Bushmeat genetics: setting up a reference framework for the DNA typing of African forest bushmeat

Bushmeat genetics: setting up a reference framework for theDNA typing of African forest bushmeat

PHILIPPE GAUBERT,* FLOBERT NJIOKOU,† AYODEJI OLAYEMI,‡ PAOLO PAGANI,§ SYLVAIN

DUFOUR,¶ EMMANUEL DANQUAH,** MAC ELIKEM K. NUTSUAKOR,** GABRIEL NGUA,††

ALAIN-DIDIER MISSOUP,‡‡ PABLO A. TEDESCO,§§ R�EMY DERNAT¶¶ and

AGOSTINHO ANTUNES***†††

*Institut des Sciences de l’Evolution de Montpellier – UM2-CNRS-IRD, Universit�e Montpellier 2, Place Eug�ene Bataillon – CC 64,

34095 Montpellier Cedex 05, France, †Laboratoire de Parasitologie et d’Ecologie, Facult�e des Sciences, Universit�e de Yaound�e I, BP

812 Yaound�e, Cameroon, ‡Natural History Museum, Obafemi Awolowo University, Ho 220005 Ile-Ife, Osun State, Nigeria,

§Dutch Wildlife Health Centre, Faculty of Veterinary Medicine, Yalelaan 1, 3584 CL Utrecht, The Netherlands, ¶SYLVATROP,

26 route de Vannes, Nantes, France, **Department of Wildlife and Range Management, Faculty of Renewable Natural Resources,

Kwame Nkrumah University of Science and Technology, University Post Office, Kumasi, Ghana, ††Amigos de la Naturaleza y del

Desarrollo de Guinea Ecuatorial (ANDEGE), Barri�o Ukomba, S/N, Bata, Equatorial Guinea, ‡‡Biologie de l’Evolution -

Mammalogie, D�epartement de Biologie des Organismes Animaux, Facult�e des Sciences, Universit�e de Douala, BP 24157 Douala,

Cameroon, §§D�epartement Milieux et Peuplements Aquatiques, Mus�eum National d’Histoire Naturelle, UMR Biologie des

ORganismes et des Ecosyst�emes Aquatiques (UMR BOREA IRD 207-CNRS 7208-UPMC-MNHN), 43 rue Cuvier, FR-75231

Paris Cedex, France, ¶¶Institut des Sciences de l’Evolution – CNRS UMR 5554, Plateforme Bioinformatique LabEx, Universit�e

Montpellier 2, Place Eug�ene Bataillon, 34095 Montpellier Cedex 05, France, ***CIMAR/CIIMAR, Centro Interdisciplinar de

Investigac�~ao Marinha e Ambiental, Universidade do Porto, Rua dos Bragas, 177, 4050-123 Porto, Portugal, †††Departamento de

Biologia, Faculdade de Ciencias, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal

Abstract

The bushmeat trade in tropical Africa represents illegal, unsustainable off-takes of millions of tons of wild game –

mostly mammals – per year. We sequenced four mitochondrial gene fragments (cyt b, COI, 12S, 16S) in >300 bush-

meat items representing nine mammalian orders and 59 morphological species from five western and central African

countries (Guinea, Ghana, Nigeria, Cameroon and Equatorial Guinea). Our objectives were to assess the efficiency of

cross-species PCR amplification and to evaluate the usefulness of our multilocus approach for reliable bushmeat spe-

cies identification. We provide a straightforward amplification protocol using a single ‘universal’ primer pair per

gene that generally yielded >90% PCR success rates across orders and was robust to different types of meat prepro-

cessing and DNA extraction protocols. For taxonomic identification, we set up a decision pipeline combining similar-

ity- and tree-based approaches with an assessment of taxonomic expertise and coverage of the GENBANK database.

Our multilocus approach permitted us to: (i) adjust for existing taxonomic gaps in GENBANK databases, (ii) assign

to the species level 67% of the morphological species hypotheses and (iii) successfully identify samples with uncer-

tain taxonomic attribution (preprocessed carcasses and cryptic lineages). High levels of genetic polymorphism across

genes and taxa, together with the excellent resolution observed among species-level clusters (neighbour-joining trees

and Klee diagrams) advocate the usefulness of our markers for bushmeat DNA typing. We formalize our DNA typ-

ing decision pipeline through an expert-curated query database – DNABUSHMEAT – that shall permit the automated

identification of African forest bushmeat items.

Keywords: Africa, bushmeat, decision pipeline, DNA typing, mammals, mtDNA

Received 12 March 2014; revision received 17 September 2014; accepted 19 September 2014

Introduction

Bushmeat is the wild game (mostly mammals), that is

hunted by local communities for subsistence and trade.

Although considered illegal in many countries, the bush-Correspondence: Philippe Gaubert, Fax: +3 346 714 3622; E-mail:

[email protected]

© 2014 John Wiley & Sons Ltd

Molecular Ecology Resources (2014) doi: 10.1111/1755-0998.12334

meat market is a flourishing economic activity that sup-

ports a multimillion dollar worldwide economy (Nasi

et al. 2008). Following recent socioeconomic transforma-

tions, including increased pressures from burgeoning

human populations, and commercial logging, but also

the generalized use of firearms, the volume of bushmeat

hunting has reached unsustainable levels (Fa et al. 2005;

Nasi et al. 2008; Jenkins et al. 2011). This ‘bushmeat crisis’

is particularly visible in western and central Africa,

where bushmeat has traditionally been the main source

of animal protein and revenue for rural populations (Asi-

bey 1977). Off-takes exceed several millions of tons each

year (Davies 2002; Brown & Davies 2007), and in Central

Africa, 100% of the targeted mammalian species were

considered to be hunted at unsustainable levels (Nasi

et al. 2008).

The challenges and imperatives of addressing the

bushmeat crisis on the African continent are numerous,

including the need to secure sustainable access to natural

protein resources, while concurrently reducing the

occurrence and mitigating the effects of zoonotic pan-

demics that can be spread through the bushmeat market

(Kilonzo et al. 2013). Understanding and mitigating the

bushmeat market first relies on the accurate identifica-

tion of the species being traded, notably for conserva-

tionists and national wildlife corps engaged in the

control of bushmeat activities (Ogden et al. 2009). A large

percentage of the bushmeat sold in markets consists of

smoked and processed meat (Willcox & Nambu 2007),

which is difficult or impossible to identify accurately. As

a consequence, distinguishing between legal and illegal

trade is difficult or impossible and surveys to estimate

the impact of bushmeat activities using standard proto-

cols (i.e. phenotypic recognition of carcasses) likely

underestimate the number of individuals and species

involved (Olayemi et al. 2011).

Forensic science techniques, including DNA typing

methods such as ‘forensically informative nucleotide

sequencing’ (FINS), have been applied successfully to

species-level identification of illegally hunted wildlife

(Thommasen et al. 1989; Bartlett & Davidson 1992; Ver-

ma & Singh 2003; Baker 2008). The application of DNA

typing (using a short series of informative mitochondrial

gene fragments) and barcoding (targeting the standard

barcode fragment of cytochrome c oxidase 1 ‘COI’;

Hebert et al. 2003) for the identification of African bush-

meat has demonstrated a potential for DNA-based spe-

cies identification. However, these studies have been

limited taxonomically to a few mammalian orders and/

or geographically restricted to a given region (Malisa

et al. 2006; Eaton et al. 2010; Ghobrial et al. 2010; Ntie

et al. 2010; Bitanyi et al. 2011; Olayemi et al. 2011; Minh�os

et al. 2013). Most importantly, the heterogeneity of the

molecular markers and PCR techniques used in these

studies may not be sufficient for standardized species

identification across the African continent.

Here, we propose a reference framework for the

DNA typing of African forest bushmeat (i.e. mammals)

that would facilitate the implementation of a standard-

ized DNA-based species identification tool. To circum-

vent the potential caveats of DNA typing nonvouchered

specimens (for ethical reasons, no carcasses were bought

from bushmeat markets), we relied on the production of

multiple FINS and the use of a taxonomic expert ‘loop’

to validate the genetic identification of the sampled

animals. Through a collaborative regional framework

across five African countries (Guinea, Ghana, Nigeria,

Cameroon and Equatorial Guinea), we sequenced four

mitochondrial gene fragments in >300 bushmeat items

representing nine mammalian orders. Our first objective

was to assess the PCR amplification efficiency of our

mtDNA markers on bushmeat samples (i.e. their ability

to amplify a wide range of species from potentially poor

quality and degraded samples) across different methods

of DNA extraction. Second, we evaluated the efficacy of

our mitochondrial DNA (mtDNA) sequences to distin-

guish and identify species (i.e. we assessed their status

of FINS) through an original decision pipeline. Finally,

we produced a web-assisted query database – DNABUSH-

MEAT – that can serve as a reference framework for the

DNA-based identification of African forest bushmeat

species.

