AlateMiocenesubtropical-dry£orafromthenorthern Altiplano...

A late Miocene subtropical-dry £ora from the northernAltiplano, Bolivia

Kathryn M. Gregory-WodzickiLamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964-8000, USA

Received 18 October 2000; accepted 24 September 2001

Abstract

A variety of evidence suggests that the Altiplano of the Central Andes, the second highest and largest plateau onearth, underwent significant uplift in the late Miocene^Pliocene. The most important datum supporting recent uplift isa collection of the 10.660 0.06 Ma Jakokkota flora from west-central Bolivia, which implies a paleoelevation no morethan 16000 1200 m; today the site has an elevation of almost 4000 m. In order to test the reliability of this estimate,the present study analyzes a new collection of the Jakokkota flora from a lacustrine unit that is 0.2^0.5 Myr youngerthan the previously analyzed collection from a fluvial unit. Climate estimates based on leaf morphology for the twocollections are statistically indistinguishable; the combined flora has a mean annual temperature of 21.50 2.0‡C and amean annual precipitation of 5500 180 mm. The similarity of the climate estimates for the two floras suggests thatthere was not a significant climate change between them, nor a significant bias in the leaf morphology due to differingtaphonomic processes. The climate estimate for the combined flora thus presents a representative picture of the lateMiocene climate of the northern Altiplano. If one assumes that the climate of the tropics has not changed significantlysince the late Miocene, as is suggested by marine isotopic data, then the paleoclimate of the Jakokkota flora implies apaleoelevation of 11600 600 m. Thus, the Jakokkota flora supports the hypothesis of a young age for theAltiplano. 5 2002 Elsevier Science B.V. All rights reserved.

Keywords: Central Andes; Miocene; leaves; paleoclimatology; Andean Orogeny

1. Introduction

The Altiplano, which is the second highest andlargest plateau on earth and forms the heart ofthe Central Andes, is perhaps a very young fea-ture; a variety of evidence suggests that morethan half its modern elevation of 3700 m wascreated after the middle Miocene. For example,

erosion rates and the deposition of evaporites sug-gest that the Central Andes began to in£uencerainfall patterns by about 15 Ma, and fossil £orasand erosion surface remnants suggest that the Al-tiplano was at elevations of no more than V1500m as recently as 10 Mya (Gregory-Wodzicki,2000a).The age of the Altiplano is of interest for sev-

eral reasons. First of all, the Altiplano has a ma-jor in£uence on regional climate, and perhaps onglobal climate. It anchors the location of thesouth paci¢c subtropical anticyclone, strengthens

0031-0182 / 02 / $ ^ see front matter 5 2002 Elsevier Science B.V. All rights reserved.PII: S 0 0 3 1 - 0 1 8 2 ( 0 1 ) 0 0 4 3 4 - 5

E-mail address: [email protected](K.M. Gregory-Wodzicki).

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Palaeogeography, Palaeoclimatology, Palaeoecology 180 (2002) 331^348

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the austral summer anticyclone over Bolivia, andenhances convective rainfall in the Central Andes(Meehl, 1992; Lenters and Cook, 1995, 1997).Determining the uplift history of the Altiplanois thus critical to understanding the evolution ofthe South American climate and biota. Secondly,the Altiplano is tectonically a rather enigmaticfeature: a plateau formed at a non-collisionalmargin. De¢ning the timing of uplift can helpconstrain the dynamic processes responsible forits formation.As of now, the most important piece of evi-

dence for the young-Altiplano hypothesis is the10.660 0.06 Ma Jakokkota £ora from west-cen-tral Bolivia (Fig. 1). This £ora is the most pre-cisely dated Miocene £ora from the Central An-des, and was deposited during the most recentphase of Andean orogeny. An analysis of theleaf morphology of the £ora suggests that thesite, which today has a mean annual temperature(MAT) of 8.3‡C, was signi¢cantly warmer in thepast, with a paleoMAT of 18.6^21.00 2.5‡C. Ifone assumes that the climate of the tropics haschanged little since the late Miocene, as suggestedby marine isotope data, the paleotemperatureimplies a paleoelevation of 590^16100 1200 m(Gregory-Wodzicki et al., 1998; Gregory-Wod-zicki, 2000a), which is signi¢cantly lower thanthe modern elevation of 3940 m.But does this estimate present a representative

picture of the late Miocene climate and elevationof the Altiplano? Collected from a £uvial horizonless than 25 cm thick, the Jakokkota £ora is therecord of probably no more than 10 000 yr.Though short-term climate variation before theice ages was perhaps of a smaller magnitudethan we observe during the ice ages, it probablystill was signi¢cant; for example, marine isotopedata suggest that in the Pliocene short-term tem-perature £uctuations were on the order of 1.5^4‡C(King, 1996). Thus it is possible that this collec-tion of the Jakokkota £ora may not typify the lateMiocene paleoenvironment.This study attempts to provide a more charac-

teristic estimate of late Miocene climate by ana-lyzing a sample from a new horizon at the Jakok-kota locality, a lacustrine unit found 10 m abovethe previously collected £uvial horizon. Analysis

of the £ora of this younger horizon, here calledthe upper Jakokkota £ora, can improve ourunderstanding of late Miocene climate in twoways. First of all, by providing a larger sampleof the Jakokkota £ora, it reduces the error of theclimate estimate; several authors have shown thatthe accuracy of climate estimates based on leafmorphology increases with the increasing numberof species in a sample (Wolfe, 1971; Povey et al.,1994; Wilf, 1997; Burnham et al., 2001). Sec-ondly, by sampling a di¡erent time horizon anddepositional environment, the new collection willprovide some measure of short-term climate var-iation and will provide some constraints on errordue to di¡erent taphonomic processes.In this study, the climate of the upper Jakok-

kota £ora is analyzed using the method of Wolfe(1993). First, the leaf morphology of the upperJakokkota £ora is scored, and then these scoresare input into models that relate leaf morphologyto MAT and mean annual precipitation. At thismoment, it is di⁄cult to determine which climateleaf morphology models provide the most accu-rate estimates of climate for Bolivian paleo£oras,so the assumption is made that the most accurateresults will be from those models that provide themost accurate climate estimates for modern vege-tation from Bolivia. The climate estimates for theupper Jakokkota £ora are then compared to thosefrom a similar analysis of the lower Jakokkota£ora. The climate of the combined £ora is thenused to calculate the paleoelevation at which itgrew.

