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Soil physical conditions limit palm and tree basalarea in Amazonian forestsThaise Emilio a , Carlos A. Quesada b , Flávia R.C. Costa c , William E. Magnusson c ,Juliana Schietti a , Ted R. Feldpausch d , Roel J.W. Brienen d , Timothy R. Baker d ,Jerome Chave e , Estebán Álvarez f , Alejandro Araújo g , Olaf Bánki h , Carolina V.Castilho i , Eurídice N. Honorio C. d j , Timothy J. Killeen k , Yadvinder Malhi l , Erick M.Oblitas Mendoza m , Abel Monteagudo n , David Neill o , Germaine Alexander Parada f ,Antonio Peña-Cruz n , Hirma Ramirez-Angulo p , Michael Schwarz q , Marcos Silveira r ,Hans ter Steege s , John W. Terborgh t , Raquel Thomas u , Armando Torres-Lezama p ,Emilio Vilanova p & Oliver L. Phillips da Programa de Pós-Graduação em Ecologia, Instituto Nacional de Pesquisas da Amazônia(INPA) , Manaus , Brasilb Coordenação de Pesquisa em Dinâmica Ambiental, INPA , Manaus , Brasilc Coordenação de Pesquisa em Biodiversidade, INPA , Manaus , Brasild School of Geography , University of Leeds , Leeds , UKe Laboratoire Evolution et Diversité Biologique, CNRS and Université Paul Sabatier ,Toulouse , Francef Medellín Botanical Garden, Medellín , Colombiag Museo de Historia Natural Noel Kempff Mercado , Santa Cruz , Boliviah Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam ,Amsterdam , Nederlandsi EMBRAPA, Centro de Pesquisa Agroflorestal de Roraima, Boa Vista , Roraima , Brasilj Instituto de Investigaciones de la Amazonía Peruana , Iquitos , Peruk Conservation International , Arlington , USAl Environmental Change Institute, School of Geography and the Environment, Universityof Oxford , Oxford , UKm Programa de Capacitação Institucional, INPA , Manaus , Brasiln Jardín Botánico de Missouri , Oxapampa , Perúo Universidad Estatal Amazónica, Facultad de Ingeniería Ambiental , Puyo , Pastaza ,Ecuadorp Instituto de Investigaciones para el Desarrollo Forestal (INDEFOR,) Universidad de LosAndes , Mérida , Venezuelaq University of Applied Sciences Eberswalde , Eberswalde , Germanyr Centro de Ciências Biológicas e da Natureza, Universidade Federal do Acre , RioBranco , Brasils Naturalis Biodiversity Center , Leiden , Netherlandst Nicholas School of the Environment and Earth Sciences, Duke University , Durham , USAu Iwokrama International Centre for Rain Forest Conservation and Development ,Iwokrama , GuyanaAccepted author version posted online: 04 Feb 2013.Published online: 08 Mar 2013.

To cite this article: Thaise Emilio , Carlos A. Quesada , Flávia R.C. Costa , William E. Magnusson , Juliana Schietti , TedR. Feldpausch , Roel J.W. Brienen , Timothy R. Baker , Jerome Chave , Estebán Álvarez , Alejandro Araújo , Olaf Bánki ,Carolina V. Castilho , Eurídice N. Honorio C. , Timothy J. Killeen , Yadvinder Malhi , Erick M. Oblitas Mendoza , AbelMonteagudo , David Neill , Germaine Alexander Parada , Antonio Peña-Cruz , Hirma Ramirez-Angulo , Michael Schwarz ,

Marcos Silveira , Hans ter Steege , John W. Terborgh , Raquel Thomas , Armando Torres-Lezama , Emilio Vilanova & OliverL. Phillips (2014) Soil physical conditions limit palm and tree basal area in Amazonian forests, Plant Ecology & Diversity,7:1-2, 215-229, DOI: 10.1080/17550874.2013.772257

To link to this article: http://dx.doi.org/10.1080/17550874.2013.772257

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Plant Ecology & Diversity, 2014Vol. 7, Issues 1–2, 215–229, http://dx.doi.org/10.1080/17550874.2013.772257

Soil physical conditions limit palm and tree basal area in Amazonian forests

Thaise Emilioa*, Carlos A. Quesadab , Flávia R.C. Costac , William E. Magnussonc , Juliana Schiettia , Ted R. Feldpauschd ,Roel J.W. Brienend , Timothy R. Bakerd , Jerome Chavee , Estebán Álvarezf , Alejandro Araújog , Olaf Bánkih ,Carolina V. Castilhoi , Eurídice N. Honorio C.d,j , Timothy J. Killeenk , Yadvinder Malhil , Erick M. Oblitas Mendozam,Abel Monteagudon , David Neillo , Germaine Alexander Paradaf , Antonio Peña-Cruzn , Hirma Ramirez-Angulop ,Michael Schwarzq , Marcos Silveirar , Hans ter Steeges , John W. Terborght , Raquel Thomasu , Armando Torres-Lezamap,Emilio Vilanovap and Oliver L. Phillipsd

aPrograma de Pós-Graduação em Ecologia, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brasil; bCoordenação dePesquisa em Dinâmica Ambiental, INPA, Manaus, Brasil; cCoordenação de Pesquisa em Biodiversidade, INPA, Manaus, Brasil; dSchoolof Geography, University of Leeds, Leeds, UK; eLaboratoire Evolution et Diversité Biologique, CNRS and Université Paul Sabatier,Toulouse, France; fMedellín Botanical Garden, Medellín, Colombia; gMuseo de Historia Natural Noel Kempff Mercado, Santa Cruz,Bolivia; hInstitute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, Nederlands; iEMBRAPA, Centro dePesquisa Agroflorestal de Roraima, Boa Vista, Roraima, Brasil; jInstituto de Investigaciones de la Amazonía Peruana, Iquitos, Peru;kConservation International, Arlington, USA; lEnvironmental Change Institute, School of Geography and the Environment, University ofOxford, Oxford, UK; mPrograma de Capacitação Institucional, INPA, Manaus, Brasil; nJardín Botánico de Missouri, Oxapampa, Perú;oUniversidad Estatal Amazónica, Facultad de Ingeniería Ambiental, Puyo, Pastaza, Ecuador; pInstituto de Investigaciones para elDesarrollo Forestal (INDEFOR,) Universidad de Los Andes, Mérida, Venezuela; qUniversity of Applied Sciences Eberswalde,Eberswalde, Germany; rCentro de Ciências Biológicas e da Natureza, Universidade Federal do Acre, Rio Branco, Brasil; sNaturalisBiodiversity Center, Leiden, Netherlands; tNicholas School of the Environment and Earth Sciences, Duke University, Durham, USA;uIwokrama International Centre for Rain Forest Conservation and Development, Iwokrama, Guyana

(Received 7 March 2012; final version received 18 May 2012)

Background: Trees and arborescent palms adopt different rooting strategies and responses to physical limitations imposedby soil structure, depth and anoxia. However, the implications of these differences for understanding variation in the relativeabundance of these groups have not been explored.Aims: We analysed the relationship between soil physical constraints and tree and palm basal area to understand how thephysical properties of soil are directly or indirectly related to the structure and physiognomy of lowland Amazonian forests.Methods: We analysed inventory data from 74 forest plots across Amazonia, from the RAINFOR and PPBio networks forwhich basal area, stand turnover rates and soil data were available. We related patterns of basal area to environmental variablesin ordinary least squares and quantile regression models.Results: Soil physical properties predicted the upper limit for basal area of both trees and palms. This relationship was directfor palms but mediated by forest turnover rates for trees. Soil physical constraints alone explained up to 24% of palm basalarea and, together with rainfall, up to 18% of tree basal area. Tree basal area was greatest in forests with lower turnover rateson well-structured soils, while palm basal area was high in weakly structured soils.Conclusions: Our results show that palms and trees are associated with different soil physical conditions. We suggest thatadaptations of these life-forms drive their responses to soil structure, and thus shape the overall forest physiognomy ofAmazonian forest vegetation.

Keywords: ecological limiting factors; life-forms; palm-dominated forests; quantile regression; soil structure; tropical forest;vegetation types

Introduction

Amazonian forests play an important role in the globalcarbon cycle but how much carbon is stored in these ecosys-tems is still uncertain. Variation in biomass is directlyrelated to variation in stand basal area and stand-level wooddensity. Mean stand-level wood density is dependent bothon species composition (Baker et al. 2004) and environ-mental factors (Patiño et al. 2009), such as soil fertilityand climate. The environmental correlates of basal area areless clear. There is important site-to-site variability (Malhiet al. 2006) that appears to be less correlated with broad-scale regional predictors than with local predictors such as

*Corresponding author. Email: thaise.emilio@gmail.com

disturbance or the presence (or absence) of limiting soilproperties.

Soil and climate have been widely investigated tounderstand forest structure and composition in Amazonia(Laurance et al. 1999; Roggy et al. 1999; Malhi et al. 2006;ter Steege et al. 2006; Quesada et al. 2012) and elsewhere(Paoli et al. 2007; Slik et al. 2010). Soil physical condi-tions in particular, such as water-holding capacity, drainage,and physical structure, are important factors that affect treegrowth, mortality and floristic composition in the tropicalforests worldwide (Dietrich et al. 1996; Ferry et al. 2010;Gourlet-Fleury et al. 2011; Quesada et al. 2012). However,

© 2013 Botanical Society of Scotland and Taylor & Francis

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216 T. Emilio et al.

their influence in determining large-scale patterns of foreststructure and composition in the tropics is poorly known.

