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29 VOL. 521 2022: 29 37
http://dx.doi.org/10.1590/1809-4392202101413
ORIGINAL ARTICLE
ACTA
AMAZONICA
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.
Fast and novel botanical exploration of a 320-km
transect in eastern Amazonia using DNA barcoding
William MILLIKEN1* , Guillaume ODONNE2, Julien ENGEL3, François-Michel LE TOURNEAU4, Uxue
SUESCUN5, Jérôme CHAVE5
1 Royal Botanic Gardens, Kew, Richmond, TW9 3AB, UK
2 Centre National de la Recherche Scientique (CNRS), LEEISA (Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens), CNRS, Université de Guyane,
IFREMER, 97300 Cayenne, French Guiana
3 Institut de Recherche pour le Développement (IRD), AMAP, Université de Montpellier, CIRAD, CNRS, INRAE, Boulevard de la Lironde, TA A-51/PS2, F-34398 Montpel-
lier Cedex 5, France
4 Centre National de la Recherche Scientique (CNRS)/The University of Arizona, International Research Laboratory Interdisciplinary Global and Environmental
Studies (iGLOBES), 845 N Park avenue, 85719, Tucson, AZ, United States
5 Centre National de la Recherche Scientique (CNRS)/Laboratoire Évolution et Diversité Biologique (EDB), CNRS, UPS, IRD, Université Paul Sabatier, 31062 Toulouse, France
* Corresponding author: W.Milliken@kew.org; https://orcid.org/0000-0002-3926-6661
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 identication.
We also collected fertile herbarium specimens from other species (trees/shrubs/herbs) inside and outside the plots. e selected
DNA barcodes were useful at the family level but failed to identify specimens at the species level. Based on DNA barcoding
identication, 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 identied 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 identication of specimens at lower taxonomic levels in oristic inventories in the Amazon region.
KEYWORDS: DNA barcoding, French Guiana-Brazil border, matK, rbcLa, tree inventory, Tumucumaque
Exploração botânica rápida e inovadora de um transecto de 320 km no
leste da Amazônia usando código de barras de DNA
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 identicação 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 identicar espécimes em nível de espécie. Com base na identicação 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 identicadas 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 identicação de espécimes até níveis taxonômicos mais baixos em inventários orísticos na região amazônica.
PALAVRAS-CHAVE: código de barras de DNA, fronteira Guiana Francesa-Brasil, inventário de árvores, matK, rbcLa, Tumucumaque
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
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INTRODUCTION
e Amazon region harbours the richest ora 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).
e 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 ora 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.
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. e
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). is 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 identication 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/). is method has
proven to be eective for the identication 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
anking 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 dicult, and several strategies have been tested
specically for Neotropical plants (Gonzalez et al. 2009; Kress
et al. 2009). e goal of this contribution is not to debate the
utility of DNA barcodes for plant identication, but rather
to use this approach to aid the taxonomic identication
process. Hollingsworth et al. (2009) recommended the use
of a combination of two plastid DNA regions, the rst 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 ora 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
ora. However, they did not assess the identication potential
of the DNA barcodes they surveyed.
e primary goal of this contribution was to evaluate
the potential of the DNA barcoding approach to aid in the
taxonomic identication process in a poorly known tropical
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 dierence between remote-sensed ground elevation (SRTM) and
ground elevation (mean dierence = 10.6 ± 7.9 standard deviation). This gure
is in colour in the electronic version.
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
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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
e 2015 expedition involved 20 people (including 15
from the 3rd 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. e 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. e 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, Scientic
House, Terminus Rd, Chichester, UK), we monitored
environmental conditions throughout the route.
e 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).
e 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).
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 identication.
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.
e herbarium vouchers were deposited at Kew (K) and
Cayenne (CAY). e herbarium specimens were directly
identied 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 identications to support DNA
identication.
e DNA samples from the cambium of trees and lianas
in the plots were used for preliminary identication. ese
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-lter 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,
Figure 3. Example of daily uctuation 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.
Figure 2. Air temperature along the transect measured by a Tinytag unit, with
daily uctuations.
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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 buer. Total DNA extraction was
performed with a Biosprint 15 workstation (Qiagen, CA)
following the manufacturer’s protocols. PCR amplication
was performed for the two plastid DNA barcoding regions
selected. e rbcLa marker is the rst half of the rbcL gene
and was amplified using classic primers: 1F and 724R
(Gonzalez et al. 2009). e matK region was amplied 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). e PCR reaction mix included
0.2 µl of GoTaq 51 U/µl (Promega), 10 ml of 5 x buer, 1
µl of 20 µM for each primer, 1 µl of dNTP 10 µM, 1 ml of
DNA template and H2O for a nal volume of 50 µl. PCR
products were puried with a MinElute PCR Purication
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. e 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. e 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.
e 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. e 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).
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 amplied for rbcLa, and 220 (93.6%) for
matK, with 170 samples (72.3%) amplied for both markers.
Matching to the NCBI reference database revealed that most
of the specimens could be condently identied to family.
