Fast and novel botanical exploration of a 320-km transect in eastern Amazonia using DNA barcoding

ABSTRACT

We explored a 320-km transect in the Tumucumaque mountain range along the border between southern French Guiana and Brazil, sampling all trees and lianas with DBH ≥ 10 cm in seven 25 x 25-m plots installed near seven boundary milestones. We isolated DNA from cambium tissue and sequenced two DNA barcodes (rbcLa and matK) to aid in species identification. We also collected fertile herbarium specimens from other species (trees/shrubs/herbs) inside and outside the plots. The selected DNA barcodes were useful at the family level but failed to identify specimens at the species level. Based on DNA barcoding identification, the most abundant families in the plots were Burseraceae, Fabaceae, Meliaceae, Moraceae, Myristicaceae and Sapotaceae. One third of the images of sampled plants posted on the iNaturalist website were identified by the community to species level. New approaches, including the sequencing of the ITS region and fast evolving DNA plastid regions, remain to be tested for their utility in the identification of specimens at lower taxonomic levels in floristic inventories in the Amazon region.

KEYWORDS:
DNA barcoding; French Guiana-Brazil border; matK; rbcLa; tree inventory; Tumucumaque

RESUMO

Um transecto de 320 km foi explorado na Serra do Tumucumaque, ao longo da fronteira entre o sul da Guiana Francesa e o Brasil por meio da amostragem de todas as árvores e lianas com DAP ≥ 10 cm em sete parcelas de 25 x 25 m instaladas perto de sete marcos fronteiriços. Isolamos DNA de tecido cambial e sequenciamos dois códigos de barra de DNA (rbcLa e matK) para auxiliar na identificação das espécies. Também coletamos espécimes de herbário férteis de outras espécies (árvores/arbustos/ervas) dentro e fora das parcelas. Os códigos de barra de DNA selecionados foram úteis em nível de família, mas não conseguiram identificar espécimes em nível de espécie. Com base na identificação de DNA barcoding, as famílias mais abundantes nas parcelas foram Burseraceae, Fabaceae, Meliaceae, Moraceae, Myristicaceae e Sapotaceae. Um terço das imagens de plantas amostradas postadas no website iNaturalist foram identificadas em nível de espécie. Novas abordagens, incluindo o sequenciamento da região ITS e regiões de DNA plastidial de rápida evolução, ainda precisam ser testadas quanto à sua utilidade na identificação de espécimes até níveis taxonômicos mais baixos em inventários florísticos na região amazônica.

PALAVRAS-CHAVE:
código de barras de DNA; fronteira Guiana Francesa-Brasil; inventário de árvores; matK; rbcLa; Tumucumaque

INTRODUCTION

The Amazon region harbours the richest flora of the planet, yet many areas remain under-collected (Prance et al. 2000; Hopkins 2007). As a result, there is still considerable uncertainty about the total number of tree species occurring in this region (Cardoso et al. 2017; Ter Steege et al. 2019). In eastern Amazonia, the upper Jarí River is one of the most poorly sampled areas, which is mostly due to the challenging terrain of the Tumucumaque mountain range (Ter Steege et al. 2013; Zizka et al. 2018), where the tallest trees of Amazonia were recently detected (Gorgens et al. 2019).

In 2015, a survey team hiked 320 km along the border between French Guiana and Brazil, from the trijunction point with Suriname towards the Oiapoque River (Figure 1). The goals of the expedition were to clarify the exact location of the border (Le Tourneau et al. 2016), to explore the Tumucumaque mountain range, and to test the feasibility of a rapid botanical inventory based primarily on tissue collections and techniques requiring only lightweight equipment. In the past, progress in the knowledge of Amazonian flora has been obtained by establishing transects (Tuomisto et al. 2003; Pitman et al. 2008) and by setting up permanent sampling plots (Blundo et al. 2021). However, the French Guiana-Brazil border remains largely underexplored.

Figure 1
Environmental context of the 320-km transect. A - Transect route (in red) overlaid on a topographic map of Eastern Amazonia (SRTM product at 1 arc second resolution; downloaded from the USGS Earth Explorer website); blue-to-red colour ranges from 125 to 775 m a.s.l. within the study area; B - Location of the transect area along the border between southern French Guiana and Brazil; C - Ground elevation along the transect measured by a hand-held GPS unit; D - Plot of the difference between remote-sensed ground elevation (SRTM) and ground elevation (mean difference = 10.6 ± 7.9 standard deviation). This figure is in colour in the electronic version.

In the RAINFOR network of permanent sampling sites (http://www.rainfor.org/en/map), there is a dearth of data between the mouth of the Jari River and the Nouragues Research Station, which are more than 500 km apart. The Amazon Tree Diversity Network includes more information on the tree diversity of the Tumucumaque mountain range but is limited to the trijunction region (https://atdn.myspecies.info/node/2456). This part of the border between Brazil and French Guiana had been surveyed by the French National Geographical Institute (Institut Geographique National - IGN) in 1956-57. Later, in 1961-62 the binational Brazil/France border commission oversaw the construction of seven milestones on selected sites to demarcate the border (Le Tourneau 2017).

One method to facilitate the identification of sterile plant material is DNA barcoding, which consists of extracting and sequencing short orthologous DNA sequences for each collected plant, and comparing the obtained sequences to a publicly available reference database (GenBank, maintained by the National Center for Biotechnology Information; https://www.ncbi.nlm.nih.gov/genbank/). This method has proven to be effective for the identification of animal species (Hebert et al. 2003), due to the existence of a mitochondrial DNA region called cytochrome oxidase 1 (CO1). CO1 is a good DNA barcode because it is short enough for Sanger sequencing; it can be sequenced using the same pair of primers flanking the sequence in a wide range of taxonomic groups, and it is variable enough to discriminate between sister species (Hebert et al. 2003).

In plants, the search for universal DNA barcodes has been more difficult, and several strategies have been tested specifically for Neotropical plants (Gonzalez et al. 2009; Kress et al. 2009). The goal of this contribution is not to debate the utility of DNA barcodes for plant identification, but rather to use this approach to aid the taxonomic identification process. Hollingsworth et al. (2009) recommended the use of a combination of two plastid DNA regions, the first part of the rbcL gene (henceforth rbcLa), and a large fragment of the matK gene. Recently, Lima et al. (2018) conducted a survey on the publicly available DNA sequences from tree species of the flora of São Paulo state in Brazil and generated new sequences of three of the most widely used plant DNA barcodes (rbcL, matK and ITS) for 609 tree species of that flora. However, they did not assess the identification potential of the DNA barcodes they surveyed.

The primary goal of this contribution was to evaluate the potential of the DNA barcoding approach to aid in the taxonomic identification process in a poorly known tropical area, and to highlight the use of samples from cambium tissue for this purpose. Cambium tissue, as an alternative to collecting inaccessible canopy leaves, has already been used in previous DNA barcoding studies (Colpaert et al. 2005; Tibbits et al. 2006; Gonzalez et al. 2009; Novaes et al. 2009). We report on the results of our botanical exploration in this little explored area of Amazonia along the border of Brazil and French Guiana, including environmental conditions, forest structure and tree inventory. We explored the utility of extracting DNA from cambium tissue and the botanical identification potential of two widely used plant DNA barcodes, namely rbcLa and matK.

MATERIAL AND METHODS

The 2015 expedition involved 20 people (including 15 from the 3^rd Infantry Regiment of the Foreign Legion, part of the Forces Armées de Guyane) and lasted six weeks, with a weekly re-supply of food (Kew youtube 2016). Milestones were separated by 23-68 km, and given the rugged terrain, the team moved about 10 km per day. The route of the expedition was pre-planned but had to be adapted daily depending on local terrain conditions.

A hand-held GPS unit (Garmin 62) logged the location of the route. The elevation data provided a good opportunity to test the altimetry data from the Shuttle Radar Topography Mission (SRTM) at 30 m resolution in a little-explored area, and on rugged terrain. Using a Tinytag data logger (temperature and humidity; Gemini data loggers, Scientific House, Terminus Rd, Chichester, UK), we monitored environmental conditions throughout the route.

The route of the expedition is plotted in Figure 1 (a-b). Elevation measurements recorded with the GPS unit show that the terrain of the Tumucumaque range is rugged, with elevation varying between 200 m to 600 m a.s.l. (Figure 1c). Visual impression of the forest along the route are shown with panoramic photographs (Supplementary Material, Figure S1). Comparing ground data and SRTM data showed that the match in elevation was generally quite good, within 7.9 m (standard deviation), but with a systematic bias: SRTM tends to overestimate the elevation by about 10.6 m (Figure 1d). The Tinytag data logger showed that mean temperature did not display a trend along the transect, but varied principally due to daily variations, from 21°C to 27°C, with a few peaks above 30°C when the team reached tabletop inselbergs (Figure 2). Air humidity was consistently high, reaching 100% at night with a minimum around 80% at mid-day (Figure 3).

Figure 2
Air temperature along the transect measured by a Tinytag unit, with daily fluctuations.

Figure 3
Example of daily fluctuation of temperature and humidity during the transect route, measured by a Tinytag unit. The data are from 29 Jun 2015, at an altitude of 387 m a.s.l.

Seven randomised 25 x 25-m (0.0625 ha) plots were established around the seven country-boundary milestones. In each plot, each free-standing stem ≥ 10 cm DBH was sampled for cambium using a cleaned knife, as a rapid alternative to collecting herbarium specimens from trees for identification. Each sampled tree or liana was measured for DBH, and the tree height was recorded on a visual estimation.

We also sampled fertile material (herbarium specimens) from plants inside and outside the plots, during the march, and from some trees that were felled for a helicopter landing. The herbarium vouchers were deposited at Kew (K) and Cayenne (CAY). The herbarium specimens were directly identified by taxonomists based on morphological features. Field images associated with 167 herbarium specimens were also placed on the iNaturalist website (www.inaturalist.org/observations/willmilliken) to determine whether the specimens could be identified by other experts without knowledge of the corresponding herbarium vouchers. As we did not collect fertile specimens from the trees in the plots, we could not use herbarium identifications to support DNA identification.