Structural dominance by palms (and other life-forms,such as lianas and bamboos) has been used in the Brazilianforest classification system (IBGE 2012) to distinguish for-est types (see Appendix 1, Emilio et al. 2010), and todevelop improved allometric equations for biomass calcula-tion in the Brazilian Amazon (Nogueira et al. 2008). Palm-dominated forests cover 20% of Brazilian Amazonia (IBGE1998) and large extensions of other Amazonian coun-tries. Given this widespread occupation of forest by palms,understanding how palms vary in abundance could help tobetter understand basal area and biomass variation acrossAmazonian forests. At a single site in the Amazon, Castilhoet al. (2006) reported that tree biomass was higher in well-drained clay soils while arborescent palm biomass washigher in poorly drained, sand-rich soils. This suggests thatsoil physical conditions may have different effects on thesetwo plant life-forms. The aim of this study was to investi-gate the relationships between soil physical properties andthe basal area of both trees and palms across Amazonia.

In addition to the direct effect that soil physical prop-erties may exert on plant roots, soil physical constraintscan also indirectly affect forest basal area by increasingturnover. Quesada et al. (2012) showed that Amazonianforests have greater turnover rates where soil propertiesconstrain root development (e.g. shallow impediment lay-ers, high bulk density, anoxic horizon). At local scales, theproportion of stems that die uprooted or snapped off byother falling trees is generally greater in sandy and water-logged soils (Toledo et al. 2011). The dominant modes ofmortality may also vary depending on whether the plant isa tree or a palm. In Ecuador, dicotyledonous trees mostlydied uprooted and snapped while arborescent palms mostlydied standing and snapped (Gale and Barfod 1999).

The differences in the response of plant life-forms, suchas palms and trees, to soil physical properties and distur-bance are likely to be related to differences in their physio-logical and morphological adaptations, particularly growthstrategies and the root system. Palms lack vascular cam-bium and do not show secondary growth. To compensate,primary tissues continually increase in their mechanicalstrength with gradual lignification of fibrous and parenchy-matous tissue, resulting in stronger stems as palm heightincreases (Tomlinson 2006). The absence of secondarygrowth in palms may be advantageous against wind dam-age, but prevents dormancy, implying that they must havespecial adaptations (e.g. aerenchyma, pneumatophores) todeal with seasonally stressful conditions (Tomlinson 2006).In contrast, cambial dormancy is a common strategy inmany tree species (Zuidema et al. 2012) and allows themto occupy seasonally unfavourable environments, such asseasonally dry or waterlogged forests, with or withoutmorphological adaptations to such conditions.

Palms and dicotyledonous trees also differ in theirroot systems. Trees develop roots that can reach depthsof up to 10 m to access water (Nepstad et al. 1994). Thedevelopment of deep roots provides mechanical stability

and helps trees to prevent water deficit where soils are deepand well structured. Palms do not have extensive root sys-tems, but they compensate this disadvantage by developinghigh root water pressures (Davis 1961). This may confera competitive advantage in shallow or compacted soils.In soils that limit root development, trees may be moresusceptible to anchorage problems, especially if associatedwith steep topography (Gale and Barfod 1999; Toledo et al.2011). In contrast, palms are more stable than trees due totheir stem anatomy (Tomlinson 1990) that allows them toremain strongly anchored to the ground, even without deeproots. In addition, palms have smaller canopies and largeleaves instead of woody branches. Palm leaves are less car-bon expensive to rebuild than tree branches, so their lossif hit by a falling branch or tree may be expected to haverelatively minor impact on the structure and stability of theplant, and its carbon balance. Hence, palms appear to bebetter adapted to grow in highly dynamic forests.

In summary, we would expect that trees are at an advan-tage in deep, well-drained soils where their extensive rootsystems provide good anchorage and are able to extensivelyexploit soil resources. On the other hand, palms may out-compete trees in shallow, poorly drained soils, since theymay be able to cope better with water-saturated soil andlimited rooting space. Therefore, one could expect that theresponses of palms and trees to soil physical propertiesshows opposite patterns, and this could result in a signif-icant shift in the relative contribution of trees and palmsto forest structure across Amazonia as a function of soilproperties. So far, no attempt has been made to understandwhat could explain the relative contribution of arborescentpalms and trees to forest structure and physiognomy acrossAmazonia.

Here, we use a unique set of permanent study plotsacross Amazonia to analyse the relationship between soilphysical constraints and basal area of trees and palms inorder to better understand how soil physical limitationsaffect the structure and physiognomy of Amazonian forests.We also explore the relationship among precipitation, soilfertility and forest turnover and basal area variation in treesand palms.

Methods

Vegetation data

We compiled forest-structure data from the RAINFORForest Plots database (Lopez-Gonzalez et al. 2011, down-loaded from http://www.forestplots.net (Lopez-Gonzalezet al. 2012)) and the PPBio database (Pezzini et al. 2012,downloaded from http://ppbio.inpa.gov.br (PPBio 2012)).We used data from 74 Amazonian plots that have both plantand soil data. Most plots are 1 ha in area (see Table 1 forplot dimensions and data sources). In each of these plots,all stems (trees and palms) with a diameter at breast height(DBH) ≥ 10 cm were measured and identified to at leastfamily level. The basal areas of trees and arborescent palmswere calculated and used as response variables in regressionanalyses.

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Soil physical conditions limit palm and tree basal area 217

Tabl

e1.

Bas

alar

ea(B

A)

and

envi

ronm

enta

ldat

afr

om74

Am

azon

ian

fore

stpl

ots

used

inth

isst

udy.

Soi

lphy

sica

lcon

stra

ints

core

s

Plo

tcod

eL

atit

ude

Lon

gitu

deA

ltit

ude

(ma.

s.l.)

Plo

tare

a(h

a)Pa

lmB

A(m

2ha

−1)

Palm

BA

(%)

Tre

eB

A(m

2ha

−1)

Tre

eB

A(%

)S

oil

anox

iaTo

pogr

aphy

Soi

lde

pth∗

Soi

lst

ruct

ure∗

Pre

cipi

tati

on(m

mye

ar−1

)IE

Fore

stcl

ass∗∗

Turn

over

rate

s(%

)

AL

F-0

1R−9

.598

30−5

5.93

690

269

1.00

0.18

2.42

7.27

97.5

80

04

123

562.

041.

73A

LF

-02R

−9.5

7840

−55.

9176

027

71.

003.

7914

.15

22.9

985

.85

00

42

2356

2.23

AL

M-0

1R−1

1.80

000

−71.

4700

040

02.

004.

0413

.41

26.0

886

.59

22

41

2395

6.57

2.33

AL

P-1

1R−3

.950

00−7

3.43

000

114

0.44

2.31

8.07

26.3

291

.93

03

31

2784

3.62

TF

-33.

09A

LP

-12R

−3.9

5000

−73.

4400

012

50.

40.

341.

3225

.37

98.6

83

01

127

842.

46T

F-2

2.05

AL

P-2

1R−3

.950

00−7

3.44

000

114

0.48

1.15

4.49

24.4

895

.51

33

43

2784

0.4

TF

-32.

58A

LP

-22R

−3.9

5000

−73.

4400

012

50.

442.

047.

4925

.18

92.5

10

34

127

845.

13T

F-2

2.12

AL

P-3

0R−3

.950

00−7

3.43

000

130

1.00

0.21

0.94

22.1

99.0

60

04

327

840.

49T

F-1

1.27

BO

G-0

1R−0

.700

00−7

6.48

000

257

1.00

1.32

4.39

28.7

895

.61

03

12

3133

12.4

72.

65B

OG

-02R

−0.7

0000

−76.

4700

028

41.

001.

947.

7423

.12

92.2

60

41

231

338.

483.

80C

AX

-01R

−1.7

4000

−51.

4600

015

1.00

0.01

0.03

3399

.97

00

44

2206

1.81

TF

-11

0.86

CA

X-0

2R−1

.740

00−5

1.46

000

151.

000.

020.

0728

.47

99.9

30

04

422

061.

56T

F-1

11.

74C

AX

-06R

−1.7

2000

−51.

4600

020

1.00

0.02

0.05

36.7

999

.95

00

44

2206

2.28

TF

-12

1.27

CR

P-0

1R−1

4.54

000

−61.

5000

035

01.

000

0.00

19.0

510

0.00

03

33

1364

7.66

3.03

CR

P-0

2R−1

4.54

000

−61.

5000

035

01.

000.

180.

7623

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99.2

40

23

313

645.

223.

13C

UZ

-01R

−12.

5000

0−6

8.97

000

190

1.00

1.75

7.44

21.7

792

.56

20

30

2098

8.69

FAT-

22.

53C

UZ

-02R

−12.

5000

0−6

8.97

000

190

1.00

4.01

14.1

024

.42

85.9

02

03

020

988.

07FA

T-2

2.43

CU

Z-0

3R−1

2.50

000

−68.

9600

019

01.

003.

2212

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22.2

987

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30

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2098

6.82

FAT-

12.

68C

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−12.

5000

0−6

8.96

000

190

1.00

3.96

14.2

723

.885

.73

20

30

2098

8.12

FAT-

12.

90D

KE

-02P

−2.9

5000

−59.

9400

085

1.00

0.11

0.54

20.4

199

.46

00

44

2197

1.75

DK

E-0

3P−2

.950

00−5

9.93

000

100

1.00

0.04

0.17

23.3

999

.83

00

43

2197

1.13

DO

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R−1

0.57

000

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3100

020

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001.

264.

9524

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95.0

51

03

019

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922.

00D

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02R

−10.

5500

0−6

8.31

000

203

1.00

0.06

0.38

15.5

499

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30

20

1902

5.62

4.32

EL

D-0

1R6.

