Article
Genetic Diversity and Population Structure of Capirona
(Calycophyllum spruceanum Benth.) from the Peruvian Amazon
Revealed by RAPD Markers
Carla L. Saldaña 1 , Johan D. Cancan 1, Wilbert Cruz 1 , Mirian Y. Correa 2 , Miriam Ramos 3, Eloy Cuellar 3 and
Carlos I. Arbizu 1,*
1 Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina
1981, Lima 15024, Peru; carla18317@gmail.com (C.L.S.); clg.johan@gmail.com (J.D.C.);
wilbertcruz2002@gmail.com (W.C.)
2 Facultad de Ciencias Biológicas, Universidad Nacional Pedro Ruíz Gallo, Juan XXIII No 391,
Lambayeque 14013, Peru; miryacormel15@gmail.com
3 Facultad de Ciencias Forestales, Universidad Nacional Agraria La Molina, Av. La Molina s/n, Lima 15024,
Peru; miriam.piaforestal@gmail.com (M.R.); eloycuellar@lamolina.edu.pe (E.C.)
* Correspondence: carbizu@inia.gob.pe
Abstract: Capirona (Calycophyllum spruceanum Benth.) is a tree species of commercial importance
widely distributed in South American forests that is traditionally used for its medicinal properties
and wood quality. Studies on this tree species have been focused mainly on wood properties,





 propagation, and growth. However, genetic studies on capirona have been very limited to date.
Currently, it is possible to explore genetic diversity and population structure in a fast and reliable
Citation: Saldaña, C.L.; Cancan, J.D.;
Cruz, W.; Correa, M.Y.; Ramos, M.; manner by using molecular markers. We here used 10 random amplified polymorphic DNA (RAPD)
Cuellar, E.; Arbizu, C.I. Genetic markers to analyze the genetic diversity and population structure of 59 samples of capirona that were
Diversity and Population Structure of sampled from four provinces located in the eastern region of the Peruvian amazon. A total of 186
Capirona (Calycophyllum spruceanum bands were manually scored, generating a 59 × 186 presence/absence matrix. A dendrogram was
Benth.) from the Peruvian Amazon generated using the UPGMA clustering algorithm, and, similar to the principal coordinate analysis
Revealed by RAPD Markers. Forests (PCoA), it showed four groups that correspond to the geographic origin of the capirona samples
2021, 12, 1125. https://doi.org/ (LBS, Irazola, Masisea, Iñapari). Similarly, a discriminant analysis of principal components (DAPC)
10.3390/f12081125 and STRUCTURE analysis confirmed that capirona is grouped into four clusters. However, we
also noticed that a few samples were intermingled. Genetic diversity estimation was conducted
Academic Editors: Carol A. Loopstra
considering the four groups (populations) identified by STRUCTURE software. AMOVA revealed the
and Timothy A. Martin
greatest variation within populations (71.56%) and indicated that variability among populations is
Received: 14 July 2021 28.44%. Population divergence (Fst) between clusters 1 and 4 revealed the highest genetic difference
Accepted: 20 August 2021 (0.269), and the lowest Fst was observed between clusters 3 and 4 (0.123). RAPD markers were
Published: 22 August 2021 successful and effective. However, more studies are needed, employing other molecular tools. To
the best of our knowledge, this is the first investigation employing molecular markers in capirona
Publisher’s Note: MDPI stays neutral in Peru considering its natural distribution, and as such it is hoped that this helps to pave the way
with regard to jurisdictional claims in towards its genetic improvement and the urgent sustainable management of forests in Peru.
published maps and institutional affil-
iations. Keywords: amazon forest; capirona; genetic diversity; molecular markers; population structure
Copyright: © 2021 by the authors. 1. Introduction
Licensee MDPI, Basel, Switzerland. Calycophyllum spruceanum (Benth.) (Rubiaceae) is a fast-growing forest species [1] with
This article is an open access article a natural origin in the Amazon basin, covering the territories of Bolivia, Peru, Colombia,
distributed under the terms and and Brazil [2]. Capirona is a valuable timber forest species in the Peruvian Amazon that
conditions of the Creative Commons is widely used by local communities and is exported around the world. It has a high-
Attribution (CC BY) license (https:// density, durable wood; therefore, it is used for the construction of economically valuable
creativecommons.org/licenses/by/ products [3]. Capirona has high potential for cultivation in agroforestry systems, which
4.0/).
Forests 2021, 12, 1125. https://doi.org/10.3390/f12081125 https://www.mdpi.com/journal/forests
Forests 2021, 12, 1125 2 of 12
counters unsustainable land use due to activities such as slash-and-burn agriculture, and
this species represents an alternative to increase rural incomes and development [4]. Thus,
capirona is currently the object of research in the Peruvian Amazon basin in order to estab-
lish participatory research with local communities, optimizing genetic and silvicultural
management strategies including community conservation methods [1] since it is known
that the indiscriminate use of forest species is generating deforestation in various regions
of the planet [5–7]. Genetic diversity studies are indispensable for conducting conservation
programs and sustainable management. Moreover, these studies based on molecular mark-
ers provide important information on the genetic makeup of the population because they
are independent of environmental factors [8]. A demographic study would not provide this
kind of information, influencing decision making. In recent years, molecular markers such
as DNA-based markers like random-amplified polymorphic DNA (RAPD), microsatellite
or simple sequence repeat (SSR) and inter simple sequence repeat (ISSR) have been used in
studies for the analysis of phylogeny, inter-species relationships and genetic diversity for
forest species like Pinus leucodermis, Cedrela odorata Eucalyptus globulus, Swietenia macrophylla,
Bertholletia excelsa and Populus deltoides [9–14].