The DNA samples from the cambium of trees and lianas in the plots were used for preliminary identification. These samples did not include the cork, but they did include the cork cambium, the phloem and the vascular cambium together, as recommended by Tibbits et al. (2006). Samples were individually wrapped in tea-filter paper, numbered, and immediately stored in an airtight plastic container with dried silica gel, following a procedure previously described in Gonzalez et al. (2009). A bark slash on each sampled tree, with the tree number, was also photographed. After the end of the trip, samples were shipped for DNA extraction and sequencing.

For DNA analysis, up to 30 mg of dry tissue of each cambium sample was ground for two minutes in a TissueLyser mixer-mill disruptor (Qiagen, California, USA) using tungsten beads. Lysis incubation was carried out at 65 °C for two hours, using a CTAB 1% PVP buffer. Total DNA extraction was performed with a Biosprint 15 workstation (Qiagen, CA) following the manufacturer’s protocols. PCR amplification was performed for the two plastid DNA barcoding regions selected. The rbcLa marker is the first half of the rbcL gene and was amplified using classic primers: 1F and 724R (Gonzalez et al. 2009). The matK region was amplified using two combinations of primers: 390F and 1326R (Cuénoud et al. 2002); 3F_Kim and 1R_Kim (Dunning and Savolainen 2010; Lima et al. 2018). The PCR reaction mix included 0.2 µl of GoTaq 51 U/µl (Promega), 10 ml of 5 x buffer, 1 µl of 20 µM for each primer, 1 µl of dNTP 10 µM, 1 ml of DNA template and H₂O for a final volume of 50 µl. PCR products were purified with a MinElute PCR Purification Kit (Qiagen, CA).

Cycle sequencing reactions were performed in 10 µl reactions using 1 µl of BigDye Terminator cycle sequencing chemistry (v3.1; ABI; Warrington, Cheshire, UK) and run on an ABI sequencer. The two genetic regions were sequenced in both forward and reverse directions. DNA fragments were visually inspected and assembled with Geneious v.8 and curated manually if necessary. The DNA sequences were then matched with BLAST against the NCBI reference nucleotide collection using Megablast, a plugin available in Geneious. Default options of Megabast were used, and for each sequence, the top hit was visually inspected in the resulting lookup table. The 193 rbcLa sequences were 328-681 nucleotides (nt) in length (three were less than 500 nt). Two sequences had a pairwise sequence similarity < 97%, and they were removed from subsequent analyses. The 227 matK sequences were 123-804 nucleotides in length (nine were less than 500 nt). Two sequences had a pairwise sequence similarity < 97%, and they were also removed from subsequent analyses. All sequences were submitted to NCBI, and GenBank accession numbers are available in the Supplementary Material, Tables S1, S2.

RESULTS

Overall, 279 trees were sampled in a total area of 0.4375 ha over the seven plots (Table 1), resulting in an estimated density of 642 trees with DBH ≥ 10 cm per hectare. In addition, 15 lianas were sampled from the plots. Average tree height was 16 m, and average basal area was 38.2 (24 - 58.5) m² ha^-1, with a tendency to smaller basal area in plots at higher elevations (387- 556 m a.s.l.) than the plots at lower elevations (285-366 m a.s.l.). A brief description of the plots is provided in the Supplementary Material (Figure S3).

Table 1
Basal area, maximal DBH, number of trees ≥ 10 cm DBH, and average tree height in each of seven plots (25 m x 25 m) sampled along a 320-km transect on the Brazil-French Guiana border. GPS coordinates of the plots are in WGS 84.

Plot	Basal area (m2 ha-1)	DBH max (cm)	Tree count	Average tree height (m)	Longitude	Latitude
1	29.95	65.5	47	15	-54.436817	2.209524
2	27.47	44.5	41	17	-54.190377	2.176846
3	52.53	38.3	50	18	-53.973027	2.207217
4	23.96	68.0	34	15	-53.774145	2.368937
5	37.13	110.0	33	15	-53.550010	2.251592
6	37.54	105.0	42	14	-53.359604	2.344864
7	58.58	120.0	36	19	-53.281768	2.187473
Mean	38.2		40.1	16.1

We extracted DNA sequences for 235 individuals (84.2% of the cambium samples). Mean DNA concentration was 12 ng µL^-1, range = 3 - 25 ng µL^-1. Of the 235 samples, 197 (83.8%) were amplified for rbcLa, and 220 (93.6%) for matK, with 170 samples (72.3%) amplified for both markers. Matching to the NCBI reference database revealed that most of the specimens could be confidently identified to family.

Comparing the identifications based on either rbcLa and matK revealed a corresponding match per sample in 98.8% at the family level, 59.1% at the generic level and 10.7% at the species level. Tree taxa identified at the species level were Balizia pedicellaris (DC.) Barneby & J.W.Grimes, Diospyros tetrandra Hiern, Leonia glycycarpa Ruiz & Pav., Ormosia arborea (Vell.) Harms, Pseudopiptadenia suaveolens (Miq.) J.W.Grimes, Rhabdodendron amazonicum (Spruce ex Benth.) Huber, Siparuna decipiens (Tul.) A.DC, Theobroma cacao L. and Trymatococcus oligandrus (Benoist) Lanj., and one species of liana (Hippocratea volubilis L.). Interestingly, one sample was identified by both barcodes as Pouteria campechiana (Kuhn) Baehni, which is not native to the Amazon region (Awang-Kanak and Bakar 2018). Due to the low taxonomic resolution of the DNA barcoding, the overall botanical results are reported at family resolution.

The most abundant family across the tree samples was Burseraceae (21%), all of them attributed to genus Protium (Daly and Fine 2018), followed by Fabaceae (11%), Meliaceae (8%), Moraceae (8%), Myristicaceae (6%), Sapotaceae (6%) and Vochysiaceae (5%) (Table 2). Together, these families contributed over 50% of all tree individuals in the seven plots. We did not include a survey of lianas, due to the small sample size (Supplementary Material, Tables S1 and S2).

Table 2
List of tree families identified in seven 25x25-m plots along the Brazil-French Guiana border. The numbers refer to individual tree counts; INDET refers to trees that could not be identified through DNA barcoding.

Family	Plot							Total
	1	2	3	4	5	6	7
Burseraceae	16	8	5	2	7	8	10	56
Fabaceae	2	2	8	6	4	5	2	29
Meliaceae	3	2	7	3	3	2	1	21
Moraceae	4	2	5	2	3	5		21
Myristicaceae			3	6	3		4	16
Sapotaceae	10	1			1	4		16
Vochysiaceae		2	3			7		12
Malvaceae	4	1	1		2	2		10
Apocynaceae		1	1	2	1	2	1	8
Olacaceae		1	1	2			4	8
Arecaceae	1	3					3	7
Anacardiaceae		2	3			1		6
Urticaceae			2			2	2	6
Chrysobalanaceae		3		1	1			5
Lauraceae	1	1	2	1				5
Lecythidaceae	1			2	1			4
Ebenaceae					3			3
Euphorbiaceae		2		1				3
Nyctaginaceae		1	1		1			3
Salicaceae			1		1		1	3
Siparunaceae				3				3
Annonaceae	1	1						2
Rhabdodendraceae			2					2
Rubiaceae		2						2
Sapindaceae					2			2
Violaceae		1				1		2
Clusiaceae						1		1
Erythroxylaceae			1					1
Humiriaceae							1	1
Lacistemataceae				1				1
Myrtaceae		1						1
Ochnaceae	1							1
Putranjivaceae							1	1
Simaroubaceae		1						1
Ulmaceae						1		1
INDET	3	3	4	2		1	6	19

Thirty-five species (31.8% of cambium samples sequenced with one or both markers) were given tentative identifications (Table 3), based on: 1) DNA barcodes (removing alternative identification for species that are not present in French Guiana or neighbouring countries); 2) conformation from the bark slash (Figure 4) by one of the authors (J. Engel); 3) the species was also collected as a herbarium specimen on the expedition, though not from within the plots. Burseraceae and Meliaceae, however, could only be confidently assigned to family level.

Figure 4
Examples of bark slash sampled from trees along the Brazil-French Guiana border for DNA barcoding analysis from cambium tissue. A - Protium sp.; B - Arecaceae; C - Helicostylis pedunculata Benoit; D - Swartzia sp. (possibly Swartzia cf. canescens Torke, based on the bark slash); E - Sapotaceae; F - Sapotaceae; G - Vochysiaceae; H - Conceveiba guianensis Aubl.; I - Brosimum alicastrum S

Of the 289 herbarium vouchers collected during the expedition, 160 were identified by botanists from CAY and K to species (55%) and 27 to genus only (9%). Of the 167 images of vouchered specimens placed on iNaturalist in 2015, 57 (34%) were identified to species in 2021 and 58 (35%) to genus only (Supplementary Material, Figure S2). Among the determined species, 48 were marked as ‘Research Grade’, meaning that two experts or knowledgeable people have reviewed the observation and agreed.

DISCUSSION

The tree composition (at family level) in our plots was comparable with that of the Nouragues Station in Central French Guiana (Poncy et al. 2001), but differs from that of Northern French Guiana, with an under-representation of Chrysobalanaceae and Lecythidaceae. This corresponds to other observations on the decrease in Lecythidaceae species-richness, and the increase in Burseraceae, from north to south in French Guiana (Guitet et al. 2015). Comparing data with other plot surveys in the Amazon, the average basal area was high (Phillips et al. 2004). In other parts of French Guiana, basal areas of 30-35 m² ha^-1 are not uncommon, while lower basal areas (25-30 m² ha^-1) are sometimes found in ancient anthropogenic areas (Odonne et al. 2019). A discussion about the herbarium specimens collected on the expedition (available on request from the authors), and ecology relating to these specimens, are described in Le Tourneau et al. (2016) and in the Supplementary Material (Figure S1, Figure S3).

Collection of cambium samples for DNA barcoding is quicker, particularly for larger trees, than leaf tissue collection. It also means that leafless trees can be surveyed, e.g., in the dry season. Compared to wood, it was found that the cambium had a higher concentration of DNA than the heartwood or sapwood, although it also had larger amounts of PCR reaction inhibitors (Tang et al. 2011). The cambium samples that we collected were rapidly dried with silica gel, corresponding to the best long-term approach for DNA preservation (Mangaravite et al. 2020), and we indeed found that DNA quality was good, despite the fast collection approach adopted in this work.