1148

4−6

1.41

159

210

0.25

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0031

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100.

000

24

425

221.

110.

91

(Con

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ed)

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Tabl

e1.

(Con

tinu

ed)

Soi

lphy

sica

lcon

stra

ints

core

s

Plo

tcod

eL

atit

ude

Lon

gitu

deA

ltit

ude

(ma.

s.l.)

Plo

tare

a(h

a)Pa

lmB

A(m

2ha

−1)

Palm

BA

(%)

Tre

eB

A(m

2ha

−1)

Tre

eB

A(%

)S

oil

anox

iaTo

pogr

aphy

Soi

lde

pth∗

Soi

lst

ruct

ure∗

Pre

cipi

tati

on(m

mye

ar−1

)IE

Fore

stcl

ass∗∗

Turn

over

rate

s(%

)

HC

C-2

2R−1

4.53

000

−60.

7300

074

71.

002.

228.

7923

.04

91.2

10

03

314

794.

742.

60E

LD

-02R

6.11

470

−61.

4115

518

00.

250

0.00

37.5

710

0.00

02

44

2522

0.76

0.54

EL

D-0

3R6.

4005

9−6

1.08

833

380

0.25

00.

0016

.64

100.

000

20

125

222.

071.

81E

LD

-04R

6.08

836

−61.

4005

835

00.

250

0.00

27.1

610

0.00

02

01

2522

3.27

1.36

FM

H-0

1R5.

1700

0−5

8.69

000

981.

000

0.00

42.3

510

0.00

00

43

2822

1.09

0.49

FM

H-0

2R5.

1700

0−5

8.69

000

122

1.00

00.

0036

.26

100.

000

04

328

221.

001.

28F

MH

-03R

5.18

000

−58.

7000

011

51.

000

0.00

31.9

210

0.00

00

43

2822

0.34

0.89

HC

C-2

1R−1

4.53

000

−60.

7400

072

91.

001.

666.

7323

.01

93.2

70

02

114

797.

683.

76IP

M-8

2P−5

.630

00−6

2.19

000

671.

001.

718.

0019

.66

92.0

01

03

225

742.

44IP

M-8

3P−5

.640

00−6

2.18

000

671.

001.

114.

4623

.79

95.5

41

14

225

743.

29IW

O-0

3R4.

5300

0−5

8.78

000

100

1.00

0.04

0.11

35.2

899

.89

00

43

2248

0.78

0.40

IWO

-12R

4.73

000

−58.

7200

061

1.00

0.1

0.40

24.7

299

.60

10

41

2405

1.80

2.91

JAS

-02R

−1.0

6981

−77.

6163

145

21.

002.

188.

3124

.05

91.6

90

32

236

453.

052.

89JA

S-0

3R−1

.077

13−7

7.60

966

384

1.00

1.06

3.75

27.1

996

.25

02

32

3645

2.66

2.11

JAS

-04R

−1.0

7323

−77.

6122

343

01.

002.

396.

5534

.11

93.4

50

32

236

453.

241.

95JE

N-1

1R−4

.880

00−7

3.63

000

151

1.00

0.2

0.77

25.6

699

.23

01

44

2642

1.98

TF

-11.

77JE

N-1

2R−4

.900

00−7

3.63

000

122

1.00

1.17

4.76

23.4

395

.24

20

23

2642

1.45

TF

-11.

04JE

N-1

3R−4

.920

00−7

3.53

000

145

1.00

4.58

19.6

918

.68

80.3

13

03

226

421.

782.

06L

FB

-01R

−14.

5800

0−6

0.83

000

245

1.00

0.95

3.56

25.7

296

.44

00

44

1479

1.25

2.70

LF

B-0

2R−1

4.58

000

−60.

8300

022

71.

001.

143.

9327

.89

96.0

70

04

414

791.

522.

73L

SL

-01R

−14.

4000

0−6

1.14

000

189

1.00

0.4

2.21

17.6

897

.79

40

23

1460

2.08

2.51

LS

L-0

2R−1

4.40

000

−61.

1400

019

01.

000.

481.

8924

.93

98.1

13

03

314

602.

551.

39M

NU

-03R

−11.

9000

0−7

1.40

000

312

2.00

4.08

14.6

323

.885

.37

00

20

2477

1.53

3.74

MN

U-0

4R−1

1.91

000

−71.

4000

031

22.

003.

814

.50

22.4

185

.50

12

31

2477

3.63

2.52

MN

U-0

5R−1

1.88

000

−71.

4100

031

22.

002.

096.

2431

.42

93.7

63

04

124

778.

521.

91

(Con

tinu

ed)

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Soil physical conditions limit palm and tree basal area 219

Tabl

e1.

(Con

tinu

ed)

Soi

lphy

sica

lcon

stra

ints

core

s

Plo

tcod

eL

atit

ude

Lon

gitu

deA

ltit

ude

(ma.

s.l.)

Plo

tare

a(h

a)Pa

lmB

A(m

2ha

−1)

Palm

BA

(%)

Tre

eB

A(m

2ha

−1)

Tre

eB

A(%

)S

oil

anox

iaTo

pogr

aphy

Soi

lde

pth∗

Soi

lst

ruct

ure∗

Pre

cipi

tati

on(m

mye

ar−1

)IE

Fore

stcl

ass∗∗

Turn

over

rate

s(%

)

MN

U-0

6R−1

1.89

000

−71.

4000

031

22.

255.

0416

.34

25.8

183

.66

30

41

2477

8.59

2.40

MT

H-0

1R−8

.880

00−7

2.79

000

246

1.00

2.92

14.9

616

.685

.04

04

32

1655

8.04

2.93

NO

U-0

2R4.

0800

0−5

2.67

000

110

1.00

0.04

0.12

33.1

499

.88

00

44

3280

1.57

1.27

NO

U-1

0R4.

0800

0−5

2.67

000

110

1.00

00.

0023

.23

100.

000

04

432

801.

742.

01N

OU

-12R

4.08

000

−52.

6700

011

01.

000.

61.

9729

.84

98.0

30

24

432

802.

131.

85N

OU

-17R

4.08

000

−52.

6700

011

01.

000.

471.

3135

.33

98.6

90

23

232

802.

401.

02N

OU

-21R

4.08

000

−52.

6700

011

01.

000.

240.

7731

.03

99.2

30

24

332

801.

321.

27P

OR

-01R

−10.

8200

0−6

8.78

000

268

1.00

1.26

4.11

29.3

995

.89

02

31

1720

1.41

2.01

PO

R-0

2R−1

0.80

000

−68.

7700

026

81.

001.

948.

7820

.15

91.2

20

21

217

201.

322.

61S

UC

-01R

−3.2

5000

−72.

9100

010

71.

001.

093.

9226

.75

96.0

81

34

228

133.

77T

F-1

2.12

SU

C-0

2R−3

.250

00−7

2.90

000

981.

000.

72.

5326

.93

97.4

70

44

128

133.

71T

F-1

2.64

SU

C-0

3R−3

.250

00−7

2.92

000

118

1.00

0.03

0.11

26.6

299

.89

30

31

2813

6.23

TF

-12.

31TA

M-0

1R−1

2.84

000

−69.

2900

020

51.

006.

6623

.58

21.5

876

.42

01

41

2523

2.96

FAT-

32.

64TA

M-0

2R−1

2.83

000

−69.

2900

021

01.

006.

120

.90

23.0

979

.10

00

20

2391

3.56

FAT-

32.

01TA

M-0

4R−1

2.84

000

−69.

2800

021

00.

422.

187.

1328

.41

92.8

73

03

125

234.

14S

W2.

67TA

M-0

5R−1

2.83

000

−69.

2700

022

01.

000.

51.

9025

.78

98.1

01

03

123

914.

47T

F-2

2.61

TAM

-06R

−12.

8400

0−6

9.30

000

200

1.00

7.5

22.4

525

.91

77.5

51

33

025

235.

67FA

T-3

2.39

TAM

-07R

−12.

8300

0−6

9.26

000

225

1.00

0.49

2.02

23.7

497

.98

01

42

2391

3.38

TF

-22.

67T

IP-0

3R−0

.640

00−7

6.15

000

221

1.00

0.44

1.67

25.9

598

.33

30

30

2987

7.83

3.30

YA

N-0

1R−3

.440

00−7

2.85

000

132

1.00

0.64

2.18

28.7

797

.82

13

41

2786

9.21

3.12

YA

N-0

2R−3

.430

00−7

2.84

000

109

1.00

0.42

1.36

30.5

398

.64

12

43

2786

5.72

1.65

ZA

R-0

1R−4

.010

00−6

9.91

000

126

1.00

0.71

4.22

16.1

95.7

81

03

328

370.

711.

14Z

AR

-02R

−4.0

0000

−69.

9000

012

61.

001.

78.

7917

.64

91.2

10

03

228

373.

511.

10Z

AR

-03R

−3.9

9000

−69.

9000

013

01.

000.

41.

8221

.58

98.1

80

13

227

621.

931.

41Z

AR

-04R

−3.9

9000

−69.

9100

014

61.

040.

632.

4325

.397

.57

01

32

2762

2.60

1.22

∗ Soi

ldep

than

dst

ruct

ure

scor

esin

vert

edfr

omQ

uesa

daet

al.(

2010

).∗∗

Mod

ified

from

And

erso

net

al.2

009

(ava

ilab

lefo

r23

plot

s).

pD

ata

dow

nloa

ded

from

http

://p

pbio

.inpa

.gov

.br;

Rda

tado

wnl

oade

dfr

omht

tp:/

/ww

w.f

ores

tplo

ts.n

et

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220 T. Emilio et al.

