South African Journal of Botany 89 (2013) 42–57

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South African Journal of Botany

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Stability structures tropical woody plant diversity more than seasonality: Insights into the ecology of high legume-succulent-plant biodiversity A.T. Oliveira-Filho a, D. Cardoso b, B.D. Schrire c, G.P. Lewis c, R.T. Pennington d, T.J. Brummer e, J. Rotella f, M. Lavin g,⁎

a Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil b Herbário HUEFS, Universidade Estadual de Feira de Santana, Av. Transnordestina, s/n, Novo Horizonte, 44036-900 Feira de Santana, Bahia, Brazil c Herbarium, Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3AB, UK d Tropical Biology Group, Royal Botanic Garden Edinburgh, 20a Inverleith Row, EH3 5LR, UK e Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USA f Ecology, Montana State University, Bozeman, MT 59717, USA g Plant Sciences and Plant Pathology, Montana State University, Bozeman, MT 59717, USA

article info abstract

Available online 2 August 2013 Phylogenies of legume taxa are ecologically structured along a tropical seasonality gradient, which suggests phylogenetic niche conservatism. This seasonality gradient spans Neotropical wet forests, savannas, and highly Edited by B-E Van Wyk seasonal drought-prone woody vegetation known as the succulent biome. Ecological phylogenetic structure was investigated using a community phylogenetic approach. We further analyzed bioclimatic and other Keywords: independent variables that potentially explained phylogenetic beta diversity among 466 floristic sites that Leguminosae spanned the savanna and succulent biomes in eastern South America. Explanatory variables were selected Mean-nearest-taxon distances using variance inflation factors, information criteria, and the ability to explain both species and phylogenetic Phylogenetic beta diversity b Phylogenetic community ecology beta diversity. A model involving annual precipitation suggests that a threshold of 1200 mm explains commu- Savanna nity phylogenetic structure along the savanna–succulent biome transition. Variables involving temperatures or Seasonally dry tropical forests measures of seasonality were notably lacking from top-ranked models. The abundance and diversity of legumes Succulent biome across the tropical seasonality gradient suggest that a high nitrogen metabolism confers an advantage in one of two ways, both of which are related to rapid growth rates. Legumes adapted to the succulent biome may be responding to regular post-dry-season leaf-flush opportunities. Legumes adapted to the savanna biome may be responding to intermittent post-disturbance growing opportunities. A seasonal predominance of leaf flushing by woody plants implicates the role of ecological stability in the succulent biome because of the need to recover the cost of regenerating short-lived leaves. Ecological stability may be the fundamental cause of ecological phylogenetic structure across the tropical seasonality gradient and required for maintaining high levels of both leaf-flushing legume and succulent plant biodiversity. © 2013 SAAB. Published by Elsevier B.V. All rights reserved.

1. Introduction transoceanically from presumed ancestral areas was a major theme of Raven and Polhill (1981). Legume biogeography has been a focus of study since the seminal Ensuing legume biogeographical studies have continued to highlight overview of Raven and Polhill (1981) where the diversity of the family, the abundance and diversity of the family in the tropics of Africa and particularly in Africa and South America, was underscored. Legume the Americas (e.g., Schrire et al., 2005a). With the many studies that abundance and diversity are so great throughout the tropical expanses have accumulated since Raven and Polhill (1981),thedirectionnow of these two continents that an origin of the family in western Gondwana has been to explain why legume phylogenies are so highly structured was invoked. The abundance and diversity of the family elsewhere were with respect to geography and especially ecology (e.g., Lavin et al., assumed to represent subsequent dispersal from the west Gondwana 2004; Lavin, 2006; Schrire et al., 2005a, 2009; Pennington et al., 2009, center of origin. Indeed, the apparent ease with which legumes dispersed 2010; Särkinen et al., 2012). The assertion by Raven and Polhill (1981) that legumes are highly mobile is still considered an accurate statement. ⁎ Corresponding author. Movement occurs more readily among continents than among certain E-mail addresses: ary.oliveira.fi[email protected] (A.T. Oliveira-Filho), ecologies, which is the essence of phylogenetic niche conservatism [email protected] (D. Cardoso), [email protected] (B.D. Schrire), (Donoghue, 2008). [email protected] (G.P. Lewis), [email protected] (R.T. Pennington), [email protected] (T.J. Brummer), [email protected] (J. Rotella), In tropical Africa and the Americas, however, legume diversity [email protected] (M. Lavin). remarkably spans a seasonality gradient from wet forests, savannas,

0254-6299/$ – see front matter © 2013 SAAB. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.sajb.2013.06.010 blt ftescuetboe(rsaoal r rpclfrss DF)acrigto according SDTFs) forests; tropical dry seasonally (or biome succulent Brazil the within of domain ability geographical cerrado The outlined. 2. Fig. 2008 Fine, and Graham 1. Fig. hr h eeaini ihydcdosadteosto h e esni akdb conspicuous a marked season wet by is the post-dry-season onset of the and deciduous leaf highly is vegetation the where season highly occupies which succu biome, and savanna savanna grass-rich suc the families the abunda of plant indicates interface in an Brown the habits latitudes. allow at growth low that succulent at with conditions gradient species edaphic precipitation of and accumulation the climatic with of areas tropical end signi a wettest for enough the occupies eieiuu n iha nosiuu otdysao leaf post-dry-season inconspicuous an with and semideciduous a from Map oaino h netgtd46SuhAmerican South 466 investigated the of Location fi atbidpo ims u r nuht ufrdogtadintermittent and drought suffer to enough dry but biomass of buildup cant crr ta.(2005a) al. et Schrire .Bu ersnstetmeaeboe hc occu which biome, temperate the represents Blue ).

6 6 5 5 4 4 −35 −40 −45 −50 −55 −60 −65 −25 −20 −15 −10 −5 etboe nesenSuhAmerica. South eastern in biomes lent eitn h imso h ol htms togysaegoa atrso euepyoeei eadvriy(sensu diversity beta phylogenetic legume of patterns global shape strongly most that world the of biomes the depicting ..Oier-ih ta./SuhArcnJunlo oay8 21)42 (2013) 89 Botany of Journal African South / al. et Oliveira-Filho A.T. fl fl ush. wt otysvnaboe sotie ihatikln ( line thick a with outlined is biome) savanna mostly (with rsi ie ihrsett h aan n succule and savanna the to respect with sites oristic steAaaee rmlaee atca,adEpobaee h focu The Euphorbiaceae. and Cactaceae, Bromeliaceae, Agavaceae, the as h c fwoypatgot bs thicket (bush growth plant woody of nce ishg lvtosadl and elevations high pies fi e e hw h itiuino h ucln im,wihocpe ihydrought-prone highly occupies which biome, succulent the of distribution the shows Red re. äknne l (2011) al. et Särkinen savanna biome(n=263) savanna biomeenclave(n=3) succulent biomeenclave(n=54) succulent biome(n=146) ,wolns n oet) little forests), and woodlands, s, ttds re signi Green atitudes. fte46sts 0 r rmtescuetbiome succulent the from are 200 sites, 466 the Of . tboe.Tebreso oii n aauyare Paraguay and Bolivia of borders The biomes. nt – fl 57 s n 6 r rmtesvnaweetevgtto is vegetation the where savanna the from are 266 and ush BE 2012 IBGE, fi stetoia e oetboe which biome, forest wet tropical the es .Teha oosrfrt h prob- the to refer colors heat The ). fti td so lmtcdistinctions climatic on is study this of s 0.2 0.4 0.6 0.8 fl mal ims,adasigni a and biomass, ammable llwlttd ra htaewet are that areas latitude low al fi cant 43 44 A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57

Leguminosae (351 species) along the Paleogene Tethyan Sea boundaries, which corresponded Myrtaceae (129 species) not only to the prevailing semi-arid climate of the period, but also Malvaceae (59) Rubiaceae (73) to the time of rapid divergence of the legume family (Lavin et al., Bignoniaceae (32) Vochysiaceae (27) 2005). Apocynaceae (40) Euphorbiaceae (78) Why legumes are uniquely abundant and diverse across this 466 sites) Malpighiaceae (31) tropical seasonality gradient is a question we attempt to address Anacardiaceae (19) Annonaceae (44) here. Explaining the high levels of legume diversity in African and over Sapindaceae (30) Erythroxylaceae (35) American tropical wet forests has long been of interest (e.g., Hedin Combretaceae (17) et al., 2009) but such explanations need to encompass the high levels Cactaceae (39) Arecaceae (30) of legume diversity that occur in adjacent seasonally dry vegetation. Our community phylogenetic approach to addressing this question was designed to detect large-scale biogeographic patterns of beta diversity, the geographic turnover in species- and clade-diversity, and attendant environmental correlates, rather than to focus on mechanisms that maintain local diversity (e.g., competitive exclu- fi log 10 (sum: number of arborescent species in each family per site sion and ecological ltering; Webb et al., 2002). 01234

