META-ANALYSIS

Global dependence of field-observedleaf area index in woody species onclimate: a systematic reviewAtsuhiro Iio1*, Kouki Hikosaka2,3, Niels P. R. Anten4, Yoshiaki Nakagawa5 and

Akihiko Ito1

1Center for Global Environmental Research,

National Institute for Environmental Studies,

16-2 Onogawa, Tsukuba 305-8506, Japan,2Graduate School of Life Sciences, Tohoku

University, Aoba Sendai 980-8578, Japan,3CREST, Japan Science and Technology,

Chiyoda, Tokyo 102-0076, Japan, 4Center for

Crop System Analysis, Wageningen University,

PO Box 430, 6700 AK, Wageningen, The

Netherlands, 5Graduate School of Life and

Environmental Sciences, University of

Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki

305-8572, Japan

ABSTRACT

Aim Leaf area index (LAI) is one of the key variables related to carbon, water andnutrient cycles in terrestrial ecosystems, but its global distribution patterns remainpoorly understood. We evaluated the dependence of LAI on mean annual tempera-ture (MAT) and wetness index (WI; a ratio of annual precipitation to potentialevapotranspiration) for three plant functional types (PFTs: deciduous broadleaf,DB; evergreen conifer, EC; evergreen broadleaf, EB) at the global scale.

Location Global.

Methods We developed a new global database of unprecedented size (2606 pub-lished values) of field-observed LAI (site-specific maximum) values for vegetationof woody species. To maximize the generic applicability of our analysis, we stand-ardized the definition of LAI, and corrected or excluded potentially erroneous dataobtained from indirect optical methods.

Results The global dependence of LAI on MAT showed a reverse S-shapedpattern, in which LAI peaked at around 8.9 and 25.0°C and was lowest at around−10.0 and 18.8°C. The dependence on WI followed a saturation curve levelling offat around log WI = 0.30. LAI for evergreen forests increased linearly with increasingWI, but that for DB showed a curvilinear pattern saturating at log WI = 0.03. ECforests had higher LAI values than those of DB forests under cool conditions(MAT ≤ 8.9°C), but similar values under temperate conditions (MAT = 8.9–18.8°C).

Main conclusions This analysis of global LAI−climate relationships supports thegeneral belief that temperature limits LAI under cool conditions whereas wateravailability plays a predominant role under other conditions. We also found thatthese patterns differed significantly between PFTs, suggesting that the LAI of dif-ferent PFTs may respond differently to climate change. Our study provides a broadempirical basis for predicting the global distribution of LAI and for analysing theeffects of global climate change on vegetation structure and function.

KeywordsGlobal scale, leaf area index, plant functional type, temperature, wetness index,woody species.

*Correspondence: Atsuhiro Iio, Center forEducation and Research in Field Science,Agricultural Faculty, Shizuoka University, Ohya,Shizuoka, 422-8529, Japan.E-mail: aaiio@ipc.shizuoka.ac.jp

INTRODUCTION

Leaf area index (LAI), the amount of leaf area per unit ground

area, is one of the dominant factors controlling plant produc-

tivity, expressed as, for example, gross or net primary produc-

tion (e.g. Kira, 1991; Luo et al., 2004), because it is a major

determinant of the amount of light intercepted by a canopy and

hence of photosynthesis. LAI also strongly influences the water

and nutrient cycles in forest ecosystems (e.g. Granier et al., 2000;

Erisman & Draaijers, 2003; Schulze, 2006). Consequently, most

ecosystem process-based models that simulate carbon, water

and nutrient cycles include LAI as a key parameter (e.g. Running

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Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2014) 23, 274–285

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& Coughlan, 1988; Sellers et al., 1996; Liu et al., 1997; Ito &

Oikawa, 2002).

Although it is widely recognized that LAI is sensitive to

climate variables, there are only a limited number of studies

investigating LAI−climate relationships under field conditions

(e.g. Grier & Running, 1977; Battaglia et al., 1998; Luo et al.,

2004; Ladd et al., 2009; Schleppi et al., 2010). Furthermore,

there is considerable variation between the LAI−climate rela-

tionships reported in different studies. For example, Grier &

Running, (1977) showed that LAI was linearly dependent on

mean annual precipitation (MAP), while Schleppi et al. (2010)

found a saturation effect. Since these studies were conducted

on a regional scale, differences can probably be ascribed to

limited numbers of observations, shallow gradients for envi-

ronmental variables and/or site-specific effects such as soil

nutrient content and geography. The global dependence of

LAI on climate variables thus remains unclear. Quantifying

this dependence could make a large contribution to a better

understanding of the factors determining the global distribu-

tion of LAI, and thus to reliable predictions of carbon and

water cycles, vegetation–climate feedback effects and climate

change.

