The effects of late Quaternary climate and pCO2 change on C 4 plant abundance in the south-central...

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Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357

The 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

Received 28 July 2003; accepted 25 September 2003

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.

D 2004 Elsevier B.V. All rights reserved.

Keywords: C3; C4; Pleistocene; Holocene; Mammal; Soil; Paleosol; Carbon isotope; Oxygen isotope; Vegetation; GCM; Texas

1. Introduction east, arid subtropical in the west, and temperate/

Today, Texas exhibits strong gradients in climate

and vegetation. Climate is humid subtropical in the

0031-0182/$ - see front matter D 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.palaeo.2003.09.034

* Corresponding author. Tel.: +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

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 207 (2004) 331–357332

coast 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-

P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 333

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

two 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

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


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.

2. 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

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).

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 3.0xfor 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,� 2 9 . 4 F 0 . 4x, � 1 4 . 0 F 0 . 3x, a n d

� 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 fraction-

ation, 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 the

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.

Table 1

pCO2 and d13C values for atmosphere, plants and animals used in

mass balance calculations and vegetation modeling

Late

1990s

Holocene Post-

LGM

LGM/

Pre-LGM

Atmospheric pCO2

(ppmV)

350 280 235 200

d13C atmospheric CO2 � 8.0 � 6.5 � 6.8 � 7.1

d13C C3 plants � 27.5 � 26.0 � 26.3 � 26.6

d13C C4 plants � 12.5 � 11.0 � 11.3 � 11.6

d13C 100% C3 enamel � 13.5 � 12.0 � 12.3 � 12.6

d13C 100% C4 enamel 1.5 3.0 2.7 2.4

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.


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

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

Table 2

Isotope data for each taxon organized by site and age

Species/locality n d13CF 1r d18OF 1r %C4F 1r

Pre-Last Glacial Maximum sites

Clear Creek Fauna

Mammuthus columbi 2 � 1.8F 0.6 27.5F 0.3 72F 4

Equus sp. 4 � 5.8F 1.0 30.0F 1.9 45F 7

Coppell

Equus sp. 2 � 4.4F 0.1 30.2F 0.3 55F 1

Easely Ranch

Mammuthus columbi 1 � 0.8 30.2 79

Equus sp. 3 � 2.8F 1.4 28.7F 0.5 65F 9

Ingleside

Bison sp. 6 � 0.4F 1.1 30.5F 1.0 81F 7


Equus fraternus 6 � 2.2F 1.3 27.7F 1.0 69F 9

Equus pacificus 10 � 1.3F 0.8 29.7F 1.3 75F 5

Equus complicatus 8 � 2.2F 1.1 30.1F 2.0 69F 7

Leo Boatright Pit

Bison sp. 2 � 1.2F 0.7 29.1F 0.4 76F 5


Equus sp. 2 � 4.1F1.4 29.8F 1.2 57F 9

Moore pit

Bison sp. 3 � 2.2F 1.1 27.5F 1.1 69F 7


Equus sp. 5 � 5.5F 1.4 30.4F 1.2 47F 9

Quitaque Creek

Equus sp. 2 � 1.9F 0.6 27.4F 0.3 71F 4

Valley Farms

Bison sp. 2 � 0.7F 0.7 29.1F1.2 79F 5


Equus sp. 2 � 6.4F 0.5 29.7F 1.4 41F 3

Waco Mammoth Site


Equus sp. 1 � 4.7 30.3 53

Last Glacial Maximum sites

Congress Avenue


Equus sp. 2 � 4.3F 0.4 28.7F 0.9 55F 3

Friesenhahn Cave

Bison sp. 5 � 1.5F 1.2 29.4F 0.9 74F 8


Equus sp. 3 � 4.0F 0.1 28.4F 0.1 57F 1

Howard Ranch

Bison sp. 1 2.8 30.8 >100

Table 2 (continued)

