A Biogeographical and Conservation Approach

Integrating community phylogenetics and phylogenetic beta diversity to understand Amazonian trees community assembly: a biogeographical and conservation approach

By Juan Ernesto Guevara Andino

A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy In Integrative Biology in the Graduate Division of the University of California, Berkeley

Committee in charge: Professor Paul V.A. Fine, Chair Professor David Ackerly Professor Rosemary Gillespie Professor Brent Mishler

Spring 2017

Abstract Integrating community phylogenetics and phylogenetic beta diversity to understand Amazonian trees community assembly: a biogeographical and conservation approach By Juan Ernesto Guevara Andino Doctor of Philosophy in Biology University of California, Berkeley Professor Paul V.A. Fine, Chair

Understanding the tree species composition and turnover in Amazon forests has fascinated ecologists and evolutionary biologists since the first botanical expeditions from the 19th century. More recently, the advent of community phylogenetics and phylogenetic beta diversity methods has been demonstrated to be powerful tools to investigate the patterns and causes of Amazonian tree species assemblies. However, the lack of a comprehensive sampling of tree species communities using well- standardized taxonomy across gradients of soils, geology and climate has precluded making conclusions about the relative importance of these environmental filters on the patterns of species and lineage composition and turnover. In addition, due to current threats that Amazon forests are experiencing an ecologically informed delineation of floristic boundaries is urgently needed to guide conservation decisions. In this dissertation, I present the results of intensive floristic sampling at two spatial scales continental and regional, and show that the complementary implementation of phylogenetic beta diversity and community phylogenetics can help us to better describe and analyze floristic patterns of Amazon tree communities. Additionally, I used this combination of methods to determine high priority conservation areas that are currently experiencing severe threats in Ecuador Amazon.

Table of contents Introduction…………………………………………………………………...... ii Acknowledgements…...……………………………………………………...... v Chapter 1 Low Phylogenetic Beta Diversity and Geographic Neo-endemism in Amazonian White-sand Forests……………………………………………………………...... 1 Abstract…………………………………………………………………………………….…...... 1 Introduction………………………………………………………………………………………..1 Methods……………………………………………………………………………………………2 Results………………………………………………………………………………………...…...5 Discussion…………………………………………………………………………………………7 Acknowledgements………………………………………………………………………………10 References………………………………………………………………………………………..11 Tables…………………………………………………………………………………………….16 Figure Legends……………...……………………………………………………………………17 Figures……………………………………………………………………………………………18 Appendices……………………………………………………………………………………….23

Chapter 2 Incorporating phylogenetic information for the definition of floristic subregions in hyper-diverse Amazon forests: implications for conservation...... 40 Abstract……………………………………………………………………………………...…...40 Introduction…………………………………………………………………………………...... 40 Methods………………………………………………………………………………………...... 42 Results………………………………………………………………………………………...... 46 Discussion…………………………………………………………………………………...... 47 Acknowledgements…………………………………………………………………………...... 50 References……………………………………………………………………………………...... 50 Tables………………………………………………………………………………………...... 56 Figure Legends……………………………………………………………………………...... 59 Figures………………………………………………………………………………………...... 60 Appendices…………………………………………………………………………………...... 65

Chapter 3 Climatic and geomorphological control on the phylogenetic and taxonomic beta diversity patterns of Amazon tree communities………………………………...... 117 Abstract………………………………………………………………………………………....117 Introduction…………………………………………………………………………………...... 117 Methods……………………………………………………………………………………...... 119 Results………………………………………………………………………………………...... 123 Discussion……………………………………………………………………………………....124 Acknowledgements…………………………………………………………………………...... 127 References…………………………………………………………………………………...... 127 Tables………………………………………………………………………………………...... 132 Figure Legends...……………………………………………………………………………...... 134 Figures………………………………………………………………………………………...... 135 Appendices…………………………………………………………………………………...... 139

Introduction The merging of community ecology and phylogenetics has been one of the major advances in evolutionary biology and ecology in the last 15 years. Because there is an intrinsic relation between the patterns of distribution and abundance of the species and the evolutionary processes that underlie them, the study of community phylogenetics is fundamental for our understanding about the emergence and maintenance of diversity. This relationship becomes evident when we consider evolutionary time in the origin and maintenance of biodiversity because the rate of change in diversity is intrinsically linked to the rates at which both a clade originates and goes extinct (Graham and Fine 2008). Community ecology attempts to explain the patterns and processes that determine the co-occurrence and abundance of species in a community and increasingly, a large amount of publications linking evolutionary history and genealogical relations of the species to patterns of community assembly and diversity. Since Darwin’s postulates in the Origin of Species, two basic trends have been strongly linked to phylogeny. The first one posits that “closely related species tend to not co-occur in the same community due to interspecific competition” (Elton 1946, Webb 2000) and the second states that if related species tend to co-occur in local communities they must exhibit similar ecological requirements (Darwin 1871, Ackerly 2003). However, this dichotomization ignores the complexity of the interplay between evolutionary and ecological processes on the spatial and taxonomic patterns of community assembly and phylogenetic community structure. For the sake of argument, processes like speciation important in the determination of regional diversity could determine that close relatives inhabit the same local community (i.e., phylogenetic clustering) with very divergent ecological traits (i.e., trait lability) if allopatric speciation is involved (Cavender-Bares et al. 2009, Vamosi et al. 2009). By the same token if sympatric populations experienced an adaptive radiation to novel, divergent environments, closely related species with divergent traits will inhabit the same region but distantly related species will occupy the same local community (i.e., phylogenetic eveness) (Guillespie et al. 2004). In this sense, the study of phylogenetic community structure allows us to explore what kinds of mechanisms are responsible for shaping the community assemblage. Furthermore, the link between community structure and phylogenetic structure is also related to the evolution of traits and the biogeographical sorting of lineages across heterogeneous environments (Ackerly 2003, Ackerly 2004). Nevertheless, because two communities could potentially exhibit a similar pattern of phylogenetic community structure but different species composition, the analysis of phylogenetic community structure per se could not provide accurate information about the role of evolutionary process as diversification and the role of geographic barriers and biotic interactions as drivers of composition of local communities (Graham et al. 2009) In this sense, the analysis of phylogenetic beta diversity (phylobetadiversity) allow us to analyze how the phylogenetic relatedness of regional or local communities changes across environmental and spatial gradients in response to different biogeographic histories of regional species pools (Webb 2000, Graham and Fine 2008, Kraft et al. 2007, Fine and Kembel 2011, Kembel and Hubbell 2006). Because phylobetadiversity takes into account changes in phylogenetic community assembly and the geographic distribution of phylogenetic diversity it adds an additional factor to the analysis, which is inherently linked to the quantification of changes in phylogenetic patterns across space as a function of environmental gradients and geographic barriers (Graham and Fine 2008).

In this dissertation, I employ the complementary use of phylogenetic community structure and phylogenetic beta diversity metrics to describe and analyze floristic patterns of Amazonian tree communities from both biogeographic and conservation perspectives. At the same time, I aim to disentangle the drivers of both species and lineage composition of Amazon tree communities combining extensive fieldwork with community phylogenetics, geology and climatic data.

The first chapter is focused on a biogeographical analysis of Amazonian white-sand forests at a regional scale. I present the results of the most comprehensive data set known to date for Amazonian white-sand forests in order to elucidate the biogeographic relationships of these tree communities across Amazon basin. First, I describe floristic relationships in Amazonian white sand forests at the regional scale by using the largest number of one-hectare plots ever compiled for these forests, including all Amazonian regions. Secondly, I evaluated the role of dispersal and putative in situ radiation using community phylogenetic methods and phylogenetic beta diversity metrics. The results indicate that closely related lineages tend to occupy white-sand forests within the same region leading to patterns of phylogenetic clustering at local and regional scales. In addition, I found low phylogenetic beta diversity and neo-endemism in white-sand tree species communities; a result that contradicts the dominant paradigm that these are unique communities composed mainly of paleo-endemic species. In the second chapter, I explored different community phylogenetic methods including phylogenetic endemism and phylogenetic beta diversity metrics in order to define floristic sectors of the Ecuadorian Amazon. I combined two recently developed community phylogenetic methods that incorporate individual abundances, evolutionary distinctiveness and clade imbalance with phylogenetic endemism metrics to define high priority conservation areas in the lowland Ecuadorian Amazon. I showed that the spatial patterns of phylogenetic beta diversity and phylogenetic endemism are consistent with the definition of three floristically distinct units that may be assigned as floristic sub regions in Ecuador, and probably also includes areas of Peru and Colombia. Large areas with high levels of phylogenetic endemism and evolutionary distinctiveness in Ecuadorian Amazon forests remain without formal protection. Furthermore, these areas are severely threatened by proposed plans of oil and mining extraction at large scale and should be prioritized in conservation planning for this region. In the third chapter, I analyze the role of climate, geomorphology and soils on the patterns of both taxonomic and phylogenetic beta diversity of Amazon forests tree communities. In order to test the role of climatic, soils and geomorphological variables we compiled data from 80 floristic inventory plots recently established in the Ecuadorian Amazon. The results demonstrated that climate is a significantly better predictor of phylogenetic and taxonomic beta diversity than are geomorphology and soils.. These results challenge the well-cited hypothesis that geology is the main driver of floristic composition in the Western Amazon. Taken together the results presented in this thesis demonstrate that the complementary use of community phylogenetic and phylogenetic beta diversity methods provides useful information for analyses focused on the biogeography and conservation of Amazonian tree communities. At the same time, I provide new insights in the discussion about the role of climate vs. geology and soils as environmental filters determining the floristic composition and lineages and species turnover of Amazonian plant communities.

iii

References

Ackerly, D. (2003) Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant Sci. 164(3 Suppl.):S165–S184.

Ackerly, D. (2004) Functional strategies of chaparral shrubs in relation to seasonal water deficit and disturbance Ecological Monographs, 74(1), 25–44.

Cavender-Bares, J., Kozak, K. H., Fine, P.V.A. & Kembel, S.W. (2009) The merging of community ecology and phylogenetic biology. Ecology Letters 12: 1–23.

Darwin, C. (1859). The Origin of Species. Modern Library, New York.

Elton, C. (1946). Competition and the structure of ecological communities. J. Anim. Ecol., 15, 54– 68.

Fine, P.V.A & Kembel, S. (2011) Phylogenetic community structure and phylogenetic turnover across space and edaphic gradients in western Amazonian tree communities. Ecography, 34(4), 552-556.

Gillespie, R.G. (2004) Community assembly through adaptive radiation in Hawaiian spiders. Science 303: 356–359.

Graham, C. H. & Fine, P.V.A. (2008) Phylogenetic beta diversity: linking ecological and evolutionary processes across space in time. Ecology Letters 11, 1265-1277.

Graham, C.H., Parra, J. L., Rahbeck, C. & Mcguire, J.A. (2009) Phylogenetic structure in tropical hummingbird communities. Proceedings of the National Academy of Sciences of the United States of America, 106, 19673-19678.

Kembel, S.W. & Hubbell, S. P. (2006) The phylogenetic structure of a neotropical forest tree community. Ecology 87 (7)S: S86-S99.

Kraft, N.J.B, Cornwell, W., Webb, C.O. & Ackerly, D. (2007) Trait evolution, Community Assembly, and the Phylogenetic Structure of Ecological Communities. The American Naturalist 170:271-283. Vamosi, S.M., Heard, S.B., Vamosi, J.C. & Webb, C.O. (2009) Emerging patterns in the comparative analysis of phylogenetic community structure. Mol. Ecol. 18: 572–592.

Webb, Campbell O. (2000) Exploring the phylogenetic structure of ecological communities: An example for rain forest trees. American Naturalist 156(2):145-15

Acknowledgements

Since I first began studying plants at the Catholic University of Ecuador, I have been fascinated by community ecology, diversity and biogeography of Amazonian plants. The first field trips introduced me in a sea of queries about these topics, and courses related to Biogeography, Floristics, Ecology, Evolution and Taxonomy made me cogitate about the processes that drive the maintenance and origin of the diversity in Amazon forests. These interests granted me the opportunity to gain a position in 1999 on the Yasuní Forest Dynamics Project, a 50-ha plants inventory in Amazonian Ecuador established by the Smithsonian Tropical Research Institute, Aarhus University and the Catholic University of Ecuador. After a lapse of internship, I achieve the leadership of a taxonomy field team for almost two years. This allowed me to have the opportunity to gain firsthand experience of many concepts about tropical plant ecology and work with some outstanding Amazon ecologists like my advisor Paul V.A. Fine, Nigel C.A. Pitman, Robin Foster or Hans ter Steege. Nigel Pitman convinced me to talk with Paul who at the time of my application to UC Berkeley was Assistant Professor. He could have not been so right about choosing UC Berkeley for a graduate program as well as choosing Paul as advisor. I am profoundly indebted to Paul, who believed in my ideas and supported me in every stage of my dissertation. I do not think I could have found a more supportive advisor. Through these years, he demonstrated to be as open minded as one could think to support my dissertation project even though it is not related to the system he works with. I have benefited from his big picture perspective, comments and engagement. I am also deeply grateful for his support in non-academic issues at some difficult stages of my dissertation, without his help I could have not accomplished all the goals I have attained. I have also had the privilege to interact with David Ackerly, member of my dissertation committee and chair of my qualifying exam. His intellectual vigor and curiosity during the many discussions we had helped to keep the things on perspective and challenged me to developed new skills. I am really grateful with him for the opportunity to share my thoughts, chat about many topics in evolutionary biology, community assembly, and ecology; this undoubtedly established the foundation of many of the ideas I present in this thesis. Brent Mishler, Rosemary Gillespie and Bruce Baldwin all served on my dissertation committee and I am more than grateful for the probing questions, cogent arguments and insightful comments that we shared during the intellectual discussions we had through these years. The Fine lab has been the best place I could have found as a graduate student. The camaraderie, sense of humor and support of my lab mates were crucial not just to develop my ideas but also to create an environment that helps me to deal with the contingencies of a PhD program. I am indebted to Tracy Misiewicz, Chris di Vitorio and Seth Kaupinen for their advice and support in the first years of my PhD program. Finding balance in a graduate program could be a difficult task and I was very lucky to have really good friends here in Berkeley. Gabriel Damasco, Clarissa Fontes and Betsabe Castro not just shared their friendship but also they open the doors of their house, Roxy Cruz and Jason Vleminckx provide me support during difficult times and I am grateful for that. I value all the discussions about phylogenetics, ecology and evolution I had with many of my department colleagues. I am especially indebted with Gabriel Damasco, Diego Salazar and Jason

Vleminckx with whom I have many conversations that helped me to develop new perspectives for analysis. I am also grateful to Dori Contreras, Jenna Judge and Seth Kaupinen for their insightful comments and observations to the first chapter of my dissertation. Carlos Ceron, Walter Palacios, Nigel C.A. Pitman and Hugo Mogollón shared data without which would have been impossible to accomplish the goals for the second and third chapters of dissertation. Nigel C.A. Pitman has been fundamental in my development as ecologist, I appreciate the advices, talks and insightful comments he shared with me through these years. I also would like to acknowledge to Hans ter Steege that provide me advices that helped me to clarify statistical concepts and develop programming skills. Working in isolated areas of Amazon can be a monumental task and I owe special thanks to the many field assistants and indigenous communities in Ecuadorian Amazon that provide logistic support. In particular, I am indebted to Camilo Kajekai who supported my work in the Cordillera del Condor lowlands, Danilo Simba and Juan Carlos Ceron were my assistants during most of my field work in Ecuador, and I appreciate their enthusiasm, willingness and patience. Oliver Phillips, Alberto Vicentini, Roel Brienen, Dairon Cárdenas López, Florian Wittmann, Eurídice Honorio, Francisca de Matos, Juliana Stropp, María Cristina Peñuela Mora, Iêda Leão do Amaral, Rachel Thomas, Natalia Targhetta, Hans ter Steege, Pascal Petronelli, Bill Magnusson, Álvaro Duque, Carolina Castilho, Chris Baraloto, Fredy Francisco Ramírez Arévalo, Eliana Jiménez, Gabriel Damasco Do Vale, Abel Monteagudo, Rodolfo Vásquez, Paul Maas, Paul Fine from the Amazon Tree Diversity Network provides me data for the first chapter of my dissertation. I am thankful to my parents Julio and Meri who gave me the encouragement to pursue a graduate degree, my brothers Esteban and Hostmaro for their support, sense of humor and advice. Finally, I am eternal grateful to my wife Gabriela Eaton who supports me through these years even leaving our home country to start a life here in United States. Despite the difficulties, her love and support have been fundamental through this path and I am very lucky to have her in my life. I love you. My sweet little girls Emma and Mia are the source of inspiration, love, peace, joy and strength for keeping me focused in my goals and I am the luckiest man in the world for having both in my life.

Chapter 1 Low Phylogenetic Beta Diversity and Geographic Neo-endemism in Amazonian White- sand Forests Originally published as Guevara, J. E., Damasco, G., Baraloto, C., Fine, P. V. A., Peñuela, M. C., Castilho, C., Vincentini, A., Cárdenas, D., Wittmann, F., Targhetta, N., Phillips, O., Stropp, J., Amaral, I., Maas, P., Monteagudo, A., Jimenez, E. M., Thomas, R., Brienen, R., Duque, Á., Magnusson, W., Ferreira, C., Honorio, E., de Almeida Matos, F., Arevalo, F. R., Engel, J., Petronelli, P., Vasquez, R. and ter Steege, H. (2016), Low Phylogenetic Beta Diversity and Geographic Neo-endemism in Amazonian White-sand Forests. Biotropica, 48: 34–46.

Abstract Over the past three decades, many small-scale floristic studies of white-sand forests across the Amazon basin have been published. Nonetheless, a basin-wide description of both taxonomic and phylogenetic alpha and beta diversity at regional scales has never been achieved. We present a complete floristic analysis of white-sand forests across the Amazon basin including both taxonomic and phylogenetic diversity. We found strong regional differences in the signal of phylogenetic community structure with both overall and regional Net Relatedness Index and Nearest Taxon Index values found to be significantly positive leading to a pattern of phylogenetic clustering. Additionally, we found high taxonomic dissimilarity but low phylogenetic dissimilarity in pairwise community comparisons. These results suggest that recent diversification has played an important role in the assembly of white-sand forests causing geographic neo-endemism patterns at the regional scale. Key words: Amazon, white sands, phylogenetic beta diversity, neo-endemism, recent diversification Introduction White sand forests have been referred to as a classic case study of habitat specialization (Anderson 1981) and a source of high endemism in both animal and plant communities of Amazonian forests (Frazier et al. 2008, Fine et al. 2005, Fine et al. 2010, Misiewicz and Fine 2014, Berry et al. 1995). We have learned much about the composition of this flora over the past three decades thanks to the many small-scale and regional floristic studies of white-sand forests across the Amazon basin that have been published (Duivenvoorden and Lips 1995, Coomes and Grubb 1998, Fine et al. 2010, Stropp et al. 2011, Damasco et al. 2012, Peñuelas-Mora 2014). Nonetheless, there has never been a basin-wide description of both the taxonomic and phylogenetic alpha and beta diversity of Amazonian white-sand forests. Most of the plants found in white-sand forests are rare or absent in other forest types (Vormisto et al. 2000, Fine et al. 2010, Stropp et al. 2011). This specialization to the white-sand habitat has evolved repeatedly, with some species belonging to lineages that are generally restricted to this habitat (i.e., Potalia, Pagamea; Frasier et al. 2008, Vicentini in press) and other species that appear to have evolved white-sand specialization relatively recently, descending from non-white-sand ancestors (i.e., Protium, Fine et al. 2005, Fine et al. 2014, Fine & Baraloto, in press).

Strong environmental gradients, like white-sand habitats adjacent to other more nutrient- rich soil habitats, are thought to promote high levels of both phylogenetic and taxonomic beta diversity across habitats reflecting the high turnover of lineages at both shallow and deep phylogenetic scales. Such turnover of entire clades might occur if certain traits associated with white-sand specialization are conserved and phylogenetic niche conservatism is responsible for such patterns (Graham and Fine 2008, Anacker and Harrison 2012). Alternatively, in situ diversification may cause some groups to have many more species in certain regions according to each lineage’s life history and biogeography (Ricklefs 2006). A remarkable example of this phenomenon is the diversification of the Protieae tribe which exhibits higher net diversification rates in Western Amazonia compared to Central Amazonia and the Guiana Shield with many species associated with white-sand habitats (Fine et al. 2014). The role of dispersal also likely plays an important role driving patterns of taxonomic and phylogenetic beta diversity of white sand forests. Species with differential seed dispersal capabilities may occupy new regions with white-sand forests promoting gene flow between populations which in turn may influence allopatric speciation and extinction probabilities. Thus, across the Amazon basin, specialization and speciation events through time in white-sand forests have been influenced by 1) the frequency of lineages to evolve white-sand specialization in different regions and 2) the dispersal capability of already-specialized white-sand taxa from white-sands in one region to white-sand forests in other regions. Here we present a new and more complete depiction of the floristic relationships of white- sand forests across the Amazon basin using measures of both taxonomic and phylogenetic diversity. In addition we test the relative role of dispersal and in situ radiation in the patterns of Phylogenetic beta diversity (PBD) of white-sand forests within and across regions. If little opportunity for dispersal and differential extinction rates have promoted entire lineages or clades to be absent in certain regions we would expect a significant increase in observed phylogenetic and compositional dissimilarities compared with expected phylogenetic dissimilarity as geographic distance increases. In this case we would predict a strong pattern of turnover in taxa due to the long term disparate evolutionary histories of these taxa which in turn should lead to strong spatial replacement of lineages (Fig. 1); meaning that clusters of closely related lineages should be present in multiple distinct regions of the Amazon basin. Alternatively, if small-ranged taxa have evolved by in situ radiation, we expect a higher compositional dissimilarity than expected. Furthermore, if such in situ radiation has occurred recently in evolutionary time, we would expect a limited time to accumulate new distinct lineages leading to a pattern of geographic neo-endemism. Finally, we predict low values of both taxonomic and phylogenetic beta diversity if there have been many opportunities for dispersal among different local communities of white- sand forests maintaining gene flow and genetic cohesion across species with widespread geographic ranges (Fig. 1). Methods Study Area White-sand forests occur across the entire Amazon basin spanning approximately 200,000 km2 (Hammond 2005, ter Steege et al. 2013). Our study area includes three main regions: Northwestern Amazonia (NWA), Central Amazonia (CA) and the Guiana Shield (GS) (Fig.2). The data set includes 91 plots, ranging from 0.1 ha to 1 ha, from white-sand forests in Colombia, Ecuador, Peru, Brazil, Guyana, French Guiana and Suriname, representing a longitudinal gradient of almost

3000 km. Because white-sand forests sometimes include vegetation types that structurally are not “forested” we excluded plots from our analysis that had extremely low canopy cover (chamizales or caatinga scrub in Anderson 1981, Fine et al. 2010) and white-sand savannas (campinas; Adeney et al., in press). All plots were at least 0.1 ha in size and included all trees with dbh > 5cm per ha (most plots were 1 ha and had a dbh cut-off of 10 cm dbh). In each plot, all individual trees were tagged and identified at the species level in the field when possible. Voucher specimens for most of the species and morphospecies were collected and subsequently deposited in Herbario Nacional del Ecuador (QCNE), Missouri Botanical Garden (MO), Field Museum (F), Museo Herbario Amazonense (AMAZ), Herbário Instituto Nacional de Pesquisas da Amazonia (INPA) and ORSTOM-Cayenne Herbarium (CAY). Analysis We compiled 44,579 individual trees from the 91 white-sand plot inventories. For the analysis of taxonomic beta diversity, we used a subset of 38,721 individuals from the dataset, taking into account only species with valid names (1,256 named species). The omission of unnamed “morphospecies” from meta-analysis has been hypothesized to not bias the detection of ecological patterns (Lennon et al. 2001, Lennon et al. 2004, Pos et al. 2014). In order to avoid the well-known problem resulting from the use of phylogenetic trees without fully resolved branches (Swenson et al. 2006), a subset of 420 species (representing 18,163 individuals) for which we have a molecular phylogeny was used for the analysis of phylogenetic beta diversity. The molecular phylogeny used in our analysis of phylogenetic beta diversity, is a pruned version of the one used by Zanne et al. (2014) (Fig. S1). PBD was calculated with the Phylo Sorenson index as a measure of the degree of phylogenetic relatedness between pairs of local communities. The Phylo Sorenson index measures the fraction of branch lengths (Phylogenetic distance) shared by two communities or samples (Bryant et al. 2008, Graham et al. 2009). However, in order to match the metrics used to evaluate taxonomic beta diversity, we used the complement of the Phylo Sorenson index to establish a phylogenetic dissimilarity metric (1-Phylo Sorenson).

1 ( + 𝑖𝑖𝑖𝑖 ) 𝐵𝐵𝐵𝐵 2 𝑃𝑃ℎ𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑖𝑖𝑖𝑖= 𝑖𝑖 𝑗𝑗 Where BLij is the sum of branches length 𝐵𝐵𝐵𝐵shared 𝐵𝐵𝐵𝐵by communities i and j, BLi is the sum of the length for branches present only in community i and BLj is the sum of the length for branches present only in community j. To test the influence of geographic distance as a predictor of both compositional beta diversity (CBD) and PBD, we fitted a Loess curve to our data. This non-parametric regression method fits a regression surface to the data based on multivariate smoothing and does not consider any a priori regression function between the descriptor and the response variable. By using a local fitting regression we can estimate a wide variety of smoothing functions such as polynomial functions based on Ordinary Least Squares regression (Cleveland and Levin 1998). In order to test if CBD is a good predictor of PBD, we compared the observed values of PBD (1-Phylo Sorenson) with CBD. Additionally, we compared the observed PBD to the expected values of PBD based on a null model that makes random draws from the regional species pool (here defined as the total number of species in our plot network). This null model maintains species

richness for each local community and the number of species shared between communities with equal probability to colonize them. The basic assumption under this model is that species in the phylogeny have equal probability of colonizing a local community in such a way that dispersal limitation or long distance dispersal has only minor effects on the assembly of communities. Thus when interpreting the results, if the observed values of PBD are less than the expected values based on the null model, we infer that pairs of compared communities are composed of lineages that are closely related. Conversely, if values of PBD are greater than expected based on the null model pairs of communities, then pairs of communities are composed of lineages that include distant relatives. Paired t-tests were performed in order to detect significant differences between observed PBD and expected PBD. Non Metric Multidimensional (NMDS) Analysis with both taxonomic and phylogenetic dissimilarity matrices was performed in order to have a graphical depiction of the floristic relationships of white-sand plots across different regions of the Amazon basin. We used two dimensions in the ordination and 100 random starting iterations in order to obtain the lowest stress value that determines the best solution for that ordination. The difference between 1 and Sorensen index (1-Sorenson) was used as the dissimilarity metric for the NMDS ordination based on taxonomy, while the difference between 1 and Phylosorenson (1-Phylosorenson) was used to create a phylogenetic distance matrix for the phylogenetic NMDS ordination. To determine if species co-occurring in the same local community (plot) are more closely related than expected by chance, we calculated the Mean Phylogenetic Distance (MPD) - the pairwise comparison of phylogenetic distances between all species in the local community (Webb 2000). This value was compared with the expectation based on a null model; if MPD(obs) is < MPD(null) the communities are phylogenetically even and conversely if MPD(obs) > MPD(null) the communities are phylogenetically clustered. The net relatedness index, hereafter NRI, measures the phylogenetic clumpedness of taxa over the entire community phylogeny (Webb 2000). By contrast, the phylogenetic nearest taxa index, hereafter NTI, measures the extent to which taxa are “locally clustered” within a clade irrespective of the relation among those clades (Webb 2000). In order to test the effect of ecological dominance and composition in the phylogenetic community structure of white-sand local communities we used taxon (presence/absence) and individuals (relative abundance) based analysis to calculate both NRI and NTI. Therefore:

NRIi = Net Relatedness Index based on tree species abundance

NTIi = Nearest Taxon Index based on tree species abundance

NRIt = Net Relatedness Index based on taxon information

NTIi = Nearest Taxon Index based on taxon information To test whether there is a significant difference in the patterns of NRI and NTI at regional scale we compared observed NRI and NTI values for each region to those calculated using a null model. The null model maintains both the species richness and abundance constant for each sample while species or individuals are drawn without replacement from the list of all species in the phylogeny pool. This model assumes that null communities are structured by random draws of the 420 species present in the phylogeny.

All analyses were carried out with the functions of the packages ecodist, picante, labdsv and custom functions in the R software platform (R Core Development Team 2011). Results Floristic patterns From a total of 44,579 individuals observed, we recorded 1,482 morphospecies identified at the family level, 672 at genus level, 1,256 at species level and 17 remained undetermined at all levels. We found on average a lower diversity in white-sand plots compared with the terra firme forests, a pattern that has been reported repeatedly (Anderson 1981, Fine et al. 2010, Gentry1986, Stropp et al. 2011, ter Steege 2000). However, patterns of diversity varied across regions; white-sand forests from GS have fewer species than plots from NWA and CA (Table 1). Fabaceae was by far the most dominant family across the basin accounting for 26% of the total stems (11,618 individuals) followed by Chrysobalanaceae with almost 10% of individuals (4,288 ind.), Malvaceae with 8% (3,482 ind.), Sapotaceae with 6% (2,596 ind.) and Euphorbiaceae (2,471 ind.) with 5%. A different pattern arises when we consider (valid) species richness per family; the greatest number of species was concentrated in Fabaceae (234 spp.) followed by Lauraceae (86 spp.), Sapotaceae (81 spp.), Chrysobalanaceae (78 spp.) and Rubiaceae (76 spp.). At the genus level, Licania with 46 (valid) species was the most species rich genus in white- sand forests. Genera like Pouteria (36 spp.), Protium (34 spp.), Swartzia (30 spp.), Inga (30 spp.) and Ocotea (28 spp.) were also remarkably diverse. With the exception of Licania (3,228 individuals), species-poor genera dominate white sand forests at both local and regional scales. Eperua (6,101 ind.) was by far the most abundant genus in white sand forests followed by Catostemma (1,960 ind.), Pachira (1,611 ind.) and Micrandra (1,494 ind.). The most common species was Eperua falcata, accounting for 3,343 individuals, almost 8% of the total number of individuals. The five most common species account for 19% of the number of individuals (8,407 ind.), a number that was two-fold lower than patterns of dominance reported in Peru (Fine et al. 2010). The NMDS ordination based on taxonomic and phylogenetic dissimilarity matrices produced contrasting patterns (Fig. 3). The ordination based on taxonomic dissimilarity matrix exhibited a clear gradient along the axis by defining four floristic regions (Fig. 3A). The first one corresponded to some of the plots defined in axis 2 of the ordination and located in the core of the Guiana Shield. These plots were characterized by the predominance of Fabaceae, especially species from genera like Eperua, Dicymbe, Elizabetha, Dicorynia, Alexa or Aldina, as the results of the indicator species analysis demonstrates (Appendix S1). Other remarkably conspicuous elements of this region were Catostemma (Malvaceae s.l.), Cyrilla racemiflora (Cyrillaceae), the extremely rare Cyrillopsis paraensis (Ixonanthaceae), and two species from the genus Acioa (Chrysobalanaceae). The second group of plots defines the northeastern portion of Central Amazonia white sand forests; the great majority of these plots are located in Viruá National Park. This group of plots, mainly defined along axis 1 of the ordination, constitutes a region of confluence of the two regional floras of GS and CA, but was also characterized by the extremely rare and species-poor genera Elvasia (Ochnaceae), Euphronia (Euphroniaceae), Excellodendron (Chrysobalanaceae) and Chaetocarpus (Euphorbiaceae). A third floristically distinctive group in the ordination included communities spanning the Amazon, with plots from the Guiana Shield, Central and Western Amazonia. These plots are remarkably similar in floristic terms despite the large distance between them. Some of the most predominant groups in this cluster of plots include

5 genera like Adiscanthus (Rutaceae), Lorostemon (Clusiaceae), Ambelania (Apocynaceae), Pachira (Malvaceae s.l.) and Cinchonopsis (Rubiaceae) (Appendix S1). Finally, plots located in the Upper Rio Negro, including areas of Colombia, Brazil and Venezuela; mostly define the fourth floristic region. Conspicuous groups in this region are Couma (Apocynaceae), Parahancornia (Apocynaceae), Haploclathra (Clusiaceae), Micrandra (Euphorbiaceae), Dicymbe (Fabaceae s.l.), Eperua (Fabaceae s.l.) Emmotum (Icacinaceae) as the results of the Indicator Species Analysis at genus level demonstrated. When the phylogenetic distance matrix was used as input for the NMDS, a different pattern arises. The gradient along axis 1 of the NMDS still remained, yet the regional clusters disappeared (Fig. 3B). More than 50% of the local communities in CA were structured by lineages phylogenetically closely related to those lineages that contribute strongly to the local communities in GS. Furthermore all the NWA white sand plots were composed of lineages phylogenetically closely related to lineages that dominate GS white-sand plots. Only a small fraction of CA plots including those of Upper Rio Negro and Viruá National Park were structured by distantly related lineages compared to all GS and NWA plots, meaning that the this group of CA plots were composed by evolutionary distinctive lineages.

Compositional and phylogenetic dissimilarity Overall analysis of phylogenetic beta diversity demonstrates that a weak but significant correlation of phylogenetic dissimilarity and geographic distance exists in white-sand forests across the Amazon basin (Mantel r=0.18, p=0.003). When we consider each regional flora independently we found on average a higher phylogenetic dissimilarity in Central Amazonian and Western Amazonian white-sand plots than in white-sand plots of the Guiana Shield (Fig. 4; Table 1). However, the white-sand forest plots in the Guiana Shield exhibited a higher correlation of phylogenetic dissimilarity with respect to geographic distance (Mantel r=0.54, p=0.001). White- sand plots in Central (Mantel r=0.35, p=0.001) and Western Amazonia (Mantel r=0.38, p=0.002) exhibit similar values for this relationship. Additionally, the influence of the regional pool in patterns of phylogenetic turnover is evident. White sand forests in NWA and CA are more distinct from one another than either of them is with the plots from the Guiana Shield (Fig. 4B, C). Overall phylogenetic dissimilarity was also significantly lower between plots than taxonomic dissimilarity (Fig. 4D, H). Thus, pairs of local communities exhibited higher compositional turnover than phylogenetic turnover with respect to geographic distance, meaning that on average plots that were spatially close were taxonomically more dissimilar than phylogenetically dissimilar (Fig. 4). The slope of both compositional and phylogenetic dissimilarity decreased considerably at geographic distances larger than 500 km as the Loess curve shows in Fig. 4. However, the compositional dissimilarity continued to increase with distance while phylogenetic dissimilarity tends to decrease. In other words, even if pairs of communities are separated by thousands of kilometers, they are generally composed of the same lineages. Similarly, the observed values of phylogenetic dissimilarity were significantly lower than those expected based on the null model (t=78.80, p<0.0001) (Fig. 4H). Phylogenetic community structure We found non-random phylogenetic community structure for white-sand forests at regional scales which can be attributed to large scale evolutionary processes shaping the assembly of these

habitats. Overall, values of NRI and NTI were significantly positive, but some regional differences arose when considering regional pools separately (Fig. 5, Table S2). However, overall values of NTIi were not significantly different from our null expectation. The predominant pattern in white- sand forests in Western and Central Amazonia, and in the Guiana Shield, was that NRI values were significantly positive when relative abundance of species (NRIi) was taken into account (Fig. 5B; Table S2).

The NTIi also indicated regional differences in how taxa within local community are distributed towards the tips of the community phylogeny. For instance, close relatives within the same clade significantly structured communities of white-sand forests in Central Amazonia and the Guiana Shield. This is in contrast to patterns found for Northwestern Amazonian white-sand forests that on average exhibited values close to zero, suggesting a random pattern of phylogenetic structure. When overall NRIt values are considered, a remarkably different pattern arises. Most of the local communities of white-sand forests across the basin exhibit negative values indicating that communities are phylogenetically overdispersed across the whole community phylogeny. Interestingly, the values for NTI indicated that communities were phylogenetically clustered towards the tips of the phylogeny of the regional pool (Fig. 5; Table S2).

Discussion Floristic patterns Previous attempts to characterize the species composition and structure of white-sand forests have been done only at local or within one region of Amazonia. Our results are the first attempt to describe floristic patterns of white-sand forests across the entire Amazon basin. It is important to note, however, that the plot network, while extensive and covering a large geographical extent that includes most of the major white-sand forests of the Amazon basin, certainly has overrepresented certain regions (i.e., the Guiana Shield) compared to others and this spatial bias likely has influenced our results and interpretations somewhat. The NMDS ordinations indicated two gradients in species composition. The first one defined a longitudinal gradient in species composition with plots from Guiana, Suriname and French Guiana at the left of the ordination and the CA and NWA plots in the center and right side of the ordination (Fig. S2). This pattern reflects a strong spatial component. The Loess regression comparing longitude with the first axis of the NMDS ordination explained 94% of variation in floristic data (Fig. S2). Furthermore, the floristic composition changed very rapidly from plots in the Guiana Shield to Central Amazonia and then more gradually from plots in Central Amazonia to plots in Western Amazonia. Terborgh and Andressen (1998) proposed that, due to different climates and geologic histories leading to edaphic gradients across the Amazon, differences in tree-species composition of forests in Northwestern Amazonia should be remarkably different than those of Central Amazonia. Based on the results of the NMDS, and compositional beta diversity, we found support for the hypothesis of strong species turnover across a longitudinal gradient (Fig. S2). Turnover in white-sand forests also showed a latitudinal gradient in the NMDS ordination, especially for white-sand plots from Central Amazonia (Fig. S3). This latitudinal gradient in floristic composition is mostly defined along axis 2 of the NMDS and suggests that there are two distinct white-sand floras of comparatively high diversity on each side of the Amazon basin. This pattern appears to be driven in large part by the high diversity, dominance and endemism of Fabaceae and Chrysobalanaceae in Guiana Shield forests (ter Steege and Hammond 2001, ter Steege et al. 2006).

