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Perspectives in Plant Ecology, Evolution and Systematics 14 (2012) 205–216

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Perspectives in Plant Ecology, Evolution and Systematics

journa l homepage: www.elsevier .de/ppees

Research article

The influence of fire on phylogenetic and functional structure of woodysavannas: Moving from species to individuals

Marcus V. Cianciaruso a,∗, Igor A. Silva b, Marco A. Batalha b, Kevin J. Gaston c, Owen L. Petchey d

a Department of Ecology, Universidade Federal de Goiás, CP 131, 74001-970Goiânia, GO, Brazilb Department of Botany, Universidade Federal de SãoCarlos, CP 676, 13565-905São Carlos, SP, Brazilc Environment andSustainability Institute, University of Exeter, Penryn, Cornwall TR10 9EZ, UK d Institute for Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190 8057 Zurich, Switzerland

a r t i c l e i n f o

Article history:

Received 22 May 2010

Received in revised form 28 October 2011

Accepted 26 November 2011

Keywords:

Community assembly

Environmental filtering

Intraspecific variability

Plant traits

a b s t r a c t

Fire isakey determinant of tropical savanna structure and functioning. High fire frequencies are expected

to assemble closely related species with a restricted range of functional trait values. Here we deter-mined the effect of fire on phylogenetic and functional diversity of woody species and individuals in

savanna communities under different fire frequencies. We found phylogenetic signals for one third of the functional traits studied. High numbers of fires simultaneously led to phylogenetic overdispersion

and functional clustering when communities were represented by mean trait values with all traits thatputatively should be affected or respond to fire. This finding is important, because it shows that the

relationship between ecological processes and the phylogenetic structure of communities is not straight-forward. Thus, we cannot always assume that close relatives are more similar in their ecological features.

However, when considering a different set of traits representing different plant strategies (fire resis-tance/avoidance, physiological traits and regeneration traits), the results were not always congruent.When asking how communities are assembled in terms of individuals (not species) the outcome was

different from the species-based approach, suggesting that the realised trait values – rather than mean

species trait values – have an important role in driving community assembly. Thus, intraspecific traitvariability should be taken into account if we want fully to improve our mechanistic understanding of assembly rules in plant communities.

© 2011 Elsevier GmbH. All rights reserved.

Introduction

A fundamental goal in ecology is to understand the processby which local communities are assembled (Weiher and Keddy,1999; Pavoine and Bonsall, 2011). In recent years, community

assembly rules have received increased attention from ecolo-gists. A wide variety of processes may play important roles,including mutualism, facilitation, dispersal limitation and randomcolonisation-extinction events (see Leibold et al., 2004; Pausas and

Verdú, 2010; Pavoine and Bonsall, 2011).Two processes in particularhave received much attention: envi-

ronmental filtering and competitive interactions among species(Webb et al., 2002; Kraft et al., 2007; Vamosi et al., 2009; Mouchet

et al., 2010; Pausas and Verdú, 2010). These processes haveopposing (but not exclusive) effects on functional similarity andphylogenetic relatedness of co-occurring species. Environmental

∗ Corresponding author.

E-mail address: [email protected](M.V. Cianciaruso).

filters select those species that can persist within a community

on the basis of their tolerance to the prevailing abiotic conditions.The result is an assembly of species with similar characteristicsand niches (Fukami et al., 2005; Pausas and Verdú, 2010). Con-sequently, a restricted range of species trait values is viewed as

evidence of environmental filtering (Weiher et al., 1998; Pausasand Verdú, 2008). Competitive interactions, by contrast, are pre-dicted to result in co-occurring species with dissimilar traits, andthis can be interpreted as evidence for limiting similarity (niche

differentiation among species) (Weiher et al., 1998; Webb et al.,2002). Thepredictedeffects of these two processes on phylogeneticrelatedness depend on how functional traits evolved in species lin-eages. Phylogenetic signal in functional traits (i.e., traits are more

similar among closely related species; sensu Losos,2008) and envi-ronmental filtering should result in species more closely relatedthan expected by chance(i.e., phylogeneticclustering). By contrast,species living in communities driven by competition should be

less closely related (i.e., phylogenetic overdispersion; Webb et al.,2002; Cavender-Bares et al., 2006; Kraft et al., 2007; Vamosi et al.,2009; but see Mayfield and Levine, 2010). However, if functional

1433-8319/$– see front matter © 2011 Elsevier GmbH. All rights reserved.

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http://localhost/var/www/apps/conversion/tmp/scratch_5/dx.doi.org/10.1016/j.ppees.2011.11.004http://localhost/var/www/apps/conversion/tmp/scratch_5/dx.doi.org/10.1016/j.ppees.2011.11.004http://www.sciencedirect.com/science/journal/14338319http://www.elsevier.de/ppeesmailto:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_5/dx.doi.org/10.1016/j.ppees.2011.11.004http://localhost/var/www/apps/conversion/tmp/scratch_5/dx.doi.org/10.1016/j.ppees.2011.11.004mailto:[email protected]://www.elsevier.de/ppeeshttp://www.sciencedirect.com/science/journal/14338319http://localhost/var/www/apps/conversion/tmp/scratch_5/dx.doi.org/10.1016/j.ppees.2011.11.004