LAI may be different for different plant functional types

(PFTs). For example, many authors have stated that evergreen

forests have higher LAIs than deciduous forests (e.g. Gower

et al., 1993; Asner et al., 2003; Larcher, 2003). This has been

ascribed to clumping of foliage, which allows deeper penetration

of light (Niinemets & Anten, 2009), and a longer leaf life span

(Gower et al., 1993; Niinemets, 2010). However, previous

studies have not analysed between-PFT variation in LAI in rela-

tion to climate variables. The extent to which these putative

between-PFT differences are consistent across the globe there-

fore remains unclear.

Recent advances in satellite-based remote sensing enable us to

use globally continuous LAI datasets. However, these remote

sensing techniques suffer from serious uncertainties that arise

from difficulties in both reflectance measurements (e.g. satura-

tion in dense canopies) and empirical assumptions made during

data processing, especially with respect to cloud contamination,

canopy architecture and vegetation classification (Garrigues

et al., 2008). Comprehensive analysis of field-observed LAI is

therefore necessary for a reliable evaluation of the dependence

of LAI on climate variables. However, we are currently aware of

only one previous study that has analysed field-observed LAI on

a global scale, that of Asner et al. (2003).

Asner et al. (2003) collected data from 1008 stands and

showed that LAI varies greatly between biomes and PFTs.

However, they did not study the dependence of LAI on climate

variables, so their results are of limited use for predicting the

responses of LAI to climate change. Furthermore, they did not

use a standardized definition of LAI. This lack of a clear defini-

tion is particularly problematic for species like conifers that have

non-flat leaves, because in these cases the LAIs can vary more

than three-fold depending on the definitions used (Barclay,

1998). Standardization of the definition of LAI is therefore nec-

essary for reliable analysis.

Another difficulty facing those attempting comprehensive

analysis of LAI is that the accuracy of estimates of LAI is strongly

dependent on the method of measurement used (Gower et al.,

1999; Bréda, 2003; Jonckheere et al., 2004). Indirect optical (IR)

methods, which estimate LAI from measurements of the inter-

ception of light by the canopy, are generally considered to

underestimate LAI because they cannot detect the effect of

foliage clumping (which is expressed as the clumping index, Ω).

Thus, detailed classification of measurement methods and cor-

recting the bias of IR estimates of LAI (by applying a typical Ωvalue from standard literature) is necessary before analysis, but

this was not done in the study of Asner et al. (2003).

In this study, we developed a new global database of LAI for

woody species of unprecedented size (about 2600 observations).

In it, we used a standardized definition of LAI and corrected the

bias in IR estimates of LAI. Our primary objectives were to

characterize the global dependence of LAI on climate variables

and to determine the extent to which this relationship differs

between PFTs.

METHODS

Data compilation

A list of abbreviations used here for terms and variables is shown

in Table 1. We obtained a total of 2606 observations of field LAI

from 554 literature sources (Appendices S1 and S2 in Support-

ing Information), all published between 1932 and 2011 (Fig. 1).

Most of the data were plot-based. As far as possible, we chose

site-specific maximum LAI values, and therefore excluded LAI

data points with low values that were obviously due to dry

season measurements, disturbances or stands being immature

or old and declining. In addition, we excluded LAI data affected

by artificial treatments such as CO2 enrichment, continuous

fertilization and irrigation. We included data from plantations,

and divided the vegetation status for records in the dataset into

three subgroups: ‘plantation’ (trees had been planted), ‘natural

forest’ (trees had grown naturally) and ‘not described’. Plot-

related information, such as the name of the dominant species,

latitude, longitude, elevation and climate variables, was also

included. PFTs of species were classified into six groups based on

leaf phenology and leaf shape (Table 2, Fig. 2c,d). The biome

type of each plot was derived from a gridded global dataset of

the Köppen–Geiger classification (Peel et al., 2007).

We standardized the definition of LAI as half of the total leaf

surface area (HSA) per unit ground area (Chen & Black, 1992).

For conversion from the other definitions to HSA, we employed

the factors published by Cannell (1982); the ratio of HSA to

projected leaf area is 1.4 for pine, 1.15 for other conifers and 1.0

for broadleaf species. When the definition was not provided in

the source literature, we assumed that: (1) LAI for flat broadleaf

species was defined on a projected leaf area basis, because other

definitions are rarely employed for this leaf type, and (2) LAI for

conifer species measured with indirect optical instruments was

defined as HSA (Stenberg et al., 1994). The data that did not fit

these assumptions were excluded.

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Measurement methods for LAI were separated into four

groups: (1) direct methods (D, e.g. litter trapping, allometric

approaches); (2) indirect optical methods that assumed foliage

randomness (IR, i.e. measurements of canopy gap fraction using

a plant canopy analyser, fish-eye cameras or quantum sensors);

(3) indirect optical methods that allowed for non-randomness

of foliage, i.e. clumping (IC, combining measurements of gap

fraction and of gap-size distribution in the canopy or direct

calibration against D estimates of LAI); and (4) other methods

(OT, e.g. point quadrat sampling, litter trapping for evergreen

species and other empirical approaches). Details of each meth-

odology and the theory underlying it can be found elsewhere

(e.g. Gower et al., 1999; Bréda, 2003; Jonckheere et al., 2004).

