The Effects of Late Quaternary Climate and Pco2 Change on C4 Plant Abundance in the South-Central United States Paul L

Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 www.elsevier.com/locate/palaeo

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

a Department of Earth Sciences, University of California, Santa Cruz, CA 95064, USA b Department 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/ continental in the north. Temperature varies strongly Today, Texas exhibits strong gradients in climate from north to south, whereas rainfall changes from and vegetation. Climate is humid subtropical in the east to west. Intersecting climatic gradients couple with geology and topography to create vegetation zones (Fig. 1, Appendix A). Moving west across * Corresponding author. Tel.: +1-831-459-5861. northern and central Texas, the pine and hardwood E-mail addresses: [email protected] (P.L. Koch), forests of the east give way to oak woodlands mixed [email protected] (N.S. Diffenbaugh), [email protected] (K.A. Hoppe). with tallgrass prairie, and then to mixedgrass and 1 Tel.: +1-831-459-3504. shortgrass prairie intermingled with shrublands on 2 Tel.: +1-650-723-9191. 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. coast has scattered forests, prairie, and wetlands. Like many parts of the globe, the south-central US Shrublands occur inland of the coast in southern was subject to large environmental fluctuations in the Texas, and woodlands and shrublands occur on the Quaternary. Noble gas analyses suggest that the mean plateaus of central Texas. In mountainous western annual temperature in the south-central US was f 5 Texas, which is within the Chihuahuan desert, basins jC lower at the Last Glacial Maximum (LGM) (Stute have lowland desert grass- and shrublands and higher et al., 1995). Quantitative estimates of past precipita- altitudes have forests. 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 periods with lower amounts of moisture, and C3 plants that are qualitative and variable (Mock and Bartlein, should dominate wetter areas or periods (Polley et al., 1995; Wilkins and Currey, 1997; Musgrove et al., 1993; Huang et al., 2001). Differences in WUE no 2001). From the last interglacial to the Holocene, the doubt contribute to dominance by C3 trees in areas concentration of atmospheric CO2 was lower than pre- with substantial rainfall, like eastern Texas. Yet within Industrial values ( f 180 vs. 280 ppmV), though grasslands, which are at least seasonally dry, recent values have risen sharply to >360 ppmV in the last work has shown that C4 grass production is positively two centuries due to human activities (Leuenberger et correlated with mean annual and growing season al., 1992; Petit et al., 1999; Monnin et al., 2001). precipitation (Paruelo and Lauenroth, 1996; Epstein Given the strong climatic gradients that in part shape et al., 1997; Yang et al., 1998).C4 grasses are a vegetation zones in the region today, we might expect greater fraction of biomass in grasslands that are that regional biomes would be sensitive to the large wetter, not drier. C4 dicots are more abundant in dry climatic and atmospheric shifts of the Quaternary. areas, but they comprise a small fraction of biomass Pollen and plant macrofossils provide the most direct (2% to 5%) (Ehleringer et al., 1997). Thus, the measure of how vegetation responded to Pleistocene distribution of C3 and C4 plants on grasslands is climate and atmospheric changes. Unfortunately, pol- affected by moisture, but not as expected from simple len and plant macrofossil sites are uncommon in Texas, ideas about differences in WUE. and the state often ‘falls between the cracks’ in synoptic Carbon-concentrating ability also makes C4 plants studies of past climate and vegetation (e.g., Thompson less prone to photorespiration, a process in which and Anderson, 2000; Williams et al., 2000). Prior work fixed carbon is oxidized without an energy yield for does reveal two points of interest, however. First, the plant. Photorespiration rates in C3 plants rise with pollen data have led to conflicting views of the LGM temperature, but are low and invariant in C4 plants. As vegetation of northern and central Texas as either a a result, the quantum yield (i.e., carbon gain per pine-spruce woodland or a grassland (Bryant and photon absorbed) for C3 plants drops as temperature Holloway, 1985; Hall and Valastro, 1995). Second, rises, but remains constant for C4 plants (Ehleringer et the type of grasses comprising Pleistocene biomes is al., 1997). This temperature sensitivity in yield likely unclear. Plants can use the C3,C4, or Crassulacean acid explains why C4 grasses dominate grasslands with a metabolism (CAM) photosynthetic pathways. These warm growing season (>22 jC), whereas C3 grasses plants differ in many key attributes that affect, among dominate where the growing season is cool (Ehler- other things, biogeography, competitive abilities, rates inger, 1978; Paruelo and Lauenroth, 1996; Tieszen et of carbon fixation, and susceptibility to predation al., 1997). By similar logic, we might expect that C3 (Ehleringer et al., 1997).C4 photosynthesis is common grasses would dominate under cool Pleistocene cli- in grasses, but also occurs in sedges and weedy herbs, mates. The situation is complicated, however, because and rarely in woody dicots. Most trees, shrubs, and experiments have shown that quantum yield is affect- herbs, and many grasses are C3 plants. CAM occurs ed by atmospheric pCO2 as well as temperature. chiefly in succulent plants. Because of differences in Quantum yield drops with decreasing pCO2 in C3 their sensitivities to environmental factors, plants using plants, but is insensitive to pCO2 changes in C4 plants C3 versus C4 photosynthetic pathways may have had (Ehleringer et al., 1997). Thus, lower pCO2 in the different geographic ranges in the Quaternary (Ehler- Pleistocene would have favored C4 plants, whereas inger et al., 1997). lower temperatures would have favored C3 plants. C4 plants have structural and enzymatic adapta- Given these complex interactions, predicting the tions that allow them to concentrate CO2 at the site of proportions of C3 and C4 plants will require quantita- carbon fixation. As a consequence, C4 plants have tive modeling. Collatz et al. (1998) conducted a global greater water use efficiency (WUE) than C3 plants. climate–vegetation-modeling study of C4 plant distri- That is, photosynthetic carbon gain relative to tran- bution under lower pCO2 with a LGM climate simu- spirational water loss is higher in C4 than in C3 plants. lated using a general circulation model. The south- If this greater efficiency translates to a competitive central US was the only area in North America where advantage, C4 plants should dominate areas or time they simulated a change in %C4 biomass between the 334 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357

LGM and today. They modeled a shift from the mixed following discussion is based on Schwarcz and C4 –C3 grasslands of today to 100% C4 grass in the Schoeninger, 1991; Koch, 1998). Highly crystalline LGM, implying that the effects of lower pCO2 would tooth enamel is resistant to diagenetic alteration. The overwhelm the impact of lower temperatures. d13C value of apatite carbonate is highly correlated Testing model results with pollen data is not with that of bulk diet. For wild herbivores, the possible. Grass and other non-arboreal pollen types fractionation between diet and apatite carbonate is are only identifiable to the family level, whereas f 14x(Cerling and Harris, 1999). Globally, the plants vary in photosynthetic pathway at the genus current average d13C value ( F 1 standard deviation) or tribe level. C4 and C3 grass biomes can be distin- is 27.5 F 3.0xfor C3 plants and 12.5 F guished using phytoliths (Fredlund and Tieszen, 1994, 2.0xfor C4 plants (Ehleringer and Monson, 1993; 1997). This method has been applied to a Holocene Cerling and Harris, 1999). In southern Texas, the 13 site in Texas (Fredlund et al., 1998),buttoour current average d C values for C3 woody plants, C3 knowledge, there are no published records extending forbs, C4 grasses, and CAM plants are 26.9 F back to the Pleistocene in Texas. 0.6x, 29.4 F 0.4x, 14.0 F 0.3x,and Isotopic analysis offers another approach to assess- 15.6 F 0.2x, respectively (Boutton et al., 1998). ing the photosynthetic physiology of vegetation that is We use enamel d13C values to estimate the per- especially important when pollen, phytolith, and mac- centage of C4 plants in the diet (X) with a mass rofossil data are sparse. C3 and C4 plants have balance equation. different stable carbon isotope values (d13C3). As discussed in more detail below, these differences are 13 13 passed on to materials derived from plants, such as ð100Þd Csample ¼ð100 X Þd C100% C3 enamel soil organic matter and animal tissues, offering a þðX Þd13C ð1Þ proxy for the photosynthetic physiology of vegetation. 100% C4 enamel Here, we determine the d13C value of tooth enamel from fossil mammals thought to have been grazers To obtain d13C values for animals on end-member 13 (i.e., animals with diets of grass and other herbaceous diets, we first estimate the d C values of C3 and C4 plants). We include a surviving taxon known to be a plants in the past, and then account for the metabolic committed grazer, the bison (Van Vuren, 1984; Cop- fractionation between diet and enamel apatite (Table pedge and Shaw, 1998), as well as extinct horses and 1).C3 plants vary by at least 6x in relation to mammoths. To assess temporal or spatial mixing of differences in environmental conditions and function- fossils at sites, we examine enamel oxygen isotope al group, whereas differences among C4 plants are 18 values (d O). Finally, we compare %C4 estimates smaller and more related to phylogeny and physiology from enamel to those derived from climate–vegeta- (Tieszen, 1991; Ehleringer and Monson, 1993). With- tion models. 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 2. Reconstructing paleoenvironments using mam- point (Table 1). Plant d13C values also vary with shifts 13 malian isotope values in the d C of atmospheric CO2 (Marino et al., 1992; Leavitt, 1993). These shifts are quantified using d13C 2.1. Isotopic controls in plants and animals measurements for CO2 from ice cores (Table 1). We performed a few simple tests to explore the We measured the isotopic composition of carbon- sensitivity of %C4 estimates to errors in assumptions ate in the mineral hydroxylapatite in tooth enamel (the underlying the mass balance calculations. For our calculations, we have assumed a diet to apatite