020406080 2. Materials and methods family occupancy rank 2.1. Study area and site characteristics Fig. 3. Rank occupancy of families, as scored for arborescent species that were present in each of the 466 floristic sites. After the Leguminosae, the next 15 most common families are listed in the order of rank occupancy. In addition, the total number of Woody plant diversity was sampled along the tropical seasonality species that was sampled for each family is indicated in parentheses. The number of gradient that spanned the savanna and succulent biomes in eastern times a plant family was scored for the presence of an arborescent species has a general South America (Figs. 1–2). The succulent biome, which predominates positive relationship with the total number of species sampled for that family. within the caatinga geographical domain, comprises woodlands and bush-thickets with typically open canopies, trees and shrubs with to highly drought-prone woody vegetation that harbors an abundance highly deciduous leaves and slender trunks, some plants with succulent, and diversity of succulent plant taxa, the succulent biome (Schrire et al., sclerophyllous, or spiny evergreen leaves, and soils that are often 2005a). The diversity of legumes along this gradient has been attributed nutrient-rich, rocky, and without much water holding capacity. The in part to the origins of this family, and its often deciduous habit, highly seasonal drought-prone succulent biome rarely involves the in highly seasonal tropical vegetation (McKey, 1994). Schrire et al. accumulation of grass biomass and the vegetation is thus not fire-prone (2005b) underscored the abundance of highly seasonal vegetation (e.g., Schrire et al., 2005a; Pennington et al., 2009; Cardoso and Queiroz,

Fig. 4. Boxplots depicting the number of arborescent species per floristic site for the 15 most abundant and diverse families (see Fig. 3). Solid horizontal bars represent the median value, the boxes contain 50% of the data points, and the whiskers encompass essentially 100% of the data points. Legumes are by far the most species diverse family in both the succulent biome (i.e., the 200 sites harboring vegetation with a conspicuous leaf flush at the onset of the wet season) and the savanna biome (i.e., the 266 sites with vegetation having an inconspicuous leaf flush at the onset of the wet season). A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57 45

Fig. 5. Rank occupancy of species for each of the 15 most abundant and diverse families (see Fig. 3). Species within each family have been categorized into those scored as present in the succulent biome (i.e., the 200 sites with a conspicuous post-dry-season leaf flush) and those present in the savanna biome (i.e., the 266 sites with an inconspicuous post-dry-season leaf flush). The y-axis of each panel is similar to that in Fig. 3 except that species within each family are ranked.

2010). The savanna biome, which predominates within the cerrado classify each of the 466 sample sites into one of these two biomes geographical domain, typically occurs on substrates with high (Queiroz, 2006; Oliveira-Filho, 2009; Oliveira-Filho et al., 2013). These water-holding capacity and often comprises open woodlands and included 1) the caatinga versus cerrado geographical domain (e.g., the intervening grasslands, woody geoxylic subshrubs with enlarged caatinga geographical domain harbors mostly succulent biome whereas underground xylopodia or lignotubers, and trees with at most the cerrado mostly savanna biome; Fig. 2), 2) a semi-arid versus seasonal semi-deciduous leaves and often fire-adapted trunks (e.g., Ratter et al., climatic regime (e.g., the succulent biome is mostly associated with a 2006; Simon et al., 2009). Where the succulent and savanna biomes semiarid climate, which is characterized by precipitation rates less interface, substrate can be strongly biome-determining because rocky than evapotranspiration rates for most of the year, or generally where soils with little water-retention capacity in an otherwise savanna drought is extensive and precipitation erratic; Peel et al., 2007), 3) leaf biome can render a succulent-biome enclave. All instances of savanna flush at the onset of rain either pronounced or inconspicuous (e.g., in biome in our study, for example, occur on loamy soils with high the highly deciduous succulent biome, more than 60% of leaf biomass is water-retention capacity. shed during the dry season and the onset of the wet season triggers a A total of 466 floristic sites (Fig. 2) included a sample of 1714 pronounced leaf flush, whereas in the mostly semideciduous cerrado arborescent species. An occupancy matrix (i.e., with species incidences often less than 50% of the leaf biomass is shed during the dry season rather than abundances; www.montana.edu/mlavin/data/ary_species_ and the onset of the wet season is associated with an inconspicuous matrix.txt) was analyzed in conjunction with a site characteristic leaf flush), 4) shallow versus deep soils (e.g., the succulent biome matrix (www.montana.edu/mlavin/data/ary_site_matrix.txt)derived is typically associated with shallow rocky soils with low-water- from TreeAtlan (Oliveira-Filho, 2010; Santos et al., 2012; Oliveira-Filho retention capacity that exacerbates droughty conditions, whereas et al., 2013). the savanna biome is generally associated with deep soils having high-water-retention capacity), 5) eutrophic versus mesotrophic ver- 2.2. Explanatory variables sus dystrophic soil fertility (e.g., the succulent biome often has soils that are not well weathered and thus high in nutrients, whereas the savanna Elevation and 19 bioclimatic variables were extracted from the biome often has well weathered soils low in nutrients), and 6) a sandy 0.5 arc minute resolution WorldClim 1.4 data layers (Hijmans et versus loamy soil texture (the succulent biome often has sandy soils al., 2005) for each of the 466 geo-referenced floristic sites using with less water-retention capacity than the loamy soils typical of the the ‘raster’ package (Hijmans and Van Etten, 2012)ofprogram savanna). R(R Development Core Team, 2013). The bioclimatic variables A bias against detecting a strong climatic difference between sites included 11 temperature and eight precipitation measures. Other from the savanna and succulent biomes was inherent in the study design. variables included ‘distance from the ocean’ (km) and ‘mean dura- A total of 54 floristic sites represented enclaves of succulent biome lying tion of water deficit’ (the number of days that evapotranspiration within an expanse of savanna (Fig. 2). Three sites represented savanna rates exceed precipitation rates), the latter derived from WorldClim 1.4 enclaves within an expanse of succulent biome (Fig. 2). Biome enclaves data (Hijmans et al., 2005). do not differ climatically from the surrounding expanse of the alternative Variables also included categories that served as proxies for the biome because biomes in these instances are substrate-determined succulent and savanna biomes and that therefore could be used to (Ratter et al., 1978; Furley and Ratter, 1988). 46 A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57

2.3. The community phylogeny Covariates remaining after the filtering steps of the VIF analysis were incorporated into models that explained Bray–Curtis and MNT dis- The community phylogeny was generated with Phylomatic tances. Competing models comprising different subsets of covariates