Previous research determined the genetic variation of capirona by using amplified
fragment length polymorphisms (AFLP) among nine populations from the Peruvian Ama-
zon [1]. They demonstrated that variation among individuals within populations is pre-
dominant. However, they did not employ samples from the Madre de Dios department,
which is considered a place where capirona exists naturally [15]. On the other hand,
Tauchen et al. [3] identified the genetic variability of capirona at the level of DNA polymor-
phism evaluated by non-specific ITS primers of plant materials from Pucallpa, department
of Ucayali, in the Peruvian Amazon. Their results showed that morphological diversity
is greater than genetic diversity of capirona. They also found that environmental factors
had a greater impact on the phenotype in those accessions. Finally, their results are in line
with the claims of previous studies on C. spruceanum, suggesting greater variation within
provenances than among provenances. Capirona is an orphan forest species, and no further
studies of genetic diversity with molecular markers have been reported.
RAPD is an especially useful DNA-based method for initial research of genetic diver-
sity in plant species. These studies are crucial for the initiation of conservation programs,
and the rapidity of working with RAPD markers is an advantage because they can deliver
crucial information in a short time [16–18]. They have been used to differentiate cultivars
and also on genetic mapping [19–21]. Although some researchers have shown reservations
regarding the usefulness of RAPD, basically due its poor reproducibility [22,23], other
studies mentioned that RAPD can be employed for genetic analyses [24–28]. Additionally,
other studies demonstrated a high level of reproducibility [29]. A great potential for the
analysis of polymorphism and genetic diversity has also been demonstrated by RAPD
markers as they are easy to test and require low concentrations of DNA [17].
The objective of this study was to determine the genetic diversity and population
structure of capirona from primary forests located in the departments of Ucayali, San
Martín and Madre de Dios located in the Peruvian Amazon to help pave the way towards
its modern breeding program, the sustainable conservation of this tree species in the near
future.
2. Materials and Methods
2.1. Plant Material
We collected 59 samples of capirona from Ucayali, San Martín and Madre de Dios
departments in the Peruvian Amazon considering their natural range of distribution. Leaf
samples were collected in paper envelopes, stored in airtight containers with silicone gel,
and transported to the National Institute of Agricultural Innovation (INIA) for genomic
DNA extraction. Further details of the samples examined in this study are available in
Table 1.
Forests 2021, 12, 1125 3 of 12
Table 1. Codes and geographic information of the 59 samples of capirona from the Peruvian Amazon characterized in this
study.
Code Region Department Latitude Longitude Code Region Department Latitude Longitude
cap018 Irazola Ucayali 8◦51′ S 75◦7′ W cap057 LBS San Martín 6◦28′ S 76◦20′ W
cap019 Irazola Ucayali 8◦51′ S 75◦7′ W cap058 LBS San Martín 6◦28′ S 76◦20′ W
cap020 Irazola Ucayali 8◦51′ S 75◦7′ W cap059 LBS San Martín 6◦28′ S 76◦20′ W
cap021 Irazola Ucayali 8◦51′ S 75◦7′ W cap060 LBS San Martín 6◦28′ S 76◦20′ W
cap022 Irazola Ucayali 8◦51′ S 75◦7′ W cap061 LBS San Martín 6◦28′ S 76◦20′ W
cap023 Irazola Ucayali 8◦51′ S 75◦7′ W cap062 LBS San Martín 6◦28′ S 76◦20′ W
cap024 Irazola Ucayali 8◦51′ S 75◦7′ W cap063 LBS San Martín 6◦28′ S 76◦20′ W
cap030 Masisea Ucayali 8◦41′ S 73◦37′ W cap064 LBS San Martín 6◦28′ S 76◦20′ W
cap031 Masisea Ucayali 8◦42′ S 73◦40′ W cap065 LBS San Martín 6◦28′ S 76◦20′ W
cap032 Masisea Ucayali 8◦42′ S 73◦40′ W cap066 LBS San Martín 6◦28′ S 76◦20′ W
cap033 Masisea Ucayali 8◦43′ S 73◦40′ W cap067 LBS San Martín 6◦28′ S 76◦20′ W
cap034 Masisea Ucayali 8◦43′ S 73◦40′ W cap068 LBS San Martín 6◦28′ S 76◦20′ W
cap035 Masisea Ucayali 8◦43′ S 73◦40′ W cap069 LBS San Martín 6◦28′ S 76◦20′ W
