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www.elsevier.com/locate/palaeo
Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx
FThe effects of late Quaternary climate and pCO2 change on
C4 plant abundance in the south-central United States
Paul L. Kocha,*, Noah S. Diffenbaugha,1, Kathryn A. Hoppeb,2
aDepartment of Earth Sciences, University of California, Santa Cruz, CA 95064, USAbDepartment of Geological and Environmental Sciences, Stanford University, Stanford, CA 94305, USA
OReceived 28 July 2003; accepted 25 September 2003
ECTED PRO
Abstract
The late Quaternary was a time of substantial environmental change, with the past 70,000 years exhibiting global changes in
climate, atmospheric composition, and terrestrial floral and faunal assemblages. We use isotopic data and couple climate and
vegetation models to assess the balance between C3 and C4 vegetation in Texas during this period. The carbon isotope
composition of fossil bison, mammoth, and horse tooth enamel is used as a proxy for C3 versus C4 plant consumption, and
indicates that C4 plant biomass remained above 55% through most of Texas from prior to the Last Glacial Maximum (LGM)
into the Holocene. These data also reveal that horses did not feed exclusively on herbaceous plants, consequently isotopic data
from horses are not reliable indicators of the C3–C4 balance in grassland biomes. Estimates of C4 percentages from coupled
climate–vegetation models illuminate the relative roles of climate and atmospheric carbon dioxide (CO2) concentrations in
shaping the regional C4 signal. C4 percentages estimated using observed modern climate–vegetation relationships and late
Quaternary climate variables (simulated by a global climate model) are much lower than those indicated by carbon isotope
values from fossils. When the effect of atmospheric CO2 concentration on the competitive balance between C3 and C4 plants is
included in the numerical experiment, however, estimated C4 percentages show better agreement with isotopic estimates from
late Quaternary mammals and soils. This result suggests that low atmospheric CO2 levels played a role in the observed
persistence of C4 plants throughout the late Quaternary.
RD 2004 Published by Elsevier B.V.31
ORKeywords: C3; C4; Pleistocene; Holocene; Mammal; Soil; Paleosol; Carbon isotope; Oxygen isotope; Vegetation; GCM; Texas1. Introduction east, arid subtropical in the west, and temperate/
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CToday, Texas exhibits strong gradients in climate
and vegetation. Climate is humid subtropical in the
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0031-0182/$ - see front matter D 2004 Published by Elsevier B.V.
doi:10.1016/j.palaeo.2003.09.034
* Corresponding author. Fax: +1-831-459-5861.
E-mail addresses: [email protected] (P.L. Koch),
[email protected] (N.S. Diffenbaugh),
[email protected] (K.A. Hoppe).1 Tel.: +1-831-459-3504.2 Tel.: +1-650-723-9191.
continental in the north. Temperature varies strongly
from north to south, whereas rainfall changes from
east to west. Intersecting climatic gradients couple
with geology and topography to create vegetation
zones (Fig. 1, Appendix A). Moving west across
northern and central Texas, the pine and hardwood
forests of the east give way to oak woodlands mixed
with tallgrass prairie, and then to mixedgrass and
shortgrass prairie intermingled with shrublands on
the Texas Panhandle (Diamond et al., 1987). The
PALAEO-03309; No of Pages 27
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Fig. 1. Map showing localities and modern Texas vegetation zones. Vegetation zones are described in Appendix A. The area marked with gray
shading on the Texas–New Mexico border is the region from which soil samples from Holliday (2000) were collected (Locality 21). BP—
Blackland Prairie; OWP—Oak Wood and Prairie; CSP—Coast Sand Plains.
P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx2
UNcoast has scattered forests, prairie, and wetlands.
Shrublands occur inland of the coast in southern
Texas, and woodlands and shrublands occur on the
plateaus of central Texas. In mountainous western
Texas, which is within the Chihuahuan desert, basins
have lowland desert grass- and shrublands and higher
altitudes have forests.
Like many parts of the globe, the south-central US
was subject to large environmental fluctuations in the
Quaternary. Noble gas analyses suggest that the mean
annual temperature in the south-central US was f 5
jC lower at the Last Glacial Maximum (LGM) (Stute
et al., 1995). Quantitative estimates of past precipita-
tion are unavailable, but lake levels, fossil assemb-
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P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx 3
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lages, and speleothem growth rates offer estimates
that are qualitative and variable (Mock and Bartlein,
1995; Wilkins and Currey, 1997; Musgrove et al.,
2001). From the last interglacial to the Holocene, the
concentration of atmospheric CO2 was lower than pre-
Industrial values (f 180 vs. 280 ppmV), though
values have risen sharply to >360 ppmV in the last
200 centuries due to human activities (Leuenberger et
al., 1992; Petit et al., 1999; Monnin et al., 2001).
Given the strong climatic gradients that in part shape
vegetation zones in the region today, we might expect
that regional biomes would be sensitive to the large
climatic and atmospheric shifts of the Quaternary.
Pollen and plant macrofossils provide the most direct
measure of how vegetation responded to Pleistocene
climate and atmospheric changes. Unfortunately, pol-
len and plant macrofossil sites are uncommon in Texas,
and the state often ‘falls between the cracks’ in synoptic
studies of past climate and vegetation (e.g., Thompson
and Anderson, 2000; Williams et al., 2000). Prior work
does reveal two points of interest, however. First,
pollen data have led to conflicting views of the LGM
vegetation of northern and central Texas as either a
pine-spruce woodland or a grassland (Bryant and
Holloway, 1985; Hall and Valastro, 1995). Second,
the type of grasses comprising Pleistocene biomes is
unclear. Plants can use the C3, C4, or Crassulacean acid
metabolism (CAM) photosynthetic pathways. These
plants differ in many key attributes that affect, among
other things, biogeography, competitive abilities, rates
of carbon fixation, and susceptibility to predation
(Ehleringer et al., 1997). C4 photosynthesis is common
in grasses, but also occurs in sedges and weedy herbs,
and rarely in woody dicots. Most trees, shrubs, and
herbs, and many grasses are C3 plants. CAM occurs
chiefly in succulent plants. Because of differences in
their sensitivities to environmental factors, plants using
C3 versus C4, photosynthetic pathways may have had
different geographic ranges in the Quaternary (Ehler-
inger et al., 1997).
C4 plants have structural and enzymatic adapta-
tions that allow them to concentrate CO2 at the site of
carbon fixation. As a consequence, C4 plants have
greater water use efficiency (WUE) than C3 plants.
That is, photosynthetic carbon gain relative to tran-
spirational water loss is higher in C4 than in C3 plants.
If this greater efficiency translates to a competitive
advantage, C4 plants should dominate areas or time
ED PROOF
periods with lower amounts of moisture, and C3 plants
should dominate wetter areas or periods (Polley et al.,
1993; Huang et al., 2001). Differences in WUE no
doubt contribute to dominance by C3 trees in areas
with substantial rainfall, like eastern Texas. Yet within
grasslands, which are at least seasonally dry, recent
work has shown that C4 grass production is positively
correlated with mean annual and growing season
precipitation (Paruelo and Lauenroth, 1996; Epstein
et al., 1997; Yang et al., 1998). C4 grasses are a
greater fraction of biomass in grasslands that are
wetter, not drier. C4 dicots are more abundant in dry
areas, but they comprise a small fraction of biomass
(2% to 5%) (Ehleringer et al., 1997). Thus, the
distribution of C3 and C4 plants on grasslands is
affected by moisture, but not as expected from simple
ideas about differences in WUE.
Carbon-concentrating ability also makes C4 plants
less prone to photorespiration, a process in which
fixed carbon is oxidized without an energy yield for
the plant. Photorespiration rates in C3 plants rise with
temperature, but are low and invariant in C4 plants. As
a result, the quantum yield (i.e., carbon gain per
photon absorbed) for C3 plants drops as temperature
rises, but remains constant for C4 plants (Ehleringer et
al., 1997). This temperature sensitivity in yield likely
explains why C4 grasses dominate grasslands with a
warm growing season (>22 jC), whereas C3 grasses
dominate where the growing season is cool (Ehler-
inger, 1978; Paruelo and Lauenroth, 1996; Tieszen et
al., 1997). By similar logic, we might expect that C3
grasses would dominate under cool Pleistocene cli-
mates. The situation is complicated, however, because
experiments have shown that quantum yield is affect-
ed by atmospheric pCO2 as well as temperature.
Quantum yield drops with decreasing pCO2 in C3
plants, but is insensitive to pCO2 changes in C4 plants
(Ehleringer et al., 1997). Thus, lower pCO2 in the
Pleistocene would have favored C4 plants, whereas
lower temperatures would have favored C3 plants.
Given these complex interactions, predicting the
proportions of C3 and C4 plants will require quantita-
tive modeling. Collatz et al. (1998) conducted a global
climate–vegetation-modeling study of C4 plant distri-
bution under lower pCO2 with a LGM climate simu-
lated using a general circulation model. The south-
central US was the only area in North America where
they simulated a change in %C4 biomass between the
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P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx4
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LGM and today. They modeled a shift from the mixed
C4–C3 grasslands of today to 100% C4 grass in the
LGM, implying that the effects of lower pCO2 would
overwhelm the impact of lower temperatures.
Testing model results with pollen data is not
possible. Grass and other non-arboreal pollen types
are only identifiable to the family level, whereas
plants vary in photosynthetic pathway at the genus
or tribe level. C4 and C3 grass biomes can be distin-
guished using phytoliths (Fredlund and Tieszen, 1994,
1997). This method has been applied to a Holocene
site in Texas (Fredlund et al., 1998), but to our
knowledge, there are no published records extending
back to the Pleistocene in Texas.
Isotopic analysis offers another approach to assess-
ing the photosynthetic physiology of vegetation that is
especially important when pollen, phytolith, and mac-
rofossil data are sparse. C3 and C4 plants have
different stable carbon isotope values (d13C3). As
discussed in more detail below, these differences are
passed on to materials derived from plants, such as
soil organic matter and animal tissues, offering a
proxy for the photosynthetic physiology of vegetation.
Here, we determine the d13C value of tooth enamel
from fossil mammals thought to have been grazers
(i.e., animals with diets of grass and other herbaceous
plants). We include a surviving taxon known to be a
committed grazer, the bison (Van Vuren, 1984; Cop-
pedge and Shaw, 1998), as well as extinct horses and
mammoths. To assess temporal or spatial mixing of
fossils at sites, we examine enamel oxygen isotope
values (d18O). Finally, we compare %C4 estimates
from enamel to those derived from climate–vegeta-
tion models.
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NCO2. Reconstructing paleoenvironments using mam-
malian isotope values
2.1. Isotopic controls in plants and animals
We measured the isotopic composition of carbon-
ate in the mineral hydroxylapatite in tooth enamel (the
U 230231
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3 d13C=[((13C/12Csample H 13C/12Cstandard)� 1)� 1000], and the
standard is VPDB. d18O values follow the same convention, where
ratios are 18O/16O and the standard is VSMOW. Units are parts per
thousand (x).
