The effects of late Quaternary climate and pco 2 change on C 4 plant abundance in the south-central United States

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1 Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) The effects of late Quaternary climate and pco 2 change on C 4 plant abundance in the south-central United States Paul L. Koch a, *, Noah S. Diffenbaugh a,1, Kathryn A. Hoppe b,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 C 3 and C 4 vegetation in Texas during this period. The carbon isotope composition of fossil bison, mammoth, and horse tooth enamel is used as a proxy for C 3 versus C 4 plant consumption, and indicates that C 4 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 C 3 C 4 balance in grassland biomes. Estimates of C 4 percentages from coupled climate vegetation models illuminate the relative roles of climate and atmospheric carbon dioxide (CO 2 ) concentrations in shaping the regional C 4 signal. C 4 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 CO 2 concentration on the competitive balance between C 3 and C 4 plants is included in the numerical experiment, however, estimated C 4 percentages show better agreement with isotopic estimates from late Quaternary mammals and soils. This result suggests that low atmospheric CO 2 levels played a role in the observed persistence of C 4 plants throughout the late Quaternary. D 2004 Elsevier B.V. All rights reserved. Keywords: C 3 ;C 4 ; Pleistocene; Holocene; Mammal; Soil; Paleosol; Carbon isotope; Oxygen isotope; Vegetation; GCM; Texas 1. Introduction Today, Texas exhibits strong gradients in climate and vegetation. Climate is humid subtropical in the * Corresponding author. Tel.: addresses: pkoch@es.ucsc.edu (P.L. Koch), ndiffenbaugh@es.ucsc.edu (N.S. Diffenbaugh), khoppe@pangea.stanford.edu (K.A. Hoppe). 1 Tel.: Tel.: east, arid subtropical in the west, and temperate/ 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 /$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi: /j.palaeo

2 332 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 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. 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 precipitation are unavailable, but lake levels, fossil assemb-

3 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 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 CO 2 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, pollen 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 C 3,C 4, 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).C 4 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 C 3 plants. CAM occurs chiefly in succulent plants. Because of differences in their sensitivities to environmental factors, plants using C 3 versus C 4 photosynthetic pathways may have had different geographic ranges in the Quaternary (Ehleringer et al., 1997). C 4 plants have structural and enzymatic adaptations that allow them to concentrate CO 2 at the site of carbon fixation. As a consequence, C 4 plants have greater water use efficiency (WUE) than C 3 plants. That is, photosynthetic carbon gain relative to transpirational water loss is higher in C 4 than in C 3 plants. If this greater efficiency translates to a competitive advantage, C 4 plants should dominate areas or time periods with lower amounts of moisture, and C 3 plants should dominate wetter areas or periods (Polley et al., 1993; Huang et al., 2001). Differences in WUE no doubt contribute to dominance by C 3 trees in areas with substantial rainfall, like eastern Texas. Yet within grasslands, which are at least seasonally dry, recent work has shown that C 4 grass production is positively correlated with mean annual and growing season precipitation (Paruelo and Lauenroth, 1996; Epstein et al., 1997; Yang et al., 1998). C 4 grasses are a greater fraction of biomass in grasslands that are wetter, not drier. C 4 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 C 3 and C 4 plants on grasslands is affected by moisture, but not as expected from simple ideas about differences in WUE. Carbon-concentrating ability also makes C 4 plants less prone to photorespiration, a process in which fixed carbon is oxidized without an energy yield for the plant. Photorespiration rates in C 3 plants rise with temperature, but are low and invariant in C 4 plants. As a result, the quantum yield (i.e., carbon gain per photon absorbed) for C 3 plants drops as temperature rises, but remains constant for C 4 plants (Ehleringer et al., 1997). This temperature sensitivity in yield likely explains why C 4 grasses dominate grasslands with a warm growing season (>22 jc), whereas C 3 grasses dominate where the growing season is cool (Ehleringer, 1978; Paruelo and Lauenroth, 1996; Tieszen et al., 1997). By similar logic, we might expect that C 3 grasses would dominate under cool Pleistocene climates. The situation is complicated, however, because experiments have shown that quantum yield is affected by atmospheric pco 2 as well as temperature. Quantum yield drops with decreasing pco 2 in C 3 plants, but is insensitive to pco 2 changes in C 4 plants (Ehleringer et al., 1997). Thus, lower pco 2 in the Pleistocene would have favored C 4 plants, whereas lower temperatures would have favored C 3 plants. Given these complex interactions, predicting the proportions of C 3 and C 4 plants will require quantitative modeling. Collatz et al. (1998) conducted a global climate vegetation-modeling study of C 4 plant distribution under lower pco 2 with a LGM climate simulated using a general circulation model. The southcentral US was the only area in North America where they simulated a change in %C 4 biomass between the