cap037 Masisea Ucayali 8◦49′ S 73◦44′ W cap070 Iñapari Madre de Dios 10◦60′ S 69◦53′ W
cap038 Masisea Ucayali 8◦50′ S 73◦45′ W cap071 Iñapari Madre de Dios 10◦60′ S 69◦53′ W
cap039 Masisea Ucayali 8◦50′ S 73◦47′ W cap072 Iñapari Madre de Dios 10◦57′S 69◦54′ W
cap042 Masisea Ucayali 8◦50′ S 73◦47′ W cap073 Iñapari Madre de Dios 10◦59′ S 69◦53′ W
cap043 Masisea Ucayali 8◦50′ S 73◦47′ W cap074 Iñapari Madre de Dios 10◦59′ S 69◦54′ W
cap044 Masisea Ucayali 8◦50′ S 73◦47′ W cap075 Iñapari Madre de Dios 10◦59′ S 69◦53′ W
cap045 Masisea Ucayali 8◦53′ S 73◦52′ W cap076 Iñapari Madre de Dios 10◦58′ S 69◦53′ W
cap046 Masisea Ucayali 8◦53′ S 73◦52′ W cap078 Iñapari Madre de Dios 10◦58′ S 69◦54′ W
cap047 Masisea Ucayali 8◦40′ S 74◦11′ W cap079 Iñapari Madre de Dios 10◦58′ S 69◦54′ W
cap048 Masisea Ucayali 8◦41′ S 74◦12′ W cap080 Iñapari Madre de Dios 10◦58′ S 69◦54′ W
cap049 Masisea Ucayali 8◦41′ S 74◦12′ W cap081 Iñapari Madre de Dios 10◦58′ S 69◦54′ W
cap050 LBS San Martín 6◦28′ S 76◦20′ W cap082 Iñapari Madre de Dios 10◦58′ S 69◦54′ W
cap051 LBS San Martín 6◦28′ S 76◦20′ W cap084 Iñapari Madre de Dios 10◦58′ S 69◦54′ W
cap052 LBS San Martín 6◦28′ S 76◦20′ W cap085 Iñapari Madre de Dios 10◦60′ S 69◦53′ W
cap053 LBS San Martín 6◦28′ S 76◦20′ W cap086 Iñapari Madre de Dios 11◦0′ S 69◦53′ W
cap054 LBS San Martín 6◦28′ S 76◦20′ W cap087 Iñapari Madre de Dios 11◦0′ S 69◦53′ W
cap055 LBS San Martín 6◦28′ S 76◦20′ W
2.2. DNA Amplification
We extracted genomic DNA by using the CTAB method with minor modifications [30,31].
All procedures were performed in 1.5 and 2 mL plastic microfuge tubes. About 0.1 g dry
leaves were grounded in liquid nitrogen and suspended in 600 mL of 2× CTAB buffer
containing 0.2% b-mercaptoethanol followed by incubating at 65 ◦C for 60 min, then an
equal volume of chloroform:isoamylalcohol (24:1, v/v) was added and the sample was
shaken gently and then centrifuged. The supernatant was extracted, then 10× CTAB
buffer and chloroform:isoamylalcohol (24:1, v/v) was added again. The supernatant was
extracted and then mixed with ice cold isopropanol. DNA was recovered as a pellet by
centrifugation, washed with ice-cold ethanol twice (70 and 90%), and then DNA was dried
in the air. Finally, DNA was resuspended in nuclease-free water. The RNA contamination
in all samples was removed by digesting the extract with RNase-A (100 µg ml−1) for
30 min at 37 ◦C. DNA quality and quantity were checked in 1% agarose gels using Gelred
(Biotium®, Fremont, CA, USA) and by standard spectrophotometry.
Forests 2021, 12, 1125 4 of 12
A total of 10 RAPD markers (Operon Technologies Inc., Alameda, CA, USA) were
employed to assess genetic diversity among 59 samples of C. spruceanum (Table S1). Am-
plification was achieved with 10 µL reaction volume containing 5 ng DNA, kit Kapa HiFi
Hotstart ReadyMix, and 0.2 µM of primers. The PCR amplification was performed at
an initial denaturation temperature of 94 ◦C for 4 min followed by 40 cycles of 1 min
denaturation at 94 ◦C, 45 s annealing at 37 ◦C and 2 min extension at 72 ◦C with a final
extension of 10 min at 72 ◦C [32] in a Thermal Cycler Simplyone (Applied Biosystems™,
Foster City, CA, USA). Amplified products were separated on 1% (w/v) agarose gel in TBE
buffer by electrophoresis and were then visualized with Gelred staining and photographed
using the gel documentation system. The size of the amplification products was estimated
by comparing the amplicons with a 100 bp ladder (New England Biolabs, Ipswich, MA,
USA).
2.3. Data Analysis
The RAPD band patterns were scored visually for the presence (1) or absence (0) of
various molecular weights. Only polymorphic, clear and consistent bands were considered
for the analysis [32]. Similar to the procedure employed by Chia [33], loci with more than
10% missing data were excluded from the analysis. Polymorphic information content (PIC)
from dominant markers was calculated u[sing the followin]g equation:
PIC = 1− f i2 + (1− f i)2 (1)
where, fi is the frequency of the amplified band (1) and (1 − fi) is frequency of absence of
band (0) [34].
