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.
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.
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.
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.
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.
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'.
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.
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.
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
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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
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|Publication:||International Forestry Review|
|Date:||Sep 1, 2015|
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