Bremond et al 2012-GCB-Neotropical C3-C4 grass distributions

Neotropical C3/C4 grass distributions – present, past andfutureLAURENT BREMOND* † , ARNOUD BOOM ‡ and CHARLY FAVIER§

*Paleoenvironments and Chronoecology, Ecole Pratique des Hautes Etudes, Institut de Botanique, 163 rue Broussonet, 34090

Montpellier, France, †Centre for Bio-Archaeology and Ecology, Universite Montpellier 2, CNRS Institut de Botanique, 163 rue

Broussonet, 34090 Montpellier, France, ‡Department of Geography, University of Leicester, University Road, Leicester, LE1 7RH,

UK, §Institut des Sciences de l’Evolution, Universite Montpellier 2, CNRS, IRD, place Eugene Bataillon, 34095 Montpellier cedex

5, France

Abstract

Changes in C4 grass distribution and abundance are frequently observed in Quaternary, Holocene and future envi-

ronmental-change scenarios. However, the factors driving these dynamics are not fully understood, and conflicting

theories have been reported. In this paper, we present a very large dataset of modern altitudinal distribution profiles

of C3 and C4 grasses covering the entire Neotropical Andes, which was compared with actual climate data. The

results of multivariate analysis demonstrate that, in the Neotropical Andes, mean annual temperature is the main fac-

tor governing the modern altitudinal distribution of C3 and C4 grass species. The C3 and C4 grass distributions were

compared with simulations based on the Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM), which

allowed the present grass distribution to be estimated. Finally, the DGVM was employed to simulate past and future

scenarios, using the IPCC’s climate projections for 2100 and PMIP2 models for the Holocene Optimum (HO,

6000 years BP) and the Last Glacial Maximum (LGM, 21 000 years BP). The results were found to be significantly dif-

ferent from those obtained using a simple photosynthetic model. According to LPJ forced with the PMIP2 models for

the LGM, during the LGM, the C4 grasses would not have reached higher altitudes than found in the present day.

Keywords: altitudinal gradient, Andes, C4 grass distribution, grass dynamics

Received 14 December 2011 and accepted 2 February 2012

Introduction

Global and regional distributions of C3 and C4 grasses

follow consistent geographical patterns: C4 grasses pref-

erentially occupy low latitudes and altitudes, resulting

in geographical crossovers between grasslands domi-

nated by C3 grasses and C4 grasses (Sage & Monson,

1999). This has been explained by a greater efficiency of

C4 photosynthesis at high light intensities, high temper-

atures, low stomatal conductance and low atmospheric

CO2 concentration (Ehleringer, 1978). These factors

mean C4 plants have a competitive advantage in open,

warm and dry environments, and during periods with

reduced atmospheric CO2 (e.g. glacial periods). The rel-

ative importance of these environmental drivers is still

actively debated, also in the context of altitudinal grass

distribution in tropical elevation gradients. However,

compared with latitudinal gradients, altitudinal gradi-

ents are geographically circumscribed within one

climatic and biogeographical region (Korner, 2000).

All studies have demonstrated a strong decline in the

richness and biomass of C4 species with increasing

altitude: for example, Livingstone & Clayton (1980) in

Africa, Chazdon (1978) in Costa Rica, Tieszen et al.

(1979) and Young & Young (1983) in Mount Kenya, and

Rundel (1980) in Hawaii. In general, the main factor

governing the C4 grass species distributions along ele-

vational transects is evidenced to be temperature. But

depending on the locality or the study, different sec-

ondary environmental variables are identified. For

instance, Cabido et al. (1997) and Cavagnaro (1988)

respectively conclude that in Central Argentina, rainfall

is or is not a secondary factor. Two studies of Mount

Kenya showed a similar level of disagreement: Tieszen

et al. (1979) proposed that minimum temperature, irra-

diance and available soil moisture were the selective

forces for the observed grass distributions on an open

grassland vegetation transect, whereas Young & Young

(1983) showed a significant positive correlation

between the relative abundance of C4 grasses and soil

moisture, but significant negative correlation between

percent C4 composition and percent soil covered by

vegetation.

The C4 distribution is anticipated to be particularly

sensitive to past and current global changes in atmo-

spheric CO2 levels and climate. For instance, an appar-

ent global increase in C4 plant biomass during theCorrespondence: Laurent Bremond, tel. + 33 4 99 23 21 80,

fax + 33 4 67 54 35 37, e-mail: [email protected]

© 2012 Blackwell Publishing Ltd 1

Global Change Biology (2012), doi: 10.1111/j.1365-2486.2012.02690.x

Miocene/Pliocene boundary has been related to a

decrease in atmospheric CO2 levels, together with

changes in precipitation regimes, uplift topography

and fires/CO2 interactions (e.g. Pagani et al., 1999;

Bond et al., 2003; Keeley & Rundel, 2005). The Last

Glacial Maximum (further LGM) has also been consid-

ered and the majority of studies have suggested that

there was a higher C4 biomass during the LGM com-

pared with the present-day. Despite all the work per-

formed to date, the main driving factors are still under

debate (e.g. Huang et al., 2001; Liu et al., 2005; Schefusz

et al., 2005; Sinninghe Damste et al., 2011).

