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

15 Pages·2007·0.49 MB·English

PlantEcology&Diversity,2013
http://dx.doi.org/10.1080/17550874.2013.772257
SoilphysicalconditionslimitpalmandtreebasalareainAmazonianforests
ThaiseEmilioa*,CarlosA.Quesadab,FláviaR.C.Costac,WilliamE.Magnussonc,JulianaSchiettia,TedR.Feldpauschd,
RoelJ.W.Brienend,TimothyR.Bakerd,JeromeChavee,EstebánÁlvarezf,AlejandroAraújog,OlafBánkih,
CarolinaV.Castilhoi,EurídiceN.HonorioC.d,j,TimothyJ.Killeenk,YadvinderMalhil,ErickM.OblitasMendozam,
AbelMonteagudon,DavidNeillo,GermaineAlexanderParadaf,AntonioPeña-Cruzn,HirmaRamirez-Angulop,
MichaelSchwarzq,MarcosSilveirar,HansterSteeges,JohnW.Terborght,RaquelThomasu,ArmandoTorres-Lezamap,
EmilioVilanovap andOliverL.Phillipsd
aProgramadePós-GraduaçãoemEcologia,InstitutoNacionaldePesquisasdaAmazônia(INPA),Manaus,Brasil;bCoordenaçãode
PesquisaemDinâmicaAmbiental,INPA,Manaus,Brasil;cCoordenaçãodePesquisaemBiodiversidade,INPA,Manaus,Brasil;dSchool
ofGeography,UniversityofLeeds,Leeds,UK;eLaboratoireEvolutionetDiversitéBiologique,CNRSandUniversitéPaulSabatier,
Toulouse,France;fMedellínBotanicalGarden,Medellín,Colombia;gMuseodeHistoriaNaturalNoelKempffMercado,SantaCruz,
3 Bolivia;hInstituteforBiodiversityandEcosystemDynamics,UniversityofAmsterdam,Amsterdam,Nederlands;iEMBRAPA,Centrode
1
0 PesquisaAgroflorestaldeRoraima,BoaVista,Roraima,Brasil;jInstitutodeInvestigacionesdelaAmazoníaPeruana,Iquitos,Peru;
2
h kConservationInternational,Arlington,USA;lEnvironmentalChangeInstitute,SchoolofGeographyandtheEnvironment,Universityof
rc Oxford,Oxford,UK;mProgramadeCapacitaçãoInstitucional,INPA,Manaus,Brasil;nJardínBotánicodeMissouri,Oxapampa,Perú;
a
M oUniversidadEstatalAmazónica,FacultaddeIngenieríaAmbiental,Puyo,Pastaza,Ecuador;pInstitutodeInvestigacionesparael
2 DesarrolloForestal(INDEFOR,)UniversidaddeLosAndes,Mérida,Venezuela;qUniversityofAppliedSciencesEberswalde,
1
7 Eberswalde,Germany;rCentrodeCiênciasBiológicasedaNatureza,UniversidadeFederaldoAcre,RioBranco,Brasil;sNaturalis
5 BiodiversityCenter,Leiden,Netherlands;tNicholasSchooloftheEnvironmentandEarthSciences,DukeUniversity,Durham,USA;
00: uIwokramaInternationalCentreforRainForestConservationandDevelopment,Iwokrama,Guyana
at
] (Received7March2012;finalversionreceived18May2012)
ht
c
e
r Background:Treesandarborescentpalmsadoptdifferentrootingstrategiesandresponsestophysicallimitationsimposed
Ut
bysoilstructure,depthandanoxia.However,theimplicationsofthesedifferencesforunderstandingvariationintherelative
y
r abundanceofthesegroupshavenotbeenexplored.
a
br Aims:Weanalysedtherelationshipbetweensoilphysicalconstraintsandtreeandpalmbasalareatounderstandhowthe
Li physicalpropertiesofsoilaredirectlyorindirectlyrelatedtothestructureandphysiognomyoflowlandAmazonianforests.
y Methods:Weanalysedinventorydatafrom74forestplotsacrossAmazonia,fromtheRAINFORandPPBionetworksfor
sit whichbasalarea,standturnoverratesandsoildatawereavailable.Werelatedpatternsofbasalareatoenvironmentalvariables
r
ve inordinaryleastsquaresandquantileregressionmodels.
ni Results:Soilphysicalpropertiespredictedtheupperlimitforbasalareaofbothtreesandpalms.Thisrelationshipwasdirect
U
[ forpalmsbutmediatedbyforestturnoverratesfortrees.Soilphysicalconstraintsaloneexplainedupto24%ofpalmbasal
by areaand,togetherwithrainfall,upto18%oftreebasalarea.Treebasalareawasgreatestinforestswithlowerturnoverrates
d onwell-structuredsoils,whilepalmbasalareawashighinweaklystructuredsoils.
e
d Conclusions:Ourresultsshowthatpalmsandtreesareassociatedwithdifferentsoilphysicalconditions.Wesuggestthat
a
o adaptations of these life-forms drive their responses to soil structure, and thus shape the overall forest physiognomy of
nl
w Amazonianforestvegetation.