Comparing the identications 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 identied 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, eobroma cacao L.
and Trymatococcus oligandrus (Benoist) Lanj., and one species
of liana (Hippocratea volubilis L.). Interestingly, one sample
was identied 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.
e 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).
irty-ve species (31.8% of cambium samples sequenced
with one or both markers) were given tentative identications
(Table 3), based on: 1) DNA barcodes (removing alternative
identication 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
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
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DISCUSSION
e 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 diers from that of
Northern French Guiana, with an under-representation of
Chrysobalanaceae and Lecythidaceae. is 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 leaess 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). e 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
Table 2. List of tree families identied 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 identied 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
was also collected as a herbarium specimen on the expedition,
though not from within the plots. Burseraceae and Meliaceae,
however, could only be condently assigned to family level.
Of the 289 herbarium vouchers collected during the
expedition, 160 were identied 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 identied 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.
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
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Table 3. Species identications (likely determination) of species sampled in the Tumucumaque range (Brazil-French Guiana border) using rbcLa and matK DNA barcodes.
‘Collected’ refers to numbered W. Milliken specimens stored at Kew (K) and Cayenne (CAY); Slash ID is J. Engel’s preliminary identication based on images of the bark slash.
Likely determination Family rbcLa matK Slash ID Collected
Balizia pedicellaris (DC.) Barneby & J.W.Grimes Fabaceae Balizia pedicellaris Balizia pedicellaris
Brosimum alicastrum Sw. Moraceae Brosimum rubescens Brosimum alicastrum Brosimum
Brosimum guianense (Aubl.) Huber ex Ducke Moraceae Brosimum rubescens Brosimum guianense Brosimum
Brosimum lactescens (S.Moore) C.C.Berg Moraceae Brosimum alicastrum Brosimum lactescens Brosimum
Carapa guianensis Aubl. Meliaceae Swietenia mahagoni Carapa guianensis C. guianensis
Casearia javitensis Kunth. Salicaceae Casearia javitensis C. javitensis
Chaunochiton kappleri (Sagot ex Engl.) Ducke Olacaceae Chaunochiton kappleri C. kappleri
Conceveiba guianensis Aubl. Euphorbiaceae Conceveiba terminalis Conceveiba martiana C. guianensis
Cupania scrobiculata Rich. Sapindaceae Cupania scrobiculata Synima cordieri * Cupania
Diplotropis purpurea(Rich.) Amsho Fabaceae D iplotropis purpurea D. pupurea
Erythroxylum macrophyllum Cav. Erythroxylaceae Erythroxylum novogranatense Erythroxylum macrophyllum 5263
Geissospermum argenteum Woodson Apocynaceae Vallesia antillana * Geissospermum laeve G. argenteum 5261
Guarea sylvatica C.DC. Meliaceae Guarea silvatica Guarea pterorhachis G. silvatica
Gustavia hexapetala (Aubl.) Sm. Lecythidaceae Gustavia hexapetala Gria s cauliora * G. hexapetala
Helicostylis pedunculata Benoist Moraceae Helicostylis pedunculata Castilla elastica Moraceae
Hippocratea volubilis L. ** Celastraceae Celastraceae sp. Hippocratea volubilis Celastraceae
Hymenaea courbaril L. Fabaceae Hymenaea courbaril
Iryanthera sagotiana (Benth.) Warb. Myristicaceae Iryanthera sagotiana Haematodendron glabrum * I. sagotiana
Lacistema aggregatum (P.J.Bergius) Rusby Lacistemataceae Lacistema robustum * Lacistema aggregatum
Leonia glycycarpa Ruiz & Pav Violaceae Leonia glycycarpa Leonia glycycarpa L. glycycarpa
Macoubea guianensis Aubl. Apocynaceae Macoubea guianensis M. guianensis
Maquira calophylla (Poepp. & Endl.) C.C.Berg Moraceae Maquira calophylla Castilla elastica * Moraceae
Minquartia guianensis Aubl. Olacaceae Minquartia guianensis Minquartia
Naucleopsis guianensis (Mildbr.) C.C.Berg Moraceae Naucleopsis guianensis Castilla elastica * Moraceae
Pouteria campechiana (Kunth) Baehni * Sapotaceae Pouteria campechiana * Pouteria campechiana * Pouteria
Protium excelsior Byng & Christenh. Burseraceae Protium excelsior Burseraceae
Pseudopiptadenia suaveolens (Miq.) J.W.Grimes Fabaceae Pseudopiptadenia suaveolens Pseudopiptadenia
suaveolens
Ptychopetalum olacoides Benth. Olacaceae Ptychopetalum olacoides Ptychopetalum petiolatum * P. olacoides
Rhabdodendron amazonicum (Spruce ex Benth.)
