of morphotypes in the Great Plains (Fredlund & Tieszen, 1994) and across China (Lu et al., 2005) is consistent with the continent-spanning gradients in those studies. However, we did find important differences in our assemblages highlighted in the CCA study.

CCA revealed that there is a good agreement between the vegetation growing on the plots and morphotypes found in soil (Fig. 7). For example, plots with Stipa-type bilobate phytoliths in soils are also the ones that support Stipa today. Plots with a high incidence of silicified hairs (HT) had a high occurrence of Bromus tectorum, which has heavily silicified hairs. Plots with a high incidence of epidermal polygonal phytoliths from shrubs are the ones with high shrub content today. Contrary to our expectations, there was less correspondence between shrub cover and blocky forms. Blinnikov (2005) reported that blocky forms occur primarily in Artemisia and perhaps in other shrubs from Asteraceae family in the region. However, ‘blocky’ is a catch-all category of phytoliths


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that are large in size and 3-dimensional in appearance. Certain types of grass bulliform cells also may resemble these. Keystone-shaped bulliforms, which are very distinct, were not found in this study, but some blocky forms may have been contributed by grasses. Also, our collection of forbs from the area is far from complete. It is possible that some yet unstudied taxa contribute blocky forms in non-shrub communities.

The encouraging result was that plants above ground seem to contribute morphotypes in situ. Few studies exist that prove this connection (Kerns et al., 2001). Generally, phytoliths are expected to provide a rather local signal, since it is assumed that there is little post-depositional translocation (Piperno, 1988). Fredlund and Tieszen (1994), however, found that in the Great Plains, only about 50% of phytoliths from modern soils could be considered truly local (i.e., from within a few meters of a 4x4 m plot); the rest came in from within one hectare. While our study does not dispute that claim, in our study, it seems that there is a strong correspondence between the presence of certain phytolith producers with distinct shapes (e.g., Stipa) in our plots that do deposit these primarily locally (within 6x6 m plots). In most cases, we did not find a large number of foreign phytoliths that we could not link to the present vegetation.

Another issue with phytolith records in soils is inheritance. There were some plots in our study that had mismatches between the morphotypes in soils and species’ composition on the plots. For example, plot B8 (Boardman preserve) had a high incidence of blocky phytoliths presumably from shrubs, but no shrubs in the surface record. However, history of the site (TNC staff, pers. comm.) indicates that a recent fire may have destroyed the pre-existing sagebrush on this site, which takes longer to recover than grasses. Phytoliths in the upper 2 cm of the soil profile have likely accumulated in the past few decades, and thus are more reflective of the longer-term period of vegetation on the site, and not necessarily only current vegetation. This longer-term phytolith accumulation does cloud the modern-day phytolith record in soils somewhat, but this should be acceptable for paleoecological studies where temporal resolutions are much larger (Blinnikov et al., 2002).

We also found that annual exotic weeds (Bromus tectorum) may influence modern-day phytolith records in our study area. Seed phytoliths, presumably from Bromus, and silicified hairs common in the species were found on plots on which B. tectorum occurs with regularity. This highlights the detective power of phytoliths, but also dilutes the assemblage of morphotypes from native grasses. Although due care was taken to sample only native grasslands, the reality is that B. tectorum in the Columbia Basin ecosystem is an ever-increasing problem, even on the preserves where our study was performed. However, the presence of B. tectorum doesn’t invalidate our study: this species has not been present for a great enough time period to have a significant impact on the phytolith record.

CONCLUSIONS The first attempt to quantitatively analyze phytolith soil assemblages and total opal concentrations (TOC) in the Palouse area of the Columbia Basin, Washington State has shown several benefits and limitations. The use of TOC as an indicator of vegetation cover and NDVI has been successful, but reveal only a weak positive relationship for the vegetation cover and moderately strong for the NDVI (R2 of 0.22 for plant cover and 0.49


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for NDVI). Morphotypes in soils indicate the above-ground plant taxa on our 6 x 6 m plots. Our results show that TOC is important and morphotype assemblages do reasonably approximate the vegetation on a given plot over a timescale of at least a few decades. Short-term fluctuations in vegetation attributes are muted (averaged) in the phytolith assemblages. Presence of exotic weeds (Bromus tectorum) has been detected in the modern assemblages, but does not mitigate our results. Most assemblages from grasslands and shrublands in our area are broadly similar. There are no morphotypes that would distinguish one vegetation type from another, but overall, existence of Stipa-type bilobates even in small amounts (3-5%) of the total is a strong indicator that Stipa and Oryzopsis are present in the modern vegetative cover. Likewise, the presence of epidermal polygonal forms strongly suggests that shrubs are to be found as well. Silicified hairs found in the assemblages suggest the presence of Bromus tectorum, an exotic weed. More mesic grasslands can be distinguished from more arid ones on the basis of higher proportions of plates and blocky forms in the former, while the arid ones will have a higher incidence of short trapeziforms and rods. Very few phytoliths were contributed by Asteraceae and other forbs, although contribution to the assemblages by these species seems likely. The issues of inheritance and translocation of phytoliths in post-depositional period could not be conclusively resolved in this limited study. Some of our morphotypes may have come from outside the plots or were inherited from previous times (e.g., when shrubs were removed in a crown fire). The issues should be studied further in a series of controlled experiments with long-term mixtures. REFERENCES