Our low success in species identification using DNA barcodes from cambium samples nevertheless confirm that the selected markers do not fully resolve plants down to species level in all plant families (Gonzalez et al. 2009). Our inability to identify Burseraceae and Meliaceae beyond family level was probably since both families include clades that radiated recently (Fine et al. 2014; Koenen et al. 2015), and therefore the DNA barcodes selected in this study are unable to discriminate the species in these clades sufficiently.

One illustration of this problem is the identification of one of our specimens as Pouteria campechiana. In NCBI, many species in the genus Pouteria have the same matK and rbcL sequences, perhaps due to a relatively recent radiation of this clade within Sapotaceae, subfamily Chrysophylloideae (De Faria et al. 2017). Our BLAST search against NCBI selected one of the possible species and happened to select one that does not occur in our study area, highlighting one of the problems of relying too much on DNA barcodes for species identification. To avoid this type of geographical bias, we could have downloaded the full NCBI database, select only the species known to occur in the study region, and then run the BLAST search on the regional subset. However, several species of Pouteria cooccur in this region, and this procedure would merely reduce geographical inconsistencies, and not resolve the issue with species identification for Sapotaceae.

Adding more DNA barcodes to the ones selected here, such as the Internal Transcribed Spacers region of nuclear ribosomal DNA (ITS) or the plastid trnH-psbA intergenic spacer might, in some families, increase the rate of correct identification (Gonzalez et al. 2009; Hollingsworth et al. 2009; Costion et al. 2011; Bolson et al. 2015). In an analysis of plant fragments from Brazilian caves (mainly roots), the ITS2 spacer was believed to be the best marker for identification (Ramalho et al. 2018). More recent studies have shown that ITS2 is likely to become recognised as the standard DNA barcode for plants (Moorhouse-Gann et al. 2018; Miao et al. 2019). However, the ITS region presents specific challenges for plants: the ribosomal cluster which carries the ITS region is present in multiple copies in the plant cell, and many of these copies are non-functional, but appear to be retained in the cell (Feliner and Rosselló 2007; Group et al. 2011). Non-functional ITS copies appear to have a lower GC content and are preferentially selected during PCR and sequencing, creating potential biases (Besnard et al. 2009).

Identification of trees in forest plots, using herbarium specimens, continues to be problematic. In an analysis of 60 plots in Western Amazonia, over the last 30 years, 25% of specimens were misidentified, and in some difficult genera 50% were incorrect (Baker et al. 2017). One of the issues with species identification within our plots (through DNA barcoding) is that there are large numbers of plant species that have not yet been placed in the NCBI reference collection. Only 31% of known plants have sequences in Genbank, and these were fewer near the Equator (Cornwell et al. 2019), where our survey was carried out. Of the species-rich flora of São Paulo (southeastern Brazil), 58% of tree species have at least one barcoding sequence available, including 35.5% with ITS data (Lima et al. 2018). Based on current accumulation rates, it is possible that 100% species coverage will be achieved within the next 20 years for the São Paulo tree flora, but nevertheless this may not be enough for complete identification in specific taxonomic groups of communities with closely related taxa (Lima et al. 2018). Southern Brazil has been more densely explored and studied than Amazonia, so a high coverage of DNA sequences for the flora of our study area is far from being reached.

Accurate iNaturalist identification of plant images in a poorly known Amazonian region requires trained researchers. Good-quality images, as shown here, can improve the knowledge of species distribution without collecting herbarium specimens. However, given that our images were available to researchers over five years, and less than half are now identified to species, this is not a rapid way to assess biodiversity. New computer-based image identification resources (AI/machine learning) will probably improve and accelerate biodiversity knowledge (Wäldchen and Mäder 2018), but this will require more ‘training’ of images from poorly known taxa (Van Horn et al. 2018).

The under-sampling of inter-fluvial areas of Amazonia remains a major hurdle to biodiversity discovery, and future research should prioritize these less accessible areas in a more systematic way to improve conservation planning. In terms of sampling plant diversity, technological development in communication and automated monitoring could bring down the costs of sampling in the future (Mulatu et al. 2017; Draper et al. 2020).

CONCLUSIONS

Our study demonstrates that lightweight expeditions can benefit from the advances in novel biodiversity monitoring, yet the impossibility to collect herbarium specimens from trees in such conditions is an impediment to species discovery. Identification of trees using DNA from cambium samples and two DNA barcodes (rbcLa and matK) yielded low success at the species level. Our identifications at the family level are insufficient for comparable surveys across Amazonia. Using DNA barcoding to aid species identification will require further development, not only of sampling methods but also the necessary knowledge to support it (a baseline of accurate and reproducible DNA barcodes). We hope that this research, and the discovery of new techniques, will stimulate increased research in eastern Amazonia.

ACKNOWLEDGMENTS

We are grateful to supporters and participants in the expedition and research, including the Parc Amazonien de Guyane, Forces Armées de Guyane (3rd Foreign Infantry Regiment), French National Centre for Scientific Research (CNRS), Ministry of the Interior, National Geographic Institute (IGN), National Museum of Natural History (MNHN) and the IRD Herbarium in Cayenne (CAY). Sponsors included Airbus Defence and Space, Arianespace, Cofely Endel, Kew Foundation, and the National Centre for Space Studies (CNES). This work has benefited from an “Investissement d’Avenir” grant managed by Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01).

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• CITE AS:

  Milliken, W.; Odonne, G.; Engel, J.; Le Tourneau, F.; Suescun, U.; Chave, J. 2022. Fast and novel botanical exploration of a 320-km transect in eastern Amazonia using DNA barcoding. Acta Amazonica 52: 29-37.

SUPPLEMENTARY MATERIAL

(only available in the electronic version)

Milliken et al. Fast and novel botanical exploration of a 320-km transect in eastern Amazonia using DNA barcoding

Figure S1
Examples of panoramas of the vegetation along the 320-km transect along the border between southern French Guiana and Brazil. A - 2.324275N, 54.5391W, creek between hills, with Euterpe oleracea Mart., Socratea exorrhiza (Mart.) H.Wendl. (Arecaceae), Symphonia globulifera L.f. (Clusiaceae) and Rapatea paludosa Aubl. (Rapateaceae); B - 2.279289N, 54.5253W, granite inselberg, with Mandevilla surinamensis (Pulle) Woodson (Apocynaceae), Oreopanax capitatus (Jacq.) Decne. & Planch. (Araliaceae), Topobea parasitica Aubl. (Melastomataceae), Clusia palmicida Rich. (Clusiaceae) and Sapium argutum (Müll. Arg.) Huber (Euphorbiaceae); C - 2.256044N, 54.4826W, forest on ridge, with Astrocaryum sciophyllum (Miq.) Pulle (Arecaceae); D - 2.207178N, 54.438W, granite inselberg. In damp areas on the rock the species included Sipanea wilson-brownei R.S. Cowan (Rubiaceae), Paepalanthus oyapockensis Herzog (Eriocaulaceae), Rhynchospora subdicephala T. Koyama (Cyperaceae), Utricularia hispida spp. (Lentibulariaceae), Sinningia incarnata (Aubl.) D.L.Denham (Gesneriaceae), among others; E - 2.168647N, 54.3391W, forest on ridge, with Astrocaryum sciophyllum (Miq.) Pulle (Arecaceae); F - 2.166903N, 54.2006W, forest on ridge, with the massive Huberodendron swietenioides (Gleason) Ducke (Malvaceae); G - 2.207217N, 53.973027W, forest on ridge, dominated by Fabaceae and Meliaceae. Fluted trunks on the left are Minquartia guianensis Aubl. (Olacaceae); H - 2.300701N, 53.885363W, forest on ridge with young stems of Oenocarpus sp. (Arecaceae); I - 2.347966N, 53.804337W, swamp forest dominated by Euterpe oleracea Mart. (Arecaceae); J - 2.369397N, 53.772392W, hilltop forest on inselberg, close to Milestone 4, with Ananas comosus (L.) Merr. (Bromeliaceae), and Syagrus inajai (Spruce) Becc. (Arecaceae) which are possible clues of past human occupations.

Figure S2
Examples of specimen identification by other botanists, five years after the images became available on iNaturalist (https://www.inaturalist.org/home). A - Rhabdodendron amazonicum (Spruce ex Benth.) Huber; B - Styrax pallidus A.DC.; C - Elleanthus graminifolius (Barb.Rodr.) Løjtnant; D - Nautilocalyx pictus (Hook.) Sprague; E - Sinningia incarnata (Aubl.) D.L.Denham; F - Maieta poeppigii Mart. ex Cogn.; G - Sapium argutum (Müll.Arg.) Huber; H - Carpotroche longifolia (Poepp.) Benth.

