Modelling responses of western Amazonian palms to soil nutrients

15 Pages·2016·1.58 MB·English

Journal of Ecology doi: 10.1111/1365-2745.12708
Modelling responses of western Amazonian palms to
soil nutrients
Rodrigo Ca(cid:1)mara-Leret†,1, Hanna Tuomisto2, Kalle Ruokolainen2, Henrik Balslev1 and
Søren Munch Kristiansen*,3
1Sectionfor Ecoinformatics &Biodiversity,Department of Bioscience,Aarhus University, NyMunkegade114,
DK-8000 AarhusC,Denmark; 2Department of Biology, University of Turku, FI-20014Turku, Finland; and3Department
of Geoscience, AarhusUniversity, Høegh-Guldbergs Gade2, DK-8000 AarhusC,Denmark
Summary
1. Understanding the responses of individual plant species along different edaphic gradients is a key
question in ecology, with implications to community assembly, functioning of forest ecosystems,
niche theory and conservation planning. In tropical rain forests, responses to soil nutrients have been
described only for a handful of species. Even abundant and conspicuous components of the forest,
such as the palms (Arecaceae), remain largely unknown in this respect.
2. We inventoried all palm species and analysed soil chemistry in 96 sites across western Amazonia
to (i) assess the relative importance of some micronutrients vs. macronutrients as drivers of patterns
in palm species composition, (ii) model the response shapes of 61 palm taxa and of canopy vs.
understorey palms along gradients of selected soil nutrients, (iii) determine if the response shapes of
conspecific palm varieties differ in relation to the most important observed soil nutrient and (iv)
assess if the rank order of the most abundant canopy species changes along edaphic gradients.
3. Patterns in palm species composition were best explained by Mehlich-III extractable exchange-
able bases (Ca, K, Mg) and phosphorus (P), with the different palm species clearly separating along
the soil cation concentration gradient. All 61 palm taxa exhibited statistical responses along soil
nutrient gradients. Response shapes in relation to exchangeable bases varied among bimodal skewed
(51% of species), unimodal skewed (47%) and monotonic (2%).
4. There were no significant differences between canopy vs. understorey species in their mean
response shapes to the exchangeable bases, but canopy species had significantly higher mean opti-
mum value for soil P and greater mean niche width along the Ca, K, Mg, P and boron (B) gradients.
Varieties of the same species of Bactris, Desmoncus and Geonoma generally had different response
shapes and different optima for the exchangeable bases.
5. Synthesis. Among the soil variables we studied, macronutrients (especially exchangeable bases
and P) emerged as more important than micronutrients in predicting species abundances in palm
communities of western Amazonian non-inundated forests. Non-Gaussian responses were predomi-
nant, conspecific palm varieties exhibited different response types, and the rank order of dominance
of canopy palms varied along the gradients of exchangeable bases and P. Together, these findings
advance niche theory about palms and may be used for generating better predictive models of palm
species distributions and for experimental studies that search for the physiological mechanisms
underlying inter- and infra-specific trade-offs along edaphic gradients.
Key-words: Arecaceae, ecological response curves, exchangeable bases, gradient analysis,
Huisman–Olff–Fresco models, phosphorus, plant community ecology, resource gradients, tropical
forest, tropical soils
*Correspondenceauthor.E-mail:[email protected]
†PresentAddress:Identification&NamingDepartment,RoyalBotanicGardens,Kew,Richmond,SurreyTW93AE,UK.
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety
2 R. C(cid:1)amara-Leret et al.
responses to environmental factors has practical implications
Introduction
for, e.g. enhancing simulation models about climate change
Understanding how the occurrence and abundance of a spe- impacts onvegetation (Austin et al.1994), predicting species’
cies vary along environmental gradients is a fundamental goal abundances and occurrences based on environmental data
of ecology, as the question is directly derivable from the con- (Bongers et al. 1999), predicting environmental conditions on
cepts ofniche (Hutchinson 1957)andgradient analysis (Whit- the basis of floristic inventories (Sir(cid:1)en, Tuomisto & Navarrete
taker 1956). Although in tropical rain forests the importance 2013) and generating better floristic maps for conservation
of soil factors as controls of plant species composition at the planning(Zuquim et al.2014).
community level is well-established (Phillips et al. 2003; The high richness and density of Amazonian palm commu-
Tuomisto, Ruokolainen & Yli-Halla 2003; Kristiansen et al. nities largely results from the high diversity of understorey
2012), the responses of individual species along soil nutrient species of the genera Geonoma and Bactris (Balslev et al.
gradients remain largely unknown (but see Tuomisto 2006; 2011). Species in these genera may exhibit great morphologi-
Condit et al.2013; Zuquimet al.2014). cal variation. Subspecific varieties are readily recognised in
Here,we investigatehowtheabundancesof alargenumber some areas, but in others morphological differentiation is not
of Amazonian palm species vary along different soil nutrient possible due to intermediate forms (Henderson 2000, 2011).