o
D Keywords:ecologicallimitingfactors;life-forms;palm-dominatedforests;quantileregression;soilstructure;tropicalforest;
vegetationtypes
Introduction disturbance or the presence (or absence) of limiting soil
Amazonian forests play an important role in the global properties.
carboncyclebuthowmuchcarbonisstoredintheseecosys- Soil and climate have been widely investigated to
tems is still uncertain. Variation in biomass is directly understand forest structure and composition in Amazonia
relatedtovariationinstandbasalareaandstand-levelwood (Lauranceetal.1999;Roggyetal.1999;Malhietal.2006;
density. Mean stand-level wood density is dependent both ter Steege et al. 2006; Quesada et al. 2012) and elsewhere
on species composition (Baker et al. 2004) and environ- (Paoli et al. 2007; Slik et al. 2010). Soil physical condi-
mental factors (Patiño et al. 2009), such as soil fertility tionsinparticular,suchaswater-holdingcapacity,drainage,
andclimate.Theenvironmentalcorrelatesofbasalareaare andphysicalstructure,areimportantfactorsthataffecttree
lessclear.Thereisimportantsite-to-sitevariability(Malhi growth, mortality and floristic composition in the tropical
et al. 2006) that appears to be less correlated with broad- forests worldwide (Dietrich et al. 1996; Ferry et al. 2010;
scaleregionalpredictorsthanwithlocalpredictorssuchas Gourlet-Fleuryetal.2011;Quesadaetal.2012).However,
*Correspondingauthor.Email:[email protected]
©2013BotanicalSocietyofScotlandandTaylor&Francis
2 T.Emilioetal.
theirinfluenceindetermininglarge-scalepatternsofforest andhelpstreestopreventwaterdeficitwheresoilsaredeep
structureandcompositioninthetropicsispoorlyknown. andwellstructured.Palmsdonothaveextensive rootsys-
Structural dominance by palms (and other life-forms, tems,buttheycompensatethisdisadvantagebydeveloping
suchaslianasandbamboos)hasbeenusedintheBrazilian high root water pressures (Davis 1961). This may confer
forestclassificationsystem(IBGE2012)todistinguishfor- a competitive advantage in shallow or compacted soils.
est types (see Appendix 1, Emilio et al. 2010), and to In soils that limit root development, trees may be more
developimprovedallometricequationsforbiomasscalcula- susceptibletoanchorageproblems,especiallyifassociated
tionintheBrazilianAmazon(Nogueiraetal.2008).Palm- withsteeptopography(GaleandBarfod1999;Toledoetal.
dominatedforestscover20%ofBrazilianAmazonia(IBGE 2011).Incontrast,palmsaremorestablethantreesdueto
1998) and large extensions of other Amazonian coun- their stem anatomy (Tomlinson 1990) that allows them to
tries.Giventhiswidespreadoccupationofforestbypalms, remainstronglyanchoredtotheground,evenwithoutdeep
understandinghowpalmsvaryinabundancecouldhelpto roots. In addition, palms have smaller canopies and large
better understand basal area and biomass variation across leavesinsteadofwoodybranches.Palmleavesarelesscar-
Amazonianforests.AtasinglesiteintheAmazon,Castilho bon expensive to rebuild than tree branches, so their loss
etal.(2006)reportedthattreebiomasswashigherinwell- if hit by a falling branch or tree may be expected to have
drained clay soils while arborescent palm biomass was relativelyminorimpactonthestructureandstabilityofthe
higherinpoorlydrained,sand-richsoils.Thissuggeststhat plant, and its carbon balance. Hence, palms appear to be
3 soilphysicalconditionsmayhavedifferenteffectsonthese betteradaptedtogrowinhighlydynamicforests.
1
20 two plant life-forms. The aim of this study was to investi- Insummary,wewouldexpectthattreesareatanadvan-
h gatetherelationshipsbetweensoilphysicalpropertiesand tage in deep, well-drained soils where their extensive root
c
r
a thebasalareaofbothtreesandpalmsacrossAmazonia. systemsprovidegoodanchorageandareabletoextensively
M
2 In addition to the direct effect that soil physical prop- exploit soil resources. On the other hand, palms may out-
1
7 erties may exert on plant roots, soil physical constraints compete trees in shallow, poorly drained soils, since they
5
0: can also indirectly affect forest basal area by increasing may be able to cope better with water-saturated soil and
at 0 turnover. Quesada et al. (2012) showed that Amazonian limitedrootingspace.Therefore,onecouldexpectthatthe
] forests have greater turnover rates where soil properties responses of palms and trees to soil physical properties
cht constrain root development (e.g. shallow impediment lay- shows opposite patterns, and this could result in a signif-
e
Utr ers,highbulkdensity,anoxichorizon).Atlocalscales,the icant shift in the relative contribution of trees and palms
y proportion of stems that die uprooted or snapped off by to forest structure across Amazonia as a function of soil
r
a other falling trees is generally greater in sandy and water- properties.Sofar,noattempthasbeenmadetounderstand
r
b
Li logged soils (Toledo et al. 2011). The dominant modes of whatcouldexplaintherelativecontributionofarborescent
y mortalitymayalsovarydependingonwhethertheplantis palmsandtreestoforeststructureandphysiognomyacross
rsit a tree or a palm. In Ecuador, dicotyledonous trees mostly Amazonia.