Materials and methods

Sampling data

We collected 302 samples of African mammalian species

from bushmeat markets and other sources in Guinea,

Ghana, Nigeria, Cameroon and Equatorial Guinea

(Fig. 1; Table S1, Supporting Information). The sample of

Orycteropus afer (our sole representative of Tubulidenta-

ta) came from South Africa. Our sample set covered

eight taxonomic orders of African mammals commonly

found in bushmeat markets, including Artiodactyla (14

species; n = 53), Carnivora (17 species; n = 88), Pholidota

(three species; n = 27), Primates (15 species; n = 25),

Rodentia (seven species; n = 70), Erinaceomorpha (one

species; n = 1), Lagomorpha (one species; n = 1) and

Tubulidentata (one species; n = 1). A ninth mammalian

order (Hyracoidea) was subsequently identified in a set

of taxonomically unidentified samples from Guinea (see

below). We followed an opportunistic sampling strategy,

collecting small pieces of tissues (ear or tongue, in gen-

eral) from the available carcasses at the time of our sur-

veys. To assess the impact of sample quality on our

DNA typing approach, both freshly killed and smoked

specimens were sampled. Whenever possible, pictures of


2 P . GAUBERT ET AL .

the animals were taken to confirm species identification

(Fig. 1). The preliminary morphological identification of

species was based on the field guide of Kingdon (1997).

In some cases, we relied on local knowledge for

identification (e.g. attributing local names to the ani-

mals). Taxonomy was further refined/updated following

the recent edition of the Mammals of Africa (Kingdon

et al. 2013) and Colyn et al. (2010) for the new species of

blue duiker (Philantomba walteri). We also included 36

samples from Guinea that did not have any species

Tropical rainforest

1000 km

GuineaGhana

Cameroon

Nigeria

EquatorialGuinea

n = 72

n = 108n = 39

n = 22

n = 60

(a)

(b)

(d)

(c)

(g)(f)(e) (h)

(i) (j) (k) (l)

Fig. 1 Geographic coverage of our African bushmeat study. Samples (n) were collected from various bushmeat markets and game sell-

ing places in five countries from tropical Africa (Orycteropus afer from South Africa is not shown). Photographs of sampled individuals

for species identification as follows: a – Civettictis civetta; b – processed Manis gigantea; c – Manis tricuspis; d – Thryonomys swinderianus; e

– beheaded Cephalophus ogilbyi (Cameroon); f – Lepus victoriae; g – anus of Civettictis civetta (Ghana); h – stall of smoked meat; i – Xerus

erythropus (Nigeria); j – processed Hylochoerus meinertzhageni (Guinea); k – smokedMandrillus sphinx; l – processed Gorilla gorilla (Equato-

rial Guinea).


DNA TYPING OF AFRICAN FOREST BUSHMEAT 3

attribution, to test the usefulness of our approach in

identifying unrecognizable species representatives. Two

nonmammalian species, a hooded vulture (Necrosyrtes

monachus) and a Nile monitor (Varanus niloticus) were

also sampled and included in the analyses.

DNA extraction, amplification and sequencing

We extracted genomic DNA using either an ABI PRISM

6100 Nucleic Acid PrepStation (Applied Biosystems,

Carlsbad, CA, USA) following the manufacturer’s rec-

ommendations, or a standard CTAB procedure (Rogers

& Bendich 1988). We systematically amplified four

mitochondrial genes to ensure a broad nucleotide

sequence coverage that would maximize our chance of

reaching DNA-based taxonomic identification. We used

the ‘universal’ primer pair L14724-H15149 (following

Olayemi et al. 2011) to amplify by PCR the first 402 bp

of cytochrome b (cyt b). We aligned a series of GEN-

BANK sequences representative of the mammalian

orders under study (data not shown) to design single,

mammalian-universal primer pairs amplifying

384–658 bp fragments of cytochrome c oxidase I (COI)

and ribosomal subunits 12S and 16S (Table 1). Con-

served primer pairs were designed using consensus

sequences for each gene on the Primer3 web platform

(http://primer3.ut.ee/). Our targeted COI fragment

corresponds to the ‘standard barcode’ region developed

for animals (Hebert et al. 2003). PCRs were carried out

in a 20-lL final volume, containing ~50 ng of template

DNA, 0.1 mg/mL BSA, 0.25 9 4 mM dNTPs,

0.2 9 2 lM primers, 59 PCR direct loading buffer with

MgCl2 and 0.5–1.5 U Taq DNA polymerase (Q-BIO-

gene, Illkirch, France). PCR cycling conditions included

a first step of denaturation (94 °C, 2 min), followed by

35 cycles of denaturation (92 °C, 30 s), annealing (30 s;

see Table 1 for T°), extension (72 °C, 30 s) and a final

extension step (72 °C, 15 min). PCR products were

directly sequenced in both directions on 3730xl DNA

Analyzer 96-capillary sequencers (Applied Biosystems,

Foster City, CA, USA) at Genoscope, Evry, France. All

the sequences were deposited in GENBANK under

accession nos KJ192435–KJ193529.

DNA typing analytical procedures

Validation of nucleotide sequences—The detection of puta-

tive pseudogenes in gene fragments with open reading

frame (cyt b and COI) was performed by translating

nucleotide sequence alignments into proteins with MEGA

5.2.1. (Tamura et al. 2011) and checking for stop codons

and indels. We also checked for heterozygosity, atypical

branch lengths and dubious phylogenetic branching of

the sequences (see below ‘Taxonomic assignment proce-

dure’), a method also applicable to genes unconstrained

by open reading frames such as rRNAs (Triant &

DeWoody 2007).

We checked for potential contamination from

exogenous DNA by assessing the congruence of taxo-

nomic assignments (i) between sequences and morpho-

logical identification and (ii) among sequences

themselves (for a single individual). A taxonomic conflict

among sequences was considered to have occurred when

a DNA-based identification was in disagreement with

another for the same taxonomic level [e.g. ‘Dendrohyrax

dorsalis’ (Hyracoidea) vs. ‘Perodicticus potto’ (Primate)].

By contrast, two assignments such as ‘D. dorsalis’ and

‘Dendrohyrax’ would not be considered to be in conflict.

The morphological identification of the species and/or

the taxonomic identification supported by the majority

of the genes was used to eventually identify and remove

the exogenous DNA fragments (cross-species contamina-

tion).

Taxonomic assignment procedure—Morphological species

identification was made through consensus of the

co-authors via direct observations (joint presence in the

field) or shared photographic material (‘primary mor-

phological species hypothesis’). If cross-validation could

not be carried out, the identification was considered

secondary (‘secondary morphological species hypothe-

sis’), except when the sequence derived from such

Table 1 Details of the single primer pairs used to amplify the four mitochondrial DNA fragments across mammals

Primer pairs Sources Annealing T° (C) Product size (bp)

cyt b GVL14724 50 GATATGAAAAACCATCGTTG 30 Modified from Kocher et al. (1989) 50 402

H15149 50 CTCAGAATGATATTTGTCCTCA 30 Irwin et al. (1991)

COI bush-COIF 50 CACAAACCACAAAGAYATYGG 30 This study 50 658

bush-COIR 50 TCAGGGTGTCCAAARAAYCA 30 This study

12S bush-12SF 50 GGGATTAGATACCCCACTATGC 30 This study 52 384–430

bush-12SR 50 GTGACGGGCGGTGTGT 30 This study

16S bush-16SF 50 CGCCTGTTTACCAAAAACATC 30 This study 52 510–527

bush-16SR 50 AATCGTTGAACAAACGAACC 30 This study



representatives grouped within the same genetic clusters

with other sequences representing primary morphologi-

cal hypotheses of species. The taxonomic identification

of the sample set from Guinea was considered

‘unknown’.

