Plant Functional Traits and the Distribution of West African Rain Forest Trees along the Rainfall...

Plant Functional Traits and the Distribution of West African Rain Forest Trees alongthe Rainfall Gradient

Surya K. Maharjan1,2, Lourens Poorter1,2,5, Milena Holmgren2, Frans Bongers1, Jan J. Wieringa3, and William D. Hawthorne4

1 Forest Ecology and Forest Management Group, Wageningen University, PO Box 47, 6700 AA Wageningen, The Netherlands

2 Resource Ecology Group, Wageningen University, PO Box 47, 6700 AA Wageningen, The Netherlands

3 National Herbarium of The Netherlands—Wageningen University Branch, Biosystematics Group, Wageningen University,

General Foulkesweg 37, 6703 BL Wageningen, The Netherlands

4 Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK

ABSTRACT

Plant morphological and physiological traits affect the way plants tolerate environmental stresses and therefore play an important role in shaping species distributionpatterns in relation to environmental gradients. Despite our growing knowledge on the role of functional traits in structuring plant communities, few studies havetested their importance at large scales in the wet tropics. Here, we describe the distribution patterns of the most important West African rain forest timber species alongthe regional rainfall gradient and relate them to their functional traits. We found that the distribution patterns of 25 out of the 31 studied species (80%) weresignificantly related to mean annual rainfall. Shade tolerance and drought resistance were identified as the main strategy axes of variation. Wood density and leafdeciduousness emerged as the best predictor traits of species position along the rainfall gradient, explaining respectively 32 and 15 percent of the variation. Species traitstended to show stronger relationships with estimated optimum annual rainfall for each species than to the extreme rainfall conditions where they occur. The significantrole of rainfall in shaping timber species distribution and the strong relationships between species traits and rainfall indicate that changes in climate, especially decliningrainfall, could have strong effects on species composition and abundance in these tropical forests.

Key words: deciduousness; drought tolerance; plant strategy; rainfall; shade tolerance; species distribution; upper Guinea; wood density.

FUNCTIONAL RESPONSE TRAITS DETERMINE PLANT GROWTH, SURVIVAL

AND REPRODUCTIVE SUCCESS, and as a result, they are expected to play

an important role in shaping species distribution patterns along en-

vironmental gradients. The study of how functional response traits

affect the way plants tolerate environmental stresses and cope withdisturbances has played a central role in the development of eco-

logical theories (Grime 1974, 1977; Keddy 1992; Westoby 1998).

Most large-scale generalizations have compared the predominance

of morphological traits across vegetation types and evaluated their

potential adaptive role along environmental gradients (Ackerly

2004, Thuiller et al. 2004). In contrast, more detailed analysis

within vegetation types have concentrated on relationships between

functional traits at smaller-scale environmental gradients related to,for example, topography and exposition (Ackerly et al. 2002) and

irradiance and soil fertility (Veenendaal et al. 1996, Agyeman et al.1999). As reliable environmental response curves are lacking for

most tropical species, our knowledge of the importance of func-

tional traits for species partitioning of environmental gradients is

particularly poor for the tropics.

This study focused on the rainfall gradient, which is an im-

portant environmental factor determining plant species distribu-tion (Swaine 1996, Bongers et al. 1999, Engelbrecht et al. 2007),

composition (Hall & Swaine 1976, Bongers et al. 2004, Toledo

et al. 2011) and richness (ter Steege et al. 2006) in tropical forests.

Rainfall in West Africa has declined more rapidly than in any other

rain forest region during the past four decades (Malhi & Wright

2004), which could severely impact plant and animal diversity in

the region.

Plant species vary considerably in their morphological andphysiological traits giving rise to a continuum of plant strategies

(Reich et al. 2003). One fundamental axis of evolutionary special-

ization across ecosystems and biomes is that of high resource cap-

ture (rapid acquisition of resources) at one end of the spectrum and

high resource conservation (conservative use of resources) at the

other (Diaz et al. 2004). Consequently, along the rainfall gradient

rapid water acquisition and conservative water use could be impor-

tant strategies for the success of plant species. Conservative wateruse can be achieved through efficient use of limited water (i.e.,drought tolerance) or through drought avoidance (Markesteijn &

Poorter 2009). Drought-tolerant species are, in general, character-

ized by narrow and small leaves (Fonseca et al. 2000, Thuiller et al.2004), low specific leaf area (SLA) (Fonseca et al. 2000, Niinemets

2001) and high wood density (Searson et al. 2004, Poorter &

Markesteijn 2008). Drought-avoiding species are characterized by

deciduous leaves (Borchert 1994, Markesteijn & Poorter 2009) toavoid transpirational water loss in the dry season, and low wood

density (Borchert 1994) that allows for water storage in stems.

