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Article

The Efficiency of DNA Barcoding in the Identification of Afromontane Forest Tree Species

by
David Kenfack
1,*,
Iveren Abiem
2,3 and
Hazel Chapman
3
1
Smithsonian Tropical Research Institute, 10th & Constitution Avenue NW, Washington, DC 20013-7012, USA
2
Department of Plant Science and Biotechnology, University of Jos, P.M.B. 2084, Jos 930001, Nigeria
3
School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
*
Author to whom correspondence should be addressed.
Diversity 2022, 14(4), 233; https://doi.org/10.3390/d14040233
Submission received: 16 February 2022 / Revised: 16 March 2022 / Accepted: 21 March 2022 / Published: 23 March 2022
(This article belongs to the Special Issue Plant DNA Barcodes, Community Ecology, and Species Interactions)
Browse Figure
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Abstract

:
The identification of flowering plants using DNA barcoding proposed in last decades has slowly gained ground in Africa, where it has been successfully used to elucidate the systematics and ecology of several plant groups, and to understand their evolutionary history. Existing inferences on the effectiveness of DNA barcoding to identify African trees are mostly based on lowland forests, whereas adjacent montane forests significantly differ from the latter floristically and structurally. Here, we tested the efficiency of chloroplast DNA barcodes (rbcLa, matK, and trnH-psbA) to identify Afromontane Forest tree species in a 20.28 ha permanent plot in Ngel Nyaki, Taraba state, Nigeria. We collected, identified, and vouchered 274 individuals with diameter at breast height ≥ 1 cm belonging to 101 morphospecies, 92 genera, and 48 families. rbcLa and matK used alone or in combination performed better than in lowland forests, with the best species discrimination obtained with the two-locus combination of matK + rbcLa. The intragenic spacer trnH-psbA was too variable to align and could not be tested using the genetic distance method employed. Classic DNA barcode can be a powerful tool to identify Afromontane tree species, mainly due to the non-prevalence in these communities of species—rich genera (low species-to-genus ratio) that constitute the biggest challenge of DNA barcoding of flowering plants.

1. Introduction

Africa includes the second largest tropical forest block in the world, considered as one of the most important pool of biological diversity [1]. Yet, African forests are threatened by expanding human activities such as industrial logging, mining, agriculture, and road networks [2,3], but are also highly susceptible to the impact of climate change [4]. Despite the growing international concern about the future of these forests, the diversity, the ecology and the evolutionary processes that have shaped African forests remain relatively poorly understood, compared to the Amazon forest block [5]. In this regard, there is an urgent need to increase our efforts in documenting and describing the diversity of these forests as many of the species might go extinct before they are discovered. Therefore, large-scale biodiversity inventories of African forests will be critical to develop sound conservation strategies for these forests [6]. During the past decades, significant progress has been made in the study of the biodiversity of African forests using classic floristic inventories and long-term monitoring plots grouped into two main networks, the African Tropical Rainforest Observation Network (AfriTRON, http://www.afritron.org/) (accessed on 10 February 2022)and the Africa program of the Forest Global Earth Observatory Network (ForestGEO, https://forestgeo.si.edu/, accessed on 10 February 2022). In forest inventories, the species are identified merely on the basis of morphological characters, and this is challenging even for expert botanists. Often, up to 30% of the individuals in the plots remain unidentified for years [7] due to the absence during field surveys of flowers and fruits that are needed to achieve accurate identifications [8].
Biological identification through “DNA barcode” was proposed, first in the animal kingdom [9,10] and later on for land plants [11,12] as a molecular method that could supplement morphological identifications. DNA barcodes are short and standardized fragments of DNA that should be easy to amplify and to sequence, and that can rapidly and reliably distinguish species from each other. DNA barcoding slowly gained ground in Africa, with over 21,000 vascular plants and 3000 animal records in the Barcode of Life Data System in 2019 [13,14], and has been used to elucidate the systematics and ecology of several plant groups, e.g., [15,16,17]. Existing African DNA barcodes for plants have been concentrated in forest ecosystems in Southern and West Africa [14,18] and more recently in savanna ecosystems [13]. Furthermore, inferences on the effectiveness of DNA barcode to identify African forest trees have been mostly based on lowlands. Whereas montane forests significantly differ floristically and structurally from lowland forests, the effectiveness of DNA barcoding in identifying tree species in these forests is still lacking.
We constructed a local DNA barcode database to aid the identification of tree species and reconstruct their community phylogeny in a 20.28 ha plot located in montane forest in Northeastern Nigeria. Here, we test the ability of this DNA barcode to identify the plot species and genera.

