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Floristics and biogeography of vegetation in seasonally dry tropical regions/Floristique et biogeographie de la vegetation dans les regions a saisons seches/Floristica y biogeografia de la vegetacion de regiones tropicales estacionalmente secas.

INTRODUCTION

This paper examines the relationships amongst different formations of vegetation in seasonally dry regions throughout the tropics, especially in their floristic composition, and also in terms of their ecology. Our approach is to undertake a novel pantropical analysis of the floristic composition of dry forest, savanna and moist forest formations, and to place the results in the context of their structure and key ecological attributes, such as propensity to burn. We stress that it is not our intention to re-visit labyrinthine debates of the definition of vegetation formations (e.g. Gentry 1995, Leimgruber et al. 2011, McShea and Davies 2011, Torellos-Raventos et al. 2013, Veenendaal et al. 2014) or to attempt to make precise definitions of "seasonally dry tropical forest" or "tropical savannas" on different continents. Our analyses address fundamental biogeographic questions, such as whether there is coherence in floristic composition in vegetation formations that are structurally and ecologically similar across continents. However, in the context of the papers in this volume, another goal is to help to understand the generality of case studies in ecology and conservation from a particular seasonally dry tropical region. For example, can the lessons of a study of resilience to fire in "tropical dry forest" in Indochina be applied to "tropical dry forest" in South America?

Major vegetation formations in seasonally dry regions of the tropics

In understanding the vegetation of lowland tropical regions, the distinction between savanna and forests is critical. We take the view that savannas are distinguished from other tropical forest formations by the presence of more or less continuous C4 grass cover and the prevalence of natural fire. This grass-layer and proneness to fire is found even in savannas with a dense tree canopy, such as the "cerradao", a sub-formation within the savannas ("cerrados") of Brazil (Oliveira-Filho and Ratter 2002). This contrasts with closed canopy forests, including wet forests and seasonally dry tropical forests (SDTF), where grasses are infrequent in the understory and where natural fire is rare. The distinction of savanna and forests by the key factors of C4 grass presence and prevalence to fire is followed by many workers at a global scale (Lehmann et al. 2011, McShea and Davies 2011, Ratnam et al. 2011, Scholes and Walker 1993, Suresh et al. 2011) and it is widely accepted in the Neotropics (Pennington et al. 2000, 2006).

When climate is sufficiently dry in the tropics, moist forest gives way to savannas and SDTF (Lehmann et al. 2011, Murphy and Lugo 1986, Pennington et al. 2006, Staver et al. 2011). In the Neotropics, SDTF experiences [less than or equal to] 1600 mm rainfall a year, has a dry period of at least 5-6 months where precipitation is [less than or equal to] 100 mm/month and is mostly deciduous (Murphy and Lugo 1986, Pennington et al. 2006). It grows on relatively fertile, often calcareous soils, and where soils are poor and acid it is replaced by savanna, which differs in its evergreen trees (Pennington et al., 2006). Neotropical savannas, and those on other continents, can be found under wetter conditions than SDTF (up to 2500 mm rainfall/yr; Lehmann et al. 2011, Staver et al. 2011).

It is perhaps unclear whether vegetation with the attributes of neotropical SDTF outlined above exists on other continents (Lock 2006). Monsoon vine thickets of northern Australia have attributes of SDTF--having a closed canopy and being largely deciduous (Bowman 2000). The dry scrub on the Horn of Africa and similar regions in Arabia and northwest India, which are rich in legumes and succulents, have been considered similar to cactus-rich drier formations of SDTF in the Neotropics, and they have been classified as a global "succulent biome" by Schrire et al. (2005). These formations have led to suggestions that there may be a "global metacommunity" of SDTF that has some plant lineages specific to it (Pennington et al., 2009). However, elsewhere, extensive areas of vegetation in seasonally dry regions of Asia and Africa that are named "forest" or "woodland" are C4 grass-rich and fire-prone --and hence in our view a form of savanna. Examples are dry deciduous forest in India (Suresh et al. 2011), Miombo woodland in southern Africa (Campbell et al. 1995, Chidumayo 2013) and deciduous dipterocarp forest in continental Asia (Bunyavejchewin 1983, Bunyavejchewin et al. 2011).

In this paper we analyse forest inventory plot data from across the tropics using clustering and ordination methods to explore the relationships in floristic composition of diverse vegetation formations from South America, Africa, India, and Indochina that could be broadly classified as tropical savannas or SDTF. We include moist or wet forest plots from each continent to provide broader biogeographic context. The analyses are used to address the following related questions:

1. Is there floristic commonality of savannas and dry forests amongst continents? This examines, for example, the suggestion that there may be a global metacommunity of SDTF (Pennington et al. 2009).

2. Alternatively, do different formations (savannas, moist forests, SDTF) cluster floristically by continent? If they do cluster geographically, this would refute the global metacommunity hypothesis and suggest independent evolutionary assembly of the vegetation formations on each continent.

3. Do formations in Africa and Asia that are termed "dry forests" or "woodland" show floristic relationships to Neotropical SDTF or savannas? A closer relationship with Neotropical savannas might be expected if they are grass-rich and fire prone.

METHODS

Data Sources

To compare the floristic composition of different woody tropical vegetation formations within and amongst continents, we assembled tree inventory data from South America, Africa, and Asia--three continents with major portions of their area in the tropics (Fig. 1). We aimed to obtain data from formations in dry as well as moist areas. Woody formations in drier areas have been subject to many designations, including SDTF, deciduous forests, woodlands, and savannas, while forests from moist areas have been more consistently referred to as wet, moist, or rain forest. We classified plots into major vegetation formations based on available metadata (i.e. from the associated journal article for published plots or from the data provider for unpublished plots). We did not include data from islands as they may confound analyses because of divergent floristic composition resulting from isolation. We therefore did not include tropical Malesia (e.g. Borneo, New Guinea, etc.), which, while comprising an extensive part of the Asian tropics, are also of lesser interest in this study as they have little dry vegetation. The inventory data primarily consisted of 1 ha plots that measured the diameter of and identified all trees > 10 cm diameter at breast height (DBH), except where noted below or in Table S1. We only included plots where > 90% of stems were identified to genus.

South America

Our plot data for South America primarily comes from RAINFOR plots curated in the ForestPlots.net database (Lopez-Gonzalez et al. 2011; www.forestplots.net; extraction date: Sept. 30th, 2013), which consists of a network of 1 ha tree plots that monitor the composition, structure, and biomass of forests across the Amazon. The RAINFOR plot network extends into drier areas at its eastern and southern borders, and we restricted analyses to plots in wet or moist forest (n = 60), SDTFs (n = 10), and savannas (n = 10) in the southern and eastern Amazon and neighbouring areas (found in Bolivia and Brazil). Additionally, we surveyed the published literature, obtaining tree plot data for SDTFs from the Brazilian states of Bahia, Goias, Distrito Federal, Mato Grosso, and Mato Grosso do Sul (n = 12; details in Table S1). Across all South American plots, 3.6% of individual stems were not identified to genus.

Africa

Our data for Africa come primarily from the AfriTRON network (Lewis et al. 2013), which is also curated in the ForestPlots.net database (Lopez-Gonzalez et al. 2011; www. forestplots.net; extraction date June 13th, 2013) and which consists of 1 ha plot data from primarily wet or moist forests in the Congo basin and the West African Guinean region (n = 64). Several plots from the database are located in savanna at the northern edge of African wet forests (n = 5; see Fig. 1). Additionally we obtained data from five 1 ha plots in savanna in Sierra Leone (from Ottamba Killimi National Park; M.P. Bessike Balinga, unpubl. data). Two of the major habitat types in tropical Africa proper (between 23[degrees]S and 23[degrees]N) that have commonly been considered as a form of tropical dry forest are Miombo woodlands, which occur across Africa south of the Congo basin (Campbell et al. 1995, Chidumayo 2013), and thornveld or scrub forest in the Horn of Africa (Schrire et al. 2005). We were unable to obtain 1 ha plot data from the Horn of Africa, but do include data from a 10 ha plot in Miombo woodlands in the southeastern Democratic Republic of the Congo (J. Ilunga-Muledi and P. Meerts, unpubl. data), which we subdivided into 1 ha plots to allow for comparison with the other 1 ha plot data. Across all African plots, 2.8% of individual stems were not identified to genus.

Asia

There are extensive forested regions in tropical Asia in both India and Indochina. We obtained forest plot data for India from two primary sources: 1) a series of 1 ha plots from wet evergreen forest (n = 15) in the Western Ghats and dry evergreen forest (n = 16) from across southeastern India (Anbarashan and Parthasarathy 2008, 2013, Ayyappan and Parthasarathy 2001, Chittibabu and Parthasarathy 2000, Mani and Parthasarathy 2005, Muthuramkumar et al. 2006, Parthasarathy and Karthikeyan 1997a, 1997b, Parthasrathy and Sethi 1997, Srinivas and Parthasarathy 2000, Venkateswaran and Parthasarathy 2003) and 2) a series of 25 m x 25 m plots in wet evergreen (n = 155) and deciduous forest (n = 44) in the Western Ghats that sample individuals > 10 cm in circumference at breast height (N. Page, unpubl. data). We combined neighbouring plots together where possible to approach the sample size, in terms of individuals, present in 1 ha plots (see Table S1).