We corrected the potential bias associated with IR methods in

order to increase the reliability and general applicability of our

analysis. Although the source of the bias is mainly attributable to

clumping of foliage in the canopy and to interception of radia-

tion by woody elements, we assumed that the contribution of

the latter factor to LAI estimates was relatively small (Kucharik

et al., 1997). Consequently, each LAI value obtained from an IR

method (effective leaf area index, LAIe) was converted to ‘true’

LAI using the formula:

LAI LAIe= Ω (1)

where Ω is the total clumping index. Ω can be expressed in terms

of the element clumping index (Ωe; leaf and shoot element for

broadleaf species and conifers, respectively) and the within-

shoot (i.e. needle) clumping index (γe):

Ω Ω= e eγ . (2)

For flat broadleaves, γe is equal to 1.0. These clumping indices

were derived from the global database compiled by Pisek et al.

(2011). Since we found that the effects of climate on clumping

indices were generally marginal (i.e. correlations were weak;

Appendix S3), we used the global mean values of Ωe and γe (0.85

and 1.52, respectively) for correcting LAIe.

Climate variables

Mean annual temperature (MAT; °C) and MAP (mm) were

derived from the gridded global dataset CRU CL 2.0 (10′ × 10′),

containing monthly means (long-term average, 1961–90; New

et al., 2002). The net radiation (Rn; MJ m−2 day−1), vapour pres-

sure deficit (VPD; kPa) and Penman–Monteith potential evapo-

transpiration (PET; mm) were also calculated from global

climate variables derived from CRU CL 1.0 (0.5° × 0.5°) and 2.0

according to FAO-56 guidelines (Allen et al., 1998). We also

calculated the ratio of MAP to PET to give the wetness index

(WI), which represents plot water availability. The climate vari-

ables for each plot were summed (precipitation, PET) or aver-

aged (other variables) across all months of the year. When MAT

and MAP were given in the source literature we used those

values. In addition, we calculated the mean temperature and

wetness index during the growing season (MGT and MGW,

respectively; the definition of the growing season is given in

Appendix S4).

Data analysis

LAI was log10-transformed to meet assumptions of normality

and homogeneity for regression analysis, as is common practice

in global meta-analysis of leaf traits (e.g. Wright et al., 2004). In

addition, since the dependences of LAI on WI showed curvilin-

ear relationships (data not shown), WI values were also

log10-transformed to linearize the relationships. To avoid

misinterpretation of the results, we determined outliers by

applying an interquartile range approach (Appendix S4) to data

pooled for all PFTs, and removed these outliers from all statis-

tical analyses presented here. Mean LAI values were compared

among biomes, PFTs, vegetation states and measurement

methods using one-way ANOVA, followed by Tukey’s post hoc

comparisons when effects were significant.

Given the significant correlations among climate variables

(Appendix S5), we characterized climate on the basis of two

variables, MAT and WI, which are critical in shaping vegetation

distribution and structure. Since the global dependences of LAI

on MAT and WI were nonlinear (Fig. 2), we divided each rela-

Table 1 A list of abbreviations of terms and variables.

Term or

variable Explanation Unit

D Direct method

IR Indirect optical method assuming leaf

randomness

IC Indirect optical method taking into account

leaf clumping

O Other methods

PFT Plant functional type

DB Deciduous broad leaf

DC Deciduous conifer

EB Evergreen broadleaf

EC Evergreen conifer

sDB Semi (dry)-deciduous

Mix Mixture of various functional types

HSA Half of the total leaf surface area

GLMM General linear mixed model

PRC Partial regression coefficient

LAI Leaf area index m2 m−2

LAIe Effective leaf area index (i.e. IR estimates

of LAI)

m2 m−2

Ω Clumping index

Ωe Element clumping index

γe Within-shoot (needle) clumping index

WI Wetness index

MAT Mean annual temperature °C

MAP Mean annual precipitation mm

MGT Mean temperature during growing season °C

MGWI Mean wetness index during growing season

PET Potential (reference) evapotranspiration mm

VPD Vapour pressure deficit kPa

Rn Net radiation MJ m−2 day−1

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tionship into groups according to the positions of the points of