(Webb and Donoghue, 2005) and Phylocom version 4.2 (Webb et al., were evaluated using AICc implemented in the R packages MuMIn 2008) using the angiosperm backbone tree (svn.phylodiversity.net/tot/ (Bartoń,2013), ‘bestglm’ (McLeod and Xu, 2012), and ‘pgirmess’ megatrees/R20100701.new). The taxonomic list of 1714 species submit- (Giraudoux, 2012). The relative plausibility of each model was evaluat- ted to Phylomatic followed the latest legume classification (Lewis et al., ed by examining differences between the AICc value for the best model 2005; Jobson and Luckow, 2007; Queiroz, 2008; Silva et al., 2012; and values for every other model (ΔAICc, Burnham and Anderson, 2002; Cardoso et al., 2012a) and the Angiosperm Phylogeny Group III classifica- Johnson and Omland, 2004). Models with ΔAICc b 2wereconsidered tion (APG, 2009; www.montana.edu/mlavin/data/ary_phylomatic_list. strongly supported by the data, and models with ΔAICc N 10 were con- txt). Age estimates from Wikstrom et al. (2001; svn.phylodiversity.net/ sidered to have essentially no support from the data. We measured the tot/megatrees/ages) were used to scale the branch lengths to millions relative likelihood of a model given the data and the model list using of years (Ma) using Phylocom (see www.montana.edu/mlavin/data/ Akaike weights (Wic), which are normalized values that sum to 1 across ary_bladj_tree.nex). all models. Variables present in the top-ranking model and associated with significant coefficients were included in the model used for predic- tion mapping. 2.4. Hypothesis shaping our approach and the attendant response variable The ultimate step in the selection of explanatory variables was dependent on whether they explained both patterns of species beta Phylogenetic relationships among tropical woody legumes are diversity (PCO-transformed Bray–Curtis distances) and phylogenetic well structured by a tropical seasonality gradient with wet-forests beta diversity (PCO-transformed MNT distances). Variables that did and savannas at one end and the succulent biome at the other not explain both were considered to be important short-term ecological more highly seasonal and drought-prone end (e.g., Simon et al., determinants (e.g., if they explained species beta diversity) or other- 2009, 2011; Pennington et al., 2009, 2010; Queiroz and Lavin, wise spurious in explaining phylogenetic beta diversity. 2011; Simon and Pennington, 2012; Cardoso et al., 2012b; Hughes et For prediction mapping, the predictive capacity of the AIC -selected al., 2013). Consequently, biome distribution modeling was performed c highest ranking logistic model was determined by the area under using a response variable that included both community phylogenetic the receiver operator characteristic curve (AUC analysis) using the R distances among sites in addition to a binomial response variable that package ‘ROCR’ (Sing et al., 2012). The AUC was partitioned into was determined during this study to be most explanatory of community independent and joint contributions from variables using regression phylogenetic distances (e.g., the potential binomial response variables commonality analysis (Nimon and Reio, 2011). The AIC -selected being: caatinga versus cerrado geographical domain, conspicuous ver- c logistic model was analyzed in conjunction with raster layers of the sus inconspicuous post-dry-season leaf flush, semi-arid versus seasonal most explanatory covariates (e.g., altitude and bioclimatic variables) climatic regime). Selecting a binomial response variable from among using the ‘raster’ package in order to produce prediction maps. these ecological categories that serve as proxies for the succulent and Prediction maps were generated for point estimates and confidence savanna biomes facilitated biome distribution modeling and prediction limits of the probability of the succulent biome. Confidence intervals on mapping (e.g., Särkinen et al., 2011). predicted probabilities of occurrence were obtained using prediction Bray–Curtis and mean-nearest-taxon (MNT) distances (Webb et tools in the ‘raster’ package. Confidence intervals were first constructed al., 2002) were our measures of beta diversity, which we defined in on the log-odds scale and then back-transformed to the 0–1scale, two ways. Species beta diversity, as measured by Bray–Curtis distance, which ensured that values were constrained within the 0–1 limits. is the turnover in species composition among the 466 floristic sites, a Standard errors of predicted probabilities were also obtained using the use consistent with Hubbell (2001). Phylogenetic beta diversity (sensu predict function of the ‘raster’ package and working on the real parameter Graham and Fine, 2008), as measured by MNT distance, represents the scale. Estimates incorporated uncertainty in the model's coefficients turn-over in clade composition among the floristic sites. using the delta method (Seber, 1982). Graphical output was generated The community phylogenetic metric, MNT distance, was generated using ‘raster’ and ‘ggplot2’ (Wickham and Chang, 2012). with the R package ‘picante’ (Kembel et al., 2012). This metric provided the greatest variance while retaining a monotonic relationship with 3. Results corresponding Bray–Curtis distances (Oliveira-Filho et al., 2013). For use as a response variable, Bray–Curtis and MNT distances were 3.1. Alpha diversity transformed into values along the first two principal coordinate (PCO) axes for each of the 466 floristic sites. Both PCO-transformed The 1714 arborescent species sampled from the 466 floristic sites Bray–Curtis and MNT distances were used in the model selection belonged to 453 genera and 95 families. Leguminosae was by far the analysis (see below). These analyses were performed with the R most commonly sampled family and the most species-rich with 351 packages ‘labdsv’ (Roberts, 2012)and‘vegan’ (Oksanen et al., 2012). total arborescent species scored for 9541 occupancies (Fig. 3). Myrtaceae was the second most abundant and diverse family with 129 species 2.5. Selection among the explanatory variables scored for 1826 occupancies (Fig. 3). High levels of legume diversity were found in both the savanna and succulent biomes. Sites categorized Bioclimatic variables, elevation, longitude, and latitude were as succulent biome (i.e., having a conspicuous post-dry-season leaf evaluated for collinearity using variance inflation factors (VIFs), flush) had a median of 24 legume species (maximum = 64 legume implemented in the R package ‘AED’ (Zuur et al., 2009). The variable species) whereas those categorized as savanna biome (i.e., having an with the highest VIF value was eliminated, VIF values for remaining inconspicuous post-dry-season leaf flush) had a median of 16 species variables were evaluated, and the process repeated until VIF values (Fig. 4; maximum = 31 legume species). These numbers are much for remaining explanatory variables were b3. This top-down approach greater than for the other top 15 most species-rich families (Fig. 4). to variable selection via VIF analysis was complemented with a Similarly, sites categorized as “caatinga” had a median of 24 arborescent bottom-up approach whereby the variable involved in the highest legume species whereas sites from the “cerrado” (or from savanna land- ranking single-variable model, using Akaike's information criterion scapes outside of the caatinga proper) had a median of 15 legume species. adjusted for small sample sizes (AICc, Akaike, 1973), was retained during Sites categorized for sandy substrates had a median of 31 legume species the analysis of VIFs. whereas those with loamy substrates had a median of 19 legume species. A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57 47

Table 1

Rankings of individual categorical variables. Variables explaining PCO1-transformed Bray–Curtis response variable are ordered by ΔAICc. Identically ranked top models involving the other response variables are indicated in boldface. The number of parameters (K), ΔAICc, and model weights (Wic) are reported. For Bray–Curtis distances, PCO axis 1 captured 25% whereas PCO axis 2 captured 7% of the variation. For MNT distances, PCO axis 1 captured 31% and PCO axis 2 captured 8% of the variation.

PCO 1 (Bray–Curtis) PCO 1 (MNT) PCO 2 (Bray–Curtis) PCO 2 (MNT)

K ΔAICc Wic K ΔAICc Wic K ΔAICc Wic K ΔAICc Wic Post-dry-season leaf flush (conspicuous or inconspicuous) 3 0 1 3 0 1 3 131 0 3 0 0.95 Geographical domain (caatinga or cerrado) 3 417 0 3 277 0 3820 3180 Substrate nutrients (dystrophic or mesotrophic or eutrophic) 4 559 0 4 385 0 4 0 1 4 7 0.02 Regime (semi-arid or seasonal) 3 637 0 3 473 0 3 100 0 3 18 0 Substrate (deep or rocky) 3 683 0 3 441 0 3 43 0 3 18 0 Loam or sand-loam or sand texture 4 708 0 4 532 0 4 34 0 4 8 0.02 Intercept only 2 936 0 2 741 0 2 136 0 2 16 0

The legume family also had the greatest evenness of arborescent ranked for PCO-axis-2-transformed MNT distances, PCO axis 2 did not species in both the savanna and succulent biomes (Fig. 5). The 200 resolve consistent rankings for Bray–Curtis and MNT distances. PCO axis sites classified as having a conspicuous post-dry-season leaf flush 2 was most likely not capturing evolutionarily important ecological arbored a total of 332 legume species. The 266 sites classified as variation (e.g., variables potentially reflecting phylogenetic niche having an inconspicuous leaf flush harbored a total of 142 legume conservatism). For example, soil fertility was the most explanatory of species. No other family approached these levels of species richness Bray–Curtis distances along PCO axis 2, but not so of MNT distances and evenness (Fig. 5). along PCO axis 2. Variation in soil fertility, therefore, may be more readily adapted to than be part of the cause of phylogenetic niche conservatism. Individual continuous variables that best explained species beta 3.2. Beta diversity and phylogenetic beta diversity (PCO-axis-1-transformed Bray–Curtis and MNT distances) included primarily annual precipitation followed The identification of categorical variables that most strongly by precipitation during the wettest quarter and then precipitation explained patterns of phylogenetic beta diversity among the 466 during the wettest month (Table 2). Beta diversity as reflected floristic sites clearly resolved the post-dry-season leaf-flush cate- along PCO axis 2 was not consistently explained by any variable gory, conspicuous versus inconspicuous, as the most explanatory (Table 2;e.g.,note‘latitude’ as explanatory of Bray–Curtis distances (Table 1). This variable was top ranked when the response was and ‘mean temperature during the driest quarter’ as most explanatory both a measure of species beta diversity (PCO-axis-1-transformed of MNT distances along PCO axis 2). PCO axis 2 did not capture evolution- Bray–Curtis distances) and phylogenetic beta diversity (PCO-axis-1- arily significant variation and much less variation than PCO axis 1. For transformed MNT distances). Although this leaf-flush category was top Bray–Curtis distances, PCO axis 1 captured 25% whereas PCO axis 2

Table 2

Rankings of individual continuous variables. Variables explaining PCO1-transformed Bray–Curtis response variable are ordered by ΔAICc. Identically ranked top models involving the other response variables are indicated in boldface. Number of parameters K = 3 for models with a PCO-transformed response and K = 2 for the binomial response (see Table 1).