With increasing rainfall, forests also tend to become taller and

cast a deeper and more persistent shade. To regenerate successfully

in these wetter, more shaded environments species should be more

shade tolerant (Smith & Huston 1989), and have larger seeds toReceived 29 January 2010; revision accepted 23 September 2010.5Corresponding author; e-mail: lourens.poorter@wur.nl

BIOTROPICA 43(5): 552–561 2011 10.1111/j.1744-7429.2010.00747.x

552 r 2011 The Author(s)

Journal compilation r 2011 by The Association for Tropical Biology and Conservation

provide the seedling with sufficient seed reserves to survive in shade

(Grubb 1998, Leishman et al. 2000). Competition for light favors

larger maximum plant heights (Fonseca et al. 2000), and due to

little wind within closed canopy forests seed dispersal is more oftenby animals (Bullock 1995). Clearly, plants have to cope with mul-

tiple environmental conditions, and they can do so through differ-

ent traits. A question, therefore, is how these traits are associated,

and what plant strategies can be distinguished.

Here we studied the distribution patterns of 31 West African

rain forest timber species in relation to the rainfall gradient, and

related these patterns to 25 traits that have been identified in the

literature as important for plant growth, survival and reproduction.The use of timber species has several advantages: first, forest inven-

tories have been done at a sufficiently large scale and intensity to get

reliable distribution data; second, these species have been invento-

ried in several countries covering a large rainfall gradient; third, a

considerable amount of trait information is available in the litera-

ture; and fourth, it is important to know how these commercially

important species may respond to climate change. The selection of

timber species may, to some extent, also affect the results as timberspecies may not cover the full range of species traits found among

tropical trees (see ‘Methods’).

We addressed the following questions: (1) How do tree species

respond to the rainfall gradient in terms of abundance? (2) How are

functional traits associated and what plant strategies can be recog-

nized? (3) Which functional traits are the best predictors of species

distribution along the rainfall gradient?

METHODS

STUDY AREA.—This study focused on a steep rainfall gradient across

West African rain forests. Rainfall varies from ca 4000 mm/yr near

the coast in Liberia to ca 1000 mm/yr at the forest–savanna bound-

ary to the north and east (Poorter et al. 2004). Along the rainfall

gradient, vegetation changes from wet evergreen, to moist ever-green, moist semi-deciduous and dry semi-deciduous forests (Hall

& Swaine 1981, Bongers et al. 2004). At the driest end of the gra-

dient, there are some tiny fragments of dry evergreen forests in

Ghana, but these were not included in this study.

SPECIES SELECTION.—For this study, 31 timber species (see caption

of Fig. 2) were selected, for which forest inventory data were avail-able at the species level, and that were reliably identified in each

country. Several other important timber species were inventoried at

genus level only (e.g., Entandrophragma, Erythrophleum, Khaya and

Parinari spp.) but were not included in the analysis because trait

variations among congeners were relatively large. The use of timber

species may affect the results if such species do not cover the full

range of species traits found among tropical trees. Our selected

timber species do cover almost the full possible range in wood den-sity (0.25–1.09 g/cm3) but are, however, relatively large (the max-

imum observed height ranges from 30 to 60 m, while the forest

canopy is at 40 m) so understory species are under-represented.

SPECIES DISTRIBUTION DATA.—Species distribution was analyzed us-

ing forest inventory data from 176 forest inventory sites in Sierra

Leone (8), Liberia (26), Ivory Coast (37) and Ghana (105). Data

for Sierra Leone were collected by Small (1953), Savill and Fox(1967) and Davies (1987); for Liberia by German Forestry Mission

to Liberia (GFML 1967a, 1967b; Sachtler & Hamer 1967a,

1967b; Sachtler 1968) and Liberian Forest Service; for Ivory Coast

by SODEFOR (Clement & Guinaudeau 1973; SODEFOR 1978,

1979) and for Ghana by Wong (1989), Hawthorne and Abu Juam

(1995) and Hawthorne (1995, 1996). Inventoried area of each site

varied from 10 to 4500 ha. The abundance values for species were

expressed as number of trees 4 30 cm diameter at breast height perkm2 to have comparable abundance values for all sites. See Bongers

et al. (2004) for more details.

Using forward multiple regression, the abundance of a species

was regressed against environmental variables that are likely to in-

fluence large-scale species distribution patterns, such as annual rain-

fall, soil water holding capacity (WHC), cation (i.e., calcium,

magnesium and potassium) availability, altitude and their quadratic

terms. By including quadratic terms of the environmental variables,nonlinear responses of species to the environment could be mod-

eled (Jongman et al. 1987). Rainfall data were derived from a rain-

fall map based on data from 578 weather stations in West Africa,

using the inverse distance weighting interpolation method in Arc

View (Poorter et al. 2004). To increase the spatial resolution we

included as many weather stations as possible, with the drawback

that the length of the monitoring period and the years covered var-

ied between stations. WHC and cation availability data were calcu-lated based on the FAO soil map of Africa and a quantitative review

of chemical analyses of soil profiles (Batjes 1997). Soil depth and

soil texture were taken as parameters to determine WHC and it was

assumed that sandy soils have WHC of 75 mm/m, loamy soils of

100 mm/m and clayey soils of 125 mm/m. Altitude data were de-

rived from the Digital Chart of the World (ESRI 1984). To esti-

mate environmental conditions of a forest site, we used its center.