2. Geology and age of the Jakokkota £ora

The Jakokkota £ora is located in the northernAltiplano of Bolivia (Fig. 1), in member 6 of theMiocene Mauri Formation (Sirvas and Torres,1966; Suarez Soruco and Diaz Martinez, 1996).Leaf impressions are found in two di¡erent units.The lower Jakokkota £ora, which was discoveredby Berry (1922b) and further described by Greg-ory-Wodzicki et al. (1998), is found in a white,ash-rich claystone to ¢ne-grained sandstone, in-terpreted to be a £uvially reworked ash deposit(Fig. 2).

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K.M. Gregory-Wodzicki / Palaeogeography, Palaeoclimatology, Palaeoecology 180 (2002) 331^348332

The upper Jakokkota £ora is found in an 8 mthick sequence of reddish-tan to green laminatedsandstones, siltstones, and mudstones that weredeposited 10 m above the lower Jakokkota £ora(Fig. 2). Individual laminae range from 1 mm to3 cm in thickness, and grade from siltstone tocoarse sandstone at the base to clay at the top.The laminated bedding and graded layers suggestthat these deposits are lacustrine.A fall ash located 3 m above the lower £ora has

an age of 10.660 0.06 Ma, based on single-crystallaser fusion analysis of sanidine, while a fall ashjust below the upper Jakokkota £ora has less pre-cise ages of 11.35 0 0.69 Ma on biotite and12.740 0.69 Ma on hornblende (Gregory-Wod-zicki et al., 1998) (Fig. 2). A fall ash 29 m abovethe upper Jakokkota £ora has ages of 10.810 0.72

Ma (biotite) and 11.310 0.53 Ma (hornblende)(Fig. 2). The low precision of the biotite andhornblende ages probably re£ects alteration;these minerals alter much more quickly than sa-nidine. Thus the 10.660 0.06 Ma age is consideredthe most accurate and reliable age derived fromthe ash falls.The upper Jakokkota £ora is probably not sig-

ni¢cantly younger than the lower Jakokkota £ora.The only physical sign of a depositional hiatus, anunconformity just above the lower Jakokkota£ora (Fig. 2), is associated with £ame structures,which suggest soft-sediment loading and thus onlya minor time lapse. If the biotite and hornblendeages of the two fall ashes are compared, they sug-gest an average sedimentation rate of around20^50 m/Ma. This would suggest that the 10 m

Fig. 1. (A) Relief map of the Central Andes (USGS 30 arc-second DEM data as processed by the Cornell Andes Project) show-ing Miocene fossil £oras: 1, Los Litres; 2, Chucal; 3, Potos|¤; 4, Goterones and Boca Pupuya; 5, Jakokkota; 6, Pislepampa.(B) Morphotectonic provinces of the Central Andes, showing the location of the Altiplano and Eastern Cordillera (after Jordanet al., 1983). The heavy line indicates the location of the continental divide.

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K.M. Gregory-Wodzicki / Palaeogeography, Palaeoclimatology, Palaeoecology 180 (2002) 331^348 333

di¡erence between the lower and upper £oraswould represent approximately 0.2^0.5 Ma.

3. Materials and methods

3.1. Collection and description

Fossil leaves and leaf fragments were collectedfrom two locations in the lacustrine facies, with acombined volume of approximately 1 m2. Whendry, these sediments are characterized by perva-sive crackle fracturing. In order to preserve thecollected fossils, most specimens were painted onthe base and sides with a clear plastic coating.Once in the lab, the specimens were photographedand split into morphospecies based on venationcharacteristics. Identi¢cations were made usingcomparisons to modern herbarium material.The morphology of the leaves in each morpho-

species was scored after the method of Wolfe(1993). In order to be consistent with the scorefor the lower Jakokkota £ora, leaves that werevery close in size to the next larger size categorywere scored in both categories in order to com-pensate for the size reduction observed betweencanopy and litter samples (Greenwood, 1992;Gregory and McIntosh, 1996). The morphospe-cies scores were summed and divided by the totalnumber of morphospecies to derive a site score.Further details about the scoring method are giv-en in Table 1 and in Wolfe (1993).

3.2. Estimating paleoclimate

The climate of the upper Jakokkota £ora wascalculated using models based on relationshipsobserved in modern vegetation between leaf mor-phology and climate, such as the increase in thepercentage entire-margined species with increasingtemperature and the increase in leaf size with in-creasing precipitation (Fig. 3). A large number of

Fig. 2. Generalized stratigraphic column of fossil locality,showing the lower and upper Jakokkota £oras (leaf sym-bols). Radiometric ages from Gregory-Wodzicki et al. (1998).

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such models exist, which vary both in terms of thedata set and statistical method used, and there hasbeen much discussion in the literature aboutwhich provide the most accurate climate estimates(Wolfe, 1995; Jordan, 1997; Stranks and Eng-land, 1997; Wilf, 1997; Wilf et al., 1998, 1999;Wolfe and Uemura, 1999; Gregory-Wodzicki,

2000b; Kowalski, 2001; Greenwood et al., in re-view; Jacobs, in review).Choosing an appropriate predictor data set is

of primary importance, because the closer its re-lationships between leaf morphology and climateto those of the site to be analyzed, the more ac-curate the climate estimates will be (Gregory-

Table 1Morphologic character state scores for the lower, upper, and combined Jakokkota £oras

LMCS Lower J. score Upper J. score Comb(%) (%)

1. TLob 1.6 0.0 1.02. NoT 71.0 77.3 73.93. TRg 17.7 15.9 16.34. TCl 8.1 11.4 8.75. TRnd 16.1 11.4 14.15. TAct 12.9 11.4 12.06. TCmp 0.0 0.0 0.07. Size: Nan 7.6 8.3 7.5

Le1 14.1 14.6 13.9Le2 33.3 38.9 37.8Mi1 39.1 32.6 36.1Mi2 6.0 5.6 4.7Mi3 0.0 0.0 0.0Me1 0.0 0.0 0.0Me2 0.0 0.0 0.0Me3 0.0 0.0 0.0