To date, modelling studies have mainly focused on

quantum yields that quantify the light-use efficiency at

low irradiance. C3 quantum yield decreases as tempera-

ture increases and intercellular CO2 concentration

decreases, while C4 quantum yield does not change.

The switch between C3 and C4 dominance has been

commonly expressed as a crossover temperature, at

which C3 and C4 quantum yields are identical. The

crossover temperature is dependent on atmospheric

CO2 levels, and therefore varies with time (Ehleringer

& Bjorkman, 1977; Farquhar, 1989; Ehleringer et al.,

1997). Models based on this scheme (Ehleringer et al.

(1997), Collatz et al. (1998) Still et al. (2003)) have been

used to estimate global distribution of C3 and C4

grasses and to explain changes in C3/C4 palaeo-data

records (Boom et al., 2001). A different model was

developed by Winslow et al. (2003) that estimates the

C3 grass biomass distribution based on the seasonal

timing of water availability and growing seasons with

respect to the different requirements of C3 and C4

plants.

So far, dynamic global vegetation models (DGVMs)

have not been used to model C3/C4 competitive rever-

sal or to estimate climate changes impacts on C3/C4

grass distributions. Within DGVMs, photosynthesis

models are adapted from Farquhar et al. (1980) and

Collatz et al. (1992) (hereafter referred to as Farquhar/

Collatz models) and generalized for global modelling.

Therefore, DGVMs allow the competitive reversal of C3

and C4 grasses to be modelled not only under light-

limited environments but also in open environments

where most grasses grow.

The aims of this study were to investigate the main

climatic factors controlling the C3 and C4 grass altitudi-

nal distribution under the present climate conditions in

the Tropical Andes, and evaluate the impact of climate

changes on this distribution. To achieve these objec-

tives, we compiled a dataset of the grass species rich-

ness distribution throughout the whole Tropical Andes

and in the surrounding lowlands, calculated current

altitudinal distribution profiles of C3 and C4, and inves-

tigated the correlation between the distribution profiles

and potential climatic drivers. Subsequently, crossovers

were computed from the data and compared with val-

ues predicted using the model of Collatz et al. (1998)

and the LPJ-DGVM model for the study area. Both

models were used to simulate the C3 and C4 distribu-

tions under CO2 and climate conditions computed for

the Holocene Optimum [HO, 6 ky before present (BP)]

and the LGM (21 ky BP), using PMIP2 models in the

LPJ-DGVM. Finally, the models were used to make pre-

dictions for 2100 using IPCC AR4 models to force the

LPJ-DGVM.

Materials

Grass floristic composition

Grass species richness distribution data were compiled from

all the available literature for four intertropical countries of

South America (Fig. 1, Table S1): Colombia (Pinto-Escobar &

Mora-Osejo, 1966), Ecuador (Jørgensen & Ulloa Ulloa, 1994;

Jørgensen & Leon-Yanez, 1999), Peru (Tovar, 1993) and Bolivia

(Renvoize, 1998). For each grass species, the limits of the alti-

tudinal distribution corresponded to the lowest and highest

altitude where it was observed. All altitudes are quoted as

heights a.s.l. The data from Colombia only included the Poa-

ceae distributions in the province of Cundinamarca (139 spe-

cies, 300–4200 m a.s.l.), the lowland department of Guaviare

(89 species; 100–300 m a.s.l.; D. Giraldo-Canas, unpublished

data) and at several scattered locations (67 species; 0–4900 m

a.s.l.; Giraldo-Canas, 2003, 2004, 2005; Garcia-Ulloa et al.,

2005). For the other countries, the grass distribution covered

the whole national territories: Ecuador (219 species, 0–5100 m

a.s.l.), Peru (482 species, 0–5250 m a.s.l.) and Bolivia (694

species, 0–5300 m a.s.l.).

Photosynthetic pathways were assigned to each species

according to Watson & Dallwitz (1992). However, because in

Watson & Dallwitz (1992) pathways are only listed at the

genus level, additional sources were used to classify genera,

such as Panicum, which exhibit both C4 and C3 photosynthetic

pathways (Hattersley, 1983; Brown et al., 1985; Oguro et al.,

1985; Cabido et al., 1997; Wooller et al., 2001; Belmonte &

De Agrasar, 2002). C3 and C4 grass species richness and pro-

portion of C4 species were calculated for each country and the

whole study area at 100 m elevation intervals. Bootstrapped

95% confidence intervals for each country were derived via

1000 bootstrap replications of the original data.