gradients. Palms are in many ways an important component Conspecific varieties of Geonoma and Bactris often co-occur
of tropical rain forests and, therefore, a well-suited model in the same habitat (Costa et al. 2009), but whether their
group for this purpose. There are 2400 species of palms on coexistence can be explained by edaphic niche partitioning or
Earth, 730 of these are tropical American and 152 grow in even sympatric divergent evolution via habitat specialisation
Amazonia (Henderson 1995). Amazonian palms are key pro- (Savolainen et al. 2006) remains unknown. Antagonistic pat-
viders of ecosystem services to indigenous and local commu- terns in topographic preferences among some understorey
nities (C(cid:1)amara-Leret et al. 2014), and many species are palm species have been documented in individual sites (Sven-
commercialised both locally and internationally (Brokamp ning 1999), but such patterns are not consistent across sites
et al. 2011). Amazonian palm communities are remarkable (Vormisto, Tuomisto & Oksanen 2004). While reproductive
both in terms of local species density, which often reaches isolation for some of the nine Geonoma macrostachys mor-
30–40 species ha(cid:1)1, and in stem density, which may reach photypes has been confirmed (Listabarth 1993), establishing
8000 stems ha(cid:1)1 (Balslev et al. 2011). Furthermore, palms genetic distinctness at regional scales is problematic (Roncal,
are disproportionately well represented among the dominant Francisco-Ortega & Lewis 2007). Henderson (2011) high-
Amazonian tree species (Pitman et al. 2001; ter Steege et al. lighted three hypotheses, which are not mutually exclusive, to
2013). explain the distribution of polymorphic palm species: refugia,
Clarifying a species’ response along environmental gradi- rapid expansion (Cronk 1998) and resource polymorphism
ents (i.e. determining its environmental niche) is of theoreti- (Smith & Sk(cid:1)ulason 1996). The three conditions sensu Smith
cal, methodological and practical importance. First, & Sk(cid:1)ulason (1996) that promote resource polymorphism and
understanding the niche requirements of plants is important that may lead to divergent evolution are found in Amazonia:
for understanding community assembly and the functioning of (i) unoccupied edaphic niches, which arise from river dynam-
forest ecosystems, including why some species are abundant ics, tectonics or volcanism (Salo et al. 1986; Hoorn et al.
and others are rare at a site. Species that are oligarchic (Pit- 2010), (ii) high habitat heterogeneity (Tuomisto et al. 1995)
man et al. 2001) or hyperdominant (ter Steege et al. 2013) and (iii) relaxation of interspecific competition, which occurs
should, in principle, exhibit flatter responses along environ- in the form of differences in phenology and pollinator spec-
mental gradients than subdominant species, yet cross-taxon trum (Listabarth 1993). In Geonoma, these resource polymor-
studies are still remarkably scant. Second, response shape phisms ‘may represent an intermediate step in sympatric
affects the applicability of ordination methods. Canonical cor- speciation’(Henderson 2011, p.22).
respondence analysis is one of the most widely used ordina- Earlier studies in lowland tropical rain forests have sug-
tion methods and one of its basic assumptions is that species gested that soil P may be the most important nutrient-limiting
responses are Gaussian (ter Braak 1986). Non-Gaussian tree growth (Vitousek 1984), although other nutrients (e.g. N
responses (e.g. skewed or bimodal) may occur when inter- and K) may be colimiting (Mirmanto et al. 1999; Wright
specific competition displaces the mode from the physiologi- et al. 2011; Alvarez-Clare, Mack & Brooks 2013; Fisher
cal optimum (Austin 1976), but even physiological responses et al. 2013). Research in Central America has suggested that
may be non-Gaussian (Austin & Austin 1980). There is evi- palm distributions are best explained by exchangeable bases,
dence that non-Gaussian distributions are frequent or even P and N (Clark et al. 1995; Andersen, Turner & Dalling
predominant in plant communities (Austin 1976; Minchin 2010; Baribault, Kobe & Finley 2012), and tree distributions
1989). In the tropics, skewed response curves have been by P and to a lesser degree Ca (Condit et al. 2013). On the
foundforvariousplantgroups:thistlesalongsoilnutrientgra- other hand, several studies on Amazonian trees, ferns and
dients in Australia (Austin et al. 1985), trees along P gradi- Melastomataceae have found that exchangeable bases explain
ents in Cameroon and Panama (Gartlan et al. 1986; Condit compositional differences better than P does (Phillips et al.
et al. 2013) and Polybotrya ferns along edaphic gradients in 2003; Tuomisto, Ruokolainen & Yli-Halla 2003; Tuomisto
Amazonia (Tuomisto 2006). Third, knowledge of species et al. 2003). Furthermore, a niche breadth analysis of trees in
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety,JournalofEcology
Modelling responses of Amazonian palmstosoil nutrients 3
three large (25–50 ha) Neotropical tree plots (John et al. Universal Transverse Mercator coordinates at the mid-point of each
2007) suggested that both exchangeable bases and several transectusingahandheldGPS.
micronutrients can be more important controllers of tree spe- In each 5m95m subunit, we identified and counted all palm
cies distributions than P. It has even been proposed that B individuals of all life-history stages (seedlings, juveniles, subadults
andadults).Rametsofclonalandmultistemmedspecieswerecounted
may be a uniquely important soil gradient for tropical plants,
as separate individuals. Individuals were determined to the lowest
as its concentration seems to range from severe scarcity to
possible taxonomic level, i.e. variety in Bactris and Geonoma, and
toxicity(Steidinger 2015).
subspecies in Desmoncus, and this classification was maintained in
Widespread palm species in Amazonia tend to be tall and
theanalyses.WeunifiednomenclatureaccordingtotheWorldCheck-
have wide tolerances to soil fertility (Ruokolainen & Vor- listofPalms(Govaerts&Dransfield2005).Voucherspecimenswere
misto 2000). At local scales, different canopy species may collectedanddepositedinnationalherbariainBogot(cid:1)a(COL),Iquitos
exhibit different responses to soil fertility. For example, (AMAZ), La Paz (UMSA), Lima (USM), Manaus (INPA) and Quito
Oenocarpus bataua was found to be more abundant in poor (QCA),withduplicatesfromallregionsinAarhus(AAU)(herbarium
soils and Iriartea deltoidea in rich soils in a study in Ecuador acronymsaccordingtoThiers2015).