We obtained data from Indochina from two sources. From Cambodia, we sourced data from a series of 0.1 ha plots from the central plains region (Theilade et al. 2011, I. Theilade, unpubl. data). We combined neighbouring plots within the same habitat type to create a total of 10 'plots' with sufficient sample size. The majority of the plots were in wet evergreen forest (n = 7), such as riverine, swamp, or tall dipterocarp forest, while three plots were in deciduous or semi-deciduous forest such as dry dipterocarp forest and sralao forest, a habitat dominated by trees of the genus Lagerstroemia (Lythraceae). From Vietnam, we obtained data for four 1 ha plots in evergreen forest from Cat Tien National Park from the literature (Blanc et al. 2000). Across all Asian plots, 0.3% of individual stems were not identified to genus.

Data Standardization and Analyses

We ran all datasets through the Taxonomic Name Resolution Service v3.2 (http://tnrs.iplantcollaborative.org; Boyle et al. 2013), which corrects misspellings and standardizes synonyms based on several botanical databases, most importantly, in this instance, the Missouri Botanic Garden's Tropicos database (http://www.tropicos.org). As few species are found on more than one continent, we did not find species-level analyses to be appropriate for comparing floristic similarity of vegetation formations within and amongst continents. In contrast, no family was restricted to a single vegetation formation or a single continent, and we therefore did not consider that analyses at this taxonomic level would be useful for comparisons either; most plots show high floristic similarity with little variation in values. Consequently, we conducted all analyses at the genus level, excluding individuals that were not identified to genus. The final matrix for analysis comprised 1078 genera, 269 plots, and 120,691 individual trees.

We used the Sorensen distance (Sorensen 1948) to determine how divergent individual pairs of plots were in their genus composition. The Sorensen distance for each pair of plots was calculated as (A+B-2*J)/(A+B) where A is the number of genera in plot A, B is the number of genera in plot B, and J is the number of genera shared between plots A and B. We used the Sorensen distance matrix as the basis for a hierarchical clustering analysis of plots. We implemented the clustering using the recluster package (Dapporto et al. 2013) in the R Statistical Environment v. 3.0.1 (R Core Development Team 2013). This approach is advantageous because it adds plots randomly to the clustering analysis, repeats this process as many times as the user decides (in our case 100 times, which was well above the threshold at which a stable solution was reached), and generates a consensus tree from all random addition replicates, thus avoiding biases in plot entry order to which other clustering approaches are susceptible (Dapporto et al. 2013). We additionally conducted a bootstrap analysis, resampling the same number of genera in the original plots with replacement 1000 times, to assess support for the clusters obtained. Finally, we used multiple agglomeration methods to link clusters, including single linkage, complete linkage, average linkage, and Ward's minimum variance method (Borcard et al. 2011).

We also used the Sorensen distance matrix as the basis for ordination of plots using non-metric multidimensional scaling (NMDS) in the vegan package (Oksanen et al. 2013) of the R Statistical Environment. We began the analysis with two axes and added axes until the stress value dropped below 0.1, an arbitrary threshold that indicates a reasonably stable solution (Borcard et al. 2011). In all cases, we used 20 random starts and ensured convergence among runs. All of the above analyses were repeated using Jaccard and Simpson distances among plots to assess the robustness of results to different distance indices.

In order to compare directly the influence of continent versus vegetation formation on floristic similarity, we conducted a series of analyses of variance of distance matrices, equivalent to permutational MANOVA (Anderson 2001), using functions in the vegan package (Oksanen et al. 2013). We used continental region and vegetation formation as explanatory variables, both individually and together. The moist vegetation formation was found in all continental regions, while dry vegetation formations varied: savanna and SDTF in South America, savanna and Miombo woodland in Africa, deciduous and dry evergreen forest in India, and deciduous forest in Indochina. Given uncertainty about whether dry vegetation formations on different continents actually represent the same units, we compared analyses with plots assigned to their original vegetation formation versus various possible combinations of dry formations. The simplest scheme consisted of assigning all plots from dry formations to a single category to contrast with moist forest. This categorisation allowed us to assess statistically a potential interaction between continent and vegetation type. We also considered schemes where different dry formations in Africa and Asia were lumped with Neotropical savanna or SDTF. Additionally, we conducted an analysis where each vegetation formation in each continental region was given a distinct vegetation category (e.g. the savannas of South America and Africa were assigned to different categories). Lastly, given observed floristic differentiation between India and Indochina, we conducted analyses both where these were distinguished in continental region assignments and where they were lumped together as 'Asia'.

RESULTS

Plots from different continents consistently show high Sorensen distances, with a minimum value of 0.71, indicating that the two most similar plots from different continents share 29% of their genera, and a modal value of 1.00, indicating that most plots from different continents do not share any genera at all. In contrast, plots within continents show a broad range of Sorensen distances from 0.10 to 1.00.

All of the plots from a given vegetation formation on a given continent cluster together, and we refer to these primary clusters as ecogeographic units (Fig. 2). The relationships of ecogeographic units show some support for the role of geography in determining floristic similarity, while vegetation formations from different continents never cluster together. For example, all of the plots from South America form a well-supported cluster (>70% bootstrap support), and the three major vegetation formations are clearly distinct from each other. African wet forests are sister to the rest of the ecogeographic units from Asia and Africa. The relationships of the remaining ecogeographic units from Africa and Asia are unclear (Fig. 2). Moist and deciduous forests from Indochina cluster together, rather than with the corresponding vegetation formation from India, showing that geography is important even within Asia. These results were robust to the agglomeration method used to link clusters.

Our ordination analyses also suggest the pre-eminence of geography in determining floristic relationships (Fig. 3A), while also demonstrating the clear importance of vegetation formation (Fig. 3B). We used an NMDS ordination with four axes, as this was the lowest number of axes that had a stress value under 0.1 (stress = 0.088). The first two axes clearly segregate plots from different continents, irrespective of their vegetation type. If an NMDS ordination is conducted with only two axes (results not shown, stress = 0.185), an identical result is obtained, suggesting that geography is the first factor that determines the floristic similarity of plots. The third and fourth axes separated plots in moist forests from those in savannas and other dry formations (e.g. SDTFs, Miombo woodlands). That all continents show this moist versus dry segregation within the same ordination does suggest that there is some floristic signal for moist versus drier formations that is the same on each continent. Nevertheless, African savannas are clearly floristically distinct from South American savannas, while there is also limited support, especially from the clustering analysis, for segregation of the different dry forest/ woodland categories on different continents.

Analyses of variance of the Sorensen distances among plots also showed a predominant influence of geography. Continent alone explained 27.3% of the variation in distance values, while the original vegetation formation delimitations explained 19.6%. When continent and vegetation formation were combined in a multivariate analysis, continent explained 27.3% and vegetation formation 18.3%. Any other possible scheme of combining savanna and dry forest formations resulted in less variation explained by vegetation formation. If we lumped all dry formations into one category to allow for an assessment of interaction between continent and vegetation formation, we found that continent explained 27.2%, habitat 5.7%, and their interaction 8.6%. The best model, in terms of percentage of variation explained (49.3%), was that which distinguished all vegetation formations on different continents as belonging to different categories. When India and Indochina were lumped together as one continental region, nearly identical results were obtained, although the amount of variation explained by continent was reduced by an average of 2%. All results were qualitatively similar when Jaccard or Simpson distances were used instead of Sorensen distances for analyses.

DISCUSSION

The floristics and biogeography of vegetation in seasonally dry regions of the tropics

Moist forests in the Neotropics, Africa, and Asia are typically considered the same biome, despite differences in floristic composition (Pennington et al. 2009). However, inter-continental floristic and ecological comparisons of SDTF are exceedingly rare, and so the idea of a global "dry forest" biome is still controversial and poorly tested. A previous intercontinental analysis of the biogeography of the Leguminosae (Schrire et al. 2005) suggested the existence of a "succulent" biome, which encompasses regions corresponding to SDTF in both the Neotropics and the Paleotropics, whereas a floristic comparison of African and Neotropical SDTF showed that the vegetation of the two continents, despite their similarity in physiognomy, is made up of different assemblages of families and genera (Lock 2006).

The fundamental floristic units found in our hierarchical clustering analysis consist of individual vegetation formations within continents (Fig. 2). Similar vegetation formations from different continents (e.g. savanna) clearly do not cluster together, thus falsifying the hypothesis that there are global metacommunities for different vegetation formations. Meanwhile, there is a substantial signal for geography in the clustering results. For example, the three vegetation formations from South America, while clearly distinct from each other, form a strongly supported cluster, and all plots from Indochina cluster together rather than with plots of the corresponding vegetation formation from India (Fig. 2). The ordination analyses also support the pre-eminence of geography in determining the floristic similarity of vegetation formations (Fig. 3A). Finally, our analyses of variance of Sorensen distance values further highlight the importance of geography and clearly demonstrated that vegetation formations on different continents are more divergent in floristic composition than any vegetation formations within continents.

Our clustering results also suggest that South America is more isolated from Africa and Asia than either of the latter two continents are from each other (Fig. 2). This conclusion is supported by our NMDS analysis, the first axis of which clearly separated South American plots from African and Asian plots (Fig. 3A). Indeed, of the 477 genera found in South American plots, 67 are found in African plots and 64 are found in Asian plots, while African and Asian plots share 96 genera overall (with 389 and 396 total genera respectively).