Soil sampling and determination of chemicaland physical properties

Soil sampling and analysis were undertaken by thePPBio/HIDROVEG and RAINFOR projects (Malhi et al.2002), using equivalent protocols (PPBio/HIDROVEG:http://ppbio.inpa.gov.br/manuais; RAINFOR: www.geog.leeds.ac.uk/projects/rainfor/projdocs.html). One soil pitwas dug in the dominant soil type, where soil descriptionswere made. In addition, samples were taken at 5–10 com-plementary points with a hand-held auger adapted tocollect undisturbed soil samples (Eijkelkamp AgrisearchEquipment BV, Giesbeek, The Netherlands). Samplingpoints followed a random stratified distribution so as toobtain representative soil collections of each area. The soilswere sampled up to 2 m deep, but chemical data reportedhere are for surface samples only (0–30 cm), while theentire profile was considered for soil physical properties.For a detailed description of the methods see Quesada et al.(2010).

Effective cation exchange capacity (IE) is used hereas a proxy for general soil fertility since there are strongrelationships between IE (hereafter called fertility), soilP and total elemental composition (Quesada et al. 2010).Samples were analysed for exchangeable cations by thesilver-thiourea method (Pleysier and Juo 1980), and thesum of concentrations for exchangeable Ca, Mg, K, Na andAl are reported.

Soil descriptions followed a standard protocol (Jahnet al. 2006), with special attention to the measurement ofeffective soil depth, depth to C horizon (where possible),horizon distribution (i.e. identification and depth of visi-ble soil diagnostic horizons), colour, distribution of rocks,concretions (i.e. presence of coarse, hard material in thesoil as petroplinthite, gravel, or other hardened material),ironstone layers or other hardpans, redox features, root dis-tribution, drainage capacity, soil hardness, soil structure(i.e. aggregate distribution, type and stability), organic mat-ter content and topographic position of the pit. Three bulkdensity samples were collected from the pit walls at thesame depths as for the soil samples (0–5, 5–10, 10–20,20–30, 30–50, 50–100, 100–150, 150–200 cm).

For quantifying the magnitude of root-limiting soilphysical properties (hereafter soil physical constraints), weused the same approach as in Quesada et al. (2010, 2012),assigning sequential scores to different levels of physicallimitations. This was done by reading the field descriptionsof soil and assigning to each category a score (Table 2;see details in Quesada et al. 2010). These scored cate-gories provide information on topography, soil depth, soilstructure and anoxic conditions in a semi-quantitative form.To aid interpretation, here we inverted Quesada’s origi-nal scale for soil depth and structure, so that shallower,poorly structured soils had lower scores, while deeper, well-structured soils had higher scores (Table 2). We used each

Table 2. Soil physical constraint scores modified from Quesada et al. (2010).

Soil physical constraint rating categories Score1

(1) Effective soil depth (soil depth, hardpans)Shallow soils (< 20 cm) 0Less shallow (20–50 cm) 1Hardpan or rock that allows vertical root growth; other soils between 50 and 100 cm

deep.2

Hardpan, rocks or C horizon ≥ 100 cm deep 3Deep soils ≥ 150 cm 4

(2) Soil structureVery dense, very hard, very compact, without aggregation, root restrictive 0Dense, compact, little aggregation, lower root restriction 1Hard, medium to high density and/or with weak or block-like structure 2Loose sand, slightly dense; well aggregated in sub-angular blocks, discontinuous pans 3Good aggregation, friable, low density 4

(3) TopographyFlat 0◦ 0Gently sloping 1–8◦ 1Gently undulating 8–19◦ 2Steep 20–44◦ 3Very steep > 45◦ 4

(4) Anoxic conditionsUnsaturated conditions 0Deep saturated zone (maximum of high saturation > 100 cm deep); deep redox features 1Deep saturated zone (maximum of high saturation 50 cm deep); redox features 2Seasonally flooded; soils with high clay content and very low porosity and/or dominated

by plinthite3

Constantly flooded; patches of stagnated water 4

1Soil depth and structure scores inverted from Quesada et al. (2010).

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Soil physical conditions limit palm and tree basal area 221

soil physical constraint characteristic as an independentexplanatory variable in regression analyses.

Forest turnover

Forest turnover was calculated as the proportion of stems(trees and palms combined) entering and leaving the plotper year. Annual mortality and recruitment rates wereestimated separately using standard procedures, based onlogarithmic models. These models assume a constant prob-ability of mortality and recruitment through each inventoryperiod (Swaine et al. 1987; Phillips et al. 2004), and theywere corrected for census interval following the recommen-dations by Lewis et al. (2004). We then considered the meanvalue of mortality and recruitment over the entire period asthe forest turnover rate for each plot, which we present as apercentage of all stems present.

Data analysis

We used ordinary least square regressions (OLS) toexamine the relationships between tree basal area, palmbasal area, environmental predictor variables, and turnoverrates. Environmental variables included the soil chemi-cal and physical properties described above, and annualprecipitation obtained from the interpolated WorldClimdataset (Hijmans et al. 2005), which varied from1333–4113 mm year−1 across our study area. The inter-polations of WorldClim dataset for Amazonia are basedon the few meteorological stations that are available forthis region (Hijmans et al. 2005). However, as the stationsare well spaced, the interpolation could well represent thelarge-scale precipitation trends that we analysed.

To select the model that best explained tree and palmbasal area variation, we carried out an exhaustive searchincluding all predictor variable combinations, using addi-tive linear models. Interactions between soil physical con-straints, rainfall and soil fertility, were also tested. Akaike’sInformation Criterion (AIC) was adopted as a measure ofgoodness of fit. AIC penalises parameter-rich models toprevent over-fitting. This procedure is preferable to sequen-tial searching protocols in avoiding Type-I error because themodels are not explicitly compared through statistical tests(MacNally 2000). We then ranked our 74 models from best(i.e. lowest) to worst (i.e. highest) AIC value. The �AIC ofa model is the difference between the AIC of a model tothat of the best model. Models with �AIC <2 were consid-ered as informative as the best model and the importanceof explanatory variables in these models was determinedaccording to their frequency of occurrence in the subsetof the best models (Richards 2005). After the best modelswere chosen, path analysis was used to determine the directand indirect effects of the environmental variables on palmand tree basal area.

Given that soil physical constraints are highlyrelated to forest turnover, some authors have assumed

a causal relationship between them (Quesada et al.2012). Nevertheless, to untangle the complex relationshipsbetween soil properties, stem turnover and forest basal area,simple direct relationships may not adequately describethe system, as both direct and indirect effects may occur.Therefore, we also built a path model that included thebest environmental predictors selected by the �AIC crite-ria, combined with the turnover rate to better understandthe direct and indirect effects of soil physical properties onpalm and tree basal area.

We performed quantile regressions (QR) in additionto OLS, as basal area variation was not homogeneous inrelation to the environmental variables in some cases. QR(Koenker and Bassett 1978) is a method for estimatingrelationships between variables for all portions of a prob-ability distribution without ignoring any part of the data.This method is robust to outliers and skewed distributions(Cade et al. 1999). In addition, fitting higher percentiles ofresponse variables as a function of the independent vari-able should estimate the upper limit set by the measuredindependent factors. This approach was undertaken mainlybecause, if an independent variable can be considered alimiting factor, the models estimated for the upper quan-tiles should have better predictive values than OLS models(Cade and Noon 2003). To evaluate for which cases QRshould be a better predictive model than OLS, we car-ried out the joint test of equality of slopes described byKoenker and Bassett (1982). This test evaluates if the slopesof QR and OLS differ from each other. If so, the distributionis heteroscedastic and the QR model should be consid-ered instead of the OLS model. We used the QR fitted foreach independent variable separately and the quantiles withtau = 0.25, tau = 0.50 and tau = 0.90, for this test.

We also attempted to understand the variation in for-est physiognomy in response to soil physical constraints.We adopted the forest classification of Anderson et al.(2009), who used a region-growing technique and non-supervised classification algorithm to classify forest plotsfrom Landsat 7/ETM+ and SRTM images and deter-mine forest physiognomy at a local-scale resolution, anda vegetation map provided by IBGE (1998) for the palm-dominated forests map presented in Figure 4. The local-scale forest classification was used only for small win-dows surrounding the ALP, CAX, CUZ, JEN and TAMstudy areas (Table 1), as it could not be generalised toother areas. As far as we know, there is no vegetationmap available for the entire Amazon with an appropri-ate resolution to allow us to distinguish palm-dominatedfrom other terra-firme forests across all study areas. TheBrazilian RADAMBRASIL vegetation map (Brasil 1978)is not useful to distinguish vegetation types at the localscale because only the dominant vegetation classes at ascale of 1:250,000 were mapped (Emilio et al. 2010). Forother Amazonian countries, available vegetation maps arenot comparable or the vegetation-class resolution is toocoarse. Anderson et al. (2009) distinguished seven foresttypes in the RAINFOR sites, including one-third of the

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222 T. Emilio et al.

plots included here. For this study, we grouped Anderson’svegetation units into four classes: terra-firme forests wherelarge palms do not contribute greatly to the forest canopy(TF), terra-firme forests where large palms do contributegreatly to the forest canopy (TFP), Mauritia swamps (SW),and forests over alluvial terraces (FAT). There are fewexamples of the SW and FAT categories and it is difficultto formally test the relationship between soil conditionsand forest structure. Therefore, we only explored theserelationships graphically without use of formal statisticalmethods.

Statistical analyses were carried out by using the R sta-tistical platform (R Development Core Team 2011) andthe quantreg package (Koenker 2011). Maps were preparedwith ArcGis 9.0.