inflection between groups. The inflection points were objec-

tively determined using a bent-line model (i.e. by fitting a bent

line with segment regression; Chappell, 1989). For pooled data

for all PFTs, the LAI−MAT relationship was divided into three

temperature groups with the inflection points at 8.9 and 18.8 °C

(Fig. 2a). Going from low to high temperature, the groups were

denoted ‘Cool’, ‘Temperate’ and ‘Warm’, respectively. The

LAI−WI relationship was divided into two groups with the

inflection point at log WI = 0.30. The groups below and above

the boundary were denoted ‘Dry’ and ‘Wet’, respectively. The

LAI−MAT relationship for deciduous broadleaves (DB) and

evergreen conifers (EC) was also divided into two groups with

the points of inflection at 8.6 and 9.7 °C, respectively, but that

for evergreen broadleaves (EB) was not divided into groups

(Fig. 2e). The LAI−WI relationship for DB was divided into two

groups with the inflection point at log WI = 0.03, but those for

EB and EC were not divided into groups (Fig. 2f). The nomen-

clature used for the climate groups for each individual PFT

followed that used for the all-PFT pooled data.

Figure 1 (a) Global distribution of field plots included in the leaf area index (LAI) database. (b) The relationship between mean annualwetness index (WI) and temperature (MAT), classified by mean annual precipitation (MAP).

Table 2 Statistical metrics of leaf areaindex for all pooled data, biomes, plantfunctional types and vegetation states(see Table 1 for abbreviations).

Category Mean SD Max. Min.

n

Erroneous* Outlier OriginalTotal

All 4.21 1.84 23.50 0.69 2393 104 109 2606

Biome

Tropical 4.13 a 1.90 22.40 0.70 234 28 9 271

Dry 2.71 b 2.14 12.67 0.71 107 14 50 171

Temperate 4.70 a 1.83 23.50 0.69 910 62 7 979

Continental 4.04 a 1.76 16.66 0.72 1117 0 36 1153

Polar 3.17 b 2.17 9.68 0.72 25 0 7 32

PFT

EC 4.93 a 1.82 23.50 0.75 902 0 12 914

DB 4.09 ab 1.65 12.10 0.72 809 0 11 820

EB 3.48 b 1.99 22.40 0.69 517 104 72 693

DC 2.77 c 1.85 10.81 1.18 24 0 6 30

sDB 3.95 ab 2.31 17.40 0.72 57 0 2 59

Mix 3.80 b 1.76 10.31 0.75 84 0 6 90

Vegetation state

Plantation 4.87 a 1.76 20.13 0.72 442 19 0 461

Natural 3.92 b 1.91 23.50 0.69 1298 43 90 1431

Mix 6.61 c 1.45 9.50 1.33 40 2 1 43

Not described 4.26 ab 1.71 18.50 0.75 613 40 18 671

Significant differences (α = 0.05) between mean values are indicated by different letters.*Potentially erroneous estimates of LAI by the indirect optical methods were excluded, on the basis ofthe results of general linear mixed model analysis (Appendices S8 and S9).

Global dependence of leaf area index on climate

Global Ecology and Biogeography, 23, 274–285, © 2013 John Wiley & Sons Ltd 277

The LAI−climate relationship within each climate and PFT

group was quantified using a general linear mixed model

(GLMM):

LAI MAT WI= + + +a b c· · ε (3)

where the parameters a and b are model coefficients, c is the

intercept and ε is the random effect. We employed ‘site’, which

was defined as a data group within which the geographical coor-

dinates (spatial resolution of 0.1°) were the same, as a random

effect to incorporate into the analysis the fact that plots within a

site may be non-independent due to a similarity of growth envi-

ronments. MAT and WI were significantly correlated in most of

the data groups, but the correlations were generally weak

(r2 < 0.240; Appendix S5) irrespective of climate group and PFT.

Thus, the problem of collinearity is marginal in our analyses.

After analysing the effects of MAT and WI, we evaluated whether

or not other climate variables, such as VPD, Rn, MGT and MGW,

improve the predictive power of the GLMM, based on Akaike

information criteria (AIC; Appendix S6).

The effects of measurement methods and PFTs on the

climate−LAI relationships were also analysed with a GLMM:

LAI MAT WI FAC MAT

FAC WI FAC

= + + +× + × + +a b c d

e f

· · · ·

· ε(4)

Figure 2 Dependence of leaf area index (LAI) on mean annual temperature (MAT) and on wetness index (WI): (a), (b) all data pooled;(c), (d) classified by plant functional type (PFT); (e), (f) the three major PFTs, with averaged data (1 °C and 0.1 intervals for MAT and WI,respectively) for each PFT shown as a thick dark symbol. The average data were removed if the number of data points was less than 10. Thelines indicate the best result of the bent-line model. Abbreviations: DB, deciduous broad leaf; EB, evergreen broadleaf; EC, evergreenconifer; DC, deciduous conifer; sDB, semi (dry)-deciduous; Mix, mixture of various functional types.