Explanatory variable PCO1 PCO1 PCO2 PCO2 Leaf flush Bray–Curtis MNT Bray–Curtis MNT (conspicuous or inconspicuous)

ΔAICc Wic ΔAICc Wic ΔAICc Wic ΔAICc Wic ΔAICc Wic Annual precipitation (mm; bioclimatic variable 12) 0 1 0 1 86 0 111 0 01 Precipitation during the wettest quarter (16) 36 0 29 0 85 0 114 0 34 0 Precipitation during the wettest month (13) 73 0 70 0 83 0 114 0 60 0 longitude (decimal degrees) 202 0 201 0 12 0 114 0 141 0 Water deficit (days) 223 0 219 0 62 0 110 0 147 0 Distance ocean (km) 238 0 247 0 56 0 106 0 176 0 Temperature annual range (7 = 5–6) 253 0 239 0 40 0 103 0 207 0 Precipitation during the warmest quarter (18) 255 0 262 0 61 0 63 0 197 0 Latitude (decimal degrees) 256 0 257 0 0 1 36 0 201 0 Mean of monthly diurnal temperature ranges (2) 282 0 267 0 77 0 112 0 227 0 Minimum temperature of the coldest month (6) 304 0 298 0 88 0 20 0 234 0 Isothermality (3 = 2/7) 327 0 337 0 82 0 90 0 240 0 Elevation (m) 337 0 340 0 120 0 62 0 254 0 Mean temperature of the driest quarter (9) 340 0 343 0 103 0 0 0.81 255 0 Mean temperature of the warmest quarter (10) 344 0 347 0 119 0 37 0 256 0 Coefficient of variation of monthly temperatures (4) 345 0 352 0 95 0 46 0 256 0 Mean annual temperature (1) 346 0 351 0 119 0 21 0 258 0 Maximum temperature during the warmest period (5) 346 0 353 0 117 0 30 0 258 0 Precipitation during the driest month (14) 346 0 354 0 108 0 108 0 256 0 Precipitation during the driest quarter (17) 346 0 353 0 110 0 110 0 256 0 Coefficient of variation of monthly precipitation (15) 347 0 354 0 119 0 114 0 257 0 Mean temperature during the coldest quarter (11) 347 0 353 0 116 0 3 0.19 259 0 Mean temperature during the wettest quarter (8) 347 0 353 0 111 0 66 0 259 0 Precipitation during the coldest quarter (19) 347 0 354 0 114 0 103 0 257 0 48 A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57

Table 3 diversity (Table 3) and this included annual precipitation (biocli- Rankings of multiple-variable models. These models include the three high ranking matic variable 12), mean monthly diurnal temperature range (biocli- continuous variables (Table 2) and associated variables selected via an analysis of VIF matic variable 2), isothermality (bioclimatic variable 3; the and significance of regression coefficients. Models explaining the PCO1-transformed proportion of monthly temperature variance to annual tempera- Bray–Curtis response variable are ranked by ΔAICc. Identically ranked models involving 2 the other response variables are indicated in boldface. The number of parameters (K), ture variance), and elevation (m). For this top-ranking model, R = Δ AICc, and model weights (Wic) are reported. 0.59 (p b 2e-16) for the PCO-axis-1-transformed Bray–Curtis distances 2 b Model PCO 1 of PCO 1 of MNT Leaf flush and R =0.62(p 2e-16) for PCO-axis-1-transformed MNT distances. Bray–Curtis (conspicuous or Beta diversity as measured along PCO axis 2 was not consistently inconspicuous) explained by a single model and theseresultsarethusnotdetailed.

K ΔAICc Wic K ΔAICc Wic K ΔAICc Wic Using a logistic response variable, conspicuous or inconspicuous post-dry-season leaf flush, the top-ranking model was essentially 12, 2, elevation, 3 6 0 0.98 6 0 0.93 5 2 0.27 12, 2, elevation 5 8 0.02 5 5 0.07 4 0 0.61 the same as described above but bioclimatic variable 3 was excluded 12, 2 4 26 0 4 31 0 3 3 0.11 (Table 3). The logistic model reveals that a threshold of annual pre- 16, 3, 2 5 42 0 5 50 0 4 31 0 cipitation just over 1000 mm begins to reduce the probability of the 16, 3, 2, 8 6 43 0 6 48 0 5 32 0 succulent biome (Fig. 6), all other factors being equal. The upper 12 3 63 0 3 92 2 13 0 13, 2, 3, 8, ocean distance 7 70 0 7 79 0 6 48 0 shoulder in each panel (Fig. 6) is proportional to the probability of 13, 2, 3, ocean distance 6 71 0 6 84 0 5 50 0 the succulent biome. This shoulder is best developed in the lower left hand panel where annual precipitation is low, mean monthly temperature variation the least, and elevation the lowest. This shoul- captured 7% of the variation. For MNT distances, PCO axis 1 captured 31% der diminishes rightward with increasing mean monthly temperature whereas PCO axis 2 captured 8% of the variation. range and upward with elevation gain. With a higher mean monthly Competing models with multiple continuous explanatory vari- temperature range or at higher elevations, the annual precipitation ables included top-ranked single variables retained during VIF threshold distinguishing the succulent and savanna biomes drops to analysis, bioclimatic variables 12, 16, and 13 (Table 2), along with a around 1000 mm. subset of non-collinear variables. This process ultimately identified An interaction of the top ranking categorical variable (conspicuous the top-ranked model as most explanatory of phylogenetic beta versus inconspicuous post-dry-season leaf flush) with top ranking

bioclimatic variable 2: 9 C bio variable 2 (mean monthly diurnal temp range): 12 C bioclimatic variable 2: 14 C 1.00

0.75 elevation: 909 m

0.50

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0.50

probability of succulent biome (site with a conspicuous post−dry−season leaf flush) 0.25

0.00 500 1000 1500 2000 500 1000 1500 2000 500 1000 1500 2000 bioclimatic variable 12: annual precipitation (mm)

Fig. 6. A model that predicts the succulent biome (a site with a conspicuous post-dry-season leaf flush versus a site with an inconspicuous leaf flush) and involves the three most explanatory continuous variables from the top ranking logistic model (Table 3). Annual precipitation, the most explanatory of these variables, is represented along the x-axis of each panel. The values for bioclimatic variable 2 (columns) and elevation (rows) are represented by quantiles where the middle value represents the median, the high value delimits the 95% of the data, and the low value 5% of the data. A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57 49

1714 total species, Bray−Curtis distances 1714 total species, MNT distances

succulent biome savanna biome

600 1100 1000 1500 900 700 1300 1400

600 800 1200 800 800 1500

1600 900 700

1000 1500 1300

1200 1400 PCO2 (0.08) 1100 PCO2 (0.07) 1200

1300 1400 1500 succulent biome enclave

−0.4 −0.2 0.0 0.2 savanna biome enclave −40 −20 0 20 40 60 −0.4 −0.2 0.0 0.2 0.4 −100 −50 0 50 PCO1 (0.25) PCO1 (0.31)

351 legume species, Bray-Curtis distances 351 legume species, MNT distances

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1400 700

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PCO2 (0.06) 900 1100 1300 1200 1200

1400 −20 0 20 40 −0.4 −0.2 0.0 0.2 −0.4 −0.2 0.0 0.2 0.4 −40 −20 0 20 40 PCO1 (0.23) PCO1 (0.30) annual precipitation (mm) annual precipitation (mm)

Fig. 7. The 466 floristic sites sampled for all arborescent species (top two panels) or just arborescent legume species (lower two panels) and arrayed by a PCO analysis of Bray–Curtis or MNT distances. The contour lines indicate annual precipitation (mm).

continuous variable (annual precipitation) reveals that precipitation mat- contribution of 0.21 to the AUC over 0.50 and a combined contribution ters as much within a leaf-flush category as between the two categories with each of bioclimatic variables 2 and 3 that sums to 0.15 (Table 4). (Fig. 7). This model involving only annual precipitation interacting with The prediction maps depicting the upper and lower confidence levels leaf-flush category and explaining PCO axis 1 of MNT distances has an and the standard errors further suggest that the top-ranking model is R2 = 0.850 (p b 2.2e-16) for the all-species model and an R2 =0.856 highly predictive of the succulent biome within eastern South America (p b 2.2e-16) for the legume species model (Fig. 7). This simple model (cf. Figs.2,8). also best explained patterns of both beta (Bray–Curtis) and phylogenetic beta (MNT) diversity. The contour lines indicating annual precipitation 4. Discussion (Fig. 7)werefit with generalized additive modeling and graphically align with PCO axis 1 in all cases. Here, a threshold of about 1200 mm The community phylogenetic approach taken during this study has evolutionarily distinguishes the succulent from the savanna biome, all shown that the pattern of phylogenetic niche conservatism observed other factors being equal. Clearly, the biome enclaves show community for legume taxon phylogenies, such as a clade of the genus Coursetia phylogenetic integrity not with the surrounding predominant biome species confined to the succulent biome in the caatinga region of east- but with that more distant and of similar precipitation regime and ern Brazil (Queiroz and Lavin, 2011) or subclades of the genus Mimosa leaf-flush category (Fig. 7). The leaf-flush category is likely a complex in- from the succulent biomes in Mexico, the Andean dry valleys, and the teraction of annual precipitation with multiple substrate conditions and caatinga (Simon et al., 2011), is generalizable to at least the arborescent physical disturbance regimes (e.g., fire). In all these regards, the legume if not all woody plant lineages that inhabit the tropical seasonality subset of 351 arborescent species reflected nearly identically the findings gradient in eastern South America. Indeed, our general results agree derived from all 1714 arborescent species (Fig. 7). with those of Queiroz (2006), Cardoso and Queiroz (2007), Yesson et A prediction map of the succulent biome (sites with a conspicuous al. (2007) and Villaseñor et al. (2007) in suggesting that legume post-dry-season leaf flush) that uses the top ranking logistic model but biodiversity might serve as a proxy for total plant biodiversity in biogeo- with bioclimatic variable 3 excluded (see Table 3; Fig. 6)suggeststhat graphical and ecological analyses. bioclimatic variable 12, annual precipitation, is by far the most important The tropical seasonality gradient spanned by the savanna biome at as it contributes the most to the AUC value over 0.50. Regression the wetter end and the succulent biome at the drought-prone end clearly commonality analysis shows annual precipitation to have a unique involves an ecological transition that constrains evolving lineages. 50 A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57

0.8 0.8

0.6 0.6

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succulent biome, n=200

−25 −20 −15 −10 −5 savanna biome, n=266 lower confidence level

succulent~bio12+bio2+elev+bio3, AUC=0.90 (0.82-0.96)

0.35 0.8 0.30 0.25 0.6 0.20 0.4 0.15 0.10 0.2 0.05 −25 −20 −15 −10 −5

−65 −60 −55 −50 −45 −40 −35 −65 −60 −55 −50 −45 −40 −35

Fig. 8. Prediction maps of the succulent biome (i.e., highly deciduous vegetation with a conspicuous post-dry-season leaf flush). The upper right panel includes the predictions of point estimates derived from the logistic model with the locations of the biome-assigned floristic sites superimposed. The upper right panel reflects the lower confidence level predictions of the logistic model, the lower left panel the upper confidence level predictions, and the lower right panel the standard error of the predictions. AUC quartiles = 0.82, 0.88, 0.90 (median), 0.92, 0.96.