Results

In our dataset, trees accounted for most of the basal area interra-firme Amazonian forests (94 ± 6%). However, palmscontributed up to 23% of basal area in some areas in thewestern Amazon (Table 1) with Iriartea deltoidea being thedominant arborescent palm species; Oenocarpus batauadominated in the central Amazon and Guiana Shield.

Simple OLS regressions showed that palm and treebasal area exhibited different responses to the same

environmental variables (Figure 1). Tree basal area wassignificantly positively related to soil depth and annual pre-cipitation, while palm basal area showed no relationship tothese variables. Palm basal area was related to soil fertility(r2 = 0.10, P = 0.004) while tree basal area did not show asignificant relationship with soil fertility. Soil structure wasthe only environmental variable significantly related to bothtree and palm basal area, and it defined the upper boundaryof palm and tree basal area in opposite ways. Palms attainedgreater total basal area in less-structured soils while treebasal area was greater in well-structured soils. Soil struc-ture alone explained up to 26% of the variance in palmbasal area and up to 10% of the variance in tree basal areain simple OLS regressions.

Multiple linear models showed essentially the samerelationships as the simple OLS models (Table 3). Forpalms, models including topography, soil depth and fertilitywere as informative (�AIC <2) as the simple soil-structuremodel. The inclusion of other environmental variables inthe model for palm basal area only very weakly increasedand, in some cases, even decreased explanatory power. Fortrees, best models included soil structure, with the P-valuefor this factor significant in almost all models. Precipitationwas the second best variable in the tree basal area model.Models that included both soil structure and precipita-tion explained up to 23% of tree basal area variance.

Figure 1. Simple relationships between basal area, soil physical properties and precipitation. Solid lines represent mean values predictedby ordinary linear regression (OLS, which here also coincide with the quantile regression, tau = 0.5). The dotted lines represent the valuespredicted by the linear quantile regressions, tau = 0.25 and tau = 0.9. The same model (linearised Gaussian in (c), and linear for the others)was adopted for the ordinary linear and quantile regressions. Lines are shown only when the OLS model is significant at the 0.05 level.The r2- and P-values of each OLS are presented at the top of each graph.

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Soil physical conditions limit palm and tree basal area 223

Table 3. AIC-ranked linear regression models with �AIC < 2. We evaluated 72 models, including simple models of eachexplanatory variable (soil anoxia, soil depth, soil structure, annual precipitation and fertility), all 57 combinations of the explana-tory variables in additive models, and five interaction models with precipitation and each of the other five explanatory variables.All top-rated tree and palm models include soil structure and exclude soil anoxia.

Model R2 P AICc �AICc

palm BA ∼ structure∗∗∗ 0.28 <0.0001 190.90 0.00palm BA ∼ depthns+ structure∗∗∗ 0.29 <0.0001 191.90 1.00palm BA ∼ topographyns+ structure∗∗∗ 0.28 <0.0001 192.80 1.90palm BA ∼ structure∗∗∗ + fertilityns 0.28 <0.0001 192.81 1.91

tree BA ∼ depthns+ structure∗+precipitation∗∗ 0.23 0.0003 200.48 0.00tree BA ∼ depthns+ structure∗ 0.14 0.0044 201.54 1.06tree BA ∼ depthns+ structure∗+precipitation∗∗+fertilityns 0.23 0.0008 202.32 1.84

∗∗∗, P < 0.0001; ∗∗, P < 0.001; ∗, P < 0.01 ; ns, P > 0. 01.

Topography and soil fertility did not contribute significantlyto any model.

Palm basal area variation was heterogeneous alongthe soil-structure axis (Figure 1). This variation was notreduced significantly in the multiple OLS regressions bythe addition of soil anoxia, topography, soil fertility orprecipitation as predictor variables (Table 3). Neither theinteractions between soil structure and precipitation norbetween soil structure and soil fertility was significantlyrelated to palm basal area variation (P > 0.1 for interac-tion term in all models). Tree basal area was related toboth soil structure and precipitation in the multiple OLSregressions. Like palms, variation in tree basal area washeterogeneous along the soil-structure axis and variationcould not be explained by interactions between explanatoryvariables in the multiple OLS regressions.

Soil structure explained a large fraction of the vari-ation in stand turnover rates (r2 = 0.23, P < 0.001).All low-turnover stands (0–2%) had a low proportion ofpalms and a high proportion of trees (Figure 2). Tree basalarea decreased significantly with increasing turnover (r2 =0.21, P < 0.001), while palm basal area was greatest atintermediate levels of turnover (2–3%). The relationshipbetween palm basal area and turnover rates appeared to benon-linear, and a linearised Gaussian model provided a sig-nificant fit (r2 = 0.24, P < 0.001). However, this non-linearpattern was strongly driven by one plot (DOI-02) with par-ticularly high disturbance rates. When we excluded thisplot, the quadratic term of the linearised Gaussian regres-sion became non-significant and linear regression provideda more appropriate fit to our data. The simple OLS regres-sion between palm basal area and stand turnover ratewas significant (r2 = 0.11, P = 0.005) and suggestedthat palm basal area increased with increasing turnoverrates.

A more complete picture of the relationship betweenbasal area, soil structure and turnover rate was obtainedby path analysis (Figure 3). Palm basal area was notdirectly affected by turnover rate, as the simple regressionsabove had suggested. When we accounted for the effect ofsoil structure on turnover rates, the relationship betweenpalm basal area and turnover rate became non-significant.

Figure 2. Relationship between forest stand-turnover rates and(a) palm basal area and (b) tree basal area for 60 forest plotsfor which turnover rate data was available. Dashed lines representvalues predicted by quantile regression (tau = 0.9).

Conversely, tree basal area remained significantly relatedto turnover rate, but the significant relation to soil struc-ture was lost. Our path analysis showed that the previouslyobserved response of tree basal area to soil structure wasindirect and mediated by forest turnover rates which were,in turn, mediated by soil structure. Despite the fact thatpalm and tree basal area presented opposite response pat-terns to soil structure and turnover rates, our model showedthat palm basal area was not significantly affected by treebasal area.

At larger scales, the distribution of palm basal areaat the plot level was congruent with mapped large-scale

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Figure 3. Diagram of direct and indirect effects of soil structureand forest turnover on palm and tree basal area. Arrows point toresponse variables. Standardised regression coefficients are shownalong the lines. Continuous lines indicate significant coefficientsand dashed lines non-significant ones.

forest physiognomies: plots with greater palm basal areaoccurred in and around palm-dominated forests, whileplots with lower palm basal area occurred mainly inregions where mapped palm-dominated forests are uncom-mon (Figure 4(a)). At local scales, physiognomies withhigh palm dominance occurred mainly over less-structuredsoils (Figure 4(b)). The soils under alluvial terrace andMauritia-dominated swamps were less structured thanthose under terra-firme sites (Figure 4(b)). Higher soil-structure variation was observed in terra-firme forests,where soil structure varied from well-structured friableclasses that do not impose much resistance to root pen-etration to more root-restrictive soils. In agreement withthe results suggested by our models, palm-dominated

forests (forest over alluvial terraces, Mauritia-dominatedswamp, palm-dominated terra-firme) were mostly found onpoorly structured soils (Figure 4(b)). Although our mod-els detected a relationship between tree basal area and soildepth, there were no clear differences in soil depth betweenforest physiognomies.

Discussion

Basal area partitioning and soil physical constraints

The observed relationship between soil physical constraintsand the partitioning of forest basal area between treesand palms suggest that soil-structure conditions establishthe upper limit to the basal area of palms and trees inAmazonian terra-firme forests. Soil structure was relatedin opposite ways to the basal area of trees and palms.In addition, the effect of soil structure on basal areawas direct for palms but mediated by forest turnoverrates for trees. Palm-dominated terra-firme forests occurredover less-structured soils, while terra-firme forests with-out canopy-palm dominance occurred more frequently overwell-structured soils.

Identifying the specific role of different physical con-straints imposed by soils and topography on root devel-opment is difficult, as geomorphology and soil conditionsare related throughout pedogenesis. For example, in theAmazon region, topography tends to vary regionally, oftenfollowing local geological history. Where dissected reliefoccurs, soils tend to be rejuvenated by erosion and thusconditions associated with early pedogenetic developmentprevail (i.e. limiting physical conditions such as high bulkdensity and shallow depth), as can be found in the Andes

Figure 4. (a) Spatial distribution of palm basal area in 74 forest plots, superimposed on the Brazilian map of palm-dominated forests(modified from IBGE 1998). (b) Soil structure variation between forest physiognomies for ALP, CAX, CUZ, JE and TAM sites (n = 23).Soil structure index follows Quesada et al. (2010) and forest classification follows Anderson et al. (2009). Lower values for the soil structureindex indicate less structured soils (see Table 1 for a complete description). FAT, forest over alluvial terrace; SW, Mauritia-dominatedswamp; TFP, palm-dominated terra-firme (Anderson’s TF 2 and 3); and TF, terra-firme forest.

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foothills in Peru and Ecuador (BOG plots). Where morestable, flat geomorphology dominates, soil conditions aredetermined by geological history. If the soils are old andwell drained, a highly weathered soil will occur, such asthose occurring on the plateaux of the Manaus region incentral Amazonia (DKE plots). However, if sediments areyoung and/or poorly drained, then undeveloped soils occur,as can be found at the Purus–Madeira interfluvial zone(IPM plots). In addition to this general trend, there is largevariation in soil physical properties at smaller scales, result-ing in a wealth of possible combinations to tease apart thespecific causal factors.