e
niv dieduprootedandsnappedwhilearborescentpalmsmostly Here, we use a unique set of permanent study plots
U diedstandingandsnapped(GaleandBarfod1999). across Amazonia to analyse the relationship between soil
[
y Thedifferencesintheresponseofplantlife-forms,such physical constraints and basal area of trees and palms in
b
d as palms and trees, to soil physical properties and distur- order to better understand how soil physical limitations
e
ad bancearelikelytoberelatedtodifferencesintheirphysio- affectthestructureandphysiognomyofAmazonianforests.
o
nl logicalandmorphologicaladaptations,particularlygrowth We also explore the relationship among precipitation, soil
w
strategies and the root system. Palms lack vascular cam- fertilityandforestturnoverandbasalareavariationintrees
o
D
bium and do not show secondary growth. To compensate, andpalms.
primary tissues continually increase in their mechanical
strengthwithgraduallignificationoffibrousandparenchy-
Methods
matous tissue, resulting in stronger stems as palm height
increases (Tomlinson 2006). The absence of secondary Vegetationdata
growth in palms may be advantageous against wind dam- We compiled forest-structure data from the RAINFOR
age, but prevents dormancy, implying that they must have Forest Plots database (Lopez-Gonzalez et al. 2011, down-
special adaptations (e.g. aerenchyma, pneumatophores) to loaded from http://www.forestplots.net (Lopez-Gonzalez
dealwithseasonallystressfulconditions(Tomlinson2006). et al. 2012)) and the PPBio database (Pezzini et al. 2012,
In contrast, cambial dormancy is a common strategy in downloaded from http://ppbio.inpa.gov.br (PPBio 2012)).
many tree species (Zuidema et al. 2012) and allows them Weuseddatafrom74Amazonianplotsthathavebothplant
to occupy seasonally unfavourable environments, such as and soil data. Most plots are 1ha in area (see Table 1 for
seasonally dry or waterlogged forests, with or without plot dimensions and data sources). In each of these plots,
morphologicaladaptationstosuchconditions. allstems(treesandpalms)withadiameteratbreastheight
Palms and dicotyledonous trees also differ in their (DBH) ≥ 10cm were measured and identified to at least
root systems. Trees develop roots that can reach depths familylevel.Thebasalareasoftreesandarborescentpalms
of up to 10m to access water (Nepstad et al. 1994). The werecalculatedandusedasresponsevariablesinregression
development of deep roots provides mechanical stability analyses.
Soilphysicalconditionslimitpalmandtreebasalarea 3
er%) d)
Turnovrates( 1.73 2.333.092.052.582.121.272.653.800.861.741.273.033.132.532.432.682.90 2.004.320.91 Continue
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Fcl TTTTT TTT FFFF
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y Utrecht study. TreeBA(%) 97.5885.8586.5991.9398.6895.5192.5199.0695.6192.2699.9799.9399.95100.0099.2492.5685.9087.3885.7399.4699.8395.0599.62100.00
rsity Librar usedinthis TreeBA−21(mha) 7.2722.9926.0826.3225.3724.4825.1822.128.7823.123328.4736.7919.0523.6221.7724.4222.2923.820.4123.3924.1815.5431.82
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wnloaded by [Uni mazonianforestpl PalmBAPalm−21(mha)(%) 0.182.43.7914.14.0413.42.318.00.341.31.154.42.047.40.210.91.324.31.947.70.010.00.020.00.020.000.00.180.71.757.44.0114.13.2212.63.9614.20.110.50.040.11.264.90.060.300.0
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nde ngit 5.935.911.473.433.443.443.443.436.486.471.461.461.461.501.508.978.978.968.969.949.938.318.311.41
a o 557777777755566666655666
A) L −−−−−−−−−−−−−−−−−−−−−−−−
B 000000000000000000000004
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asalar Latit −9.5−9.5−11.8−3.9−3.9−3.9−3.9−3.9−0.7−0.7−1.7−1.7−1.7−14.5−14.5−12.5−12.5−12.5−12.5−2.9−2.9−10.5−10.56.1
B
Table1. Plotcode RALF-01RALF-02RALM-01RALP-11RALP-12RALP-21RALP-22RALP-30RBOG-01RBOG-02RCAX-01RCAX-02RCAX-06RCRP-01RCRP-02RCUZ-01RCUZ-02RCUZ-03RCUZ-04PDKE-02PDKE-03RDOI-01RDOI-02RELD-01
4 T.Emilioetal.
er%) d)
Turnovrates( 2.600.541.811.360.491.280.893.76 0.402.912.892.111.951.771.042.062.702.732.511.393.742.521.91 Continue
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est∗∗s 11
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Fcl TT
4677904849805648585285332
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n)
pitatio−1year 479522522522822822822479574574248405645645645642642642479479460460477477477
cim 1222222122223332221111222
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Pr(
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2 n
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12 Marc physical Topograp 0222000001003231000000020
00:57 Soil oiloxia 0000000011010000230043013
at San
]
ht A 1000000704909553414791706
Utrec TreeB(%) 91.2100.0100.0100.0100.0100.0100.093.292.095.599.899.691.696.293.499.295.280.396.496.097.798.185.385.593.7
y
brar BA−1a) 0457641635269201667928720519116643687289689384142
y Li Tree2mh 23.37.16.27.42.36.31.23.19.23.35.24.24.27.34.25.23.18.25.27.17.24.23.22.31.
sit (
er A
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ed b BA−1a) 2 6114 869 7854 88 9
d mh 2 671011032159144080
oa al2m 2.0000001.1.1.0.0.2.1.2.0.1.4.0.1.0.0.4.3.2.