The most important ecological variable involved in this transition is a savanna than succulent biome are exemplified by Andira,Dipterygeae threshold of annual precipitation at about 1000–1200 mm (Figs. 6–7). (i.e., Dipteryx, Pterodon), Leptolobium, Tachigali, Stryphnodendron,and Arborescent Cactaceae and leaf-flushing legume trees and shrubs are Vatairea. Legume genera that tended to be sampled more in the succulent abundant and diverse below this precipitation threshold. Grass cover or than savanna biome numbered many more and are exemplified by closed canopy forests are more common above this threshold. Anadenanthera, Chloroleucon, Coursetia, Luetzelburgia, Parapiptadenia, The annual precipitation threshold of 1000–1200 mm has given rise Piptadenia, Poincianella,andPseudopiptadenia (Table 5). Of the most di- to a pattern of phylogenetic niche conservatism such that tropical woody verse families, Vochysiaceae, Malpighiaceae, and Erythroxylaceae tended species inhabiting this gradient can be said to belong to either to be sampled more in the savanna than succulent biome, whereas succulent-biome-inhabiting clade or a savanna-inhabiting clade Euphorbiaceae, Sapindaceae, and Cactaceae tended to be sampled more (e.g., Schrire et al., 2005a). In this community phylogenetic analysis, in the succulent than savanna biome (Figs. 4–5). An accurate profile of legume genera or tribes that tended to be sampled more in the such ecological phylogenetic structure, however, awaits a community phylogenetic analysis that incorporates species abundances into the Table 4 community matrix rather than just incidences, as in this study. Top ranking model (Table 3) explaining PCO-axis-1 transformed MNT distances (capturing 31% of the variation). The logistic representation of this model excludes bioclimatic variable 4.1. The concept of the succulent biome 3(seeTable 3, where the highest ranking binomial model explaining leaf flush, ΔAICc =0, includes only variables 12, 2, and elevation). For the logistic model, the AUC improvement “ ” refers to the unique contribution to the AUC value over 0.500 as determined by a regression The term tropical dry forest has been widely used to encompass commonality analysis. Contribution to the AUC over 0.500 was about 0.18 for combined mostly any kind of seasonal forest or woodland, tropical or paratropical, variables and about 0.39 total, for a final AUC ≈ 0.89. regardless of how fire-prone, frost-tolerant, or lacking in succulent Variable Estimate Std. error t value Pr(N|t|) AUC improvement for plant diversity (e.g., Cabrera and Willink, 1973; Andrade-Lima, 1982; binomial response Morrone, 2001; Veloso et al., 1991; Oliveira-Filho et al., 2006;

Intercept 97.426050 22.482089 −4.333 1.80e-05 NA Oliveira-Filho, 2009; IBGE, 2012; Chacón et al., 2012; Ghazoul, 2012). bio12 0.060735 0.002779 21.852 b2e-16 0.210 Attempts were made to more accurately circumscribe American bio2 5.900075 0.755531 7.809 3.91e-14 0.003 seasonally dry tropical forests, SDTFs, using several criteria including Elevation 0.022061 0.004347 5.075 5.63e-07 −0.002 the diversity of legumes and succulent plant taxa, low grass abundance, bio3 −0.822377 0.303136 −2.713 0.00692 −0.001 and lack of a fire regime and fertile soils (e.g., Prado and Gibbs, 1993; A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57 51

Table 5 Table 5 (continued) The incidence of the 351 legume species from the 200 succulent biome sites (those Species Number Number with a conspicuous post-dry-season leaf flush) and the 266 savanna sites (those with of times of times an inconspicuous post-dry-season leaf flush). sampled sampled Species Number Number in the in the of times of times succulent savanna sampled sampled biome biome in the in the Chloroleucon extortum Barneby & J.W.Grimes 6 0 succulent savanna Chloroleucon foliolosum (Benth.) G.P.Lewis 85 0 biome biome Chloroleucon mangense (Jacq.) Britton & Rose 2 0 fl Abarema cochleata (H.S.Irwin) Barneby & J.W.Grimes 0 1 Chloroleucon tenui orum (Benth.) Barneby & J.W.Grimes 19 0 Abarema cochliacarpos (Gomes) Barneby & J.W.Grimes 3 1 Copaifera coriacea Mart. 20 0 Abarema langsdorffii (Benth.) Barneby & J.W.Grimes 1 0 Copaifera duckei Dwyer 8 0 Acosmium cardenasii H.S.Irwin & Arroyo 12 0 Copaifera elliptica Mart. 0 5 fi Acosmium diffusissimum (Mohlenbr.) Yakovlev 1 0 Copaifera langsdorf i Desf. 54 149 Acosmium lentiscifolium Schott 26 0 Copaifera luetzelburgii Harms 5 0 Albizia inundata (Mart.) Barneby & J.W.Grimes 33 1 Copaifera magnifolia Dwyer 12 0 Albizia niopoides (Spruce ex Benth.) Burkart 43 10 Copaifera malmei Harms 0 49 Albizia pedicellaris (DC.) L.Rico 1 0 Copaifera oblongifolia Mart. 0 57 Albizia polycephala (Benth.) Killip 25 0 Copaifera reticulata Ducke 3 0 Amburana cearensis (Allemão) A.C.Sm. 82 3 Copaifera rigida Benth. 12 0 Anadenanthera colubrina (Vell.) Brenan 164 52 Copaifera sabulicola J.A.S.Costa & L.P.Queiroz 3 13 Anadenanthera peregrina (L.) Speg. 27 58 Coursetia caatingicola L.P.Queiroz 3 0 Andira cordata Arroyo 4 13 Coursetia hassleri Chodat 4 0 Andira cujabensis Benth. 2 88 Coursetia rostrata Benth. 29 0 Andira fraxinifolia Benth. 8 0 Cyclolobium brasiliense Benth. 7 4 Andira inermis (W.Wright) DC. 7 4 Dahlstedtia araripensis (Benth.) M.J.Silva & 13 1 Andira vermifuga (Mart.) Benth. 10 150 A.M.G.Azevedo Apuleia leiocarpa (Vogel) J.F.Macbr. 27 18 Dalbergia acuta Benth. 14 1 Ateleia guaraya Herzog 3 0 Dalbergia brasiliensis Vogel 2 1 Barnebydendron riedelii (Tul.) J.H.Kirkbr. 0 1 Dalbergia catingicola Harms 9 0 Bauhinia acreana Harms 1 0 Dalbergia cearensis Ducke 52 0 Bauhinia aculeata L. 36 0 Dalbergia decipularis Rizzini & A.Mattos 16 0 Bauhinia acuruana Moric. 75 0 Dalbergia elegans A.M.Carvalho 1 0 Bauhinia bombaciflora Ducke 0 1 Dalbergia foliolosa Benth. 8 0 Bauhinia brevipes Vogel 14 35 Dalbergia frutescens (Vell.) Britton 15 3 Bauhinia cacovia Wunderlin sp. nov. ined. 4 0 Dalbergia glaucescens (Mart. ex Benth) Benth. 17 0 Bauhinia cheilantha (Bong.) Steud. 93 0 Dalbergia miscolobium Benth. 3 133 Bauhinia cupulata Benth. 9 10 Dalbergia riparia (Mart.) Benth. 1 0 Bauhinia dubia G.Don 7 1 Dalbergia villosa (Benth.) Benth. 2 0 Bauhinia forficata Link 33 26 Deguelia costata (Benth.) A.M.G.Azevedo 1 0 Bauhinia holophylla (Bong.) Steud. 1 9 Deguelia nitidula (Benth.) A.M.G.Azevedo 26 0 Bauhinia leptantha Malme 1 0 Dimorphandra exaltata Schott 1 0 Bauhinia longifolia (Bong.) D.Dietr. 8 12 Dimorphandra gardneriana Tul. 13 52 Bauhinia membranacea Benth. 6 0 Dimorphandra jorgei M.F.Silva 1 0 Bauhinia mollis (Bong.) D.Dietr. 11 7 Dimorphandra mollis Benth. 6 168 Bauhinia pentandra (Bong.) Vogel 62 0 Diplotropis ferruginea Benth. 6 0 Bauhinia pulchella Benth. 35 10 Dipteryx alata Vogel 21 105 Bauhinia rufa (Bong.) Steud. 10 89 Diptychandra aurantiaca Tul. 15 59 Bauhinia subclavata Benth. 28 0 Enterolobium contortisiliquum (Vell.) Morong 80 24 Bauhinia ungulata L. 23 12 Enterolobium gummiferum (Mart.) J.F.Macbr. 1 122 Bauhinia vespertillo S.Moore 4 0 Erythrina amazonica Krukoff 3 0 Blanchetiodendron blanchetii (Benth.) Barneby & 32 0 Erythrina dominguezii Hassl. 5 0 J.W.Grimes Erythrina velutina Willd. 53 1 Bionia coriacea Benth. 4 0 Erythrina verna Vell. 7 5 Bowdichia virgilioides Kunth 23 214 Erythrostemon calycina (Benth.) L.P.Queiroz 11 0 Calliandra aeschynomenoides Benth. 7 0 Geoffroea spinosa Jacq. 28 0 Calliandra asplenioides (Nees) Renvoize 1 1 Goniorrhachis marginata Taub. 40 0 Calliandra bella Benth. 1 0 Guibourtia hymenaefolia (Moric.) J.Léonard 21 1 Calliandra calycina Benth. 4 0 Holocalyx balansae Micheli 13 0 Calliandra foliolosa Benth. 6 4 Hymenaea aurea Y.-T.Lee & Langenh. 1 0 Calliandra haematocephala Hassk. 1 0 Hymenaea courbaril L. 32 31 Calliandra harrisii (Lindl.) Benth. 2 0 Hymenaea eriogyne Benth. 43 5 Calliandra macrocalyx Harms 32 4 Hymenaea maranhensis Y.-T.Lee & Langenh. 0 3 Calliandra umbellifera Benth. 2 0 Hymenaea martiana Hayne 35 1 Cassia ferruginea (Schrad.) Schrad. ex DC. 8 0 Hymenaea parvifolia Huber 2 1 Cassia grandis L.f. 1 0 Hymenaea stigonocarpa Mart. ex Hayne 28 212 Cenostigma macrophyllum Tul. 19 15 Hymenaea velutina Ducke 14 0 Centrolobium microchaete (Mart. ex Benth.) H.C.Lima 3 0 Inga alba (Sw.) Willd. 1 2 Centrolobium sclerophyllum H.C.Lima 23 0 Inga capitata Desv. 1 0 Centrolobium tomentosum Guillem. ex Benth. 4 1 Inga cylindrica (Vell.) Mart. 1 0 Chamaecrista eitenorum (H.S.Irwin & Barneby) 60 Inga edulis Mart. 3 0 H.S.Irwin & Barneby Inga ingoides (Rich.) Willd. 7 0 Chamaecrista orbiculata (Benth.) H.S. Irwin & Barneby 1 12 Inga laurina (Sw.) Willd. 21 3 Chamaecrista xinguensis (Ducke) H.S.Irwin & Barneby 2 0 Inga lenticellata Benth. 1 0 Chamaecrista zygophylloides (Taub.) H.S.Irwin & Barneby 9 0 Inga marginata Willd. 5 1 Chloroleucon acacioides (Ducke) Barneby & J.W.Grimes 2 0 Inga nobilis Willd. 3 0 Chloroleucon dumosum (Benth.) G.P.Lewis 49 0 Inga stenopoda Pittier 3 0