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206 M.V. Cianciaruso et al. / Perspectives in Plant Ecology, Evolution andSystematics 14 (2012) 205–216

traits are convergent in species lineages, environmental filtering

should assemble communities containing distantly related species,whereas competition should remove any systematic associationsresulting in community assemblages indistinguishable from ran-dom(Webbet al.,2002). Notethat theeffects andrelativestrengths

of environmental filtering are likely to change with spatial scale(Gómez et al., 2010; Thuiller et al., 2010), though this is not a focus

of our study.Strongly parallel literatures have developed on the measure-

mentof phylogeneticand functional diversity,and of therespectiveclustering or over-dispersion of species. The concomitant use of measures of phylogenetic diversity and functional diversity thusseems likely to help in understanding the mechanisms of commu-

nity assembly (Pavoine and Bonsall, 2011; Diniz Filho et al., 2011).To date,however, there have been rather few such studies (but seePavoine et al., 2010; Meynard et al., in press; Safi et al., 2011). In anexperimental grassland, Cadotte et al. (2009) f ound more support

in using phylogenetic diversity than functional diversity to under-stand productivitypatterns.However,a meta-analysis showedthatthis is not always the case (Cadotte et al., 2008). Further, there isevidence that species traits may be more important than species

relatednessto ecosystem processes (Díaz andCabido,2001; Hooperet al., 2002). If important traits were very labile with a low phy-logenetic signal or when species exhibit considerable phenotypicplasticity, phylogenetic distances will fail to resemble ecological

(functional) distance among species (Losos, 2008).Although both phylogenetic and functional diversity have most

commonly been determined using species as the most finelyresolved taxonomic units, inclusion of intra-specific trait variation

is also important (Cianciaruso et al., 2009; Albert et al., 2010; deBello et al., 2011). Competition for resources, niche width expan-sion and, ultimately, natural selection occur at the individual level(Pachepsky et al., 2007). Plant phenotypic diversity was found to

influence ecosystem functioning in several studies (e.g., MadritchandHunter, 2002, 2003; andreferences in Schmitzet al., 2003), andthere is evidence fora role of phenotypic variability in species coex-

istence and responses to both biotic and abiotic filters ( Jung et al.,2010). Furthermore, environmental variation can cause impor-tant trait variation without species turnover (Fajardo and Piper,2011). Intraspecific variabilitycan generate trait-mediated indirectinteractions and thus influence the strength of competition, facili-

tation, and species-environment relationships (Miner et al., 2005).In flooded meadows Jung et al. (2010) f ound that plant specieswere sorted not only according to their mean trait value but alsoaccordingto their ability to ‘fit’ their trait valuesto local abiotic and

biotic requirements. This implies that a priori ‘unexpected species’(species whose mean trait values do not satisfy the abiotic andbiotic requirements) are still able to pass through environmentalfilters ( Jung et al., 2010). This said, empirical analyses of the role of

functional diversity at the individual level remain scarce.

Fire-prone communities are very useful to test communityassembly theories, because fire may act as a strong environmentalfilter for particular combinations of functional traits in communi-

ties with different fire frequencies (Pausas and Verdú, 2008; Silvaand Batalha, 2010). Savannas have experienced fire for millions of years and, as a consequence, savanna plants evolved fire-tolerancemechanisms, and sometimes even require fire to maintain their

populations (Hoffmann, 1998). Among woody species, the mainadaptations are those that allow thermal isolation of living inter-nal tissues, such as strong suberisation of the trunk and branches,clonal reproduction, and the ability to sprout vigorously from

underground organs (Miranda et al. , 2002; Pausas and Lavorel,2003). Moreover, different processes related with recruitment,flowering, dispersal, and germination might be modified by fire

(Coutinho, 1990). The role of fire in assembling plant communities

has been widelyinvestigated, especiallyin the Mediterranean basin

(see Verdú and Pausas, 2007 f or references), where it producesfunctionally and phylogenetically clustered communities (Pausasand Verdú, 2008). However, there are few studies focusing oncommunity assembly in savannas and, as far as we know, none

including intraspecific trait variation.Here, we determined the effect of fire on the phylogenetic

and functional diversity of woody species in savanna communi-ties under different fire frequencies. We used null models to test

whether levels of community phylogenetic and functional struc-ture were different from what we would expect by chance. Insummary, we asked the following questions: (i) Are the functionaltraits more similar among closely related plant species? (ii) Do

phylogenetic and functional diversities differbetween sites experi-encing different fire frequencies from that expected by the randomassembly of communities? (iii) Are there any differences betweenspecies- and individual-based approaches to community assembly

based on functional attributes?

Methods

Study area and data

The Emas National Park (ENP) is located in the Brazilian

Central Plateau, southwestern Goiás State (17◦49–18◦28S and52◦39–53◦10W), and is one of the largest and most impor-tant savanna reserves in South America, covering ca. 133,000ha.Regional climate is tropical and humid, with a wet summer and

dry winter, classified as Aw following Köppen (1931). The dry sea-son is from June to August and the wet season from September toMay. Annual rainfall and mean temperature lie around 1,745 mmand 24.6◦C, respectively. In the park, we find a gradient from

open (68.1% of its area) to closed savannas (25.1%), as well as wetgrasslands (4.9%) and riparian and semideciduous forests (1.2%)(Ramos-Neto and Pivello, 2000).

Historically, ENP was exploited by farmers for cattle ranching,and dry season burnings were used to promote forage regrowthevery year. In 1984,the parkwas completely fenced, cattle were nolonger allowed inside, and a fire exclusion policy was established(Ramos-Neto and Pivello, 2000). As a consequence, uncontrolled

wildfires occurred every 3–4 years, burning on average 80% of itstotal area (Ramos-Netoand Pivello, 2000; França et al., 2007). Since1994, when a catastrophic fire burned almost 95% of ENP’s area,approximately 10 km2 of preventive firebreaks are burned annu-

ally in the dryseason, anda firebrigade is permanently stationed inthe park to prevent anthropogenic fires during this period (Françaet al., 2007). As a result, nowadays there are few occurrences of anthropogenic burnings inside the park (almost all fires are light-