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ot(h 1.0.0.0.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.2.2.2.
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udes.l.) 7000825977012401255790222
Altitma. 7418383591211726610645384315121424221819313131
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ngit 0.731.411.081.408.698.698.700.742.192.188.788.727.617.607.613.633.633.530.830.831.141.141.401.401.41
o 6666555666557777776666777
L −−−−−−−−−−−−−−−−−−−−−−−−−
0096000000001330000000000
ntinued) Latitude 14.53006.11476.40056.08835.17005.17005.180014.5300−5.6300−5.64004.53004.7300−1.0698−1.0771−1.0732−4.8800−4.9000−4.920014.580014.580014.400014.400011.900011.910011.8800
o − − −−−−−−−
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Table1. Plotcode RHCC-22RELD-02RELD-03RELD-04RFMH-01RFMH-02RFMH-03RHCC-21PIPM-82PIPM-83RIWO-03RIWO-12RJAS-02RJAS-03RJAS-04RJEN-11RJEN-12RJEN-13RLFB-01RLFB-02RLSL-01RLSL-02RMNU-03RMNU-04RMNU-05
Soilphysicalconditionslimitpalmandtreebasalarea 5
er%)
Turnovrates( 2.402.931.272.011.851.021.272.012.612.122.642.312.642.012.672.612.392.673.303.121.651.141.101.411.22
orest∗∗ass F-1F-1F-1AT-3AT-3WF-2AT-3F-2
Fcl TTTFFSTFT
9474302127136647783121130
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n)
pitatio−1year 477655280280280280280720720813813813523391523391523391987786786837837762762
cim 2133333112222222222222222
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2 n
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12 Marc physical Topograp 0400222223401000310320011
00:57 Soil oiloxia 3000000001030031103111000
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ht A 6480393928792070583248187
Utrec TreeB(%) 83.685.099.8100.098.098.699.295.891.296.097.499.876.479.192.898.177.597.998.397.898.695.791.298.197.5
y
brar BA−1a) 81614238433033915759362580941789174957753164583
y Li Tree2mh 25.16.33.23.29.35.31.29.20.26.26.26.21.23.28.25.25.23.25.28.30.16.17.21.25. net
y [Universit PalmBA(%)( 16.3414.960.120.001.971.310.774.118.783.922.530.1123.5820.907.131.9022.452.021.672.181.364.228.791.822.43 ww.forestplots.
ed b BA−1a) 424 74649 36 8 94421 3 p://w
oad alm2mh 5.02.90.000.60.40.21.21.91.00.70.06.66.12.10.57.50.40.40.60.40.71.70.40.6 mhtt
nl P( o
w fr
Do Plotarea(ha) 2.251.001.001.001.001.001.001.001.001.001.001.001.001.000.421.001.001.001.001.001.001.001.001.001.04 etal.(2010).plots).downloaded
Altitude(ma.s.l.) 31224611011011011011026826810798118205210210220200225221132109126126130146 mQuesadaablefor23Rv.br;data
Table1.(Continued) PlotcodeLatitudeLongitude −−RMNU-0611.8900071.40000−−RMTH-018.8800072.79000−RNOU-024.0800052.67000−RNOU-104.0800052.67000−RNOU-124.0800052.67000−RNOU-174.0800052.67000−RNOU-214.0800052.67000−−RPOR-0110.8200068.78000−−RPOR-0210.8000068.77000−−RSUC-013.2500072.91000−−RSUC-023.2500072.90000−−RSUC-033.2500072.92000−−RTAM-0112.8400069.29000−−RTAM-0212.8300069.29000−−RTAM-0412.8400069.28000−−RTAM-0512.8300069.27000−−RTAM-0612.8400069.30000−−RTAM-0712.8300069.26000−−RTIP-030.6400076.15000−−RYAN-013.4400072.85000−−RYAN-023.4300072.84000−−RZAR-014.0100069.91000−−RZAR-024.0000069.90000−−RZAR-033.9900069.90000−−RZAR-043.9900069.91000 ∗Soildepthandstructurescoresinvertedfro∗∗ModifiedfromAndersonetal.2009(availpDatadownloadedfromhttp://ppbio.inpa.go