(Korning et al. 1994). In a landscape-scale study in Costa Soilsampleswerecollectedineachtransectatthebeginning,mid-
Rica, Euterpe and Prestoea showed opposite edaphic associa- dle and end, corresponding generally to subunits 1, 50 and 100. In
tions, whereas Iriartea and Socratea showed reciprocal den- total, 275 soil samples were analysed. Due to the loss of some soil
samples,12transectshadonlytwosoilsamples,andonetransecthad
sity variation in two soil types (Clark et al. 1995). Although
only one sample. In the data analyses, we used transect-wise means
illuminating, single-site studies only sample a fraction of the
ofthelaboratoryanalysisresults.
environmental gradient that species experience in their entire
Each soil sample was bulked from five topsoil subsamples
geographic ranges, so broader-scale approaches are needed to
(0–10cm depthbelow the organic layer; c. 0(cid:3)5dm3 per sample) dis-
unravel species responses properly.
tributed at the corners and the centre within a subunit. Roots and
Our objective is to contribute to our understanding on soil– stones were removed and the samples were air dried in the field and
plant relationships in tropical rain forests. We do so by mod- stored in cotton bags or plastic bottles. The soil samples were anal-
elling the statistical responses of selected palm species to soil ysed at the Agronomic Research Centre of the University of Costa
nutrientsandbyinvestigating ifsoilnutrientavailabilitycould Rica.NutrientswereextractedinMehlich-IIIsolution(Mehlich1984)
drive the assembly and composition of palm communities in andthenanalysedformacronutrients(Ca,K,Mg,P,S)andmicronu-
western Amazonian terra firme rain forests. We address these trients (B, Cu, Fe, Mn, Zn) using inductively coupled plasma-optical
questions using a large floristic and soil data set collected in emission spectrometry. The Mehlich-III method was selected because
96 transects across five countries. Specifically, we ask (i) it is a multielement extraction method designed to reflect the actual
plant nutrient availability across a wide range of soil properties with
what is the relative importance of some micronutrients vs.
reactionranging fromacidto basic(SoilSurveyStaff2014).We did
macronutrients in explaining dissimilarity in palm species
not include nitrogen (N) in our analyses because N is a volatile ele-
composition between sites; (ii) what are the response shapes
mentanditwasnotpossibletostandardisethestorageconditionsand
of 61 western Amazonian palm taxa and of understorey vs.
time between the collection and analysis of the soil samples. How-
canopy palms to soil nutrients, and which is the most impor- ever, earlier studies have suggested that N limitation is more impor-
tant nutrient out of those analysed; (iii) whether varieties tant in montane than in lowland tropical rain forests (Vitousek &
within the same palm species differ in their response shapes Matson 1988; Andersen, Turner & Dalling 2010), and that P limita-
to the most important soil nutrient and (iv) whether the rank tion may be more important in the lowlands (Vitousek 1984; Arag~ao
order of abundance of the most abundant canopy species var- etal.2009;Quesadaetal.2009).
ies in accordance with their inferred preferences for soil nutri-
ents. This is, to our knowledge, the first study to investigate DATA ANALYSES
the response shapes of a large number of Amazonian palm
Floristic similarity between transects was calculated with the equa-
taxa in relation to individual soil nutrients. We address 61
palm taxa (c. 40% of the Amazonian palm flora), including tion2W/(A+B),whereWisthesharedpalmabundancebetweentwo
transects, A is the total abundance of all palm species in the first
conspecificpairsof varietiesbelongingtothree diversegenera
transect and B the total abundancein the second transect. This equa-
(Bactris, DesmoncusandGeonoma).
tioncorrespondstothe Sørensenindexwhenappliedtopresence–ab-
sence data and to the Steinhaus index when applied to abundance
Materials and methods data (Legendre & Legendre 2012). Here, we used the number of
stems as a measure of species abundance. The equation Dissimilar-
ity=1(cid:1)Similarity was used when dissimilarity values were
FIELD SAMPLING AND SOIL ANALYSIS
needed.
During1995–2012wecollecteddataonpalmspeciesdistributionand To assess species compositional patterns, we subjected the abun-
abundance in nine regions in western Amazonia (Fig.1) (Aarhus dance-based dissimilarity matrix to non-metric multidimensional scal-
University Palm Transect Database, doi: 10.15468/r5hujc). We used ing(NMDS). Thismapsthe between-sitefloristic dissimilarity values
satellite images and field observations to place 96 transects in terra into an ordination space with a predefined number of dimensions
firme,i.e.non-inundatedterrainthatappearedtohaveold-growthfor- (Minchin1987).Weproducedatwo-dimensionalordinationusingthe
est cover and homogeneous geomorphology. Each transect was metaMDS function in the R package VEGAN (Oksanen etal. 2016),
5m9500m in size. Geographical location was recorded in and projected soil variables as vectors onto the ordination using the