3 13 13 12 13 12 fractionation of 14x.A1x error in this fraction- d C=[(( C/ Csample H C/ Cstandard) 1) Â 1000], and the standard is VPDB. d18O values follow the same convention, where ation, which would change 100% C3 and C4 enamel 13 ratios are 18O/16O and the standard is VSMOW. Units are parts per d C values by the same amount, would lead to a thousand (x). 7% error in the C4 estimate. Uncertainties about the P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 335

Table 1 between apatite and local water d18O values. Ingested 13 pCO2 and d C values for atmosphere, plants and animals used in water d18O values, in turn, co-vary with climate. mass balance calculations and vegetation modeling Meteoric water d18O values are lower in cold regions Late Holocene Post- LGM/ and seasons, and higher in warm regions and seasons. 1990s LGM Pre-LGM The surface and plant water ingested by mammals may Atmospheric pCO2 350 280 235 200 be 18O-enriched relative to meteoric water by evapo- (ppmV) 13 ration and evapo-transpiration, respectively. On long- d C atmospheric CO2 8.0 6.5 6.8 7.1 13 18 d CC3 plants 27.5 26.0 26.3 26.6 time scales, the d O value of meteoric water may shift 13 d CC4 plants 12.5 11.0 11.3 11.6 with changes in climate and/or vapor source area. 13 18 d C 100% C3 enamel 13.5 12.0 12.3 12.6 We use d O values to evaluate mixing of individ- 13 d C 100% C4 enamel 1.5 3.0 2.7 2.4 uals from different geographic areas (via migration) All values are in x relative to VPDB. Atmospheric pCO2 values 13 or different time periods (due to taphonomic process- are from Collatz et al. (1998) and Monnin et al. (2001). d C values es). During tooth formation, individuals experience for atmospheric CO2 values are from Leuenberger et al. (1992) and Indermuhle et al. (1999). Past values for C and C plants are different climates and physiological states, generating 3 4 18 estimated by adding the difference between modern and past d O variability within populations. Using a large atmospheric CO2 to modern plant isotope values. Past values for C3 collection of teeth from a deer population, Clementz x 18 and C4 enamel are estimated by adding 14 to plant values and Koch (2001) showed that enamel d O values (Cerling and Harris, 1999). These calculations assume that the have a standard deviation (1r) of 1.3x that is stable fractionations between atmosphere and plant and between plant and z animal have not changed with time. when the sample size is 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., 13 4 d C values for C3 and C4 plants in the past are 1996). We suggest that if 1r is z 2x for a species harder to assess, because they may lead to non- at a locality, the collection may contain individuals uniform shifts in %C4 estimates due to changes in that are either time-averaged or spatially mixed due to the spacing between end-member d13C values. As a migration. quick check on this uncertainty, we recalculated %C4 estimates assuming end-member d13Cvaluesfor 2.2. Fossil materials modern C3 and C4 plants from the study of Boutton et al. (1998) on southern Texas plants (i.e., C3 Locations, ages, and other data for fossil localities plants, 28x;C4 plants, 14x). Recalculated are supplied in Appendix B. For temporal compar- %C4 estimates differed from the values reported here isons, we parse sites into four temporal bins: before by 3% to 5%. These simple tests suggest that the the Last Glacial Maximum (Pre-LGM, 70 to 25 14C 5 14 %C4 estimates may exhibit uncertainties of 5% to ky), LGM (25 to 15 C ky), Pleistocene after the 10% around the values reported here. Phillips and LGM (Post-LGM, 15 to 10 14C ky), and Holocene Gregg (2001) present a rigorous statistical method (10 to 0 14C ky). LGM, Post-LGM, and Holocene for assessing uncertainty in source partitioning when sites are constrained by 14C ages. Age constraints for using isotope mass balance models, but it requires Pre-LGM sites are weaker. The few Pre-LGM sites data on variance in end-member isotope values that is not available here. 4 18 18 We have added samples and species since publication of The d O value of apatite is controlled by the d O Bocherens et al. (1996). The d18O standard deviations for herbivore values of oxygen fluxes into and out of the body, by tooth enamel apatite are: African elephant, 0.6x, n = 11; black fractionations associated with biomineralization, and rhinoceros, 1.6x, n = 5; Grants gazelle, 1.7x, n = 5; plains zebra, by physiological factors that alter flux magnitudes and 1.8x, n = 7; common wildebeest, 1.9x, n =8. 5 fractionations (the following discussion is based on When discussing time, we will use units of 1000 radiocarbon years before present (14C ky) or 1000 calendar years before present Koch, 1998; Kohn and Cerling, 2002). Ingested water (cal ky). Conversions for key dates are: 29 cal ky = 25 14C ky; 21 cal is the chief isotopically variable source of oxygen to ky = 18 14C ky; 17 cal ky = 15 14C ky; 14 cal ky = 12 14C ky; 12 cal large mammals, and there is a strong correlation ky = 10 14Cky(Kitagawa and van der Plicht, 1998; Fiedel, 1999). 336 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357

Table 2 Table 2 (continued) Isotope data for each taxon organized by site and age Species/locality n d13C F 1rd18O F 1r %C4 F 1r 13 18 Species/locality n d C F 1rdO F 1r %C4 F 1r Last Glacial Maximum sites Pre-Last Glacial Maximum sites Howard Ranch Clear Creek Fauna Equus sp. 4 3.2 F 3.2 28.4 F 2.2 63 F 21 Mammuthus columbi 2 1.8 F 0.6 27.5 F 0.3 72 F 4 Equus sp. 4 5.8 F 1.0 30.0 F 1.9 45 F 7 Laubach Cave, Level 2 Coppell Mammuthus columbi 1 3.0 30.2 64 Equus sp. 2 4.4 F 0.1 30.2 F 0.3 55 F 1 Post-Last Glacial Maximum sites Easely Ranch Ben Franklin Mammuthus columbi 1 0.8 30.2 79 Mammuthus columbi 3 2.1 F 1.3 29.6 F 0.3 68 F 9 Equus sp. 3 2.8 F 1.4 28.7 F 0.5 65 F 9 Equus sp. 4 4.7 F 1.7 29.0 F 1.9 51 F 11

Ingleside Blackwater Draw Bison sp. 6 0.4 F 1.1 30.5 F 1.0 81 F 7 Bison sp. 1 1.4 28.1 91 a Mammuthus columbi 8 1.5 F 0.6 29.6 F 0.8 74 F 4 Bison sp. 3 0.1 F 1.2 26.5 F 1.4 83 F 8 Equus fraternus 6 2.2 F 1.3 27.7 F 1.0 69 F 9 Mammuthus columbi (l) 2 0.6 F 0.3 28.6 F 0.7 78 F 2 Equus pacificus 10 1.3 F 0.8 29.7 F 1.3 75 F 5 Mammuthus columbi (m) 3 8.2 F 0.8 23.3 F 1.0 27 F 5 a Equus complicatus 8 2.2 F 1.1 30.1 F 2.0 69 F 7 Mammuthus columbi 4 1.0 F 1.0 27.9 F 2.7 75 F 7 Equus sp. 2 5.7 F 0.6 27.0 F 0.9 44 F 4 Leo Boatright Pit Bison sp. 2 1.2 F 0.7 29.1 F 0.4 76 F 5 Bonfire Shelter Mammuthus columbi 4 3.8 F 2.2 28.6 F 1.5 59 F 15 Bison sp. 2 0.3 F 0.5 27.4 F 1.1 84 F 3 Equus sp. 2 4.1 F 1.4 29.8 F 1.2 57 F 9 Mammuthus columbi 1 2.8 29.5 63