(continued on next page) (continued on next page) 52 A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57

Table 5 (continued) Table 5 (continued) Species Number Number Species Number Number of times of times of times of times sampled sampled sampled sampled in the in the in the in the succulent savanna succulent savanna biome biome biome biome

Inga striata Benth. 1 1 Muellera fluvialis (Lindm.) Burkart 4 0 Inga subnuda Salzm. ex Benth. 1 0 Muellera montana (A.M.G.Azevedo) M.J.Silva & A.M.G.Azevedo 14 0 Inga vera Willd. 20 2 Muellera nudiflora (Burkart) M.J.Silva & A.M.G.Azevedo 7 0 Leptolobium dasycarpum Vogel 14 183 Muellera obtusa (Benth.) M.J.Silva & A.M.G.Azevedo 5 0 Leptolobium elegans Vogel 0 78 Muellera virgilioides (Vogel) M.J.Silva & A.M.G.Azevedo 9 1 Leptolobium nitens Vogel 1 0 Myrocarpus fastigiatus Allemão 1 0 Leucochloron foederale (Barneby & J.W.Grimes) Barneby & 10 Myroxylon peruiferum L.f. 4 0 J.W.Grimes Ormosia arborea (Vell.) Harms 1 1 Leucochloron incuriale (Vell.) Barneby & J.W.Grimes 1 1 Ormosia fastigiata Tul. 2 0 Leucochloron limae Barneby & J.W.Grimes 10 0 Parapiptadenia blanchetii (Benth.) Vaz & M.P.Lima 6 0 Libidibia ferrea (Mart. ex Tul.) L.P.Queiroz 64 0 Parapiptadenia excelsa (Griseb.) Burkart 1 0 Libidibia paraguariensis (D.Parodi) comb. ined. 5 0 Parapiptadenia pterosperma (Benth.) Brenan 1 0 Lonchocarpus cultratus (Vell.) A.M.G.Azevedo & H.C.Lima 11 0 Parapiptadenia rigida (Benth.) Brenan 5 0 Lonchocarpus pluvialis Rusby 6 0 Parapiptadenia zehntneri (Harms) M.P.Lima & H.P.Lima 38 0 Lonchocarpus praecox Mart. ex Benth. 3 0 Parkia pendula (Willd.) Benth. ex Walp. 1 2 Lonchocarpus sericeus (Poir.) DC. 34 0 Parkia platycephala Benth. 11 29 Luetzelburgia andradelimae H.C.Lima 12 0 Parkinsonia aculeata L. 25 0 Luetzelburgia auriculata (Allemão) Ducke 36 4 Peltogyne confertiflora (Mart. ex Hayne) Benth. 18 24 Luetzelburgia bahiensis Yakovlev 21 0 Peltogyne discolor Vogel 1 0 Luetzelburgia neurocarpa D.B.O.S.Cardoso, L.P.Queiroz & H.C.Lima 1 0 Peltogyne pauciflora Benth. 25 0 Luetzelburgia praecox (Harms ex Kuntze) Harms 1 8 Peltophorum dubium (Spreng.) Taub. 53 6 Luetzelburgia purpurea D.B.O.S.Cardoso, L.P.Queiroz & H.C.Lima 1 0 Piptadenia adiantoides (Spreng.) J.F.Macbr. 1 0 Machaerium acutifolium Vogel 70 181 Piptadenia gonoacantha (Mart.) J.F.Macbr. 13 9 Machaerium amplum Benth. 9 0 Piptadenia irwinii G.P.Lewis 4 0 Machaerium brasiliense Vogel 18 8 Piptadenia macradenia Benth. 1 0 Machaerium eriocarpum Benth. 2 0 Piptadenia paniculata Benth. 2 0 Machaerium floridum (Mart. ex Benth.) Ducke 8 1 Piptadenia robusta Pittier 2 0 Machaerium fruticosum Hoehne 2 0 Piptadenia stipulacea (Benth.) Ducke 69 0 Machaerium fulvovenosum H.C.Lima 7 0 Piptadenia viridiflora (Kunth) Benth. 65 0 Machaerium hirtum (Vell.) Stellfeld 55 28 Pithecellobium diversifolium Benth. 31 0 Machaerium isadelphum (E.Mey) Amsh. 2 2 Pithecellobium roseum (Vahl) Barneby & J.W.Grimes 0 1 Machaerium nyctitans (Vell.) Benth. 7 0 Pityrocarpa moniliformis (Benth.) Luckow & R.W.Jobson 89 3 Machaerium opacum Vogel 13 108 Pityrocarpa obliqua (Pers.) Brenan 1 0 Machaerium ovalifolium Glaz. ex Rudd 2 0 Plathymenia reticulata Benth. 51 178 Machaerium paraguariense Hassl. 7 0 Platycyamus regnellii Benth. 3 1 Machaerium pilosum Benth. 4 1 Platymiscium floribundum Vogel 48 3 Machaerium punctatum (Poir.) Pers. 21 3 Platymiscium pinnatum (Jacq.) Dugand 3 0 Machaerium saraense Rudd 3 0 Platymiscium pubescens Micheli 25 0 Machaerium scleroxylon Tul. 31 12 Platypodium elegans Vogel 59 81 Machaerium sp. nov. 7 0 Poecilanthe falcata (Vell.) Heringer 15 0 Machaerium stipitatum (DC.) Vogel 21 3 Poecilanthe grandiflora Benth. 21 0 Machaerium vestitum Vogel 14 12 Poecilanthe subcordata Benth. 4 0 Machaerium villosum Vogel 22 15 Poecilanthe ulei (Harms) Arroyo & Rudd 22 0 Martiodendron mediterraneum (Mart. ex Benth.) R.Koeppen 4 6 Poeppigia procera C.Presl. 39 0 Martiodendron parviflorum (Amshoff) R.Koeppen 0 2 Poincianella bracteosa (Tul.) L.P.Queiroz 37 1 Melanoxylon brauna Schott 3 0 Poincianella echinata (Lam.) comb. ined. 1 0 Microlobius foetidus (Jacq.) M.Souza & G.Andrade 4 0 Poincianella gardneriana (Benth.) L.P.Queiroz 30 0 Mimosa acutistipula Benth. 31 1 Poincianella laxiflora (Tul.) L.P.Queiroz 13 0 Mimosa adenophylla Taub. 5 1 Poincianella microphylla (Mart. ex G.Don) L.P.Queiroz 28 0 Mimosa arenosa (Willd.) Poir. 59 0 Poincianella pluviosa (DC.) L.P.Queiroz 48 0 Mimosa bimucronata (DC.) Kuntze 1 0 Poincianella pyramidalis (Tul.) L.P.Queiroz 43 0 Mimosa caesalpiniifolia Benth. 12 0 Prosopis flexuosa DC. 3 0 Mimosa claussenii Benth. 0 11 Prosopis rubriflora Hassl. 1 0 Mimosa exalbescens Barneby 5 2 Pseudopiptadenia bahiana G.P.Lewis & M.P.Lima 9 0 Mimosa gemmulata Barneby 47 0 Pseudopiptadenia brenanii G.P.Lewis & M.P.Lima 10 1 Mimosa hexandra Micheli 13 0 Pseudopiptadenia contorta (DC.) G.P.Lewis & M.P.Lima 10 0 Mimosa irrigua Barneby 9 0 Pseudopiptadenia warmingii (Benth.) G.P.Lewis & M.P.Lima 2 0 Mimosa laticifera Rizzini & A.Mattos 0 16 Pterocarpus monophyllus Klitg., L.P.Queiroz & G.P.Lewis 3 0 Mimosa lepidophora Rizzini 4 0 Pterocarpus rohri Vahl 5 0 Mimosa lewisii Barneby 6 0 Pterocarpus villosus (Mart. ex Benth.) Benth. 12 0 Mimosa manidea Barneby 0 3 Pterocarpus zehntneri Harms 13 0 Mimosa ophtalmocentra Mart. 42 0 Pterodon abruptus (Moric.) Benth. 7 0 Mimosa paraibana Barneby 5 0 Pterodon emarginatus Vogel 16 35 Mimosa pithecolobioides Benth. 1 1 Pterodon pubescens (Benth) Benth. 3 107 Mimosa pseudosepiaria Harms 8 0 Pterogyne nitens Tul. 53 8 Mimosa pteridifolia Benth. 4 5 Riedeliella graciliflora Harms 5 0 Mimosa sericantha Benth. 4 2 Riedeliella magalhaesii (Rizzini) H.C.Lima & Vaz 1 0 Mimosa setosa Benth. 1 0 Samanea inopinata (Harms) Barneby & J.W.Grimes 5 0 Mimosa tenuiflora (Willd.) Poir. 115 15 Samanea tubulosa (Benth.) Barneby & J.W.Grimes 9 0 Mimosa verrucosa Benth. 17 2 Senegalia amazonica (Benth.) Seigler & Ebinger 3 0 Moldenhawera emarginata (Spreng.) L.P.Queiroz & Allkin 1 2 Senegalia bahiensis (Benth.) Seigler & Ebinger 47 0 Muellera campestris (Mart. ex Benth.) M.J.Silva & A.M.G.Azevedo 8 0 Senegalia kallunkiae (J.W.Grimes & Barneby) Seigler & Ebinger 3 0 A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57 53