Climate data

To compare with floristic data, we explore altitudinal varia-

tion of several climatic factors that have been shown previ-

ously to affect C4 grass distribution (i.e. Sage & Monson, 1999;

Murphy & Bowman, 2007; Von Fischer et al., 2008): mean

annual temperature (MAT), mean temperature of the cold-

est month (MTCO), mean temperature of the warmest month

(MTWA), mean annual precipitation [MAP; see MAP

distribution in Fig. S1 (Supporting Information) and Table S2],

© 2012 Blackwell Publishing Ltd, Global Change Biology, doi: 10.1111/j.1365-2486.2012.02690.x

2 L. BREMOND et al.

mean annual evaporation (MAE), PmE (MAP � MAE) and

seasonal water availability [SWA, a proxy for the distribution

of rainfall in the C4 vs. C3 growing seasons (Winslow et al.,

2003)]. Elevation and climate data were extracted from

Worldclim 1.4 1-km database (Hijmans et al., 2005). The

MATs, MTCO and MTWA were clearly related to elevation

for all four countries (Fig. 1) and closely correlated because of

the relatively low seasonal range of temperatures. Therefore,

we use only MAT as temperature factor. No clear trends were

discernable for the other available climate parameters (diurnal

temperature range, frost day frequency, relative humidity,

sunshine duration, wet day frequency and wind speed),

except that there was a general tendency for decreased rainfall

at higher elevations, which would affect the C3/C4 grass spe-

cies distribution.

Methods

Floristic C3/C4 crossover

The floristic C3/C4 crossover temperature is defined as the

temperature at which the proportion of C4 species is 0.5. Below

the crossover temperature (Tx), C3 grass species dominate and

amount to more than half of the grass species. To estimate Tx

for each country and the area as a whole, the proportion of C4

species pC4 was expressed as a logistic function of MAT, T:

Colombia Ecuador Peru Bolivia

Mean annualtemperature (°C)




Elevation (m) Elevation (m) Elevation (m) Elevation (m)

0 2000 4000 6000

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Mea

nte

mpe

ratu

re, °

CN

umbe

r of C

4 and

C3

gras

s sp

ecie

sFr

actio

nC4

gra

ss s

peci

esFr

actio

nC4

gra

ss b

iom

ass

(a)

(b)

(c)

(d)

Fig. 1 (a) Mean temperature of the warmest month (orange dots) and the coldest month (blue dots) along elevation gradients for the

four studied countries (data from New et al. (2002)). (b) Number of C4 (green dots) and C3 (blue dots) grass species along the elevation

gradients. Data are compiled from existing literature for each country. (c) C4 grass species as a fraction of the total vs. the mean annual

temperature. Each point represents the mean value of the number of species in 100-m intervals. Plain lines are the 95% confidence inter-

val estimate by bootstrapping. (d) C4 grass biomass as a fraction of the total biomass estimated using the LPJ-DGVM (C3 and C4 grass

PFTs were included, but not tree PFTs).


DISTRIBUTION OF C4 GRASSES 3

pC4 ðTÞ ¼p0C4

1þ exp½�kðT � T0Þ� ð1Þ

Eqn (1) describes sigmoidal growth from pC4= 0 at low tem-

peratures to pC4¼ p0C4

at high temperatures. The parameter T0

determines the range of temperatures where sigmoidal

growth occurs, whereas k affects the slope of the growth. The

crossover temperature, Tx, can be estimated from Eqn (1)

using the following relationship:

pC4ðTxÞ ¼ 0:5 ð2Þ

Because of the strong correlation between MAT and eleva-

tion, Tx is related to a crossover elevation, which marks the

transition from lower elevations where C4 grass species domi-

nate to upper elevations where C3 species dominate.

Statistical analyses

To evaluate climate controls on the distribution of the C4 grass

species, we performed stepwise logistic regressions to estab-

lish a parsimonious multiple climatic variable model of the

proportion of C4 species. We performed these regressions on a

compilation of the data for the four countries where the pro-

portion of C4 species is inferior to 60%. This excludes C3

grasses occurring in tropical lowland forest understory due to

low irradiance conditions and not to climate directly. Stepwise

regression performs an automated choice of variables to be

included to minimize Akaike information criterion (AIC)

value. Models were ranked for suitability using AIC, which

favours both model fit and model simplicity. Lower values of

AIC indicate greater support for a model, relative to other

models in the same candidate set.