4 334 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) LGM and today. They modeled a shift from the mixed C 4 C 3 grasslands of today to 100% C 4 grass in the LGM, implying that the effects of lower pco 2 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. C 4 and C 3 grass biomes can be distinguished 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 assessing the photosynthetic physiology of vegetation that is especially important when pollen, phytolith, and macrofossil data are sparse. C 3 and C 4 plants have different stable carbon isotope values (d 13 C 3 ). 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 d 13 C 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; Coppedge 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 (d 18 O). Finally, we compare %C 4 estimates from enamel to those derived from climate vegetation models. 2. Reconstructing paleoenvironments using mammalian isotope values 2.1. Isotopic controls in plants and animals We measured the isotopic composition of carbonate in the mineral hydroxylapatite in tooth enamel (the 3 d 13 C=[(( 13 C/ 12 C sample H 13 C/ 12 C standard ) 1) 1000], and the standard is VPDB. d 18 O values follow the same convention, where ratios are 18 O/ 16 O 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 d 13 C 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 d 13 C value ( F 1 standard deviation) is 27.5 F 3.0xfor C 3 plants and 12.5 F 2.0xfor C 4 plants (Ehleringer and Monson, 1993; Cerling and Harris, 1999). In southern Texas, the current average d 13 C values for C 3 woody plants, C 3 forbs, C 4 grasses, and CAM plants are 26.9 F 0.6x, 29.4 F 0.4x, 14.0 F 0.3x, and 15.6 F 0.2x, respectively (Boutton et al., 1998). We use enamel d 13 C values to estimate the percentage of C 4 plants in the diet (X) with a mass balance equation. ð100þd 13 C sample ¼ð100 X Þd 13 C 100% C3 enamel þðx Þd 13 C 100% C4 enamel ð1þ To obtain d 13 C values for animals on end-member diets, we first estimate the d 13 C values of C 3 and C 4 plants in the past, and then account for the metabolic fractionation between diet and enamel apatite (Table 1). C 3 plants vary by at least 6x in relation to differences in environmental conditions and functional group, whereas differences among C 4 plants are smaller and more related to phylogeny and physiology (Tieszen, 1991; Ehleringer and Monson, 1993). Without d 13 C 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 d 13 C values also vary with shifts in the d 13 C of atmospheric CO 2 (Marino et al., 1992; Leavitt, 1993). These shifts are quantified using d 13 C measurements for CO 2 from ice cores (Table 1). We performed a few simple tests to explore the sensitivity of %C 4 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 fractionation, which would change 100% C 3 and C 4 enamel d 13 C values by the same amount, would lead to a 7% error in the C 4 estimate. Uncertainties about the