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Strömberg, C.A.E. (2004) Using phytolith assemblages to reconstruct the origin and spread of grass-dominated habitats in the great plains of North America during the late Eocene to early Miocene. Palaeogeography, Palaeoclimatology, Palaeoecology, 207, 239-275.

Sweeney, M.R., Busacca, A.J. and Gaylord, D.R. (2005) Topographic and climatic influences on accelerated loess accumulation since the last glacial maximum in the Palouse, Pacific Northwest, USA. Quaternary Research, 63, 261-273.

Sweeney, M.R., Gaylord, D.R. and Busacca, A.J. (in press) Evolution of Eureka Flat: A dust-producing engine of the Palouse loess, USA. Quaternary International.

Ter Braak, C.J.F. (1986) Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology, 67, 1167-1179.

Verma, S.D., & Rust, R.H. (1969) Observations on opal phytoliths in a soil biosequence in southeastern Minnesota. Proceedings-Soil Science Society of America, 33, 749-751.

Wang, X., Qian, Y., Jiang, J., Lei, J. & Zhang, W. (2003) The distribution of ephemeral vegetation on the longitudinal dune surface and its stabilization significance in the Gurbantunggut Desert. Acta Geographica Sinica, 58, 605-609.

Western U.S. climate historical summaries. Retrieved November 28, 2004 from Desert Research Institute website Western Region al Climate Center. Web site: http://www.wrcc.dri.edu/index.html.

Whitlock, C., Sarna-Wojcicki, A.M., Bartlein, P.J., & Nickmann, R.J. (2000) Environmental history and tephrostratigraphy at Carp Lake, southwestern Columbia Basin, Washington, USA. Palaeogeography, Palaeoclimatology, Palaeoecology, 155, 7-29.

Whitmore, J., Williams, J.W., Shuman, B., Bartlein, P.J., Minckley, T., Viau, A.E., Webb III, T., Shafer, S., Anderson, P., Brubaker, L., Gajewski, K. & Sawada, M. (2005) Modern pollen data from North America and Greenland for multi-scale paleoenvironmental applications. Quaternary Science Reviews, 24, 1828-1848.

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Zender, C.S., Bian, H., & Newman, D. (2003) Mineral Dust Entrainment And Deposition (DEAD) model: Description and 1990s dust climatology. Journal of Geophysical Research, 108, 4416, doi: 10.1029/2002JD002775. ACKNOWLEDGEMENTS The authors wish to thank the St. Cloud State Diatom Herbarium for the generous use of their facility. We also wish to thank Dr. Lewis Wixon, Dr. Matt Julius, Chad Yost, and Bruce Wigget for their feedback and contributions to this study. This paper was supported by NSF grant #0214508 (P.I.s: A. Busacca, C. Zender), and SCSU research grants awarded to P. Reyerson and M. Blinnikov. BIOSKETCHES Paul Reyerson is a Ph.D. graduate student at the University of Wisconsin-Madison, Department of Geography. His research focuses on paleoreconstructions of semi-arid temperate Quaternary grasslands through the use of phytolith analysis. His other interests include terrestrial silica cycles, vegetation dynamics, and climate change. Mikhail Blinnikov is a professor at St. Cloud State University. His research interests include phytolith analysis, biogeography of Quaternary grasslands, alpine plant community ecology, remote sensing/landscape change analysis, conservation of protected natural areas, and the geography of the North American Orthodox church. Alan Busacca is a professor at Washington State University. His principal themes of the research are in Pedology and Quaternary Studies, including rates and processes of soil development, soils and global change, use of soils to interpret geologic history, genesis of soils from volcanic materials and loess, and soil pollution hazards. Alan is currently a vineyard siting consultant. David Gaylord is a Professor of Geology in the School of Earth and Environmental Sciences at Washington State University. He has focused his research on clastic sedimentology and stratigraphy, including volcanogenic sedimentology as it relates to reconstructing sedimentary depositional systems and their provenance, evaluating environmental hazards, and discerning Quaternary climate change from the study of sand dune, sand sheet, and loess deposits. Richard Rupp works in Dept. of Crop & Soil Sciences at Washington State University. His research areas include assessing land cover change in the Columbia Basin region of Washington State. Mark Sweeney is an assistant professor in the Earth Sciences Department at the University of South Dakota. His research focuses on wind blown deposits, specifically the formation of dust and loess deposits. He has recently conducted research linking climate change to sand dune activity and dust formation and deposition in the Palouse loess of the Pacific Northwest.