We then used RStudio software v1.2.5033 to calculate genetic distances based on
Provesti’s coefficient [35], then a dendrogram was generated using the UPGMA clustering
algorithm with 1000 bootstrap replicates from poppr package v2.9.2 [36]. We also used
ade4 v1.7–16 and adegenet v.2.1.3 packages in Rstudio to conduct a principal coordinate
analysis (PCoA) and a discriminant analysis of the principal component analysis (DAPC),
respectively, in order to infer the capirona population structure. Number of populations
(K) were set from 1 to 10 by k-means clustering with 100,000 iterations. Selection of the
most likely number of clusters was based on the lowest Bayesian Information Criterion
(BIC) value.
Population structure was also estimated using the STRUCTURE program v.2.3.4 [37]
with ten runs for each number of populations (K value) ranging from 1 to 10 with a burn-
in length of 50,000 Monte Carlos iterations, which was followed by 150,000 iterations.
An admixture model with no previous population information was considered; all other
parameters were set to default values. Estimation of the most likely number of clusters was
calculated by the Evanno method [38]. Membership probabilities ≥ 0.8 or the maximum
membership probability was adopted to divide the capirona samples into different clusters.
Population structure plots were generated with R package pophelper v.2.3.1 [39]. We used
tess3r package v1.1.0 [40] to display the STRUCTURE membership coefficient matrix of the
estimated K on a geographic map of Peru.
We used the number of populations determined by STRUCTURE to conduct an
analysis of molecular variance (AMOVA) using the R package poppr. In addition, three
genetic diversity indices were calculated using the same package: (i) Shannon–Wiener
index, (ii) Simpson’s index, and (iii) Nei’s gene diversity (expected heterozygosity). The
degree of gene differentiation among clusters in terms of allele frequencies (Fst) was
estimated using the following formula:
Fst = 1 − (Hs/Ht) (2)
where HS is the average expected heterozygosity estimated from each cluster and Ht is
total gene diversity or expected heterozygosity in the total cluster as estimated from the
pooled allele frequencies.
Forests 2021, 12, 1125 5 of 12
3. Results
3.1. RAPD Analysis
The 10 RAPD primers employed in this work revealed 16 to 23 fragments in 59 samples
of capirona, with 29.4 fragments being the average. Of the total 186 fragments, 100% were
polymorphic (Figure S1). Polymorphic information content (PIC) ranged from 0.20 to 0.38
(Table 2).
Table 2. Genetic diversity based on 10 RAPD markers among four clusters inferred by the STRUC-
TURE analysis.
Cluster N H Lambda He PIC PPL
1 6 1.79 0.83 0.12 0.20 51.08%
2 18 2.89 0.94 0.3 0.29 83.33%
3 19 2.94 0.95 0.41 0.38 96.24%
4 16 2.77 0.94 0.29 0.20 83.33%
Total 59 4.08 0.98 0.41
N: population size, H: Shannon–Wiener index of diversity, Lambda: Simpson’s index, He: Nei’s 1978 expected
heterozygosity, PIC: polymorphic information content, PPL: percentage of polymorphic loci.
3.2. Genetic Diversity and Population Structure
Forests 2021, 12, x FOR PEER REVIEAW total of 186 bands were manually scored, generating a 59 × 186 p6r eofs e13n ce/absence
 data matrix. A phylogenetic tree based on Provesti’s genetic distances showed that all
members of capirona were placed in four main clusters according to their geographic
localitoyf :c(a1p)irsoanma aprele cslubsetelroend gaicncogrdtoinIgñ tao pthaeriir (geexocgerappthcica por0ig6i9n), ,e(x2c)epsta fmorp slaemspflreos mcapL0B20S,, (3) samples
collecctaepd02a1t, cMapa0s3i5s aenad acanpd08(54 ()Fiagnur“e a3)d. mMoixretouvreer,” Ficgluurset Se4r swhoiwths SsTaRmUpCTleUsRfEr ommemdbeifrf-erent origin
but shsohiwp ipnrgopaorptiroenvs atole cnlucseteorsf sspaamtiaplllye sinbteerlpoonlagted into a map resulting from analysis in TESS3 [40]. Spatial interpolation of membershipi nmgattroix Iirnafezrorelda a(sFsiiggnuerde c1a)p.irRoneag isoamns- Irazola and
Masispelaesb freolmon IgraztoolaU toc acyluaslteird 1e mpaairntlmy. eInn tth.eI snecaodndd ictliuosnte,r,o suamr pdleesn bderloonggrianmg tos hMoawsiseeda that cluster
Masisaerae ianncldudIeñda. pClaursitearr 3e ctohmeporinseldy scamlupslteesr fsrosmu LpBpSo, arntedd clwustiethr 4a inbclouodtesdt rsamppvleasl furoemo f more than
70% (FIñiagpuarrei. 1).
 
Figure 1. DeFnigdurroe g1r. aDmendbraosgeradmo bnasPedro ovne Psrtoiv’sesgtie´sn geetnicetdic idsitsatannccee andd ththe eUPUGPMGAM clAustcelruinsgt emreinthgodm oef t5h9 osadmopfle5s 9ofs caampirpolneas of capirona
using 10 RAuPsDingm 1a0r kReArPsD.  Nmuarmkebres.r sNaubmobveres tahbeovber athnec hbreasncrheepsr erespernestebnot obotsottrsatrpapv vaalulueess,, wiitthh oonnlyl yvavluaelus ehsighhiegr hthearnt h70a%n 70% shown.
shown. 