5 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) Table 1 pco 2 and d 13 C values for atmosphere, plants and animals used in mass balance calculations and vegetation modeling Late 1990s Holocene Post- LGM LGM/ Pre-LGM Atmospheric pco (ppmv) d 13 C atmospheric CO d 13 CC 3 plants d 13 CC 4 plants d 13 C 100% C 3 enamel d 13 C 100% C 4 enamel All values are in x relative to VPDB. Atmospheric pco 2 values are from Collatz et al. (1998) and Monnin et al. (2001). d 13 C values for atmospheric CO 2 values are from Leuenberger et al. (1992) and Indermuhle et al. (1999). Past values for C 3 and C 4 plants are estimated by adding the difference between modern and past atmospheric CO 2 to modern plant isotope values. Past values for C 3 and C 4 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. d 13 C values for C 3 and C 4 plants in the past are harder to assess, because they may lead to nonuniform shifts in %C 4 estimates due to changes in the spacing between end-member d 13 C values. As a quick check on this uncertainty, we recalculated %C 4 estimates assuming end-member d 13 C values for modern C 3 and C 4 plants from the study of Boutton et al. (1998) on southern Texas plants (i.e., C 3 plants, 28x; C 4 plants, 14x). Recalculated %C 4 estimates differed from the values reported here by 3% to 5%. These simple tests suggest that the %C 4 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 d 18 O value of apatite is controlled by the d 18 O values 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 d 18 O values. Ingested water d 18 O values, in turn, co-vary with climate. Meteoric water d 18 O 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 18 O-enriched relative to meteoric water by evaporation and evapo-transpiration, respectively. On longtime scales, the d 18 O value of meteoric water may shift with changes in climate and/or vapor source area. We use d 18 O values to evaluate mixing of individuals from different geographic areas (via migration) or different time periods (due to taphonomic processes). During tooth formation, individuals experience different climates and physiological states, generating d 18 O variability within populations. Using a large collection of teeth from a deer population, Clementz and Koch (2001) showed that enamel d 18 O 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 d 18 O 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 Fossil materials Locations, ages, and other data for fossil localities are supplied in Appendix B. For temporal comparisons, we parse sites into four temporal bins: before the Last Glacial Maximum (Pre-LGM, 70 to C ky), 5 LGM (25 to C ky), Pleistocene after the LGM (Post-LGM, 15 to C ky), and Holocene (10 to 0 14 C ky). LGM, Post-LGM, and Holocene sites are constrained by 14 C ages. Age constraints for Pre-LGM sites are weaker. The few Pre-LGM sites 4 We have added samples and species since publication of Bocherens et al. (1996). The d 18 O 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. 5 When discussing time, we will use units of 1000 radiocarbon years before present ( 14 C ky) or 1000 calendar years before present (cal ky). Conversions for key dates are: 29 cal ky = C ky; 21 cal ky = C ky; 17 cal ky = C ky; 14 cal ky = C ky; 12 cal ky = Cky(Kitagawa and van der Plicht, 1998; Fiedel, 1999).

6 336 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) Table 2 Isotope data for each taxon organized by site and age Species/locality n d 13 C F 1r d 18 O F 1r %C4 F 1r Pre-Last Glacial Maximum sites Clear Creek Fauna Mammuthus columbi F F F 4 Equus sp F F F 7 Coppell Equus sp F F F 1 Easely Ranch Mammuthus columbi Equus sp F F F 9 Ingleside Bison sp F F F 7 Mammuthus columbi F F F 4 Equus fraternus F F F 9 Equus pacificus F F F 5 Equus complicatus F F F 7 Leo Boatright Pit Bison sp F F F 5 Mammuthus columbi F F F 15 Equus sp F F F 9 Moore pit Bison sp F F F 7 Mammuthus columbi F F F 6 Equus sp F F F 9 Quitaque Creek Equus sp F F F 4 Valley Farms Bison sp F F F 5 Mammuthus columbi F F F 15 Equus sp F F F 3 Waco Mammoth Site Mammuthus columbi F F F 5 Equus sp Last Glacial Maximum sites Congress Avenue Mammuthus columbi Equus sp F F F 3 Friesenhahn Cave Bison sp F F F 8 Mammuthus columbi F F F 9 Equus sp F F F 1 Howard Ranch Bison sp >100 Table 2 (continued) Species/locality n d 13 C F 1r d 18 O F 1r %C4 F 1r Last Glacial Maximum sites Howard Ranch Equus sp F F F 21 Laubach Cave, Level 2 Mammuthus columbi Post-Last Glacial Maximum sites Ben Franklin Mammuthus columbi F F F 9 Equus sp F F F 11 Blackwater Draw Bison sp Bison sp. a F F F 8 Mammuthus columbi (l) F F F 2 Mammuthus columbi (m) F F F 5 Mammuthus columbi a F F F 7 Equus sp F F F 4 Bonfire Shelter Bison sp F F F 3 Mammuthus columbi Cave Without a Name Bison sp Kincaid Shelter Mammuthus columbi Equus sp F F F 11 Schulze Cave, Level C2 Mammuthus columbi Holocene sites Blackwater Draw Bison sp. a F F F 9 Keller Springs Bison sp Schulze Cave, Level C1 Bison sp 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 14 C 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