Although species composition varies among regions, we found a geographically idiosyncratic dominance of a small number of white-sand specialist lineages. For instance, Protium spp., Pachira spp. And Caraipa spp.exhibited high dominance across the east-west gradient of WS patches. In some plots, this dominance may account for more than 70% of total tree abundance (Fine et al. 2010). In contrast, lineages as Eperua spp., Aldina spp. or Licania spp, drive the patterns of white sand forests composition and structure along the north-south gradient including Guiana Shield. Compositional and phylogenetic beta diversity From our results, it is clear that the regional species pool has a strong influence on shaping patterns of both CBD and PBD. The distance-decay curves of CBD and PBD for central and Northwestern Amazonian white-sand plots were steeper than for Guiana Shield plots. This may be due to higher levels of dominance of a small number of lineages in Guiana Shield white-sand forests, which may be mediated by the combination of local adaptations, environmental suitability and, dispersal capability (Emerson and Gillespie 2008, Fine and Kembel 2011, Struwe et al.1997). White-sand specialist taxa may be able to colonize and become established more easily in the larger tracts of white-sand forest in the Guiana Shield, compared to the patchy distribution of suitable habitat in other regions. Dispersal limitation should be less severe if a contiguous suitable environment is available. Moreover, if low levels of disturbance permit environments to remain unchanged, lineages can persist over long periods of time, and therefore low phylogenetic turnover would be expected to occur in large areas with relatively stable environmental conditions (Emerson and Gillespie 2008). Satellite images from the upper Rio Negro and other areas of the Guiana Shield show that the extent of white-sand forest in this region is massive and can reach thousands of hectares (Stropp et al. 2011, Adeney et al., this issue). In contrast, the great majority of the plots established in central and Northwestern Amazonia are scattered over large tracts of adjacent terra firme forests making dispersal to neighboring white-sand habitats more challenging for white-sand specialists (Adeney et al., this issue). Alternatively, one could explain these striking differences in PBD as the result of the different geological origins of white sand forest across the basin and the different biogeographic histories of Amazonian forests. While WS habitat in Northwestern Amazonia is interspersed in a geologically more heterogeneous landscape that varies from Cretaceous to Pliocene sediments, most of the white-sand soils in the Guiana Shield are derived from Precambrian geological formations (Horn et al. 2010, Wessenlingh et al. 2006). Andean uplift has undoubtedly played a major role in the evolution of white-sand habitat patches becoming fragmented in waves of deposition of newer sediments which in turn would influence divergence times for different lineages that arrived to these habitat islands in Northwestern Amazonia. Nonetheless, this kind of close match between geological history and lineage formation would not be found if plants are good dispersers and easily colonize distant patches of suitable habitat. We found evidence for regional neo-endemism in Amazonian white sand forests. CBD was much higher than PBD meaning that local communities occurring in different regions have very different taxonomic composition even though they are derived from the same lineages. Moreover, pairwise comparisons of plots across the basin revealed significantly lower PBD values compared to the null expectation, indicating that local communities across the entire Amazon basin tend to share close relatives. In this context, our results indicate that most of the white sand specialists sappear to be geographic neo-endemics with small distributional ranges. This is also

consistent with a plausible scenario of in situ radiation in white sand forests by sequential allopatric speciation by white-sand specialist lineages across the basin. Therefore, pairs of compared local communities can be very different in floristic terms but at the same time exhibit low PBD if the communities compared are within in situ radiation centers (Fine and Kembel 2011, Graham and Fine 2008). This appears to be the case for Guiana Shield and Northwestern Amazonia white-sand forests that presumably are centers of radiation for many Fabaceae clades (i.e. Aldina, Eperua, Dicymbe, Dimorphandra, Clathrotropis, Swartzia) as well as other groups like Chrysobalanaceae and Inga, respectively. Some of these clades have three-fold the number of species in these regions compared with other regions of the Amazon basin (Bardon et al. 2013, Richardson et al. 2001). The source-sink model of diversification assumes that a source region will exhibit higher origination rates relative to other regions and also higher levels of endemism. This model could explain the potential origination centers for these clades (Ricklefs and Schluter 1993, Rosenzweig and Sandlin 1997, Goldberg et al. 2005). A plausible scenario under the source-sink model, assuming that the Guiana Shield is the source and central and Northwestern Amazonia are sinks, would have allowed more time for ancient lineages to expand their ranges from the Guiana Shield towards the west, thus sink communities would be composed predominantly by early diverging lineages. However the average age distribution and estimates of speciation, extinction and dispersal rates of clades between regions need to be simulated in order to support a plausible source-sink scenario (Goldberg et al. 2005, Roy and Goldberg 2007). Phylogenetic community structure Our results indicate that on average Amazon white-sand forests are phylogenetically clustered at regional scales (Fig. 5; Table S2). While our results of phylogenetic clustering at regional scales is in agreement with previous studies (Fine and Kembel 2011, Eiserhardt et al. 2013); we found contrasting results in the phylogenetic community structure of white-sand forests at the subregional scale. We argue that these results are not just the by-product of differences in phylogenetic resolution and spatial scale (Vamosi et al. 2009), but instead are the result of the strong influence of large scale evolutionary process underlying the nature of these communities (Cardillo 2011). For instance, the strength of environmental filtering may also be different in Western Amazon compared to the Guiana Shield, and this effect could be driving some of the differences in local phylogenetic structure. In this way because the difference in soil fertility between white-sand and terra firme forests is less marked in the Guiana shield (Hammond 2005, Fine and Baraloto, in press), there may be a larger species pool able to colonize WSF in the Guianas. Therefore, we would expect more close relatives inhabiting white sand communities in Guiana Shield forests with respect Western Amazonia white sand forests because as the species pool able to colonize increase the probability for two species randomly chosen from this pool will increase as well. Convergent adaptation to white-sand habitats could create a pattern of phylogenetic evenness if traits enhancing fitness evolved independently in distant relatives (Fine and Kembel 2011). It has been largely argued that phylogenetic clustering is the product of environmental filtering when close relatives share ecological resemblance in traits that allow them to coexist in sympatry (Webb 2000, Cavender-Bares et al. 2004). Alternatively, phylogenetic clustering in local communities could also be the result of many different processes including competition, pollinator facilitation, adaptive radiation and herbivore-plant interactions (Cavender-Bares et al. 2009, Emerson and Gillespie 2008, Gillespie 2004, Mayfield and Levine 2010). Thus, the spatial and

phylogenetic scale of our analysis allows us to posit that biogeographical processes may be more important in the determination of phylogenetic structure of white-sand forests than processes operating at local scale (Ricklefs 2007, Hardy and Senterre 2007). Taken together, the results of overall phylogenetic clustering at regional scales together with low phylogenetic beta diversity support the hypothesis of regional neo-endemism mediated by recent diversification on white sand forests across the Amazon basin. Our results contradict the main paradigm of white-sand forests as habitats structured by early divergent lineages that have colonized “ancient” white-sand habitat islands that predate the Middle Miocene (Hoorn et al. 2010, Frasier et al. 2008, Pennington and Dick 2010, Fine and Kembel 2011). The arguments given for this hypothesis are that the sediments that originated in Guiana, and Brazilian Shield Precambrian geological formations were widely distributed across the basin previous to the Miocene in such a way that large portions of today’s Amazon basin were covered by extremely poor quarzitic sandstones (Struwe et al. 1997, Hoorn 1993). In this way similar soil conditions as current white-sand habitats predate the diversification of many Amazonian tree clades promoting early divergence, colonization and habitat specialization mediated by strong conservatism of traits that enhanced fitness in this stressful environment (Struwe et al. 1997). On the basis of this assumption, we would expect early diverging lineages to dominate white-sand habitats. Nonetheless, Fine et al. (2005) demonstrated that most of the members of the tribe Protieae that inhabit white sand forest evolved from close relatives on adjacent terra firme forest suggesting that, at least in some lineages, recent ecological divergence from richer to poorer soils had occurred (Fine & Baraloto, in press). Similar patterns of neo- endemism have been suggested for Peruvian white-sand bird communities resulting from recent in situ diversification in the Western Amazon (Álvarez Alonso et al. 2013, Matos et al. in press). While endemism could be promoted by habitat specialization, our results show that geographical endemism driven by dispersal limitation of white-sand specialist lineages may also be important in explaining floristic dissimilarity across white-sand patches. Rather than a single flora that originated in the Guianas, specialization to sandy soils appears to have evolved independently in different plant lineages over many different white-sand patches across the Amazonian landscape. Because our analyses have some limitations, our assumptions should be considered cautiously, and our intent is that subsequent analyses will help us to identify the evolutionary processes that underlie the patterns we describe. The degree of patch isolation, asymmetries in speciation, extinction and dispersal rates, or any specialization mediated by niche conservatism for white-sand environments could influence the divergence of plant lineages and yield patterns of geographic endemism. Therefore, to understand the floristic variation and the species turnover of white-sand forests across the Amazon basin, it is fundamental to investigate the processes responsible for local dominance, the evolutionary mechanisms for geographic endemism, and the relationship between ecological filters and species traits that allows some successful lineages from the regional species pool to establish and become common in local communities.

Acknowledgements JEG, GD, and PVAF were supported by NSF DEB 1254214. We thank the staff at the herbaria AMAZ, INPA, GUY, MO and QCNE for their invaluable support. Nigel C.A. Pitman, Jenna Judge and Dori Contreras provide insightful comments to earlier versions of the manuscript. We are grateful to Peru's Instituto Nacional de Recursos Naturales, Ecuador's Ministerio de Ambiente (MAE), Brazil’s Instituto de Pesquisas da Amazonia (INPA), French Guiana’s Institute Researche

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Table 1. Structural and ecological attributes of the 91 white-sand plots from the Amazon Tree Diversity network (ATDN). Number of species and individuals are based on the total number of trees sampled (including unidentified morphospecies).

Western Central Guiana Overall Amazonia Amazonia Shield Number of ha sampled 20 23 48 91 Number of individuals 8789 8121 27011 44579 Number of species 594 545 914 1482 Number of families 59 63 54 77 Number of hyperdominant species sensu ter Steege et al. 2013, Pitman et al. 2001 10 11 17 34 Mean species per plot 59 55 60 60 Mean stems per plot 470 353 565 490 Number of singletons 174 167 200 318

% stems that belongs single most common species 12.1 3.7 12.4 7.6 % stems that belongs 5 most common species 29.5 14 30.3 19 Mean taxonomic dissimilarity (1- Sorenson) 0.84 0.84 0.86 0.9 Mean phylogenetic dissimilarity (1- Phylosorenson ) 0.52 0.6 0.48 0.56

Figure legends Figure 1. Three potential scenarios for compositional beta diversity (CBD) and phylogenetic beta diversity (PBD) in Amazonian white sand forests. The red dashed line represents the null expectation of no influence of geographic distance in the patterns of taxonomic and phylogenetic beta diversity. A) High taxonomic and phylogenetic turnover with respect to the null expectation due to longterm disparate evolutionary histories of the taxa occupying white-sand forests across the Amazon basin. B) Potential scenario for in situ radiation of small ranged species. In this case we expect a higher compositional dissimilarity with respect to both observed and expected phylogenetic dissimilarity. C) Long distance dispersal capabilities over long periods of evolutionary time among different local communities of white sand forests would cause lower values for both taxonomic and phylogenetic beta diversity. Figure 2. Map of the locations of the 91 ATDN plots established in white-sand forests across Amazon basin. Light blue: North Western Amazonia, Blue: Upper Rio Negro basin, Magenta: Guiana Shield, Black: Central Amazonia. Figure 3. Non Metric Multidimensional ordination for 91 white-sand plots across the Amazon basin: A) NMDS based on compositional data dissimilarity matrix B) NMDS based on phylogenetic distance matrix. Ellipses represent the 95% confidence interval in grouping plots as part of a particular cluster of similar floristic units. Abbreviations for regions are as follows; NWA = North Western Amazonia, CA = Central Amazonia, GS= Guiana Shield, URN = Upper Rio Negro, VIR = Virua National Park. Figure 4. Phylogenetic dissimilarity as a function of geographic distance in white-sand forests across the Amazon basin. Phylogenetic dissimilarity is measured as the complement of Phylosorenson index (1-Phylosor). The lines represent a Loess non-parametric regression that best describes the patterns of distance decay curve in phylogenetic beta diversity. The results of t-tests to determine significant difference between observed average PBD and expected average PBD are shown. Confidence intervals for both observed and expected overall phylogenetic dissimilarity lowess curves are shown in figure 4H. Figure 5. Phylogenetic local community structure in white-sand forests across the Amazon basin. The red dashed lines represent the confidence interval that is the null expectation under the “richness” null model; asterisks represent both significantly positive and negative values of Near Relatedness Index (NRI) and Nearest Taxon Index (NTI) based on a t-test (p<0.001).

Figure 1.

Figure 2.

Figure 3.

A B

Figure 4.

Figure 5.

Appendices Table S1. Table S1. Plot codes, countries and geographic coordinates for the 91 plot network used in this study. Size of each plot, tree species richness and abundance per plot is also shown.

Plot Size Plot Code Country Latitude Longitude Individuals Species (hectares) 24MR_01 Guiana 6.14761 -58.6595 525 55 1 24MR_02 Guiana 6.14761 -58.6613 519 43 1 ALP_40 Peru -3.94111 -73.4394 1209 47 1 ARA_PR Colombia -0.71175 -72.0633 251 17 0.4 BIT_02 Suriname 5.08293 -56.1966 455 42 1 BIT_03 Suriname 5.08293 -56.197 414 37 1 BP90M Guiana 5.55 -58.74 704 27 1.486 BSH_01 Suriname 5.14205 -55.7835 459 33 1 BSH_02 Suriname 5.14508 -55.7836 300 31 1 BSH_03 Suriname 5.14508 -55.7827 432 31 1 CAQ_04WS Colombia -1.03745 -71.522 335 27 0.3 CHBR_02 Guiana 4.95 -58.35 651 62 1 CHBR_03 Guiana 4.94996 -58.368 547 39 1 CIJH_01 Peru -4.86194 -73.6192 885 63 1 CNG_01 Venezuela 0.833333 -66.1667 530 92 1 CUI_01 Brazil -2.5958 -60.2111 657 160 1 DUCKE_WS Brazil -2.94138 -59.9399 255 98 0.5 FTC_01 Guiana 6.62791 -58.8726 479 43 1 FTC_03 Guiana 6.629712 -58.8726 505 55 1 GOL_01 Suriname 5.2225 -55.6548 459 44 1 ICANA_02 Brazil 1.4808 -68.711 723 115 1 INI_19mh_CP Colombia 3.60608 -67.5494 691 161 1 INI_19mh_RA Colombia 3.87524 -67.8638 409 107 0.6 INI_20mh_CP1 Colombia 3.80028 -67.8299 831 175 1.4 INI_20mh_CP2 Colombia 3.55349 -67.6692 514 95 0.7 INI_20mh_CP3 Colombia 3.09361 -67.787 854 96 1 INI_47_RT Colombia 3.6206 -67.8215 309 46 0.5 INI_48_CV Colombia 3.13687 -67.785 591 59 0.5 INIRIDA Colombia 3.79167 -67.8197 564 108 1 IWO_01 Guiana 4.63095 -58.7389 583 34 1 IWO_09 Guiana 4.61071 -58.7338 688 38 1 JEN_12 Peru -4.89869 -73.6284 765 125 1 KAKO_R Guiana 5.72917 -60.6158 393 17 1 KAMPA Ecuador -3.02722 -77.9139 269 93 0.25 KAPUT_01 Ecuador -3.02632 -77.9139 245 112 0.25

KAPUT_02 Ecuador -3.007 -77.916 103 42 0.25 KUR_02 Guiana 4.6534 -58.6862 728 43 1 LO4T500 Brazil -2.94807 -59.9251 351 136 0.5 French LST2 Guiana 5.480093 -53.596 492 87 1 MB_WS Guiana 6.186934 -58.55 919 59 1.5 MHFR_01 Guiana 5.17887 -58.7039 543 40 1 MHFR_02 Guiana 5.16393 -58.7121 509 42 1 MHFR_03 Guiana 5.1826 -58.7008 592 35 1 MHFR_04 Guiana 5.17916 -58.7035 609 41 2.3 MORAB_WS2 Guiana 6.187835 -58.55 490 64 1 MSH_01 Peru -3.78333 -73.5 1502 460 1 NIR_01 Suriname 4.98276 -56.9984 651 23 1 NIR_02 Suriname 4.98457 -56.9984 675 42 1 PF_AMC Peru -3.94778 -73.4117 251 30 0.1 PF_AMD Peru -3.94167 -73.4389 343 34 0.1 PF_ANV Peru -3.74139 -74.1328 259 25 0.1 PF_JEB Peru -5.3 -76.2667 250 41 0.1 PF_JH1 Peru -4.85 -73.6 253 62 0.1 PF_JH2 Peru -4.8491 -73.6 221 37 0.1 PF_MAT Peru -5.855 -73.754 328 34 0.1 PF_MB Peru -4.26667 -77.2333 228 70 0.1 PF_TA1 Peru -3.98333 -73.0667 227 30 0.1 PNNP_02 Brazil 0.4 -66.3 465 56 0.25 RCAMP_02 Brazil -2.58333 -60.0333 813 106 1 RDS_C1A Brazil -2.19031 -59.0233 329 70 0.5 RDS_C1B Brazil -2.18722 -59.0214 231 25 0.5 RDS_C2A Brazil -2.18461 -59.0185 298 35 0.5 RDS_C2B Brazil -2.18156 -59.023 204 29 0.5 RDS_C3A Brazil -2.18022 -59.0207 452 28 0.5 RDS_C3B Brazil -2.18025 -59.0159 334 52 0.5 SCR_04 Venezuela 1.95169 -67.0047 824 47 0.6 SGC_04 Brazil -0.10065 -66.8804 547 102 1 SGC_05 Brazil -0.11069 -66.8813 632 94 1 SGC_06 Brazil -0.16551 -67.0105 609 86 1 SI_01 Brazil -0.41 -64.95 583 131 1 SIMCR_01 Suriname 5.31887 -54.9422 693 41 1 SIMCR_02 Suriname 5.31512 -54.9411 908 29 1 SNCR_B Suriname 5.23333 -56.8 261 47 0.47 ST_CUTH Guiana 6.36806 -58.0781 526 70 1 Virua_01_2500 Brazil 1.486675 -61.0247 344 45 1 Virua_01_4500 Brazil 1.486692 -61.0427 449 58 1 Virua_02_3500 Brazil 1.477782 -61.034 366 48 1

Virua_02_4500 Brazil 1.477649 -61.0427 406 56 1 Virua_04_500 Brazil 1.459419 -61.0068 249 63 1 Virua_05_1500 Brazil 1.450463 -61.0158 297 43 1 Virua_05_2500 Brazil 1.450479 -61.0247 339 41 1 Virua_05_3500 Brazil 1.450508 -61.0337 577 31 1 Virua_05_4500 Brazil 1.450511 -61.0427 144 21 1 Virua_05_500 Brazil 1.450442 -61.0067 260 61 1 Virua_06_1500 Brazil 1.441456 -61.0156 440 77 1 Virua_06_2500 Brazil 1.441467 -61.0247 115 18 1 Virua_06_4500 Brazil 1.441442 -61.0427 211 21 1 WIN_A Suriname 5.25 -57.0667 274 26 0.54 WIN_EI Suriname 5.250901 -57.0667 352 72 0.68 ZAR_01 Colombia -4.00683 -69.9061 865 62 1 ZF2_CAMP Brazil -2.6001 -60.2126 658 161 1

Table S2. Table S2. Mean values of standardized phylogenetic diversity (NTI and NRI), positive values indicate phylogenetic clustering and negative values phylogenetic evenness. Significance between observed values vs. expected values under null model was evaluated with a paired t- test (*=p<0.05, **=p<0.01, ***=p.001, NS=non-significant).

Western Central Guiana Shield Overall mean Amazonia Amazonia (mean) (mean) (mean)

NRIt 1.77181 *** -0.2935 NS -0.2042 NS -0.0572 NS

NTIt 3.82881 *** 1.02366 *** 1.3311 *** 1.77151 ***

NRIi 0.82314 *** 0.29798 ** 1.26399 *** 0.91717 **

NTIi 0.61272 NS 0.98791 *** 0.86831 *** 1.0885 ***

Appendix S1. Indicator Species Analysis for 91 plot network established in Amazonian white- sand forests (CA= Central Amazonia, GS = Guiana Shield, NWA= North Western Amazonia). Bold genus names and numbers represent taxa significantly associated to one or more regions based on the following attributes: IV$maxcls = Region in which species has maximum indicator value, IV$indcls = Indicator value, IV$pval = the probability of obtaining the highest indicator value based on 1000 iterations.

CA GS NWA IV$maxcls IV$indcls IV$pval Abarema 0.25 0.395833 0.263158 2 0.133359 0.8615 Abuta 0.041667 0.020833 0 1 0.033333 0.6999 Acioa 0 0.041667 0 2 0.041667 0.5492 Acosmium 0 0.0625 0 2 0.0625 0.3542 Adiscanthus 0 0 0.052632 3 0.052632 0.2094 Agonandra 0.166667 0 0 1 0.166667 0.0051 Aiouea 0.083333 0.020833 0 1 0.074074 0.1359 Albizia 0 0.020833 0 2 0.020833 1 Alchornea 0.041667 0.0625 0.052632 2 0.032095 0.8514 Alchorneopsis 0.125 0 0.052632 1 0.091851 0.072 Aldina 0.41667 0.20833 0.05263 1 0.3016 0.0062 Alexa 0 0.0625 0 2 0.0625 0.3199 Alibertia 0 0 0.052632 3 0.052632 0.2114 Allantoma 0.041667 0 0.157895 3 0.124928 0.0315 Allophylus 0 0.020833 0.052632 3 0.02406 0.8576 Amaioua 0 0.166667 0.052632 2 0.135538 0.0893 1.00E- Amanoa 0.41667 0.04167 0 1 0.41389 04 Ambelania 0 0.041667 0 2 0.041667 0.3921 Ampelocera 0 0.020833 0.052632 3 0.042531 0.224 Anacardium 0.166667 0 0.157895 3 0.100861 0.1676 Anaxagorea 0.041667 0.145833 0.105263 2 0.086589 0.4327 3.00E- Andira 0.5 0.20833 0.05263 1 0.3914 04 Aniba 0.291667 0.479167 0.157895 2 0.349288 0.0122 Annona 0.375 0.166667 0.157895 1 0.337536 0.0051 Anomalocalyx 0.041667 0.020833 0 1 0.027778 1 Anthodiscus 0 0.020833 0.105263 3 0.092993 0.0722 Antonia 0 0.041667 0 2 0.041667 0.5466 Aparisthmium 0.166667 0.041667 0 1 0.15 0.0206 Apeiba 0 0.0625 0.052632 3 0.029376 0.807 Aptandra 0.125 0.041667 0.052632 1 0.030579 0.9566 Archytaea 0 0.020833 0 2 0.020833 1 Aspidosperma 0.416667 0.895833 0.684211 2 0.586925 0.0099 Astronium 0.166667 0.145833 0 2 0.100962 0.3254 Attalea 0.041667 0 0.052632 3 0.029376 0.7101

Bathysa 0 0 0.052632 3 0.052632 0.2189 Batocarpus 0 0 0.052632 3 0.052632 0.2074 Beilschmiedia 0 0 0.052632 3 0.052632 0.2061 Blastemanthus 0 0.041667 0 2 0.041667 0.5477 Bocageopsis 0.166667 0.166667 0.263158 3 0.174082 0.1271 Bocoa 0 0.020833 0 2 0.020833 1 Bombax 0 0.020833 0 2 0.020833 1 Bonyunia 0 0.020833 0 2 0.020833 1 Botryarrhena 0 0.020833 0.052632 3 0.017667 0.8597 Brosimum 0.666667 0.625 0.421053 2 0.236354 0.7283 Buchenavia 0.666667 0.145833 0.315789 1 0.571383 1.00E-04 Byrsonima 0.458333 0.208333 0.473684 3 0.243214 0.1162 Cabralea 0 0 0.052632 3 0.052632 0.2171 Calliandra 0.125 0 0 1 0.125 0.0245 Calophyllum 0.375 0.208333 0.368421 3 0.233583 0.1418 Calycolpus 0 0.208333 0 2 0.208333 0.0117 Calycophyllum 0 0.0625 0.052632 3 0.049497 0.5751 Calyptranthes 0.041667 0.020833 0.105263 3 0.085061 0.0929 Campsiandra 0 0.041667 0 2 0.041667 0.3849 Caraipa 0.25 0.29167 0.42105 3 0.32565 0.0178 Carapa 0.083333 0.041667 0 1 0.037037 0.7785 Cariniana 0.125 0 0.05263 1 0.1131 0.0496 Carpotroche 0 0 0.052632 3 0.052632 0.2065 Caryocar 0.291667 0.229167 0.263158 1 0.109337 0.8459 Casearia 0.166667 0.083333 0.210526 1 0.078729 0.6319 Cassipourea 0 0.020833 0.052632 3 0.015595 1 Cathedra 0 0 0.105263 3 0.105263 0.0416 1.00E- Catostemma 0.29167 0.625 0 2 0.54056 04 Cecropia 0.083333 0.1875 0 2 0.1125 0.2163 Cedrelinga 0 0.020833 0 2 0.020833 1 Centronia 0 0 0.157895 3 0.157895 0.0083 Cespedesia 0 0 0.052632 3 0.052632 0.2168 Chaetocarpus 0.16667 0.04167 0 1 0.1592 0.0159 Chamaecrista 0.04167 0.25 0 2 0.23881 0.0136 Chaunochiton 0.333333 0.166667 0 1 0.146893 0.2099 Cheiloclinium 0 0.041667 0 2 0.041667 0.3896 Chlorocardium 0 0.020833 0 2 0.020833 1 2.00E- Chrysophyllum 0.33333 0.45833 0.84211 3 0.55434 04 Cinchonopsis 0 0 0.052632 3 0.052632 0.2064 Cinnamomum 0 0 0.052632 3 0.052632 0.2158 Clarisia 0 0.041667 0 2 0.041667 0.3845 Clathrotropis 0 0.1875 0 2 0.1875 0.0226

Coccoloba 0 0.041667 0 2 0.041667 0.4891 Cochlospermum 0 0.020833 0 2 0.020833 1 Compsoneura 0 0.02083 0.21053 3 0.19508 0.0048 Conceveiba 0.166667 0.25 0.052632 1 0.137622 0.5315 Cordia 0.208333 0.0625 0 1 0.166667 0.0292 Cordiera 0 0 0.052632 3 0.052632 0.2121 Corythophora 0.083333 0 0.052632 1 0.051075 0.4201 Couepia 0.458333 0.416667 0.315789 2 0.281166 0.2631 Couma 0.45833 0.25 0.1579 1 0.33949 0.0102 Couratari 0.29167 0.04167 0.05263 1 0.27439 0.0021 Crepidospermum 0.083333 0 0 1 0.083333 0.1078 Croton 0 0 0.052632 3 0.052632 0.2122 Crudia 0 0.020833 0 2 0.020833 1 Cupania 0 0.041667 0.473684 3 0.458201 1.00E-04 Cybianthus 0.291667 0.166667 0.368421 3 0.215281 0.1837 Cynometra 0 0.020833 0 2 0.020833 1 Cyrilla 0 0.02083 0 2 0.02083 1 Cyrillopsis 0 0.02083 0 2 0.02083 1 Dacryodes 0.166667 0.104167 0.105263 1 0.130335 0.2253 Dendrobangia 0 0 0.105263 3 0.105263 0.0449 1.00E- Dendropanax 0.04167 0.16667 0.52632 3 0.49055 04 Dialium 0 0.0625 0.052632 2 0.048801 0.5316 Dichapetalum 0.041667 0 0 1 0.041667 0.4725 7.00E- Diclinanona 0 0.0625 0.26316 3 0.25245 04 Dicorynia 0 0.104167 0 2 0.104167 0.1255 4.00E- Dicymbe 0 0.08333 0.47368 3 0.35662 04 Dicypellium 0.041667 0 0 1 0.041667 0.4756 Digomphia 0 0 0.052632 3 0.052632 0.2193 Dimorphandra 0.083333 0.041667 0.105263 3 0.028458 0.9749 Diospyros 0.083333 0.1875 0 2 0.143382 0.1212 Diploon 0 0 0.052632 3 0.052632 0.2165 Diplotropis 0.375 0.125 0.10526 1 0.23061 0.0233 Dipteryx 0 0.0625 0.052632 2 0.045925 0.5904 Discophora 0 0 0.210526 3 0.210526 0.0016 Dodecastigma 0 0 0.052632 3 0.052632 0.2082 Drypetes 0 0.041667 0 2 0.041667 0.3808 Duguetia 0.083333 0.0625 0 1 0.047619 0.623 Dulacia 0.125 0.020833 0.105263 3 0.055676 0.5213 Duroia 0.66667 0.375 0 1 0.43609 0.0015 Ecclinusa 0.08333 0.29167 0 2 0.27632 0.0075 Elaeagia 0 0 0.052632 3 0.052632 0.2127

Elaeoluma 0.208333 0.125 0.157895 1 0.104816 0.4883 Elizabetha 0 0.04167 0 2 0.04167 0.3784 Elvasia 0.125 0 0 1 0.125 0.0227 7.00E- Emmotum 0.25 0.27083 0.63158 3 0.46387 04 Endlicheria 0.333333 0.229167 0.263158 1 0.166414 0.3041 Enterolobium 0.041667 0.020833 0 1 0.027778 1 1.00E- Eperua 0.20833 0.79167 0 2 0.76406 04 Ephedranthus 0.083333 0 0.052632 1 0.063333 0.1751 Eriotheca 0.291667 0.166667 0 1 0.137255 0.236 Erisma 0.041667 0.125 0 2 0.09375 0.1972 Erythroxylum 0.166667 0 0.315789 3 0.253159 0.0027 8.00E- Eschweilera 0.58333 0.375 0.21053 1 0.49326 04 Eugenia 0.291667 0.145833 0.526316 3 0.215863 0.111 Euphronia 0.04167 0.02083 0 1 0.03846 0.6941 Euterpe 0.208333 0.083333 0.263158 3 0.200157 0.0556 1.00E- Exellodendron 0.375 0 0 1 0.375 04 Faramea 0.166667 0 0.105263 1 0.122468 0.0716 Ferdinandusa 0.375 0.145833 0.473684 1 0.22581 0.133 Ficus 0.083333 0.145833 0.052632 2 0.105378 0.2435 Froesia 0 0.020833 0 2 0.020833 1 Fusaea 0.041667 0.0625 0 2 0.051136 0.4451 Garcinia 0 0.333333 0.210526 2 0.19503 0.1238 Gavarretia 0.041667 0 0.105263 3 0.081167 0.0989 Geissospermum 0 0.041667 0 2 0.041667 0.4983 Glandonia 0.041667 0.104167 0 2 0.093202 0.2028 Gloeospermum 0 0.020833 0.052632 3 0.037706 0.6027 Glycydendron 0.041667 0 0 1 0.041667 0.4686 Godoya 0 0 0.052632 3 0.052632 0.2021 Gordonia 0 0.020833 0 2 0.020833 1 Goupia 0.25 0.166667 0 1 0.126866 0.2469 Guapira 0 0.1875 0 2 0.1875 0.0189 Guarea 0.083333 0.083333 0.157895 3 0.061121 0.7227 Guatteria 0.416667 0.395833 0.578947 1 0.219238 0.4974 Gustavia 0 0.104167 0 2 0.104167 0.1185 Handroanthus 0 0.10417 0.26316 3 0.19987 0.0148 Haploclathra 0 0.10417 0.21053 3 0.20219 0.0148 Hebepetalum 0.208333 0.208333 0.052632 1 0.109303 0.4801 Heisteria 0.208333 0.166667 0.105263 2 0.077596 0.8369 Helicostylis 0.166667 0.1875 0.157895 1 0.066412 0.9655 Henriettella 0 0.020833 0 2 0.020833 1 Henriquezia 0 0.041667 0 2 0.041667 0.55

Heterostemon 0.083333 0.0625 0 1 0.051282 0.55 Hevea 0.54167 0.3125 0.73684 3 0.37225 0.0195 Hieronyma 0 0.020833 0.052632 3 0.037706 0.6059 Himatanthus 0.041667 0.229167 0 2 0.159722 0.0681 Hirtella 0.416667 0.104167 0.052632 1 0.254258 0.0138 Hortia 0.041667 0.104167 0.052632 2 0.08194 0.299 Humiria 0.291667 0.25 0.210526 2 0.109229 0.8355 Humiriastrum 0 0.208333 0.105263 2 0.077614 0.683 Hura 0 0 0.052632 3 0.052632 0.2048 Hydrochorea 0 0.020833 0.052632 3 0.047892 0.2209 Hylocarpa 0 0.041667 0 2 0.041667 0.3885 Hymenaea 0.04167 0 0.1579 3 0.12493 0.0329 Hymenolobium 0.20833 0.1875 0.36842 3 0.24647 0.0285 Ilex 0.04167 0.20833 0.47368 3 0.25054 0.0287 Inga 0.375 0.60417 0.42105 2 0.31219 0.0886 Iriartella 0.083333 0 0 1 0.083333 0.1133 Iryanthera 0.208333 0.291667 0.473684 3 0.215016 0.2704 Isertia 0.041667 0 0 1 0.041667 0.4714 Ixora 0 0.020833 0.052632 2 0.016267 1 Jacaranda 0.125 0.166667 0.105263 3 0.051875 0.9883 Jacqueshuberia 0 0 0.052632 3 0.052632 0.2121 Kutchubaea 0.166667 0 0.157895 3 0.100861 0.1817 Lacistema 0.041667 0 0.052632 3 0.029376 0.7214 Lacmellea 0.25 0.0625 0.105263 1 0.21236 0.0195 Lacunaria 0.041667 0.083333 0 2 0.055556 0.4716 Ladenbergia 0.041667 0.083333 0.157895 3 0.079361 0.364 Laetia 0 0.083333 0.052632 2 0.073077 0.3241 Lecointea 0 0 0.052632 3 0.052632 0.2106 Lecythis 0.29167 0.52083 0.05263 2 0.37408 0.0032 Leonia 0 0.041667 0.052632 3 0.029376 0.5942 Leopoldinia 0 0.145833 0 2 0.145833 0.0403 Leptolobium 0.16667 0.02083 0 1 0.16279 0.0083 Licania 1 0.97917 0.68421 2 0.57092 0.0217 Licaria 0.208333 0.5 0.263158 2 0.371787 0.0193 Lissocarpa 0 0.041667 0.052632 2 0.027681 0.9374 Lorostemon 0 0 0.05263 3 0.05263 0.2068 Loxopterygium 0 0.020833 0 2 0.020833 1 Luehea 0 0.020833 0 2 0.020833 1 Lueheopsis 0 0.145833 0.210526 3 0.107962 0.2538 Lunania 0 0 0.052632 3 0.052632 0.2048 Mabea 0.04167 0 0.21053 3 0.18175 0.0053 Machaerium 0.083333 0 0 1 0.083333 0.112 Macoubea 0.33333 0.04167 0.42105 3 0.24239 0.0275

Macrolobium 0.29167 0.35417 1 3 0.39125 0.0462 Macrosamanea 0.083333 0.020833 0 1 0.066667 0.1884 Magnolia 0 0 0.052632 3 0.052632 0.2114 Mahurea 0 0.020833 0 2 0.020833 1 Malouetia 0.041667 0 0 1 0.041667 0.4716 Manicaria 0 0.020833 0 2 0.020833 1 Manilkara 0.58333 0.39583 0.05263 1 0.45351 0.0012 Maprounea 0 0.104167 0.052632 2 0.079167 0.3219 Maquira 0.29167 0.08333 0 1 0.27726 0.0019 Marlierea 0 0.1875 0.36842 3 0.29604 0.0049 Marmaroxylon 0 0 0.105263 3 0.105263 0.043 Matayba 0.375 0.479167 0.578947 2 0.344516 0.1007 Matisia 0.041667 0 0 1 0.041667 0.4814 Mauritia 0.208333 0.166667 0.368421 3 0.179535 0.1849 1.00E- Mauritiella 0.29167 0.04167 0.52632 3 0.44061 04 Maytenus 0 0.041667 0.157895 3 0.11992 0.0492 Melicoccus 0 0 0.1579 3 0.1579 0.0099 Meliosma 0 0.041667 0.105263 3 0.066047 0.2769 Meriania 0.125 0 0 1 0.125 0.0245 Mezilaurus 0.25 0.166667 0.315789 2 0.10284 0.9139 Miconia 0.125 0.145833 0.210526 3 0.098794 0.4806 Micrandra 0.16667 0.25 0.31579 2 0.21393 0.1895 Micropholis 0.5 0.416667 0.578947 3 0.20524 0.7543 Minquartia 0 0.145833 0.052632 2 0.110833 0.1229 Mollia 0 0.104167 0 2 0.104167 0.1177 Monopteryx 0 0.1875 0.05263 2 0.18259 0.028 Mora 0 0.020833 0 2 0.020833 1 Moronobea 0 0.0625 0.157895 3 0.083515 0.2919 Mouriri 0.25 0.145833 0.157895 2 0.101372 0.6685 Mucoa 0 0.145833 0.210526 3 0.126844 0.1671 Myrcia 0.375 0.3125 0.105263 2 0.203531 0.2004 Myrciaria 0 0 0.10526 3 0.10526 0.0409 Myrocarpus 0 0.020833 0 2 0.020833 1 Myrsine 0 0 0.052632 3 0.052632 0.2082 Naucleopsis 0 0.041667 0.157895 3 0.099071 0.1394 Nectandra 0.083333 0.104167 0.157895 3 0.053 0.8737 Neea 0.25 0.354167 0.526316 3 0.142787 0.8889 Neocouma 0 0.104167 0 2 0.104167 0.1025 7.00E- Ocotea 0.45833 0.83333 0.57895 2 0.59917 04 Oenocarpus 0.375 0.1875 0.421053 3 0.154162 0.5229 Ormosia 0.29167 0.64583 0.26316 2 0.47946 0.0022 Osteophloeum 0 0.08333 0.26316 3 0.19713 0.0132

Otoba 0 0.041667 0 2 0.041667 0.5395 1.00E- Ouratea 0.45833 0.04167 0.10526 1 0.42204 04 Oxandra 0 0.104167 0.210526 3 0.159893 0.0385 1.00E- Pachira 0.54167 0.60417 0.89474 3 0.72507 04 Pagamea 0.29167 0.25 0.21053 1 0.17484 0.263 Palicourea 0 0.083333 0 2 0.083333 0.2029 Panopsis 0.125 0.020833 0.052632 1 0.101396 0.1301 Parahancornia 0 0.270833 0.368421 3 0.173443 0.2065 Parinari 0.291667 0.145833 0 1 0.152174 0.1548 3.00E- Parkia 0.5 0.25 0.73684 3 0.54365 04 Pausandra 0 0.083333 0 2 0.083333 0.1964 Paypayrola 0.041667 0.041667 0 1 0.03125 0.6168 Peltogyne 0.125 0.25 0.105263 2 0.110221 0.4975 Pentaclethra 0 0.020833 0.052632 3 0.047892 0.2281 Pera 0.375 0.25 0.263158 1 0.126934 0.7856 Persea 0 0.0625 0.36842 3 0.26394 0.0017 Pithecellobium 0 0.020833 0.052632 3 0.043936 0.2217 Platonia 0.125 0.29167 0 2 0.21875 0.0325 Platycarpum 0.29167 0.02083 0.31579 1 0.22092 0.0339 Podocalyx 0.083333 0.020833 0 1 0.060606 0.3871 Poecilanthe 0 0.020833 0 2 0.020833 1 Pogonophora 0.041667 0.020833 0 1 0.027778 1 Poraqueiba 0.041667 0.041667 0 2 0.025 0.7587 Posoqueria 0.041667 0 0.052632 3 0.029376 0.7204 Pourouma 0.166667 0.145833 0.210526 3 0.078487 0.8084 Pouteria 0.45833 0.85417 0.57895 2 0.48054 0.0138 Pradosia 0.25 0.479167 0.105263 1 0.175834 0.6547 Prestoea 0 0.020833 0 2 0.020833 1 Protium 0.583333 0.770833 0.842105 3 0.267917 0.942 Prunus 0 0.02083 0.1579 3 0.13949 0.0251 Pseudolmedia 0.083333 0.104167 0.421053 3 0.378947 3.00E-04 Pseudomonotes 0.041667 0 0 1 0.041667 0.4739 Pseudopiptadenia 0 0.020833 0 2 0.020833 1 Pseudosenefeldera 0 0 0.1579 3 0.1579 0.0098 Pseudoxandra 0.166667 0.0625 0.052632 1 0.098573 0.1945 Psychotria 0 0.041667 0 2 0.041667 0.498 Pterocarpus 0.125 0.166667 0.105263 2 0.088785 0.5587 Ptychopetalum 0.20833 0.02083 0 1 0.17157 0.0097 Qualea 0.083333 0.166667 0 2 0.134146 0.1124 Quiina 0.041667 0.104167 0.052632 2 0.073799 0.3913 Recordoxylon 0 0.020833 0 2 0.020833 1

2.00E- Remijia 0.04167 0.0625 0.42105 3 0.39393 04 Retiniphyllum 0.041667 0 0 1 0.041667 0.4716 Rhabdodendron 0.083333 0.208333 0 2 0.122863 0.241 Rhigospira 0.041667 0.0625 0 2 0.057292 0.3715 Rhodostemonodaphne 0.083333 0.020833 0.052632 1 0.044289 0.5342 Richeria 0.16667 0 0.05263 1 0.12247 0.0389 Rinorea 0.083333 0 0.052632 1 0.051075 0.4175 Roucheria 0.16667 0.125 0.52632 3 0.38167 0.0013 Roupala 0 0 0.10526 3 0.10526 0.0437 Rudgea 0 0 0.21053 3 0.21053 0.0014 Ruizodendron 0 0 0.052632 3 0.052632 0.2015 1.00E- Ruizterania 0.66667 0.20833 0.05263 1 0.5943 04 Sacoglottis 0.41667 0.25 0.21053 1 0.36796 0.0118 Salacia 0.083333 0 0.052632 1 0.051075 0.423 Sandwithia 0.041667 0.0625 0 2 0.035326 0.6917 Sarcaulus 0 0 0.052632 3 0.052632 0.212 Scheelea 0 0.020833 0.052632 3 0.037706 0.6145 Schefflera 0.08333 0.25 0.05263 2 0.19257 0.0541 Schistostemon 0 0.083333 0 2 0.083333 0.2159 Sclerolobium 0.083333 0 0 1 0.083333 0.108 Scleronema 0.20833 0.08333 0.05263 1 0.16219 0.0502 Senefeldera 0 0 0.10526 3 0.10526 0.0435 Sextonia 0 0.041667 0 2 0.041667 0.3895 Simaba 0.375 0.395833 0 2 0.20507 0.21 Simarouba 0.208333 0.166667 0 1 0.104167 0.4125 Simira 0 0 0.21053 3 0.21053 0.0018 Siparuna 0.04167 0.04167 0.26316 3 0.24258 0.0013 1.00E- Sloanea 0.20833 0.33333 0.84211 3 0.70821 04 Socratea 0 0.020833 0.052632 3 0.02406 0.8602 Spondias 0 0.020833 0 2 0.020833 1 Stachyarrhena 0 0 0.105263 3 0.105263 0.0419 Stenostomum 0 0 0.052632 3 0.052632 0.2107 Sterculia 0 0.166667 0.052632 2 0.114208 0.1447 Sterigmapetalum 0.25 0.0625 0.052632 1 0.224409 0.0074 Stryphnodendron 0 0 0.052632 3 0.052632 0.2122 Stylogyne 0 0 0.10526 3 0.10526 0.0432 6.00E- Swartzia 0.625 0.83333 0.47368 2 0.68131 04 Symphonia 0.041667 0.020833 0.052632 3 0.02406 0.7802 Tabebuia 0.041667 0.104167 0 2 0.098536 0.202 Tabernaemontana 0.041667 0.020833 0 1 0.027778 1 Tachigali 0.25 0.25 0.47368 3 0.38248 0.007

Talisia 0.20833 0.39583 0.05263 2 0.38161 0.0027 Tapirira 0.54167 0.625 0.26316 2 0.40772 0.0097 Tapura 0.166667 0.333333 0.105263 2 0.109827 0.8372 Taralea 0 0.125 0.26316 3 0.18427 0.0342 Terminalia 0.04167 0.39583 0.05263 2 0.37708 0.0012 Ternstroemia 0.04167 0.125 0.26316 3 0.19193 0.0372 Tetrameranthus 0.20833 0 0.05263 1 0.16267 0.0145 Tetrastylidium 0 0 0.052632 3 0.052632 0.2116 Tetrazygia 0 0.020833 0 2 0.020833 1 Theobroma 0.166667 0.020833 0.105263 1 0.13045 0.0826 Thyrsodium 0.041667 0.020833 0.052632 3 0.033024 0.5382 Toulicia 0 0.104167 0.052632 2 0.092981 0.1877 Touroulia 0 0.041667 0 2 0.041667 0.4943 Tovomita 0.375 0.520833 0.684211 3 0.353535 0.0696 5.00E- Trattinnickia 0.04167 0.47917 0.10526 2 0.44324 04 Trichilia 0.375 0.104167 0.263158 3 0.142676 0.3442 Trymatococcus 0.25 0.0625 0.05263 1 0.18785 0.0201 Unonopsis 0.166667 0.0625 0 1 0.161994 0.0284 Vantanea 0.083333 0.0625 0.105263 3 0.040747 0.8496 Vatairea 0.083333 0.041667 0 1 0.041667 0.6044 Vataireopsis 0 0.020833 0 2 0.020833 1 Virola 0.583333 0.291667 0.736842 1 0.292369 0.2558 Vismia 0 0.041667 0 2 0.041667 0.4912 Vitex 0.25 0.083333 0 1 0.140244 0.1318 2.00E- Vochysia 0.58333 0.14583 0.10526 1 0.48246 04 Vouacapoua 0.041667 0 0 1 0.041667 0.4735 Vouarana 0.041667 0 0 1 0.041667 0.4663 Wallacea 0 0.020833 0 2 0.020833 1 Wettinia 0 0 0.052632 3 0.052632 0.2097 Xylopia 0.625 0.45833 0.1579 1 0.37555 0.0147 Zanthoxylum 0.041667 0.041667 0.052632 2 0.019547 0.9384 Zygia 0.25 0.04167 0.05263 1 0.19375 0.0183

Figure S1. Phylogenetic reconstruction for 420 species recorded in a 91 one hectare plot network in Amazonian white sand forests. Phylogenetic tree is based on GenBank accessions for 7 gene regions (18S rDNA, 26S rDNA, ITS, matK, rbcL, atpB, and trnL-F) and represents a pruned version of the phylogenetic tree used in Zanne et al. 2014.