Huber Rhabdodendraceae Rhabdodendron amazonicum Rhabdodendron
amazonicum 5291
Simarouba amara Aubl. Simaroubaceae Simarouba amara S. amara
Siparuna decipiens (Tul.) A.DC. Sipuarunaceae Siparuna decipiens Siparuna decipiens S. decipiens
Sterculia pruriens (Aubl.) K.Schum. Malvaceae Sterculia pruriens Sterculia apetala S. pruriens 5252
Tapirira obtusa (Benth.) J.D.Mitch. Anacardiaceae Tapirira obtusa Tapirira guianensis Tapirira 5234
Touroulia guianensis Aubl. Ochnaceae Touroulia guianensis 5314
Trymatococcus oligandrus (Benoist) Lanj. Moraceae Trymatococcus oligandrus Trymatococcus oligandrus Trymatococcus 5297
Virola michelii Heckel Myristicaceae Myristica fragrans * Virola michelii V. michelii (or
V. kwatae)
* Not present in French Guiana or neighbouring countries
** Liana
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
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quality was good, despite the fast collection approach adopted
in this work.
Our low success in species identication using DNA
barcodes from cambium samples nevertheless conrm 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 suciently.
One illustration of this problem is the identication 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
identication. 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 identication 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
identication (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 identication
(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 specic 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).
Identication 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 misidentied, and in some dicult genera
50% were incorrect (Baker et al. 2017). One of the issues
with species identication 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 ora
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
ora, but nevertheless this may not be enough for complete
identication in specic 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 ora 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 ve years, and less than half are
now identied 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).
e under-sampling of inter-uvial 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
benet 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.
Identication of trees using DNA from cambium samples and
two DNA barcodes (rbcLa and matK) yielded low success at
the species level. Our identications at the family level are
insucient for comparable surveys across Amazonia. Using
DNA barcoding to aid species identication 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.
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
36 VOL. 521 2022: 29 37
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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 Scientic 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). is work has beneted from an
“Investissement d’Avenir” grant managed by Agence Nationale
de la Recherche (CEBA, ref. ANR-10-LABX-25-01).
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ACCEPTED: 17/12/2021
ASSOCIATE EDITOR: Ricarda Riina
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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.
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
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Figure S2. Examples of specimen identication by other botanists, ve 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.
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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 guianensisAmsho, 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 identications 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 (identied 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), (identied 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 atland close to the seventh milestone. Dominated by Burseraceae, with both Protium spp. and Protium excelsior (rbcLa), as in Plot 5.
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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 eld. 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 suulta 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 suulta 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
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
ACTA
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VOL. 521 2022: 29 37
Query Query
coverage
Sequence
length
% Identical
sites
% Pairwise
identity %GC Bit-score Grade
(%) Organism Accession
number
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 Hir tella suulta 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
Table S1. Continued
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
ACTA
AMAZONICA
VOL. 521 2022: 29 37
Query Query
coverage
Sequence
length
% Identical
sites
% Pairwise
identity %GC Bit-score Grade
(%) Organism Accession
number
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 oribunda 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 suulta 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
Table S1. Continued
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
ACTA
AMAZONICA
VOL. 521 2022: 29 37
Query Query
coverage
Sequence
length
% Identical
sites
% Pairwise
identity %GC Bit-score Grade
(%) Organism Accession
number
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 suulta 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 albiora 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 albiora 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
Table S1. Continued
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
ACTA
AMAZONICA
VOL. 521 2022: 29 37
Query Query
coverage
Sequence
length
% Identical
sites
% Pairwise
identity %GC Bit-score Grade
(%) Organism Accession
number
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 albiora 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 P tychopetalum 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 eld. 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
Table S1. Continued
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
ACTA
AMAZONICA
VOL. 521 2022: 29 37
Query Query
coverage
Sequence
length
% Identical
sites
% Pairwise
identity %GC Bit-score Grade
(%) Organism Accession
number
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 uniora 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 parviora 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
Table S2. Continued
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
ACTA
AMAZONICA
VOL. 521 2022: 29 37
Query Query
coverage
Sequence
length
% Identical
sites
% Pairwise
identity %GC Bit-score Grade
(%) Organism Accession
number
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 Minquar tia guianensis KU247535
3.4 81.24 749 100.0 100.0 33.0 1384.26 90.6 Inga paraensis KX374525
Table S2. Continued
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
ACTA
AMAZONICA
VOL. 521 2022: 29 37
Query Query
coverage
Sequence
length
% Identical
sites
% Pairwise
identity %GC Bit-score Grade
(%) Organism Accession
number
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 cauliora 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 Hir tella 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 Minquar tia 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 A spidosperma 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
Table S2. Continued
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
ACTA
AMAZONICA
VOL. 521 2022: 29 37
Query Query
coverage
Sequence
length
% Identical
sites
% Pairwise
identity %GC Bit-score Grade
(%) Organism Accession
number
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 grandiora 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
Table S2. Continued
MILLIKEN et al. Botanical DNA barcoding in the eastern Amazon
ACTA
AMAZONICA
VOL. 521 2022: 29 37
Query Query
coverage
Sequence
length
% Identical
sites
% Pairwise
identity %GC Bit-score Grade
(%) Organism Accession
number
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 grandiora 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 Q ualea grandiora 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 Minquar tia 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
Table S2. Continued
