The Neogene transition from C3 to C4 grasslands in North
America: stable carbon isotope ratios of fossil phytoliths
Author(s): Francesca A. McInerney, Caroline A. E. Strömberg, and James W. C.
White
Source: Paleobiology, 37(1):23-49. 2011.
Published By: The Paleontological Society
DOI: 10.1666/09068.1
URL: http://www.bioone.org/doi/full/10.1666/09068.1
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Paleobiology, 37(1), 2011, pp. 23–49
The Neogene transition from C3 to C4 grasslands in North
America: stable carbon isotope ratios of fossil phytoliths
Francesca A. McInerney, Caroline A. E. Strömberg, and James W. C. White
Abstract.—C4 grasses form the foundation of warm-climate grasslands and savannas and provide
important food crops such as corn, but their Neogene rise to dominance is still not fully understood.
Carbon isotope ratios of tooth enamel, soil carbonate, carbonate cements, and plant lipids indicate a
late Miocene–Pliocene (8-2 Ma) transition from C3 vegetation to dominantly C4 grasses at many sites
around the world. However, these isotopic proxies cannot identify whether the C4 grasses replaced
woody vegetation (trees and shrubs) or C3 grasses. Here we propose a method for reconstructing the
carbon isotope ratio of Neogene grasses using the carbon isotope ratio of organic matter trapped in
plant silica bodies (phytoliths). Although a wide range of plants produce phytoliths, we hypothesize
that in grass-dominated ecosystems the majority of phytoliths will be derived from grasses, and will
yield a grass carbon isotope signature. Phytolith extracts can be contaminated by non-phytolith silica
(e.g., volcanic ash). To test the feasibility of the method given these potential problems, we examined
sample purity (phytolith versus non-phytolith silica), abundance of grass versus non-grass phytoliths,
and carbon isotope ratios of phytolith extracts from late Miocene–Pliocene paleosols of the central
Great Plains. Isotope results from the purest samples are compared with phytolith assemblage
analysis of these same extracts. The dual record spans the interval of focus (ca. 12-2 Ma), allowing us,
for the first time, to investigate how isotopic shifts correlate with floral change.
We found that many samples contained high abundances of non-biogenic silica; therefore, only a
small subset of ‘‘pure’’ samples (.50% of phytoliths by volume) with good preservation were
considered to provide reliable carbon isotope ratios. All phytolith assemblages contained high
proportions (on average 85%) of grass phytoliths, supporting our hypothesis for grass-dominated
communities. Therefore, the carbon isotope ratio of pure, well-preserved samples that are dominated
by grass biosilica is considered a reliable measure of the proportion of C3 and C4 grasses in the
Neogene.
The carbon isotope ratios of the pure fossil phytolith samples indicate a transition from
predominantly C3 grasses to mixed C3-C4 grasses by 5.5 Ma and then a shift to more than 80% C4
grasses by 3-2 Ma. With the exception of the Pliocene sample, these isotopic data are broadly
concordant with phytolith assemblages that show a general increase in C4 grasses in the late Miocene.
However, phytolith assemblage analysis indicates lower relative abundance of C4 grasses in overall
vegetation than do the carbon isotopes from the same phytolith assemblages. The discrepancy may
relate to either (1) incomplete identification of (C4) PACMAD phytoliths, (2) higher production of nondiagnostic phytoliths in C4 grasses compared to C3 grasses, or (3) biases in the isotope record toward
grasses rather than overall vegetation. The impact of potential incomplete characterization of (C4)
PACMAD phytoliths on assemblage estimates of proportion of C4, though important, cannot reconcile
discrepancies between the methods. We explore hypothesis (2) by analyzing a previously published
data set of silica content in grasses and a small data set of modern grass leaf assemblage composition
using analysis of variance, independent contrasts, and sign tests. These tests suggest that C4 grasses
do not have more silica than C3 grasses; there is also no difference with regard to production of nondiagnostic phytoliths. Thus, it is most likely that the discrepancy between phytolith assemblages and
isotope ratios is a consequence of hypothesis (3), that the isotope signature is influenced by the
contribution of non-diagnostic grass phytoliths, whereas the assemblage composition is not.
Assemblage-based estimates of % C4 within grasses, rather than overall vegetation, are in
considerably better agreement with the isotope-based estimates. These results support the idea that,
in grass-dominated assemblages, the phytolith carbon isotope method predominantly records shifts in
dominant photosynthetic pathways among grasses, whereas phytolith assemblage analysis detects
changes in overall vegetation. Carbon isotope ratios of fossil phytoliths in conjunction with phytolith
assemblage analysis suggest that the late Neogene expansion of C4 grasses was largely at the expense
of C3 grasses rather than C3 shrubs/trees. Stable isotopic analysis of phytoliths can therefore provide
unique information about grass community changes during the Neogene, as well as help test how
grass phytolith morphology relates to photosynthetic pathway.
Francesca Avril McInerney (formerly Francesca A. Smith).*{ Department of the Geophysical Sciences,
University of Chicago, 5734 South Ellis Avenue, Chicago, Illinois 60637
Caroline A. E. Strömberg.{ Department of Biology and the Burke Museum of Natural History and Culture,
University of Washington, Box 351800, Seattle Washington 98195, E-mail: [email protected]
James W. C. White. Institute for Arctic and Alpine Research, University of Colorado at Boulder, Campus Box
450, Boulder, Colorado 80309-0450
*Present address: Department of Earth and Planetary Sciences, Northwestern University, 1850 Campus
Drive, Evanston, Illinois 60208. E-mail: [email protected]
’ 2011 The Paleontological Society. All rights reserved.
0094-8373/11/3701–0002/$1.00
24
MCINERNEY, STRÖMBERG, AND WHITE
{
Corresponding authors, who contributed equally: McInerney collected samples, extracted phytoliths
and measured d13C values; Strömberg analyzed phytolith morphology, purity and preservation of
assemblages, and silica production in modern grasses.
Accepted:
29 June 2010
Introduction
Presently, grass-dominated ecosystems represent up to 40% of Earth’s vegetated surface,
and domesticated grass cereals form the
majority of the world’s food supply (Gibson
2009). Grasses today use either the C3 (CalvinBenson) or the C4 (Hatch-Slack) photosynthetic pathway. C3 grasses prefer elevated
atmospheric CO2, cooler temperatures, and
moister conditions, whereas the C4 grasses
can tolerate low atmospheric CO2, warmer
temperatures, and dryer conditions (Sage et
al. 1999). C3 grasses also thrive in climates
with cool-season precipitation, whereas C4
grasses thrive in climates with warm season
precipitation (Sage et al. 1999). As a result,
tropical to subtropical low-altitude savannas
and grasslands tend to be dominated by C4
grasses; C3 grasses are mainly found in
temperate grasslands (Teeri and Stowe 1976;
Hattersley 1983; Epstein et al. 1997; Tieszen et
al. 1997) and at high altitudes (Chazdon 1978;
Tieszen et al. 1979; Boutton et al. 1980; Rundel
1980). Expansive C4-grass dominated vegetation is a geologically recent phenomenon.
Although C4 photosynthesis is now thought
to have evolved multiple times within the
PACMAD grass clade (Panicoideae, Arundinoideae, Chloridoideae, Micrairoideae, Aristidoideae and Danthonioideae [Duvall et al.
2007]) starting in the Early Oligocene (Sánchez-Ken et al. 2007; Christin et al. 2008;
Vicentini et al. 2008; Bouchenak-Khelladi et
al. 2009), the emergence of C4-dominated
grasslands did not occur until the late
Miocene–Pliocene (for review, see Jacobs et
al. 1999; Osborne and Beerling 2006; Tipple
and Pagani 2007; Osborne 2008; Edwards and
Smith 2010; Edwards et al. 2010).
Because the carbon isotope signatures of C4
and C3 grasses differ, the explosive rise to
dominance of C4 grasses is well documented
in the geochemical record. Stable carbon
isotope ratios measured in paleosol carbonates, carbonate cements, leaf-wax n-alkanes,
and ungulate tooth enamel from around the
world indicate that, during a geologically
brief period of time (8-2 Ma), C4 grasses
became prominent in vegetation and diets of
herbivores at low to midlatitudes (Quade et
al. 1989; Kingston et al. 1994; Morgan et al.
1994; Wang et al. 1994; Quade and Cerling
1995; MacFadden et al. 1996; Cerling et al.
1997; Freeman and Colarusso 2001; Fox and
Koch 2003, 2004; Sanyal et al. 2005; Ségalen et
al. 2007; Passey et al. 2009). In most of these
records, the shift is not actually a simple shift
from C3 to C4, but is rather an expansion of
carbon isotope values, suggesting the presence of pure C3, pure C4, and mixed C3-C4
plant habitats (for review, see Tipple and
Pagani 2007; Osborne 2008; Edwards et al.
2010).
Initially, the transition from exclusively C3
to more prevalent C4 communities was
interpreted as a change from ecosystems
dominated by C3 trees and shrubs to those
dominated by C4 grasses (Cerling et al. 1993;
Wang et al. 1994), based in part on the
analogy of late Miocene grasslands to African
savannas (predominantly C4 grasses and C3
shrubs and trees) and the absence of modern
midlatitude pure C3 grasslands. Counter to
this, functional morphology of herbivores,
plant silica (phytolith) assemblage data, and
paleosols all indicate that C3 grass-dominated
habitats existed in some regions, including
the Great Plains, prior to the late Miocene
(e.g., Janis 1993; Wang et al. 1994; Retallack
1997; Jacobs et al. 1999; Passey et al. 2002;
Strömberg 2005). The pattern suggests that
the expansion of C4 grasses may have
occurred at the expense mainly of C3 grasses.
However, this hypothesis has not been explicitly tested because the previously applied
carbon isotope techniques cannot distinguish
between C3 grasses and C3 shrubs and trees,
and therefore cannot detect whether the C3-C4
shift occurred within grass communities or as
a result of C4 grasses replacing C3 woody
vegetation. This paper aims to remedy this
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
problem by exploring stable carbon isotope
ratios of phytoliths as a tool to examine late
Miocene/Pliocene changes in relative abundances of C3 and C4 grasses within grass
communities in the central Great Plains.