Modelled values of C3/C4 crossover temperature

Collatz et al. (1998) proposed a theoretical value for Tx as the

temperature at which the quantum yield for photosynthetic

CO2 fixation is equal for the C3 and C4 pathways when stomata

are fully open. Considering the Farquhar/Collatz models for

C3 and C4 photosynthesis, Tx can be expressed mathematically

as the temperature for which the derivative of the light-limited

rate of photosynthesis with respect to the flux of incident pho-

tosynthetically active radiation (PAR), (dA/dI), is equal for C3

and C4 photosynthesis (see Appendix S1). As grass species

often grow in open environments where light is not limited

and other environmental factors besides temperature may

affect the distribution, quantum yield crossovers may not nec-

essarily imply floristic crossovers. Therefore, we also consid-

ered the LPJ-DGVM, which models carbon and water cycles in

plants by taking account of photosynthesis, stomatal conduc-

tance, phenology and population dynamics with respect to

monthly climate (temperature, rainfall and insolation), atmo-

spheric CO2 content and physical parameters of soil. DGVMs

regard plant species as divided into plant functional types

(PFTs) and simulate the biomass of each PFT. Here, we only

considered C3 grass and C4 grass PFTs, and thus the resulting

LPJ model predicted the biomass of grasslands in each region

in the absence of competition from trees. The main differences

between C3 and C4 PFTs in the LPJ model are the response of

photosynthesis to temperature and leaf CO2 pressure (Farqu-

har/Collatz models), and the relationship between leaf CO2

pressure and water stress. In DGVM, species diversity in a

PFT is implicitly taken into account by the possibility of adap-

tation of physiological parameters to environmental condi-

tions. For example, leaf structure is assumed to adapt to

maximize the net daily rate of photosynthesis and optimize the

use of available light. Additionally, a temperature-controlled

factor is incorporated in the model to limit photosynthesis at

low and high temperatures, the optimal temperature ranges

being different for each PFT. This comes to assume that among

all PFT species are selected those that can optimally adapt to

local environment and that temperature limits the possibility

of adaption. Additional bioclimatic limits of PFTs included in

the original model were omitted in this study.

The LPJ model was run on a 0.5° grid mesh covering the

study area (parameters are given in Table 1 and further details

can be found in Sitch et al. (2003)). Input values for the present

climate were taken from CRU data for 1961–1990 (New et al.,

2002), and crossover temperature was estimated by fitting the

proportion of C4 grass biomass to total grass biomass vs. MAT

with a logistic function.

The effects of CO2 levels and climate change in the past

[PMIP2 scenarios 6 and 21 ky BP (Braconnot et al., 2007)] and

in the future [IPCC SRES scenarios A2 and B1 (Nakicenovic

et al., 2000)] were investigated using an ensemble simulation

by forcing the LPJ model with all available climatic simula-

tions for each period and emissions scenario data (downloaded

from http://pmip2.lsce.ipsl.fr/ and http://www.ipcc-data.org/).

This allows to derive a prediction of the evolution of modelled

crossover temperature and altitudes with climate change, that

can be compared with Collatz et al. (1998) estimations.

Results

Relationship between species and elevation

The floristic data show the greatest species richness

occurred at low elevation (Fig. 1b). Moreover, the C3

Table 1 Summary of the photosynthetic parameter values

that differ between C3 and C4 grasses or which have been

changed from the values reported by Sitch et al. (2003). Other

parameter values and model parameterization are as

described in Sitch et al. (2003)

Parameter C3 grass C4 grass

Intrinsic quantum efficiency

of CO2 uptake

0.08 0.053

Optimal temperature range

for photosynthesis

10–30 °C 20–45 °C

Leaf respiration as fraction of

maximum Rubisco capacity

0.015 0.02

Temperature limits for

establishment

No No


4 L. BREMOND et al.

and C4 grass species distributions reveal that C3 species

were present at all altitudes, but C4 species decreased

in number above 2500 m and were essentially absent

above 4000 m.

Generally, the C4-grass distribution with respect to

temperature exhibited a similar sigmoidal pattern for

all countries (Fig. 1c), except for slight differences, e.g.

the slope of the curve for Bolivia was lower than for the

other countries. The maximum proportion of C4 species

was around 0.7 for Bolivia and Ecuador, whereas it was

0.8 for Colombia and Peru. The parsimony and sam-

pling heterogeneity of the Colombian dataset resulted

in anomalous C3/C4 richness patterns compared with

the other countries, for which data were available for

the whole territory. In particular, the apparent decline

of C4 grasses at the lowest sites in Colombia may be an

artefact due to over-representation of Amazonian forest

C3 grasses in the available data.

Statistical relationships with climate variables

Stepwise logistic regression showed that MAT is the

best predictor of the proportion of C4 grass species and

explained 88% of the deviance (Table 2). Other climatic

variables did not account for the discrepancies among

the four countries.

Predicted biomass of C4 grasses using the LPJ model

For all the studied countries, the proportion of total bio-

mass of C4 grasses simulated using the LPJ model was

sigmoidal with temperature and varied from 0 to 1

(Figs 1d and 2a). However, the slopes of the curves

were steeper near the crossover temperature than the

corresponding sigmoidal fits to the floristic data

(Figs 1c and 2b) because the LPJ model predicted an

abrupt transition of C4 biomass between high and low

temperatures.

Table 2 Percentage deviance explained and AIC values for

logistic models of proportion of C4 species against different

combination of climatic factors. Models are ranked in ascend-

ing order of the model selection criterion (AIC)

Factors % Deviance explained AIC

MAT 87.9 56.099

MAT + SWA 90.4 57.224

MAT + MAE 88.0 58.043

MAT + PmE 87.9 58.074

MAT + MAP 87.9 58.081

MAT + SWA + MAE 92.2 58.628

MAT + SWA + PmE 92.0 58.681

MAT + SWA + MAP 91.9 58.73

MAT MAP SWA MAE 92.2 60.621

MAE 73.2 67.13

SWA 38.2 69.76

Null 0.0 89.86

PmE 15.5 91

MAP 0.8 92.71

AIC, Akaike information criterion; MAT, mean annual tem-

perature; MAP, mean annual precipitation; MAE, mean

annual evaporation; PmE, mean annual precipitation minus

mean annual evaporation; SWA, seasonal water availability.