Mechanisms for basal area partitioning

Our best models consistently selected soil structure as apredictor of basal area, but not the other variables or theirinteractions. At small scales (i.e. plot level) topographyand soil conditions interact and potentially increase thestress caused by limited anchorage and rooting space. Otherproperties, such as soil structure, depth and hydrologicalproperties also interact to limit water movement and wateravailability. However, the relative importance of soil struc-ture compared with topography and soil anoxia in our finalmodels suggests that the process underlying the partitioningof basal area distribution between palms and trees may notbe directly related to drainage. Our results are more consis-tent with the hypothesis of self-maintaining forest dynamicfeedback mechanisms initiated by edaphic conditions, orig-inally proposed by Quesada et al. (2012). This hypothesissuggests that in forest stands where soils are deep and wellstructured as a result of millennia of soil weathering, lackof root anchorage will not shorten the lifetime of trees(Quesada et al. 2012). Trees growing in these soil condi-tions are also on average taller (Feldpausch et al. 2011),which may affect below-canopy light levels and palm abun-dance. In contrast, in areas where soils are less developed(i.e. western Amazonia, see Quesada et al. 2010), they areusually less weathered and thus more fertile, but they alsohave physical proprieties, such as shallow impediment lay-ers, high bulk density, and anoxic horizons that restrict rootdevelopment and anchorage (Quesada et al. 2010). In thesesites, mortality rates are higher and disturbances more fre-quent, resulting in more dynamic forests. Also, trees areon average shorter (Feldpausch et al. 2011), tend to growfaster as a result of the more fertile soils and increased lightavailability from more open canopies, invest less in wooddensity, die faster (Phillips et al. 2004), and are more likelyto die broken or fallen than standing (Chao et al. 2009).This is in accordance with our data, which show that palm-dominated forests occur more frequently in western andsouthern Amazonia (Figure 4) where soils show higher lev-els of soil physical limitation to root development (Quesadaet al. 2010) and forests have higher dynamism (Phillipset al. 2004; Quesada et al. 2012). This finding suggeststhat, in these environments, palm morphological adapta-tions may be more advantageous than those of trees. This

means that soil physical constrains may not only select fordifferent tree growth strategies but also influence life-formpartitioning in Amazonian forests.

Plant functional strategies

While soil structure is an important limiting factor for bothtrees and palms, morphological and physiological differ-ences result in trees and palms reaching maximum basalarea at opposite ends of the soil structure gradient. Theabsence of secondary growth in palms makes them moresusceptible to vascular system disruption. Unlike trees,palms cannot build new xylem vessels when embolismdamages part of their vascular system (Tomlinson 1990).Therefore, palms must adopt more conservative water-balance strategies, such as rapid stomatal closure whenevaporative demand is greater than the soil water contentcan supply (Sperry 1986; Renninger and Phillips 2010).This strategy may cause palms to grow more slowly thantrees. Annual mean adult growth in height varies between0.08 and 0.8 m in palms (Henderson 2002). We are notaware of studies that registered the growth in height foradult trees in tropical forest, but juvenile tropical treescan grow in height between 0.7–1.5 m per year on aver-age (Clark and Clark 2001). This is equivalent to the meanannual juvenile growth rate of the palm species Euterpeprecatoria Mart. (Zuidema 2000), a species with one ofthe highest adult growth rates registered for Amazonianpalms (Henderson 2002). This slower growth may result ina competitive disadvantage for palms, so that trees, whennot affected by unfavourable soil conditions, can outper-form palms. However, the unbranched crown architecture –and hence small canopy volume – associated with high stemmechanical resistance will probably increase the ability ofpalms to succeed in more dynamic places where soils arealso less favourable for trees.

Beyond the differences in life-form, differences amongspecies in functional strategies could also help explainthe observed differences in basal area. Some palm speciesmay outperform others in their ability to persist andthrive in areas of high turnover (Montúfar et al. 2011).Iriartea deltoidea Ruiz & Pav., Attalea butyracea (Mutisex L.f.) Wess. Boer, Oenocarpus bataua Mart., Euterpeprecatoria Mart., Socratea exorrhiza (Mart.) H.Wendl. andAstrocaryum murumuru Wallace each contributed at least10 times more to basal area than any other palm speciesrecorded in our plots. Four of these dominant species(I. deltoidea, O. bataua, E. precatoria and S. exorrhiza)develop stilt roots. The sparsely distributed stilt roots ofS. exorrhiza allow individuals to physically move acrossthe forest floor – changing their original rooting positionas the plant grows – likely conferring an advantage tothis species in a dynamic environment by avoiding fallentrunks and by acquiring light (Bodley and Benson 1980).In its adult phase, the ability of S. exorrhiza to developa second set of stilt roots may favour this species, allow-ing it to re-emerge from coarse woody debris commonly

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present after disturbance (Avalos 2004). The clumped stilt-root architecture of E. precatoria, O. bataua and I. del-toidea does not allow for that kind of disturbance avoid-ance. However, at least in the case of E. precatoria, stiltroots improve trunk stability and may help the stem todevelop extreme ‘slenderness’, with unusually high height–diameter relationships (Avalos and Otárola 2010). As withbuttresses in many trees, stilt roots in palms reduce effectivebole length and therefore reduce the risk of structural fail-ure due to buckling (Young and Perkocha 1994). In shallow,less-structured and/or hydromorphic soils, the presenceof stilt roots and buttresses thus help prevent tree andpalm uprooting. Deep roots can also provide anchorage,but shallow or hydromorphic soils restrict the root growthand prevent their development. Stilt roots may thereforeconfer an advantage to species with this adaptation overthose species that rely only on deep roots for anchor-age. In addition, the ability of stilt-root palms to generatenew adventitious roots at the stem base (Tomlinson 2006)may allow these palms to obtain water and nutrients moreefficiently than buttressed trees in these environments.

Geographical patterns of palm dominance

Biogeography may also play an important role in palm-species distribution at large spatial scales (Eiserhardt et al.2011) and also in overall palm basal area in each region.Biogeographical factors may also interact with soil factorseven in the absence of clear biogeographical boundariesfor Amazonian palms. The species pool of palms, soilconstraints and disturbance regime in each region shouldinteract to define the relative contribution of palms to over-all forest basal area. In our study plots, Iriartea deltoideaand Attalea butyracea were the most abundant arbores-cent palm species, so it is not surprising that the spatialpattern of palm basal area distribution shown here substan-tially coincides with the spatial distribution of these twospecies. These two species were very abundant in our studyplots probably because our survey effort was concentratedalong the periphery of the Amazon basin. Along the easternand northern edges of the Amazon basin, oligarchic forestsdominated by other palm species occur. Euterpe oleraceastands are common over fluvisols in the Amazonian estu-aries of Pará and Amapá states in Brazil, Attalea speciosastands occur near to the Amazon borders in the Brazilianstates of Maranhão (north), Piauí (north-east), and Goiás(central Brazil) in forest sites subject to intensive distur-bance, Oenocarpus bataua forms extensive aggregations inseasonal swamp forests on gleyic podzols in central andwestern Amazonia and dense stands of Mauritia flexuosaoccur over dystric histosols of the Orinoco, Ucayali andMarañon Rivers (Peters et al. 1989). All these oligarchicforests are associated with young (fluvisols, histosol), lowphysical quality (gleyic podzol) soils or high-disturbanceforests, consistent with our hypothesis that poor soil-structure and more dynamic forests lead to higher palmbasal area. Furthermore, our data also show that, within thesame region, greater palm basal area is usually attained on

the more poorly structured soils (Table 1). Together, thesedata lead us to suggest that, even though different palmspecies may be dominant in regions with distinct speciespools and biogeographical history, palm dominance is alsoaffected by local soil physical constraints.

Conclusions

Trees attained highest basal area in deep, well-structuredsoils that experienced high rainfall. On the other hand, treesmay fail to persist in all available physical space in areaswhere limiting soil characteristics occur, making space forother life-forms. Our study indicates that palms are animportant life-form and they occupy forest space wheresoils are less developed and less structured. In order tounderstand the complex puzzle of variation in forest struc-ture across the vast Amazon basin and beyond, we mustidentify the various potential environmental and histori-cal controls on forest ecosystems. Our study identified onepiece of this puzzle by demonstrating that (1) soil physi-cal constraints establish the upper bound for palm and treebasal area, (2) life-forms with contrasting rooting strate-gies, such as palms and trees, achieve greater basal areaat opposite ends of the soil-structure gradient, (3) forestdynamism may be reflected by the differences betweenpalm and tree basal area, and (4) distinct combinations ofpalm and tree basal areas imposed by soil structure and/ordisturbance regimes may be large enough to be observedin forest physiognomy. What remains to be understood iswhat determines the variation in basal area below the lim-its imposed by soil physical constraints, and how otherlife-forms, such as herbs, bamboos and lianas deal withsoil–rainfall environmental niche envelopes. A more com-plete understanding of this will help develop more realisticmodels of forest response to changing land use and climatethat take into account the fact that there are more than justtrees and rainfall in Amazonian forests.