6 T.Emilioetal.
Soilsamplinganddeterminationofchemical Soil descriptions followed a standard protocol (Jahn
andphysicalproperties et al. 2006), with special attention to the measurement of
Soil sampling and analysis were undertaken by the effective soil depth, depth to C horizon (where possible),
PPBio/HIDROVEG and RAINFOR projects (Malhi et al. horizon distribution (i.e. identification and depth of visi-
2002), using equivalent protocols (PPBio/HIDROVEG: ble soil diagnostic horizons), colour, distribution of rocks,
http://ppbio.inpa.gov.br/manuais; RAINFOR: www.geog. concretions (i.e. presence of coarse, hard material in the
leeds.ac.uk/projects/rainfor/projdocs.html). One soil pit soil as petroplinthite, gravel, or other hardened material),
wasduginthedominantsoiltype,wheresoildescriptions ironstonelayersorotherhardpans,redoxfeatures,rootdis-
were made. In addition, samples were taken at 5–10 com- tribution, drainage capacity, soil hardness, soil structure
plementary points with a hand-held auger adapted to (i.e.aggregatedistribution,typeandstability),organicmat-
collect undisturbed soil samples (Eijkelkamp Agrisearch tercontentandtopographicpositionofthepit.Threebulk
Equipment BV, Giesbeek, The Netherlands). Sampling density samples were collected from the pit walls at the
points followed a random stratified distribution so as to same depths as for the soil samples (0–5, 5–10, 10–20,
obtainrepresentativesoilcollectionsofeacharea.Thesoils 20–30,30–50,50–100,100–150,150–200cm).
were sampled up to 2m deep, but chemical data reported For quantifying the magnitude of root-limiting soil
here are for surface samples only (0–30cm), while the physicalproperties(hereaftersoilphysicalconstraints),we
entire profile was considered for soil physical properties. usedthesameapproachasinQuesadaetal.(2010,2012),
13 ForadetaileddescriptionofthemethodsseeQuesadaetal. assigning sequential scores to different levels of physical
20 (2010). limitations.Thiswasdonebyreadingthefielddescriptions
ch Effective cation exchange capacity (I ) is used here of soil and assigning to each category a score (Table 2;
r E
Ma as a proxy for general soil fertility since there are strong see details in Quesada et al. 2010). These scored cate-
2 relationships between I (hereafter called fertility), soil gories provide information on topography, soil depth, soil
1 E
7 P and total elemental composition (Quesada et al. 2010). structureandanoxicconditionsinasemi-quantitativeform.
5
0: Samples were analysed for exchangeable cations by the To aid interpretation, here we inverted Quesada’s origi-
at 0 silver-thiourea method (Pleysier and Juo 1980), and the nal scale for soil depth and structure, so that shallower,
] sumofconcentrationsforexchangeableCa,Mg,K,Naand poorlystructuredsoilshadlowerscores,whiledeeper,well-
cht Alarereported. structuredsoilshadhigherscores(Table2).Weusedeach
e
r
Ut
y
r
a
r
b Table2. SoilphysicalconstraintscoresmodifiedfromQuesadaetal.(2010).
Li
sity Soilphysicalconstraintratingcategories Score1
r
e
v (1) Effectivesoildepth(soildepth,hardpans)
Uni Shallowsoils(<20cm) 0
[ Lessshallow(20–50cm) 1
y
b Hardpanorrockthatallowsverticalrootgrowth;othersoilsbetween50and100cm 2
d deep.
e
ad Hardpan,rocksorChorizon≥100cmdeep 3
nlo Deepsoils≥150cm 4
w (2) Soilstructure
o
D Verydense,veryhard,verycompact,withoutaggregation,rootrestrictive 0
Dense,compact,littleaggregation,lowerrootrestriction 1
Hard,mediumtohighdensityand/orwithweakorblock-likestructure 2
Loosesand,slightlydense;wellaggregatedinsub-angularblocks,discontinuouspans 3
Goodaggregation,friable,lowdensity 4
(3) Topography
Flat0◦ 0
Gentlysloping1–8◦ 1
Gentlyundulating8–19◦ 2
Steep20–44◦ 3
Verysteep>45◦ 4
(4) Anoxicconditions
Unsaturatedconditions 0
Deepsaturatedzone(maximumofhighsaturation>100cmdeep);deepredoxfeatures 1
Deepsaturatedzone(maximumofhighsaturation50cmdeep);redoxfeatures 2
Seasonallyflooded;soilswithhighclaycontentandverylowporosityand/ordominated 3
byplinthite
Constantlyflooded;patchesofstagnatedwater 4
1SoildepthandstructurescoresinvertedfromQuesadaetal.(2010).
Soilphysicalconditionslimitpalmandtreebasalarea 7
soil physical constraint characteristic as an independent a causal relationship between them (Quesada et al.
explanatoryvariableinregressionanalyses. 2012).Nevertheless,tountanglethecomplexrelationships
betweensoilproperties,stemturnoverandforestbasalarea,
simple direct relationships may not adequately describe
Forestturnover the system, as both direct and indirect effects may occur.
Therefore, we also built a path model that included the
Forest turnover was calculated as the proportion of stems
best environmental predictors selected by the (cid:2)AIC crite-
(trees and palms combined) entering and leaving the plot
ria, combined with the turnover rate to better understand
per year. Annual mortality and recruitment rates were
thedirectandindirecteffectsofsoilphysicalpropertieson
estimated separately using standard procedures, based on
palmandtreebasalarea.
logarithmicmodels.Thesemodelsassumeaconstantprob-
We performed quantile regressions (QR) in addition
abilityofmortalityandrecruitmentthrougheachinventory
to OLS, as basal area variation was not homogeneous in
period (Swaine et al. 1987; Phillips et al. 2004), and they
relation to the environmental variables in some cases. QR
werecorrectedforcensusintervalfollowingtherecommen-
(Koenker and Bassett 1978) is a method for estimating
dationsbyLewisetal.(2004).Wethenconsideredthemean
relationships between variables for all portions of a prob-
valueofmortalityandrecruitmentovertheentireperiodas
ability distribution without ignoring any part of the data.
theforestturnoverrateforeachplot,whichwepresentasa
This method is robust to outliers and skewed distributions
percentageofallstemspresent.