ning fires), and fire frequency at a given point is around 6–7 years

on average.We used a long time-series of satellite images to map fire scars

within ENP from 1973 to 2009, creating a fire map from which we

can determine the number of fires in this period for any locationin the park. Using this information, we selected sites coveredwith savanna vegetation that experience high (HiFi), intermediate(MidFi) and low (LowFi) fire frequencies. We randomly placed 64

plots (each a 25 m2 quadrat) across the study sites: 21 plots inHiFi sites, 21 in MidFi sites, and 22 in LowFi sites. There was nooverlap in fire frequency among each classification. On average,the number of fires between 1973 and 2009 (mean± standard

deviation) was 16.00±1.12 in HiFi plots; 10.50±1.15 in MidFiplots; and 7.00±1.03 in LowFi plots. In each plot, from September2009 to January 2010, we sampled all woody individuals with

stem diameter equal to or higher than 3 c m at soil level. We

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M.V. Cianciaruso et al./ Perspectives in Plant Ecology, Evolutionand Systematics 14 (2012) 205–216 207

Aspidosperma tomentosumPalicourea rigidaTabebuia aurea

Tabebuia ochraceaSchefflera malmei Eremanthus erythropappusPiptocarpha rotundifoliaDiospyros hispidaPouteria ramifloraPouteria tortaStyrax ferrugineusCaryocar brasilienseKielmeyera coriaceaErythroxylum campestreErythroxylum suberosumErythroxylum tortuosumByrsonima coccolobifoliaByrsonima verbasci foliaOuratea acuminataOuratea spectabilisCasearia sylvestrisPlenckia populneaConnarus suberosusRourea induta Acosmium dasycarpumMachaerium acutifolium Albi zia niopoides Anadenanthera peregrinaStryphnodendron adstri ngensMimosa amnisatri Dimorphandra mollisHymenaea stigonocarpaSclerolobium aureumLafoensia pacari Miconia albicansEugenia aurataEugenia bimarginataEugenia punicifoliaPsidium laruotteanumMyrcia bella

Myrcia camapuensisMyrcia obovataMyrcia guianensisMyrcia lasianthaQualea parvifloraEriotheca graci lipesEriotheca pubescensDavilla ellipticaRoupala montana

0204060100140

Fig. 1. Thephylogenetic tree assembled forthe cerrado species in allsampledplots(HighFi,MidFi, andLowFi) in Emas NationalPark, central Brazil. Therelationship among

species was based on the current Phylomatictree (treeR20080147, Webband Donoghue, 2005). Theageof marked nodeswerebased onminimumagesof nodesdetermined

for genus, families and higher orders from fossil data (Wikström et al., 2001). Other nodes were placed evenly between dated nodes and dated nodes. We improved tree

resolution by consulting recent phylogenies of Fabaceae (Simon et al., 2009), Malpighiales (Wurdack and Davis, 2009), and Myrtaceae (Costa, 2009). In these clades, we

evenly placed thegenusnodesabove family dated nodes (only including brancheswith more than 80% of support). The scale is in million years.

used Plantminer (Carvalho et al., 2010a) to search for families(following the Angiosperm Phylogeny Group III, 2009), authors,

and synonyms concerning our species list. The soil in the studyareas is a typical Oxisol, poor in nutrients, well drained, and acidic.

Phylogenetic data

We constructed a phylogenetic tree for all sampled species(Fig. 1) using the Phylomatic software, a phylogenetic database

and toolkit for the assembly of phylogenetic trees (Webb andDonoghue, 2005). Phylogenetic distances among species from dif-ferent families were estimated from the current Phylomatic tree(R20080147). The backbone of the Phylomatic tree is the phylo-

genetic relationship among Angiosperm Phylogeny Group orders(Stevens, 2001). Phylomatic tree is a supertree assembled by hand,rather than by an automated supertree algorithm, and conflict-

ing branching patterns were resolved subjectively. It is, however,

intended to represent a pragmatic approximation of the true phy-

logeny of seed plants (Webb and Donoghue, 2005). We used a less

conservative Phylomatic tree which includes some branches withbootstrap support less than 80% and more details of the decisionsinvolved in phylogenetic tree construction are given at the Phy-

lomatic website (http://www.phylodiversity.net/phylomatic/). Inorder to solve some of the politomies we improved tree resolutionby consulting recent phylogenies of some clades: Fabaceae (Simonet al. , 2009), Malpighiales (Wurdack and Davis, 2009), and Myr-

taceae (Costa, 2009). In this case, only branches with more than80% of support were included. Branch lengths were based on mini-mum ages of nodes determined forfamilies and higherordersfromfossil data (Wikström et al., 2001). We placed undated nodes in the

tree evenly between dated nodes with the Branch Length Adjust-ment algorithm in Phylocom (Webb et al. , 2008). This algorithmtook the phylogeny generated by Phylomatic, fixed the root node

at 137 million years before present (i.e., the age of the eudicots

http://www.phylodiversity.net/phylomatic/http://www.phylodiversity.net/phylomatic/

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Table 1

Traits used to measure functional diversityin savanna woody species at Emas National Park, Brazil.