7 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) in southern Texas, is aged biostratigraphically (Lundelius, 1972a). Average d 13 C and d 18 O 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 identified 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 specimens are only identified to genus in museum collections 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 Museum (University of Texas, Austin), Shuler Museum of Paleontology (Southern Methodist University), Department of Biology at Midwestern State University, and Strecker Museum of Natural History (Baylor University) Isotopic methods Prior to sampling, the outer layer of enamel and adhering dentin or cementum were removed by grinding. 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 enamel 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 CO 2 generated by reaction were separated cryogenically. Reaction time for each sample was >10 min. The 1r value for 97 laboratory calcite standards (Carrera Marble) included with these samples was < 0.1x for d 13 C and d 18 O. 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 d 13 C and 0.2x for d 18 O. 3. Numerical reconstruction of vegetation cover We coupled climate and vegetation models to reconstruct the balance between C 3 and C 4 vegetation on three time planes: 0, 14 and 21 cal ky (equal to 0, 12 and C ky). Climate fields for the three periods were constructed from a combination of observed and simulated data. For 0 14 C ky, we used the modern observed record of New et al. (2000) (archived at which is a global, gridded dataset (0.5j latitude 0.5j longitude) generated from climate station normals for 1931 to For 12 and C ky, we constructed climate fields using the climate model simulations of Kutzbach et al. (1996) (archived at gov/paleo/paleo.html). These simulations were generated using the National Center for Atmospheric Research Community Climate Model (CCM1) with a mixed layer ocean at R15 resolution ( f 4.4j latitude 7.5j longitude) (Wright et al., 1993; Kutzbach 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 simulations 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 14 C ky experiments and between the 18 and 0 14 C ky experiments were added to the New et al. (2000) modern data set, yielding the 12 and C ky climate fields that were used to estimate %C 4 biomass.

8 338 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) We used two methods to estimate %C 4 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 variables that was developed from 73 sites in central North America (Paruelo and Lauenroth, 1996). The regression for %C 4 grass biomass is: %C 4 ¼ 0:9837 þ 0:000594ðMAPÞ þ 1:3528ðJJA=MAPÞþ0:2710ðlnMATÞ ð2þ where lnmat is the natural logarithm of mean annual temperature, MAP is mean annual precipitation, and JJA/MAP is the fraction of mean annual precipitation falling in the summer (June, July, August). At most of the sites, the functional types not explained by this regression are C 3 plants (i.e., forbs, shrubs, C 3 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 d 13 C value to C 4 plants, their presence may lead to erroneously high estimates of %C 4 biomass from mammalian isotopic data relative to the regression method. The regression method relates %C 4 biomass to climate, but not pco 2. To explore the effects of late Quaternary changes in pco 2 on the C 3 C 4 balance, we adapted the approach of Collatz et al. (1998), who calculated crossover temperature (the mean monthly temperature at which C 4 grasses would fix carbon faster than C 3 grasses) as a function of pco 2. To facilitate comparison with %C 4 estimates from mammalian data, which integrate the d 13 C of vegetation from the growing season, we calculated the percentage of growing season months in which C 4 grasses would be favored over C 3 grasses as our measure of %C 4 biomass. We refer to this approach to estimating %C 4 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 available at monthly resolution, we developed a relationship 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: ncdc.noaa.gov/oa/documentlibrary/freezefrost/frostfreemaps.html) with maps of mean monthly temperature (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 C 3 C 4 crossover temperature for each time period. Following Collatz et al. (1998), we set pco 2 = 350 ppmv today and pco 2 = 200 ppmv at C ky, yielding crossover temperatures of 22 and 11 jc, respectively. We set pco 2 = 235 ppmv at Cky(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 changing temperature and pco 2 on different types of plants. Third, at grid point we evaluate whether each growing season month is dominated by C 3 or C 4 plants. Competitive superiority is assessed solely on the basis of expected differences in photosynthetic rate under different climatic and atmospheric conditions, ignoring other potential competitive and environmental factors. To qualify as a C 4 month, the mean monthly temperature must exceed the crossover temperature. Like Collatz et al. (1998), we impose an added constraint for a C 4 month, that mean monthly precipitation is >25 mm. All other growing season months are C 3 months, either because they are too cold or too dry. For each grid point, dividing the number of C 4 growing season months by the total number of growing season months gives the estimated %C 4 biomass. We must note one feature of the mechanistic method. Based on modern data, we set the 15 jc criterion for defining a growing season month. This value is above the C 3 C 4 crossover temperature at 18 and 12, but not at 0 14 C ky. Consequently, differences in %C 4 between the modern and Pleistocene cases may be due to changes in crossover temperature, monthly temperature, or monthly precipitation. When comparing the 18 and C ky results, however,