Our ordination analyses show a common floristic signal across continents for segregation of wet versus dry vegetation formations (evident in Fig. 3B), but the analyses do not allow us to classify the different dry formations across continents with respect to each other. Only in the Neotropics are savanna and SDTF clearly distinguished in our floristic analyses (Fig. 2), which corroborates their a priori distinction here and in the literature (e.g., Pennington et al. 2006). However, it is evident that plots classified a priori as savanna in South America and Africa do not show great floristic similarity (Fig. 3B), while the various dry forest formations from different continents all fall out as separate clusters in our clustering analyses (Fig. 2). Furthermore, the analyses of variance demonstrate that the best categorisation scheme incorporates different categories for superficially similar vegetation formations on different continents (e.g. savanna from South America should comprise a separate category from savanna in Africa). In other words, the analyses suggest that based on floristics there are not any common vegetation units across continents.

Thus, it seems that we cannot use these floristic analyses to determine whether the various dry vegetation formations in Africa and Asia correspond better to Neotropical savanna or SDTF. Rather, to classify palaeotropical dry vegetation types as savanna vs. SDTF (sensu Neotropical definitions), one would have to rely on information besides woody plant floristic composition, such as the presence vs. absence of C4 grasses and succulents or the frequency of fires (e.g., Torellos-Raventos et al. 2013). For example, based upon their ecological characteristics of richness in C4 grasses and propensity to burn, we suggest that many formations termed "forest" or "woodland" in Africa and Asia, including all of those analysed here, are better considered as savannas. We acknowledge that there are many types of vegetation in Africa and Asia that we have not assessed, e.g. Baikiaea (Leguminosae) woodlands (Piearce 1984) and Cryptosepalum (Leguminosae) dry forests in Angola, Democratic Republic of Congo, and Zambia (White 1983) and the coastal woodlands of Mozambique and Tanzania (Burgess and Clarke 2000), which may not have a propensity to burn and may be analogous to SDTF (sensu Neotropical definitions). The frequency of fires as a determinant of vegetation type in the tropics is supported by the observation that anthropogenic fires in SDTFs lead to their substitution by savannas (Saha and Howe 2003, Wanthongchai and Goldammer 2011). Conversely, in the absence of fire, savanna vegetation may eventually grow into a closed canopy forest that can then exclude C4 grasses and fire, particularly on more fertile soils (Durigan 2006, Lawes et al. 2011, Woinarski et al. 2004).

Tropical savannas are geologically young, dating from the late Miocene (Beerling and Osborne 2006, Cerling et al. 1997, Jacobs et al. 1999), and SDTF in the Neotropics, though older, postdates the origin of tropical moist forests (Becerra 2005, Pennington et al. 2006). The antiquity of tropical moist forests relative to drought-adapted formations implies that the continentally structured floristic patterns we have found are largely a result of isolated continental floras evolving independently to occupy a seasonally dry environmental niche, rather than the result of the same drought-adapted lineages dispersing across the globe to reach dry environments. This result implies that though intercontinental migration has undoubtedly been important in tropical plant biogeography (e.g., Pennington and Dick 2004), the effect of in situ diversification on continents may have been greater. This can be illustrated by considering that only 88 of 477 genera (~20%) in our South American plots are even found in Africa and Asia. The fact that many eudicot families that are dominant in tropical vegetation date only to the late Cretaceous (Magallon et al. 1999) implies that the origin of most of their genera--a lower taxonomic level--will be later and therefore post-dates Gondwanan vicariance. Hence, long-distance dispersal is likely to have been important in the biogeography of these trans-continental genera. A corollary suggestion is that the genera restricted to the Neotropics in our dataset (c. 80%) are likely to have had a neotropical origin. However, we note that this is a very approximate estimate as some of the 20% of widespread genera may also have had a neotropical origin, and some of the 80% apparently restricted to the Neotropics may be found in Africa and Asia outside of the plots we examined.

Recent work on the evolution of plant lineages found in the savannas of South America and Africa corroborate the view of in situ continental evolution (Maurin et al. 2014, Simon et al. 2009, Simon and Pennington 2012). Plants occupying these savannas have sister groups in the other vegetation types of each continent such as moist forests and SDTF. Woody lineages occupying the savannas in Africa and South America are not the result of a dispersal of fire adapted species from another part of the global savanna biome, but are instead a result of multiple local lineages evolving fire adaptations and expanding into the savanna niche. It seems that the evolutionary barrier preventing the entry of lineages into savannas is relatively weak, and that plants from other types of vegetation have evolved the fire adaptations (such as root-sprouting and corky bark) needed to survive fire-prone savannas relatively easily (Pennington and Hughes 2014, Simon and Pennington 2012). Our results showing clustering of different vegetation types, including savanna, by continent, support this idea of local lineages evolving in situ to fill niches in other environments.

Implications for conservation and management

Dry forests have been defined in many different ways. In the context of this journal volume, it is worth considering that CIFOR has adopted the FAO's concept of "dry forests" (FAO, 2001), which encompasses both formations that we would classify as SDTF and as savanna.

In ecological terms, SDTF and savannas have features in common that are related to rainfall seasonality. Rainfall is a dominant ecological force affecting temporal patterns of biological activity such as growth and reproduction, which are synchronised with water availability (McLaren and McDonald 2005, Murphy and Lugo 1986, Silva et al. 2011). Litter production is also influenced by seasonality and occurs during the dry season, when litterfall is at its maximum (Murphy and Lugo 1986), with cascading effects on the timing of essential nutrient fluxes, microbial dynamics, and vegetation growth in savannas and dry forests (Lawrence 2005). However, despite these similarities, SDTF and savannas are ecologically distinct in the Neotropics (Pennington et al. 2000; see above), especially in the prevalence of natural fires, which are much more frequent in savannas. Therefore, in terms of fire resistance, dry forests and savannas require different management strategies. For example, fire is an essential tool to maintain savanna structure and biodiversity, since in its absence the woody plant cover increases (Durigan 2006, Lawes et al. 2011, Woinarski et al. 2004). In contrast, a neotropical SDTF is adversely affected by fire, because its woody plants, especially the succulent element from the Cactaceae family, lack the necessary adaptations to fire.

As our results have demonstrated, we cannot use floristic analyses to relate neotropical SDTF and savannas with palaeotropical dry vegetation. Dry forests that are physiognomically similar to neotropical SDTF (sensu Pennington et al. 2000) may cover only a small part of Africa (Lock 2006). Some possible examples are the deciduous bushlands and thickets of the Horn of Africa, which may be considered ecologically equivalent to the caatinga dry forest in northeastern Brazil (Lock 2006). In Asian dry forests, fire-sensitive succulents are almost absent and, due to their propensity to burn, we suggest that many Asian "dry forests" should be classified as savannas.

Although fire is considered a natural feature of "dry forests" in Africa and Asia, its frequency is now probably much higher than it has been historically (McShea and Davies 2011, Timberlake et al. 2010), with possible negative consequences such as invasion by alien species (Hiremath and Sundaram 2005). When burning frequency is inappropriate, dry forests in the tropics often degrade to more open formations or convert to other land-use systems (Wanthongchai and Goldammer 2011). In this context, and regardless of the classification adopted, management systems need to be carefully designed to incorporate the peculiarities of each landscape and their different levels of resistance to fire. Consequently, more research is needed, particularly to address the spatial and temporal effects of burning, so as to design appropriate fire management systems (Wanthongchai and Goldammer 2011).

A second, longer term management and conservation issue is the spectre of climate change, and how this may change the distributions of moist forests, savannas and SDTFs. In this context, the key differences in soil preference of neotropical SDTF and savannas needs consideration in models such as dynamic global vegetation models (DGVMs). For example, if climates warm and become more seasonal in moist forest areas, SDTF species will not spread into these areas unless fertile soils are present. Whilst consideration of soil variables has been included in some discussions of palaeovegetation changes (e.g., Pennington et al. 2000; Slik et al. 2011), it has yet to be used in hind-casting of quantitative species-distribution models (e.g., Werneck et al. 2012) or in DGVMs.

The result presented here--different vegetation types clustering floristically by continent--means that pantropical biological generalisations should be drawn with care, even within the ecologically defined savanna and SDTF categories. For example, while tropical savannas can be globally defined by an abundance of C4 grasses and propensity to burn, because they contain different woody plant lineages on each continent, it may be hard to generalise studies of resilience or ecosystem rehabilitation from one continent to another. With regard to forest management, the lack of floristic identity between neotropical and paleotropical SDTF and savannas makes cross-continental comparison in some contexts almost meaningless. For example, a species-specific analysis and a demographic approach are preconditions for evaluating whether timber and non-timber forest products harvesting is sustainable or not (Sutherland 2001).

Our findings that global SDTF or savanna biomes may not exist from the floristic standpoint are not in disagreement with the proposition of a global conservation plan or strategy for seasonally dry tropical regions. Both SDTF and savannas have experienced extensive deforestation (Aide et al. 2013), so the adoption of a broad concept is strategic to call attention to tropical dry biomes, which have been neglected historically in both research and conservation efforts. Many of the global threats to SDTF and savannas are similar (e.g., mineral exploration, expansion of agricultural frontiers) and successful experiences to protect the remaining vegetation, as well as contributions to sustainable livelihoods in dry areas, certainly need to be shared. Because SDTF and savannas often occur as mosaics together and with other vegetation types, conservation strategies should consider their inter-connections and links with other types of vegetation and land-use systems at the landscape level. However, we emphasise that any conservation strategy for SDTF and savannas should take into account the distinctiveness of their flora in each tropical region.