Trait Unit Functional significance

1. Plant height m Associated with competitive vigour, whole plant fecundity, tolerance or avoidance

of disturbances and top-kill

2. Basal area m Competitive vigour, survival ability after fire3. Bark thickness mm Protection of vital tissues against damage, thick barks can decrease mortality by

fire or accelerate post-fire recovery

4. Specific leaf area mm2 mg−1 Highly correlated with several physiological traits related to resource uptake anduse efficiency and plant growth strategies

5. Leaf size mm2 Ecologicalstrategy, with respect to environmental nutrient stress and disturbances

6. Leaf toughness mg g−1 Related to flammability, resistance to physical hazard, disturbed environments

7. wood density mg mm−3 Structural strength, resistance against physical damage

8. Leaf N mg g−1Maximum photosynthetic rate, LNC:LPC ratio related to carbon cycling processes

9. Leaf P mg g−1

10. Leaf K mg g−1Competitive vigour, persistence after environmental disturbances

11. Resprouting due to top-kill Frequency12. Pollination syndromes Categorical (bees, small i nsects, moths,

flies, birds, bats) Regeneration traits linked to (re)colonisingability and disturbances

13. Dispersal mode Categorical (anemochory, zoochory,

and autochory)

clade) and fixed other nodes we had age estimative from Wikström

et al. (2001) (Fig. 1). It then sets all other branch lengths by placing

the nodes evenly between dated nodes, and between dated nodesand terminals (Webb et al., 2008). This has the effect of minimisingvariance in branch length, within the constraints of dated nodes. It

thus produces a pseudo-chronogram that canbe usefulfor estimat-ing phylogenetic distance (in units of time) between taxa mainlyforthe analysis of phylogeneticcommunitystructure (Webb, 2000;Webb et al., 2008; Vamosi et al., 2009).

We investigated whether the functional traits of the sampledspecies tended to present a phylogenetic signal (i.e., trait similar-ity between species is related to phylogenetic relatedness, Losos,2008) with a test based on the variance of phylogenetically inde-

pendent contrasts (Blomberg et al., 2003). If related species aresimilar to each other, the magnitude of independent contrasts willgenerally be similar across the tree, resulting in a small variance

of contrast values (Blomberg et al., 2003). The observed contrastvariances are compared to the expectations under a null model of randomly swapping trait values across the tips of the tree, with1000 randomisations. For a detailed description of comparativeanalyses using phylogenetically independent contrast, see Garland

et al. (1992). We excluded from this analysis the pollination anddispersal traits, because they are categorical variables. We did thisanalysis with the package ‘picante’ (Kembel et al., 2010) for R (R Development Core Team, 2010).

Functional traits

We used 13 plant traits (Table 1) that were relatively easy

to measure and represented functional characteristics related to

disturbances such as fire and drought (Cornelissen et al., 2003;Pausas and Paula, 2005). All traits, except pollination syndromes,were measured or determined according to the protocol pro-

posed by Cornelissen et al. (2003). We determined pollinationsyndromes using data available for cerrado species (Gottsbergerand Silberbauer-Gottsberger, 2006). In this study we adopted thespecies functional attribute approach proposed by Violle et al.

(2007). An attribute is the particular value taken by a trait at anyplace and time. That is, within a species, trait values may exhibitvariation along environmental gradients or through time (Violleet al., 2007). Here, functional information was plot-specific, and

we considered all the individuals sampled, allowing us to includeintraspecific differences among species (Cianciaruso et al., 2009).Thus, we collected andanalysed functional trait data forall individ-

uals sampled in the plots.Because data on pollination anddispersal

modes was gathered from the literature, there was no intraspecific

variability for them.

Phylogenetic and functional structure of communities

To find out whether fire constrained the phylogenetic structureofthe savanna plantcommunities we calculatedthree continuouslydistributed metrics of phylogenetic diversity: phylogenetic diver-sity index (PD; Faith, 1992), mean pairwise phylogenetic distance

(MPD), and mean nearest taxon distance (MNTD; Webb, 2000). PDis the sum of branch lengths of the phylogenetic tree connectingall species within a community. MPD is the mean pairwise phy-logenetic distance between each species in the community, and it

is considered a tree-wide, or basal, measure of the phylogeneticrelatedness of co-occurring species (Webb, 2000). Lastly, MNTDquantifies the phylogenetic distance between each species and its

nearest neighbouron thephylogenetic tree with which it co-occursin the local assemblage. Thus, MNTD can be viewed as a termi-nal metric of the phylogenetic relatedness of co-occurring species(Webb, 2000).

To assess the functional structure of communities we used the

same metrics above. This is straightforward since a functional den-drogram and phylogenetic tree can be represented by an identicaldata structure, thus, any index applied to a phylogenetic tree canalso be applied to a functional dendrogram or to trait-based dis-

tance matrices (Pavoine and Bonsall, 2011). For example, one of the functional measures we used (termed FD, Petchey and Gaston,2002, 2006) has its roots on Faith’s PD (Faith, 1992). Therefore,FD is defined as the sum of branch lengths of the functional den-

drogram necessary to connect all the species present in a local

community. Using the trait-based distance matrix (which is usedto calculated the functional dendrogram), MPD and MNTD canbe defined, respectively, as the mean pairwise functional distance

between each species in the assemblage (i.e., the mean functionaldistance, MFD) and the functional distance between each speciesand its nearest neighbour on the distance matrix with which it co-occurs in the local community (i.e., the mean nearest functional

distance, MNFD).Constructing a functional dendrogram requires that functional

traits be transformed into functional distances, and that distancesbe transformed into a dendrogram. Since we used both qualitative

and quantitative plant traits, we used a generalisation of Gowerdistance (Pavoine et al., 2009) dedicated to the treatment of mixeddata and then UPGMA clustering. Because one of our aims was to

understand whether fire selects for species (or individuals) more

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similar in their biological attributes than expected by chance, we

built species-based and individual-based dendrograms. For thespecies-level analysis, for each trait and each species with morethan two individuals, we calculated species average traitvalues andconstructed a species-level functional dendrogram. For singleton

species, we used the measured value of each trait. We assembleda trait matrix in which each species was labelled according to the

fire treatment in which trait values were measured (HiFi, MidFi,LowFi), since individuals from the same species but from different

sites may exhibit differences in trait values (see Cianciaruso et al.,2009 f or references). Thus, a species may occur in all three fire fre-quencies, and exhibit different trait values in each. By doing thiswe included the intraspecific variability of species because we had