A. Iio et al.

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where FAC is a categorical factor, i.e. either measurement

method (D versus IR after application of Ω correction) or PFT

(DB, EB or EC), parameters a to e are model coefficients and f is

the intercept. The effect of the categorical factors was evaluated

with a null hypothesis test (t-test) of parameters c to e which

represent the effect of the categorical on the intercept, MAT

response and WI response, respectively. We first examined the

effect of Ω correction on IR estimates of LAI. If the LAI esti-

mates from corrected IR differed significantly from the D esti-

mates, we regarded them as potentially erroneous and excluded

them from all other analyses. We also examined the effect of

vegetation state (‘plantation’ versus ‘natural forest’) but found

that differences between them were mostly marginal (Appendix

S7). All statistical analyses were performed using spss software

(IBM spss 19.0, Armonk, NY, USA) and the R software package

(R Core Team, 2012). Further details of the methodology are

described in Appendix S4.

RESULTS

To exclude the potential bias introduced by indirect optical

methods that assume foliage randomness (IR), we first exam-

ined the effect of clumping correction (i.e. applying a typical

clumping index, Ω, to IR estimates; D versus corrected IR) on

the LAI−climate relationships for individual PFTs (Appendix

S8). Since the IR estimates of LAI were generally lower than the

D estimates, applying Ω correction lessened the bias in most

data groups. However, it still caused significant underestimation

of LAI for EB forests in the MAT range 10−20 °C (Appendix S9).

Although significant differences in the slope of the LAI−MAT

relationship were also found for EC under cool conditions, the

LAI difference for a given MAT was marginal because the two

regression lines crossed at the midpoint of the MAT range (Fig.

e in Appendix S8). We therefore excluded the IR estimates of LAI

for EB in the MAT range 10–20 °C from all subsequent analyses

as being erroneous. In the other data groups, the corrected IR

estimates of LAI were included in the analyses.

The global mean LAI value in the database was 4.21 (Table 2).

LAI differed significantly among biomes, with ranking (high to

low) being temperate ≈ tropical ≈ continental > polar ≈ dry

climate. Although we classified the dataset into six PFTs, more

than 93% of the total records were obtained from three PFTs:

EC, DB and EB. The mean LAI was lower for EB than for DB and

EC, while the standard deviation was markedly larger for EB

than for the other PFTs. Plantations had significantly higher

LAIs than natural vegetation.

The global dependence of LAI on MAT showed a reverse

S-shaped pattern which was highest at around 8.9 and 25.0 °C

and lowest at around −10.0 and 18.8 °C (Fig. 2a). The depend-

ence on WI was curvilinear, saturating at around log WI = 0.30

(Fig. 2b). These dependences were quantified using a GLMM

applied to individual climate groups (equation 3, Fig. 3).

Although LAI showed strong (P < 0.01) and positive correla-

tions with MAT under cool dry conditions, the opposite trend

was found under temperate dry conditions. Under warm dry

conditions, LAI was weakly correlated with MAT. The depend-

ences of LAI on MAT were generally marginal under wet con-

ditions, irrespective of temperature group. With respect to WI,

significantly positive dependences of LAI were found under dry

conditions irrespective of temperature group (Fig. 3b). By con-

trast, LAI was negatively correlated with WI under warm wet

conditions.

The dependence of LAI on climate variables differed among

the three major PFTs (Fig. 2e,f). Although the LAI−MAT rela-

tionship for DB and EC showed a bell-shaped pattern peaking at

MAT = 9.8 and 7.9 °C, respectively, no correlation was found for

EB. With respect to WI, the LAI for evergreen forests increased

linearly with increasing WI, but that for DB showed a curvilin-

ear pattern, saturating at around log WI = 0.03. To examine

these differences in more detail, the LAI−climate relationships

for individual PFTs were compared for the same climate groups

(Fig. 4, Table 3). EB data under cool conditions (MAT < 8°C)

were excluded from the analysis due to the small number of data

points available. Under cool wet conditions, the slope of the

LAI−WI relationship was significantly steeper for EC than for

DB (Fig. 4b,d). Consequently, LAI in a given climate condition

was generally higher for EC than for DB. However, such differ-

ences disappeared under temperate conditions, due mainly to an

increase in LAI for DB relative to EC in a given WI (Fig. 4d,e).

The slope of the LAI−WI relationship was significantly greater

for EC than for EB. Consequently, LAI in a given climate variable

was higher for EC than for EB (Fig. 4c,f). LAI for DB also tended

to be higher than that for EB, though GLMM analysis showed

that the differences were generally marginal (Fig. 4a,e).

When VPD and Rn were incorporated into the model, and

when MGT and MGWI, respectively, were employed instead of

MAT and WI, there was no improvement in the predictive power

for most of the climate groups (Appendix S6), indicating that

MAT and WI explained most of the variance in LAI.