ACKNOWLEDGEMENTS

KGD, RTP, TRB, and OLP acknowledge the National Environment Research Council (U.K.) Standard Grant NE/ I028122/1, and KGD and RTP thank CIFOR, through their funding from USAID's Biodiversity Bureau for financial support. KGD was funded by an NSF International Research Fellowship (OISE-1103573) during the time this research was completed. This paper is in part a product of the RAINFOR network, supported by a Gordon and Betty Moore Foundation grant, the European Union's Seventh Framework Programme (GEO-CARBON; ERC grant "Tropical Forests in the Changing Earth System), and a Natural Environment Research Council (NERC) Urgency Grant and NERC Consortium Grants AMAZONICA (NE/F005806/1) and TROBIT (NE/D005590/1). RJWB is funded independently by Research Fellowship (NE/I021160/1). SLL is funded by a Royal Society Fellowship. OLP is supported by an ERC Advanced Grant and a Royal Society Wolfson Research Merit Award. This work was partially supported by a grant from the Brazilian National Council for Scientific and Technological Development (CNPq)/Long Term Ecological Research (PELD) project (Proc. 403725/2012-7). We wholeheartedly acknowledge the contributions from numerous field assistants, local botanists and rural communities to collecting the field data summarized here. Most are thanked elsewhere, especially in Phillips et al. (2009) and Lewis et al. (2013). We thank Georgia Pickavance for support with the ForestPlots.net database and Joana Ricardo for work supporting RAINFOR collaborators. We thank Christopher Baraloto and three anonymous reviewers for helpful suggestions that improved the manuscript.

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TABLE S1. Metadata and relevant publications for all plots used
in analyses

Continent       Country         Vegetation Type             Plot Code

Africa          DR Congo        Miombo Woodlands            mikembol
Africa          DR Congo        Miombo Woodlands            mikembo2
Africa          DR Congo        Miombo Woodlands            mikembo3
Africa          DR Congo        Miombo Woodlands            mikembod
Africa          DR Congo        Miombo Woodlands            mikembo5
Africa          DR Congo        Miombo Woodlands            mikembo6
Africa          DR Congo        Miombo Woodlands            mikembo7
Africa          DR Congo        Miombo Woodlands            mikembo8
Africa          DR Congo        Miombo Woodlands            mikembo9
Africa          DR Congo        Miombo Woodlands            mikembo10
Africa          Cameroon        Savanna                     MDJ.02
Africa          Cameroon        Savanna                     MDJ.04
Africa          Cameroon        Savanna                     MDJ.06
Africa          Cameroon        Savanna                     MDJ.08
Africa          Cameroon        Savanna                     MDJ.09
Africa          Sierra Leone    Savanna                     OKNP01
Africa          Sierra Leone    Savanna                     OKNP02
Africa          Sierra Leone    Savanna                     OKNP03
Africa          Sierra Leone    Savanna                     OKNP01
Africa          Sierra Leone    Savanna                     OKNP05
Africa          Ghana           Wet Forest                  ASU.01
Africa          Ghana           Wet Forest                  ASU.02
Africa          Ghana           Wet Forest                  ASU.88
Africa          Ghana           Wet Forest                  ASU.99
Africa          Ghana           Wet Forest                  BBR.14
Africa          Ghana           Wet Forest                  BBR.16
Africa          Ghana           Wet Forest                  BBR.17
Africa          Cameroon        Wet Forest                  BIS.01
Africa          Cameroon        Wet Forest                  BIS.02
Africa          Cameroon        Wet Forest                  BIS.03
Africa          Cameroon        Wet Forest                  BIS.04
Africa          Cameroon        Wet Forest                  BIS.05
Africa          Cameroon        Wet Forest                  BIS.06
Africa          Ghana           Wet Forest                  BOR.05
Africa          Ghana           Wet Forest                  BOR.06
Africa          Cameroon        Wet Forest                  CAM.01
Africa          Cameroon        Wet Forest                  CAM.02
Africa          Cameroon        Wet Forest                  CAM.03
Africa          Ghana           Wet Forest                  CAP.09
Africa          Ghana           Wet Forest                  CAR10
Africa          Liberia         Wet Forest                  CVL.01
Africa          Liberia         Wet Forest                  CVL.11
Africa          Cameroon        Wet Forest                  DJK.01
Africa          Cameroon        Wet Forest                  DJK.02
Africa          Cameroon        Wet Forest                  DJK.03
Africa          Cameroon        Wet Forest                  DJK.04
Africa          Cameroon        Wet Forest                  DJK.05
Africa          Cameroon        Wet Forest                  DJK.06
Africa          Cameroon        Wet Forest                  DJL.01
Africa          Cameroon        Wet Forest                  DJL.02
Africa          Cameroon        Wet Forest                  DJL.03
Africa          Cameroon        Wet Forest                  DJL.04
Africa          Cameroon        Wet Forest                  DJL.05
Africa          Cameroon        Wet Forest                  DJL.06
Africa          Ghana           Wet Forest                  DRA.04
Africa          Ghana           Wet Forest                  DRA.05
Africa          Cameroon        Wet Forest                  EJA.04
Africa          Cameroon        Wet Forest                  EJA.05
Africa          Ghana           Wet Forest                  ESU.18
Africa          Ghana           Wet Forest                  ESU.20
Africa          Ghana           Wet Forest                  FUR.07
Africa          Ghana           Wet Forest                  FUR.08
Africa          Liberia         Wet Forest                  GBO.01
Africa          Liberia         Wet Forest                  GBO.11
Africa          Liberia         Wet Forest                  GBO.20
Africa          Gabon           Wet Forest                  LM
Africa          Gabon           Wet Forest                  MDC.01
Africa          Gabon           Wet Forest                  MDC.02
Africa          Gabon           Wet Forest                  MDC.03
Africa          Gabon           Wet Forest                  MDC.04
Africa          Gabon           Wet Forest                  MDC.05
Africa          Cameroon        Wet Forest                  MDJ.01
Africa          Cameroon        Wet Forest                  MDJ.03
Africa          Cameroon        Wet Forest                  MDJ.07
Africa          Cameroon        Wet Forest                  MDJ.10
Africa          Equat. Guinea   Wet Forest                  MMI.01
Africa          Equat. Guinea   Wet Forest                  MMI.02
Africa          Ghana           Wet Forest                  TBE.05
Africa          Ghana           Wet Forest                  TBE.08
Africa          Ghana           Wet Forest                  TBE.09
Africa          Ghana           Wet Forest                  TON.01
Africa          Ghana           Wet Forest                  TON.08
Africa          Gabon           Wet Forest                  WKA.09
Africa          Gabon           Wet Forest                  WKA.10
Asia            India           Deciduous Forest            Akovil
Asia            India           Deciduous Forest            Bathery
Asia            India           Deciduous Forest            Bela
Asia            India           Deciduous Forest            Bondla
Asia            India           Deciduous Forest            Dand
Asia            India           Deciduous Forest            Mbolly
Asia            India           Deciduous Forest            Mulla
Asia            India           Deciduous Forest            Mund
Asia            India           Deciduous Forest            Nadke
Asia            India           Deciduous Forest            Phan
Asia            India           Deciduous Forest            Sthoppu
Asia            India           Deciduous Forest            Tansa
Asia            India           Deciduous Forest            Thek
Asia            India           Deciduous Forest            Tkad
Asia            India           Deciduous Forest            Top
Asia            India           Deciduous Forest            Tyanai
Asia            India           Deciduous Forest            Uthanni
Asia            India           Deciduous Forest            Vasant
Asia            Cambodia        Deciduous Forest            Cambodial
                                  (Dry Dipterocarp)
Asia            Cambodia        Deciduous Forest (Sralao)   Cambodia5
Asia            Cambodia        Deciduous Forest (Sralao)   Cambodia6
Asia            India           Dry Evergreen Forest        TDEF.AK
Asia            India           Dry Evergreen Forest        TDEF.AR
Asia            India           Dry Evergreen Forest        TDEF.CK
Asia            India           Dry Evergreen Forest        TDEF.KK
Asia            India           Dry Evergreen Forest        TDEF.KR
Asia            India           Dry Evergreen Forest        TDEF.MM
Asia            India           Dry Evergreen Forest        TDEF.OR
Asia            India           Dry Evergreen Forest        TDEF.PP
Asia            India           Dry Evergreen Forest        TDEF.PT
Asia            India           Dry Evergreen Forest        TDEF.RP
Asia            India           Dry Evergreen Forest        TDEF.SK
Asia            India           Dry Evergreen Forest        TDEF.SP
Asia            India           Dry Evergreen Forest        TDEF.SPD
Asia            India           Dry Evergreen Forest        TDEF.SR
Asia            India           Dry Evergreen Forest        TDEF.TM