8. AEmg 4.0 11.8 5.69. Apex: ARnd 54.0 70.6 59.7

AAct 46.0 29.4 40.3AAtn 0.0 0.0 0.0

10. Base: BCd 1.2 2.3 1.9BRnd 28.0 68.2 42.8BAct 70.8 29.5 55.3

11. L:W ratio: LW6 1:1 0.0 2.3 1.1L:W 1^2:1 21.1 25.0 21.4L:W 2^3:1 40.6 20.5 36.2L:W 3^4:1 20.6 22.7 22.1L:W s 4:1 17.8 27.3 19.2

12. Shape: SOb 19.4 11.6 17.0SElp 64.4 79.0 69.1SOv 16.1 9.4 13.8

Numbers in LMCS (leaf morphology character state column) denote categories. Some categories have only two character states,for example ‘lobed’ and ‘not lobed’ are in the category ‘lobed’. For simplicity, only one character state is usually reported. ‘teethacute’ and ‘teeth round’ are an exception, as both are reported. Other categories, such as size, contain several character statesand all are reported. Quanti¢cation of physiognomic score for given leaf form: (1) if present, character state receives score of1 divided by number of character states present for form in that category; (2) if absent, character scored 0; (3) if partly present,scored as 0.5 divided by number of character states in category. Form scores then added for each character state and divided bytotal number of forms to derive physiognomic score. See Wolfe (1993) for further details of scoring and de¢nitions. Abbrevia-tions: TLob, teeth lobed, NoT, no teeth; TRg, teeth regularly spaced; TCl, teeth closely spaced; TRnd, teeth round; TAct, teethacute; TCmp, teeth compound; Le1,2, leptophyllous 1,2; Mi1,2,3, microphyllous 1,2,3; Me1,2, mesophyllous 1,2; AEmg, apexemarginate; ARnd, apex round; AAct, apex acute; AAtn, apex attenuate; BCd, base cordate; BRnd, base round; BAct, baseacute; L:W, length to width ratio; Sob, shape obovate; SElp, shape elliptical; SOv, shape ovate.

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Fig. 3. (A) Mean annual temperature vs. percentage entire-margined species for di¡erent data sets: Bolivia+Peru (composed ofthe Bolivian sites from Gregory-Wodzicki, 2000b and two samples from Bolivia and Peru from Wilf, 1997); East Asia (datafrom caliper measurements by D.R. Greenwood of data published in Wolfe, 1979); CLAMP 3B; subalpine sites from CLAMP3A; and Africa. See Table 2 for sources of data. Regression lines: Solid line, Bolivia+East Asia; dashed line, CLAMP 3B.(B) Mean annual precipitation vs. percentage species with Mesophyllous 1+Mesophyllous size 2 leaves for di¡erent data sets: Bo-livia, CLAMP 3B mid-latitude sites (s 24‡ latitude), CLAMP 3B ^ tropical (6 24‡ latitude), and Africa. See Table 2 for datasources. Regression lines: Solid line, Bolivia+Africa; dashed line, CLAMP 3B ^ tropical.

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Wodzicki, 2000b; Jacobs, in review). The maindata sets that have been collected are listed inTable 2. The most extensive is the Climate LeafAnalysis Multivariate Program (CLAMP) dataset of Wolfe (1993, 1995), which includes 173 sitesmostly from North America and Asia, scored for31 di¡erent leaf morphology character states.Other data sets contain sites from equatorial Afri-ca, Ecuador, East Asia, New Zealand/Australia,and Bolivia.Several studies suggest that the relationships

between leaf morphology and climate are not uni-versal, but vary from region to region (Wolfe,1993; Stranks and England, 1997; Jacobs, in re-view). For example, subalpine vegetation appearsto have a di¡erent relationship between leaf mar-gin and temperature than vegetation from warmerclimates (Fig. 3). The three major leaf morphol-ogy domains identi¢ed so far are 1: North Amer-ica, Caribbean, Japan, Bolivia, 2: Australia andNew Zealand, and 3: subalpine zones (Wolfe,1993; Kennedy, 1998; Gregory-Wodzicki,2000b). Jacobs (in review) and Kowalski (2001)

suggest that the ¢rst domain should be furtherdivided into tropical and mid-latitude subsets;they ¢nd that the CLAMP data set, which hassites predominately from mid-latitudes, is inap-propriate for estimation of temperature and pre-cipitation for sites from equatorial Africa andequatorial South America.Choosing an appropriate data set for the Ja-

kokkota £ora is complicated by the fact that wedo not yet understand why there are di¡erentcorrelations between climate and leaf morphologyin these di¡erent domains; the di¡erences ob-served could be due to di¡erences in the climate,environmental conditions, £oristic composition,or scoring style. For example, the di¡erencesthat Kowalski (2001) observes between CLAMPsamples and her samples from Ecuador could bedue to the in£uence of cold season temperatureson the higher latitude CLAMP sites (Jacobs, inreview), or could be due to di¡erent samplingstrategies; Kowalski (2001) sampled herbariumspecimens that were mostly from trees s 5 cmdiameter at breast height, while CLAMP samples

Table 2Leaf morphology data sets in the literature

Data set N Character states Sampling strategy Source

CLAMP 3Aa 173 31 of Wolfe CLAMP Wolfe, 1993, 1995CLAMP 3Ba 144 31 of Wolfe CLAMP Wolfe, 1993, 1995Western Hemisphere, Africa 50 MlnA primarily data from £oral

manuals, primarily all woodydicots

Wilf et al., 1998

East Asia 34 NoT ^ Wolfe, 1979Equatorial Africa 30 15 of Jacobs, 31 of Wolfe herbarium samples, primarily

all woody dicots, thoughsome samples lack lianas

Jacobs, 1999; Jacobs, inreview

New Zealand 30 31 of Wolfe CLAMP Stranks and England, 1997;Kennedy, 1998

Ecuador 30 29 of Wolfe herbarium samples, primarilyonly trees s 5 cm dbh

Kowalski, 2001

SE Australia, New Zealand 13 31 of Wolfe CLAMP Jordan, 1997Bolivia 12 31 of Wolfe CLAMP Gregory-Wodzicki, 2000bAustralia 8 NoT leaf litter Greenwood, 1992Western Hemisphere 7 NoT live foliage, all woody dicots Wilf, 1997

Abbreviations: N, number of sites; MlnA, mean of the natural log leaf area; NoT, no teeth (entire-margined).a CLAMP refers to the Climate Leaf Analysis Multivariate Program data set of Wolfe (1993, 1995). CLAMP 3A refers to the

most recent version of the data set, which has percent occurrence data for 31 di¡erent leaf morphology character states from 173sites. Most CLAMP 3A sites are from North America and Japan, with some sites from Caribbean and South Paci¢c islands.CLAMP 3B is a subset of CLAMP 3A that excludes 29 subalpine sites; these sites are known to be outliers (Wolfe, 1993). Inthe CLAMP sampling strategy, live foliage from at least 30 species of woody dicotyledons is collected from an area of 1^5 hec-tares, generally in a riparian zone. See Wolfe (1993) for more details.