2. Materials and Methods

2.1. Study Site and Sampling

The tissue samples for DNA extraction were collected from the 20.28 hectares (260 × 780 m) Ngel Nyaki Forest Dynamics plot, where all trees with diameter at breast height (dbh) > 1 cm had previously been measured, mapped tagged and morphologically identified [19]. The plot (07°04005′′ N, 11°03024′′ E) is located within the Ngel Nyaki Forest Reserve on the Mambilla Plateau, Taraba State, Nigeria, with elevation ranging from 1588 m to 1690 m, and is part of the Forest Global Earth Observatory (ForestGEO) network [20]. The mean annual rainfall is 1800 mm while the mean annual temperature is 19 °C. The vegetation of the area is a mosaic of grassland and montane forest [21].
The morphological identifications of the trees were performed in the field by non-professional taxonomists, but were partially checked by the first author. The resulting checklist comprised 105 morphospecies including 74 (71%) identified to species level, 22 (21%) to genus, and 9 (9%) unidentified, even to family level. Of the 105 species (with dbh > 1 cm) recorded in the Ngel Nyaki plot, we sampled 99 belonging to 90 genera and 47 families. Two additional woody species growing in the vicinity of the plot, Dracaena cf. deisteliana Engl. (Asparagaceae) and Pittosporum viridiflorum Sims (Pittosporaceae), were added to our sample, making a total of 101 species in 92 genera and 48 families. We collected leaf tissue from 1 (for species that were represented by a single individual in the plot) to 4 individuals per species. The samples were collected in the field and were immediately dried in silica-gel. They consisted of 5–50 cm2 of leaf tissue. Voucher specimens accompanying the leaf tissue were also collected and are deposited at the National Museum of Natural History in Washington.

2.2. DNA Extraction and Sequencing

All laboratory work was carried out at the Canadian Centre for DNA Barcoding (CCDB) (https://ccdb.ca/, accessed on 10 February 2022) and following their protocols. Total genomic DNA was extracted from silica dried leaf material using the CCDB protocol (https://ccdb.ca/site/wp-content/uploads/2016/09/CCDB_DNA_Extraction-Plants.pdf, accessed on 10 February 2022). DNA barcode regions rbcLa, matK and the trnH-psbA intergenic spacer were amplified using CCDB standard PCR primers and protocols (https://ccdb.ca/site/wp-content/uploads/2016/09/CCDB_Amplification-Plants.pdf, accessed on 10 February 2022) with the primers available at https://ccdb.ca/site/wp-content/uploads/2016/09/CCDB_PrimerSets-Plants.pdf (accessed on 10 February 2022) Voucher details and GenBank accession numbers for all sequences are listed in BOLD (http://www.boldsystems.org/) (accessed on 10 February 2022).

2.3. Testing the DNA Barcode Accuracy

Prior to evaluating the identification success of the two barcode regions, we used the Basic Local Alignment Search Tool (BLAST) [22] to compare our sequences to those available in GenBank (https://www.ncbi.nlm.nih.gov/genbank/, accessed on 10 February 2022), with the aim of confirming our identifications and identifying our unknowns. After matching our sequences in GenBank, the morphospecies names were updated only after comparison of their voucher specimens to either the type specimens available online or to other herbarium specimens and photographs in Tropicos (https://www.tropicos.org/, accessed on 10 February 2022).
To test the barcode efficiency, we followed [18]. Our DNA barcoding refence database (assumed to be exhaustive in terms of species) had 274 individuals and was used to assign individual trees to species or genera. The test was performed on species represented by at least two individuals in the database, so that we could have a query and a matching sample. The coding genes matK and rbcLa were aligned and manually adjusted using ClustalW in the Molecular Evolutionary Genetic Analysis software version 7.0.26 [23]. After the global alignment, we computed pairwise genetic distances among all sequences in the dataset using the Kimura’s 2-parameter model [24]. The analysis was also performed in Mega7. In the resulting matrix, each sample (query) was assigned to a species or a genus of the sample (matching) from which it is separated with the least genetic distance (excluding itself). The identification was (1) correct when the query sample matched only the samples of its species of genus; (2) multiple if the query sample matched several species or genera including its correct one; and (3) wrong when the query sample matched species or genera different from its own [18]. We were not able to align trnH-psbA because it was too variable among the 48 plant families in the study. Hence this locus was not used in the test of DNA barcode accuracy analyses.