The regression analysis showed that in 25 out of 31 studied speciesrainfall is the most important environmental factor determining

species distribution and abundance. Of course, small-scale differ-

ences in soil conditions might explain smaller-scale distribution

patterns of trees, but here we focus on large-scale gradients.

The species-specific regression equations were used to show

how species respond to rainfall. Rainfall response curves were

drawn by varying rainfall only, while keeping the other environ-

mental variables in the regression equation constant, by using theaverage environmental conditions across the 176 forest inventory

sites in the equation (WHC: 70.3 mm/m; cation availability:

1.4 cmol/kg; altitude: 219 m). As the original forest inventory data

covered the rainfall range from 1200 to 3400 mm/yr, the species

response curves were made by interpolating abundance of each

timber species at 10 mm rainfall intervals between 1000 (forest–

savanna boundary) and 3400 mm/yr rainfall.

Species response curves represent the realized niche of eachspecies along an environmental gradient. The shape of the response

curve was described using minimum (Rmin), optimum (Ropt),

maximum (Rmax) and amplitude (Ramp) of annual rainfall (all in

Functional Traits Affect Species Distribution 553

mm/yr). Ramp is the difference between Rmax and Rmin and repre-

sents the range of annual rainfall where a species occurs. Ropt

represents annual rainfall level at which abundance of a species is

maximum (i.e., an estimation of ecological optimum). If theresponse curve would have completely fallen within the studied

rainfall gradient (as is the case for Petersianthus macrocarpus), then

the Ropt and rainfall amplitude could have been obtained from the

regression coefficients of the Gaussian curves. As several response

curves were cut-off at either the lower (e.g., Triplochiton scleroxylon)

or the higher (e.g., Tetraberlinia tubmaniana) end of the rainfall

gradient, rainfall parameters were derived in a different way: the

tenth and 90th percentiles for each species were taken as lower andupper limits of the niche (thus avoiding the more noisy extremes)

and their corresponding rainfall values are referred to as Rmin

and Rmax (Fig. 1). In cases where Ropt was located below Rmin (e.g.,T. scleroxylon; Fig. 1B), Ropt was assumed to have the same value as

Rmin; and in cases where Ropt was located above Rmax (e.g., T. tub-maniana), Ropt was assumed to have the same value as Rmax.

SPECIES TRAIT DATA.—To analyze the association of traits and how

to distinguish strategies amongst tree species, we selected 25 traits(Table 1) that are important for plant growth, survival and repro-

duction. Reproductive traits included the color and size of flowers

and fruits (as this determines the type of pollinators and dispersers),

FIGURE 1. Illustration of process of calculation of four rainfall parameters

(Rmin, Ropt, Rmax and Ramp) using the species response curves. Area under the

response curve is considered to be 100 percent. Dotted lines represent lower

(where lower 10% of species occurs), optimum (where abundance of species is

maximum) and upper (where upper 10% of species occurs) limits of realized

niche. Black dots along the horizontal axis corresponding to those dotted lines

represent the annual rainfall levels at those points, i.e., Rmin, Ropt and Rmax. Ramp

is simply the range between Rmin and Rmax. See methods section for more

detailed explanation.

TABLE 1. Twenty-five functional response traits selected for this study. Given are

trait names, trait attributes and their abbreviations used in categorical

principle component analysis (CATPCA). White, yellow, orange, pink,

yellowish-green, cream-color and silvery-gray were grouped as light color,

and red, blue, green, brown, purple and black were grouped as dark

color for flower and fruit color.

Species traits Trait attributes Abbreviations

Reproductive traits

Flower size 1: Small (length o 1 cm) Fl_Size

2: Medium (length 1–4 cm)

3: Large (length 4 4 cm)

Flower color 1: Light colored flower Fl_Light

2: Dark colored flower Fl_Dark

Fruit length (cm) Fr_Len

Fruit width (cm) Fr_Wid

Fruit color 1: Light colored fruit Fr_Light

2: Dark colored fruit Fr_Dark

Fruit type 1: Fleshy Fleshy

2: Dry Dry

Dispersal mode 1: Explosive D_Exp

2: Wind D_Win

3: Animals D_Ani

Seed traits

Number of seeds 1: One seed Nr_Seed

2: Few seeds (2–10)

3: Many seeds (4 10)

Seed length (cm) S_Len

Seed width (cm) S_Wid

Seedling type 1: Cryptocotylar epigeal reserve CER

2: Cryptocotylar hypogeal reserve CHR

3: Phanerocotylar epigeal

foliaceous

PEF

4: Phanerocotylar epigeal reserve PER

Leaf traits

Deciduousness 1: Deciduous Deci

2: Evergreen Ever

Leaf type 1: Simple Simp

2: Compound Comp

Leaf texture 1: Papery/herbaceous LT_Pap

2: Coriaceous LT_Cor

Leaf hairs 1: Without hairs No_hair

2: With hairs Hair

Leaf length (cm) L_Len

Leaf width (cm) L_Wid

Specific leaf area (cm2/g) SLA

Leaf N-concentration (mg/g) Nmass

Leaf P-concentration (mg/g) Pmass

Stem traits

Modulus of elasticity (kg/cm2) MOE

Wood density (g/cm3) WD

Size related traits

Maximum diameter (m) Dmax

Maximum height (m) Hmax

554 Maharjan, Poorter, Holmgren, Bongers, Wieringa, and Hawthorne

seed size, and number and dispersal syndrome. Leaf traits that

are important for the water and heat balance are leaf type (simple/

compound), size, texture, hairiness, SLA, nutrient concentrationsand deciduousness. SLA and nutrient concentration are important