Fossil Phytolith d13C: A New Approach to
Reconstructing Neogene Grass Communities.—
Phytoliths are biosilica bodies precipitated in
cells and tissue in living plants. Their morphology allows for differentiation of ecologically important groups of plants, such as
grasses and woody/herbaceous dicotyledons
(Piperno 1988; Strömberg 2004). Analysis of
phytolith assemblages in fossil soils has
become a useful tool in reconstructing vegetation type (e.g., forest versus grassland) in
Quaternary as well as in more ancient strata
(e.g., Piperno and Becker 1996; Boyd et al.
1998; Blinnikov et al. 2002; Delhon et al. 2003;
Barboni et al. 2007; Strömberg et al. 2007a,b).
Many land plants produce phytoliths, but
they are particularly abundant in grasses and
a few other monocotyledons (e.g., palms,
gingers, sedges; Piperno 1988, 2006). Indeed,
grass shoots may contain as much as 11% dry
weight silica (Hodson et al. 2005). As a result,
phytolith assemblages from ecosystems that
contain abundant grasses tend to be heavily
dominated by grass phytoliths (e.g., Piperno
1988; Strömberg 2004); in phytolith assemblage analysis, this bias is compensated for by
counting only certain grass phytoliths (grass
silica short cells; GSSCs) that are considered
the most diagnostic of grasses and the least
environmentally variable (e.g., Piperno 1993;
Strömberg 2004, 2005).
Morphological analysis of phytoliths can
also be used to infer the relative dominance of
C3 and C4 grasses in recent and Quaternary
ecosystems, where the modern grass flora is
well studied and can reliably be used as an
analog (Twiss 1992; Alexandre et al. 1997;
Fredlund and Tieszen 1997a; Bremond et al.
2008a,b). However, morphology cannot always be applied dependably in deep time
records for distinguishing C3 and C4 grasses.
Certain C4-dominated clades such as chloridoids are fairly readily identified but the
detailed correlation between grass phylogeny,
phytolith morphology, and photosynthetic
pathway has not yet been established because
25
of the multiple independent originations of C4
within grasses, the remaining uncertainties in
relationships among different C4 lineages
(Sinha and Kellogg 1996; Christin et al. 2008;
Vicentini et al. 2008; Bouchenak-Khelladi et
al. 2009), and the paucity of detailed, threedimensional morphological descriptions of
GSSCs (e.g., Piperno and Pearsall 1998).
Consequently, we cannot assume that Neogene phytoliths that resemble modern C4
grass phytoliths indicate a Neogene C4 grass
(Smith and Anderson 2001; Strömberg 2004).
In contrast, the carbon isotope ratio of
Neogene fossil grass phytoliths does not rely
on assumptions about the evolution of C4 and
GSSC morphology, and should accurately
mirror the relative abundance of C4 grasses.
We hypothesize, on the basis of the
overwhelming silica production in grasses,
that stable carbon isotope ratios of phytolith
assemblages from grass-dominated vegetation mainly reflect grasses. They should
therefore be able to provide a direct record
of proportions of C3 and C4 grasses (versus
C3/C4 of total vegetation) (Smith and White
2004), as well as valuable insight into the
relationship between phytolith morphology
and photosynthetic pathway within grasses in
the geologic past. This information is vital for
testing what environmental factors may have
triggered the shift from C3 to C4.
Because C3 and C4 grasses use different
mechanisms to acquire CO2 for photosynthesis, they fix carbon with different isotope
ratios. Carbon isotope ratios are expressed
using d notation with the units of per mil (%)
or parts per thousand: d13C (%) 5 [(Rsample/
Rstandard) 2 1] 3 1000, where R 5 13C/12C for
the sample and a standard (VPDB, Vienna
Pee Dee Belemnite). The d13C values for
modern, post-industrial C3 grasses range
between 222% and 234%, with a mode of
226.7%, whereas C4 grasses range from 29%
to 216%, with a mode of 212.6% (n 5 351
species) (Vogel 1993). Phytoliths trap trace
amounts of organic carbon (0.09–1% by
weight) from the plants in which they form
(Wilding 1967; Kelly et al. 1991; Mulholland
and Prior 1993; Smith and Anderson 2001).
Carbon isotopic analysis of phytoliths exploits the isotopic difference created by the
26
MCINERNEY, STRÖMBERG, AND WHITE
two photosynthetic pathways to generate a
record of the relative dominance of C3 and C4
plant phytoliths in a soil assemblage.
Modern calibrations to characterize d13C
ratios, both in the plants themselves (setting
initial d13C) and in the soils where phytoliths
accumulate (taphonomic effects), are discussed elsewhere (Kelly et al. 1991, 1998; Lü
et al. 2000; McClaren and Umlauf 2000; Smith
and White 2004). Of note, however, is that
phytoliths from C4 grasses are more 13Cdepleted than phytoliths from C3 grasses
relative to plant tissue. This has been attributed to the inclusion of lipids that are
generally more 13C-depleted in C4 plants than
C3 plants relative to bulk tissue (Kelly et al.
1991; Smith and Anderson 2001; Smith and
White 2004). The empirically derived endmember for a pure C3 grassland phytolith
assemblage preserved in a soil is 227 6 6%
(90% C.I.) and for a pure C4 grassland
phytolith assemblage is 215 6 4% (90%
C.I.) (Smith and White 2004). These large
confidence intervals will undoubtedly be
reduced through additional calibration
points. However, these end-members successfully capture the range of observations for all
published modern and Quaternary phytolith
assemblages (Smith and White 2004). Because
these end-members are based on pre-industrial phytolith assemblages, they pre-date the
depletion of 13C in atmospheric CO2 caused
by fossil fuel burning.
Phytolith d13C ratios have been used previously to reconstruct proportions of C3 and C4
grasses in ancient environments, but application has been restricted to the Quaternary
(Kelly et al. 1991; Fredlund 1993; McClaren
and Umlauf 2000). Other authors have also
compared phytolith assemblage data with
SOM d13C in modern-submodern soils (e.g.,
Fredlund and Tieszen 1997b; Baker et al. 2000;
Kerns et al. 2001).
This study constitutes a first attempt at
applying the isotopic method to Neogene
fossil phytoliths. More specifically, we measure the carbon isotope ratios of organic
matter occluded in silica that we extracted
from paleosols from Nebraska and Kansas, in
the central Great Plains of the United States.
Contamination of silica extracts by non-
phytolith material is a serious concern for
carbon isotope measurements on total extracts. We quantitatively assess the degree of
contamination by non-phytoliths silica, and
identify samples that are phytolith dominated. We tie the record of stable isotope ratios of
the purest samples to information about
vegetation structure by comparing these data
with results from assemblage analysis of the
same samples (see companion paper Strömberg and McInerney 2011 [this volume]). The
dual record, the first of its kind, spans the
critical interval, late Miocene/Pliocene (ca.
12–2 Ma). We focus on answering the following questions: (1) How can the carbon isotope
ratios of silica extracts be used to reliably
reconstruct the photosynthetic pathways of
Neogene grasses? (2) What is the Neogene
history of C3 and C4 grasses recorded in the
d13C of fossil phytoliths? and (3) How does it
compare with phytolith assemblage composition from the same samples?
To address question (1) we analyze composition and preservation of the fossil phytolith extracts used for isotopic and morphological analysis to understand the degree to
which isotopic values are influenced by (a)
non-phytolith silica (non-biogenic and other
biogenic silica such as diatoms), (b) nondiagnostic phytoliths (phytoliths not useful
for assemblage analysis), and (c) preservational state. We quantify the proportion of
phytoliths that are derived from grasses to
test the hypothesis that assemblages from
grass-dominated ecosystems will be primarily from grasses. Using two modern data
sets, we also examine whether there is
differential production of plant silica in C3
and C4 grasses, which could introduce biases
in the record of stable carbon isotopes. These
tests allow us to develop a protocol for
evaluating fossil phytolith samples for d13C
analysis. To address question (2) we apply
this protocol to samples from Nebraska and
Kansas to produce a fossil phytolith d13C
record of the abundance of C3 and C4 grasses
within Neogene communities. To address
question (3) we compare these results directly with the assemblage composition of the
same samples (Strömberg and McInerney
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
2010) to integrate morphological and isotopic
approaches.
Methods
Samples and Sites
Forty-six samples from paleosols ranging in
age from ca. 18 to 2 Ma were collected from
Nebraska and Kansas by one of us (F.A.M.);
only 24 yielded significant phytoliths and are
discussed here. Sample locations and age
control are described in the companion paper
on phytolith assemblage analysis (Strömberg
and McInerney 2011 [this volume]).
Extraction of Phytoliths from
Paleosol Sediment
Phytoliths were extracted from sediments
using a modified procedure from Kelly
(1989). Sediments were dried at 50uC overnight, or until dry, disaggregated with mortar
and pestle and sieved to remove the .2 mm
size fraction, including rare, macroscopic
organic matter. To remove carbonate, 50 ml
of 2N HCl was added to 50 g of sediment in a
hot water bath at 70uC followed by 25-ml
additions until the reaction ceased. Samples
were rinsed three times with distilled water,
settled through centrifuging, and decanted.
To remove some organic matter, H2O2 (30%)
was added in 2-ml increments to samples in a
hot water bath at 70uC until the reaction
ceased. Samples were rinsed three times as
above. Clays were deflocculated through
shaking (4 hours on shaker table) with the
dispersant sodium pyrophosphate (0.1 molar,
buffered to pH 7 with HCl) (Bates et al. 1978),
which also served to solubilize any humic
fraction. Repeated settling and siphoning
until supernatant was clear successfully removed clays (,2 mm), as evidenced by
particle size analysis of the siphoned clays.
The sample was wet sieved to remove the
.250 mm fraction and the 2–250 mm fraction
was freeze-dried.