Bold value denotes the Best AIC score.

Mean annual temperature (°C)

Colombia

BoliviaPeru

Compiled data

Model MAT

Compiled data

Ecuador


Frac

tion

ofC4

gra

ss s

peci

esFr

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n of

C4 g

rass

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mas

s

0

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1

0

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0.6

0.8

1

30 25 20 15 10 5 0

30 25 20 15 10 5 0

(a)

(b)

Fig. 2 (a) Number of C4 grass species as a fraction of the total

vs. mean annual temperature for each of the four countries and

the mean value of the whole dataset (light green dots) calcu-

lated from the floristic data. Best C4 grass species nonlinear

model according to mean annual temperature (MAT). (b) C4

grass biomass as a fraction of the total vs.mean annual tempera-

ture estimated by the LPJ model compiled from the results for

all four countries.



Crossover temperature

The crossover temperatures (Tx) calculated from floris-

tic data were close for all four countries (Fig. 3 and

Table 3). The value of Tx was about 15.5–16 °C for

Colombia, Ecuador and Bolivia, but only 12.4 °C for

Peru. Tx calculated from the C4 grass biomass simu-

lated using the LPJ model varied from 14.0 to 17.0 °C.Regression fits of the floristic data (Fig. 2a) and LPJ

simulated data (Fig. 2b) compiled for all four countries

gave Tx values of 15.3 and 15.1 °C respectively. The

crossover temperature calculated using the equation

reported by Collatz et al. (1998) (Supporting informa-

tion) was between 17.2 and 22 °C, about 2–5 °C higher

than the values obtained using the floristic data or the

LPJ model.

Past and future crossover

In general, values for Tx estimated by the Collatz et al.

(1998) model for the past and future scenarios were

clearly different from the values estimated by the LPJ

model (Fig. 3). The value of Tx simulated for 6 ky BP by

the LPJ model (15.2 °C) was within the range estimated

by the Collatz et al. model (13.6–18.4 °C; Fig. 3a). How-

ever, for 21 ky BP, Tx estimated by the LPJ model was

13.7 °C compared to only 6.1–10.9 °C by the Collatz

et al. model. The present crossover point was found to

correspond to an altitude of 2650 m (Fig. 3b). The LPJ

model estimated that the crossover point would be

lower at 2032 m for the 21 ky BP scenario, whereas the

Collatz et al. model gave a higher value of 2880–3820 m. According to the LPJ model, Tx in 2100 (17 and

18.3 °C for scenarios B1 and A2, respectively, corre-

sponding to altitudes of 2679 and 2752 m respectively)

would not differ significantly from the present day.

However, the Collatz et al. (1998) model predicted Tx to

be 25.2–30 °C for the B1 scenario (corresponding to 0–600 m) and 30.9–35.7 °C for scenario A2. The latter tem-

perature range is above the current range in the studied

area.

Discussion

The floristic approach: independence towards short-termclimate

The floristic assessments used in this study are limited

because they do not provide ecological information such

as cover, biomass or relative dominance. The results of

several surveys have suggested that the number of C3/

C4 species may be a poor indicator of local abundance.

For example, Paruelo & Lauenroth (1996) observed that,

in a site in Colorado (USA), the relative abundance of C3


Scenarios

A2

Present6 k21 k

B1

21K 6K Present B1 A20

Collatz model

CO2 partial pressure (Pa)

A2

Present LPJ6 k21 k

Present floristic

Collatz model

B1

Cro

ssov

erm

ean

an

nu

al te

mp

erat

ure

, Tx

(°C

)El

evat

ion

(m)

Frac

tio

n o

f C

4 g

rass

bio

mas

s

4000

3000

2000

1000

1

0.8

0.6

0.4

0.2

30

20

10

00 20 40 60 80 100

30 25 20 15 10 5 0

(a)

(b)

(c)

Fig. 3 (a) Response of the crossover temperature (Tx) to differ-

ent partial pressures of atmospheric CO2, according to the

Collatz et al. (1998) model and the LPJ model for past, present

and future climate conditions. The past conditions were simu-

lated by the LPJ model using the PMIP2 climate reconstructions

for 6 and 21 ky BP (Last Glacial Maximum). The simulations for

the future were run using the IPCC scenarios for 2100. (b) Eleva-

tion of the Tx according to the Collatz et al. (1998) model and the

LPJ for the different scenarios. (c) C4 grass biomass as a fraction

of the total vs. mean annual temperature estimated by the LPJ

model for past, present and future climate scenarios.


6 L. BREMOND et al.

species of 59% accounted for only 10% of total biomass. It

has also been observed a C3/C4 grass ground-cover

crossover considerably lower (by about 500 m) than the

floristic crossover Argentina (Cabido et al., 1997) and

Hawaii (Rundel, 1980). However, on an alpha scale (sensu

Whittaker, 1977), the species composition of local com-

munities, and in particular, the distribution of C3 vs. C4

grasses, was shown to depend not only on macroclimate

but also on the interaction of microclimate, site history,

herbivory, soil conditions, burning, topography, seed

dispersal and competition processes (Winslow et al.,

2003; Whittaker, 2010). Therefore, the data from these rel-

atively sparse censuses are likely to reflect local condi-

tions at a given time rather than results on a wider scale.