3. Results

3.1. Sequencing Success

DNA sequencing success was tested on 274 individual trees, representing 101 species. Sequencing success was lowest for matK and highest for rbcLa. Reliable contigs were obtained for only 78.9% of all individuals sequenced for matK, 95.3% trnH-psbA, but 97.5% for rbcLa, which corresponded to all the species represented in the database for rbcLa and trnH-psbA, but only to 93.1% of the species for matK (Table 1). The percentages of species represented by at least two individuals for matK, rbcLa and trnH-psbA in the database were 70.3%, 87.2% and 84.2% respectively.

3.2. Taxonomic Update Using BLAST

The identification of 13 morphospecies was updated using the heuristic search in GenBank. Of the nine morphospecies for which the family was unknown, seven were identified to species level and two placed in different plant families. Furthermore, the identification of four other morphospecies was updated. The first morphospecies placed in the Argocoffeopsis Lebrun (Rubiaceae) Lebrun was updated to Psilanthus mannii Hook. within the same family. A morphospecies thought to belong to the genus Beilschmiedia Nees (Lauraceae) was transferred to the family Sapotaceae. The identification of a morphospecies thought to be Lannea barteri (Oliv.) Engl. (Anacardiaceae) was updated to Brucea antidysenterica J.F. Mill. (Simaroubaceae). Finally, the morphospecies Hannoa klaineana Pierre ex Engl. (Simaroubaceae) was updated to Ekebergia capensis Sparrm.

3.3. Barcode Accuracy

The accuracy of two of the three barcode markers (matK and rbcLa) in identifying montane forest trees is presented in Table 2. The analyses were performed on all available samples for each marker. When used individually, highest success for the identification of species was obtained with matK (98.3%). The two makers performed slightly better when combined. At genus level, the same trend was maintained, but with even better performances. Here, matK and the rbcLa + matK combination successfully identified all the samples to the genus, while rbcLa alone was successful to identify 98.4% of samples to genus (Table 2).

4. Discussion

4.1. Recoverability of DNA Barcode Used

The two DNA barcodes rbcLa and matK used in this study have long been recognized having sufficient variation to discriminate among land plant species [11,25,26]. Among the three barcodes, matK had the lowest rate of recovery (79%), consistent with prior studies [18,27,28]. In contrast, rbcLa and trnH-psbA had higher rates of recovery (above 95%). However, it is worth pointing out that the rates of recovery were in general higher than in prior studies, probably due to the efficiency of the Canadian Centre for DNA Barcoding that has optimized protocols for higher rates of recovery. For example, recovery rates around 70% have been reported for matK in several studies [8,27,29], while sequencing and amplification success for rbcLa and trnH-psbA is often below 94% e.g., [8,27,30].

4.2. Tree Species Identification Using DNA Barcode in Ngel Nyaki Montane Forest

The morphological identification of the trees in the Ngel Nyaki plot was almost entirely performed by non-professional taxonomists who however accurately identified to species 69% of all tree species occurring in the plot. Only four species were wrongly identified. The DNA barcode was instrumental in updating the identification of 12% of the species in the plot for which prior sequences were available in Genbank. Due to the lack of adequate library in Genbank, 21% of the species in the plot for which good quality barcode sequences were generated could still not be identified to species level. Hence, molecular techniques such as DNA barcode may not replace traditional taxonomic techniques as suggested by some studies [31], but can only supplement it.
This study showed the efficiency of the two barcode loci rbcLa and matK in accurately assigning Afromontane forest tree species to a correct species or genera. When used alone, best results for species identification were obtained with matK (98%) compared to rbcLa (94%). These values are slightly higher than those reported in most lowland forests [8,18,27,30]. The combination of the two markers matK + rbcLa improved the barcoding success to 99%, a result consistent with those in most lowland forests. Barcoding success was even better at genus level, rbcLa alone identifying 98% of all genera, while matK and the combination matK + rbcLa accurately identified all the samples to genus.
The genetic distance method that we used did not allow us to test the accuracy of the intergenic spacer trnH-psbA. This locus, easy to amplify and short, is known to be very variable among angiosperms and thus is widely used in plant species identification [32]. In general, trnH-psbA locus is more variable than matK and rbcL and we assume its performance in the identification of montane forest species would even be greater. matK and rbcLa were variable enough that their combination to trnH-psbA was no more relevant.