functional trait information from species occurring in different firetreatments (i.e., “among-sites” spFD, Cianciaruso et al., 2009). Toinclude both the differences in individual traits among and within-sites (fire treatments), we used data on all individuals sampled

in the 64 plots to construct an individual-based dendrogram. Thisdendrogram was assembled using an individual level trait matrix.Using this approach we captured the functional variation simulta-neously at both the species and individual level (i.e., iFD approach,

see Cianciaruso et al., 2009).Since choiceof traitscan influence measures of functional diver-

sity (Petchey et al., 2009), we constructed independent functionaldendrograms using different setsof traits thought to represent dis-

tinct ecological strategies (see Cornelissen et al., 2003; Pausas andPaula, 2005 f or references). The three trait sets were (i) fire resis-tance/avoidance (plant height,basal area,resprouting aftertop-kill,and bark thickness); (ii) physiological response to disturbances

(stem specific density, leaf size, SLA, leaf toughness, leaf N, P andK contents); and (iii) regenerating traits (pollination and dispersalmode). We calculated the same above-mentioned metrics for eachof these trait sets. Analyses were repeated separately foreach func-

tional dendrogram but for the individual-based dendrogram withregenerative traits because there was no intraspecific variabilityfor this data. In this case spFD and iFD approaches should converge

(Cianciaruso et al., 2009).

Data analysis

We used null models to determine whether coexisting specieswere more or less similar than expected by chance in phylogeneticand functional terms. We compared the observed values of each

metric found under each fire treatment (HiFi, MidFi and LowFi)with the corresponding mean value obtained from 1000 randomlygenerated null communities using an independent swap algorithm(Gotelli and Entsminger, 2001). By doing this we maintained the

observed species richness and abundance in the random commu-nities. This null model combines good Type I error rates with thepower to detect niche-based assembly processes (Kembel, 2009).

Our species pool (or individual pool in the case of iFD) was definedasall species (orindividuals) found in allthe 64 plots.For allmetricswe calculated the standardised effect sizes against null communi-ties as

standardised effect=−(obsMetric − rndMetric)

sd.rndMetric

where obsMetric is the observed value of the metric under analy-sis, rndMetric is the mean metric value of null communities, andsd.rndMetric is the standard deviation of the 1000 random valuesof the metric.

ForMPD andMNTDthesestandardisedeffect sizevalues arealsoknown as the nearest relative index (NRI) and the nearest taxonindex (NTI), respectively (Webb, 2000). Values of standard effects

greater than zero indicate phylogenetic clustering and values lower

Fig. 2. Standardised effect sizes of phylogenetic diversity (PD; circles) and mean

phylogenetic distance (MPD; squares) in woody savanna communities and their

95%confidenceintervalsfor eachfirefrequency evaluated. Symbolsin blackindicate

values significantly different from zero (random).

than zero indicate phylogenetic overdispersion (Webb et al., 2002;Vamosi et al., 2009).

To test whether standard effects presented significant devia-tions from a null expectation (mean= 0) we used a one sample

t -test. These metrics and the null model approach have been usedwidely in explaining community assembly, searching for signals

of environmental filter or limiting similarity in plant assemblages(e.g., Webb, 2000; Swenson et al., 2006; Kraft et al., 2007). In addi-tion, FD (and by extension PD) was one ofthe best metrics to detectassembly rules using null models under different simulation sce-

narios (Mouchet et al., 2010). We calculated all the metrics and didall the analysis and randomisations using the functions ‘ses.pd’,‘ses.mpd’ and ‘ses.mntd’ of the ‘picante’ package (Kembel et al.,2010) for R (R Development Core Team, 2010).

Results

In total we sampled 347 individuals, belonging to 49 species.There were 15 species in the HiFi plots, 26 species in the MidFi and38 species in the LowFi (Fig. 1; Appendix 1). All metrics we used to

describe the phylogenetic and functional structure of communitiesled us to the same conclusions. So, for the sake of clarity hereafterwe just present the results for FD/PD and MPD/MFD.

Trait evolution and phylogenetic structure of communities

We found significant phylogenetic signals for one third of thefunctional traits analysed (Table 2). Strong phylogenetic signals

were observed in N, P, and K leaf content, and leaf toughness, suchthat closely related species tended to have similar values for thesetraits (Table 2). The other traits were randomly distributed acrossthe phylogeny (Table 2).

High fire frequency (HiFi plots) were composed of species that

were more distantly related than expected by chance, that is, theywere phylogenetically overdispersed (Fig. 2). We did not observeanysignificant phylogenetic structure in the MidFi and LowFi plots(Fig. 2).

Functional structure of communities: species level approach

When we used the 13 traits (Table 1) all metrics showedthat species co-occurring in HiFi plots were more similar in their

functional attributes than expected by chance (Fig. 3). However,

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Table 2

Tests for phylogenetic signal in functional traits of cerrado woody species, in Emas National Park, central Brazil. The observed variance and the mean of random variances

of the phylogenetic independent contrasts (PICs) are given. Significant values at ˛= 0.05 arepresented in bold face.

Functional trait k Observed variance of PICs Mean of random variances of PICs P

Plant height 0.69 0.070 0.080 0.240

Basal area 0.43 0.000 0.000 0.950Bark thickness 0.57 1.080 0.970 0.780

Specific leaf area 0.78 0.340 0.420 0.100

Leaf size 0.66 0.104 0.111 0.320Leaf toughness 0.86 0.030 0.043 0.002

Stem specific density 0.71 0.001 0.002 0.180

Leaf N content 1.13 1.370 2.380 0.001

Leaf P content 0.82 0.119 0.196 0.001

Leaf K content 0.81 0.380 0.520 0.022

Resprouting due to top-kill 0.71 0.012 0.014 0.220

species were assembled at random with respect to their functionalattributes under MidFi and LowFi (Fig. 3). When using the set of traits representing resistance or avoidance to fire we found func-

tionalclustering forHiFi andLowFiplots whereas in MidFi plots thevalues were not different from random (Fig. 3). When using traitsrepresenting physiological response to disturbances we found thatHiFi andMidFiplotswere composed of species that were more sim-

ilar than expected by chance,whereas at LowFi we found a randompattern(Fig.3). Finally,when usingthe two regeneratingtraits (pol-lination and dispersal mode) we did not find any deviations fromrandomness (Fig. 3).