DISCUSSION

The LAI−climate relationship

The global LAI−WI relationship showed a saturation pattern,

with the inflection point at around log WI = 0.30 (Fig. 2b). Since

irrigation of forests in dry regions increases the LAI (Dobbertin

et al., 2010), the significant positive LAI−WI relationship

observed under dry conditions indicates that the LAIs are

limited by site water availability. Although a decrease in LAI

under water-limited conditions reduces light harvesting by the

canopy, it also reduces water loss from the leaves, thus allowing

higher stomatal conductance and photosynthesis per unit leaf

area. Optimality modelling of forest carbon exchange indicates

that this strategy may increase carbon gain under water-limited

conditions (McMurtrie et al., 2008; Sterck et al., 2011). Under

wet conditions, the slope of the LAI−WI relationship was mark-

edly lower than that under dry conditions (Fig. 3b). This may

indicate that resources other than water limit biomass produc-

tion and thus LAI. However, under warm wet conditions, LAI

was negatively correlated with WI. This is probably associated

with the negative effect of heavy precipitation on LAI (Santiago

Global dependence of leaf area index on climate

Global Ecology and Biogeography, 23, 274–285, © 2013 John Wiley & Sons Ltd 279

et al., 2000; Schuur et al., 2001), which most likely results from

indirect effects such as low irradiance due to frequent cloud and

fog and reductions in plant nutrient uptake due to nutrient

leaching from the soil and to hypoxic limitation of litter decom-

position and root activity.

The present study found that the global relationship between

LAI and MAT shows a reverse S-shaped curve with the highest

points at 8.9 and 25.0 °C and the lowest at −10.0 and 18.8 °C

(Fig. 2a). Such a pattern has not been observed in any previous

study; for example, a bell-shaped pattern was reported for

LAI−MAT relationships by Battaglia et al. (1998 and Luo et al.

(2004). This discrepancy probably lies at least partly in the fact

that the previous studies were made at a regional scale and for

limited ranges of MAT. Our dataset suggests that the pattern of

global LAI−climate relationships is more complicated than has

been believed up to now.

A positive LAI−MAT relationship under cool conditions is to

be expected, because it is known that LAI tends to be lower

at high latitudes (Van Cleve et al., 1983; Larcher, 2003). This

may be explained partly by nutrient availability, since soil

nitrogen (N) availability increases with increasing MAT in cool

climates (Craine et al., 2009) because the decomposition and

mineralization processes are limited by low soil temperature

(Robinson, 2002), and LAI increases with increasing nitrogen

availability (Anten et al., 1995; Hikosaka, 2003; McMurtrie

et al., 2008). Lower Rn at high latitudes may also be associated

with the positive LAI−MAT relationship, because Rn decreases

with decreasing MAT across our entire dataset (r2 = 0.741,

P < 0.001), and because LAI decreases with decreasing light

availability (Hikosaka, 2003).

A significant depression in LAI was found at around

MAT = 19 °C under dry conditions (Figs 2a & 3). This depres-

sion may be ascribed to the fact that many of the world’s drier

regions that are characterized by low LAI (e.g. open woodland in

savanna; warm Mediterranean climates) tend to have an annual

MAT of around 19 °C (Fig. 1b). Although a significant negative

dependence of LAI on MAT was also found, exclusion of the

MAT term from the model (equation 3) had little influence on

the predictive power (AIC increased by only 0.8), indicating that

contribution of MAT to the depression is marginal. We thus

concluded that the depression of LAI observed at around 19 °C

is associated with water availability.

It should be mentioned that our data tend to concentrate on

those regions where ecologists have conducted extensive studies,

Figure 3 Result of general linear mixedmodel analysis (equation 3) for thedependence of leaf area index (LAI) onclimate variables. (a) Partial regressioncoefficient (PRC) of the mean annualtemperature (MAT) term. (b) PRC of thewetness index (WI) term. ns, P ≥ 0.05;*P < 0.05; **P < 0.01; ***P < 0.001. Otherparameters are shown in Appendix S11.

A. Iio et al.

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such as the USA, Europe, Australia and Japan (Fig. 1a). Conse-

quently, under temperate and warm conditions, data were rela-

tively sparser from wet conditions than from dry conditions

(Fig. 1b), indicating that results from our dataset may overem-

phasize the depression of LAI at around 19 °C. We thus stress the

importance of making additional measurements in warm and

wet climates, such as the humid subtropics, which are mainly

distributed in the southern part of China and in Brazil and

Africa.

Our results support the general ideas underlying previous

studies that temperature and water availability play the pre-

dominant roles in controlling LAI. However, previous studies

characterized global LAI−climate relationship at the biome scale

(Asner et al., 2003; Larcher, 2003), which was too coarse a level

to predict global distribution of LAI. We characterized the

details of the dependence of LAI on climate at the plot scale and

our results can be summarized as follows: (1) water availability

limits LAI at log WI < 0.30; (2) temperature limits LAI at

Figure 4 Dependence of leaf area index (LAI) on mean annual temperature (MAT) and on wetness index (WI), classified by climate groupand plant functional type (PFT). When comparing deciduous broadleaf (DB) with evergreen conifer (EC) and broadleaf (EB), dry and wetconditions for evergreen species were defined as log WI ≤ 0.1 and > −0.1, respectively, and cool conditions for EB were defined as8 °C < MAT ≤ 22 °C. Data were fitted by a general linear mixed model (y = ax + b; the regression parameters are not shown).