ED PROOF
following discussion is based on Schwarcz and
Schoeninger, 1991; Koch, 1998). Highly crystalline
tooth enamel is resistant to diagenetic alteration. The
d13C value of apatite carbonate is highly correlated
with that of bulk diet. For wild herbivores, the
fractionation between diet and apatite carbonate is
f 14x(Cerling and Harris, 1999). Globally, the
current average d13C value (F 1 standard deviation)
is � 27.5F 3.0x for C3 plants and � 12.5F2.0xfor C4 plants (Ehleringer and Monson, 1993;
Cerling and Harris, 1999). In southern Texas, the
current average d13C values for C3 woody plants, C3
forbs, C4 grasses, and CAM plants are� 26.9F0.6x,� 29.4F 0.4x, � 14.0F 0.3x, and
� 15.6F 0.2x, respectively (Boutton et al., 1998).
We use enamel d13C values to estimate the per-
centage of C4 plants in the diet (X) with a mass
balance equation.
ð100Þd13Csample ¼ ð100� X Þd13C100% C3 enamel
þ ðX Þd13C100% C4 enamel ð1Þ
To obtain d13C values for animals on end-member
diets, we first estimate the d13C values of C3 and C4
plants in the past, and then account for the metabolic
fractionation between diet and enamel apatite (Table
1). C3 plants vary by at least 6x in relation to
differences in environmental conditions and function-
al group, whereas differences among C4 plants are
smaller and more related to phylogeny and physiology
(Tieszen, 1991; Ehleringer and Monson, 1993). With-
out d13C data on fossil plants, we cannot constrain this
variability, so for our mass balance calculations, we
use the modern global mean values as our starting
point (Table 1). Plant d13C values also vary with shifts
in the d13C of atmospheric CO2 (Marino et al., 1992;
Leavitt, 1993). These shifts are quantified using d13Cmeasurements for CO2 from ice cores (Table 1).
We performed a few simple tests to explore the
sensitivity of %C4 estimates to errors in assumptions
underlying the mass balance calculations. For our
calculations, we have assumed a diet to apatite
fractionation of 14x. A 1x error in this frac-
tionation, which would change 100% C3 and C4
enamel d13C values by the same amount, would lead
to a 7% error in the C4 estimate. Uncertainties about
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5 When discussing time, we will use units of 1000 radiocarbon
years before present (14C ky) or 1000 calendar years before present
(cal ky). Conversions for key dates are: 29 cal ky = 25 14C ky; 21 cal
ky = 18 14C ky; 17 cal ky = 15 14C ky; 14 cal ky = 12 14C ky; 12 cal
ky = 10 14C ky (Kitagawa and van der Plicht, 1998; Fiedel, 1999).
4 We have added samples and species since publication of
Bocherens et al. (1996). The d18O standard deviations for herbivore
tooth enamel apatite are: African elephant, 0.6x, n= 11; black
rhinoceros, 1.6x, n= 5; Grants gazelle, 1.7x, n= 5; plains
zebra, 1.8x, n= 7; common wildebeest, 1.9x, n= 8.
t1.1 Table 1
pCO2 and d13C values for atmosphere, plants and animals used in
mass balance calculations and vegetation modelingt1.2
Late
1990s
Holocene Post-
LGM
LGM/
Pre-LGMt1.3
Atmospheric pCO2
(ppmV)
350 280 235 200t1.4
d13C atmospheric CO2 � 8.0 � 6.5 � 6.8 � 7.1t1.5d13C C3 plants � 27.5 � 26.0 � 26.3 � 26.6t1.6d13C C4 plants � 12.5 � 11.0 � 11.3 � 11.6t1.7d13C 100% C3 enamel � 13.5 � 12.0 � 12.3 � 12.6t1.8d13C 100% C4 enamel 1.5 3.0 2.7 2.4t1.9
All values are in x relative to VPDB. Atmospheric pCO2 values
are from Collatz et al. (1998) and Monnin et al. (2001). d13C values
for atmospheric CO2 values are from Leuenberger et al. (1992) and
Indermuhle et al. (1999). Past values for C3 and C4 plants are
estimated by adding the difference between modern and past
atmospheric CO2 to modern plant isotope values. Past values for C3
and C4 enamel are estimated by adding 14x to plant values
(Cerling and Harris, 1999). These calculations assume that the
fractionations between atmosphere and plant and between plant and
animal have not changed with time.t1.10
P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx 5
UNCORREC
the d13C values for C3 and C4 plants in the past are
harder to assess, because they may lead to non-
uniform shifts in %C4 estimates due to changes in
the spacing between end-member d13C values. As a
quick check on this uncertainty, we recalculated %C4
estimates assuming end-member d13C values for
modern C3 and C4 plants from the study of Boutton
et al. (1998) on southern Texas plants (i.e., C3
plants, � 28x; C4 plants, � 14x). Recalculated
%C4 estimates differed from the values reported here
by 3% to 5%. These simple tests suggest that the
%C4 estimates may exhibit uncertainties of 5% to
10% around the values reported here. Phillips and
Gregg (2001) present a rigorous statistical method
for assessing uncertainty in source partitioning when
using isotope mass balance models, but it requires
data on variance in end-member isotope values that
is not available here.
The d18O value of apatite is controlled by the d18Ovalues of oxygen fluxes into and out of the body, by
fractionations associated with biomineralization, and
by physiological factors that alter flux magnitudes and
fractionations (the following discussion is based on
Koch, 1998; Kohn and Cerling, 2002). Ingested water
is the chief isotopically variable source of oxygen to
large mammals, and there is a strong correlation
ED PROOF
between apatite and local water d18O values. Ingested
water d18O values, in turn, co-vary with climate.
Meteoric water d18O values are lower in cold regions
and seasons, and higher in warm regions and seasons.
The surface and plant water ingested by mammals may
be 18O-enriched relative to meteoric water by evapo-
ration and evapo-transpiration, respectively. On long-
time scales, the d18O value of meteoric water may shift
with changes in climate and/or vapor source area.
We use d18O values to evaluate mixing of individ-
uals from different geographic areas (via migration)
or different time periods (due to taphonomic process-
es). During tooth formation, individuals experience
different climates and physiological states, generating
d18O variability within populations. Using a large
collection of teeth from a deer population, Clementz
and Koch (2001) showed that enamel d18O values
have a standard deviation (1r) of 1.3x that is stable
when the sample size is z 5 individuals. Study of
tooth enamel from mammal populations in Kenya
supports the conclusion that a d18O standard deviation
of 1.5x to 2.0x is typical (Bocherens et al.,
1996).4 We suggest that if 1r is z 2x for a species
at a locality, the collection may contain individuals
that are either time-averaged or spatially mixed due to
migration.
2.2. Fossil materials
Locations, ages, and other data for fossil localities
are supplied in Appendix B. For temporal compar-
isons, we parse sites into four temporal bins: before
the Last Glacial Maximum (Pre-LGM, 70 to 25 14C
ky),5 LGM (25 to 15 14C ky), Pleistocene after the
LGM (Post-LGM, 15 to 10 14C ky), and Holocene
(10 to 0 14C ky). LGM, Post-LGM, and Holocene
sites are constrained by 14C ages. Age constraints for
Pre-LGM sites are weaker. The few Pre-LGM sites
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t2.1 Table 2
Isotope data for species for each taxon organized by site and aget2.2
Species/locality n d13CF 1r d18OF 1r %C4F 1rt2.3
Pre-Last Glacial Maximum sitest2.4Clear creek faunat2.5Mammuthus columbi 2 � 1.8F 0.6 27.5F 0.3 72F 4t2.6Equus sp. 4 � 5.8F 1.0 30.0F 1.9 45F 7t2.7Coppellt2.8Equus sp. 2 � 4.4F 0.1 30.2F 0.3 55F 1t2.9
Easely rancht2.10Mammuthus columbi 1 � 0.8 30.2 79t2.11Equus sp. 3 � 2.8F 1.4 28.7F 0.5 65F 9t2.12
Inglesidet2.13Bison sp. 6 � 0.4F 1.1 30.5F 1.0 81F 7t2.14Mammuthus columbi 8 � 1.5F 0.6 29.6F 0.8 74F 4t2.15Equus fraternus 6 � 2.2F 1.3 27.7F 1.0 69F 9t2.16Equus pacificus 10 � 1.3F 0.8 29.7F 1.3 75F 5t2.17Equus complicatus 8 � 2.2F 1.1 30.1F 2.0 69F 7t2.18
Leo boatright pitt2.19Bison sp. 2 � 1.2F 0.7 29.1F 0.4 76F 5t2.20Mammuthus columbi 4 � 3.8F 2.2 28.6F 1.5 59F 15t2.21Equus sp. 2 � 4.1F1.4 29.8F 1.2 57F 9t2.22
Moore pitt2.23Bison sp. 3 � 2.2F 1.1 27.5F 1.1 69F 7t2.24Mammuthus columbi 9 � 2.7F 0.9 28.8F 0.7 66F 6t2.25Equus sp. 5 � 5.5F 1.4 30.4F 1.2 47F 9t2.26
Quitaque creekt2.27Equus sp. 2 � 1.9F 0.6 27.4F 0.3 71F 4t2.28
Valley farmst2.29Bison sp. 2 � 0.7F 0.7 29.1F1.2 79F 5t2.30Mammuthus columbi 2 � 5.3F 2.2 27.1F 0.9 49F 15t2.31Equus sp. 2 � 6.4F 0.5 29.7F 1.4 41F 3t2.32
Waco Mammoth sitet2.33Mammuthus columbi 14 � 2.7F 0.8 29.9F 0.8 66F 5t2.34Equus sp. 1 � 4.7 30.3 53t2.35
t2.36Last Glacial Maximum sitest2.37Congress avenuet2.38Mammuthus columbi 1 � 1.0 28.7 77t2.39Equus sp. 2 � 4.3F 0.4 28.7F 0.9 55F 3t2.40
Friesenhahn cavet2.41Bison sp. 5 � 1.5F 1.2 29.4F 0.9 74F 8t2.42Mammuthus columbi 16 � 1.8F 1.4 29.7F 0.7 72F 9t2.43Equus sp. 3 � 4.0F 0.1 28.4F 0.1 57F 1t2.44
Howard rancht2.45Bison sp. 1 2.8 30.8 >100t2.46
t2.47Table 2 (continued)
Species/locality n d13CF 1r d18OF 1r %C4F 1r t2.48
Last Glacial Maximum sites t2.49Howard ranch t2.50Equus spp. 4 � 3.2F 3.2 28.4F 2.2 63F 21 t2.51
Laubach cave, level 2 t2.52Mammuthus columbi 1 � 3.0 30.2 64 t2.53
t2.54Post-Last Glacial Maximum sites t2.55Ben Franklin t2.56Mammuthus columbi 3 � 2.1F1.3 29.6F 0.3 68F 9 t2.57Equus sp. 4 � 4.7F 1.7 29.0F 1.9 51F11 t2.58
Blackwater draw t2.59Bison sp. 1 1.4 28.1 91 t2.60Bison sp.a 3 0.1F1.2 26.5F 1.4 83F 8 t2.61Mammuthus columbi (l) 2 � 0.6F 0.3 28.6F 0.7 78F 2 t2.62Mammuthus columbi (m) 3 � 8.2F 0.8 23.3F 1.0 27F 5 t2.63Mammuthus columbia 4 � 1.0F 1.0 27.9F 2.7 75F 7 t2.64Equus sp. 2 � 5.7F 0.6 27.0F 0.9 44F 4 t2.65
Bonfire shelter t2.66Bison spp. 2 0.3F 0.5 27.4F 1.1 84F 3 t2.67Mammuthus columbi 1 � 2.8 29.5 63 t2.68
Cave without a name t2.69Bison spp. 1 � 3.8 28.0 57 t2.70
Kincaid Shelter t2.71Mammuthus columbi 1 � 1.8 30.1 70 t2.72Equus spp. 2 � 3.9F 1.6 28.0F 3.0 56F 11 t2.73
Schulze cave,
level C2 t2.74Mammuthus columbi 1 � 4.2 29.1 54 t2.75
t2.76Holocene sites t2.77Blackwater draw t2.78Bison sp.a 8 0.1F1.3 26.2F 2.6 81F 9 t2.79
t2.80Keller springs t2.81Bison sp. 1 0.2 26.7 81 t2.82
Schulze cave, level C1 t2.83Bison sp. 1 � 1.8 25.9 68 t2.84
The 1r for taxa with only two individuals per site is the difference
between the values divided by 2. t2.85(l), local.; (m), migratory, from Hoppe (in press).
a Data from Connin et al. (1998).