Figure S2. Longitudinal gradient in species composition in 91 plots established in white-sand forests across the Amazon basin.

Figure S3. Spatial variation of floristic dissimilarities in white-sand forests across the Amazon basin. The dots size represents NMDS axis 1 score values with smaller dots representing negative values and larger dots representing positive values.

NMDS1 WHITE SAND FOREST

Chapter 2 Incorporating phylogenetic information for the definition of floristic districts in hyper- diverse Amazon forests: implications for conservation

Abstract Using complementary metrics to evaluate phylogenetic diversity can facilitate the delimitation of floristic units and conservation priority areas. In this study, we describe the spatial patterns of phylogenetic alpha and beta diversity, phylogenetic endemism, and evolutionary distinctiveness of the hyperdiverse Ecuador Amazon forests and define priority areas for conservation. We established a network of 80 one-hectare plots in the Ecuadorian Amazon. In these plots we tagged, collected, and identified every single adult tree with dbh ≥ 10 cm. These data were combined with a regional community phylogenetic tree to calculate different phylogenetic diversity (PD) metrics in order to create spatial models. We used Loess regression to estimate the spatial variation of taxonomic and phylogenetic beta diversity as well as phylogenetic endemism and evolutionary distinctiveness. We found evidence for the definition of three floristic districts in the Ecuadorian Amazon, supported by both taxonomic and phylogenetic diversity data. In addition, large areas that include measures of high phylogenetic endemism (PE) and evolutionary distinctiveness (ED) remain without formal protection and could be severely threatened in the next 10 years. The spatial patterns of phylogenetic beta diversity and phylogenetic endemism are consistent with the definition of three floristically distinct units that may be assigned as floristic districts in Ecuador that probably also includes areas of Peru and Colombia. Areas with high levels of phylogenetic endemism and evolutionary distinctiveness in Ecuadorian Amazon forests are unprotected. Furthermore these areas are severely threatened by proposed plans of oil and mining extraction at large scale and should be prioritized in conservation planning for this region. Keywords: Amazon, Ecuador, conservation, evolutionary distinctiveness, floristics, phylogenetic beta diversity, phylogenetic endemism

INTRODUCTION Ever since Wallace one of the main goals of biogeography has been the delimitation of biotic regions in order to circumscribe areas that are characterized not only by the same species pool but also potentially by the same evolutionary, geological-historical, and ecological processes. Thus, the spatial classification of biodiversity has strong implications for the understanding of the evolutionary and ecological processes underlying patterns of alpha and beta diversity (Kreft and Jetz 2010, Li et al. 2015). Located within the South America’s Piedemonte del Napo region, the Ecuadorian Amazon has been recognized as one of the most biodiverse areas around the world (Bass et al.2010, Funk et al.2012, Myers et al. 2000) and is especially famous for possessing the highest levels of tree and

shrub diversity across the Amazon basin (Pitman et al. 2001, ter Steege et al.2013, ter Steege et al. 2016, Valencia et al.2004). Floristic inventories in Ecuadorian Amazon have also been influential in our understanding of the concept of hyperdominance and patterns of relative abundance of species in the Amazon as well as floristic disruptions triggered by geology (Pitman et al. 2008, Higgins et al. 2011), suggesting that the assembly of the lowland Amazonian tree flora is the result of the interplay between edaphic specialization mediated by geological history and oligarchic tree communities However, besides these efforts to determine both floristic and abundance patterns in Ecuador Amazon tree flora (Pitman et al. 2001, Pitman et al. 2002, Macía and Svenning 2005, Valencia et al. 2004), our understanding of the Ecuadorian Amazonian flora is quite limited due to significant geographic gaps in floristic assessments across the region. To date the most complete floristic assessment of Ecuador Amazon used both herbarium data and a one hectare plot network to delineated 4 floristic sub-regions in Ecuador Amazon (Guevara et al. 2016). However, there has been no systematic attempt to define floristic regions using approaches that include both compositional and phylogenetic diversity, which is likely to provide additional insights to improve research-based conservation policies. In his pioneering work Faith et al. (1992) posited the concept of phylogenetic diversity as the sum of branch lengths of a phylogenetic tree along a minimum spanning path connecting the tips of the tree present in a location to its root. This measure has been the cornerstone of subsequent methods looking for the identification of regions of high phylogenetic endemism and/or evolutionary distinctiveness (Redding and Moers 2006, Rossauer et al. 2009, Forest et al. 2007; Mishler et al. 2014). Applied in a biogeographical-conservation context PD provides a way to detect regions that contain assemblages of species that share the same evolutionary history and help us to elucidate the historical events that may have shaped these assemblages (Whittaker et al. 2005, Kraft et al. 2010). Recent works have developed indexes such as Phylogenetic Endemism (PE) defined as the sum of branch lengths geographic range that a clade of the regional phylogenetic tree occupies in a particular region (Rossauer et al. 2009). Because phylogenetic endemism works as an analogy of weighted endemism described as a relative measure of endemism we can use this index to better understand floristic changes across regions and define conservation priority areas simultaneously more effectively that using taxonomy alone (Li et al. 2015, Laffan et al. 2010). Spatial models with taxonomic and phylogenetic diversity metrics Several software packages for the spatial analysis of biodiversity have been developed in the past a ten years (e.g., Biodiverse, GDM) (Ferrier et al. 2007, Laffan et al. 2010), changing and improving radically our understanding of the spatial distribution of both taxonomic and phylogenetic diversity. The great majority of these analyses use a moving window approach that predefine a window around a group (e.g., site collection, plots) in the dataset to then calculate any appropriate statistic for each group based on the neighborhoods that fall within such window (Laffan et al. 2010). However as a caveat one must consider that when there is not complete spatial coverage within a region there is no way to predict values of taxonomic and phylogenetic turnover across space. Therefore, we used a different approach to predict the spatial variation of both richness and abundance-based metrics for taxonomic and phylogenetic diversity. Here we present the results of an extensive one hectare plot network that represents the most comprehensive spatial sampling of the trees of the Ecuadorian Amazon to date in order to evaluate the patterns of floristic affinities in this hyper-diverse region and provide insights in conservation priorities from a phylogenetic context. In addition we address the following questions: (1) Do the

41 results of our analyses support previously published floristic classifications for Ecuadorian Amazon? (2) To what extent are differences in species composition (taxonomic dissimilarity) across the region congruent with differences in phylogenetic composition (phylogenetic dissimilarity)? (3) Are regions with high phylogenetic diversity (PD) areas with extraordinary evolutionary distinctiveness or endemism? (4) Are areas characterized by high PD currently under formal protection? METHODS Study Area Our study was carried out in the Ecuadorian lowland Amazon (Figure 1). We defined lowland Amazonia based on three parameters proposed as diagnostic factors for the definition of vegetation units for the Vegetation Map of Ecuador (Ministerio de Ambiente del Ecuador 2013) (SThis area includes two protected areas in the north, Yasuní National Park and Cuyabeno Reserve, whereas the southern portion of Ecuador Amazon contains no formal protected areas although it is inhabited by at least 6 different groups of indigenous people. These groups have inhabited this area for many centuries and have ancestral control over their land, however there is no legal status assigned by the Ecuadorian government to these territories. Toward the northern portion of Yasuní National Park, the interfluvial landscape is mostly dominated by rolling hills interrupted by terrain depressions or baixios that vary in extent and levels of drainage (Pitman 2000). This landscape is interrupted by the Napo River that divides the most northern portion of the Ecuadorian Amazon from the rest. High and low terraces from Pleistocene origin dominate the northern and southern riverbanks of the Aguarico River whereas the northern riverbank of the Napo River mainly consists of palm-dominated swamps (Ministerio de Ambiente del Ecuador 2013). The Pastaza River represents a geomorphological break in the landscape of Ecuador Amazon. South of this river the landscape is characterized by extensive plains of terra firme forests interspersed by swamps that are sometimes but not always dominated by palms. This area is known as the Pastaza fan which corresponds to a massive volcanoclastic alluvial fan deposited during the Holocene (Rasanen et al. 1987; Bernal et al. 2011). Finally, we sampled the lowland forests adjacent to the Cordillera del Condor, which is one of the areas of the Ecuadorian Amazon that remains most poorly explored in terms of floristic inventories. We sampled one plateau at 300-400 m on quarzitic sandstones (white sands) that represents the lowest altitude of Cordillera del Condor in Ecuadorian Amazon and also the first record of white sand habitats for the country. Tree community data We established a network of 60 one-hectare plots from 2000-2016, across a longitudinal and latitudinal gradients in Ecuadorian Amazon including terra firme and white sands forests (Table 1). Our plot network includes many areas not previously visited by other botanical researchers, namely the lower portion of Cordillera del Condor and the Pastaza river watershed in Ecuador. In each plot we recorded, tagged and identified all trees with diameter at breast height (dbh) ≥ 10 cm. Botanical collections for every tree species were collected and duplicates were deposited and compared with botanical specimens from five herbaria (MO, QCNE, QCA, QAP and F). Most of the new records and new species have been confirmed by taxonomic specialists from each group but in other cases our extensive experience in Amazonian tree species identification allows us to be confident about the accuracy of the taxonomy across the plot network. Finally, in order to perform phylogenetic and statistical analyses we excluded unnamed morphospecies, which has

been demonstrated to have weak effects on the detection of ecological patterns (Lennon et al. 2001, Lennon et al. 2004, Pos et al. 2014). Phylogenetic tree We created a phylogenetic tree for 1687 operational taxonomic units (OTUs) using as backbone the tree R20120829 (Li et al. 2015) from Phylomatic (Webb and Donoghue 2005), which is based on the Angiosperm Phylogeny Group’s system (APGIII 2009). In order to assign branch lengths we used the BLADJ algorithm in Phylocom (Webb et al. 2008) based on inferred nodes ages (Wikstrom 2001). Despite the fact that our regional phylogenetic tree is not fully resolved, we argue that many polytomies in some rich species and abundant clades (e.g. Guatteria, Inga) represent hard polytomies that may be impossible to resolve (Pennington et al., in press.). Furthermore, recent studies have demonstrated that there is no significant difference between supertrees based on inferred node ages and trees using DNA in order to detect patterns at community or regional scale (Swenson et al. 2009). However, in order to compare the results based on phylogenetic trees without fully resolved branches (Swenson et al. 2009), a subset of 480 species for which we have a molecular phylogeny was also included for a separate analysis of phylogenetic beta diversity. Molecular data for the three chloroplast markers ITS, rbcL and matK were obtained from the public repository Genbank, Taxonomic and phylogenetic alpha diversity metrics To estimate species diversity at each location/plot we used Fisher’s alpha index which calculates the number of species in a sample relative to the number of individuals therein based on the following formula:

= ln (1 + ) 𝑛𝑛 𝑆𝑆 𝛼𝛼 Where S is the number of species, FA is the Fisher’s value𝛼𝛼 per assemblage and N is the number of individuals per plot. I decided to use this parametric method due to its statistical properties and considered the best choice according to the nature of the system I am investigating. Fisher’s alpha is an asymptotic non-parametric estimator of species richness that has a good discriminatory power to detect richness under the assumption that communities sampled are characterized by a non- clustered spatial distribution of species (Schulte et al. 2005). In order to evaluate the standardized effect size of PD in each local community we calculated the ses.mpd value for each plot using the independent swap algorithm as the null model (Gotelli 2000) implemented in the “picante” package in R (Kembel et al. 2010).This metric measures the standardized effect of mean pairwise phylogenetic distance between communities. Positive values over a 1.96 confidence interval determine communities were mainly structured by more closely related species (phylogenetic clustering) than expected by chance and negative values less than - 1.96 confidence interval were communities assembled by more distantly related species than expected by chance (eveness) (Webb 2000)

Taxonomic and phylogenetic beta diversity

Investigating how phylogenetic relatedness among communities changes across environmental and spatial gradients allows us to make inferences about the different biogeographical histories of regional species pools with the strong analytical power of phylogenies (Graham and Fine 2008). For instance, high levels of Taxonomic Beta Diversity (TBD) can be congruent with high levels of Phylogenetic Beta Diversity (PBD) if allopatric speciation by vicariance has promoted geographical separation of two areas for long periods of time, which in turn has led to long disparate evolutionary histories of communities in both areas. Conversely high levels of TBD can be related to low PBD indicating that recent events of speciation via parapatry or sympatry may be the drivers of community assembly. We must also consider that species abundances might be correlated with phylogeny if traits associated to habitat specialization allow species of one or few clades to become abundant in a particular habitat or region. As a consequence of the variation in species abundance across geographic distance, variation in the proportional representation of species traits will also vary among communities (Cadotte et al. 2010b). Abundance weighted phylogenetic metrics are essential to understand whether PD is concentrated in few dominant clades that would represent a great proportion of regional floras and therefore predictors of floristic breaks among regions. TBD was calculated as the difference (1-Sorenson index) to obtain a measure of taxonomic dissimilarity whereas PBD was calculated with the Phylo Sorenson index as a measure of the degree of phylogenetic relatedness between pairs of local communities. In order to be consistent with the metrics used to evaluate taxonomic beta diversity, we used the complement of the Phylo Sorenson index to establish a phylogenetic dissimilarity metric (1-Phylo Sorenson) (Bryant et al. 2008, Graham et al. 2009). In order to test whether TBD is a good predictor of PBD, we compared the observed and expected values of PBD. In order to do this, we calculated the expected values of PBD based on a null model that makes random draws from the regional species pool (here defined as the total number of species in our plot network). This null model randomizes the community data matrix with the independent swap algorithm developed by Gotelli (2000), maintaining species occurrence frequency and sample species richness. Thus if the observed values of PBD are less than the expected values based on the null model, we infer that pairs of compared communities are composed of lineages that are closely related. Conversely, if values of PBD are greater than expected based on the null model, then pairs of communities are composed of lineages that include distant relatives. Both Mantel tests and Multi Response Permutation Procedure were performed to test the significance of the correlation between patterns of taxonomic beta diversity and phylogenetic beta diversity as well as the significance of the difference between groups of sites based on permutation tests of among- and within-group dissimilarities (Legendre and Legendre 2012, Mielke 1991).

Ordination Non Metric Multidimensional Scaling (NMDS) with both taxonomic and phylogenetic dissimilarity matrices was performed in order to have a graphical depiction of the floristic relationships of the 80 one-hectare plots in the Ecuador Amazon basin. We used the first two dimensions in the ordination and 1000 random starting iterations in order to obtain the lowest stress value that determines the best solution for that ordination. Cluster Analysis

In order to compare the delineation of the floristic units with previous methods we performed several agglomerative clustering analyses based on of both taxonomic and phylogenetic dissimilarity matrices to group sites into discrete floristic units.. We obtained the best clustering solution by following three basic steps: 1) the best cluster solution may represent a dendrogram in which groups of sites form clusters spatially coherent, 2) By the comparison of the cophenetic coefficients (the clustering algorithm with a highest cophenetic coefficient would represent the best clustering solution) and 3) By comparing the results of the previous steps with the recluster,region algorithm from the R package recluster (Daporto et al. 2015) (See supporting information for details). Evolutionary distinctiveness and phylogenetic endemism Finally we calculated the Weighted Phylogenetic Endemism, Abundance Weighted Evolutionary Distinctiveness (AED) and Imbalance at Clade Level (IAC) following the algorithms developed by Rossauer et al. (2009) and Cadotte et al. (2010b) respectively. Weighted Phylogenetic Endemism (WPE) is defined as the sum of branch lengths divided divided by the clade range for each branch on the spanning path linking a set of taxa to the root of the tree (Rossauer et al. 2009). AED measures the evolutionary distinctiveness of species based on abundance and phylogenetic distances according to the following formula:

= ( , , ) 𝜆𝜆𝑒𝑒 𝐴𝐴𝐴𝐴𝐴𝐴 � 𝑒𝑒 Therefore ED is not just proportional to the phylogenetic𝑒𝑒∈𝑠𝑠 𝑇𝑇 𝑖𝑖 𝑟𝑟 𝑛𝑛 distances but also to the distribution of individuals in a particular e edge of length k in the set s(T,i,r) that connects species i to the root, r and Se is the number of species that descend from edge e. Finally, the AIC index measures the relative deviation in the abundances distribution of individuals in any clade based on the null expectation that individuals are evenly partitioned between clade splits (Cadotte et al. 2010b). ^ = 𝑆𝑆 ∑𝑖𝑖=1 𝑛𝑛𝑖𝑖 − 𝑛𝑛𝑖𝑖 𝐴𝐴𝐴𝐴𝐴𝐴 Where ni is the number of lineages originating at node𝑣𝑣 k out of v nodes in the set s(T,k,r). This is the number of nodes between node k and the r root in the tree T, meanwhile ^ni is the expected abundance of species i. Spatial Model We divided Ecuadorian Amazon in 0.5 degree grid cells (55 km x 55 km) which is a spatial scale that allows us to have a balance between accuracy and detail when performing the spatial analysis (Kreft and Jetz 2010, Keil et al. 2012). In each of this grid cells we calculated the mean values of both PBD and TBD for each plot with respect any other in the plot network. Then we used this average to . Because a finer grain size could lead us to increase the sampling bias introduced by the non-uniform distribution of plots intermediate grid cell size may avoid underestimation of phylogenetic and taxonomic beta diversity values. This is mainly determined by the fact that increasing the grain size we would reduce the number of grid cells and includes plots in contrasting

45 habitats (terra firme vs white sands) at landscape scale therefore reducing the predicted values of both beta and phylogenetic beta diversity (Keil et al. 2012). A Loess spatial regression model was used to predict both taxonomic and phylogenetic turnover to obtain the most accurate fit we used default parameters for our loess regression: a 0.75 span was used to find the best smoothing average and a degree 2 polynomial was set to reduce variance. We used this method due to its inherent flexibility compared with other interpolation techniques. Because our data is irregularly distributed loess interpolation allows us fitting locally individual values of taxonomic and phylogenetic diversity across space using the average of each of these values at location x with grids cells in the neighborhood of x. In order to perform this, loess method sets the size of the neighborhood with respect to location x with the parameter α. All the analyses were performed with the packages picante (Kembel et al. 2010), vegan (Oksanen et al.2015) and recluster (Daporto et al. 2015) and custom functions on the R platform.

RESULTS Alpha diversity patterns The highest Fisher’s alpha values were found in a cluster of plots at the intersection of a latitudinal band between 0.5 and 0.8 degrees and a longitudinal band between 76 and 76.5 degrees (Fig. S1). This peak of taxonomic diversity is congruent with peaks of phylogenetic alpha diversity across the region (Fig. S1, Table1). Floristic affinities in the Ecuadorian Amazon Taxonomic-based NMDS analysis (stress function 0.1048091) led to the definition of three floristic sub regions (Figure 3), which is corroborated with the results of the cluster analysis based on the average method (cophenetic coefficient = 0.881765). A similar pattern was found with the phylogenetic-based non-metric multidimensional analysis (NMDS) (stress function 0.1019252) led to the definition of three floristically distinct sub-regions in the Ecuadorian Amazon. The forests located in the inter-fluvial area between Napo and Aguarico rivers (Aguarico-Putumayo basin), the Napo-Pastaza basin and the Cordillera del Condor lowlands (Fig. 2). When the phylogenetic dissimilarity matrix is considered as input for cluster analysis, the best clustering method resulted in the definition of three clusters of sites (cophenetic coefficient = 0.827194) that share floristics affinities. A similar result arose when the taxonomic dissimilarity matrix is considered in the analysis (Table 2, Fig. S2, S3). This is supported by the 70% of explained dissimilarity produced by the regionalization of three floristic districts based on the recluster.region analysis (Table 3). MRPP analysis based on Phylosorenson values support the delimitation of three floristically distinct units as shown by the delta values (Table 1). Thus, there is highly significant difference between groups of sites according to the biogeographical subdivision supporting the delimitation of three floristic sub-regions in Ecuador Amazon (Table 1). Beta diversity patterns The spatial distribution of taxonomic and phylogenetic alpha diversity was quite different. We found a tight correlation between TBD and PBD (r=0.9043, p≤ 0.001), which indicates that phylogenetic dissimilarity can be predicted by taxonomy (Table 1, Fig. S4). Nevertheless, we

found a weaker correlation between taxonomy and phylogeny when the standardized ses.mpd index was included in analysis (r=0.3016, p=0.002). When comparing the observed values of phylogenetic turnover against the expected values based on our null model we found lower observed phylogenetic turnover than expected (Fig. S4). Evolutionary Distinctiveness and Phylogenetic endemism High WPE values were concentrated in areas such as Condor Cordillera lowlands and the Aguarico-Putumayo basin whereas the lowest values of WPE were concentrated in the southern portion of the Napo-Pastaza basin. The spatial distribution of WPE showed that some areas to the southeast of the Ecuadorian Amazon basin are predicted to represent areas with high WE. On average high AED values were concentrated in areas such as Napo-Pastaza basin and the Aguarico- Putumayo-Caqueta region (Fig. 5A). The spatial distribution of AED shows that a great portion of the southern Ecuadorian Amazon is characterized by moderate to high levels of evolutionary distinctiveness. A different pattern arises when the southernmost part of Ecuador Amazon is considered, the spatial distribution of AED is considered, low AED values were concentrated in areas that correspond to Cordillera del Condor region (Fig. 5).

We also found significant differences in the spatial distribution of Imbalance of Abundances at Clade level (IAC). This is confirmed with the spatial distribution of abundances across the Ecuador Amazon, as there is a disproportionate dominance of clades such as Arecaceae, Moraceae, Fabaceae or Myristicaceae in areas of the Napo-Pastaza basin. We also found higher than predicted IAC values in regions that correspond to the lowland of Cordillera del Condor and some areas of the Pastaza fan (Fig. 5C). DISCUSSION Floristic patterns The taxonomic-based floristic analysis differs significantly with the previous classification proposed by Guevara et al. (2016). Our results are confirmed by the phylogenetic-based classification that shows a similar pattern. The main difference with the floristic divisions proposed by Guevara et al. (2016) is the strong floristic affinities between the two previously separated Pastaza basin and Napo-Curaray sub regions. In fact, the former is nested into the latter as shown by the results of the cluster analysis and the ordination (Fig. 3, Fig.S1). We propose to name this district as the Napo-Pastaza basin due to the major watersheds that shape the landscape of this region. This district is characterized by high levels of tree alpha diversity; the results of our tree inventory shows that peaks of tree diversity can be found in the forests located in the Yasuní National Park area. Some groups such as Inga, Ocotea, Pouteria, Virola, Eugenia and Calyptranthes are species rich genera that exhibit peaks of diversity in Yasuní National Park. The spatial distribution of phylogenetic beta diversity (phylogenetic dissimilarity) is congruent with the floristic subdivision that results from clustering analysis. However, phylogenetic turnover values show stronger patterns than expected from the null model in the most southern and northern portion of Ecuadorian Amazonia, which is congruent with the delineation of Condor Cordillera lowlands and Aguarico-Putumayo floristic sub regions. Some elements from regions with biogeographic affinities with the Guiana Shield have been recorded only in the Aguarico- Putumayo district. These unusual trees include genera such as Sterigmapetalum, Chaunochiton, Neoptychocarpus Macoubea, Podocalyx, Pogonophora, Bothryarrena, Clathrotropis, Ruizterania

and Neocalyptrocalyx . Almost 90 % of the new records in the Aguarico-Putumayo district include species that are locally abundant in areas of the Middle Caquetá in the Colombian Amazon and in areas near Manaus, Brazil (Duivenvoorden et al.1995; Duque et al. 2002; De Oliveira and Daly 1999; De Oliveira and Mori 1999, Pitman et al. 2003). Thus, we think the northeastern portion of the Ecuadorian Amazon, may represent the westernmost edge of Amazon with floristic influences of Central Amazonia and the Guiana Shield region. We propose that this floristic district may include areas of Colombian and Peruvian Amazon along a west-east axis but towards the north bank of the Napo River. This is consistent with earlier studies that have posited that a strong floristic disruption between forest located to the west and those located to the east of the Ecuador- Peru border might also represent a shift in geological formations from nutrient rich Miocene to nutrient poor Pleistocene based sediments (Pitman et al. 2008, Higgins et al. 2011). Our loess regression model predicts high spatial turnover of lineages in the lowland forests (< 500 masl) adjacent to the Cordillera del Condor district (Fig. 4.). This region is one of the most floristically distinct areas within Ecuadorean Amazon (Fig. 3). The confluence of several floras, including widely distributed elements of the Amazon piedmont, the flora of Guyana Shield forests and the region of Iquitos, Peru on mixed soils determine the patterns we found in this region. Some taxa predominant in this area include the genera Centronia, Pachira, Micrandra, Diclinanonna, Parkia, Aspidosperma and Sterigmapetalum (Appendix S1). Can PBD be predicted by TBD? Our results highlight the benefits of the use of complementary phylogenetic methods to determine strong turnover in floristic composition and also their importance for conservation purposes. We found that the observed levels of lineages turnover (PBD) are significantly lower than expected. A similar pattern has been found in two regional analyses of North American Angiosperms and white-sand forests across the Amazon basin (Qian et al. 2013, Guevara et al. 2016). Lower PBD than TBD may be the result of the spatial turnover of species that are nested in similar clades which in turns lead to floras mainly composed of the same phylogenetic components. Our results support the hypothesis that PBD can be predicted by TBD, and lower PBD than expected based on null TBD may be suggestive of recent divergence across strong environmental gradients or biogeographic boundaries promoting speciation for subsets of regional species pool (Graham et al. 2009). This suggests a potential scenario in which parapatric speciation might be a general process shaping Amazon forest composition. Evidence for a generalized pattern of speciation mediated by divergent natural selection for habitat specialization has been found in species of the genus Protium (Misiewicz and Fine 2014, Fine et al. 2013). Nonetheless, current evidence suggests that allopatric speciation after dispersal might be a major evolutionary driver of speciation in Amazon tree lineages (Dexter et al. 2017). Therefore it will be important to carry out subsequent research in other clades to elucidate if these can be considered general mechanisms for the formation of species pool in Amazonian forests (Fine and Baraloto 2016). The predicted spatial distribution of PBD reflects strong turnover of lineages across regions in the Ecuador Amazon as the results of loess model showed (Fig. S2). This spatial structure of PBD not only represents spatial variability in lineages composition but should also represent variability in the set of traits for subsets of the regional species pool. Are regions with high levels of PD areas with high levels of evolutionary distinctiveness and endemism?

The spatial distribution of WPE and PBD determined that communities located in Cordillera del Condor lowlands may be characterized by high levels of WPE and PBD meaning that there is a high replacement of lineages with short geographic ranges compared with communities in the other floristic districts of Ecuador Amazon (Fig. 4, Fig. 5). High levels of WPE can be explained by the presence of white-sand specialist taxa recently diverged from adjacent terra firme sister clades (Fine et al. 2013, Misiewicz and Fine 2014). Low levels of AED are also congruent with this scenario because species and clades sharing low evolutionary distinctiveness may be dominant in this region. Simultaneously, the spatial distribution of IAC and AED determined that communities in the Napo-Tigre watershed exhibit the highest IAC values meaning that there is significant phylogenetic imbalance in the distribution of abundance and certain unique clades dominate this area. Moreover because this region is also characterized by high AED values one might argue that the most abundant species and clades are sharing disproportionately long branch lengths corresponding to common species from clades that have extremely ancient divergent times from one another. The abundance and diversity of Magnoliids, Arecaceae and Moraceae, which are remarkably dominant in this region might explain this pattern. Moreover, genera such as Ocotea, Virola, Otoba and the monotypic palm genus Iriartea exhibit peaks of abundance in areas like Yasuni National Park. Conversely, low values of AED in areas corresponding to Condor Cordillera lowlands and the adjacent forest of Pastaza fan watershed are consistent with the hypothesis that the composition of these forests is characterized by turnover of recently diverged lineages. We found that taxa dominant in the white-sand forest of the surroundings of Iquitos and the upper Morona river watershed and taxa which are also dominant in medium elevation plateaus of El Condor Cordillera are important floristic components of the regional flora of Cordillera del Condor lowlands (Fine et al. 2010). Implications for conservation The inclusion of an evolutionary approach in any analysis of beta diversity can contribute significantly to scientific research based conservation policies. Because species centric conservation research solely takes into consideration a snapshot of the fractal nature of the tree of life without including phylogenetic data we miss all the information that genealogical relationships between organisms can give us. Currently, many conservation priority-setting exercises tend to be solely focused on species level data and have proved to be a poor predictor of both species richness and threatened species identification (Orme et al. 2005). We found that despite a high correlation between species richness and PD, the predicted spatial distribution that incorporates phylogenetic information shows critical new details. For example, areas that currently are unprotected and exhibit high Fisher’s alpha values are also areas with relative high PD. These areas include the lowlands of Cordillera del Condor and the Pastaza fan watershed. Regarding the predicted spatial patterns of PBD, we found that areas with high replacement of lineages could be considered as important priorities for conservation purposes because so many phylogenetically distant lineages coexist across the landscape. In the Ecuadorian Amazon, the sub- regions with the highest values of phylogenetic turnover correspond to areas that include national parks (e.g., Cuyabeno reserve in the Aguarico-Putumayo-Caquetá district) but also areas that are under some level of threat. For example, the lowlands of Condor Cordillera region and the Pastaza basin are regions threatened by massive plans for new hydroelectric dams, large scale gold mining projects and oil extraction (Finer et al. 2008, Finer et al. 2012).

The spatial distribution of AED and WPE showed contrasting patterns with areas of the Ecuadorian Amazon that have no formal protection characterized by high levels of WEP and low levels of AED. Areas such as the Cordillera del Condor lowlands represent areas with low evolutionary distinctiveness meaning that the loss of species due to deforestation, mining or infrastructure development would represent a loss of unique lineages that have recently evolved. Furthermore, this loss would be related to the loss of lineages with restricted geographic ranges and that represent short branches of the regional phylogenetic tree. While we acknowledge that most of the evolutionary lineages contained in the regional species pool of Ecuador Amazon is currently harbored within National Parks there are some caveats to consider. For example, 45% of Yasuni National Park, located in the Napo-Tigre watershed, overlaps with existing oil concessions and meanwhile 22% of the Cuyabeno Reserve in the Aguarico-Putumayo-district is currently also open for oil concessions (Lessmann et al. 2016). Even more alarming is the fact that 19 of the 25 ecosystems of lowland Ecuador Amazon are found within areas that are open for oil exploration, particularly towards the southern portion of the Pastaza fan watershed and Cordillera del Condor lowlands where Ecuador’s greatest amount of evolutionary distinctiveness and phylogenetic endemism is concentrated.

Recently, Lessmann et al. (2014) assigned a low to medium range in conservation priority to areas that correspond to the southern floristic districts we described here as regions containing both unique and geographically restricted evolutionary information. The approach used by these authors to define conservation priorities areas included richness maps based on species distribution models and maps of environmental vulnerability. However we think that our results represent significant improvements upon these models. Here we have demonstrated that areas with high tree alpha diversity could be assigned as areas of low-medium level of priority in a conservation context if the same areas exhibit low AED values. Moreover, we have shown that areas characterized by the dominance of recently diverged lineages with restricted ranges correspond to floristically unique units located towards the south of the Ecuadorian Amazon. This highlights the necessity to develop new conservation plans for this region taking into account the current and potential pervasive negative effects of mining, damn construction and oil extraction.

Acknowledgements

This work was benefited by the Lewis and Clark Fund from the American Philosophical Society, the Garden Club of America award for Tropical Botany, the National Secretary of Science and Technology of Ecuador (Senescyt) grant for field research and the Summer Research Award from the University of California, Berkeley. We are also indebted to the Shuar, Shiwiar, Achuar, Waorani and Kichwa indigenous communities of the Ecuadorian Amazon, without whose support this work would have been impossible to carry out. Milton Tirado, Ophelia Wong and Rodrigo Sierra collected data for three plots (Juyuintsa, Yutsuntsa and Sawastian). David Ackerly provided valuable suggestions to improve the manuscript.

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Table 1. Results of Multiple Response Permutation Procedure and Mantel tests for TBD and PBD.

MRPP Within r MRPP expected groups Mantel observed delta A test δ value value statistic p-value

Phylogenetic beta diversity

Phylogenetic beta diversity-Taxonomic beta diversity 0.912 < 0.001 Null Phylogenetic beta diversity-Taxonomic beta diversity 0.445 < 0.001 Basal phylogenetic beta diversity-Taxonomic beta diversity 0.281 < 0.001 Definition of 3 floristic regions based on taxonomic beta diversity 0.754 0.8159 0.07585 0.00009 Definition of 3 floristic regions based on phylogenetic beta diversity 0.4766 0.5154 0.07536 0.00009

Table 2. Cophenetic coefficient for 7 seven clustering methods based on 80 one hectare plots in Ecuador Amazon. Bold numbers represents cluster algorithms with the highest cophenetic coefficient.

Taxonomic based Phylogenetic based cophenetic cophenetic coefficient coefficient Clustering Average method 0.827194 0.881765 Ward method 0.544901 0.680091 Complete method 0.793065 0.818753 Centroid method 0.786403 0.77734 Single method 0.599358 0.872216 Median method 0.429821 0.861772 Mcquitty method 0.809923 0.872216

Table 3. Silhouette values and explained dissimilarity of PBD based on recluster.region analysis for 80 one hectare plots in Ecuador Amazon. Silhouette values are expressed as strength of clustering in a correct group with positive values close to 1 determining strong support for clusters of plots and negative values close to -1 determining little support for clusters.

Strength of clustering in a Explained Number of clusters correct grouping dissimilarity 2 0.128871 0.475517 3 0.100864 0.696756 4 0.104702 0.730144

Figure legends Figure 1. Map of locations of the 80 one hectare plots used in this study. Figure 2. Map of floristic affinities in the Ecuadorian Amazon tree communities with colored dots representing plots that belongs to a particular floristic unit. A) Floristic regionalization proposed by Guevara et al. 2016, this classification is congruent with the major hydrographic basins and geomorphology as follows: Aguarico-Putumayo basin (blue dots), the Napo- Curaray basin (yellow dots), the Pastaza basin (orange dots), and the Cordillera del Condor lowlands (red dots). B) Proposed floristic regionalization based on patterns of PBD and TBD; colored dots represent the results of the best cluster analysis based on phylogenetic dissimilarity matrix, the size of the dots represents NMDS axis 1 scores. Figure 3. Non-metric multidimensional ordinations based on A) taxonomic dissimilarity matrix and B) phylogenetic dissimilarity matrix for 80 one hectare plot network in Ecuador Amazon. Phylogenetic dissimilarity based NMDS ordination defines three floristically distinct districts; the Aguarico-Putumayo district (pink dots), the Napo-Pastaza district (blue dots) which includes plots in the Pastaza fan (orange dots), and the Cordillera del Condor lowlands (black dots). Spiders diagram represents associated groups of plots; sites are connected to the centroid of each class, in this case a floristic region defined on the basis of the results of cluster analysis. Ellipses represent the 95% confidence interval in grouping plots as part of a particular group of similar floristic units based on cluster analysis. Figure 4. Spatial variation of different measures of phylogenetic beta diversity across Ecuador Amazon. Spatial interpolation was based on loess regression with 0.5 degree grid cell span. Dots A) Taxonomic beta diversity measured as 1-Sorenson as a proxy of taxonomic dissimilarity. B) Weighted Phylogenetic Endemism C) Phylogenetic beta diversity measured as 1-Phylosorenson as a proxy of phylogenetic dissimilarity. D) Null phylogenetic beta diversity measured as 1- Phylosorenson based on 1000 randomized matrices using swap algorithm. Red and orange colors represent higher values for each metric while lighter yellow and light blue colors represent lower values for each metric. Figure 5. Spatial distribution of different phylogenetic diversity metrics A) Weighted Phylogenetic endemism , B) Imbalance of abundances at clade level and C) Abundance Weighted Evolutionary Distinctiveness. Red and orange colors represent higher values for each metric while lighter yellow and light blue colors represent lower values for each metric.