AcknowledgementsThe National Council for Scientific and TechnologicalDevelopment (CNPq) awarded stipends to TE, and a majorgrant from the Gordon and Betty Moore Foundation toRAINFOR supported collection of much of the data analysedhere. Significant funding for fieldwork was also receivedfrom NERC-TROBIT, EU, AMAZONICA, CNPq Universal,CNPq/FAPEAM PRONEX, PPBio Amazônia oriental andCNPq/FAPESP/FAPEAM HIDROVEG project. OP is supportedby the European Research Council project ‘Tropical Forests in theChanging Earth System’. We thank the many field workers whocollected plant and soil data from the 74 RAINFOR and PPBiostudy sites distributed across eight countries. Data repositoriesfor PPBio sites are maintained by PPBio and CENBAM. Ourspecial thanks to S. Almeida, L. Arroyo, J.G. Barroso, V. Bezard,L. Blanc, P. Castro, W. Castro, F. Cornejo, C. Czimczik, A.Etienne, R. Farias, S. Fauset, G. van der Heijden, F. Kwasie,A. de la Fuente, B. Leudet de la Vallee, J. Lloyd, B. Michel,D. Moura de Freitas, P. Naisoo, O. N’Gwete, E.-A. Nicolini,T. Paine, M.C. Penuela-Mora, A. Roopsind, E. Rutishauser, R.Sante, D. Sasaki, G. da Silva Henicka, G.B. da Silva Oliveira, J.Teran Aguilar and R. Vásquez who participated in data collection

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in many sites and to G. López-Gonzalez for her work curatingand managing the ForestPlots database. We also thank the FloraProgram at the Fundação Ecológica Cristalino, Instituto Nacionalde Pesquisas da Amazônia, Medellin Botanical Garden, MuseuGoeldi, National University of Colombia, and University ofAcre for their support of field teams and their activities. Thismanuscript was partly developed during the 2011 RAINFOR(Gordon and Betty Moore Foundation-UFAC) workshop in RioBranco, Acre, Brazil. We thank C.L. Bechtoldt for help withEnglish revisions.

Notes on contributorsThaise Emilio is a Ph.D. student, working with plant speciesand vegetation distribution across Amazonia. Her research inter-ests include palms and how environmental conditions drive theirdistribution and abundance.

Carlos A. Quesada is a post-doctoral research associate. Hisresearch interests include biogeochemical cycles, Amazoniansoils and their interaction with forest structure and dynamics.

Flávia R.C. Costa is a researcher working on the ecologicaldeterminants of plant species and community distributions, andanthropogenic influences on plant communities.

William E. Magnusson studies the ecological and evolutionarydeterminants of Amazonian organisms.

Juliana Schietti is a Ph.D. candidate. Her research interests focuson understanding tropical forest structure and functioning andtheir relations with soil hydrology and climate.

Ted R. Feldpausch’s research interests include the ecology oftropical forests and savannas; effects of global change on for-est and savanna composition, structure and carbon accumulation;land-use change, forest disturbance and recovery.

Roel J.W. Brienen is a researcher who is broadly interested intropical forest dynamics and impacts of global change on tropicalforest functioning.

Timothy R. Baker’s research focuses on tropical forests and aimsto understand past, current, and future patterns of carbon storageand biodiversity in these ecosystems.

Jerome Chave is a senior staff member and is the science coor-dinator of the Nouragues research station. His field of scientificexpertise is spatial patterns of plant diversity in Amazonia andbiogeochemical cycles in tropical rain forests.

Estebán Álvarez is a Ph.D. student and is currently working withforest diversity, biomass, and dynamics in complex environmentalgradients in Colombia.

Alejandro Araújo Murakami is a researcher focusing on Bolivia’sfloristic diversity, with a particular interest in ethnobotany andforest ecology. He has worked in Amazonian ecosystems, Chaco-Amazonia transitions (Chiquitanos), and Andean forests.

Olaf Bánki is a conservation biologist and forest ecologist with aninterest in drivers of change in forest composition and tree diver-sity along ecological and geographical gradients in the GuianaShield.

Carolina V. Castilho is a biologist interested in long-term eco-logical studies, and uses permanent plots to evaluate changesin biomass, structure, and tree species composition in tropicalforests.

Eurídice N. Honorio C. is a Ph.D. student and her research focuseson tropical plant taxonomy in Peru, and she is currently interestedin phylogeographic patterns of Amazonian trees.

Timothy J. Killeen is a conservation biologist.

Yadvinder Malhi is a professor. His research interests focus onunderstanding the ecology and functioning of tropical forestsworldwide, and their interactions with climate change.

Erick M. Oblitas Mendoza has been studying the carbon stocks ofAmazonian soils. He is currently looking at carbon stocks alongan altitudinal gradient from the Andes to the Amazon.

Abel Monteagudo was a M.Sc. student funded by the AmazonForest Inventory Network (RAINFOR) at the time of the study;currently he is working on a project to understand carbon balancein the Amazon. He is particularly interested in the study of thedistribution and diversity of trees in the Amazon.

David Neill’s research interests include taxonomic studies of theflora of Ecuador, particularly in the Amazon and Cordillera delCóndor regions, and quantitative inventories of forest compositionand structure in one-hectare inventory plots, with a focus on exam-ining relationships between forest floristic composition, structureand dynamics and geological substrate and soil characteristics.

Germaine Alexander Parada is currently working on floristicinventories of Andean forests in Santa Cruz, Bolivia. He is inter-ested in the ecology of the Amazon rainforest and the Andes ingeneral.

Hirma Ramirez-Angulo’s main research interests include thedynamics of tropical forests, with a focus on the application ofsimulation models to predict forest dynamics.

Michael Schwarz is a M.Sc. student. His research interests includeclimate change and tropical forests, climate mitigation in develop-ing countries, and climate project finance schemes like REDD andCDM.

Marcos Silveira has been working with plant and forest ecology,with a special interest in factors driving species composition.

Hans ter Steege is a senior researcher and he works on treealpha- and beta-diversity in the Amazon and Guiana Shield andis interested in how local and regional processes drive diversity.

John W. Terborgh is a research professor. For 36 years, he hasoperated a research station in the Manu National Park, Peru wherePeruvian and international scientists study a wide range of basicand applied problems. His current interests lie in the field ofplant–animal interactions and the mechanisms that sustain thehigh diversity of tropical tree communities.

Armando Torres-Lezama is a forester. His main research inter-ests include sustainable rural development and tropical forestdynamics.

Emilio Vilanova’s research interests include tropical forestdynamics, with an emphasis on biomass and carbon estimations,and options for sustainable management of tropical forests.

Oliver L. Phillips co-ordinates the RAINFOR project. His long-term research goal is to understand the dynamics of carbon andbiodiversity across the world’s tropical forests, how these changewith our changing climate, and how they may feed back on thewhole planet.

ReferencesAnderson LO, Malhi Y, Ladle RJ, Aragao LEOC, Shimabukuro

Y, Phillips O, Baker TR, Costa ACL, Espejo JS, HiguchiN, et al. 2009. Influence of landscape heterogeneity onspatial patterns of wood productivity, wood specific den-sity and above ground biomass in Amazonia. Biogesciences6:1883–1902.

Avalos G. 2004. Production of a second set of stilt roots inarborescent palms: a solution to a puzzle. Palms 48:83–85.

Avalos G, Otárola MF. 2010. Allometry and stilt root structure ofthe neotropical palm Euterpe precatoria (Arecaceae) across

Dow

nloa

ded

by [

the

Bod

leia

n L

ibra

ries

of

the

Uni

vers

ity o

f O

xfor

d] a

t 09:

05 0

3 M

arch

201

4

228 T. Emilio et al.

sites and successional stages. American Journal of Botany97:388–394.

Baker TR, Phillips OL, Malhi Y, Almeida S, Arroyo L, DiFiore A, Erwin T, Killeen TJ, Laurance SG, Laurance WF,et al. 2004. Variation in wood density determines spatial pat-terns in Amazonian forest biomass. Global Change Biology10:545–562.

Bodley JH, Benson FC. 1980. Stilt-root walking by and Iriarteoidpalm in the Peruvian Amazon. Biotropica 12:67–71.

Brasil. 1978. Projeto RADAMBRASIL. Geologia, geomorfolo-gia, pedologia, vegetação e uso potencial da terra. Riode Janeiro (Brazil): Departamento Nacional de ProduçãoMineral.

Cade BS, Noon BR. 2003. A gentle introduction to quantileregression for ecologists. Frontiers in Ecology and theEnvironment 1:412–420.

Cade BS, Terrell JW, Schroeder R. 1999. Estimating effectsof limiting factors with regression quantiles. Ecology80:311–323.

Castilho CVD, Magnusson WE, Nazare O de A R, LuizaoRCC, Luizao FJ, Lima AP, Higuchi N. 2006. Variationin aboveground tree live biomass in a central Amazonianforest: effects of soil and topography. Forest Ecology andManagement 234:85–96.

Clark DA, Clark DB. 2001. Getting the canopy: tree height growthin a neotropical rain forest. Ecology 82:1460–1472.

Chao K-J, Phillips OL, Monteagudo A, Torres-Lezama A,Vásquez-Martínez R. 2009. How do trees die? Mode ofdeath in northern Amazonia. Journal of Vegetation Science20:260–268.

Davis TA. 1961. High root-pressures in palms. Nature192:277–278.

Dietrich WE, Windsor DM, Dunne T. 1996. Geology, climate andhydrology of Barro Colorado Island. In: Leigh EG, Rand AS,Windsor DM, editors. The ecology of a tropical rain forest:seasonal rhythms and long term changes. 2nd ed. Washington(DC): Smithsonian Institution Press. p. 101–108.

Eiserhardt WL, Svenning J-C, Kissling WD, Balslev H. 2011.Geographical ecology of the palms (Arecaceae): determinantsof diversity and distributions across spatial scales. Annals ofBotany 108:1391–1416.

Emilio T, Nelson BW, Schietti J, Desmoulière SJ-M, EspíritoSanto HMV, Costa FRC. 2010. Assessing the relation-ship between forest types and canopy tree beta diversity inAmazonia. Ecography 33:738–747.

Feldpausch TR, Banin L, Phillips OL, Baker TR, Lewis SL,Quesada CA, Affum-Baffoe K, Arets EJMM, Berry NJ, BirdM, et al. 2011. Height-diameter allometry of tropical foresttrees. Biogeosciences 8:1081–1106.