Table 3 Result of general linear mixedmodel analysis (equation 4) for the effectof plant functional types (PFTs) on leafarea index−climate relationships.

Data group Model term n

Climate PFT MAT × PFT WI × PFT PFT DB EC EB

Cool dry EC vs. DB ns ns ns 198 352

Cool wet EC vs. DB ns *** ns 367 506

Temperate dry DB vs. EC ns ns ns 96 123

Temperate wet DB vs. EB ns ns * 96 283

Temperate dry DB vs. EC ns ns ns 148 257

Temperate wet DB vs. EB ns ns ns 148 261

Temperate EC vs. EB *** ** *** 293 430

When comparing deciduous broadleaf (DB) with evergreen conifer (EC) and broadleaf (EB), dry andwet conditions for evergreen species were defined as log WI ≤ 0.1 and > −0.1, respectively, and coolconditions for EB were defined as 8 °C < MAT ≤ 22 °C.Significance for each model term is shown as follows: ns, P ≥ 0.05; *P < 0.05; **P < 0.01; ***P < 0.001.

Global dependence of leaf area index on climate

Global Ecology and Biogeography, 23, 274–285, © 2013 John Wiley & Sons Ltd 281

MAT < 8.9 °C; and (3) heavy precipitation limits LAI in warm

and humid climates (MAT > 18.8 °C and WI > 0.30). The

LAI−climate relationships investigated here may help us to gain

a better understanding of the global distribution of LAI and

ecosystem CO2 fluxes and their possible responses to climate

change over the next few centuries.

Differences in LAI−climate relationshipsbetween PFTs

It has been widely recognized that LAIs of EC forests are higher

than those of DB forests within a given biome (Asner et al.,

2003; Larcher, 2003) and site (Gower et al., 1993). In this study,

LAI under cool conditions was generally higher for EC than for

DB (Table 3, Fig. 4), which is consistent with this idea. One

explanation for the difference in LAI between PFTs may be the

fact that foliage clumping is higher in EC than in DB. Since

foliage clumping increases irradiance in deeper canopy layers

(Niinemets & Anten, 2009), such plants can maintain high LAIs

(Hikosaka & Hirose, 1997). Longer leaf life span in EC than in

DB has also been believed to contribute to this difference,

because an increase in leaf life span enables plants to support a

greater foliage area (Gower et al., 1993; Niinemets, 2010). The

fact that LAI was higher for EC than for EB under temperate

conditions is also consistent with these assumptions. However,

the above-mentioned differences in LAI between DB and EC

disappeared under temperate conditions, suggesting that the

idea that EC forests have larger LAIs than DB forests is not

globally applicable. The lack of between-PFT differences under

temperate conditions was mainly associated with an increase in

LAI for DB in temperate conditions relative to cool conditions

(Fig. 4d,e). Although there is no theory that explains why EC

have similar LAI to DB in these conditions, many studies have

reported an empirical relationship that more productive canopy

tends to have higher LAI (e.g. Fassnacht & Gower, 1997; Luo

et al., 2004). A longer growing season in temperate conditions

than in cool conditions would particularly benefit DB species,

which tend to have higher photosynthetic capacities than EC

species. By contrast, for evergreen species, carbon loss due to

foliage respiration during the dormant season would increase

with increasing growth temperature. These contrasts in physio-

logical and phenological characteristics between PFTs may

explain the lack of the differences in LAI between DB and EC.

We also found that not only EC but also EB showed similar LAI

to that of DB, although leaf life span is generally longer in EB

than in DB. This suggests that leaf life span is not one of the

principal factors accounting for between-PFT variations in LAI.

PFT-dependent variations in LAI were relatively small com-

pared with the climatic effects, suggesting that climate has a

more important influence on LAI than does PFT. However,

LAI−climate relationships were apparently different between

PFTs, especially for WI (Table 3). For example, LAI values for

evergreen species were linearly correlated with WI, while the

LAI−WI relationship for DB exhibited a saturation pattern

(Fig. 2f). This result suggests that LAIs for individual PFTs may

respond differently to climate change. These differential

responses may, in turn, have important implications for regional

and global patterns of carbon, water and nutrient cycling under

climate change as well as for feedback between vegetation and

climate.