Figure 1.

Figure 2.

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Figure 3.

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Figure 4.

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Figure 5.

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Appendices Methods Clustering algorithms In order to obtain the cluster analysis that best represent the floristics affinities of the tree communities we studied we decided to use seven well known hierarchical cluster algorithms. These methods include Unweighted Pair-Group Arithmetic Average (UPGMA), the Weighted Pair-Group Arithmetic Average (WPGMA) or McQuitty method, Single Linkage, Complete Linkage, Ward, the median and centroid. In order to compare the results of the best cluster algorithm based on conventional hierarchical clustering methods we decide to compare these results with a unbiased method of regionalization recently developed by Daporto et al. (2015). We decided to use the recluster.region function from the package recluster (Daporto et al. 2013) due to its sensitiveness to small to mid-spatial scales analyses. This function produces n trees by randomly re-ordering the original row order of the dissimilarity matrix and then the trees are trimmed to different nodes producing an increasing number of clusters. A final hierarchical clustering is applied generating an interval of maximum and minimum number of clusters and a consensus tree is generated. The results are summarized in a matrix providing number of clusters for each solution (k), the associated mean number of clusters obtained by node cuts (clust), the silhouette (silh) value and the explained dissimilarity (ex.diss). Silhouette values range between -1 and +1, with a negative value indicating that most cells/plots are probably located in an incorrect cluster. These values measures the strength of the partition of objects (plots/cells) by comparing the minimum distance between a particular plot and the most similar plot belonging to any other cluster and the mean distance of that plot with other belonging to the same cluster (Daporto et al. 2015). All the analyses were carried out with the packages recluster (Daporto et al. 2015) and the package vegan (Oksanen et al. 2016) from the R platform.

Literature cited Daporto, L., Ramazzotti, M., Fattorini, S., Talavera, G., Vila, R. & Dennis, R.L.H. (2013) recluster: an unbiased clustering procedure for beta-diversity turnover. Ecography, 36, 1070– 1075.

Appendix S1. Indicator Species Analysis for 80 one plot network established in Ecuador Amazon forests (APC= Aguarico-Putumayo-Caqueta basin, CCL = Cordillera del Condor lowlands, NP= Napo-Pastaza basin, PF= Pastaza fan basin). Colored species names represent taxa significantly associated (p ≤ 0.05) to one or more regions based on the following attributes: IV$maxcls = Region in which species has maximum indicator value, IV$indcls = Indicator value, IV$pval = the probability of obtaining the highest indicator value based on 1000 iterations.

IV$maxcl AP CCL NP IV$indcls IV$pval s Abarema_adenophora 0 0.67 0 2 0.67 0.001 Abarema_jupunba 0.33 0 0.13 1 0.27 0.064 Abarema_laeta 0 0 0.03 3 0.03 1 Abuta_grandifolia 0.07 0 0.03 1 0.04 0.701 Acacia_immense 0 0 0.03 3 0.03 1 Acacia_loretensis 0 0 0 4 0.07 0.46 Acacia_multipinnata 0.07 0 0 1 0.07 0.461 Acalypha_diversifolia 0 0 0.03 3 0.03 1 Acanthosyris_annonagustata 0 0 0.08 3 0.08 0.42 Acanthosyris_sp_nov 0 0 0.03 4 0.05 0.912 Aegiphila_haughtii 0 0 0.13 3 0.1 0.474 Aegiphila_integrifolia 0 0 0.03 4 0.04 0.912 Aegiphila_vochy 0.07 0 0 1 0.07 0.453 Agonandra_peruviana 0.07 0 0.1 3 0.04 1 Agonandra_silvatica 0.2 0 0.08 1 0.16 0.251 Agouticarpa_isernii 0 0 0 4 0.07 0.449 Agouticarpa_velutina 0.07 0 0 1 0.07 0.458 Aiouea_brasiliensis 0.07 0 0 1 0.07 0.456 Aiouea_cinnamo 0 0 0 4 0.07 0.461 Aiouea_grandifolia 0 0 0 4 0.13 0.165 Aiouea_impressa 0.07 0 0.05 1 0.04 0.851 Aiouea_laevis 0 0 0 4 0.07 0.462 Aiphanes_ulei 0 0 0 4 0.07 0.459 Albizia_niopoides 0.13 0 0 1 0.13 0.174 Albizia_pedicellaris 0 0 0.03 3 0.03 1 Albizia_racpub 0.07 0 0 1 0.07 0.461 Albizia_subdimidiata 0.13 0 0 1 0.13 0.15 Alchornea_discolor 0.33 0 0.03 1 0.32 0.017 Alchornea_glandulosa 0.07 0 0.21 4 0.21 0.184 Alchornea_latifolia 0.2 0 0.08 1 0.14 0.329 Alchornea_triplinervia 0.13 0 0.15 1 0.07 0.831 Alchornea_webster 0 0 0.08 3 0.08 0.465 Alchorneopsis_floribunda 0.07 0 0.26 3 0.21 0.185

Alibertia_edulis 0.13 0 0 1 0.09 0.328 Alibertia_isernii 0 0 0.18 3 0.18 0.233 Alibertia_itayensis 0.07 0 0.18 3 0.14 0.418 Alibertia_macrophylla 0.07 0 0 1 0.07 0.464 Alibertia_schwackei 0.07 0 0 1 0.07 0.462 Alibertia_verrucosa 0.13 0 0.03 1 0.06 0.815 Allophyllus_glabra 0 0 0.03 3 0.03 1 Allophylus_amazonicus 0.13 0 0.18 1 0.1 0.757 Allophylus_angustatus 0.07 0 0 1 0.07 0.468 Allophylus_divaricatus 0 0 0.21 3 0.21 0.084 Allophylus_floribundus 0 0 0.15 3 0.13 0.352 Allophylus_green 0 0 0.1 3 0.1 0.338 Allophylus_membranaceous 0 0 0.05 4 0.05 0.612 Allophylus_pilosus 0 0 0.1 3 0.07 0.715 Allophylus_punctatus 0 0 0.23 3 0.13 0.355 Alseis_dosel 0 0 0.05 3 0.05 0.453 Alseis_eggersii 0 0 0 4 0.13 0.161 Alseis_labatioides 0 0 0.05 3 0.05 0.445 Alseis_lugonis 0.07 0 0.33 3 0.25 0.162 Amaioua_corymbosa 0.13 0 0 1 0.13 0.16 Amanoa_guianensis 0.07 0 0.03 1 0.03 1 Ampelocera_edentula 0.07 0 0.41 3 0.2 0.302 Ampelocera_indet 0 0 0.03 3 0.03 1 Ampelocera_longissima 0 0 0.23 3 0.19 0.141 Amyris_macrocarpa 0.07 0 0.05 1 0.02 1 Anacardium_blood 0 0 0.03 4 0.04 1 Anacardium_excelsum 0 0.67 0 2 0.67 0.002 Anacardium_giganteum 0.13 0 0.03 1 0.11 0.292 Anacardium_kerosine_sp._nov._ined. 0.2 0 0.15 1 0.12 0.57 Anacardium_sp_824 0.07 0 0 1 0.07 0.457 Anaxagorea_brevipes 0.27 0 0.03 1 0.14 0.323 Anaxagorea_dolichocarpa 0 0 0.05 3 0.05 0.682 Anaxagorea_phaeocarpa 0.07 0 0.03 1 0.03 1 Andira_indet 0 0 0.03 3 0.03 1 Andira_inermis 0.2 0 0.13 1 0.09 0.745 Andira_juyuintsa 0 0 0 4 0.07 0.45 Andira_macrocarpa 0.13 0 0.08 1 0.05 0.893 Andira_macrothyrsa 0.13 0 0.13 1 0.1 0.463 Andira_multistipula 0.07 0 0.1 3 0.06 0.821 Andira_surinamensis 0 0 0.03 4 0.11 0.277 Aniba_amarillento 0 0 0.03 3 0.03 1 Aniba_coto 0.07 0 0 1 0.03 1 Aniba_guianensis 0.13 0 0.18 4 0.07 0.978

Aniba_hostman 0 0.33 0 2 0.33 0.041 Aniba_hostmanniana 0.13 0 0.28 3 0.19 0.23 Aniba_megaphylla 0 0 0.03 3 0.03 1 Aniba_panurensis 0.33 0 0.08 1 0.31 0.029 Aniba_puchury-minor 0 0 0.05 3 0.03 0.954 Aniba_riparia 0.2 0 0.21 3 0.11 0.768 Aniba_rosaeodora 0.07 0 0 1 0.07 0.457 Aniba_taubertiana 0 0 0.08 3 0.08 0.474 Aniba_williamsii 0.13 0 0.03 1 0.08 0.562 Annona_ambotay 0 0 0.18 3 0.08 0.819 Annona_corky_sp._nov._ined. 0 0 0.03 3 0.03 1 Annona_dolichopetala 0 0 0.13 4 0.08 0.642 Annona_duckei 0 0 0.23 3 0.12 0.524 Annona_edulis 0.07 0 0 1 0.07 0.452 Annona_exsucca 0 0 0.03 3 0.03 1 Annona_glomerulifera 0 0 0.13 3 0.13 0.329 Annona_helosioides 0 0 0.08 3 0.08 0.485 Annona_hypoglauca 0.07 0 0.03 1 0.06 0.391 Annona_neochrysocarpa 0 0 0 4 0.13 0.167 Annona_papilionella 0.07 0 0.38 3 0.29 0.101 Anthodiscus_klugii 0.2 0 0.1 1 0.13 0.387 Anthodiscus_peruanus 0 0 0 4 0.13 0.166 Antrocaryon_amazonicum 0 0 0.03 3 0.03 1 Aparisthmium_cordatum 0.2 0 0.08 4 0.07 0.793 Apeiba_aspera 0.6 0.33 0.9 3 0.5 0.005 Apeiba_tibourbou 0 0 0.18 3 0.18 0.165 Aptandra_tubicina 0.2 0 0.1 1 0.11 0.479 Apuleia_ferruginea 0.07 0 0 1 0.07 0.46 Apuleia_leiocarpa 0 0 0.03 3 0.03 1 Ardisia_densapunta 0 0 0.03 3 0.03 1 Ardisia_guianensis 0 0 0 4 0.07 0.467 Ardisia_sp1 0.13 0 0 1 0.13 0.17 Aspidosperma_249 0.07 0 0 1 0.07 0.456 Aspidosperma_darienense 0.13 0 0.21 3 0.14 0.384 Aspidosperma_excelsum 0.13 0.67 0.03 2 0.43 0.026 Aspidosperma_fendleri 0.27 0 0.1 1 0.08 0.847 Aspidosperma_indet 0 0 0.03 3 0.03 1 Aspidosperma_inundatum 0.07 0 0.08 1 0.04 0.836 Aspidosperma_rigidum 0.07 0 0.33 3 0.21 0.186 Aspidosperma_sandwithianum 0.07 0 0 1 0.07 0.459 Aspidosperma_schultesii 0 1 0 2 1 0 Aspidosperma_sp_* 0.07 0 0 1 0.03 1 Aspidosperma_spruceanum 0 0.33 0.03 2 0.26 0.104

Aspidosperma_tsuirim 0 0 0 4 0.07 0.455 Astrocaryum_chambira 0.33 0 0.72 3 0.47 0.033 Astrocaryum_jauari 0.2 0 0 1 0.2 0.082 Astrocaryum_murumuru 0.07 0 0.74 3 0.56 0.018 Astronium_graveolens 0.07 0 0 4 0.04 0.776 Attalea_butyracea 0 0 0.05 3 0.05 0.774 Attalea_maripa 0.13 0 0.13 1 0.07 0.724 Bactris_riparia 0.07 0 0 1 0.07 0.463 Banara_arguta 0 0 0.03 3 0.03 1 Banara_nitida 0 0 0.13 4 0.07 0.785 Bathysa_peruviana 0 0 0 4 0.07 0.456 Batocarpus_amazonicus 0 0 0.05 4 0.04 0.856 Batocarpus_costaricensis 0.07 0 0.15 4 0.08 0.834 Batocarpus_orinocensis 0.33 0 0.46 3 0.18 0.604 Bauhinia_arborea 0 0 0.31 3 0.17 0.343 Bauhinia_brachycalyx 0 0 0.46 3 0.4 0.045 Bauhinia_rutilans 0 0 0.03 3 0.03 1 Bauhinia_tarapotensis 0 0 0.03 4 0.06 0.394 Beilschmiedia_costaricensis 0 0.33 0 2 0.33 0.043 Beilschmiedia_pendula 0.27 0 0.1 1 0.12 0.462 Bellucia_pentamera 0 0 0.08 3 0.08 0.47 Bellucia_spruceana 0 0 0.03 4 0.05 0.912 Bellucia_subandina 0 0 0.03 3 0.03 1 Bixa_platycarpa 0 0 0.05 4 0.11 0.322 Bixa_urucurana 0.07 0 0.26 3 0.22 0.127 Borojoa_claviflora 0 0 0.03 4 0.04 1 Borojoa_fruits 0 0 0.08 3 0.08 0.479 Borojoa_retidomatia 0 0 0.03 3 0.03 1 Borojoa_sp1 0.07 0 0 1 0.07 0.451 Borojoa_stipularis 0.13 0 0 1 0.13 0.159 Borojoa_twin 0 0 0.05 3 0.05 0.456 Botryarrhena_pendula 0.07 0 0 1 0.07 0.459 Brosimum_2468 0.07 0 0 1 0.07 0.456 Brosimum_acutifolium 0.2 0 0.21 1 0.12 0.494 Brosimum_alicastrum 0 0 0.03 4 0.12 0.23 Brosimum_guianense 0.13 0 0.41 4 0.18 0.464 Brosimum_indet 0 0 0.03 3 0.03 1 Brosimum_lactescens 0.73 0 0.49 1 0.5 0.04 Brosimum_potabile 0.13 0 0 1 0.1 0.262 Brosimum_rubescens 0.27 0 0.05 1 0.16 0.297 Brosimum_utile 0.2 0.67 0.38 2 0.28 0.145 Brownea_grandiceps 0.2 0 0.69 3 0.53 0.023 Brownea_jaramilloi 0 0 0.36 3 0.36 0.045

Brownea_macrophylla 0 0 0.08 3 0.06 0.714 Browneopsis_ucayalina 0 0 0.38 4 0.27 0.145 Buchenavia_2337 0.07 0 0 1 0.07 0.465 Buchenavia_almargen-pechispi 0.07 0 0 1 0.07 0.459 Buchenavia_amazonia 0.2 0 0.13 1 0.07 0.843 Buchenavia_congesta 0.13 0 0.1 1 0.06 0.934 Buchenavia_defoliated 0 0 0.03 3 0.03 1 Buchenavia_erismabnerv 0 0 0 4 0.07 0.456 Buchenavia_grandis 0.27 0 0 1 0.27 0.048 Buchenavia_indet 0 0 0.03 3 0.03 1 Buchenavia_macrophylla 0 0.33 0.08 2 0.27 0.034 Buchenavia_oxycarpa 0.07 0 0 1 0.07 0.455 Buchenavia_parvifolia 0.2 0 0.1 1 0.12 0.414 Buchenavia_reticulata 0 0.33 0.03 2 0.29 0.062 Buchenavia_seri 0.07 0 0 1 0.07 0.461 Buchenavia_suaveolens 0.07 0 0 1 0.07 0.457 Buchenavia_viridiflora 0.13 0.33 0 1 0.1 0.241 Bunchosia_argentea 0.07 0 0.23 3 0.11 0.679 Bunchosia_hookeriana 0.07 0 0 1 0.07 0.458 Byrsonima_arthropoda 0.07 0.67 0.03 2 0.53 0.006 Byrsonima_japurensis 0.2 0 0.1 1 0.15 0.21 Byrsonima_kampa 0 0.33 0 2 0.33 0.041 Byrsonima_krukoffii 0 0 0 4 0.07 0.457 Byrsonima_otra 0 0 0.05 3 0.05 0.689 Byrsonima_putumayensis 0 0 0.31 3 0.16 0.322 Byttneria_sp_nov 0 0 0.03 3 0.03 1 Cabralea_canjerana 0.13 0 0.33 3 0.19 0.261 Calatola_costaricensis 0.07 0 0.21 3 0.08 0.787 Calliandra_guildingii 0 0 0.03 4 0.05 0.913 Calliandra_large 0 0 0.05 3 0.05 0.45 Calliandra_trinervia 0 0 0.18 3 0.13 0.388 Calophyllum_brasiliense 0.07 0 0.05 1 0.02 1 Calophyllum_longifolium 0.27 0 0 1 0.27 0.043 Calycophyllum_obovatum 0.07 0 0 4 0.07 0.48 Calycophyllum_spruceanum 0 0 0.08 3 0.05 0.851 Calyptranthes_65 0 0 0.03 3 0.03 1 Calyptranthes_bipennis 0.07 0 0.03 1 0.06 0.399 Calyptranthes_branch 0 0 0.08 3 0.08 0.467 Calyptranthes_brosimoid 0.07 0 0 1 0.07 0.461 Calyptranthes_cortezanegra 0.07 0 0 1 0.07 0.458 Calyptranthes_densiflora 0 0 0.03 3 0.03 1 Calyptranthes_doradita_2319 0.07 0 0 1 0.07 0.448 Calyptranthes_forsteri 0.07 0 0.03 1 0.05 0.912

Calyptranthes_macrophylla 0.07 0 0.03 1 0.05 0.913 Calyptranthes_maxima 0.13 0 0.15 3 0.09 0.729 Calyptranthes_minicom?n_ 0.07 0 0 1 0.07 0.467 Calyptranthes_mistic 0.07 0 0 1 0.07 0.46 Calyptranthes_nearbranch 0 0 0.08 3 0.08 0.48 Calyptranthes_paniculata 0.13 0 0.03 1 0.06 0.578 Calyptranthes_plicata 0 0 0 4 0.07 0.45 Calyptranthes_pseudorugosa_222 0.07 0 0 1 0.07 0.464 Calyptranthes_rabbit 0 0 0.03 3 0.03 1 Calyptranthes_rugosa_21 0.07 0 0 1 0.07 0.46 Calyptranthes_ruiziana 0.07 0 0 1 0.07 0.453 Calyptranthes_salacia 0 0 0.1 3 0.1 0.29 Calyptranthes_tessmannii 0.07 0 0 1 0.07 0.455 Campomanesia_lineatifolia 0.07 0 0.08 3 0.04 0.842 Capirona_decorticans 0.2 0 0.1 1 0.08 0.769 Capparidastrum_osmanthum 0.07 0 0.15 4 0.12 0.466 Capparis_detonsa 0 0 0.08 3 0.05 0.744 Capparis_güepia_sp._nov 0.07 0 0 1 0.07 0.459 Caraipa_grandifolia 0.07 0 0 1 0.07 0.458 Cariniana_multiflora 0 1 0 2 0.94 0 Carpotroche_longifolia 0 0 0 4 0.07 0.462 Caryocar_glabrum 0.13 1 0.1 2 0.86 0.001 Caryocar_villosum 0 0 0.03 3 0.03 1 Caryodaphnopsis_fosteri 0.07 0 0.05 4 0.08 0.52 Caryodaphnopsis_tomentosa 0.07 0 0.08 1 0.04 0.837 Caryodendron_orinocense 0 0 0.28 3 0.18 0.381 Casearia_2387 0.13 0 0 1 0.13 0.161 Casearia_aculeata 0 0 0.03 3 0.03 1 Casearia_arborea 0.2 0 0.31 3 0.19 0.275 Casearia_combaymensis 0.13 0 0.1 1 0.07 0.727 Casearia_decandra 0.13 0 0.08 1 0.1 0.397 Casearia_fasciculata 0 0 0.03 3 0.03 1 Casearia_indet 0 0 0.03 3 0.03 1 Casearia_javitensis 0.13 0.33 0.31 3 0.14 0.542 Casearia_sylvestris 0 0 0.23 3 0.23 0.091 Casearia_tubiflora 0 0 0.1 3 0.1 0.284 Casearia_uleana 0.13 0 0.18 3 0.09 0.64 Casearia_ulmifolia 0.13 0 0.08 1 0.08 0.572 Cassia_cowanii 0 0 0.1 4 0.04 0.866 Castilla_ulei 0.07 0 0.36 4 0.18 0.426 Cathedra_acuminata 0.13 0 0.21 3 0.11 0.702 Cecropia_angustifolia 0.07 0 0 1 0.07 0.442 Cecropia_distachya 0.33 0 0.05 1 0.26 0.036

Cecropia_engleriana 0.07 0 0.38 3 0.27 0.13 Cecropia_ficifolia 0.27 0 0.56 3 0.27 0.223 Cecropia_herthae 0.07 0 0.46 3 0.41 0.036 Cecropia_hispid 0 0 0.08 3 0.05 0.739 Cecropia_indet 0 0 0.03 3 0.03 1 Cecropia_latiloba 0.13 0 0 1 0.12 0.227 Cecropia_marginalis 0 0 0.05 4 0.11 0.291 Cecropia_membranacea 0 0 0.1 4 0.27 0.043 Cecropia_peru 0.13 0 0 1 0.13 0.165 Cecropia_putumayonis 0 0 0.21 3 0.21 0.126 Cecropia_rio 0 0 0.05 3 0.05 0.686 Cecropia_sciadophylla 0.33 0 0.64 3 0.3 0.21 Cedrela_fissilis 0.07 0 0.33 3 0.2 0.205 Cedrela_odorata 0.07 0 0.15 4 0.2 0.115 Cedrelinga_cateniformis 0.4 0 0.15 4 0.17 0.367 Ceiba_pentandra 0.07 0 0.1 4 0.1 0.636 Ceiba_samauma 0.13 0 0.13 3 0.05 0.988 Celtis_schippii 0 0 0.59 3 0.39 0.067 Centronia_laurifolia 0 1 0 2 1 0 Cespedesia_spathulata 0.2 0 0.1 3 0.06 0.869 Cestrum_megalophyllum 0 0 0.18 3 0.18 0.164 Cestrum_racemosum 0 0 0.08 3 0.08 0.456 Chaunochiton_kappleri 0.07 0 0 1 0.07 0.452 Chelyocarpus_ulei 0 0 0.08 3 0.08 0.477 Chimarrhis_gentryana 0.2 0 0 1 0.13 0.183 Chimarrhis_glabriflora 0 0 0.05 4 0.32 0.022 Chimarrhis_hookeri 0 0 0.21 4 0.17 0.253 Chionanthus_implicatus 0.07 0 0.1 4 0.07 0.661 Chione_sylvicola 0 0 0.05 3 0.05 0.454 Chlorocardium_blanco 0 0 0 4 0.07 0.455 Chlorocardium_galeras 0 0 0 4 0.07 0.471 Chlorocardium_rodiei 0 0 0 4 0.07 0.462 Chlorocardium_subopo 0.07 0 0 1 0.07 0.474 Chlorocardium_venenosum 0 0 0.03 4 0.03 1 Chomelia_polyantha 0 0 0.05 3 0.05 0.682 Chomelia_tenuiflora 0.13 0 0.18 1 0.09 0.666 Chromolucuma_angostistipul 0.07 0 0.05 1 0.04 0.92 Chrysochlamys_bracteolata 0.07 0 0.03 4 0.09 0.398 Chrysochlamys_intersecpromin 0.07 0 0 1 0.07 0.461 Chrysochlamys_membranacea 0.2 0 0.44 4 0.39 0.073 Chrysochlamys_sp 0 0 0.03 3 0.03 1 Chrysochlamys_ulei 0 0 0 4 0.07 0.456 Chrysophyllum_amazonicum 0.2 0 0.26 1 0.12 0.563

Chrysophyllum_argenteum 0.07 0 0.31 3 0.12 0.674 Chrysophyllum_bombycinum 0.07 0 0.03 1 0.05 0.916 Chrysophyllum_colombianum 0.13 0 0.03 1 0.12 0.231 Chrysophyllum_cuneifolium 0.13 0 0.21 1 0.11 0.506 Chrysophyllum_indet 0 0 0.03 3 0.03 1 Chrysophyllum_lucentifolium 0 0 0.13 3 0.1 0.469 Chrysophyllum_manaosense 0.33 0 0.1 1 0.28 0.062 Chrysophyllum_ovale 0 0 0.03 3 0.03 1 Chrysophyllum_pomiferum 0.07 0 0.15 4 0.1 0.719 Chrysophyllum_sanguinolentum 0.27 1 0 2 0.95 0 Chrysophyllum_venezuelanense 0.07 0 0.31 3 0.19 0.213 Cinchonopsis_amazonica 0 0.33 0 2 0.33 0.045 Cinnamomum_napoense 0 0 0.03 4 0.05 0.909 Cinnamomum_triplinerve 0.07 0 0.05 1 0.04 0.857 Citharexylum_juli 0 0 0.03 3 0.03 1 Citharexylum_poeppigii 0 0 0.1 3 0.1 0.359 Citronella_incarum 0 0 0.18 3 0.1 0.712 Clarisia_biflora 0.2 0 0.41 3 0.21 0.3 Clarisia_racemosa 0.4 0 0.41 4 0.18 0.571 Clathrotropis_macrocarpa 0.07 0 0 1 0.07 0.464 Clavija_indet 0 0 0.03 3 0.03 1 Clavija_procera 0 0 0.05 3 0.05 0.451 Clerodendron_tessmannii 0.07 0 0.03 1 0.05 0.914 Clitoria_arborea 0.07 0 0.03 1 0.05 0.908 Clusia_amazonica 0 0 0.03 3 0.03 1 Coccoloba_broch 0.07 0 0 1 0.07 0.461 Coccoloba_coronata 0.07 0 0.33 3 0.12 0.707 Coccoloba_densifrons 0.2 0 0.74 3 0.32 0.175 Coccoloba_fallax 0 0 0.18 3 0.18 0.168 Coccoloba_fenestrada 0.07 0 0 1 0.03 1 Coccoloba_hirsuta 0.07 0 0 1 0.07 0.453 Coccoloba_immense 0 0 0.05 3 0.05 0.688 Coccoloba_latifolia 0.07 0 0 1 0.07 0.461 Coccoloba_lehmannii 0 0 0.13 4 0.11 0.567 Coccoloba_lovely 0 0 0.03 3 0.03 1 Coccoloba_mollis 0.2 0 0.21 1 0.14 0.357 Coccoloba_ninfi 0 0 0.05 3 0.05 0.458 Coccoloba_paraensis 0.07 0 0 1 0.03 1 Coccoloba_shoe 0 0 0.05 3 0.05 0.451 Coccoloba_spruceana 0 0 0 4 0.07 0.469 Cochlospermum_orinocense 0 0 0 4 0.07 0.45 Colubrina_arborescens 0 0 0.18 3 0.15 0.311 Colubrina_spinosa 0 0 0.03 4 0.05 0.909

Compsoneura_capitellata 0.4 1 0.31 2 0.4 0.037 Compsoneura_sprucei 0.2 0.33 0.18 1 0.1 0.707 Compsoneura_tsuirim 0 0 0 4 0.07 0.462 Compsoneura_ulei 0 0 0.08 3 0.08 0.46 Conceveiba_1 0 0 0.31 3 0.31 0.053 Conceveiba_guianensis 0.53 0 0.05 1 0.41 0.034 Conceveiba_indet 0 0 0.03 3 0.03 1 Conceveiba_martiana 0 0 0.03 3 0.03 1 Conceveiba_rhytidocarpa 0.2 0 0.08 1 0.13 0.396 Conceveiba_split 0 0 0.1 3 0.1 0.297 Conceveiba_terminalis 0 0 0 4 0.07 0.458 Connarus_dubia 0 0 0.03 3 0.03 1 Cordia_1 0 0 0.03 3 0.03 1 Cordia_2279 0.07 0 0 1 0.07 0.46 Cordia_3 0 0 0.05 3 0.05 0.681 Cordia_alliodora 0 0 0.31 4 0.16 0.329 Cordia_bullata 0 0 0 4 0.07 0.457 Cordia_chamissoniana 0 0 0.08 3 0.08 0.464 Cordia_collococa 0 0 0.18 3 0.18 0.192 Cordia_gracilipes 0 0 0.03 3 0.03 1 Cordia_handsome 0 0 0.03 3 0.03 1 Cordia_hebeclada 0.13 0 0.13 4 0.04 1 Cordia_mexiana 0.07 0 0 1 0.07 0.453 Cordia_neat 0 0 0.08 3 0.08 0.465 Cordia_nig 0 0 0.08 4 0.04 0.768 Cordia_nodosa 0 0 0.08 4 0.08 0.542 Cordia_ripicola 0 0 0 4 0.07 0.456 Cordia_sericicalyx 0 0 0 4 0.07 0.461 Cordia_split 0 0 0.05 3 0.05 0.689 Cordia_trachyphylla 0.07 0 0.03 1 0.05 0.913 Cordia_ucayaliensis 0.2 0 0.28 3 0.14 0.477 Corythophora_retic_Lecythis_corrugata_cf 0 0.33 0 2 0.33 0.038 . Couepia_chrysocalyx 0 0 0.15 4 0.24 0.089 Couepia_guianensis 0.07 0 0 1 0.05 0.768 Couepia_kaputna 0 0.33 0 2 0.33 0.04 Couepia_macrophylla 0 0 0.15 3 0.12 0.429 Couepia_obovata 0.27 0 0.1 1 0.16 0.264 Couepia_parillo 0.2 0 0.03 1 0.18 0.2 Couepia_subcordata 0.07 0.33 0 2 0.28 0.082 Couma_guianensis 0.13 0.33 0 2 0.17 0.197 Couma_macrocarpa 0.2 0 0.03 1 0.06 0.679 Couratari_guianensis 0.2 0 0.23 3 0.08 0.996

Couratari_oligantha 0.13 0 0 1 0.13 0.159 Couratari_stellata 0 0 0.03 4 0.05 0.386 Couroupita_guianensis 0.2 0 0.23 4 0.08 0.937 Coussapoa_asperifolia 0.07 0 0 1 0.07 0.452 Coussapoa_indet 0 0 0.03 3 0.03 1 Coussapoa_jatun-sachensis 0.07 0 0 4 0.09 0.332 Coussapoa_longepedunculata 0.07 0 0.03 1 0.05 0.909 Coussapoa_orthoneura 0.47 0 0.49 1 0.24 0.269 Coussapoa_ovalifolia 0.07 0 0.05 1 0.04 0.854 Coussapoa_trinervia 0.27 0 0.1 1 0.18 0.232 Coussarea_amplifolia 0 0 0.03 4 0.05 0.909 Coussarea_cephaeloides 0 0 0.08 3 0.08 0.462 Coussarea_crassa 0 0 0.03 3 0.03 1 Coussarea_klugii 0 0 0.08 4 0.12 0.315 Coussarea_obliqua 0 0 0.05 4 0.04 0.854 Coussarea_paniculata 0 0 0.03 4 0.04 1 Coussarea_racemosa 0.07 0 0 1 0.07 0.464 Coussarea_tenuiflora 0.13 0 0.03 1 0.08 0.299 Coussarea_tigershark 0 0 0.03 3 0.03 1 Cremastosperma_cauliflorum 0 0 0.03 3 0.03 1 Cremastosperma_gracilipes 0.07 0 0 1 0.07 0.458 Cremastosperma_longicuspe 0 0 0 4 0.07 0.463 Cremastosperma_megalophyllum 0 0 0 4 0.2 0.109 Cremastosperma_monospermum 0.07 0 0 1 0.07 0.455 Cremastosperma_napoense 0 0 0 4 0.13 0.163 Crepidospermum_indet 0 0 0.03 3 0.03 1 Crepidospermum_prancei 0.07 0 0 1 0.07 0.464 Crepidospermum_rhoifolium 0.53 0 0.41 1 0.3 0.126 Croton_cuneatus 0.27 0 0 1 0.27 0.062 Croton_lechleri 0 0 0.08 3 0.06 0.669 Croton_matourensis 0.07 0 0 1 0.07 0.461 Croton_sampatik 0 0 0.05 4 0.05 0.716 Croton_tessmannii 0 0 0.15 3 0.07 0.747 Croton_yellow 0 0 0.03 3 0.03 1 Crudia_glaberrima 0.27 0 0.1 1 0.2 0.137 Cryptocarya_aschersoniana 0 0 0.03 3 0.03 1 Cryptocarya_yasuniensis 0.07 0 0.21 3 0.17 0.216 Cupania_burnham 0 0 0.05 3 0.05 0.451 Cupania_cinerea 0.2 0 0.13 1 0.13 0.402 Cupania_dentate 0 0 0.03 3 0.03 1 Cupania_livida 0.07 0 0.08 1 0.04 0.925 Cupania_livida/fulvida_cf._green 0 0 0.15 3 0.13 0.341 Cupania_rufescens 0 0 0.03 3 0.03 1

Cupania_scrobiculata 0.07 0 0.1 3 0.08 0.628 Cyathea_lasiosora 0 0 0.03 3 0.03 1 Cybianthus_2351 0.07 0 0 1 0.07 0.462 Cybianthus_amplus 0 0.67 0 2 0.67 0.001 Cybianthus_cuatrecasasii 0 0 0 4 0.07 0.462 Cybianthus_longifolius 0 0 0.03 3 0.03 1 Dacryodes_belemensis 0.13 0 0 1 0.12 0.2 Dacryodes_chimantensis 0.33 0 0 1 0.3 0.02 Dacryodes_cupularis 0.07 0 0.03 1 0.06 0.391 Dacryodes_gorda 0 0 0.05 3 0.05 0.678 Dacryodes_indet 0 0 0.03 3 0.03 1 Dacryodes_peruviana 0.33 0 0.44 4 0.4 0.056 Dacryodes_sclerophylla 0.07 0.67 0 2 0.51 0.008 Dacryodes_tsuirim 0 0 0 4 0.07 0.46 Dalbergia_frutescens 0.2 0 0.21 4 0.3 0.093 Dalbergia_monetaria 0.07 0 0 1 0.07 0.446 Daphnopsis_equatorialis 0 0 0.05 4 0.09 0.471 Dendrobangia_boliviana 0.33 0 0.1 1 0.29 0.034 Dendrobangia_multinervia 0.07 0 0.13 3 0.08 0.534 Dendropanax_arboreus 0.2 0 0.1 4 0.15 0.346 Dendropanax_caucanus 0.2 0.33 0.41 3 0.18 0.529 Dendropanax_cernua 0 0 0.03 4 0.06 0.394 Dendropanax_indet 0 0 0.03 3 0.03 1 Dendropanax_macropodus 0 0.33 0 2 0.21 0.123 Dendropanax_punctate 0 0 0.03 3 0.03 1 Dendropanax_truncate 0 0 0.13 3 0.13 0.321 Dendropanax_umbellatus 0 0 0 4 0.07 0.457 Dialium_guianense 0.33 0 0.44 1 0.17 0.639 Diclinanona_calycina 0 0.67 0 2 0.67 0.001 Diospyros_artanthifolia 0.13 0 0.31 3 0.2 0.197 Diospyros_capreifolia 0.07 0 0.03 1 0.05 0.908 Diospyros_ekodul 0 0 0.05 3 0.05 0.449 Diospyros_nanay 0.07 0 0 1 0.07 0.454 Diospyros_poeppigiana 0 0 0.03 3 0.03 1 Diospyros_pseudoxylopia 0.13 0 0.28 3 0.16 0.305 Diospyros_sericea 0 0 0.03 3 0.03 1 Diploon_cuspidatum 0 0 0.18 3 0.18 0.16 Diplotropis_martiusii 0 0.33 0 2 0.33 0.045 Diplotropis_purpurea 0.07 0 0.03 1 0.06 0.386 Dipteryx_micrantha 0.2 0 0.03 1 0.18 0.181 Discophora_guianensis 0.2 0 0.23 1 0.09 0.895 Drypetes_amazonica 0.27 0 0.44 3 0.31 0.11 Drypetes_fanshawei 0 0 0.28 3 0.28 0.067

Drypetes_gentryi 0 0 0 4 0.13 0.167 Drypetes_heisteria 0 0 0.03 3 0.03 1 Drypetes_variabilis 0.2 0 0.41 3 0.14 0.685 Duguetia_2244 0.07 0 0 1 0.07 0.455 Duguetia_flagellaris 0 0 0.03 3 0.03 1 Duguetia_hadrantha 0.13 0 0.23 3 0.16 0.291 Duguetia_macrophylla 0.07 0 0.03 1 0.06 0.391 Duguetia_odorata 0.2 0 0.03 1 0.13 0.326 Duguetia_quitarensis 0.07 0 0.31 3 0.26 0.09 Duguetia_spixiana 0.33 0 0.28 1 0.26 0.192 Duguetia_surinamensis 0.13 0 0.05 1 0.1 0.443 Dulacia_candida 0.07 0 0.05 1 0.05 0.597 Duroia_eriopila 0 0 0.03 3 0.03 1 Duroia_hirsuta 0.13 0 0.18 1 0.08 0.875 Duroia_petiolaris 0.13 0 0 1 0.11 0.31 Dussia_delgada_sp._nov 0.07 0 0.13 3 0.1 0.478 Dussia_glauca_sp._nov. 0.07 0 0 1 0.07 0.461 Dussia_gorda 0 0 0.13 3 0.13 0.324 Dussia_tessmannii 0.4 0 0.28 1 0.21 0.275 Dystovomita_crasa_brasiliensis_cf. 0.13 0 0.03 1 0.13 0.179 Ecclinusa_guianensis 0.07 0 0.13 3 0.08 0.529 Ecclinusa_lanceolata 0.07 0 0.03 1 0.03 1 Ecclinusa_ramiflora 0 0 0.03 4 0.05 0.915 Elaeagia_kampa 0 0.33 0 2 0.33 0.039 Elaeoluma_glabrescens 0 0.33 0 2 0.28 0.083 Endlicheria_almargen-rufa 0.07 0 0 1 0.07 0.455 Endlicheria_anomala 0.2 0 0 1 0.2 0.079 Endlicheria_bracteata 0.07 0 0 1 0.07 0.467 Endlicheria_canescens 0.33 0 0.1 1 0.25 0.052 Endlicheria_chalisea 0.07 0 0.05 1 0.02 1 Aniba perutilis 0 0.67 0 2 0.67 0.001 Endlicheria_directonervia 0.07 0 0.03 1 0.05 0.909 Endlicheria_dori 0.07 0 0.08 3 0.06 0.673 Endlicheria_dysodantha 0 0 0.1 3 0.1 0.378 Endlicheria_ferruginosa 0 0 0 4 0.07 0.45 Endlicheria_formosa 0.07 0 0.15 3 0.05 0.933 Endlicheria_gracilis 0.07 0 0.05 1 0.04 0.847 Endlicheria_griseo-sericea 0.07 0 0 1 0.04 0.767 Endlicheria_indet 0 0 0.03 3 0.03 1 Endlicheria_klugii 0.07 0 0.03 1 0.05 0.914 Endlicheria_krukovii 0.07 0 0.1 4 0.06 0.93 Endlicheria_lorastemon 0 0 0.08 3 0.08 0.463 Endlicheria_metallica 0.27 0 0.05 1 0.24 0.065