In contrast to C4 relative abundance, which fluctuates on

a year-to-year basis in response to short-term climate

changes, Murphy & Bowman (2007) suggested that C4

relative species richness is largely stable over time and is

insensitive to climatic changes, especially when envis-

aged at the gamma scale (Whittaker, 2010). The floristic

data used in this study were gathered from a large num-

ber of observations of grass species richness distribution

by numerous independent botanists over several dec-

ades, incorporating a large number of grass species

(almost 700 in Bolivia) and on a wide spatial and climatic

scale. We can therefore assume that local environmental

conditions have a minor influence on these data com-

pared with the macroclimate, although the heterogeneity

and parsimony of the data for Colombia may neverthe-

less have affected the results in this case.

At this scale, we observed a close relationship

between the number of C4 grass species and potential

C4 grass biomass in grasslands in the results from both

the LPJ simulations and the floristic approach. This is in

agreement with the commonly observed positive rela-

tionship between diversity and productivity at global

and continental scales (Mittelbach et al., 2001; Aarssen,

2004; Gillman & Wright, 2006). In contrast, unimodal

relationships are typically observed on regional scales,

due to small-scale heterogeneity in environmental

conditions (Aarssen, 2004).

The C4 grass species distribution: relationship withtemperature

The global latitudinal distribution of C4 grass species

richness has previously been linked to several envi-

ronmental parameters, but in all cases, the contribu-

tion of C4 species to the floral or total biomass has

been shown to correlate strongly with some index of

temperature (Sage et al., 1999). In temperate zones,

such as the Great Plains in North America, the cli-

mate in July (average of daily high temperature and

month’s total rainfall) explains the distribution of C4

species (Teeri & Stowe, 1976; Von Fischer et al., 2008).

In Australia, the proportion of C4 species has been

shown to correlate strongly with MAT (Hattersley,

1983). However, where grasslands exhibit two distinct

growing seasons (i.e. winter–spring and summer),

precipitation and its seasonal distribution are an

important secondary factor (Paruelo & Lauenroth,

1996). One explanation is that in temperate latitudes,

C3 grasses start their growth early and have sole

access to water stored in the soil from melting

snow and spring rains. In contrast, C4 grasses start

growing later in the year as temperatures warm, and

Table 3 Comparison of crossover temperatures for each country calculated using the floristic approach and the LPJ model. Values

of the best-fit parameters obtained by fitting the floristic data to a sigmoidal function are also shown in the lower panel (all parame-

ters have z-test P-values <10�3)

Colombia Ecuador Peru Bolivia Compiled data

Floristic Tx, °C 15.9 15.4 12.4 15.9 15.3

Altitude, m 2425 2387 3006 2379 2461

MAP (SD) 1832 (563) 1245 (460) 704 (333) 768 (297) 1038 (690)

LPJ Tx, °C 17.0 16.2 15.05 14.0 15.4

Altitude, m 2192 1981 2656 3108 2545

MAP (SD) 2007(569) 1650 (678) 702 (398) 595 (214) 956 (618)

Sigmoidal fits to floristic data

pC4ðTÞ ¼ p0

C4

1þexp½�kðT�T0Þ�p0C4

0.799 ± 0.010 0.743 ± 0.008 0.758 ± 0.010 0.708 ± 0.006 0.718 ± 0.005

k (°C�1) 0.716 ± 0.040 0.525 ± 0.023 0.623 ± 0.048 .0379 ± 0.012 0.424 ± 0.011

T0 (°C) 15.31 ± 0.09 14.02 ± 0.10 11.35 ± 0.14 13.60 ± 0.10 13.50 ± 0.07

R2 0.992 0.992 0.979 0.996 0.997

MAP, mean annual precipitation; LPJ, Lund-Potsdam-Jena; SD, standard deviation.



subsequently both grasses will have access to avail-

able water, primarily from rainfall (Winslow et al.,

2003). The recent study of Murphy and Bowman

(Murphy & Bowman, 2007) confirms that the relative

C4 species richness has a high correlation with tem-

perature, whereas C4 relative abundance correlates

with precipitation (SWA) because it is more sensitive

to short-term climate: SWA fluctuates on a year-to-

year basis more than temperature in the tropics.

Our data represent the largest study of C3/C4 grass

distributions on the South American continent and

cover the entire latitudinal range of the Tropical Andes.