Functional structure of communities: individual level approach

Using all traits we found a consistent pattern of functional clus-

tering in MidFi and LowFi plots and randomness for HiFi plots(Fig. 4). However, when using trait subsets we found a differentpicture: in LowFi, individuals were more similar in their fire resis-tance/avoidance attributes than expected by chance (Fig. 4) but

were randomly assembled in HiFi and MidFi plots. For the physi-ological traits, individuals were more similar in the HiFi plots andassembled at random in MidFi and LowFi plots (Fig. 4).

Discussion

Our main finding was that fire frequency, the functional traitsconsidered, and intraspecific trait variability can each affect thesignature of community assembly in the savanna communities

studied. High number of fires simultaneously led to phylogeneticoverdispersion and functional clustering patterns when we useda species-level approach (i.e., species being represented by meantrait values) with all traits that putatively should be affected or

respond to fire. However, different sets of traits, representingdifferent strategies of plants sometimes led to different results. Fur-thermore, analyses of individuals (not species) produced differentconclusions, suggesting that including intraspecific variability may

produce shifts on how we understand community assembly.

Fig. 3. Species-based standardised effect sizes of functional diversity (FD; circles) and mean functional distance (MFD; squares) in woody savanna communities and their

95% confidenceintervals foreach firefrequency and trait set evaluated. Symbols in black indicate values significantly different from zero (random).

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Fig. 4. Individual-based standardised effect sizes of functional diversity (FD; circles) and mean functional distances (MFD; squares) in woody savanna communities and

their 95% confidence intervalsfor eachfire frequency and traitset evaluated. Symbols in black indicate values significantly different from zero (random).Regenerative traits

were not analysed because there was no intra-specific variability for them.

Trait evolution and phylogenetic structure of communities

Wherever phylogenetic signals are found in plant traits, envi-ronmental filters are predicted to assemble phylogenetically

related species, causing phylogenetic clustering (Kraft et al., 2007;Vamosi et al. , 2009). In contrast to this prediction and to previ-ousfindings showing phylogeneticclustering at local scales(Webb,2000; Cavender-Bares et al., 2006; Swenson et al., 2006), includ-ing in other fire-prone communities (e.g., Verdú and Pausas, 2007

for Mediterranean vegetation), we found that co-occurring woodyspecies subjected to high numbers of fires were less related thanexpected by chance, whereas fire frequencies had no apparenteffect on the phylogenetic structure of these communities. This

can be explained by the fact that we observed phylogenetic signalsin only one third of the functional traits studied. These traits (leaf toughness,leafN, P,and K concentrations),in spite oftheirphyloge-netic signals, were not enough to produce phylogenetic clustering

pattern in our communities. However, they comprise about 58% of

the traits used to understand the ‘physiological response to distur-bances’ strategy. Indeed, for this set of traits, a phylogenetic signalwas detected (there was phylogenetic clustering of trait values).

Silva and Batalha (2010), when looking for phylogenetic sig-nals in the community structure of six savannas in Brazil alsofound that fire did not promote phylogenetic clustering. Theypostulated that the observed phylogenetic overdispersion wasmainly due to the persistence of long-lived resprouting speciesfrom different plant lineages. Resprouting ability is an importantdimensionof the“persistenceniche” ofplants that explains thesur-

vival of individuals subjected to environmental constraints suchas fire or drought (Bond and Keeley, 2005). In general, most of the plants of the Brazilian savannas are able to resprout afterrecurrent fires (Miranda et al., 2002; Gottsberger and Silberbauer-

Gottsberger, 2006). For example, comparing to other fire-prone

communities in South America, such as the southern temperate

grasslands in which around half of its species are resprouters(Overbeck and Pfadenhauer, 2007), in the Brazilian savannas, morethan 90% of species can be classified as resprouters (Gottsberger

and Silberbauer-Gottsberger, 2006). Thus, the presence of a largenumberof species able to resprout, belonging to different plant lin-eages, may prevent fire from assembling closely related plants inthe savannas we studied. In the Mediterranean vegetation, alter-natively, some specific adaptations to fire (for example, obligate

seeders - plants that can only regenerate after fire from seeds)are concentrated in few lineages (such as Cistaceae and Fabaceae,Verdú and Pausas, 2007) and consequently frequent fires pro-mote phylogenetic clustering. Phylogenetic overdispersion may be

a commoncharacteristic of plant communities in Braziliansavannainspiteoffire(Silvaand Batalha, 2009a). Thus,frequentfires didnotexclude entireclades from localassemblages; instead, fire excludesspecies relatively evenly across the phylogenetic tree.

This finding is conceptually important for studies investigat-

ing the role of fire in these savannas because in fact disassemblingby removal of individuals, and consequently species, is proba-bly much more important than the establishment (assembling) of

individuals that pursuit particular traits suited to one or anotherfire regime. In other fire prone communities, such as Mediter-ranean heathlands, sclerophyllous shrublands, and temperate pineforests, distinct strategies of colonisation and space occupation, forexample, resprouters versus obligate seeder species, explain com-munityassemblyafterfires (Pausas and Lavorel, 2003). However, inthese vegetation types, fire recurrence is estimated to be between

25 and 40 years or more (DeBano et al., 1998). The fire recur-rence period is shorter in tropical savannas, and woody plants arethought to face a strong bottleneck to seedling survival due to fire,drought, competition with grasses and herbivory (Miranda et al.,

2002; Gottsberger and Silberbauer-Gottsberger, 2006; Cianciaruso

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et al., 2010). Despite being an important aspect of plant popula-

tion dynamics, the different establishment strategies after fire arerelatively poorly understood for Neotropical savanna species.