Endlicheria_miniopacangulo 0 0 0.03 4 0.05 0.912 Endlicheria_mishuyacensis 0.07 0 0.03 4 0.11 0.33 Endlicheria_panicu 0.07 0 0 1 0.07 0.464 Endlicheria_paniculata 0.07 0 0.1 3 0.07 0.715 Endlicheria_peqobov_aff._mishuyacensis 0 0 0 4 0.07 0.468 Endlicheria_rubriflora 0 0 0 4 0.07 0.456 Endlicheria_ruforamula 0 0 0.21 3 0.21 0.119 Endlicheria_sericea 0 0 0.05 3 0.05 0.45 Endlicheria_sp 0 0 0.03 3 0.03 1 Endlicheria_sprucei 0.07 0 0.05 1 0.04 0.851 Endlicheria_tessmannii 0 0 0.03 3 0.03 1 Endlicheria_tschudyana 0 0 0.03 3 0.03 1 Endlicheria_verde 0 0 0.03 3 0.03 1 Endlicheria_verdeclaro 0 0 0.03 3 0.03 1 Endlicheria_verdemetalico 0 0 0.03 3 0.03 1 Endlicheria_verticillata 0.13 0 0.05 1 0.07 0.643 Enterolobium_barnebianum 0 0 0.03 4 0.06 0.4 Enterolobium_indet 0 0 0.03 3 0.03 1 Enterolobium_schomburgkii 0.07 0 0 1 0.07 0.459 Eriotheca_florencia_macrophylla_cf. 0.07 0 0 1 0.07 0.467 Eriotheca_globosa 0.2 0 0.33 1 0.16 0.516 Eriotheca_macrophylla 0.07 0 0 1 0.07 0.463 Erisma_uncinatum 0.27 0 0.31 1 0.16 0.395 Erythrina_amazonica 0.07 0 0.1 4 0.06 0.929 Erythrina_poeppigiana 0 0 0.03 3 0.03 1 Erythroxylum_citrifolium 0.07 0.33 0 2 0.29 0.046 Erythroxylum_divaricatum 0.07 0 0 1 0.07 0.459 Erythroxylum_macrophyllum 0.2 0 0.15 1 0.14 0.386 Erythroxylum_squamatum 0 0 0.03 3 0.03 1 Eschweilera_2372 0.07 0 0 4 0.05 0.482 Eschweilera_2458 0.07 0 0 1 0.07 0.468 Eschweilera_andina 0.07 0 0.31 4 0.29 0.105 Eschweilera_bracteosa 0.13 0 0.36 3 0.27 0.11 Eschweilera_chartaceifolia 0.13 0 0 1 0.13 0.158 Eschweilera_coriacea 0.67 0.33 0.67 1 0.28 0.355 Eschweilera_decolorans 0.2 0 0.03 1 0.15 0.277 Eschweilera_gigantea 0 0 0.31 3 0.31 0.044 Eschweilera_grandiflora 0.07 0 0 1 0.07 0.463 Eschweilera_indet 0 0 0.03 3 0.03 1 Eschweilera_intersec-juyu 0 0 0 4 0.13 0.167 Eschweilera_itayensis 0.4 0 0 1 0.39 0.016 Eschweilera_juruensis 0.07 0 0.33 3 0.25 0.101 Eschweilera_kaputna 0 0.33 0 2 0.33 0.043

Eschweilera_laevicarpa 0.27 0.33 0.13 2 0.12 0.624 Eschweilera_ovalifolia 0.27 0 0.21 1 0.15 0.38 Eschweilera_parvifolia 0.47 0.33 0.26 1 0.34 0.067 Eschweilera_rufifolia 0.53 0 0.46 1 0.27 0.289 Eschweilera_tessmannii 0.33 0.33 0.03 1 0.2 0.143 Esenbeckia_amazonica 0 0 0.13 3 0.13 0.188 Eugenia_11 0 0 0.03 3 0.03 1 Eugenia_736 0 0 0.03 3 0.03 1 Eugenia_anastomosans 0.07 0 0 1 0.07 0.459 Eugenia_black 0.07 0 0 1 0.07 0.46 Eugenia_blacksheep 0 0 0.03 3 0.03 1 Eugenia_chicaretic 0.07 0 0 1 0.03 1 Eugenia_cuspidifolia 0.13 0 0.05 1 0.1 0.439 Eugenia_dittocrepis 0.07 0 0 1 0.03 1 Eugenia_doublering 0 0 0.03 3 0.03 1 Eugenia_egensis 0.13 0 0.13 3 0.07 0.784 Eugenia_eschweil_ 0.07 0 0 1 0.07 0.456 Eugenia_feijoi 0.13 0.33 0.18 2 0.12 0.52 Eugenia_festoendida 0.07 0 0 1 0.07 0.458 Eugenia_florida 0.27 0 0.28 3 0.12 0.728 Eugenia_galalonensis 0 0 0.05 3 0.05 0.46 Eugenia_heterochroma 0.13 0 0.03 1 0.11 0.272 Eugenia_indet 0 0 0.03 3 0.03 1 Eugenia_kaputna 0 0.33 0 2 0.33 0.04 Eugenia_lambertiana 0 0 0.08 4 0.05 0.754 Eugenia_lime 0 0 0.08 3 0.08 0.479 Eugenia_macro-oerst 0 0 0.03 3 0.03 1 Eugenia_macrocalyx 0 0 0.03 3 0.03 1 Eugenia_margin 0 0 0.03 3 0.03 1 Eugenia_marowynensis 0 0 0.08 3 0.08 0.462 Eugenia_medioperp-rojo 0.07 0 0.03 1 0.05 0.911 Eugenia_multirimosa 0 0 0.05 3 0.05 0.685 Eugenia_muricata 0.07 0 0.08 4 0.03 0.905 Eugenia_panosadorada 0 0 0.03 3 0.03 1 Eugenia_patens 0.07 0 0.03 1 0.05 0.914 Eugenia_patrisii 0.07 0 0 1 0.07 0.46 Eugenia_pinprick 0 0 0.03 3 0.03 1 Eugenia_schunkei 0.07 0 0.26 3 0.14 0.433 Eugenia_soft 0 0 0.03 3 0.03 1 Eugenia_sovran 0 0 0.23 3 0.23 0.083 Eugenia_stipitata 0 0 0.05 3 0.05 0.458 Eugenia_tetrasticha 0.07 0 0.03 1 0.05 0.91 Eugenia_tomentdelicada 0.07 0 0 1 0.07 0.452

Eugenia_yasuniana 0 0 0.08 3 0.08 0.489 Euplassa_inaequalis 0.07 0 0 1 0.07 0.452 Euplassa_occidentalis 0.13 0 0 1 0.13 0.168 Euterpe_precatoria 0.67 0 0.62 3 0.39 0.142 Exostema_maynense 0 0 0.08 3 0.08 0.478 Faramea_735 0 0 0.03 3 0.03 1 Faramea_anisocalyx 0 0 0.03 3 0.03 1 Faramea_capillipes 0 0.33 0 2 0.33 0.043 Faramea_eurycarpa 0 0 0.03 3 0.03 1 Faramea_glandulosa 0.07 0 0.08 3 0.04 0.838 Faramea_indet 0 0 0.03 3 0.03 1 Faramea_multiflora 0 0 0.05 3 0.05 0.457 Faramea_occidentalis 0 0 0.03 3 0.03 1 Faramea_parvibractea 0.07 0 0 1 0.07 0.455 Faramea_spathacea 0 0 0.03 3 0.03 1 Faramea_tamberlikiana 0 0 0.03 3 0.03 1 Faramea_torquata 0.07 0 0.08 3 0.05 0.706 Ferdinandusa_“kaputna” 0 0.33 0 2 0.33 0.04 Ferdinandusa_elliptica 0.2 0 0 1 0.2 0.123 Ferdinandusa_guainiae 0.13 0.33 0.03 2 0.14 0.217 Ferdinandusa_uaupensis 0 0.33 0 2 0.33 0.041 Ficus_brevibracteata 0 0 0.08 3 0.08 0.485 Ficus_broadwayi 0 0 0.03 3 0.03 1 Ficus_casapiensis 0.13 0 0 1 0.13 0.163 Ficus_castellviana 0.07 0 0 4 0.11 0.266 Ficus_cuatrecasasiana 0 0 0.03 3 0.03 1 Ficus_donnell-smithii 0.13 0 0 1 0.09 0.328 Ficus_gomelleira 0.07 0 0.05 1 0.02 1 Ficus_guianensis 0.07 0 0.05 1 0.05 0.603 Ficus_indet 0 0 0.03 3 0.03 1 Ficus_insipida 0.13 0 0.15 3 0.1 0.638 Ficus_juno 0 0 0.03 4 0.06 0.395 Ficus_macbridei 0 0 0.05 4 0.17 0.236 Ficus_mathewsii 0.13 0 0 1 0.13 0.168 Ficus_maxima 0 0 0.21 3 0.21 0.163 Ficus_nymphaefolia/ypselophlebia_cf. 0 0 0.03 4 0.05 0.911 Ficus_piresiana 0.13 0 0.26 3 0.19 0.177 Ficus_pulchella 0 0 0.03 4 0.05 0.913 Ficus_schippii 0.07 0 0.08 3 0.04 0.847 Ficus_schultesii 0 0 0.05 3 0.05 0.448 Ficus_tonduzii 0 0 0.05 3 0.05 0.455 Ficus_trianae 0.07 0 0 4 0.06 0.492 Ficus_trigona 0 0 0.03 3 0.03 1

Ficus_trigonata 0 0 0.03 3 0.03 1 Ficus_uiant 0.07 0 0 1 0.07 0.461 Ficus_ursina 0 0 0.05 3 0.05 0.459 Froesia_diffusa 0 0 0.03 3 0.03 1 Fusaea_longifolia 0.4 0 0.03 1 0.3 0.034 Garcinia_brasiliensis 0.2 0 0.1 1 0.14 0.364 Garcinia_condor 0 0.33 0 2 0.33 0.044 Garcinia_intermedia 0.13 0 0 1 0.13 0.165 Garcinia_macrophylla 0.27 0 0.18 1 0.16 0.324 Garcinia_madruno 0.2 0 0.18 4 0.12 0.581 Garcinia_sp 0.07 0 0 1 0.07 0.46 Geissospermum_sp 0.07 0 0 1 0.07 0.454 Genipa_americana 0.07 0 0.03 1 0.05 0.908 Gloeospermum_equatoriense 0 0 0.03 4 0.04 0.914 Gloeospermum_longifolium 0.07 0 0.03 4 0.05 0.688 Gloeospermum_sclerophyllum 0 0 0 4 0.07 0.456 Gloeospermum_sphaerocarpum 0 0 0.03 3 0.03 1 Glycydendron_amazonicum 0.2 0 0.33 3 0.2 0.228 Gordonia_fruticosa 0.07 0 0 1 0.07 0.464 Graffenrieda_intermedia 0 0 0.03 4 0.02 1 Graffenrieda_sp._1 0 0 0.03 3 0.03 1 Grias_neuberthii 0 0 0.62 4 0.36 0.092 Grias_peruviana 0 0 0 4 0.33 0.013 Guadua_chinese 0 0 0.03 4 0.06 0.394 Guapira_clásica 0 0 0.03 3 0.03 1 Guapira_granclásica 0 0 0.03 3 0.03 1 Guapira_relief 0 0 0.03 3 0.03 1 Guapira_sp1 0.07 0 0 1 0.07 0.464 Guapira_sp2 0.07 0 0 1 0.07 0.458 Guapira_tomenrufa 0 0 0 4 0.07 0.458 Guarea_155 0 0 0.03 3 0.03 1 Guarea_1933_ 0 0 0.03 3 0.03 1 Guarea_2426 0.07 0 0 1 0.07 0.449 Guarea_4 0 0 0.03 3 0.03 1 Guarea_carapoides 0 0 0 4 0.2 0.128 Guarea_carinata 0 0 0.21 3 0.12 0.445 Guarea_ecuadoriensis 0.07 0 0.18 3 0.1 0.606 Guarea_eschweilera_sp._nov._ined.? 0 0 0.03 3 0.03 1 Guarea_fistulosa 0 0 0.05 3 0.05 0.679 Guarea_glabra 0 0 0.05 3 0.05 0.457 Guarea_gomma 0.13 0 0.46 3 0.38 0.041 Guarea_grandifolia 0.07 0 0.44 4 0.32 0.107 Guarea_guentheri 0.07 0 0.26 3 0.21 0.124

Guarea_guidonia 0.07 0 0.21 3 0.15 0.432 Guarea_heavy_sp._nov._ined.? 0 0 0.03 3 0.03 1 Guarea_indet 0 0 0.03 3 0.03 1 Guarea_kunthiana 0.27 0 0.62 3 0.31 0.195 Guarea_macro 0.07 0 0 1 0.07 0.455 Guarea_macrophylla 0.4 0.33 0.54 3 0.21 0.781 Guarea_percy 0 0 0.05 3 0.05 0.459 Guarea_pterorhachis 0 0 0.54 3 0.29 0.161 Guarea_pubescens 0.2 0 0.21 1 0.09 0.824 Guarea_purusana 0.07 0 0.38 3 0.34 0.07 Guarea_silvatica 0.33 0 0.46 3 0.15 0.881 Guatteria__84 0.07 0 0 1 0.07 0.462 Guatteria_15 0.13 0 0 1 0.13 0.166 Guatteria_1976 0 0 0.03 3 0.03 1 Guatteria_676_224 0.13 0 0 1 0.13 0.168 Guatteria_698 0.07 0 0 1 0.07 0.459 Guatteria_amazonica 0 0 0 4 0.07 0.456 Guatteria_asimet 0 0 0 4 0.07 0.462 Guatteria_asplundiana 0.33 0 0.36 1 0.18 0.369 Guatteria_brevicuspis 0.07 0 0.13 3 0.05 0.913 Guatteria_citriodora 0.2 0 0 1 0.2 0.136 Guatteria_diana 0 0 0.03 3 0.03 1 Guatteria_dicli 0.07 0 0 1 0.07 0.46 Guatteria_ecuadorensis 0 0 0 4 0.07 0.462 Guatteria_gentryi 0 0.67 0 2 0.67 0.002 Guatteria_glaberrima 0.6 0 0.49 4 0.2 0.628 Guatteria_guianensis 0.07 0 0.08 3 0.06 0.65 Guatteria_hispida 0.13 0 0.05 1 0.1 0.444 Guatteria_impreso 0.07 0 0 1 0.07 0.454 Guatteria_indet 0 0 0.03 3 0.03 1 Guatteria_megalophylla 0 0 0.13 4 0.09 0.69 Guatteria_multivenia 0.13 0 0.23 4 0.32 0.061 Guatteria_myrsinac 0.07 0 0 1 0.07 0.448 Guatteria_pastazae 0 0 0 4 0.13 0.149 Guatteria_planerdorita 0 0 0.05 3 0.05 0.461 Guatteria_prominintersec 0.13 0 0 1 0.13 0.163 Guatteria_puncticulata 0 0 0 4 0.07 0.46 Guatteria_punctommarron 0 0 0.03 3 0.03 1 Guatteria_recurvisepala 0.27 0 0.23 1 0.1 0.747 Guatteria_schomburgkiana 0.07 0 0.03 1 0.03 1 Guatteria_seri 0 0 0 4 0.07 0.458 Guatteria_sp1 0.07 0 0 1 0.07 0.457 Guatteria_sp2 0.07 0 0 1 0.07 0.465

Guatteria_sp5_* 0 0 0.03 3 0.03 1 Guatteria_streambed 0 0 0.18 3 0.1 0.62 Guatteriopsis_ramiflora 0.07 0 0.05 1 0.04 0.948 Guazuma_ulmifolia 0 0 0.08 3 0.08 0.478 Guettarda_ovo 0 0 0.03 3 0.03 1 Gustavia_hexapetala 0.33 0 0.51 3 0.17 0.79 Gustavia_longifolia 0.33 0 0.82 3 0.53 0.017 Gustavia_macarenensis 0 0 0 4 0.27 0.051 Gymnosporia_urbaniana 0 0 0.08 4 0.08 0.476 Hasseltia_floribunda 0 0 0.51 3 0.45 0.024 Hasseltia_hasseltomen 0 0 0.05 3 0.05 0.453 Hebepetalum_humiriifolium 0.07 0 0 1 0.07 0.46 Heisteria_acuminata 0 0 0.1 4 0.07 0.721 Heisteria_indet 0 0 0.03 3 0.03 1 Heisteria_latifolia 0.07 0 0 1 0.03 1 Heisteria_megalophylla 0 0 0 4 0.13 0.168 Heisteria_nitida 0.07 0 0.36 3 0.32 0.072 Heisteria_spruceana 0.27 0 0.13 1 0.15 0.393 Helicostylis_elegans 0.2 0 0.03 1 0.16 0.177 Helicostylis_scabra 0.07 0 0 1 0.07 0.466 Helicostylis_tomentosa 0.27 0.33 0.41 4 0.16 0.736 Helicostylis_turbinata 0.27 0 0.03 1 0.15 0.226 Heliocarpus_americanus 0 0 0.03 4 0.07 0.463 Henriettea_stellaris 0.07 0 0 1 0.07 0.451 Herrania_nitida 0.07 0 0 1 0.07 0.466 Hevea_guianensis 0.27 0.33 0.21 4 0.18 0.332 Hieronyma_alchorneoides 0.13 0 0.41 3 0.21 0.368 Hieronyma_kaputna 0 0.33 0 2 0.33 0.04 Hieronyma_oblonga 0.4 0 0.26 1 0.24 0.243 Himatanthus_bracteatus 0.13 0 0.03 4 0.13 0.319 Himatanthus_bracteatus/sucuuba_cf. 0.2 0 0.28 1 0.12 0.63 Himatanthus_indet 0 0 0.03 3 0.03 1 Himatanthus_sucuuba 0 0 0.05 4 0.19 0.12 Himatanthus_tarapotensis 0 0 0 4 0.07 0.467 Hippotis_albiflora 0 0 0.03 3 0.03 1 Hippotis_brevipes 0 0 0 4 0.2 0.117 Hippotis_mollis 0 0 0 4 0.13 0.163 Hirtella_“pub” 0 0.33 0 2 0.33 0.04 Hirtella_aequatoriensis 0 0.33 0 2 0.33 0.043 Hirtella_bicornis 0.13 0 0.03 1 0.12 0.222 Hirtella_elongata 0.33 0 0 1 0.33 0.01 Hirtella_excelsa 0 0 0.18 3 0.15 0.289 Hirtella_gracilipes 0.07 0 0 1 0.07 0.46

Hirtella_indet 0 0 0.03 3 0.03 1 Hirtella_macrophylla 0.13 0 0 1 0.07 0.571 Hirtella_magnifolia 0.07 0 0 1 0.07 0.455 Hirtella_pilosissima 0 0 0.1 3 0.1 0.358 Hirtella_racemosa 0 0 0.03 4 0.05 0.914 Hirtella_triandra 0.2 0 0.1 1 0.08 0.756 Homalium_racemosum 0 0 0.03 3 0.03 1 Huberodendron_swietenioides 0 0 0 4 0.07 0.458 Huertea_glandulosa 0.07 0 0.28 4 0.18 0.327 Humiriastrum_diguense 0 0.33 0 2 0.33 0.039 Hura_crepitans 0 0.33 0 2 0.22 0.118 Hydrochorea_corymbosa 0.4 0 0.18 1 0.32 0.062 Hymenaea_oblongifolia 0.47 0.33 0.21 1 0.22 0.227 Hymenolobium_heterocarpum 0 0 0.03 3 0.03 1 Hyperacanthus_manolo 0 0 0.05 3 0.05 0.685 Hyperacanthus_wilson 0 0 0.05 3 0.05 0.452 Ilex_inundata 0.13 0 0.15 3 0.09 0.676 Ilex_laureola 0.07 0.33 0 2 0.28 0.081 Inga_1964 0 0 0.03 3 0.03 1 Inga_2123 0.07 0 0 1 0.07 0.457 Inga_2parsinala 0.07 0 0 1 0.07 0.459 Inga_3oscura 0 0 0.21 3 0.21 0.122 Inga_6cuadra_aff._gracilifolia 0.13 0.33 0.46 3 0.23 0.248 Inga_acreana 0.27 0.33 0.38 4 0.16 0.715 Inga_acrocephala 0.07 0 0 1 0.07 0.462 Inga_acrocephala/rusbyi_cf. 0 0 0.08 3 0.08 0.46 Inga_acuminata 0.07 0 0.13 3 0.1 0.503 Inga_alata 0.2 0 0.38 3 0.24 0.214 Inga_alavelu 0.07 0 0 1 0.07 0.463 Inga_alba 0.2 0.33 0.36 3 0.11 0.879 Inga_aliena 0 0 0.03 3 0.03 1 Inga_auristellae 0.53 0 0.36 1 0.31 0.141 Inga_bourgonii 0 0 0.13 3 0.13 0.335 Inga_brachyrhachis 0.27 0 0.08 1 0.16 0.247 Inga_capitata 0 0 0.54 3 0.36 0.074 Inga_cayennensis 0 0 0.18 4 0.13 0.401 Inga_chartacea 0.07 0 0.21 3 0.15 0.314 Inga_ciliata 0 0 0.21 3 0.13 0.419 Inga_cinnamomea 0 0 0.15 3 0.15 0.278 Inga_cordatoalata 0.27 0 0.26 1 0.15 0.431 Inga_coruscans 0.2 0 0.33 3 0.19 0.278 Inga_cylindrica 0 0 0.03 3 0.03 1 Inga_delgadaoerst 0.07 0 0 1 0.07 0.449

Inga_densiflora 0 0 0 4 0.07 0.462 Inga_edulis 0 0 0.15 3 0.07 0.776 Inga_glomeriflora 0 0 0.1 3 0.1 0.383 Inga_gracilior 0.07 0 0.03 4 0.08 0.564 Inga_heterophylla 0 0 0.05 3 0.05 0.45 Inga_ilta 0 0 0.15 3 0.15 0.266 Inga_indet 0 0 0.03 3 0.03 1 Inga_laurina 0 0 0.05 4 0.07 0.543 Inga_leiocalycina 0.2 0 0.46 3 0.36 0.081 Inga_lenticelosa 0.07 0 0 1 0.07 0.457 Inga_marginata 0.07 0 0.59 3 0.31 0.12 Inga_multijuga 0.07 0 0.08 1 0.05 0.802 Inga_multinervis 0 0 0.18 4 0.32 0.065 Inga_nobilis 0.2 0 0.23 4 0.17 0.423 Inga_obidensis 0 0 0.03 3 0.03 1 Inga_oerstediana 0.07 0 0.33 3 0.16 0.393 Inga_paraensis 0.07 0 0 1 0.07 0.465 Inga_pavonis 0 0 0.03 4 0.06 0.397 Inga_pezizifera 0 0 0.03 4 0.2 0.125 Inga_poeppigiana 0.13 0 0.13 1 0.05 0.955 Inga_pruriens 0 0 0.08 3 0.08 0.466 Inga_psittacorum 0 0 0.03 3 0.03 1 Inga_punctata 0 0.33 0.18 2 0.13 0.485 Inga_ruiziana 0.13 0 0.56 3 0.26 0.274 Inga_rusbyi 0.47 1 0.23 2 0.5 0.013 Inga_sapindoides 0 0 0.1 3 0.1 0.347 Inga_sarayacuensis 0.07 0 0.05 1 0.03 0.952 Inga_sarmentosa 0 0 0.03 3 0.03 1 Inga_semialata 0 0 0.05 3 0.05 0.772 Inga_sertulifera 0 0 0.26 3 0.16 0.273 Inga_sp_1 0.07 0 0 4 0.12 0.248 Inga_sp_10 0.07 0 0 1 0.07 0.462 Inga_sp_12 0 0 0.03 3 0.03 1 Inga_sp_13 0 0 0.03 3 0.03 1 Inga_sp_14 0 0 0.03 3 0.03 1 Inga_sp_2 0.07 0 0 1 0.03 1 Inga_sp_3 0.07 0 0 1 0.03 1 Inga_sp_4 0.07 0 0 1 0.03 1 Inga_sp_5 0.07 0 0 4 0.05 0.77 Inga_sp_6 0 0 0 4 0.07 0.459 Inga_spectabilis 0.07 0 0.23 3 0.19 0.193 Inga_splendens 0 0 0.23 3 0.23 0.087 Inga_stenoptera 0.07 0 0.1 1 0.05 0.809

Inga_stenoptera/striolata_cf. 0.27 0 0.13 1 0.18 0.211 Inga_stipulacea 0 0 0.05 3 0.05 0.453 Inga_striata/venusta_aff. 0 0 0.03 3 0.03 1 Inga_striolata 0.13 0 0 1 0.13 0.168 Inga_suaveolens 0.13 0 0.26 3 0.13 0.581 Inga_tenuistipula 0 0 0.21 3 0.21 0.12 Inga_tessmannii 0.07 0 0.08 4 0.25 0.046 Inga_thibaudiana 0.33 0 0.36 4 0.21 0.402 Inga_umbellifera 0.33 0 0.33 3 0.21 0.289 Inga_umbratica 0.13 0 0.28 3 0.15 0.474 Inga_velutina 0.27 0 0.23 1 0.16 0.368 Inga_venusta 0.07 0 0 1 0.07 0.459 Inga_vera 0.07 0 0 1 0.07 0.461 Inga_vismiifolia 0 0 0.21 3 0.1 0.712 Inga_yacoana 0 0 0.23 3 0.23 0.092 Inga_yarina 0.07 0 0 1 0.07 0.463 Inga_yasuniana 0 0 0.03 4 0.04 1 Iriartea_deltoidea 0.27 0 0.79 3 0.53 0.022 Iryanthera_crassifolia 0.07 0.67 0 2 0.45 0.014 Iryanthera_grandis 0.07 0 0.13 4 0.08 0.732 Iryanthera_hostmannii 0.6 0.33 0.18 1 0.25 0.194 Iryanthera_indet 0 0 0.03 3 0.03 1 Iryanthera_juruensis 0.6 0 0.62 1 0.27 0.338 Iryanthera_laevis 0.4 0 0.1 1 0.32 0.058 Iryanthera_lancifolia 0.4 0.33 0.03 1 0.17 0.282 Iryanthera_macrophylla 0.07 0.33 0 2 0.14 0.123 Iryanthera_tessmannii 0.4 0 0 1 0.11 0.437 Iryanthera_tsuirim 0 0 0 4 0.07 0.456 Iryanthera_ulei 0.07 0 0 1 0.07 0.459 Isertia_rosea 0.07 0 0.08 3 0.05 0.914 Ixora_killipii 0.07 0 0.05 4 0.07 0.637 Jacaranda_copaia 0.2 0 0.26 4 0.11 0.747 Jacaranda_glabra 0 0 0.03 3 0.03 1 Jacaratia_digitata 0 0 0.41 3 0.25 0.173 Jacaratia_spinosa 0 0 0.03 4 0.25 0.055 Klarobelia_napoensis 0 0 0.23 3 0.23 0.059 Kutchubaea_semisericea 0 0 0.1 3 0.1 0.291 Kutchubaea_sericantha 0 0.67 0 2 0.67 0.001 Lacistema_aggregatum 0.07 0.33 0.08 2 0.12 0.355 Lacistema_indet 0 0 0.03 3 0.03 1 Lacistema_med 0 0 0.1 3 0.07 0.637 Lacistema_nena 0.13 0 0.21 3 0.1 0.714 Lacmellea_floribunda 0.07 0 0.13 1 0.04 1

Lacmellea_lactescens 0 0 0.21 3 0.13 0.396 Lacmellea_oblongata 0.07 0 0 1 0.07 0.451 Lacmellea_tsuirim 0 0 0 4 0.07 0.46 Lacunaria_crenata 0.13 0 0.1 1 0.09 0.542 Lacunaria_green 0 0 0.05 3 0.05 0.447 Lacunaria_jenmanii 0.13 0 0 1 0.13 0.17 Ladenbergia_acutifolia 0 0.33 0 2 0.33 0.04 Ladenbergia_amazonensis 0 0.33 0 2 0.33 0.044 Ladenbergia_muzonensis 0 0.33 0 2 0.33 0.043 Ladenbergia_oblongifolia 0 0 0 4 0.13 0.168 Laetia_procera 0.33 0 0.36 3 0.2 0.321 Laetia_suaveolens 0.07 0 0 1 0.07 0.452 Lafoensia_acuminata 0 0 0.03 3 0.03 1 Laxoplumeria_tessmannii 0 0 0.08 3 0.08 0.466 Lecointea_amazonica 0 0 0.03 3 0.03 1 Lecointea_peruviana 0 0 0.13 3 0.09 0.502 Lecythis_oval 0 0.33 0 2 0.21 0.085 Lecythis_rios-daza 0 0 0.1 3 0.1 0.295 Lecythis_tsuirim 0 0 0 4 0.07 0.451 Lecythis_zabucajo 0.07 0 0.03 1 0.05 0.913 Leonia_crassa 0 0 0.56 3 0.47 0.024 Leonia_glycycarpa 0.27 0 0.49 4 0.3 0.169 Leonia_indet 0 0 0.03 3 0.03 1 Leonia_racemosa 0.07 0 0.1 1 0.05 0.93 Licania_subcordata 0 0.33 0 2 0.33 0.044 Licania_2296 0.07 0 0 1 0.07 0.452 Licania_255 0.07 0 0 1 0.07 0.459 Licania_apetala 0.27 0 0 1 0.21 0.109 Licania_arborea 0 0 0.05 4 0.04 0.854 Licania_bitterlime 0 0 0.03 3 0.03 1 Licania_bracteo-lanuginosa 0.13 0 0 1 0.13 0.168 Licania_brittoniana 0.4 0 0.33 1 0.26 0.139 Licania_canescens 0.07 0 0 1 0.07 0.457 Licania_caudata 0.07 0 0.08 3 0.04 0.835 Licania_cuyabenensis 0.13 0 0.1 1 0.11 0.437 Licania_durifolia 0.2 0 0.18 3 0.1 0.76 Licania_egleri 0 1 0 2 1 0 Licania_elliptica 0 0 0.1 3 0.1 0.311 Licania_granvillei 0.27 0 0.03 1 0.25 0.058 Licania_guianensis 0.07 0 0.1 4 0.03 0.992 Licania_harlingii 0.33 0 0.38 3 0.12 0.89 Licania_heteromorpha 0.2 0 0 1 0.15 0.185 Licania_hypoleuca 0.33 0 0 1 0.21 0.109

Licania_indet 0 0 0.03 3 0.03 1 Licania_intrapetiolaris 0.07 0 0 1 0.07 0.464 Licania_kaputna 0 0.33 0 2 0.33 0.038 Licania_krukovii 0 0 0.05 3 0.05 0.45 Licania_kunthiana 0 0.33 0 2 0.33 0.041 Licania_lata 0.07 0 0.1 3 0.06 0.812 Licania_latexrojo 0 0 0.03 3 0.03 1 Licania_latifolia 0 0 0.05 3 0.05 0.457 Licania_leite-cerosa_canescens 0.13 0 0 1 0.1 0.266 Licania_longipedicellata 0.07 0 0.03 1 0.06 0.393 Licania_longistyla 0.07 0 0.1 4 0.05 0.918 Licania_macrocarpa 0.13 0 0.1 1 0.07 0.718 Licania_micrantha 0.13 0 0 1 0.09 0.332 Licania_octandra 0.27 0 0.1 1 0.24 0.093 Licania_pallida 0.13 0.33 0.03 2 0.25 0.068 Licania_reticulata 0.13 0 0.1 3 0.06 0.817 Licania_silvae 0 0 0.13 3 0.13 0.326 Licania_sothersiae 0.07 0 0 1 0.07 0.467 Licania_sp_1 0 0 0.03 4 0.06 0.392 Licania_triandra 0 0.33 0 2 0.33 0.045 Licania_unguiculata 0.13 0 0 1 0.13 0.164 Licania_urceolaris 0.33 0 0.08 1 0.28 0.064 Licania_velutina 0.07 0 0.1 3 0.06 0.821 Licania_yasuni 0 0 0.03 3 0.03 1 Licaria_angosta 0.07 0 0 1 0.07 0.456 Licaria_aurea 0.13 0 0 1 0.13 0.16 Licaria_cannella 0.2 0 0.26 3 0.14 0.433 Licaria_chiquita 0.07 0 0 1 0.07 0.455 Licaria_exserta 0 0 0.03 3 0.03 1 Licaria_guianensis 0.33 0.33 0.31 3 0.11 0.832 Licaria_triandra 0.13 0 0.13 3 0.06 0.942 Lindackeria_indet 0 0 0.03 3 0.03 1 Lindackeria_paludosa 0.07 0 0.21 3 0.14 0.442 Lissocarpa_stenocarpa 0 0 0 4 0.07 0.46 Lonchocarpus_seorsus 0 0 0.46 3 0.4 0.035 Tovomita callophylophylla 0 0.33 0 2 0.33 0.041 Lozania_indet 0 0 0.03 3 0.03 1 Luehea_cymulosa 0.27 0 0.1 1 0.2 0.139 Luehea_deciduous 0 0 0.03 3 0.03 1 Luehea_white 0.07 0 0.03 1 0.06 0.395 Lueheopsis_hoehnei 0.07 0 0 1 0.07 0.455 Lueheopsis_rosea 0.07 0 0 1 0.07 0.455 Lunania_parviflora 0 0 0.18 4 0.18 0.184

Mabea_acutissima 0 0.67 0 2 0.67 0.001 Mabea_indet 0 0 0.03 3 0.03 1 Mabea_klugii 0 0 0.13 4 0.1 0.605 Mabea_macbridei 0.2 0 0 1 0.19 0.102 Mabea_maynensis 0 0 0.03 3 0.03 1 Mabea_nitida 0.27 0 0.1 1 0.2 0.163 Mabea_occidentalis 0 0 0.05 3 0.04 0.948 Mabea_piriri 0.4 0 0.36 1 0.2 0.3 Mabea_speciosa 0 0 0.08 3 0.06 0.627 Mabea_subsessilis 0 0.67 0 2 0.67 0.002 Machaerium_1 0 0 0.03 3 0.03 1 Machaerium_aristulatum 0 0 0.03 3 0.03 1 Machaerium_floribundum 0.13 0 0.15 3 0.09 0.668 Machaerium_leiophyllum 0.07 0 0.03 1 0.06 0.397 Maclura_tinctoria 0 0 0.05 3 0.05 0.461 Macoubea_guianensis 0.07 0 0 1 0.07 0.453 Macrolobium_acaciifolium 0.27 0 0.08 1 0.24 0.075 Macrolobium_angustifolium 0.27 0 0.28 3 0.2 0.29 Macrolobium_archeri 0.07 0 0.23 3 0.21 0.108 Macrolobium_colombianum 0 0 0.03 3 0.03 1 Macrolobium_gracile 0.27 1 0.1 2 0.6 0.003 Macrolobium_ischnocalyx 0.13 0 0.15 3 0.1 0.646 Macrolobium_limbatum 0.27 0.67 0.08 2 0.23 0.089 Macrolobium_microcalyx 0.13 0 0 1 0.13 0.171 Macrolobium_multijugum 0.13 0 0 1 0.12 0.188 Macrolobium_novel 0 0 0.03 3 0.03 1 Macrolobium_stenocladum 0 0 0.03 3 0.03 1 Macrolobium_suaveolens 0 1 0 2 1 0 Macrolobium_yasuni 0 0 0.05 3 0.05 0.77 Magnolia_rimachii 0 0 0 4 0.27 0.043 Malouetia_flavescens 0 0 0.03 4 0.06 0.388 Manilkara_inundata 0.07 0 0 1 0.07 0.455 Maquira_calophylla 0.07 0 0.54 3 0.45 0.027 Maquira_guianensis 0.2 0 0.1 1 0.11 0.573 Margaritaria_nobilis 0.13 0 0.54 3 0.44 0.029 Marila_alternifolia 0.07 0 0.05 4 0.13 0.366 Marila_grandiflora 0 0 0.05 3 0.05 0.78 Marila_indet 0 0 0.03 3 0.03 1 Marila_laxiflora 0 0 0.03 3 0.03 1 Marila_myrsinac 0 0 0.08 3 0.08 0.479 Marila_pluricostata 0.07 0 0 1 0.07 0.459 Marila_tomentosa 0 0 0.13 3 0.13 0.324 Marlierea_blancanueva 0.07 0 0.03 1 0.04 1

Marlierea_schomburgkiana 0 0 0.05 3 0.05 0.677 Marlierea_sp 0.07 0 0 1 0.07 0.453 Marlierea_subulata 0.07 0 0 1 0.07 0.462 Matayba_adenanthera 0 0 0.03 4 0.05 0.913 Matayba_guianensis 0 0.67 0 2 0.67 0.001 Matayba_indet 0 0 0.03 3 0.03 1 Matayba_macrostylis 0.07 0 0 1 0.07 0.465 Matayba_peruviana 0 0 0 4 0.07 0.46 Matayba_purgans 0.07 0.33 0.03 2 0.26 0.099 Matisia_arteagensis 0 0 0.03 3 0.03 1 Matisia_bracteolosa 0.33 0 0.46 3 0.27 0.215 Matisia_cordata 0.07 0 0.31 3 0.27 0.084 Matisia_idroboi 0.07 0 0.18 4 0.06 0.961 Matisia_indet 0 0 0.03 3 0.03 1 Matisia_lasiocalyx 0.13 0 0.03 4 0.31 0.058 Matisia_lomensis 0.07 0 0.18 3 0.11 0.533 Matisia_longiflora 0 0 0.36 3 0.26 0.158 Matisia_malacocalyx 0.47 0 0.56 3 0.35 0.153 Matisia_obliquifolia 0 0 0.56 3 0.51 0.018 Matisia_ochrocalyx 0.07 0 0 4 0.19 0.081 Mauritia_flexuosa 0.13 0 0.1 3 0.08 0.66 Mauritiella_armata 0 0 0.03 3 0.03 1 Mayna_parvifolia 0 0 0.05 3 0.05 0.447 Maytenus_amazonica 0.13 0 0 1 0.12 0.199 Maytenus_cuero 0.07 0 0 1 0.07 0.455 Maytenus_guyanensis 0 0 0.05 4 0.04 0.853 Maytenus_indet 0 0 0.03 3 0.03 1 Maytenus_macrocarpa 0.13 0 0.21 3 0.09 0.883 Maytenus_peking 0 0 0.03 3 0.03 1 Melicoccus_novogranatensis 0.13 0 0.33 3 0.25 0.139 Melicoccus_oliviformis 0.07 1 0 2 0.98 0 Meliosma_dudosa 0 0.33 0 2 0.33 0.041 Meliosma_herbertii 0.13 0 0.28 3 0.18 0.247 Meliosma_indet 0 0 0.03 3 0.03 1 Meliosma_loretoyacuensis 0.2 0 0.03 1 0.09 0.578 Meliosma_palustris 0.07 0 0 4 0.06 0.472 Meliosma_polyneura 0 0 0.08 3 0.08 0.482 Meliosma_vasquezii 0 0 0.08 3 0.08 0.483 Memora_cladotricha 0.2 0 0.03 1 0.14 0.296 Metteniusa_tessmanniana 0 0 0.08 3 0.08 0.468 Mezilaurus_campaucola 0 0.67 0 2 0.67 0.001 Mezilaurus_itauba 0.07 0 0 1 0.07 0.461 Mezilaurus_opaca 0.07 0 0 1 0.03 1