Figure S3
Brief description of the seven 25x25-m plots sampled for tree cambium along a 320-km transect along the Brazil-French Guiana border (further details in Table 1). 1 - Forest approximately 100 m from an open granite inselberg, on shallow soil, with few large trees and a low canopy (approx. 15 m). The most abundant tree families were Burseraceae (Protium spp.) and Sapotaceae; 2 - Trees larger than in Plot 1 (DBH and height), and more diverse in families and species and with deeper soil. Several trees were felled next to the clearing (for a helicopter landing), where collections were made. These included: Aspidosperma excelsum Benth. (Apocynaceae), Oenocarpus bacaba Mart. (Arecaceae), Protium morii Daly, Protium robustum (Swart) D.M. Porter, Protium spruceanum (Benth.) Engl. (Burseraceae), Caryocar microcarpum Ducke (Caryocaraceae), Dicorynia guianensisAmshoff, Ormosia amazonica Ducke, Swartzia panacoco (Aubl.) R.S.Cowan (Fabaceae), Goupia glabra Aubl. (Goupiaceae), Licaria debilis (Mez) Kosterm (Lauraceae), Eschweilera coriacea (DC.) S.A.Mori (Lecythidaceae), Trichilia micrantha Benth. (Meliaceae), Trymatococcus oligandrus (Moraceae), Iryanthera sp. (Myristicaceae), Touroulia guianensis Aubl. (Ochnaceae), Rhabdodendron amazonicum (Spruce ex Benth.) Huber (Rhabdodendraceae), Talisia carinata Radlk., Toulicia sp. (Sapindaceae), Manilkara huberi (Ducke) Standl. (Sapotaceae), Styrax cf. macrophyllus Schott ex Pohl (Styracaceae), Coussapoa angustifolia Aubl. and Pourouma minor Benoist (Urticaceae). It is likely that Protium sp. in the DNA identifications may have been one of the three species collected, that all the Arecaceae (DNA) were Oenocarpus bacaba, and that Swartzia sp. (DNA) was S. panacoco (identified correctly by matK); 3 - This was the plot highest in tree density, and second for basal area, with 52.53 m² ha^-1, mostly small trees (DBH max = 38.3 cm) of Fabaceae and Meliaceae. Located on top of a little plateau, partly on a slope, and likely an old secondary forest, with few species represented by several individuals, but Protium sp. (Burseraceae), (identified as P. decandrum (Aubl.) Marchand by rbcLa determination) appeared three times and Rhabdodendron amazonicum (Spruce ex Benth.) Huber (Rhabdodendraceae) twice; 4 - Located at the top of an inselberg, but on a draining substrate (not directly on the rocky outcrop), with an open understory and a low tree density (544 trees ha^-1). The largest tree was measured on this plot. Dominated by Fabaceae and Myristicaceae, with three individuals of Carapa guianensis Aubl. (Meliaceae); 5 - Located on a ridge. Plot with the fewest trees (density of 528 tree ha^-1). Dominated by Burseraceae, with both Protium spp. and Protium excelsior Byng & Christenh. (rbcLa); 6 - Plot with the lowest canopy, with open understory and many small diameter stems. Dominated by Burseraceae and Vochysiaceae, in which Erisma uncinatum Warm. (rbcLa) is probably the most abundant species; 7 - Low-density plot (576 tree ha^-1) on a well-drained flatland close to the seventh milestone. Dominated by Burseraceae, with both Protium spp. and Protium excelsior (rbcLa), as in Plot 5.

Table S1
Blast results for rbcLa DNA barcodes against the NCBI online database for samples from seven 25-m² plots along a 320-km transect on the Brazil-French Guiana border. ‘Query’ refers to the tree label as given in the field. The two last columns report the best hit sequence (organism name and accession) to the DNA barcode.