Moore pit Cave Without a Name Bison sp. 3 2.2 F 1.1 27.5 F 1.1 69 F 7 Bison sp. 1 3.8 28.0 57 Mammuthus columbi 9 2.7 F 0.9 28.8 F 0.7 66 F 6 Equus sp. 5 5.5 F 1.4 30.4 F 1.2 47 F 9 Kincaid Shelter Mammuthus columbi 1 1.8 30.1 70 Quitaque Creek Equus sp. 2 3.9 F 1.6 28.0 F 3.0 56 F 11 Equus sp. 2 1.9 F 0.6 27.4 F 0.3 71 F 4 Schulze Cave, Valley Farms Level C2 Bison sp. 2 0.7 F 0.7 29.1 F 1.2 79 F 5 Mammuthus columbi 1 4.2 29.1 54 Mammuthus columbi 2 5.3 F 2.2 27.1 F 0.9 49 F 15 Equus sp. 2 6.4 F 0.5 29.7 F 1.4 41 F 3 Holocene sites Blackwater Draw a Waco Mammoth Site Bison sp. 8 0.1 F 1.3 26.2 F 2.6 81 F 9 Mammuthus columbi 14 2.7 F 0.8 29.9 F 0.8 66 F 5 Equus sp. 1 4.7 30.3 53 Keller Springs Bison sp. 1 0.2 26.7 81 Last Glacial Maximum sites Congress Avenue Schulze Cave, Level C1 Mammuthus columbi 1 1.0 28.7 77 Bison sp. 1 1.8 25.9 68 Equus sp. 2 4.3 F 0.4 28.7 F 0.9 55 F 3 The 1r for taxa with only two individuals per site is the difference between the values divided by 2. Friesenhahn Cave (l), local.; (m), migratory, from Hoppe (2004). Bison sp. 5 1.5 F 1.2 29.4 F 0.9 74 F 8 a Data from Connin et al. (1998). Mammuthus columbi 16 1.8 F 1.4 29.7 F 0.7 72 F 9 Equus sp. 3 4.0 F 0.1 28.4 F 0.1 57 F 1 with 14C ages have large errors. Most Pre-LGM sites Howard Ranch occur on terraces in northeastern Texas thought to be Bison sp. 1 2.8 30.8 >100 older than the LGM but younger than the last interglacial (Ferring, 1990). Ingleside, a coastal site P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 337 in southern Texas, is aged biostratigraphically (Lun- Prism gas source mass spectrometers with an ISO- delius, 1972a). CARB automated carbonate system. Samples were Average d13C and d18O values for tooth enamel dissolved by reaction in stirred 100% phosphoric acid from mammoth (Mammuthus), bison (Bison), and at 90 jC. Water and CO2 generated by reaction were horse (Equus) from each site are reported in Table separated cryogenically. Reaction time for each sam- 2, which also includes data from Connin et al. (1998) ple was >10 min. The 1r value for 97 laboratory for Blackwater Draw, NM. The mammoth samples calcite standards (Carrera Marble) included with these probably represent a single species, Mammuthus samples was < 0.1x for d13Candd18O. This columbi, as this is the only mammoth reliably identi- standard has been calibrated relative to NBS 18 and fied in late Quaternary deposits from Texas (FAUN- 19. The standard deviation for 15 laboratory enamel MAP, 1996). The situation is less clear for bison and standards included with these samples was 0.1x for horse, due to the rapid evolution of new forms and d13C and 0.2x for d18O. taxonomic disagreements (Guthrie, 1990; Dalquest and Schultz, 1992; MacFadden, 1992). Most specimens are only identified to genus in museum collec- 3. Numerical reconstruction of vegetation cover tions and will be treated as such here. In Appendix C, we list specimen number, tooth sampled, and the most We coupled climate and vegetation models to specific taxonomic data available for each specimen. reconstruct the balance between C3 and C4 vegeta- Samples were provided by the Texas Memorial Mu- tion on three time planes: 0, 14 and 21 cal ky (equal seum (University of Texas, Austin), Shuler Museum to 0, 12 and 18 14C ky). Climate fields for the three of Paleontology (Southern Methodist University), periods were constructed from a combination of Department of Biology at Midwestern State Univer- observed and simulated data. For 0 14C ky, we used sity, and Strecker Museum of Natural History (Baylor the modern observed record of New et al. (2000) University). (archived at www.ipcc-ddc.cru.uea.ac.uk), which is a global, gridded dataset (0.5j latitude Â 0.5j longi- 2.3. Isotopic methods tude) generated from climate station normals for 1931 to 1990. For 12 and 18 14C ky, we constructed Prior to sampling, the outer layer of enamel and climate fields using the climate model simulations of adhering dentin or cementum were removed by grind- Kutzbach et al. (1996) (archived at www.ngdc.noaa. ing. Enamel powders were generated either by drilling gov/paleo/paleo.html). These simulations were gen- under a microscope or by crushing enamel fragments erated using the National Center for Atmospheric in an agate mortar and pestle. When collecting enamel Research Community Climate Model (CCM1) with a samples, we tried to sample in a fashion that cut mixed layer ocean at R15 resolution ( f 4.4j lat- across growth lamellae so the sample would be itude Â 7.5j longitude) (Wright et al., 1993; Kutz- representative of a substantial fraction of the time of bach et al., 1996). In constructing Pleistocene tooth crown formation. At the same time, we were climate fields, we used the anomaly technique of trying to minimize damage to the specimens, so Kutzbach et al. (1998). In this method, differences complete homogenization of the enamel record in between experimental and control climate simula- each tooth was impossible. tions are added to a modern observed climate data Powders were soaked for 24 h in 2% NaOHCl to set, which allows the resolution of the simulated remove organic contaminants, rinsed five times with climate to far exceed that of the climate model. And de-ionized water, reacted with 1.0 N acetic acid because it relies on climate model sensitivity, the buffered with calcium acetate (pH 5) for 24 h to method reduces biases in the simulated climate. remove diagenetic carbonate, then rinsed a final five Differences between the 12 and 0 14C ky experi- times with de-ionized water and freeze dried (Koch et ments and between the 18 and 0 14C ky experiments al., 1997). were added to the New et al. (2000) modern data set, Carbon and oxygen isotope compositions of enam- yielding the 12 and 18 14C ky climate fields that el powders were measured on Micromass Optima or were used to estimate %C4 biomass. 338 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357

We used two methods to estimate %C4 biomass able at monthly resolution, we developed a relation- from these climate fields, and applied these methods ship between mean monthly temperature and the at all sites within the region except grid cells currently occurrence of a day with growth limiting frost within occupied by forest biome. The first method (referred that month. By comparing maps of the probability of to as the regression method) uses a quantitative spring and fall frost (archived at: http://www. relationship between the above-ground productivity ncdc.noaa.gov/oa/documentlibrary/freezefrost/frost- of different plant functional types and climate varia- freemaps.html) with maps of mean monthly temper- bles that was developed from 73 sites in central North ature (New et al., 2000), we determined that in the America (Paruelo and Lauenroth, 1996). The regres- south-central US there is only a 10% probability of a sion for %C4 grass biomass is: 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. %C4 ¼0:9837 þ 0:000594ðMAPÞ The second step is to assess the C3 –C4 crossover þ 1:3528ðJJA=MAPÞþ0:2710ðlnMATÞð2Þ temperature for each time period. Following Collatz et al. (1998), we set pCO2 = 350 ppmV today and 14 where lnMAT is the natural logarithm of mean pCO2 = 200 ppmV at 18 C ky, yielding crossover annual temperature, MAP is mean annual precipita- temperatures of 22 and 11 jC, respectively. We set 14 tion, and JJA/MAP is the fraction of mean annual pCO2 = 235 ppmV at 12 Cky(Monnin et al., 2001), precipitation falling in the summer (June, July, Au- yielding a crossover temperature of 14 jC (Collatz et gust). At most of the sites, the functional types not al., 1998). These crossover temperatures are based on explained by this regression are C3 plants (i.e., forbs, laboratory experiments exploring the effects of chang- shrubs, C3 grasses). At two sites (Texas Panhandle, ing temperature and pCO2 on different types of southwest Texas), CAM plants comprise a substantial plants. percentage of the modern flora (16% and 38% Third, at grid point we evaluate whether each respectively). Because Texas CAM plants are similar growing season month is dominated by C3 or C4 13 in d C value to C4 plants, their presence may lead to plants. Competitive superiority is assessed solely on erroneously high estimates of %C4 biomass from the basis of expected differences in photosynthetic mammalian isotopic data relative to the regression rate under different climatic and atmospheric condi- method. tions, ignoring other potential competitive and envi- The regression method relates %C4 biomass to ronmental factors. To qualify as a C4 month, the mean climate, but not pCO2. To explore the effects of late monthly temperature must exceed the crossover tem- Quaternary changes in pCO2 on the C3 –C4 balance, perature. Like Collatz et al. (1998), we impose an we adapted the approach of Collatz et al. (1998), who added constraint for a C4 month, that mean monthly calculated crossover temperature (the mean monthly precipitation is >25 mm. All other growing season temperature at which C4 grasses would fix carbon months are C3 months, either because they are too faster than C3 grasses) as a function of pCO2.To cold or too dry. For each grid point, dividing the facilitate comparison with %C4 estimates from mam- number of C4 growing season months by the total malian data, which integrate the d13C of vegetation number of growing season months gives the estimated from the growing season, we calculated the percent- %C4 biomass. age of growing season months in which C4 grasses We must note one feature of the mechanistic would be favored over C3 grasses as our measure of method. Based on modern data, we set the 15 jC %C4 biomass. We refer to this approach to estimating criterion for defining a growing season month. This %C4 biomass as the mechanistic method. value is above the C3 –C4 crossover temperature at 18 This mechanistic method has several steps. First, and 12, but not at 0 14C ky. Consequently, differences we estimate the number of growing season months at in %C4 between the modern and Pleistocene cases each grid point in each simulation. Growing season may be due to changes in crossover temperature, is largely set by the last frost of spring and first frost monthly temperature, or monthly precipitation. When of fall. Because climate simulations are only avail- comparing the 18 and 12 14C ky results, however, P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 339 differences can only result from changes in simulated to mixing. This is the case for mammoths at Valley moisture, because the mechanistic method will con- Farms and Leo Boatright Pit, for bison at Moore Pit, sider any month that is warm enough to have plant and for horses at Clear Creek. Despite hints of spatial growth as dominated by C4 plants. and/or temporal mixing at these four sites, we include data from all individuals at these sites in our statistical analyses. 4. Results of isotopic analysis Overall, d18O variability offers little support for the hypothesis that these sites are subject to strong tem- 4.1. Testing for temporal or spatial averaging at fossil poral or spatial mixing. The one clear exception localities (Blackwater Draw) shows that mixing leaves obvious signs, at least in regions near highlands. We consider d18O standard deviations for taxa at a site are the other sites spatially and temporally discrete. typically < 1.5x(Table 2). At sites with z 5 individuals per taxon, 1r values are almost always V 2x, 4.2. Temporal and spatial trends in isotopic values the cut-off for spatially or time averaged populations. Blackwater Draw is the exception. For the mammoths Temporally binned means ( F 1r) for the three taxa from this site, 1r =3x, and the data are bimodal; five are presented in Table 3. ANOVA does not reveal individuals have high values (28.9 F 1.1x) and four significant differences in mean d13C values among 13 have low values (23.5 F 0.9x). d C values differ time periods for bison ( F3,32 = 0.85, p = 0.48). Differ- strongly between these groups and are positively ences in means for mammoths and horses are more correlated with d18O values (R = 0.72). Positive cor- pronounced, but still not significant at the p V 0.05 relation would be expected if the sample contains a level ( F2,66 = 2.50, p = 0.09 and F2,59 = 2.36, p = 0.10, mix of individuals from warm regions or time periods respectively). Inspection suggests that low d13C val- (represented by high values) and individuals from ues for Pre-LGM mammoths and the contrast between cool regions or times (represented by low values). Pre-LGM and Post-LGM horses contribute to these For the mammoths measured here, Hoppe (2004) lower p values. For horses, the large number of tested for spatial versus temporal mixing using specimens from the Pre-LGM Ingleside site may bias 87Sr/86Sr ratios. 87Sr/86Sr ratios in herbivores track our results. Ingleside is further south than any other differences in soil available Sr, which in turn are site, it is the only coastal site, and horse d13C values controlled by bedrock geology and atmospheric de- here are substantially higher than values at other sites position (Hoppe et al., 1999). Mammoths with low (Table 2). Excluding Ingleside, the Pre-LGM mean for d18O and d13C values have higher 87Sr/86Sr values, as horses is 4.6 F 1.7x, and differences among time expected if they are immigrants from mountains to the periods are no longer significant ( F2,35 = 1.01, west. We exclude these animals from further statistical p = 0.38).6 tests. One mammoth from the study by Connin et al. Differences in mean d18O values among time peri- 18 (1998) also has a low d O value (24.2x) and is ods are not significant for mammoths ( F2,66 = 1.35, 13 excluded, though it had a high d C value similar to p = 0.26) or horses ( F2,59 = 2.69, p = 0.08). Mean bison non-migratory individuals. Holocene bison from d18O values, in contrast, differ significantly among the 18 Blackwater Draw also show high d O variability four time periods ( F3,32 = 8.79, p = 0.0002). Post hoc (1r =2.6x), and a positive correlation between tests (Scheffe´’s method) show that Holocene bison d13Candd18Ovalues(R = 0.51), which suggests d18O values are significantly lower than values for Pre- some mixing for this population as well. Because LGM and LGM bison. the data do not show strong bimodality, however, we leave them in our statistical analyses. 6 d18O variability is not a reliable marker of mixing Note that while three different species of Equus co-occur at Ingleside, their mean d13C values are statistically indistinguishable when a site contains < 5 individuals per taxon. Still, 18 ( F2,21 = 2.41, p = 0.11), whereas their mean d O values do differ some sites with low numbers of specimens contain significantly ( F2,21 = 4.55, p = 0.02) due to the low values for E. 18 13 outliers with low d O and d C values, which points fraternus (Table 2). 340 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357