for the carbon gain and growth potential of the plant. Modulus of

elasticity is related to stem stability, wood density is related to stem

construction costs, water storage and cavitation resistance, and

maximum height and diameter determine species competitive abil-

ity for light. The shade tolerance guild (shade-bearer, nonpioneer

light demander and pioneer; sensu Hawthorne 1995) is not a func-

tional trait but was included to reflect the overall strategy of a spe-cies as shade tolerance is important for species success in closed

tropical forests. Species trait data were assembled from published

sources (i.e., Siepel et al. 2004, Hawthorne & Jongkind 2006) and

herbarium specimens available at National Herbarium of The

Netherlands, Wageningen University Branch. Data on SLA (leaf

area divided by leaf mass), leaf nitrogen concentration (Nmass) and

leaf phosphorous concentration (Pmass) were measured using dried

leaf samples obtained from herbarium specimens.SLA is generally calculated as fresh leaf area divided by oven-

dried leaf mass. For this study, SLA was calculated as dried leaf area

divided by dried leaf mass (leaves were oven-dried initially and

stored at room temperature). In case of compound leaves, leaf area

was measured as the leaflet area. To evaluate the potential error in

SLA determination using herbarium leaves, we compared in a sep-

arate analysis of 32 West African species the leaf mass of freshly

oven-dried leaves with the leaf mass of oven-dried leaves stored atroom temperature. Freshly oven-dried leaves were, on average, 5

percent lighter than oven-dried leaves stored at room temperature.

To evaluate leaf area shrinkage during herbarium drying, fresh

leaves of 21 taxa were collected from the tropical greenhouse, and

leaf areas were measured before and after oven drying. Dry leaf areas

were, on average, 12 percent less than fresh leaf areas. The dried leaf

samples were analyzed for Nmass and Pmass. Owing to limited avail-

ability of duplicate specimens only 13 species could be analyzed.Leaf samples were weighed, crushed into powder, digested in tubes

using H2SO4–salicylic acid–H2O2 and selenium (Novozamsky

et al. 1983), and N- and P-content were then measured using

Skalar San-plus auto analyzer.

Species were classified into different seedling types (Garwood

1996), based on the exposure of the cotyledons (exposed: phanero-

cotylar [P]; hidden in the seed: cryptocotylar [C]), the position of

the cotyledons (aboveground: epigeal [E]; belowground: hypogeal[H]), and whether the cotyledons are photosynthetically active

(leaf-like and active: foliaceous [F]; thick and not active: reserve

[R]). Four seedling types were found in this study (for seedling type

abbreviations, see Table 1).

DATA ANALYSIS.—A categorical principle component analysis

(CATPCA) was used to evaluate trait associations and plant strat-

egies. CATPCA is a nonlinear equivalent of PCA (Linting 2007).

Twenty-three traits of 25 species with complete trait data were in-cluded in the analysis. As Nmass and Pmass data were not available for

all species, they were not included in the main analysis but were

later related to the species scores along the first two CATPCA axes.

To evaluate whether species traits predict species distribution

along the rainfall gradient a Pearsons correlation was used to relate

species traits to rainfall parameters. Where necessary, species traits

and rainfall parameters were log10-transformed before analysis to

improve normality and homoscedasticity. For categorical traits,differences in rainfall levels among categories were tested using a t-test or ANOVA. To identify the best predictors of species position

along the rainfall gradient a multiple categorical regression (CAT-

REG) analysis was used, which is a nonlinear equivalent of linear

multiple regression analysis (van der Kooij 2007). Rainfall param-

eters were used as the dependent variables and species traits

as the independent variables, as these traits should determine the

position of the species along the rainfall gradient. Continuoustraits were standardized by multiplying by ten and rounding them

to integers. Traits with limited number of observations (Nmass

and Pmass) were not included in the regression analysis. All

statistical analyses were done using PASW Statistics 17 (SPSS Inc.,

Chicago).

RESULTS

SPECIES RESPONSE TO THE RAINFALL GRADIENT.—Of the 31 timber

species included in this study, 25 species responded significantly to

the rainfall gradient. Of these 25 species, 14 species (56%) had sta-tistically normal response curves along the rainfall gradient with

clear minima, optima and maxima (e.g., P. macrocarpus, Pycnanthusangolensis; Fig. 2). The abundance of seven species (28%) decreased

along the rainfall gradient (e.g., Nesogordonia papaverifera,

T. scleroxylon; Fig. 2) and the abundance of four species (16%)

increased along the rainfall gradient (e.g., Anopyxis klaineana,

T. tubmaniana; Fig. 2).

TRAIT ASSOCIATIONS AND PLANT STRATEGIES.—CATPCA was used to

evaluate associations among species traits. First and second CAT-

PCA axes explained respectively 23 percent and 15 percent of trait

variation (Fig. 3A). Shade-bearers (e.g., Mammea africana,Turraeanthus africanus) and pioneers (e.g., Alstonia boonei, Ceibapentandra) differed in their position along the first CATPCA axis