Genetic diversity and Fst estimation were conducted considering the four clusters 
(populations) identified by STRUCTURE software, taking into account that RAPD are 
dominant markers. The Nei´s genetic diversity [46] estimates ranged from 0.12 to 0.41 
among the four populations sampled across the Peruvian Amazon, showing a considera-
ble similarity between the samples studied here. The Shannon–Wiener index ranged from 
1.79 to 2.94, and Simpson´s index from 0.83–0.95, indicating high genetic diversity for all 
four populations of capirona. The percentage of polymorphic loci per cluster ranged from 
51.08% (cluster 1) to 96.24% (cluster 3) with an average of 78.5% (Table 2). Population 
divergence (Fst) between clusters 1 and 4 revealed the highest genetic difference (0.269), 
and the lowest Fst was observed between clusters 3 and 4 (Table 3). 
  
 
Forests 2021, 12, 1125 6 of 12
Principal coordinate analysis (PCoA) showed that the first two axis explained 22.8%
Forests 2021, 12, x FOR PEER REVIEWo f the variation and revealed that all 59 individuals are separated into four popula7 tioof n1s3, 
 according to their geographic locality (Irazola, Masisea, LBS, Iñapari). Two samples
from Masisea are mixed with the Irazola population, and two samples from Iñapari are
intermingled within the LBS group (Figure 2a). To explore the genetic structure of capirona
Tfraobmle t3h. eThPee rFust vviaalnueAs mamaoznogn tbhaes fionu,rw cleuustseerds itnhfeerfirendd b.cyl uSTstReUrsCfTuUnRctEio annatloysdies.t ermine the best
K valueClfuosrteoru r capirona sam1p les, discoverin2g that K = 4 is th3 e most likely nu4m ber of
groups acc1o rding to the BIC cri teria (Figure S2). In accordance wi th the dendrogra m result,
discrimina2n t analysis of pri0n.1c8ip2 al components (DAPC) also ev idenced that all  samples
of capiron3a are separated in0.t1o98f our clusters0(.F12ig5 ure 2b, Table S 2). Moreover, F igure S3
demonstra4t ed that capirona0.s2a6m9 ples also clu0s.1t6e7r ed according0.1to23t heir geograph ic origin
mainly. However, four samples from cluster 1 (cap030, cap033, cap035, cap065) grouped
withiTnhteh eanMaalysissies aoafn mdoLlBecSuploarp uvlaartiiaonnc.eS i(mAiMlaOrlyV,Asa)m apllloewcas pth06e9 sftruodmy coluf sgteenr e4twic evrearpialatcioend 
wiitthhiinn athned LbBeStwpeoepnu lcaltuisotne.rsS. aAmMplOeVcaAp 0r8ev5ebaelleodn gthsatto 2c8lu.4s4t%er o3,f bthuet  ittowtaal svianrtieartmioinn gwleads 
fwouitnhdi n btehtewIeñeanp aclrui sptoerpsu olaf tcioanp.irona while 71.56% was within clusters (Table 4). ––
 
Figure 2. (a) Principal coordinate analysis (PCoA) of 59 samples of capirona from the eastern region of the Peruvian Amazon
Figure 2. (a) Principal coordinate analysis (PCoA) of 59 samples of capirona from the eastern region of the Peruvian Am-
abzaosend boanseRdA oPnD RAmPaDrk emrsa.rPkeerrsc.e Pnteargceens toafgteost aolf vtoartaial nvcaerieaxnpclea ienxepdlabiyneeda cbhyc eoaocrhd icnoaotredainreatneo aterde ninotpeadr einn tphaerseens.thPeospeus.l aPtoiopn-
usylamtiboonl ssyanmdbcoolslo arnsdin cdoilcoartse ignedoigcraatpe hgiecoagffiralipahtiiocn a.f(fbil)iaDtiiosncr.i m(bi)n Danistcarnimaliynsaisnto fanparilnycsiips aolfc porminpcoipneanl tcso(mDpAoPnCe)notsf (5D9 AsaPmCp) loesf 
5o9f scaampiprolensa o. fT chaepiarxoensar. eTphree saexnets trheeprfierssetnttw thoel ifniresat rtwdios clirnimeairn danistcsri(mLDin)a. nEtasc (hLDcir).c Elearcehp crierscelen rtseparcelsuesnttesr aa cnldusetaecr hansdym eabcohl 
sryepmrbesoel nrtesparnesienndtisv aidnu inald. iNviudmuable. rNs urempbresresn rtepthresdeinffte trheen tdciflufesrtenrst cidluesntteirfise iddebnytDifiAedP Cby.  DAPC. 