Uncertainty in our analysis

Although LAI was found to be significantly correlated with

MAT, WI and PFTs, there was still a fair degree of unexplained

variation in our global LAI data set. This variation could stem

from methodological inaccuracies and/or biotic and abiotic

factors that are not considered in this analysis. Regarding meth-

odological uncertainties, different experimental designs used in

the source studies, such as plot sizes and number of samplings,

could have been responsible for some of the unexplained vari-

ation in our database. Furthermore, the extensive time period

over which the source literature was published (1932−2011)

could lead to uncertainty because measurement techniques,

especially for the IR approach, have changed considerably over

time and because ambient CO2 concentration has increased by

more than 50 p.p.m. in the past 50 years, which may have

affected LAI (Norby et al., 2005; McCarthy et al., 2006). The fact

that only 6% of the total number of records explicitly included

understorey LAI (grass/herb/shrub layers) may also cause uncer-

tainty, especially in the case of regions of sparse vegetation such

as savanna and tundra. However, existing data showed that

understorey LAI was fairly constant at around 1.0 irrespective of

overstorey LAI (Appendix S10) and climate (data not shown),

probably because of severe environmental limitations on LAI in

sparse vegetation. Consequently, inclusion of the understorey

LAI in GLMM analysis (i.e. overstorey LAI plus 1.0) did not

affect our results. We thus conclude that the uncertainty due

to our not having explicitly considered understorey LAI is

marginal.

With respect to biotic and abiotic factors, between-site differ-

ences in soil nutrient availability, a factor which is known to

affect LAI (Anten et al., 1995; Hikosaka, 2003), could also

account for some of the variation in LAI in our dataset. Further-

more, although we excluded data where LAI values were obvi-

ously low due to forest stands being either immature or old and

declining, differences in stand age could have caused the vari-

ation in LAI, because in some cases LAIs gradually decrease after

a peak age and the rate of decline can differ considerably

between species and growth conditions (Ryan et al., 1997).

Physiological variability among species within each PFT could

also be an important factor contributing to the variability in

LAI. Moreover, natural and human disturbance (e.g. change of

land use, air pollution, wildfire, typhoon-mediated windfall) in

the past few decades could have caused variation in LAI. All of

these factors probably contributed to the variation in our LAI

analysis, but it is difficult to evaluate these uncertainties. Never-

theless, we obtained strongly statistically significant results

(P < 0.01), most of which are consistent with previous ideas. We

thus believe that these uncertainties are unlikely to cause signifi-

cant changes in the observed patterns of global relationships

between LAI, climates and PFTs presented in this paper.

A. Iio et al.

Global Ecology and Biogeography, 23, 274–285, © 2013 John Wiley & Sons Ltd282

ACKNOWLEDGEMENTS

We thank many researchers for providing additional informa-

tion about their published studies. We also thank Dr D. Kabeya

and Dr K. Nishina for helpful suggestions about statistical analy-

sis. This study was supported by a Grant-in-Aid for Scientific

Research on Innovative Areas in Japan ‘Comprehensive studies

of plant responses to high CO2 world by an innovative consor-

tium of ecologists and molecular biologists’ (no. 22114513).

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Note: Additional references for the LAI dataset (Appendix S1)

can be found in Appendix S2 at [http://onlinelibrary.wiley.com/

doi/10.1111/geb.12133/suppinfo].

SUPPORTING INFORMATION

Additional supporting information may be found in the online

version of this article at the publisher’s web-site.

Appendix S1 Data set.

Appendix S2 Reference list for the data set.

Appendix S3 Dependency of clumping indices on mean annual

temperature and on wetness index.

Appendix S4 Full description of the Material and Methods.

Appendix S5 Correlation coefficient and significance of linear

regression analysis between all climate variables and leaf area

index.

Appendix S6 Comparison of generalized linear mixed

models for leaf area index−climate relationship based on

the AIC.

A. Iio et al.

Global Ecology and Biogeography, 23, 274–285, © 2013 John Wiley & Sons Ltd284

Appendix S7 Differences in leaf area index−climate relation-

ships according to vegetation state.

Appendix S8 Differences in leaf area index−climate relation-

ships according to measurement method and clumping

correction.

Appendix S9 Result of generalized linear mixed model analysis

for the effect of measurement methods on leaf area index−climate relationships.

Appendix S10 The relationship between understorey and

overstorey leaf area index.

Appendix S11 Result of generalized linear mixed model analysis

of dependency of leaf area index on mean annual temperature

and wetness index.

BIOSKETCH

A. Iio is a post-doctoral researcher at the National

Laboratory of Environmental Studies (NIES) in Japan.

His current research interest is global meta-analysis of

key parameters of ecosystem process-based models.

Author contributions: K.H., N.A and A. Ito conceived

the study. A. Iio collated data, undertook analysis and

wrote the manuscript. Y.N. helped with programming

and statistical analysis. K.H., N.A and A. Ito revised the

manuscript and contributed to the final version.

Editor: Ian Wright

Global dependence of leaf area index on climate

Global Ecology and Biogeography, 23, 274–285, © 2013 John Wiley & Sons Ltd 285