(t-test, t = 4.97, Po 0.001), with pioneers having higher axis

scores. This indicates that the first axis corresponds to a gradient in

shade tolerance. Shade-bearers on the left side of the axis were char-

acterized by evergreen leaves, relatively small stature, large seeds and

seedlings with reserve type cotyledons (CHR, CER and PER seed-

lings, Table 1) while pioneer species on the right side of the axiswere characterized by deciduous leaves, large stature, small and nu-

merous seeds and seedlings with foliaceous photosynthetically ac-

tive cotyledons (PEF seedlings).

TABLE 1. Continued

Species traits Trait attributes Abbreviations

Guild 1: Shade-bearer SB

2: Nonpioneer light demander NPLD

3: Pioneer P

Functional Traits Affect Species Distribution 555

At the top of the second CATPCA axis were mainly low-rain-

fall species (e.g., Ricinodendron heudelotii, T. scleroxylon) occurring

in the areas with Ropt � 1500 mm/yr (Fig. 3B). At the bottom of

the second CATPCA axis were mainly high rainfall species (e.g., A.klaineana and T. tubmaniana) occurring in the areas with Ropt

Z3000 mm/yr (Fig. 3B). Indeed the species scores along the sec-

ond CATPCA axis were significantly and negatively correlated to

rainfall parameters (i.e., Rmin, Ropt and Rmax; Table 2) and this axis

thus corresponds to a gradient in drought resistance. Low-rainfall

species at the top of the axis were characterized by large leaf size

(large leaf width and length), high SLA and high leaf nutrient

FIGURE 3. Categorical principle component analysis (CATPCA) loading plots

(A) and species scores (B) of 25 timber species based on 23 traits. The first and

second axes explained 23 percent and 15 percent of the trait variation, respectively.

(A) Lines with dot originating from origin represent loadings for continuous and

ordinal traits while dots represent centroids for categorical traits. (A) Nmass and Pmass

(indicated by asterisk) were not included in the CATPCA, but were later related to

the CATPCA axes scores. (B) Open symbols represent species occurring in areas

with Ropt � 1500 mm/yr; closed symbols represent species occurring in areas with

Ropt Z3000 mm/yr and gray symbols represent species occurring in-between. Trait

abbreviations used in loading plots are given in Table 1. Species abbreviations used

in species scores plots refer to first three letters of the genus name and first three

letters of the species name. Species list is included in the caption of Fig. 2.

FIGURE 2. Species response curves of 31 timber species along the rainfall gra-

dient. (A) Species with maximum abundance Z100 per km2; (B) species with

maximum abundance Z20 per km2 and o 100 per km2; and (C) species with

maximum abundance o 20 per km2. Numbers refer to the 31 species selected

for the study, i.e., 1: Alstonia boonei; 2: Amphimas pterocarpoides; 3: Anopyxis

klaineana; 4: Anthonotha fragrans; 5: Antiaris toxicaria; 6: Canarium schwein-

furthii; 7: Ceiba pentandra; 8: Distemonanthus benthamianus; 9: Funtumia afric-

ana; 10: Gilbertiodendron preussii; 11: Guarea cedrata; 12: Guibourtia ehie; 13:

Heritiera utilis; 14: Klainedoxa gabonensis; 15: Lophira alata; 16: Lovoa trichilio-

ides; 17: Mammea africana; 18: Nauclea diderrichii; 19: Nesogordonia papaverif-

era; 20: Petersianthus macrocarpus; 21: Piptadeniastrum africanum; 22:

Pycnanthus angolensis; 23: Rhodognaphalon brevicuspe; 24: Ricinodendron heu-

delotii; 25: Terminalia ivorensis; 26: Terminalia superba; 27: Tetraberlinia tub-

maniana; 28: Tieghemella heckelii; 29: Triplochiton scleroxylon; 30: Turraeanthus

africanus; and 31: Zanthoxylum gilletii.

556 Maharjan, Poorter, Holmgren, Bongers, Wieringa, and Hawthorne

concentrations (high Nmass and Pmass), whereas high-rainfall species

at the bottom of the axis were characterized by high wood density,

high modulus of elasticity, seeds dispersed by active expulsion and

PER seedling types.

SPECIES TRAITS VS. THE RAINFALL GRADIENT.—Of the 15 continuous

species traits related to rainfall parameters, a similar number of

species traits (3–4) were significantly correlated to Rmin, Ropt and

Rmax whereas none was significantly correlated to Ramp (Table 2).