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety,JournalofEcology
4 R. C(cid:1)amara-Leret et al.
(a)
Region
(no. transects):
I. Guaviare (8)
II. Caquetá (12)
III. Yasuní (12)
IV. Kapawi (11)
V. Iquitos (10)
VI. Orellana (11) Elevation
VII. Ucayali (9) 5000 m
VIII. Juruá (12)
IX. La Paz (11) 0 m
(b) Exchangeable bases (c) Phosphorus
1·0 1·0
0·5 0·5
2
S 0·0 0·0
D
M –0·5 –0·5
N
–1 –1
–1·5 –1·5 Fig.1. (a) Map of northwestern South
America showing the locations of the 96
–2 –1 0 1 –2 –1 0 1 sampled transects and their division to nine
regions. (b–e) Non-metric multidimensional
(d) Boron (e) scaling (NMDS) of the transects on the basis
1·5 of the dissimilarity of their palm floras (as
NMDS 2 ––10001–·····100555 ––01100–·····155050 ++ +C+IMCZ+C+EgnMS+a+nC+uKP++++ +++++++ ++++++++++++++++++++++++++++B+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++F++++e+++++++ pipt(pcemvnheoxrhra)deeooncloauijphcscTesenoeapacuhinnrhttn=rteiitgeoineorodera0gnNnuatg(cid:3)iab2sMtwoeo,hll0onefie4Datgh.tnsroSsoaodtTfrptiehohblh(Mtnereadhivdgcs)eeaiedatSnhhrslibiatalaemiotomrcbiierfonheolgeenha-nct(iIsneaoCo.IruwInrawCrsso+ieletioofKlhtxsiaghlnhtott+-rhidaoutaoMareewrcnarxsptrgncna)oob)si.brw,rsfeelicotetSfnhelwre,ertemrrsie(aeo((aeedbtscir)donsss))i.
the soil variable and NMDS axes 1 and 2.
–2 –1 0 1 –2 –1·5 –1 –0·5 0·0 0·5 1 [Colour figure can be viewed at
NMDS 1 NMDS 1 wileyonlinelibrary.com]
envfitfunction.Wecalculatedsquaredcorrelationcoefficients(R2)for computing the Euclidean distances. Before combining soil variables
each predictor in relation to the NMDS axes and determined their intoonedistancematrix,eachvariablewasstandardisedtozeromean
significance with a Monte Carlo permutation test using 999 random and unit variance. To control for spatial autocorrelation and thereby
permutations. avoidinflatingtheapparentimportanceoftheenvironmentalvariables
Mantel tests were run to find out whether dissimilarity in species included in the analyses, we also ran partial Mantel tests, where the
composition correlated with dissimilarity in soil nutrient concentra- correlation with logarithmically transformed geographical distances
tion. These analyses were done in parallel using presence–absence was partialled out before calculating the correlation between floristic
and abundance data to assess to what degree taking species abun- and edaphic dissimilarities. Spatial autocorrelation in floristic data
dances into account affects the results. Euclidean distance matrices emerges because dispersal of seeds is spatially restricted, which
were computed for all soil variables together and for each of them makes geographically close sites floristically more similar to each
individually. All soil variables were log-transformed prior to other than sites that are located further apart. This distance decay in
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety,JournalofEcology
Modelling responses of Amazonian palmstosoil nutrients 5
similarityisexpectedtobeapproximatelylogarithmic(Hubbell2001). transect and 61 taxa were present in at least 10 transects.
Environmentalvariablesareoftenspatiallyautocorrelatedaswell,both Pair-wise floristic similarity values between transects ranged
thosethatwereincludedintheanalysesandthosethatwerenot.There- from 0(cid:3)00 to 0(cid:3)96 (mean = 0(cid:3)35) when calculated with pres-
fore,geographicaldistancescanalsoserveassurrogatesfordifferences ence–absence data (Sørensen index) and from 0(cid:3)00 to 0(cid:3)92
in unmeasuredenvironmental variables.The standardised formof the (mean = 0(cid:3)15) when calculated with abundance data (Stein-
Mantelstatistic(r )wasused,whichisequivalenttothePearsoncorre-
M hausindex).
lation coefficient between two dissimilarity matrices. In all cases, we
The concentration of Mehlich-III extractable macronutrients
established the statistical significance of each correlation at the
P<0(cid:3)001levelwithaMonteCarlopermutationtestusing999random varied greatly among regions and transects (Table 1,
Fig. 1b–e). The concentration of exchangeable bases (Ca, K,
permutations.Allanalysesweremadeusingthemantelfunctioninthe
RpackageVEGAN(Oksanenetal.2016). Mg)varied400-foldandwasmainlydeterminedbyCa,which
Species’ responsesalong environmental gradients have been tested accounted for 14–93% (mean = 51%) of the sum. Phosphorus
withavarietyofmethods.Here,wefocusontheHuisman–Olff–Fresco concentrationvaried30-fold.Micronutrientconcentrationsalso
(HOF) models and extendedHOF models(eHOF) becausethey have varied greatly among transects: B concentrations varied four-
beenfoundtoperformwellforparametricquestionsofshape(Oksanen fold,Fe10-fold, Cu30-fold, Zn60-fold andMn c. 1800-fold.
& Minchin 2002). HOF models (Huisman, Olff & Fresco 1993) are Correlation values of environmental vectors with the NMDS
logistic regression models of hierarchically increasing complexity.
axes showed that palm species composition and abundance
They test statistically whether a response is flat (model I), or signifi-
werestronglyrelatedtothegradientsofallsoilvariablesexcept
cantlymonotonic(II),plateau(III),symmetricunimodal(IV)orskewed
B(Fig. 1e,TableS4).ConcentrationofBwasonlyweaklycor-
unimodal(V).Importantly,HOFmodelsprovidereliableestimatesof
relatedwiththeothersoilvariables(r = 0(cid:3)19orbelow),except
the niche properties of species that differ in abundance(Lawesson &
with Fe (r = 0(cid:3)61) and S (r = 0(cid:3)63). Phosphorus correlated
Oksanen 2002). Extended HOF models (eHOF) further allow testing
forbimodalresponseswitheithertwoequaloptima(modelVI)ortwo moderately with the other soil variables (0(cid:3)35 ≤ r ≤ 0(cid:3)57) but
unequaloptima(VII)(Jansen&Oksanen2013). strongly with Zn (r = 0(cid:3)77). The remaining soil variables (Ca,
We fitted the seven hierarchical logistic regression models to the Cu, K, Mg, Mn, S, Zn and Fe) correlated moderately-to-
palm species abundance data using the R package eHOF (Jansen & stronglywitheachother(0(cid:3)43 ≤ r ≤ 0(cid:3)87;Fig.S2,TableS5).