Query	Query coverage	Sequence length	% Identical sites	% Pairwise identity	%GC	Bit-score	Grade (%)	Organism	Accession number
1.1	100.00	681	100.0	100.0	43.0	1258.69	100.0	Trichilia sp.	KC628372
1.13	99.41	677	100.0	100.0	41.7	1251.3	99.7	Trymatococcus oligandrus	FJ038126
1.15	99.71	679	100.0	100.0	43.7	1255	99.9	Pouteria decorticans	FJ038177
1.16	99.71	679	100.0	100.0	41.8	1255	99.9	Helicostylis pedunculata	FJ038121
1.18	100.00	681	99.7	99.7	43.3	1247.61	99.9	Chrysophyllum oliviforme	L12607
1.2	99.71	679	100.0	100.0	43.6	1255	99.9	Chrysophyllum sanguinolentum	FJ038163
1.21	99.27	676	100.0	100.0	42.8	1249.46	99.6	Swartzia sp.	FJ038056
1.23	99.85	680	99.9	99.9	44.3	1251.3	99.9	Ocotea venulosa	KF981236
1.27	99.71	679	99.9	99.9	43.6	1249.46	99.8	Chrysophyllum sanguinolentum	FJ038163
1.3	99.71	679	100.0	100.0	43.9	1255	99.9	Sterculia pruriens	FJ038104
1.32	99.71	679	99.6	99.6	41.8	1238.38	99.6	Helicostylis pedunculata	FJ038121
1.33	100.00	681	99.7	99.7	43.5	1247.61	99.9	Manilkara zapota	EU980807
1.34	99.27	676	100.0	100.0	42.8	1249.46	99.6	Swartzia sp.	FJ038056
1.36	99.71	679	100.0	100.0	43.6	1255	99.9	Chrysophyllum prieurii	GQ428633
1.4	99.71	679	100.0	100.0	43.6	1255	99.9	Chrysophyllum sanguinolentum	FJ038163
1.41	100.00	681	99.9	99.9	42.7	1253.15	99.9	T.guianensis chloroplast	Z75690
1.43	91.48	623	97.8	98.8	45.1	1099.88	95.1	Fusaea longifolia	GQ428542
1.48	100.00	681	99.9	99.9	43.8	1253.15	99.9	Theobroma cacao	JQ228389
1.7	99.85	680	99.9	99.9	43.1	1251.3	99.9	Abarema brachystachya	KF981222
2.1	100.00	681	100.0	100.0	42.9	1258.69	100.0	Hirtella suffulta	KX180070
2.11	100.00	681	99.6	99.6	42.9	1242.07	99.8	Trichilia emetica	AY128244
2.12	100.00	681	99.9	99.9	43.2	1253.15	99.9	Hexopetion mexicanum	JX903251
2.13	99.71	679	100.0	100.0	43.6	1255	99.9	Chrysophyllum prieurii	GQ428633
2.17	100.00	681	99.9	99.9	43.4	1255	100.0	Ferdinandusa speciosa	AM117226
2.18	100.00	681	99.6	99.6	42.9	1242.07	99.8	Trichilia emetica	AY128244
2.19	99.71	679	99.7	99.7	42.7	1243.92	99.7	Erisma uncinatum	FJ038209
2.2	92.22	628	98.9	99.4	45.2	1134.96	95.8	Fusaea longifolia	GQ428542
2.2	99.71	679	100.0	100.0	43.9	1255	99.9	Protium decandrum	FJ037977
2.21	95.89	653	100.0	100.0	43.0	1206.98	97.9	Hirtella suffulta	KX180070
2.23	99.71	679	99.9	99.9	42.9	1249.46	99.8	Pachycormus discolor	GU935437
2.24	97.65	665	100.0	100.0	42.7	1229.14	98.8	Swartzia sp.	FJ038056
2.28	95.89	653	99.7	99.8	42.6	1199.6	97.8	Brosimum rubescens	GQ428590
2.3	99.71	679	100.0	100.0	43.9	1255	99.9	Protium decandrum	FJ037977
2.3	100.00	681	99.9	99.9	43.6	1253.15	99.9	Leonia glycycarpa	FJ670179
2.31	99.71	679	99.7	99.7	43.9	1245.76	99.7	Protium decandrum	FJ037977
2.32	99.85	680	100.0	100.0	42.4	1256.84	99.9	Aparisthmium cordatum	KF981218
2.33	99.71	679	99.9	99.9	43.9	1249.46	99.8	Protium decandrum	FJ037977
2.35	99.71	679	100.0	100.0	43.9	1255	99.9	Sterculia pruriens	FJ038104
2.36	99.71	679	100.0	100.0	43.9	1255	99.9	Protium decandrum	FJ037977
2.37	100.00	681	99.1	99.2	42.9	1227.3	99.6	Loxopterygium huasango	GU935431
2.38	100.00	681	99.9	99.9	43.2	1253.15	99.9	Hexopetion mexicanum	JX903251
2.39	100.00	681	100.0	100.0	44.3	1258.69	100.0	Simarouba amara	EU043036
2.4	99.41	677	99.9	99.9	41.7	1245.76	99.6	Trymatococcus oligandrus	FJ038126
2.4	99.71	679	100.0	100.0	43.9	1255	99.9	Protium decandrum	FJ037977
2.42	99.71	679	100.0	100.0	43.9	1255	99.9	Protium decandrum	FJ037977
2.5	100.00	681	100.0	100.0	42.9	1258.69	100.0	Hirtella suffulta	KX180070
2.6	99.71	679	99.9	99.9	43.9	1249.46	99.8	Protium decandrum	FJ037977
2.7	99.71	679	100.0	100.0	43.3	1255	99.9	Himatanthus sp.	GQ428618
2.8	91.78	625	99.4	99.6	42.9	1140.5	95.7	Diplotropis purpurea	GQ428606
2.9	99.71	679	99.9	99.9	43.9	1249.46	99.8	Protium decandrum	FJ037977
2B*	100.00	681	98.2	98.2	41.1	1192.21	99.1	Millettia leptobotrya	KJ440056
2C*	100.00	681	99.4	99.4	43.8	1236.53	99.7	Bignonia capreolata	HQ384884
2D*	100.00	681	99.4	99.4	44.3	1236.53	99.7	M.sanderi chloroplast	X91764
2G*	100.00	681	99.3	99.3	43.6	1234.68	99.7	Martinella obovata	L36444
2H*	100.00	681	99.9	99.9	44.2	1253.15	99.9	Securidaca bialata	EU644682
2I*	99.71	679	100.0	100.0	43.9	1255	99.9	Arrabidaea pubescens	AF102641
2J*	100.00	681	99.4	99.4	44.1	1236.53	99.7	Maripa paniculata	AY101046
2K*	100.00	681	99.9	99.9	44.1	1253.15	99.9	Eriandra fragrans	AM234170
3.1	99.71	679	100.0	100.0	43.0	1255	99.9	Guarea silvatica	FJ038158
3.11	99.71	679	100.0	100.0	45.1	1255	99.9	Iryanthera sagotiana	FJ038128
3.16	99.71	679	100.0	100.0	43.4	1255	99.9	Pourouma tomentosa	FJ038203
3.18	99.41	677	100.0	100.0	41.7	1251.3	99.7	Trymatococcus oligandrus	FJ038126
3.2	100.00	681	99.9	99.9	43.5	1253.15	99.9	P.roxburghii chloroplast	Z70152
3.24	99.71	679	100.0	100.0	43.9	1255	99.9	Protium decandrum	FJ037977
3.25	100.00	681	99.3	99.3	42.9	1230.99	99.6	Loxopterygium huasango	GU935431
3.26	99.71	679	100.0	100.0	43.9	1255	99.9	Sterculia pruriens	FJ038104
3.27	99.71	679	100.0	100.0	44.2	1255	99.9	Protium sagotianum	FJ037982
3.3	100.00	681	99.9	99.9	42.1	1253.15	99.9	Brosimum alicastrum	AF500346
3.3	100.00	681	100.0	100.0	43.0	1258.69	100.0	Trichilia sp.	KC628372
3.31	99.71	679	99.4	99.4	43.9	1232.84	99.6	Protium decandrum	FJ037977
3.32	100.00	681	99.1	99.1	43.9	1225.45	99.6	Erythroxylum novogranatense	KX256287
3.34	99.71	679	100.0	100.0	43.4	1255	99.9	Aspidosperma cruentum	FJ037963
3.35	100.00	681	99.1	99.1	44.2	1225.45	99.6	Rhabdodendron amazonicum	Z97649
3.36	99.71	679	98.4	99.0	44.5	1214.37	99.4	Licaria guianensis	GQ428566
3.37	99.71	679	100.0	100.0	43.0	1255	99.9	Guarea silvatica	FJ038158
3.39	99.71	679	100.0	100.0	42.7	1255	99.9	Diplotropis purpurea	GQ428606
3.4	100.00	681	99.4	99.4	42.9	1236.53	99.7	Swartzia cardiosperma	AM234259
3.41	99.71	679	100.0	100.0	42.3	1255	99.9	Brosimum guianense	GQ428589
3.42	99.71	679	100.0	100.0	42.7	1255	99.9	Swartzia sp.	FJ038056
3.43	99.71	679	100.0	100.0	43.0	1255	99.9	Guarea silvatica	FJ038158
3.44	99.71	679	99.3	99.3	43.9	1229.14	99.5	Protium decandrum	FJ037977
3.46	99.71	679	100.0	100.0	43.0	1255	99.9	Guarea silvatica	FJ038158
3.47	99.41	677	100.0	100.0	41.7	1251.3	99.7	Trymatococcus oligandrus	FJ038126
3.48	100.00	681	99.4	99.4	42.9	1236.53	99.7	Swartzia cardiosperma	AM234259
3.49	99.71	679	100.0	100.0	43.0	1255	99.9	Guarea silvatica	FJ038158
3.5	100.00	681	99.1	99.1	44.2	1225.45	99.6	Rhabdodendron amazonicum	Z97649
3.5	99.41	677	100.0	100.0	41.7	1251.3	99.7	Trymatococcus oligandrus	FJ038126
3.6	100.00	681	100.0	100.0	44.1	1258.69	100.0	Tapirira obtusa	GU935446
3.7	99.71	679	100.0	100.0	43.4	1255	99.9	Pourouma tomentosa	FJ038203
3.8	99.27	676	100.0	100.0	44.1	1249.46	99.6	Tapirira obtusa	GU935446
3.9	99.85	680	99.9	99.9	43.1	1251.3	99.9	Abarema brachystachya	KF981222
4.1	99.71	679	100.0	100.0	42.3	1255	99.9	Neea floribunda	FJ038135
4.1	100.00	681	100.0	100.0	44.9	1258.69	100.0	Myristica fragrans	AY298839
4.11	99.71	679	100.0	100.0	42.3	1255	99.9	Brosimum rubescens	GQ428590
4.12	100.00	681	100.0	100.0	43.9	1258.69	100.0	Swietenia mahagoni	FN599465
4.13	99.71	679	100.0	100.0	45.1	1255	99.9	Iryanthera sagotiana	FJ038128