The phylogenetic overdispersion of communities under highlevels of disturbance (e.g., high fire frequencies) is consistent with

the environmental filtering hypothesis if species under frequentfire share similar traits due to convergence (Weiher et al., 1998;

Fukami et al., 2005; Pausas and Verdú, 2008). Our findings par-tially corroborate this view, since we found functional clustering

associated with high fire frequency (Fig. 3) despite the observedphylogenetic overdispersion (Fig. 2). Nevertheless, because thephylogenetic structure of communities depends on the distribu-tion of different traits among lineages (which can have different

evolutionary signals, as we found here) the phylogenetic frame-work discussedabove canbe improved by identifying specifictraits(or set of traits) that confer to different species groups the abil-ity to survive under different environmental conditions (Mayfield

et al.,2006; Verdú etal.,2009). Only after accountingfor such infor-mation can we more thoroughly unveil the forces that structurenatural communities.

Functional structure of communities: species level approach

WefoundthatHiFi plotswere composed byspecies more similar

in their functional traits (functional clustering) than that of MidFiand LowFi plots (Fig. 3). Similar findings have been observed inother fire-prone communities around the world. In Mediterraneanwoodlands, for instance, an increase in fire frequency reduced the

functional diversity of plant communities due to the eliminationof species without effective mechanisms for post-fire regenera-tion (Pausas and Verdú, 2008). In Brazilian savannas, however,most of the species sprout promptly after fires (Gottsberger and

Silberbauer-Gottsberger, 2006), and the low functional diversityis mainly due to the reduction in the range of trait values of thespeciesinHiFiplots.Areductioninthetraitrangewasalsoobserved

between neighbour woody species in an annually burned savanna(Silva and Batalha, 2009b).

None of the set of traits (i.e., traits related to resistance, phys-iological response, and regeneration) seems to be favored by fire(Fig. 3). Frequent fires decreased the range of traits associated

with physiological responses to disturbance. Thus, frequent firespredominantly assembled species with similar physiological func-tional traits: leaf traits and stem specific density. Leaf traits arealsoimportant in the mediation of plant-herbivore interactionsand

represent a fundamental dimension of the plant defenses againstherbivory. Herbivory may also actas an environmental filter select-ing species with similar defenses against herbivory (Harley, 2003).The fact that ourresults shows that fire assembles more similar leaf

traitsin an area than expected by chance encourages investigations

of the role of fire in indirectly mediating an important top-downprocess in these savannas.

Interestingly, traits related to resistance exhibited functional

clustering at both ends of the fire frequency gradient: species weremore similar in their resistance traits under high and low fire fre-quency. This could simply be explained by the fact that the plantswere taller and had greater basal area than expected by chance

under low fire frequency, but were shorter and thinner under highfire frequency (Miranda et al., 2002). Knowing the position of thespecies in trait space is an important issue, but up to now thereare few metrics which incorporate thisaspect (but see Pavoine and

Bonsall, 2011).Finally, regenerative traits were not affected by fire. In Brazilian

savannas, where sprouting is a widespread strategy (Gottsberger

and Silberbauer-Gottsberger, 2006), it is perhaps unsurprising that

other regenerative traits, such as dispersal and pollination modes,

play a secondary role in the regeneration of the populations.

Functional structure of communities: individual level approach

When we considered the variation of traits among individuals,the functional diversity was lower in areas with low fire frequen-

cies (Fig. 4). In other words, co-occurring individuals were moresimilar in MidFi and in LowFi plots than those in HiFi plots. This is

the opposite pattern to that in the species based analyses, suggest-ing that accounting for intraspecific variation in the trait diversityis important for fully understanding how communities are assem-bled. This functional clustering of individuals may be attributed

to the great number of co-occurring individuals of the same pop-ulation in plots with lower fire frequencies. Fires commonly killsseedlings and saplings, decreasing the clustered pattern of spatialdistribution of thepopulation (Greig-Smith,1991). Thus, the exclu-

sionof fire may decrease the trait variability at small spatial scales.Also, at low fire frequencies individuals may have sufficient timeto recover from the last fire and start to spread vegetatively viaclonal reproduction (San Jose et al., 1991; Hoffman, 2002). In this

case, many of the individuals within the same species we sampledat MidFi and LowFi plots were in fact the same individuals whichcould additionally explain the relatively low functional clusteringobserved.

However, when we considered the set of traits representingdifferent plant strategies, the results were similar to those of thespecies level approach. Frequent fires also decreased the range of traits associated with resistance and physiological responses to

disturbance.Despite having observed that in some cases both species and,

especially, individuals were randomly assembled with respect totheir traits, overall we found that coexisting species were more

similar in their traits than expected by chance. While the envi-ronmental conditions where functional clustering was observedvaried, these findings provide evidence against neutral theories of

biodiversity(Hubbell, 2001). These theories predict that thespeciestraits are unimportant for coexistence patterns. On the other hand,we also found some situations in which species and individualswere a randomsample from thespecies (individuals)pool.Thiswasnot always congruent between species-based and individual-based

approaches and depended on the traits under analysis. Indeed, acritical point in understanding the functional structure of commu-nities is the choice of functional traits with which organisms aredistinguished. Usually the recommendation is to use all the traits

that are thought to be important in the context under investiga-tion (Petchey and Gaston, 2006). Another possibility would be touse single-traitapproaches (e.g., Lavoreletal.,2007) orasetoftraitsrelated to more specific plant ecological strategies, as we did here.