Oksanen2013)andselected themost appropriate modelaccordingto
the Akaike Information Criterion corrected for small samples (AICc)
and a bootstrap approach (999 permutations) to guarantee model sta- MANTEL AND PARTIAL MANTEL TESTS
bility. The change in palm species abundance along each log-trans-
Thematrixcorrelationbetweenoveralledaphicandfloristicdis-
formed soil nutrient gradient was analysed for all palm taxa that
similarity was 0(cid:3)64 when presence–absence data were used,
occurred in at least 10 transects. The soil variables were log trans-
and 0(cid:3)49 when abundance data were used (P < 0(cid:3)001 in both
formed before analysis because a unit change in nutrient availability
is likely more important for plants when the nutrient is scarce than cases) (Table S6). All correlations between floristic difference
whenitisabundant.Thespeciesoptimaandnichewidthsalongeach anddifferenceinindividualedaphicvariableswerestatistically
soilnutrientgradientwerederivedfromtheeHOFmodels.Wetested significantforbothpresence–absencedataandabundancedata.
for statistical differences between the mean optimum response of In all cases, presence/absence data yielded higher correlation
canopyvs.understoreypalmstosoilnutrientsusingWelch’st-testfor coefficientsthanabundancedatadid.Sumofbases,CaandMg
unpairedsamples.Thistestwasalso usedtotestforstatisticaldiffer- yieldedthehighestcorrelationcoefficients(r = 0(cid:3)57–0(cid:3)58for
M
ences between palms with no skew, positive or negative skewed presence–absence, r = 0(cid:3)42–0(cid:3)45 for abundance data). Mn,
M
responses to soil nutrients. All statistical analyses were conducted in Cu, S and Zn yielded intermediate correlations (r = 0(cid:3)47–
R3.1.3(RDevelopmentCoreTeam,2015). M
0(cid:3)52 for presence–absence, r = 0(cid:3)32–0(cid:3)38 for abundance
M
data), and B, K, Fe and P yielded the lowest (but still signifi-
Results cant) correlations (r = 0(cid:3)20–0(cid:3)39 for presence–absence,
M
r = 0(cid:3)12–0(cid:3)30 for abundance data). Correlations between
M
GENERAL FLORISTIC AND EDAPHIC PATTERNS floristicandgeographicdissimilaritymatriceswerestatistically
significant (r = 0(cid:3)62 for both presence–absence and abun-
About 100 300 palm individuals, representing 118 taxa, were M
dancedata).Dissimilaritiesinallsoilvariableswerealsocorre-
found in the 96 inventoried transects (Table S1, Supporting
Information). Each transect contained 195–2904 individuals lated with the log-transformed geographical distances, but the
(mean (cid:4) SD: 1045 (cid:4) 553) and 8–36 taxa (19 (cid:4) 6) correlations were relatively low (rM = 0(cid:3)09–0(cid:3)25). All correla-
tions between floristic andedaphic distance matrices remained
(Table S2). Species accumulation curves showed a clear ten-
statistically significant after removing the effect of log-trans-
dency to level off in all transects (Fig. S1), indicating that the
formedgeographicdistancewithpartialManteltests,andwere,
500-m-long transects were adequate to sample local palm spe-
hence,notmerelyduetospatialautocorrelationinthedata.
cies composition in western Amazonia. The most frequent
species were Euterpe precatoria, Socratea exorrhiza and
RESPONSE SHAPES
O. bataua, each of which was present in at least 75 transects
and in all nine regions (Table S3). The most abundant species All 61 palm taxa responded to soil nutrients, as assessed with
were O. bataua and Lepidocaryum tenue, each with over eHOF models (Fig. 2, Fig. S3, Table S7). The most frequent
14 000 individuals. Sixteen taxa were found in only one response shape was bimodal with unequal optima (51% of