P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx6
with 14C ages have large errors. Most Pre-LGM sites
occur on terraces in northeastern Texas thought to be
older than the LGM but younger than the last
interglacial (Ferring, 1990). Ingleside, a coastal site
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UNCORREC
in southern Texas, is aged biostratigraphically (Lun-
delius, 1972a).
Average d13C and d18O values for tooth enamel
from mammoth (Mammuthus), bison (Bison), and
horse (Equus) from each site are reported in Table
2, which also includes data from Connin et al. (1998)
for Blackwater Draw, NM. The mammoth samples
probably represent a single species, Mammuthus
columbi, as this is the only mammoth reliably identi-
fied in late Quaternary deposits from Texas (FAUN-
MAP, 1996). The situation is less clear for bison and
horse, due to the rapid evolution of new forms and
taxonomic disagreements (Guthrie, 1990; Dalquest
and Schultz, 1992; MacFadden, 1992). Most speci-
mens are only identified to genus in museum collec-
tions and will be treated as such here. In Appendix C,
we list specimen number, tooth sampled, and the most
specific taxonomic data available for each specimen.
Samples were provided by the Texas Memorial Mu-
seum (University of Texas, Austin), Shuler Museum
of Paleontology (Southern Methodist University),
Department of Biology at Midwestern State Univer-
sity, and Strecker Museum of Natural History (Baylor
University).
2.3. Isotopic methods
Prior to sampling, the outer layer of enamel and
adhering dentin or cementum were removed by grind-
ing. Enamel powders were generated either by drilling
under a microscope or by crushing enamel fragments
in an agate mortar and pestle. When collecting enamel
samples, we tried sample in a fashion that cut across
growth lamellae so the sample would be representa-
tive of a substantial fraction of the time of tooth crown
formation. At the same time, we were trying to
minimize damage to the specimens, so complete
homogenization of the enamel record in each tooth
was impossible.
Powders were soaked for 24 h in 1 ml 2% NaOHCl
to remove organic contaminants, rinsed five times
with de-ionized water, reacted with 1 ml 1.0 N acetic
acid buffered with calcium acetate (pH 5) for 24 h to
remove diagenetic carbonate, then rinsed a final five
times with de-ionized water and freeze dried (Koch et
al., 1997).
Carbon and oxygen isotope compositions of enam-
el powders were measured on Micromass Optima or
Prism gas source mass spectrometers with an ISO-
CARB automated carbonate system. Samples were
dissolved by reaction in stirred 100% phosphoric acid
at 90 jC. Water and CO2 generated by reaction were
separated cryogenically. Reaction time for each sam-
ple was >10 min. The 1r value for 97 laboratory
calcite standards (Carrera Marble) included with these
samples was < 0.1x for d13C and d18O. This
standard has been calibrated relative to NBS 18 and
19. The standard deviation for 15 laboratory enamel
standards included with these samples was 0.1x for
d13C and 0.2x for d18O.
ED PROO3. Numerical reconstruction of vegetation cover
We coupled climate and vegetation models to
reconstruct the balance between C3 and C4 vegeta-
tion on three time planes: 0, 14 and 21 cal ky (equal
to 0, 12 and 18 14C ky). Climate fields for the three
periods were constructed from a combination of
observed and simulated data. For 0 14C ky, we used
the modern observed record of New et al. (2000)
(archived at www.ipcc-ddc.cru.uea.ac.uk), which is a
global, gridded dataset (0.5j latitude� 0.5j longi-
tude) generated from climate station normals for
1931 to 1990. For 12 and 18 14C ky, we constructed
climate fields using the climate model simulations of
Kutzbach et al. (1996) (archived at www.ngdc.noaa.
gov/paleo/paleo.html). These simulations were gen-
erated using the National Center for Atmospheric
Research Community Climate Model (CCM1) with a
mixed layer ocean at R15 resolution (f 4.4j lat-
itude� 7.5j longitude) (Wright et al., 1993; Kutz-
bach et al., 1996). In constructing Pleistocene
climate fields, we used the anomaly technique of
Kutzbach et al. (1998). In this method, differences
between experimental and control climate simula-
tions are added to a modern observed climate data
set, which allows the resolution of the simulated
climate to far exceed that of the climate model. And
because it relies on climate model sensitivity, the
method reduces biases in the simulated climate.
Differences between the 12 and 0 14C ky experi-
ments and between the 18 and 0 14C ky experiments
were added to the New et al. (2000) modern data set,
yielding the 12 and 18 14C ky climate fields that
were used to estimate %C4 biomass.
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UNCORREC
We used two methods to estimate %C4 biomass
from these climate fields, and applied these methods
at all sites within the region except grid cells currently
occupied by forest biome. The first method (referred
to as the regression method) uses a quantitative
relationship between the above-ground productivity
of different plant functional types and climate varia-
bles that was developed from 73 sites in central North
America (Paruelo and Lauenroth, 1996). The regres-
sion for %C4 grass biomass is:
%C4 ¼ �0:9837þ 0:000594ðMAPÞþ 1:3528ðJJA=MAPÞ þ 0:2710ðlnMATÞ ð2Þ
where lnMAT is the natural logarithm of mean
annual temperature, MAP is mean annual precipita-
tion, and JJA/MAP is the fraction of mean annual
precipitation falling in the summer (June, July, Au-
gust). At most of the sites, the functional types not
explained by this regression are C3 plants (i.e., forbs,
shrubs, C3 grasses). At two sites (Texas Panhandle,
southwest Texas), CAM plants comprise a substantial
percentage of the modern flora (16% and 38%
respectively). Because Texas CAM plants are similar
in d13C value to C4 plants, their presence may lead to
erroneously high estimates of %C4 biomass from
mammalian isotopic data relative to the regression
method.
The regression method relates %C4 biomass to
climate, but not pCO2. To explore the effects of late
Quaternary changes in pCO2 on the C3–C4 balance,
we adapted the approach of Collatz et al. (1998), who
calculated crossover temperature (the mean monthly
temperature at which C4 grasses would fix carbon
faster than C3 grasses) as a function of pCO2. To
facilitate comparison with %C4 estimates from mam-
malian data, which integrate the d13C of vegetation
from the growing season, we calculated the percent-
age of growing season months in which C4 grasses
would be favored over C3 grasses as our measure of
%C4 biomass. We refer to this approach to estimating
%C4 biomass as the mechanistic method.
This mechanistic method has several steps. First,
we estimate the number of growing season months at
each grid point in each simulation. Growing season
is largely set by the last frost of spring and first frost
of fall. Because climate simulations are only avail-
ED PROOF
able at monthly resolution, we developed a relation-
ship between mean monthly temperature and the
occurrence of a day with growth limiting frost within
that month. By comparing maps of the probability of
spring and fall frost (archived at: http://www.
ncdc.noaa.gov/oa/documentlibrary/freezefrost/frost-
freemaps.html) with maps of mean monthly temper-
ature (New et al., 2000), we determined that in the
south-central US there is only a 10% probability of a
day with frost if the mean monthly temperature is
above 15 jC. Hence, we define the growing season
as any month with a mean temperature above 15 jC.The second step is to assess the C3–C4 crossover
temperature for each time period. Following Collatz
et al. (1998), we set pCO2 = 350 ppmV today and
pCO2 = 200 ppmV at 18 14C ky, yielding crossover
temperatures of 22 and 11 jC, respectively. We set
pCO2 = 235 ppmV at 12 14C ky (Monnin et al., 2001),
yielding a crossover temperature of 14 jC (Collatz et
al., 1998). These crossover temperatures are based on
laboratory experiments exploring the effects of chang-
ing temperature and pCO2 on different types of
plants.
Third, at grid point we evaluate whether each
growing season month is dominated by C3 or C4
plants. Competitive superiority is assessed solely on
the basis of expected differences in photosynthetic
rate under different climatic and atmospheric condi-
tions, ignoring other potential competitive and envi-
ronmental factors. To qualify as a C4 month, the mean
monthly temperature must exceed the crossover tem-
perature. Like Collatz et al. (1998), we impose an
added constraint for a C4 month, that mean monthly
precipitation is >25 mm. All other growing season
months are C3 months, either because they are too
cold or too dry. For each grid point, dividing the
number of C4 growing season months by the total
number of growing season months gives the estimated
%C4 biomass.
We must note one feature of the mechanistic
method. Based on modern data, we set the 15 jCcriterion for defining a growing season month. This
value is above the C3–C4 crossover temperature at 18
and 12, but not at 0 14C ky. Consequently, differences
in %C4 between the modern and Pleistocene cases
may be due to changes in crossover temperature,
monthly temperature, or monthly precipitation. When
comparing the 18 and 12 14C ky results, however,
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differences can only result from changes in simulated
moisture, because the mechanistic method will con-
sider any month that is warm enough to have plant
growth as dominated by C4 plants.
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6 Note that while three different species of Equus co-occur at
Ingleside, their mean d13C values are statistically indistinguishable
( F2,21 = 2.41, p= 0.11), whereas their mean d18O values do differ
significantly ( F2,21 = 4.55, p= 0.02) due to the low values for E.
fraternus (Table 2).
UNCORREC
4. Results of isotopic analysis
4.1. Testing for temporal or spatial averaging at fossil
localities
d18O standard deviations for taxa at a site are
typically < 1.5x(Table 2). At sites with z 5 indi-
viduals per taxon, 1r values are almost always V 2x,
the cut-off for spatially or time averaged populations.