Table 5 (continued) Prado, 2000; Pennington et al., 2000). We suggest that the succulent Species Number Number biome (sensu Schrire et al., 2005a)isamoreprecisenameforthehighly of times of times drought-prone tropical metacommunity (sensu Hubbell, 2001)thatisa sampled sampled distinct theater of evolution not only for legumes and succulent plants in the in the succulent savanna but also for a large assortment of other plant taxa (e.g., De-Nova et al., biome biome 2011; Thiv et al., 2011; Angulo et al., 2012).Thesucculentbiomeisa well-characterized subset of SDTFs that is restricted to the highly Senegalia langsdorffii (Benth.) Seigler & Ebinger 69 0 Senegalia lewisii (Bocage & Miotto) L.P.Queiroz 3 0 drought-prone end of the tropical seasonality gradient where annual Senegalia limae (Bocage & Miotto) L.P.Queiroz 5 0 precipitation is less than 1200 mm. Senegalia loretensis (J.F.Macbr.) Seigler & Ebinger 3 1 The finding that annual precipitation is somehow responsible for Senegalia martii (Benth.) Seigler & Ebinger 11 0 community phylogenetic structure across a tropical seasonality gra- Senegalia monacantha (Willd.) Seigler & Ebinger 10 0 Senegalia piauhiensis (Benth.) Seigler & Ebinger 22 0 dient was also found by Cardoso et al. (unpublished data),whoused Senegalia polyphylla (DC.) Britton & Rose 90 14 the Vataireoid legume phylogeny (Cardoso et al., 2013)tostudythis Senegalia recurva (Benth.) Seigler & Ebinger 1 0 same gradient. Annual precipitation was the only highly explanatory Senegalia riparia Britton & Rose ex Britton & Killip 28 0 variable in common to this community phylogenetic analysis and the Senegalia santosii (G.P.Lewis) Seigler & Ebinger 1 0 Vataireoid phylogenetic analysis. Temperature and climate season- Senegalia tenuifolia (L.) Britton & Rose 10 4 Senegalia velutina (DC.) Seigler & Ebinger 3 0 ality variables were not found to be explanatory of phylogenetic Senna acuruensis (Benth.) H.S.Irwin & Barneby 45 0 beta diversity (this study) or of ecological phylogenetic structure Senna affinis (Benth.) H.S.Irwin & Barneby 1 0 (Cardoso et al., unpublished data). This suggests that use of terms Senna aristeguietae H.S.Irwin & Barneby 2 0 related to “seasonality” does not necessarily imply an evolutionari- Senna cana (Nees & Mart.) H.S.Irwin & Barneby 28 10 Senna catingae (Harms) L.P.Queiroz 6 0 ly determining facet of a seasonal environment. Senna cearensis Afr.Fern 14 1 Senna corifolia (Benth.) H.S.Irwin & Barneby 2 0 4.2. Community- versus taxon-phylogenetic approaches in biogeography Senna gardnerii (Benth.) H.S.Irwin & Barneby 15 0 Senna georgica H.S.Irwin & Barneby 1 0 A community phylogenetic approach to the study of how ecology Senna macranthera (DC. ex Collad.) H.S.Irwin & Barneby 88 5 Senna martiana (Benth.) H.S.Irwin & Barneby 23 0 shapes phylogeny has advantages over the taxon phylogeny approach. Senna multijuga (Rich.) H.S.Irwin & Barneby 19 1 In the case of Cardoso et al. (unpublished data), the study of the Senna pendula (Willd.) H.S.Irwin & Barneby 6 2 Vataireoid clade (Leguminosae) was fruitful because that particular Senna quinquangulata (Rich.) H.S.Irwin & Barneby 0 1 clade happened to be one of the few that was distributed across the Senna reticulata (Willd.) H.S.Irwin & Barneby 1 0 Senna rizzinii H.S.Irwin & Barneby 24 0 entirety of the tropical seasonality gradient, from wet forests, savannas, Senna rugosa (G.Don) H.S.Irwin & Barneby 15 23 to the succulent biome. Yet, the Vataireoid clade was taxonomically Senna silvestris (Vell.) H.S.Irwin & Barneby 14 17 small in size (27 species) and geographically concentrated in northern Senna spectabilis (DC.) H.S.Irwin & Barneby 113 18 and eastern South America (Cardoso et al., 2013). This allowed for the Senna splendida (Vogel) H.S.Irwin & Barneby 41 2 sampling of 1263 collection localities that fully spanned the tropical Senna trachypus (Benth.) H.S.Irwin & Barneby 16 0 Senna velutina (Vogel) H.S.Irwin & Barneby 9 9 seasonality gradient in eastern South America. Few taxon phylogenies Sesbania sesban (L.) Merrill 2 0 are so broadly distributed ecologically within a relatively small geograph- Sesbania virgata (Cav.) Pers. 6 0 ic region so as to be amenable to the study of how ecological variables Stryphnodendron adstringens (Mart.) Cov. 0 107 have influenced a particular phylogeny. Stryphnodendron coriaceum Benth. 4 18 Stryphnodendron fissuratum Mart. 2 0 The main issue with the study of the Vataireoid phylogeny Stryphnodendron guianense (Aubl.) Benth. 0 2 (Cardoso et al., unpublished data), however, was that phylogeneti- Stryphnodendron obovatum Benth. 0 95 cally divergent clades were situated in similar ecologies. The genera Stryphnodendron polyphyllum Mart. 1 10 Luetzelburgia and Sweetia, for example, are not most closely related Stryphnodendron rotundifolium Mart. 2 5 among Vataireoid subclades yet both are highly concentrated in the Swartzia acutifolia Vogel 1 0 Swartzia apetala Raddi 9 3 succulent biome. The correspondence between ecological similarity Swartzia flaemingii Vogel 23 23 and phylogenetic relatedness can thus be obscured even when a phy- Swartzia jorori Harms 11 1 logeny is strongly ecologically structured. Cardoso et al. (unpublished Swartzia macrostachya Benth. 9 0 data) resorted to a binomial response variable (succulent biome versus Swartzia myrtifolia J.E.Sm. 1 0 Swartzia pilulifera Benth. 1 1 savanna-wet-forest biome) as a substitute for phylogenetic distance in Sweetia fruticosa Spreng. 47 5 order to effectively carry out a model selection analysis. The ecologically Tabaroa caatingicola L.P.Queiroz, G.P.Lewis & M.F.Wojc. 6 0 structured Vataireoid phylogeny was used only to postulate a hypothe- Tachigali aurea Tul. 7 178 sis of ecological phylogenetic structure, whereas a logistic modeling fl Tachigali densi ora (Benth.) L.G.Silva & H.C.Lima 1 0 approach was needed to test it. Tachigali subvelutina (Benth.) Oliveira-Filho 13 110 Tachigali vulgaris L.G.Silva & H.C.Lima 0 26 A community phylogenetic approach best addresses questions Taralea oppositifolia Aubl. 1 0 involving the influence of ecology on phylogeny because particular Trischidium limae (R.S.Cowan) H.E.Ireland 1 0 taxonomic groups or clades of interests are typically not well distrib- Trischidium molle (Benth.) H.E.Ireland 40 1 uted along an ecological gradient of interest (Pennington et al., Vachellia albicorticata (Burkart) Seigler & Ebinger 2 0 Vachellia farnesiana (L.) Wight & Arn. 55 0 2009). In a community phylogenetic analysis such as this one, particu- Vachellia macracantha (Humb. & Bonpl. ex Willd.) 10 lar taxa or clades do not have to be especially well sampled (e.g., only a Seigler & Ebinger few species of Luetzelburgia, Sweetia,andVatairea were sampled during Vatairea macrocarpa (Benth.) Ducke 11 178 this analysis, but the assemblage of the 27 species of the Vataireoid Vatairea sericea Ducke 0 8 clade was scarcely sampled; Table 5). Taxon sampling concerns are Zapoteca formosa (Kunth) H.M.Hern. 3 0 Zapoteca portoricensis (Jacq.) H.M.Hern. 3 0 circumvented with a community phylogenetic analysis as long as the Zollernia cowanii Mansano 1 0 community phylogeny relates all sampled species in the data set and Zollernia ilicifolia (Brongn.) Vogel 3 0 sample sites include representative levels of biodiversity (e.g., they are Zygia latifolia (L.) Fawc. & Rendle 17 0 not highly human-perturbed) and span an ecological gradient thought Zygia pithecolobioides (O.Kuntze) Barneby & J.W.Grimes 1 0 to impose phylogenetic constraints. 54 A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57