Ferry B, Morneau F, Bontemps J-D, Blanc L, Freycon V. 2010.Higher treefall rates on slopes and waterlogged soils result inlower stand biomass and productivity in a tropical rain forest.Journal of Ecology 98:106–116.

Gale N, Barfod AS. 1999. Canopy tree mode of death in westernEcuadorian forests. Journal of Tropical Ecology 15:415–436.

Gourlet-Fleury S, Rossi V, Rejou-mechain M, Freycon V, FayolleA, Saint-André L, Cornu G, Gérard J, Sarrailh J-Michel,Flores O, et al. 2011. Environmental filtering of dense-wooded species controls above-ground biomass stored inAfrican moist forests. Journal of Ecology 99:981–990.

Henderson A. 2002. Evolution and ecology of palms. Bronx (NY):The New York Botanical Garden Press.

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A.2005. Very high resolution interpolated climate surfacesfor global land areas. International Journal of Climatology25:1965–1978.

IBGE. 1998. Recursos Naturais e Meio Ambiente: Uma visão doBrasil.

IBGE. 2012. Manual Técnico da Vegetação Brasileira. 2ndedition. (Manuais técnicos em geociências Series; vol 1).Rio de Janeiro (Brazil): Instituto Brasileiro de Geografia eEstatística.

Jahn R, Blume H-P, Asio VB, Spaargaren O, Schad P. 2006.Guidelines for soil description. Rome (Italy): FAO.

Koenker R, Bassett G. 1982. Robust tests for heteroscedasticitybased on regression quantiles. Econometrica 50:43–61.

Koenker R, Bassett G. 1978. Regression quantiles. Econometrica46:33–50.

Koenker R. 2011. quantreg: Quantile Regression. Available onlineat http://cran.r-project.org/package=quantreg.

Laurance WF, Fearnside PM, Laurance SG, Delamonica P,Lovejoy TE, Rankin-de Merona JM, Chambers JQ, Gascon C.1999. Relationship between soils and Amazon forest biomass:a landscape-scale study. Forest Ecology and Management118:127–138.

Lewis SL, Phillips OL, Sheil D, Vinceti B, Baker TR, Brown S,Graham AW, Higuchi N, Hilbert DW, Laurance WF, et al.2004. Tropical forest tree mortality, recruitment and turnoverrates: calculation, interpretation and comparison when censusintervals vary. Journal of Ecology 92:929–944.

Lopez-Gonzalez G, Lewis SL, Burkitt M, Phillips OL. 2011.ForestPlots.net: a web application and research tool to man-age and analyse tropical forest plot data. Journal of VegetationScience 22:610–613.

Lopez-Gonzalez G, Lewis SL, Phillips OL, Burkitt M. 2012.Forest Plots Database. Available online at www.forestplots.net(accessed 18 June 2012).

MacNally R. 2000. Regression and model-building in conser-vation biology, biogeography and ecology: the distinctionbetween – and reconciliation of – “predictive” and “explana-tory” models. Biodiversity & Conservation 8:655–671.

Malhi Y, Phillips OL, Lloyd J, Baker T, Wright J, Almeida S,Arroyo L, Frederiksen T, Grace J, Higuchi N, et al. 2002. Aninternational network to monitor the structure, compositionand dynamics of Amazonian forests (RAINFOR). Journal ofVegetation Science 13:439–450.

Malhi Y, Wood D, Baker TR, Wright J, Phillips OL, CochraneT, Meir P, Chave J, Almeida S, Arroyo L, et al. 2006. Theregional variation of aboveground live biomass in old-growthAmazonian forests. Global Change Biology 12:1107–1138.

Montúfar R, Anthelme F, Pintaud J-C, Balslev H. 2011.Disturbance and resilience in tropical American palm popu-lations and communities. The Botanical Review 77:426–461.

Nepstad DC, Carvalho CR, Davison EA, Jipp PH, Lefebvre PA,Negreiros GH, Silva ED, Stone TA, Trumbire SE, VieiraS. 1994. The role of deep roots in the hydrological andcarbon cycles of Amazonian forests and pastures. Nature372:666–669.

Nogueira EM, Fearnside PM, Nelson BW, Barbosa RI, KeizerEWH. 2008. Estimates of forest biomass in the BrazilianAmazon: new allometric equations and adjustments tobiomass from wood-volume inventories. Forest Ecology andManagement 256:1853–1867.

Paoli GD, Curran LM, Slik JWF. 2007. Soil nutrients affect spatialpatterns of aboveground biomass and emergent tree density insouthwestern Borneo. Oecologia 155:287–299.

Patiño S, Lloyd J, Paiva R, Baker TR, Quesada CA, Mercado LM,Schmerler J, Schwarz M, Santos AJB, Aguilar A, et al. 2009.Branch xylem density variations across the Amazon Basin.Biogeosciences 6:545–568.

Peters CM, Balick MJ, Kahn F, Anderson AB, Peters CM, KahnF, Anderson AB. 1989. Oligarchic forests of economic plantsin Amazonia: utilization and conservation of an importanttropical resource. Conservation Biology 3:342–349.

Pezzini FF, Hendrigo PHA de M, Oliveira DMS de, AmorimRXD, Figueiredo FOG de, Drucker DP, Rodrigues FR de O,

Dow

nloa

ded

by [

the

Bod

leia

n L

ibra

ries

of

the

Uni

vers

ity o

f O

xfor

d] a

t 09:

05 0

3 M

arch

201

4

Soil physical conditions limit palm and tree basal area 229

Zuquim G, Emilio T, Costa FRC, et al. 2012. The BrazilianProgram for Biodiversity Research ( PPBio ) InformationSystem. Biodiversity & Ecology 4: 265–274.

Phillips OL, Baker TR, Arroyo L, Higuchi N, Killeen TJ,Laurance WF, Lewis SL, Lloyd J, Malhi Y, Monteagudo A,et al. 2004. Pattern and process in Amazon tree turnover,1976–2001. Philosophical transactions of the Royal Societyof London. Series B, Biological Sciences 359:381–407.

Pleysier JL, Juo ASR. 1980. A single-extraction method usingsilver-thiourea for measuring exchangeable cations and effec-tive CEC in soils with variable charges. Soil Science129:205–211.

PPBio (Programa de Pesquisa em Biodiversicade). 2012.Repositório de dados do PPBio. Available online at http://ppbio.inpa.gov.br (accessed 18 June 2012).

Quesada CA, Lloyd J, Schwarz M, Patiño S, Baker TR, CzimczikC, Fyllas NM, Martinelli L, Nardoto GB, Schmerler J,et al. 2010. Variations in chemical and physical proper-ties of Amazon forest soils in relation to their genesis.Biogeosciences 7:1515–1541.

Quesada CA, Phillips OL, Schwarz M, Czimczik CI, Baker TR,Patiño S, Fyllas NM, Hodnett MG, Herrera R, Almeida S,et al. 2012. Basin-wide variations in Amazon forest struc-ture and function are mediated by both soils and climate.Biogeosciences 9:2203–2246.

R Development Core Team. 2011. R: A language and environ-ment for statistical computing. Available online at http://www.r-project.org/.

Renninger HJ, Phillips N. 2010. Wet- vs. dry-season transpira-tion in an Amazonian rain forest palm Iriartea deltoidea.Biotropica 42:470–478.

Richards SA. 2005. Testing ecological theory using theinformation-theoretic approach: examples and cautionaryresults. Ecology 86:2805–2814.

Roggy JC, Prevost MF, Garbaye J, Domenach AM. 1999.Nitrogen cycling in the tropical rainforest of French Guiana:

comparison of two sites with contrasting soil types 15N.Journal of Tropical Ecology 15:1–22.

Slik JWF, Aiba S-I, Brearley FQ, Cannon CH, Forshed O,Kitayama K, Nagamasu H, Nilus R, Payne J, Paoli G, et al.2010. Environmental correlates of tree biomass, basal area,wood specific gravity and stem density gradients in Borneo’stropical forests. Global Ecology and Biogeography 19:50–60.

Sperry JS. 1986. Relationship of xylem embolism to xylempressure potential, relationship stomatal closure, and shootmorphology in the palm Rhapis. Plant Physiology 80:110–116.

Swaine MD, Liebermanf D, Putzj FE. 1987. The dynamics of treepopulations in tropical forest: a review. Journal of TropicalEcology 3:359–366.

ter Steege H, Pitman NCA, Phillips OL, Chave J, Sabatier D,Duque A, Molino J-F, Prevost M-F, Spichiger R, CastellanosH, et al. 2006. Continental-scale patterns of canopy treecomposition and function across Amazonia. Nature 443:444–447.

Toledo JJ de, Magnusson WE, Castilho CV, Nascimento HEMM.2011. Tree mode of death in Central Amazonia: effects ofsoil and topography on tree mortality associated with stormdisturbances. Forest Ecology and Management 263:253–261.

Tomlinson PB. 1990. The structural biology of palms. New York(NY): Oxford University Press.

Tomlinson PB. 2006. The uniqueness of palms. Botanical Journalof the Linnean Society 151:5–14.

Young TP, Perkocha V. 1994. Treefalls, crown asymmetry, andbuttresses. Journal of Ecology 82:319–324.

Zuidema PA. 2000. Demography of exploited tree species inthe Bolivian Amazon. Scientific series; vol 2. Riberira Alta(Bolivia): PROMAB.

Zuidema PA, Brienen RJW, Schöngart J. 2012. Tropical forestwarming: looking backwards for more insights. Trends inEcology & Evolution 23:11–12.

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