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety,JournalofEcology
6 R. C(cid:1)amara-Leret et al.
2)7)12)19)7)06)02)5)5)6) 42)0)39)6)84)21)23)43)40)40)
(cid:3)60(cid:3)27(cid:3)37(cid:3)34(cid:3)60(cid:3)86(cid:3)23(cid:3)47(cid:3)55(cid:3)60 –(cid:3)0(cid:3)48–(cid:3)3(cid:3)39–(cid:3)0–(cid:3)7–(cid:3)2–(cid:3)4–(cid:3)8–(cid:3)8
11–– 1–– 258 4– 2– 626814
–– 81– 15––– 1208294331
(cid:3)32(723(cid:3)80(517(cid:3)75(201(cid:3)14(125(cid:3)92(747(cid:3)83(195(cid:3)93(102(cid:3)76(887(cid:3)66(942(cid:3)89(517 Zn (cid:3)(cid:3)029(0(cid:3)(cid:3)109(0(cid:3)(cid:3)188(1(cid:3)(cid:3)195(0(cid:3)(cid:3)055(0(cid:3)(cid:3)402(1(cid:3)(cid:3)094(0(cid:3)(cid:3)168(0(cid:3)(cid:3)403(1(cid:3)(cid:3)189(0
(cid:3)2(cid:3)9(cid:3)6(cid:3)0(cid:3)0(cid:3)8(cid:3)2(cid:3)4(cid:3)6(cid:3)1
S 1 22151122
(cid:3)–(cid:3)4751449)(cid:3)–(cid:3)56)4821(cid:3)–(cid:3)9411764)(cid:3)–(cid:3)3041684)(cid:3)–(cid:3)461830)(cid:3)–(cid:3)13033656)(cid:3)–(cid:3)5941774)(cid:3)–(cid:3)3711577)(cid:3)–(cid:3)10498927)(cid:3)–(cid:3)3048927) –(cid:3)3201)–(cid:3)11736)–(cid:3)230812)–(cid:3)24845)–(cid:3)91651)–(cid:3)7642243)–(cid:3)9958)–(cid:3)337038)–(cid:3)3226827)–(cid:3)342243)
P (cid:3)824((cid:3)932((cid:3)1200((cid:3)930((cid:3)627((cid:3)2704((cid:3)1073((cid:3)793((cid:3)3455((cid:3)1412( (cid:3)24(02(cid:3)54(03(cid:3)94(16(cid:3)93(38(cid:3)64(02(cid:3)46(37(cid:3)09(12(cid:3)94(06(cid:3)57(50(cid:3)17(02
1 n (cid:3)1(cid:3)2(cid:3)1(cid:3)8(cid:3)2(cid:3)0(cid:3)5(cid:3)6(cid:3)0(cid:3)2
g. M 127 2319158
Fi
e
e
s
s,
ocation –(cid:3)009)–(cid:3)041)(cid:3)464)(cid:3)417)–(cid:3)099)–(cid:3)763)(cid:3)323)–(cid:3)269)–(cid:3)409)–(cid:3)763)
l 44–– 66– 694
geographical Mg (cid:3)(cid:3)007(00(cid:3)(cid:3)014(00(cid:3)(cid:3)150(03(cid:3)(cid:3)161(02(cid:3)(cid:3)018(00(cid:3)(cid:3)407(05(cid:3)(cid:3)052(01(cid:3)(cid:3)078(00(cid:3)(cid:3)164(03(cid:3)(cid:3)121(00 –(cid:3)237344)–(cid:3)943143)–(cid:3)736316)–(cid:3)32661)–(cid:3)542417)–(cid:3)750798)–(cid:3)848179)–(cid:3)564336)–(cid:3)443882)–(cid:3)64336)
8761854421
Amazonia.For (cid:3)008)(cid:3)042)(cid:3)020)(cid:3)083)(cid:3)024)(cid:3)081)(cid:3)025)(cid:3)025)(cid:3)049)(cid:3)083) Fe (cid:3)(cid:3)32350(282(cid:3)(cid:3)32762(257(cid:3)(cid:3)25773(118(cid:3)(cid:3)19783(643(cid:3)(cid:3)39591(325(cid:3)(cid:3)18488(108(cid:3)(cid:3)34949(171(cid:3)(cid:3)51336(244(cid:3)(cid:3)30094(182(cid:3)(cid:3)31664(643 (cid:1)1inmgkg.
–––––––––– s
western (cid:3)05(003(cid:3)25(003(cid:3)14(011(cid:3)35(014(cid:3)17(009(cid:3)48(018(cid:3)19(015(cid:3)12(005(cid:3)27(015(cid:3)23(003 element
in K (cid:3)0(cid:3)0(cid:3)0(cid:3)0(cid:3)0(cid:3)0(cid:3)0(cid:3)0(cid:3)0(cid:3)0 her
ns ot
gio all
re 2)9)5)6)1)6)5)1)3)6) of
nine –(cid:3)06–(cid:3)22–(cid:3)14–(cid:3)46–(cid:3)07–(cid:3)52–(cid:3)23–(cid:3)10–(cid:3)20–(cid:3)52 ons
nutrientsin (cid:3)–(cid:3)005009)(cid:3)–(cid:3)006084)(cid:3)–(cid:3)0311592)(cid:3)–(cid:3)0231528)(cid:3)–(cid:3)007150)(cid:3)–(cid:3)074287)(cid:3)–(cid:3)009876)(cid:3)–(cid:3)006999)(cid:3)–(cid:3)021035)(cid:3)–(cid:3)0054287) Cu (cid:3)(cid:3)041(019(cid:3)(cid:3)051(021(cid:3)(cid:3)089(046(cid:3)(cid:3)285(145(cid:3)(cid:3)031(022(cid:3)(cid:3)266(041(cid:3)(cid:3)073(028(cid:3)(cid:3)043(016(cid:3)(cid:3)137(053(cid:3)(cid:3)115(016 Concentrati
cro 7(3(0(0(3(8(2(7(6(6( (cid:1)1.