Most traits (Pmass, modulus of elasticity, wood density) showed a

stronger correlation to Ropt than to Rmin or to Rmax (Table 2). Thissuggests that Ropt is a better rainfall parameter to explain trait vari-

ations than Rmin or Rmax. Ropt was significantly correlated to Rmin

(Pearson’s r = 0.87, Po 0.01) and Rmax (Pearson’s r = 0.79, Po 0.01) indicating that Ropt is a good indicator of both Rmin and

Rmax. In addition, Ropt also reflects the average position of species

along the rainfall gradient. Thus, only Ropt was used for further an-

alyses. Only three out of 15 continuous species traits were signifi-

cantly correlated to Ropt (Table 2). Wood density and modulus ofelasticity increased significantly with increasing rainfall while Pmass

decreased significantly with increasing rainfall (Table 2).

Deciduous species occurred in significantly lower-rainfall areas

than evergreen species (t-test: t29 = 2.76, Po 0.05; Fig. 4A). Ropt

differed among dispersal modes (ANOVA: F2, 29 = 4.55, Po 0.05):

species dispersed by wind or animals tended to be more common in

low-rainfall areas than those dispersed by active expulsion of seeds

(Fig. 4B). Ropt also differed among seedling types (ANOVA:F3, 27 = 3.30, Po 0.05). Species with seedlings with foliaceous pho-

tosynthetically active cotyledons (PEF seedlings) occurred in lower

rainfall areas than those with seedlings with reserve type cotyledons

(CER, CHR and PER seedlings; Fig. 4C).

Ropt was regressed against species traits using a multiple CAT-

REG analysis to evaluate the relative importance of species traits

in explaining species distribution along the rainfall gradient. Wood

density and deciduousness emerged as the best traits explaining spe-cies distribution along the rainfall gradient. Ropt was positively asso-

ciated with wood density (CATREG: b = 0.43, partial R2 = 0.32,

Po 0.01; Fig. 5) and was negatively associated with deciduousness

(CATREG: b =� 0.41, partial R2 = 0.15, Po 0.01; Fig. 5).

DISCUSSION

SPECIES RESPONSE TO THE RAINFALL GRADIENT.—The majority of the

study species responded significantly to the rainfall gradient

TABLE 2. Pearson’s correlation between species scores along first two categorical

principle component analysis (CATPCA) axes and rainfall parameters,

and species traits and rainfall parameters. N indicates the number of

species included in the correlation analysis. Correlations in bold are sig-

nificant at Po 0.05 and those in bold and italic are significant at

Po 0.01. Diameter, fruit length, fruit width, seed length, seed width,

SLA, Pmass and Rmin were log10-transformed.

Rmin Ropt Rmax Ramp N

Strategy axis

CATPCA axis 1 � 0.24 � 0.35 � 0.30 � 0.10 25

CATPCA axis 2 � 0.40 � 0.49 � 0.57 � 0.16 25

Species traits

Flower size � 0.17 � 0.17 � 0.33 � 0.18 31

Fruit length 0.06 0.08 0.07 0.01 31

Fruit width 0.27 0.33 0.11 � 0.14 31

Leaf length � 0.01 0.02 � 0.12 � 0.03 31

Leaf N-content � 0.65 � 0.50 � 0.14 0.42 13

Leaf P-content � 0.72 � 0.78 � 0.59 0.04 13

Leaf width � 0.14 � 0.18 � 0.43 � 0.30 31

Maximum diameter � 0.03 � 0.03 0.04 0.08 31

Maximum height � 0.10 � 0.19 � 0.05 0.02 31

Modulus of elasticity 0.47 0.57 0.50 0.10 30

Number of seeds 0.02 0.05 0.05 0.03 31

Seed length 0.16 0.29 0.17 0.04 30

Seed width 0.13 0.29 0.26 0.14 30

Specific leaf area � 0.34 � 0.24 � 0.19 0.14 31

Wood density 0.47 0.57 0.44 0.05 29

FIGURE 4. Relationships between optimum annual rainfall (Ropt) and categorical species traits. Differences between the categories were tested using t-test and

ANOVA. Bar diagrams with same letters at the top are not significantly different while those with different letters at the top are significantly different (LSD post-hoc

test, Po 0.05). Error bars show mean� 1 SE. (A) Deci and Ever correspond to deciduous and evergreen species respectively. (B) Explosive, Wind and Animal

correspond to species dispersed by active expulsion of seeds, by wind and by animals, respectively. (C) CER, CHR, PEF and PER correspond to cryptocotylar epigeal

reserve, cryptocotylar hypogeal reserve, phanerocotylar epigeal foliaceous and phanerocotylar epigeal reserve seedling types, respectively.

Functional Traits Affect Species Distribution 557

indicating the important role of rainfall in shaping species distribu-

tion patterns in West Africa (cf. Swaine 1996, Bongers et al. 1999,

Holmgren & Poorter 2007). As expected, the abundance of many

species followed bell-shaped response curves along the rainfall gra-

dient with clear minima, optima and maxima (Fig. 2). Many spe-

cies had widespread distribution ranges represented by bell-shaped

response curves extending over the entire rainfall gradient (e.g., P.macrocarpus, P. angolensis; Fig. 2) and some had local distributionranges restricted to low-rainfall areas (e.g., N. papaverifera, T.scleroxylon; Fig. 2) or high-rainfall areas (e.g., A. klaineana, T. tub-maniana; Fig. 2) (cf. Holmgren & Poorter 2007).