Phytoliths were separated from the mineral
fraction using heavy-liquid flotation in a
sodium polytungstate solution set to a specific gravity of 2.3 (Madella et al. 1998).
Approximately 5 g of the 2–250 mm fraction
were loaded into 50-ml centrifuge bottles with
27
30 ml of heavy liquid and were dispersed
using an ultrasonic probe. Separation was
achieved through either centrifuging at
2000 rpm for 20 minutes or gravity settling
overnight. The material that floated was
removed with a pipette and constitutes the
silica extract. The flotation was repeated two
to three times. The extract was rinsed three
times with distilled water and freeze-dried.
To remove organic matter from the exterior
of particles, extracts were heated to 80uC in
50 ml of concentrated sulfuric acid (98%) for
three hours. After cooling, 2 ml H2O2 (30%)
was added and samples were returned to the
hot plate. H2O2 additions were repeated two
to three more times. Samples were filtered
onto ashed glass-fiber filters, rinsed with
distilled water, and oven dried at 70uC. For
each sample, a slide was made for analysis of
biosilica yield and phytolith assemblages.
Purity of Phytolith Yield
Heavy-liquid flotation of sediment samples
yields all light biogenic (e.g., phytoliths,
diatoms) and non-biogenic (e.g., volcanic
ash) silica (Fig. 1). Although many nonphytolith particles might represent plant
silica, unlike morphologically distinct phytoliths they cannot be assigned an unambiguous plant origin. Amorphous silica grains
might also be pedogenic (Chadwick et al.
1989) and trap soil organic matter. The d13C of
soil organic matter can become enriched in
13
C over long time periods (Ehleringer et al.
2000), so that more positive d13C values could
reflect pedogenic processes rather than C4
vegetation (Smith and White 2004). The
purity of this total yield, that is, the proportion made up by phytoliths, should therefore
reflect the ‘‘purity’’ of the isotopic signal.
To study purity of the sample yields we
chose random fields of view by scanning the
slide and making arbitrary stops (with eyes
closed). All particles in each field of view
were scored as the particle types P (phytoliths), OB (other biosilica, including diatoms,
sponge spicules, and chrysophyte cysts), and
NB (volcanic ash and other non-biogenic
silica). Particles within each of these categories were scored in size bins, measured as the
maximum dimension ,10 mm, 10–50 mm, and
28
MCINERNEY, STRÖMBERG, AND WHITE
FIGURE 1. Microscope image of heavy liquid yield.
Phytoliths marked with ‘‘P’’; remaining particles nonbiogenic silica. A, ‘‘Pure’’ sample (LS26), with few nonbiogenic silica particles. Note etched elongate psilate and
acicular hair cell in upper left corner. B, Non-‘‘pure’’
sample (Dawes), with the only phytolith (an etched
echinate sphere) in the field of view. Scale bar, 10 mm.
.50 mm. These boundaries were chosen to
reflect the tendency of phytoliths to fall in the
10–50 mm size class. At least 250 (254–463)
individual particles were counted. We investigated the composition of samples in terms of
particle size classes and particle types, looking both at raw counts and counts weighted
to reflect the volumetric contribution of silica
of particles of different size classes, because
the latter should be more important in
influencing the source of the isotopic signal
(amount of occluded organic material). The
volumetric contribution was calculated as
follows. For the ,10 mm and 10–50 mm size
classes/bins, particle volume was estimated
as proportional to a sphere with a diameter
(d) reflecting the arithmetic average of these
classes (5 mm and 30 mm, respectively); for the
.50 mm size class/bin, a sphere diameter of
60 mm was used. Note, however, that using a
diameter of 50 and 70 mm, respectively, gave
very similar results. The sample counts were
weighted by multiplying the relative abundance of particles in each size bin with a
factor of (d/2)3 to reflect their volumetric
contribution.
Phytolith assemblage counts only take into
account so-called diagnostic phytoliths, that
is, phytoliths typical of various forest indicator taxa and grass sublineages (see Strömberg
2004, but see Bremond et al. 2005a for a
different approach). To compare the phytolith
assemblage analysis results with the isotopic
analysis, we examined which kinds of phytoliths contribute most to the samples in terms
of relative abundances of particles and volume, respectively. We divided the phytoliths
into different classes (see below for definition), namely diagnostic phytoliths (DIP), nondiagnostic (potential) grass phytoliths (NDG),
and non-diagnostic phytoliths (NDO), and
plotted their relative abundance and volumetric contribution. We also looked at the contribution of grass phytoliths (GSSC + NDG)
relative to all taxonomically assigned phytoliths (DIP + NDG).
Preservation of Phytoliths
We examined the preservational status of
phytoliths semiquantitatively by surveying
the presence and abundance of (a) occluded
carbon, (b) fine ornamentation, (c) etching, (d)
fragmentation of phytoliths, (e) structural/
textural alteration of phytoliths, and (f)
secondary silicification (Strömberg 2003;
Strömberg et al. 2007b). As noted by Strömberg et al. (2007b), the resulting preservational classes are less quantitatively defined
than the classification of Fredlund and Tieszen’s (1997b). Instead, they attempt to consider the many different ways in which
transport, pedogenesis, and diagenesis may
affect a biosilica assemblage and its isotopic
signal.
Isotopic (d 13C) Analysis of Phytoliths
All isotopic analyses were performed at
Stable Isotope Lab at INSTAAR, University of
Colorado, Boulder. Phytoliths (30–70 mg)
were loaded into ashed vycor sample tubes
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
with CuO (50 mg) and silver wire, evacuated
and flame sealed. Ampules were heated to
960uC for 4 hours. The resultant CO2 was
introduced to the Sira dual-inlet mass spectrometer via a cracker manifold, and the CO2
was collected cryogenically in a triple trap
and then in a cold finger, allowing very small
samples to be run (,6–10 mg of carbon)
(Smith and White 2004). Both pure CO2 gas
and organic materials were run as standards.
The standard deviation of the pure CO2 gas
was 0.07% (n 5 33). The organic standards
(corn and glycine) were prepared and analyzed in the same way as the phytoliths;
the standard deviation for the corn was 0.2%
(n 5 10) and for the glycine was 0.1% (n 5 8).
Thus, the phytolith d13C values are reported
with a precision of 60.2%.
Carbon isotope ratios are used to estimate
% C4 contribution using a simple mixing
model with the established end-member
values of 227% for C3 and 215% for C4
(Smith and White 2004).
Phytolith Classification and
Assemblage Analysis
We selected, on the basis of quality of
preservation, 14 samples for quantitative
phytolith analysis and vegetation reconstruction. LS21 was included despite rather poor
preservation, because it provided the best
available sample older than ca. 10 Ma. Phytoliths were classified according to Strömberg
(2004, 2005) and Strömberg et al. (2007b). For
full methods, see Strömberg and McInerney
(2011 [this volume]).
The main categories used for classifying
phytoliths are as follows:
1. Forest indicator morphotypes (FI TOT)
from palms, woody or herbaceous dicotyledons, conifers, ferns, etc.
2. Grass silica short cells (GSSC) produced
exclusively by grasses (Poaceae). Within
GSSCs we distinguish among morphotypes typical of (a) closed-habitat grasses
in the Bambusoideae + Ehrhartoideae
(BE) clade, and a variety of basal grasses
(CH TOT) (GPWG 2001); (b) open-habitat
grasses in the Pooideae (diagnostic pooid
29
[POOID-D] + non-diagnostic pooid
[POOID-ND]); (c) C4 grasses in the Panicoideae (PAN) and Chloridoideae
(CHLOR); (d) other C3/C4 open-habitat
grasses in the PACMAD clade (Panicoideae + Arundinoideae + Chloridoideae +
Micrairoideae + Aristidoideae + Danthonioideae) (PACMAD general); and (e)
other (unidentified) Poaceae (OTHG).
3. Phytoliths from wetland plants, such as
sedges (Cyperaceae) (AQ).
4. Non-diagnostic (potential) grass phytoliths (NDG).
5. Non-diagnostic and unclassified phytoliths (NDO).
The NDG class encompasses (a) phytolith
morphotypes unique to grasses (e.g., cuneiform bulliform cells) and (b) phytoliths
abundantly produced by grasses (e.g., elongate sinuous, echinate, and dendritic and
acicular hair cells) (Alexandre et al. 1997;
Barboni et al. 1999), but commonly found in
other monocotyledons, conifers, or both
(Strömberg 2003; Carnelli et al. 2004). The
production of some NDG forms is strongly
controlled by environmental factors such as
water availability and evapotranspiration
(Sangster and Parry 1969; Bremond et al.
2005b; Piperno 2006; Madella et al. 2009).
NDO morphotypes are found in many vascular plant taxa and are not useful for
inferring vegetation (see also Piperno 1993;
Strömberg 2004). Both NDG and NDO are
commonly excluded from phytolith assemblage analysis of vegetation structure (e.g.,
Piperno 1993; Strömberg 2005; Strömberg et
al. 2007a,b).
Vegetation structure was inferred by calculating (1) a rough tree-cover estimate, FI-t,
which is the ratio of forest indicator phytoliths (FI TOT) to the sum of FI TOT and
diagnostic grass phytoliths (GSSC) and (2) the
relative contribution to GSSC from grasses
with preference for open versus closed
habitats (Strömberg 2005; Strömberg et al.
2007b). The relative abundances of grasses
with different photosynthetic pathways were
estimated by comparing different classes of
GSSCs. Two measures of C4 grasses were
used: (1) GSSCs typical of the Panicoideae
30
MCINERNEY, STRÖMBERG, AND WHITE
and Chloridoideae (PAN + CHLOR), providing a rough minimum estimate of C4 grass
abundance; and (2) GSSCs produced by all
PACMAD grasses (PACMAD TOT), providing a rough maximum estimate of C4 grass
abundance. The relative importance of C4
grasses in grass communities was determined
as a proportion (%) of each of these measures
of C4 GSSCs relative to counts of all identified GSSCs (GSSC 2 OTHG 5 CH TOT +
POOID-D + POOID-ND + PACMAD TOT).