TableT 4h. eAEnavlaynsinso ofm meothleocdul[a3r8 v]adrieapniccet e(AdMthOaVt tAh)e ubseinsgt  K10v RaAluPeD( nmuamrkbeersr ooff thpeo gpeunleatticio vnasri)aitsiotnw oof 
5f9o rsaomurpldeas toaf sceatp. iHroonwa aemveorn, gth tehen efoxutrl acrlugsetsetrps einafkerirseadt bKy =ST4R. UWCaTpUleRsEa nandaGlyasgisg. iotti (2006) [41],
Frantz et al. (2009) [42] and Janes et al. (2017) [43] mentioned that the Evanno method
tends Stoouurncde erestimatedft he numbeSrSo f genetic clusMteSr s. Similar Etostp.Vraerv. ious studie%s  [44,45],
wBeeotwbteaeinn ecdluastfearsls e high3e st peak a5t8K4.1=4 2 with th1e9E4v.7a1n no metho1d1d.6u9 e to a stron2g8.4re4j%ec tion
Within clusters 55 1617.01 29.40 29.4 71.56% 
Total 58 2201.15 37.95 41.09 100.00% 
 
Forests 2021, 12, 1125 7 of 12
of the null hypothesis of no structure (K = 1). On the other hand, DAPC indicated that
K = 4 is the most likely number of populations (Figure S2), which is concordant with the
dendrogram (Figure 1) and PCoA (Figure 2a). Therefore, further analyses followed K = 4.
STRUCTURE analysis exhibited admixture for a few samples. Most samples of capirona are
clustered according to their geographic origin, except for samples cap020, cap021, cap035
and cap085 (Figure 3). Moreover, Figure S4 shows STRUCTURE membership proportions
to clusters spatially interpolated into a map resulting from analysis in TESS3 [40]. Spatial
interpolation of membership matrix inferred assigned capirona samples from Irazola to
Forests 2021, 12, x FOR PEER REVIEW
 cl
 uster 1 mainly. In the second cluster, samples belonging to Masisea are included. C8lu osft e1r3 
3 comprised samples from LBS, and cluster 4 included samples from Iñapari.
 
FFiigguurree 33.. PPooppuullaattiioonn ssttrruuccttuurree ooff 5599 ssaammpplleess ooff ccaappiirroonnaa iinnffeerrrreeddb byyt thheeS STTRRUUCCTTUURREEa annaalylyssisisu ussiningg1 100R RAAPPDDm maarrkkeerrss. . 
4. DiGsceunsestiiocnd iversity and Fst estimation were conducted considering the four clusters
(popuGlaetnieotnics )diidveenrstiitfiye sdhbedysS lTigRhUt oCnT tUhRe Eevsoolfutwtioanrea,ryta pkriensgsuinreto ona ctchoeu anlltetlhesa tanRdA tPhDe marue-
dtaotmioinn aranttem a alrokceurss .mTighhetN heaiv’se guenndeetricgodnive eorvsietry a[ 4p6e]reiostdim ofa tteims rea [n4g7e]d. Tforo tmhe0 b.1e2stt oof0 o.4u1r 
akmnoowngletdhgeef,o tuhrisp iosp tuhlea tfiiornsts sstaumdyp ltehdaat cermospslothyes PmeorulevciuanlaAr maarzkoenr,ss thoo wesitnimg aatceo pnosipduelraatbiolen 
ssitmruiclaturirtey abnedtw geeennettihce dsiavmerpsilteys osft ucdapieidrohnear efr.oTmh ea Swhiadnen roann–gWe ioefn neratiunrdael xdrisatnrigbeudtiforno min 
1P.7e9rut.o M2.o9l4e,caunladr Smimaprkseorns’ sbiansdedex ofnro DmN0A.8 3(R–0A.9P5D, i, nAdFicLaPti,n SgShRi)g ahreg eunseetdic tdoi vsteursdiyty gfeonreatlilc 
fvoaurriaptoiopnu liant ifoonrsesotf scpaepciireosn [a1.,T8,h1e6,p4e8r]c. eAndtavgaenotafgpeosl yomf tohrepshei cmloecthi opders colvuestre or trhaenrgse idncfrluomde 
5th1.e0i8r %be(tctelur srteeprr1es)etnota9t6i.o2n4 %of (tchleu svtaerria3t)iowni pthreasnenatv weriathgien osfpe7c8i.e5s% [1(2T]a. bRlAe P2)D.  mPoaprkuelarsti aorne 
daipvpelrigeden fcoer (inFistti)abl esttwudeieens oclfu gsetneresti1c danivder4sriteyv iena lfeodretsht espheigciheess ltikgee pnientiec (dPiifnfuerse lneucceo(d0e.r2m6i9s)),, 
abnigd-ltehaef lmowahesotgFasnt yw (aSswoibetseenrivae mdabcertowpheyelnlac) luanstde rtso3rnainlldo 4(C(Tedarbellein3g)a.  cateniformis) [9,12,16]. 