Mezilaurus_retic 0.07 0 0 1 0.07 0.465 Mezilaurus_sprucei 0.07 0 0 1 0.07 0.462 Mezilaurus_triunca 0 0 0.13 3 0.13 0.32 Miconia_1 0 0 0.08 3 0.08 0.405 Miconia_2192 0.07 0 0 1 0.07 0.463 Miconia_2231 0.07 0 0 1 0.07 0.457 Miconia_2255 0.07 0 0 1 0.07 0.465 Miconia_242 0.07 0 0 1 0.07 0.45 Miconia_abbreviata 0.07 0 0.03 1 0.05 0.912 Miconia_ampla 0.07 0 0 1 0.07 0.448 Miconia_bubalina 0.07 0 0.05 4 0.04 0.919 Miconia_cazal 0 0 0 4 0.07 0.462 Miconia_cazaletii 0.07 0 0.03 1 0.06 0.391 Miconia_centrodesma 0 0 0.03 3 0.03 1 Miconia_cernua 0 0 0.03 3 0.03 1 Miconia_decurrens 0 0 0.08 3 0.08 0.486 Miconia_elata 0.07 0 0.26 3 0.18 0.189 Miconia_estrobico 0 0 0.03 3 0.03 1 Miconia_flask 0 0 0.03 3 0.03 1 Miconia_france 0 0 0.05 3 0.05 0.686 Miconia_glaucescens 0.13 0 0.03 1 0.08 0.556 Miconia_grandifolia 0 0 0.15 3 0.15 0.23 Miconia_hylophila 0 0 0 4 0.13 0.165 Miconia_indet 0 0 0.03 3 0.03 1 Miconia_insularis 0 0.67 0 2 0.61 0.003 Miconia_kallesp16 0 0 0.1 3 0.1 0.29 Miconia_klugii 0.07 0 0 1 0.07 0.469 Miconia_lamprophylla 0.07 0 0 4 0.07 0.47 Miconia_longifolia 0.07 0.33 0 2 0.28 0.081 Miconia_matthaei 0 0 0.03 3 0.03 1 Miconia_minutiflora 0 0.33 0 2 0.33 0.042 Miconia_multispicata 0.07 0 0.15 3 0.11 0.491 Miconia_napoana 0 0 0.21 3 0.15 0.36 Miconia_oval 0 0 0.1 3 0.1 0.391 Miconia_pilgeriana 0.13 0 0.13 3 0.07 0.75 Miconia_prasina 0.07 0 0 1 0.07 0.466 Miconia_pterocaulon 0.07 0 0.03 1 0.05 0.91 Miconia_punctata 0.27 0 0.23 1 0.12 0.586 Miconia_rust 0 0 0.03 3 0.03 1 Miconia_saramago 0 0 0.03 3 0.03 1 Miconia_splendens 0.27 0 0.05 1 0.24 0.099 Miconia_subspicata 0 0 0.03 3 0.03 1 Miconia_symplectocaulos 0 0 0.03 3 0.03 1

Miconia_ternatifolia 0 0 0.03 3 0.03 1 Miconia_tipica5ha 0 0 0.13 3 0.13 0.319 Miconia_trinervia 0 0 0.1 4 0.09 0.475 Miconia_turku 0 0 0.03 3 0.03 1 Miconia_white 0 0 0.08 3 0.08 0.466 Micrandra_elata 0 0 0 4 0.07 0.459 Micrandra_rossiana 0 0 0 4 0.07 0.454 Micrandra_spruceana 0 1 0 2 1 0 Micropholis__2452 0.07 0 0 4 0.05 0.485 Micropholis_brochidodroma 0.13 0.67 0.08 2 0.49 0.015 Micropholis_casiquiarensis 0 0 0 4 0.07 0.458 Micropholis_egensis 0.2 0.33 0.33 2 0.15 0.532 Micropholis_guyanensis 0.33 0.33 0.31 1 0.15 0.592 Micropholis_indet 0 0 0.03 3 0.03 1 Micropholis_melinoniana 0.13 0 0.13 4 0.18 0.223 Micropholis_sanctae-rosae 0.13 0 0 1 0.13 0.164 Micropholis_venulosa 0.4 0.33 0.59 3 0.26 0.258 Minquartia_guianensis 0.4 0 0.59 4 0.29 0.235 Mollia_gracilis 0 0 0.15 3 0.15 0.233 Mollia_lepidota 0 0 0.1 3 0.1 0.316 Mollinedia_ovata 0 0 0.08 3 0.08 0.479 Mollinedia_panther 0 0 0.05 3 0.05 0.456 Mollinedia_repanda 0 0 0 4 0.13 0.16 Mollinedia_tomentosa 0 0 0.03 3 0.03 1 Moronobea_coccinea 0.07 0.33 0 2 0.28 0.083 Mosannona_papillosa 0 0 0.03 3 0.03 1 Mouriri_2454 0.07 0 0 1 0.07 0.449 Mouriri_acutiflora 0.2 0 0.05 1 0.17 0.148 Mouriri_grandiflora 0.07 0 0.08 3 0.04 1 Mouriri_huberi 0.07 0 0 4 0.09 0.311 Mouriri_icarus 0 0 0.03 3 0.03 1 Mouriri_laxiflora 0.13 0 0.08 1 0.11 0.383 Mouriri_myrtifolia 0.07 0 0 1 0.07 0.458 Mouriri_myrtilloides 0.2 0 0.1 1 0.15 0.276 Mouriri_nigra 0.4 0 0.13 1 0.32 0.062 Mouriri_oligantha 0 0 0.03 3 0.03 1 Mouriri_vernicosa 0.2 0 0.03 1 0.15 0.249 Moutabea_aculeata 0 0 0.03 3 0.03 1 Myrcia_aliena 0 0 0.15 3 0.15 0.237 Myrcia_chiquita 0.07 0 0 1 0.07 0.456 Myrcia_guianensis 0.07 0 0 1 0.07 0.456 Myrcia_indet 0 0 0.03 3 0.03 1 Myrcia_obumbrans 0.07 0 0.05 1 0.03 0.951

Myrcia_portuguese 0 0 0.03 3 0.03 1 Myrcia_ridgely 0 0 0.13 3 0.13 0.325 Myrcia_splendens 0 0 0.08 3 0.08 0.465 Myrcia_walter 0 0 0.03 3 0.03 1 Myrciaria_amazonica 0 0 0.03 3 0.03 1 Myrciaria_dubia 0.2 0 0 1 0.2 0.131 Myrciaria_dubius 0.13 0 0 1 0.13 0.158 Myrciaria_floribunda 0.07 0 0.08 3 0.04 0.839 Myrciaria_sp1 0.07 0 0 4 0.04 0.767 Myroxylon_balsamum 0 0 0.08 4 0.15 0.271 Naucleopsis_concinna 0.07 0.33 0 2 0.17 0.194 Naucleopsis_glabra 0.33 0 0.23 4 0.25 0.182 Naucleopsis_herrerensis 0.33 0 0.15 1 0.19 0.198 Naucleopsis_humilis 0 0 0.03 3 0.03 1 Naucleopsis_imitans 0.2 0 0.13 1 0.14 0.435 Naucleopsis_indet 0 0 0.03 3 0.03 1 Naucleopsis_krukovii 0.13 0 0.44 3 0.31 0.115 Naucleopsis_oblongifolia 0.47 0 0 1 0.45 0.022 Naucleopsis_ternstroemiiflora 0.13 0 0 1 0.13 0.168 Naucleopsis_ulei 0.13 0 0.41 4 0.19 0.387 Nealchornea_yapurensis 0.2 0 0.03 4 0.15 0.261 Nectandra_242 0.07 0 0 1 0.07 0.457 Nectandra_75 0.07 0 0.03 1 0.06 0.39 Nectandra_anib 0.07 0 0 1 0.07 0.448 Nectandra_canescens 0.07 0 0.08 1 0.05 0.763 Nectandra_cissiflora 0.27 0 0.26 1 0.14 0.556 Nectandra_coeloclada 0.13 0 0.1 1 0.07 0.611 Nectandra_crasa 0.07 0 0 1 0.07 0.455 Nectandra_crassiloba 0.2 0.33 0.33 3 0.17 0.341 Nectandra_cuneatocordata 0 0 0.05 3 0.05 0.456 Nectandra_fragrans 0 0.33 0 2 0.33 0.04 Nectandra_gracilis 0.13 0 0.08 1 0.08 0.521 Nectandra_indet 0 0 0.03 3 0.03 1 Nectandra_lineata 0.07 0 0.03 4 0.17 0.148 Nectandra_lineatifolia 0 0 0.03 4 0.12 0.224 Nectandra_longiretic 0.07 0 0 1 0.07 0.458 Nectandra_matthewsii 0.07 0 0 1 0.07 0.464 Nectandra_maynensis 0.13 0 0 1 0.13 0.165 Nectandra_membranacea 0.13 0.33 0.13 2 0.15 0.326 Nectandra_oppositifolia 0.07 0 0.1 3 0.07 0.629 Nectandra_opub 0 0 0.03 3 0.03 1 Nectandra_parviflora 0 0 0.05 3 0.05 0.454 Nectandra_paucinervia 0.07 0 0.26 3 0.24 0.081

Nectandra_pearcei 0 0 0.13 4 0.13 0.338 Nectandra_purpurea 0.07 0 0 1 0.07 0.458 Nectandra_reticulata 0.07 0 0.08 4 0.14 0.371 Nectandra_roja 0 0 0 4 0.07 0.463 Nectandra_rojaretic 0.13 0 0 1 0.13 0.161 Nectandra_sp1 0.07 0 0 1 0.07 0.47 Nectandra_sp2 0.07 0 0 1 0.07 0.461 Nectandra_viburnoides 0 0 0.1 3 0.08 0.586 Neea_“kaputna” 0 0.33 0 2 0.33 0.041 Neea_188 0 0 0.03 3 0.03 1 Neea_bajio 0.07 0 0.05 1 0.04 0.85 Neea_chala 0.07 0 0 1 0.07 0.465 Neea_común_23 0.27 0 0.46 3 0.24 0.22 Neea_crasa_1922 0.13 0 0.03 1 0.1 0.288 Neea_divaricata 0.13 0 0.1 1 0.09 0.761 Neea_falsagarci_ 0.13 0 0.03 1 0.12 0.224 Neea_fuzzy 0 0 0.05 3 0.05 0.677 Neea_garci 0.07 0 0 1 0.03 1 Neea_grancrasa 0.07 0 0 1 0.07 0.458 Neea_green 0.13 0 0.28 3 0.15 0.365 Neea_indet 0 0 0.03 3 0.03 1 Neea_laxa 0.13 0 0 1 0.07 0.567 Neea_macrophylla 0.07 0 0 1 0.04 0.772 Neea_mini_2223 0.07 0 0 1 0.07 0.455 Neea_pantano 0 0 0.03 3 0.03 1 Neea_psychotrioides 0.07 0 0 4 0.31 0.019 Neea_pubcafeintersec_186/_Guapira_cuspi 0.2 0 0.03 1 0.17 0.17 data Neea_pubiroja 0.07 0 0 1 0.07 0.451 Neea_sp 0.13 0 0 1 0.13 0.157 Neea_sp1 0.07 0 0 1 0.07 0.455 Neea_spruceana 0.07 0 0.33 3 0.24 0.148 Neea_stilted 0.07 0 0.05 4 0.04 0.829 Neea_superbrochi_2211 0.07 0 0 1 0.07 0.453 Neea_supercrasa 0.07 0 0.05 1 0.03 0.948 Neea_verde_1839 0 0 0.03 3 0.03 1 Neea_verticillata 0 0 0.05 4 0.04 0.85 Neea_virens 0 0 0 4 0.07 0.462 Neoptychocarpus_killipii 0.07 0 0 1 0.07 0.468 Neosprucea_grandiflora 0 0 0.13 3 0.09 0.518 Ochroma_pyramidale 0 0 0.08 3 0.08 0.477 Ocotea_aciphylla 0.53 0.33 0.05 1 0.36 0.054 Ocotea_amazonica 0.07 0 0.08 4 0.22 0.098

Ocotea_argyrophylla 0.47 0.33 0.15 1 0.26 0.119 Ocotea_bofo 0.07 0 0.05 1 0.05 0.889 Ocotea_cernua 0.07 0 0.21 4 0.16 0.32 Ocotea_cujumary 0.13 0 0 1 0.13 0.171 Ocotea_cuneifolia 0.13 0 0.05 1 0.12 0.284 Ocotea_floribunda 0.27 0 0.13 1 0.18 0.205 Ocotea_hirtostyla 0.07 0 0.03 1 0.02 1 Ocotea_indet 0 0 0.03 3 0.03 1 Ocotea_javitensis 0.07 0.33 0.31 4 0.11 0.832 Ocotea_laurita 0 0 0.05 3 0.05 0.453 Ocotea_lenitae 0.07 0 0 1 0.07 0.455 Ocotea_leucoxylon 0.2 0 0.1 4 0.14 0.37 Ocotea_lisagroovy 0.07 0 0.21 3 0.18 0.192 Ocotea_longifolia 0.07 0 0.05 4 0.13 0.322 Ocotea_neblinae 0 0.67 0 2 0.67 0.001 Ocotea_negripuberula 0 0.33 0 2 0.33 0.043 Ocotea_oblonga 0.2 0 0.13 1 0.12 0.409 Ocotea_obovata 0.07 0 0.03 3 0.02 1 Ocotea_olivacea 0.07 0 0.03 1 0.05 0.633 Ocotea_opaca 0 0 0.03 3 0.03 1 Ocotea_ovalifolia 0.07 0 0 1 0.07 0.469 Ocotea_peqsericea 0.27 0 0.23 1 0.12 0.505 Ocotea_quixos 0.13 0.67 0.13 2 0.36 0.034 Ocotea_ramacanalada 0 0 0.03 4 0.12 0.185 Ocotea_scalariformis 0.07 0 0.05 1 0.04 0.852 Ocotea_sp_1__1851 0 0 0.03 3 0.03 1 Ocotea_sp_2__236 0.07 0 0 1 0.07 0.461 Ocotea_sp_3 0.13 0 0 1 0.13 0.163 Ocotea_sp_4 0.07 0 0 1 0.07 0.461 Ocotea_sp_nov_ceano 0 0.33 0 2 0.33 0.041 Ocotea_tessmannii 0.13 0 0.05 1 0.08 0.504 Ocotea_ucayalensis 0.07 0.33 0.13 2 0.17 0.185 Oenocarpus_bataua 0.67 0 0.67 1 0.29 0.412 Oenocarpus_kampa 0 0.33 0 2 0.33 0.043 Oenocarpus_mapora 0.07 0.33 0.03 2 0.13 0.256 Ophiocaryon_heterophyllum 0 0 0 4 0.13 0.164 Ophiocaryon_manausense 0.13 0 0 1 0.13 0.166 Ormosia_amazonica 0.07 0 0.05 1 0.02 1 Ormosia_elata 0.07 0 0.1 3 0.03 1 Ormosia_grandiflora 0 0 0.03 3 0.03 1 Ormosia_kaputna 0 0.67 0 2 0.67 0.002 Ormosia_paraensis 0.13 0 0.05 1 0.12 0.285 Osteophloeum_platyspermum 0.4 1 0.15 2 0.49 0.018

Otoba_glycycarpa 0.33 0 0.69 3 0.53 0.035 Otoba_parvifolia 0.13 0 0.62 3 0.26 0.293 Ouratea_amplifolia 0.07 0 0.08 1 0.05 0.733 Ouratea_aromatica 0 0 0.03 3 0.03 1 Ouratea_crasa-serrulada 0.2 0 0.03 1 0.18 0.187 Ouratea_pendula 0 0 0.05 3 0.03 0.949 Ouratea_williamsii 0.07 0 0 1 0.07 0.467 Oxandra_dull 0.07 0 0 1 0.07 0.456 Oxandra_euneura 0.33 0 0 1 0.3 0.02 Oxandra_iromenga 0 0 0.03 3 0.03 1 Oxandra_mediocris 0.13 0 0.23 3 0.13 0.417 Oxandra_riedeliana 0 0 0.21 3 0.21 0.111 Oxandra_xylopioides 0.2 0 0.03 4 0.14 0.346 Pachira_aquatica 0.07 0 0.03 4 0.1 0.414 Pachira_nitida 0 0.67 0 2 0.67 0.001 Pachira_insignis 0.07 0 0.33 3 0.15 0.499 Pachira_punga-schunkei 0.13 0 0.33 3 0.15 0.6 Palicourea_lasiantha 0 0 0 4 0.07 0.455 Panopsis_rubescens 0.2 0.33 0 2 0.16 0.195 Paradrypetes_subintegrifolia 0 0 0.1 4 0.05 0.781 Parahancornia_peruviana 0 0.33 0 2 0.33 0.04 Parathesis_amazonica 0 0 0.03 3 0.03 1 Parathesis_palaciosii 0 0 0.03 3 0.03 1 Parinari_klugii 0.4 0 0.08 1 0.18 0.232 Parinari_neartin 0.07 0 0.03 1 0.04 0.912 Parinari_parilis 0 0 0.1 3 0.1 0.291 Parkia_balslevii 0.33 0 0.26 1 0.18 0.24 Parkia_igneiflora 0 0.33 0 2 0.33 0.047 Parkia_multijuga 0.13 0.67 0.26 4 0.22 0.247 Parkia_nitida 0.33 0 0.15 1 0.24 0.082 Parkia_panurensis 0.07 0 0 1 0.07 0.458 Parkia_pendula 0.2 0 0 1 0.2 0.104 Parkia_velutina 0.27 0 0.33 3 0.11 0.857 Patinoa_paraensis/sphaerocarpa_cf. 0 0 0.31 4 0.15 0.405 Paullinia_xestophylla 0 0 0.05 3 0.05 0.454 Pausandra_indet 0 0 0.03 3 0.03 1 Pausandra_trianae 0 0 0.36 3 0.25 0.154 Paypayrola_bract 0 0 0 4 0.07 0.456 Pentagonia_amazonica 0 0 0.23 3 0.19 0.173 Pentagonia_indet 0 0 0.03 3 0.03 1 Pentagonia_macrophylla 0 0 0.13 3 0.08 0.788 Pentagonia_pentamera 0 0 0 4 0.07 0.454 Pentagonia_spathicalyx 0 0 0.33 3 0.27 0.096

Pentagonia_wurdackii 0 0 0.03 3 0.03 1 Pentaplaris_huaoranica 0 0 0.31 3 0.31 0.047 Peperomia_hispidula 0 0 0 4 0.07 0.454 Pera_benensis 0 0 0.05 4 0.04 0.855 Pera_bicolor 0.07 0 0 1 0.07 0.45 Pera_decipiens 0 0 0.03 3 0.03 1 Pera_glabrata 0 0 0.03 4 0.05 0.911 Pera_indet 0 0 0.03 3 0.03 1 Pera_kaputna_arborea_cf. 0 0.67 0 2 0.67 0.001 Perebea_246 0.07 0 0 1 0.07 0.457 Perebea_angustifolia 0.13 0 0.15 3 0.07 0.774 Perebea_guianensis 0.27 0 0.56 3 0.21 0.564 Perebea_indet 0 0 0.03 3 0.03 1 Perebea_mollis 0.2 0 0.21 1 0.11 0.603 Perebea_rubra 0.13 0 0 1 0.13 0.162 Perebea_tessmannii 0.07 0 0.23 4 0.16 0.325 Perebea_xanthochyma 0.27 0 0.64 3 0.31 0.146 Persea_838 0.13 0 0 1 0.13 0.172 Persea_areolatocostae 0.07 0 0 1 0.07 0.466 Persea_pseudofasciculata 0 0 0.03 3 0.03 1 Phragmotheca_ecuadorensis 0 0 0.18 3 0.18 0.173 Phyllanthus_attenuatus 0 0 0.03 4 0.13 0.231 Phyllanthus_juglandifolius 0 0 0.03 3 0.03 1 Phytelephas_macrocarpa 0 0 0 4 0.07 0.463 Phytelephas_tenuicaulis 0 0 0.21 4 0.09 0.687 Picramnia_faboideae 0 0 0.03 3 0.03 1 Picramnia_juniniana 0.07 0 0.08 3 0.03 1 Picramnia_latifolia 0 0 0.03 3 0.03 1 Picramnia_magnifolia 0 0 0.03 3 0.03 1 Picramnia_spruceana 0.07 0 0.1 3 0.04 0.854 Piper_bellidifolium 0.07 0 0 1 0.07 0.453 Piper_longicaudatum 0 0 0.03 3 0.03 1 Piper_reticulatum 0 0 0.1 3 0.07 0.71 Piptadenia_pteroclada 0.13 0 0.08 4 0.23 0.104 Platymiscium_stipulare 0.07 0 0.13 4 0.05 0.922 Pleuranthodendron_indet 0 0 0.03 3 0.03 1 Pleuranthodendron_lindenii 0 0 0.33 3 0.17 0.315 Pleurothyrium_616 0 0 0.03 3 0.03 1 Pleurothyrium_aureocordato 0.07 0 0 1 0.07 0.46 Pleurothyrium_bifidum 0 0 0.15 3 0.15 0.263 Pleurothyrium_cinereum 0.07 0 0.05 1 0.03 0.949 Pleurothyrium_cuneifolium 0 0 0.1 3 0.04 0.851 Pleurothyrium_glabrifolium 0.07 0 0 1 0.07 0.458

Pleurothyrium_indet 0 0 0.03 3 0.03 1 Pleurothyrium_insigne 0.13 0 0.15 3 0.08 0.832 Pleurothyrium_parviflorum 0.07 0 0.18 3 0.08 0.696 Pleurothyrium_tomentellum 0 0 0.05 3 0.05 0.772 Pleurothyrium_trianae 0.2 0 0.36 3 0.26 0.125 Pleurothyrium_williamsii 0.07 0 0.08 3 0.06 0.629 Plinia_7 0 0 0.1 3 0.1 0.281 Plinia_almargen 0.07 0 0.03 1 0.05 0.909 Plinia_myro 0.07 0 0 1 0.07 0.452 Plinia_tan 0 0 0.08 3 0.08 0.488 Plinia_unop 0 0 0 4 0.07 0.45 Pogonophora_schomburgkiana 0.07 0 0 1 0.07 0.454 Porcelia_mediocris 0 0 0.1 4 0.08 0.647 Posoqueria_latifolia 0 0 0.18 3 0.14 0.324 Posoqueria_maxima 0 0 0.03 3 0.03 1 Posoqueria_panamensis 0 0 0.13 3 0.13 0.321 Poulsenia_armata 0 0 0.15 4 0.14 0.381 Pourouma_“fina” 0 0.33 0 2 0.33 0.045 Pourouma_acuminata 0 0.33 0 2 0.33 0.042 Pourouma_bicolor 0.53 0.67 0.64 3 0.26 0.371 Pourouma_cecropiifolia 0.07 0 0.33 4 0.23 0.235 Pourouma_cucura 0.33 0 0.03 1 0.3 0.016 Pourouma_deeplob 0 0 0.08 3 0.08 0.41 Pourouma_defiant 0 0 0.03 3 0.03 1 Pourouma_floccosa 0.07 0 0.08 3 0.05 0.812 Pourouma_guianensis 0.2 0 0.26 3 0.09 0.939 Pourouma_indet 0 0 0.03 3 0.03 1 Pourouma_melinonii 0.27 0 0 1 0.24 0.057 Pourouma_minor 0.2 0 0.46 4 0.35 0.108 Pourouma_mollis 0.2 0 0.08 1 0.18 0.131 Pourouma_myrmecophila 0.07 0 0 1 0.07 0.462 Pourouma_napoensis 0.13 0 0.13 4 0.05 0.995 Pourouma_petiolulata 0 0 0.13 4 0.07 0.74 Pourouma_tomentosa 0.33 0 0.54 3 0.27 0.191 Pouteria_226 0.07 0 0 1 0.07 0.457 Pouteria_2424_219_246 0.07 0 0 1 0.07 0.46 Pouteria_564 0.07 0 0.03 1 0.05 0.915 Pouteria_6 0 0 0.08 3 0.08 0.487 Pouteria_angostaloopy 0 0 0.05 3 0.05 0.775 Pouteria_aubrevillei 0.2 0 0.15 1 0.11 0.647 Pouteria_baehniana 0.07 0.33 0.31 2 0.17 0.367 Pouteria_bangii 0.27 0 0.1 1 0.13 0.379 Pouteria_bilocularis 0.2 0 0.21 1 0.1 0.803

Pouteria_bullata 0 0 0.03 3 0.03 1 Pouteria_caimito 0.07 0 0.18 3 0.11 0.586 Pouteria_calistophylla 0.2 0 0.05 1 0.14 0.291 Pouteria_campechiana 0.13 0 0.1 1 0.06 0.93 Pouteria_cladantha 0.13 0 0 1 0.1 0.265 Pouteria_coriacea 0.27 0 0 1 0.27 0.039 Pouteria_cuspidata 0.73 0.33 0.26 1 0.27 0.178 Pouteria_durlandii 0.27 0.33 0.28 1 0.13 0.556 Pouteria_ephedrantha 0.07 0 0.08 1 0.05 0.671 Pouteria_filipes 0.2 0 0.03 1 0.18 0.184 Pouteria_final 0.07 0 0 1 0.07 0.461 Pouteria_glomerata 0.2 0 0.18 1 0.11 0.572 Pouteria_gracilis 0.07 0 0.21 3 0.15 0.378 Pouteria_guianensis 0.13 0 0.23 3 0.13 0.539 Pouteria_hispida 0.27 0 0.18 1 0.18 0.181 Pouteria_indet 0 0 0.03 3 0.03 1 Pouteria_jariensis 0.27 0 0.03 1 0.14 0.223 Pouteria_kanga 0 0 0.03 3 0.03 1 Pouteria_kaputna 0 0.33 0 2 0.33 0.041 Pouteria_krukovii 0.07 0 0.1 3 0.07 0.631 Pouteria_laevigata 0.27 0 0.03 1 0.26 0.078 Pouteria_lobo 0 0 0.18 3 0.18 0.164 Pouteria_macrophylla 0.2 0 0 1 0.18 0.101 Pouteria_membra 0.07 0 0 1 0.07 0.443 Pouteria_minga 0.07 0 0.08 3 0.05 0.776 Pouteria_multiflora 0.07 0 0.31 1 0.05 0.989 Pouteria_nemorosa 0 0 0.03 3 0.03 1 Pouteria_nudipetala 0 0 0.03 3 0.03 1 Pouteria_oblanceolata 0.27 0.33 0.03 1 0.12 0.449 Pouteria_obovpubdorada 0 0 0 4 0.13 0.164 Pouteria_pariry 0 0 0 4 0.07 0.453 Pouteria_pear 0 0 0.03 3 0.03 1 Pouteria_peclargo 0 0 0.03 3 0.03 1 Pouteria_petiolata 0 0 0.13 3 0.13 0.338 Pouteria_petroleo 0 0 0.05 3 0.05 0.45 Pouteria_platyphylla 0.2 0 0.08 4 0.19 0.183 Pouteria_procera 0.4 0 0.1 1 0.34 0.037 Pouteria_procera/sclerocarpa_cf. 0 0 0.05 3 0.05 0.688 Pouteria_procker 0 0 0.03 3 0.03 1 Pouteria_prominulous 0 0 0.03 3 0.03 1 Pouteria_pubescens 0 0 0.08 3 0.08 0.474 Pouteria_pubretic 0.07 0 0 1 0.07 0.465 Pouteria_redondita-retic_2281 0.13 0 0 1 0.13 0.167

Pouteria_reticulata 0.53 0 0.54 1 0.3 0.18 Pouteria_rojita_P1_554 0 0 0 4 0.07 0.458 Pouteria_rostrata 0.27 0 0.18 1 0.15 0.345 Pouteria_sclerocarpa 0.13 0 0 1 0.13 0.164 Pouteria_sericea 0 0 0 4 0.13 0.168 Pouteria_simulans 0.07 0 0 1 0.07 0.458 Pouteria_sp_1 0.07 0 0 1 0.07 0.448 Pouteria_sp_2 0.07 0 0 1 0.07 0.462 Pouteria_sp_3 0.07 0 0 1 0.07 0.452 Pouteria_sp_4 0 0 0.03 3 0.03 1 Pouteria_sp_5 0 0 0.03 3 0.03 1 Pouteria_sp_6 0 0 0.03 3 0.03 1 Pouteria_suela 0 0 0.08 3 0.05 0.769 Pouteria_t-shaped 0.07 0 0.13 3 0.08 0.529 Pouteria_tenuipetiole 0.07 0 0.21 3 0.13 0.339 Pouteria_torta 0 0 0.36 3 0.26 0.141 Pouteria_tortachica 0 0 0.03 3 0.03 1 Pouteria_trilocularis 0.27 0 0.36 3 0.2 0.298 Pouteria_trinervia 0.07 0 0 1 0.07 0.457 Pouteria_unknow 0 0 0.03 3 0.03 1 Pouteria_vernicosa 0.2 0 0.03 4 0.15 0.248 Pradosia_”crasa” 0 0.33 0 2 0.33 0.041 Pradosia_atroviolacea 0.2 0.33 0.28 2 0.13 0.413 Protium_229 0.07 0 0 1 0.04 0.77 Protium_291 0.07 0 0 1 0.07 0.466 Protium_aidanum 0 0 0.41 3 0.41 0.034 Protium_altsonii 0.07 0 0 1 0.07 0.467 Protium_amazonicum 0.6 0 0.44 4 0.39 0.074 Protium_apiculatum 0 0.33 0 2 0.33 0.046 Protium_aracouchini 0.6 0 0.23 1 0.39 0.059 Protium_cuspi 0 0 0 4 0.07 0.454 Protium_dacry 0.07 0 0 1 0.07 0.462 Protium_divaricatum 0.2 0 0 1 0.19 0.103 Protium_gallosum 0 0.67 0 2 0.67 0.001 Protium_glabrescens 0.4 0 0.44 3 0.27 0.162 Protium_guianense 0.2 0 0.05 1 0.17 0.194 Protium_indet 0 0 0.03 3 0.03 1 Protium_kaputna 0 0.33 0 2 0.24 0.082 Protium_meridionale 0 0 0.15 3 0.15 0.266 Protium_miniature 0 0 0.03 3 0.03 1 Protium_nodulosum 0.53 0 0.59 1 0.24 0.489 Protium_opacum 0.07 0 0.1 1 0.06 0.747 Protium_polybotryum 0.2 0 0.03 1 0.13 0.331

100

Protium_puncticulatum 0 0 0.03 3 0.03 1 Protium_rubrum 0.2 0 0.05 1 0.17 0.13 Protium_sagotianum 0.33 0 0.44 3 0.18 0.663 Protium_sect._Icicopsis_sp._nov._ined. 0 0 0.46 3 0.46 0.014 Protium_smooth 0 0 0.05 3 0.05 0.446 Protium_spruceanum 0.13 0 0.03 1 0.13 0.195 Protium_subserratum 0.4 0 0.13 1 0.3 0.077 Protium_tenuifolium 0 0 0.03 3 0.03 1 Protium_trifoliolatum 0.13 0 0.05 1 0.11 0.287 Protium_unifoliolatum 0.07 0 0.03 1 0.06 0.394 Protium_uruts-kunchae 0 0.33 0 2 0.33 0.044 Prunus_237 0.07 0 0 1 0.07 0.455 Prunus_debilis 0 0 0.26 3 0.14 0.362 Pseudobombax_munguba 0.07 0 0.03 1 0.06 0.391 Pseudolmedia_indet 0 0 0.03 3 0.03 1 Pseudolmedia_laevigata 0.6 1 0.54 2 0.36 0.114 Pseudolmedia_laevis 0.47 0 0.69 4 0.22 0.705 Pseudolmedia_macrophylla 0.2 0.33 0.15 2 0.08 0.835 Pseudolmedia_rigida 0.07 0 0.51 3 0.37 0.066 Pseudomalmea_diclina 0.07 0 0.46 3 0.38 0.049 Pseudopiptadenia_suaveolens 0.27 0 0.28 1 0.16 0.382 Pseudoxandra_polyphleba 0.2 0 0.13 1 0.11 0.515 Psidium_acutangulum 0 0 0.05 3 0.05 0.444 Psychotria_crimson 0 0 0.05 3 0.05 0.459 Psychotria_flaviflora 0 0 0.03 3 0.03 1 Psychotria_mathewsii 0.07 0 0 1 0.07 0.466 Pterocarpus__brown 0 0 0.05 3 0.05 0.447 Pterocarpus_228 0.07 0 0 1 0.07 0.453 Pterocarpus_amazonum 0.4 0 0.26 1 0.25 0.13 Pterocarpus_indet 0 0 0.03 3 0.03 1 Pterocarpus_rohrii 0.27 0 0.51 4 0.24 0.357 Pterygota_amazonica 0 0 0 4 0.13 0.163 Qualea_acuminata 0.2 0 0 1 0.2 0.101 Qualea_paraensis 0.13 0 0.1 4 0.19 0.188 Quararibea_amazonica 0.07 0 0.13 1 0.05 0.845 Quararibea_spatulata 0.07 0 0.26 3 0.19 0.157 Quararibea_wittii 0 0 0.41 3 0.31 0.096 Quiina_amazonica 0 0 0.1 3 0.04 0.849 Quiina_aulestia 0 0 0.05 3 0.05 0.682 Quiina_florida 0 0 0.13 3 0.07 0.694 Quiina_grandifolia 0 0 0 4 0.07 0.453 Quiina_macrophylla 0 0 0.03 3 0.03 1 Quiina_obovata 0 0 0 4 0.07 0.461

101

Quiina_rhytidopus 0 0 0 4 0.07 0.46 Randia_armata 0.13 0 0.15 1 0.09 0.632 Raputiarana_subsigmoidea 0 0 0 4 0.07 0.455 Rauvolfia_polyphylla 0.07 0 0 1 0.07 0.455 Rauvolfia_praecox 0 0 0 4 0.2 0.13 Remijia_chelomaphylla 0 0.33 0 2 0.33 0.041 Rhamnidium_elaeocarpum 0 0 0.18 3 0.1 0.701 Rhigospira_quadrangularis 0.07 0 0 4 0.06 0.478 Rhodostemonodaphne_crenaticupula 0.07 0 0 1 0.07 0.466 Rhodostemonodaphne_grandis 0.07 0 0.13 3 0.1 0.492 Rhodostemonodaphne_kunthiana 0.27 0 0.23 1 0.12 0.731 Rhodostemonodaphne_licanioides 0 0 0 4 0.2 0.135 Rhodostemonodaphne_napoensis 0.07 0 0 1 0.07 0.453 Rhodostemonodaphne_praeclara 0 0 0.08 3 0.08 0.483 Rhodostemonodaphne_synandra 0 0 0.03 3 0.03 1 Richeria_grandis 0.47 0 0.46 3 0.24 0.276 Richeria_sp 0.07 0 0 1 0.07 0.461 Richeria_sp1 0.07 0 0 1 0.07 0.456 Rinorea_apiculata 0 0 0.31 3 0.19 0.257 Rinorea_lindeniana 0.07 0 0.21 3 0.17 0.218 Rinorea_viridifolia 0 0 0.41 3 0.39 0.042 Rosenbergiodendron_longiflorum 0.07 0 0 1 0.07 0.458 Roucheria_punctata 0 0 0 4 0.07 0.455 Roucheria_schomburgkii 0.27 0 0 1 0.21 0.169 Roupala_montana 0.07 0.67 0.05 2 0.62 0.004 Rourea_amazonica 0 0 0 4 0.07 0.464 Ruagea_insignis 0 0 0.08 3 0.08 0.482 Rudgea_2238 0.07 0 0 1 0.07 0.455 Rudgea_bracteata 0 0 0.1 3 0.1 0.417 Rudgea_indet 0 0 0.03 3 0.03 1 Rudgea_lanceifolia 0 0 0.03 3 0.03 1 Rudgea_obesiflora 0 0 0 4 0.07 0.463 Rudgea_panurensis 0 0 0.03 3 0.03 1 Rudgea_verticillata 0.07 0 0 4 0.11 0.266 Rudgea_viburnoides 0 0 0.05 4 0.04 0.859 Ruizodendron_ovale 0.07 0 0.21 4 0.26 0.104 Ruizterania_cassiquiarensis 0 0.33 0 2 0.33 0.038 Ruizterania_trichanthera 0.07 0 0 1 0.04 0.704 Ryania_speciosa 0 0 0.08 3 0.08 0.476 Sacoglottis_amazonica 0.07 0 0 1 0.04 0.763 Sacoglottis_guianensis 0.2 0 0 1 0.2 0.133 Sagotia_racemosa 0 0 0.18 3 0.18 0.149 Salacia_2258_ 0.07 0 0 1 0.07 0.461

102

Salacia_cuero 0 0 0 4 0.07 0.46 Salacia_elliptica 0 0 0.05 3 0.05 0.685 Salacia_juruana/micrantha 0 0 0.13 4 0.07 0.73 Sapium_ciliatum 0 0 0.23 3 0.23 0.055 Sapium_garcinia 0 0 0.1 3 0.1 0.428 Sapium_glandulosum 0.07 0 0.36 3 0.12 0.761 Sapium_indet 0 0 0.03 3 0.03 1 Sapium_laurifolium 0.13 0 0.05 1 0.11 0.289 Sapium_marmieri 0 0 0.41 3 0.19 0.35 Sarcaulus_brasiliensis 0.13 0 0.38 3 0.32 0.075 Sarcaulus_brilla 0 0 0.03 3 0.03 1 Sarcaulus_kaputna 0 0.33 0 2 0.33 0.041 Sarcaulus_oblatus 0 0 0 4 0.07 0.456 Sarcaulus_sp 0.07 0 0 1 0.07 0.456 Sarcaulus_sp2 0.07 0 0 1 0.07 0.455 Sarcaulus_vestitus 0.13 0 0.03 1 0.07 0.548 Sarcaulus_wurdackii 0 0 0.03 3 0.03 1 Schefflera_morototoni 0.13 0 0.31 4 0.13 0.659 Schizolobium_parahyba 0.07 0 0.08 4 0.25 0.054 Schoepfia_lucida 0 0 0.08 3 0.08 0.479 Semaphyllanthe_megistocaula 0.13 0 0.18 3 0.09 0.765 Senefeldera_inclinata 0.2 0.67 0.08 2 0.16 0.385 Senegalia_polyphylla 0 0 0.05 4 0.05 0.577 Senna_bacillaris 0 0 0.03 3 0.03 1 Senna_trolliiflora 0 0 0.03 3 0.03 1 Simaba_guianensis 0.4 0 0.21 1 0.31 0.073 Simaba_orinocensis 0.13 0 0.05 1 0.09 0.452 Simaba_paraensis 0 0 0.1 3 0.07 0.718 Simaba_polyphylla 0.13 0 0.23 1 0.08 0.86 Simarouba_amara 0.13 0 0.26 1 0.09 0.889 Simira_cordifolia 0.2 0 0.23 4 0.24 0.176 Simira_harmful 0 0 0.03 3 0.03 1 Simira_kampa 0 0.33 0 2 0.33 0.042 Simira_rubescens 0.07 0 0.08 4 0.16 0.2 Simira_time-lapse 0.07 0 0.03 3 0.02 1 Simira_wurdackii 0 0 0.1 3 0.1 0.342 Siparuna_cervicornis 0.07 0.33 0.1 2 0.2 0.164 Siparuna_cristata 0 0 0.05 4 0.09 0.459 Siparuna_cuspidata 0.2 0 0.41 3 0.28 0.145 Siparuna_decipiens 0.27 0 0.56 3 0.47 0.036 Siparuna_guianensis 0 0 0.05 4 0.12 0.227 Siparuna_macrotepala 0 0 0.03 3 0.03 1 Siparuna_poeppigii 0 0 0.03 3 0.03 1