Blackwater Draw is the exception. For the mammoths
from this site, 1r = 3x, and the data are bimodal; five
individuals have high values (� 28.9F 1.1x) and
four have low values (23.5F 0.9x). d13C values
differ strongly between these groups and are positive-
ly correlated with d18O values (R = 0.72). Positive
correlation would be expected if the sample contains
a mix of individuals from warm regions or time
periods (represented by high values) and individuals
from cool regions or times (represented by low
values). For the mammoths measured here, Hoppe
(in press) tested for spatial versus temporal mixing
using 87Sr/86Sr ratios. 87Sr/86Sr ratios in herbivores
track differences in soil available Sr, which in turn are
controlled by bedrock geology and atmospheric de-
position (Hoppe et al., 1999). Mammoths with low
d18O and d13C values have higher 87Sr/86Sr values, as
expected if they are immigrants from mountains to the
west. We exclude these animals from further statistical
tests. One mammoth from the study by Connin et al.
(1998) also has a low d18O value (24.2x) and is
excluded, though it had a high d13C value similar to
non-migratory individuals. Holocene bison from
Blackwater Draw also show high d18O variability
(1r = 2.6x), and a positive correlation between
d13C and d18O values (R = 0.51), which suggests
some mixing for this population as well. Because
the data do not show strong bimodality, however, we
leave them in our statistical analyses. d18O variability
is not a reliable marker of mixing when a site
contains < 5 individuals per taxon. Still, some sites
with low numbers of specimens contain outliers with
low d18O and d13C values, which points to mixing.
ED PROOF
This is the case for mammoths at Valley Farms and
Leo Boatright Pit, for bison at Moore Pit, and for
horses at Clear Creek. Despite hints of spatial and/or
temporal mixing at these four sites, we include data
from all individuals at these sites in our statistical
analyses.
Overall, d18O variability offers little support for the
hypothesis that these sites are subject to strong tem-
poral or spatial mixing. The one clear exception
(Blackwater Draw) shows that mixing leaves obvious
signs, at least in regions near highlands. We consider
the other sites spatially and temporally discrete.
4.2. Temporal and spatial trends in isotopic values
Temporally binned means (F 1r) for the three taxaare presented in Table 3. ANOVA does not reveal
significant differences in mean d13C values among
time periods for bison (F3,32 = 0.85, p = 0.48). Differ-
ences in means for mammoths and horses are more
pronounced, but still not significant at the pV 0.05
level (F2,66 = 2.50, p = 0.09 and F2,59 = 2.36, p = 0.10,
respectively). Inspection suggests that low d13C val-
ues for Pre-LGM mammoths and the contrast between
Pre-LGM and Post-LGM horses contribute to these
lower p values. For horses, the large number of
specimens from the Pre-LGM Ingleside site may bias
our results. Ingleside is further south than any other
site, it is the only coastal site, and horse d13C values
here are substantially higher than values at other sites
(Table 2). Excluding Ingleside, the Pre-LGM mean for
horses is � 4.6F 1.7x, and differences among time
periods are no longer significant ( F2,35 = 1.01,
p= 0.38).6
Differences in mean d18O values among time peri-
ods are not significant for mammoths (F2,66 = 1.35,
p= 0.26) or horses (F2,59 = 2.69, p = 0.08). Mean bison
d18O values, in contrast, differ significantly among the
four time periods (F3,32 = 8.79, p = 0.0002). Post hoc
tests (Scheffe’s method) show that Holocene bison
d18O values are significantly lower than values for Pre-
LGM and LGM bison.
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t3.1 Table 3
Summary of isotopic data for each genus binned by aget3.2
Bison sp. Mammuthus sp. Equus sp.t3.3
Pre-LGMt3.4Number of specimens 13 40 45t3.5Number of sites 4 7 9t3.6Mean d13CF 1r � 1.0F 1.2 � 2.6F 1.4 � 3.1F 2.0t3.7Mean d18OF 1r 29.4F 1.6 29.2F 1.1 29.5F 1.6t3.8
t3.9LGMt3.10Number of specimens 6 18 9t3.11Number of sites 2 3 3t3.12Mean d13CF 1r � 0.8F 2.1 � 1.9F 1.4 � 3.7F 2.0t3.13Mean d18OF 1r 29.6F 1.0 29.7F 0.7 28.8F 1.4t3.14
t3.15Post-LGMt3.16Number of specimens 7 11 8t3.17Number of sites 3 5 3t3.18Mean d13CF 1r � 0.2F 1.8 � 1.9F 1.2 � 4.7F 1.6t3.19Mean d18OF 1r 27.2F 1.3 29.3F 0.8 28.3F 2.3t3.20
t3.21Holocenet3.22Number of specimens 10 N.A. N.A.t3.23Number of sites 3 N.A. N.A.t3.24Mean d13CF 1r � 0.1F1.3 N.A. N.A.t3.25Mean d18OF 1r 26.2F 2.3 N.A. N.A.t3.26
t3.27Totalt3.28Mean d13CF 1r � 0.6F 1.5 � 2.3F 1.4 � 3.4F 2.0t3.29Mean d18OF 1r 28.1F 2.2 29.3F 1.0 29.2F 1.7t3.30
All calculations include data from this study and Connin et al.
(1998), but exclude migratory mammoths.t3.31
P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx10
UNCORRECWe did not detect strong temporal isotopic trends
in these three taxa. If spatial isotopic gradients are
present, however, uneven spatial sampling might
mask temporal trends. We assess this idea using a
least-squares linear regression analysis and by in-
spection. The only significant relationship
( pV 0.05) between d13C and latitude is for horses,
with lower values at higher latitudes (Fig. 2C), but
this relationship collapses if specimens from Ingle-
side are omitted. The relationship between d13C and
longitude is significant for bison (Fig. 2D) and
mammoths (Fig. 2E), but not for horses (with or
without Ingleside). In all cases, d13C values increase
to the west. Inspection of Fig. 2 reveals strong
isotopic overlap among individuals in each region,
irrespective of their age, both where there are
significant spatial gradients (e.g., Fig. 2D and E)
and where gradients are lacking (e.g., Fig. 2A, B
and F).
ED PROOF
There are significant relationships between d18Oand latitude for bison ( p = 0.0003, R2 = 0.32) and
mammoths ( p = 0.009, R2 = 0.10), but not horses ( p =
0.61, R2 < 0.01). There are significant relationships
between d18O and longitude for bison ( p = 0.0004,
R2 = 0.31) and horses ( p = 0.002, R2 = 0.14), but not
mammoths ( p = 0.67, R2 < 0.01). Where significant
trends exist, values decrease from east to west or from
south to north by f 3x. Yet low R2 values indicate
that even these significant isotopic gradients are vari-
able. Plots of d18O values vs. latitude and longitude (not
shown) reveal no strong temporal differences within
regions.
4.3. Differences among taxa
Inspection of Tables 2 and 3 reveals differences in
mean d13C and d18O values among taxa. For the
following analysis, we include horse d13C data from
Ingleside; the described pattern is stronger if these data
are excluded. For d13C, Bison>Mammuthus> Equus,
whereas for d18O, Bison <MammuthuscEquus (Ta-
ble 3). ANOVA reveals that differences in mean values
among taxa are highly significant (for d13C, F2,164 =
33.30, p < 10� 12; for d18O, F2,164 = 7.22, p < 0.001).
Post hoc comparison (Scheffe’s method) shows that all
pairwise differences are significant for d13C. For d18O,Bison is significantly different from Mammuthus and
Equus, but the differences between the latter two taxa
are not significant.
Finally, we examined differences in mean isotope
values between species at localities where they
co-occur. We have data for bison versus mammoth
or bison versus horse at seven sites. At these
sites, bison d13C values are, on average, 1.9F1.6xhigher than mammoth values and 4.2F1.9xhigher than horse values. There are 12 sites
where mammoth and horse co-occur; mammoth d13Cvalues are, on average, 2.4F 1.2xhigher than
horse values. A similar within-site analysis of d18Ovalues reveals no significant differences between
taxa.
4.4. Summary of isotopic results from fossil mammals
(1) d18O gradients of f 3x occur across the
region, with lower values in northern/western
areas. Data from MacFadden et al. (1999a) and
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Fig. 2. Plots of d13C versus latitude (A, B, and C) and longitude (D, E, and F) for bison (A and D), mammoth (B and E) and horses (C and F). In
each panel, open circles are Pre-LGM, gray filled circles are LGM, and black filled circles are Post-LGM. Open diamonds for bison are
Holocene. Migratory mammoths are circled (B and E); horses from Ingleside are enclosed by a rectangle. Significance values ( p) and
coefficients of determination (R2) are supplied for linear regression of d13C on latitude or longitude. For horses, regressions were calculated both
with and without data from Ingleside.
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Hoppe (in press) suggest these d18O gradients
continue at higher latitudes.
(2) Despite the presence of these gradients, popula-
tions at most localities show low d18O variability,
suggesting they have not experienced strong
spatial mixing. This situation is violated Black-
water Draw, which includes migratory mam-
moths from highlands to the west (Hoppe, in
press). There are hints of mixing at some Pre-
LGM sites with small sample sizes.
(3) Spatial gradients in d13C values are significant, and
generally suggest that d13C values are higher to the
west.
(4) There is no compelling evidence for large temporal
shifts in d13C or d18O values in the region as a
whole, or in different sub-regions, though the latter
conclusion is weak due to sparse data coverage.
(5) There are significant differences in d13C value
among taxa, with Bison>Mammuthus>Equus.
Because extant bison are grazers, fossil bison
provide the most reliable evidence regarding the
d13C of herbaceous vegetation. While the lower
average d13C value for mammoths indicates they
typically consumed a greater fraction of C3 plants
than bison, at some sites mammoths yield %C4
estimates that are similar to bison. In contrast,
horse d13C values are almost always much lower
than those for bison, suggesting that horses were
consistently eating a large fraction of C3 plants, in
settings where grazing bison were consuming
almost entirely C4 diets. Pleistocene horses were
not obligate grazers, but rather had more diverse
diets that contained a mix of C3 trees and shrubs,
as well as the largely C4 grass ingested by bison
and mammoths. Prior studies have shown that
fossil horses are not obligate grazers (Koch et al.,
1998; MacFadden et al., 1999b). Consequently,
we cannot use d13C values from horses to quantify
the C3–C4 balance among herbaceous plants, as
we can with bison and mammoth, but we can use
them as a rough proxy for the overall proportion
of C3 versus C4 plants on the landscape.
726
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U5. Results of vegetation modeling
In Fig. 3, we show modern and simulated MAT,
MAP, JJA/MAP data, which are the three climatic
ED PROOF
parameters used in the regression method to estimate
%C4 biomass (see Eq. (2)). In the modern, MAT
shows a meridional gradient, with values decreasing
from 22 jC in the south to 12 jC in the north (Fig.
3A). MAP exhibits a steep zonal gradient, with
values decreasing from 1400 to 300 mm/year from
east to west (Fig. 3B). Variation in JJA/MAP is also
steep zonally, but in the opposite direction, with
values increasing from 20% to 50% from east to
west (Fig. 3C). Using the regression method, this
climatology yields estimated C4 grass biomass of
55% to 85%, with lower values in the west and
higher values in the east (Fig. 3D). While agreement
varies regionally, these estimated values for %C4
biomass are lower (15–20%) than those observed
in the regional calibration data set of Paruelo and
Lauenroth (1996).