The accuracy of DNA-based species assignment

methods is dependent on the level of taxonomic repre-

sentation and the amount of within-species genetic

diversity represented in the nucleotide databases. Tradi-

tionally used tools such as BLAST (Ye et al. 2006) are

known to result in false positives in cases of taxonomic

underrepresentation (Ross et al. 2008). To follow a

rigorous and conservative approach, we set up a

decision pipeline (Fig. 2) combining (i) similarity-based

taxonomic assignments by querying the GENBANK data-

base (http://www.ncbi.nlm.nih.gov, accessed November

2013) with MEGABLAST (Ng & Peng Pang 2010) using

default parameters, (ii) a tree-based approach determin-

ing the most inclusive phylogenetic attribution of a

given sequence, using the BLAST Treeview widget

(Neighbour Joining and Reroot option whenever

necessary), (iii) an assessment of the accuracy of the tax-

onomic identification of the GENBANK sequences used

in the taxonomic assignment procedure (i.e. whether the

sequence labels have been validated by expert taxo-

nomic studies; see Appendix S2, Supporting Informa-

tion, and below for further details) and (iv) an

assessment of taxonomic coverage (i.e. whether or not

the species sequenced was already represented in

GENBANK). Studies used as ‘expert’ reference sources

were restricted to those that included voucher speci-

mens, morphological description of species or which

focused on a single species (or a few congeneric species).

These steps resulted in DNA-based assignments of accu-

rate to less accurate taxonomic categories, ranging from

‘hard’ and ‘soft’ species (respectively, species identified

from expert- and nonexpert-generated sequences) to

genus and in some cases the most inclusive phylogenetic

level below the genus (named after Wilson & Reeder

2005). For example, the taxonomic assignment of a

sequence would be considered a hard species if it

grouped within or was the sister group of a monophy-

letic species-level cluster, under the met condition of

appropriate GENBANK taxonomic coverage and expert

validation of the taxonomic identity of the sequence(s)

with which it shared ≥95% maximum identity (per cent

similarity between the query and subject sequences over

the length of the coverage area). At the opposite

extreme, a sequence that did not group within or as sis-

ter group to a monophyletic species-level cluster would

be assigned to its most inclusive phylogenetic level

(genus, subfamily, etc.), independently of its maximum

identity value. Final classification (DNA-based taxo-

nomic assignment) was determined by choosing the low-

est phylogenetic level provided by the four genes, as

long as none of these were in conflict with each other.

For instance, a sample that was classified as ‘Neotragus

pygmaeus’ with two genes, and as Bovidae and Bovinae

for the other two, would be assigned to the species

N. pygmaeus. Alternatively, a sample that best matched

‘Manis’ and ‘Mammalia’, each with two of the four

genes, would be assigned to the genus Manis.

Nucleotide sequencescyt b, 12S, COI, 16S

Within or sister-group of a monophyletic species-level cluster

BLAST Treeview

Appropriate GENBANK taxonomic coverage + expert validation of the taxonomic identity of the sequence

MEGABLAST in GENBANK

Most inclusive phylogenetic level

≥95% maximum identity

Hard species Soft species Genus

Yes

No

Yes

Yes

No No

≥95% maximum identity

NoYes

Fig. 2 Decision pipeline used to taxo-

nomically assign the nucleotide sequences

generated from bushmeat animals.



Utility and diagnostic level of the four mitochondrial genes for

DNA typing African forest bushmeat—Correlations among

gene amplification success and species, quality of sam-

ples and extraction methods were estimated using the

Chi2 test (for groups with n > 5). We further evaluated

the effect of these variables on PCR success applying

generalized linear models (GLMs) with binomial distri-

bution errors fitting success or failure (as a response vari-

able) for each mitochondrial gene fragment. Models

were performed with the ‘glm’ function in the R package

(R Development Core Team 2013). We also assessed the

success of species-level assignment per mammalian

orders and genes as well as the reasons why species-level

assignment was not reached using GENBANK as a refer-

ence database.

Cyt b and COI sequences were aligned by eye with

BIOEDIT 7.1.3 (Hall 1999). We used the MUSCLE web-

platform (http://www.ebi.ac.uk/Tools/msa/muscle/;

Edgar 2004) with default settings to align the 12S and

16S fragments. Regions with indels were removed before

analysis. DNA polymorphism estimates (polymorphic

sites and polymorphic informative sites) were calculated

for each mammalian order and gene fragment in DNASP

5.10.01 (Librado & Rozas 2009). We used the ‘sliding

window’ option to give a visual representation of the dis-

tribution of polymorphic sites (S) along genes (window

length: 20; step size: 10).

As an alternative way of estimating the usefulness of

our sequences in DNA typing bushmeat, we assessed the

level of taxonomic clustering in our data sets across

mammalian orders using neighbour-joining (NJ) trees

built in MEGA with the K2P distance model (Kimura 1980)

and 1000 bootstrap replications (Felsenstein 1985). We

also used the model selection option in MEGA to select the

best fit model (using the BIC criterion; Keane et al. 2006)

per gene partition per mammalian order and ran maxi-

mum-likelihood (ML) tree searches with five discrete

categories for the gamma distribution (whenever appli-

cable). Node support was estimated through 500 boot-

strap replications.

Genetic distances among and within mammalian

orders were calculated in MEGA using K2P distance and

1000 bootstrap replications for standard error estimates.

The choice of the K2P model allowed the comparison of

our results with genetic distance estimates from previous

bushmeat studies in the framework of the genetic species

concept in mammals (Bradley & Baker 2001; Baker &

Bradley 2006). In addition, we used an approach based

on the vectorization of nucleotide sequences that extracts

DNA diagnostic patterns in the form of indicator vectors

and visually represents the patterns as ‘Klee diagrams’

(Sirovich et al. 2009, 2010). Those latter constitute an

alternative to phylogenetic trees for representing nucleo-

tide sequence clustering, where Klee diagrams are heat

maps of the indicator- vector correlation matrixes. For

this purpose, distance trees were calculated using p-dis-

tance (Nei & Kumar 2000) in MEGA. Output files were

generated via the web-based program TREEPARSER

and visualized using the Indicator Vector program

(http://phe.rockefeller.edu/barcode/klee.php).

Results

We obtained 1157 nucleotide sequences of the 1208 pos-

sible sequences from the 302 mammalian samples (i.e. 51

PCR amplifications failed). Only 69 sequences required a

second round of PCR and the success rate of first-round

PCR amplification across mammalian orders was gener-

ally > 90% (Fig. S3, Supporting Information). Lower

levels of PCR success occurred with COI in Erinaceomor-

pha, Pholidota and Carnivora. Amplification of pseudog-

enes (NUMTs) was most common in Pholidota (26%)

and Carnivora (20%) for COI and was also observed

when amplifying cyt b in Primates (10%). Although the

rate of successful COI amplification was the most dissim-

ilar compared with the three other genes, the difference

was not significant. Among orders, patterns of PCR

amplification success of the four genes were significantly

different when comparing Pholidota to Artiodactyla and

Primates. Binomial GLMs highlighted the significantly

lower level of PCR success concerning COI in Pholidota

and Carnivora (Table S4, Supporting Information).

Smoked samples were not particularly subject to failed

amplification, although they were involved in most of

the failed PCRs in Artiodactyla and Carnivora (Fig. S5,

Supporting Information). There was no significant

difference between CTAB and robot extraction PCR suc-

cess rates. PCR success rates from CTAB DNA extrac-

tions ranged from 96% (COI) to 100% (cyt b, 12S, 16S),

whereas robot-based DNA extractions yielded rates from

88% (COI) to 96–98% (cyt b, 12S, 16S). The nonmammali-

an species Necrosyrtes monachus and Varanus niloticus

yielded amplification products for 12S and 16S, and COI,

12S and 16S, respectively.

Primary morphological species hypotheses repre-

sented the majority of our data set (53 species hypotheses

out of 59; Table 2). The success rate of DNA-based spe-

cies-level assignment across mammals was ≥50% for

each of the four genes (Fig. 3). Cyt b and 16S had the

highest and lowest rates (73 and 50%), respectively. The

main reason that species-level assignments failed was

incomplete taxonomic representation in GENBANK

(reaching 86% for COI), and to a lesser extent, the

nonmonophyly of species-level reference sequences

(14–28%). This trend was generally similar within each

taxonomic group, with cyt b performing better or equally

better than the other three mtDNA genes in Artiodactyla,

Carnivora, Pholidota and Rodentia. COI was generally



Table

2DNA-based

taxonomic

assignmen

tofbush

meatsamplesusingourdecisionpipeline(Fig.2)

andGENBANK

asareference

datab

ase.