Asia            India           Dry Evergreen Forest        TDEF.VP
Asia            India           Wet Forest                  Ach
Asia            India           Wet Forest                  AG.1
Asia            India           Wet Forest                  AG.2
Asia            India           Wet Forest                  AG.3
Asia            India           Wet Forest                  Agu
Asia            India           Wet Forest                  Amb
Asia            India           Wet Forest                  Ans
Asia            India           Wet Forest                  Bhi
Asia            India           Wet Forest                  Bra
Asia            India           Wet Forest                  COURT.1
Asia            India           Wet Forest                  Kat
Asia            India           Wet Forest                  KMTR
Asia            India           Wet Forest                  KO.KS
Asia            India           Wet Forest                  KO.MS
Asia            India           Wet Forest                  KO.PS
Asia            India           Wet Forest                  KO.VS
Asia            India           Wet Forest                  Koy
Asia            India           Wet Forest                  KS
Asia            India           Wet Forest                  Kud
Asia            India           Wet Forest                  Mat
Asia            India           Wet Forest                  Nel
Asia            India           Wet Forest                  Nil
Asia            India           Wet Forest                  Par
Asia            India           Wet Forest                  Per
Asia            India           Wet Forest                  Push
Asia            India           Wet Forest                  Rad
Asia            India           Wet Forest                  Radha
Asia            India           Wet Forest                  Sch
Asia            India           Wet Forest                  SilVal
Asia            India           Wet Forest                  Sub
Asia            India           Wet Forest                  Tal
Asia            India           Wet Forest                  Tatte
Asia            India           Wet Forest                  VA.AK
Asia            India           Wet Forest                  VA.IP
Asia            India           Wet Forest                  VA.LM
Asia            India           Wet Forest                  Valp
Asia            India           Wet Forest                  Vara
Asia            India           Wet Forest                  Vazh
Asia            India           Wet Forest                  VG.ha1
Asia            India           Wet Forest                  VG.ha10
Asia            India           Wet Forest                  VG.ha20
Asia            India           Wet Forest                  VG.ha30
Asia            Vietnam         Wet Forest                  VietnamA
Asia            Vietnam         Wet Forest                  VietnamB
Asia            Vietnam         Wet Forest                  VietnamC
Asia            Vietnam         Wet Forest                  VietnamE
Asia            India           Wet Forest                  Vish
Asia            India           Wet Forest                  Wyn
Asia            Cambodia        Wet Forest (Riverine)       Cambodia2
Asia            Cambodia        Wet Forest (Riverine)       Cambodia3
Asia            Cambodia        Wet Forest (Riverine)       Cambodia4
Asia            Cambodia        Wet Forest (Swamp)          Cambodia7
Asia            Cambodia        Wet Forest (Tall            Cambodia8
                                  Dipterocarp)
Asia            Cambodia        Wet Forest (Tall            Cambodia9
                                  Dipterocarp)
Asia            Cambodia        Wet Forest (Tall            Cambodia10
                                  Dipterocarp)
South America   Brazil          Savanna                     IBGE
South America   Boh via         Savanna                     LFB.03
South America   Brazil          Savanna                     NXV.01
South America   Brazil          Savanna                     NXV.02
South America   Brazil          Savanna                     NXV.03
South America   Brazil          Savanna                     NXV.05
South America   Brazil          Savanna                     NXV.09
South America   Brazil          Savanna                     SMT.01
South America   Brazil          Savanna                     SMT.02
South America   Brazil          Savanna                     SMT.03
South America   Boh via         SDTF                        ACU.01
South America   Boh via         SDTF                        ACU.02
South America   Bolivia         SDTF                        CRP.01
South America   Bolivia         SDTF                        CRP.02
South America   Bolivia         SDTF                        OTT.01
South America   Bolivia         SDTF                        OTT.02
South America   Bolivia         SDTF                        OTT.03
South America   Bolivia         SDTF                        SRQ.01
South America   Brazil          SDTF                        TA_BA
South America   Brazil          SDTF                        TA_DF
South America   Brazil          SDTF                        TA_GO
South America   Brazil          SDTF                        TA_GO_A
South America   Brazil          SDTF                        TA_GO_C
South America   Brazil          SDTF                        TA_GO_D
South America   Brazil          SDTF                        TA_GO_E
South America   Brazil          SDTF                        TA_GO_F
South America   Brazil          SDTF                        TA_MS_A
South America   Brazil          SDTF                        TA_MS_B
South America   Brazil          SDTF                        TA_MS_D
South America   Brazil          SDTF                        TA_MT
South America   Boh via         SDTF                        TUC.01
South America   Boh via         SDTF                        TUC.03
South America   Brazil          Wet Forest                  ALF.01
South America   Brazil          Wet Forest                  ALF.02
South America   Boh via         Wet Forest                  BBC.01
South America   Boh via         Wet Forest                  BBC.02
South America   Boh via         Wet Forest                  BEE.01
South America   Boh via         Wet Forest                  BEE.05
South America   Boh via         Wet Forest                  CHO.01
South America   Boh via         Wet Forest                  CHO.02
South America   Brazil          Wet Forest                  DOI.01
South America   Brazil          Wet Forest                  DOI.02
South America   Brazil          Wet Forest                  FEC.01
South America   Brazil          Wet Forest                  FLO.01
South America   Boh via         Wet Forest                  FOB.01
South America   Boh via         Wet Forest                  HCC.11
South America   Boh via         Wet Forest                  HCC.12
South America   Boh via         Wet Forest                  HCC.21
South America   Boh via         Wet Forest                  HCC.22
South America   Boh via         Wet Forest                  HCC.23
South America   Boh via         Wet Forest                  HCC.24
South America   Brazil          Wet Forest                  JFR.01
South America   Brazil          Wet Forest                  JFR.02
South America   Brazil          Wet Forest                  JFR.09
South America   Bohvia          Wet Forest                  KEN.01
South America   Bohvia          Wet Forest                  LCA.13
South America   Bohvia          Wet Forest                  LCA.16
South America   Bohvia          Wet Forest                  LCA.29
South America   Bohvia          Wet Forest                  LCA.30
South America   Bohvia          Wet Forest                  LFB.01
South America   Bohvia          Wet Forest                  LFB.02
South America   Bohvia          Wet Forest                  LGB.01
South America   Bohvia          Wet Forest                  LSL.01
South America   Bohvia          Wet Forest                  LSL.02
South America   Bohvia          Wet Forest                  MBT.01
South America   Bohvia          Wet Forest                  MBT.05
South America   Bohvia          Wet Forest                  MBT.08
South America   Bohvia          Wet Forest                  MVE.01
South America   Bohvia          Wet Forest                  NCR.01
South America   Bohvia          Wet Forest                  NCR.02
South America   Bohvia          Wet Forest                  NEN.01
South America   Bohvia          Wet Forest                  NEN.02
South America   Bohvia          Wet Forest                  NLT.01
South America   Bohvia          Wet Forest                  NLT.02
South America   Brazil          Wet Forest                  NXV.06
South America   Brazil          Wet Forest                  NXV.07
South America   Brazil          Wet Forest                  NXV.08
South America   Brazil          Wet Forest                  PEA.01
South America   Brazil          Wet Forest                  PEA.02
South America   Brazil          Wet Forest                  POR.01
South America   Brazil          Wet Forest                  POR.02
South America   Brazil          Wet Forest                  RBR.01
South America   Bohvia          Wet Forest                  RET.06
South America   Bohvia          Wet Forest                  RET.08
South America   Boh via         Wet Forest                  SCT.01
South America   Boh via         Wet Forest                  SCT.06
South America   Brazil          Wet Forest                  SIP.01
South America   Brazil          Wet Forest                  TAN.02
South America   Brazil          Wet Forest                  TAN.03
South America   Brazil          Wet Forest                  TAN.04
South America   Brazil          Wet Forest                  VCR.01
South America   Brazil          Wet Forest                  VCR.02