9 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) differences can only result from changes in simulated moisture, because the mechanistic method will consider any month that is warm enough to have plant growth as dominated by C 4 plants. 4. Results of isotopic analysis 4.1. Testing for temporal or spatial averaging at fossil localities d 18 O standard deviations for taxa at a site are typically < 1.5x(Table 2). At sites with z 5 individuals 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 (28.9 F 1.1x) and four have low values (23.5 F 0.9x). d 13 C values differ strongly between these groups and are positively correlated with d 18 O values (R = 0.72). Positive correlation would be expected if the sample contains a mix of individuals from warm regions or time periods (represented by high values) and individuals from cool regions or times (represented by low values). For the mammoths measured here, Hoppe (2004) tested for spatial versus temporal mixing using 87 Sr/ 86 Sr ratios. 87 Sr/ 86 Sr ratios in herbivores track differences in soil available Sr, which in turn are controlled by bedrock geology and atmospheric deposition (Hoppe et al., 1999). Mammoths with low d 18 O and d 13 C values have higher 87 Sr/ 86 Sr 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 d 18 O value (24.2x) and is excluded, though it had a high d 13 C value similar to non-migratory individuals. Holocene bison from Blackwater Draw also show high d 18 O variability (1r =2.6x), and a positive correlation between d 13 C and d 18 O 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. d 18 O 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 d 18 O and d 13 C 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, d 18 O variability offers little support for the hypothesis that these sites are subject to strong temporal 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 Temporal and spatial trends in isotopic values Temporally binned means ( F 1r) for the three taxa are presented in Table 3. ANOVA does not reveal significant differences in mean d 13 C values among time periods for bison ( F 3,32 = 0.85, p = 0.48). Differences in means for mammoths and horses are more pronounced, but still not significant at the p V 0.05 level ( F 2,66 = 2.50, p = 0.09 and F 2,59 = 2.36, p = 0.10, respectively). Inspection suggests that low d 13 C values 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 d 13 C values here are substantially higher than values at other sites (Table 2). Excluding Ingleside, the Pre-LGM mean for horses is 4.6 F 1.7x, and differences among time periods are no longer significant ( F 2,35 = 1.01, p = 0.38). 6 Differences in mean d 18 O values among time periods are not significant for mammoths ( F 2,66 = 1.35, p = 0.26) or horses ( F 2,59 = 2.69, p = 0.08). Mean bison d 18 O values, in contrast, differ significantly among the four time periods ( F 3,32 = 8.79, p = ). Post hoc tests (Scheffé s method) show that Holocene bison d 18 O values are significantly lower than values for Pre- LGM and LGM bison. 6 Note that while three different species of Equus co-occur at Ingleside, their mean d 13 C values are statistically indistinguishable ( F 2,21 = 2.41, p = 0.11), whereas their mean d 18 O values do differ significantly ( F 2,21 = 4.55, p = 0.02) due to the low values for E. fraternus (Table 2).