Species/locality n d13CF 1r d18OF 1r %C4F 1r


Howard Ranch

Equus sp. 4 � 3.2F 3.2 28.4F 2.2 63F 21

Laubach Cave, Level 2


Post-Last Glacial Maximum sites

Ben Franklin

Mammuthus columbi 3 � 2.1F1.3 29.6F 0.3 68F 9

Equus sp. 4 � 4.7F 1.7 29.0F 1.9 51F11

Blackwater Draw

Bison sp. 1 1.4 28.1 91

Bison sp.a 3 0.1F1.2 26.5F 1.4 83F 8

Mammuthus columbi (l) 2 � 0.6F 0.3 28.6F 0.7 78F 2

Mammuthus columbi (m) 3 � 8.2F 0.8 23.3F 1.0 27F 5

Mammuthus columbia 4 � 1.0F 1.0 27.9F 2.7 75F 7

Equus sp. 2 � 5.7F 0.6 27.0F 0.9 44F 4

Bonfire Shelter

Bison sp. 2 0.3F 0.5 27.4F 1.1 84F 3


Cave Without a Name

Bison sp. 1 � 3.8 28.0 57

Kincaid Shelter


Equus sp. 2 � 3.9F 1.6 28.0F 3.0 56F 11

Schulze Cave,

Level C2


Holocene sites

Blackwater Draw

Bison sp.a 8 0.1F1.3 26.2F 2.6 81F 9

Keller Springs

Bison sp. 1 0.2 26.7 81

Schulze Cave, Level C1

Bison sp. 1 � 1.8 25.9 68

The 1r for taxa with only two individuals per site is the difference

between the values divided by 2.

(l), local.; (m), migratory, from Hoppe (2004).a Data from Connin et al. (1998).


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


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 to sample in a fashion that cut

across growth lamellae so the sample would be

representative 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 2% NaOHCl to

remove organic contaminants, rinsed five times with

de-ionized water, reacted with 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.

3. 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.

http:\\www.ipcc-ddc.cru.uea.ac.uk

http:\\www.ngdc.noaa.gov\paleo\paleo.html


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-

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,

http:\\www.ncdc.noaa.gov\oa\documentlibrary\freezefrost\frostfreemaps.html


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.

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).

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 positively

correlated with d18O values (R = 0.72). Positive cor-

relation 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 (2004)

tested for spatial versus temporal mixing using87Sr/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. 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.

Table 3

Summary of isotopic data for each genus binned by age

Bison sp. Mammuthus sp. Equus sp.

Pre-LGM

Number of specimens 13 40 45

Number of sites 4 7 9

Mean d13CF 1r � 1.0F 1.2 � 2.6F 1.4 � 3.1F 2.0

Mean d18OF 1r 29.4F 1.6 29.2F 1.1 29.5F 1.6

LGM



Mean d13CF 1r � 0.8F 2.1 � 1.9F 1.4 � 3.7F 2.0

Mean d18OF 1r 29.6F 1.0 29.7F 0.7 28.8F 1.4

Post-LGM



Mean d13CF 1r � 0.2F 1.8 � 1.9F 1.2 � 4.7F 1.6

Mean d18OF 1r 27.2F 1.3 29.3F 0.8 28.3F 2.3

Holocene

Number of specimens 10 N.A. N.A.

Number of sites 3 N.A. N.A.

Mean d13CF 1r � 0.1F1.3 N.A. N.A.

Mean d18OF 1r 26.2F 2.3 N.A. N.A.

Total

Mean d13CF 1r � 0.6F 1.5 � 2.3F 1.4 � 3.4F 2.0

Mean d18OF 1r 28.1F 2.2 29.3F 1.0 29.2F 1.7

All calculations include data from this study and Connin et al.

(1998), but exclude migratory mammoths.


We 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

inspection. 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 in-

crease 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).

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

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.



Hoppe (2004) 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, 2004).

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.

5. Results of vegetation modeling

In Fig. 3, we show modern and simulated MAT,

MAP, JJA/MAP data, which are the three climatic

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

http:\\ngwww.ucar.edu\ncl\index.html

MA

T (

˚C)

MA

P (

mm

/yr)

JJA

/MA

P (

frac

tion)

C4

Gra

ss (

frac

tion)

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.


http:\\ngwww.ucar.edu\ncl\index.html

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

logy207(2004)331–357

344


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.

6. 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,


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,

Fig. 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 Plains with C4 estimates >110% from B an

diagenesis is the likely explanation.

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

d calculations in the text because they deviate so greatly that sample


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 biomass 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.

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


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-

Fig. 6. %C4 biomass estimates reconstructed from Pre-LGM mammalian is

by filled circles, squares, and triangles, respectively.

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

otope data. Estimates from bison, mammoth and horses are indicated


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 includes 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

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.

7. 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

Appendix A (continued)

Regions Dominant plants

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


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.
of C3 and C4 grasses.
Coastal Sand

Plains (CSP)

Live oak woodland with tallgrass (mostly C4)

understory; tall grasslands, saltgrass marsh.