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are from living foliage from all woody dicotyle-dons from a limited area. For now, it is probablybest to compare the fossil Bolivian £oras withdata sets that provide the most accurate climateestimates for a collection of 12 modern foliagesamples from Bolivia, namely the Bolivia andequatorial Africa data sets (Gregory-Wodzicki,2000b; Jacobs, in review).After one has chosen an appropriate predictor

data set for a fossil £ora, then one must choose atype of statistical analysis. Methods that havebeen proposed include: linear regression analysis(Wolfe, 1971; Wilf, 1997; Wilf et al., 1998), multi-variate regression analysis (Wing and Greenwood,1993; Gregory and McIntosh, 1996; Wiemann etal., 1998; Jacobs, 1999), canonical correspon-dence analysis (Wolfe, 1995), and nearest-neigh-bor analysis (Stranks and England, 1997). A studyby Gregory-Wodzicki (2000b) suggests that multi-ple regression analysis tends to produce the mostaccurate estimates for small data sets with a nar-row range of environmental variation that havesimilar relationships to the £ora to be analyzed,and linear regression or canonical correspondenceanalyses produce the most accurate estimateswhen using the larger and more varied CLAMPdata set. If a similar predictor data set is notavailable, then nearest-neighbor analysis can stillproduce accurate paleoclimate estimates.Wilf (1997) advocates linear regression analysis,

arguing that the percent entire-margined speciescharacter state explains most of the variation intemperature, and the size character state explainsmost of the variation in precipitation, thus theadditional character states in the CLAMP dataset only add noise. While it is indeed true that

these character states explain a large part of thevariation, studies by both Gregory-Wodzicki(2000b) and Jacobs (1999; in review) show thatadditional character states can improve results.More study is needed on which character statesprovide useful information.For the paleoclimatic analysis of the Jakokkota

£oras, this study uses the database/statisticalmethod combinations that produced the most ac-curate estimates for 12 modern foliage samplesfrom Bolivia (Gregory-Wodzicki, 2000b). Thesemodels are listed in Table 3, and include a multi-ple regression analysis based on the Bolivia data-base for MAT, a multiple regression analysisbased on the Bolivia database plus the Africa da-tabase of Jacobs (in review) for mean annual pre-cipitation, and a linear regression based on theBolivian database for mean growing season pre-cipitation.

3.3. Estimating paleoelevation from paleoclimate

Because of the cooling of the earth’s atmo-sphere with increasing elevation, one way to esti-mate the paleoelevation of a fossil £ora locality isto compare its mean annual temperature (MAT)to the MAT at sea level, and then apply a terres-trial lapse rate. Another approach uses variationsin moist enthalpy, a climatic variable that is afunction of temperature and relative and speci¢chumidity (Forest et al., 1995, 1999). However, thislatter method has only been veri¢ed for NorthAmerica, so this study will use the temperature-based paleoaltimeter.To estimate the paleoelevation of the Jakokko-

ta £ora using this method, one needs to correct

Table 3Equations used to estimate the paleoclimate of the Jakokkota £ora

Method Data set Equation F SE r2

Mean annual temperature (MAT)MRA Bolivia 0.306(NoT)30.15(Mi1)+4.42 239 0.7 98Mean annual precipitation (MAP)LRA Bolivia+Africa exp(6.302+1.354(Me1+2)) 188 180 82Mean growing season precipitation (MGSP)LRA Bolivia 3.79(Me1)+58.2 22 160 66

Models listed produced the most accurate estimates for modern Bolivian £oras of Gregory-Wodzicki (2000b). Method: LRA, lin-ear regression analysis; MRA, multiple regression analysis. Equation: See Table 1 for abbreviations of character states.

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the sea level MAT for factors such as global cli-mate change and continental drift that could havecaused the temperature to change since the lateMiocene. One way to do this is to calculate sealevel MAT from a coastal paleo£ora that is coevalto the Jakokkota £ora, after the method of Ax-elrod and Bailey (1976), Meyer (1992) and Wolfe(1992):

Zp ¼MATpc3MATpi

Qþ Sp ð1Þ

where Zp = paleoelevation (m); MATpc = paleomean annual temperature at sea level (‡C);MATpi = paleoMAT from a coeval inland site(‡C); Q= ‘the empirical relationship betweenmean annual temperature at the surface and alti-tude’ (Forest et al., 1995), equivalent to the ter-restrial lapse rate of Wolfe (1992) and Meyer(1992) and Sp = paleo sea level relative to modernsea level (m). However, this method cannot beused for the Jakokkota £ora, because there areno early late Miocene sea level £oras from theCentral Andes that have been analyzed in termsof their quantitative paleoclimate.

Alternatively, one corrects the sea level MATby adjusting the modern sea level MAT for anychanges in temperature since the late Mioceneafter the following equation:

Zp ¼ðMATmc3vMATgc3vMATcdÞ3MATpi

Qþ Sp

ð2Þ

where MATmc = the modern sea level MAT, ob-served or projected (‡C); and vMATgc,vMATcd = the change in MAT for the sea levelsite since deposition of the fossil £ora due to glob-al climate change and latitudinal continental drift,respectively (‡C).Gregory-Wodzicki et al. (1998) modi¢ed Eq. 1

in a slightly di¡erent way; instead of correctingthe modern sea level MAT for post-Jakokkotaclimate change, they corrected the modern MATat the Jakokkota site. But the above equation iseasier to use, because values for Q, the terrestriallapse rate, are typically calculated in reference tocoastal or low-elevation sites (see below).The global climate change term (vMATgc) for

the late Miocene to the present is apparently rel-

Fig. 4. Elevation vs. mean annual temperature (MAT) for modern climate stations in the Altiplano region of the Central Andes(15^24‡S), projected to the latitude of the Jakokkota £ora (17‡10P). Circles represent climate stations from the Altiplano, EasternCordillera, and eastern lowlands, while triangles represent sites on the west coast. The projected sea level temperature is calcu-lated by solving the regression equation for an elevation of 0 m. MATs calculated from climate data from Vose et al., 1992.