3 (Cadeetal.1999).Inaddition,fittinghigherpercentilesof
1
0
2 response variables as a function of the independent vari-
ch able should estimate the upper limit set by the measured
Mar Dataanalysis independentfactors.Thisapproachwasundertakenmainly
2 We used ordinary least square regressions (OLS) to because, if an independent variable can be considered a
1
7 examine the relationships between tree basal area, palm limiting factor, the models estimated for the upper quan-
5
0: basalarea,environmentalpredictorvariables,andturnover tilesshouldhavebetterpredictivevaluesthanOLSmodels
at 0 rates. Environmental variables included the soil chemi- (Cade and Noon 2003). To evaluate for which cases QR
] cal and physical properties described above, and annual should be a better predictive model than OLS, we car-
ht
c precipitation obtained from the interpolated WorldClim ried out the joint test of equality of slopes described by
e
Utr dataset (Hijmans et al. 2005), which varied from KoenkerandBassett(1982).Thistestevaluatesiftheslopes
y 1333–4113mmyear−1 across our study area. The inter- ofQRandOLSdifferfromeachother.Ifso,thedistribution
r
ra polations of WorldClim dataset for Amazonia are based is heteroscedastic and the QR model should be consid-
b
Li on the few meteorological stations that are available for ered instead of the OLS model. We used the QR fitted for
y this region (Hijmans et al. 2005). However, as the stations eachindependentvariableseparatelyandthequantileswith
rsit are well spaced, the interpolation could well represent the tau=0.25,tau=0.50andtau=0.90,forthistest.
e
niv large-scaleprecipitationtrendsthatweanalysed. We also attempted to understand the variation in for-
U To select the model that best explained tree and palm est physiognomy in response to soil physical constraints.
[
y basal area variation, we carried out an exhaustive search We adopted the forest classification of Anderson et al.
b
d including all predictor variable combinations, using addi- (2009), who used a region-growing technique and non-
e
ad tive linear models. Interactions between soil physical con- supervised classification algorithm to classify forest plots
o
nl straints,rainfallandsoilfertility,werealsotested.Akaike’s from Landsat 7/ETM+ and SRTM images and deter-
w
o Information Criterion (AIC) was adopted as a measure of mine forest physiognomy at a local-scale resolution, and
D
goodness of fit. AIC penalises parameter-rich models to a vegetation map provided by IBGE (1998) for the palm-
preventover-fitting.Thisprocedureispreferabletosequen- dominated forests map presented in Figure 4. The local-
tialsearchingprotocolsinavoidingType-Ierrorbecausethe scale forest classification was used only for small win-
modelsarenotexplicitlycomparedthroughstatisticaltests dows surrounding the ALP, CAX, CUZ, JEN and TAM
(MacNally2000).Wethenrankedour74modelsfrombest study areas (Table 1), as it could not be generalised to
(i.e.lowest)toworst(i.e.highest)AICvalue.The(cid:2)AICof other areas. As far as we know, there is no vegetation
a model is the difference between the AIC of a model to map available for the entire Amazon with an appropri-
thatofthebestmodel.Modelswith(cid:2)AIC<2wereconsid- ate resolution to allow us to distinguish palm-dominated
ered as informative as the best model and the importance from other terra-firme forests across all study areas. The
of explanatory variables in these models was determined Brazilian RADAMBRASIL vegetation map (Brasil 1978)
according to their frequency of occurrence in the subset is not useful to distinguish vegetation types at the local
of the best models (Richards 2005). After the best models scale because only the dominant vegetation classes at a
werechosen,pathanalysiswasusedtodeterminethedirect scale of 1:250,000 were mapped (Emilio et al. 2010). For
andindirecteffectsoftheenvironmentalvariablesonpalm other Amazonian countries, available vegetation maps are
andtreebasalarea. not comparable or the vegetation-class resolution is too
Given that soil physical constraints are highly coarse. Anderson et al. (2009) distinguished seven forest
related to forest turnover, some authors have assumed types in the RAINFOR sites, including one-third of the