During the past four decades, West Africa has experienced a

severe decline in rainfall (Malhi & Wright 2004) and even

a stronger decline in precipitation might be expected with a future

reduction in forest cover. As a result, species restricted to high-rainfall

areas (e.g., A. klaineana, T. tubmaniana) are likely to be more vulner-able, whereas species restricted to low-rainfall areas (e.g., N. papaverif-era, T. scleroxylon) are likely to become more abundant.

TRAIT ASSOCIATIONS RELATED TO SHADE TOLERANCE.—CATPCA in-

dicated that the first two strategy axes correspond to shade tolerance

and drought resistance, respectively. Shade tolerance emerged as the

primary strategy axis, possibly because all forest types have horizon-

tal (from the understory to gap centers) and vertical (from the un-

derstory to the canopy) gradients in light availability. The shadetolerance axis separated shade-tolerators (shade-bearers) from light-

demanding pioneers, with the nonpioneer light demanders being in

between (Fig. 3A). Shade-tolerators were characterized by evergreen

leaves, a relatively small stature, large seeds and hypogeal cotyle-

dons. These traits may enhance their performance under low-light

conditions. The presence of evergreen long-lived leaves ensures effi-

cient and conservative use of carbon resources in the shaded under-

story (Givnish 1988, Walters & Reich 1999, Poorter 2009). Therelatively small stature of these species is indicative of species’ ability

to tolerate asymmetric competition for light in moist forests. Large

seeds allows these species to produce larger seedlings with better

seedling survival in the shaded understory (Leishman et al. 2000,

Myers & Kitajima 2007). Experimental evidence shows that hypo-

geal cotyledons present among seedlings of shade-tolerators further

enhance seedling survival in the shaded understory (Baraloto &Forget 2007).

Light demanders were characterized by traits that enable these

species to take advantage of the higher amount of light available in

open areas. In high-light environments, water stress is more severe,

and most light-demanding species had a deciduous leaf habit by

which severe water stress during the dry season might be avoided.

Deciduous species are capable of more efficient light capture per

unit leaf mass (Antunez et al. 2001, Eamus & Prior 2001, Ruiz-Robleto & Villar 2005) because of their high SLA (Poorter &

Markesteijn 2008). This allows faster growth during the wet season,

which may partially compensate for a shorter growing season. The

relatively large stature of light-demanding species provides a com-

petitive advantage for light. The small seeds facilitate wind dis-

persal, which is common among species with emergent crowns that

colonize open areas (Bullock 1995, Holmgren & Poorter 2007).

The large number of seeds facilitates the colonization of new openareas (Dalling & Hubbell 2002). Small seeds have few reserves, and

the resulting seedlings tend to have foliaceous photosynthetically

active cotyledons to ensure optimum use of high irradiance, and

rapid heterotrophic growth (Kitajima 1992).

TRAIT ASSOCIATIONS RELATED TO DROUGHT RESISTANCE.—Drought

resistance (i.e., the capacity of a plant to withstand periods of dry-

ness; Larcher 1980) emerged as the secondary strategy axis. Thedrought resistance axis separated low-rainfall species from high-

rainfall species (Fig. 3B). Low-rainfall species were characterized by

large leaves, high SLA, high leaf nutrient concentration, low wood

density and modulus of elasticity, whereas high-rainfall species were

characterized by the opposite suite of traits (Fig. 3). A low wood

density enables low-rainfall species to store water in their stem to

enable flowering during the dry season and leaf flushing at the end

of dry season (Borchert 1994). During the wet season, this storedwater is not only used for daily transpiration (Meinzer et al. 2004)

but, more importantly, the high capacitance might buffer plants

against daily fluctuations in xylem tension and leaf water potential

(Meinzer et al. 2008). At even lower rainfall, it is expected that spe-

cies would have high wood density to resist drought-induced cav-

itation and avoid vessel implosion (Pratt et al. 2007, Markesteijn

et al. 2011). Low-rainfall species were also characterized by high leaf

nutrient concentrations (cf. Santiago et al. 2004), which implieshigher photosynthetic capacity because most of the leaf N and P are

allocated to photosynthetic enzymes. High leaf nutrient concentra-

tions reduce leaf internal CO2 concentrations and a strong CO2

gradient is created between leaf interior and the atmosphere allow-

ing fast diffusion of CO2 for a given stomatal aperture, thus

minimizing stomatal water loss (Wright et al. 2001). This enables

low-rainfall species to maintain a given photosynthetic capacity for

minimum use of water. Leaf deciduousness was not strongly asso-ciated with this multivariate drought resistance axis, although as an

individual trait it is one of the best predictors of species’ position

along the rainfall gradient (see below). Leaf area was higher for

FIGURE 5. Relationship between optimum annual rainfall and wood density

for 15 deciduous species (open symbols) and 14 evergreen species (closed sym-

bols). Regression line, coefficient of determination (R2) and significance level (P)

are shown.

558 Maharjan, Poorter, Holmgren, Bongers, Wieringa, and Hawthorne

low-rainfall species, whereas often the opposite is found (Fonseca etal. 2000).