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atively minor; marine isotope data for tropicallatitudes suggest that there has been either a cool-ing or warming on the order of 1‡C in sea surfacetemperatures since the late Miocene (Savin, 1977;Savin et al., 1985; Savin and Woodru¡, 1990). Asfor the continental drift term (vMATcd), platetectonic reconstructions of Smith et al. (1981) sug-gest that the Central Andean area was V2‡ lat-itude further south 10 Mya (Smith et al., 1981).Marine stable isotope data suggest that the lateMiocene latitudinal gradient was about 3/4 of themodern-day gradient (Loutit et al., 1983). Thus ifwe take the modern temperature gradient in theeastern lowlands of Bolivia and Argentina of0.44‡C/‡ latitude (Gregory-Wodzicki et al.,1998), and reduce this by 3/4 to 0.33‡C/‡ latitudeto simulate the Miocene, the area would havewarmed 0.7‡C since the Jakokkota £ora was de-posited 10 Ma. According to the eustatic curvesof Haq et al. (1987), sea level at 10.7 Ma (Sp) wasabout 50 m higher than today.Meyer (1992) and Wolfe (1992) have compiled

terrestrial lapse rates, with the intent of providingan average value for Q for use in Eq. 1. Meyer(1992) found a mean value of 5.9 0 1.1‡C/km forclimate stations from 39 areas of high topo-graphic relief from around the world, while Wolfe(1992) found a signi¢cantly lower average value of

3.0‡C/km for hundreds of climate stations fromthe western US.This large di¡erence is due to the di¡erent

methodologies used in these compilations for cal-culating terrestrial lapse rate. Meyer (1992) per-formed linear regressions between stations in arestricted geographical area, preferably an areawith high relief, while Wolfe (1992) comparedthe MAT of each inland climate station to theMAT of the west coast of the US at that samelatitude. MATs along the west coast are lowerthan would be expected for their latitude becauseof the upwelling of cold water o¡shore. Thus,comparing these unusually cool coastal sites tothe inland sites produced lower terrestrial lapserates than the linear regressions of Meyer (1992).Meyer (1992) observed a standard error for his

database of 1.1‡C/km. A large part of this varia-tion is due to topography. In a column of free air,the average moist adiabatic lapse rate is 6.0‡C/km. For an isolated peak, surface air mixes withthe surrounding air, and thus temperatures for agiven elevation tend to be fairly similar to those inthe column of free air, though with variation dueto albedo, local topography, and the nature andsource of the air mass (Meyer, 1992). However,the temperature for a given elevation from abroad area of high elevation tends to be higher

Table 4Terrestrial lapse rates for di¡erent physiographies

Region Elevation range TLR Source(m) (‡C/km)

Sea level vs. elevated base levelTexas^New Mexico 2^2652 5.3 Axelrod and Bailey, 1976Western US 2^3460 3.0a Wolfe, 1993Eastern Mexico NR 4.7 Meyer, 1992Eastern Bolivia^Altiplano 135^4071 4.4 this studyBrazil 40^2195 5.5 Axelrod and Bailey, 1976Tibetan Plateau SL^4000 1.4^2.9b Wolfe, 1993Elevated base level vs. isolated peaksArizona (eastern) 670^2438 7.6 Meyer, 1992Arizona (Grand Canyon) 759^2560 6.8 Meyer, 1992New Mexico (northern) 1705^2484 7.8 Meyer, 1992New Mexico (southern) 1277^2652 7.9 Meyer, 1992Utah 1423^2652 6.8 Meyer, 1992Colorado (southwestern) 1958^2841 8.1 Meyer, 1992

TLR, terrestrial lapse rate.a This value is low because inland sites were compared to coastal sites a¡ected by upwelling.b These sites are from river valleys, which tend to have lower lapse rates than more open areas.

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than that in a column of free air, because theelevated surface acts as a heat source.Thus, if lowland sites are compared with up-

land sites from an elevated base level, then lapserates tend to be low. For example, comparing theeastern lowlands of Bolivia with the Altiplanosuggests a lapse rate of 4.4‡C/km, and comparinglowland sites in eastern Mexico with sites fromthe elevated interior gives a lapse rate of 4.7‡C/km (Meyer, 1992) (Fig. 4, Table 4). On the otherhand, comparing lowland sites from an elevatedbase level with highland sites from isolated peaksgives high terrestrial lapse rates. For example,lapse rates for the Colorado Plateau, RockyMountain area of the Western US have lapserates between 6.8 and 8.1‡C/km (Table 4).In terms of choosing a terrestrial lapse rate for

Eq. 2, it is probably most appropriate to use themodern lapse rate of 4.4‡C/km between the east-ern lowlands of Bolivia and the Altiplano. Theconstant of this regression represents the MATat 0 m elevation, or the ‘projected sea level tem-perature’ of Meyer, 1992, and can be used to rep-resent sea level MAT in Eq. 2.Using the average lapse rate of Meyer of

5.9 0 1.1‡C/km would not be appropriate, as thisvalue is calculated for lowland areas of elevatedbase level compared to highland areas from iso-lated peaks. The Jakokkota £ora did not grow onan isolated peak; it grew in an area of low relief.Therefore, the terrestrial lapse rate for this regionhas probably been lower than Meyer’s averagevalue ever since the Altiplano was elevated abovesea level. Wolfe (1994) observes that 500^1000 mof elevation is enough to create the elevated baselevel e¡ect.Another approach for choosing a lapse rate for

Eq. 2 would be to use the modern terrestrial lapserate between the west coast of South America andthe Altiplano. However, like the sites from thewest coast of the US, sites along the west coastof South America are unusually cool for theirlatitude because of the cold-water Humboldt cur-rent (Fig. 4). Using this comparison would neces-sitate correcting for the evolution of ocean circu-lation in the southeastern Paci¢c. Thus, itprobably involves less error to use the terrestriallapse rate for the eastern slope of the Andes.