This seems to be more informative because it allows us to under-

stand better different aspects of the relationship between speciestraits and the environment.

Finally, we showed that deciding how to summarise trait infor-

mation(i.e., usingspecies traitaveragesor individualvalues), is alsoimportant. There is an increasing recognition of the importanceof including intraspecific variability in functional traits (Ackerlyand Cornwell, 2007; de Bello et al., 2011). Indeed, in some cases,

we found different results in species-based and individual-basedanalyses. Recently, Jung et al. (2010) f ound evidence for the impor-tance of intraspecific trait variability in detecting habitat filteringand niche differentiation processes in meadow plant communities.

Here, by using an individual-level approach to functional diver-sity, we corroborated the view that realised trait values – ratherthan species mean trait values – have an important role in driving

community assembly ( Jung et al., 2010, Messier et al., 2010). This

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happens because species that are ‘unexpected’ to occur in a given

assemblage according to its mean trait values (species-level traitvalues) may infact occurdue to some plastic response (intraspecifictrait variability). Thus, approaches that incorporate intraspecifictrait variation could improve our capacity to understand commu-

nity assembly processes and the evolution of functional traits inplant communities (Pachepskyet al., 2001, 2007; Cianciaruso et al.,

2009; Albert et al., 2010).This finding raises an old question about the basic biological

unit we should use in community ecology: species or individu-als? Some authors suggest that intraspecific functional variabilitycan be considered as negligible, and others that individuals are themost significant ecological unit (e.g., Albert et al., 2010; de Bello

et al., 2011). We agree with Albertet al.(2010) whenthey state thatthis decision cannot be generic; it of course depends on the levelsof intraspecific variation present in the system under study. Thiswill depend on both the kind of traits and also the kind of organ-

isms. For example, plants are much more plastic than animals (seeSchlichting,1986). Additionally, spatial and temporal scales, as wellas the feasibility of acquiring functional attributesat the individuallevel are likely to influence whether individual level trait values

are collected. At local scales such refined information seems moreimportant to consider, whereas at larger scales differences amongspecies could be more important.

Implications for firemanagement strategies and concluding

remarks

Conservation-oriented studies frequently focus on species

diversity, and less attention is given to species traits or phyloge-neticdiversity,andveryrarelytoboth(butsee Mayfield et al., 2006;Carvalho et al., 2010b; Devictor et al., 2010). Habitat modificationsand management practices that change functional and phyloge-

netic diversity are likely to have large impacts on communityprocesses and evolutionary history. Perhaps, the most importantconsequence of changes in species traits and relatedness have to

do with the largely unknown feedbacks of the altered environmentto further changes in biodiversity and ecosystem processes. There-fore,neglectingcommunity patterns and aspects such as functionaldiversity and evolutionary history of species, may render seriouslong-term conservation efforts impossible and futile (Ernst et al.,

2006).Here, we showed that fire frequency has important conse-

quences for the phylogenetic and functional structure of the

savannas studied. Whereas we observed functional clustering

under HiFi, which is in accordance with the observed pattern forother fire-prone vegetation, this was accompanied by phyloge-netic overdispersion. This is important, because it shows that therelationship between ecological processes and the phylogenetic

structure of communities is not always straightforward, that is, wecannot always use PD as a surrogate for FD, with the assumption

that close relatives are always similar in their ecological features(see also Losos, 2008). Also, we showed that including intraspecific

variability into the community assembly framework is relevantand should improve our understanding of how community areassembled. Nevertheless, even if there is evidence that species’intraspecific variability can regulate important ecosystem pro-

cesses and that realised trait values – rather than mean speciestrait values – have an important role in driving community assem-bly, as we showed here, most published studies continue to ignorethis scale of information.

Acknowledgements

We are grateful to Fapesp, for financial support; to Fapesp andCapes, for the scholarships granted to the first author; to CNPq, for

the scholarship granted to the second author; to Helena França, forproviding us the updated firehistory map of Emas National Park; tothe park staff, for logistical assistance; to C.S. Gonçalves, D.M. Silva,M.V. Forzani, N.A. Escobar, N.B. Rossati, P. Loiola, P. Zava, T. Vecchi,

and V. Dantas, for valuable help in the field; and to Jasper Slingsby,SaraBlanchard, and two anonymousreferees,which comments andsuggestions improved the manuscript. O.L.P. was partly funded bythe Royal Society and the University of Zurich.

Appendix 1.

Species sampled in Emas National Park, central Brazil(17◦49–18◦28S, 52◦39–53◦10W) with the respective number of

individuals (n) and average trait values for each fire frequency.

H = height (m), BA = basal area (m2), Brk= bark thickness (mm),SLA= specific leaf area (mm2 mg−1),LSz = leafsize(mm2),Tgh=leaf toughness (N), Woo = wood density (mg mm–3), N= leaf nitro-

gen content (mg g−1), P = leaf phosphorus content (mgg−1),K=leaf potassium content (mgg−1), top = resprouting due to top-kill (fre-quency),pol = polination mode (be= bees, si = smallinsects,fl = flies,mo= moths, hb= humming birds, ba= bats), dis = dispersal mode

(ane = anemochory, autochory= auto, zoo= zoochory).

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http://www.plantminer.com/

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http://www.mobot.org/MOBOT/research/APweb/http://www.r-project.org/http://localhost/var/www/apps/conversion/tmp/scratch_5/dx.doi.org/10.1111/j.1469-185X.2010.00171.xhttp://eufirelab.org/http://dx.doi.org/10.1111/j.1466-8238.2010.00647.x

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