The unidentified Poaceae (OTHG) fraction of
GSSCs was excluded because their taxonomic
affiliation or photosynthetic pathway cannot
be assigned. The relative abundance of C4
grasses in the vegetation as a whole was
inferred as the proportion (%) of C4 phytoliths
out of GSSC 2 OTHG scaled to the relative
abundance of all GSSCs out of FI TOT +
GSSC. In effect, OTHG are assumed to be
composed of the same proportion of C3 and C4
as the sum of CH TOT, POOID-D, POOID-ND,
and PACMAD TOT GSSCs. We consider this a
reasonable assumption given that most OTHG
are broken or otherwise obscure specimens
that would normally likely fall into one of the
known GSSC classes (see Strömberg and
McInerney 2011 [this volume]). Scaling avoids
introducing a bias against C4 grasses by
removing the OTHG that cannot be assigned
a photosynthetic pathway, but that contribute
to the grass phytolith fraction.
Comparing Isotopic and Phytolith
Assemblage Data
We examined the statistical correlation
between estimates of % C4 based on d13C
values and phytolith assemblage composition
(see above). Carbon isotope ratios of pure
samples were converted to % C4 by using a
simple mixing model; values ,0% and
.100% were set to 0% and 100%, respectively.
These values were compared with estimates
of the proportion of potential C4 grasses in the
vegetation, calculated as the scaled percentage of either panicoid + chloridoid (minimum
estimate) or all PACMAD (maximum estimate) GSSCs relative to vegetation overall
and to grasses only. R statistical software
was used for these analyses (http://www.
R-project.org). In addition, the different
estimates for % C4 grasses were compared
for each of the pure samples individually.
Amount of Silica in C3 and C4 Grasses
Amount of silica was used as a proxy for
occluded organic carbon, which determines
the isotopic signal in phytoliths. We investigated whether shoots of C4 grasses have more
phytoliths (higher silica concentration) than
C3 grasses in several ways. For these tests, we
used a data set assembled by Hodson et al.
(2005) on grass shoot silicon (Si) concentration
of 148 grass species (3 Bambusoideae, 4
Ehrhartoideae, 63 PACMAD, 78 Pooideae).
Note that % Si is closely correlated with %
opal A (SiO2?(nH2O)), the material that
composes phytoliths, making silicon concentration a reasonable approximation of amount
of silica deposited in grasses (Piperno 1988;
Hodson et al. 2005). The data were originally
assembled from the literature as raw shoot
silica concentrations (%, on a dry weight
basis); from these raw data, mean relative
shoot Si concentrations were calculated using
Restricted Maximum Likelihood (REML) to
adjust for inter-treatment differences (Hodson
et al. 2005). For a full description of this
method, see Broadley et al. (2001, 2003).
Using these data, we first tested whether
shoot Si concentrations differ between C3 and
C4 grasses, regardless of phylogenetic position, and between different clades of grasses.
Plotting and Shapiro-Wilks’ test (p % 0.05)
show that the data are right-skewed and thus
violate the assumption of normality (data not
shown). Nonparametric tests (Mann-Whitney
U-test, Kruskal-Wallis test) were therefore
performed using R software to test for
differences in mean Si shoot concentration
between C3 and C4 grasses and among
various clades within Poaceae and PACMAD
(Table 1). ANOVAs were also performed for
comparison (data not shown).
Secondly, we used phylogenetic comparative analytical methods to test whether
evolution of C4-ness correlates with an increase in shoot Si concentration, using a
phylogenetic tree from Bouchenak-Khelladi
et al. (2009) (Fig. S1 in the online supplementary information at http://dx.doi.org/10.
1666/09068.s1). The tree encompasses a
TABLE 1. Result from various tests of the hypothesis that C4 grasses produce more silica than C3 grasses are unable to reject the null hypothesis that they do not.
See text for explanation.
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
31
32
MCINERNEY, STRÖMBERG, AND WHITE
Bayesian consensus backbone based on 90
taxa and three plastid DNA regions, which
helped constrain a parsimony search of 417
morphological characters for all 800 grass
genera (Bouchenak-Khelladi et al. 2009). It
was selected because it is the most detailed
phylogeny of grasses to date, it captures most
of the taxa (144/148) sampled in the Hodson
et al. (2005) data set, and it also contains
information about branch lengths. Mesquite
software (Maddison and Maddison 2009) was
used to prune it to include only taxa in
Hodson et al. (2005). Note that there are
several species per genus in the Hodson et al.
(2005) data set. For the most part, these
species were modeled as polytomies under
the assumption that Poaceae genera are
monophyletic unless otherwise indicated in
the literature; however, this assumption may
not always be prudent (e.g., Christin et al.
2008; Vicentini et al. 2008).
Two phylogenetic comparative analyses
were carried out. Following Osborne and
Freckleton (2009), we used a generalized
linear model in which photosynthetic pathway (C3 versus C4) was the categorical
predictor and mean relatively shoot Si concentration was the continuous, dependent
variable. We controlled for phylogenetic
dependence by calculating Pagel’s lambda
(Pagel 1999; see Freckleton et al. 2002).
Lambda determines and controls for phylogenetic correlation, and typically varies between 0 (no phylogenetic dependence) and 1
(highest degree of phylogenetic dependence)
(Freckleton et al. 2002). Calculations were
performed in R from scripts provided by R.
Freckleton (personal communication 2009).
We also conducted independent phylogenetic contrast analysis using phylocom
software (http://www.phylodiversity.net/
phylocom/; Webb et al. 2008). Phylocom is
appropriate for this data set because it is
able to reconstruct trait distribution across
polytomies. Our phylogeny provides only
four contrasts; we tested the significance of
these contrasts using a t-test and a sign test
(Table 1).
In addition, we tested whether, in each
treatment (set of environmental conditions),
C4 grasses accumulate more silica than C3
grasses. This test used a subset of the raw
shoot silica concentration data in Hodson et
al. (2005), namely studies in which at least one
C3 and one C4 grass had been grown under
similar soil and climatic conditions. For each
treatment, we calculated the difference between the within-treatment average Si shoot
concentration for C4 and C3 grasses, respectively, and conducted a two-sided sign test.
We also calculated all possible differences
between C4 and C3 grasses for each treatment,
and conducted a two-sided sign test for these.
In both cases, the H0 was that there is no significant difference between shoot silica concentration, which should result in an equal
number of positive and negative differences.
We repeated this analysis using only openhabitat grasses, Pooideae and PACMADs,
after it was found that Bambusoideae and
Ehrhartoideae often have very elevated shoot
silica levels compared with other grasses (see
Hodson et al. 2005). On the basis of the results
from these analyses, we also carried out a onesided sign test for whether C3 grasses produce
more silica than C4 grasses (Table 1). All
analyses were performed in R.
Finally, we studied phytolith assemblages
extracted from leaves of 23 species (24 samples, with two samples for Nassella pulchra) in
Strömberg’s modern reference collection
(Strömberg 2003, unpublished data) to determine whether there are detectable differences
in relative abundance of non-diagnostic (potential) grass phytoliths (NDG) and in nonGSSC phytolith morphotypes between C3 and
C4 grasses and between major grass clades,
namely the BE + Pooideae (BEP) clade (GPWG
2001) versus PACMAD and Pooideae versus
PACMAD. Plotting indicates that the nonGSSC and NDG frequencies may not be
normally distributed (data not shown), but
the small data set precludes firm conclusions
in this regard. One-way ANOVAs were
conducted on raw NDG and non-GSSC frequencies (%) to detect differences (Table 1).
Results
Purity of Samples
Particle Size Composition of Sample Overall.—
All samples but one (LS26) consist dominant-
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
33
FIGURE 2. Composition of heavy liquid yield. A–B. Particle size (,10, 10–50, .50 mm) composition. A, Unweighted
(numeric) relative abundances of particles. B, Relative abundances of particles, using a volumetric weighting scheme,
providing a measure of the volumetric contribution of particles of different size. This should be a more relevant
measure of which particle sizes influence the isotopic signal. C, D. Particle type (P 5 phytoliths, OB 5 other biogenic
silica, NB 5 non-biogenic silica) composition. C, Unweighted (numeric) relative abundance. D, Volumetrically
weighted relative abundance (see above). E, F. Particle type composition, focusing on phytolith types (DIP 5 diagnostic
phytoliths, NDG 5 non-diagnostic (potential) grass phytoliths, NDO 5 non-diagnostic phytoliths). E, Unweighted
(numeric) relative abundance. F, Volumetrically weighted relative abundance (see above).
ly (&50%) of very small particles (longest
dimension ,10 mm) (Table S1A, Fig. 2A), and
often with a longest dimension closer to 1–
2 mm (data not shown). The fraction of the
particles with a longest dimension of .50 mm
is less than 10% in all samples. In contrast, the
volumetric contribution of the ,10 mm size
class is very small, often ,3%, whereas
particles of 10–50 mm make up the chief part
of the silica in the sample, and particles
.50 mm often contribute 40% or more (Table
S1B, Fig. 2B).
34
MCINERNEY, STRÖMBERG, AND WHITE
Particle Class Composition of Sample Overall.—
Most of the samples are made up chiefly of
non-biogenic silica particles (NB) and contain
less than 20% phytoliths (Table S1A, Fig. 2C).
Exceptions include five samples in which
over 30% (31–62%) of the particles are
phytoliths: LS26, LS27, LS25, LS13, and KimA2. Other biogenic silica particles (OB) make
up very small fractions (%5%) of most
samples.
Volumetrically, phytoliths contribute a
higher relative proportion to the samples
(29% on average) than they do numerically
(16% on average) (Table S1B, Fig. 2D). Nevertheless, phytoliths make up less than 40% in
most samples. The five phytolith-rich samples, however, are volumetrically phytolithdominated, with 56% to 74% (average 5 64%),
phytoliths by volume. These samples are
considered the most ‘‘pure’’ in terms of their
phytolith content (Table S1B).