Simulated MAT is lower than today at 12 14C ky,
decreasing from 20 jC in the south to 10 jC in the
north (Fig. 3E). Simulated MAP is lower too, drop-
ping from 1100 to 200 mm/year from east to west
(Fig. 3F). Simulated JJA/MAP is lower across the
entire region at 12 14C ky, but the gradient is steeper
than today (4% to 44%, east to west) (Fig. 3G). As
all three of the climate variables that influence %C4
have lower values at 12 14C ky than today, C4
percentages estimated using the regression method
are much lower, with values ranging from 10% to
30% (Fig. 3H).
Simulated MAT is substantially lower than present
at 18 14C ky across the entire region, decreasing from
18 to 4 jC from south to north (Fig. 3I). The gradient
in simulated MAP is similar to that at 12 14C ky (Fig.
3J). Simulated JJA/MAP shows enhanced meridional
variability at 18 14C ky, increasing from 4% to 56%,
south to north (Fig. 3K). Simulated JJA/MAP is
higher in NW Texas and New Mexico at 18 14C ky
than today or at 12 14C ky. C4 biomass estimates
generated using the regression method are 0% to 20%
over most of the region, with higher values (25% to
45%) in northern Texas where simulated JJA/MAP
values are high (Fig. 3L).
In Fig. 4, we map the number of growing season
months based on modern and simulated climate data
(Fig. 4A, C, and E), as well as estimates of the
fraction of the growing season dominated by C4
grasses generated using the mechanistic method
(Fig. 4B, D, and F). Today, the number of growing
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UNCORRECTED PROOF
ARTICLE IN PRESSM
AT
(˚C
)M
AP
(m
m/y
r)JJ
A/M
AP
(fr
actio
n)C
4 G
rass
(fr
actio
n)
25°N
30°N
35°N
105°W 100°W 95°W105°W 100°W 95°W105°W 100°W 95°W
25°N
30°N
35°N
25°N
30°N
35°N
25°N
30°N
35°N
0 14C kyr 18 14C kyr12 14C kyr
A
B
C
D
E
F
G
H
I
J
K
L
Fig. 3. Climate fields used to estimate C4 grass biomass by the regression method and resulting biomass estimates for 0, 12, and 18 14C ky.
Climate data and resulting %C4 estimates are on a 0.5� 0.5j grid, and are contoured using the NCAR Command Language (NCL)
gsn_csm_contour_map_ce function (http://ngwww.ucar.edu/ncl/index.html). Estimated grass fractions are shown only for non-forest points, as
defined by Ramankutty and Foley (1999). Forested grid cells are shaded gray.
P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx 13
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UNCORRECTED PROOF
AR
TIC
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IN P
RE
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Fig. 4. Number of growing season months (A, C, and E) and fraction of growing season months dominated by C4 grass (B, D, and F) estimated using the mechanistic method for 014C kyr (A–B), 12 14C kyr (C–D) and 18 14C kyr (E–F). Colored stars show C4 biomass fractions observed in modern grasslands (Paruelo and Lauenroth, 1996). Colored squares
and circles show C4 biomass fractions estimated from d13C values of fossil mammoths and bison, respectively. For isotopic estimates of C4 biomass, values greater than 100% are
mathematically possible, but they indicate either a problem with the mass balance model (incorrect end-member values, incorrect assumptions regarding fractionation) or sample
diagenesis. White areas in A, C, and D indicate forested grid cells (as defined by Ramankutty and Foley, 1999). In B, D and F, white areas indicate forested grid cells plus cells with
no growing season months.
P.L.Koch
etal./Palaeogeography,Palaeoclim
atology,Palaeoeco
logyxx
(2004)xxx–
xxx14
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UNCORREC
season months changes markedly, from 12 months in
the far south to 5 months on the Texas Panhandle and
in eastern New Mexico, though 7 to 9 months is
characteristic for most of the region (Fig. 4A). Central
Texas has the highest estimated percentage of C4
growing season months (50% to 80%), with lower
values on the Gulf Coast (50% to 70%) and the
Panhandle (40% to 60%) (Fig. 4B).
The stars in Fig. 4B show observed %C4 biomass
at sites in the study by Paruelo and Lauenroth (1996).
In most of central and southern Texas, the mechanistic
method yields %C4 biomass estimates within 10% to
20% of observations. At a site in central Texas, the
method over-estimates C4 biomass by 30%. The low
%C4 biomass value recorded at this site in Paruelo and
Lauenroth (1996) is at odds with results from other
studies (Epstein et al., 1997; Tieszen et al., 1997).
Isotopic study of soils and biomass from southern
Texas has revealed historical shifts toward greater C3
(i.e., shrub) biomass, probably due to grazing (Bout-
ton et al., 1998); it is possible a similar phenomenon
has impacted this site. On the Texas Panhandle, the
mechanistic method under-estimates the amount of C4
vegetation by 20% to 30%. Overall, however, the
method yields %C4 estimates in reasonable agreement
with observational data.
The simulated growing season at 12 14C ky drops
from 9 to 4 months from south to north, though 5 to 7
months characterizes most of the region (Fig. 4C). C4
biomass estimates generated using the mechanistic
method are higher than modern on the coast (60%
to 80%) and on the Panhandle (60% to 100%), but
lower than modern in central Texas (40% to 70%)
(Fig. 4D). These differences are due to the differential
effects of temperature and moisture. Today, on the
Gulf Coast and Panhandle, some cool growing season
months are C3 dominated. %C4 biomass rises in these
regions in the 12 14C ky case because of the lower
crossover temperature, which causes every growing
season month to be warm enough for C4 plants, and
because simulated moisture is sufficient through most
of the growing season. In central Texas, %C4 biomass
drops because the 12 14C ky climate simulation is
drier than today in the growing season; the decrease in
crossover temperature is overwhelmed by the drop in
moisture.
The simulated growing season at 18 14C ky drops
from 8 to 2 months from south to north, though 4 to
6 months typifies most of the region (Fig. 4E).
Estimated C4 biomass is lower than at 0 or 12 14C
ky on the coast (40% to 80%) and in central Texas
(30% to 60%), due largely to lower moisture levels in
the growing season (Fig. 4F). On the Texas Panhan-
dle, C4 estimates at 18 14C ky are lower than at 1214C ky because some growing season months are too
dry for C4 plants, but higher than at 0 14C ky due to
loss of cool C3 months with the drop in crossover
temperature.
ED PROOF6. Discussion
6.1. Comparisons of mammalian and soil isotopic
data
Isotopic data from mammoths and bison yield
high estimates of C4 grass consumption across much
of Texas and eastern New Mexico from the Pre-LGM
to the Holocene. If horse data are a rough proxy for
the overall abundance of C4 versus C3 plants, the
region may have consistently had greater than
f 45% C4 vegetation. The persistence of biomes
with substantial C4 biomass in the face of late
Quaternary climate change is surprising and merits
further verification.
As a step toward verification, we compare esti-
mates of %C4 biomass from mammalian isotope data
to those from isotopic study of soil and paleosol or
buried soil organic matter. Soils and mammal teeth
record data on different spatial and temporal scales.
Soils offer localized data integrated over centuries,
whereas mammals feed over a large area but form
enamel over a few years. Soils integrate carbon from
all above-ground biomass, whereas mammals have
preferred diets. Finally, soil organic records often
come from river valleys and terraces and may slightly
over-represent the C3 trees and shrubs that inhabit
these ecosystems, either through direct input from
above-ground biomass or through inheritance from
the fluvial parent material. Still, a consistent, large
mismatch between soils and all mammalian taxa
would be troubling, whereas rough agreement would
be mutually supportive.
We know of three well-dated records of soil
organic d13C values spanning the Pleistocene–Holo-
cene boundary in our study area. Holliday (1997,
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P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx16
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2000) analyzed organic carbon from soils on playas
and dunes on the southern High Plains (34.4 to
32.8jN; 101.6 to 103.5jW). Dune soils are well
drained, but probably formed during wetter intervals
when plant growth stabilized the dunes. We will not
consider data from more poorly drained playa soils,
as they may inherit organic matter from aquatic
plants. At the Aubry Clovis site (33.3jN, 97.1jW,
Fig. 1), Humphrey and Ferring (1994) analyzed
organic carbon from flood plain soils and other
sources. Again, we only use their soil data to
estimate %C4 biomass. Finally, Nordt et al. (2002)
analyzed soil organic matter from a buried alluvial
soil sequence from the Medina River valley (29.3jN,98.5jW, Fig. 1).
To estimate %C4 biomass from soil d13C values,
we use end-member d13C values for C3 and C4 plants
(Table 1) and an equation analogous to Eq. (1). On the
southern High Plains, soil d13C values indicate sub-
stantial C4 biomass at the LGM (65F 20%) and Post-
LGM (80F 26%), with slightly less C4 in the Holo-
cene (58F 19%) (Fig. 5A). At Blackwater Draw,
Post-LGM bison and non-migratory mammoths yield
dietary estimates consistent with %C4 estimates from
soils, whereas diets for early Holocene bison contain
more C4 plants than soils (Fig. 5A). In northeastern
Texas, soil values indicate that plant cover was similar
in the Post-LGM (56.7F 5%C4) and Holocene
(57.5F 7%C4) (Fig. 5B). At Ben Franklin, the closest
Post-LGM mammal site, %C4 estimates from mixed
feeding horses and grazing mammoths are similar to
those from soils, whereas at Keller Springs, a Holo-
cene site, a single bison consumed more C4 vegetation
(Fig. 5B).
The Medina River soil sequence in south-central
Texas provides a high resolution and variable
record of %C4 biomass over the past 15 14C ky
(Fig. 5C). LGM data from mammoths and bison in
the region suggest grasslands with 75% C4 biomass,
UNCFig. 5. %C4 biomass estimates from soil organic matter and mammalian dand south-central Texas (C). Estimates from soil organics are indicated by
filled circles, squares, and triangles, respectively. Mammal data in A are f
Springs; in C data are from Congress Avenue, Friesenhahn Cave, Laubach
Friesenhahn Cave, we report the mean (symbol)F 1r (bar) for each taxon
see. Isotopic estimates of C4 biomass >100% indicate either a problem wi
samples from the southern High with C4 estimates >110% from B and c
diagenesis is the likely explanation.
ED PROOF
and horse data suggest an overall C4 biomass of
55% to 60% (Fig. 5C), but all these data predate
the earliest Medina River records. Mammal diets
from the latest Pleistocene and earliest Holocene,
when the Medina River record shows a rapid rise
in %C4, have 10% to 30% more C4 plants than the
soils.
The match between soil organic and mammalian
isotope records is good in light of potential biasing
factors. For most of the early Holocene and Post-
LGM, soil and mammal data point to C4 biomass
>40%. When mammal data do not match soil data,
mammals typically yield higher %C4 estimates. This
mismatch may reflect over-representation of C3
plants in sediments with soil organic matter, or the
fact that all these mammals (even horses with more
catholic feeding habits) may under-sample C3 tree
and shrubs. We conclude that a substantial C4
biomass persisted throughout the late Quaternary in
the south-central US, albeit with local, short-term
drops, as seen in the high-resolution Medina River
record.