Table 3 There are significant relationships between d18O Summary of isotopic data for each genus binned by age and latitude for bison ( p = 0.0003, R2 =0.32) and Bison sp. Mammuthus sp. Equus sp. mammoths ( p = 0.009, R2 = 0.10), but not horses ( p = Pre-LGM 0.61, R2 < 0.01). There are significant relationships Number of specimens 13 40 45 between d18O and longitude for bison ( p =0.0004, Number of sites 4 7 9 2 2 13 F F F F R = 0.31) and horses ( p = 0.002, R = 0.14), but not Mean d C 1r 1.0 1.2 2.6 1.4 3.1 2.0 2 Mean d18O F 1r 29.4 F 1.6 29.2 F 1.1 29.5 F 1.6 mammoths ( p = 0.67, R < 0.01). Where significant trends exist, values decrease from east to west or from LGM south to north by f 3x. Yet low R2 values indicate Number of specimens 6 18 9 that even these significant isotopic gradients are vari- Number of sites 2 3 3 able. Plots of d18O values vs. latitude and longitude (not Mean d13C F 1r 0.8 F 2.1 1.9 F 1.4 3.7 F 2.0 Mean d18O F 1r 29.6 F 1.0 29.7 F 0.7 28.8 F 1.4 shown) reveal no strong temporal differences within regions. Post-LGM Number of specimens 7 11 8 4.3. Differences among taxa Number of sites 3 5 3 Mean d13C F 1r 0.2 F 1.8 1.9 F 1.2 4.7 F 1.6 Mean d18O F 1r 27.2 F 1.3 29.3 F 0.8 28.3 F 2.3 Inspection of Tables 2 and 3 reveals differences in mean d13Candd18O values among taxa. For the Holocene following analysis, we include horse d13C data from Number of specimens 10 N.A. N.A. Ingleside; the described pattern is stronger if these data Number of sites 3 N.A. N.A. 13 13 F F are excluded. For d C, Bison>Mammuthus> Equus, Mean d C 1r 0.1 1.3 N.A. N.A. 18 c Mean d18O F 1r 26.2 F 2.3 N.A. N.A. whereas for d O, Bison < Mammuthus Equus (Ta- ble 3). ANOVA reveals that differences in mean values 13 Total among taxa are highly significant (for d C, F2,164 = Mean d13C F 1r 0.6 F 1.5 2.3 F 1.4 3.4 F 2.0 33.30, p <10 12; for d18O, F = 7.22, p < 0.001). 18 F F F F 2,164 Mean d O 1r 28.1 2.2 29.3 1.0 29.2 1.7 Post hoc comparison (Scheffe´’s method) shows that all All calculations include data from this study and Connin et al. pairwise differences are significant for d13C. For d18O, (1998), but exclude migratory mammoths. Bison is significantly different from Mammuthus and Equus, but the differences between the latter two taxa We did not detect strong temporal isotopic trends are not significant. in these three taxa. If spatial isotopic gradients are Finally, we examined differences in mean isotope present, however, uneven spatial sampling might values between species at localities where they mask temporal trends. We assess this idea using co-occur. We have data for bison versus mammoth a least-squares linear regression analysis and by or bison versus horse at seven sites. At these inspection. The only significant relationship sites, bison d13C values are, on average, 1.9 F ( p V 0.05) between d13C and latitude is for horses, 1.6xhigher than mammoth values and 4.2 F with lower values at higher latitudes (Fig. 2C), but 1.9xhigher than horse values. There are 12 sites this relationship collapses if specimens from Ingle- where mammoth and horse co-occur; mammoth d13C side are omitted. The relationship between d13C and values are, on average, 2.4 F 1.2xhigher than longitude is significant for bison (Fig. 2D) and horse values. A similar within-site analysis of d18O mammoths (Fig. 2E), but not for horses (with or values reveals no significant differences between without Ingleside). In all cases, d13C values in- taxa. crease to the west. Inspection of Fig. 2 reveals strong isotopic overlap among individuals in each 4.4. Summary of isotopic results from fossil mammals region, irrespective of their age, both where there are significant spatial gradients (e.g., Fig. 2D and (1) d18O gradients of f 3x occur across the E) and where gradients are lacking (e.g., Fig. 2A, region, with lower values in northern/western B and F). areas. Data from MacFadden et al. (1999a) and P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 341

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

Hoppe (2004) suggest these d18O gradients parameters used in the regression method to estimate continue at higher latitudes. %C4 biomass (see Eq. (2)). In the modern, MAT (2) Despite the presence of these gradients, popula- shows a meridional gradient, with values decreasing tions at most localities show low d18O variability, from 22 jC in the south to 12 jC in the north (Fig. suggesting they have not experienced strong 3A). MAP exhibits a steep zonal gradient, with spatial mixing. This situation is violated Black- values decreasing from 1400 to 300 mm/year from water Draw, which includes migratory mam- east to west (Fig. 3B). Variation in JJA/MAP is also moths from highlands to the west (Hoppe, 2004). steep zonally, but in the opposite direction, with There are hints of mixing at some Pre-LGM sites values increasing from 20% to 50% from east to with small sample sizes. west (Fig. 3C). Using the regression method, this 13 (3) Spatial gradients in d C values are significant, and climatology yields estimated C4 grass biomass of generally suggest that d13C values are higher to the 55% to 85%, with lower values in the west and west. higher values in the east (Fig. 3D). While agreement (4) There is no compelling evidence for large temporal varies regionally, these estimated values for %C4 shifts in d13Cord18O values in the region as a biomass are lower (15–20%) than those observed whole, or in different sub-regions, though the latter in the regional calibration data set of Paruelo and conclusion is weak due to sparse data coverage. Lauenroth (1996). (5) There are significant differences in d13C value Simulated MAT is lower than today at 12 14Cky, among taxa, with Bison>Mammuthus>Equus. decreasing from 20 jC in the south to 10 jC in the Because extant bison are grazers, fossil bison north (Fig. 3E). Simulated MAP is lower too, drop- provide the most reliable evidence regarding the ping from 1100 to 200 mm/year from east to west d13C of herbaceous vegetation. While the lower (Fig. 3F). Simulated JJA/MAP is lower across the average d13C value for mammoths indicates they entire region at 12 14C ky, but the gradient is steeper typically consumed a greater fraction of C3 plants than today (4% to 44%, east to west) (Fig. 3G).As than bison, at some sites mammoths yield %C4 all three of the climate variables that influence %C4 14 estimates that are similar to bison. In contrast, have lower values at 12 Ckythantoday,C4 horse d13C values are almost always much lower percentages estimated using the regression method than those for bison, suggesting that horses were are much lower, with values ranging from 10% to consistently eating a large fraction of C3 plants in 30% (Fig. 3H). settings where grazing bison were consuming Simulated MAT is substantially lower than present 14 almost entirely C4 diets. Pleistocene horses were at 18 C ky across the entire region, decreasing from not obligate grazers, but rather had more diverse 18 to 4 jC from south to north (Fig. 3I). The gradient 14 diets that contained a mix of C3 trees and shrubs, in simulated MAP is similar to that at 12 Cky(Fig. as well as the largely C4 grass ingested by bison 3J). Simulated JJA/MAP shows enhanced meridional and mammoths. Prior studies have shown that variability at 18 14C ky, increasing from 4% to 56%, fossil horses are not obligate grazers (Koch et al., southtonorth(Fig. 3K). Simulated JJA/MAP is 1998; MacFadden et al., 1999b). Consequently, higher in NW Texas and New Mexico at 18 14Cky 13 14 we cannot use d C values from horses to quantify than today or at 12 Cky.C4 biomass estimates the C3–C4 balance among herbaceous plants, as generated using the regression method are 0% to 20% we can with bison and mammoth, but we can use over most of the region, with higher values (25% to them as a rough proxy for the overall proportion 45%) in northern Texas where simulated JJA/MAP of C3 versus C4 plants on the landscape. values are high (Fig. 3L). In Fig. 4, we map the number of growing season months based on modern and simulated climate data 5. Results of vegetation modeling (Fig. 4A, C, and E), as well as estimates of the fraction of the growing season dominated by C4 In Fig. 3, we show modern and simulated MAT, grasses generated using the mechanistic method MAP, JJA/MAP data, which are the three climatic (Fig. 4B, D, and F). Today, the number of growing lmt aaadrslig%C resulting and data Climate s_s_otu_a_efnto ( function gsn_csm_contour_map_ce eie by defined i.3 lmt ilsue oetmt C estimate to used fields Climate 3. Fig.