It is probably reasonable to assume that lapserates have not changed signi¢cantly through time,at least since the Altiplano attained between 500and 1000 m of elevation. For example, Wolfe etal. (1997) analyzed the paleoelevation of 14 mid-dle and late Miocene £oras from California andNevada using the enthalpy method of Forest et al.(1995, 1999), which does not rely on lapse rate,and found an average error of 0.66‡C/km betweenmodern lapse rates and Miocene lapse rates. Thiserror is surprisingly low considering that duringthis period the Great Basin collapsed to abouthalf its former elevation.Formal errors for the elevation calculation are

a combination of the errors for the MAT estimatefor the Jakokkota £ora, the estimates of MATchange due to global climate change and conti-nental drift, and the terrestrial lapse rate. Forthe present day, the lapse rate term probablyhas an error of around 0.5‡C/km, based on thestandard deviation for the lapse rates from areasof elevated base level in Table 4. However, theerror of 0.7‡C/km observed by Wolfe et al.(1997) for the Miocene of the western US is prob-ably more appropriate when using this equationfor the Miocene.

4. Results

4.1. Floristic composition and leaf morphology

The fossil leaf impressions from the upper Ja-kokkota £ora were split into 24 morphospecies(Fig. 5). The most abundant form was Anacardia-ceae sp. 1, which made up almost half of thecollected specimens (Table 5). This same formwas also quite common in the lower Jakokkota£ora, making up almost a 10th of the fossil forms.A second species of Anacardiaceae, this onetoothed, was less common. Today, this family ismostly found in the tropics and subtropics (Kil-leen et al., 1993).A species of Berberis is present in the upper

Jakokkota £ora, although it appears to be a dif-ferent morphospecies than the Berberis encoun-tered in the lower Jakokkota £ora. Today, thereare 28 species in Bolivia, which are found in mon-

PALAEO 2793 21-5-02


tane forest and cloud forest, ranging from 1700 to3800 m (Killeen et al., 1993).At least ¢ve legumes are present, as evidenced

by the striated pulvinuses on the fossil leaves. Le-gumes were also common in the lower Jakokkota£ora, though only one form, form 6, appears to beshared by both £oras. This family is common inthe dry subtropical forests of eastern Bolivia. OneMyrtaceae is present; this is an important familyin subtropical environments in South America.

Zizyphus sp. was another common form. To-day, there are four species in Bolivia; they range

from 250 to 850 m, and are found both in xericand humid forests.The specimens of Polylepis sp. were very poorly

preserved, but the teeth were identical to betterpreserved specimens in the lower Jakokkota £ora.Today this genus is found from elevations be-tween 1700 and 5200 m, in xeric to humid envi-ronments. In total, seven of the 24 morphospeciesin the upper Jakokkota £ora were also encoun-tered in the lower Jakokkota £ora (Table 5).The leaf morphologic score for the upper Ja-

kokkota £ora is given in Table 1, along with the

Fig. 5. Leaf morphospecies from the upper Jakokkota £ora: (a) Polylepis sp. ; (b) Form 18; (c) Leguminosae (Form 6); (d) Legu-minosae (Form 62); (e), (f) Zizyphus sp.; (g) Form 8; (h) Anacardiaceae sp. 1; (i) Form 25; (j) Form 48; (k) Form 51; (l) Form54; (m) Form 55; (n) Form 58; (o) Leguminosae (Form 59); (p) Anacardiaceae sp. 2; (q), (r) Leguminosae (Form 57); (s) Form53; (t) Form 52; (u) Leguminosae (Form 61); (v) Myrtaceae; (w) Form 68; (x) Form 64; (y), (z) Berberis sp. ; (aa) Leguminosae(Form 60).

PALAEO 2793 21-5-02


score for the 32 morphospecies of the lower Ja-kokkota £ora and the 48 morphospecies of thecombined £ora. The score of the lower Jakokkota£ora is slightly modi¢ed from that given in Greg-ory-Wodzicki et al. (1998); one fragmentaryspecimen that was not considered a separate mor-phospecies was reclassi¢ed based on the discoveryof numerous similar material in the upper Jakok-

kota £ora. In morphologic space, the fossil £orasare most similar to modern microphyllous scrubfrom the dry interandean valleys of Bolivia.The scores for margin characteristics vary

somewhat between the two levels ; the percentageof entire-margined species is about 6% higher inthe upper Jakokkota £ora than in the lower. Ingeneral, the size distribution of the two £oras issimilar; both have mostly small leaves, with nonelarger than the microphyllous 2 category, thoughthe average size of the upper Jakokkota £ora isslightly smaller. The distribution of length towidth ratios is di¡erent, with the upper Jakokkota£ora having more species in the equant and elon-gate categories than the lower Jakokkota £ora,which is dominated by leaves in the L:W 2^3:1category.The most notable di¡erences occur in the apex

and base categories. The lower Jakokkota £ora isdominated by species with acute bases, and hassubequal numbers of species with acute androunded apices. On the other hand, the upperJakokkota £ora is dominated by species withround bases and round apices.

4.2. Climate analysis

When the leaf morphologic scores for the Ja-kokkota £oras are plugged into the regressionmodels in Table 3, the results consistently suggesta subtropical-dry climate (Table 6). MAT esti-mates range from 20.1 0 0.7‡C for the lower £orato 23.0 0 0.7‡C for the upper £ora. The combined£ora has a MAT of 21.5 0 0.7‡C. These MATs areconsiderably warmer than the modern MAT of8.3‡C for the Charan‹a station, which is 60 kmto the southwest of Cerro Jakokkota (Vose etal., 1992).The formal error given for these estimates is

probably too low, due to the small size of the

Table 6Climate estimates for the lower, upper, and combined Jakokkota £oras

Climate variable Lower Upper Combined

MAT (‡C) 20.10 2.5 23.00 2.7 21.50 2.0MAP (mm) 5500 180 5500 180 5500 180MGSP (mm) 5800 160 5800 160 5800 160

Estimates calculated using the equations listed in Table 3.