Greycellsindicatethat

taxonomic

assignmen

tdid

notreachsp

ecieslevel

Ord

er

Morphological

species

hypothesis

n

DNA-based

taxonomicassignmen

t

cytb

12S

COI

16S

DNA-based

taxonomic

consensu

s

Artiodactyla

Syn

ceruscaffer*

2Syn

ceruscaffer

Syn

ceruscaffer

Syn

ceruscaffer

Syn

ceruscaffer

Syn

ceruscaffer

Artiodactyla

Neotragusbatesi*

2Neotragusbatesi

Neotragusbatesi

Neotragusbatesi

Neotragusbatesi

Neotragusbatesi

Artiodactyla

Neotraguspygm

aeus

2Neotraguspygm

aeus

Neotraguspygm

aeus

Bovidae

Bovinae

Neotraguspygm

aeus

Artiodactyla

Tragelaphusscriptus

3Tragelaphusscriptus

Tragelaphusscriptus

Tragelaphusscriptus

Tragelaphusscriptus

Tragelaphusscriptus

Artiodactyla

Tragelaphusspekii

2Tragelaphusspekii

Tragelaphusspekii

Tragelaphusspekii

Tragelaphusspekii

Tragelaphusspekii

Artiodactyla

Cephalophusdorsalis

2Cephalophusdorsalis

Cephalophus

Cephalophusdorsalis

Cephalophusdorsalis

Cephalophusdorsalis

Artiodactyla

Cephalophusogilbyi

4Cephalophusogilbyi

Cephalophus

Cephalophus

Cephalophus

Cephalophusogilbyi

Artiodactyla

Philantomba

maxwelli

6Philantomba

maxwelli

Philantomba

maxwelli

Philantomba

maxwelli

Philantomba

maxwelli

Philantomba

maxwelli

Artiodactyla

Philantomba

monticola

2Philantomba

monticola

Philantomba

Philantomba

monticola

Philantomba

monticola

Philantomba

monticola

Artiodactyla

Philantomba

walteri

3Philantomba

walteri

Philantomba

Philantomba

walteri

Philantomba

Philantomba

walteri

Artiodactyla

Uniden

tified

duiker

1*2

Philantomba

walteri

Philantomba

Philantomba

walteri

Philantomba

Philantomba

walteri

Artiodactyla

Uniden

tified

duiker

2*2

Susscrofa

Susscrofa

Susscrofa

Susscrofa

Susscrofa

Artiodactyla

Hylochoerus

meinertzhageni

1Phacochoerusafricanus

Phacochoerusafricanus




Artiodactyla


10Phacochoerusafricanus





Artiodactyla

Potam

ochoerusporcus1*

5Potam

ochoerusporcus

Potam

ochoerusporcus

Potam

ochoerusporcus

Potam

ochoerusporcus

Potam

ochoerusporcus

Artiodactyla

Potam

ochoerusporcus2*

4Susscrofa

Susscrofa

Susscrofa

Susscrofa

Susscrofa

Carnivora

Canisadustus

5Canisadustus

Canis

Canis

Canis

Canisadustus

Carnivora

Nandiniabinotata

21Nandiniabinotata

Feliform

iaNandiniabinotata

Feliform

iaNandiniabinotata

Carnivora

Profelisaurata

1Felidae

Felidae

Felidae

Felidae

Felidae

Carnivora

Caracalcaracal

2Felidae

Felidae

Caracalcaracal

Felidae

Caracalcaracal

Carnivora

Proailurusserval

1Felidae

Felidae

Felidae

Felidae

Felidae

Carnivora

Felissilvestris

1Felissilvestris

Felissilvestris

Felissilvestris

Felissilvestris

Felissilvestris

Carnivora

Pantherapardus

1NA

Pantherapardus

Pantherapardus

Pantherapardus

Pantherapardus

Carnivora

Crossarchus

platycephalus

10Crossarchus

platycephalus

Herpestidae

Crossarchus

Herpestidae

Crossarchusplatycephalus

Carnivora

Herpestes

ichn

eumon

3Herpestes

ichn

eumon

Herpestidae

Herpestidae

Herpestidae

Herpestes

ichn

eumon

Carnivora

Civettictiscivetta

11Civettictiscivetta

Viverrinae

Civettictiscivetta

Viverrinae

Civettictiscivetta

Carnivora

Genetta

cfthierryi

1Genetta

thierryi

Feliform

iaNA

Viverrinae

Genetta

thierryi

Carnivora

Genetta

thierryi

2Genetta

thierryi

Feliform

iaNA

Viverrinae

Genetta

thierryi

Carnivora

Genetta

servalina

2Genetta

servalina

Feliform

iaFeliform

iaViverrinae

Genetta

servalina

Carnivora

Genetta

cfpardina

3Large-sp

otted

gen

et

complex

Feliform

iaFeliform

iaViverrinae

Large-sp

otted

gen

et

complex

Carnivora

Genetta

pardina

9Large-sp

otted

gen

et

complex

Feliform

iaFeliform

iaViverrinae

Large-sp

otted

gen

et

complex

Carnivora

Genetta

maculata

2Large-sp

otted

gen

et

complex

Feliform

iaFeliform

iaViverrinae

Large-sp

otted

gen

et

complex



Table

2(C

ontinued

)

Ord

er

Morphological

species

hypothesis

n

DNA-based

taxonomicassignmen

t

cytb

12S

COI

16S

DNA-based

taxonomic

consensu

s

Carnivora

Genetta

sp.1

10Large-sp

otted

gen

et

complex

Feliform

iaFeliform

iaViverrinae

Large-sp

otted

gen

et

complex

Carnivora

Genetta

sp.2

2Genetta

thierryi

Feliform

iaNA

Viverrinae

Genetta

thierryi

Philodota

Manisgigantea

5Mam

malia

Manis

Mam

malia

Manis

Manis

Philodota

Manistetradactyla

5Mam

malia

Manis

Manis

Manis

Manis

Philodota

Manistricuspis

17Manistricuspis

Manistricuspis

Manistricuspis

Manistricuspis

Manistricuspis

Primates

Cercocebu

storquatus

1Cercocebu

storquatus

Lophocebu

salbigena

Cercopithecinae

Lophocebu

salbigena

Cercocebu

storquatus

Primates

Cercopithecus

erythrogaster

1Cercopithecus

erythrogaster

Cercopithecuserythrogaster


Cercopithecus


Primates

Cercopithecusmona

1Cercopithecusmona

Cercopithecusmona

Cercopithecusmona

Cercopithecus

Cercopithecusmona

Primates

Cercopithecussp

.1*

1Cercopithecus

petaurista

Cercopithecuspetaurista


Cercopithecus


Primates

Cercopithecussp

.2

1Cercopithecus

erythrogaster



Cercopithecus


Primates

Chlorocebussabaeus

5Chlorocebussabaeus

Chlorocebussabaeus

Chlorocebussabaeus

Chlorocebussabaeus

Chlorocebussabaeus

Primates

Colobussatanus

2Colobus

Colobus

Colobus

Colobus

Colobus

Primates

Erythrocebu

spatas

2Erythrocebu

spatas†

Erythrocebu

spatas

Erythrocebu

spatas

Erythrocebu

spatas

Erythrocebu

spatas

Primates

Lophocebu

salbigena*

1Lophocebu

sLophocebu

salbigena

Lophocebu

salbigena

NA

Lophocebu

salbigena

Primates

Mandrillussphinx

2Mandrillussphinx

Mandrillussphinx

Mandrillussphinx

Mandrillussphinx

Mandrillussphinx

Primates

Galagosenegalensis*

1Galag

idae

Galagosenegalensis

(isolates1,2)

Galago

Galagosenegalensis

Galagosenegalensis

(isolate)

Primates

Galagoidesdemidovii

1Galag

idae

Galagoidesdemidovii

(isolates2,3,6,7)

Amniota

Galag

idae

Galagoidesdemidovii

(isolate)