C4 Grass (fraction) JJA/MAP (fraction) MAP (mm/yr) MAT (˚C) 0° 0° 95°W 100°W 105°W aaktyadFly(1999) Foley and Ramankutty 0 14 ..Kc ta./Plegorpy aaolmtlg,Pleeooy27(04 331–357 (2004) 207 Palaeoecology Palaeoclimatology, Palaeogeography, / al. et Koch P.L. y 18 C kyr 4 siae r na0.5 a on are estimates http://ngwww.ucar.edu/ncl/index.html 4 oetdgi el r hddgray. shaded are cells grid Forested . rs ims ytergeso ehdadrsligboasetmtsfr0 2 n 18 and 12, 0, for estimates biomass resulting and method regression the by biomass grass D C B A 0° 0° 95°W 100°W 105°W Â 0.5 12 j 14 rd n r otue sn h CRCmadLnug (NCL) Language Command NCAR the using contoured are and grid, C kyr .Etmtdgasfatosaesonol o o-oetpit,as points, non-forest for only shown are fractions grass Estimated ). H G F E 0° 0° 95°W 100°W 105°W 14 C kyr K L I J 14 25°N 30°N 35°N 25°N 30°N 35°N 25°N 30°N 35°N 25°N 30°N 35°N Cky. 343 344 ..Kc ta./Plegorpy aaolmtlg,Pleeooy27(04 331–357 (2004) 207 Palaeoecology Palaeoclimatology, Palaeogeography, / al. et Koch P.L.

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 0 14 14 14 C kyr (A–B), 12 C kyr (C–D) and 18 C kyr (E–F). Colored stars show C4 biomass fractions observed in modern grasslands (Paruelo and Lauenroth, 1996). Colored squares 13 and circles show C4 biomass fractions estimated from d C 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 et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 345 season months changes markedly, from 12 months in 6 months typifies most of the region (Fig. 4E). 14 the far south to 5 months on the Texas Panhandle and Estimated C4 biomass is lower than at 0 or 12 C in eastern New Mexico, though 7 to 9 months is ky on the coast (40% to 80%) and in central Texas characteristic for most of the region (Fig. 4A). Central (30% to 60%), due largely to lower moisture levels in Texas has the highest estimated percentage of C4 the growing season (Fig. 4F). On the Texas Panhan- 14 growing season months (50% to 80%), with lower dle, C4 estimates at 18 C ky are lower than at 12 values on the Gulf Coast (50% to 70%) and the 14C ky because some growing season months are too 14 Panhandle (40% to 60%) (Fig. 4B). dry for C4 plants, but higher than at 0 Ckydueto The stars in Fig. 4B show observed %C4 biomass loss of cool C3 months with the drop in crossover at sites in the study by Paruelo and Lauenroth (1996). temperature. 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 6. Discussion method over-estimates C4 biomass by 30%. The low %C4 biomass value recorded at this site in Paruelo and 6.1. Comparisons of mammalian and soil isotopic Lauenroth (1996) is at odds with results from other data studies (Epstein et al., 1997; Tieszen et al., 1997). Isotopic study of soils and biomass from southern Isotopic data from mammoths and bison yield Texas has revealed historical shifts toward greater C3 high estimates of C4 grass consumption across much (i.e., shrub) biomass, probably due to grazing (Bout- of Texas and eastern New Mexico from the Pre-LGM ton et al., 1998); it is possible a similar phenomenon to the Holocene. If horse data are a rough proxy for has impacted this site. On the Texas Panhandle, the the overall abundance of C4 versus C3 plants, the mechanistic method under-estimates the amount of C4 region may have consistently had greater than vegetation by 20% to 30%. Overall, however, the f 45% C4 vegetation. The persistence of biomes method yields %C4 estimates in reasonable agreement with substantial C4 biomass in the face of late with observational data. Quaternary climate change is surprising and merits The simulated growing season at 12 14C ky drops further verification. from 9 to 4 months from south to north, though 5 to 7 As a step toward verification, we compare esti- months characterizes most of the region (Fig. 4C).C4 mates of %C4 biomass from mammalian isotope data biomass estimates generated using the mechanistic to those from isotopic study of soil and paleosol or method are higher than modern on the coast (60% buried soil organic matter. Soils and mammal teeth to 80%) and on the Panhandle (60% to 100%), but record data on different spatial and temporal scales. lower than modern in central Texas (40% to 70%) Soils offer localized data integrated over centuries, (Fig. 4D). These differences are due to the differential whereas mammals feed over a large area but form effects of temperature and moisture. Today, on the enamel over a few years. Soils integrate carbon from Gulf Coast and Panhandle, some cool growing season all above-ground biomass, whereas mammals have months are C3 dominated. %C4 biomass rises in these preferred diets. Finally, soil organic records often regions in the 12 14C ky case because of the lower come from river valleys and terraces and may slightly crossover temperature, which causes every growing over-represent the C3 trees and shrubs that inhabit season month to be warm enough for C4 plants, and these ecosystems, either through direct input from because simulated moisture is sufficient through most above-ground biomass or through inheritance from of the growing season. In central Texas, %C4 biomass the fluvial parent material. Still, a consistent, large drops because the 12 14C ky climate simulation is mismatch between soils and all mammalian taxa drier than today in the growing season; the decrease in would be troubling, whereas rough agreement would crossover temperature is overwhelmed by the drop in be mutually supportive. moisture. We know of three well-dated records of soil The simulated growing season at 18 14C ky drops organic d13C values spanning the Pleistocene–Holo- from 8 to 2 months from south to north, though 4 to cene boundary in our study area. Holliday (1997, 346 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357

2000) analyzed organic carbon from soils on playas and horse data suggest an overall C4 biomass of and dunes on the southern High Plains (34.4 to 55% to 60% (Fig. 5C), but all these data predate 32.8jN; 101.6 to 103.5jW). Dune soils are well the earliest Medina River records. Mammal diets drained, but probably formed during wetter intervals from the latest Pleistocene and earliest Holocene, when plant growth stabilized the dunes. We will not when the Medina River record shows a rapid rise consider data from more poorly drained playa soils, in %C4, have 10% to 30% more C4 plants than the as they may inherit organic matter from aquatic soils. plants. At the Aubry Clovis site (33.3jN, 97.1jW, The match between soil organic and mammalian Fig. 1), Humphrey and Ferring (1994) analyzed isotope records is good in light of potential biasing organic carbon from flood plain soils and other factors. For most of the early Holocene and Post- sources. Again, we only use their soil data to LGM, soil and mammal data point to C4 biomass estimate %C4 biomass. Finally, Nordt et al. (2002) >40%. When mammal data do not match soil data, analyzed soil organic matter from a buried alluvial mammals typically yield higher %C4 estimates. This soil sequence from the Medina River valley (29.3jN, mismatch may reflect over-representation of C3 98.5jW, Fig. 1). plants in sediments with soil organic matter, or the 13 To estimate %C4 biomass from soil d C values, fact that all these mammals (even horses with more 13 we use end-member d C values for C3 and C4 plants catholic feeding habits) may under-sample C3 tree (Table 1) and an equation analogous to Eq. (1). On the and shrubs. We conclude that a substantial C4 southern High Plains, soil d13C values indicate sub- biomass persisted throughout the late Quaternary in stantial C4 biomass at the LGM (65 F 20%) and Post- the south-central US, albeit with local, short-term LGM (80 F 26%), with slightly less C4 in the Holo- drops, as seen in the high-resolution Medina River cene (58 F 19%) (Fig. 5A). At Blackwater Draw, record. Post-LGM bison and non-migratory mammoths yield dietary estimates consistent with %C4 estimates from 6.2. Extent of grasslands at the LGM soils, whereas diets for early Holocene bison contain more C4 plants than soils (Fig. 5A). In northeastern Early pollen studies suggested that forests covered Texas, soil values indicate that plant cover was similar the plains of northern, western, and central Texas at in the Post-LGM (56.7 F 5%C4) and Holocene the LGM (reviewed by Bryant and Holloway, 1985). (57.5 F 7%C4) (Fig. 5B). At Ben Franklin, the closest Recently, it has been argued that high conifer pollen Post-LGM mammal site, %C4 estimates from mixed counts at some LGM sites are due to preservational feeding horses and grazing mammoths are similar to bias, and that the High Plains and Edwards Plateau those from soils, whereas at Keller Springs, a Holo- were dominated by grass, not trees (Hall and Valastro, cene site, a single bison consumed more C4 vegetation 1995). The presence of animals thought to be grazers (Fig. 5B). at LGM sites supports this idea (Graham, 1987), and The Medina River soil sequence in south-central data on the diets of these animals helps to resolve the Texas provides a high resolution and variable issue. 14 record of %C4 biomass over the past 15 Cky LGM bison and mammoths from the plains and the (Fig. 5C). LGM data from mammoths and bison in edge of the Edwards Plateau have diets with 64% to the region suggest grasslands with 75% C4 biomass, 100% C4 plants. Even horse diets always have more