If all particles in the sample contain
approximately the same concentration of
encased organic carbon, the carbon isotope
signature should be proportional to the
volume. If so, the isotopic signal in most
samples derives mainly from non-biogenic
silica rather than phytoliths, but the subset of
pure samples derive their carbon isotope
signatures primarily from the volumetrically
dominant phytoliths.
Phytolith Type Composition in Samples.—The
phytolith fraction of the samples (P 5 DIP +
NDG + NDO) commonly consists of roughly
30–50% (average 5 34%) diagnostic phytoliths (DIP, relevant to the phytolith assemblage count) (Table S1A, Fig. 2E). Non-diagnostic (potential) grass phytoliths (NDG)
commonly make up ,5–30% (average 5
13%) of the phytolith assemblages. Nondiagnostic phytoliths (NDO) routinely contribute .50% (average 5 53%, maximum 5
88%) of the total phytolith count. Relative to
the total particle count (includes OB and NB),
DIP accounts for 0.3–20% (average 5 5.6%);
NDG makes up 0.3–12% (average 5 2%);
NDO contributes 1.6–31% (average 5 8%).
When the samples are weighted volumetrically, the diagnostic phytoliths (DIP) contribute the same, on average, to the phytolith
fraction as when simply considered relative to
counts (average 5 34%), but show a broader
range, commonly between ,20–60%, (Table
S1B, Fig. 2F). Relative to the total sample, the
average volumetric contribution of DIP is
larger than the per-count contribution (average 5 9%). Volumetric weighting increases
the contribution of non-diagnostic (potential)
grass phytoliths (NDG) relative to the phytolith volume (range: ,5–50%, average 5 22%)
and to the total sample volume (range: ,0.5–
30%, average 5 7%). In contrast, the nondiagnostic phytoliths (NDO) decrease their
contribution relative to the volume of phytoliths (range: 20–90%; average 5 45%) and
increase their contribution to the total sample
volume (range: 1–43%, average 5 13%) over
their contribution to total particle count.
The five pure samples numerically contain
an average of 17% DIP, 7% NDG, and 24%
NDO (Table S1A, Fig. 2E), and volumetrically
contain 19% DIP, 19% NDG, 26% NDO (Table
S1B, Fig. 2F) relative to the sample as a whole.
The phytolith counts for the pure samples are
on average 34% DIP, 13% NDG, and 53% NDO
and the phytolith volume is on average 30%
DIP, 30% NDG, and 40% NDO. Thus, in the
pure samples, the phytolith volume will be
dominated by the non-diagnostic phytoliths
(NDG + NDO 5 70% of phytolith volume)
rather than diagnostic phytoliths (DIP).
Proportion of Grass Phytoliths in Samples.—To
estimate the proportion of phytoliths derived
from grasses, we focus on the diagnostic
phytoliths (DIP) and the non-diagnostic (potential) grass phytoliths (NDG, including bulliform cells, wavy elongates) because unlike
NDO these can be confidently identified as
grasses or non-grasses. Grass phytoliths, GSSC
and NDG, constitute 62–95% (average 5 85%)
of the assignable phytoliths (DIP + NDG)
(Table S2). Thus, the vast majority of the
phytoliths that can be assigned to a taxonomic
group are derived from grasses.
Sources of Contamination.—An important
potential contaminant to be considered is
non-biogenic (NB) silica such as volcanic ash
because it can have the same specific gravity
as biogenic silica, and thus cannot be separated from phytoliths through heavy-liquid
separation. Both phytoliths and volcanic ash
contain very low concentrations of carbon
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
35
FIGURE 3. Stable isotope values (d13C, % VPDB) of phytoliths samples designated by purity, as estimated from
volumetrically weighted relative frequencies (%) of phytoliths (compared to other biogenic silica and non-biogenic
silica) in the samples. Pure 5 .50% phytoliths (volumetrically weighted; Table S1B); less pure 5 20–50% phytoliths;
not pure 5 ,20% phytoliths. Note that sample purity is not always a good predictor if phytoliths are well enough
preserved for a quantitative assemblage analysis. Thick, dashed lines connect samples collected at a single outcrop. For
age ranges of samples see Table S2). Pure C3 and C4 end-member values derived from phytoliths from soils (Smith and
White 2004) are indicated with arrows at top.
36
MCINERNEY, STRÖMBERG, AND WHITE
(0.01–0.10% by weight). To investigate how
the presence of volcanic ash in phytolith
yields might bias isotopic values, a pure
Miocene volcanic ash sample from Greenwood Canyon, Morrill County, Nebraska,
was prepared and measured in the same
way as the phytolith extracts. This sample
yielded a d13C value of 225.1%, close to the
value for C3 phytoliths (227%). Thus contamination by volcanic ash would contribute
to the isotopic signature of a sample prepared
using heavy liquid separation and bias results
against C4 grasses. Characterization of abundance of volcanic ash and secondary silica in
the five pure samples ranged from ‘‘not
observed’’ to ‘‘moderately abundant’’ (Table
S2).
Preservation of Phytoliths
The preservation of phytoliths in the
samples ranges from pristine, with detailed
ornamentation and visibly intact occluded
carbon, to very poor, with either heavily
etched or severely structurally altered phytoliths (Table S2). Phytoliths are often fragmented to some degree. Secondarily precipitated
silica is present in most samples and often
abundant. The samples with the highest
phytolith content (LS25–LS27, LS13, and
Kim-A2) also have well-preserved phytolith
assemblages. However, this is not always the
case: LS21 and LS41 have relatively high
phytolith content (by volume; Fig. 2B), but
exhibit poor phytolith preservation.
Isotopic (d13C) Analysis of Phytoliths
Carbon isotope ratios of extracts show a
large range of values, no singular trend with
time, and no consistent differences between
sites (Table S2, Fig. 3). This lack of a coherency is evident when all samples are considered, without regard for sample purity. A
more coherent pattern emerges when only
pure (.50% phytoliths, volumetrically
weighted) samples are examined: LS26,
LS27, LS25, LS13, and Kim-A2. These samples
range from 227.9% to 217.0% in d13C values,
with the oldest samples (LS13, 7-6 Ma;
KimA2, 5-9 Ma) being the most negative, the
youngest (3-2 Ma) the most positive, and the
intermediate samples having intermediate
values (Fig. 3).
The Neogene data fall largely within the
range of the end-members in grasslands soils
(227% for a pure C3 phytolith assemblage and
215% for a pure C4 phytolith assemblage)
established by Smith and White (2004), suggesting that the end-member values can also
be applied to Neogene phytolith records.
Using a simple mixing model, we interpret
the Neogene record of d13C for pure phytolith
samples in terms of proportions of C3 and C4
grass (Table S3, Fig. 3) as follows: at the
beginning of the late Hemphillian (7-6 Ma),
the grasses in the Great Plains were 100% C3.
During the late Hemphillian, C4 grasses
abundance rose to 28% and became increasingly dominant, reaching over 50% in Nebraska. By the late Blancan (3-2 Ma), C4 grasses
were even more dominant, constituting over
80% of the grass community in Nebraska.
Phytolith Analysis
In order to compare isotope results to
morphological analysis, vegetation changes
are summarized briefly here; see Strömberg
and McInerney (2011 [this volume]) for a
complete account. The tree cover index, FI-t,
shows higher ratios in samples older than
9 Ma (.35% with one exception) and lower
values (,20% with one exception) in samples
younger than 9 Ma (Table S2), reflecting
increased contributions from phytoliths of
open-habitat grasses over palms and other
forest indicator taxa (Fig. 4). Grass communities were dominated by open-habitat grasses
(94–100% of identified GSSCs) and all samples are interpreted to have included both
pooid (C3) and panicoid (predominantly C4)
plus chloridoid (C4) PACMAD grasses. Panicoid and chloridoid grasses increased in
abundance from ,10% PAN + CHLOR of
the total assemblage (using scaled values, see
above) to 26–28% in the latest Miocene, only
to decrease again in the Pliocene to 10%.
PACMAD grasses as a whole showed the
same pattern, with (scaled) frequencies %30%
of the total assemblage in the early late
Miocene, climbing to abundances 44–46% in
the latest Miocene, and dropping to 25% in
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
37
FIGURE 4. Vegetation analysis of Miocene phytolith assemblages from Nebraska and Kansas, compared with stable
carbon isotope values. Quantitatively analyzed phytolith assemblages represented as pie charts; assemblages from
Kansas marked with ‘‘KS.’’ There is not a clear correspondence between d13C values and relative abundance of
potential C4 grass phytoliths, measured either as phytoliths typical of panicoids and chloridoids (% PAN + CHLOR) or
as phytoliths typical of all PACMAD grasses (% PACMAD TOT). The indeterminate or non-diagnostic GSSCs (OTHG)
have been omitted to emphasize relative changes in GSSC composition among diagnostic classes of phytoliths (see
Table S2 for actual relative abundances). Assemblages considered relatively pure on the basis of frequency of
phytoliths in samples (volumetrically weighted %, see text) marked with a heavier gray outline. Other assemblages are
considered less pure or not pure (see Table S1B, Fig. 2). Black, horizontal arrow points to actual isotopic value of
sample LS19. Dashed gray vertical arrows denote uncertainty in age of assemblage.
the single Pliocene assemblage. The late
Miocene increase in panicoids, chloridoids,
and PACMADs in general is due to the
increase of these grasses relative to other
grasses in grass communities and the overall
shift to more grass-dominated communities.
Phytoliths indicative of wetlands are generally rare (Table S2).
38
MCINERNEY, STRÖMBERG, AND WHITE
ence the isotopic signal in many of the nonpure samples.