103

Siparuna_pube 0.07 0 0 1 0.07 0.463 Sloanea_“hirsu” 0 0.33 0 2 0.33 0.043 Sloanea_“kaputna” 0 0.33 0 2 0.33 0.044 Sloanea_“lau” 0 0.33 0 2 0.33 0.045 Sloanea_1 0 0 0.13 3 0.13 0.32 Sloanea_21 0.07 0 0 1 0.07 0.457 Sloanea_2111 0.07 0 0 1 0.07 0.463 Sloanea_2229 0.07 0 0 1 0.07 0.461 Sloanea_2236 0.07 0 0 1 0.07 0.454 Sloanea_2254 0.07 0 0 1 0.07 0.451 Sloanea_2388 0.07 0 0 1 0.07 0.459 Sloanea_245 0.07 0 0 1 0.07 0.453 Sloanea_282 0.07 0 0 1 0.07 0.464 Sloanea_499 0.07 0 0 1 0.07 0.459 Sloanea_bark 0 0 0.08 3 0.08 0.462 Sloanea_brachytepala 0.2 0.33 0 2 0.15 0.196 Sloanea_catostemoide 0.07 0 0 1 0.07 0.463 Sloanea_cordia 0 0 0.18 3 0.18 0.153 Sloanea_durissima 0.2 0 0 1 0.17 0.144 Sloanea_erismoides 0 0 0.03 3 0.03 1 Sloanea_falsebark 0 0 0.03 3 0.03 1 Sloanea_family 0 0 0.03 3 0.03 1 Sloanea_floribunda 0.2 0.67 0 2 0.27 0.046 Sloanea_fragante 0 0 0.05 3 0.05 0.449 Sloanea_froesii 0.13 0 0 1 0.1 0.266 Sloanea_grandiflora 0 0 0.15 4 0.08 0.756 Sloanea_granulosa 0.2 0 0 1 0.2 0.107 Sloanea_guianensis 0.27 0 0 1 0.18 0.186 Sloanea_hirtella 0 0 0.03 3 0.03 1 Sloanea_indet 0 0 0.03 3 0.03 1 Sloanea_lasiocoma 0 0.33 0 2 0.33 0.04 Sloanea_laxiflora 0.13 0 0 1 0.07 0.511 Sloanea_longif 0 0 0.03 3 0.03 1 Sloanea_macrophylla 0.07 0 0 1 0.07 0.465 Sloanea_meianthera 0.13 0.33 0.26 2 0.11 0.637 Sloanea_monosperma 0.13 0 0 1 0.13 0.163 Sloanea_multiflora 0.07 0.33 0.03 2 0.2 0.125 Sloanea_obtusifolia 0.07 0 0.03 1 0.05 0.915 Sloanea_oppd 0 0 0.03 3 0.03 1 Sloanea_opposite 0 0 0.03 3 0.03 1 Sloanea_oppositifolia 0.07 0 0 1 0.07 0.461 Sloanea_oppunifoliolatum 0.07 0 0 1 0.07 0.454 Sloanea_peculiar 0 0 0.03 3 0.03 1

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Sloanea_pinguino 0 0 0.03 3 0.03 1 Sloanea_pubescens 0.4 0 0.1 1 0.26 0.08 Sloanea_robusta 0.07 0.33 0.05 2 0.3 0.033 Sloanea_rufa 0.07 0 0.03 4 0.1 0.343 Sloanea_spathulata 0.2 0 0.08 1 0.14 0.349 Sloanea_stipi 0.07 0 0 1 0.07 0.455 Sloanea_superbul_281 0.07 0 0 1 0.07 0.462 Sloanea_synandra 0.27 0.33 0.18 2 0.09 0.91 Sloanea_terniflora 0.13 0 0.03 1 0.12 0.186 Sloanea_tuerckheimii 0.07 0 0.1 3 0.06 0.827 Sloanea_ugly 0 0 0.03 3 0.03 1 Socratea_exorrhiza 0.2 0 0.51 3 0.15 0.879 Socratea_rostrata 0 0.33 0 2 0.33 0.041 Solanum_altissimum 0.07 0 0.1 3 0.07 0.634 Solanum_appressum 0 0 0.03 3 0.03 1 Solanum_lepidotum 0 0 0.03 3 0.03 1 Sorocea_guilleminiana 0.47 0 0 1 0.47 0.012 Sorocea_indet 0 0 0.03 3 0.03 1 Sorocea_muriculata 0 0 0.05 4 0.16 0.255 Sorocea_pubivena 0.47 0 0.44 1 0.22 0.343 Sorocea_sarco 0 0 0.05 3 0.05 0.681 Sorocea_steinbachii 0.33 0 0.72 3 0.55 0.015 Spondias_mombin 0.13 0 0.62 3 0.4 0.048 Stachyarrhena_mezinerv 0.07 0 0 1 0.07 0.459 Stenostomum_acreanum 0 0.33 0.03 2 0.31 0.046 Stephanopodium_peruvianum 0 0 0.15 3 0.15 0.254 Sterculia_apeibophylla 0.4 0 0.41 1 0.25 0.222 Sterculia_apetala 0 0 0.18 4 0.2 0.169 Sterculia_colombiana 0.47 0 0.72 3 0.39 0.084 Sterculia_corrugata 0 0 0.03 3 0.03 1 Sterculia_frondosa 0.13 0 0.51 3 0.45 0.025 Sterculia_guapayensis 0.07 0 0 4 0.11 0.267 Sterculia_indet 0 0 0.03 3 0.03 1 Sterculia_killipiana 0.13 0 0 1 0.13 0.16 Sterculia_peruviana 0 0 0 4 0.07 0.458 Sterculia_pruriens 0 0 0.03 3 0.03 1 Sterculia_rebeccae 0.13 0 0 1 0.13 0.162 Sterculia_tessmannii 0.4 0 0.44 3 0.23 0.287 Sterigmapetalum_obovatum 0.2 0 0 1 0.2 0.145 Strychnos_panurensis 0 0 0.08 3 0.08 0.462 Stryphnodendron_porcatum 0.07 0 0.15 3 0.08 0.829 Styrax_argenteus 0 0 0.03 3 0.03 1 Swartzia_amplifolia 0 0 0 4 0.07 0.456

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Swartzia_arborescens 0.2 0 0.18 1 0.1 0.626 Swartzia_benthamiana 0.07 0 0.03 1 0.05 0.912 Swartzia_bombycina 0.13 0 0.08 1 0.07 0.657 Swartzia_cardiosperma 0.27 0 0.1 1 0.18 0.181 Swartzia_cuspi 0 0.33 0 2 0.33 0.041 Swartzia_falsatangarana 0.07 0 0 1 0.07 0.468 Swartzia_feeble 0 0 0.08 3 0.08 0.481 Swartzia_haughtii 0 0 0.03 3 0.03 1 Swartzia_indet 0 0 0.03 3 0.03 1 Swartzia_leptopetala 0 0 0 4 0.07 0.463 Swartzia_macrosema 0.07 0 0.18 3 0.15 0.306 Swartzia_multijuga 0.07 0 0.05 4 0.07 0.67 Swartzia_myrtifolia 0.07 0 0 1 0.07 0.463 Swartzia_polyphylla 0.13 0 0.05 1 0.07 0.625 Swartzia_racemosa 0.27 0 0 1 0.27 0.043 Swartzia_reticulata 0 0.33 0 2 0.33 0.043 Swartzia_rojitaseri 0.07 0 0 1 0.07 0.461 Swartzia_simplex 0 0 0.03 4 0.06 0.407 Swartzia_yutsuntsa 0 0 0 4 0.07 0.452 Symmeria_paniculata 0.13 0 0 1 0.13 0.164 Symphonia_globulifera 0.47 0 0.36 1 0.25 0.232 Symplocos_arechea 0 0 0.03 3 0.03 1 Tabebuia_indet 0 0 0.03 3 0.03 1 Tabebuia_moby 0 0 0.03 4 0.06 0.389 Tabebuia_ochracea 0.07 0 0 1 0.07 0.463 Tabebuia_serratifolia 0 0 0.13 3 0.1 0.512 Tabebuia_vanilla 0 0 0.03 4 0.06 0.388 Tabernaemontana_sananho 0.07 0 0.03 1 0.06 0.396 Tachigali_chrysaloides 0 0 0.03 3 0.03 1 Tachigali_chrysophylla 0.07 0 0.08 4 0.08 0.56 Tachigali_formicarum 0.27 0 0.1 1 0.15 0.361 Tachigali_inconspicua 0 0.33 0 2 0.33 0.038 Tachigali_paniculata 0.07 0 0 1 0.07 0.456 Tachigali_paraensis 0 0 0.05 4 0.12 0.249 Tachigali_schultesiana 0.07 0 0 1 0.07 0.458 Tachigali_setifera 0.13 0 0 1 0.13 0.171 Tachigali_vasquezii 0 0 0.03 4 0.04 1 Talauma_fine 0 0 0.08 3 0.08 0.471 Talisia_12 0.07 0 0 4 0.04 0.773 Talisia_696 0 0 0.03 3 0.03 1 Talisia_72 0 0 0.03 3 0.03 1 Talisia_agria 0 0 0.03 3 0.03 1 Talisia_asimet_24 0.07 0 0 1 0.07 0.453

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Talisia_cerasina 0 0 0.18 3 0.18 0.208 Talisia_equis 0.07 0 0 1 0.07 0.466 Talisia_gigantesca 0 0 0.05 3 0.05 0.693 Talisia_gigapulvi 0 0 0.05 3 0.05 0.448 Talisia_megaphylla 0 0 0.13 3 0.13 0.318 Talisia_microphylla 0.2 0 0 1 0.2 0.141 Talisia_pachycarpa 0 0 0.03 3 0.03 1 Talisia_praealta 0.07 0 0 1 0.07 0.462 Talisia_princeps 0 0 0.08 3 0.08 0.474 Talisia_sp_1 0 0 0.03 3 0.03 1 Talisia_velvet 0 0 0.08 3 0.08 0.49 Tapirira_gloomy 0 0 0.03 3 0.03 1 Tapirira_guianensis 0.6 0.33 0.56 1 0.32 0.18 Tapirira_immense 0 0 0.05 3 0.05 0.682 Tapirira_indet 0 0 0.03 3 0.03 1 Tapirira_obtusa 0 0 0.13 3 0.1 0.46 Tapirira_retusa 0.07 0 0 1 0.03 1 Tapura_amazonica 0.27 0 0 1 0.27 0.05 Tapura_juruana 0.27 0 0.49 3 0.26 0.197 Tapura_large 0 0 0.05 3 0.05 0.454 Tapura_peruviana 0 0 0.03 4 0.05 0.911 Terminalia_amazonia 0 0 0.08 4 0.12 0.28 Terminalia_dichotoma 0.13 0 0 1 0.13 0.163 Terminalia_indet 0 0 0.03 3 0.03 1 Terminalia_oblonga 0.07 0 0.23 3 0.15 0.34 Tessmannianthus_heterostemon 0 0 0.15 3 0.12 0.416 Tetragastris_indet 0 0 0.03 3 0.03 1 Tetragastris_panamensis 0.6 0 0.41 4 0.29 0.208 Tetrameranthus_globulifer 0 0 0.08 3 0.08 0.449 Tetrathylacium_macrophyllum 0.13 0 0.49 3 0.31 0.137 Tetrorchidium_macrophyllum 0.07 0 0.21 3 0.16 0.297 Theobroma_bicolor 0 0 0 4 0.13 0.158 Theobroma_cacao 0 0 0.1 4 0.23 0.093 Theobroma_colonial 0 0 0.1 3 0.1 0.375 Theobroma_glauco-membra_sp._nov. 0.07 0 0 1 0.07 0.456 Theobroma_glaucum 0 0 0.03 3 0.03 1 Theobroma_speciosum 0.33 0 0.64 3 0.44 0.037 Theobroma_subincanum 0.53 0.33 0.77 3 0.34 0.17 Thyrsodium_dik 0 0 0.03 3 0.03 1 Thyrsodium_herrerense 0 0.33 0 2 0.33 0.044 Tocoyena_williamsii 0.13 0 0.1 3 0.06 0.814 Tontelea_micrantha 0 0 0.03 3 0.03 1 Toulicia_reticulata 0.13 0 0.03 1 0.11 0.278

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Tovomita_1857 0 0 0.03 3 0.03 1 Tovomita_1999 0.07 0 0 1 0.07 0.449 Tovomita_calophyllophylla 0 0.67 0 2 0.67 0.001 Tovomita_choisyana 0.07 0 0 1 0.07 0.455 Tovomita_grata 0.07 0 0 1 0.03 1 Tovomita_indet 0 0 0.03 3 0.03 1 Tovomita_macrophylla 0.07 0 0.03 1 0.04 1 Tovomita_mitopsis 0 0.33 0 2 0.33 0.043 Tovomita_tyana 0 0 0.03 3 0.03 1 Tovomita_umbellata 0.07 0.67 0 2 0.27 0.045 Tovomita_weddelliana 0 0 0.1 4 0.17 0.196 Trattinnickia_boliviana 0.13 0 0.03 1 0.06 0.853 Trattinnickia_glaziovii 0.27 0 0.15 1 0.24 0.105 Trattinnickia_lawrancei 0 0 0.13 3 0.13 0.288 Trema_interregima/micrantha_cf. 0 0 0.13 3 0.13 0.325 Trichilia__2275 0.07 0 0 1 0.07 0.461 Trichilia_adolfi 0 0 0.18 3 0.15 0.292 Trichilia_cipo 0.07 0 0.1 4 0.14 0.333 Trichilia_d-day 0 0 0.05 3 0.05 0.677 Trichilia_elegans 0.07 0 0.05 3 0.03 0.952 Trichilia_elsae 0 0 0.13 4 0.19 0.172 Trichilia_euneura 0 0 0.1 4 0.04 0.852 Trichilia_güeppí 0.07 0.33 0 2 0.31 0.041 Trichilia_inaequilatera 0 0 0.03 3 0.03 1 Trichilia_indet 0 0 0.03 3 0.03 1 Trichilia_laxipaniculata 0.07 0 0.21 3 0.18 0.211 Trichilia_maynasiana 0.07 0 0.23 4 0.26 0.113 Trichilia_micrantha 0 0 0.15 3 0.12 0.456 Trichilia_obovata 0 0 0.03 3 0.03 1 Trichilia_pachypoda 0.13 0 0 4 0.1 0.459 Trichilia_pallida 0 0 0.28 3 0.2 0.183 Trichilia_pittieri 0 0 0.08 4 0.12 0.25 Trichilia_pleeana 0 0 0.15 3 0.08 0.75 Trichilia_poeppigii 0.07 0 0.31 3 0.27 0.091 Trichilia_quadrijuga 0.2 0 0.31 3 0.19 0.242 Trichilia_rubra 0 0 0.13 4 0.17 0.181 Trichilia_septentrionalis 0.27 0 0.54 3 0.34 0.095 Trichilia_solitudinis 0 0 0.44 3 0.39 0.042 Trichilia_walter 0 0 0.05 3 0.05 0.461 Trigynaea_duckei 0 0 0.18 3 0.14 0.325 Trigynaea_lagaropoda 0 0 0 4 0.2 0.138 Trigynaea_triplinervis 0.07 0 0.05 1 0.04 0.858 Triplaris_americana 0.07 0 0 1 0.07 0.463

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Triplaris_dugandii 0 0 0.18 3 0.15 0.273 Triplaris_weigeltiana 0.2 0 0.28 1 0.16 0.454 Trophis_caucana 0 0 0.03 3 0.03 1 Trymatococcus_amazonicus 0.2 0 0.15 1 0.12 0.564 Turpinia_occidentalis 0 0 0.23 4 0.13 0.444 Unonopsis_elegantissima 0.07 0 0 1 0.07 0.45 Unonopsis_floribunda 0.13 0 0.49 3 0.31 0.134 Unonopsis_magnifolia 0.07 0 0 1 0.07 0.454 Unonopsis_peruviana 0 0 0 4 0.07 0.457 Unonopsis_qca 0.07 0 0 1 0.07 0.463 Unonopsis_spectabilis 0.13 0 0 1 0.12 0.18 Unonopsis_veneficiorum 0 0 0.08 4 0.11 0.363 Urera_baccifera 0 0 0.03 3 0.03 1 Urera_caracasana 0 0 0.15 4 0.18 0.177 Vantanea_guianensis 0.2 0 0.05 1 0.16 0.257 Vantanea_parviflora 0.13 0.33 0 2 0.28 0.055 Vantanea_peruviana 0.13 0.33 0 2 0.19 0.158 Vatairea_erythrocarpa 0 0 0.03 4 0.25 0.063 Vatairea_fusca 0.13 0 0 1 0.13 0.166 Vatairea_guianensis 0.07 0 0 1 0.07 0.459 Vataireopsis_iglesiasii 0 0 0.03 3 0.03 1 Virola_albidiflora 0.07 0 0 1 0.07 0.463 Virola_caducifolia 0 0 0 4 0.07 0.456 Virola_calophylla 0.53 0 0.38 1 0.36 0.084 Virola_decorticans 0.13 0 0.15 3 0.06 0.993 Virola_divergens 0 0 0.05 3 0.05 0.457 Virola_duckei 0.27 0 0.59 3 0.38 0.066 Virola_elongata 0.73 0.33 0.56 1 0.44 0.049 Virola_flexuosa 0.27 0 0.46 4 0.21 0.452 Virola_indet 0 0 0.03 3 0.03 1 Virola_marlenei 0 1 0 2 1 0 Virola_minutiflora 0.07 0 0 1 0.07 0.468 Virola_mollissima 0.07 0 0.13 3 0.05 0.936 Virola_multinervia 0.27 0 0.46 4 0.42 0.057 Virola_obovata 0.2 0 0.46 3 0.37 0.072 Virola_parvifolia 0.07 0 0 1 0.03 1 Virola_pavonis 0.6 0 0.69 1 0.3 0.294 Virola_peruviana 0.33 0 0.38 4 0.23 0.346 Virola_sebifera 0.2 0 0.05 4 0.12 0.411 Virola_surinamensis 0.4 0 0.28 1 0.19 0.345 Virola_theiodora 0 0 0.03 3 0.03 1 Virola_tsuirim 0 0 0 4 0.07 0.452 Virola_vino 0 0 0.03 3 0.03 1

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Vismia_baccifera 0 0 0.03 4 0.06 0.386 Vismia_cayennensis 0 0 0 4 0.07 0.45 Vismia_confertiflora 0 0 0.03 3 0.03 1 Vismia_floribunda 0 0 0.03 4 0.05 0.913 Vismia_lauriformis 0.07 0 0.03 1 0.05 0.398 Vismia_lemonade 0 0 0.03 3 0.03 1 Vismia_macrophylla 0.13 0 0.08 1 0.1 0.395 Vismia_myrsinac 0 0 0.05 3 0.05 0.765 Vismia_palate 0 0 0.03 4 0.03 1 Vismia_pozuzoensis 0 0 0.03 3 0.03 1 Vismia_punctate 0 0 0.03 3 0.03 1 Vismia_sprucei 0 0 0.15 3 0.15 0.301 Vismia_weedy 0 0 0.13 3 0.13 0.326 Vitex_bicolor 0 0 0.1 3 0.1 0.287 Vitex_cymosa 0 0 0.05 4 0.04 0.854 Vitex_indet 0 0 0.03 3 0.03 1 Vitex_pseudolea 0 0 0.03 3 0.03 1 Vitex_schunkei 0.07 0 0.15 3 0.09 0.726 Vitex_serratifolia 0 0 0.03 3 0.03 1 Vitex_triflora 0.27 0 0.08 1 0.22 0.079 Vochysia_aff_grandis 0.13 0.33 0 2 0.19 0.136 Vochysia_biloba 0.07 0 0.03 3 0.01 1 Vochysia_braceliniae 0.07 0 0.05 4 0.21 0.101 Vochysia_ferruginea 0.13 0 0 1 0.13 0.166 Vochysia_floribunda 0.07 0 0 1 0.07 0.461 Vochysia_grandis 0.13 0 0.23 4 0.09 0.802 Vochysia_indet 0 0 0.03 3 0.03 1 Vochysia_leguiana 0 0 0.05 3 0.04 0.778 Vochysia_lomatophylla 0 0 0.03 3 0.03 1 Vochysia_splendens 0 0.33 0 2 0.28 0.082 Vochysia_vismiifolia 0.07 0.33 0 2 0.31 0.039 Vouarana_anomala 0 0 0.13 3 0.13 0.328 Warscewiczia_coccinea 0 0 0.31 3 0.24 0.18 Warszewiczia_cordata 0 0 0.15 3 0.08 0.731 Warszewiczia_schwackei 0.07 0 0 1 0.07 0.459 Wettinia_drudei 0.07 0 0 4 0.06 0.481 Wettinia_longipetala 0 0.33 0 2 0.33 0.042 Wettinia_maynensis 0.13 0 0.59 3 0.24 0.368 Witheringia_herbaceous 0 0 0.03 3 0.03 1 Wittmackanthus_stanleyanus 0.07 0 0.31 3 0.19 0.233 Xylopia_938 0.07 0 0 1 0.07 0.465 Xylopia_aromatica 0 0 0.03 3 0.03 1 Xylopia_calophylla 0.07 0 0.03 1 0.03 1

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Xylopia_cuspidata 0.07 0 0.05 3 0.03 0.952 Xylopia_diospyros 0 0 0.03 3 0.03 1 Xylopia_hiena_1492 0.07 0 0.03 1 0.03 1 Xylopia_holosericea 0 0 0 4 0.07 0.455 Xylopia_ligustrifolia 0.2 0 0.21 3 0.12 0.544 Xylopia_multiflora 0.13 0 0 4 0.08 0.448 Xylopia_nitida 0 0 0 4 0.07 0.464 Xylopia_parviflora 0.2 0 0 1 0.2 0.107 Xylopia_pubescent 0 0 0.08 3 0.08 0.481 Xylopia_sericea 0.47 0 0.1 1 0.35 0.057 Xylopia_surinamensis 0.07 0 0 1 0.07 0.463 Xylosma_dubia 0 0 0.03 3 0.03 1 Xylosma_intermedia 0.07 0 0.15 3 0.11 0.491 Yasunia_sessiliflora 0.07 0 0.08 4 0.05 0.892 Zanthoxylum_1962 0 0 0.03 3 0.03 1 Zanthoxylum_acuminatum 0 0 0.03 4 0.06 0.389 Zanthoxylum_climb 0 0 0.1 3 0.1 0.381 Zanthoxylum_formiciferum 0 0.33 0 2 0.33 0.039 Zanthoxylum_riedelianum 0.07 0 0.15 3 0.12 0.437 Zanthoxylum_sp 0.07 0 0 1 0.07 0.461 Zanthoxylum_sprucei 0.07 0 0.18 3 0.09 0.763 Zapoteca_amazonica 0 0 0 4 0.13 0.162 Zizyphus_cinnamomum 0 0 0.18 3 0.15 0.31 Zygia_cataractae 0.07 0 0.1 3 0.09 0.515 Zygia_cataractae/juruana_cf. 0.2 0 0.1 1 0.14 0.3265 Zygia_coccinea 0 0 0.38 3 0.33 0.0424 Zygia_heteroneura 0 0 0.08 3 0.08 0.4553 Zygia_igapo 0.07 0 0 1 0.07 0.4523 Zygia_inaequalis 0.07 0 0.03 1 0.07 0.3771 Zygia_juruana 0.13 0 0 1 0.13 0.1615 Zygia_lathetica 0.13 0 0.13 4 0.05 0.9891 Zygia_latifolia 0 0 0.03 3 0.03 1 Zygia_longifolia 0.13 0 0.03 1 0.12 0.2703 Zygia_unexpected 0 0 0.05 3 0.05 0.4468

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Figure S1. Comparison between Taxonomic Diversity (measured as Fisher’s alpha index) and Phylogenetic diversity (measured as ses.mpd index) in Ecuador Amazon. Black lines represent the best fit for the relationship between latitude-longitude vs. Fisher’s alpha and Rao’s index based on loess interpolation.

(a) (b)

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Figure S2. Heatmap and dendrogram of a 80 one hectare plot network in Ecuador Amazon. Phylogenetic beta diversity was measured as phylogenetic dissimilarity (1-Phylosoreson) and results show the best supported cluster algorithm (Average method). Yellow box represents plots in inundated forests located across the entire Ecuadorean Amazon therefore is not considered a floristic sub-region.

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Figure S3. Heatmap and dendrogram of a 80 one hectare plot network in Ecuador Amazon. Taxonomic beta diversity was measured with a dissimilarity matrix (1-Sorenson) and hierarchical clustering was performed with Ward’s algorithm. The colors in the boxes correspond to the 3 floristic districts. Yellow box represents plots in inundated forests located across the entire Ecuadorean Amazon therefore is not considered a floristic sub-region.

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Figure S4. Correlation between PBD and TBD measured as 1- Phylosorenson and 1- Sorenson respectively. Blue dots represents observed values of PBD while gray dots represents null values of PBD based on 1000 randomizations of presence-absence matrix (sites by species) using swap algorithm. A) Results for the regional species phylogeny based on the Phylomatic backbone tree; B) Results for the regional species phylogeny of 480 tree species.

(a) (b)

Observed Observed Expected Expected

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116

Chapter 3 Climatic and geomorphological control on the phylogenetic and taxonomic beta diversity patterns of Amazon tree communities

Abstract Climate, geomorphology and soils are considered strong predictors of Phylogenetic Beta Diversity (PBD) and Taxonomic Beta Diversity (TBD) of Amazonian tree communities at different spatial scales. In order to test the role of climatic, soils and geomorphological variables we compiled 80 floristic inventories plots established in Ecuadorian Amazon. Then we used 28 climatic, edaphic and geomorphological variables to perform Generalized Additive Models, variation partitioning via Redundancy Analysis and Multiple Regression on Matrices (MRM) in order to assess the contribution of climate, soils and geomorphology as drivers of PBD and TBD. We found that climate was a better predictor of PBD and TBD than geomorphology and soils. The influence of climate was stronger at broader spatial scales meanwhile geomorphology and soils appear to be better predictors of species turnover at mid and fine spatial scales but a weak predictor of lineages turnover at all spatial scales. Keywords: Amazon, Ecuador, floristic inventories, phylogenetic beta diversity, taxonomic beta diversity, climatic control, geomorphology.

Introduction The complicated geological history of Amazonian soil types have been proposed as the main predictor of the species turnover in plant communities at both local and landscape scales (Tuomisto et al. 2002; Phillips et al. 2003; Higgins et al. 2011). Some of these studies have hypothesized that species turnover along the west-east axis of Amazon basin is determined by pervasive soil composition differences (Terborgh & Andressen 1998; ter Steege et al. 2006). By the same token there is also evidence for an abrupt change in soils composition promoted by strong differences in geological units along this longitudinal axis (Pitman et al. 2008; Higgins et al. 2011). More recently, it has been proposed that the synergistic effect of the west-east rainfall gradient along the equatorial band of South America along with Andean orogeny might promote lineage and species turnover across this west-east axis (Antonelli et al. 2009; Fine & Kembel 2011; Kerkhoff et al. 2014). However, there are no studies testing the combined effect of geology, soil resource availability, regional climatic variables, and dispersal limitation on the structure and composition of Amazonian tree communities along this longitudinal gradient. Furthermore, the scarcity of studies that include phylogenies at the community level constrains our ability to understand the historical and evolutionary processes that underlay the patterns of phylogenetic turnover among tree communities at regional scales (Fine & Kembel 2011). The “geological control hypothesis” posits that Amazon forests are partitioned in large floristic units on the basis of geological formations. Under this scenario differences in soils composition should lead to significant and abrupt changes in plant species composition across landscapes if mosaic sympatry or parapatric speciation processes are involved (Mallet 2008; Fine

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et al. 2013; Tuomisto et al. 2016). In addition, the “geological control hypothesis” posits that contemporary geological and edaphic patterns in Western and Central Amazon are the result of both gradual and abrupt transitions from nutrient-rich Miocene-Pleistocene sediments in areas close to Andean foothills to poor Pliocene-Pleistocene sediments to the east of the basin (Rossetti et al. 2005; Higgins et al. 2011). Because the nutrient-rich sediments became increasingly available through erosion during the Pleistocene, clades already adapted to poor soil conditions could have adapted to the novel environments resulting in rapid divergence of many lineages via parapatric speciation. We expect that younger sediments corresponding to nutrient rich soils would be predominant in Ecuadorian Amazon contrasting with poorer soils to the east of the basin (Sombroek 1990). Evidence suggests that during the middle Miocene the landscape of Western Amazonia was determined by the deposition of sediments from the Andes and the embayment of the Pebas systems (Hoorn et al. 2010; Rasanen et al. 1990). The nutrient rich sediments of the Pebas formation were ubiquitous across the landscape of Western Amazonia as the product of a low energy marine or lacustrine system. This geological dynamic changed dramatically during the late Miocene (10-5 Ma) as the Andean uplift continued during this period the nutrient rich sediments were covered with cation-poor fluvial sediments (Hoorn et al. 2010; Wessenlingh & Salo 2006). Some examples of geological formations that have originated from deposition of cation-poor sediments are the Nauta Formation in Peru and the Içá Formation in Brazil (Rossetti et al. 2005; Hoorn et al. 2010; Higgins et al. 2011). Finally during the Plio-Holocene (5Ma-present) the landscape of Western Amazonia changed from a depositional dynamics to an increasingly high energy system characterized by fluvial erosion. In such high energy conditions river incision and denudation eroded the previously deposited cation-poor sediments exposing the cation-rich Miocene sediments of the Pebas Formation. The role of climate on the patterns of tree species turnover has been addressed recently showing that precipitation gradients drives phylogenetic and taxonomic turnover across the Neotropics including the Amazon forests (ter Steege et al. 2006; Hardy et al. 2012; Esquivel- Muelbert et al. 2016). For example, it has been demonstrated that a great proportion of tree species in the Western Neotropics have an affiliation with ever-wet conditions and therefore exhibit range restriction to this extreme of the climate gradient, whereas other species of trees appear to be restricted to dry environments. Furthermore, a gradient in seasonal dry length from Western to southeastern Amazonia is responsible for the geographic variation in tree species composition across this longitudinal gradient (ter Steege et al. 2006) These results corroborate the idea that climate envelope might act as the main environmental filter determining the regional species pool (Engelbrecht et al. 2007; Lessard et al. 2011; Lessard et al. 2012; Pennington et al. 2009). Two opposing theories related to climate have arisen to explain community assembly in Amazon forests. The first one propose that climatic stability since the Eocene might be fundamental to promote lineage divergence via allopatric speciation and niche conservatism in areas close to the Andes (Hutter et al. 2013, Antonelli et al. 2009) This potentially is related to an overrepresentation of small ranged species in this area (Morueta-Holmes et al. 2013). On the other hand recent Quaternary climatic changes have been proposed to be important determining species proliferation either via allopatric or parapatric speciation (Haffer and Prance 2001), One of the main constraints in investigating the relative importance of geology and climate on tree species turnover in Amazon forests is the lack of systematic sampling of tree communities

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along geological units, soil, and climate gradients. It is critical to have a dataset that includes taxonomic standardization, a unified criterion for morphospecies delimitation, intensive soil sampling, and accurate geomorphological and climatic data. Therefore, our aim in this study was to disentangle the influence of these variables on the patterns of taxonomic and phylogenetic turnover in Amazonian tree communities by integrating intensive fieldwork, geological maps, climatic variables, phylogenetic beta diversity and phylogenetic community structure methods. Methods Study Area In order to study the relationships between geology, soils, and climate with the patterns of phylogenetic and taxonomic beta diversity we selected the Ecuadorian Amazon located on a mosaic of geological formations that date from the Cretaceous to the Pleistocene. The Napo River is the major physiographic barrier that divides the northern portion of the Ecuador Amazon from the southern portion. The landscape of the area is mostly dominated by rolling hills interrupted by terrain depressions or baixios that vary in extent and levels of drainage (Pitman 2001). High and low terraces from Pleistocene origin dominate the northern and southern river banks of Aguarico River whereas the northern river bank of Napo River is mainly covered by palm-dominated swamps (Ministerio de Ambiente del Ecuador 2013). The Pastaza River represents a geomorphological break in the landscape of Ecuador Amazon. South of this river the landscape is characterized by extensive plains of terra firme forests interspersed by swamps that are sometimes, but not always, dominated by palms. This area is known as the Pastaza fan which corresponds to a massive volcanoclastic alluvial fan deposited during the Holocene (Rasanen et al. 1987; Bernal et al. 2011). Finally, we sampled the lowland forests adjacent to the Cordillera del Condor which is one of the areas of Ecuadorian Amazon that remains poorly explored in terms of floristic inventories. We sampled one plateau on quarzitic sandstones that represents the lowest altitudes of Cordillera del Condor. The main geological unit of this area is the Tena formation which has been dated as Cretaceous origin (Cristophoul et al. 2012). Tree community sampling We established 15 one-hectare plots in both terra firme and white sand forests in the Ecuadorian Amazon (Fig. 1). At each one-hectare plot we recorded, tagged, and identified all the trees with diameter breast height (dbh) above or equal to 10 cm, the final dataset includes 34,874 individual trees. Herbarium specimens for every tree species were collected and duplicates were deposited and compared with botanical specimens from four herbaria (QCNE, QCA, QAP, F). Then we curated and standardized the taxonomy of the vouchers collected in this study with all the specimens collected in previous studies which include 65 one hectare plots; 46 in terra firme, 11 in varzea, 4 in swamps, and 5 in igapo forests (Pitman et al. 2001, Ceron and Reyes 2003; ter Steege et al. 2013; Pos et al. 2014). Therefore for the sake of analysis we used an 80 one hectare plot dataset. We confirmed the taxonomy of almost every species collected in the field with the specialists in each group. In many other cases our extensive experience in Amazonian tree species identification allows us to be confident about the accuracy of the taxonomy in our plot network. We established the plot network on the basis of the most current geological map generated by Instituto de Investigaciones Geológicas Mineras Metalúrgicas of Ecuador

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(http://www.geoinvestigacion.gob.ec). We established 8 plots on alluvial terraces of the Aguarico River towards the north of the Ecuadorian Amazon; the age of these units ranges from Pliocene to Pleistocene origin (Laraque et al.2009) The landscape is mostly characterized by large areas that correspond to Pleistocene alluvial terraces that occasionally suffer flooding events. These geomorphological units are interrupted only by high terraces with flat surfaces that have not suffered erosion of their surfaces (Saunders 2008; Wesselingh et al. 2006). Two additional plots were established in old alluvial terraces of Napo River, these units as well as the units located in the Aguarico River are high terraces that presumably constitute old flood plains of the previously mentioned rivers (Saunders 2008). Ten plots were established in the Pastaza megafan which is a massive alluvial deposit located in the southwestern Ecuadorian Amazon, evidence suggests that the modern megafan complex dates from the Pliocene-Pleistocene and recent alluvial processes have occurred between the last 180 000-30 000 yrs. (Rasanen et al. 1990; Bernal et al. 2011). Five plots were established in areas belonging to plateaus originated during the Cretaceous period; these geomorphological units are located in the lowest part of Cordillera del Condor below 500 m. The remaining 55 one hectare plots were established in the Yasuní national Park and the Tigre- Corrientes watershed. The landscape in both areas is characterized by the predominance of geomorphological units such as highly dissected hills occasionally interrupted by valleys (Pitman 2000 The landscape is dominated by Curaray and Chambira formations from Miocene and Mio- Pliocene origin respectively and soils are characterized by higher nutrients content (Pitman et al. 2008) Finally, we decided to exclude unnamed morphospecies from the phylogenetic and statistical analyses due to weak effects on the detection of ecological patterns at larger spatial scales (Pos et al. 2014).

Phylogenetic tree We created a phylogenetic tree for 1,687 operational taxonomic units (OTUs) (Fig.S1) using as backbone the tree R20120829 (Li et al. 2015) from Phylomatic (Webb and Donoghue 2005), then in order to assign branch lengths we used the BLADJ algorithm in Phylocom (Webb et al. 2008) based on inferred node ages (Wikstrom 2001). This phylogenetic tree is based on Angiosperm Phylogeny Group’s system (APGIII 2009).

Phylogenetic and taxonomic beta diversity The change in phylogenetic relationships between local communities across space (PBD) was measured using the Phylosorenson index measures the fraction of branch lengths (Phylogenetic distance) shared by two communities or samples (Graham et al. 2009). For the sake of congruence with the metrics used to evaluate taxonomic beta diversity we used the complement of Phylo Sorenson index to establish a phylogenetic dissimilarity metric (1-Phylosorenson) as follows:

= 1 1 ( + 𝑖𝑖𝑖𝑖 ) 𝐵𝐵𝐵𝐵 2 𝑃𝑃ℎ𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 − 𝐵𝐵𝐵𝐵𝑖𝑖 𝐵𝐵𝐿𝐿𝑗𝑗

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Where, BLij is the branch length common to both communities i and j, and BLi and BLj are the total branch lengths of community i and j, respectively (Bryant et al. 2008). Taxonomic dissimilarity was measured using the Sorenson index as follows: 2a = (2 + 𝑖𝑖𝑖𝑖 + )

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑖𝑖𝑖𝑖 𝑖𝑖 𝑗𝑗 Where aij is the fraction of species shared in communities𝑎𝑎 𝑏𝑏 i and𝑐𝑐 j and bi and cj are the proportion of species just present in communities i and j respectively. A null model that randomize community data matrix by drawing species from the regional phylogeny with equal probability was performed using the function phylogeny.pool implemented in the “picante” package in R (Kembel et al. 2010, Webb et al. 2008). Climatic variables In order to assess the role of climatic and geological-geomorphological variables in the patterns of taxonomic beta diversity (TBD) and phylogenetic beta diversity (PBD) we used 19 climatic variables from Bioclim at 30 seconds of resolution as initial set of variables (Table S1). Then we performed a Principal Component Analysis using a correlation matrix to avoid collinearity between the variables. We decided to use this approach instead of selecting those climatic variables exhibiting high correlations to avoid missing valuable information. Finally we selected the two first axes of the PCA that explained most of the variation, 65.29% and 20.56% respectively. We also performed a forward selection procedure Multiple Regression on Distance Matrices (MRM) for the 19 climatic variables selecting 13 final variables that were used in this analysis. Geological maps and soils To correlate geology as predictor of PBD in Amazon tree communities we used geological maps and digital elevation model (DEM) to create a consensus map by overlaying the rasterized map of geology with the DEM at 90m of resolution. Then we plotted the results of a NMDS based on a phylogenetic dissimilarity matrix using the complement of Phylosorenson and Sorenson indexes (1-Phylosorenson, 1-Sorenson) on the consensus map produced by the sum of geological map and the DEM. Because this approach is purely descriptive we decided to use geomorphological variables as a proxy of historical events in analogy to geology. Because geomorphology encloses the geological processes that have shaped the distribution of species we consider this as good proxy for historical events. Finally, soil properties at each plot location were measured to obtain nine edaphic variables, soil texture was measured in percentage meanwhile cation content was measured in parts per million (ppm) (Table S2). All the analyses to obtain soil properties were done in the laboratory of the Facultad de Geologia, Minas y Petroleo from (Labgeimpa) Universidad Central del Ecuador.

Geomorphological variables Four variables were used in the analysis describing the geomorphology and land cover features in the vicinity of the forest plots. Digital terrain elevation data for the Ecuadorian Amazon was obtained from the Shuttle Radar Topography Mission (SRTM) distributed by the USGS through the Earth Explorer platform (https://earthexplorer.usgs.gov/). The SRTM dataset has worldwide

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coverage of void filled elevation data at a resolution of 1 arc-second (30 meters). Topographic slope in degrees was calculated from the elevation data using the Spatial Analyst extension in ArcGIS 10.3 software from ESRI (Environmental Systems Resource Institute). Hierarchical Slope Position identifies topographic exposure (ridge, slope, valley bottom, etc) by applying moving windows with increasing radii to a digital elevation model (DEM) (Murphy et al. 2010). The exposure is a ridge if the elevation of the center cell in the window is higher than the average of the cells in the window. The opposite case corresponds to a valley bottom or toe slope. Hierarchical integration is done by starting with exposure values for the largest (user defined) window and adding values from smaller windows if their absolute standardized values exceed the values of the larger scale map. The variable was calculated using the Geomorphometry and Gradient Metrics (version 2.0) for ArcGIS (Evans et al. 2014) using windows of radii between 2 and 10 pixels with increments of two pixels. Land cover information was obtained from a mosaic of Landsat images for the period 2010-2014 that had been created for the Ministry of the Environment of Ecuador. The mosaic was created using the approach described in Hansen et al. (2013) that includes (i) image resampling, (ii) conversion of raw digital values (DN) to top of atmosphere (TOA) reflectance, (iii) cloud/shadow/water screening and quality assessment (QA), and (iv) image normalization. A principal components analysis was performed using the three RGB bands of the mosaic and the first component, that explained 96.4% of the variance, was used for further analysis.