Primates

Gorillagorilla1

1Gorillagorilla

Gorillagorilla

Gorillagorilla

Gorillagorilla

Gorillagorilla

Primates

Gorillagorilla2*

1Pan

troglodytes

Pan

troglodytes

Pan

troglodytes

Pan

troglodytes

Pan

troglodytes

Primates

Pan

troglodytes

2Pan

troglodytes

Pan

troglodytes

Pan

troglodytes

Pan

troglodytes

Pan

troglodytes

Primates

Perodicticuspotto*

1Perodicticuspotto

Perodicticus

Perodicticuspotto

Perodicticuspotto

Perodicticuspotto

Primates

Unidentified

prim

ate

1Cercopithecus

erythrogaster



Cercopithecus


Roden

tia

Anom

aluruspelii

1Anom

alurus

Anom

alurus

Anom

alurus

Anom

alurus

Anom

alurus

Roden

tia

Atherurusafricanus1

10Atherurusafricanus

Atherurusafricanus

Amniota

Hystricidae

Atherurusafricanus

Roden

tia

Atherurusafricanus2

2Hystricidae

Atherurusafricanus

Amniota

Hystricidae

Atherurusafricanus

Roden

tia

Atherurusafricanus3*

1Susscrofa

Susscrofa

Susscrofa

Susscrofa

Susscrofa

Roden

tia

Cricetomys

emini*

2Cricetomys

sp.3

Cricetomys

Cricetomys

Muroidea

Cricetomys

sp.3

Roden

tia

Cricetomys

sp.1

1Cricetomys

gambianus

Cricetomys

Cricetomys

Muroidea

Cricetomys

gambianus

Roden

tia

Cricetomys

sp.2

1Cricetomys

sp.1

Cricetomys

Cricetomys

Muroidea

Cricetomys

sp.1

Roden

tia

Heliosciurus

rufobrachium

5Heliosciurus

rufobrachium

Heliosciurus

Sciuridae

Sciuridae

Heliosciurusrufobrachium

Roden

tia

Xeruserythropus

3Xeruserythropus

Xerinae

Mam

malia

Xerini

Xeruserythropus



Table

2(C

ontinued

)

Ord

er

Morphological

species

hypothesis

n

DNA-based

taxonomicassignmen

t

cytb

12S

COI

16S

DNA-based

taxonomic

consensu

s

Roden

tia

Thryonom

ys

swinderianus1

25Thryonom

is

swinderianus

Thryonom

issw

inderianus

Heliosciurusgambianus

Thryonom

issw

inderianus

Thryonom

yssw

inderianus

Roden

tia

Thryonom

ys

swinderianus2*

18Susscrofa

Susscrofa

Susscrofa

Susscrofa

Susscrofa

Primates‡

Unknown1

3Cercopithecinae

Cercocebu

satys

Cercopithecinae

Cercocebu

satys

Cercocebu

satys

Primates‡

Unknown2

1Chlorocebussabaeus

Chlorocebussabaeus

Chlorocebussabaeus

Chlorocebussabaeus

Chlorocebussabaeus

Primates‡

Unknown3

11Cercopithecuscampbelli†

Cercopithecus

Cercopithecuscampbelli

Cercopithecus

Cercopithecuscampbelli

Primates‡

Unknown4

1Cercopithecuspetaurista



Cercopithecus


Hyracoidea‡

Unknown5

1Dendrohyrax

dorsalis

Dendrohyrax

dorsalis

Dendrohyrax

dorsalis

Dendrohyrax

dorsalis

Dendrohyrax

dorsalis

Artiodactyla‡

Unknown6

5Cephalophusdorsalis

Cephalophus

Cephalophusdorsalis

Cephalophusdorsalis

Cephalophusdorsalis

Artiodactyla‡

Unknown7

1Cephalophussilvicultor

Cephalophus

Cephalophussilvicultor



Artiodactyla‡

Unknown8

1Cephalophus

Cephalophus

Cephalophus

Cephalophus

Cephalophus

Artiodactyla‡

Unknown9

2Philantomba

maxwelli

Philantomba

maxwelli

Philantomba

maxwelli

Philantomba

maxwelli

Philantomba

maxwelli

Artiodactyla‡

Unknown10

1Tragelaphusscriptus

Tragelaphusscriptus

Tragelaphusscriptus

Tragelaphusscriptus

Tragelaphusscriptus

Carnivora‡

Unknown11

4Civettictiscivetta

Viverrinae

Civettictiscivetta

Viverrinae

Civettictiscivetta

Carnivora‡

Unknown12

3Large-sp

otted

gen

et

complex

Feliform

iaFeliform

iaViverrinae

Large-sp

otted

gen

et

complex

Pholidota‡

Unknown13

2Manistricuspis

Manistricuspis

Manistricuspis

Manistricuspis

Manistricuspis

Tubuliden

tata

Orycteropusafer

1Orycteropusafer

Orycteropusafer

Orycteropusafer

Orycteropusafer

Orycteropusafer

Erinaceo

morpha

Atelerixalbiventris

1Metazoa

Atelirixalbiventris

NA

Erinaceinae

Atelerixalbiventris

Lag

omorpha

Lepusvictoriae

1Lepus

Lepus

Lepus

Lepus

Lepus

*Secondarymorphological

hypothesisofsp

ecies.

†Softsp

eciesassignmen

t.

‡Ord

erattributionafterDNA-based

taxonomicassignmen

t.



0

2

4

6

8

10

12

14

16

0

2

4

6

8

10

12

14

16

0

2

4

6

8

10

12

14

16

0

10

20

30

40

50

60

70

0

2

4

6

8

10

12

14

10

0

2

4

6

8

10

12

14

16

0

2

4

6

8

10

12

14

10

Complete data set Artiodactyla

Carnivora Pholidota

RodentiaPrimates

Unknown taxa and other mammalian orders

cyt b

12S

COI

16S

1 2 3 4

1 2 3 4 1 2 3 4

1 2 3 41 2 3 4

1 2 3 4

1 2 3 4

Fig. 3 Success of species-level assignment per taxonomic groups and genes together with factors responsible for failure in reaching

species-level assignment. Assignments are calculated per morphological species hypothesis. 1 – quantity of species-level assignment

reached; quantity of species-level assignment not reached because of: 2 – incomplete taxonomic representation in GENBANK; 3 – low

expert knowledge or inappropriate taxonomic labelling of sequences (e.g. ‘Anomalurus sp.’) in GENBANK; 4 – nonmonophyly of the

sequences belonging to a same species in GENBANK.



ranked second or third in species-level assignment

success. 12S performed better than any other genes in

Primates. Artiodactyla and Primates were the orders

with the highest number of successful species-level

assignments per genes. There were more failed species-

level assignments than successes in Carnivora and

Pholidota and equal levels of success in Rodentia. The

nonmonophyly of species-level GENBANK sequences

(especially for 16S) was the main reason species-level

assignments failed in Primates. Sixty-nine per cent of the

samples of unknown taxonomic origin were identified at

the species level.

Overall, our decision pipeline allowed assigning to

the species level 45 of the 69 taxonomic hypotheses

(Table 2). Cyt b sequences provided ‘soft species’ sup-

port for Erythrocebus patas and Cercopithecus campbelli

(Primates), which in contrast were recovered as ‘hard

species’ (i.e., more accurately distinguished) with the

other genes. Seven of the morphological species identifi-

cations conflicted with their DNA-based assignments.

Series of smoked duikers, Potamochoerus porcus (Artio-

dactyla), Atherurus africanus and Thryonomys swinderianus

(Rodentia) were assigned to Sus scrofa. H. meinertzhageni

(Artiodactyla) was assigned to Phacochoerus africanus, one

individual of Gorilla gorilla (Primates) was classified as

Pan troglodytes and two specimens of Cricetomys emini

(Rodentia) were assigned to a different species-level line-

age of Cricetomys. Conflicting assignments among genes

were observed in one Primate (Cercocebus torquatus) and

one Rodentia (T. swinderianus). Some representatives of

A. africanus could not be classified to the species level,

whereas some from other geographic origins were cor-

rectly classified.

DNA polymorphism levels were high in each of the

four genes, especially in COI and cyt b (Table S6, Sup-

porting Information). Mean number of polymorphic sites

(S) in the total data sets ranged from 8.4 (16S) to 9.4

(12S), 9.6 (COI) and 11.8 (cyt b). The distribution of S

along the genes was more regular in COI and cyt b than

in 12S and 16S (Fig. 4). Those trends were also consistent

within mammalian orders (data not shown).

Average interspecies genetic distances within mam-

malian orders were the highest for cyt b (min–max:

23.3–39.5%), and then decreased from COI (20.8–27.8%)

to 12S (11.6–22.4%) and 16S (9–19.6%). Cyt b had the wid-

est range of genetic distance values (Fig. 5). NJ and ML

trees yielded very similar patterns of sequence cluster-

ing, with NJ trees performing slightly better (i.e. having

in some cases better supported clusters; Figs S7 and S8,

Supporting Information). Trees provided a very good

0

2

4

6

8

10

12

14

16

18

0

2

4

6

8

10

12

14

16

18

0

2

4

6

8

10

12

14

16

18

0

2

4

6

8

10

12

14

16

18

cyt b (n = 295) 12S (n = 298)

16S (n = 295)COI (n = 265)

0 bp 402 0 bp

0 bp 658 0 bp

418

602

Fig. 4 Sliding window view of the distribution of polymorphic sites along the four mitochondrial genes within African mammals. The

curve describes the distribution of the mean number of polymorphic sites across all taxa in each 20 bp along the genes (sliding

window = 10 bp).



level of resolution among the five mammalian orders

represented by more than one species (Bovidae, Carniv-

ora, Pholidota, Primates and Rodentia). Cyt b trees

clustered into 55 well-supported, distinct monophyletic

groups corresponding to species-level lineages. In Artio-

dactyla, Cephalophus dorsalis was not monophyletic in the

12S and 16S trees, nor Cephalophus ogilbyi in the 12S tree.