Continent       Latitude   Longitude   Total       Number     Min. Size
                                       Area (ha)   of Plots   (cm DBH)

Africa          -11.48     27.67       1           1          10
Africa          -11.48     27.67       1           1          10
Africa          -11.48     27.67       1           1          10
Africa          -11.48     27.67       1           1          10
Africa          -11.48     27.67       1           1          10
Africa          -11.48     27.67       1           1          10
Africa          -11.48     27.67       1           1          10
Africa          -11.48     27.67       1           1          10
Africa          -11.48     27.67       1           1          10
Africa          -11.48     27.67       1           1          10
Africa          6.16       12.82       1           1          10
Africa          6.00       12.87       1           1          10
Africa          6.00       12.89       1           1          10
Africa          6.21       12.75       1           1          10
Africa          6.01       12.89       0.4         1          10
Africa          9.67       12.14       1           1          10
Africa          9.70       12.15       1           1          10
Africa          9.61       12.48       1           1          10
Africa          9.76       12.49       1           1          10
Africa          9.83       12.38       1           1          10
Africa          7.14       -2.45       1           1          10
Africa          7.13       -2.47       1           1          10
Africa          7.16       -2.45       1           1          10
Africa          7.13       -2.47       1           1          10
Africa          6.71       -1.29       1           1          10
Africa          6.70       -1.29       1           1          10
Africa          6.69       -1.28       1           1          10
Africa          3.30       12.48       1           1          10
Africa          3.29       12.48       1           1          10
Africa          3.22       12.49       1           1          10
Africa          3.21       12.50       1           1          10
Africa          3.31       12.49       1           1          10
Africa          3.31       12.49       1           1          10
Africa          5.35       -1.83       1           1          10
Africa          5.35       -1.84       1           1          10
Africa          2.36       9.93        1           1          10
Africa          2.31       9.92        1           1          10
Africa          2.42       9.90        1           1          10
Africa          4.85       -2.04       1           1          10
Africa          4.80       -2.05       1           1          10
Africa          6.19       -8.18       1           1          10
Africa          6.19       -8.18       1           1          10
Africa          3.33       12.72       1           1          10
Africa          3.33       12.72       1           1          10
Africa          3.36       12.72       1           1          10
Africa          3.36       12.73       1           1          10
Africa          3.32       12.76       1           1          10
Africa          3.33       12.76       1           1          10
Africa          3.12       13.58       1           1          10
Africa          3.12       13.59       1           1          10
Africa          3.04       13.62       1           1          10
Africa          3.05       13.62       1           1          10
Africa          3.03       13.58       1           1          10
Africa          3.03       13.61       1           1          10
Africa          5.16       -2.38       1           1          10
Africa          5.21       -2.44       1           1          10
Africa          5.75       8.99        1           1          10
Africa          5.75       8.99        1           1          10
Africa          5.86       -0.80       1           1          10
Africa          5.83       -0.78       1           1          10
Africa          5.56       -2.39       1           1          10
Africa          5.58       -2.39       1           1          10
Africa          5.39       -7.62       1           1          10
Africa          5.39       -7.59       1           1          10
Africa          5.41       -7.59       1           1          10
Africa          -0.19      11.58       1.2         15         10
Africa          0.62       10.41       1           1          10
Africa          0.62       10.41       1           1          10
Africa          0.62       10.42       1           1          10
Africa          0.47       10.28       1           1          10
Africa          0.46       10.29       1           1          10
Africa          6.17       12.83       1           1          10
Africa          5.98       12.87       1           1          10
Africa          6.01       12.89       1           1          10
Africa          6.00       12.89       0.4         1          10
Africa          1.39       9.92        1           1          10
Africa          1.37       9.97        1           1          10
Africa          7.01       -2.05       1           1          10
Africa          7.02       -2.07       1           1          10
Africa          7.02       -2.06       1           1          10
Africa          6.07       -2.12       1           1          10
Africa          6.04       -2.10       1           1          10
Africa          -1.14      11.07       1           1          10
Africa          -1.14      11.07       1           1          10
Asia            9.52       77.45       0.125       2          3.18
Asia            11.70      76.36       0.125       2          3.18
Asia            14.95      74.15       0.125       2          3.18
Asia            15.43      74.10       0.125       2          3.18
Asia            15.16      74.63       0.375       6          3.18
Asia            10.37      76.88       0.125       2          3.18
Asia            9.53       77.25       0.125       2          3.18
Asia            8.68       77.35       0.125       2          3.18
Asia            14.99      74.21       0.125       2          3.18
Asia            18.65      73.00       0.375       6          3.18
Asia            9.56       77.57       0.125       2          3.18
Asia            19.60      73.24       0.125       2          3.18
Asia            9.59       77.17       0.125       2          3.18
Asia            10.13      76.70       0.125       2          3.18
Asia            10.49      76.84       0.125       2          3.18
Asia            8.53       77.50       0.125       2          3.18
Asia            10.13      76.72       0.125       2          3.18
Asia            15.40      74.26       0.125       2          3.18
Asia            12.92      105.61      0.5         10         10
Asia            13.45      105.61      0.5         10         10
Asia            13.44      105.53      0.6         12         10
Asia            11.69      79.67       1           1          10
Asia            10.45      79.08       1           1          10
Asia            11.51      79.71       1           1          10
Asia            11.72      79.67       1           1          10
Asia            10.46      79.05       1           1          5
Asia            10.48      79.11       1           1          10
Asia            13.60      79.92       1           2          10
Asia            12.55      79.87       1           1          10
Asia            11.53      79.70       1           1          10
Asia            10.00      78.81       1           1          10
Asia            11.50      79.70       1           1          10
Asia            9.98       78.81       1           1          10
Asia            11.67      79.70       1           1          10
Asia            11.73      79.64       1           1          10
Asia            11.72      79.68       1           1          10
Asia            11.94      79.39       1           1          10
Asia            9.11       77.19       0.25        4          3.18
Asia            13.52      75.08       1           1          10
Asia            13.52      75.08       1           1          10
Asia            13.52      75.08       1           1          10
Asia            13.51      75.08       0.375       6          3.18
Asia            15.94      74.00       0.375       6          3.18
Asia            15.01      74.38       0.5         8          3.18
Asia            19.06      73.54       0.1875      3          3.18
Asia            12.08      75.80       1.25        20         3.18
Asia            9.25       77.25       1           1          10
Asia            14.27      74.75       0.6875      11         3.18
Asia            8.59       77.35       0.6875      11         3.18
Asia            11.33      78.38       2           1          10
Asia            11.33      78.38       2           1          10
Asia            11.33      78.38       2           1          10
Asia            11.33      78.38       2           1          10
Asia            17.44      73.71       0.1875      3          3.18
Asia            10.47      76.83       0.375       6          3.18
Asia            13.24      75.16       0.5         8          3.18
Asia            12.09      75.76       0.1875      3          3.18
Asia            10.53      76.68       0.125       2          3.18
Asia            11.44      76.39       0.1875      3          3.18
Asia            10.42      76.71       0.1875      3          3.18
Asia            9.49       77.19       0.5625      9          3.18
Asia            12.59      75.68       0.25        4          3.18
Asia            16.37      73.87       0.125       2          3.18
Asia            16.33      73.90       0.1875      3          3.18
Asia            8.88       77.14       0.4375      7          3.18
Asia            11.12      76.44       0.625       10         3.18
Asia            12.63      75.65       0.1875      3          3.18
Asia            12.36      75.48       0.125       2          3.18
Asia            10.12      76.77       0.1875      3          3.18
Asia            10.40      77.45       1           25         10
Asia            10.40      77.45       0.8         20         10
Asia            10.40      77.45       0.8         20         10
Asia            10.34      76.91       0.375       6          3.18
Asia            10.42      76.87       0.1875      3          3.18
Asia            10.30      76.67       0.375       6          3.18
Asia            10.42      76.87       1           1          10
Asia            10.42      76.87       1           1          10
Asia            10.42      76.87       1           1          10
Asia            10.42      76.87       1           1          10
Asia            11.43      107.33      1           1          10
Asia            11.43      107.33      1           1          10
Asia            11.43      107.33      1           1          10
Asia            11.43      107.33      1           1          10
Asia            16.94      73.79       0.125       2          3.18
Asia            11.84      75.81       0.125       2          3.18
Asia            13.35      105.62      0.9         18         10
Asia            13.25      105.58      0.5         10         10
Asia            13.43      105.55      0.45        9          10
Asia            13.34      105.60      0.95        19         10
Asia            13.34      105.61      0.4         8          10

Asia            13.25      105.58      0.55        11         10

Asia            13.43      105.59      1.1         22         10

South America   -15.92     -17.88      3           4          10
South America   -14.58     -60.83      1           1          10
South America   -14.71     -52.35      1           1          10
South America   -14.70     -52.35      1           1          10
South America   -14.71     -52.35      0.5         1          5
South America   -14.71     -52.35      0.5         1          5
South America   -14.69     -52.35      0.5         1          5
South America   -12.82     -51.77      1           1          10
South America   -12.82     -51.77      1           1          10
South America   -12.82     -51.77      1           1          10
South America   -15.25     -61.25      1           1          10
South America   -15.25     -61.24      1           1          10
South America   -14.54     -61.50      1           1          10
South America   -14.54     -61.50      1           1          10
South America   -16.39     -61.21      1           1          10
South America   -16.39     -61.21      1           1          10
South America   -16.42     -61.19      1           1          10
South America   -14.40     -62.30      1           1          10
South America   -13.50     -14.24      1           25         5
South America   -15.50     -47.30      1           25         5
South America   -13.15     -46.66      1           25         5
South America   -14.06     -46.49      1           25         5
South America   -13.66     -46.75      2.4         60         5
South America   -13.83     -46.70      1           25         5
South America   -13.52     -46.50      1           25         5
South America   -13.69     -46.74      1           25         5
South America   -19.03     -57.68      0.3         80         5
South America   -19.03     -57.68      0.4         78         5
South America   -19.21     -57.79      0.1         20         5
South America   -14.35     -52.35      1           25         5
South America   -18.52     -60.81      1           1          10
South America   -18.52     -60.81      1           1          10
South America   -9.60      -55.94      1           1          10
South America   -9.58      -55.92      1           1          10
South America   -14.30     -60.53      1           1          10
South America   -14.30     -60.53      1           1          10
South America   -16.53     -64.58      1           1          10
South America   -16.53     -64.58      1           1          10
South America   -14.39     -61.15      1           1          10
South America   -14.34     -61.16      1           1          10
South America   -10.57     -68.31      1           1          10
South America   -10.55     -68.31      1           1          10
South America   -10.07     -67.62      1           1          10
South America   -12.81     -51.85      1           1          10
South America   -13.57     -61.02      1           1          10
South America   -13.91     -60.82      1           1          10
South America   -13.91     -60.82      1           1          10
South America   -14.53     -60.74      1           1          10
South America   -14.53     -60.73      1           1          10
South America   -14.56     -60.75      1           1          10
South America   -14.57     -60.75      1           1          10
South America   -10.48     -58.47      0.93        1          10
South America   -10.53     -58.50      0.525       1          10
South America   -10.47     -58.51      0.975       1          10
South America   -16.02     -62.73      1           1          10
South America   -15.68     -62.78      1           1          10
South America   -15.68     -62.78      1           1          10
South America   -15.68     -62.77      1           1          10
South America   -15.68     -62.77      1           1          10
South America   -14.58     -60.83      1           1          10
South America   -14.58     -60.83      1           1          10
South America   -14.80     -60.39      1           1          10
South America   -14.40     -61.14      1           1          10
South America   -14.40     -61.14      1           1          10
South America   -10.07     -65.89      1           1          10
South America   -10.03     -65.63      1           1          10
South America   -9.94      -65.75      1           1          10
South America   -15.01     -61.13      1           1          10
South America   -14.64     -61.16      1           1          10
South America   -14.71     -61.15      1           1          10
South America   -13.63     -60.89      1           1          10
South America   -13.63     -60.89      1           1          10
South America   -13.65     -60.82      1           1          10
South America   -13.65     -60.83      1           1          10
South America   -14.72     -52.36      0.47        1          5
South America   -14.72     -52.36      0.47        1          5
South America   -14.72     -52.36      0.47        1          5
South America   -12.15     -50.83      1           1          5
South America   -12.32     -50.74      1           1          5
South America   -10.82     -68.78      1           1          10
South America   -10.80     -68.77      1           1          10
South America   -11.00     -61.95      1           1          10
South America   -10.97     -65.72      1           1          10
South America   -10.97     -65.72      1           1          10
South America   -17.09     -64.77      1           1          10
South America   -17.09     -64.77      1           1          10
South America   -11.41     -55.32      1           1          10
South America   -13.09     -52.38      1           1          10
South America   -12.82     -52.36      1           1          10
South America   -12.92     -52.37      1           1          10
South America   -14.83     -52.16      1           1          10
South America   -14.83     -52.17      1           1          10