10 340 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) Table 3 Summary of isotopic data for each genus binned by age Bison sp. Mammuthus sp. Equus sp. Pre-LGM Number of specimens Number of sites Mean d 13 C F 1r 1.0 F F F 2.0 Mean d 18 O F 1r 29.4 F F F 1.6 LGM Number of specimens Number of sites Mean d 13 C F 1r 0.8 F F F 2.0 Mean d 18 O F 1r 29.6 F F F 1.4 Post-LGM Number of specimens Number of sites Mean d 13 C F 1r 0.2 F F F 1.6 Mean d 18 O F 1r 27.2 F F F 2.3 Holocene Number of specimens 10 N.A. N.A. Number of sites 3 N.A. N.A. Mean d 13 C F 1r 0.1 F 1.3 N.A. N.A. Mean d 18 O F 1r 26.2 F 2.3 N.A. N.A. Total Mean d 13 C F 1r 0.6 F F F 2.0 Mean d 18 O F 1r 28.1 F F F 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 ( p V 0.05) between d 13 C and latitude is for horses, with lower values at higher latitudes (Fig. 2C), but this relationship collapses if specimens from Ingleside are omitted. The relationship between d 13 C and longitude is significant for bison (Fig. 2D) and mammoths (Fig. 2E), but not for horses (with or without Ingleside). In all cases, d 13 C values increase to the west. Inspection of Fig. 2 reveals strong isotopic overlap among individuals in each region, irrespective of their age, both where there are significant spatial gradients (e.g., Fig. 2D and E) and where gradients are lacking (e.g., Fig. 2A, B and F). There are significant relationships between d 18 O and latitude for bison ( p = , R 2 =0.32) and mammoths ( p = 0.009, R 2 = 0.10), but not horses ( p = 0.61, R 2 < 0.01). There are significant relationships between d 18 O and longitude for bison ( p =0.0004, R 2 = 0.31) and horses ( p = 0.002, R 2 = 0.14), but not mammoths ( p = 0.67, R 2 < 0.01). Where significant trends exist, values decrease from east to west or from south to north by f 3x. Yet low R 2 values indicate that even these significant isotopic gradients are variable. Plots of d 18 O values vs. latitude and longitude (not shown) reveal no strong temporal differences within regions Differences among taxa Inspection of Tables 2 and 3 reveals differences in mean d 13 C and d 18 O values among taxa. For the following analysis, we include horse d 13 C data from Ingleside; the described pattern is stronger if these data are excluded. For d 13 C, Bison>Mammuthus> Equus, whereas for d 18 O, Bison < Mammuthus c Equus (Table 3). ANOVA reveals that differences in mean values among taxa are highly significant (for d 13 C, F 2,164 = 33.30, p <10 12 ; for d 18 O, F 2,164 = 7.22, p < 0.001). Post hoc comparison (Scheffé s method) shows that all pairwise differences are significant for d 13 C. For d 18 O, 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 d 13 C values are, on average, 1.9 F 1.6xhigher than mammoth values and 4.2 F 1.9xhigher than horse values. There are 12 sites where mammoth and horse co-occur; mammoth d 13 C values are, on average, 2.4 F 1.2xhigher than horse values. A similar within-site analysis of d 18 O values reveals no significant differences between taxa Summary of isotopic results from fossil mammals (1) d 18 O gradients of f 3x occur across the region, with lower values in northern/western areas. Data from MacFadden et al. (1999a) and

11 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) Fig. 2. Plots of d 13 C 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 (R 2 ) are supplied for linear regression of d 13 C on latitude or longitude. For horses, regressions were calculated both with and without data from Ingleside.