In Carnivora, Genetta pardina and Genetta maculata clus-

tered in a single group for each of the four genes. In Pri-

mates, the two specimens of Cercopithecus petaurista were

not monophyletic or clustered with weak support in the

12S, COI and 16S analyses. The distribution of intra- ver-

sus interspecies genetic distances was nonoverlapping in

Carnivora, Pholidota and Rodentia for all genes, and in

Artiodactyla and Primates for cyt b, COI and 16S (Fig. 6).

The strength of the tree-based segregation among and

within mammalian orders was also apparent in the heat

maps of indicator-vector correlation matrixes (Figs 7 and

S9, Supporting Information).

Discussion

Success in PCR amplification of the four mtDNAmarkers across taxonomic and sample quality ranges

The application of forensic science and conservation

genetics techniques to the study and regulations of wild-

life trade requires genetic markers that are amplifiable

cyt b 12S(n = 295) (n = 298) (n = 265) (n = 294)

K2P

dis

tanc

es

16SCOI

0.4

0.3

0.2

0.1

0.0

Fig. 5 Box plots summarizing the distribution of average inter-

specific genetic distances within each of the four mitochondrial

genes among African mammals.

0

5

10

15

20

25

02468

1012141618

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 270

1

2

3

0

5

10

15

20

01234567

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

Artiodactyla(15 species)

Pholidota(3 species)

Primates(16 species)

Carnivora(12 species)

Rodentia(9 species)

*

*

*

cyt bCOI12S16S

Fig. 6 Distribution of genetic distances per mitochondrial gene among species within five mammalian orders. X-axis represents K2P

distances in percentage (1 = 0–0.99%; 2 = 1–1.99%; and so on). Dashed lines delimit intraspecific genetic distance distributions. Catego-

ries where intra- and interspecies genetic distances overlap are marked with an asterisk.



across a wide taxonomic range to be broadly useful

(Verma & Singh 2003). However, investigations of the

African bushmeat trade have focused on only a limited

taxonomic set and/or geographic spectrum of species,

and a limited number of gene fragments. Although a

variety of DNA typing techniques have been proposed,

the majority of the bushmeat studies have so far relied

on the direct sequencing of a heterogeneous series of

‘barcoding’ fragments, including: (i) COI and 12S for Pri-

mates in Guinea Bissau (Minh�os et al. 2013), (ii) cyt b and

control region for Artiodactyla (Bovinae) in Central

Africa (Ntie et al. 2010), and (iii) COI for Primates and

Artiodactyla in the Republic of Congo (Eaton et al. 2010)

and for Artiodactyla in Tanzania (Bitanyi et al. 2011,

2012). These studies used taxon-specific series or cock-

tails of primer pairs that cannot be expanded to investi-

gations across a wide range of mammalian orders.

Building on the preliminary work of Olayemi et al.

(2011), we provide a single primer pair per gene for the

amplification of the cyt b, 12S, COI and 16S fragments

across species from nine mammalian orders that are

commonly involved in the African bushmeat trade

(Artiodactyla, Carnivora, Pholidota, Primates, Rodentia,

Erinaceomorpha, Lagomorpha, Tubulidentata, and Hyr-

acoidea). In our study, PCR success rates across orders

exceeded 90% and a second round of PCRs was rarely

necessary. These rates are remarkably high given the

variety of factors that can contribute to lower rates of

amplification of cross-species primers (Housley et al.

2006). The only significant exception was the weaker suc-

cess of COI amplification in Carnivora and Pholidota,

mostly attributable to the amplification of pseudogenes.

Nuclear integration of mtDNA resulting in nuclear mito-

chondrial pseudogenes (NUMTs) is a relatively common

cyt bCercopithecus campbelli (15)

Cercopithecus mona (3)Erythrocebus patas (7)

Chlorocebus sabaeus (5)

Cercopithecus erythrogaster (2)Cercopithecus petaurista (4)

Lophocebus albigena (8)Cercocebus torquatus (1)

Mandrillus sphinx (9)Cercocebus atys (16)

Gorilla gorilla (12)Pan troglodytes (13)

Colobus satanus (6)

P. potto (14)

Galago senegalensis (10)Gs. demidovii (11)

0.03

12SCercopithecus campbelli (15)

Cercopithecus mona (3)

Cercopithecus erythrogaster (2)

Cercopithecus petaurista (4)Cercopithecus petaurista (4)

Erythrocebus patas (7)Chlorocebus sabaeus (5)

Lophocebus albigena (8)

Cercocebus torquatus (1)Mandrillus sphinx (9)

Cercocebus atys (16)

Gorilla gorilla (12)Pan troglodytes (13)

Colobus satanus (6)

P. potto (14)

Galago senegalensis (10)Galagoides demidovii (11)

0.02

15

5

7

3

4

2

16

19

6

8

13

12

1410

11

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.21 0.9 0.8 0.7 0.6 0.5 0.4 0.3

5

7

3

4

2

16

19

6

8

13

12

1410

11

15

Fig. 7 Neighbour-joining trees (left) and Klee diagrams (right) showing species-level genetic clusters and correlation of

indicator vectors within Primates for cyt b and 12S. All the collapsed clusters (NJ trees) are supported by bootstrap values > 75%.

Scale bar (NJ trees) corresponds to % divergence. Vertical scale bar (Klee diagrams) expresses the level of similarity among

sequences. Numbers in the Klee diagrams correspond to the species in the NJ trees. Gs. demidovii = Galagoides demidovii;

P. potto = Perodicticus potto. For 12S, the correlation between the two sequences of C. petaurista was less ‘hot’ than for cyt b, mirroring

their nonmonophyly in the 12S NJ tree.



problem in COI amplification (Song et al. 2008; Buhay

2009). Indeed, the regular distribution of site polymor-

phism inherent to COI (Fig. 4) made it more difficult to

define conserved internal primers. This is a well-known

issue affecting vertebrate taxa (Vences et al. 2005) that

was notably reflected in the multiprimer pair approach

of previous bushmeat genetic studies (see above). The

degenerate nature of our COI primer pair in 30 probablyexplains the higher level of pseudogene amplification

observed in some mammalian orders (Dawnay et al.

2007; Ivanova et al. 2007). Nevertheless, considering the

fact that we used ‘universal’ primers to amplify four

mitochondrial genes, the overall level of pseudogene

amplification was low in our study (see Zhang & Hewitt

1996).

PCR amplification was not affected by the type of

sample (fresh, smoked or dry) or the DNA extraction

method (robot versus CTAB). Although a few degraded

(smoked) samples prevented PCR amplification, the

heating techniques used to smoke and dry meat did not

denature DNA and sequences of c. 650 bp could be

routinely amplified, as has been previously observed for

smoked fishes (Smith et al. 2008). This is an encouraging

result regarding our ability to amplify middle-sized

mtDNA fragments whatever the preprocessing of bush-

meat items (see Eaton et al. 2010).

We expect that the design of ‘universal’ mtDNA

markers in combination with the use of a multilocus

approach and a decision pipeline will be a useful

contribution to the implementation of standardized

FINS-based DNA typing procedures for the study and

monitoring of African bushmeat markets. The utility of

our straightforward PCR protocol should be even more

far-reaching as it might successfully amplify additional

taxa that are involved in the bushmeat trade, such as

Chiroptera (data not shown) and at least some reptiles

and birds (this study). However, further analyses involv-

ing additional mammalian species and orders will have

to be conducted to accurately assess the taxonomic

amplification range of our mtDNA markers.

Usefulness of our approach in generating FINSapplicable to bushmeat items

Our approach is original and broadly useful because our

protocol includes a decision pipeline that provides a

level of confidence in species assignments. Most wildlife

forensic approaches have focused on the validation of

their laboratory protocols (Dawnay et al. 2007), but to

our knowledge, generally did not include a rigorous

decision pipeline for the taxonomic assignment of FINS.

To avoid nonreproducible protocols of DNA-based spe-

cies identification (Ogden et al. 2009), we provide a step-

by-step decision pipeline to validate sequence identities

(Fig. 2). Our approach explicitly relies on an expert

validation of GENBANK sequences, so doubtful or mis-

labelled reference sequences that may blur the assign-

ment process (Bridge et al. 2003) could be filtered. We

also took into account taxonomic coverage and intraspe-

cific diversity representation in GENBANK, which can

cause inaccurate sequence assignment (Munch et al.