To date, only a small number of studies have used molecular markers to examine genetic 
diversity within and between populations of capirona, and this study represents the first 
population structure assessment in capirona from the Peruvian Amazon. We report that 
genetic diversity of capirona based on RAPD markers is high among four populations 
sampled across the Peruvian Amazon. These estimates are in agreement with the results 
obtained by Russell et al. [1]. On the contrary, Dávila-Lara et al. [48] reported lower ge-
netic diversity parameters across 13 populations of capirona in Nicaragua (Central Amer-
ica). This discrepancy may be explained due to different sampling origins. The Amazon 
basin is known as the center of origin of this forest species [2], therefore this geographic 
region might hide plenty of beneficial genes as germplasm around this center because it 
has had the opportunity to evolve adaptation capacities to multiple environmental chal-
lenges over a longer period of time. Therefore, it is expected that the capirona samples 
 
Forests 2021, 12, 1125 8 of 12
Table 3. The Fst values among the four clusters inferred by STRUCTURE analysis.
Cluster 1 2 3 4
1
2 0.182
3 0.198 0.125
4 0.269 0.167 0.123
The analysis of molecular variance (AMOVA) allows the study of genetic variation
within and between clusters. AMOVA revealed that 28.44% of the total variation was found
between clusters of capirona while 71.56% was within clusters (Table 4).
Table 4. Analysis of molecular variance (AMOVA) using 10 RAPD markers of the genetic variation
of 59 samples of capirona among the four clusters inferred by STRUCTURE analysis.
Source df SS MS Est.Var. %
Between
clusters 3 584.14 194.71 11.69 28.44%
Within
clusters 55 1617.01 29.40 29.4 71.56%
Total 58 2201.15 37.95 41.09 100.00%
4. Discussion
Genetic diversity sheds light on the evolutionary pressure on the alleles and the
mutation rate a locus might have undergone over a period of time [47]. To the best of our
knowledge, this is the first study that employs molecular markers to estimate population
structure and genetic diversity of capirona from a wide range of natural distribution in
Peru. Molecular markers based on DNA (RAPD, AFLP, SSR) are used to study genetic
variation in forest species [1,8,16,48]. Advantages of these methods over others include
their better representation of the variation present within species [12]. RAPD markers are
applied for initial studies of genetic diversity in forest species like pine (Pinus leucodermis),
big-leaf mahogany (Swietenia macrophylla) and tornillo (Cedrelinga cateniformis) [9,12,16].
To date, only a small number of studies have used molecular markers to examine genetic
diversity within and between populations of capirona, and this study represents the first
population structure assessment in capirona from the Peruvian Amazon. We report that
genetic diversity of capirona based on RAPD markers is high among four populations
sampled across the Peruvian Amazon. These estimates are in agreement with the results
obtained by Russell et al. [1]. On the contrary, Dávila-Lara et al. [48] reported lower genetic
diversity parameters across 13 populations of capirona in Nicaragua (Central America).
This discrepancy may be explained due to different sampling origins. The Amazon basin
is known as the center of origin of this forest species [2], therefore this geographic region
might hide plenty of beneficial genes as germplasm around this center because it has had
the opportunity to evolve adaptation capacities to multiple environmental challenges over a
longer period of time. Therefore, it is expected that the capirona samples from the Amazon
basin possess higher genetic diversity. Moreover, the low genetic diversity of samples from
Nicaragua might be explained by its biogeographic history, Pleistocene range dynamics
and recent anthropogenic deforestation [48]. In addition, Tauchen et al. [3] used nuclear
ribosomal internal transcribed spacers (nrITS) to molecularly characterize populations
of capirona and failed to separate individuals by multivariate analysis. They concluded
that morphological diversity is higher than the genetic one in this tree species. Similarly,
Montes et al. [49] analyzed the morphological diversity of Callycophyllum spruceanum in 11
natural populations of the Peruvian Amazon. They grew seeds of capirona and analyzed
stem-growth and branch-wood traits and found no significant structuring populations.
The different values obtained for the three diversity indices (Shannon–Wiener, Simp-
son and Nei’s indices) may be explained due to the fact that whereas Nei’s index measures
Forests 2021, 12, 1125 9 of 12
the level of heterozygosity, Shannon–Wiener and Simpson assumes that any difference
between the analyzed individuals means a different species. Nei’s genetic diversity index
ranged from 0.12 to 0.41, indicating high genetic diversity, which is concordant for speci-
mens from natural populations as they are not affected by artificial selection. Maintaining
high genetic diversity in natural populations is important because it reduces the risk of
local extinction under natural conditions [50,51]. Similarly, the Shannon–Wiener index is
slightly high (1.79 to 2.94) and demonstrates the wealth and abundance among capirona
clusters. Likewise, Simpson index varied from 0.83 to 0.95, revealing that it is very likely
that two individuals of capirona randomly selected from a cluster will belong to the same
species. Although genetic relationships between species may be relatively low (low Nei’s
index), the diversity of the sequences and therefore of the population can still be relatively
high (high Simpson and Shannon indices) [46,52]. Similarly, a recent study on Guazuma
crinita reported low Nei’s gene diversity and high Shannon information index [50]. In
addition, our expected heterozygosity is similar to the populations of tornillo (Cedrelinga
cateniformis) [31].