The results confirm that temperature is the main factor

which determines the C4 grass distribution along

altitude gradients in the tropics. The general pattern of

C3/C4 grass distribution is in agreement with previous

studies of altitudinal gradients in intertropical regions

in Africa (Tieszen et al., 1979; Livingstone & Clayton,

1980; Young & Young, 1983), Hawaii (Rundel, 1980;

Edwards & Still, 2008), Costa Rica (Chazdon, 1978) or

Argentina (Rundel, 1980; Cabido et al., 1997). The rela-

tionship between the relative C4 species richness and

temperature along altitudinal gradients is characterized

by sigmoidal curves. These curves can be divided into

three zones. In the first zone, which corresponds to low

temperatures (<10 °C), C3 species dominate without

significant competition from low-temperature intoler-

ant C4 species. The second zone corresponds to temper-

atures from 10 to 20 °C and here the slope of the curve

indicates the degree to which the C3 and C4 grasses

compete. Finally, above 20 °C, C3 grasses are still pres-

ent, but C3 and C4 grasses occupy different ecological

niches: C3 grasses will dominate in tropical understo-

reys where C4 grasses are light-limited (Long, 1999),

whereas C4 grasses dominate in all open places because

of higher water-use efficiency, which is crucial under

high solar irradiance and high temperature conditions.

Tropical forests at low elevations under warm tempera-

tures allow the presence of C3 species, but there are no

corresponding situations which favour C4 grasses at

high altitude, resulting in the dissymmetric shape of

the curves shown in Fig. 2. A few Andean high-elevation

vegetation types which include C4 species have been

described, but they are exceptional, possibly due to

warm microclimate conditions but also previously con-

sidered to be relict communities from glacial time

(Boom et al., 2001).

The floristic data suggested Tx for the Neotropical

Andes was 15.3 °C, compared to 15.4 °C from the LPJ

simulations. Both values are significantly lower than

the values of 21–25 °C estimated by Ehleringer et al.

(1997) for worldwide C4 grasses. However, they are clo-

ser to the values obtained using the Collatz et al. (1998)

model of C4 grass distribution, which estimates that Tx

under current atmospheric CO2 levels is 17–22 °C. Thediscrepancy in the values of Tx predicted by the LPJ

and Collatz et al. (1998) models arises because, even

though both models only consider competition for

atmospheric carbon as the driver of C3/C4 repartition,

they model it quite differently. In line with Ehleringer

(1978), who explained the C3/C4 climatic repartition as

due to differences in quantum yields at low tempera-

tures disfavouring C4 photosynthesis, Collatz et al.

(1998) defined Tx as the temperature at which light-lim-

ited photosynthesis is equal for C3 and C4 pathways. In

contrast, the LPJ model includes several other pro-

cesses, such as the Rubisco-limited component of pho-

tosynthesis, reduction of CO2 pressure in leaves

induced by stomatal closure in response to water stress,

leaf phenology (i.e. periods when photosynthesis is

absent and acquired carbon is allocated to grow leaves)

and inhibition of photosynthesis at low and high tem-

peratures, which interplay and give rise to the different

estimated crossover temperature. For instance, we per-

formed simulations for theoretical grasslands, by

removing tree PFTs from the model. This is justified by

the fact that, with the notable exception of lowland C3

species, most grass species reviewed in this study grow

in open environments maintained by humans. The

inclusion of tree PFTs in the model results in a domi-

nance of trees up to the treeline. However, omitting tree

PFTs allows the potential biomass of grasslands under

any climate to be estimated. In this situation, the light

intensity reaching the grass canopy would be high and

Rubisco-limited photosynthesis would play an impor-

tant role. Importantly, the LPJ model can incorporate

the effect of the timing and amount of precipitation,

mainly through estimations of stomatal closures and

subsequent variations in leaf CO2 partial pressures.

Using the floristic data, the crossover temperatures

were close for all countries (15.4–15.9 °C) except Peru

(12.4 °C). Statistical analyses did not evidence that the

other climatic factors explained this discrepancy. Several

studies have, however, indicated that precipitation and

its seasonal distribution is an important secondary fac-

tor, after temperature, governing the distribution of C3

and C4 species (Paruelo & Lauenroth, 1996; Murphy &

Bowman, 2007; Von Fischer et al., 2008). Sage et al. (1999)

noted that aridity affects altitude trends, with C4 plants

reaching higher altitudes on drier sites. In LPJ simula-

tions with different levels of MAP from 100 to 2700 mm,

crossover temperature is only sensitive to MAP for

MAP < 500 mm (Fig. S2). This indicates that rainfall

weakly affects crossover temperatures in the intertropi-

cal zones, except in very arid places. Thus, the influence

of the drier climate along the Pacific Ocean coast com-

pared with the Amazonian basin may explain the low

crossover temperature in Peru (see distribution of MAP


8 L. BREMOND et al.

in Fig. S1). It would have been interesting to compare

the floristic Tx for the West and East sides of the Andes,

but comparable data were not available. The previous

studies along altitudinal gradients in intertropical

regions are very few and the data are sparse or very

local. We suppose that at this scale, microclimate condi-

tions can play as secondary factors over the C4 grass dis-

tributions. But along a large-scale altitude gradient,

where other climatic factors than temperatures are not

necessarily correlated with altitude, MAT become the

best predictor of the proportion of C4 grass species and

explain almost 90% of the deviance when other climatic

variables failed to explain the remaining 10%.