South 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.

Edwards Plateau

and Llano Uplift

Live oak-ash-juniper woodlands with

understory of tallgrass (mostly C4) species on

the 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.

Rolling 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.

High Plains Mixed- to shortgrass (mostly C4) prairies;

juniper, mesquite, oak, or sandsage

shrublands on sandy/rocky substrates;

aquatic vegetation in playas.

Trans 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

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.
shortgrass habitats and shrub (creosotebush,
tarbush, acacia, mimosa, yucca) at lower

elevations. Mostly C4 grasses at lower

elevations; more C3 grasses at higher

elevations.

Definitions Woody vegetation is dominated by trees or

Appendix A. Description of modern vegetation in

Texas

Regions 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, hackberry,

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.

Herbaceous 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.

Appendix B. Site information

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 >35,000 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, N 30j37V, W 97j37V 15,850F 500 Level 1: bone Cave

Level 2 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, 1994) 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).zcal year BP, all other dates in 14CBP.

P.L.Koch

etal./Palaeogeography,Palaeoclim

atology,Palaeoeco

logy207(2004)331–357

352

Appendix C (continued)

13 18


Appendix C

Taxon Specimen # Pt. d13C d18O


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


Ingleside

E. pacificus TMM 30967-1518 frag � 0.89 30.27

TMM 30967-1540 r P4 � 3.36 31.93

TMM 30967-2225 frag � 1.44 30.37

TMM 30967-2226 frag � 0.84 27.62

TMM 30967-2229 frag � 1.25 30.36

Leo Boatright Pit

Bison TMM 30907-13 M1or2 � 1.86 28.72

TMM 30907-33 M1or2 � 0.48 29.49

Mammuthus TMM 30907-10 CT � 2.77 28.68

TMM 30907-29 M2or3 � 3.01 30.35

TMM 30907-40 M3 � 2.32 28.58

TMM 30907-79 dP2or3 � 7.01 26.73

Equus TMM 30907-95 Mx � 5.48 31.04

TMM 30907-114 M � 2.67 28.61

Moore Pit

Bison SMP-60178 CTx � 2.06 27.93

SMP-60608 M � 3.39 26.24

SMP-60849 Px � 1.28 28.43

Mammuthus SMP-60345 CT � 2.20 29.12

SMP-60351 CT � 1.29 29.67

SMP-60844 CT � 3.22 28.33

SMP-62287 CT � 1.70 29.71

SMP-62357 CT � 2.70 28.22

SMP-62358 CT � 3.91 28.52

SMP-62359 CT � 3.59 28.52

SMP-70153 CT � 2.33 29.06

SMP-70161 CT � 3.38 27.60

Equus SMP-60124 r M2 � 6.46 28.69

E. midlandensis SMP-60130 r M1? � 3.93 31.98

Equus SMP-60188 CT � 7.44 30.82

SMP-60240 r M2 � 4.96 30.15

SMP-60855 l M2 � 4.93 30.17

Quitaque Creek

Equus MSU-2036 CT � 1.37 27.05

MSU-2819 M3 � 2.52 27.70

Valley Farms

Bison TMM 31030-2A Mx 0.01 30.26

TMM 31030-2B Mx � 1.33 27.96

Mammuthus TMM 31030-3 dP2or3 � 7.56 26.19

TMM 31030-8 M2� 3 � 3.10 28.04

Equus TMM 31030-27 l P3or4 � 5.86 28.28

TMM 31030-28 l P3or4 � 6.89 31.07

Waco Mammoth site

Mammuthus SMNH WACO-B CT � 1.75 30.43

SMNH WACO-C CT � 2.45 29.96

SMNH WACO-D CT � 2.10 29.88

(continued on next page)