The comparison of isotope-based and assemblage-based C4 estimates in individual
pure samples shows that the assemblagebased C4 abundance values in overall vegetation are lower than the carbon isotopederived estimates for all but one sample, LS13
(Table S3, Fig. 6A). When % C4 is calculated
within grasses, the proportion of C4 increases
and agreement between the methods improves considerably for the three intermediate aged samples, Kim-A2, LS27, and LS26
(Table S3, Fig. 6B). However, for youngest
sample, LS25, carbon isotope ratios reconstruct 83% C4, whereas the maximum assemblage estimate within grasses is only 30% C4
(Table S3, Fig. 6B).
Amount of Silica in C3 and C4 Grasses
FIGURE 5. Correlation between estimates of C4 biomass,
based on potential C4 grass GSSC abundances in
vegetation as a whole (FI TOT + GSSC) and carbon
isotope values, respectively. A, Percent panicoid +
chloridoid GSSC, providing a minimum estimate of
potential C4 grasses, calculated as: [(PAN + CHLOR)/
(GSSC 2 OTHG)]*[GSSC/(FI TOT + GSSC)]. B, Percent
PACMAD GSSC, providing a maximum estimate of
potential C4 grasses, calculated as: [(PACMAD TOT)/
(GSSC 2 OTHG)]*[GSSC/(FI TOT + GSSC)].
Comparing Isotopic and Phytolith
Assemblage Data
Regression analysis using all samples,
regardless of sample purity, shows that there
is no significant correlation between the
relative abundance of C4 grasses in vegetation
overall estimated by the frequency of potential C4 grass phytoliths and the d13C values,
regardless of whether panicoid + chloridoid
or all PACMAD GSSCs are used as C4 grass
indicators (Fig. 5; data not shown). This
suggests that non-biogenic silica may influ-
Nonparametric tests (Mann-Whitney Utest) showed that there is no significant
difference (p . 0.05) in silica concentration
between C3 and C4 grasses or between major
clades (BEP versus PACMAD, Pooideae
versus PACMAD) of grasses (Table 1). Kruskal-Wallis tests further demonstrate no significant differences (p . 0.05) among clades
within Poaceae—except when PACMADs are
treated as a group—or within PACMADs. In
contrast, box plots indicate that grasses in the
Bambusoideae and Ehrhartoideae have substantially more Si in their shoots than Pooideae and PACMADs (Fig. 7B). This corresponds well with semiquantitative observations of extracted phytoliths (Strömberg
2003), and an ANOVA with Tukey Honest
Significant Differences test indicates that
Bambusoideae, but not Ehrhartoideae, produces significantly more silica than other
clades when PACMADs are lumped ( p ,
0.05; data not shown). However, the low
number of samples available so far complicates any firm conclusions with regard to
inter-clade variability when these non-phylogenetic methods are used. Parametric tests
(ANOVAs) performed on the data give
generally the same outcome as the nonparametric tests (data not shown).
Pagel’s lambda is 0.48 and thus significantly different from both 0 and 1, indicating that
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
39
FIGURE 6. Abundance of C4 based on phytolith composition and carbon isotope ratios for five pure samples.
Assemblage composition estimates are for % C4 in overall vegetation (A) or in grasses only (B). Composition estimates
assume either that only GSSCs typical of chloridoids and panicoids derived from C4 grasses (minimum estimate) or
that all GSSCs typical of PACMAD grasses derived from C4 grasses (maximum estimates). Error bars indicate 95%
confidence intervals (Table S3).
there is some phylogenetic correlation in the
data. However, controlling for this dependence, there is no significant difference in
mean shoot Si concentration between C3 and
C4 grasses (Table 1). Similarly, analysis of the
few (four) phylogenetic independent contrasts could not reject the null hypothesis that
changes in mean Si shoot concentration are
40
MCINERNEY, STRÖMBERG, AND WHITE
FIGURE 7. Box plots showing distribution of mean Si
shoot concentration between different grasses. A, C3 and
C4 grasses, regardless of clade are not statistically
significantly different in their silica production. B,
Different clades within Poaceae, with Bambusoideae
being potentially statistically significant from Ehrhartoideae, PACMADs (when lumped), and Pooideae (Table 1).
not correlated with shifts in photosynthesis
type (Table 1). Instead, in three out of four
contrasts the lineage with conserved (C3)
photosynthetic pathway had higher mean Si
shoot concentration at a C3-C4 split, counter to
expectation that C4 lineages should have
higher silica content.
Sign tests of within-treatment differences in
raw shoot silica concentration showed that C3
grasses contain significantly more silica than
C4 grasses under a given set of environmental
conditions (treatments) (Table 1). This is true
FIGURE 8. Box plots showing that there is no statistically
significant difference in the production of various nonGSSC phytolith types between modern C3 and C4 grasses
from Strömberg’s reference collection (Strömberg 2003,
unpublished data; see Table S4). A, Non-diagnostic
(potential) grass phytoliths (NDG). B, Non-GSSC phytoliths.
whether all grasses, or just open-habitat
grasses (Pooideae and PACMAD) are studied.
Box plots and one-way ANOVAs between
untransformed data on % NDG and non-GSSC
demonstrate that, there is no significant
difference in the relative abundance of NDG
or non-GSSC phytoliths in assemblages produced in C3 and C4 grasses (Table 1, Fig. 8).
This is true whether all grasses are used or the
comparison is restricted to Pooideae and
PACMADs. Box plots show that, on average,
there is a higher frequency of non-GSSC
phytoliths and a lower frequency of NDG
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
phytoliths in BEP or Pooideae compared to
(C3/C4) PACMADs (data not shown, but see
Table S4); however, none of these differences
are statistically significant in our data set,
though this is likely due to limited sample size.
Discussion
Can the Carbon Isotope Ratios of Phytoliths Be
Used to Reconstruct the Photosynthetic Pathways
of Neogene Grasses?—Most extracts analyzed
here are dominated by volcanic ash and other
non-biogenic silica (NB), with phytoliths
representing less than 30% by volume, with
the exception of five pure samples that
contain 56–74% phytoliths by volume (Fig.
2D). The d13C of volcanic ash suggests such
contamination would bias against C4 signatures. Pedogenic silica, on the other hand,
could bias against C3 signatures (Ehleringer et
al. 2000; Smith and White 2004). Thus,
samples with high proportions of non-biogenic silica (.50%) are likely to provide
spurious results. However, samples with
.50% phytoliths by volume should derive
their signature dominantly from phytoliths
and are amenable to isotopic measurement.
In addition to potential contaminants introduced by non-plant silica, the preservational
status of the phytoliths may influence the
isotopic value. Etching, structural/textural
alteration of silica, and fragmentation may
compromise the integrity of the isotopic
composition of the carbon occluded in the
fossil phytoliths. The secondarily precipitated
silica is another sign of dissolution of silica,
which may have led to potential exposure of
occluded organic material to groundwater,
possibly altering the isotopic signal. For this
reason we recommend determining preservation in addition to purity and applying
quality standards prior to isotopic analysis.
Although a range of preservational quality
was observed, the five pure samples also had
good to pristine preservation.
A pure, well-preserved sample should
provide a reliable measure of the carbon
isotopic composition of vegetation, but does
it specifically record the composition of
grasses? In our sample set, the vast majority,
85% on average, of the phytoliths with
assignable origins are derived from grasses.
41
This estimate excludes the proportion of
phytoliths that cannot be confidently assigned
a taxonomic origin, the non-diagnostics
(NDO) that constitute an average of 44% of
the phytolith fraction. However, smooth
elongates and rods, which make up a substantial portion of the NDO component (data
not shown), are known to be produced in
many Poaceae taxa (e.g., Twiss et al. 1969;
Rovner 1971; Piperno 1988; Runge 1999;
Strömberg 2003). Therefore, it is very likely
that grasses were the source for a large
proportion of the NDOs in the Great Plains
samples, in particular in assemblages that are
clearly grass dominated (with low FI-t ratio),
such as the five pure samples (FI-t from 5% to
19%). Analysis of a data set of 99 Cenozoic
assemblages reported by Strömberg (2005)
shows that proportion NDOs (of all phytoliths) and GSSCs (of FI TOT + GSSC) are
correlated ( p 5 0.001748), supporting this
argument, although the explanatory power is
low (Multiple R2: 0.1026). Assuming that the
non-diagnostics are produced at least in
proportion with the assignable phytoliths,
then 85% on average of phytoliths would
derive from grasses. If so, the pure samples
with .50% phytoliths should provide a
dominantly grass signal.
On the basis of the samples analyzed here,
the following guidelines for sample assessment are recommended. In order to use carbon
isotope ratios of heavy liquid extracts to
reconstruct proportion of C3 and C4 grasses,
measurements should be made on samples
with (1) .50% phytoliths by volume, (2) good
preservation, and (3) grass-dominated phytolith assemblages. Once these criteria are met,
the sample should provide a reliable measure
of the carbon isotope ratio of ancient grasses.
Vegetation History of the Central Great Plains
Based on d13C of Fossil Phytoliths.—Among the
samples measured here, five samples qualify
for providing a vegetation analysis on the
basis of their purity, preservation, and grass
phytolith content. The history of grasses in
the central Great Plains based on the d13C
values of these samples is one of a transition
during the latest Miocene–Pliocene from pure
C3, to mixed C3-C4 by ca. 5.5 Ma and then to
.80% C4 grasses by ca. 2.5 Ma (Fig. 6).
42
MCINERNEY, STRÖMBERG, AND WHITE
These findings corroborate previous work
on carbon isotope ratios of horse tooth enamel
from the Great Plains, which indicate C3dominated ecosystems during much of the
Miocene until 6.6 Ma, when C4 grasses began
contributing a significant portion of horse diet
(Wang et al. 1994; Passey et al. 2002). Stable
carbon isotopes from soil carbonates, on the
other hand, suggest that a shift to clearly C4
dominated ecosystems (.50% C4) did not
occur until the Pliocene at ca. 2.5 Ma (Fox and
Koch 2003, 2004; Martin et al. 2008). Prior to
this, from the Early Miocene until at least
6.6 Ma, the Great Plains region was dominated by C3 vegetation with variable, low levels
of C4 grasses (,10–40%, on average 20%),
consistent with our LS13 and Kim-A2, which
indicate 0–28% C4 in grass communities. Note
that no isotopic data from soil carbonates are
available for 6.4–4.2 Ma, when our samples
show mixed C3-C4 (,50% C4) vegetation;
hence this apparent discrepancy may be a
sampling artifact.