6.2. Extent of grasslands at the LGM
Early pollen studies suggested that forests covered
the plains of northern, western, and central Texas at
the LGM (reviewed by Bryant and Holloway, 1985).
Recently, it has been argued that high conifer pollen
counts at some LGM sites are due to preservational
bias, and that the High Plains and Edwards Plateau
were dominated by grass, not trees (Hall and Valastro,
1995). The presence of animals thought to be grazers
at LGM sites supports this idea (Graham, 1987), and
data on the diets of these animals helps to resolve the
issue.
LGM bison and mammoths from the plains and the
edge of the Edwards Plateau have diets with 64% to
100% C4 plants. Even horse diets always have more
13C values for the southern High Plains (A), northeastern Texas (B),
crosses; estimates from bison, mammoth and horses are indicated by
rom Blackwater Draw; in B data are from Ben Franklin and Keller
Cave, Schulze Cave, Cave Without a Name, and Kincaid Shelter. For
, and offset the symbols slightly vertically so that they are easier to
th the mass balance model or sample diagenesis. We excluded three
alculations in the text because they deviate so greatly that sample
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UNCORRECTED PROOF
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than 55% C4 plants. We might argue these animals are
vagrant grazers moving through a forested environ-
ment, but the persistence of d13C gradients across the
region and the lack of d18O evidence for migration
make this ad hoc explanation unlikely. High C4
percentages in the diets of large resident animals are
inconsistent with the presence of an extensive region-
al forest, and instead point to more open vegetation.
We are not suggesting that forests were entirely
absent, however. In addition to grazing mammoths
and bison, Friesenhahn Cave also has mastodon, deer,
and tapir with d13C values suggesting f 100% C3
diets (Koch, 1998). A likely scenario is that, as is the
case today, forests occurred in canyons and riparian
zones.
6.3. Model–model and model–data comparison
The regression method and the mechanistic meth-
od use the same climate data, but yield different
estimates of %C4 biomass for 12 and 18 14C ky
(compare Fig. 3H with Fig. 4D and Fig. 3L with Fig.
4F), with much lower estimates for the regression
method. These differences in Pleistocene %C4 esti-
mates are much greater than those between the two
methods when estimating modern C4 using current
climate data (compare Fig. 3D with 4B). Mammal
and most soil isotope data yield %C4 estimates that
are also significantly higher than those obtained
using the regression method. For example, isotopic
data from Blackwater Draw bison, mammoth and
soils all suggest that the biomass in the Post-LGM
had 75% to 90% C4 plants. The regression method,
in contrast, yields %C4 estimates of only 20% to
30%.
We consider three reasons why the regression
method yields erroneously low estimates. One possi-
bility is that the regression model, which is trained on
data that span the entire Great Plains, is poorly
calibrated for the south-central US. We think this
explanation is unlikely given the good fit between
regression-based %C4 estimates and those observed
at calibration sites in Texas. While calibration bias
may explain the 10% to 20% under-estimation of
%C4 in Texas using modern climate data (see Section
5), the errors in the modern estimates are small
relative to the errors associated with Pleistocene
estimates.
ED PROOF
A second possibility is that the simulated climate
data are wrong. As the mean temperature of these
simulations agrees with proxy data (Stute et al., 1995),
precipitation is a more likely culprit. If simulated
LGM and Post-LGM climates had more moisture
overall (20% to 50%) or a greater fraction of summer
rainfall, they would yield reasonable %C4 estimates.
Are the simulations too dry by this amount? Proxy
data offer qualitative estimates of past precipitation.
At the LGM, higher lake levels in western and
northern Texas (Mock and Bartlein, 1995; Wilkins
and Currey, 1997) and accelerated speleothem growth
rates from south-central Texas point to greater
amounts of precipitation and/or a more positive bal-
ance of precipitation-to-evaporation than today (Mus-
grove et al., 2001). If not solely due to reduced
evaporation under cooler climates, these data show
that the simulated LGM climates do under-estimate
regional MAP. In contrast, speleothem, mammalian,
and sedimentological data from the plateau region
point to moisture levels as low as or lower than
modern in the Post-LGM (Toomey et al., 1993;
Musgrove et al., 2001). On the High Plains, some
authors argue for Post-LGM aridity (Haynes, 1991);
others argue for Post-LGM moisture levels interme-
diate between high LGM and low Holocene values
(Holliday, 2000). Still, the Post-LGM climate simula-
tion is a closer match to moisture proxies, yet it too
produces %C4 estimates that are unreasonably low.
We think it unlikely that errors in simulated moisture
are the main explanation for the failure of the regres-
sion method.
A final possibility is that because the regression
method fails to account for the effect of changes in
atmospheric pCO2 on plant physiology, it cannot
capture the fact that C4 plants out-compete C3 plants
at lower temperatures under the lower pCO2 atmos-
pheres of the Pleistocene. The mechanistic method,
which accounts for the effect of pCO2 on the C3–C4
crossover temperature, does a much better job match-
ing isotopic data from mammals and soils, so we favor
this plausible explanation.
We examine the fit between %C4 estimates from
mammalian isotopic data and the mechanistic method
in Fig. 4. At most sites, the isotopic and mechanistic
%C4 estimates are similar for the Post-LGM (Fig. 4D)
and LGM (Fig. 4F), rarely differing by more than
20%. Where there is disagreement, isotopic estimates
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1021
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P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx 19
are typically higher. The mismatch is greatest at
Bonfire Shelter in the Post-LGM, where the mecha-
nistic method yields low %C4 estimates because of
great aridity in the climate simulation. Either the
simulation is too dry, or the mechanistic method fails
to capture plant dynamics in deserts, or these animals
had diets biased toward C4 grass in a system domi-
nated by C3 shrubs. The comparison between mech-
anistic %C4 estimates and those from Holocene bison
(Fig. 4B) is complicated by the recent rise in atmo-
spheric pCO2, which may have reduced C4 plant
abundance in the modern relative to the Holocene
(Collatz et al., 1998) and by our sparse Holocene
bison data. Still, estimates from isotopic data and the
mechanistic method are similar in southern and north-
UNCORRECT
Fig. 6. %C4 biomass estimates reconstructed from Pre-LGM mammalian is
by filled circles, squares, and triangles, respectively.
OF
eastern Texas, whereas bison diets on the southern
High Plains have somewhat more C4 biomass than
that estimated using the mechanistic method.
Our model–model and model–data comparisons
indicate that regression methods based on modern
plant climatology that ignore the effects of pCO2 on
the rate of carbon fixation may not apply outside of
the Holocene (Cowling and Sykes, 1999). As current-
ly implemented, the more successful mechanistic
method is insensitive to changes in precipitation, other
than requiring a minimum monthly precipitation of 25
mm for C4 growth. We would prefer a method that
simultaneously accounts for the effects of changes in
temperature, moisture and pCO2 on %C4 biomass, but
at present, we know of no quantitative treatment of
ED PRO
otope data. Estimates from bison, mammoth and horses are indicated
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UNCORREC
this subject. Likewise, because diurnal temperature
data are not routinely saved and/or reported from
paleoclimatic simulation studies, to implement the
mechanistic method we had to approximate growing
season from monthly mean temperature. A better
approach would be to use daily simulation data to
determine the start and end of the growing season,
which very likely include days with temperatures
below 15 jC. Even so, this coarse mechanistic method
produces results that are remarkably consistent with
isotopic data from mammals and soil organic matter.
Authors have debated the primacy of changes in
climate versus pCO2 in driving changes in the C3–C4
balance (e.g., Ehleringer et al., 1997; Cowling and
Sykes, 1999; Huang et al., 2001; Nordt et al., 2002).
A key role for climate has been argued for south-
central Texas because isotopically reconstructed %C4
estimates increase from LGM to Holocene (as might
be expected from a rise in temperature), rather than
decrease (as might be expected from the rise in
atmospheric pCO2) (Nordt et al., 2002). Yet co-vari-
ation between %C4 biomass and temperature does not
preclude an important role for changes in pCO2. The
failure of the regression method when applied to LGM
and Post-LGM climates suggests that without the
competitive advantage supplied by lower pCO2, C4
plants would have been minor elements in Pleistocene
floras in the region. And the mechanistic method,
which accounts for the effects of pCO2, yields
increases in %C4 biomass between the Pleistocene
and Holocene in much of central Texas. The rise in C4
biomass from Pleistocene to Holocene likely does
reflect a rise in growing season temperature and/or
an increase in moisture, but the relatively low magni-
tude of this rise is a reflection of the offsetting impact
of a rise in atmospheric pCO2. The difference between
our results from the regression and the mechanistic
method can serve as a rough proxy for the magnitude
of this offsetting effect. The influences of atmospheric
pCO2, and seasonal temperature and moisture on
photosynthesis are so complex that predicting the
change in vegetation is difficult without an explicit
climate–vegetation model.
6.4. Composition of Pre-LGM floras
Our Pre-LGM data are coarsely dated, and we
currently lack simulated paleoclimate data to drive
OOF
vegetation models, though such simulations have re-
cently been conducted (Barron and Pollard, 2002).
Consequently, our study of Pre-LGM vegetation is
limited to inspection of broad trends in map view
(Fig. 6).
Focusing first on data from more mixed feeding
horses, %C4 is high at Ingleside on the Gulf Coast
(70% to 80%), intermediate in northeast Texas (40%
to 60%), then high again on the plains (60% to 80%).
Estimates from mammoths are similar at Ingleside,
and slightly higher in northeast Texas (40% to 80%)
and on the Plains (70% to 80%). Pre-LGM bison
consume more C4 grass than horses or mammoths at
Ingleside (80% to 90%) and northeast Texas (70% to
80%). The spatial patterning and absolute values are
similar to those at the LGM and Post-LGM for all
three taxa, again emphasizing the stability of regional
vegetation.
ED P7. Conclusion
Our study has six key results about late Quaternary
mammals and plants.
(1) Pleistocene bison were committed grazers, and
mammoths ate dominantly herbaceous plants,
with a minor supplement of trees and shrubs.
These taxa provide evidence on the C3–C4
balance of regional grasslands. Horses ate mixed
diets, and may be better viewed as rough monitors
of the overall C3–C4 balance.
(2) Data from fossil mammals do not support the idea
that forests spread across the plains and plateau
region at the LGM.
(3) Data from fossil mammals show spatial gradients
in C4 grass abundance, with maxima on the Gulf
coast and the High Plains, and minima on the
edge of the Edwards Plateau and in northeastern
Texas. These trends apply at all time periods for
which we have data.
(4) Mammalian carbon isotope data do not reveal
dramatic temporal shifts in C3–C4 balance
between cool glacial and warm Holocene times.
(5) Mammalian isotope data are in good agree-
ment with isotopic results from soil organic
matter, and from the results of climate–
vegetation models that account for the effects
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1120
1121
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A1.1
A1.2
A1.3
A1.4
Appendix A (continued) A1.5
Regions Dominant plants A1.6
Gulf Prairies
and Marshes
Water oak-live oak forest and mixed oak
woodlands; riparian forests same as OWP;
tallgrass (mostly C4) prairie with sparse oak
cover; saline and freshwater marshes with
sedges, rushes, reeds, aquatic forbs and a mix
P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx 21
of changes in atmospheric CO2 concentration
and climate.