Continent       Number of     Citation
                Individuals

Africa          402           S1
Africa          441           S1
Africa          439           S1
Africa          509           S1
Africa          376           S1
Africa          381           S1
Africa          496           S1
Africa          404           S1
Africa          623           S1
Africa          468           S1
Africa          135           S2
Africa          212           S2
Africa          309           S2
Africa          240           S2
Africa          43            S2
Africa          270           S3
Africa          225           S3
Africa          382           S3
Africa          227           S3
Africa          285           S3
Africa          347           S2, S4
Africa          364           S5
Africa          149           S6
Africa          115           S5
Africa          490           S7
Africa          566           S7
Africa          455           S7
Africa          330           S8
Africa          491           S8
Africa          331           S8
Africa          434           S8
Africa          325           S8
Africa          436           S8
Africa          337           S7
Africa          430           S7
Africa          403           S8
Africa          419           S8
Africa          404           S8
Africa          516           S8
Africa          508           S8
Africa          503           S8
Africa          458           S8
Africa          314           S8
Africa          407           S8
Africa          343           S8
Africa          477           S8
Africa          371           S8
Africa          432           S8
Africa          351           S8
Africa          435           S8
Africa          429           S8
Africa          613           S8
Africa          320           S8
Africa          496           S8
Africa          422           S7
Africa          409           S7
Africa          556           S8
Africa          559           S8
Africa          450           S7
Africa          541           S6
Africa          576           S7
Africa          563           S7
Africa          364           S8
Africa          424           S8
Africa          339           S8
Africa          488           S8
Africa          531           S8
Africa          547           S8
Africa          518           S8
Africa          506           S8
Africa          521           S8
Africa          558           S4
Africa          418           S4
Africa          449           S4
Africa          183           S4
Africa          416           S8
Africa          634           S8
Africa          493           S7
Africa          356           S6
Africa          490           S6
Africa          458           S7
Africa          484           S7
Africa          546           S8
Africa          602           S8
Asia            188           S9
Asia            193           S9
Asia            93            S9
Asia            166           S9
Asia            284           S9
Asia            206           S9
Asia            200           S9
Asia            148           S9
Asia            182           S9
Asia            740           S9
Asia            114           S9
Asia            100           S9
Asia            327           S9
Asia            231           S9
Asia            63            S9
Asia            111           S9
Asia            158           S9
Asia            281           S9
Asia            302           S10
Asia            253           S10
Asia            203           S10
Asia            748           S11
Asia            511           S12
Asia            347           S13
Asia            654           S14
Asia            855           S12
Asia            358           S12
Asia            934           S11
Asia            870           S15
Asia            687           S16
Asia            522           S12
Asia            696           S16
Asia            470           S12
Asia            292           S13
Asia            359           S13
Asia            390           S14
Asia            803           S13
Asia            132           S9
Asia            600           S17
Asia            311           S17
Asia            580           S17
Asia            214           S9
Asia            242           S9
Asia            410           S9
Asia            87            S9
Asia            714           S9
Asia            546           S18
Asia            537           S9
Asia            514           S9
Asia            813           S19
Asia            1190          S19
Asia            1138          S19
Asia            1309          S19
Asia            161           S9
Asia            252           S9
Asia            356           S9
Asia            101           S9
Asia            66            S9
Asia            106           S9
Asia            93            S9
Asia            381           S9
Asia            151           S9
Asia            78            S9
Asia            223           S9
Asia            234           S9
Asia            399           S9
Asia            111           S9
Asia            77            S9
Asia            79            S9
Asia            611           S20
Asia            395           S20
Asia            484           S20
Asia            211           S9
Asia            120           S9
Asia            127           S9
Asia            285           S21
Asia            360           S21
Asia            381           S21
Asia            387           S21
Asia            384           S22
Asia            416           S22
Asia            425           S22
Asia            522           S22
Asia            78            S9
Asia            66            S9
Asia            475           S10
Asia            285           S10
Asia            294           S10
Asia            486           S23
Asia            188           S10
Asia            280           S10
Asia            510           S10
South America   305           S24, S25
South America   204           S2
South America   385           S2
South America   571           S2
South America   1045          S2
South America   1179          S2
South America   916           S2
South America   381           S2
South America   444           S2
South America   209           S2
South America   336           S2, S4
South America   406           S24, S25
South America   456           S24, S25
South America   497           S24, S25
South America   410           S2, S4
South America   169           S2, S4
South America   250           S2
South America   291           S24, S25
South America   881           S26
South America   1189          S26
South America   734           S26
South America   756           S27
South America   609           S28
South America   536           S29
South America   842           S30
South America   920           S31
South America   320           S32
South America   410           S32
South America   80            S33
South America   813           S26
South America   828           S2, S4
South America   152           S2, S4
South America   506           S2, S4
South America   537           S2, S4
South America   515           S24, S25
South America   537           S24, S25
South America   571           S24, S25
South America   544           S24, S25
South America   623           S24, S25
South America   519           S24, S25
South America   466           S2, S4
South America   244           S2, S4
South America   411           S2, S4
South America   608           S2, S4
South America   224           S24, S25
South America   534           S24, S25
South America   690           S24, S25
South America   556           S24, S25
South America   609           S24, S25
South America   638           S24, S25
South America   488           S24, S25
South America   383           S24, S25
South America   168           S24, S25
South America   382           S24, S25
South America   438           S24, S25
South America   420           S24, S25
South America   441           S24, S25
South America   397           S24, S25
South America   425           S24, S25
South America   559           S2, S4
South America   525           S2, S4
South America   598           S24, S25
South America   494           S24, S25
South America   612           S24, S25
South America   448           S24, S25
South America   490           S24, S25
South America   437           S24, S25
South America   567           S24, S25
South America   475           S24, S25
South America   532           S24, S25
South America   561           S24, S25
South America   500           S24, S25
South America   456           S24, S25
South America   304           S24, S25
South America   480           S24, S25
South America   395           S24, S25
South America   571           S24, S25
South America   1600          S24, S25
South America   1311          S24, S25
South America   527           S2, S4
South America   501           S2, S4
South America   565           S24, S25
South America   523           S24, S25
South America   523           S24, S25
South America   391           S24, S25
South America   335           S24, S25
South America   349           S24, S25
South America   489           S24, S25
South America   577           S24, S25
South America   567           S2, S4
South America   523           S2, S4
South America   532           S2, S4


SUPPLEMENTAL MATERIAL REFERENCES

S1. Ilimga Muledi, J. and Meerts, P. Unpublished data.

S2. Torello-Raventos, M., Feldpausch, T.R., Veenendaal, E., Schrodt, E, Saiz, G., Domingues, T.F., et al. (2013). On the delineation of tropical vegetation types with an emphasis on forest/savanna transitions. Plant Ecol. Divers., 6, 101-137.

S3. Bessike Balinga, M. P. Unpublished data.

S4. Feldpausch, T.R., Lloyd, J., Lewis, S.L., Brienen, R.J.W., Gloor, M., Monteagudo Mendoza, a., et al. (2012). Tree height integrated into pantropical forest biomass estimates. Biogeosciences, 9, 3381-3403.

S5. Lloyd, J. Unpublished data, TROBIT project.

S6. Fauset, S. Unpublished data.

S7. Fauset, S., Baker, T.R., Lewis, S.L., Feldpausch, T.R., Affum-Baffoe, K., Foli, E.G., et al. (2012). Drought-induced shifts in the Holistic and functional composition of tropical forests in Ghana. Ecol. Lett., 15, 1120-9.

S8. Lewis, S.L., Lopez-Gonzalez, G., Sonke, B., AffumBaffoe, K., Baker, T.R., Ojo, L.O., et al. (2009). Increasing carbon storage in intact African tropical forests. Nature, 457, 1003-6.

S9. Page, N. Unpublished data.

S10. Theilade, I. Unpubhshed data.

S11. Venkateswaran, R. and Parthasarathy, N. (2003). Tropical dry evergreen forests on the Coromandel coast of India: structure, composition and human disturbance. Ecotropica, 9, 45-58.

S12. Mani, S. and Parthasarathy, N. (2005). Biodiversity assessment of trees in five inland tropical dry evergreen forests of peninsular India. Syst. Biodivers., 3, 1-12.

S13. Anbarashan, M. and Parthasarathy, N. (2013). Tree diversity of tropical dry evergreen forests dominated by single or mixed species on the Coromandel coast of India. Trop. Ecol., 54, 179-190.

S14. Parthasarathy, N. and Karthikeyan, R. (1997a). Plant biodiversity inventory and conservation of two tropical dry evergreen forests on the Coromandel coast, south India. Biodivers. Conserv., 6, 1063-1083.

S15. Parthasarathy, N. and Sethi, P. (1997). Trees and liana species diversity and population structure in a tropical dry evergreen forest in south India. Trop. Ecol., 38, 19-30.