However, there are important distinctions
between these different isotopic records
based on, respectively, phytoliths, soil carbonate, or tooth enamel. Whereas phytoliths
have the potential to provide an unbiased
signature of the grass community, soil carbonates record overall vegetation and tooth
enamel records diet. For example, in Hemphillian and younger equids, diet appears to
have been a C3-C4 mixture (Wang et al. 1994;
Passey et al. 2002), but it is not clear whether
the C3 component consisted of grasses and/or
shrubs/trees. The same is true for earlier in
the Miocene, where both soil carbonate and
tooth enamel carbon isotope ratios indicate
predominantly C3 vegetation, but cannot
clearly indicate the presence of C3 grasses.
The d13C results for phytolith samples LS13
and KimA2 indicate that grass communities
were dominated by C3 grasses. These are the
first isotopic data to unambiguously demonstrate that the late Miocene/Pliocene experienced mainly a transition from C3 grasses to
C4 grasses, rather than from C3 shrubs/trees
to C4 grasses.
Vegetation History of the Central Great Plains
Based on Phytolith Assemblage Analysis.—Phytolith analysis points to a scenario where late
Miocene habitats in the central Great Plains
became more uniformly open, and the expansion of potential C4 grasses (and PACMAD
grasses in general) resulted in mixed C3-C4
grassland habitats with variable grass community composition until potentially the
early late Pliocene (Fig. 4). The proportion of
C4 grasses in the phytolith assemblages
between 12 and 9 Ma ranges from 2% to
26% of vegetation and rises to 26–46% of
vegetation by 5.5 Ma. The Pliocene phytolith
assemblage, LS25, indicates a decrease in the
relative abundance of C4 to only 10–25% of
vegetation.
Comparing Isotopic and Phytolith Assemblage
Data.—For the pure samples, both the isotopic
and assemblage records indicate an increase in
the abundance of C4 grasses in the late
Miocene samples, LS13, Kim-A2, LS27, and
LS26. However, assemblage estimates of the
proportion of C4 in the overall vegetation tend
to be lower than the isotope estimates in all but
one case (Fig. 6A). In particular, the estimates
for the Pliocene sample, LS25, do not agree,
with the isotope ratio reconstructing 83% C4
and phytolith composition suggesting a maximum of 25% C4 in overall vegetation.
Because the isotopic data and assemblage
data come from the same samples, no
differences in time or space averaging between the records exist that would explain
this disagreement. Instead, we hypothesize
that this discrepancy is due to (1) inadequate
classification of PACMAD GSSCs, (2) greater
production of non-diagnostic phytoliths by C4
grasses than C3 grasses, or (3) differences in
the sensitivity of each method to non-diagnostic phytoliths leading to carbon isotope
ratios reflecting grasses rather than overall
vegetation.
The first hypothesis is that GSSC classification is inadequate for identifying all PACMADs, on the one hand, and for distinguishing C4 PACMADs from other PACMADs on
the other, causing us to underestimate the
amount of C4 grasses in grass communities.
Our study no doubt suffers from both of these
problems to some extent. It is well known that
there can be considerable overlap in morphotypes produced by, for example, pooid and
PACMAD grasses (e.g., Piperno and Pearsall
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
1998; Piperno 2006). Quantitative analysis of a
small number of modern grasses revealed that
the leaf phytolith assemblages of certain
PACMAD grasses contain as much as ,30%
(average 5 16%; n 5 11) non-PACMAD
morphotypes, most of which are non-diagnostic pooid (POOID-ND) forms (Strömberg
unpublished data). For pooids, the redundancy seems to be less severe, with a maximum of
10% (average 5 2%, n 5 7) typical PACMAD
forms. There is potentially even more overlap
in grass fruit and seed phytolith assemblages
(Mulholland 1989). Because of this overlap, we
can expect that a higher proportion of phytoliths classified as pooid were produced by
PACMADs than vice versa which would, in
general, bias a straight interpretation of morphotype class relative abundance data against
PACMADs and potentially against C4 grasses.
As for differentiating C3 and C4 PACMADs, much work is needed to establish a
robust classification scheme that calibrates
phytolith morphology against phylogeny and
photosynthetic pathway. In part, this is
because comprehensive phylogenies of grasses and in particular PACMADs have only
recently become available (e.g., BouchenakKhelladi et al. 2008, 2009; Christin et al. 2008;
Vicentini et al. 2008; Edwards and Smith
2010), and the number and relationships of
different C4 lineages within the PACMAD
clade are still somewhat obscure. In addition,
information on detailed three-dimensional
morphology of PACMAD GSSCs to analyze
in such a phylogenetic framework is rare
(Fredlund and Tieszen 1994; Piperno and
Pearsall 1998; Strömberg 2005). Thus, whereas
the GSSC makeup of chloridoids is fairly well
understood, other, newly recognized C4 lineages are not yet clearly distinguishable from
C3 PACMADs on the basis of phytoliths.
In sum, we cannot yet determine whether
none, some, or all PACMAD grasses in our
samples were C4. Furthermore, it is likely that
our assemblage-based estimates of the proportion of PACMAD grasses are overall too
low, perhaps as much as 30%. Nevertheless,
this imprecision in GSSC identification is
insufficient for explaining the incongruity
between morphology and isotopes in certain
samples, notably LS25, which has a d13C value
43
indicating 83% C4 but contains 25% PACMAD GSSC morphotypes (and 10% panicoid
+ chloridoid GSSCs) in total (Table S3). Even
if all PACMAD grasses were C4 and 30% of
their phytoliths were misclassified as POOIDND because of redundancy, the maximum C4
estimate would only increases to 33%. Thus, it
is unlikely that inadequate PACMAD GSSC
classification is the whole story.
Our second hypothesis is that non-diagnostic phytolith morphotypes are produced in
different quantities by C3 and C4 grasses and
thereby bias the isotope signature. As mentioned earlier, non-diagnostic (potential)
grass phytoliths (NDG) and non-diagnostic
phytoliths (NDO) contribute substantially to
each of the phytolith assemblages in terms of
silica volume (and occluded carbon). It has
been suggested that C4 grasses produce more
phytoliths than do C3 grasses (Kaufman et al.
1985; Bouchenak-Khelladi et al. 2009) because
of differences in the production of either
GSSC or non-diagnostic grass phytoliths, or
both. On the basis of their study of 20 grasses,
Kaufman et al. (1985) asserted that C4 grasses
produce more GSSCs than C3 grasses. However, the proportions of GSSCs of C3 and C4
grasses have been shown to reflect C3-C4
composition in modern grass communities
relatively closely (e.g., Fredlund and Tieszen
1994, 1997b; Alexandre et al. 1997; Bremond et
al. 2005a; Barboni et al. 2007), indicating that
magnitude of GSSC production (or contribution to soil phytolith assemblages) is similar
for C3 and C4 grasses. The formation of GSSCs
appears to be under genetic control, so
environmental variation would not cause
differences in GSSC production in either C3
and C4 grasses (see Blackman and Parry 1968;
Dorweiler and Doebley 1997; Zheng et al.
2003; Piperno 2006). Therefore, we hypothesize that any disparity between the amount of
silica deposited in C3 and C4 grasses should
primarily relate to non-diagnostic (non-GSSC)
morphotypes (NDG, NDO, or both).
Our analyses of the differences in mean Si
shoot concentration in grasses reject this
hypothesis, showing no significant difference
between phytolith production between C3
and C4 grasses, across Poaceae as a whole,
among open-habitat grasses (Pooideae and
44
MCINERNEY, STRÖMBERG, AND WHITE
PACMADs), or within the PACMAD clade
(Table 1, Fig. 7). When C3 and C4 grasses are
grown under similar conditions, our analyses
show that C3 grasses produce significantly
more silica than do C4 grasses, even when the
high-producing bamboos and ehrhartoids are
excluded (Table 1). Phylogenetic comparative
methods failed to show that the evolution of
C4 photosynthesis resulted in increased levels
of mean Si shoot concentration in grasses. In
fact, three out of four independent contrasts
showed higher mean Si shoot concentrations
in C3 grasses, suggestive of a different
pattern.
There are several possible reasons for why
our results contrast with the findings of
Kaufman et al. (1985) and Bouchenak-Khelladi et al. (2009). First, whereas Kaufman et al.
(1985) and Bouchenak-Khelladi et al. (2009)
were interested in numbers of phytoliths per
leaf area, we are first and foremost seeking to
understand the amount of silica (or rather,
occluded carbon translating into strength of
isotope signal) produced by different grasses.
It is not clear that these two are equivalent.
However, our preliminary examination of the
morphotype composition of phytolith assemblages extracted from modern grass leaves
suggests that, counter to previous work
(Kaufman et al. 1985; Bouchenak-Khelladi et
al. 2009), there is also no significant difference
between C3 and C4 grasses in the relative
number of NDG and all non-GSSC phytoliths
produced in leaf phytolith assemblages.
Second, previous studies comparing silica
production in C3 and C4 grasses have failed to
take phenotypic plasticity in this trait into
consideration (Kaufman et al. 1985; Bouchenak-Khelladi et al. 2009). As discussed earlier,
environmental conditions appear to be important for determining the production of
silica in plants in general (e.g., Epstein 1999),
and in grasses in particular (e.g., Jones and
Handreck 1965; Sangster and Parry 1969;
Miller Rosen and Weiner 1994; Bremond et
al. 2005b; Madella et al. 2009). Besides soil
chemistry, water regime may be controlling
much of the variation in silica deposition in
plants (e.g., Jones and Handreck 1965; Sangster and Parry 1969; Miller Rosen and Weiner
1994; Bremond et al. 2005b; Madella et al.