8 T.Emilioetal.
plotsincludedhere.Forthisstudy,wegroupedAnderson’s environmental variables (Figure 1). Tree basal area was
vegetationunitsintofourclasses:terra-firmeforestswhere significantlypositivelyrelatedtosoildepthandannualpre-
large palms do not contribute greatly to the forest canopy cipitation,whilepalmbasalareashowednorelationshipto
(TF), terra-firme forests where large palms do contribute thesevariables.Palmbasalareawasrelatedtosoilfertility
greatlytotheforestcanopy(TFP),Mauritiaswamps(SW), (r2=0.10,P=0.004)whiletreebasalareadidnotshowa
and forests over alluvial terraces (FAT). There are few significantrelationshipwithsoilfertility.Soilstructurewas
examples of the SW and FAT categories and it is difficult theonlyenvironmentalvariablesignificantlyrelatedtoboth
to formally test the relationship between soil conditions treeandpalmbasalarea,anditdefinedtheupperboundary
and forest structure. Therefore, we only explored these ofpalmandtreebasalareainoppositeways.Palmsattained
relationships graphically without use of formal statistical greater total basal area in less-structured soils while tree
methods. basal area was greater in well-structured soils. Soil struc-
StatisticalanalyseswerecarriedoutbyusingtheRsta- ture alone explained up to 26% of the variance in palm
tistical platform (R Development Core Team 2011) and basalareaandupto10%ofthevarianceintreebasalarea
thequantregpackage(Koenker2011).Mapswereprepared insimpleOLSregressions.
withArcGis9.0. Multiple linear models showed essentially the same
relationships as the simple OLS models (Table 3). For
palms,modelsincludingtopography,soildepthandfertility
3 Results wereasinformative((cid:2)AIC<2)asthesimplesoil-structure
1
20 Inourdataset,treesaccountedformostofthebasalareain model. The inclusion of other environmental variables in
ch terra-firmeAmazonianforests(94±6%).However,palms the model for palm basal area only very weakly increased
r
Ma contributed up to 23% of basal area in some areas in the and,insomecases,evendecreasedexplanatorypower.For
2 westernAmazon(Table1)withIriarteadeltoideabeingthe trees,bestmodelsincludedsoilstructure,withtheP-value
1
7 dominant arborescent palm species; Oenocarpus bataua forthisfactorsignificantinalmostallmodels.Precipitation
5
0: dominatedinthecentralAmazonandGuianaShield. was the second best variable in the tree basal area model.
at 0 Simple OLS regressions showed that palm and tree Models that included both soil structure and precipita-
] basal area exhibited different responses to the same tion explained up to 23% of tree basal area variance.
ht
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e
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Ut
y
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a
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Figure1. Simplerelationshipsbetweenbasalarea,soilphysicalpropertiesandprecipitation.Solidlinesrepresentmeanvaluespredicted
byordinarylinearregression(OLS,whichherealsocoincidewiththequantileregression,tau=0.5).Thedottedlinesrepresentthevalues
predictedbythelinearquantileregressions,tau=0.25andtau=0.9.Thesamemodel(linearisedGaussianin(c),andlinearfortheothers)
wasadoptedfortheordinarylinearandquantileregressions.LinesareshownonlywhentheOLSmodelissignificantatthe0.05level.
Ther2-andP-valuesofeachOLSarepresentedatthetopofeachgraph.
Soilphysicalconditionslimitpalmandtreebasalarea 9
Table3. AIC-rankedlinearregressionmodelswith(cid:2)AIC<2.Weevaluated72models,includingsimplemodelsofeach
explanatoryvariable(soilanoxia,soildepth,soilstructure,annualprecipitationandfertility),all57combinationsoftheexplana-
toryvariablesinadditivemodels,andfiveinteractionmodelswithprecipitationandeachoftheotherfiveexplanatoryvariables.
Alltop-ratedtreeandpalmmodelsincludesoilstructureandexcludesoilanoxia.
Model R2 P AICc (cid:2)AICc
palmBA∼structure∗∗∗ 0.28 <0.0001 190.90 0.00
palmBA∼depthns+structure∗∗∗ 0.29 <0.0001 191.90 1.00
palmBA∼topographyns+structure∗∗∗ 0.28 <0.0001 192.80 1.90
palmBA∼structure∗∗∗+fertilityns 0.28 <0.0001 192.81 1.91
treeBA∼depthns+structure∗+precipitation∗∗ 0.23 0.0003 200.48 0.00
treeBA∼depthns+structure∗ 0.14 0.0044 201.54 1.06
treeBA∼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.
Topographyandsoilfertilitydidnotcontributesignificantly
toanymodel.
3
1 Palm basal area variation was heterogeneous along
0
2 the soil-structure axis (Figure 1). This variation was not
h
c reduced significantly in the multiple OLS regressions by
r
a
M the addition of soil anoxia, topography, soil fertility or
2 precipitation as predictor variables (Table 3). Neither the
1
7 interactions between soil structure and precipitation nor
5
0: between soil structure and soil fertility was significantly
0
at related to palm basal area variation (P > 0.1 for interac-
ht] tion term in all models). Tree basal area was related to
ec both soil structure and precipitation in the multiple OLS
r
Ut regressions. Like palms, variation in tree basal area was
y heterogeneous along the soil-structure axis and variation
r
a
br couldnotbeexplainedbyinteractionsbetweenexplanatory
Li variablesinthemultipleOLSregressions.
y
sit Soil structure explained a large fraction of the vari-
er ation in stand turnover rates (r2 = 0.23, P < 0.001).