Table 5Floristic composition of the upper Jakokkota £ora

Form # sp. %

Anacardiaceaesp. 1a 313 47.1sp. 2 3 0.5BerberidaceaeBerberis sp. 29 4.4LeguminosaeLegume (6)a 77 11.6Legume (57) 49 7.4Legume (59) 31 4.7Legume (60) 7 1.1Legume (62) 18 2.7Myrtaceae undet. 1 0.2RhamnaceaeZizyphus sp.a 33 5.0RosaceaePolylepis sp.a 20 3.0Incertae cedisForm 8a 2 0.3Form 18a 3 0.5Form 25a 18 2.7Form 48a 5 0.8Form 51 7 1.1Form 52 15 2.3Form 53 3 0.5Form 54 13 2.0Form 55 1 0.2Form 58 1 0.2Form 61 5 0.8Form 64 9 1.4Form 68 1 0.2

sp., number of specimens.a Also present in lower Jakokkota £ora.

PALAEO 2793 21-5-02


modern Bolivia data set; as more samples areadded to the database, the error will probablyincrease. Wilf (1997) shows that for samples ofless than about 75 species, the error associatedwith using a small sample of fossil morphospeciesto represent a large £ora is greater than the aver-age formal error, and is thus a better approxima-tion of the actual error. Using the equation ofWilf (1997), the sampling error for the lower Ja-kokkota £ora with 32 species is 2.5‡C, for theupper Jakokkota £ora with 24 species is 2.7‡C,and for the combined £ora with 48 species is2.0‡C (Table 6). Given these errors, the MATsestimated for the di¡erent £oras are statisticallyindistinguishable.Estimates of the mean annual precipitation

(MAP) and mean growing season precipitationsuggest a xeric climate. MAP is estimated as5500 180 mm and the mean growing season pre-cipitation as 5800 160 mm for the upper, lower,and combined £oras (Table 6). The estimates forthese variables are the same for all of the £orasbecause the linear models rely on the mesophyl-lous 1 and 2 leaf size categories, and none of the£oras has leaves of these sizes. The estimates ofmean growing season precipitation are probably agood approximation of the MAP, because thegrowing season for this variable is de¢ned byWolfe (1993) as the number of months with amean monthly temperature s 10‡C. The warmpaleotemperatures of the Jakokkota £ora suggestthat the growing season, so de¢ned, was year-round. Today, the MAP of the Charan‹a stationis around 300 mm and the mean growing seasonprecipitation is 150 mm, with a marked dry sea-son during the winter (Vose et al., 1992).

The assumption that the Jakokkota £ora wouldhave had relationships between leaf morphologyand climate most similar to the modern Bolivian£oras is supported by the fact that the nearestneighbors to the Jakokkota £oras in morphologicspace, as calculated by canonical correspondenceanalysis, are Bolivian samples.

4.3. Elevation analysis

When the MAT of the combined £ora alongwith the values for global climate change, conti-nental drift, sea level change, and terrestrial lapserate discussed above are plugged into Eq. 2, apaleoelevation of 11600 600 m is calculated (Ta-ble 7). This paleoelevation is considerably lowerthan the modern elevation of the Jakokkota siteof 3940 m. Because this paleoelevation is abovethe 500^1000 m needed to produce an elevatedbase level e¡ect, it is probably reasonable to usethe modern terrestrial lapse rate in the calcula-tion.This paleoelevation estimate for the combined

Jakokkota £ora is comparable to the paleoeleva-tion estimated by Gregory-Wodzicki et al. (1998)for the lower Jakokkota £ora of 590^16100 1200m. The error is less for the new estimate for thecombined £ora because of more accurate esti-mates of MAT and terrestrial lapse rate.

5. Discussion

5.1. Implications of climate analysis

The climate estimates for the upper and lower

Table 7Elevation estimate for the combined Jakokkota £ora

Factor Value

Modern MAT at coast (projected) (‡C) 27.1MAT change due to global climate change (‡C) 00 1MAT change due to continental drift (‡C) 0.7Jakokkota paleoMAT (‡C) 21.50 2.0Late Miocene sea level (m) 50Terrestrial lapse rate (‡C/km) 4.4 0 0.7Paleoelevation (m) 11600 600a

a Error calculated using the quotient variance equation.

PALAEO 2793 21-5-02


Jakokkota £oras are statistically indistinguish-able, and suggest that both £oras grew under asubtropical-dry climate. The small di¡erences inthese estimates are due to the morphological var-iation between the two £oras. Namely, the upper£ora has a higher percentage of entire-marginedspecies, which translates into a higher paleotem-perature.There are two factors besides climate change

that could explain the di¡erences in leaf morphol-ogy observed between the two £oras. Firstly, somevariation could be due to sampling error. Severalstudies show that the smaller the vegetation sam-ple, the greater the error of the temperature esti-mate (Wolfe, 1971; Povey et al., 1994; Wilf, 1997;Burnham et al., 2001); most authors suggest thatat least 20^30 species are needed to obtain accu-rate climate estimates. The upper Jakokkota £orais on the small side, with only 24 species, and only22 of these have margin information. Thus errorsin the morphologic score could be large. Note,however, that the combined £ora with 48 speciesis a statistically robust sample.Another factor that could cause the morpho-

logic scores of the two £oras to vary is taphono-my. The two di¡erent levels of the Jakokkota£ora represent di¡erent paleoenvironments; thelower Jakokkota £ora was deposited in a low-energy stream, while the upper Jakokkota £orawas deposited in a strati¢ed lake. Studies byGreenwood (1992) and Roth and Dilcher (1978)show that depositional processes have an impor-

tant a¡ect on leaf size; they found that averageleaf size drops both with increasing distancefrom the shore in lake deposits, and with increas-ing amounts of transportation in stream depos-its.Less is known about the e¡ects of taphonomic

processes on other leaf morphology characterstates. Burnham et al. (2001) show that the per-cent entire-margined species tends to be lower inriparian versus terra ¢rme vegetation, but bothJakokkota £oras represent riparian vegetation,so this is unlikely to be a factor.Given these sources of error, and the fact that

the di¡erences in climate estimates between thetwo £oras are within the sampling error, it wouldbe an overinterpretation to suggest that a signi¢-cant climate change occurred between upper andlower Jakokkota time. Thus, the climate estimatefor the combined £ora of MAT=21.5 0 2.0‡C andMAP=5500 180 mm should present a represen-tative picture of latest middle Miocene climate forthe northern Altiplano.This temperature estimate is similar to temper-

ature estimates for other Miocene £oras from theCentral Andes (Table 8), which, with the excep-tion of the Chucal £ora of northwestern Chile andGoterones £ora of coastal Chile, are all estimatedto have been subtropical or tropical. Together,these £oras suggest that a large portion of whatis now the Central Andes was covered by subtrop-ical-dry forest. Today, the Altiplano is covered bypuna, that is, alpine tundra.