(b)mi Ca (cid:3)00(cid:3)03(cid:3)43(cid:3)48(cid:3)02(cid:3)264(cid:3)12(cid:3)22(cid:3)49(cid:3)51 molkgc
d
n c
a n
s i
ofsoil(a)macronutrient Exchangeable++bases(CaKMg) (cid:3)(cid:3)–(cid:3)019(013025)(cid:3)(cid:3)–(cid:3)071(012144)(cid:3)(cid:3)–(cid:3)595(0722071)(cid:3)(cid:3)–(cid:3)677(0572012)(cid:3)(cid:3)–(cid:3)057(024265)(cid:3)(cid:3)–(cid:3)3102(1454940)(cid:3)(cid:3)–(cid:3)193(0371223)(cid:3)(cid:3)–(cid:3)317(0171293)(cid:3)(cid:3)–(cid:3)686(0741295)(cid:3)(cid:3)–(cid:3)660(0124939) B (cid:3)(cid:3)–(cid:3)047(041054)(cid:3)(cid:3)–(cid:3)048(04059)(cid:3)(cid:3)–(cid:3)039(024052)(cid:3)(cid:3)–(cid:3)037(023054)(cid:3)(cid:3)–(cid:3)057(045072)(cid:3)(cid:3)–(cid:3)062(042091)(cid:3)(cid:3)–(cid:3)047(029061)(cid:3)(cid:3)–(cid:3)071(04087)(cid:3)(cid:3)–(cid:3)054(038068)(cid:3)(cid:3)–(cid:3)051(023091) blebases,Ca,KandMg
ges gea
n n
ra ha
d c
n x
Table1.Meansa Site (a)Macronutrients1.Guaviare(cid:1)2.Caqueta(cid:1)ı3.Yasun4.Kapawi5.Iquitos6.Orellana7.Ucayali(cid:1)8.Jurua9.LaPazMean(range) Site (b)Micronutrients1.Guaviare(cid:1)2.Caqueta(cid:1)ı3.Yasun4.Kapawi5.Iquitos6.Orellana7.Ucayali(cid:1)8.Jurua9.LaPazMean(range) Concentrationofe
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety,JournalofEcology
Modelling responses of Amazonian palmstosoil nutrients 7
Fig.2. Examples of different response
shapes of palms to soil nutrients, fitted using
extended Huisman–Olff–Fresco models
(eHOF): (II) monotonic, (III) plateau, (IV)
unimodal symmetric, (V) unimodal skewed,
(VI) bimodal with two equal optima and
(VII) bimodal with unequal optima. Roman
numerals in parenthesis indicate the most
appropriate eHOF model selected according
to the Akaike Information Criterion corrected
for small samples sizes and a bootstrap
approach (999 permutations). The lower
horizontal boxplot (y=0) represents
absences and the upper boxplot represents
occurrences of the respective species along
the gradient. All species showed some
response, so eHOF model I (no response) is
notshown.
palm taxa), followed by unimodal skewed (47%); monotonic macrocarpa.Themeanoptimumofcanopypalmswasstatisti-
responses were rare (2%) and unimodal symmetric (i.e. Gaus- callyindistinguishable fromthe mean optimum ofunderstorey
sian) or plateau were not found at all (Table S8). No differ- palms for both exchangeable bases and B. In contrast, canopy
ences in response shapes were found among the different age palmshadsignificantlyhigher mean optimumfor Pandlarger
classes within taxa (i.e. seedlings, juveniles and adults; data meannichewidthalongtheexchangeablebases,PandBgradi-
not shown). Among unimodal skewed responses, species with entsthanunderstoreypalmsdid(Fig. 4).
a negative skew had significantly higher mean optima for Most conspecific varieties of Bactris, Desmoncus and Geo-
exchangeable bases than those with apositiveskew (Fig.S4). noma differed in their response shape along the exchangeable
Therewere14canopyand47understoreyspecieswith≥10occur- bases gradient (Fig. 5). For example, Bactris hirta var. hirta
rences in our sample (Table S8). Among canopy palms, the most exhibited a bimodal response with unequal optima and was
commonresponseshapewasbimodalwithunequaloptima(57%), mostabundantinsoilsofintermediateconcentrationofextrac-
followedbyunimodalskewed(29%)andbimodalwithequaloptima table bases, whereas B. hirta var. lakoi showed a unimodal
(7%). In understorey palms, unimodal skewed responses were pre- skewed response and was most abundant in base-poor soils
dominant(53%),followedbybimodalwithunequaloptima(43%). (Fig. 5a). A similar difference was seen between conspecific
varieties of G. macrostachys (Fig. 5g and j). Model selection
in both cases was robust, as evidenced by the large difference
DISTRIBUTION ALONG EDAPHIC GRADIENTS
in explanatory power (AICc) between the best and the next-
Mostpalmtaxahadbroadandoverlappingrealisednichesalong bestmodel(TableS10).Incontrast,differencesinexplanatory
the different soil nutrient gradients (Fig. 3, Table S9). About powerbetweenmodelsweresmallinBactrismarajavar.mar-
54%ofalltaxahadoptimainthelowendoftheexchangeable aja(Fig. 5e)andDesmoncusmitis subsp.mitis (Fig. 5f). Still,
basesgradient(Fig. 3a).Speciestypicallygrowinginwhitesand for D. mitis subsp. mitis both the best (IV) and next-best (V)
forests(i.e.Astrocaryumgynacanthum,Attaleamicrocarpa,Iri- model were different than the best model of D. mitis subsp.
artella setigera and Oenocarpus bacaba) exhibited the lowest leptospadix (VII). In both Geonoma maxima and Geonoma
optimaforexchangeablebases.Palmswithintermediateoptima stricta, two conspecific varieties showed bimodal responses
forexchangeablebasesincludedAttaleamaripa,E. precatoria with unequal optima centred at the intermediate to base-rich
and O. bataua, whereas palms with high optima included soils, and one variety had a skewed response and an optimum
Aphandra natalia, Attalea phalerata and Phytelephas in base-poor soils. Some of the conspecific varieties never
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety,JournalofEcology
8 R. C(cid:1)amara-Leret et al.
(a) (b) (c)
Fig.3. Theoptimumandrangeof61westernAmazonianpalmtaxafor(a)exchangeablebases(Ca+K+Mg),(b)phosphorusand(c)boron.The
verticalgreydashedlineineachfigureindicatesthemeanoptimum.Blackhorizontallinesindicatecanopypalms;greyhorizontallinesindicate
understoreypalms.ForunabbreviatedspeciesnamesseeTableS9.
occurred in the same transect, whereas others could be found (Fig. 3,TableS9). Typically,O. batauawas themost abundant
togetherinupto19transects(TableS11). canopy palm in soils of low-to-moderate concentration of
Thefourmostabundantcanopypalmsinoursample(E. pre- exchangeablebasesandP.Itsabundancedecreasedsharplyinthe
catoria, I. deltoidea, O. bataua and S. exorrhiza) also had the morefertilesoils,whereothercanopypalmsandespeciallyI. del-
widest niches along the exchangeable bases and P gradients toideareplaceditasdominant(Fig. 6).