4.14	99.71	679	100.0	100.0	43.9	1255	99.9	Protium decandrum	FJ037977
4.15	100.00	681	100.0	100.0	44.2	1258.69	100.0	Lacistema robustum	JX664056
4.16	99.71	679	100.0	100.0	42.3	1255	99.9	Gustavia hexapetala	FJ038089
4.18	99.85	680	100.0	100.0	41.9	1256.84	99.9	Ormosia arborea	KF981227
4.19	99.71	679	100.0	100.0	45.4	1255	99.9	Siparuna decipiens	FJ038200
4.2	100.00	681	100.0	100.0	43.9	1258.69	100.0	Swietenia mahagoni	FN599465
4.2	99.71	679	99.9	99.9	42.9	1249.46	99.8	Swartzia benthamiana	FJ038055
4.22	99.71	679	100.0	100.0	45.4	1255	99.9	Siparuna decipiens	FJ038200
4.23	100.00	681	100.0	100.0	44.9	1258.69	100.0	Myristica fragrans	AY298839
4.25	99.71	679	100.0	100.0	45.4	1255	99.9	Siparuna decipiens	FJ038200
4.26	100.00	681	100.0	100.0	42.9	1258.69	100.0	Hirtella suffulta	KX180070
4.27	99.85	680	100.0	100.0	42.8	1256.84	99.9	Balizia pedicellaris	KF981225
4.29	100.00	681	100.0	100.0	43.8	1258.69	100.0	M.laxum chloroplast	X91765
4.3	100.00	681	100.0	100.0	44.9	1258.69	100.0	Myristica fragrans	AY298839
4.3	99.71	679	100.0	100.0	43.4	1255	99.9	Aspidosperma cruentum	FJ037963
4.31	99.71	679	99.9	99.9	42.9	1249.46	99.8	Swartzia benthamiana	FJ038055
4.32	99.71	679	100.0	100.0	45.1	1255	99.9	Iryanthera sagotiana	FJ038128
4.33	99.71	679	100.0	100.0	44.8	1255	99.9	Virola kwatae	FJ038129
4.4	99.71	679	100.0	100.0	43.3	1255	99.9	Ptychopetalum olacoides	FJ038139
4.6	100.00	681	100.0	100.0	42.6	1258.69	100.0	Gavarretia terminalis	AY794953
4.7	99.71	679	100.0	100.0	42.3	1255	99.9	Brosimum rubescens	GQ428590
4.8	100.00	681	100.0	100.0	43.9	1258.69	100.0	Swietenia mahagoni	FN599465
4.9	99.71	679	99.9	99.9	42.9	1249.46	99.8	Swartzia benthamiana	FJ038055
5.1	100.00	681	99.3	99.3	44.2	1230.99	99.6	Matisia cordata	AJ233117
5.1	99.71	679	99.4	99.4	42.1	1232.84	99.6	Naucleopsis guianensis	GQ428596
5.13	100.00	681	100.0	100.0	43.2	1258.69	100.0	Diospyros tetrandra	EU980756
5.15	50.22	342	99.4	99.6	46.5	625.288	74.9	Casearia javitensis	JQ626018
5.16	100.00	681	99.9	99.9	43.5	1253.15	99.9	P.roxburghii chloroplast	Z70152
5.17	100.00	681	100.0	100.0	44.9	1258.69	100.0	Myristica fragrans	AY298839
5.19	99.71	679	100.0	100.0	43.3	1255	99.9	Diospyros carbonaria	FJ038021
5.2	99.71	679	99.4	99.4	42.1	1232.84	99.6	Naucleopsis guianensis	GQ428596
5.2	99.71	679	100.0	100.0	43.6	1255	99.9	Tetragastris altissima	FJ037987
5.21	100.00	681	99.6	99.6	42.9	1242.07	99.8	Trichilia emetica	AY128244
5.22	99.71	679	100.0	100.0	43.3	1255	99.9	Diospyros carbonaria	FJ038021
5.23	99.71	679	100.0	100.0	44.2	1255	99.9	Protium sagotianum	FJ037982
5.24	100.00	681	99.9	99.9	42.1	1253.15	99.9	Brosimum alicastrum	AF500346
5.26	48.16	328	95.4	96.6	42.7	551.422	72.4	Trichilia euneura	JQ625863
5.29	99.71	679	100.0	100.0	43.7	1255	99.9	Pouteria decorticans	FJ038177
5.3	53.01	361	94.5	96.5	45.4	590.202	74.8	Tilia x	KX163059
5.31	100.00	681	99.0	99.0	44.8	1219.91	99.5	Aganosma marginata	AJ419730
5.32	100.00	681	99.3	99.3	44.2	1230.99	99.6	Matisia cordata	AJ233117
5.33	99.71	679	100.0	100.0	44.2	1255	99.9	Protium sagotianum	FJ037982
5.34	99.71	679	100.0	100.0	43.6	1255	99.9	Tetragastris altissima	FJ037987
5.35	99.71	679	100.0	100.0	43.9	1255	99.9	Protium decandrum	FJ037977
5.36	100.00	681	99.1	99.1	43.8	1225.45	99.6	Vallesia antillana	AJ419767
5.4	99.71	679	100.0	100.0	43.6	1255	99.9	Tetragastris altissima	FJ037987
5.5	100.00	681	99.9	99.9	43.5	1253.15	99.9	Sclerolobium sp.	AM234242
5.6	100.00	681	99.9	99.9	42.9	1253.15	99.9	Hirtella suffulta	KX180070
5.7	99.71	679	100.0	100.0	44.2	1255	99.9	Protium sagotianum	FJ037982
5.8	99.71	679	100.0	100.0	43.9	1255	99.9	Cupania scrobiculata	FJ038156
6.1	99.71	679	100.0	100.0	43.6	1255	99.9	Tetragastris altissima	FJ037987
6.1	99.85	680	99.3	99.3	41.9	1229.14	99.6	Ormosia arborea	KF981227
6.12	99.71	679	99.4	99.4	42.1	1232.84	99.6	Naucleopsis guianensis	GQ428596
6.13	99.71	679	100.0	100.0	43.7	1255	99.9	Aspidosperma marcgravianum	FJ037965
6.15	99.71	679	100.0	100.0	43.4	1255	99.9	Pourouma tomentosa	FJ038203
6.16	99.71	679	100.0	100.0	42.9	1255	99.9	Ruizterania albiflora	FJ038212
6.17	99.85	680	99.9	99.9	42.5	1251.3	99.9	Guapira opposita	KF981271
6.19	99.71	679	100.0	100.0	43.4	1255	99.9	Pourouma tomentosa	FJ038203
6.2	99.71	679	100.0	100.0	43.6	1255	99.9	Tetragastris altissima	FJ037987
6.20	97.21	662	99.7	99.7	41.8	1212.52	98.5	Trymatococcus oligandrus	FJ038126
6.22	100.00	681	100.0	100.0	43.0	1258.69	100.0	Cotinus obovatus	GU935422
6.23	99.41	677	100.0	100.0	43.1	1251.3	99.7	Trichilia sp.	KC628372
6.24	99.71	679	99.6	99.6	42.7	1240.22	99.7	Erisma uncinatum	FJ038209
6.25	100.00	681	100.0	100.0	43.5	1258.69	100.0	Manilkara zapota	EU980807
6.27	99.71	679	99.6	99.6	42.7	1240.22	99.7	Erisma uncinatum	FJ038209
6.28	100.00	681	97.8	98.1	42.1	1188.52	99.1	Ampelocera hottleyi	AF500335
6.3	100.00	681	100.0	100.0	43.8	1258.69	100.0	Pachira aquatica	AJ233119
6.3	100.00	681	100.0	100.0	43.5	1258.69	100.0	Sclerolobium sp.	AM234242
6.31	100.00	681	100.0	100.0	43.5	1258.69	100.0	Sclerolobium sp.	AM234242
6.32	99.71	679	100.0	100.0	42.9	1255	99.9	Ruizterania albiflora	FJ038212
6.33	99.71	679	99.6	99.6	42.7	1240.22	99.7	Erisma uncinatum	FJ038209
6.34	99.71	679	99.6	99.6	42.7	1240.22	99.7	Erisma uncinatum	FJ038209
6.35	100.00	681	99.9	99.9	43.3	1253.15	99.9	Pouteria campechiana	KX426215
6.36*	99.71	679	99.7	99.7	43.3	1243.92	99.7	Celastraceae sp.	FJ037994
6.38	99.41	677	99.9	99.9	41.7	1245.76	99.6	Maquira calophylla	FJ038123
6.4	99.71	679	100.0	100.0	42.3	1255	99.9	Brosimum guianense	GQ428589
6.41	99.71	679	100.0	100.0	43.6	1255	99.9	Tetragastris altissima	FJ037987
6.42*	99.71	679	99.7	99.7	43.3	1243.92	99.7	Celastraceae sp.	FJ037994
6.43	100.00	681	100.0	100.0	43.5	1258.69	100.0	Sclerolobium sp.	AM234242
6.44	100.00	681	100.0	100.0	43.8	1258.69	100.0	Pachira aquatica	AJ233119
6.45	100.00	681	100.0	100.0	43.5	1258.69	100.0	Manilkara zapota	EU980807
6.6	99.71	679	100.0	100.0	44.2	1255	99.9	Protium sagotianum	FJ037982
6.7	99.71	679	99.9	99.9	41.5	1249.46	99.8	Maquira calophylla	FJ038123
6.8	99.71	679	100.0	100.0	42.9	1255	99.9	Ruizterania albiflora	FJ038212
7.1	100.00	681	100.0	100.0	43.8	1258.69	100.0	M.laxum chloroplast	X91765
7.1	97.65	665	100.0	100.0	43.8	1229.14	98.8	Pseudopiptadenia suaveolens	FJ038053
7.13	95.89	653	100.0	100.0	43.2	1206.98	97.9	Trichilia emetica	AY128244
7.15	99.71	679	100.0	100.0	45.1	1255	99.9	Iryanthera sagotiana	FJ038128
7.16	99.71	679	100.0	100.0	42.6	1255	99.9	Lecythis idatimon	FJ038090
7.19	98.97	674	100.0	100.0	44.1	1245.76	99.5	Protium decandrum	FJ037977
7.2	99.71	679	100.0	100.0	43.9	1255	99.9	Protium decandrum	FJ037977
7.26	99.71	679	99.9	99.9	43.6	1251.3	99.8	Tetragastris altissima	FJ037987
7.27	99.71	679	100.0	100.0	43.3	1255	99.9	Ptychopetalum olacoides	FJ038139
7.3	99.71	679	100.0	100.0	43.6	1255	99.9	Tetragastris altissima	FJ037987
7.4	99.71	679	100.0	100.0	45.1	1255	99.9	Iryanthera sagotiana	FJ038128
7.5	97.21	662	100.0	100.0	44.0	1223.6	98.6	Leonia glycycarpa	FJ670179
7.7	97.36	663	99.5	99.7	45.2	1214.37	98.5	Iryanthera sagotiana	FJ038128