(6) Regression models based on the climatology of
modern plants that do not consider the effects of
changes in atmospheric CO2 concentration dra-
matically under-estimate the amount of C4
vegetation in the region.
TED PROOF
of C3 and C4 grasses. A1.7Coastal Sand
Plains (CSP)
Live oak woodland with tallgrass (mostly C4)
understory; tall grasslands, saltgrass marsh. A1.8South Texas
Brushland
Texas ebony-anacua forest; shrublands with
ceniza, acacia, ebony or mesquite and
pricklypear; riparian forests same as OWP;
C4 grassland with mesquite; subtropical
plants in far south. A1.9Edwards Plateau
and Llano Uplift
Live oak-ash-juniper woodlands with
understory of tallgrass (mostly C4) species on A1.10the eastern plains and the canyonland at the
southeastern border; mesquite-juniper shrublands
on western plateau; live oak woodland
on Llano Uplift; riparian forests same as
OWP; mixed- and shortgrass (mostly C4)
with forbs and mesquite on western plateau. A1.11Rolling Plains Tall- and mixedgrass (mostly C4) prairies
with forbs; juniper, mesquite, oak, or
sand-sage shrublands on sandy/rocky
substrates; riparian forests same as OWP. A1.12High Plains Mixed- to shortgrass (mostly C4) prairies;
juniper, mesquite, oak, or sandsage
shrublands on sandy/rocky substrates;
aquatic vegetation in playas. A1.13Trans Pecos The northern extension of the Chihuahuan
desert. Ponderosa pine-Douglas fir forests at
high elevations; oak, juniper, pinyon pine,
cottonwood, or mesquite woodlands at
middle elevations; desert mixed- and
C
Acknowledgements
This research would not have been possible
without the generous assistance and samples provided
by Dale Winkler (Shuler Museum of Paleontology,
Southern Methodist University, Dallas, TX), Ernie
Lundelius, Melissa Winans, and Pam Owen (Texas
Memorial Museum, University of Texas, Austin, TX),
Fred Stangl (Department of Biology, Midwestern
State Univ., Witchita Falls, TX), and Calvin Smith
(Strecker Museum of Natural History, Baylor Univer-
sity, Waco, TX). We thank Dan Bryant, Geoff
Koehler, Rachel Zisook and Beth Zotter for assistance
with sample preparation and analysis. We thank Pat
Holroyd, Beverley Johnson, and Lee Nordt for
providing thoughtful reviews of this paper, though
any mistakes are of course of our own making.
Finally, we thank Caroline Stromberg and Bob
Feranec for inviting us to participate in this volume
and for accepting our late submission. This research
was supported by NSF-EAR 9316371 and 9725854 to
PLK.
E shortgrass habitats and shrub (creosotebush,tarbush, acacia, mimosa, yucca) at lower
elevations. Mostly C4 grasses at lower
elevations; more C3 grasses at higher
elevations. A1.14Definitions Woody vegetation is dominated by trees or
RRAppendix A. Description of modern vegetation in
Texas
UNCORegions Dominant plants
Piney Woods Mixed hardwood forest in lowlands; mixed
pine-hardwood forest on uplands; diverse
shrubs, vines, forbs and C3 and C4 grasses on
forest floor; C4 grasses in large open areas;
trees and shrubs dominate swamps.
Oak Woods and
Prairies (OWP)
Short oak forests and woodlands associated
with tallgrasses (mostly C4) and prairie forbs;
diverse understory shrubs and vines; riparian
forests of elm, sugarberry, ash, oak, hack
berry, ash, and pecan.
Blackland
Prairies
Tallgrass (mostly C4) prairie with forbs;
riparian forests same as OWP.
(from Diamond
et al., 1987):
shrubs, which form z 25% of the plant
canopy. Forest—trees z 3 m tall form >60%
of canopy; Woodland—trees form 25–60%
of canopy; Shrubland—shrubs 0.5–3 m tall
form >25% of canopy. A1.15Herbaceous vegetation is dominated by
grasses, graminoids or forbs with < 25%
woody plant canopy. Tallgrass—dominated
by grasses >1 m tall; Mixedgrass—dominated
by grasses 0.5 to 1 m tall; Shortgrass—
dominated by grasses < 0.5 m tall; Marsh—
dominated by herbaceous vegetation with water
at the surface 50% of the year. A1.16
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A2.18A2.19A2.20A2.21
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A2.23
A2.24
A2.25
A2.26
A2.27
A2.28
A2.29
A2.31A2.33A2.35
P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx22
Appendix B. Site information
UNCORRECTED PROOF
Site Lat, Long Age Type of date, level Depositional
environment
Pre-LGM Clear Creek N 33j15V, W 97j00V 28,840F 4740 Alluvium? Fluvial terrace
Coppell N 32j57V, W 97j00V 30,000–75,000z Terrace Correlation Fluvial terrace
Easley Ranch N 33j 59V, W 99j 51V 30,000–75,000z Faunal Correlation Sinkhole/
terrace fill
Ingleside N 27j52V, W 97j12V 30,000–75,000z Faunal Correlation Pond
Leo Boatright Pit N 32j07V, W 96j00V 30,000–75,000z Terrace Correlation Fluvial terrace
Moore Pit N 32j44V, W 96j44V 30,000–75,000z Terrace Correlation Fluvial terrace
Quitaque Creek N 34j15V, W 100j30V >35,000y Soil organics Fluvial terrace
Valley Farms N 32j15V, W 96j15V 30,000–75,000z Terrace Correlation Fluvial terrace
Waco Mammoth Site N 31j36V, W 97j11V 28,000 Not reported Fluvial channel
LGM Congress Avenue N 30j15V, W 97j45V 15,970F 860 Grey-green clay,
Area B: sed.
organics
Pond/Fluvial
17,220F 1870 Red-brown clay,
Area A: sed. organics
18,330F 1400 No level info
Friesenhahn Cave N 29j37V, W 98j22V 17,800F 880 Level 3B: bone Cave
19,600F 710 Level 3A: bone
Howard Ranch N 34j22V, W 99j45V 16,775F 565,
19,098F 74
shell Sinkhole lake
Laubach Cave,
Level 2
N 30j37V, W 97j37V 15,850F 500 Level 1: bone Cave
23,230F 430 Level 3: bone
Post-LGM Ben Franklin N 33j22V, W 95j45V 9500 charcoal Fluvial terrace
11,135 shell
Bonfire Shelter N 29j49V, W 101j33V 10,100F 300,
10,230F 160
Bone Bed 2:
charcoal,
Cave
Cave without a
Name
N 29j53V, W 98j37V 10,980F 190 bone, no level
info
Cave
Kincaid Shelter N 29j22V, W 99j28V 10,025F 185 84–90U level:
shell
Cave
10,065F 185 90–96U level:
shell
10,365F 110 96–102U level:
shell
Post-LGM/
Holocene
Blackwater Draw, NM N 34j14V, W 103j25V Multiple dated
levels
sed. organics Pond
Schulze Cave N 30j15V, W 99j52V 9,310F 310 Level C2: faunal
correlation, bone
Cave
9,680F 700 Level C1: faunal
correlation, bone
Keller Springs N 32j58V, W 96j48V Holocene Faunal Correlation Not reported
Number for the locality in the FAUNMAP Database (FAUNMAP, 1996) and citations for each locality are as follows: Clear Creek—135,
Slaughter and Ritchie (1963); Easley Ranch, Dalquest and Schultz (1992); Ingleside—Lundelius (1972a); Leo Boatright Pit—127, Stovall and
McAnulty (1950); Moore Pit—Slaughter (1966); Quitaque Creek—689, Dalquest (1964) and Dalquest and Schultz (1992); Valley Farms—125,
Stovall and McAnulty (1950); Waco Mammoth Site—Fox et al. (1992); Congress Avenue—165, Lundelius (1992); Friesenhahn Cave—727,
Graham (1976); Howard Ranch—134, Dalquest (1965) and Dalquest and Schultz (1992); Laubach Cave—719, Lundelius (1985); Ben
Franklin—136, Slaughter and Hoover (1963); Bonfire Shelter—122, Robinson (1997); Cave without a Name—159, Lundelius (1967); Kincaid
Shelter—800, Lundelius (1967); Schulze Cave—155, Dalquest et al. (1969); Blackwater Draw, NM—Lundelius (1972b), Stanford et al. (1986)
and Haynes (1995).y 14C year BP.z cal year BP.