Table 8Paleoclimate and paleoelevation estimates for Miocene fossil £oras from the Central Andes

Flora Lat. Long. Age Method MAT MAP PaleoZ Refs(Ma) (‡C) (mm) (m)

1. Los Litres 33.30 70.55 21.2^26.6 M subtrop. 700^800 ^ 12. Chucal 18.9 68.9 25^19 A temperate dry 10000 1500 2, 33. Potos|¤ 19.61 65.74 20.8^13.8 M 21.60 2.1 5000 400 0^13200 1200 4, 54. Goterones 33.96 71.87 19^10 M 15.50 2.0 1500+ 0 1, 6, 74. Boca Pupuya 33.96 71.87 19^10 M 21.70 2.0 humid 0 1, 6, 75. Jakokkota 17.17 69.17 10.660 0.06 M 21.50 2.5 5500 180 550^16000 1200 4, 8, 96. Pislepampa 17.18 66.03 7^6 A, M V20 1000^1500 1200^1400 10, 11

Numbers denote the location of the £oras on Fig. 1. Method, method used to estimate paleoclimate and paleoelevation; A, mod-ern analog; M, leaf morphology. MAT, mean annual temperature; MAP, mean annual precipitation; PaleoZ, paleoelevation.Refs, references: 1, Hinojosa and Villagran, 1997; 2, Charrier et al., 1994; 3, Mun‹oz and Charrier, 1996; 4, Gregory-Wodzickiet al., 1998; 5, Berry, 1939; 6, Troncoso, 1991; 7, Hinojosa, 2000, in preparation; 8, Berry, 1922b; 9, this study; 10, Berry,1922a; 11, Graham et al. (in press).

PALAEO 2793 21-5-02


The interpretation of an arid climate in thenorthern Altiplano in the late Miocene is consis-tent with the accumulation of evaporite depositsin the southern Altiplano Puna of Argentinastarting at 15 Ma, which is thought to mark theonset of the modern dry climate regime (Vander-voort et al., 1995). The higher amounts of rainfallestimated for the Pislepampa £ora from the east-ernmost slopes of the Eastern Cordillera as com-pared to the other £oras from further west sug-gests the presence of gradient in rainfall from eastto west, and thus a rain shadow in the Altiplanoarea by at least the late Miocene.

5.2. Implications of elevation analysis

The warmth of the late Miocene Jakokkota siteas compared with the modern cool climate regimecan not be explained by global climate change orcontinental drift, and thus is most likely due to asigni¢cantly lower elevation than at present. Thelapse rate-based calculation used in this studyestimates a paleoelevation of 11600 600 m, whichis almost 2800 m lower than the modern eleva-tion.This paleoelevation is consistent with paleoele-

vation estimates of other paleo£oras from the Al-tiplano and Eastern Cordillera (Table 8). The 21Ma Chucal £ora from the Chilean Altiplano hasan estimated paleoelevation of 10000 1500 m,while the early^middle Miocene Potos|¤ and 6^7Ma Pislepampa £oras from the Eastern Cordillerahave paleoelevation estimates of 0^13200 1200and 1200^14000 1000 m, respectively (Fig. 1, Ta-ble 8) (Mun‹oz and Charrier, 1996; Gregory-Wod-zicki, 2000a; Graham et al., in press). The paleo-elevation of the Jakokkota £ora is also consistentwith paleoelevations estimated using other paleo-altimeters; Kennan et al. (1997) estimated thatremnants of a 10 Ma erosion surface that capsthe Eastern Cordillera formed at an elevation of1000^1500 m.If these data are correct, they imply that the

Altiplano and Eastern Cordillera underwent sig-ni¢cant amounts of uplift since the late Miocene.The Jakokkota £ora suggests that on the order of2/3 of the modern elevation of the Altiplano wascreated since the early late Miocene, and the ero-

sion surfaces and the Pislepampa £ora suggestthat on the order of 1/3^1/2 of the modernelevation of the Eastern Cordillera was createdsince the late late Miocene (Graham et al., inpress).Such signi¢cant amounts of recent uplift would

make the Central Andes the youngest of the ma-jor world orogens; the Western Cordillera ofNorth America is thought to be Eocene^Creta-ceous in age (Chase et al., 1998; Wolfe et al.,1998), while the Himalayas and southern TibetanPlateau appear to have attained their modern el-evations by at least 10 Ma (Garzione et al., 2000;Rowley et al., 2001).

6. Conclusions

The analysis of the upper Jakokkota £ora sug-gests the following conclusions:

1. The upper Jakokkota £ora is probably notmore than 0.2^0.5 Ma older than the 10.66 0 0.06Ma lower Jakokkota £ora.2. There was not a signi¢cant climate change

between the two levels of the Jakokkota £ora;leaf morphologic analysis suggests a mean annualtemperature of 20.1 0 2.5‡C and a mean annualprecipitation of 5500 180 mm for the lower Ja-kokkota £ora, and 23.0 0 2.7‡C and 5500 180mm for the upper Jakokkota £ora.3. The paleoclimate estimate for the combined

Jakokkota £ora is MAT=21.5 0 2.0‡C andMAP=5500 180 mm. Given the apparent lackof climate change and taphonomic bias betweenthe two levels, this estimate is likely a good rep-resentation of the late Miocene climate of thenorthern Altiplano.4. Comparison with other Miocene fossil £oras

suggests that subtropical-dry forest covered alarge portion of the Altiplano. The area is nowcovered by alpine tundra.5. Using a lapse rate-based calculation, the

paleoelevation of the Jakokkota £ora was11600 600 m. This estimate implies that on theorder of 2/3 of the modern elevation of the Al-tiplano was created since the early late Mio-cene.

PALAEO 2793 21-5-02


Acknowledgements

The author was supported by U.S. NationalScience Foundation grant EAR-99-09114. Manythanks to J. Argollo for assistance with relocatingthe Jakokkota site and for use of laboratoryfacilities at the Universidad Mayor de San Andres,to A. Auza for assistance in the field, to M. Neefor assistance in identifying the fossil forms, and toD.R. Greenwood and J.A. Wolfe for reviewswhich significantly improved the manuscript. Thisis Lamont-Doherty Earth Observatory contribu-tion number 6319.

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