2008; see below). Nevertheless, we acknowledge that

going through the expert validation of a given sequence

may be a subjective process, notably when the available

literature does not perfectly meet our criteria (e.g. when

there is no reference to vouchers or expert taxonomic

knowledge of the authors). This potential drawback will

be less burdensome with the addition of new expert-

generated sequences in GENBANK, a task to which our

study contributes. Our decision pipeline may also benefit

from an empirical assessment of the conservative, 95%

similarity threshold that we used to minimize false

positives of species-level matches, given the potential

arbitrariness of this value (Meier et al. 2006).

Our study confirmed the usefulness of a multilocus

typing approach (Elias et al. 2007; Fr�ezal & Leblois 2008;

Kim et al. 2014). The independent amplification of four

mtDNA genes facilitated accurate species-level assign-

ment by overcoming random, gene-dependent gaps in

GENBANK taxonomic coverage. It also permitted the

assessment of taxonomic assignment inconsistencies

among amplifications for a given sample (notably due to

pseudogene amplification or cross-species contamina-

tion). The success in reaching species-level identification

from our mtDNA sequences when querying GENBANK

was fair to good, ranging from 50% (16S) to 73% (cyt b).

In total, 67% of the morphological species hypotheses

could be DNA assigned to the species level (i.e. ‘hard

species’ assignments). In addition, the distribution of

genetic distances, together with the good level of resolu-

tion of the NJ/ML trees and the Klee diagrams showed

that in most cases, we obtained satisfying results in

distinguishing among recently diverged species (e.g.

Philantomba spp.). Thus, we hypothesize that our rate of

species-level assignment would be higher with a larger

taxonomic representation of the queried database.

The great majority of interspecific distances were

higher than intraspecific distances (Fig. 6), further

confirming the utility of the four mtDNA genes. COI dis-

tances among congeneric species averaged 11.4% (from

4.3 to 20.7%), which is in line with previously published

ranges (e.g. Hebert et al. 2003: 9.6%; Eaton et al. 2010:

9.8%). For cyt b, the trend was similar (mean = 11%;

range = 4.4–22.7%), and a large proportion of pairwise

genetic distances were above the 11% threshold that can

be indicative of species recognition among mammals

(Bradley & Baker 2001; Baker & Bradley 2006). Although

our sample set included a small proportion of sister



species, the high pairwise distances were indicative of

the utility of cyt b and COI in identifying between closely

related species (Philantomba walteri and P. maxwelli: 8.2

and 4.9%, respectively; C. caracal and P. aurata: 13.6 and

8.4%; M. tetradactyla and M. tricuspis: 18.3 and 14.5%).

The only cases of intra- versus interspecific distance

overlap concerned phylogenetically proximate species

groups (Philantomba spp., Cercopithecus spp.) with 12S

and 16S, but these were generally distinguishable via

clustering methods (NJ/ML trees and Klee diagrams).

Such clustering methods are probably the best comple-

ment to similarity-based approaches when DNA typing

biodiversity in the context of incomplete taxonomic cov-

erage of reference databases (Puillandre et al. 2009).

However, we acknowledge that a denser taxonomic sam-

pling including more sister species among mammalian

orders would be useful to improve accuracy between

inter- and intraspecific genetic distance estimates.

DNA typing proved useful in (i) resolving the taxo-

nomic identity of smoked/processed carcasses and cryp-

tic species and (ii) correcting misidentifications in the

field. We reached species-level assignment in 69% of the

Guinean samples of unknown taxonomic attribution. In

addition, a pool of smoked items sold in southwestern

Nigeria under different labels (‘duiker’, Potamochoerus

porcus, P. africanus or Thryonomys swinderianus) was

DNA assigned to S. scrofa. This finding is not trivial for

the Muslim populations of the area, and we suspect that

traders may increase their margins by selling domestic

animals (pigs) at the price of wild games.

Other conflicts between morphological species

hypotheses and DNA assignment occurred with pro-

cessed carcasses (only parts of the body were available)

that could not be distinguished by eye from closely

related species (e.g. ‘H. meinertzhageni’ and ‘Gorilla gor-

illa’ actually represented P. africanus and P. troglodytes,

respectively). However, with H. meinertzhageni, the

assignment to P. africanus may reflect natural interspe-

cies hybridization, incomplete lineage sorting or the poor

delineation of species boundaries (see Gongora et al.

2011). Another case where FINS proved their usefulness

was in the assignment of phenotypically undistinguish-

able (cryptic) species of Cricetomys into three distinct cyt

b species-level lineages (Olayemi et al. 2012). Similarly,

our specimens representing G. senegalensis and Galagoides

demidovii were assigned to specific 12S lineages (none of

the two species were monophyletic); FINS providing

here a glimpse of the complex systematics of Galagidae

(Bearder & Masters 2013).

Another advantage of our decision pipeline approach

was that it could provide a minimum phylogenetic level

of assignment for problematic taxa that could not be

identified to the species level (see concern raised by

Nilsson et al. 2005). For instance, an unknown sample

from Guinea was assigned to the genus Cephalophus,

although it was subject to conflicting species-level

assignments (‘Unknown 8’; Table 2). Similar to what has

been observed in most DNA typing studies, the main

reason for not reaching species-level identification was

incomplete taxonomic representation in GENBANK (see

Puillandre et al. 2009). Our best performing gene was cyt

b (73%), supporting the idea that cyt b is, to date, the best

marker to identify mammalian species (Parson et al.

2000; Bradley & Baker 2001; Eaton et al. 2010; Olayemi

et al. 2011; Naidu et al. 2012). On the other hand, COI

reached 64% of species-level identification and was

poorly represented mainly in Carnivora, Primates and

Rodentia. The four mtDNA genes were in general com-

plementary in achieving species-level identification

across different taxa (e.g. 12S performed better in Pri-

mates than any other genes). Weak representation of

intraspecific diversity in GENBANK was also responsi-

ble for less accurate assignment of species identification

(Munch et al. 2008) – e.g. what we identified as ‘soft

species’ for E. patas and C. campbelli (cyt b). Here, the for-

mulation of clearly defined species assignment hypothe-

ses – such as ‘soft species’ – will help focus the direction

of future investigations on the status of those lineages

with uncertain taxonomic attributions.

The nonmonophyly of the species-level sequences

present in GENBANK was the second leading factor

causing the failure of species assignment. This was

mostly the case for 12S and 16S (especially in Primates),

suggesting that their lower rates of evolution were

responsible for incomplete lineage sorting among

recently diverged species (Simon et al. 1994). The failure

to obtain monophyletic species-level sequences may

also originate from an incongruence between morpho-

logical species hypotheses and gene trees, such as in

large-spotted genets – here involving Genetta pardina

and Genetta maculata (Gaubert 2003; Olayemi et al.

2011). Incorrect labelling and poor quality control of

GENBANK sequences may also involve the inconclu-

sive, nonmonophyly of retrieved species-level

sequences (Nilsson et al. 2006; Bertheau et al. 2011). In

addition, mislabelling can result in wrong species

assignment attribution, when only a single sequence is

available to represent a species in GENBANK. Here, we

suggest that (i) the mitogenome of ‘Manis tetradactyla’

AJ421454/NC004027 actually represents the species

M. tricuspis (also see Olayemi et al. 2011), and (ii) the

COI sequences of ‘Heliosciurus gambianus’ JX426127-8

belong to the species T. swinderianus (Table 2). The

apparent conflict around the genetic assignment of the

primary morphological hypothesis C. torquatus for

L. albigena (12S and 16S) also originated from the

nonupdated mislabelling of GENBANK sequences (see

Guschanski et al. 2013).



DNABUSHMEAT: an expert knowledge, web-assistedquery database for the identification of African forestbushmeat

DNA typing (including barcoding) is an efficient tool to

trace the dynamics of hidden or hardly accessible animal

trades (Baker 2008; Baker et al. 2010). It is also a useful

tool to reassess the taxonomic identity of mislabelled,

traded species (‘market substitution’; Wong & Hanner

2008), which can lead to the identification of protected

species (Milius 1998). The use of FINS has become a

widely approved approach by the forensic genetics com-

munity (Carracedo et al. 2000). Given that >50% of

African forest mammals are considered game species (Fa

et al. 2002) and that misidentifications of carcasses may

reach 59% in some cases (Minh�os et al. 2013), we propose

to deliver a web-accessible tool for the DNA-based iden-

tification of African forest bushmeat.