13 Fig. 5. %C4 biomass estimates from soil organic matter and mammalian d C values for the southern High Plains (A), northeastern Texas (B), and south-central Texas (C). Estimates from soil organics are indicated by crosses; estimates from bison, mammoth and horses are indicated by filled circles, squares, and triangles, respectively. Mammal data in A are from Blackwater Draw; in B data are from Ben Franklin and Keller Springs; in C data are from Congress Avenue, Friesenhahn Cave, Laubach Cave, Schulze Cave, Cave Without a Name, and Kincaid Shelter. For Friesenhahn Cave, we report the mean (symbol) F 1r (bar) for each taxon, and offset the symbols slightly vertically so that they are easier to see. Isotopic estimates of C4 biomass >100% indicate either a problem with the mass balance model or sample diagenesis. We excluded three samples from the southern High Plains with C4 estimates >110% from B and calculations in the text because they deviate so greatly that sample diagenesis is the likely explanation. P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 347 348 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 than 55% C4 plants. We might argue these animals are A second possibility is that the simulated climate vagrant grazers moving through a forested environ- data are wrong. As the mean temperature of these ment, but the persistence of d13C gradients across the simulations agrees with proxy data (Stute et al., 1995), region and the lack of d18O evidence for migration precipitation is a more likely culprit. If simulated make this ad hoc explanation unlikely. High C4 LGM and Post-LGM climates had more moisture percentages in the diets of large resident animals are overall (20% to 50%) or a greater fraction of summer inconsistent with the presence of an extensive region- rainfall, they would yield reasonable %C4 estimates. al forest, and instead point to more open vegetation. Are the simulations too dry by this amount? Proxy We are not suggesting that forests were entirely data offer qualitative estimates of past precipitation. absent, however. In addition to grazing mammoths At the LGM, higher lake levels in western and and bison, Friesenhahn Cave also has mastodon, deer, northern Texas (Mock and Bartlein, 1995; Wilkins 13 and tapir with d C values suggesting f 100% C3 and Currey, 1997) and accelerated speleothem growth diets (Koch, 1998). A likely scenario is that, as is the rates from south-central Texas point to greater case today, forests occurred in canyons and riparian amounts of precipitation and/or a more positive bal- zones. ance of precipitation-to-evaporation than today (Mus- grove et al., 2001). If not solely due to reduced 6.3. Model–model and model–data comparison evaporation under cooler climates, these data show that the simulated LGM climates do under-estimate The regression method and the mechanistic meth- regional MAP. In contrast, speleothem, mammalian, od use the same climate data, but yield different and sedimentological data from the plateau region 14 estimates of %C4 biomass for 12 and 18 Cky point to moisture levels as low as or lower than (compare Fig. 3H with Fig. 4D and Fig. 3L with Fig. modern in the Post-LGM (Toomey et al., 1993; 4F), with much lower estimates for the regression Musgrove et al., 2001). On the High Plains, some method. These differences in Pleistocene %C4 esti- authors argue for Post-LGM aridity (Haynes, 1991); mates are much greater than those between the two others argue for Post-LGM moisture levels interme- methods when estimating modern C4 biomass using diate between high LGM and low Holocene values current climate data (compare Fig. 3D with 4B). (Holliday, 2000). Still, the Post-LGM climate simula- Mammal and most soil isotope data yield %C4 tion is a closer match to moisture proxies, yet it too estimates that are also significantly higher than those produces %C4 estimates that are unreasonably low. obtained using the regression method. For example, We think it unlikely that errors in simulated moisture isotopic data from Blackwater Draw bison, mammoth are the main explanation for the failure of the regres- and soils all suggest that the biomass in the Post- sion method. LGM had 75% to 90% C4 plants. The regression A final possibility is that because the regression method, in contrast, yields %C4 estimates of only method fails to account for the effect of changes in 20% to 30%. atmospheric pCO2 on plant physiology, it cannot We consider three reasons why the regression capture the fact that C4 plants out-compete C3 plants method yields erroneously low estimates. One possi- at lower temperatures under the lower pCO2 atmos- bility is that the regression model, which is trained on pheres of the Pleistocene. The mechanistic method, data that span the entire Great Plains, is poorly which accounts for the effect of pCO2 on the C3 –C4 calibrated for the south-central US. We think this crossover temperature, does a much better job match- explanation is unlikely given the good fit between ing isotopic data from mammals and soils, so we favor regression-based %C4 estimates and those observed this plausible explanation. at calibration sites in Texas. While calibration bias We examine the fit between %C4 estimates from may explain the 10% to 20% under-estimation of mammalian isotopic data and the mechanistic method %C4 in Texas using modern climate data (see Section in Fig. 4. At most sites, the isotopic and mechanistic 5), the errors in the modern estimates are small %C4 estimates are similar for the Post-LGM (Fig. 4D) relative to the errors associated with Pleistocene and LGM (Fig. 4F), rarely differing by more than estimates. 20%. Where there is disagreement, isotopic estimates P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 349 are typically higher. The mismatch is greatest at eastern Texas, whereas bison diets on the southern Bonfire Shelter in the Post-LGM, where the mecha- High Plains have somewhat more C4 biomass than nistic method yields low %C4 estimates because of that estimated using the mechanistic method. great aridity in the climate simulation. Either the Our model–model and model–data comparisons simulation is too dry, or the mechanistic method fails indicate that regression methods based on modern to capture plant dynamics in deserts, or these animals plant climatology that ignore the effects of pCO2 on had diets biased toward C4 grass in a system domi- the rate of carbon fixation may not apply outside of nated by C3 shrubs. The comparison between mech- the Holocene (Cowling and Sykes, 1999). As current- anistic %C4 estimates and those from Holocene bison ly implemented, the more successful mechanistic (Fig. 4B) is complicated by the recent rise in atmo- method is insensitive to changes in precipitation, other spheric pCO2, which may have reduced C4 plant than requiring a minimum monthly precipitation of 25 abundance in the modern relative to the Holocene mm for C4 growth. We would prefer a method that (Collatz et al., 1998) and by our sparse Holocene simultaneously accounts for the effects of changes in bison data. Still, estimates from isotopic data and the temperature, moisture and pCO2 on %C4 biomass, but mechanistic method are similar in southern and north- at present, we know of no quantitative treatment of

Fig. 6. %C4 biomass estimates reconstructed from Pre-LGM mammalian isotope data. Estimates from bison, mammoth and horses are indicated by filled circles, squares, and triangles, respectively. 350 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 this subject. Likewise, because diurnal temperature vegetation models, though such simulations have re- data are not routinely saved and/or reported from cently been conducted (Barron and Pollard, 2002). paleoclimatic simulation studies, to implement the Consequently, our study of Pre-LGM vegetation is mechanistic method we had to approximate growing limited to inspection of broad trends in map view season from monthly mean temperature. A better (Fig. 6). approach would be to use daily simulation data to Focusing first on data from more mixed feeding determine the start and end of the growing season, horses, %C4 is high at Ingleside on the Gulf Coast which very likely includes days with temperatures (70% to 80%), intermediate in northeast Texas (40% below 15 jC. Even so, this coarse mechanistic method to 60%), then high again on the plains (60% to 80%). produces results that are remarkably consistent with Estimates from mammoths are similar at Ingleside, isotopic data from mammals and soil organic matter. and slightly higher in northeast Texas (40% to 80%) Authors have debated the primacy of changes in and on the Plains (70% to 80%). Pre-LGM bison climate versus pCO2 in driving changes in the C3 –C4 consume more C4 grass than horses or mammoths at balance (e.g., Ehleringer et al., 1997; Cowling and Ingleside (80% to 90%) and northeast Texas (70% to Sykes, 1999; Huang et al., 2001; Nordt et al., 2002). 80%). The spatial patterning and absolute values are A key role for climate has been argued for south- similar to those at the LGM and Post-LGM for all central Texas because isotopically reconstructed %C4 three taxa, again emphasizing the stability of regional estimates increase from LGM to Holocene (as might vegetation. 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- 7. Conclusion ation between %C4 biomass and temperature does not preclude an important role for changes in pCO2. The Our study has six key results about late Quaternary failure of the regression method when applied to LGM mammals and plants. and Post-LGM climates suggests that without the competitive advantage supplied by lower pCO2,C4 (1) Pleistocene bison were committed grazers, and plants would have been minor elements in Pleistocene mammoths ate dominantly herbaceous plants, floras in the region. And the mechanistic method, with a minor supplement of trees and shrubs. which accounts for the effects of pCO2, yields These taxa provide evidence on the C3 –C4 increases in %C4 biomass between the Pleistocene balance of regional grasslands. Horses ate mixed and Holocene in much of central Texas. The rise in C4 diets, and may be better viewed as rough monitors biomass from Pleistocene to Holocene likely does of the overall C3–C4 balance. reflect a rise in growing season temperature and/or (2) Data from fossil mammals do not support the idea an increase in moisture, but the relatively low magni- that forests spread across the plains and plateau tude of this rise is a reflection of the offsetting impact region at the LGM. of a rise in atmospheric pCO2. The difference between (3) Data from fossil mammals show spatial gradients our results from the regression and the mechanistic in C4 grass abundance, with maxima on the Gulf method can serve as a rough proxy for the magnitude coast and the High Plains, and minima on the of this offsetting effect. The influences of atmospheric edge of the Edwards Plateau and in northeastern pCO2 and seasonal temperature and moisture on Texas. These trends apply at all time periods for photosynthesis are so complex that predicting the which we have data. change in vegetation is difficult without an explicit (4) Mammalian carbon isotope data do not reveal climate–vegetation model. dramatic temporal shifts in C3 –C4 balance between cool glacial and warm Holocene times. 6.4. Composition of Pre-LGM floras (5) Mammalian isotope data are in good agreement with isotopic results from soil organic Our Pre-LGM data are coarsely dated, and we matter, and from the results of climate– currently lack simulated paleoclimate data to drive vegetation models that account for the effects P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 351