2009). Most recently, Madella et al. (2009)
showed that both amplified evapotranspiration rates and water availability increase the
amount of non-GSSC phytoliths produced in
grasses. Among NDG forms, various long
cells, stomata, and bulliforms are found to be
particularly environmentally sensitive (Sangster and Parry 1969; Bremond et al. 2005b;
Madella et al. 2009).
Neither of the previous studies (Kaufman et
al. 1985; Bouchenak-Khelladi et al. 2009)
reports the growing conditions of the grasses
measured; thus, it is unclear whether differences in genetic makeup or in climatic or
edaphic growing conditions are being measured. Here, we attempt to account for environmental variation by using REML adjusted
data (Hodson et al. 2005) as well as exploring
within-treatment comparisons. Nevertheless,
we acknowledge that substantially more work
is needed before the question of differential
silica deposition in C3 and C4 grasses is
answered. Available data, in the Hodson et
al. (2005) data set are clearly biased toward the
Northern Hemisphere and temperate, as opposed to subtropical-tropical, climates, and
may not capture the extremes of possible
phenotypes relating to hydrological regime.
What is clear, however, is that we cannot
assume that C4 grasses contributed disproportionately to the Miocene–Pliocene soil phytolith
assemblages, thereby biasing the isotopic record in favor of C4 grasses. Similarly, it is
premature to conclude that silica-accumulating
C4 grasses and hypsodont ungulates formed
part of a coevolutionary arms race, as has been
suggested by Bouchenak-Khelladi et al. (2009).
The third hypothesis is that the two
methods have different sensitivities to nondiagnostic phytoliths (NDG and NDO). In
phytolith analysis, the most abundant—and
largest—grass phytoliths, namely the NDGs,
such as bulliforms and wavy elongates, are
commonly excluded because it is not clear
that their abundance accurately represents
biomass of different plants (see ‘‘Methods’’).
In contrast, the isotopic value of a phytolith
assemblage will be controlled by the volume
of silica contributed by different plants. In
this context, the non-diagnostic phytoliths
may play a significant role.
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
Given our analysis suggesting no significant difference in the production of NDG
phytoliths in C3 and C4 grasses, the NDG
contribution should faithfully reflect relative
abundances of C3 and C4 grasses. The same is
true for the proportion of the NDOs produced
by grasses, which is likely to be substantial in
assemblages from grass-dominated vegetation (e.g., Strömberg 2003).
Among the pure samples, NDG and NDO
combined on average account for 70% of the
phytolith volume and for 45% of the total
sample volume. As a consequence, we should
expect a non-linear relationship between abundance of plants in the vegetation as estimated
by phytolith analysis and the volume of silica
contributed by different plants, as measured by
isotopic analysis. In other words, the carbon
isotope ratio will provide a record primarily of
grasses, whereas assemblage data provide a
characterization of the overall vegetation. As a
result, trees and shrubs that are C3 will have
a larger influence on the assemblage analysis
than on the isotope analysis.
This methodological difference, namely
that assemblage data exclude non-diagnostics
and isotope measurements necessarily include them, may in itself explain the tendency
for the assemblage method to yield lower
proportions of C4 than the isotope method
(Fig. 6A). If we use the assemblage data to
reconstruct the proportion of C4 grasses,
assigning (PAN + CHLOR) as a lower bound
and (PACMAD TOT) as an upper bound, in
the grasses only we see much better agreement (Fig. 6B). The estimates based on isotopic ratio for LS26, LS27, and Kim-A2 are close
to estimates based on the assemblage analysis
using all PACMAD phytoliths as C4 indicators. This could indicate that most PACMAD
grasses were C4, that GSSC classification is
too coarse to properly distinguish all phytoliths produced by C3/C4 PACMAD grasses,
or both. These hypotheses can be tested when
a more detailed understanding of distribution
of GSSC morphologies among modern PACMADs has been achieved and with additional
fossil phytolith assemblages comparing the
d13C values and assemblage composition.
The major discrepancy in the interpretation
of the C4 component in the grass community
45
lies with LS25. Even when only grasses are
considered, the assemblage suggests only 12–
30% C4, whereas the isotope signature suggests 83% C4 (Fig. 6B). Because LS25 is ‘‘pure’’
and well preserved, it is not clear why there is
such a difference. Analysis of more pure, wellpreserved samples will shed light on any
systematic differences that cannot be explained by the factors examined in this paper.
Conclusions
We analyzed the stable carbon isotope
ratios and assemblage composition of silica
extracted from Miocene and Pliocene paleosols from Nebraska and Kansas to answer the
following questions: (1) How can the carbon
isotope ratios of silica extracts be used to
reliably reconstruct the photosynthetic pathways of Neogene grasses? (2) What is the
Neogene history of C3 and C4 grasses recorded in the d13C of fossil phytoliths? and (3)
How does it compare with phytolith assemblage composition from the same samples
(Strömberg and McInerney 2011 [this volume])? The study showed that although
many Miocene–Pliocene paleosols from the
Great Plains did contain preserved fossil
phytoliths, they were often in such low
concentrations or too poorly preserved to
provide a reliable carbon isotope value. Five
samples contained high concentrations of
phytoliths (.50% by volume) with good
preservation, very few diatoms or other forms
of biosilica, and showed a strong dominance
(,85%) of grass phytoliths. Thus, this subset
of samples was considered of high enough
quality to yield reliable measurements reflective of the isotope signature of ancient
grasses. We conclude that it is vital to check
the heavy liquid yield used for d13C measurements under the microscope for signs of
contamination and alteration, which might
compromise the isotopic integrity of the
occluded carbon, before attempting analysis
of the stable isotope ratios. To reconstruct a
history of the C3-C4 abundance in grasses, we
recommend measuring the carbon isotope
ratio only of pure (.50% phytoliths by
volume), well-preserved samples that contain
a high proportion of grass phytoliths. Recent
improvements in extraction techniques
46
MCINERNEY, STRÖMBERG, AND WHITE
should enable higher sample throughput and
greater applicability of this approach (Madella et al. 1998; Katz et al. 2010).
Stable carbon isotope ratios of the five highquality samples indicate a shift from C3dominated grasses in the late Miocene to
mixed C3-C4 grasses by around 5.5 Ma to
.80% C4 grasses in the Pliocene. These results
agree broadly with previous studies of tooth
enamel and soil carbonate from the Great
Plains (Wang et al. 1994; Passey et al. 2002;
Fox and Koch 2003, 2004; Martin et al. 2008),
but provide a record primarily of grasses,
distinct from other vegetation. Specifically,
they show that the expansion of C4 grasses in
the late Miocene in the central Great Plains
was at the expense of C3 grasses rather than
C3 shrubs/trees.
Phytolith assemblage counts also indicate
an increase in C4 grasses during roughly the
same time frame, but smaller than that
suggested by isotopic data. In addition, the
assemblage data point to a decrease rather
than an increase in C4 abundance in the
Pliocene. We suggest that some of these
discrepancies are attributable to our current
inability to recognize all phytoliths from C4
grasses among C3/C4 PACMADs, and to
redundancy in GSSC production between
pooids and PACMADs, which could lead us
to underestimate the proportion of PACMAD
grasses. Another important factor is the
influence of non-diagnostic phytoliths on the
carbon isotope signature. Specifically, nondiagnostic grass phytoliths (NDG) and the
non-diagnostics (NDO) are excluded in the
assemblage analysis because they are thought
to disproportionately represent grasses and
because of the problem of redundancy,
respectively. Meanwhile, they make up a
substantial part of the silica analyzed for
carbon isotopes (,45% by volume in the pure
samples). Because both NDG and NDO are
produced in abundance by grasses, the
isotope signature likely overrepresents grasses relative to other components of the
vegetation, whereas assemblage analysis provides an overview of the total vegetation. Our
analysis suggests that C3 and C4 grasses do
not differ in their production of non-diagnostics, and thus non-diagnostics should faith-
fully reflect the grass C3-C4 composition. The
two methods provide largely similar reconstructions of C4 grass abundance when
phytolith assemblage values are calculated
within the grass community rather than
overall vegetation. However, the discrepancies with the Pliocene sample need to be
further investigated.
In grass phytolith-dominated assemblages,
the inherent bias in the isotope method enables
the reconstruction of the isotope signature of
ancient grasses distinct from other kinds of
vegetation. Thus, this bias is in fact an asset of
the method. Combined with assemblage analysis of phytoliths, carbon isotope ratios can
provide information about several important
aspects of ancient vegetation and climate that
cannot be accessed through other methods.
Comparing the estimates of C4 grass abundance from the two methods can also help
calibrate the classification of PACMAD fossil
grass phytoliths (Strömberg and McInerney
2011 [this volume]). A final advantage of the
method is that it can potentially provide
information about C4 grass biomass in fossil
soils devoid of carbonate nodules.
Acknowledgments
We would like to thank B. Vaughn, M.
Dreier, and R. Kihl for their assistance in the
laboratory. R. Diffendal, J. Thomasson, R.
Zakzewski, M. Voorhies, D. Terry, and M.
Perkins provided invaluable help with sampling localities and field information. E. Kelly
provided critical training in phytolith extraction. D. Ackerly, M. Broadley, A. Mead, J.
Hille Ris Lambers, J. Felsenstein, C. Osborne,
R. Freckleton, D. Hurley, and C. Webb
provided much-valued advice about various
statistical and phylogenetic analyses. R.
Freckleton generously supplied R-code and
Y. Bouchenak-Khelladi kindly provided the
phylogenetic tree and branch lengths. Thank
you to R. Diffendal, S. Kidwell, D. Jablonski,
G. Retallack, G. Wilson, M. Clementz, and L.
Bremond, who gave their thoughtful comments on versions of the manuscript. This
research was funded in part by grants from
Sigma Xi, the Hinds Fund (University of
Chicago), and a U.S. Environmental Protection Agency STAR fellowship to F.A.M.
NEOGENE GRASSLAND C3-C4: PHYTOLITH d13C
C.A.E.S. was supported by a Smithsonian
Fellowship during part of this work.
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