Past and future: role of CO2

The concept that temperature-CO2 drives C4 plant

abundance has motivated several studies aimed at

past climate and vegetation reconstructions. In partic-

ular, the Miocene/Pliocene boundary has been inves-

tigated because it represents a significant event with

regard to biome evolution (e.g. Segalen et al., 2007;

Edwards et al., 2010). However, because reconstruc-

tion of the environmental conditions for this time per-

iod is complex, the drivers of this major change are

still much debated. The more recent Pleistocene epoch

has been associated with significant changes in atmo-

spheric CO2 levels (Petit et al., 1999). The LGM has

been particularly studied and interpretations of the

relationships between vegetation and climate changes

are varied and often controversial. Only a few palaeo-

C4 reconstructions have been reported to date, but the

majority indicate that C4 biomass was higher during

the LGM than in the present day. Our simulation

with the LPJ-DGVM forced with PMIP2 scenarios sug-

gested that Tx during the LGM in the Neotropics was

2.7–7.8 °C higher than predicted by the Collatz et al.

(1998) model. The results of the LPJ simulations imply

that C4 grasses would not have reached as high an

altitude as in the present day (2650 m today vs.

2032 m during the LGM). Furthermore, the low atmo-

spheric CO2 levels would not have compensated for

the decrease in temperature and precipitation. These

new results raise several questions, for instance: Are

simple photosynthetic models such as those proposed

by Collatz et al. (1998) or Ehleringer et al. (1997) real-

istic in terms of light-use and water-use efficiency, as

even if temperature is the main factor governing the

present grass distributions in intertropical regions,

precipitation will counterbalance the effect of chang-

ing CO2 levels? Very few palaeoenvironmental studies

from the Neotropics have shown that C4 grasses reach

higher elevations during glacial periods (Guillet et al.,

1988; Mora & Pratt, 2001, 2002; Boom et al., 2002). In

Africa, and at Mt. Kenya specifically (Street-Perrott

et al., 2004), isotopic and palaeoecological data have

shown that C4 plants reached higher elevations.

Huang et al. (2001) concluded that regional climate

exerts a strong effect on the relative abundance of C3

and C4 plants, and that in the absence of favourable

moisture and temperature conditions, low CO2 levels

alone are not sufficient to drive an increase in the

number of C4 plants. Other palaeo-studies have

revealed that the combination of lower atmospheric

CO2 levels and increased moisture stress favours C4

plants (e.g. Sinninghe Damste et al., 2011).

The LPJ simulations for the LGM may also not reflect

reality for different reasons. Whereas using only grass

PFTs in the model enabled more accurate predictions of

present-day grass distributions, for the LGM, this may

be an oversimplification because the tree/grass ratios

were likely to have been significantly different. In

addition, the LPJ model has only been validated with

modern data (Sitch et al., 2003) and therefore may

underestimate the role of atmospheric CO2 concentra-

tions on the vegetation distribution. An interesting

challenge for the future would be to explore a field site

close to the present C3/C4 grass transition, which could

potentially act as a record of the changes in grass cover

during the Holocene.

In summary, these data represent the largest study

of C3/C4 grass distributions on the South American

continent, covering the entire latitudinal range of the

Tropical Andes. Our analysis confirms that tempera-

ture is the main factor determining modern C3/C4

grass distributions along altitudinal gradients in the

tropics. Because partial pressure of CO2 is constant

over the earth, its role in influencing grass distribu-

tions is difficult to evaluate. However, by examining

botanical evidence and using both a simple photo-

synthetic model and a complex DGVM model, we

have demonstrated the role of precipitation over that

of CO2 levels. Specific palaeoenvironmental C3/C4

dynamic reconstructions may, in the future, help

elucidate the effect of CO2 levels.

Acknowledgements

This work has been initiated during an Intra-European MarieCurie Fellowships (MEIF-CT-2005-024625). The authors thankOlivier Gauthier for constructive discussion on statistics. Weacknowledge the international modeling groups for providingtheir data for analysis, the Laboratoire des Sciences du Climat etde l‘Environnement (LSCE) for collecting and archiving themodel data. The PMIP 2 Data Archive is supported by CEA,CNRS and the Programme National d‘Etude de la Dynamiquedu Climat (PNEDC). The analyses were performed using ver-sion 01-22-2011 of the database. More information is availableon http://pmip2.lsce.ipsl.fr/. We thank Martin Sykes and VeikoLehsten for making LPJ model available to us.



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Supporting Information

Additional Supporting Information may be found in theonline version of this article:

Table S1. Altitudinal distribution of grass species.Table S2. Climate parameters.Figure S1. Distribution of mean annual precipitations(MAP) in the four studied countries.Figure S2. Crossover temperature vs. mean annual precipi-tation (MAP) in the four countries and estimated by LPJ.Appendix S1. Details of the model of Collatz et al. (1998).

Please note: Wiley-Blackwell are not responsible for the con-tent or functionality of any supporting materials suppliedby the authors. Any queries (other than missing material)should be directed to the corresponding author for thearticle.



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