Reproductive traits such as flower size and color did not load

high on the drought axis of the CATPCA. Reproductive traits donot affect drought resistance directly, but other studies found that

their relative occurrence does vary in forest communities along the

rainfall gradient, because of differences in the effectiveness or fre-

quency of pollinator- and dispersal-vectors. Dry forest species tend

to have ‘conspicuous’ flowers (that are large or bright) pollinated by

specialist pollinators such as large bees, hummingbirds or hawk-

moths (Gentry 1982), and tend to be wind-dispersed (thus taking

advantage of the seasonally open canopy). Wet forest species tend tobe animal dispersed (Bullock 1995).

TRAIT–RAINFALL RELATIONSHIPS AND BEST PREDICTOR TRAITS.—Of the

25 studied species traits, six traits showed significant relationships to

Ropt. Of these six traits, wood density and deciduousness emerged as

the best predictors of species distribution along the rainfall gradient

explaining 47 percent of the variation in Ropt. We expected that spe-cies traits would determine the minimum environmental conditions

that a species can tolerate. This apparently was not the case, because

species traits tended to show stronger relationships to Ropt than to

Rmin or any other rainfall parameter (Table 2). This suggests that traits

shape the realized niche, and especially the optimal conditions (i.e., in

this case Ropt) under which a species occurs.

Moist forests in high-rainfall areas cast deeper shade than dry

semi-deciduous forests in low-rainfall areas (Coomes & Grubb2000) due to high leaf area index and less seasonality of leaf cover

(Parker et al. 2005). Higher wood density provides greater struc-

tural stability and greater resistance against physical damage caused

by falling debris (van Gelder et al. 2006) and pathogens (Augs-

purger & Kelly 1984) in the shaded understory of moist forests.

Lower wood density, on the other hand, enhances growth in gaps

(Muller-Landau 2004, Poorter 2008) and enables species to store

water in their stem to enable flowering during the dry season andleaf flushing at the end of dry season (Borchert 1994). Wood den-

sity showed a significant positive relationship to rainfall (Table 2;

Fig. 5) and modulus of elasticity followed wood density because

they are closely related (Pearson’s r = 0.91, Po 0.001, N = 24; cf.van Gelder et al. 2006).

Deciduous species occurred in significantly lower rainfall areas

than evergreen species (Figs. 4A and 5). Shedding of leaves during

the dry season helps the plant to prevent water loss through leaftranspiration and enhances drought survival (Poorter & Mark-

esteijn 2008), which makes deciduous species more adapted to dry

forests (Givnish 2002). Evergreen leaves with long life-span, on the

other hand, ensure efficient and conservative use of carbon

resources in the shaded understory of moist forests (Poorter 2009).

Deciduousness is one of the strategies to survive in drier areas,

and another strategy, is to make evergreen, sclerophyllous leaves

(Ackerly 2004, Markesteijn & Poorter 2009). In dry forests, thisevergreen strategy is mostly confined to understory and subcanopy

species and rarely found for canopy species, which were the subject

of this study.

Dispersal mode, seedling type and Pmass showed significant re-

lationships with Ropt probably because they are important for spe-

cies success in high-light conditions of dry and open forests vs. the

low-light conditions of wet and closed forests. Wind dispersal isadvantageous in drier forests with a seasonally deciduous and open

canopy which is more exposed to wind (Bullock 1995). Foliaceous

photosynthetically cotyledons can contribute to carbon gain in

drier forests with a higher light availability, whereas reserve type

cotyledons can support seedlings in the shaded understory of wetter

forests where there is little carbon gain. Pmass might be higher in

low-rainfall areas for several reasons: First, dry forest species might

be more light demanding, and light-demanding species may benefitfrom P investment in leaves to realize a higher carbon gain. Second,

P investment in photosynthetic enzymes may draw down the inter-

nal CO2 concentration and enhance water use efficiency. Third, dry

forest soils are less leached and leaf nutrient concentrations may

therefore be higher.

In conclusion, the majority of the studied species responded

significantly to the rainfall gradient indicating the vital role of rain-

fall in shaping species distribution patterns. Based on the trait as-sociations, shade-tolerance and drought-avoidance were identified

as the key strategies for the success of timber species in West Africa.

Wood density and deciduousness emerged as the best predictors of

species distribution along the rainfall gradient. In addition to the

morphological traits evaluated here, physiological traits such as

the ability to tolerate low leaf water potentials and low leaf water

content are also good predictors of species drought tolerance and

distribution along rainfall gradients (Baltzer et al. 2008, Kursaret al. 2009).

ACKNOWLEDGMENTS

The authors express their sincere gratitude to the ECOSYN project

for providing access to the database; National Herbarium of The

Netherlands, Wageningen University Branch for providing access

to herbarium specimens; Carel Jongkind for making leaf samplesavailable for leaf analysis; Cajo ter Braak and Anita van der Kooij

for statistical advice, and Bryan Finegan, Louis Santiago, and an

anonymous reviewer for helpful comments. SKM was supported

by an Erasmus Mundus scholarship and LP by a grant from the

Wageningen graduate school Production Ecology and Resource

Conservation.

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