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Charlie Zender is an Associate Professor of Earth System Science at the University of California, Irvine. His interests include interactions of mineral dust with the environment, and the physics of the atmospheric boundary layer.


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FIGURES AND TABLES

Figure 1. Map of the Columbia Basin including vegetation zones and sample sites within the study area. Washington vegetation data modified from Daubenmire (1972); Oregon vegetation data modified from Gaines et al. (2004). Location information is in decimal degrees. Mean annual temperature and mean annual precipitation data modified from Desert Research Institute, Western U.S. Climate Historical Summaries (2004):

Marcellus (M) 47º.23N, 118º.41W MAT 9.06 ºC, MAP 301 mm Kahlotus Ridge (K) 46º.70N, 118º.55W MAT 10.39 ºC, MAP 258 mm Boardman (BO) 45º.65N, 119º.92W MAT 11.89 ºC, MAP 172 mm Lindsay Prairie (L) 45º.59N, 119º.65W MAT 11.70 ºC, MAP 187 mm


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Table 1. Individual morphotype nomenclature used in this study and plant species they derive from. Modified in part from Blinnikov

(2005). Abbreviation Description following International Code of Phytolith Nomenclature Taxa in Which Observed PR Plate, short trapeziform in 3D, rectangular in 2D, epidermal short cells (ESC) Grasses WS Short trapeziform in 3D, lobate <4 lobes on each side in 2D, sinuate margin, ESC Poa, Koeleria WL Long trapeziform in 3D, lobate >4 lobes on each side in 2D, sinuate margin, ESC Stipa, Calamogrostis LR Rod, very long trapeziform in 3D, mostly smooth on each side in 2D, epidermal Grasses, most heavy in long

cells (ELC) Festuca and Agropyron LD Rod, serrated, very long trapeziform in 3D, mostly aculeate, serrated in 2D, ELC Festuca, Agropyron SD Elongated, flat, dendriform, epidermal from seed, ELC Bromus, domesticated cereals RO Rondel, oblong/oval: short trapeziform in 3D, oblong in top view 2D, ESC Agropyron RP Rondel, pyramidal: truncated pyramidal in 3D, square top view 2D ESC Agropyron, Stipa RK Rondel, keeled: short trapeziform in 3D, oval in top view 2D ESC Agropyron, Festuca, Stipa BS Bilobate Stipa-type: trapeziform in 3D, two lobes on each side in top view, ESC Stipa, Oryzopsis BB Bilobate: two lobes with shaft both in top and side view Stipa, Panicoid grasses SA Saddle: concave in top view, concave in side view, ESC Stipa (in our region) HT Trichome: acicular/lanceolate psillate epidermal appendage (trichome) Grasses, forbs (multicell trichomes in Asteraceae) HH Hair, hair base Grasses, forbs and shrubs BL Blocky: cubic or globose, mesophyll and possibly epidermal Artemisia and other shrubs EP Polygonal: irregular polygonal in top view, very flat on the side Forbs and shrubs


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Table 2. Observed total and functional groups’ vegetation cover (%, measured visually in the field) on 37 modern plots ranked by decreasing total cover.

Plot Total Agropyron Festuca Poa Stipa3 Bromus Poaceae Asteraceae Forbs4 Shrubs spicatum idahoensis1sandbergii2 tectorum sum sum sum sum L4 98 15 0 3 10 40 68 2 3 25 M10 87 1 40 5 0 0 46 1 4 36 BO3 76 5 2 0 1 40 48 1 2 25 M5 75 0 10 1 0 1 12 3 5 55 K6 73 30 26 3 0 5 64 2 3 4 L1 71 10 0 3 2 50 65 5 1 0 M3 71 0 30 2 0 1 33 2 3 33 K5 70 15 45 0 0 0 60 2 3 5 K2 60 0 40 7 0 1 48 1 3 8 BO5 59 15 6 0 0 25 46 1 1 11 M1 58 0 16 2 0 1 19 5 4 30 M4 57 0 50 5 0 0 55 0 2 0 K4 55 0 40 3 0 0 43 6 5 1 L5 52 10 0 5 20 10 45 3 3 1 K1 51 0 41 5 0 0 46 1 3 1 K3 51 0 30 5 0 0 35 1 3 12 L2 50 15 0 1 1 30 47 2 1 0 K7 49 25 10 5 0 0 40 1 7 1 BO1 48 5 1 0 1 10 17 0 1 30 K8 44 5 31 2 0 0 38 0 4 2 K9 44 20 12 5 0 3 40 0 3 1 L6 42 0 0 3 5 20 28 5 6 3 L3 41 0 0 2 5 20 27 2 4 8 M8 36 0 15 5 0 0 20 1 4 11 M7 35 0 20 5 0 0 25 0 2 8 M9 33 0 26 1 0 0 27 1 3 2