Our mtDNA data sets were released in DNABUSHMEAT

(http://mbb.univ-montp2.fr/MBB/DNAbushmeat), an

expert-curated query database that will provide a refer-

ence framework for the DNA typing of African forest

bushmeat. DNABUSHMEAT uses the cluster computing

capacities of the Montpellier Bioinformatics Biodiversity

platform (http://mbb.univ-montp2.fr/MBB/). At the

moment, DNABUSHMEAT includes 60 species representing

110–150 sequences for each mtDNA alignment (cyt b,

COI, 12S, 16S). As far as possible, we used a comprehen-

sive coverage of intraspecific variability by including a

series of different haplotypes for each species in the ref-

erence data sets. Users can locally blast the four databas-

es by pasting or attaching sequences in fasta format,

using BLASTn and ‘discontiguous MEGABLAST’ to allow the

return of best hits with similarity values <75% (Ma et al.

2002). We recommend users to check for the presence of

putative pseudogenes in their nucleotide sequences (see

Materials and methods) before submitting their data to

DNABUSHMEAT. Expect value (Altschul & Gish 1996) and

model of evolution were set by default to e-10 and K2P,

respectively, but can be modified by users. DNABUSH-

MEAT returns a set of 30 reference sequences that repre-

sent the best hits with the query, which are summarized

in a table including values of similarity (%) and coverage

(%) between the query and the best hit sequences,

together with Expect values. The phylogenetic position

of the query within the selected set(s) of reference

sequences may be visualized by clicking on the result

lines of the table for each query. DNABUSHMEAT provides

a neighbour-joining tree generated with the APE v. 3.1.1

package (Paradis et al. 2004) using K2P distances and 500

bootstrap pseudo-replicates, after aligning the set of best

sequences with MUSCLE. We used the Jstree package

(http://lh3lh3.users.sourceforge.net/jstree.shtml) to pro-

vide tree-drawing options, such as rerooting trees (by

clicking on a specific node) and circular versus phylo-

gram tree views. DNABUSHMEAT returns ‘results’ as a

downloadable zip file that contains alignment and tree

files by query, together with the blast output table for the

whole set of queries and the complete alignment and tree

including all the queries per mtDNA database.

Conclusion

Reliance on a single DNA marker may render species

identification problematic, especially in cases of recently

derived taxa (e.g. incomplete lineage sorting) and

hybridization. The rigorous decision pipeline that we

applied to our multiple mtDNA FINS should contribute

to the set-up of an efficient reference framework for the

DNA typing of African forest bushmeat. We recommend

using each of the four mtDNA genes to increase the reli-

ability of the taxonomic assignment. The maintenance of

expert-curated DNA alignments dedicated to the identi-

fication of bushmeat items such as DNABUSHMEAT will

help to develop more complete reference databases

across taxa, geographic areas, and eventually genes (e.g.

nuclear genes to uncover potential hybrids). Since col-

lecting voucher specimens in the context of bushmeat

trade is ethically problematic, ‘voucher photographs’

taken with cellular phone technologies (Teacher et al.

2013) could be used in support of the phenotypic diagno-

sis of bushmeat species. Our approach will also help

identify hidden genetic diversity within bushmeat mar-

kets. For example, surveys of animal trade centres have

led to the discovery of new mammalian species (Baker

2008), notably in Africa (Colyn et al. 2010), especially in

species with broad geographic distribution and extensive

population structure. Our decision pipeline extended to

a denser taxonomic and geographic coverage may help

identify new lineages of poorly studied taxa from inac-

cessible African rainforests, an urgent task given the

unsustainable rate of bushmeat hunting.

Acknowledgements

Field research was conducted under the approval of the Min-

ist�ere de la Recherche Scientifique et de l’Innovation of Camer-

oon, Direccion general de proteccion y guarderia forestal of

Equatorial Guinea, Direction Nationale des Eaux et Forets of

Guinea, Wildlife Division of Ghana and Ministry of Environ-

ment of Osun State in Nigeria. Angeles Mang Eyene (Instituto

National de Desarrollo Forestal y Manejo de �Areas Protegidas)

and the Institut de Recherche pour le D�eveloppement (IRD) of

Yaound�e supported the field logistics in Equatorial Guinea and

Cameroon, respectively. Field assistance in Ghana was pro-

vided by Christophe Voisin (Mus�eum National d’Histoire Natu-

relle, Paris). We thank Khalid Belkhir and Julien Veyssier (ISEM

cluster computing and Montpellier Bioinformatics Biodiversity

platform – LabEx CeMEB) for their contribution in building

the DNABUSHMEAT website and providing access to cluster



resources. Cameron Coffran and Mark Stoeckle (The Rockefeller

University) gave invaluable help with the use of TREEPARSER.

PG was funded by the Action Transversale Mus�eum 2012–2013‘Biodiversit�e actuelle et fossile. Crises, stress, restaurations et

panchronismes: le message syst�ematique’ and the Soci�et�e des

Amis du Mus�eum National d’Histoire Naturelle et du Jardin

des Plantes. Laboratory work was supported by the ‘Consor-

tium National de Recherche en G�enomique’, and the ‘Service de

Syst�ematique Mol�eculaire’ of the Mus�eum National d’Histoire

Naturelle (CNRS – UMS 2700) as part of agreement 2005/67

‘Macrophylogeny of life’ between Genoscope and the Mus�eum

National d’Histoire Naturelle, and by the project @SPEED-ID

‘Accurate SPEcies Delimitation and Identification of eukaryotic

biodiversity using DNA markers’ of the French Barcode of life

initiative. AA was supported by the European Regional Devel-

opment Fund (ERDF) through the COMPETE – Operational

Competitiveness Programme and Fundac�~ao para a Ciencia e a

Tecnologia (FCT) under the projects PEst-C/MAR/LA0015/

2013, PTDC/AAC-AMB/104983/2008 (FCOMP-01-0124-FEDER-

008610) and PTDC/AAC-AMB/121301/2010 (FCOMP-01-0124-

FEDER-019490). We thank Warren E. Johnson for significantly

improving the manuscript and four anonymous reviewers for

their valuable comments on an earlier draft. This is publication

ISE-M 2014-136.

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chondrial DNA markers. Trends in Ecology & Evolution, 11, 247–251.

P.G., F.N. and A.A. conceived the study. P.G. and

P.T. conducted the analyses. R.D. conceived the

DNABUSHMEAT website. P.G., F.N., A.O., P.P., S.D., E.D.,

M.E.K.N., G.N. and A-D.M. surveyed and sampled the



bushmeat markets. P.G. drafted the manuscript. All

authors contributed to the final manuscript.

Data Accessibility

Details on the samples used in the study are available in

Table S1 (Supporting information). DNA sequences:

GENBANK accession nos KJ192435–KJ193529. Nucleo-

tide sequence files used for tree-based analyses are

deposited on Dryad under doi: 10.5061/dryad.j2h11.

Web address of the DNABUSHMEAT site: http://mbb.univ-

montp2.fr/MBB/DNAbushmeat.

Supporting Information

Additional Supporting Information may be found in the online

version of this article:

Table S1 Details of the samples used in the study, with PCR

success and DNA-based taxonomic assignments.

Appendix S2 Bibliographic references attached to the

GENBANK sequences that were used to establish the species-

level expertise (highest similarity level) participating to the deci-

sion pipeline (see Fig. 2).

Fig. S3 Success rate in PCR amplification of the four genes

across mammalian orders.

Table S4 Binary generalized linear models results fitting gene

amplification success as a function of taxonomy, quality of sam-

ple and type of extraction.

Fig. S5 Success rate in PCR amplification per genes and mam-

malian orders and groups, depending on tissue types.

Table S6 DNA polymorphism estimates calculated among taxo-

nomic groups and genes.

Fig. S7 Synthetic neighbour-joining trees for the five mammalian

orders represented by more than one species based on cyt b (7a),

12S (7b), COI (7c) and 16S (7d).

Fig. S8 Synthetic maximum-likelihood trees for the five mam-

malian orders represented by more than one species based on

cyt b (8a), 12S (8b), COI (8c) and 16S (8d).

Fig. S9 Klee diagrams showing the correlation of species-level

indicator vectors among nine mammalian orders (9a) and within

five orders (9b- Artiodactyla; 9c- Carnivora; 9d- Pholidota; 9e-

Primates; 9f- Rodentia).



Bushmeat genetics: setting up a reference framework for the DNA typing of African forest bushmeat

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