Statistical Analysis We performed forward selection procedure with permutation test at 5% of significance in geomorphological and climatic variables to determine which variables must be included as significant predictors of taxonomic and lineages turnover in subsequent an analysis (Legendre et al. 2009, Peres-Neto and Legendre 2010). The variation in both phylogenetic and taxonomic composition of the tree communities was partitioned with respect to climate, geomorphology, and soils. Canonical redundancy analysis with axis 1 of the PCA using climate,oils and the geomorphological variables and the axis 1 of the NMDS based on taxonomic and phylogenetic dissimilarity was performed for variation partitioning. For the analysis we treated used a full model including all the explanatory variables (climate, soils, geomorphology) and also partial models using one variable as explanatory and the others as covariables in the model. We decided to use this approach because RDA analysis allows us to account for the unique contribution of climate and geomorphology. Finally multiple regressions on distance matrices analysis (MRM) was performed to compare the results of the Generalized Additive Model (GAM) with those obtained using dissimilarity matrices. In contrast with partial Mantel tests in which different environmental variables are combined into a single multivariate distance, one of the advantages of MRM is that we can separate the individual effect of environmental variables (Lichstein 2007). Forward selection of geomorphological and climatic variables was performed with the “packfor” library (Dray et al. 2009). Phylogenetic and taxonomic beta diversity analyses were carried out with the “picante” (Kembel et al. 2010) and “vegan” (Oksanen et al. 2007) libraries from the R

122 statistical language (R Development Core Team 2007). Redundancy analyses were carried out with the “vegan” (Oksanen et al. 2007) package from R and MRM analysis were carried out with the package ecodist (Goslee & Urban 2007). Results The influence of geology, soils, and climate on PBD and TBD The results of the SRTM and geology maps revealed that, even though there is a correlation between geology and turnover in taxonomic and phylogenetic composition between those plots located in the high terraces of Aguarico and Napo rivers and those plots on plateaus of Cordillera del Condor, strong overlap between floristics and geology also correlates with the composition of Amazon tree communities (Fig. 1and Fig. 2). We found that plots located on the Cordillera del Condor plateaus represent tree communities phylogenetically and taxonomically differentiated from the rest of the Ecuadorian Amazon tree communities (Fig.2). The predominant geological formation in this area is the Tena Formation, which dates from the Cretaceous. The forests located on alluvial terraces of Aguarico and Napo Rivers also represent floristically distinct tree communities, although there is significant overlap in terms of composition and geology between these forests and those located toward the southern bank of Napo River. These plots are located in areas that we identified as alluvial deposits from Quaternary origin. Plots located in areas such as Yasuní national Park that correspond to Curaray Formation (Miocene origin) are both taxonomic and phylogenetically more similar to those plots located in areas toward the south of Yasuní on Chambira or Mera formations from Mio-Pliocene and Plio-Pleistocene origin respectively. Moreover we found that the forests on the Pastaza megafan characterized by soils derived from the Mera formation (Pleistocene) share taxonomic and phylogenetic relationships with tree communities located on the rolling plains and hilly areas of Yasuní and the Tigre and Corrientes watersheds (Fig. 1). These results are strenghtened when adding soil information to the analysis. For instance, plots with high content of sand and P can be found on areas that a priori correspond to different geological units such as the plots located in Cordillera del Condor in a Cretaceaous geological unit, Yasuni National Park on Miocene geological units, and alluvial terraces from the Pleistocene located in the north and south banks of the Aguarico River (Fig. 3, Table 1). When we considered the influence of each variable associated with morphology and soils as predictors of PBD we found that soils are significantly associated with lineage turnover, while variables associated with geomorphology such as DEM are significantly associated with taxonomic turnover,(Table 1). The GAM models accounting for TBD gave different results regarding the spatial the influence of soils, climate and geomorphology Soils were highly significant predictors of TBD when geomorphology was also considered in the model (p = 0.0000595). DEM was the only individual geomorphological variable that was found to be a good predictor of TBD when climate was also included in the model (Table 1). There was a highly significant association between climate and lineage turnover among the communities and this association was highly significant (Table 1). The results of the MRM also showed that climate was the main predictor of PBD even when soil distances were included in the analysis (Table 2). TBD patterns were also better predicted by climate (r = 0.264, p=0.0001) than by soils (r = 0.08, p=0.002).

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Partitioning the influence of geomorphology, soils and climate on PBD and TBD patterns The variation partitioning analysis via RDA determined that climate individually explained %19 of the variation in species turnover while geomorphology explained a significantly smaller fraction (Fig. 4). The fraction explained by soils for the variation in of species turnover was 3% meanwhile the shared fraction of all environmental variables contributed to explain a larger fraction of the variation in species turnover (Fig. 4). Geomorphology alone explains a small fraction of variation in lineages composition (3 %; Fig. 4). When considered alone, climate explained the greatest fraction of the variation in the lineages turnover of the forests we studied the results of the the partitioning analysis via RDA shows (Fig. 4). Meanwhile, geomorphology explained just a small fraction of the variation in the patterns of PBD (3% of explained variation). The shared fraction of all environmental variables accounts for almost 20% of explaining (Fig. 4). A large fraction of variation in both lineages and species composition is not explained by geomorphology, soils or climate (67 % and 53% respectively).

Discussion Evidence for the role of soil heterogeneity and its associated geology in the patterns of plants species composition has been demonstrated for groups such as ferns, herbs (e.g. Zingiberaceae), shrubs (e.g. Melastomataceae), palms and trees (Tuomisto et al. 2002, Phillips et al. 2003, Vormisto et al. 2004, Higgins et al. 2013, Figuereido et al. 2013). We found contrasting results with regard to the prevailing hypothesis of geological control over Amazon tree communities. While geomorphological variables and associated soil conditions explained a large fraction of both species and lineage turnover, we found that geology alone is a weak predictor of PBD and TBD. Previous studies have concluded that the role of geology is fundamental in explaining floristic patterns in Amazonian plant communities. Such studies have pointed out the dichotomization between older Miocene Pebas formation (or the analogous Solimoes formation in Brazil), associated with rich nutrients, and the younger nauta formation (Ica formation for Brazilian Amazon) associated with poor nutrient sediments (Sombroek et al. , Higgins et al. 2011, Tuomisto et al. 2016). While this dichotomization might be useful to explain the patterns seen in those studies, we believe the geological history of Western Amazonia and particularly in Ecuadorian Amazon is more complicated, making it hard to assume a clear relationship between geology and underlying soil conditions and changes in tree species composition. Widespread removal of the younger and poor-nutrient sediments derived from the Ica and nauta formation (Brazil and Peru respectively), instead of recent deposition of nutrient rich sediments derived from the Andean orogeny, might be the predominant processes generating the current mosaic of soils and consequently the patterns of TBD seen in Western Amazonia forests (Higgins et al. 2011; Tuomisto et al. 2016). However, recent deposition of both nutrient-rich sediments and fluvial or deltaic cation-poor sediments occurring since the Pleistocene could have produced the complex geological patterns which in turn could have promoted the floristic patterns we observed in this study. The present day Pastaza mega fan is a massive depositional area that covers 51400 km2 located in southwestern portion of Ecuadorian Amazon characterized by recent deposition of volcanoclastic and sedimentary material (Bernal et al. 2011). Plots located in this area are both taxonomically and phylogenetically similar to forests located towards the south of Napo River on the Miocene Curaray formation (Fig. 1b, Fig. 2). On the other hand, alluvial terraces on the Aguarico and Napo rivers with high content of sand derived from Plio-Pleistocene depositional processes share phylogenetic components with plots in the Condor Cordillera with

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similar sandy soils but originated from the older Tena Formation that predates the Cretaceous (Fig. 2c,d) While we agree that erosional processes may be important for removing the most recent cation- poor sediments deposited during the Pleistocene (Latubrese et al. 2010), deposition of younger sediments is also important, particularly in the Pastaza megafan and the alluvial terraces of the Aguarico and Napo rivers (Bes de Berc et al. 2005; Laraque et al. 2009). Furthermore, plots located in the area of Yasuni National park on clay soils derived from the Miocene Curaray formation are phylogenetically related to plots in areas with similar soil characteristics but derived from younger geological formations such as the Chambira formation (Mio-Pliocene origin) and the Pastaza fan (Pleistocene origin). We found that plots located in the same geological formation but sharing no geomorphological features in common (e.g., terraces vs. rolling plains) also exhibit differences in soil conditions. Our results are in agreement with a myriad of studies that have found a strong correlation between soil differences and both species and phylogenetic turnover (Fine and Kembel 2011; Tuomisto et al. 2002; Tuomisto et al. 2016; Higgins et al 2011). However, differences in edaphic conditions are not necessarily related to the underlying geological formations that potentially produce the observable and measurable sediments. For instance, we found that plots with unusual high content of white sands or brown sands located in Cordillera del Condor are edaphically similar to plots located in alluvial terraces towards the north of Napo River, but both phylogenetically and taxonomically dissimilar (Fig. 3). Moreover, we found that geomorphological features (e.g. plateaus) originating from completely different geological units share a high proportion of lineages producing low values of PBD (Fig. 2). Both gradual change and abrupt shifts in geology and underlying soils conditions might operate simultaneously as drivers of PBD and TBD, not just in Ecuadorian Amazon but we predict that the same complexity may also occur along the Western Amazonia, being more predominant in areas close to the Andean foothills. Our results support the idea of a strong role of soils in the pattern of TBD but a weaker relationship of soils and geomorphology on the patterns of PBD. Two main ideas may help to explain the patterns we found. First of all, in a geologically and edaphically complex system such as the one we studied is more plausible that parapatric or “mosaic sympatric” speciation may operate at smaller spatial scales as drivers of species proliferation (Mallet 2008). Divergent natural selection on the boundaries of soils habitats with strong differences should trigger adaptations to one or another side leading to stronger species turnover than lineage turnover in tree communities between soils habitats associated with a particular geological formation. On the other hand, we would expect higher values of PBD if trait conservatism for the soil niche axis is prevalent in the tree community and large clades are turning over between soil types. Secondly, while we agree soils have a strong influence at local and landscape scale in the patterns of TBD we posit this effect might not be strong for tree communities at regional scales and across edaphic gradients. Evidence for niche lability related to soil conditions have been found in tropical tree communities meaning that niche conservatism related to habitat specialization might not be a ubiquitous pattern (Kembel 2013, Fine et al. 2006). This has strong implications in detecting patterns of both lineage and species turnover because if niche axis related to soils is labile in most

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of Amazon tree lineages this might produce low phylogenetic beta diversity across soil gradients (Anacker and Harrison 2012). We found that climate was the most important predictor of both species and lineage turnover at broader spatial scales. These results are in agreement with recent evidence that posit the idea of climatic affiliation or drought sensitivity as driver of tree species distribution and composition (Esquivel- Muelbert et al. 2016, Eisehardt et al. 2013). Limited climatic niche evolution or mesoclimatic niche conservatism might prevent lineages and species from colonizing other climates than those ones to which they are adapted due to physiological constraints. Mesoclimatic niche conservatism related to annual precipitation has been found to be a strong predictor of lineage turnover at regional scales in tropical forests of Panama and India (Hardy et al. 2012). The tree communities in Ecuadorian Amazon may follow this pattern (Fig. S3, Fig. S4); plots toward the east of the basin show a strong association with precipitation seasonality and mean temperature of the driest quarter, while plots in the lowlands of Cordillera del Condor, and western plots are more associated with annual precipitation of the wettest quarter and precipitation of the wettest month (Fig. 3b). A similar pattern is observable along longitude when considering temperature (Guevara et al. 2016). Esquivel-Muelbert et al. (2016) demonstrated that precipitation affiliation was stronger at the species level, than genus or family level, which could be related to recent events of speciation mediated by Quaternary climatic fluctuations. This climatic affiliation was also related to the wettest extreme of the gradient the authors investigated, which are stronger in groups such as Brownea, Wettinia, Ardisia or Phragmotheca, which in turn are genera with high relative abundance in the western plots of our study. While climatic stability has been argued to drive the patterns of plant species distribution in the neotropics and in the Amazon (Morueta-Holmes et al. 2014), our results posits the idea that regional or even landscape climatic fluctuations might contribute to the patterns of lineage and species composition in Western Amazonia. For instance, strong lineages and species turnover along a longitudinal axis in Amazon forests may be associated to the west-east gradient in precipitation. In this way one should expect lineages adapted to wetter conditions to dominate forests to the west of basin while lineages recently adapted to drier conditions should be more dominant toward the east. Furthermore, our results could be interpreted as evidence to support the assertion that even small gradients in precipitation or length of dry period could have stronger implications for species distributions than soil differences (Engelbrecht et al. 2007, Hardy et al. 2012). In fact, the spatial scale at which we reported a strong influence of climate on floristic composition is considerably smaller than the spatial gradient reported by ter Steege et al. 2006 which includes the entire Amazon basin. Together our results support the idea that the synergistic effect of the west-east rainfall gradient along the equatorial band of South America, and the Andean orogeny promoting an edaphically and geological complex landscape, trigger the lineage and species turnover across the west-east axis in Western Amazonia (Kerkoff et al. 2014, Antonelli et al. 2011, Esquivel-Muelbert et al. 2016). The Andean orogeny changed dramatically both the climate and the edaphic landscape of Amazonian forests, creating geographic barriers that might impede dispersal of lineages (e.g. Pebas system hypothesis) while simultaneously creating extreme edaphic heterogeneity and patchiness (Wessenlingh and Salo 2006, Hoorn et al. 2010). Nonetheless, further studies should also focus on testing the role of Neogene-Quaternary climatic fluctuations produced by the Andean

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uplift (mostly precipitation) in the patterns of PBD and TBD by testing explicitly climatic niche conservatism via ancestral climate reconstructions. Thus we posit that effects of geomorphology and soils do not just occur at large or mid-spatial scales as previously reported (Higgins et al. 2011, Tuomisto et al. 2016) but may also be important at finer spatial scales. On the other hand, climate may be responsible for patterns seen at larger scales, by filtering out lineages with potential physiological constrains related to temperature and drought sensitivity. Thus climate, rather than geomorphology or soils, might operate as the major driver for the composition of the regional species pool by influencing speciation and extinction processes (Lessard et al. 2011, Mittlebach & Schemske 2015). Certainly, geomorphology and geology play an important role in Amazonian tree species composition and this influence can be observed at spatial scales larger than the one we used in our study (Mendes et al. 2016, Tuomisto et al. 2016). However, the oversimplified dichotomization of old vs. young geological formations, and the supposedly underlying soil differences that lead to abrupt shift in species composition, should not be considered as the general driver for Amazonian tree species assembly. Acknowledgements We are indebted to the indigenous communities inhabiting Ecuadorian Amazon without which would have been impossible to carry out this research. This work was benefited by the Garden Club of America award for Tropical Botany, the National Secretary of Science and Technology of Ecuador (Senescyt) grant for field research, the Summer Research Award from the University of California, Berkeley and the Lewis and Clark Fund for Exploration and Field Research from the American Philosophical Society. We thank the Ministry of Environment of Ecuador for granting permit MAE-DNB-CM-2015-0017.

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Table 1 Generalized Additive Model results showing the effect of soils, climate and three geomorphological variables (dem, slope and vshp) on Phylogenetic Beta Diversity and Taxonomic Beta Diversity patterns in tree species communities of the Ecuadorian Amazon. The significance of the effect of each individual variable is also shown; climate was the strongest predictor of both PBD and TBD (p < 0.001) at every spatial scale. Symbols *** highly significant at p < 0.0001, ** significant at p < 0.005 and * significant at p < 0.001.

Climate Soils Slope Vhsp dem (PC1) (PC1) Model Phylogenetic dissimilarity ~ Soils + 0.00292 - 0.2640 0.62681 0.41101 Geomorphology ** Phylogenetic dissimilarity ~ Climate + 0.0000932 *** - 0.1489 0.00538 0.02167 Geomorphology Phylogenetic dissimilarity ~ Climate + Soils 0.000752 *** 0.200527 0.9617 0.80153 0.36434 + Geomorphology

Taxonomic dissimilarity ~ Soils + 0.0000595 - 0.1074 0.6312 0.2187 Geomorphology *** Taxonomic dissimilarity ~ Climate + 0.00578 0.0000014 *** - 0.6352 0.38298 Geomorphology ** Taxonomic dissimilarity ~ Climate + Soils 0.000541 *** 0.012009 0.5538 0.78026 0.59758 + Geomorphology

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Table 2. Multiple Regression on Distance Matrices analysis based on environmental and geographic distance matrices. Pearson correlation for soils, climatic and geographic distances were all significant at p < 0.05, highly significant correlations with the highest r values are shown in bold.

MRM r coefficient p-value

Taxonomic Beta Diversity

TBD~ Soils + Climate + Geography 0.352 0.0001 Soils 0.08 0.002 Climate 0.264 0.0001 Geographic distance 0.302 0.0001

Phylogenetic Beta Diversity

PBD~ Soils + Climate + Geography 0.323 0.0001 Soils 0.05 0.007 Climate 0.237 0.0001 Geographic distance 0.291 0.0001

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Figure legends Figure 1. Study site map showing the geological map of Ecuadorian Amazon overlapped to a SRTM model of the region. Symbols and colors represent the plot locations and the results of non- metric multidimensional analysis using the results of a hierarchical cluster analysis (Average method resulted in the highest coefficient method) based on a phylogenetic dissimilarity matrix (1-Phylosorenson). Figure 2. NMDS ordination results based on Taxonomic Beta Diversity (TBD) and Phylogenetic Beta Diversity (PBD) with colored dots representing different geological formations, geomorphological features and geographic locations according to NMDS axis 1 and axis 2 values. Boxes a) and b) ordinations showing groups of phylogenetically and taxonomically similar plots, convex hulls represent the 90% of confidence intervals of grouping plots on the basis of the geological formations at a; boxes c) and d) ordinations represent groups of plots phylogenetically and taxonomically similar on the basis of geological formations, convex hulls represent 90% confidence intervals of grouping plots based on the results of hierarchical cluster analysis (Average method); boxes e) and f) represent ordinations showing groups of plots phylogenetically and taxonomically similar on the basis of geomorphological features, convex hulls represent 90% confidence intervals of grouping plots by geological formations. Figure 3. Principal Component Analysis of 40 one hectare plots in Ecuadorian Amazon; a) PCA analysis based on soils variables with colored dots indicating the geological formation in which the plots are located; b) PCA analysis based on 19 climatic variables from Wordclim database, colored dots represent the floristic subregion the plots belongs to using a hierarchical cluster analysis (Average method) based on taxonomic and phylogenetic dissimilarity matrices. Figure 4. Variation partitioning analysis using Canonical Redundancy Analysis (RDA) showing the fraction of variation in lineages and taxonomic turnover explained by climate, geomorphology and soils; the fractions represent the individual contribution after controlling for the effects of remaining predictors as co-variables in the RDA analysis. Significance of the effect of each predictor was tested via ANOVA analysis on each RDA model with a Monte Carlo permutation test. Climate contribution was highly significant at p < 0.001 while soils contribution was significant at p < 0.05 to explain patterns of TBD, climate contribution was significant at p< 0.05 to explain patterns of PBD.

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Figure 1.

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Figure 2.

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Figure 3.

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Figure 4.

Climate TBD Soils

Geomorphology

Shared (all environment) Unexplained PBD

0% 20% 40% 60% 80% 100%

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Appendices Table S1. Climatic variables used in this study after a forward selection with permutation analysis for 80 one hectare plots established in Ecuadorian Amazonia. Climatic variables were obtained from the Worldclim database at 30 seconds spatial resolution. Abbreviations of the climatic variables are as follows: Bio3 = Isothermality, Bio4 = Temperature Seasonality, Bio5 = Max Temperature of Warmest Month, Bio8 = Mean Temperature of Wettest Quarter, Bio9 = Mean Temperature of Driest Quarter, Bio10 = Mean Temperature of Warmest Quarter, Bio12 = Annual Precipitation, Bio13 = Precipitation of Wettest Month, Bio14 = Precipitation of Driest Month, Bio15 = Precipitation Seasonality, Bio16 = Precipitation of Wettest Quarter, Bio18 = Precipitation of Warmest Quarter, Bio19 = Precipitation of Coldest Quarter.

Bio Bio Bio Bio Bio Bio Bio Bio Bio Bio Bio Bio Bio

3 4 5 8 9 10 12 13 14 15 16 18 19 ALTA_FLOR 82 616 313 247 261 262 2719 325 150 24 908 463 804 ALTA_FLOR2 83 600 313 248 261 262 2699 319 149 24 897 458 799 BALSAURA 88 490 302 241 248 250 3320 338 210 15 960 671 871 BATABURO 87 453 307 246 253 254 3177 346 202 18 971 630 834 BOG_01 86 457 307 246 252 254 3166 332 204 16 953 642 833 BOG_02 86 457 307 246 252 254 3166 332 204 16 953 642 833 BOG_03 86 457 307 246 252 254 3166 332 204 16 953 642 833 BOG_04 86 457 307 246 252 254 3166 332 204 16 953 642 833 BOG_05 86 457 307 246 252 254 3166 332 204 16 953 642 833 BOG_06 86 451 308 247 253 255 3135 329 200 16 943 631 829 BUFEO 87 507 309 246 255 255 3090 328 203 15 898 628 835 CAN_01 85 455 308 246 252 254 3169 330 203 15 952 644 831 CEIBA 86 473 309 247 254 255 3122 328 196 16 939 623 832 CHIWIAS 85 554 316 254 250 261 2633 272 182 13 767 586 635 CHUCULA 81 653 316 250 264 265 2701 317 137 27 927 430 836 CONAMBO 87 489 309 247 255 256 3166 344 208 17 941 627 860 CYB_LG_1 86 465 310 251 256 258 3273 345 189 19 989 605 922 CYB_LG_2 86 465 310 251 256 258 3273 345 189 19 989 605 922 DICAM_01 86 473 310 250 256 257 3025 326 180 18 919 571 851 DICAM_02 86 473 310 250 256 257 3025 326 180 18 919 571 851 DICARO_01 86 472 310 247 257 258 3000 325 177 18 916 560 850 DICARO_02 86 472 310 247 257 258 3000 325 177 18 916 560 850 DICARO_03 86 472 310 247 257 258 3000 325 177 18 916 560 850 EC_JUY 86 501 313 249 260 260 2991 328 193 16 890 605 836 GUEPI 85 575 313 250 262 262 2741 327 138 26 918 440 872 GUEPPI 85 575 313 250 262 262 2741 327 138 26 918 440 872 HERRADURA 86 473 309 247 254 255 3122 328 196 16 939 623 832 HOATZIN 81 653 317 251 265 266 2692 317 137 27 925 428 834 JAGUAR 86 466 309 247 254 255 3116 329 197 16 939 621 832 JAS_02 89 403 296 236 242 243 3654 415 199 22 1194 680 1145 JAS_03 89 403 296 236 242 243 3654 415 199 22 1194 680 1145

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JAS_04 89 403 296 236 242 243 3654 415 199 22 1194 680 1145 JOY_SACH 88 398 304 243 243 251 3488 383 207 17 1068 742 957 KAMBA_01 85 551 312 252 245 258 2402 257 162 16 748 513 569 KAMBA_02 85 551 312 252 245 258 2402 257 162 16 748 513 569 KAMPA 84 559 314 253 246 260 2402 257 161 16 747 509 562 KAPAWI1 86 527 313 250 258 259 2858 289 192 13 814 610 790 KAPUT_01 84 559 314 253 246 260 2402 257 161 16 747 509 562 KAPUT_02 84 559 314 253 246 260 2402 257 161 16 747 509 562 KAPUT_03 85 551 312 252 245 258 2402 257 162 16 748 513 569 KM21 86 445 306 244 251 252 3230 334 207 15 967 661 842 KURINTZA 86 519 308 245 254 255 3155 338 208 16 923 632 858 PAKINTZA 85 544 312 250 255 257 2911 293 198 13 811 621 740 PANTANO 86 466 309 247 254 255 3116 329 197 16 939 621 832 PARCHE 81 669 317 251 265 266 2691 318 136 27 927 425 838 PAYAMINO 89 393 302 242 242 250 3273 354 182 18 1015 690 924 PILIMOSCA 81 669 317 251 265 266 2691 318 136 27 927 425 838 PIR_01 86 450 308 247 253 255 3142 329 200 16 944 634 830 SALADERO 85 486 307 245 252 254 3138 337 199 17 952 617 850 SANGUIJELA 86 466 309 247 254 255 3116 329 197 16 939 621 832 SAWASTIAN 86 528 315 254 259 260 2721 271 188 12 763 596 682 SEWAYA 85 446 309 248 253 255 3196 337 190 17 963 613 882 SHI_01 86 449 307 245 251 253 3212 330 205 15 959 660 831 SHI_02 86 449 307 245 251 253 3212 330 205 15 959 660 831 SHI_03 86 449 307 245 251 253 3212 330 205 15 959 660 831 SHIRAM_E 85 539 315 255 250 261 2577 268 178 14 762 566 615 SHR_01 88 451 304 243 250 252 3200 349 183 20 995 641 941 SHUSHUFINDI 86 437 307 245 251 253 3455 366 224 16 1050 705 859 STA_TERESITA 84 581 314 249 261 262 2666 315 145 24 885 447 789 TARANGARO 88 466 299 238 244 246 3536 389 198 19 1094 674 934 TIGRILLO 87 449 308 248 254 255 3106 328 194 16 936 615 835 TINKIAS 86 501 312 250 258 258 2915 306 191 14 842 614 773 TIP_01 86 489 309 249 256 256 3046 327 185 17 924 586 841 TIP_02 85 483 310 250 256 257 3009 326 180 18 918 567 845 TIP_03 86 489 309 249 256 256 3046 327 185 17 924 586 841 TIP_04 86 457 307 246 252 254 3186 329 206 15 954 655 825 TIP_05 85 483 310 250 256 257 3009 326 180 18 918 567 845 TSUIRIM 85 576 305 244 250 252 2612 272 167 15 779 545 660 TUKUP 85 544 316 256 250 262 2558 262 176 13 750 560 610 VILLANO 89 470 298 238 245 246 3569 389 199 19 1106 685 934 WASURAK 85 545 315 253 257 259 2729 277 187 13 775 598 676 YARINA 81 669 317 251 265 266 2691 318 136 27 927 425 838 YAS_JC 83 630 312 246 260 261 2740 324 153 24 911 473 811 YASUNI_JG 86 472 310 247 257 258 3000 325 177 18 916 560 850 YUTURI 86 462 310 247 257 258 2984 324 174 19 916 552 853

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ZANCUDO 83 608 315 251 263 264 2663 309 142 26 900 431 815 ZANCUDO_02 83 599 315 250 263 263 2669 310 143 26 899 433 818 ZOJECHOE 85 545 311 248 260 260 2813 321 149 24 922 472 873 ZOJECHOE_02 85 531 312 250 261 261 2789 316 150 23 909 472 856

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Table S2. Soil variables for a subset of 40 one-hectare plots in Ecuadorian Amazon that were used in this study. Principal nutrients content (P = phosphorous, K = Potasium, Ca = Calcium, Mg = Magnesium) were measured in parts per million (ppm) meanwhile soil texture (sand, silt, clay) and organic matter were measured as percentage.

Orgmat P K Ca Mg Sand Silt Clay pH (%) (ppm) (ppm) (ppm) (ppm) (%) (%) (%) ALTA_FLOR 3.78 2.7 4.5 116.3 4 21.5 46.2 25.6 28.2 ALTA_FLOR2 3.63 2.96 4.5 172.1 4 31.5 11.9 66.7 21.4 BOG_01 4.3 4.36 1.8 50 117 20 15 41 44 BOG_02 6.1 4.75 5.2 110 182 20 47 25 28 BOG_03 4.6 1.94 0.3 50 87 34 51 23 26 BOG_04 5 5.31 3 60 115 19 20 20 60 BOG_05 6.1 2.33 0.2 200 358 62 35 21 44 BOG_06 4 2.67 0.2 40 23 12 27 25 48 CAN_01 3.8 3.93 1.5 50 30 14 14 32 54 DICAM_01 3.7 2.12 3.2 50 52 4 1 25 70 DICAM_02 3.8 21.92 3.5 50 146 15 1 26 71 DICARO_01 4.6 2.85 2.8 30 203 20 13 39 48 DICARO_02 4.2 5.31 4 40 165 21 9 43 48 DICARO_03 4.3 4.34 3.5 40 180 21 1 27 72 GUEPI 4.95 9.11 6.053 63 53 23 70 17 13 GUEPPI 4.85 0.94 4.28 65 50 19 53 30 17 KAMBA_01 3.25 1.01 2.1 30 3 8.5 80 16 4 KAMBA_02 3.47 1.59 1.3 29.4 5 9.2 74.3 1.4 24.3 KAMPA 3.47 2 4.5 59.4 4 8.9 73.3 23.3 3.4 KAPAWI 3.82 4.65 0.097 69.4 2 11.4 65.4 17.8 16.8 KAPUT_01 3.16 1.78 4.5 72.6 4 23.4 78.3 13.3 8.4 KAPUT_02 3.32 3.74 4.8 46 4 7.96 78.2 1 21.7 KAPUT_03 4.09 3.98 2.44 34.6 4 7 62.5 35 2 PARCHE 3.29 2.99 1 159.6 5 52.7 1 1 98 PIR_01 3.7 3.48 1.2 40 20 21 27 37 36 SHI_01 4.2 4.55 1 40 50 21 15 25 60 SHI_02 3.9 4.91 0.2 40 25 21 15 17 68 SHI_03 3.9 5.23 1 50 25 21 27 19 54 STA_TERESITA 3.9 3.5 4.3 41 30 21 44 46 10 TIP_01 4 4.24 0.5 40 40 21 31 27 42 TIP_02 4.1 5.31 2 50 47 21 11 29 60 TIP_03 4.7 4.38 3.2 50 145 21 21 47 32 TIP_04 4.3 8.7 4 50 41 21 3 35 62 TIP_05 4.5 5.36 1.5 40 78 21 11 31 58 YARINA 3.29 2.99 1 159.6 5 52.7 1 1 98 YASUNI_JG 4.3 4.62 1.8 30 172 21 1 37 62 YUTURI 4.6 2.8 3.5 56 189.5 21 50 34 16 ZANCUDO 4 3.4 7.8 56 200 21 66 22 12 ZANCUDO_02 3.7 3.2 4.5 50.4 4 4.4 11.1 52.8 36.1 ZOJECHOE 4.83 2 4 65.8 636 20.5 30 69 1 ZOJECHOE_02 4.71 2.5 0.83 115.3 303 15.1 33 64 3

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Table S3. General attributes for the 80 one hectare plot network, the results for axis 1 and 2 of a non-metric multidimensional analysis based on taxonomic (TBD) and phylogenetic beta diversity (PBD) and the age of the geological formation is also shown. Both the type of formations and the age of each geological formation were obtained from the geological map developed by Instituto de Investigaciones Geológicas Mineras Metalúrgicas of Ecuador.

N. of Individual Fores NMDS NMDS Geology Latitude Longitude species s t 1 PBD 1 TBD ALTA_FLOR Pleistocene -0.89 -75.46 233 476 TF -0.120 -0.084 ALTA_FLOR2 Pleistocene -0.90 -75.46 131 313 TF -0.194 0.036 BALSAURA Pliocene -1.93 -77.28 119 566 TF -0.063 0.028 BATABURO Miocene -1.20 -76.72 197 452 TF -0.069 -0.072 BOG_01 Miocene -0.70 -76.48 206 507 TF 0.129 -0.178 BOG_02 Miocene -0.70 -76.47 212 513 TF 0.165 -0.190 BOG_03 Miocene -0.70 -76.47 196 501 TF 0.134 -0.167 BOG_04 Miocene -0.70 -76.48 216 582 TF 0.111 -0.183 BOG_05 Miocene -0.70 -76.48 199 504 TF 0.204 -0.201 BOG_06 Miocene -0.67 -76.43 194 576 TF 0.003 -0.093 Mio- BUFEO -2.20 -76.77 102 338 TF -0.061 0.072 Pliocene CAN_01 Miocene -0.63 -76.47 254 687 TF 0.119 -0.221 Mio- CEIBA -0.66 -76.38 112 435 VA 0.141 0.024 Pliocene CHIWIAS Pleistocene -2.67 -77.50 128 398 TF 0.122 -0.102 CHUCULA Pleistocene -0.63 -75.24 90 480 VA 0.085 0.159 CONAMBO Miocene -1.82 -76.70 173 715 TF -0.026 -0.079 CYB_LG_1 Pleistocene -0.02 -76.18 140 428 TF -0.129 0.045 CYB_LG_2 Pleistocene -0.02 -76.18 146 545 TF -0.251 0.123 Mio- DICAM_01 -0.57 -76.13 32 164 SW 0.226 0.242 Pliocene Mio- DICAM_02 -0.57 -76.13 66 382 SW 0.213 0.159 Pliocene Mio- DICARO_01 -0.57 -76.12 208 458 VA 0.169 -0.193 Pliocene Mio- DICARO_02 -0.57 -76.12 205 505 VA 0.147 -0.146 Pliocene DICARO_03 Miocene -0.57 -76.12 49 760 SW -0.048 0.241 EC_JUY Miocene -2.13 -76.20 163 442 TF -0.139 0.006 GUEPI Miocene -0.11 -75.52 154 463 TF -0.123 0.064 GUEPPI Miocene -0.11 -75.53 59 249 IG 0.043 0.205 Mio- HERRADURA -0.66 -76.38 65 307 VA 0.169 0.108 Pliocene HOATZIN Miocene -0.61 -75.23 98 409 IG 0.196 0.026 Mio- JAGUAR -0.67 -76.39 114 331 VA 0.145 -0.009 Pliocene JAS_02 Pleistocene -1.07 -77.61 180 537 TF 0.051 -0.107 JAS_03 Miocene -1.08 -77.61 182 536 TF 0.035 -0.147 JAS_04 Pleistocene -1.07 -77.62 136 534 VA 0.053 -0.075

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Mio- JOY_SACH -0.31 -76.89 66 92 TF 0.244 -0.001 Pliocene KAMBA_01 Cretaceous -3.02 -77.92 72 519 PZ -0.508 0.351 KAMBA_02 Cretaceous -3.02 -77.92 139 516 PZ -0.169 0.019 KAMPA Cretaceous -3.02 -77.91 85 244 PZ -0.415 0.333 KAPAWI1 Pliocene -2.52 -76.83 141 355 TF -0.179 0.043 KAPUT_01 Cretaceous -3.02 -77.91 101 212 PZ -0.444 0.281 KAPUT_02 Cretaceous -3.02 -77.92 30 80 PZ -0.569 0.504 KAPUT_03 Cretaceous -3.02 -77.92 78 488 PZ -0.427 0.312 KM21 Miocene -0.55 -76.52 241 706 TF 0.031 -0.177 Mio- KURINTZA -2.07 -76.75 128 366 TF -0.013 -0.033 Pliocene PAKINTZA Pleistocene -2.35 -77.28 104 364 TF -0.070 0.112 Mio- PANTANO -0.68 -76.40 173 479 VA 0.124 -0.087 Pliocene PARCHE Miocene -0.57 -75.23 161 507 TF -0.012 -0.005 PAYAMINO Pleistocene -0.47 -77.17 186 550 TF 0.009 -0.088 PILIMOSCA Miocene -0.56 -75.24 49 334 VA -0.070 0.295 PIR_01 Miocene -0.65 -76.45 215 518 TF 0.062 -0.127 SALADERO Miocene -0.81 -76.40 224 591 TF 0.057 -0.185 Mio- SANGUIJELA -0.67 -76.40 119 409 VA 0.136 0.016 Pliocene SAWASTIAN Pleistocene -2.64 -77.15 107 399 TF 0.187 -0.068 SEWAYA Pleistocene -0.29 -76.27 162 385 TF -0.091 0.028 SHI_01 Miocene -0.52 -76.53 231 539 TF 0.085 -0.199 SHI_02 Miocene -0.52 -76.53 234 596 TF 0.096 -0.195 SHI_03 Miocene -0.51 -76.53 194 512 TF 0.058 -0.136 SHIRAM_E Pleistocene -2.75 -77.56 94 244 TF -0.116 0.091 SHR_01 Pleistocene -1.02 -76.98 172 568 TF 0.078 -0.125 Mio- SHUSHUFINDI -0.21 -76.66 63 84 TF 0.227 0.029 Pliocene STA_TERESIT Pleistocene -0.85 -75.46 150 449 TF -0.107 0.039 A TARANGARO Pliocene -1.48 -77.33 101 151 TF 0.071 0.019 Mio- TIGRILLO -0.66 -76.36 187 493 TF 0.058 -0.125 Pliocene TINKIAS Pliocene -2.48 -76.71 95 118 TF -0.179 0.092 Mio- TIP_01 -0.63 -76.23 149 488 VA 0.192 -0.060 Pliocene Mio- TIP_02 -0.63 -76.15 213 502 TF 0.010 -0.103 Pliocene Mio- TIP_03 -0.63 -76.23 103 334 VA 0.259 0.001 Pliocene Mio- TIP_04 -0.61 -76.53 147 1060 SW 0.145 -0.017 Pliocene Mio- TIP_05 -0.63 -76.15 222 663 TF 0.131 -0.191 Pliocene TSUIRIM Paleocene -2.63 -77.80 52 241 TF 0.014 0.177

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TUKUP Pleistocene -2.80 -77.50 99 307 TF 0.070 -0.012 VILLANO Pliocene -1.47 -77.41 131 319 TF 0.097 -0.044 WASURAK Pleistocene -2.57 -77.36 139 206 TF 0.082 -0.114 YARINA Pleistocene -0.56 -75.23 158 632 TF 0.018 -0.036 YAS_JC Pleistocene -0.99 -75.45 96 430 VA 0.058 0.136 Mio- YASUNI_JG -0.55 -76.08 182 508 VA 0.178 -0.127 Pliocene YUTURI Pleistocene -0.53 -76.05 203 476 TF 0.115 -0.183 ZANCUDO Pleistocene -0.62 -75.38 188 477 TF -0.135 -0.042 ZANCUDO_02 Pleistocene -0.58 -75.42 105 439 TF -0.350 0.199 ZOJECHOE Pleistocene -0.32 -75.70 179 433 TF -0.210 0.014 ZOJECHOE_02 Pleistocene -0.34 -75.70 133 398 TF -0.262 0.098

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Figure S1. Phylogenetic tree for 1687 operational units recorded in a 80 one hectare plot network in Ecuadorian Amazon forests. Phylogenetic tree is based on Phylomatic backbone tree R20120829. Branch lengths were assigned with BLADJ algorithm in Phylocom based on inferred nodes ages (Wikstrom 2001).

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Figure S2. Correlations between PBD and TBD with respect longitude, latitude and loadings for the axis 1 from a PCA analysis based on 13 climatic variables.

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Figure S3. Longitudinal gradient in climate in Ecuadorian Amazon. Climate is represented by the loadings from a Principal Component Analysis for 13 Wordclim climatic variables at 30 seconds spatial resolution; a) and b) correlations between axis one of the PCA analysis with latitude and longitude; c) spatial variation in precipitation for Ecuadorian Amazon. Interpolation of precipitation values was done using the kriging method on the basis of monthly mean precipitation values at 1 km2 resolution; d) spatial variation in temperature for Ecuadorian Amazon. Precipitation values were interpolated using the kriging method on the basis of mean,maximum andmean temperature values at 1 km2 resolution (data from Ministerio de Ambiente del Ecuador, 2013).

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