4.3. The Efficiency of DNA Barcoding in the Context of the Afromontane Flora

DNA barcode is a powerful tool for identifying tree species to genus level. However the identification to species level is not always reliable, especially in plant communities with speciose genera [18]. For example, the identification of tree species (with dbh ≥ 1 cm) in a 50-ha plot in the highly diverse Korup National Park, Cameroon using three DNA barcode markers showed a significant decrease in their performance with increasing number of species per clade (genus) [18]. In fact, the five most speciose genera in the Korup plot Cola Schott & Endl., Diospyros L., Psychotrya L., Rinorea Aubl. and Garcinia L. have 23, 14, 13, 13 and 10 species respectively [33]. Such closely related species are more likely to hybridize, have incomplete lineage sorting and share haplotypes, all of which can lessen the ability of barcode loci to discriminate among them. At the other end of the spectrum, 165 (33%) species in Korup are represented by a single species.
The Ngel Nyaki plot had a relatively low diversity, with only 105 species in 92 genera. The most speciose genera here are Ficus L. and Psychotria L., each having three species. Five other genera have two species each, while the remaining 85 species (81%) are represented each by a single species. This species-to-genus (S/G) ratio is not specific to the Ngel Nyaki montane forest. In fact, most Afromontane forests are characterized by a low diversity of trees and low S/G ratio. For example, in Woodbush–De Hoek montane forest in South Africa, 50 species of trees with dbh > 5 cm and dbh > 10 cm in 46 genera (S/G = 1.09) were recorded within 1.5 ha circular plots [34]. Similarly, [35] in a study on trees with dbh ≥ 5 cm in dry Afromontane forests of Awi Zone, northwestern Ethiopia, recorded 18 species in 18 genera, 21 species in 21 genera, 20 species in 20 genera, 16 species in 16 genera and 23 species in 23 genera in 0.6 ha of Bari, Apini, Dabkuli, Tsahare Kan, and Kahtasa forests respectively.
We further explored the relationship between the S/G ratio and elevation, by comparing the Ngel Nyaki data other African forest sites for trees with dbh ≥ 10 cm (Table A1). The S/G ratio decreases with increasing elevation, with a correlation coefficient of −0.722 (Figure 1A). The Lambi 2 and Ngovayang mid-elevation plots in Cameroon had the highest S/G ratio (1.55 and 1.51 respectively) while higher elevation plots Bwindi 1 and Bwindi 4 had the lowest. The Lambi the Ngovayang plots seem to be outliers in our dataset. In fact, a stronger relationship with r = −0.80 is shown when these plots are removed. Higher S/G ratio of 2.6 and 3 have been reported elsewhere in the Manu forest (Peru) and Yasuni forest (Ecuador) respectively for trees with the same diameter cutoff [36]. The S/G ratio increases when smaller diameter size classes are considered and the correlation with elevation is stronger (r = −0.84, p-value = 0.007). A highest S/G ratio of 1.64 is observed for the lowland Rabi plot and 1.15 for the Ngel Nyaki plot for all trees with dbh ≥ 1 cm were measured (Figure 1B). In fact, the understory of most African forests are stocked with speciose genera of small-statured trees that never attain large size diameter classes [37,38]. Several studies have shown the decrease in tree species diversity with elevation, e.g., [39,40]. Our data also shows a decrease of generic diversity with increasing elevation (r = −0.84). This means that the low diversity in higher elevations is also due to the decrease in the number of genera, but coupled with the decrease in the number of species per genera. This result is consistent with Jaccard’s observations in the Alps [41], who noted that “with increasing altitude, the number of genera decreases less rapidly than the number of species”.