S16. Anbarashan, M. and Parthasarathy, N. (2008). Comparitive tree community analysis of two old-growth tropical dry evergreen forests of peninsular India. In: Biodivers. Impact Assess, (ed. Trivedi, PC.). Pointers Pubhshers, Jaipur, India, pp. 202-211.

S17. Srinivas, V. and Parthasarathy, N. (2000). Comparative analysis of tree diversity and dispersion in tropical lowland evergreen forest of Agumbe, central Western Ghats, India. Trop. Biodivers., 7, 45-60.

S18. Parthasarathy, N. and Karthikeyan, R. (1997). Biodiversity and population density of woody species in a tropical evergreen forest in Courtallum reserve forest, Western Ghats, India. Trop. Ecol., 38, 297-306.

S19. Chittibabu, C. V and Parthasarathy, N. (2000). Attenuated tree species diversity in human-impacted tropical evergreen forest sites at Kolli hills, Eastern Ghats, India. Biodivers. Conserv., 9, 1493-1519.

S20. Muthuramkumar, S., Ayyappan, N., Parthasarathy, N., Mudappa, D., Raman, T.R.S., Selwyn, M.A., et al. (2006). Plant community structure in tropical rainforest fragments of the Western Ghats, India. Biotropica, 38, 143-160.

S21. Ayyappan, N. and Parthasarathy, N. (2001). Patterns of tree diversity within a large-scale permanent plot of tropical evergreen forest, Western Ghats, India. Ecotropica, 7, 61-76.

S22. Blanc, L., Maury-Lecho, G. and Pascal, J.-P. (2000). Structure, floristic composition and natural regeneration in the forests of Cat Tien National Park, Vietnam: an analysis of the successional trends. J. Biogeogr., 27, 141-157.

S23. Theilade, I., Schmidt, L., Chhang, P. and McDonald, J.A. (2011). Evergreen swamp forest in Cambodia: floristic composition, ecological characteristics, and conservation status. Nord. J. Bot., 29, 71-80.

S24. Lopez-Gonzalez, G., Lewis, S.L., Burkitt, M. and Phillips, O.L. (2011). ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. J. Veg. Sci., 22, 610-613.

S25. Lopez-Gonzalez, G., Lewis, S.L., Burkitt, M., Baker T.R. and Phillips, O.L. (2013). ForestPlots.net Database. www.forestplots.net.

S26. Silva Pereira, B.A. (2008). Relacoes vegetacao-variaves amientais em florestas estacionais deciduas em afloramentos calcarios no bioma cerrado e em zonas de transicao com a caatina e com amaconia. Ph.D. Thesis, Department of Ecology, Universidade de Brasilia, Brasilia, Brazil.

S27. Carvalho, F.A. and Felfili, J.M. (2011). Variacoes temporais na comunidade arborea de uma floresta decidual sobre afloramentos calcarios no Brasil Central: composicao, estrutura e diversidade floristica 1: Introducao. Acta Bot. Brasilica, 25, 203-214.

S28. Sampaio, A.B. and Scariot, A. (2011). Edge effect on tree diversity, composition, and structure in a deciduous dry forest in central Brazil. Rev. Arvore, 35, 1121-1134.

S29. Da Silva, L.A. and Scariot, A. (2003). Composicao floristica da comunidade arborea de uma floresta estacional decidual sobre afloramento calcario (Fazenda Sao Jose, Sao Domingos, GO, Bacia do Rio Parana). Acta Bot. Brasilica, 17, 305-313.

S30. Da Silva, L.A. and Scariot, A. (2004a). Composicao e estrutura da comunidade arborea de uma floresta estacional decidual sobre alforamento calcario no Brasil central. Rev. Arvore, 28, 69-75.

S31. Da Silva, L.A. and Scariot, A. (2004b). Comunidade arborea de uma floresta estacional decidua sobre afloramento calcario na bacio do Rio Parana. Rev. Arvore, 28, 61-67.

S32. Soares de Lima, M. and Tanaka, M.O. (2010). Aspectos estruturais da comunidade arborea em remanescentes de floresta estacional decidual, em Corumba, MS, Brasil 1. Rev. Braseilera Bot., 33, 437-453.

S33. Salis, S.M., Pereira, M., Silva, D.A., Mattos, P.P.D.E., Vila, J.S., Joana, V., et al. (2004). Fitossociologia de remanescentes de floresta estacional decidual em Corumba, Estado do Mato Grosso do Sul, Brasil. Rev. Braseilera Bot., 27, 671-684.

K.G. DEXTER (1, 2) *, B. SMART (2), C. BALDAUF (3), T.R. BAKER (4), M.P. BESSIKE BALINGA (5), R.J.W. BRIENEN (4), S. FAUSET (4, 6), T.R. FELDPAUSCH (7), L. FERREIRA-DA SILVA (8), J. ILUNGA MULEDI (9), S.L. LEWIS (4, 10), G. LOPEZ-GONZALEZ (4), B.H. MARIMON-JUNIOR (11), B.S. MARIMON (11), P. MEERTS (12), N. PAGE (13), N. PARTHASARATHY (14), O.L. PHILLIPS (4), T.C.H. SUNDERLAND (15), I. THEILADE (16), J. WEINTRITT (2), K. AFFUM-BAFFOE (17), A. ARAUJO (18), L. ARROYO (19), S.K. BEGNE (20), E. CARVALHO-DAS NEVES (11), M. COLLINS (1), A. CUNI-SANCHEZ (10), M.N.K. DJUIKOUO (21), F. ELIAS (11), E.G. FOLI (22), K.J. JEFFERY (23, 24, 25), T.J. KILLEEN (18), Y. MALHI (26), L. MARACAHIPES (11, 27), C. MENDOZA (28), A. MONTEAGUDO-MENDOZA (29), P. MORANDI (11), C. OLIVEIRA-DOS SANTOS (11), A.G. PARADA (18), G. PARDO (30), K.S.-H. PEH (31, 32), R.P. SALOMAO (33), M. SILVEIRA (34), H. SINATORA -MIRANDA (35), J.W.F. SLIK (36), B. SONKE (20), H.E. TAEDOUMG (20), M. TOLEDO (37), R.K. UMETSU (11), R.G. VILLAROEL (38), V.A. VOS (30), L.J.T. WHITE (23, 24, 25) and R.T. PENNINGTON (2) *

(1) School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom

(2) Royal Botanic Garden Edinburgh, Edinburgh, United Kingdom

(3) Universidade Federal Rural do Semiarido, Mossoro, Rio Grande do Norte, Brazil

(4) School of Geography, University of Leeds, Leeds, United Kingdom

(5) Center for International Forestry Research (CIFOR), West Africa Regional Office, Ouagadougou, Burkina Faso

(6) Institute of Biology, State University of Campinas, Campinas, Sao Paulo, Brazil

(7) Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom

(8) Departamento de Botanica, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brasil

(9) Faculte des Sciences Agronomiques, Universite de Lubumbashi, Lubumbashi, Democratic Republic of the Congo

(10) Department of Geography, University College London, London, United Kingdom

(11) Universidade do Estado de Mato Grosso, Campus de Nova Xavantina, Mato Grosso, Brazil

(12) Laboratoire d'Ecologie Vegetale et Biogeochimie, Universite Libre de Bruxelles, Brussels, Belgium

(13) Center for Ecological Sciences, Indian Institute of Science, Bangalore, India

(14) Department of Ecology and Environmental Sciences, Pondicherry University, Puducherry, India

(15) Centre for International Forestry Research (CIFOR), Bogor, Indonesia

(16) Department of Food and Resource Economics, University of Copenhagen, Frederiksberg, Denmark

(17) Mensuration Unit, Forestry Commission of Ghana, Kumasi, Ghana

(18) Museo de Historia Natural Noel Kempff Mercado, Santa Cruz, Bolivia

(19) Departamento de Biologia, Universidad Autonoma Gabriel Rene Moreno, Santa Cruz, Bolivia

(20) Plant Systematic and Ecology Laboratory, University of Yaounde 1, Yaounde, Cameroon

(21) Department of Botany and Plant Physiology, University of Buea, Buea, Cameroon

(22) Forestry Research Institute of Ghana, Kumasi, Ghana

(23) Agence Nationale des Parcs Nationaux, Libreville, Gabon

(24) School of Natural Sciences, University of Stirling, Stirling, UK

(25) Institute de Recherche en Ecologie Tropicale, Libreville, Gabon

(26) School of Geography and the Environment, University of Oxford, Oxford, United Kingdom

(27) Programa de Pos-graduacao em Ecologia e Evolucao, Universidade Federal de Goias, Goiania, Goias, Brazil

(28) FOMABO, Manejo Forestal en las Tierras Tropicales de Bolivia, Sacta, Bolivia

(29) Jardin Botanico de Missouri, Oxapampa, Peru

(30) Universidad Autonoma del Beni, Riberalta, Bolivia

(31) Centre for Biological Sciences, University of Southampton, Southampton, United Kingdom

(32) Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge, United Kingdom

(33) Museu Paraense Emilio Goeldi, Belem, Brazil

(34) Laboratorio de Botanica e Ecologia Vegetal, Universidade Federal do Acre, Rio Branco, Brazil

(35) Departamento de Ecologia, Universidade de Brasilia, Brasilia, Distrito Federal, Brazil

(36) Faculty of Science, Universiti Brunei Darussalam, Brunei Darussalam

(37) Instituto Boliviano de Investigation Forestal, Santa Cruz, Bolivia

(38) Parque Nacional del Gran Chaco, Santa Cruz, Bolivia

Email: kyle.dexter@ed.ac.uk, t.pennington@rbge.org.uk
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