Appendix A (continued) of changes in atmospheric CO2 concentration and climate. Regions Dominant plants (6) Regression models based on the climatology of Gulf Prairies Water oak-live oak forest and mixed oak modern plants that do not consider the effects of and Marshes woodlands; riparian forests same as OWP; tallgrass (mostly C ) prairie with sparse oak changes in atmospheric CO2 concentration dra- 4 matically under-estimate the amount of C cover; saline and freshwater marshes with 4 sedges, rushes, reeds, aquatic forbs and a mix vegetation in the region. of C3 and C4 grasses. Coastal Sand Live oak woodland with tallgrass (mostly C4) Plains (CSP) understory; tall grasslands, saltgrass marsh. Acknowledgements South Texas Texas ebony-anacua forest; shrublands with Brushland ceniza, acacia, ebony or mesquite and pricklypear; riparian forests same as OWP; This research would not have been possible C4 grassland with mesquite; subtropical without the generous assistance and samples provided plants in far south. by Dale Winkler (Shuler Museum of Paleontology, Edwards Plateau Live oak-ash-juniper woodlands with Southern Methodist University, Dallas, TX), Ernie and Llano Uplift understory of tallgrass (mostly C4) species on Lundelius, Melissa Winans, and Pam Owen (Texas the eastern plains and the canyonland at the southeastern border; mesquite-juniper shrublands Memorial Museum, University of Texas, Austin, TX), on western plateau; live oak woodland Fred Stangl (Department of Biology, Midwestern on Llano Uplift; riparian forests same as

State Univ., Witchita Falls, TX), and Calvin Smith OWP; mixed- and shortgrass (mostly C4) (Strecker Museum of Natural History, Baylor Univer- with forbs and mesquite on western plateau. sity, Waco, TX). We thank Dan Bryant, Geoff Rolling Plains Tall- and mixedgrass (mostly C4) prairies with forbs; juniper, mesquite, oak, or Koehler, Rachel Zisook and Beth Zotter for assistance sand-sage shrublands on sandy/rocky with sample preparation and analysis. We thank Pat substrates; riparian forests same as OWP.

Holroyd, Beverley Johnson, and Lee Nordt for High Plains Mixed- to shortgrass (mostly C4) prairies; providing thoughtful reviews of this paper, though juniper, mesquite, oak, or sandsage any mistakes are of course of our own making. shrublands on sandy/rocky substrates; aquatic vegetation in playas. Finally, we thank Caroline Stro¨mberg and Bob Trans Pecos The northern extension of the Chihuahuan Feranec for inviting us to participate in this volume desert. Ponderosa pine-Douglas fir forests at and for accepting our late submission. This research high elevations; oak, juniper, pinyon pine, was supported by NSF-EAR 9316371 and 9725854 to cottonwood, or mesquite woodlands at PLK. middle elevations; desert mixed- and shortgrass habitats and shrub (creosotebush, tarbush, acacia, mimosa, yucca) at lower

elevations. Mostly C4 grasses at lower Appendix A. Description of modern vegetation in elevations; more C3 grasses at higher Texas elevations. Definitions Woody vegetation is dominated by trees or (from Diamond shrubs, which form z 25% of the plant Regions Dominant plants et al., 1987): canopy. Forest—trees z 3 m tall form >60% Piney Woods Mixed hardwood forest in lowlands; mixed of canopy; Woodland—trees form 25–60% pine-hardwood forest on uplands; diverse of canopy; Shrubland—shrubs 0.5–3 m tall form >25% of canopy. shrubs, vines, forbs and C3 and C4 grasses on Herbaceous vegetation is dominated by forest floor; C4 grasses in large open areas; trees and shrubs dominate swamps. grasses, graminoids or forbs with < 25% Oak Woods and Short oak forests and woodlands associated woody plant canopy. Tallgrass—dominated by grasses >1 m tall; Mixedgrass—dominated Prairies (OWP) with tallgrasses (mostly C4) and prairie forbs; diverse understory shrubs and vines; riparian by grasses 0.5 to 1 m tall; Shortgrass— forests of elm, sugarberry, ash, oak, hackberry, dominated by grasses < 0.5 m tall; Marsh— ash, and pecan. dominated by herbaceous vegetation with water at the surface 50% of the year. Blackland Tallgrass (mostly C4) prairie with forbs; Prairies riparian forests same as OWP. 352

Appendix B. Site information

Site Lat, Long Age Type of date, level Depositional environment

Pre-LGM Clear Creek N 33j15V,W97j00V 28,840 F 4740 Alluvium? Fluvial terrace 331–357 (2004) 207 Palaeoecology Palaeoclimatology, Palaeogeography, / al. et Koch P.L. Coppell N 32j57V,W97j00V 30,000–75,000z Terrace Correlation Fluvial terrace Easley Ranch N 33j 59V,W99j 51V 30,000–75,000z Faunal Correlation Sinkhole/terrace fill Ingleside N 27j52V,W97j12V 30,000–75,000z Faunal Correlation Pond Leo Boatright Pit N 32j07V,W96j00V 30,000–75,000z Terrace Correlation Fluvial terrace Moore Pit N 32j44V,W96j44V 30,000–75,000z Terrace Correlation Fluvial terrace Quitaque Creek N 34j15V, W 100j30V >35,000 Soil organics Fluvial terrace Valley Farms N 32j15V,W96j15V 30,000–75,000z Terrace Correlation Fluvial terrace Waco Mammoth Site N 31j36V,W97j11V 28,000 Not reported Fluvial channel

LGM Congress Avenue N 30j15V,W97j45V 15,970 F 860 Grey-green clay, Area B: sed. organics Pond/Fluvial 17,220 F 1870 Red-brown clay, Area A: sed. organics 18,330 F 1400 No level info Friesenhahn Cave N 29j37V,W98j22V 17,800 F 880 Level 3B: bone Cave 19,600 F 710 Level 3A: bone Howard Ranch N 34j22V,W99j45V 16,775 F 565, 19,098 F 74 shell Sinkhole lake Laubach Cave, N 30j37V,W97j37V 15,850 F 500 Level 1: bone Cave Level 2 23,230 F 430 Level 3: bone Post-LGM Ben Franklin N 33j22V,W95j45V 9500 charcoal Fluvial terrace 11,135 shell Bonfire Shelter N 29j49V, W 101j33V 10,100 F 300, 10,230 F 160 Bone Bed 2: charcoal, Cave Cave without a Name N 29j53V,W98j37V 10,980 F 190 bone, no level info Cave Kincaid Shelter N 29j22V,W99j28V 10,025 F 185 84–90U level: shell Cave 10,065 F 185 90–96U level: shell 10,365 F 110 96–102U level: shell Post-LGM/ Blackwater Draw, NM N 34j14V, W 103j25V Multiple dated levels sed. organics Pond Holocene Schulze Cave N 30j15V,W99j52V 9,310 F 310 Level C2: faunal correlation, bone Cave 9,680 F 700 Level C1: faunal correlation, bone Keller Springs N 32j58V,W96j48V 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). z cal year BP, all other dates in 14CBP. P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 353