5. Conclusions

Our study highlighted how DNA barcoding can be efficient in identifying tree species in an Afromontane Forest. As in lowland forests, identification success is higher at genus than at species level. Identification success was higher than in lowland forest, due to the non-prevalence of highly diverse genera in this habitat. The comparison of species-to-genus among other sites with comparable data showed that Afromontane forests tend to have a low S/G ratio for tree species, which is an advantage for the use of DNA barcode in these forests.

Author Contributions

Conceptualization, D.K.; methodology, D.K., I.A. and H.C., investigation, D.K., I.A. and H.C.; data curation, D.K. and I.A.; writing—original draft preparation, D.K.; writing review and editing, D.K., I.A. and H.C.; funding acquisition, D.K. and H.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was made possible by the generous donation made by Retired General T.Y. Danjuma to the Nigerian Montane Forest Project (NMFP) through H. Chapman. Additional support towards the plot census and DNA barcoding was provided by the Forest Global Earth Observatory (ForestGEO) of the Smithsonian Tropical Research Institute. The Chester Zoo, England, and A.G. Leventis Foundation also provided additional financial assistance to the NMFP.

Data Availability Statement

Full census data for the Ngel Nyaki plot is available upon reasonable request from the ForestGEO data portal http://ctfs.si.edu/datarequest/ and the full plant DNA barcode library is available on BOLD (http://www.boldsystems.org/) (accessed on 10 February 2022).

Acknowledgments

We wish to thank the staff of the Ngel Nyaki Montane Forest Program, especially the ForestGEO local team who carried out the tree census and collected the DNA tissue and vouchers. We are also grateful to Douglas Sheil and Robert Bitariho for their authorization to use the Bwindi data, to Terry Sunderland for sharing the Takamanda data with us and finally to Daniel Zuleta for writing the R script to estimate the barcode success from the genetic distance matrix.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Species-to-Genus ratio (S/G) among 43 African forest 1-ha plots for trees with dbh ≥ 10 cm. * denote large plots (10–50 ha) of the Forest Global Earth Observatory (ForestGEO) network. The data for each large plot was obtained by averaging the values in 1-ha subplots within the plot. S = number of species, G = number of genera. TEAM = Tropical Ecology Assessment and Monitoring.
Table A1. Species-to-Genus ratio (S/G) among 43 African forest 1-ha plots for trees with dbh ≥ 10 cm. * denote large plots (10–50 ha) of the Forest Global Earth Observatory (ForestGEO) network. The data for each large plot was obtained by averaging the values in 1-ha subplots within the plot. S = number of species, G = number of genera. TEAM = Tropical Ecology Assessment and Monitoring.
SiteCountryElevation (m)SGS/GSource
Bwindi-1Burundi147442391.08TEAM Network
Bwindi-2Burundi141928281.00TEAM Network
Bwindi-3Burundi189344411.07TEAM Network
Bwindi-4Burundi204927271.00TEAM Network
Bwindi-5Burundi210130271.11TEAM Network
Bwindi-6Burundi232125241.04TEAM Network
Bidjouka-1Cameroon39299731.36[42]
Bidjouka-2Cameroon605105731.44[42]
Korup 50-ha *Cameroon19587.248.821.79[33]
Lambi-1Cameroon396106831.28[42]
Lambi-2Cameroon627118761.55[42]
Ngovayang-1Cameroon650121801.51[42]
Rumpi-hills-11Cameroon145032311.03[43]
Takamanda-10Cameroon21010878.51.38[44]
Takamanda-11Cameroon210113801.41[44]
Takamanda-12Cameroon150105.579.51.33[44]
Takamanda-13Cameroon150118871.36[44]
Takamanda-14Cameroon1208769.51.25[44]
Takamanda-15Cameroon12091711.28[44]
Takamanda-6Cameroon320103771.34[44]
Takamanda-7Cameroon40097741.31[44]
Takamanda-8Cameroon78064501.28[44]
Takamanda-9Cameroon120071551.29[44]
Dzanga-Sanga-1Central African Republic471108851.27[45]
Dzanga-Sanga-2Central African Republic482120951.26[45]
Dzanga-Sanga-3Central African Republic39367531.26[45]
Dzanga-Sanga-4Central African Republic48996781.23[45]
Dzanga-Sanga-5Central African Republic485108841.29[45]
Edoro-1 (10-ha) *DR Congo80865.453.61.22[46]
Edoro-2 (10-ha) *DR Congo80967.455.51.21[46]
Lenda-1 (10-ha) *DR Congo80860.447.31.28[46]
Lenda-2 (10-ha) *DR Congo81949.940.81.22[46]
Monts de Cristal-1Gabon40089721.24[47]
Monts de Cristal-2Gabon30089691.29[47]
Monts de Cristal-3Gabon30099741.34[47]
Monts de Cristal-4Gabon20088711.24[47]
Monts de Cristal-5Gabon250108881.23[47]
Rabi 25-ha *Gabon4784.662.681.35[38]
Waka-10Gabon569106741.43[48]
Waka-6Gabon43883621.34[48]
Waka-7Gabon407100771.30[48]
Waka-8Gabon687107781.37[48]
Ngel Nyaki (20.28 ha) *Nigeria163941.139.51.04[19]