Population structure analysis is informative to understand genetic diversity and
facilitates subsequent association mapping studies [51]; its presence in those studies can
lead to false positive associations between markers and traits [51]. Consequently, assessing
population structure is a crucial step to conduct association between molecular markers
and traits. In our study, DAPC and STRUCTURE results revealed that 59 samples of
capirona from the Peruvian Amazon clustered into four well-defined groups associated
with their genetic structure. Moreover, PCoA gave similar results. According to Rosyara
et al. [53], DAPC exhibits the ability to control population structure in association with
mapping studies, and it is slightly better than STRUCTURE analysis for discriminating
among populations as revealed in Prunus avium [54] and Camelia sinensis [55]. The presence
of structure in these 59 samples of capirona meet our expectations since according to
the pedigree of the genotypes, all the individuals can be divided into four geographical
locations, and intentional selection of traits by farmers might affect the population structure.
However, there are a few accessions intermingled between the four clusters. This might
be due to the common process of exchanging genetic material by farmers living in close
geographic areas (Irazola, Masisea, Iñapari) or migration as it may have occurred for
sample cap069 (LBS).
Among the four populations identified in this study, cluster 1 included mainly samples
from Irazola and showed the biggest difference from the other clusters, and it had the lowest
genetic diversity. These results may suggest that samples from Irazola possess narrow
genetic diversity as they were geographically isolated and developed using limited genetic
resources. AMOVA demonstrated that the greatest variation exists within populations
of capirona. This may be explained due to the sexual propagation method of capirona.
In addition, this is expected in this species because it produces seeds that are dispersed
over long distances by wind and water. In general, this is the case for tropical trees, and
especially those that are outcrossing, pollinated and/or dispersed by wind, so they tend to
harbor high levels of genetic diversity at local and regional spatial scales [50]. Moreover,
our results agree with similar studies conducted by Russell et al. [1] and Tauchen et al. [3].
Even though there have been some efforts to study capirona at the molecular and
morphological level, further research is needed aiming at identifying putative genes in
this tree species. Next generation sequencing (NGS) is a modern tool widely used on
many plant species. However, very few investigations were conducted using NGS on
forest species [56,57]. Our next step is to develop extra molecular tools for capirona, and
other tree species using NGS techniques such as de novo transcriptome in order to identify
EST-SSR markers. Similar to other studies on forest species [58–60], the development of
those markers will benefit the genetic mapping and breeding process of capirona in the
near future. These molecular tools may also shed light on deciphering the evolution history
of the Calycophyllum genus.
Forests 2021, 12, 1125 10 of 12
5. Conclusions
In this study we demonstrated that RAPD markers were successful and effective for
the assessment of the genetic diversity and structure of C. spruceanum populations from the
Peruvian Amazon. High levels of genetic diversity were registered using different indices,
reflecting probable extensive gene flow due to long-distance seed dispersal. Moreover,
capirona samples were grouped into four clusters according to their geographic affiliation.
However, few samples were intermingled, probably due to the common activity of seed
exchange followed by farmers. More work is still needed using additional collections of
capirona from a wider geographic range and other molecular markers. In addition, extra
molecular tools should be developed for this tree species using NGS techniques in order to
promote the establishment of a modern breeding program of forest species in Peru.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/
10.3390/f12081125/s1, Table S1: List of the primers used in the RAPD analysis, their sequence and
reference. Table S2: Cluster assignment of 59 samples of capirona based on STRUCTURE and DAPC
analysis. Figure S1: RAPD banding pattern using primers OPA-10: A, SC10–20: B, OPA-02: C. Ladder
100 pb NEB. 1% agarosa. 1 ul de ADN + 9 uL de Buffer dye 1 × and 0.035 uL de gel red. Figure S2:
Number of populations. Plot of K ranging from 1 to 10. (a) All K values were obtained from adegenet
analysis (DAPC). (b) All K values were obtained from STRUCTURE analysis. Four populations were
considered in a data set of 10 RAPD markers and 59 samples of capirona. Figure S3: Population
structure of 59 samples of capirona inferred by DAPC using 10 RAPD markers. Irazola, Masisea, LBS
and Iñapari refer to the geographic origin of the samples. Figure S4: Geographic map of membership
matrix of STRUCTURE analysis employing 59 samples of capirona based on 10 RAPD markers. The
most likely number of groups is four. Black dots indicate location where capirona genotypes were
sampled.
Author Contributions: Conceptualization, C.L.S. and W.C.; methodology, C.L.S., J.D.C., M.Y.C. and
W.C.; formal analysis, C.L.S. and C.I.A.; resources, M.R. and E.C.; data curation, C.I.A.; writing—
original draft preparation, C.L.S. and C.I.A.; writing—review and editing, C.L.S. and C.I.A.; supervi-
sion, W.C. and C.I.A.; funding acquisition, M.R. and E.C. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the National Agrarian Innovation Program (PNIA)—Project
No. 120-PI. C.L.S. and C.I.A. were supported by PIP 2449640 “Creación del Servicio de Agricultura
de Precisión en los Departamentos de Lambayeque, Huancavelica, Ucayali, y San Martín 4 Departa-
mentos” and PP0068 “Reducción de la vulnerabilidad y atención de emergencias por desastres”.
Acknowledgments: We would like to thank Joel Reyes for supporting the technical activities in
the laboratory. We would also thank Haru Garcia, Yanett Chumbimune and Lhia Aguirre for their
support with sample collection.
Conflicts of Interest: The authors declare no conflict of interest.
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