• * = liana

Table S2
Blast results for matK DNA barcodes against the NCBI online database for samples from seven 25-m² plots along a 320-km transect on the Brazil-French Guiana border. ‘Query’ refers to the tree label as given in the field. The two last columns report the best hit sequence (organism name and accession) to the DNA barcode.

Query	Query coverage	Sequence length	% Identical sites	% Pairwise identity	%GC	Bit-score	Grade (%)	Organism	Accession number
1.1	83.51	770	99.6	99.7	35.1	1410.11	91.6	Trichilia martiana	JQ588367
1.1	30.91	285	99.3	99.6	38.9	520.029	65.3	Protium costaricense	GQ982071
1.11	30.91	285	99.3	99.6	38.9	520.029	65.3	Protium costaricense	GQ982071
1.12*	86.88	801	100.0	100.0	34.2	1480.29	93.4	Forsteronia acouci	EF456339
1.13	82.86	764	99.9	99.9	31.5	1408.27	91.4	Trymatococcus oligandrus	FJ037932
1.16	84.49	780	99.5	99.5	32.4	1417.5	92.0	Castilla elastica	KU856438
1.18	84.16	777	99.7	99.7	33.5	1423.04	92.0	Pouteria campechiana	KX426215
1.2	58.35	538	97.0	98.1	31.8	933.678	78.2	Sterculia multiovula	JQ626455
1.2	83.73	772	99.7	99.8	33.2	1417.5	91.8	Pouteria campechiana	KX426215
1.21	82.32	759	99.6	99.7	31.5	1387.95	91.0	Swartzia panacoco	KT876194
1.22	84.27	777	99.9	99.9	36.3	1432.27	92.1	Carapa guianensis	NC_037442
1.23	83.73	772	99.9	99.9	36.3	1421.19	91.8	Lindera benzoin	MG220609
1.27	83.95	774	99.7	99.7	33.2	1419.35	91.8	Pouteria campechiana	KX426215
1.28	80.91	746	99.9	99.9	33.6	1373.18	90.4	Pouteria campechiana	KX426215
1.29	84.27	777	99.1	99.5	34.9	1410.11	91.9	Protium pallidum	AY594476
1.3	83.19	768	99.0	99.2	33.4	1378.72	91.2	Sterculia apetala	GQ982103
1.3	84.27	777	98.6	99.3	34.9	1395.34	91.8	Protium pallidum	AY594476
1.32	81.24	749	99.7	99.7	32.3	1373.18	90.5	Castilla elastica	KU856438
1.33	83.41	770	99.2	99.2	33.2	1387.95	91.3	Pouteria campechiana	KX426215
1.34	84.27	777	99.1	99.4	31.7	1406.42	91.8	Swartzia panacoco	KT876194
1.35	84.27	777	99.9	99.9	33.5	1430.43	92.1	Pouteria campechiana	KX426215
1.36	84.27	777	100.0	100.0	33.5	1435.97	92.1	Pouteria campechiana	KX426215
1.39	82.97	765	99.1	99.2	34.9	1380.57	91.1	Protium costaricense	JQ587172
1.4	84.27	777	100.0	100.0	33.5	1435.97	92.1	Pouteria campechiana	KX426215
1.43	84.27	777	99.7	99.9	36.3	1428.58	92.1	Guatteria ouregou	KP859342
1.47	30.69	283	99.3	99.6	39.2	516.336	65.2	Protium costaricense	GQ982071
1.48	82.75	763	99.6	99.7	32.6	1395.34	91.2	Theobroma cacao	MF350235
1.5	66.81	616	97.7	98.7	34.3	1083.26	82.8	Sterculia pruriens	FJ514606
1.5	80.26	740	100.0	100.0	35.1	1367.64	90.1	Protium pallidum	AY594476
1.6	83.62	771	99.0	99.5	34.6	1395.34	91.6	Protium pallidum	AY594476
1.7	84.27	777	99.4	99.7	31.9	1417.5	92.0	Balizia sp.	KX302319
1.8	79.18	730	98.2	99.0	35.2	1299.31	89.1	Trichilia martiana	JQ588367
2.1	82.75	763	98.8	98.8	32.6	1360.25	90.8	Eugenia uniflora	KR867678
2.1	83.84	773	99.7	99.9	31.7	1421.19	91.9	Hirtella macrosepala	KX180068
2.12	75.60	697	100.0	100.0	33.4	1288.24	87.8	Sclerosperma mannii	AM114629
2.13	83.62	771	99.9	99.9	33.2	1421.19	91.8	Pouteria campechiana	KX426215
2.15	80.80	745	99.2	99.3	32.7	1347.33	90.0	Chaunochiton kappleri	DQ790179
2.16	83.19	771	98.3	98.4	32.7	1356.56	90.8	Phytelephas aequatorialis	KT312924
2.16	75.60	697	100.0	100.0	33.4	1288.24	87.8	Sclerosperma mannii	AM114629
2.17	81.45	751	99.6	99.6	34.9	1371.33	90.5	Ferdinandusa chlorantha	FJ905361
2.18	80.69	744	99.9	99.9	35.8	1369.49	90.3	Trichilia prieureana	KC627568
2.19	84.27	777	99.6	99.7	33.7	1421.19	92.0	Vochysia ferruginea	GQ982128
2.2	84.27	777	99.7	99.8	36.4	1426.73	92.0	Guatteria cf.	KP859347
2.2	19.74	182	99.5	99.7	39.6	333.517	59.7	Bursera fagaroides	KF224981
2.21	78.20	721	99.2	99.4	31.8	1310.39	88.8	Hirtella macrosepala	KX180068
2.21	69.63	642	98.4	99.1	30.7	1149.74	84.4	Hirtella macrosepala	KX180068
2.23	82.54	761	98.3	98.4	35.5	1336.25	90.4	Toxicodendron succedaneum	HQ427343
2.24	84.27	777	99.5	99.7	31.7	1419.35	92.0	Swartzia panacoco	KT876194
2.26	84.60	780	99.6	99.7	33.6	1426.73	92.1	Vochysia ferruginea	GQ982128
2.27	84.60	780	99.5	99.5	31.3	1419.35	92.0	Conceveiba martiana	FJ670011
2.28	84.82	782	99.1	99.2	31.5	1410.11	92.0	Brosimum alicastrum	GQ981947
2.31	82.21	758	100.0	100.0	36.3	1400.88	91.1	Ocotea catharinensis	KF555429
2.32	83.62	771	98.6	99.0	31.3	1376.87	91.3	Conceveiba martiana	FJ670011
2.33	83.08	766	98.6	99.1	34.9	1369.49	91.1	Protium costaricense	GQ982071
2.34	84.49	779	99.4	99.4	34.3	1411.96	91.9	Chimarrhis parviflora	GQ981964
2.35	83.19	768	99.2	99.3	33.4	1386.11	91.2	Sterculia apetala	GQ982103
2.36	83.95	774	99.7	99.8	34.8	1421.19	91.9	Protium costaricense	GQ982071
2.37	84.82	782	99.0	99.4	34.9	1411.96	92.1	Astronium graveolens	JQ586469
2.38	75.60	697	100.0	100.0	33.4	1288.24	87.8	Sclerosperma mannii	AM114629
2.4	77.98	719	99.3	99.4	31.7	1303.01	88.7	Trymatococcus oligandrus	FJ037932
2.4	80.80	745	99.5	99.7	35.2	1362.1	90.3	Protium costaricense	GQ982071
2.42	19.74	182	98.4	99.2	39.6	326.131	59.5	Bursera fagaroides	KF224981
2.5	87.20	804	99.8	99.9	31.8	1478.44	93.5	Hirtella macrosepala	KX180068
2.6	83.95	774	99.4	99.5	34.8	1406.42	91.7	Protium costaricense	GQ982071
2.7	83.73	773	99.9	99.9	34.9	1421.19	91.8	Himatanthus bracteatus	EF456366
2.9	81.56	752	99.3	99.4	35.2	1365.79	90.5	Protium costaricense	JQ587172
2A*	83.51	770	99.4	99.4	33.8	1395.34	91.4	Salacia oblonga	KX573076
2B*	84.92	783	99.1	99.2	27.8	1411.96	92.1	Deguelia negrensis	JX506607
2C*	84.92	783	99.5	99.6	34.7	1426.73	92.2	Adenocalymma validum	MG831871
2D*	85.68	790	98.9	98.9	33.9	1411.96	92.3	Forsteronia acouci	EF456339
2G*	83.62	772	98.7	98.8	34.3	1371.33	91.2	Anemopaegma foetidum	NC_037230
2H*	84.38	778	99.9	99.9	30.3	1432.27	92.1	Securidaca diversifolia	JQ588835
2I*	81.89	756	99.7	99.7	34.5	1384.26	90.8	Bignoniaceae sp.	JQ586988
2J*	82.21	758	96.0	97.6	34.4	1277.16	89.9	Maripa nicaraguensis	JQ587303
2K*	75.49	697	96.3	96.3	32.4	1142.35	85.9	Carpolobia conradsiana	JX517551
3.1	82.65	762	97.9	98.8	35.0	1343.63	90.7	Guarea pterorhachis	JQ588347
3.11	80.48	742	99.9	99.9	36.3	1367.64	90.2	Haematodendron glabrum	AY220447
3.12	84.27	777	99.9	99.9	31.3	1432.27	92.1	Tachigali sp.	KX538536
3.12	84.27	777	99.5	99.6	31.3	1421.19	91.9	Tachigali sp.	KX538536
3.13	82.00	756	99.2	99.4	36.2	1373.18	90.7	Ocotea catharinensis	KF555429
3.14	80.15	739	99.5	99.5	32.8	1341.79	89.8	Qualea rosea	JQ626462
3.15	84.27	777	99.7	99.7	32.2	1424.89	92.0	Guapira riedeliana	FN597630
3.16	82.54	761	99.5	99.6	33.4	1387.95	91.1	Pourouma bicolor	GQ982067
3.18	82.86	764	100.0	100.0	31.5	1411.96	91.4	Trymatococcus oligandrus	FJ037932
3.26	82.32	760	98.3	98.8	33.2	1345.48	90.6	Sterculia apetala	GQ982103
3.27	78.96	728	99.2	99.5	35.6	1319.63	89.2	Protium costaricense	GQ982071
3.28	80.91	746	99.7	99.7	33.6	1367.64	90.3	Vochysia ferruginea	GQ982128
3.29	80.15	739	99.5	99.5	32.8	1341.79	89.8	Qualea rosea	JQ626462
3.3	81.67	753	99.7	99.7	31.7	1380.57	90.7	Brosimum lactescens	KU856472
3.3	78.42	723	99.3	99.6	35.4	1315.93	89.0	Trichilia martiana	JQ588367
3.31	78.52	724	99.0	99.4	35.5	1308.55	89.0	Protium costaricense	GQ982071
3.32	80.04	738	99.7	99.7	32.8	1352.87	89.9	Erythroxylum macrophyllum	GQ981986
3.33	79.39	732	99.0	99.3	35.9	1327.01	89.3	Haematodendron glabrum	AY220447
3.33	77.98	719	98.7	99.2	35.7	1295.62	88.6	Haematodendron glabrum	AY220447
3.34	80.26	743	98.4	98.5	35.4	1306.7	89.4	Aspidosperma triternatum	AM295077
3.35	79.61	734	99.6	99.7	33.4	1341.79	89.6	Rhabdodendron amazonicum	JQ844136
3.36	66.92	617	99.2	99.6	37.0	1122.04	83.3	Licaria chrysophylla	JQ626395
3.37	80.69	744	98.4	99.0	35.2	1325.17	89.8	Guarea pterorhachis	JQ588347
3.39	13.34	123	94.3	97.0	32.5	202.405	55.1	Minquartia guianensis	KU247535
3.4	81.24	749	100.0	100.0	33.0	1384.26	90.6	Inga paraensis	KX374525
3.4	80.69	744	98.9	99.0	31.6	1332.55	89.8	Swartzia panacoco	KT876194
3.41	81.56	752	99.5	99.6	31.7	1371.33	90.6	Brosimum alicastrum	GQ981947
3.42	75.16	693	99.9	99.9	31.9	1277.16	87.5	Swartzia canescens	JQ626472
3.43	80.80	745	98.9	99.3	35.3	1341.79	90.0	Guarea pterorhachis	JQ588347
3.44	73.43	677	98.8	99.3	35.9	1218.06	86.3	Protium opacum	JQ626503
3.45	58.89	543	98.5	98.8	32.8	972.458	78.9	Guarea grandifolia	GQ982002
3.45	60.41	557	98.2	99.0	33.0	990.925	79.7	Guarea grandifolia	GQ982002
3.46	82.75	763	98.7	99.1	35.1	1367.64	91.0	Guarea pterorhachis	JQ588347
3.47	81.89	755	99.9	99.9	31.5	1391.65	90.9	Trymatococcus oligandrus	FJ037932
3.48	83.30	768	98.6	98.8	31.3	1365.79	91.1	Swartzia panacoco	KT876194
3.49	84.16	777	99.5	99.5	35.1	1411.96	91.8	Guarea grandifolia	GQ982002
3.5	77.77	717	99.6	99.7	33.5	1310.39	88.7	Rhabdodendron amazonicum	JQ844136