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Diversity 14 00233 g001 550
Figure 1. Correlation between the species-to-genus (S/G) ratio and elevation, (A) for trees with dbh > 10 cm in forty three 1-ha African forest plots, The correlation coefficient r = −0.722, p-value = 0.00000004635; (B) for trees with dbh > 1 cm in seven large (10–50-a) census plots, correlation coefficient r = −0.88, p-value = 0.007.
Figure 1. Correlation between the species-to-genus (S/G) ratio and elevation, (A) for trees with dbh > 10 cm in forty three 1-ha African forest plots, The correlation coefficient r = −0.722, p-value = 0.00000004635; (B) for trees with dbh > 1 cm in seven large (10–50-a) census plots, correlation coefficient r = −0.88, p-value = 0.007.
Diversity 14 00233 g001
Table
Table 1. Sequencing success of montane forest trees from Ngel Nyaki for rbcLa, matK and trnH-psbA barcode regions.
Table 1. Sequencing success of montane forest trees from Ngel Nyaki for rbcLa, matK and trnH-psbA barcode regions.
matKrbcLatrnH-psbA
Number of individuals tested274274274
Sequencing success: N ind. (% ind.)216 (78.9)267 (97.5)261 (95.3)
Sequencing success: N sp. (% sp.)94 (93.1)101 (100)101 (100)
N sp. with sequences ≥ 2 samples71 (70.3)88 (87.2)85 (84.2)
Table
Table 2. Barcoding accuracy in identifying Ngel Nyaki Afromontane forest trees at species and genus level.
Table 2. Barcoding accuracy in identifying Ngel Nyaki Afromontane forest trees at species and genus level.
Barcoding AccuracyQuery Samples
Correct IDMultiple IDWrong IDN. ind.N. sp.N. Gen.
Species identification
rbcLa93.86.1502449292
matK98.31.10.551816759
matK + rbcLa98.90.50.541867263
Genus identification
rbcLa98.41.602449292
matK100001816759
matK + rbcLa100001867263
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Kenfack, D.; Abiem, I.; Chapman, H. The Efficiency of DNA Barcoding in the Identification of Afromontane Forest Tree Species. Diversity 2022, 14, 233. https://doi.org/10.3390/d14040233

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Kenfack D, Abiem I, Chapman H. The Efficiency of DNA Barcoding in the Identification of Afromontane Forest Tree Species. Diversity. 2022; 14(4):233. https://doi.org/10.3390/d14040233

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Kenfack, David, Iveren Abiem, and Hazel Chapman. 2022. "The Efficiency of DNA Barcoding in the Identification of Afromontane Forest Tree Species" Diversity 14, no. 4: 233. https://doi.org/10.3390/d14040233

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Kenfack, D., Abiem, I., & Chapman, H. (2022). The Efficiency of DNA Barcoding in the Identification of Afromontane Forest Tree Species. Diversity, 14(4), 233. https://doi.org/10.3390/d14040233

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