Lastly, sampling particular taxonomic groups and clades to 4.3. The ecology favoring high legume-succulent-plant biodiversity understand how ecology shapes phylogeny is also problematic be- cause although a particular clade may be concentrated in a certain Where annual precipitation is less than 1200 mm along the trop- biome, individuals may occur idiosyncratically in adjacent biomes ical seasonality gradient, ecological stability, or the lack of a regular (e.g., in this analysis, the succulent-biome-inhabiting Luetzelburgia fire regime and other regular physical disturbances, must be a condi- and Sweetia were occasionally sampled in the savanna, whereas the tion for maintaining an abundance and diversity of succulent plants savanna-biome-inhabiting Vatairea was occasionally sampled in within woody vegetation (Fig. 9). In our data set these succulent the succulent biome; Table 5). A community phylogenetic analysis plants are represented by Cactaceae, but elsewhere, the succulents averages out these instances of ecological outliers. Also averaged are from other families, especially Agavaceae, Bromeliaceae, and out are particular clades that track an ecological gradient of second- Euphorbiaceae. Stability may also be an important factor in ary interest. The legume genus Machaerium, for example, is primar- maintaining an abundance and diversity of highly deciduous trees ily a wet forest genus but like other clades where a liana habit and shrubs adapted to producing regular post-drought leaf flushes, predominates (e.g., Phaseolinae; Delgado-Salinas et al., 2011), the cost of which has to be paid back over time. With a high nitrogen Machaerium extends out of the wet forest and tracks roadsides metabolism such as found in legumes, the cost can be returned soon- and similar physically disturbed habitats throughout the savanna er (McKey, 1994). and succulent biomes (Table 5). Therefore, a focus on primarily The explanatory ability of annual precipitation over other biocli- wet-forest taxa that also have an affinity to disturbance-prone hab- matic variables takes the emphasis off seasonality and deciduous- itats (e.g., Bignonieae of the Bignoniaceae; Lohmann et al., 2013) ness and suggests that the constituent lineages of the succulent should not be expected to be informative of how ecological gradi- biome not only readily endure regular and extended drought but ents involving precipitation, temperature, or altitude generally also efficiently respond to relatively trifling amounts of precipitation shape phylogenies. The liana life history might be less niche con- with a flush of new leaves. The post-dry-season leaf-flush category served (Hughes et al., 2013), but this is true to some degree only (conspicuous versus inconspicuous) was highly explanatory of phy- with respect to the tropical seasonality gradient and not necessarily logenetic beta diversity (Table 1). This categorical variable by itself true with respect to a physical-disturbance gradient where lianas explained phylogenetic beta diversity (PCO-axis-1-transformed may well be concentrated at the more frequently disturbed end. MNT distances) with an R2 = 0.631. Interacting leaf-flush category

Fig. 9. Representative localities in eastern South America that harbor the succulent biome. A. A succulent biome enclave on limestone at São Domingos, Goiás, Brazil, with the columnar Cactaceae Micranthocereus estevesii (Buining & Brederoo) F.Ritter and Bromeliaceae Encholirium eddie-estevesii Leme & Forzza at center. B. Caatinga setting (succulent biome) on limestone at Morro do Chapéu, Bahia, Brazil. The columnar Cactaceae is the caatinga endemic Pilosocereus gounellei (F.A.C.Weber ex K.Schum.) Byles & G.D.Rowley, the barrel Cactaceae is Melocactus pachyacanthus Buining & Brederoo, the Bromeliaceae is the caatinga endemic Encholirium spectabile Mart. ex Schult.f., and the leafless white-flowered tree at center-background is Coursetia rostrata Benth. (Leguminosae), a caatinga endemic. C. The arboreal caatinga (succulent biome), Parnamirim, Bahia, Brazil, with the Brazilian baobab tree, Cavanillesia arborea K.Schum. (Malvaceae, Bombacoideae), to the left. D. Chiquitano dry forest, Santa Cruz, Bolivia, with flowering Bombacoideae, Ceiba insignis (Kunth) P.E. Gibbs & Semir, and the arborescent Cactaceae Cereus sp. in the background. All photos by Domingos Cardoso. A.T. Oliveira-Filho et al. / South African Journal of Botany 89 (2013) 42–57 55 with annual precipitation (Fig. 7)resultedinanR2 ≥ 0.850 regard- probability of the South American SDTF (succulent) biome. Rafaela Forzza less of whether species beta or phylogenetic beta diversity was the and Marlon Machado kindly confirmed or identified the Bromeliaceae response, or whether the full data set or just the legume subset was and Cactaceae species from our photos. DC thanks CNPq for supporting being analyzed. This simple but highly explanatory model suggests that his research with a SWE doctoral fellowship (process 201621/2010-0) post-dry-season leaf flush should be considered as a distinctive adapta- to Montana State University, Bozeman, USA, and a postdoctoral fellow- tion involving sensitive cues for the beginning of the wet season, which ship (process 156143/2012-7) to Universidade Estadual de Feira de is sometimes coordinated with synchronous flowering (Bullock and Santana, Bahia, Brazil. We thank the organizers of the 6th International Solis-Magallanes, 1990; Guevara-de-Lampe et al., 1992; Bullock, 1995; Legume Conference held at University of Johannesburg for the invitation Machado et al., 1997; Nunes et al., 2012). to present this work. The species-rich legume family is both abundant and diverse along the entirety of the tropical seasonality gradient in Africa and the Americas References (Schrire et al., 2005a). 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