Waco Mammoth site

Mammuthus SMNH WACO-E CT � 3.08 29.11

SMNH WACO-F CT � 3.28 29.10

SMNH WACO-I CT � 2.26 30.44

SMNH WACO-K CT � 2.67 27.98

SMNH WACO-M CT � 3.50 30.00

SMNH WACO-N CT � 2.01 30.66

SMNH WACO-Q CT � 3.29 29.25

SMNH WACO-12 CT � 2.78 30.57

SMNH WACO-19 CT � 2.33 30.13

SMNH WACO-21 CT � 2.17 30.65

SMNH WACO-23 CT � 4.58 29.82

Equus no number CT � 4.72 30.34


Congress Avenue

Mammuthus TMM 43067-37 l M2or3 � 0.98 28.72

Equus TMM 43067-29 l CTx � 4.70 29.58

TMM 43067-62 l CTx � 3.88 27.83

Friesenhahn Cave

Bison TMM 933-2198 Mx � 2.10 30.98

TMM 933-3002 M3 � 1.10 29.40

TMM 933-3285 r M2 � 2.77 28.51

TMM 933-3390 r M3 0.31 28.99

TMM 933-3525 r M1or2 � 1.98 29.06

Mammuthus TMM 933-133 M2or3 � 5.09 29.93

TMM 933-296 M2or3 � 1.49 30.03

TMM 933-358 M2or3 � 1.36 29.05

TMM 933-928 CT � 2.09 29.29

TMM 933-1006 M2or3 � 0.06 29.87

TMM 933-1013 CT � 1.19 30.40

TMM 933-1309 CT � 1.70 28.14

TMM 933-1505 M3 � 0.14 30.12

TMM 933-1506 M2or3 � 1.21 29.17

TMM 933-1507 M1or2 � 3.35 29.32

TMM 933-2014 M3 � 0.02 30.01

TMM 933-2015 M2or3 � 1.39 28.91

TMM 933-2022 dPorM1 � 3.87 30.21

TMM 933-2243 M2or3 � 1.05 29.19

TMM 933-2676 CT � 1.90 30.11

TMM 933-3407 CT � 3.53 31.05

Equus TMM 933� 209A Mx � 3.89 28.39

TMM 933-209B r Mx � 4.09 28.32

TMM 933-1284 r P4 � 3.88 28.49

Howard Ranch

Bison MSU-2825 Px 2.79 30.83

Equus MSU-1671 CT � 7.51 25.49

MSU-1672 l CTx 0.22 30.31

MSU-2720 l CTx � 2.59 29.91

MSU-3016 CT � 3.11 27.98



Mammuthus TMM 40722-1 CT � 3.04 30.22

Post-Last Glacial Maximum sites

Ben Franklin

Mammuthus SMP-61233 CT � 3.54 29.96

SMP-61244 CT � 1.34 29.41

SMP-61245 CT � 1.27 29.49

Equus SMP-60731 Mx � 6.31 28.83

SMP-61236 M � 3.51 27.11

SMP-61245 CT � 6.05 28.62

SMP-61246 CT � 2.89 31.59

Blackwater Draw

Bison TMM 937-907 M2 1.40 28.14

BDM 9789a frag 1.1 25.2

BDM naa frag � 1.3 28.0

BDM naa M 0.4 26.2

Mammuthus TMM 937-46b M3 � 8.67 23.78

TMM 937-126b M � 8.64 22.16

SMP-uncatb M � 7.20 23.92

TMM 937-818 M3 � 0.92 27.83

TMM 937-E6 CT � 0.26 29.29

BDM #4a,b frag 0.3 24.2


BDM naa M � 0.8 27.6

BDM naa M � 1.6 29.8

Equus TMM 937-254 Mx � 5.14 27.90

TMM 937-738 CTx � 6.32 26.19

Bonfire Shelter

Bison TMM 40806-37 M2 � 0.21 28.53

TMM 40806-496 M2 0.74 26.26

Mammuthus TMM 40806-433 dP4? � 2.84 29.50

Cave without a Name

Bison TMM 40450-585 Mx � 3.81 28.02

Kincaid Shelter

Mammuthus TMM 908-2408 CT � 1.84 30.07

Equus TMM 908-2422 r M3 � 5.44 24.95

TMM 908-2436 r M3 � 2.31 31.02


Mammuthus MSU-7391 dP � 4.19 29.08

Holocene sites

Blackwater Draw

Bison bison BDM naa frag 0.3 26.8

BDM 9816a frag � 1.3 26.4

Bison BDM 814Aa frag 0.8 24.8

BDM naa M 1.8 27.3

Appendix C (continued) Appendix C (continued)



Holocene sites

Blackwater Draw

Bison BDM naa frag 0.9 25.6


BDM naa M 0.9 29.7


Keller Springs

Bison bison SMP-61893 l M3 0.16 26.70


Bison MSU-7324 Px � 1.85 25.90

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.

Codes 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 indeterminate. 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.

Appendix C (continued)


a Data from Connin et al. (1998).b Migratory individuals.

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