3.5	82.86	764	100.0	100.0	31.5	1411.96	91.4	Trymatococcus oligandrus	FJ037932
3.6	80.69	744	96.9	98.4	36.0	1290.08	89.6	Tapirira guianensis	KF981295
3.7	82.32	759	99.3	99.5	33.5	1380.57	90.9	Pourouma bicolor	GQ982067
3.9	80.59	743	99.5	99.7	31.6	1358.41	90.2	Balizia sp.	KX302319
4.1	84.27	777	99.9	99.9	32.2	1430.43	92.1	Guapira riedeliana	FN597630
4.1	84.27	777	100.0	100.0	36.2	1435.97	92.1	Virola michelii	AY220454
4.11	84.82	782	99.1	99.1	31.5	1408.27	92.0	Brosimum alicastrum	GQ981947
4.12	84.27	777	99.9	99.9	36.3	1430.43	92.1	Carapa guianensis	NC_037442
4.13	84.27	777	100.0	100.0	36.0	1435.97	92.1	Haematodendron glabrum	AY220447
4.15	85.57	789	99.2	99.2	31.9	1424.89	92.4	Lacistema aggregatum	FJ670025
4.16	84.27	777	97.8	97.8	32.2	1341.79	91.0	Grias cauliflora	MF359952
4.17	84.16	776	99.7	99.7	36.5	1423.04	92.0	Cinnamomum aromaticum	MF627719
4.18	84.16	776	99.9	99.9	30.0	1428.58	92.0	Ormosia arborea	KX816384
4.19	79.72	735	100.0	100.0	34.8	1358.41	89.9	Siparuna decipiens	JQ626498
4.2	84.27	777	99.9	99.9	36.3	1430.43	92.1	Carapa guianensis	NC_037442
4.2	83.73	772	100.0	100.0	31.2	1426.73	91.9	Swartzia panacoco	KT876194
4.21	80.59	743	99.6	99.7	30.1	1360.25	90.1	Ormosia arborea	KX816384
4.21	79.50	733	99.0	99.4	30.2	1327.01	89.4	Ormosia arborea	KX816384
4.22	79.83	736	100.0	100.0	34.9	1360.25	89.9	Siparuna decipiens	JQ626498
4.23	84.27	777	100.0	100.0	36.2	1435.97	92.1	Virola michelii	AY220454
4.25	79.83	736	100.0	100.0	34.9	1360.25	89.9	Siparuna decipiens	JQ626498
4.26	87.20	804	100.0	100.0	31.8	1485.83	93.6	Hirtella macrosepala	KX180068
4.27	84.27	777	100.0	100.0	32.0	1435.97	92.1	Balizia pedicellaris	KF981315
4.28	82.75	763	99.9	99.9	34.5	1404.57	91.3	Minquartia guianensis	KU247535
4.29	85.90	792	100.0	100.0	34.0	1463.67	93.0	Macoubea guianensis	GU973901
4.3	84.27	777	100.0	100.0	36.2	1435.97	92.1	Virola michelii	AY220454
4.3	83.62	774	98.7	98.7	35.5	1371.33	91.2	Aspidosperma triternatum	AM295077
4.31	83.73	772	100.0	100.0	31.2	1426.73	91.9	Swartzia panacoco	KT876194
4.32	84.27	777	100.0	100.0	36.0	1435.97	92.1	Haematodendron glabrum	AY220447
4.33	84.27	777	100.0	100.0	35.8	1435.97	92.1	Virola nobilis	GQ982126
4.34	83.95	774	99.6	99.6	34.8	1413.81	91.8	Protium costaricense	GQ982071
4.6	84.92	783	99.9	99.9	31.5	1441.51	92.4	Conceveiba martiana	FJ670011
4.7	84.82	782	99.1	99.1	31.5	1408.27	92.0	Brosimum alicastrum	GQ981947
4.8	84.27	777	99.9	99.9	36.3	1430.43	92.1	Carapa guianensis	NC_037442
4.9	83.73	772	100.0	100.0	31.2	1426.73	91.9	Swartzia panacoco	KT876194
5.1	83.30	768	99.6	99.6	33.2	1402.73	91.5	Patinoa sphaerocarpa	AY589074
5.1	81.89	755	99.7	99.8	32.2	1386.11	90.8	Castilla elastica	KU856438
5.11	84.27	777	99.5	99.5	35.4	1413.81	91.9	Synima cordieri	AY724333
5.13	84.27	777	100.0	100.0	33.2	1435.97	92.1	Diospyros tetrandra	DQ924058
5.14	84.27	777	100.0	100.0	32.3	1435.97	92.1	Hymenaea courbaril	KX538511
5.16	83.62	771	99.2	99.2	31.8	1386.11	91.4	Parkia multijuga	EU362018
5.17	84.27	777	100.0	100.0	36.2	1435.97	92.1	Virola michelii	AY220454
5.18	76.03	701	100.0	100.0	36.5	1295.62	88.0	Virola michelii	JQ626468
5.19	84.27	777	99.9	99.9	33.5	1430.43	92.1	Diospyros dichroa	DQ924011
5.2	83.95	774	99.6	99.6	34.8	1413.81	91.8	Protium costaricense	GQ982071
5.22	84.27	777	99.9	99.9	33.5	1430.43	92.1	Diospyros dichroa	DQ924011
5.23	83.95	774	99.5	99.5	34.8	1408.27	91.7	Protium costaricense	GQ982071
5.24	85.25	786	99.9	99.9	31.8	1447.05	92.6	Brosimum lactescens	KU856472
5.25	74.95	691	99.7	99.8	36.8	1269.77	87.4	Carapa guianensis	NC_037442
5.29	84.27	777	99.9	99.9	33.5	1430.43	92.1	Pouteria campechiana	KX426215
5.30	54.12	499	97.0	98.5	34.7	867.199	76.3	Iryanthera sagotiana	JQ626420
5.31	85.90	792	100.0	100.0	35.4	1463.67	93.0	Odontadenia perrottetii	EF456272
5.32	83.30	768	99.6	99.6	33.2	1402.73	91.5	Patinoa sphaerocarpa	AY589074
5.33	83.95	774	99.5	99.5	34.8	1408.27	91.7	Protium costaricense	GQ982071
5.34	83.95	774	99.6	99.6	34.8	1413.81	91.8	Protium costaricense	GQ982071
5.36	84.27	777	99.2	99.4	34.8	1408.27	91.8	Geissospermum laeve	DQ660517
5.4	77.01	710	98.2	98.6	35.9	1260.54	87.8	Protium costaricense	JQ587172
5.5	82.75	763	99.6	99.6	31.1	1395.34	91.2	Tachigali sp.	KX538536
5.6	87.20	804	100.0	100.0	31.8	1485.83	93.6	Hirtella macrosepala	KX180068
5.7	75.05	692	97.4	98.6	36.0	1210.68	86.8	Protium costaricense	GQ982071
5.7	77.11	711	96.5	97.7	35.6	1212.52	87.4	Protium costaricense	GQ982071
5.8	84.27	777	99.5	99.5	35.4	1413.81	91.9	Synima cordieri	AY724333
6.1	69.85	644	98.6	99.1	35.8	1155.28	84.5	Geissospermum laeve	DQ660517
6.1	82.97	765	99.3	99.3	30.2	1386.11	91.2	Ormosia coutinhoi	KY079016
6.13	83.95	774	99.0	99.0	35.5	1386.11	91.5	Aspidosperma triternatum	AM295077
6.14	83.95	774	99.6	99.6	34.8	1413.81	91.8	Protium costaricense	GQ982071
6.15	85.90	792	99.6	99.6	33.6	1447.05	92.8	Pourouma bicolor	GQ982067
6.16	84.60	780	98.6	98.7	32.6	1382.41	91.6	Qualea grandiflora	AF368216
6.17	84.27	777	99.7	99.7	32.2	1424.89	92.0	Guapira riedeliana	FN597630
6.19	85.90	792	99.6	99.6	33.6	1447.05	92.8	Pourouma bicolor	GQ982067
6.2	81.34	750	98.4	99.0	35.2	1336.25	90.2	Protium costaricense	JQ587172
6.2	59.87	552	99.5	99.6	33.0	1007.54	79.7	Brosimum alicastrum	GQ981947
6.22	85.47	789	98.4	98.4	34.9	1389.8	92.0	Pachycormus discolor	AY594493
6.24	80.59	743	99.7	99.7	33.5	1362.1	90.2	Vochysia ferruginea	GQ982128
6.25	84.27	777	99.9	99.9	33.6	1430.43	92.1	Micropholis gnaphaloclados	JQ413918
6.26	77.98	719	99.6	99.6	35.5	1312.24	88.8	Protium costaricense	GQ982071
6.27	84.60	780	99.6	99.7	33.6	1426.73	92.1	Vochysia ferruginea	GQ982128
6.3	83.30	768	99.7	99.7	33.1	1408.27	91.5	Eriotheca macrophylla	HQ696713
6.3	70.17	647	99.2	99.4	32.2	1175.59	84.8	Tachigali sp.	KX538536
6.31	84.27	777	99.7	99.8	31.3	1426.73	92.0	Tachigali sp.	KX538536
6.32	84.60	780	98.6	98.7	32.6	1382.41	91.6	Qualea grandiflora	AF368216
6.33	84.60	780	99.7	99.7	33.6	1430.43	92.2	Vochysia ferruginea	GQ982128
6.34	84.60	780	99.7	99.7	33.6	1430.43	92.2	Vochysia ferruginea	GQ982128
6.35	84.27	777	99.9	99.9	33.5	1430.43	92.1	Pouteria campechiana	KX426215
6.36*	84.27	777	99.9	99.9	33.5	1430.43	92.1	Hippocratea volubilis	HM230173
6.38	80.69	744	98.9	98.9	32.7	1330.71	89.8	Castilla elastica	KU856438
6.39	83.84	774	99.5	99.5	31.0	1410.11	91.7	Tachigali sp.	KX538536
6.4	31.34	289	99.7	99.7	32.2	531.109	65.5	Brosimum guianense	JQ626530
6.4	83.95	774	99.4	99.4	34.8	1406.42	91.7	Protium costaricense	GQ982071
6.41	82.43	760	99.5	99.5	35.0	1384.26	91.0	Protium costaricense	GQ982071
6.42*	83.84	773	99.7	99.8	33.5	1419.35	91.8	Hippocratea volubilis	HM230173
6.44	83.30	768	99.7	99.7	33.1	1408.27	91.5	Eriotheca macrophylla	HQ696713
6.45	84.27	777	99.9	99.9	33.6	1430.43	92.1	Micropholis gnaphaloclados	JQ413918
6.6	83.95	774	99.7	99.7	34.8	1419.35	91.8	Protium costaricense	GQ982071
6.7	51.08	471	98.9	99.0	33.8	845.039	75.1	Castilla elastica	KU856438
6.8	84.60	780	98.7	98.7	32.6	1386.11	91.7	Qualea grandiflora	AF368216
6.9	83.51	770	98.3	98.5	35.1	1362.1	91.0	Trichilia martiana	JQ588367
6.9	83.51	770	98.3	98.7	35.1	1362.1	91.1	Trichilia martiana	JQ588367
7.1	85.90	792	100.0	100.0	34.0	1463.67	93.0	Macoubea guianensis	GU973901
7.1	81.02	747	99.7	99.8	31.9	1371.33	90.4	Pseudopiptadenia suaveolens	DQ790637
7.12	80.59	743	99.9	99.9	35.4	1367.64	90.2	Minquartia guianensis	KU247535
7.13	82.21	758	99.9	99.9	35.2	1395.34	91.0	Trichilia martiana	JQ588367
7.14	78.52	724	100.0	100.0	35.5	1338.09	89.3	Protium opacum	JQ626503
7.15	84.27	777	100.0	100.0	36.0	1435.97	92.1	Haematodendron glabrum	AY220447
7.16	82.10	757	99.7	99.7	32.8	1387.95	90.9	Lecythis pneumatophora	MF359953
7.19	83.95	774	99.7	99.7	34.8	1419.35	91.8	Protium costaricense	GQ982071
7.2	78.09	720	99.7	99.8	35.6	1323.32	88.9	Protium costaricense	GQ982071
7.24	84.27	777	100.0	100.0	36.2	1435.97	92.1	Virola michelii	AY220454
7.26	83.95	774	99.2	99.3	34.8	1402.73	91.6	Protium costaricense	GQ982071
7.27	80.69	744	99.2	99.2	35.1	1341.79	89.9	Ptychopetalum petiolatum	KC627490
7.3	83.95	774	99.5	99.5	34.8	1410.11	91.7	Protium costaricense	GQ982071
7.4	84.27	777	100.0	100.0	36.0	1435.97	92.1	Haematodendron glabrum	AY220447
7.5	76.14	702	100.0	100.0	33.5	1297.47	88.1	Leonia glycycarpa	JQ626572
7.6	78.42	723	99.9	99.9	35.1	1330.71	89.1	Minquartia guianensis	KU247535
7.7	84.27	777	99.5	99.6	36.0	1421.19	91.9	Haematodendron glabrum	AY220447

• * = liana

Edited by

ASSOCIATE EDITOR:

Ricarda Riina

Publication Dates

• Publication in this collection
  16 Mar 2022
• Date of issue
  Jan-Mar 2022

History

• Received
  12 May 2021
• Accepted
  17 Dec 2021