Appendix C Appendix C (continued) Taxon Specimen # Pt. d13C d18O Taxon Specimen # Pt. d13C d18O Pre-Last Glacial Maximum sites Ingleside Pre-Last Glacial Maximum sites E. pacificus TMM 30967-1518 frag 0.89 30.27 Clear Creek TMM 30967-1540 r P4 3.36 31.93 Mammuthus SMP-60670 M3 2.40 27.18 TMM 30967-2225 frag 1.44 30.37 SMP-60705 M3 1.12 27.81 TMM 30967-2226 frag 0.84 27.62 Equus SMP-60382ms M 5.88 29.55 x TMM 30967-2229 frag 1.25 30.36 SMP-60531 l P 5.31 30.37 SMP-60827 M 7.09 27.79 Leo Boatright Pit SMP-60840 l M3 4.74 32.28 Bison TMM 30907-13 M1or2 1.86 28.72 TMM 30907-33 M 0.48 29.49 Coppell 1or2 Mammuthus TMM 30907-10 CT 2.77 28.68 Equus SMP-60292 r P 4.32 29.91 3or4 TMM 30907-29 M2or3 3.01 30.35 SMP-60442 l P 4.58 30.57 3or4 TMM 30907-40 M3 2.32 28.58 TMM 30907-79 dP 7.01 26.73 Easely Ranch 2or3 Equus TMM 30907-95 Mx 5.48 31.04 Mammuthus MSU-uncat. CT 0.78 30.18 TMM 30907-114 M 2.67 28.61 Equus MSU-1984 CT 4.06 28.29 MSU-1995 CT 1.26 28.65 Moore Pit MSU-2000 CT 3.13 29.22 Bison SMP-60178 CTx 2.06 27.93 SMP-60608 M 3.39 26.24 Ingleside SMP-60849 P 1.28 28.43 Bison TMM 30967-694 r M 0.72 29.93 x 3 Mammuthus SMP-60345 CT 2.20 29.12 TMM 30967-930 frag 0.42 31.85 SMP-60351 CT 1.29 29.67 TMM 30967-1638 M 1.97 30.12 3 SMP-60844 CT 3.22 28.33 TMM 30967-1662 Mx 0.83 31.76 3 SMP-62287 CT 1.70 29.71 TMM 30967-2473 M 0.72 29.36 SMP-62357 CT 2.70 28.22 TMM 30967-2481 l M3 1.15 30.02 3 SMP-62358 CT 3.91 28.52 Mammuthus TMM 30967-148 M 2.59 29.97 3 SMP-62359 CT 3.59 28.52 TMM 30967-165 M 1.64 30.09 SMP-70153 CT 2.33 29.06 TMM 30967-500 l M1or2 1.35 30.39 x SMP-70161 CT 3.38 27.60 TMM 30967-679 M 0.83 30.11 2or3 Equus SMP-60124 r M2 6.46 28.69 TMM 30967-1214 M 0.95 29.87 1? 2or3 E. midlandensis SMP-60130 r M 3.93 31.98 TMM 30967-1724 M 1.10 29.93 Equus SMP-60188 CT 7.44 30.82 TMM 30967-1787 r M3 1.10 28.03 2 3 SMP-60240 r M 4.96 30.15 TMM 30967-1818 M 2.25 28.55 1or2 SMP-60855 l M 4.93 30.17 E. complicatus TMM 30967-241 M 3.69 34.27 2 TMM 30967-312 P 2.15 29.00 1 Quitaque Creek TMM 30967-379 CT 1.00 28.36 x Equus MSU-2036 CT 1.37 27.05 TMM 30967-948 CTx 2.72 28.27 2 MSU-2819 M3 2.52 27.70 TMM 30967-1642 M 2.78 28.74 TMM 30967-1870 Mx 3.45 30.89 Valley Farms TMM 30967-2230 CTx 1.23 31.31 Bison TMM 31030-2A M 0.01 30.26 TMM 30967-2455 M3 0.74 29.65 x TMM 31030-2B M 1.33 27.96 E. fraternus TMM 30967-36 frag 0.98 28.99 x Mammuthus TMM 31030-3 dP2or3 7.56 26.19 TMM 30967-708 frag 1.79 28.43 x TMM 31030-8 M2 3 3.10 28.04 TMM 30967-974 r M 3.98 28.28 Equus TMM 31030-27 l P 5.86 28.28 TMM 30967-1051A frag 3.34 26.75 3or4 TMM 31030-28 l P 6.89 31.07 TMM 30967-1051B frag 2.76 26.97 3or4 TMM 30967-2223 r P 0.61 26.92 x Waco Mammoth site E. pacificus TMM 30967-242 frag 1.29 30.83 Mammuthus SMNH WACO-B CT 1.75 30.43 TMM 30967-376A frag 1.37 29.10 SMNH WACO-C CT 2.45 29.96 TMM 30967-376B frag 0.69 27.98 SMNH WACO-D CT 2.10 29.88 TMM 30967-376C frag 0.84 29.83 TMM 30967-1487 l P4 0.72 28.97 (continued on next page) 354 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357

Appendix C (continued) Appendix C (continued) Taxon Specimen # Pt. d13C d18O Taxon Specimen # Pt. d13C d18O Pre-Last Glacial Maximum sites Last Glacial Maximum sites Waco Mammoth site Mammuthus TMM 40722-1 CT 3.04 30.22 Mammuthus SMNH WACO-E CT 3.08 29.11 SMNH WACO-F CT 3.28 29.10 Post-Last Glacial Maximum sites SMNH WACO-I CT 2.26 30.44 Ben Franklin SMNH WACO-K CT 2.67 27.98 Mammuthus SMP-61233 CT 3.54 29.96 SMNH WACO-M CT 3.50 30.00 SMP-61244 CT 1.34 29.41 SMNH WACO-N CT 2.01 30.66 SMP-61245 CT 1.27 29.49 SMNH WACO-Q CT 3.29 29.25 Equus SMP-60731 Mx 6.31 28.83 SMNH WACO-12 CT 2.78 30.57 SMP-61236 M 3.51 27.11 SMNH WACO-19 CT 2.33 30.13 SMP-61245 CT 6.05 28.62 SMNH WACO-21 CT 2.17 30.65 SMP-61246 CT 2.89 31.59 SMNH WACO-23 CT 4.58 29.82 Equus no number CT 4.72 30.34 Blackwater Draw Bison TMM 937-907 M2 1.40 28.14 Last Glacial Maximum sites BDM 9789a frag 1.1 25.2 Congress Avenue BDM naa frag 1.3 28.0 a Mammuthus TMM 43067-37 l M2or3 0.98 28.72 BDM na M 0.4 26.2 b Equus TMM 43067-29 l CTx 4.70 29.58 Mammuthus TMM 937-46 M3 8.67 23.78 TMM 43067-62 l CTx 3.88 27.83 TMM 937-126b M 8.64 22.16 SMP-uncatb M 7.20 23.92 Friesenhahn Cave TMM 937-818 M3 0.92 27.83 Bison TMM 933-2198 Mx 2.10 30.98 TMM 937-E6 CT 0.26 29.29 a,b TMM 933-3002 M3 1.10 29.40 BDM #4 frag 0.3 24.2 TMM 933-3285 r M2 2.77 28.51 BDM naa frag 1.9 29.9 a TMM 933-3390 r M3 0.31 28.99 BDM na M 0.8 27.6 a TMM 933-3525 r M1or2 1.98 29.06 BDM na M 1.6 29.8 Mammuthus TMM 933-133 M2or3 5.09 29.93 Equus TMM 937-254 Mx 5.14 27.90 TMM 933-296 M2or3 1.49 30.03 TMM 937-738 CTx 6.32 26.19 TMM 933-358 M2or3 1.36 29.05 TMM 933-928 CT 2.09 29.29 Bonfire Shelter TMM 933-1006 M2or3 0.06 29.87 Bison TMM 40806-37 M2 0.21 28.53 TMM 933-1013 CT 1.19 30.40 TMM 40806-496 M2 0.74 26.26 TMM 933-1309 CT 1.70 28.14 Mammuthus TMM 40806-433 dP4? 2.84 29.50 TMM 933-1505 M3 0.14 30.12 TMM 933-1506 M2or3 1.21 29.17 Cave without a Name TMM 933-1507 M1or2 3.35 29.32 Bison TMM 40450-585 Mx 3.81 28.02 TMM 933-2014 M3 0.02 30.01 TMM 933-2015 M2or3 1.39 28.91 Kincaid Shelter TMM 933-2022 dPorM1 3.87 30.21 Mammuthus TMM 908-2408 CT 1.84 30.07 TMM 933-2243 M2or3 1.05 29.19 Equus TMM 908-2422 r M3 5.44 24.95 TMM 933-2676 CT 1.90 30.11 TMM 908-2436 r M3 2.31 31.02 TMM 933-3407 CT 3.53 31.05 Equus TMM 933 209A Mx 3.89 28.39 Schulze Cave, Level C2 TMM 933-209B r Mx 4.09 28.32 Mammuthus MSU-7391 dP 4.19 29.08 TMM 933-1284 r P4 3.88 28.49 Holocene sites Howard Ranch

Bison MSU-2825 Px 2.79 30.83 Blackwater Draw Equus MSU-1671 CT 7.51 25.49 Bison bison BDM naa frag 0.3 26.8 a MSU-1672 l CTx 0.22 30.31 BDM 9816 frag 1.3 26.4 MSU-2720 l CTx 2.59 29.91 Bison BDM 814Aa frag 0.8 24.8 MSU-3016 CT 3.11 27.98 BDM naa M 1.8 27.3 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 331–357 355

Appendix C (continued) Collatz, G.J., Berry, J.A., Clark, J.S., 1998. Effects of climate and 13 18 atmospheric CO partial pressure on the global distribution of Taxon Specimen # Pt. d C d O 2 C4 grasses: present, past, and future. Oecologia 114, 441–454. Holocene sites Connin, S.L., Betancourt, J., Quade, J., 1998. Late Pleistocene C4 plant dominance and summer rainfall in the southwestern United Blackwater Draw States from isotopic study of herbivore teeth. Quat. Res. 50, a Bison BDM na frag 0.9 25.6 179–193. a BDM na frag 1.9 20.8 Coppedge, B.R., Shaw, J.H., 1998. Diets of bison social groups on a BDM na M 0.9 29.7 tallgrass prairie in Oklahoma. Prairie Nat. 30, 28–36. a BDM na frag 0.8 28.1 Cowling, S.A., Sykes, M.T., 1999. Physiological significance of

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