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A3.2A3.3A3.4A3.5A3.6A3.7A3.8A3.9
A3.10A3.11A3.12A3.13A3.14A3.15A3.16A3.17A3.18
A3.19A3.20A3.21A3.22A3.23A3.24A3.25A3.26A3.27A3.28A3.29A3.30A3.31A3.32A3.33A3.34A3.35A3.36A3.37A3.38A3.39A3.40A3.41A3.42A3.43A3.44A3.45A3.46A3.47A3.48A3.49A3.50A3.51A3.52
Appendix C (continued) A3.5313 18
P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx 23
Appendix C
UNCORRECTED PROOF
Taxon Specimen # Pt. d13C d18O
Pre-Last Glacial Maximum sites
Clear Creek
Mammuthus SMP-60670 M3 � 2.40 27.18
SMP-60705 M3 � 1.12 27.81
Equus SMP-60382ms M � 5.88 29.55
SMP-60531 l Px � 5.31 30.37
SMP-60827 M � 7.09 27.79
SMP-60840 l M3 � 4.74 32.28
Coppell
Equus SMP-60292 r P3or4 � 4.32 29.91
SMP-60442 l P3or4 � 4.58 30.57
Easely Ranch
Mammuthus MSU-uncat. CT � 0.78 30.18
Equus MSU-1984 CT � 4.06 28.29
MSU-1995 CT � 1.26 28.65
MSU-2000 CT � 3.13 29.22
Ingleside
Bison TMM 30967-694 r M3 � 0.72 29.93
TMM 30967-930 frag 0.42 31.85
TMM 30967-1638 M3 � 1.97 30.12
TMM 30967-1662 Mx � 0.83 31.76
TMM 30967-2473 M3 � 0.72 29.36
TMM 30967-2481 l M3 1.15 30.02
Mammuthus TMM 30967-148 M3 � 2.59 29.97
TMM 30967-165 M3 � 1.64 30.09
TMM 30967-500 l M1or2 � 1.35 30.39
TMM 30967-679 Mx � 0.83 30.11
TMM 30967-1214 M2or3 � 0.95 29.87
TMM 30967-1724 M2or3 � 1.10 29.93
TMM 30967-1787 r M3 � 1.10 28.03
TMM 30967-1818 M3 � 2.25 28.55
E. complicatus TMM 30967-241 M1or2 � 3.69 34.27
TMM 30967-312 P1 � 2.15 29.00
TMM 30967-379 CTx � 1.00 28.36
TMM 30967-948 CTx � 2.72 28.27
TMM 30967-1642 M2 � 2.78 28.74
TMM 30967-1870 Mx � 3.45 30.89
TMM 30967-2230 CTx � 1.23 31.31
TMM 30967-2455 M3 � 0.74 29.65
E. fraternus TMM 30967-36 frag � 0.98 28.99
TMM 30967-708 frag � 1.79 28.43
TMM 30967-974 r Mx � 3.98 28.28
TMM 30967-1051A frag � 3.34 26.75
TMM 30967-1051B frag � 2.76 26.97
TMM 30967-2223 r Px � 0.61 26.92
E. pacificus TMM 30967-242 frag � 1.29 30.83
TMM 30967-376A frag � 1.37 29.10
TMM 30967-376B frag � 0.69 27.98
TMM 30967-376C frag � 0.84 29.83
TMM 30967-1487 l P4 � 0.72 28.97
Taxon Specimen # Pt. d C d O A3.54
Pre-Last Glacial Maximum sites A3.55Ingleside A3.56E. pacificus TMM 30967-1518 frag � 0.89 30.27 A3.57
TMM 30967-1540 r P4 � 3.36 31.93 A3.58TMM 30967-2225 frag � 1.44 30.37 A3.59TMM 30967-2226 frag � 0.84 27.62 A3.60TMM 30967-2229 frag � 1.25 30.36 A3.61
Leo Boatright Pit A3.62Bison TMM 30907-13 M1or2 � 1.86 28.72 A3.63
TMM 30907-33 M1or2 � 0.48 29.49 A3.64Mammuthus TMM 30907-10 CT � 2.77 28.68 A3.65
TMM 30907-29 M2or3 � 3.01 30.35 A3.66TMM 30907-40 M3 � 2.32 28.58 A3.67TMM 30907-79 dP2or3 � 7.01 26.73 A3.68
Equus TMM 30907-95 Mx � 5.48 31.04 A3.69TMM 30907-114 M � 2.67 28.61 A3.70
Moore Pit A3.71Bison SMP-60178 CTx � 2.06 27.93 A3.72
SMP-60608 M � 3.39 26.24 A3.73SMP-60849 Px � 1.28 28.43 A3.74
Mammuthus SMP-60345 CT � 2.20 29.12 A3.75SMP-60351 CT � 1.29 29.67 A3.76SMP-60844 CT � 3.22 28.33 A3.77SMP-62287 CT � 1.70 29.71 A3.78SMP-62357 CT � 2.70 28.22 A3.79SMP-62358 CT � 3.91 28.52 A3.80SMP-62359 CT � 3.59 28.52 A3.81SMP-70153 CT � 2.33 29.06 A3.82SMP-70161 CT � 3.38 27.60 A3.83
Equus SMP-60124 r M2 � 6.46 28.69 A3.84E. midlandensis SMP-60130 r M1? � 3.93 31.98 A3.85Equus SMP-60188 CT � 7.44 30.82 A3.86
SMP-60240 r M2 � 4.96 30.15 A3.87SMP-60855 l M2 � 4.93 30.17 A3.88
Quitaque Creek A3.89Equus MSU-2036 CT � 1.37 27.05 A3.90
MSU-2819 M3 � 2.52 27.70 A3.91
Valley Farms A3.92Bison TMM 31030-2A Mx 0.01 30.26 A3.93
TMM 31030-2B Mx � 1.33 27.96 A3.94Mammuthus TMM 31030-3 dP2or3 � 7.56 26.19 A3.95
TMM 31030-8 M2� 3 � 3.10 28.04 A3.96Equus TMM 31030-27 l P3or4 � 5.86 28.28 A3.97
TMM 31030-28 l P3or4 � 6.89 31.07 A3.98
Waco Mammoth site A3.99Mammuthus SMNH WACO-B CT � 1.75 30.43 A3.100
SMNH WACO-C CT � 2.45 29.96 A3.101SMNH WACO-D CT � 2.10 29.88 A3.102
(continued on next page)
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UNCORRECTED PROOF
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Taxon Specimen # Pt. d13C d18OA3.103
Pre-Last Glacial Maximum sitesA3.104Waco Mammoth siteA3.105Mammuthus SMNH WACO-E CT � 3.08 29.11A3.106
SMNH WACO-F CT � 3.28 29.10A3.107SMNH WACO-I CT � 2.26 30.44A3.108SMNH WACO-K CT � 2.67 27.98A3.109SMNH WACO-M CT � 3.50 30.00A3.110SMNH WACO-N CT � 2.01 30.66A3.111SMNH WACO-Q CT � 3.29 29.25A3.112SMNH WACO-12 CT � 2.78 30.57A3.113SMNH WACO-19 CT � 2.33 30.13A3.114SMNH WACO-21 CT � 2.17 30.65A3.115SMNH WACO-23 CT � 4.58 29.82A3.116
Equus no number CT � 4.72 30.34A3.117A3.118
Last Glacial Maximum sitesA3.119Congress AvenueA3.120Mammuthus TMM 43067-37 l M2or3 � 0.98 28.72A3.121Equus TMM 43067-29 l CTx � 4.70 29.58A3.122
TMM 43067-62 l CTx � 3.88 27.83A3.123
Friesenhahn CaveA3.124Bison TMM 933-2198 Mx � 2.10 30.98A3.125
TMM 933-3002 M3 � 1.10 29.40A3.126TMM 933-3285 r M2 � 2.77 28.51A3.127TMM 933-3390 r M3 0.31 28.99A3.128TMM 933-3525 r M1or2 � 1.98 29.06A3.129
Mammuthus TMM 933-133 M2or3 � 5.09 29.93A3.130TMM 933-296 M2or3 � 1.49 30.03A3.131TMM 933-358 M2or3 � 1.36 29.05A3.132TMM 933-928 CT � 2.09 29.29A3.133TMM 933-1006 M2or3 � 0.06 29.87A3.134TMM 933-1013 CT � 1.19 30.40A3.135TMM 933-1309 CT � 1.70 28.14A3.136TMM 933-1505 M3 � 0.14 30.12A3.137TMM 933-1506 M2or3 � 1.21 29.17A3.138TMM 933-1507 M1or2 � 3.35 29.32A3.139TMM 933-2014 M3 � 0.02 30.01A3.140TMM 933-2015 M2or3 � 1.39 28.91A3.141TMM 933-2022 dPorM1 � 3.87 30.21A3.142TMM 933-2243 M2or3 � 1.05 29.19A3.143TMM 933-2676 CT � 1.90 30.11A3.144TMM 933-3407 CT � 3.53 31.05A3.145
Equus TMM 933� 209A Mx � 3.89 28.39A3.146TMM 933-209B r Mx � 4.09 28.32A3.147TMM 933-1284 r P4 � 3.88 28.49A3.148
Howard RanchA3.149Bison MSU-2825 Px 2.79 30.83A3.150Equus MSU-1671 CT � 7.51 25.49A3.151
MSU-1672 l CTx 0.22 30.31A3.152MSU-2720 l CTx � 2.59 29.91A3.153MSU-3016 CT � 3.11 27.98A3.154
Taxon Specimen # Pt. d13C d18O A3.155
Last Glacial Maximum sites
Mammuthus TMM 40722-1 CT � 3.04 30.22 A3.156
Post-Last Glacial Maximum sites A3.157Ben Franklin A3.158Mammuthus SMP-61233 CT � 3.54 29.96 A3.159
SMP-61244 CT � 1.34 29.41 A3.160SMP-61245 CT � 1.27 29.49 A3.161
Equus SMP-60731 Mx � 6.31 28.83 A3.162SMP-61236 M � 3.51 27.11 A3.163SMP-61245 CT � 6.05 28.62 A3.164SMP-61246 CT � 2.89 31.59 A3.165
Blackwater Draw A3.166Bison TMM 937-907 M2 1.40 28.14 A3.167
BDM 9789a frag 1.1 25.2 A3.168BDM naa frag � 1.3 28.0 A3.169BDM naa M 0.4 26.2 A3.170
Mammuthus TMM 937-46b M3 � 8.67 23.78 A3.171TMM 937-126b M � 8.64 22.16 A3.172SMP-uncatb M � 7.20 23.92 A3.173TMM 937-818 M3 � 0.92 27.83 A3.174TMM 937-E6 CT � 0.26 29.29 A3.175BDM #4a,b frag 0.3 24.2 A3.176BDM naa frag � 1.9 29.9 A3.177BDM naa M � 0.8 27.6 A3.178BDM naa M � 1.6 29.8 A3.179
Equus TMM 937-254 Mx � 5.14 27.90 A3.180TMM 937-738 CTx � 6.32 26.19 A3.181
Bonfire Shelter A3.182Bison TMM 40806-37 M2 � 0.21 28.53 A3.183
TMM 40806-496 M2 0.74 26.26 A3.184Mammuthus TMM 40806-433 dP4? � 2.84 29.50 A3.185
Cave without a Name A3.186Bison TMM 40450-585 Mx � 3.81 28.02 A3.187
Kincaid Shelter A3.188Mammuthus TMM 908-2408 CT � 1.84 30.07 A3.189Equus TMM 908-2422 r M3 � 5.44 24.95 A3.190
TMM 908-2436 r M3 � 2.31 31.02 A3.191
Schulze Cave, Level C2 A3.192Mammuthus MSU-7391 dP � 4.19 29.08 A3.193
A3.194Holocene sites A3.195
Blackwater Draw A3.196Bison BDM naa frag 0.3 26.8 A3.197
BDM 9816a frag � 1.3 26.4 A3.198Bison BDM 814Aa frag 0.8 24.8 A3.199Bison BDM naa M 1.8 27.3 A3.200
Appendix C (continued) Appendix C (continued)
P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology xx (2004) xxx–xxx24
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1154
1155
11561157115811591160116111621163116411651166116711681169117011711172117311741175117611771178
11791180118111821183118411851186118711881189119011911192119311941195119611971198119912001201120212031204120512061207
Taxon Specimen # Pt. d13C d18OA3.201
Holocene sitesA3.202
Blackwater DrawA3.203BDM naa frag 0.9 25.6A3.204BDM naa frag � 1.9 20.8A3.205BDM naa M 0.9 29.7A3.206BDM naa frag � 0.8 28.1A3.207
Keller SpringsA3.208Bison SMP-61893 l M3 0.16 26.70A3.209
Schulze Cave, Level C1A3.210Bison MSU-7324 Px � 1.85 25.90A3.211
Museums: TMM—Texas Memorial Museum; SMP—Shuler Mu-
seum of Paleontology, Southern Methodist University; MSU—
Department of Biology, Midwestern State University; SMNH—
Strecker Museum of Natural History; BDM—Blackwater Draw Site
Museum.A3.213Codes for teeth are as follows: P, premolar; M, molar; CT, cheek
tooth; frag, tooth fragment; d, deciduous tooth; l, left; r, right;
superscript, upper tooth; subscript, lower tooth; numbers, position in
tooth row; x, position in tooth row indeterminant. When a tooth
code is not followed by a superscript or subscript number or x, we
could not determine if the tooth was an upper or a lower.A3.215A3.217A3.219
Appendix C (continued)
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a Data from Connin et al. (1998).b Migratory individuals.
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