12 342 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) Hoppe (2004) suggest these d 18 O gradients continue at higher latitudes. (2) Despite the presence of these gradients, populations at most localities show low d 18 O variability, suggesting they have not experienced strong spatial mixing. This situation is violated Blackwater Draw, which includes migratory mammoths 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 d 13 C values are significant, and generally suggest that d 13 C values are higher to the west. (4) There is no compelling evidence for large temporal shifts in d 13 Cord 18 O 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 d 13 C value among taxa, with Bison>Mammuthus>Equus. Because extant bison are grazers, fossil bison provide the most reliable evidence regarding the d 13 C of herbaceous vegetation. While the lower average d 13 C value for mammoths indicates they typically consumed a greater fraction of C 3 plants than bison, at some sites mammoths yield %C 4 estimates that are similar to bison. In contrast, horse d 13 C values are almost always much lower than those for bison, suggesting that horses were consistently eating a large fraction of C 3 plants in settings where grazing bison were consuming almost entirely C 4 diets. Pleistocene horses were not obligate grazers, but rather had more diverse diets that contained a mix of C 3 trees and shrubs, as well as the largely C 4 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 d 13 C values from horses to quantify the C 3 C 4 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 C 3 versus C 4 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 %C 4 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 C 4 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 %C 4 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 Cky, decreasing from 20 jc in the south to 10 jc in the north (Fig. 3E). Simulated MAP is lower too, dropping from 1100 to 200 mm/year from east to west (Fig. 3F). Simulated JJA/MAP is lower across the entire region at C 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 %C 4 have lower values at C ky than today, C 4 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 C 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 Cky(Fig. 3J). Simulated JJA/MAP shows enhanced meridional variability at C ky, increasing from 4% to 56%, south to north (Fig. 3K). Simulated JJA/MAP is higher in NW Texas and New Mexico at Cky than today or at C ky. C 4 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 C 4 grasses generated using the mechanistic method (Fig. 4B, D, and F). Today, the number of growing

13 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) C kyr C kyr C kyr 35 N MAT ( C) 30 N A E I 25 N MAP (mm/yr) 35 N 30 N B F J 25 N JJA/MAP (fraction) C G K 35 N 30 N 25 N C 4 Grass (fraction) D H L 35 N 30 N 25 N 105 W 100 W 95 W 105 W 100 W 95 W 105 W 100 W 95 W Fig. 3. Climate fields used to estimate C 4 grass biomass by the regression method and resulting biomass estimates for 0, 12, and Cky. Climate data and resulting %C 4 estimates are on a j grid, and are contoured using the NCAR Command Language (NCL) gsn_csm_contour_map_ce function ( Estimated grass fractions are shown only for non-forest points, as defined by Ramankutty and Foley (1999). Forested grid cells are shaded gray.

14 344 Fig. 4. Number of growing season months (A, C, and E) and fraction of growing season months dominated by C 4 grass (B, D, and F) estimated using the mechanistic method for 0 14 C kyr (A B), C kyr (C D) and C kyr (E F). Colored stars show C 4 biomass fractions observed in modern grasslands (Paruelo and Lauenroth, 1996). Colored squares and circles show C 4 biomass fractions estimated from d 13 C values of fossil mammoths and bison, respectively. For isotopic estimates of C 4 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)

15 P.L. Koch et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 207 (2004) 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 C 4 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 %C 4 biomass at sites in the study by Paruelo and Lauenroth (1996). In most of central and southern Texas, the mechanistic method yields %C 4 biomass estimates within 10% to 20% of observations. At a site in central Texas, the method over-estimates C 4 biomass by 30%. The low %C 4 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 C 3 (i.e., shrub) biomass, probably due to grazing (Boutton 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 C 4 vegetation by 20% to 30%. Overall, however, the method yields %C 4 estimates in reasonable agreement with observational data. The simulated growing season at C ky drops from 9 to 4 months from south to north, though 5 to 7 months characterizes most of the region (Fig. 4C). C 4 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 C 3 dominated. %C 4 biomass rises in these regions in the C ky case because of the lower crossover temperature, which causes every growing season month to be warm enough for C 4 plants, and because simulated moisture is sufficient through most of the growing season. In central Texas, %C 4 biomass drops because the C 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 C ky drops from 8 to 2 months from south to north, though 4 to 6 months typifies most of the region (Fig. 4E). Estimated C 4 biomass is lower than at 0 or C 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 Panhandle, C 4 estimates at C ky are lower than at C ky because some growing season months are too dry for C 4 plants, but higher than at 0 14 Ckydueto loss of cool C 3 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 C 4 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 C 4 versus C 3 plants, the region may have consistently had greater than f 45% C 4 vegetation. The persistence of biomes with substantial C 4 biomass in the face of late Quaternary climate change is surprising and merits further verification. As a step toward verification, we compare estimates of %C 4 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 C 3 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 d 13 C values spanning the Pleistocene Holocene boundary in our study area. Holliday (1997,