v
ni All low-turnover stands (0–2%) had a low proportion of
U
[ palmsandahighproportionoftrees(Figure2).Treebasal
by areadecreasedsignificantlywithincreasingturnover(r2 =
ed 0.21, P < 0.001), while palm basal area was greatest at
d
a intermediate levels of turnover (2–3%). The relationship
o
nl betweenpalmbasalareaandturnoverratesappearedtobe
w
o non-linear,andalinearisedGaussianmodelprovidedasig-
D
nificantfit(r2=0.24,P<0.001).However,thisnon-linear
Figure 2. Relationship between forest stand-turnover rates and
patternwasstronglydrivenbyoneplot(DOI-02)withpar-
(a) palm basal area and (b) tree basal area for 60 forest plots
ticularly high disturbance rates. When we excluded this forwhichturnoverratedatawasavailable.Dashedlinesrepresent
plot, the quadratic term of the linearised Gaussian regres- valuespredictedbyquantileregression(tau=0.9).
sionbecamenon-significantandlinearregressionprovided
amoreappropriatefittoourdata.ThesimpleOLSregres- Conversely, tree basal area remained significantly related
sion between palm basal area and stand turnover rate to turnover rate, but the significant relation to soil struc-
was significant (r2 = 0.11, P = 0.005) and suggested turewaslost.Ourpathanalysisshowedthatthepreviously
that palm basal area increased with increasing turnover observed response of tree basal area to soil structure was
rates. indirectandmediatedbyforestturnoverrateswhichwere,
A more complete picture of the relationship between in turn, mediated by soil structure. Despite the fact that
basal area, soil structure and turnover rate was obtained palm and tree basal area presented opposite response pat-
by path analysis (Figure 3). Palm basal area was not ternstosoilstructureandturnoverrates,ourmodelshowed
directlyaffectedbyturnoverrate,asthesimpleregressions that palm basal area was not significantly affected by tree
abovehadsuggested.Whenweaccountedfortheeffectof basalarea.
soil structure on turnover rates, the relationship between At larger scales, the distribution of palm basal area
palm basal area and turnover rate became non-significant. at the plot level was congruent with mapped large-scale
10 T.Emilioetal.
forests (forest over alluvial terraces, Mauritia-dominated
swamp,palm-dominatedterra-firme)weremostlyfoundon
poorly structured soils (Figure 4(b)). Although our mod-
elsdetectedarelationshipbetweentreebasalareaandsoil
depth,therewerenocleardifferencesinsoildepthbetween
forestphysiognomies.
Discussion
Basalareapartitioningandsoilphysicalconstraints
Theobservedrelationshipbetweensoilphysicalconstraints
and the partitioning of forest basal area between trees
Figure3. Diagramofdirectandindirecteffectsofsoilstructure
and palms suggest that soil-structure conditions establish
andforestturnoveronpalmandtreebasalarea.Arrowspointto
responsevariables.Standardisedregressioncoefficientsareshown the upper limit to the basal area of palms and trees in
alongthelines.Continuouslinesindicatesignificantcoefficients Amazonian terra-firme forests. Soil structure was related
anddashedlinesnon-significantones. in opposite ways to the basal area of trees and palms.
3 In addition, the effect of soil structure on basal area
1
0 was direct for palms but mediated by forest turnover
2
h forest physiognomies: plots with greater palm basal area ratesfortrees.Palm-dominatedterra-firmeforestsoccurred
c
ar occurred in and around palm-dominated forests, while over less-structured soils, while terra-firme forests with-
M
plots with lower palm basal area occurred mainly in outcanopy-palmdominanceoccurredmorefrequentlyover
2
1 regionswheremappedpalm-dominatedforestsareuncom- well-structuredsoils.
7
5 mon (Figure 4(a)). At local scales, physiognomies with Identifying the specific role of different physical con-
0:
0 highpalmdominanceoccurredmainlyoverless-structured straints imposed by soils and topography on root devel-
at soils (Figure 4(b)). The soils under alluvial terrace and opment is difficult, as geomorphology and soil conditions
]
ht Mauritia-dominated swamps were less structured than are related throughout pedogenesis. For example, in the
c
re those under terra-firme sites (Figure 4(b)). Higher soil- Amazonregion,topographytendstovaryregionally,often
Ut
structure variation was observed in terra-firme forests, following local geological history. Where dissected relief
y
ar where soil structure varied from well-structured friable occurs, soils tend to be rejuvenated by erosion and thus
r
b classes that do not impose much resistance to root pen- conditions associated with early pedogenetic development
Li
y etration to more root-restrictive soils. In agreement with prevail (i.e. limiting physical conditions such as high bulk
sit the results suggested by our models, palm-dominated density and shallow depth), as can be found in the Andes
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Figure4. (a)Spatialdistributionofpalmbasalareain74forestplots,superimposedontheBrazilianmapofpalm-dominatedforests
(modifiedfromIBGE1998).(b)SoilstructurevariationbetweenforestphysiognomiesforALP,CAX,CUZ,JEandTAMsites(n=23).
SoilstructureindexfollowsQuesadaetal.(2010)andforestclassificationfollowsAndersonetal.(2009).Lowervaluesforthesoilstructure
index indicate less structured soils (see Table 1 for a complete description). FAT, forest over alluvial terrace; SW, Mauritia-dominated
swamp;TFP,palm-dominatedterra-firme(Anderson’sTF2and3);andTF,terra-firmeforest.