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M2 31 0 25 1 0 0 26 0 2 3 M6 30 0 20 1 0 0 21 0 3 6 BO8 27 4 5 1 6 10 26 1 0 0 K10 27 0 21 1 0 0 22 0 3 2 BO10 26 8 0 1 5 5 19 1 3 3 BO6 24 10 0 2 2 5 19 0 0 5 L7 22 1 0 2 3 10 16 2 3 1 BO4 22 3 0 0 0 10 13 1 1 7 BO9 19 6 0 1 4 5 16 1 2 0 BO2 13 6 1 0 1 2 10 1 1 1 BO7 9 1 0 1 5 0 8 1 0 0 1 – also includes Festuca rubra (M9) and F. octoflora (M1, K1, K6, K8, K9, K10, BO1, BO3, BO5, BO8) 2 – also includes Poa bulbosa (M3, L1, L3, L4, L6, L7) and P. juncifolia (M1) 3 – Stipa-group includes mainly Stipa comata, as well as S. occidentalis (BO3) and Oryzopsis hymenoides (BO7, BO8, L7) 4 – also includes legumes


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Figure 2. Phytolith morphotype depictions. PR – Plate rectangular; WS – short trapeziform; WL – long trapeziform; LR – long smooth trapeziform (rod); LD – long serrated tapeziform (rod); SD – seed epidermal long cell; RO – Rondel oblong/oval; RP – Rondel pyramidal; RK – Rondel keeled/horned; BS – Bilobate short trapeziform Stipa-type; BB – “True” Bilobate; SA – Saddle; HT – Trichome; HH – Hair/Hair base; BL - Blocky; EP – Epidermal non-grass. Refer to Table 1 for the morphotype nomenclature and the list of taxa in which these forms are observed.

Figure 3. Regression scatterplot of in field inspection vegetation cover (VC) predicted by

TOC in modern topsoil. The regression equation is VC = 20.1 + 10.7 x TOC (R2=0.21, p=0.004). TOC is measured in percent of dry weight of extracted opal relative to bulk soil dry weight. VC is the field-estimated percent of vegetation ground cover for each sample.

Figure 4. Black & white example of a digital color infrared aerial image showing field

plots in Marcellus preserve. Pixel resolution is 0.16 m2, for a total of 250 pixels per plot.

Figure 5. Regression scatterplot of NDVI values derived from the digital aerial images

predicted by total opal concentration (TOC) in topsoil. The regression equation is NDVI = -0.432 + 0.0475 x TOC (R2=0.49, p<0.001). TOC is measured in percent of dry weight of extracted opal relative to bulk soil dry weight. NDVI is the mean value for each plot as measured from approximately 250 pixels per plot on multispectral aerial digital imagery.

Figure 6. Total opal concentration (TOC) and morphotype counts for sample plots. Bars

represent percentage of total. The regression equation used was VC = 20.1+10.7 x TOC. TOC is percent of dry weight of extracted opal relative to the bulk soil dry weight. VC is the in-field estimated percent of ground vegetation cover for each sample. Refer to Table 1 and Fig. 2 for morphotype descriptions.

Figure 7. Canonical Correspondence Analysis scatterplot (Ter Braak, 1986; PC-ORD) of

first two axes showing position of morphotype assemblages from modern plots and morphotypes as the main matrix and abundance of selected plant taxa as the environmental matrix. Refer to Table1 and Fig. 2 for individual morphotype descriptions and illustrations. Refer to Fig. 6 for morphotype percentage data per plot.


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Idaho

Was

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Columbia Basin

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Oregon

Washington

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Artemisia tridentata - Festuca

Artemisia tridentata - Agropyron

Artemisia tripartita - Festuca

Festuca - Symphoricarpos


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Ro

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Black & white example of a digital color infrared aerial image showing field plots in Marcellus preserve. Pixel resolution is 0.16 m2, for a total of 250 pixels per plot.


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For Peer Review - University of California, Irvinedust.ess.uci.edu/ppr/ppr_RBB07.pdf · Research interests: Quaternary paleoecology, paleoenvironments, vegetation dynamics, climate