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety,JournalofEcology
Modelling responses of Amazonian palmstosoil nutrients 9
(a) (b) (c)
(d) (e) (f)
Fig.4. Comparison of the optima (a–c) and
ranges (d–f) of canopyvs. understoreypalms
in response to (a, d) exchangeable bases
(Ca+K+Mg), (b, e) phosphorus and (c, f)
boron. Significance was tested using Welch’s
t-test for unpaired samples. *P<0(cid:3)05;
**P<0(cid:3)01.
agreementwithearlierstudiesontrees,ferns,Melastomataceae
Discussion
and palms in the area (Tuomisto et al. 1995; Phillips et al.
Ourlargedatabaseonpalminventorieshasallowedus,forthe 2003; Tuomisto, Ruokolainen & Yli-Halla 2003; John et al.
first time, to model and compare the response curves of 61 2007; Ruokolainen et al. 2007), as well as earlier studies on
Amazonianterrafirmepalmtaxaalongsoilnutrientgradients. palmsinmontaneforestsofPanama(Andersen,Turner&Dal-
Our results support the general hypothesis that palm species ling 2010). Phosphorus was also important in explaining the
distributionpatternsandcommunitycompositionaredrivenby floristicpatternsvisibleintheNMDSordination,butnotquite
soil nutrients. The specific results can be summarised as fol- asimportantasexchangeablebases.Thisisincontrastwiththe
lows: (i) concentration of exchangeable bases was the most widely held view that P is the most important nutrient for
important soil gradient (out of those analysed) in explaining explaining floristic patterns in tropical rain forests (Vitousek
palm species occurrences and abundances in terra firme palm 1984; Sollins 1998; Baribault, Kobe & Finley 2012; Condit
communities, (ii) the modelled species responses to soil nutri- et al. 2013). The relatively modest role that we found for B
ents were generally skewed rather than Gaussian, (iii) canopy goes even more strongly against the predicted pattern (Stei-
palms had greater tolerances than understorey palms for all dinger 2015), although even B did provide some additional
nutrientsanalysedandalsohigherPoptima,(iv)differentcon- explanatory power. One obvious complication in interpreting
specificpalmvarietiesexhibiteddifferentresponsetypestosoil theresultsrelatedtoPisthatthelaboratorymethodsvarycon-
nutrientsand(v)therankorderindominanceofthemostabun- siderably(Wuenscheret al.2015),anditisnotclearwhichof
dantcanopypalmschangedalongtheexchangeablebasesgra- themgivesthemostreliableestimateofPavailabilitytoplants.
dient.Belowwediscusseachoftheseobservationsseparately. Our mechanistic understanding of lowland Amazonian
palm–soil associations is limited because the few experimen-
tal studies that have focused on palms were made in lowland
EFFECT OF SOIL EXCHANGEABLE BASES, P AND B ON
rain forests of Costa Rica (Alvarez-Clare, Mack & Brooks
FLORISTIC COMPOSITION
2013) or in montane rain forest of Panama (Andersen et al.
The range of soil macronutrient concentrations in our study 2010; Andersen & Turner 2013; Andersen, Turner & Dalling
areaiscomparablewiththeoverallrangedocumentedinwest- 2014), which share few species with Amazonia. Large-scale
ernAmazonia(Clinebellet al.1995;Tuomisto&Ruokolainen nutrient addition experiments with canopy trees and one palm
1998; Quesada et al. 2010), and thus reflects well the great species (S. exorrhiza) have shown that positive growth
edaphic heterogeneity of terra firme forests of the western responses to the addition of P in lowland rain forests vary
Amazonbasin(Tuomisto,Ruokolainen&Yli-Halla2003).The among taxa and size classes (Alvarez-Clare, Mack & Brooks
floristic composition of palm communities in our sample was 2013). Other experiments have found that understorey palm
clearly related to the concentration of exchangeable bases, in distributions along a N gradient correspond to N uptake rates
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety,JournalofEcology
10 R.C(cid:1)amara-Leret et al.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
(j) (k) (l)
(m) (n)
Fig.5. ResponseshapesofconspecificpalmvarietiesorsubspeciestoexchangeablebasesfittedusingextendedHuisman–Olff–Frescomodels:(a,
d)Bactrishirta(var.hirta,var.lakoi),(b,e)Bactrismaraja(var.juruensis,var.maraja),(c,f)Desmoncusmitis(subsp.leptospadix,subsp.mitis),
(g,j)Geonomamacrostachys(var.macrostachys,var.acaulis),(h,k,m)Geonomamaxima(var.maxima,var.spixiana,var.chelidonura)and(i,l,
n)Geonomastricta(var.stricta,var.piscicauda,var.trailii).FordescriptionofeHOFmodels,seeFig.2.Formodelselectionresults,seeTableS10.
rather than preferences for different soil N forms in lower
SKEWED RESPONSES
montane forests (Andersen & Turner 2013). These findings
serve as a reminder that multiple elements may be limiting to No taxon had a flat response curve (eHOF type I model) for
palms(Baribault, Kobe& Finley 2012),includingmacronutri- any of the soil variables. On physiological grounds, there is
ents that we did not measure (e.g. N). The situation can be reason to expect a unimodal response shape, but this may be
further complicated by nutrient interactions (Townsend et al. asymmetric (Whittaker 1956; Austin 1976, 1990). For exam-
2011; Wright et al. 2011) and unmeasured micronutrients ple,anasymmetricresponsearisesifaspecies’yieldincreases
(Barron et al.2009). progressivelyfromlowtomediumnutrientconcentrations,but
©2016TheAuthors.JournalofEcology©2016BritishEcologicalSociety,JournalofEcology